This Handbook brings together experts in the field of leadership to provide insights into methods for leadership researc
1,442 99 4MB
English Pages 480 [472] Year 2017
Table of contents :
Front Matter......Page 1
Copyright......Page 4
Contents......Page 5
List of contributors......Page 7
Acknowledgements......Page 9
PART I INTRODUCTION......Page 11
1 Introduction and overview......Page 13
PART II MEASUREMENT AND DESIGN......Page 21
2 Implicit measures for leadership research......Page 23
3 Puppet masters in the lab: experimental methods in leadership research......Page 58
4 Assessing leadership behavior with observational and sensor-based methods: a brief overview......Page 83
5 The contribution of sophisticated facial expression coding to leadership research......Page 113
6 Behavioral genetics and leadership research......Page 137
7 Biosensor approaches to studying leadership......Page 156
PART III QUANTITATIVE METHODS AND ANALYTIC APPROACHES......Page 181
8 Mediation analysis in leadership studies: new developments and perspectives......Page 183
9 Person-oriented approaches to leadership: a roadmap forward......Page 205
10 Multi-level issues and dyads in leadership research......Page 239
11 A social network approach to examining leadership......Page 266
12 Diary studies in leadership......Page 306
13 Modeling leadership-related change with a growth curve approach......Page 327
PART IV QUALITATIVE METHODS AND ANALYTIC APPROACHES......Page 357
14 Qualitative content analysis in leadership research: principles, process and application......Page 359
15 Biographical methods in leadership research......Page 382
PART V SUMMARY......Page 411
16 Leadership in the future, and the future of leadership research......Page 413
17 Authors’ tips for doing top-quality research......Page 440
Index......Page 449
HANDBOOK OF METHODS IN LEADERSHIP RESEARCH
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HANDBOOKS OF RESEARCH METHODS IN MANAGEMENT Series Editor: Mark N.K. Saunders, University of Birmingham, UK This major series will provide the starting point for new PhD students in business and management and related social science disciplines. Each Handbook will give definitive overviews of research methods appropriate for particular subjects within management. The series aims to produce prestigious high-quality works of lasting significance, shedding light on quantitative, qualitative and mixed research methods. Each Handbook consists of original contributions by leading authorities, selected by an editor who is a recognized international leader in the field. International in scope, these Handbooks will be an invaluable guide to students embarking on a research degree and to researchers moving into a new subject area. Titles in the series include: Handbook of Research Methods on Intuition Edited by Marta Sinclair Handbook of Research Methods on Human Resource Development Edited by Mark N.K. Saunders and Paul Tosey Handbook of Research Methods on Trust Second Edition Edited by Fergus Lyon, Guido Möllering and Mark N.K. Saunders Handbook of Qualitative Research Methods on HRM Innovative Techniques Edited by Keith Townsend, Rebecca Loudoun and David Lewin Handbook of Methods in Leadership Research Edited by Birgit Schyns, Rosalie J. Hall and Pedro Neves
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Handbook of Methods in Leadership Research
Edited by
Birgit Schyns NEOMA Business School, France and Durham University Business School, Durham University, UK
Rosalie J. Hall Durham University Business School, Durham University, UK
Pedro Neves Nova School of Business and Economics, Portugal
HANDBOOKS OF RESEARCH METHODS IN MANAGEMENT
Cheltenham, UK • Northampton, MA, USA
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© Birgit Schyns, Rosalie J. Hall and Pedro Neves 2017 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA
A catalogue record for this book is available from the British Library Library of Congress Control Number: 2017947087 This book is available electronically in the Business subject collection DOI 10.4337/9781785367281
ISBN 978 1 78536 727 4 (cased) ISBN 978 1 78536 728 1 (eBook)
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Typeset by Servis Filmsetting Ltd, Stockport, Cheshire
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Contents List of contributorsvii Acknowledgementsix PART I INTRODUCTION 1 Introduction and overview Birgit Schyns, Pedro Neves and Rosalie J. Hall
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PART II MEASUREMENT AND DESIGN 2 Implicit measures for leadership research SinHui Chong, Emilija Djurdjevic and Russell E. Johnson
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3 Puppet masters in the lab: experimental methods in leadership research48 Eric F. Rietzschel, Barbara Wisse and Diana Rus 4 A ssessing leadership behavior with observational and sensor-based methods: a brief overview Alexandra (Sasha) Cook and Bertolt Meyer 5 T he contribution of sophisticated facial expression coding to leadership research Savvas Trichas
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6 Behavioral genetics and leadership research Wen-Dong Li, Remus Ilies and Wei Wang
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7 Biosensor approaches to studying leadership Aurora J. Dixon, Jessica M. Webb and Chu-Hsiang (Daisy) Chang
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PART III Q UANTITATIVE METHODS AND ANALYTIC APPROACHES 8 M ediation analysis in leadership studies: new developments and perspectives Rex B. Kline
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vi Handbook of methods in leadership research 9 Person-oriented approaches to leadership: a roadmap forward Roseanne J. Foti and Maureen E. McCusker
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10 Multi-level issues and dyads in leadership research Francis J. Yammarino and Janaki Gooty
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11 A social network approach to examining leadership Markku Jokisaari
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12 Diary studies in leadership Sandra Ohly and Viktoria Gochmann
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13 M odeling leadership-related change with a growth curve approach317 Rosalie J. Hall PART IV Q UALITATIVE METHODS AND ANALYTIC APPROACHES 14 Q ualitative content analysis in leadership research: principles, process and application Jan Schilling 15 Biographical methods in leadership research Miguel Pina e Cunha, Marianne Lewis, Arménio Rego and Wendy K. Smith
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PART V SUMMARY 16 Leadership in the future, and the future of leadership research Robert G. Lord
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17 Authors’ tips for doing top-quality research
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Index439
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Contributors Chu-Hsiang (Daisy) Chang, Michigan State University, USA SinHui Chong, Michigan State University, USA Alexandra (Sasha) Cook, Chemnitz University of Technology, Germany Miguel Pina e Cunha, Nova School of Business and Economics, Portugal Aurora J. Dixon, Michigan State University, USA Emilija Djurdjevic, University of Rhode Island, USA Roseanne J. Foti, Virginia Tech, USA Viktoria Gochmann, University of Kassel, Germany Janaki Gooty, University of North Carolina at Charlotte, USA Rosalie J. Hall, Durham University Business School, Durham University, UK Remus Ilies, National University of Singapore, Singapore Russell E. Johnson, Michigan State University, USA Markku Jokisaari, University of Turku, Finland, and Durham University Business School, Durham University, UK Rex B. Kline, Concordia University, Canada Marianne Lewis, Cass Business School, City University of London, UK Wen-Dong Li, Chinese University of Hong Kong, Hong Kong Robert G. Lord, Durham University Business School, Durham University, UK Maureen E. McCusker, Virginia Tech, USA Bertolt Meyer, Chemnitz University of Technology, Germany Pedro Neves, Nova School of Business and Economics, Portugal Sandra Ohly, University of Kassel, Germany
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viii Handbook of methods in leadership research Arménio Rego, Católica Porto Business School, Universidade Católica Portuguesa, and Instituto Universitário de Lisboa (ISCTE-IUL), Business Research Unit, Portugal Eric F. Rietzschel, University of Groningen, the Netherlands Diana Rus, Creative Peas Consultancy and University of Groningen, the Netherlands Jan Schilling, University of Applied Administrative Sciences, Hannover, Germany Birgit Schyns, NEOMA Business School, France, and Durham University Business School, Durham University, UK Wendy K. Smith, University of Delaware, USA Savvas Trichas, Open University of Cyprus and CDA College, Cyprus Wei Wang, University of Central Florida, USA Jessica M. Webb, Michigan State University, USA Barbara Wisse, University of Groningen, the Netherlands, and Durham University Business School, Durham University, UK Francis J. Yammarino, State University of New York at Binghamton, USA
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Acknowledgements We are indebted to the external reviewers for this book who gave their time generously to improve the chapters of this book. Our thanks go to: Talib AlHinai, Durham University Business School, Durham University, UK Susanna Chui, Durham University Business School, Durham University, UK Shahira Dahari, Durham University Business School, Durham University, UK Alexandra Hauser, Ludwig Maximilian University of Munich, Germany Steve Lockey, Durham University Business School, Durham University, UK Ekaterina Pogrebtsova, University of Guelph, Canada Micah Roediger, Virginia Tech, USA Dean Rosenwald, University of Pittsburgh; SolarCell Design, LLC, USA Thomas Sasso, University of Guelph, Canada Maria Joao Velez, Nova School of Business and Economics, Portugal Kenneth Wenk, University of Pittsburgh, USA Xiatong (Janey) Zheng, Durham University Business School, Durham University, UK Yuyan (Cherry) Zheng, London School of Economics, UK
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PART I INTRODUCTION
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1. Introduction and overview
Birgit Schyns, Pedro Neves and Rosalie J. Hall
This volume provides an overview of a variety of established and newer methods for leadership research. It is intended for any individuals wanting to undertake research on leaders, whether they are academics or practitioners, undergraduates, graduate students, or new or established professionals. Our goal in this volume is to help leadership researchers obtain a first insight into a specific method and its potential application to leadership research, so that they can make a decision about whether or not to delve deeper into the method and use it for their own research. We particularly encourage academics who want to try a new method and graduate students who are just starting their own research programs to read this book. This book may also prove helpful to individuals who want to better understand and assess the quality and implications of leadership research undertaken by others. It was interesting when collecting the chapters to see the great variety of methods applied in leadership research, all of which contribute to a more complete and nuanced understanding of the leadership process. Recent editorials from, for example, the Academy of Management Journal (Colquitt, 2013), stress the increasing breadth of methodological design and analysis techniques used (e.g., from inductive/qualitative to experimental research). Chen (2015) similarly highlights the breadth of designs that are covered in the Journal of Applied Psychology as well as the necessity for the method to clearly fit the research question (see also Edmondson & McManus, 2007). As Rico (2013), former editor of the European Journal of Work and Organizational Psychology, described it: “[A]fter reading a Methods section, the reader needs to be able to understand what was done and why this approach was selected” (pp. 2–3). We hope to add to that increasing variety by making more techniques accessible to scholars and by discussing when and how each method is more pertinent. In addition, Green, Tonidandel, and Cortina (2016) analysed reviewers’ comments on submissions to the Journal of Business and Psychology and found that a large number of reviewer comments referred to method/ analysis issues such as mediation/moderation or issues with multi-level models and regression analysis. These issues are commonly linked to 3
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4 Handbook of methods in leadership research rejection (though to a lesser degree than measurement issues) but are also involved in decisions to ask for “revise and resubmit.” Our volume could be helpful in making decisions about the appropriateness of design and methods for particular research questions in leadership. There were many potential techniques to choose from, and we could not cover all in a single volume. Thus, we have focused on methods, techniques and analytic approaches that are either currently valuable for understanding leadership/followership or that we believe would – if adopted – provide useful tools in the future. In addition, we have worked with our authors and reviewers to keep the presentation of the techniques as straightforward as possible, providing a detailed enough overview to get readers started with a technique, but avoiding overly technical details. Indeed, we have been very fortunate to be able to get many of our contributions from authors who have expertise in, and have published on, the topic of leadership in organizations. This means that the descriptions of methodological techniques in this volume are often embedded with recent and helpful illustrations that are particularly relevant to the study of leadership. However, we have also included a handful of other authors who are not leadership researchers per se, but contribute with their methodological expertise and rigor in areas where leadership researchers “could do better.” All authors have been asked to provide detailed guidance on the use of their featured techniques, including in many cases access to online aids and sample datasets. Our hope is that this volume may be particularly helpful not only in helping with the “how to” of a given approach, but also as a source of answering “why?” In this introductory chapter, we provide a brief overview and structural framework for the following chapters. Our ultimate goal is to motivate researchers both to try new techniques and to fine-tune their use of more common techniques. We are in a time when new methodologies are being developed, and old ones improved and made easier to use. Finally, we hope that instructors of leadership courses aimed at advanced undergraduate and graduate students will consider using all or parts of our book alongside a more content-oriented text, in order to better demonstrate the varied and creative options in the “how to” of leadership research. This handbook is divided into three main areas: Part II: Measurement and Design; Part III: Quantitative Methods and Analytic Approaches; and Part IV: Qualitative Methods and Analytic Approaches. The book finishes in Part V with a summary/conclusion chapter and a chapter on tips for leadership researchers from each author. Part II of the book focuses on measurement and design issues, and consists of six chapters. First, in Chapter 2, Chong, Djurdjevic, and Johnson provide an introduction to implicit measures for leadership research. They
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Introduction and overview 5 argue that to understand leadership, both explicit elaborate and implicit automatic processes are relevant. Most leadership research has focused on the former, though this will only capture part of what influences judgments and behaviors. Chong et al. present a taxonomy of implicit measures and outline what these are measuring. They review the extant leadership literature that has used implicit approaches and provide examples of the use of implicit measures. This chapter will help leadership researchers to make a better decision on whether or not their research would benefit from using implicit measures, and, if so, how to decide which types to use. In Chapter 3, Rietzschel, Wisse, and Rus introduce experimental methods in leadership research. They argue that experimental research can help researchers to further understand causal relationships when studying leadership. The authors provide an overview of varieties of experimental methods that are currently used in leadership research, including both the benefits of using these methods and potential issues involved in conducting experiments. We believe that a reading of this chapter will convince leadership researchers that experiments can help them draw the stronger causal conclusions that (most) field studies do not allow, as well as provide them with possible ideas about designs for experiments. Chapter 4 by Cook and Meyer outlines how observational and sensorbased methods can be used in leadership research. Their argument builds around the idea that leadership is a process and that observational methods allow for a better understanding of how leaders and followers influence each other with their respective behavior. Cook and Meyer describe different observation study approaches, including how to capture and record relevant behavior and how to analyse observational data. Their chapter outlines the advantages as well as the problems related to conducting observational studies. Readers should learn from this chapter why observational methods are useful in capturing the leadership process in its entirety, and what ways of capturing and analysing observational data are available. Following on from that, Trichas explains in Chapter 5 the benefits of sophisticated facial expression coding to leadership research. So far, this method has not been widely used in leadership research. However, Trichas uses examples of studies that have used and others that have not used (but examined similar research questions) sophisticated facial expression coding to highlight how this approach can further our understanding of the leadership process. After reading the chapter, leadership researchers should understand the value of sophisticated facial expression coding, for example, when studying emotions in leadership as well as knowing how to use this method. Li, Ilies, and Wang focus on behavioral genetics approaches in Chapter
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6 Handbook of methods in leadership research 6. They argue that genetic approaches can help leadership researchers to disentangle the nature/nurture arguments prevalent in leadership research. Introducing twin studies and a molecular genetic research approach as useful lenses through which to study leadership, they explain the contribution of those approaches as well as review examples of their application in leadership research so far. This chapter provides the reader with an overview of genetic approaches, sample studies, and application in leadership research, and an understanding of how this type of research can advance our knowledge of leadership nature versus nurture. In Chapter 7, Dixon, Webb, and Chang introduce biosensor approaches to studying leadership. They begin by describing four general categories of biosensor methods (i.e., involving collection of bodily fluids, cardiovascular activity, brain activity, and genetics/evolutionary characteristics) and their associated advantages and disadvantages for leadership research. This is followed by a review of previous biosensor-related research that contributes to our knowledge of leadership and followership, using Bass’s (2008) three general approaches for understanding leadership (leadercentric, leadership as an effect, and leader–follower interactions) as an organizing structure. Thus, this chapter gives readers a broad overview of the possibilities for integrating biosensor approaches with more traditional research methods. Part III of the book focuses on quantitative methods and analytic approaches. Here, six chapters explain how to analyse different types of quantitative data. First, Kline in Chapter 8 outlines methods for the analysis of mediation in leadership research. He particularly focuses on common misunderstandings around this topic, bringing into question the value of the results obtained using mediation in some studies. Issues comprise, amongst others, theoretical assumptions about directionality and design problems. After reading the chapter, leadership researchers should understand these issues and be able to design better studies to test mediation and/or to acknowledge more clearly the limits of their m ediation testing. In Chapter 9, Foti and McCusker introduce person-oriented approaches to leadership. This alternative to more commonly used variable-oriented approaches provides researchers with the possibility to focus on “types” or “patterns” based on individuals. Foti and McCusker review different methods within person-oriented approaches and illustrate them using examples from leadership research. This chapter provides the reader with sample research questions that require the use of person-oriented approaches, as well as an understanding of the approaches available to analyse data in this novel way. Chapter 10 by Yammarino and Gooty focuses on multi-level issues
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Introduction and overview 7 and dyads in leadership research. Very often in the study of leadership, researchers deal with so-called nested data – that is, followers nested in leaders or teams, or dyadic data such as leader–follower dyads. This chapter provides an overview of the issues as well as methods relating to dyadic data. Here, the authors discuss in depth three analytic approaches that might be applied to dyadic leadership data. The reader of this chapter will gain a better understanding of the logic of nested and dyadic data collected in leadership research and how this type of data can be meaningfully analysed. Following on from this, in Chapter 11 Jokisaari introduces a social network approach to leadership research. He argues that when studying leadership, we often ignore that leadership happens in networks of individuals. He outlines methods to address this issue and uses examples to illustrate how this approach can be applied. This includes issues of research design, sampling and data collection, methods to measure social networks, and central measures of networks for use in data analysis. Reading this chapter will provide the reader with a clear idea of how and when to apply network methods, and for which types of research questions they can be useful tools. In Chapter 12, Ohly and Gochmann focus on diary studies in leadership. Here, they argue that leadership is often assessed at one point in time but that diary studies provide the opportunity to better understand the process involved in leadership. This also includes testing the notion that leadership is a stable phenomenon. Ohly and Gochmann review existing diary studies and outline approaches that can be used as well as issues that researchers might face. In identifying relevant research questions, the authors provide the reader with guidelines on when and how to apply diary studies in leadership research. Finally, in Chapter 13, Hall explains the value of applying latent growth curve modeling to leadership research. She points out that in a substantial amount of leadership research, dynamics and change are important and latent growth curve modeling is an appropriate method to examine these changes, allowing both the identification of average change and for variability in change across individuals. After reading this chapter, the reader should be familiar with the technique, knowledgeable about issues and alternative analytic choices, and be able to reflect on appropriate designs as well as make a reasoned choice of software options. Part IV of the book summarizes some qualitative methods and analytic approaches to leadership research. The two chapters in this part outline when and how to use qualitative approaches to leadership. We appreciate that this part of our volume is short in comparison to the previous two, and acknowledge that it can only scratch the surface of qualitative
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8 Handbook of methods in leadership research methods in leadership research. The reader is encouraged to consult additional overview books in this area. First, in Chapter 14, Schilling provides an extensive overview of qualitative content analysis and how it can be applied to leadership research. He argues that qualitative approaches to leadership are somewhat undervalued currently. His chapter gives a comprehensive overview on qualitative content analysis as a systematic, rule-based process of analysing verbal and textual data (e.g., interviews, group discussions, documents). As he presents the process of qualitative content analysis step by step, and develops guidelines for researchers’ decision and action, his chapter provides the reader with a “how to” guide for approaching qualitative data in the leadership area. Following on from that, in Chapter 15, Cunha, Lewis, Rego, and Smith outline the use of biographical methods in leadership research. They define biographical methods as an umbrella term comprising approaches such as self-narratives, autobiographies, and historical biographies that explore an individual’s life story to elucidate nuanced dynamics over time. They describe the features of these methods, namely, narrative, holistic, constructivist, context-sensitive, dynamic and temporally situated, relational, self-reflexive, and contradiction-sensitive. Their chapter also provides the reader with a clear idea of how to collect biographical data and the method involved in sampling and analysing data. The chapter includes a discussion of potential issues pertaining to biographical methods. Finally, in Part V, Chapter 16, Robert Lord provides an outlook for the future of leadership research. In his chapter, he aims to improve leadership theory, methodology, and practice both in the short and the long term, including an assumption of radical change in how leadership research is approached. He addresses issues such as theory proliferation and aggregation across entities and time. In addition to improving theory, he describes how to improve the measurement of leadership and study designs. He concludes with an outlook on the potential future of leadership research. At the end of this handbook, in Chapter 17 the authors provide some handy tips for leadership researchers based on material covered in their chapters. The reader is encouraged to turn to this chapter for a summary of the key points the authors consider a “must know.” In conclusion, we hope that the readers of this book enjoy the chapters as much as we enjoyed gathering, reading, and editing them. Hopefully, this book will motivate you to understand and apply new methods in your leadership research.
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Introduction and overview 9
REFERENCES Bass, B.M. (2008). The Bass handbook of leadership: Theory, research and managerial applications. New York: Simon and Schuster. Chen, G. (2015). Editorial. Journal of Applied Psychology, 100(1), 1–4. Retrieved from http:// www.apa.org/pubs/journals/apl/ Colquitt, J.A. (2013). The last three years at AMJ – Celebrating the big purple tent. Academy of Management Journal, 56(6), 1511–1515. doi: 10.5465/amj.2013.4006 Edmondson, A.C., & McManus, S.E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1155–1179. doi: 10.5465/AMR.2007.26586086 Green, J.P., Tonidandel, S., & Cortina, J. (2016). Getting through the gate: Statistical and methodological issues raised in the reviewing process. Organizational Research Methods, 19(3), 402–432. Retrieved from http://journals.sagepub.com/doi/abs/10.1177/10944281166 31417?journalCode5orma Rico, R. (2013). Editorial letter: Publishing at EJWOP. European Journal of Work and Organizational Psychology, 22(1), 1–3. Retrieved from http://dx.doi.org/10.1080/13594 32X.2013.752247
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PART II MEASUREMENT AND DESIGN
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2. Implicit measures for leadership research SinHui Chong, Emilija Djurdjevic and Russell E. Johnson
INTRODUCTION Attitudes influence judgments and behaviors in the workplace through explicit and implicit processes (Bowling & Johnson, 2013; Uhlmann et al., 2012). Explicit processing occurs when individuals engage in deliberative and effortful analysis of the costs and benefits of a decision and behavior, while implicit processing occurs automatically and outside of individuals’ awareness (Chaiken & Trope, 1999). Despite being useful and practical, explicit measures that assess conscious work attitudes and behaviors are frequently associated with response biases that may undermine the validity of research findings (Johnson & Tan, 2009). These concerns have prompted organizational researchers to turn to implicit measures in the hope of more accurately capturing work attitudes and behaviors, especially those driven by automatic processes that reside outside people’s awareness and control (Uhlmann et al., 2012). For example, Ziegert and Hanges (2005) demonstrated that implicit racist attitudes and racist climate interacted to positively predict employment discrimination against racial minority applicants, and Stajkovic, Locke, and Blair (2006) showed that the implicit activation of a do-your-best attitude in individuals led to better goal performance than no activation. These findings highlight the promise that implicit measures hold for organizational research. In this chapter, we focus on the use of implicit measures in leadership research. Leadership scholars are among the earliest to acknowledge the importance and value of implicit processes. The most prominent is the implicit theory of leader categorization, which argues that individuals rely on their implicit expectations and prototypes of personality traits, instead of veridical leadership behaviors in the workplace, to define and categorize others as leaders (Eden & Leviatan, 1975; Rush, Thomas, & Lord, 1977). Scholars also used implicit measures, such as sentence completion tasks, to examine employees’ attitudes toward their supervisors (Burwen, Campbell, & Kidd, 1956) and individuals’ motivation to lead (Stahl, Grigsby, & Gulati, 1985). However, beyond these initial works, our review revealed that the use of implicit measures in leadership research is quite 13
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14 Handbook of methods in leadership research rare. This trend is baffling, especially in a time when implicit measures are gaining increasing popularity and credibility in organizational research (Uhlmann et al., 2012). The failure of leadership research to adopt implicit measures creates an inadequacy to capture leadership-related phenomena that operate at levels below individuals’ consciousness, and constitutes a critical methodological concern. We believe that leadership scholars’ inertia in adopting implicit measures is likely due in part to uncertainty about what implicit measures are, when to use them, and how to administer them. Therefore, our chapter aims to shed light on the utility of implicit measures for leadership research, and to produce clear and actionable knowledge for scholars to incorporate implicit measures in their works. We organize our chapter into six parts to achieve these objectives. First, we review implicit theories of leadership. Second, we highlight the rationales and possible advantages of using implicit measures for leadership research. Third, we present an established taxonomy of implicit measures, describe the assumptions they are based on, and explain the types of variables and processes they are designed to capture. We also use this taxonomy to organize and review existing leadership studies that have utilized implicit measures. Fourth, we propose a set of criteria to guide leadership scholars in their selection of appropriate implicit measures, which depends on their research question and focal constructs. Fifth, we present an empirical example of a leadership study that employed implicit measurement. Finally, we conclude by offering ideas for using implicit measures in leadership research.
LEADERSHIP THEORIES THAT INVOLVE IMPLICIT CONTENT OR PROCESSES Existing research supports a dual process model in which explicit and implicit processes operate in parallel to influence attitudes and behaviors (Chaiken & Trope, 1999). In explicit processing, individuals engage in conscious and effortful analysis in order to form judgments and decide how to behave (Fazio & Olson, 2003). Explicit attitudes and behaviors are assumed to be deliberative enough to be captured by self-report or otherreport methodologies (Uhlmann et al., 2012). For example, leadership scholars often have followers self-report their exchange quality with their leaders (Liden & Maslyn, 1998), and rate their leaders’ leader behaviors (Liden, Wayne, Zhao, & Henderson, 2008; Tepper, 2000). In contrast, implicit processes influence attitudes and behaviors in an automatic, unintentional, and sometimes even unconscious manner (Greenwald & Banaji, 1995; Wegner & Bargh, 1998). Leadership scholars generally assume
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Implicit measures for leadership research 15 that implicit processes reflect an experiential system that is slow learning but fast acting (Lord, Diefendorff, Schmidt, & Hall, 2010; McClelland, McNaughton, & O’Reilly, 1995). This means that individuals gradually learn and develop cognitive schemas about the attributes that characterize ideal leaders through socialization and life experiences (Lord, Brown, Harvey, & Hall, 2001). These schemas reside in individuals’ memory and function as a cognitive bias that spontaneously influences how individuals make sense of various aspects of leadership (Kenney, Schwartz-Kenney, & Blascovich, 1996). Implicit theories prove to be useful in explaining automatic processes in leadership-related phenomena, and leadership scholars have applied implicit theories to the following topics. Leader Categorization People make sense of the world by segmenting the environment into categories, where they classify stimuli perceived to be similar into the same category (Mervis & Rosch, 1981). Categories have unique representative symbols; for example, vehicles are mobile and primarily function to transport people or goods between places, while birds are feathery animals that lay eggs to reproduce. Categories serve important information-processing purposes by helping people simplify abundant and complex stimuli in the environment (ibid.). Building on this notion, Lord, Foti, and De Vader (1984) proposed a categorization theory to leadership. This theory argues that individuals develop leader prototypes, defined as abstract conceptions of the most representative members of the category of leaders (Rosch, 1978; Rush & Russell, 1988), from their prior experiences and social interactions with leaders. These prototypes are easily accessible and rapidly activated, and they help individuals distinguish leaders from non-leaders based on the perceived match between the target and their prototypical attributes. Research in this area demonstrated individuals’ tendency to define individuals who are intelligent and honest (Rush & Russell, 1988), white (Gündemir, Homan, De Dreu, & Van Vugt, 2014), or male (Rudman & Kilianski, 2000), as leaders. Individuals also form different male and female leader prototypes, where they expect female leaders to demonstrate sensitivity and strength and male leaders to demonstrate only strength (Johnson, Murphy, Zewdie, & Reichard, 2008). Leadership Motives Scholars have long been interested in how basic motives such as the need for power, need for affiliation, and need for achievement predict behavioral outcomes (Atkinson, 1958). Need for power refers to the desire to
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16 Handbook of methods in leadership research impact and control others’ behaviors or emotions; need for affiliation refers to the desire to build lasting relationships with others; and need for achievement refers to the concern to compete and perform better than others (ibid.). Motives such as the need for power and achievement positively predict leadership effectiveness (Locke, 1991). However, self-report and implicit measures of motives rarely correlated significantly because people are not always consciously aware of their own motives or may conceal their real motives for social desirability purpose (McClelland, Koestner, & Weinberger, 1989). According to McClelland and colleagues (1989), implicit motives reflect a primitive motivation system that predicts spontaneous and affect-charged responses while explicit motives reflect thoughtful cognitive constructs and predict socially reinforced responses in structured situations (McClelland, et al., 1989). These arguments have prompted scholars to study the influences of both explicit and implicit motives on leadership outcomes (Spangler, 1992). These studies generally supported a positive relationship between an implicit power motive and leadership outcomes (De Hoogh et al., 2005; Jacobs & McClelland, 1994). Leader Evaluation Implicit theories also serve as a framework for understanding how followers evaluate the effectiveness of leaders. Studies in this area showed that individuals rely on performance cues (i.e., successful or unsuccessful) to evaluate leaders’ effectiveness, where individuals exposed to successful performance cues were more likely to rate a leader as being considerate and exhibiting more structuring types of behaviors than individuals exposed to unsuccessful performance cues (Larson, 1982; Lord, Binning, Rush, & Thomas, 1978). Scholars attributed this phenomenon to the perceptual-memory bias, where implicit schemas function as perceptual filters that guide individuals’ attention to schema-consistent behaviors as they evaluate leaders’ effectiveness (Rush et al., 1977). Implicit Self-concepts and Leadership Implicit self-concepts have been studied in leadership research in two key ways. First, scholars have used implicit theories to explain how the activation of a certain self-identity in individuals predicts their leadership selfconcepts. For example, exposure to non-traditional roles such as a female engineer (i.e., successful women in a male-dominated career) reduces women’s implicit beliefs about how well they can lead (Rudman & Phelan, 2010). This is consistent with the backlash effect, which argues that examples of successful atypical women (i.e., female engineer) provokes upward
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Implicit measures for leadership research 17 comparison and causes perceived threat of competence among average women (Parks-Stamm, Heilman, & Hearns, 2008). In another program of research, scholars have developed conceptual models to explain how leaders influence the implicit values and self-concepts of followers (Lord & Brown, 2001). These conceptual works are built on the assumption that leaders can increase the salience of certain values and hence activate certain self-identities (i.e., individualistic, collectivistic, or relational identity) among followers through task design, communication patterns, and leader behaviors (Brewer & Gardner, 1996; Hanges, Lord, & Dickson, 2000; Johnson & Lord, 2010; Lord & Brown, 2001). The above review illustrates some of the areas where implicit theories have been applied in leadership research, and highlights the potential of implicit theories and measures to expand leadership theory. In the next section, we discuss the rationales and possible advantages of using implicit measures in leadership research.
ADVANTAGES OF IMPLICIT MEASURES FOR LEADERSHIP RESEARCH The primary objective of implicit measures is to obviate conscious processing and to capture automatic, unintentional, and/or unconscious processes underlying judgments and behaviors (De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009; Fazio & Olson, 2003). This is achieved through limiting individuals’ ability to control their responses and minimizing their awareness of what is being assessed (Uhlmann et al., 2012). There tends to be an explicit aspect to all responses, however, such that individuals can possibly still control their automatic responses to implicit measures (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005). This suggests that responses to implicit measures are likely reflecting the joint influences of both implicit and explicit processes, and it may not be possible to completely tease apart the two types of processes when assessing cognition and behavior (ibid.). Nevertheless, implicit measures capture a larger proportion of implicit processes as compared to explicit measures (Uhlmann et al., 2012). Existing meta-analyses also found that implicit measures and explicit measures only modestly correlate with each other (Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Krizan & Suls, 2008), thus supporting the incremental utility of implicit measures. Fazio and Olson (2003) contended that how much implicit and explicit measures correlate really depends on individuals’ motivation, opportunity, and ability to deliberate on their responses. If individuals have little motivation, opportunity, and ability to deliberate, their responses
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18 Handbook of methods in leadership research to explicit measures will correlate higher with their responses to implicit measures. Building on the above notion, implicit measures are most useful for studying phenomena that are theorized to operate partially or mostly below individuals’ awareness or that are not easily observed or introspected by individuals themselves (Uhlmann et al., 2012). For example, individuals are rarely aware of how their prejudices or stereotypes influence their definition and categorization of leaders and non-leaders (Chung-Herrera & Lankau, 2005; Rudman, Ashmore, & Gary, 2001). In such cases, using implicit measures addresses the limitation posed by using explicit measures to capture implicit processes. The use of implicit measures also helps to overcome instances when individuals are unwilling to report their attitudes truthfully due to social desirability concerns (Uhlmann et al., 2012). For example, individuals may not want to admit that their motivation to lead stems from their desire for power, which may sometimes be perceived as a manipulative, insincere, and undesirable motivation. With the help of implicit measures, leadership scholars fare better at getting to such underlying motives that individuals would otherwise conceal in their responses to explicit measure. In addition, some implicit measures have been demonstrated to overcome faking because they are designed to elicit spontaneous responses that are difficult to consciously control (LeBreton, Barksdale, Robin, & James, 2007). This makes them especially useful for overcoming the influence of evaluation apprehension on responses, such as when assessing followers’ satisfaction with their leader. Furthermore, individuals sometimes engage in self-deception when responding to explicit measures (Uhlmann et al., 2012). This may happen when individuals adjust their explicit attitudes from their implicit attitudes in the face of cognitive dissonance in order to protect their self-esteem (Taylor, 1983). For example, research demonstrated that exposing female participants to successful female leaders in male-dominated careers threatened their self-esteem and resulted in negative implicit evaluations of female leaders but had no effects on explicit evaluations of female leaders (Rudman & Kilianski, 2000). When conducting research in such sensitive domains, scholars would especially benefit from the adoption of implicit measures. In addition, implicit measures often predict incremental variance in criteria above and beyond corresponding explicit measures (Greenwald & Banaji, 1995; Johnson, Tolentino, Rodopman, & Cho, 2010). This is beneficial when it is important to explain unique variance in the criterion (Uhlmann et al., 2012). For example, Johnson and Saboe (2010) demonstrated that followers’ implicit self-concept positively predicted the quality of leader–member exchanges and subsequent follower outcomes such as
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Implicit measures for leadership research 19 task performance and citizenship behavior above and beyond explicit measures of self-concept. Such findings imply that scholars and organizations will benefit from incorporating implicit measures in their research in order to more comprehensively uncover and understand factors that influence leadership criteria. Taken together, these strengths of implicit measures underpin their usefulness for leadership research. To provide a comprehensive view of how implicit measures have been used in extant leadership studies, we present a taxonomy of implicit measures and use it to organize and review existing leadership research that have used implicit measures.
A TAXONOMY OF IMPLICIT MEASURES AND THEIR USE IN EXISTING LEADERSHIP RESEARCH Implicit measures can be organized in numerous ways (Fazio & Olson, 2003; Uhlmann et al., 2012). For example, implicit measures can be distinguished based on the administration format (e.g., computer-based reaction time measures vs pen-and-paper questionnaires). While such methodological properties are important, we present a taxonomy based predominantly on the conceptual differences between the measures. This taxonomy, developed by Uhlmann and colleagues (2012), classifies implicit measures into three categories based on the type of implicit content they are designed to capture. Accessibility-based measures assess the degree to which a target concept is spontaneously activated in individuals’ minds (e.g., leadership behaviors may activate and make certain self-concepts more salient in followers). Association-based measures determine whether several targets are linked as part of a cognitive schema in individuals’ memories (e.g., associating intelligence to leadership effectiveness). Interpretation-based measures evaluate the reactions to and inferences that individuals draw from ambiguous stimuli to uncover inner attitudes (e.g., assessing leadership motives through leaders’ responses on how they would react in given situations). Although interpretation-based measures are designed based on the concepts of accessibility and association, they serve to capture deeper and more complex worldviews or motives that the simpler interfaces of accessibility-based or association-based measures are inadequate in eliciting (ibid.). This taxonomy has the strength of clustering implicit measures that have substantive conceptual similarities but are being administered and delivered differently (ibid.). For example, both word completion tasks and lexical decisions tasks are accessibility-based measures that assess how salient a target concept is in individuals’ minds, but the former is a
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20 Handbook of methods in leadership research pen-and-paper task while the latter is a reaction-time task. On the other hand, although implicit association task and lexical decision task are both computer-based reaction-time tasks, they tap into different types of implicit information where the former assesses implicit linkages between multiple target concepts while the latter assesses the ease of accessibility of a single target concept. We contend that classifying measures based on their conceptual purpose and properties instead of their administration features offers a straightforward means for leadership scholars to determine which measures are more suitable for the purpose of their research. Extant findings that demonstrated significant convergence between scores from accessibility-based tasks (Johnson & Lord, 2010) further support the basis of this classification approach. Accessibility-based Implicit Measures Accessibility-based measures assess whether a single target concept is activated and accessible in individuals’ minds (Uhlmann et al., 2012). The activation of concepts can be trait or state based (ibid.). Trait-based activation exists when a target concept enjoys a generally high level of accessibility over time and across situations (Robinson & Neighbors, 2006). For example, the concept of “unpleasant” is more readily accessible and will be recognized more quickly by individuals high in negative affectivity (Johnson et al., 2010). Alternatively, state-based activation exists when a target concept becomes more accessible for a limited period of time (Robinson & Neighbors, 2006). For example, the concept of “service” may be more accessible when employees are interacting with customers versus leaders. Both trait- and state-based activations of a target concept influence how individuals make judgments and behave (ibid.). Accessibilitybased implicit measures include word or sentence completion tasks, lexical decisions tasks, and Stroop tasks. Below, we describe these measures and give examples of how they have been used in leadership research. Word- or sentence-completion tasks Participants are presented with word or sentence fragments that they have to complete to form a complete word or sentence. Word fragments are designed in a manner where they could be completed to form a word that either reflects a target construct or neutral non-target words. For example, the fragment “_OY” can be completed to form “JOY” (i.e., a word that reflects positive affectivity as the target construct) or “SOY” (i.e., a neutral non-target word). Sentence completion tasks operate in a similar way, but instead of forming words, participants form sentences by choosing one or more words from a set of options. Among the options are
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Implicit measures for leadership research 21 words that reflect the target concept and other non-target concepts, and participants are instructed to select the option that best reflects the first thought that comes to mind. Implicit scores, which reflect the proportion of target words generated or selected, are used to infer how accessible the target concept is in participants’ minds (Koopman, Howe, Johnson, Tan, & Chang, 2013). Sentence completion tasks appear to be the most commonly used accessibility-based implicit measure among leadership researchers. Several existing leadership studies have utilized the Miner Sentence Completion Scale-H (MSCS-H; Miner, 1978) to assess motivation to lead (Ebrahimi, 1997; Miner & Smith, 1982; Miner, Chen, & Yu, 1991; Stahl et al., 1985). The MSCS-H presents respondents with 35 incomplete sentences assessing six dimensions of motivation to lead (e.g., the desires to compete, assert oneself, exercise power, etc.). The options are worded to reflect high (+1), neutral (0), or low (–1) motivation to lead, and respondents have to select an option that best reflects their first thought to complete the sentence. The scores are aggregated to derive individuals’ motivation to lead scores, with higher scores representing a stronger motivation. Researchers have also used sentence completion tasks to study attitudes toward supervisors. In an early study, Burwen and colleagues (1956) used a sentence completion task to assess Air Force cadets’ attitudes toward their leaders by asking them to complete sentences with their first thoughts on how a cadet would feel or behave in different scenarios with his [sic] leader. An example item is “Whenever he saw his superior coming, he (1) threw up, (2) ducked, (3) saluted, (4) gave him a warm greeting, or (5) was very happy.” Cadets’ attitudes toward their leaders were scored and computed from their selected options to all the sentences. In another study, Konst, Vonk, and Van der Vlist (1999) asked employees to read and complete sentences describing performance-related behaviors of eight people (e.g., “J did not succeed in reaching a decision. . .”). When participants were told that these people were leaders (as opposed to subordinates), they were more likely to make inferences about the behavior’s consequences for the environment when completing the sentences. This suggests that participants viewed leaders (vs non-leaders) as having greater influence over their environments (Konst et al., 1999). Lexical decision tasks Lexical decision tasks require participants to use a computer keyboard to quickly decide whether a string of letters is an actual word or a nonsensical non-word (Meyer & Schvaneveldt, 1971). Out of all the real words presented, half of them reflect the target concept (e.g., “abuse” or “reprimand” reflect the concept of abusive supervision), and half of them
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22 Handbook of methods in leadership research are neutral, non-target words. Scholars have previously validated neutral words based on the number of letters and frequency of use in English with validated references such as the English Lexicon Project (Balota et al., 2007) and the frequency dictionary of contemporary American English (Davies & Gardner, 2013). Following up on the example, “brush” and “anonymous” can serve as the neutral counterparts of “abuse” and “reprimand,” respectively, based on their number of letters and their frequency of use in English. After participants complete the decision task, scholars compute the average within-person response time for correctly identifying words that reflect the target concept to ascertain how accessible the target concept is to participants (faster times represent higher accessibility). Researchers interested in using lexical decision tasks can download software and modifiable scripts from developer websites.1 Our review identified only one leadership study that used a lexical decisions task. In this study, Scott and Brown (2006) found that participants recognized communal trait words (e.g., kind, helpful) more readily than agentic trait words (e.g., dominating, independent) after reading a sentence describing a female (vs male) leader. Stroop tasks A final example of an accessibility-based implicit measure is the Stroop task. In traditional Stroop tasks, participants view color words (e.g., “BLUE,” “RED”) with congruent or incongruent font colors on the screen, and are required to name the font color instead of reading the word (Stroop, 1935). For example, participants presented with the word “BLUE” in red font color should answer “red” instead of “blue.” The Stroop task is designed to capture how much cognitive interference participants experience during the task. When interference is low, participants find it easier to focus on the font color instead of the meaning of the word. Over the past decades, Stroop tasks have been modified to assess interference that result from the meaning of non-color words too. The key idea is that, if the presented word is highly accessible to participants at implicit levels, participants will find it more difficult to ignore the word and focus on the font color. Hence, longer response times reflect greater accessibility of the target concept. For example, followers experiencing abusive supervision may take longer to name the font color of the word “ABUSE” as compared to the font color of a neutral counterpart such as “BRUSH.” This is because leaders’ abusive behaviors will be more accessible to them, and the word “ABUSE” will interfere with their ability to focus and name the font color. Researchers can download software and modifiable scripts for Stroop tasks online.2 The use of the Stroop task for leadership research is not common, as
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Implicit measures for leadership research 23 only one published study has used it. In this early study, Megargee (1969) investigated how well leaders with high versus low trait dominance could convey information to subordinates under time pressure. They used the original Stroop task where color words were presented in congruent or incongruent font colors, and found that leaders with high trait dominance experienced less interference and were able to more accurately convey the correct font color to subordinates as compared to leaders with low trait dominance (ibid.). Association-based Implicit Measures The second class of implicit measures is association-based measures, which assess how multiple target concepts are spontaneously linked in individuals’ memories (Uhlmann et al., 2012). They make the key assumption that activating a single target concept will trigger spreading activation to nearby related concepts in individuals’ semantic networks (Collins & Loftus, 1975). Association-based measures are especially useful for uncovering attributes that individuals associate with a given category, for example, when individuals think about leadership, they associate with it traits such as intelligence and honesty (Lord et al., 2001). Priming tasks and implicit association tests are examples of association-based implicit measures. Priming Priming measures attempt to activate target concepts by presenting participants with specific priming stimuli without their awareness (Fazio & Olson, 2003). Priming typically involves embedding the priming stimulus in filler tasks or in the physical environment, such that participants are not consciously aware of the exposure (De Houwer, et al., 2009). It is assumed that the target concept activated by the priming stimulus will influence individuals’ subsequent judgments and behaviors. Interested researchers can gain access to several types of priming tasks online.3 Priming measures appear to be a rather popular choice of implicit measure among leadership scholars. For example, Larson (1982) primed participants with information on whether a group had succeeded or failed in its task before showing them a video of an interaction between a leader and team members. Participants primed with the success information rated the leader’s effectiveness higher than those primed with the failure information because success is associated with leader effectiveness (ibid.). In another study, Baldwin, Carrell, and Lopez (1990) conducted an experiment where they primed participants with the disapproving face of their department chair or of another person, and demonstrated that
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24 Handbook of methods in leadership research articipants who were primed with the disapproving face of their departp ment chair rated their own research ideas lower as a result. Finally, Latu, Mast, Lammers, and Bombari (2013) primed female participants with photos of female leaders (e.g., Hillary Clinton, Angela Merkel) outside their awareness, and found that these female participants experienced greater empowerment and performed better on a subsequent leadership task compared to female participants primed with photos of male leaders. Scholars have also used priming measures to study stereotypical associations with female leaders (Davies, Spencer, & Steele, 2005; Rudman & Phelan, 2010). Implicit association tests Implicit association tests (IATs) present participants with pairs of words, where words representing a target concept (e.g., “leader”) are paired with either a positive concept word (e.g., “good”) or a negative concept word (e.g., “bad”). Participants have to use the computer keyboard to respond to the word pairs, where they are supposed to press a particular key when the target concept word appears with a positive concept word (e.g., “leader” and “good” appear together on the screen), and another key when the target concept word appears with a negative concept word (e.g., “leader” and “bad” appear together on the screen). When participants respond quickly to a word pair, it suggests that the two words presented in the pair are more strongly associated in memory (Greenwald, McGhee, & Schwartz, 1998). For example, if participants responded more quickly to “leader + good” as compared to “leader + bad,” it implies that participants have a positive attitude toward the leader. Researchers interested in IATs can obtain information about creating and using IATs online.4 The IAT is commonly used to study stereotypical traits or attributes that individuals associate with different categories of leaders such as male versus female leaders (Dasgupta & Asgari, 2004; Rudman & Kilianski, 2000; Rudman & Phelan, 2010), or white versus minority leaders (Gündemir et al., 2014). For example, Rudman and Kilianski (2000) demonstrated that participants responded more quickly to pairs of words linking men with high authority roles and women with low authority roles. Nevertheless, Dasgupta and Asgari (2004) showed that letting participants read biographical information about model female leaders before they completed an IAT reduced their likeliness of associating women with low authority roles. Schoel, Bluemke, Mueller, and Stahlberg (2011) also used the IAT to study the attitudes of high or low self-esteem individuals on autocratic leadership and democratic leadership, and found that high self-esteem individuals associated democratic leadership with
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Implicit measures for leadership research 25 positive valence while low self-esteem individuals associated autocratic leadership with positive valence. Interpretation-based Implicit Measures Interpretation-based implicit measures comprise the final category. Interpretation-based implicit measures capture individuals’ responses to ambiguous stimuli (Uhlmann et al., 2012). These measures assume that individuals project their chronically accessible personality, values, attitudes, or motives when they are trying to make sense of ambiguous information (ibid.). Thus, individuals’ responses are expected to be indicative of their latent personality, values, or attitudes (Tomkins & Tomkins, 1947). For example, when interpreting how a leader should behave in a team, individuals with a strong power motive are likely to find it reasonable for leaders to exert strong control over followers (James et al., 2012). Examples of interpretation-based implicit measures include thematic apperception tests and conditional reasoning tests. Thematic apperception test The thematic apperception test (TAT) requires participants to interpret pictures depicting ambiguous situations and narrate a story about what is happening in each picture (Morgan & Murray, 1935). Researchers then code the responses to infer participants’ social motives such as the needs for power, affiliation, and achievement. Previous research has found that TAT measures of achievement motives predict work outcomes such as career success and managerial success (McClelland & Boyatzis, 1982; Spangler, 1992). The TAT appears to be the only interpretation-based measure that has been used for leadership research. Leadership studies typically used the TAT to measure leadership motives such as the need for power, affiliation, and activity inhibition (House, Spangler, & Woycke, 1991; Jacobs & McClelland, 1994; McClelland & Boyatzis, 1982). For example, Jacobs and McClelland (1994) administered the TAT to entry-level staff and derived their scores for power, affiliation, and activity inhibition. They found that employees with a high need for power, a low need for affiliation, and a high activity inhibition were the most likely to reach senior management positions 12 years later for both male and female managers. In addition to leadership motives, Winter (1991) developed a coding scheme to code TAT responses for responsibility. In his study, he found that TAT measures of the need for power and responsibility collectively predicted leadership success 16 years later.
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26 Handbook of methods in leadership research Conditional reasoning test The TAT relies on coding of qualitative responses to assess a target construct, which raises concerns of validity and reliability (Lilienfeld, Wood, & Garb, 2000). However, not all interpretation-based measures face this problem. A couple of interpretation-based measures employ standardized scoring. One of these is the conditional reasoning test (CRT), which captures the reasoning process that individuals undergo to justify behavioral choices (James, 1998; James et al., 2012). The CRT is administered to participants as a cognitive ability test that contains several descriptions of situations that participants have to make logical inferences from. Each situation contains two response options that make sense and seem plausible (out of four), and one of these two options is designed to appear reasonable only to those with a specific motive (e.g., achievement motive). Participants’ responses to the situations are then scored to represent the target concept (e.g., achievement motive). The CRT has several strengths. First, the objectively scored items overcome the reliability concerns that plague the TAT (Uhlmann et al., 2012). Second, the CRT is typically robust to faking attempts as long as the purpose of the measure is not disclosed (LeBreton et al., 2007). In addition, well-validated CRTs are available for several constructs related to leadership. For example, CRTs measuring power or achievement motives (James et al., 2012) may be useful for examining leadership motives, and CRTs assessing aggression (James et al., 2005) may be appealing for scholars interested in abusive supervision. Despite this, our search failed to find any leadership studies that used the CRT. As gleaned from our discussion above, some implicit measures are more commonly used than others for leadership research. We contend that the lag in the adoption of certain implicit measures is due in part to researchers’ uncertainty about how to modify and adapt them to assess variables of interest. Therefore, in the next section, we offer some advice to guide leadership researchers in their selection and use of implicit measures for research.
CRITERIA TO GUIDE APPROPRIATE USE OF IMPLICIT MEASURES FOR LEADERSHIP RESEARCH According to Uhlmann and colleagues (2012), researchers ought to consider the following eight questions when choosing an implicit measure for their research:
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Implicit measures for leadership research 27 1. Do I need an implicit measure? 2. Which category of implicit measure should I use? 3. Can the implicit measure be flexibly adapted to assess my construct of interest? 4. What do the scores represent? 5. Does the implicit measure predict my outcome of interest? 6. Is the implicit measure reliable? 7. Is the implicit measure adaptable across cultures? 8. What resources on implicit measures are available for my study? These questions apply to leadership researchers too, and below we finetune these recommendations to specifically help leadership scholars decide how to incorporate implicit measures in their research. Do I Need an Implicit Measure? Based on our discussion earlier, there are several reasons and advantages for using implicit measures. Leadership scholars should use implicit measures if they are interested in (a) assessing a construct that operates outside an individuals’ awareness, or (b) understanding the associations among constructs in connectionist memory. They may also consider using implicit measures for the purposes of reducing the influences of evaluation anxiety or social desirability on responses, and predicting incremental variance beyond explicit measures. Which Category of Implicit Measure Should I Use? After ascertaining the need for an implicit measure, leadership researchers should select a category of implicit measures that match how the construct is conceptualized in their research questions. If they are interested in capturing what comes to individuals’ minds automatically in naturalistic settings, they should use accessibility-based implicit measures like the word/sentence-completion tasks, lexical decision tests, or Stroop tasks. For example, Scott and Brown (2006) used a lexical decision task in their study, and demonstrated that communal leadership traits (i.e., being kind) came to mind more readily than agentic traits (i.e., dominating) when individuals were evaluating a female leader. If researchers are primarily interested in assessing the connections between multiple constructs at an implicit level, they should find association-based measures such as priming and the IAT more useful. For example, Schoel and colleagues (2011) used an IAT to show that followers with lower self-esteem were more likely to associate autocratic
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28 Handbook of methods in leadership research leadership with positive valence and democratic leadership with negative valence, while followers with higher self-esteem were more likely to associate democratic leadership with positive valence and autocratic leadership with negative valence. This class of measures is especially useful for detecting individual differences in implicit attitudes and beliefs about specific leaders or general leadership style. However, it should be noted that stimuli representing concepts are often greatly simplified in priming or IAT tasks for the ease of presenting these stimuli on the computer interface (Uhlmann et al., 2012). For example, Schoel and colleagues (2011) represented autocratic leadership and democratic leadership with five words related to autocracy and five words related to democracy respectively. They obtained these words from a pilot test. This means that pilot tests have to be conducted to validate representative stimuli for the targeted constructs, and that these measures may not be well suited for capturing attitudes toward nuanced and complex targets. Also, the IAT is based on the relative comparisons between multiple target constructs in a 2 × 2 fashion typically (e.g., autocratic vs democratic leadership, and positive vs negative valence). Yet, symmetrical comparisons that fit this matrix may not always be available. Finally, leadership scholars may consider interpretation-based measures if they hope to assess complex social beliefs and motives. Most interpretation-based measures are partially structured, and allow complex thoughts to be captured (Uhlmann et al., 2012). When administering open-ended qualitative measures like the TAT, leadership scholars can adapt the coding scheme to capture multiple constructs of interest from participants’ responses. For example, Jacobs and McClelland (1994) administered the TAT to entry-level managers and coded their responses for their need for power, need for achievement, need for affiliation, and activity inhibition. They also developed a scheme to further code the power motives into themes of reactive power and resourceful power, which illustrates the malleability of interpretation-based measures. Can the Implicit Measure be Flexibly Adapted to Assess My Construct of Interest? Next, leadership scholars have to consider whether and how they are going to adapt and administer the selected measure in their research. Implicit measures may offer greater flexibility than explicit measures (Uhlmann et al., 2012). Accessibility-based implicit measures (e.g., word-completion tasks, lexical decision tasks) and association-based implicit measures (e.g., priming, IAT) are relatively easy to modify to measure other constructs. This process is usually straightforward, and involves finding alternative
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Implicit measures for leadership research 29 stimuli, such as word fragments for word completion tasks or representative words for IATs, to replace the original stimuli (see Scott & Brown, 2006 for an example of a modified lexical decision task, and Schoel et al., 2011 for an example of a modified IAT). Scripts for administering these tasks in computer software are also readily available online.5 Nevertheless, researchers should always conduct pilot tests to validate the new stimuli and to make sure that the replacement of the stimuli does not compromise on the psychometric properties of the task. In contrast, the modification of interpretation-based implicit measures is less straightforward because researchers have to create new scenarios or vignettes or develop new coding schemes in order to measure a different construct. This process is extremely laborious, and may take numerous pilot tests and several years to complete (Uhlmann et al., 2012). What Do the Scores Represent? After leadership researchers modify the implicit measure to assess their construct of interest, they should make sure they understand what the scores of the focal measure represent. For example, in an IAT, quicker reaction times represent a stronger association between two constructs, but in a Stroop task, quicker reaction times indicate lower interference and weaker accessibility of the targeted construct. A clear understanding of what the scores represent will allow leadership scholars to draw accurate conclusions from their research findings. Does the Implicit Measure Predict My Outcome of Interest? Next, leadership scholars should ask whether their selected implicit measure predicts their outcome of interest. Existing studies demonstrated mixed evidence regarding the predictive validities of implicit measures (Lilienfeld et al., 2000; Uhlmann et al., 2012). In general, our review of existing leadership studies that have used implicit measures illuminates the promise that implicit measures hold for leadership research. For example, priming task success or failure influences ratings of leadership effectiveness (Larson, 1982) and priming followers with approving or disapproving leader faces also influences their evaluation of their research ideas (Baldwin et al., 1990). In addition, IATs seem to exhibit strong predictive validities on a wide array of leadership-related associations such as white ethnicity with high leadership potential and minority ethnicity with low leadership potential (Gündemir et al., 2014), men with high authority and women with low authority (Rudman & Kilianski, 2000), and autocratic leadership with positive valence among low-esteem
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30 Handbook of methods in leadership research individuals and democratic leadership with positive valence among highesteem individuals (Schoel et al., 2011). Interpretation-based measures such as the TAT also exhibit robust predictive validity on several behavioral outcomes (Spangler, 1992). However, it should also be noted that some measures, specifically accessibility-based measures such as sentence completion tasks, have typically been used as dependent measures of constructs such as motivation to lead (Miner et al., 1991), leader adjustment (Fitzsimmons & Marcuse, 1961), and attitude toward supervisor (Burwen et al., 1956; Konst et al., 1999) instead of as predictors. This again suggests the need for leadership researchers to select an implicit measure based on the conceptualization of their construct of interest to ensure that the measure is able to appropriately assess what the researcher intends to capture. Is the Implicit Measure Reliable? After addressing the questions above, researchers should consider whether their selected implicit measure is reliable internally and temporally because this ultimately has implications for the accuracy of research findings. For implicit measures involving quantitative scoring such as the CRT, researchers can assess internal and temporal reliability by calculating the internal consistency and test-retest reliability (Uhlmann et al., 2012). To assess the internal reliability of implicit measures that rely on reaction times such as the Stroop task and IAT, researchers can separate the task into multiple trials or blocks, and calculate the extent to which the reaction times are consistent across the trials or blocks (e.g., see Greenwald et al., 1998 for an example of assessing the internal reliability of the IAT). In contrast, it is more difficult to assess the internal reliabilities of interpretation-based implicit measures and it is common for interpretation-based measures to exhibit lower internal and test-retest reliabilities than their explicit counterparts (Uhlmann et al., 2012). Relatively lower internal and test-retest reliabilities may have arisen in part due to participants’ perceived need to respond differently to each image or scenario presented in an interpretation-based task, and to provide a different response each time they participate in the task (Winter & Stewart, 1977). However, low testretest reliabilities may also indicate that the measure is assessing a state instead of a stable trait (Blair, 2002). We encourage leadership researchers to refer to well-established reliability information reported in existing literature to help them select the most reliable measure out of their chosen category of implicit measures, and to conduct further empirical tests if they encounter low internal or test-retest reliabilities with their selected implicit measures.
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Implicit measures for leadership research 31 Is the Implicit Measure Adaptable Across Cultures? Many leadership studies have non-English-speaking samples (Farh & Cheng, 2000; Liden, 2012), which requires translating and back-translating explicit measures into parallel forms in different languages (Brislin, Lonner, & Thorndike, 1973). However, there are unique challenges associated with adapting implicit measures into other languages (Uhlmann et al., 2012). For example, it may be difficult or sometimes impossible to find equivalent words that represent the construct of interest in another language for use in an IAT (Brislin et al., 1973). In addition, implicit measures such as the word completion task are not feasible in certain languages (e.g., Chinese, Korean) due to the different presentation of words in these languages (i.e., where words are not formed from alphabetical letters). However, there are some measures that have proven adaptable across cultures, such as interpretation-based measures that rely on images or vignettes (Uhlmann et al., 2012). Nevertheless, in these cases, researchers still have to expend considerable effort into modifying and validating their coding or scoring schemes to take into account cultural differences in interpretation and expressions. We advise leadership scholars to review existing literature to gain a better understanding of the cross-cultural generalizability of existing findings related to their constructs of interest. This may help to inform them of how to develop a culturally appropriate coding scheme that allows them to capture their constructs of interest accurately in another culture. What Resources on Implicit Measures are Available for My Research? The administration of some implicit measures requires the use of computers and specific software (Uhlmann et al., 2012). Researchers should then make sure that computers are available to their sample and make arrangements to install the necessary software in the computers. This can be a problem if leadership scholars are conducting field studies where respondents complete the implicit measure in their workplace. If the organization does not allow researchers to install the software to administer the implicit measure, researchers may have to abort their plans of using those implicit measures that are administered through a computer (e.g., IAT) and rely on measures that can be administered on pen and paper (e.g., word/sentencecompletion tasks, TAT, or CRT). Nevertheless, while resources may shape the decision of which implicit measure to use, leadership researchers should always ensure that the theoretical appropriateness of the selected implicit measure is not compromised. In the next section, we describe an empirical example in which a lexical
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32 Handbook of methods in leadership research decision task (LDT) was used to examine how priming individuals to think about leadership influenced the accessibility of implicit leader-related attributes, conducted by Djurdjevic and Johnson (2009).
AN EMPIRICAL EXAMPLE: ASSESSING LEADERSHIP SCHEMAS USING AN IMPLICIT MEASURE According to leader categorization theory, individuals associate certain attributes with leaders through prior experiences and social interactions with leaders, and these attributes are activated automatically to help individuals distinguish leaders from non-leaders (Lord et al., 1984). Although the theory is founded on implicit leadership theories, scholars have mostly tested it using explicit measures (e.g., participants rate how representative attributes are of leaders; Epitropaki & Martin, 2004; Lord et al., 1984; Offermann, Kennedy, & Wirtz, 1994). Doing so creates a disconnect between theory and method because implicit leadership theories posit that leader categorization operates at an implicit level, yet explicit measures require participants to engage in deliberative information processing as they access, search, and retrieve leader-related attributes from memory. Djurdjevic and Johnson’s (2009) empirical example addresses this oversight by using an implicit measure to assess the accessibility of leaderrelated attributes. The use of an implicit measure in this case offers three key advantages. First, using an implicit measure to test implicit leadership theory creates a match between theory and method because the implicit measure enables us to capture individuals’ implicit schemas with little or no biased responding due to social desirability or impression management concerns (Fazio & Olson, 2003). Second, Djurdjevic and Johnson’s (2009) findings shed light on the extent to which implicit and explicit measures of leader schemas converge. If discrepancies exist, it would imply that leader-related attributes derived from studies that used explicit measures might not be an accurate reflection of individuals’ implicit schemas. Third, Djurdjevic and Johnson’s (2009) findings may help reconcile conflicting findings from past research that used explicit measures to study implicit leader schemas. For example, certain attributes, such as masculinity and dominance, were found to be prototypical of leaders by some researchers (e.g., Lord et al., 1986; Offermann et al., 1994) yet anti-prototypical by others (e.g., Epitropaki & Martin, 2004). Such discrepancies may be the result of response biases. Djurdjevic and Johnson (2009) developed several hypotheses based on
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Implicit measures for leadership research 33 implicit theories of leader categorization. First, they hypothesized that participants primed with the concept of leadership (vs those in a control condition) would respond more quickly on the LDT to leader-related prototypical words associated with intelligence, dynamism, and dedication (H1), but more slowly to anti-prototypical words associated with tyranny (H2). This is because priming individuals to think about leadership should activate the prototypical attributes they associate with leaders, thus eliciting greater accessibility and faster recognition of these words (Lord et al., 2001). In contrast, priming leadership should suppress anti-prototypical attributes, resulting in less accessibility and slower recognition of these words (ibid.). Djurdjevic and Johnson (2009) also predicted that participants primed with the concept of leadership (vs control) would respond more quickly to the attribute words “visionary,” “innovative,” “resourceful,” and “flexible” (H3). Although these attributes are not included on explicit measures of leadership schemas, they were expected to be relevant because effective leaders foresee and adapt to changing environmental demands and provide guidance for others (Bass, 1985; Zaccaro, Foti, & Kenny, 1991). Last, Djurdjevic and Johnson (2009) also examined whether attributes associated with masculinity, dominance, and sensitivity would be more or less accessible when participants were primed with the concept of leadership. Earlier leader categorization studies found that masculinity and dominance are prototypical leader attributes (Lord et al., 1984; Offermann, et al., 1994), whereas more recent studies classified them as anti-prototypical (Epitropaki & Martin, 2004, 2005). These mixed findings might reflect a change in the concept of leadership over time (e.g., expecting leaders to be more communal) or possibly a method artifact (e.g., pressure on participants to respond in an egalitarian manner; Eagly & Karau, 2002). Djurdjevic and Johnson (2009) also hypothesized that familiarity with leadership roles, need for cognition, and participants’ gender would moderate the effects of the leadership prime on reaction times to attribute words. Specifically, familiarity with leadership roles implies highly developed and elaborate implicit leadership schemas, hence making it easier to access this information (H4). Conversely, individuals with a high need for cognition tend to react more slowly because they are predisposed to engage in deliberate and systematic thinking rather than automatic and implicit processing (H5) (Cacioppo & Petty, 1982). Finally, Lord et al. (2001) proposed that attributes such as “masculine” and “decisive” might enjoy greater accessibility among male followers while attributes such as “sensitive” and “helpful” might enjoy greater accessibility among female followers. Hence, we hypothesized the relationship between leadership prime and reaction times on agentic and communal leadership attributes to be stronger for males and females, respectively.
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34 Handbook of methods in leadership research To test these predictions, Djurdjevic and Johnson (2009) recruited a sample of 199 undergraduate participants from a large university in USA. According to prior research, implicit leadership schemas develop early in life (Lord & Maher, 1991), and are similar across students and full-time employees (Epitropaki & Martin, 2004). After reporting to the lab, participants completed explicit measures of familiarity with leadership roles and need for cognition. They were then randomly assigned into an experimental or control condition. In the experimental condition, participants were primed by instructing them to think and write a half-page description about leadership and the traits, behaviors, and qualities of an ideal leader. In the control condition, participants thought and wrote about their favorite city. Priming is an accessibility-based implicit measure, and helps to increase the accessibility of a target concept, in this case leadership, to individuals. This enabled the authors to examine whether the increased accessibility of the leadership concept influenced their reactions to leaderrelated attributes. After the priming task, participants completed a computer-based LDT, created using SuperLab Pro 2.0 (Cedrus Corp., 1999). The LDT is an accessibility-based measure that enabled Djurdjevic and Johnson (2009) to assess how accessible leader-related attributes were in participants. Leaderrelated attribute words validated in prior studies (Epitropaki & Martin, 2004; Lord et al., 1984; Offermann et al., 1994) were used to develop the LDT content. These words are presented in Table 2.1. Participants were presented with letter strings one at a time on the screen, and they had pressed “” if it was a non-word (e.g., “smeet”). A faster reaction time indicates greater accessibility of the word at implicit levels. Participants responded to a total of 98 letter strings, consisting of 31 prototypical (e.g., “intelligent”) and anti-prototypical (e.g., “domineering”) words, ten non-leader Table 2.1 Content of ILTs as captured by explicit measures Authors
Characteristics
Lord, De Vader, & Alliger (1986) Offermann, Kennedy, & Wirtz (1994)
Prototypical: Intelligence, masculinity, & dominance Prototypical (primary): Intelligence, dedication, charisma, & sensitivity Prototypical (secondary): Masculinity, tyranny, strength, & attractiveness Prototypical: Intelligence, dedication, charisma, & sensitivity Anti-prototypical: Masculinity & tyranny
Epitropaki & Martin (2004)
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Implicit measures for leadership research 35 Table 2.2 S upported EFA factor structure of the leader attribute word reaction times Factor:
Sensitivity
Intelligence
Motivation
Dominance
Tyranny
Words:
Sincere Helpful Understanding Sensitive
Clever Knowledgeable Intelligent Educated
Motivated Dedicated Strong Energetic Diligent Dynamic
Domineering Aggressive Loud Masculine Dominant
Selfish Conceited Manipulative
attribute words (e.g., “funny”), six non-attribute words (e.g., “roof”), and 47 non-words (e.g., “renkylo”). The data were cleaned following the guidelines recommended by Bassili (2001; e.g., deleted incorrect responses, and removing reaction times that fell outside of three standard deviations of participants’ mean reaction time). Djurdjevic and Johnson (2009) conducted an exploratory factor analysis (EFA) with the reaction times, and their results supported a fivefactor solution, presented in Table 2.2. These results demonstrated some consistency with those involving explicit measures of leadership schemas (Epitropaki & Martin, 2004), where factors corresponding to intelligence and sensitivity emerged. A third factor also emerged that combined the dedication and dynamism factors from Epitropaki and Martin’s (2004) study, which the authors labeled “motivation.” However, results also deviated from past findings because Epitropaki and Martin’s tyranny factor split into two factors in Djurdjevic and Johnson’s data. They labeled one factor “tyranny” (e.g., selfish, manipulative), and the other factor “dominance” (e.g., domineering, loud). Attributes from Epitropaki and Martin’s masculinity factor (e.g., masculine, aggressive) also loaded on this dominance factor. These findings suggest moderate convergence between the factor structures derived from implicit and explicit leadership schema measures. Hypotheses were tested via t-tests to compare the mean reaction times of participants in the experimental versus control conditions. As hypothesized, participants primed with leadership responded more quickly to attribute words associated with intelligence, dynamism, and dedication (e.g., clever, intelligent, motivated, dedicated, energetic). Conversely, participants in the experimental condition responded more slowly to antiprototypical attribute words (e.g., selfish, manipulative) as compared to control participants. As expected, Djurdjevic and Johnson (2009) also found that participants responded more quickly to the words “visionary,” “innovative,” “resourceful,” and “flexible” when primed to think about leadership.
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36 Handbook of methods in leadership research Of greater interest were the findings pertaining to the words that have received mixed support as prototypical of leaders, namely “masculinity,” “dominance,” and “sensitivity.” When explicit measures of leadership schemas are used, these three attributes are sometimes rated as prototypical and at other times as anti-prototypical. When the LDT was used, Djurdjevic and Johnson (2009) found that participants in the experimental condition responded faster to dominance-related words (e.g., domineering, aggressive, masculine) than individuals in the control condition, which suggests that dominance is prototypical of leadership. Djurdjevic and Johnson’s findings that involved an implicit measure therefore parallel earlier studies (e.g., Lord et al., 1984; Offermann et al., 1994), which concluded that dominance is prototypical of leadership. The results for sensitivity were less clean: some sensitive-related words appeared prototypical (e.g., sincere, sensitive) whereas others did not (e.g., understanding, helpful). Last, Djurdjevic and Johnson (2009) found support for the moderating effects of familiarity with leadership roles and need for cognition, and partial support for gender. Specifically, participants with high (vs low) familiarity with leadership roles or with low (vs high) need for cognition tended to have faster reaction times when responding to prototypical leadership attributes. With respect to gender, the authors found that female participants appeared to have slightly faster reaction times to communal attribute words (e.g., words associated with sensitivity) while males had slightly faster reaction times to agentic attribute words (e.g., words associated with dominance), but several differences did not reach statistical significance. Djurdjevic and Johnson’s (2009) study provides a nice illustration of how implicit measures can be employed for leadership research. An implicit measure was needed in this case because the process of leader categorization and the content of leadership schemas are believed to occur automatically and exist outside of awareness. While Djurdjevic and Johnson’s results demonstrated some parallels across implicit and explicit measures (e.g., intelligence and motivation are both prototypical), they also revealed some notable divergence. Specifically, sensitivity did not appear to be a prototypical attribute, and dominance and masculinity emerged as prototypical rather than anti-prototypical attributes. These findings suggest that leadership schemas examined using explicit methods of measurement do not necessarily generalize to leadership schemas at implicit levels. These findings highlight a need to understand the source of the explicit– implicit dissociation. In particular, the divergence could be a result of responding with bias (e.g., social desirability or impression management)
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Implicit measures for leadership research 37 on explicit measures. If that is the case, designing explicit measures that can reduce response bias may reconcile the explicit–implicit dissociation. Alternatively, the divergence could be due as a result of leadership schemas existing and operating in parallel on the explicit and implicit levels. If so, future research should seek to examine the relative importance of explicit and implicit leadership schemas in the prediction of follower cognitions and behaviors. The explicit–implicit dissociations also suggest the possibility that certain prototypical leader attributes have been overlooked. This is because much leadership categorization research used attributes established in prior studies to inform the design of their explicit or implicit measures. For example, Epitropaki and Martin (2004) used attributes from Lord et al. (1984) and Offermann et al. (1994) for inclusion in their explicit measure, and Djurdjevic and Johnson’s (2009) study used attributes from those prior studies for inclusion in their LDT. However, these prior studies have used explicit measures exclusively, suggesting the likelihood that some attributes operating primarily on the implicit levels may not have been captured. This suggests that the pool of leadership-related attributes that is commonly used in leader categorization studies may be incomplete or unrepresentative. Indeed, attributes that did not emerge in prior studies, such as flexibility and resourcefulness, did emerge as prototypical leader attributes when an implicit measure was used, thus providing support for this argument. In conclusion, we urge researchers to select measures designed to capture constructs at an implicit level if there is reason to believe that the focal leadership-related processes and/or content are believed to exist outside of awareness. While Djurdjevic and Johnson’s (2009) discrepant findings may be discouraging with respect to the generalizability of findings from leadership categorization research utilizing explicit measures, we believe these explicit–implicit dissociations add to our understanding of the topic. Hence, we hope this empirical example inspires greater use of implicit measures in future leadership research. With this in mind, we propose some directions for future research in the following section.
SCOUTING THE FUTURE OF IMPLICIT MEASURES FOR LEADERSHIP RESEARCH We devote this final section to discussing potential avenues for future leadership research using implicit measures. To date, researchers have typically assessed the main effects of implicit measures, often assessing whether implicit measures have incremental predictive validity beyond
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38 Handbook of methods in leadership research explicit measures (e.g., Johnson & Saboe, 2011; Johnson et al., 2010; Johnson, Chang, Meyer, Lanaj, & Way, 2013). However, as implicit research advances, we expect future research will explore how implicit and explicit processes interact (i.e., moderation), influence each other indirectly (i.e., mediation), or change over time (i.e., iteration). The following discussion is organized based on these three directions. Moderation Interactions between implicit and explicit measures can occur in several ways (Uhlmann et al., 2012). First, explicit measures may facilitate or inhibit the influence of implicit measures on outcomes. In particular, McClelland and colleagues (1989) argued that implicit and explicit processes reflect two different motivational systems that operate in parallel. Individuals may not always be aware of their implicit or unconscious intentions, or they may even engage in explicit cognition and behavior to guard against their true (implicit) intentions (ibid.). Such processes give rise to the likelihood of explicit traits or behaviors to facilitate or inhibit the influence of implicit measures on outcomes. For example, Schoel and colleagues (2011) demonstrated how self-reported explicit self-esteem moderated the implicit attitudes individuals had toward democratic and autocratic leadership. Extending this work, leadership scholars may find it fruitful to look at how various explicit measures of personality traits or environmental factors influence individuals’ implicit attitudes toward different types of leadership styles or different leaders. For example, employees in a work team with a high power distance climate may have a positive implicit attitude towards authoritarian leadership, or employees with low agreeableness may show greater implicit intention to retaliate against an abusive supervisor. The move beyond examining the main or additive effects of implicit measures on their outcomes of variables will allow researchers to elucidate how explicit and implicit processes coexist and interact to shape leaders’ or followers’ cognition and behaviors. Second, leadership scholars may study whether and how individuals engage in explicit attempts to suppress or conceal their implicit intentions or motives. For example, individuals with implicit biases against the minority actually showed greater explicit motivation to control prejudice and provided overly favorable ratings to minority targets (Olson & Fazio, 2004). These findings illustrate an explicit overcompensation effect where individuals attempt to hide their socially undesirable implicit attitudes. It will be interesting to replicate this study in the context of leadership. For example, will leaders who are implicitly biased against minorities engage in greater explicit motivation to control their prejudice and provide extra
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Implicit measures for leadership research 39 resources to their minority followers? These findings will certainly shed some light on how biases impact leader ratings of performance or leader intention to support or promote certain followers. In addition, leadership researchers can conduct research to uncover factors that moderate the correlations between implicit and explicit measures. For example, Nosek (2005) looked at the moderators of the correlations between IAT and self-report measures across a diverse array of attitudes, and demonstrated that the correlations between IAT and selfreport measures were lower in domains where social desirability concerns were high, such as in gender or racial stereotyping as opposed to consumer preferences. Applied to leadership research, we believe it will be rewarding to study such convergence and divergence of implicit and explicit measures in order to better identify specific leadership domains in which implicit measures are necessary for an accurate and comprehensive understanding of the construct of interest. Mediation Leadership scholars may also consider using a mediation framework to study how implicit and explicit processes causally influence one another to shape cognitions and behaviors. In particular, explicit processes may influence outcomes indirectly through the effects on implicit processes (Uhlmann et al., 2012). In support of this notion, affective events theory (Weiss & Cropanzano, 1996) proposes that episodes of work events elicit automatic affective responses in employees through an implicit process, and that such affective responses go on to influence explicit work behaviors. Based on this theory, scholars could examine how leader–member interactions activate implicit affective responses in both leaders and followers, and how such automatic responses spillover to impact subsequent attitudes or behaviors of the two parties. In addition, implicit processes may influence outcomes indirectly through their effects on explicit processes. For example, self-concept is often implicitly and spontaneously activated, and this activation potentially informs individuals in their explicit evaluation and reports of their self-concepts (Peters & Gawronski, 2011). In turn, explicit measures of self-concepts influence conscious thoughts and behaviors (ibid.). Therefore, leadership researchers may study how implicit values and beliefs influence the explicit judgments that individuals form in different leadership scenarios. For example, followers with low implicit self-esteem may unconsciously prefer leadership styles or behaviors that offer them greater structure and clearer instructions as compared to followers with high implicit self-esteem. In turn, their implicit self-esteem may shape how they explicitly ascribe causal attributions to
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40 Handbook of methods in leadership research certain leadership styles or behaviors, and predict their subsequent behaviors toward certain leaders. Future research on implicit–explicit mediation frameworks will help extend leadership theory and research. Iteration Finally, future leadership research may benefit from examining the iterative properties of implicit processes. One direction is to examine whether content and processes at the implicit levels are consistent over time and across situations. Scholars have argued that implicit attitudes develop early in childhood and remain stable through life (Wilson, Lindsey, & Schooler, 2000). However, several studies have also demonstrated that implicit attitudes may be malleable and can be easily manipulated by situational factors (Lowery, Hardin, & Sinclair, 2001). Such mixed findings present intriguing research questions for leadership scholars. For example, how stable are followers’ implicit leadership schemas, and to what extent do they change when followers switch leaders, jobs, or organizations? As noted by Lord et al. (2001), individuals form implicit schemas about leadership attributes through socialization and life experiences. Hence, it is possible for their implicit schemas to change as they work under different leaders or as they work in jobs or organizations with different leadership cultures. For example, a follower working under an abusive leader who actually manages to get work done effectively for a prolonged period of time may gradually disassociate tyranny as an anti-prototypical attribute. The question then falls on investigating when implicit leadership schemas start to get modified, and whether the implicit schemas can only accommodate incremental changes or also radical changes. This study of the stability of implicit leadership schemas will certainly help to expand our understanding of how implicit leadership content develops and operates to influence behaviors over time. Another potential direction is to study the temporal relationships between implicit processes, explicit processes, and behavior. Implicit processes, explicit processes, and behaviors are typically multidirectional and play out in iteration over time (Uhlmann et al., 2012). Hence, researchers may investigate whether and when implicit attitudes reflect explicit behaviors, and vice versa. For example, can newly adopted explicit cognitions and behaviors shape implicit cognitions? Applied more specifically to leadership research, an example would be whether explicit social movements such as gender equality shape followers’ implicit evaluations of lesbian, gay, bisexual, and transgender (LGBT) leaders. Such research questions could be addressed by longitudinal designs where researchers
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Implicit measures for leadership research 41 assess the constructs of interest with both implicit and explicit measures at multiple time points, and statistically analyse whether the data fits the argument of implicit attitudes at Time 1 predicting behavior at Time 2 or behavior at Time 1 predicting implicit attitude at Time 2. These findings will prove generative for understanding the potential reciprocal roles of implicit and explicit processes.
CONCLUSION Most existing leadership research is based on explicit aspects of followers and leaders and the interactions between them that are captured at a deliberative level within awareness. However, our discussion of implicit leadership content and processes suggests that implicit measures can potentially address the limitations posed by explicit leadership measures and tap into unique cognitive and affective processes that operate outside individuals’ awareness. We also believe that the relevance and applicability of implicit theories and measures will only increase in the future as complex organizational demands place higher cognitive load on leaders and followers and push a significant proportion of information processing to below conscious levels. Our literature review attests to the applicability of implicit theories in leadership research, especially in the areas of leader categorization, leadership motives, leader evaluation, and self-concepts of leaders and followers. However, our review also reveals that leadership scholars appear to be lagging in their adoption of implicit measures in their research, and that a lot more can be done with such measures in the field. We believe the use of implicit measures will enable more leadership phenomena to be empirically and accurately tested. As a means to encourage the use of implicit measures for leadership research, we provided a comprehensive set of criteria that provides actionable knowledge to guide leadership scholars in their selection and adoption of implicit measures for research. We also presented an example study that employed an implicit measure of leadership. Finally, we concluded by proposing potential directions for future leadership research that may benefit from the use of implicit measures. We hope this chapter will serve as a jump-off point for organizational researchers to explore and utilize implicit measures of leadership, and inspire a program of research examining how implicit processes influence leadership-related issues in the workplace.
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42 Handbook of methods in leadership research
NOTES 1. For example, http://www.millisecond.com/download/library/LexicalDecisionTask/; last accessed July 15, 2017. 2. For example, http://www.millisecond.com/download/library/Stroop/; last accessed July 15, 2017. 3. For example, http://www.millisecond.com/download/library/AffectivePriming/ or http:// www.millisecond.com/download/library/SubliminalPriming; last accessed July 15, 2017. 4. For example, http://www.millisecond.com/download/library/IAT; last accessed July 15, 2017. 5. For example, http://www.millisecond.com/download/library/; last accessed July 15, 2017.
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Implicit measures for leadership research 43 task performance. Journal of Personality and Social Psychology, 89(4), 469–487. doi: 10.1037/0022-3514.89.4.469 Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40(5), 642–658. doi: 10.1016/j.jesp.2004.02.003 Davies, M., & Gardner, D. (2013). A frequency dictionary of contemporary American English: Word sketches, collocates and thematic lists. New York: Routledge. Davies, P.G., Spencer, S.J., & Steele, C.M. (2005). Clearing the air: Identity safety moderates the effects of stereotype threat on women’s leadership aspirations. Journal of Personality and Social Psychology, 88(2), 276–287. doi: 10.1037/0022-3514.88.2.276 De Hoogh, A.H., Den Hartog, D.N., Koopman, P.L., Thierry, H., Van den Berg, P.T., Van der Weide, J.G., & Wilderom, C.P. (2005). Leader motives, charismatic leadership, and subordinates’ work attitude in the profit and voluntary sector. The Leadership Quarterly, 16(1), 17–38. doi: 10.1016/j.leaqua.2004.10.001 De Houwer, J., Teige-Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135(3), 347–368. doi: 10.1037/ a0014211 Djurdjevic, E., & Johnson, R. (2009). Putting the “implicit” in implicit leadership theory (ILT): Assessing ILTs using implicit measures. Paper presented at the 69th Academy of Management Annual Meeting, Chicago, IL. Eagly, A.H., & Karau, S.J. (2002). Role congruity theory of prejudice toward female leaders. Psychological Review, 109(3), 573–598. doi: 10.1037/0033-295X.109.3.573 Ebrahimi, B.P. (1997). Motivation to manage in Hong Kong: Modification and test of Miner Sentence Completion Scale-H. Journal of Managerial Psychology, 12(6), 401–414. doi: 10.1108/02683949710176151 Eden, D., & Leviatan, U. (1975). Implicit leadership theory as a determinant of the factor structure underlying supervisory behavior scales. Journal of Applied Psychology, 60(6), 736–741. doi: 10.1037/0021-9010.60.6.736 Epitropaki, O., & Martin, R. (2004). Implicit leadership theories in applied settings: Factor structure, generalizability, and stability over time. Journal of Applied Psychology, 89(2), 293–310. doi: 10.1037/0021-9010.89.2.293 Epitropaki, O., & Martin, R. (2005). From ideal to real: A longitudinal study of the role of implicit leadership theories on leader–member exchanges and employee outcomes. Journal of Applied Psychology, 90(4), 659–676. doi: 10.1037/0021-9010.90.4.659 Farh, J.-L., & Cheng, B.-S. (2000). A cultural analysis of paternalistic leadership in Chinese organizations. In J.T. Li, A.S. Tsui & E. Weldon (Eds.), Management and organizations in the Chinese context (pp. 84–127). London: Palgrave Macmillan. Fazio, R.H., & Olson, M.A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54(1), 297–327. doi: 10.1146/annurev. psych.54.101601.145225 Fitzsimmons, S., & Marcuse, F. (1961). Adjustment in leaders and non-leaders as measured by the sentence completion projective technique. Journal of Clinical Psychology, 17(4), 380–381. doi: 10.1002/1097-4679(196110)17:43.0.CO;2-A Greenwald, A.G., & Banaji, M.R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4–27. doi: 10.1037/0033-295X.102.1.4 Greenwald, A.G., McGhee, D.E., & Schwartz, J.L. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. doi: 10.1037/0022-3514.74.6.1464 Gündemir, S., Homan, A.C., De Dreu, C.K., & Van Vugt, M. (2014). Think leader, think white? Capturing and weakening an implicit pro-white leadership bias. PloS One, 9(1), e83915. doi: 10.1371/journal.pone.0083915 Hanges, P., Lord, R., & Dickson, M. (2000). An information-processing perspective on leadership and culture: A case for connectionist architecture. Applied Psychology, 49(1), 133–161. doi: 10.1111/1464-0597.00008 Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-analysis
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Implicit measures for leadership research 45 Liden, R.C. (2012). Leadership research in Asia: A brief assessment and suggestions for the future. Asia Pacific Journal of Management, 29(2), 205–212. doi: 10.1007/s10490-011-9276-2 Liden, R.C., & Maslyn, J.M. (1998). Multidimensionality of leader–member exchange: An empirical assessment through scale development. Journal of Management, 24(1), 43–72. doi: 10.1177/014920639802400105 Liden, R.C., Wayne, S.J., Zhao, H., & Henderson, D. (2008). Servant leadership: Development of a multidimensional measure and multi-level assessment. The Leadership Quarterly, 19(2), 161–177. doi: 10.1016/j.leaqua.2008.01.006 Lilienfeld, S.O., Wood, J.M., & Garb, H.N. (2000). The scientific status of projective techniques. Psychological Science in the Public Interest, 1(2), 27–66. doi: 10.1111/1529-1006.002 Locke, E.A. (1991). The essence of leadership. New York: Lexington Books. Lord, R.G., & Brown, D.J. (2001). Leadership, values, and subordinate self-concepts. The Leadership Quarterly, 12(2), 133–152. doi: 10.1016/S1048-9843(01)00072-8 Lord, R.G., & Maher, K.J. (1991). Leadership and information processing: Linking perceptions and performance. New York: Routledge. Lord, R.G., Binning, J.F., Rush, M.C., & Thomas, J.C. (1978). The effect of performance cues and leader behavior on questionnaire ratings of leadership behavior. Organizational Behavior and Human Performance, 21(1), 27–39. doi: 10.1016/0030-5073(78)90036-3 Lord, R.G., Brown, D.J., Harvey, J.L., & Hall, R.J. (2001). Contextual constraints on prototype generation and their multilevel consequences for leadership perceptions. The Leadership Quarterly, 12(3), 311–338. doi: 10.1016/S1048-9843(01)00081-9 Lord, R.G., De Vader, C.L., & Alliger, G.M. (1986). A meta-analysis of the relation between personality traits and leadership perceptions: An application of validity generalization procedures. Journal of Applied Psychology, 71(3), 402–410. Lord, R.G., Diefendorff, J.M., Schmidt, A.M., & Hall, R.J. (2010). Self-regulation at work. Annual Review of Psychology, 61(1), 543–568. doi: 10.1146/annurev.psych.093008.100314 Lord, R.G., Foti, R.J., & De Vader, C.L. (1984). A test of leadership categorization theory: Internal structure, information processing, and leadership perceptions. Organizational Behavior and Human Performance, 34(3), 343–378. doi: 10.1016/0030-5073(84)90043-6 Lowery, B.S., Hardin, C.D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81(5), 842–855. doi: 10.1037/0022-3514.81.5.842 McClelland, D.C., & Boyatzis, R.E. (1982). Leadership motive pattern and long-term success in management. Journal of Applied Psychology, 67(6), 737–743. McClelland, D.C., Koestner, R., & Weinberger, J. (1989). How do self-attributed and implicit motives differ? Psychological Review, 96(4), 690–702. doi: 10.1037/0033-295X.96.4.690 McClelland, J.L., McNaughton, B.L., & O’Reilly, R.C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457. doi: 10.1037/0033-295X.102.3.419 Megargee, E.I. (1969). Influence of sex roles on the manifestation of leadership. Journal of Applied Psychology, 53(5), 377–382. doi: 10.1037/h0028093 Mervis, C.B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32(1), 89–115. doi: 10.1146/annurev.ps.32.020181.000513 Meyer, D.E., & Schvaneveldt, R.W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227–234. doi: 10.1037/h0031564 Miner, J.B. (1978). Twenty years of research on role-motivation theory of managerial effectiveness. Personnel Psychology, 31(4), 739–760. doi: 10.1111/j.1744-6570.1978.tb02122.x Miner, J.B., & Smith, N.R. (1982). Decline and stabilization of managerial motivation over a 20-year period. Journal of Applied Psychology, 67(3), 297–305. doi: 10.1037/0021-9010.67.3.297 Miner, J.B., Chen, C.-C., & Yu, K. (1991). Theory testing under adverse conditions: Motivation to manage in the People’s Republic of China. Journal of Applied Psychology, 76(3), 343–349. doi: 10.1037/0021-9010.76.3.343
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46 Handbook of methods in leadership research Morgan, C.D., & Murray, H.A. (1935). A method for investigating fantasies: The thematic apperception test. Archives of Neurology and Psychiatry, 34(2), 289–306. doi: 10.1001/ archneurpsyc.1935.02250200049005 Nosek, B.A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134(4), 565–584. doi: 10.1037/0096-3445. 134.4.565 Offermann, L.R., Kennedy, J.K., & Wirtz, P.W. (1994). Implicit leadership theories: Content, structure, and generalizability. The Leadership Quarterly, 5(1), 43–58. doi: 10.1016/1048-9843(94)90005-1 Olson, M.A., & Fazio, R.H. (2004). Trait inferences as a function of automatically activated racial attitudes and motivation to control prejudiced reactions. Basic and Applied Social Psychology, 26(1), 1–11. doi: 10.1207/s15324834basp2601_1 Parks-Stamm, E.J., Heilman, M.E., & Hearns, K.A. (2008). Motivated to penalize: Women’s strategic rejection of successful women. Personality and Social Psychology Bulletin, 34(2), 237–247. doi: 10.1177/0146167207310027 Peters, K.R., & Gawronski, B. (2011). Mutual influences between the implicit and explicit self-concepts: The role of memory activation and motivated reasoning. Journal of Experimental Social Psychology, 47(2), 436–442. doi: 10.1016/j.jesp.2010.11.015 Robinson, M.D., & Neighbors, C. (2006). Catching the mind in action: Implicit methods in personality research and assessment. In M. Eid & E. Diener (Eds.), Handbook of multimethod measurement in psychology (pp. 115–125). Washington, DC: American Psychological Association. Rosch, E. (1978). Principles of categorization. In E. Rosch & B.B. Lloyd (Eds.), Cognition and categorization. Hillsdale, NJ: Erlbaum. Rudman, L.A., & Kilianski, S.E. (2000). Implicit and explicit attitudes toward female authority. Personality and Social Psychology Bulletin, 26(11), 1315–1328. doi: 10.1037/0022-3514.81.5.856 Rudman, L.A., & Phelan, J.E. (2010). The effect of priming gender roles on women’s implicit gender beliefs and career aspirations. Social Psychology, 41(3), 192–202. doi: 10.1027/1864-9335/a000027 Rudman, L.A., Ashmore, R.D., & Gary, M.L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81(5), 856–868. doi: 10.1037/0022-3514.81.5.856 Rush, M.C., & Russell, J.E. (1988). Leader prototypes and prototype-contingent consensus in leader behavior descriptions. Journal of Experimental Social Psychology, 24(1), 88–104. doi: 10.1016/0022-1031(88)90045-5 Rush, M.C., Thomas, J.C., & Lord, R.G. (1977). Implicit leadership theory: A potential threat to the internal validity of leader behavior questionnaires. Organizational Behavior and Human Performance, 20(1), 93–110. doi: 10.1016/0030-5073(77)90046-0 Schoel, C., Bluemke, M., Mueller, P., & Stahlberg, D. (2011). When autocratic leaders become an option – Uncertainty and self-esteem predict implicit leadership preferences. Journal of Personality and Social Psychology, 101(3), 521–540. doi: 10.1037/a0023393 Scott, K.A., & Brown, D.J. (2006). Female first, leader second? Gender bias in the encoding of leadership behavior. Organizational Behavior and Human Decision Processes, 101(2), 230–242. doi: 10.1016/j.obhdp.2006.06.002 Spangler, W.D. (1992). Validity of questionnaire and TAT measures of need for achievement: Two meta-analyses. Psychological Bulletin, 112(1), 140–154. doi: 10.1037/0033-2909.112.1.140 Stahl, M.J., Grigsby, D.W., & Gulati, A. (1985). Comparing the job choice exercise and the multiple choice version of the Miner Sentence Completion Scale. Journal of Applied Psychology, 70(1), 228–232. doi: 10.1037/0021-9010.70.1.228 Stajkovic, A.D., Locke, E.A., & Blair, E.S. (2006). A first examination of the relationships between primed subconscious goals, assigned conscious goals, and task performance. Journal of Applied Psychology, 91(5), 1172–1180. doi: 10.1037/0021-9010.91.5.1172 Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. doi: 10.1037/h0054651
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Implicit measures for leadership research 47 Taylor, S.E. (1983). Adjustment to threatening events: A theory of cognitive adaptation. American Psychologist, 38(11), 1161–1173. doi: 10.1037/0003-066X.38.11.1161 Tepper, B.J. (2000). Consequences of abusive supervision. Academy of Management Journal, 43(2), 178–190. doi: 10.2307/1556375 Tomkins, S.S., & Tomkins, E.J. (1947). The thematic apperception test: The theory and technique of interpretation. New York: Grune & Stratton. Uhlmann, E.L., Leavitt, K., Menges, J.I., Koopman, J., Howe, M., & Johnson, R.E. (2012). Getting explicit about the implicit: A taxonomy of implicit measures and guide for their use in organizational research. Organizational Research Methods, 15(4), 553–601. doi: 10.1177/1094428112442750 Wegner, D.M., & Bargh, J.A. (1998). Control and automaticity in social life. In D.T. Gilbert, S.T. Fiske & G. Lindzey (Eds.), The handbook of social psychology (4th ed., Vols. 1 and 2, pp. 446–496). New York: McGraw Hill. Weiss, H.M., & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. In B.M. Staw & L.L. Cummings (Eds.), Research in organizational behavior: An annual series of analytical essays and critical reviews (Vol. 18, pp. 1–74). Greenwich, CT: Elsevier Science/JAI Press. Wilson, T.D., Lindsey, S., & Schooler, T.Y. (2000). A model of dual attitudes. Psychological Review, 107(1), 101–126. doi: 10.1037/0033-295X.107.1.101 Winter, D.G. (1991). A motivational model of leadership: Predicting long-term management success from TAT measures of power motivation and responsibility. The Leadership Quarterly, 2(2), 67–80. doi: 10.1016/1048-9843(91)90023-U Winter, D.G., & Stewart, A.J. (1977). Power motive reliability as a function of retest instructions. Journal of Consulting and Clinical Psychology, 45(3), 436–440. doi: 10.1037/0022-006X.45.3.436 Zaccaro, S.J., Foti, R.J., & Kenny, D.A. (1991). Self-monitoring and trait-based variance in leadership: An investigation of leader flexibility across multiple group situations. Journal of Applied Psychology, 76(2), 308–315. doi: 10.1037/0021-9010.76.2.308 Ziegert, J.C., & Hanges, P.J. (2005). Employment discrimination: The role of implicit attitudes, motivation, and a climate for racial bias. Journal of Applied Psychology, 90(3), 553–562. doi: 10.1037/0021-9010.90.3.553
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3. P uppet masters in the lab: experimental methods in leadership research Eric F. Rietzschel, Barbara Wisse and Diana Rus
Leadership researchers typically aim to understand leadership processes and the impact of leadership on a variety of organizationally important outcome variables, such as individual and group emotions, motivation, cognitions, and performance. Hence, most leadership theories either explicitly propose or implicitly assume cause-and-effect relationships (cf. Yukl, 2010). Yet, a surprisingly large proportion of the amassed knowledge in the field stems from correlational field surveys (cf. Avolio, Reichard, Hannah, Walumbwa, & Chan, 2009; Bass, 1990). These correlational designs not only preclude conclusions regarding causality, but also (unnecessarily) limit researchers in developing a more nuanced understanding of leadership processes and their effects. Consequently, there is an obvious need for conducting experimental studies in leadership research (cf. Avolio et al., 2009; Brown & Lord, 1999; Day, Zaccaro, & Halpin, 2004). Our discussion of experimental methods is not a call for its undifferentiated use. Clearly, the choice of research method depends upon the nature of the question one tries to answer. In addition, in line with those organizational researchers who advocated the benefits of employing multiple research methodologies (cf. Jick, 1979), we believe that the field of leadership research would benefit from an increasing use of diverse research methods, both qualitative and quantitative. One of those methods could be experimental in nature, and the strengths of this method make it worthy of consideration. In this chapter, we therefore specifically focus on experimental research methods as an empirical approach that can help us gain a deeper understanding of leadership processes. In the sections that follow, we first review the most commonly used experimental paradigms in leadership research, such as individual laboratory experiments, group experiments, field experiments, and vignette studies. Next, we address some of the strengths and potential pitfalls associated with employing experimental methods in leadership research. Finally, we discuss the future of leadership research by highlighting some recent developments in the field and pointing out opportunities for further development and refinement of these methods. 48
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Experimental methods in leadership research 49
EXPERIMENTAL METHODS USED IN LEADERSHIP RESEARCH First, we will turn our attention to commonly used experimental paradigms and methods in leadership research and provide some examples. Specifically, we will focus on laboratory experiments with individual participants, group experiments, field experiments, and vignette experiments. Given the increasing popularity of computer-mediated paradigms (especially for laboratory experiments with individual participants and group experiments), we will devote specific attention to their use. However, as an introduction, we will start by briefly describing the prototypical experiment in the behavioral sciences. Usually, participants (individuals or groups) are invited to a laboratory, where they are asked to read, watch, or listen to certain instructions and/ or other stimulus materials (such as instructions given by a task “leader,” who may be present in person, or perhaps gives his or her messages through video or audio recordings, or in written form). These materials are often used to serve as independent variables. Different versions of these instructions or stimulus materials (the experimental manipulation) are used for different groups of participants (the experimental conditions). When the experiment has multiple independent variables that are manipulated, these are usually combined in a factorial design, where researchers “cross” the independent variables in such a way that each possible combination occurs. For example, suppose that researchers are interested in the combined effects of leadership style and feedback valence (this study would then have two independent variables). They could manipulate leadership style by presenting participants with either a transformational leader or a transactional leader, and they could manipulate feedback valence by exposing participants to either positive feedback or negative feedback. Thus, in this case the researchers would use a factorial design with 2 × 2 5 4 conditions (transformational-positive, transactional- positive, transformational-negative, and transactional-negative). Usually, participants are randomly assigned to one of these conditions (Rosenthal & Rosnow, 2008; Shadish, Cook, & Campbell, 2002) and typically perform certain tasks and/or are asked to respond to a number of questionnaires. The behavior of the participants, their task performance, and/ or questionnaire responses usually function as the main dependent variables. Statistical analysis (such as t-tests, analysis of variance, or regression analysis) is conducted to assess whether participants responded differently in the various conditions. Because everything in the experiment, except the actual manipulation, is kept constant and controlled, and because random assignment to conditions greatly diminishes the likelihood of pre-existing
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50 Handbook of methods in leadership research differences between conditions, existing effects can only reasonably be explained as being a function of the experimental manipulation(s). In the following, we focus on different categories or types of experimental paradigms that are often used in leadership research, and provide some examples. Laboratory Experiments with Individual Participants A laboratory experiment with individual participants largely follows the script of the typical experiment explained above. A good example of such an experiment is a study by Howell and Frost (1989). In this experiment, the researchers manipulated leadership style, exposing participants to charismatic, structuring, or considerate leadership. They combined this in a factorial design with a manipulation of group productivity norms, and looked at the effects on individuals’ attitudes and their performance on a decision-making task. This experiment had an elaborate set-up, involving trained confederates (actors or individuals who work for the researcher) who played two different roles (leaders and co-workers). The “leaders” were trained to follow pre-developed scripts (which specified verbal and non-verbal behaviors) for one of the different leadership styles in all their interactions with the study participants. Further, the “co-workers” were trained to display behaviors indicative of either a high or a low group productivity norm (i.e., acting enthusiastic and eager in the case of a high productivity norm, or acting bored and unwilling in the case of a low productivity norm). The experimental task consisted of an in-basket exercise and participants were subsequently asked to fill out some questionnaires. The study results showed that charismatic leadership led to good work outcomes and favorable attitudes to the leader and group, regardless of group productivity norms. In contrast, considerate and structuring leadership only led to good work outcomes and favorable attitudes when participants worked in a group with a high productivity norm. Clearly, this experiment required substantial investments in terms of time, energy, effort and money. In addition to setting up the experimental rooms and creating all of the materials (instructions, protocols, experimental tasks, and measurement instruments), the confederates needed to be trained to display the behaviors required by the experiment in a sufficiently uniform, yet believable manner. Often, such resources are simply not available. One way to reduce this resource investment is to make use of computers, rather than confederates, and this (in combination with the control and standardization advantages that computers bring) has probably led to the upsurge of computer-mediated experiments. Indeed, probably the most popular type of experimental paradigm used in leadership
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Experimental methods in leadership research 51 research is the computer-mediated laboratory experiment with individual participants. Computer-mediated variants A typical computer-mediated experiment requires participants (for instance, students or people holding a leadership position) to come to the laboratory, where they are placed in individual cubicles equipped with a computer. All subsequent instructions and/or stimuli are presented on the computer screens, and the software program records the dependent measures. An “interaction” with other participants (such as a leader or a follower) is often simulated (with messages from the “other participant” having been pre-programmed) in order to make participants believe that they are actually interacting with other individuals. Computer-mediated experiments are (often) easier to run than experiments involving actual interactions between individuals, but this does not mean that they cannot employ complex designs – in fact, using such designs may even be easier in a computer-mediated setting, since the experimental situation is easier to standardize and control. Rus, Van Knippenberg, and Wisse (2010; Study 1) report one example of such an experiment. The purpose of this study was to test the hypothesis that, with higher leader power, leaders’ self-serving behaviors would be determined more by internal states, and less by external cues. Participants (in this case undergraduate students) were seated in individual cubicles with computers, and were led to believe that they were the leader of a four-person group engaged in a computer-mediated task. This study used no less than three different manipulations. First, the researchers manipulated power, by providing participants with either more or less coercive power in the simulated group task (i.e., giving them the opportunity to fire or reprimand subordinates, or not giving them this power). Second, effective leadership beliefs (internal states) were manipulated by providing participants with a description of either a self-serving leader or a group-serving leader, and subsequently asking them to list five reasons why this leader would be effective in motivating his or her subordinates (thus instilling either self-serving or group-serving leadership beliefs in participants). Third, performance information (external cues) was manipulated by presenting participants with bogus feedback on their task performance as compared to that of their subordinates (they either did better or worse than their subordinates). These manipulations were combined in a factorial design, and participants were randomly assigned to one of the eight experimental conditions. The main dependent variable, self-serving behavior, was assessed by asking participants to divide a total of 500 points that the team could earn between themselves and their employees. Each point counted
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52 Handbook of methods in leadership research as one lottery entry for several €50 prizes. Hence, the more points they self-awarded, the higher their chances of winning one of the prizes. In line with the researchers’ expectations, the results indicated that in conditions of high power, leaders (i.e., the participants) were more self-serving when they endorsed self-serving beliefs than when they endorsed group-serving beliefs, while performance information had no effect on leader behavior. In other words, their behavior was guided more by their internal state than by external cues. The opposite was the case in conditions of low power. In experiments on leader behavior such as the one described above, participants are placed in a leader role and their behaviors, affective states and/or cognitions are subsequently assessed. In other computer-mediated experiments, participants are assigned a subordinate or follower role. They are presented with a leader (for instance via e-mails, messages or film clips) and their reaction to that leader (or their performance on a subsequent task) is assessed (e.g., Damen, Van Knippenberg, & Van Knippenberg, 2008b; Grant & Hoffmann, 2011; Van Kleef, Homan, Beersma, Van Knippenberg, & Van Knippenberg, 2009). Take, for instance, the experiments reported by Venus, Stam, and Van Knippenberg (2013). In these experiments, the hypothesis was that follower performance would depend on the match between the emotions expressed by a leader, and the regulatory focus (Higgins, 1997) implied in the leaders’ communication: followers were expected to perform best when the expressed emotions fit the regulatory focus of the leaders’ messages. Participants in these studies were shown a video clip of a leader. Each video clip showed a leader that communicated either a promotion-oriented message (e.g., referring to the importance of being “able to be flexible under fast changing conditions” and the benefits of “enthusiastic, creative subordinates that are able to cope with the complex problems of today”; Venus et al., 2013, p. 57) or a prevention-oriented message (e.g., referring to the importance of not being “inflexible and slow under fast changing conditions” and the undesirability of “conservative and bored subordinates that are not able to cope with the complex problems of today”). Moreover, this leader did so while displaying different emotional states (i.e., enthusiasm, agitation, or frustration). After participants had watched the video, they engaged in a performance task (specifically, a memory task that was presented as a test of mental ability). Venus et al. (2013) found that, as expected, performance was highest when leaders’ regulatory orientation matched their emotional display (for instance, when a promotion orientation was accompanied by enthusiasm). A recent development in computer-mediated experiments is conducting experiments via Internet platforms. Such experiments do not require participants to come to a laboratory at all; instead people can partake
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Experimental methods in leadership research 53 in the study at home or at work. Given that more and more people are online and that the speed of Internet connections keeps increasing, Internet recruitment methods have become increasingly popular among researchers. Although running experiments online has the drawback of relinquishing some experimental control (see below), research has shown that data obtained with Internet platforms like Amazon’s Mechanical Turk (MTurk) or ClearVoice are as reliable as those obtained via traditional methods (Buhrmester, Kwang, & Gosling, 2011; Mason & Suri, 2012; Paolacci, Chandler, & Ipeirotis, 2010).1 Similarly to laboratorybased computer-mediated experiments, online experiments often establish a bogus connection with “other participants” to make participants believe that they are actually interacting with other individuals. Group Experiments Sometimes, the focus of leadership research is not so much on how individuals respond to experimental manipulations, but on how groups of individuals respond to them (for example, when studying leadership in teams). This might be the case when researchers are interested in individual-level variables (such as motivation or performance) among individuals who happen to work within teams (and hence might influence each other’s responses), but more often researchers are specifically interested in the effects of leadership on variables at the team level, such as team climate (e.g., Gil, Rico, Alcover, & Barrasa, 2005) or team innovation (Eisenbeiss, Van Knippenberg, & Boerner, 2008). One possibility is to aggregate all responses to the team level, thus using the team as the unit of analysis. However, analytical techniques such as multilevel regression (e.g., Hox, 2010; also see Yammarino & Gooty, Chapter 10 this volume) allow researchers to combine variables at the individual and the team level in a single model (e.g., testing cross-level interactions between team-level variables such as climate and individual-level variables such as personality). Whenever the research question concerns variables that occur only at the team level, or that are likely to be significantly affected by the team context, researchers should consider the use of a group study. Take, for instance, a study conducted by Sy, Cȏté, and Saavedra (2005). Their study focused on the effect that the mood of a leader would have on the affective tone of the group (the “collective,” aggregated mood state of the group) and on group processes (such as coordination and investment of effort). To investigate this, they had 56 pre-existing student groups participate in their study. In each group, one randomly chosen member was assigned the leader role. To manipulate the mood of these leaders, they were asked to watch one of two versions of an eight-minute video clip,
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54 Handbook of methods in leadership research away from their group. Leaders in the positive mood condition viewed a humorous clip of David Letterman, while leaders in the negative mood condition viewed part of a TV documentary about social injustice and aggression. Thereafter, leaders were reunited with their groups and were given some time to interact and plan for the upcoming task, which was to erect a tent while wearing a blindfold. Immediately after this planning stage, group members engaged in the actual task. Group processes were coded in vivo as groups worked on the task. Group affective tone was calculated by averaging the scores of individual mood measurements within groups. The results showed, among other things, that groups had a more positive affective tone and exhibited more coordination when leaders were in a positive mood than when leaders were in a negative mood. Analyses also suggested that group affective tone mediated the effect of leader mood on group coordination. Computer-mediated variants Similarly to experiments with individual participants, group experiments are sometimes also computer-mediated and some of those experiments are even conducted via Internet platforms. In these cases, an actual (rather than simulated) connection with other participants is established to ensure group interaction. Carton, Murphy, and Clark (2014; Study 2) conducted a nice example of such an experiment. The goal was to study the combined effects of two aspects of leader communication: the degree of vision imagery (e.g., describing products as “crafted flawlessly” versus “made to perfection”) and the number of values communicated by the leader (such as “customer satisfaction,” “profitability,” and “integrity”). Specifically, the researchers wanted to test whether the combination of strong vision imagery and a small number of values would lead to better performance than other combinations. Both were experimentally manipulated, but while vision imagery was manipulated as a between-subjects factor (each participant was exposed to messages that were either strong or weak in imagery), the number of values was manipulated as a within-subjects factor (each participant was exposed to a message with a high number of values and to a message with a low number of values; the order of these messages was counterbalanced). The researchers recruited employees via the research platform ClearVoice, and assigned them randomly to one of the conditions. Participants were then placed into virtual teams of three members, and each member was given a different task in the development of a new toy for a toy company. All participants were told that it was important that their actions were congruent with the leader’s statements regarding the vision and values of the company. The quality of the toy design was the dependent variable; the sharedness of goal percep-
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Experimental methods in leadership research 55 tions within the team and coordination were the hypothesized mediators. Results showed that when leaders used strong imagery in combination with a low number of values, teams had more shared goals and better coordination, which in turn predicted better task performance. Field Experiments Although the examples discussed above take place in different settings (e.g., the laboratory versus online), none of them actually studies leaders and subordinates in their natural environment: the organization. This is what field experiments do: a field experiment aims to examine leadership processes in naturally occurring environments rather than in the laboratory. Field experiments are sometimes seen as having higher external validity than laboratory experiments, but it is often more difficult to control extraneous variables than in the lab (see below for a more extensive discussion). Moreover, it is not always possible to use true random assignment in field experiments. For example, when the manipulation entails training or an intervention (see example below), organizations may want to co-decide who gets to be trained, or which teams get assigned to an intervention condition. This effectively turns the study into a quasiexperiment, which of course limits its internal validity. The most common manipulation in field experiments on leadership is some form of leader training/development, where leaders are either assigned to a certain training program or not, and the subsequent behavior, cognitions, or feelings of their subordinates (or of the leaders themselves) are then assessed (see Avolio et al., 2009). A fairly recent example comes from Martin, Liao, and Campbell (2013), who conducted a field experiment in the United Arab Emirates. The experiment addressed the effects of two kinds of leadership (directive and empowering leadership) on task performance and proactive behaviors. Leaders who participated in the study were assigned to one of two training conditions (for directive or empowering leadership), or to a control condition without any training. Leaders were asked to keep daily logs, and subordinates (from the leaders’ work units) and customers were asked to fill out several surveys on, among other things, satisfaction with the leader, perceived task proficiency of the leaders’ work units, and proactivity of the leaders’ work units. These surveys were filled out both before and after the leadership training period (a pre-test/post-test design). Thus, the complete study design not only allowed the researcher to assess differences between the training conditions and the control group, but also to test for actual changes in the dependent variables over time. Results indicated that both directive and empowering leadership
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56 Handbook of methods in leadership research increased work unit core task proficiency. Notably, directive leadership only enhanced proactive behaviors in work units that were highly satisfied with their leaders, whereas empowering leadership had stronger effects on both core task proficiency and proactive behaviors for work units that were less satisfied with their leaders. One advantage of field experiments is that they focus on variables that are directly relevant to organizations, such as actual performance in work units. This can be an important “selling point” when contacting organizations for possible participation in a field experiment, and it is also considered a strength in the research community (e.g., by editors and reviewers). Nevertheless, the heavy time investment and the difficulties in establishing experimental control make the field experiment the most demanding of all experimental methods in leadership research. Vignette Experiments If field experiments represent one end of the “ease of use” continuum in experimental research, vignette experiments probably represent the other. A vignette experiment, sometimes also called a scenario experiment, presents a hypothetical situation, to which research participants are asked to respond. For example, they can be asked to indicate how they would perceive the presented situation or persons, or how they expect they would feel or behave in that situation. Of course, asking people how they think they would respond is not the same as measuring actual responses in a given situation, so although these kinds of experiments are conducted quite frequently in leadership research, they are usually part of a series of studies. Mostly, they are used to check whether a previously found result is robust and can be replicated by using different methods, or as an initial test of a possible causal relation that would then be replicated using a more elaborate experimental setting. For instance, having demonstrated a causal relation in lab experiments with student participants, one may conduct a vignette study with respondents from a working population to demonstrate that the obtained results generalize to this target population. Because vignette studies require very little in the way of experimental materials, they can easily be used in a variety of settings, such as the lab, the field, or even online. Then again, because vignette studies present participants with a hypothetical situation rather than immersing them in an actual task, they are more suited for measuring hypothetical reactions (i.e., how participants think they would behave in a certain situation) than for assessing actual reactions (such as work performance). Again, this means that vignette studies are best used in combination with other, more
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Experimental methods in leadership research 57 elaborate studies, especially when researchers are interested in testing behavioral hypotheses. An example of this approach is presented in Van Knippenberg and Van Knippenberg (2005), who conducted a vignette experiment on leader self-sacrifice (the degree to which leaders place the interests of the work group above their own) and leader prototypicality (the degree to which a leader is representative of a work group’s identity). The authors expected that self-sacrificing leaders would be considered to be more effective and able to push subordinates to a higher performance level than non-selfsacrificing leaders – especially if these leaders were not very prototypical (the underlying reasoning being that highly prototypical leaders get more “leeway” from their workgroup anyway, and hence do not need to display self-sacrificing behavior). Having first established these results in a computer-mediated laboratory experiment, the authors then replicated their main results in a vignette study, followed by two survey studies. In the vignette study, participants (business school students) were told that they would read about a situation in which leadership played a role, that they were to imagine themselves being in that particular situation, and that they were to answer the subsequent questions accordingly. Participants were then handed the scenario. The scenario asked them to envision that, having graduated, they had gone to work for an international consulting agency with a very good reputation. Depending on the experimental condition, their leader in that organization was portrayed as being high or low in group prototypicality. For example, in the low prototypicality condition participants read that “the leader was somewhat of an ‘outsider’ in the team, that he/she was very different from other team members and that he or she had a different background, different interests, and a different attitude toward life and work than the other team members” (Van Knippenberg & Van Knippenberg, 2005, p. 31). In contrast, participants in the high prototypicality condition read that “the leader was very representative of the kind of persons in the team, that he was very similar to the other team members, and that he had a similar background, similar interests, and a similar attitude toward life and work as the other team members.” Another paragraph in the scenario described the leader’s behavior as either self-sacrificing (vs non-self-sacrificing) by giving (vs not giving) examples of self-sacrificial behavior displayed by that leader. Participants were then asked to assess the effectiveness of the leader. In line with the other studies, results indicated that self-sacrificing leaders were seen as more effective than non-self-sacrificing leaders, but only when the leaders were low in prototypicality. The authors argue that the replication over multiple studies bolsters confidence in the finding, especially because these studies used
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58 Handbook of methods in leadership research different methodologies (i.e., laboratory experiment, scenario experiment, and cross-sectional survey). In short, there are many ways to run a leadership experiment, employing different settings (e.g., lab, field, or online), materials (e.g., confederates, computerized instructions, or scenarios), or respondents (employees, undergraduate students, or online panel respondents). Researchers can pick the type of experiment they want to do, depending on what the research question or program requires and which resources are available. In order to facilitate this choice, it may also be helpful to think about some more general advantages and pitfalls associated with the experimental method.
STRENGTHS AND PITFALLS OF EXPERIMENTS No single research method is the optimal choice for every research question or setting, and researchers need to make an informed choice as to which method best suits their goals and means. In this section, we will therefore discuss the main advantages and (possible) disadvantages of the experimental method in leadership research. Advantages of Experimental Research in Leadership Internal validity The most important advantage of experiments is their high internal validity: the combination of systematic manipulation (making sure that only one aspect of the instructions or experimental situation differs between conditions, hence ruling out so-called procedural confounds) and random assignment (which minimizes the chance of pre-existing differences between conditions, hence ruling out person-related confounds) makes it possible to eliminate alternative explanations for the effect under study, and hence allows the researcher to draw causal conclusions (Rosenthal & Rosnow, 2008; Shadish et al., 2002; Stone-Romero, 2002). Other research designs, such as correlational, qualitative, or even quasi-experimental designs (in which participants are assigned to different conditions, but not fully randomly), do not have this advantage and hence always lead to weaker conclusions with regard to causality. Certain leader behaviors may be associated with certain outcomes, or may – in a statistical sense – predict certain outcomes, but in the absence of truly experimental research, they cannot be concluded to cause or even contribute to those outcomes, since alternative explanations (e.g., a third variable, or reverse causation) cannot be ruled out. Thus, for example, a
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Experimental methods in leadership research 59 field survey study finding that, on the whole, supervisory close monitoring behavior (constantly keeping close tabs on employees; Zhou, 2003) tends to be associated with lower employee motivation and satisfaction (e.g., Rietzschel, Slijkhuis, & Van Yperen, 2014) could indicate a causal relationship, but might also be explained by, say, the presence of an unfavorable team climate (influencing both supervisor behavior and employee dissatisfaction), or by a causal relation in the reverse direction (e.g., demotivated versus motivated employees may well elicit different kinds of leader behaviors). Only by conducting an experiment in which leadership behaviors are systematically manipulated and participants are randomly assigned to conditions (i.e., to different leader behaviors), can actual causal conclusions be drawn. Testing interventions The ability to draw causal conclusions is not just important for theoretical reasons (gaining a complete and accurate understanding of the phenomenon under study; Mook, 1983) but also from a more applied perspective. Designing effective interventions (such as managerial training; e.g., Arthur, Bennett, Edens, & Bell, 2003; Collins & Holton, 2004) requires accurate knowledge of the factors that contribute to (or hinder) leader effectiveness. Organizational change efforts based on studies that do not rule out the possibility of third variables or inverse causation might turn out to be a considerable waste of time and money (e.g., Spector, 2010), and in the worst case may leave both leaders and followers disillusioned and give rise to change cynicism (Thundiyil, Chiaburu, Oh, Banks, & Peng, 2015). Moreover, testing the effectiveness of interventions (e.g., if we want to see whether a certain training indeed had the desired effect) is a causal question in itself, requiring (if possible) the use of experimental methods (Cascio & Aguinis, 2011). However, as remarked above, a real test of causality requires random assignment, and this is often not possible, especially in the context of interventions. Organizations are not always willing to allow true random assignment to intervention versus control conditions because some teams or units may experience more problems than others and hence may be seen to have a stronger need for the intervention. In such cases, it is important to measure and partial out possible differences between the experimental conditions other than the manipulation itself (see Shadish et al., 2002, for a further discussion of quasi-experimental methods). Experimental control and isolating specific factors Although experimental methods are not necessarily limited to a particular setting, many experimental studies in organizational psychology,
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60 Handbook of methods in leadership research i ncluding the field of leadership, are conducted in the laboratory (Bryman, 2011; Robson, 2011; Stone-Romero, 2009), or in what Stone-Romero (2009) calls special purpose (SP) settings. It is difficult to conduct true experiments in naturalistic settings such as organizations (non–special purpose or NSP settings; Stone-Romero, 2009), because an experiment can easily interfere with the day-to-day business on-site (e.g., Robson, 2011), and the field setting makes it difficult to achieve the desired level of experimental control. Moreover, it is almost impossible to prevent people in different conditions (but working within the same organization) from communicating with each other about the study, thus possibly leading to spillover between conditions. For example, Blumberg and Pringle (1983) report on an experiment (strictly speaking, a quasi-experiment, since people were not randomly assigned to conditions) with the introduction of autonomous workgroups in a Pennsylvania coal mine. Some groups were given high autonomy over their work tasks and scheduling, and received extensive training for this new way of working. Other groups kept working as usual (the control groups). Over time, serious tensions arose between the members of the autonomous groups and the control groups because members of the control groups felt deprived of attention and rewards. Eventually, although the intervention did seem to have the desired effects in that the autonomous workgroups improved on several aspects in their functioning (such as better intragroup coordination), the experiment had to be terminated. Although this example is rather extreme, it does illustrate why most experimental researchers tend to prefer SP settings (such as the lab) over NSP settings (such as organizations): they simply allow for more control, and – as explained above – control is essential to the experimental method. The systematic manipulation essential to true experiments further implies a relatively fine-grained focus on specific behaviors or other predictors, rather than on “clusters” of leadership behaviors that tend to cooccur. Experiments are aimed at identifying causal relationships, and this also means isolating (potential) causal factors, in order to find out which one actually contributes to the effect. Thus, researchers interested in the effects of a particular leadership style, such as charismatic leadership (e.g., Howell & Frost, 1989), will need to operationalize that style in such a way that (only) the crucial aspects are present in their manipulation (cf. Yukl, 1999). Moreover, since the independent variables in the study are at the discretion of the researchers, it is possible to study the effects of factors or behaviors that may be relatively rare or difficult to identify in reallife settings, for example because they do not take place very openly or because employees may be reluctant to report them (such as certain kinds of abusive leadership; Tepper, 2007).
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Experimental methods in leadership research 61 Testing complex models Paradoxically, it is precisely the focus on single, specific predictors that allows researchers to systematically study their combined effects as well (e.g., Fisher, 1984). By orthogonally (i.e., independently) manipulating such predictors in a factorial design (Shadish et al., 2002), researchers can see whether the effects of one predictor depend on the level of another predictor (moderation), or whether these predictors independently (and possibly additively) exert their effects. As we have seen, many of the studies described in the previous section employed such factorial designs, and the results of these studies are informative in not just showing whether certain leadership behaviors are effective (e.g., providing followers with promotion-focused or prevention-focused messages; Venus et al., 2013), but also identifying boundary conditions for those benefits (such as congruence with a leader’s emotional expression). Further, independent variables can be manipulated in different ways: for example, rather than merely testing the effects of presence versus absence of certain behaviors or factors, it is also possible to test the effects of different levels of certain independent variables – which in turn makes it possible to test non-monotonic relations, such as inverted U-shaped relations (Grant & Schwartz, 2011), where a certain predictor leads to more positive outcomes at intermediate levels than at very low or very high levels. Low time investment In addition to the potential for unique information (e.g., regarding causality), experiments can also have a practical advantage: it does not necessarily take very long to run an experiment. Of course, this only goes for some types of experiments: as described above, vignette studies may be relatively easy to run quickly, whereas experiments with more highfidelity simulations of leadership and using behavioral coding (e.g., to study group processes, as in the study by Sy et al., 2005) may require much more preparation, as well as extensive data processing. Nevertheless, once the relevant materials (such as vignettes) have been developed, many experiments can be run in a relatively short time frame. In contrast, many organizational studies, even those using relatively uncomplicated designs such as correlational surveys, require much longer time periods for data collection: access to and cooperation from the organization need to be secured, participants need to be invited, and reminders need to be sent (e.g., Robson, 2011). All in all, this can easily take months – and even then it is often highly uncertain how many respondents the researcher will end up with. Thus, experiments allow the researcher a fast route to testing specific causal hypotheses. A next step, of course, could be to conduct field
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62 Handbook of methods in leadership research studies to see whether these causal effects indeed can be observed within actual organizations. Pitfalls of Experimental Research in Leadership Despite the very clear and important advantages of the experimental research method in leadership research, there are inevitably some drawbacks as well. Mirroring the advantages described above, many of these revolve around the classic counterpart to high internal validity: (supposedly) low external validity. We will discuss several of these pitfalls below, and critically discuss whether each of them forms as serious a risk to experimental research as is sometimes assumed. Lack of external validity Systematic manipulation and experimental control are essential to safeguarding internal validity, but at the same time they can put the study’s external validity (the ability to generalize to a broader array of populations and settings) at risk. Although all experiments share this disadvantage to a certain extent, it is most salient in lab experiments. A lab experiment by definition takes place in a highly artificial setting: simplified and controlled, without much obvious resemblance to organizational reality (Bryman, 2011; Fisher, 1984; Stone-Romero, 2009). Although this is, of course, precisely the point of doing a lab experiment, it does raise questions with regard to external validity. We cannot automatically assume that effects observed in the lab will be identical, or even of the same order of magnitude, in organizations. The effects might be moderated by all kinds of personal and organizational characteristics not taken into account in the experiment. For example, the involvement of participants in lab studies begins and ends with their participation in the experiment, there is no social context to speak of (except to the extent that it is part of the experimental situation), there are no serious consequences attached to participants’ behavior, group members (mostly) have no history of working together, and participants often have only limited behavioral or other response options at their disposal (Aronson, Ellsworth, Carlsmith, & Gonzales, 1990). All of this is obviously very different from organizational reality. Although this objection to (lab) experiments is often made, one may wonder whether the concern is theoretically and empirically justified. For example, it is doubtful whether studies conducted in a real-life setting, such as an organization, have higher external validity. Stone-Romero (2009, p. 308) argues that “even when studies are conducted in NSP settings, they typically involve non-representative samples of subjects, settings, and operational definitions of manipulations and/or measures.
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Experimental methods in leadership research 63 Thus, the external validity of such studies is suspect. The fact that they were conducted in NSP settings often does nothing to strengthen external validity inferences.” Organizations, teams, supervisors, and employees differ from setting to setting, so there is no reason to assume that research results can easily be generalized when they are collected in the field as opposed to the lab. In addition, Aronson et al. (1990) argue that it is too simple to state that experiments are not “realistic,” because there are different kinds of “realism.” Most objections against (lab) experiments are based on their low mundane realism: experimental settings do not resemble real-world settings in most ways. However, Aronson and colleagues point out that mundane realism is not always important: “The mere fact that an event looks like one that occurs in the real world does not imply that it is important in the study of human behavior. Many events that occur in the real world are boring or uninfluential” (Aronson et al., 1990, p. 70). What is important, they argue, is experimental realism: the experimental situation must be “believable” for the participants in order to have an impact on behavior (e.g., when studying the effects of feedback styles, participants must believe that they will actually receive feedback on their performance; see Shalley & Perry-Smith, 2001). Empirically, moreover, the results of “artificial” experiments and “realistic” field studies tend to be more aligned than critics of the experimental method assume (e.g., Anderson, Lindsay, & Bushman, 1999; Locke, 1986). Actually, with some creativity, it often is possible to come up with experimental manipulations and tasks that resemble the real-world situation in important ways. For example, researchers interested in competitive behavior in the workplace can actually have participants engage in a competitive task (or lead them to believe they are competing; e.g., Lee, Kesebir, & Pillutla, 2016, Studies 3 and 4), and researchers interested in sales performance may ask participants to do a task that simulates what they would have to do in a computer retail store (Damen, Van Knippenberg, & Van Knippenberg, 2008a). Although experimental realism arguably is more important than mundane realism, it is usually a good idea to try to combine the two to a certain extent, and researchers increasingly do so. Another important point is that even if experiments sometimes are low on external validity, this need not be a problem, because generalization may not be what the researchers are aiming for at that moment (Mook, 1983). In many experiments, the primary goal is to establish a causal link (e.g., to replicate correlational field data, to conduct an initial test of a tentative intervention, or to test a theoretical process explanation). In such cases, the artificiality of an experiment is not problematic, since generalization simply is not the immediate concern.
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64 Handbook of methods in leadership research Use of student samples Another potential problem is the pervasive use of undergraduate students (usually psychology or business students) as respondents. The reason for relying on these populations is practical: they are available in relatively large numbers, are easily accessible within the university context (where most of this research is done), and are available at low costs (students may participate for course credits or for modest financial reimbursement). The problem with relying on student samples is that students are likely to be different from the target population (leaders or followers) in a number of respects, and this might affect results. For example, because students typically have little work experience, let alone supervisory experience, they may find it difficult to relate to situations sketched in a vignette or scenario study. Notably, this again underscores the importance of experimental realism described above: researchers need to create an experimental situation that is meaningful to these participants, and that will allow them to study the phenomenon of interest in this particular sample. Moreover, as Highhouse and Gillespie (2009, p. 258) have pointed out: “The degree to which a sample matches the population of interest does not affect one’s ability to detect a relation between variables of theoretical significance, as long as that sample is unbiased on factors relevant to the research question.” For example, the fact that most undergraduate participants score above average (as compared to the general population) on measures of cognitive ability is not problematic as long as the phenomenon under study is unrelated to cognitive ability. These differences in cognitive ability do not imply that they will respond differently to, say, a manipulation of leadership style. Of course, not all manipulations, tasks, or measurement instruments are equally suited for use in all possible populations, and use of methods that are not suited to the population can lead to floor or ceiling effects (Osborne, 2013). Suppose, for example, that a researcher wants to test the hypothesis that a certain leadership style will lead to lower performance because of reduced employee effort, but uses an experimental task that happens to be extremely easy for the undergraduate participant population. Since the task is so easy for these participants, performing well on the task will require little if any effort, and the hypothesized effects will probably not be observed. Ultimately, however, this would not be an example of research with low external validity because of the participant population sampled, but rather an example of inadequately tested research materials. Low-impact manipulations of high-impact situations All psychological research is subject to ethical guidelines (American Psychological Association, 2010). This means that there are limits to the
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Experimental methods in leadership research 65 situations participants can be exposed to. As a consequence, some situations that may occur in organizations cannot be reproduced with sufficient fidelity in the lab. For example, experimental research on unethical or abusive leadership is limited by the impossibility and undesirability of exposing participants to serious forms of such unethical behaviors (which may, in extreme cases, include verbal and even physical violence). To be able to study such phenomena at all, researchers have to resort to “lowimpact” manipulations of the same phenomena that are deemed to elicit similar responses but that can still be considered ethical. The effects of such “low-impact” manipulations may be much more subtle than those that would occur in real-world settings. Luckily, the stringent control inherent in experiments often enables researchers to pick up such subtle effects, because extraneous noise is minimized (Aronson et al., 1990). Moreover, experiments conducted in a laboratory often allow researchers to monitor participants’ mood states and other reactions better (and in real time) than studies conducted in a field or online settings do. Short-term approaches to long-term phenomena As mentioned above, one advantage of (certain types of) experiments is that they can often be run within a short time frame. Such a short-term approach can work well when studying phenomena that have immediate effects, such as affective reactions to feedback (e.g., Niemann, Wisse, Rus, Van Yperen, & Sassenberg, 2014). However, it becomes more problematic when the phenomenon of interest unfolds over a longer period of time. Some outcomes may take longer periods of time to develop (e.g., burnout; Maslach, Schaufeli, & Leiter, 2001) and some processes require repeated interaction over a prolonged period of time (e.g., the development of leader–member exchange relationships; Graen & Uhl-Bien, 1995) before their effects are visible. Moreover, even if immediate effects can be observed, there is the question of how these might further develop over time. For example, an initial strong affective reaction to negative feedback may eventually be followed by the use of emotion regulation strategies (Gross, 1998), such as reappraisal (“this feedback will help me learn”), which may then lead to very different behavioral outcomes than the initial affective response would lead the researcher to suspect. Once again, whether or not this is problematic depends on the goal of the research. If the goal is to demonstrate that negative feedback can lead to certain immediate affective and attitudinal reactions (cf. Niemann et al., 2014), the use of a short-term paradigm is fully defensible. If, however, the goal is to present an account of how negative feedback affects subordinates’ feelings and behavior over a longer period of time, additional work will probably be required.
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66 Handbook of methods in leadership research In this section, we have given an overview of the main advantages and potential pitfalls of the experimental method in leadership research. As will have become clear, whether these advantages are realized, and the pitfalls avoided, depends on the researchers’ choice for a specific type of experiment, the population they want to use, the methodological rigor of the experiment (e.g., designing manipulations without procedural confounds, and attaining high experimental realism), and on the goals of the study (e.g., with regard to generalization). In addition, researchers may want to consider recent developments in experimental research: some types of experimental research have been made possible, or much easier, by technological developments. Other types of research are now appreciated less than they used to be, due to changes in methodological and statistical norms. In the next section, we therefore present a brief look towards the future.
A LOOK TO THE FUTURE OF EXPERIMENTAL METHODS IN LEADERSHIP RESEARCH Predicting future developments in any field is an educated guess at best, and unfounded speculation at worst. That being said, in this section we will briefly discuss a number of what may be the more impactful potential developments in the field of experimental methods in leadership research (also see Lord, Chapter 16 in this volume). Upsurge in Usage and Sophistication of Technology-enabled Experiments Computer-mediated and online experiments are already being performed in leadership research. A number of converging factors suggest that such technology-enabled experiments are not only here to stay, but most likely will also see an upsurge in both usage and sophistication. Firstly, the American Psychological Association (APA) has explicitly approved their use and publications employing such designs have increasingly entered mainstream journals. Given that the initial barrier to publication has been (partially) removed, this bodes well for leadership researchers aiming to make use of such designs in the future. Secondly, increased connectivity across the world combined with rising Internet speed and the proliferation of online platforms for experimental research provide the opportunity for leadership researchers to collect data they would not have been able to collect a decade ago. As a consequence, researchers now have access to a large, ready pool of participants who are demographically diverse (e.g., in terms of ethnicity, age, culture, work
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Experimental methods in leadership research 67 experience, etc.). Indeed, Buhrmester et al. (2011) found that MTurk samples were more demographically diverse than typical American college samples. Such increased diversity in participants would not only address the often-mentioned pitfall of using undergraduate students as experimental participants, but would also allow researchers to test a different set of hypotheses as it enables them to delve into understanding the effects of leadership across a range of diverse constituents. A more refined understanding of leadership processes, especially as they play out in cross- cultural contexts, would be welcome given the increasingly dispersed nature of work and teams. In addition, the increased speed and lower costs of running experimental research online would allow leadership researchers to more quickly iterate between theory development and experimentation (cf. Mason & Suri, 2012), therefore potentially contributing to faster advances in the field. Thirdly, technological advances such as the increased sophistication of online platforms, the upsurge of gamification simulations and virtual reality developments provide opportunities to study leadership processes in vivo, within a tightly controlled environment, without losing the “richness” of the context. For instance, the increased sophistication of online platforms affords leadership researchers the opportunity to conduct group experiments that establish real-time connections among a diverse set of participants, something we could not have done in the past. In addition, gamification simulations (Hamari & Koivisto, 2015) have started to gain ground as being part and parcel of leadership development programs (e.g., Deloitte, NTT Data), and surely could be designed to be used in the more traditional, controlled laboratory settings. Such set-ups could provide researchers with the opportunity to run field experiments in vivo that would provide a wealth of data and unique opportunities to study the effects of different leadership development interventions on employee motivation, behavior, and performance. Similarly, technological advances in virtual reality simulations would provide leadership researchers with the opportunity to study leadership processes as they play out, for instance, in group settings. Changes in Methodological and Statistical Norms Whereas traditionally the norms of what constitutes good practice in (experimental) psychology research have gradually evolved over time, the last few years have seen a dramatic shift in this respect. For instance, whereas articles based on single-source, cross-sectional data or single experiments with low sample sizes used to be prevalent in reputable journals as late as the 1980s, this is no longer the case. In this respect,
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68 Handbook of methods in leadership research there has been an increasing shift towards a norm of testing the robustness of findings via replication studies and across multiple methodologies (cf. Simmons, 2014; Stone-Romero, 2009). For instance, different APA divisions and other professional organizations, such as the Society for Personality and Social Psychology, have recently provided recommendations for improving the dependability of research such as the use of larger, more powerful sample sizes, reporting effect sizes and 95 percent confidence intervals, making available research materials necessary to replicate reported results, and encouraging publication of high-quality replication studies (Funder et al., 2014). Similarly, some journals have developed new guidelines for publication, specifically focusing on the vulnerabilities of null-hypothesis testing and the need for larger samples sizes. These shifts have major implications for leadership researchers engaging in experimental research. For one, conducting multiple studies, employing different methodologies, will be the new standard (if it is not already). In addition, even within the experimental methodology, a combination of different experimental paradigms, where the potential downsides of the one paradigm are compensated for by the potential upsides of the other, appears to emerge as the new norm. For instance, the results of an online experiment would benefit from being replicated within the lab and potentially in a vignette experiment. In sum, it seems that the norms and practices within our field are shifting towards an increased focus on safeguarding the robustness of our findings.
CONCLUDING THOUGHTS In this chapter, we have provided an overview of experimental research methods in leadership research, addressing commonly used methods and designs, potential advantages and pitfalls, and future developments that seem relevant for researchers who want to conduct a leadership experiment. In doing so, we have focused mostly on researchers with a strong interest in, but little experience with experimental research. Because successfully conducting experimental research of course requires extensive theoretical knowledge (e.g., regarding the nature and scope of the variables under study) and practical skills (e.g., regarding the construction of manipulations or task instructions), in this chapter we could only brush the surface of some of the major issues. Nonetheless, we hope to have provided the reader with an overview that is both inspiring and informative.
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NOTE 1. However, other researchers, such as Harms and DeSimone (2015), do point towards possible risks of MTurk samples, and a recent article by Zhou and Fishbach (2016) experimentally addresses the possible consequences of (selective) participant attrition in online samples.
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Experimental methods in leadership research 71 Mook, D.G. (1983). In defense of external invalidity. American Psychologist, 38(4), 379–387. doi: 10.1037/0003-066X.38.4.379 Niemann, J., Wisse, B., Rus, D., Van Yperen, N.W., & Sassenberg, K. (2014). Anger and attitudinal reactions to negative feedback: The effects of emotional instability and power. Motivation and Emotion, 38(5), 687–699. doi: 10.1007/s11031-014-9402-9 Osborne, J.W. (2013). Best practices in data cleaning. Thousand Oaks, CA: Sage. Paolacci, G., Chandler, J., & Ipeirotis, P.G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision making, 5(5), 411–419. Retrieved from https:// papers.ssrn.com/sol3/papers.cfm?abstract_id51626226 Rietzschel, E.F., Slijkhuis, M., & Van Yperen, N.W. (2014). Close monitoring as a contextual stimulator: How need for structure affects the relation between close monitoring and work outcomes. European Journal of Work and Organizational Psychology, 23(3), 394–404. doi: 10.1080/1359432X.2012.752897 Robson, C. (2011). Real world research (3rd ed.). Chichester, UK: Wiley. Rosenthal, R., & Rosnow, R.L. (2008). Essentials of behavioral research: Methods and data analysis. Boston, MA: McGraw-Hill. Rus, D., Van Knippenberg, D., & Wisse, B.M. (2010). Leader power and self-serving behavior: The role of effective leadership beliefs and performance information. Journal of Experimental Social Psychology, 46(6), 922–933. doi: 10.1016/j.jesp.2010.06.007 Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton, Mifflin and Company. Shalley, C.E., & Perry-Smith, J.E. (2001). Effects of social-psychological factors on creative performance: The role of informational and controlling expected evaluation and modeling experience. Organizational Behavior and Human Decision Processes, 84(1), 1–22. doi: 10.1006/obhd.2000.2918 Simmons, D.J. (2014). The value of direct replication. Perspectives on Psychological Science, 9(1), 76–80. doi: http://dx.doi.org/10.1177/1745691613514755 Spector, B. (2010). Implementing organizational change. Upper Saddle River, NJ: Prentice Hall. Stone-Romero, E.F. (2002). The relative validity and usefulness of various empirical research designs. In S.G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 77–98). Malden, MA: Blackwell Publishing. Stone-Romero, E.F. (2009). Implications of research design options for the validity of inferences derived from organizational research. In D.A. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods. London: Sage Publications. Sy, T., Côté, S., & Saavedra, R. (2005). The contagious leader: Impact of the leader’s mood on the mood of group members, group affective tone, and group processes. Journal of Applied Psychology, 90(2), 295–305. doi: 10.1037/0021-9010.90.2.295 Tepper, B.J. (2007). Abusive supervision in work organizations: Review, synthesis, and research agenda. Journal of Management, 33(3), 261–289. doi: 10.1177/0149206307300812 Thundiyil, T.G., Chiaburu, D.S., Oh, I.S., Banks, G.C., & Peng, A.C. (2015). Cynical about change? A preliminary meta-analysis and future research agenda. Journal of Applied Behavioral Science, 51(4), 429–450. doi: 10.1177/0021886315603122 Van Kleef, G.A., Homan, A.C., Beersma, B., Van Knippenberg, D., Van Knippenberg, B., & Damen, F. (2009). Searing sentiment or cold calculation? The effects of leader emotional displays on team performance depend on follower epistemic motivation. Academy of Management Journal, 52(3), 562–580. doi: 10.5465/AMJ.2009.41331253 Van Knippenberg, B., & Van Knippenberg, D. (2005). Leader self-sacrifice and leadership effectiveness: The moderating role of leader prototypicality. Journal of Applied Psychology, 90, 25–37. doi: 10.1037/0021-9010.90.1.25 Venus, M., Stam, D., & Van Knippenberg, D. (2013). Leader emotion as a catalyst of effective leader communication of visions, value-laden messages, and goals. Organizational Behavior and Human Decision Processes, 122(1), 53–68. doi: 10.1016/j.obhdp.2013.03.009 Yukl, G. (1999). An evaluation of conceptual weaknesses in transformational and charismatic leadership theories. The Leadership Quarterly, 10(2), 285–305. doi: 10.1016/ S1048-9843(99)00013-2
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4. Assessing leadership behavior with observational and sensor-based methods: a brief overview Alexandra (Sasha) Cook and Bertolt Meyer
In order to understand interactional and interpersonal behavior, researchers need to observe it (Kerlinger, 1973). This also applies to leadership research: as leadership is a social process occurring between at least two individuals (DeRue, 2011; DeRue & Ashford, 2010; DeRue, Nahrgang, & Ashford, 2015), observations of leadership behavior are necessary for understanding how leadership is exerted, how subordinates are affected by leader behavior, and which behaviors lead to the perception of effective leadership. Accordingly, observation studies have a long tradition in leadership research (e.g., Bass, 1949, 1954). Possible applications of observations in leadership research span from leaderless group discussions in laboratory settings (Bass, 1949) to field research on behavior within existing leadership hierarchies (Luthans & Lockwood, 1984). However, despite their long tradition, today’s leadership researchers rarely observe leadership anymore (Gioia & Sims, 1986). Between 1985 and 2009, less than 2 percent of the studies in The Leadership Quarterly employed observational methods (Hiller, DeChurch, Murase, & Doty, 2011). Hence, today’s mainstream leadership research typically tries to capture leadership behavior through questionnaires (e.g., Van Knippenberg & Sitkin, 2013), resulting in the assessment of leadership behaviors through subordinate perceptions (Yukl, 2013). Several researchers have criticized that this practice is unable to capture the interactive and interpersonal nature of leadership (e.g., Luthans & Lockwood, 1984; Sims & Manz, 1984) and that survey methods are prone to systematic biases due to the subjectivity of perceptions (Hansbrough, Lord, & Schyns, 2015) and due to implicit leadership theories (e.g., Lord, Foti, & De Vader, 1984; Melwani, Mueller, & Overbeck, 2012). The call for more applications and developments of behavioral methods and measures in leadership research has been getting increasingly louder (Baumeister, 2016; Baumeister, Vohs, & Funder, 2007; Furr, 2009). Furthermore, insights into actual leadership behavior are not only valuable for researchers, but also for the application in practice, for example in team training (Taylor, Russ-Eft, & Chan, 2005) and in many leadership 73
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74 Handbook of methods in leadership research training programs employing behavior modeling for improving leadership skills (Santos, Caetano, & Tavares, 2015). With the advent of new sensor-based methods for behavior observation such as wearable sociometric badges (Olguín Olguín, 2007) and the development of new software solutions for analysing behavioral data (Mangold, 2014), we believe that observational data is now more accessible for research than ever and will thus experience a renaissance. This chapter aims at providing an overview of current techniques for observing and analysing behavior in leadership research and at giving recommendations for applying observational methods in future research. First, we describe existing coding systems that assess leadership on the basis of specific predefined sets of behaviors. Leadership coding systems provide researchers and observers with instructions on which behaviors to record. We review the coding systems regarding their content, recording techniques, and quality criteria. Subsequently, we turn to issues of coder training and reliability. This first section of the chapter provides researchers with an idea of how to conduct observations and highlights some issues and limitations regarding observational methods. In the second section, we present new technologies for capturing and recording behavior and novelties regarding the analysis of behavioral data. Specifically, we introduce sociometric badges and motion sensor recording, and illustrate their application in leadership research by providing recent examples. Subsequently, we introduce software that facilitates the coding process and the analysis of behavioral data. The second section aims at giving a thorough insight into recent developments, their respective limitations, and the ways in which these new technologies address the limitations of traditional observational methods. The chapter concludes with recommendations for researchers and with an outlook on the future (and a possible renaissance) of observational research.
OBSERVATIONAL METHODS IN LEADERSHIP RESEARCH Which behaviors lead to the perception of an individual as a leader? Which specific behaviors influence a subordinate’s perception of effective leadership? How should leaders behave to influence team effectiveness? How can someone improve their leadership? The search for effective leadership behaviors has resulted in a variety of classifications and typologies of behaviors that are assumed to constitute leadership behavior (Fleishman et al., 1991). However, the behavioral categories in these taxonomies are often described on a very abstract level (Yukl, 2013) or are defined in the
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Observational and sensor-based methods 75 form of, often positive, perceptions of outcomes (Meyer et al., 2016). To give an example, idealized influence, a subcategory of transformational leadership is described as “behavior, that increases follower identification with the leader” (Yukl, 2013, p. 313). This, however, is not a description of leader behavior, but a description of the perception of a positive effect of the leader’s behavior (Van Knippenberg & Sitkin, 2013). A similar phenomenon exists in research on emergent leadership: when assessing who emerges as a leader in a group, researchers operationalize emergent leadership almost exclusively through the team members’ perceptions, neglecting the processes that lead to these perceptions (Guastello, 2007). Additionally, questionnaires require the participant to recall past behavior, which makes the data susceptible to biases and errors (Eby, Cader, & Noble, 2003; Hansbrough et al., 2015; Rush, Thomas, & Lord, 1977). The popularity of survey research on leadership behavior may have contributed to inhibiting research on more specific behaviors (Yukl, 2012), for example through observations. The absence of observational methods can be, at least to a certain extent, explained by the fact that behavioral coding is both time-consuming and labor intensive (e.g., Eby et al., 2003). Additionally, methods for coding and analysing behavioral data are complex and are rarely taught in organizational behavior classes. The coding systems and automated behavior recording methods presented in this chapter focus on behavioral expressions of leadership behavior in a way that conceptualizes behavior as visible conduct and interaction (Bonito & Sanders, 2011). They capture more or less narrowly defined sets of behaviors, which allows researchers to describe sequences of interactional events that are visible and can be measured in an objective way. This is an important precondition for sensor-based methods. If we take a weekly team meeting with a supervisor as an example, observational methods allow capturing behaviors such as whether the supervisor provided positive or negative feedback, to whom he or she provided the feedback, or, on a more basic level, the turn taking, speaking time and listening. Observations may thus provide a more detailed picture of the communication between individuals compared to post hoc recollections by the meeting participants. Additionally, these specific behaviors can provide starting points for leadership development and training. In many commercial training programs, the importance of leader–follower communication is emphasized and specific communication behaviors are practiced (Frese, Beimel, & Schoenborn, 2003). Therefore, research on actual leader communication behaviors could provide important insights for professionals in the field of coaching and human resource development. Coding systems and automated behavior recordings have the potential to address the questions asked at the beginning of the section. We proceed
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76 Handbook of methods in leadership research with presenting coding systems that allow the structured observation and recording of leadership behavior in the following section before reviewing sensor technologies in the subsequent one. Classical Approaches: Leadership Coding Systems The use of coding systems (or coding schemes) in leadership research dates back to early studies on the leaderless group discussion technique in the first half of the twentieth century. The leaderless group discussion was developed and used as a personnel selection technique in order to evaluate candidates for leadership positions, especially in military contexts (Ansbacher, 1951; Bass, 1954). The basic procedure is simple: participants discuss a given topic while observers monitor the discussion and rate or code the participants’ behavior. The observers themselves do not take part in the discussion (Bass, 1954). While the first rating systems included very global (and subjective) ratings of behavior, such as “Who do you think led the discussion?” (Bass, 1949, p. 529), accompanied by the recording of speaking times (Bass, 1949), the systems became more elaborate and focused by coding more specific descriptions of behavior such as “clearly defined or outlined the problem” (Bass, 1954, p. 468) as predictors for future leadership status or leadership perceptions. In general, leadership coding systems aim at assessing leadership in terms of visible, and therefore observable behavior between at least two individuals. This behavior is either observed on-site or recorded for later analysis. By providing predefined behavior categories, often accompanied by concrete behavioral examples, coding schemes guide the observer by telling him or her what to look for. The observer classifies the behavior according to categories or codes and records their occurrences. The list of concrete behaviors that are to be coded, which are typically grouped into broader categories, is either derived from a theoretical framework (Bienefeld & Grote, 2014; Eby et al., 2003) or grounded in previous unstructured observations (Luthans & Lockwood, 1984). Regardless of their theoretical foundation, all systems intend to assess behaviors constituting leadership or are typical for leaders. In this chapter, we review coding systems that aim at assessing leadership itself as a variable, meaning that the results of the observations intend to indicate the degree to which an observed individual showed leadership behavior or acted as a leader. Table 4.1 lists example coding systems that can be applied across different professions, because their behavioral codes are not formulated with regard to a specific professional context. Other coding systems are designed to assess leadership in specific professional contexts such as medical teams (e.g., Künzle et al., 2010; Parker, Yule,
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Observation coding scheme for leader verbal behavior
Formal leadership hierarchies Formal leadership hierarchies Formal leadership hierarchies Formal leadership hierarchies
Leaderless teams
Leaderless teams
Leaderless teams
Context
0.71–0.97b
0.86
0.81
0.65–0.98
0.07–0.97
0.90
0.94
Interrater reliability (Cohen’s Kappa)
Notes: a. Coding systems designed for the application in laboratory studies are marked with a grey background. Coding systems with a white background are designed for the application in specific field contexts. b. Computed with Spearman–Brown interrater reliability.
Meinecke, Klonek, & Kauffeld (2016) Sims & Manz (1984)
Komaki, Zlotnick, & Jensen (1986) Luthans & Lockwood (1984)
Lord (1977)
Act4leadership
Observational Inventory for Leader Behaviors (OILB) Emergent leadership behaviors – coding sheet Functional leadership – behavior coding system Operant Supervisory Taxonomy and Index (OSTI) Leadership Observation System (LOS)
Eby et al. (2003)
Foti & Hauenstein (2007)
Name
Source
Table 4.1 Examples of existing leadership behavior coding systems including application contexts and reliability valuesa
78 Handbook of methods in leadership research Flin, & McKinley, 2011) or cockpit and cabin crews (Bienefeld & Grote, 2014). We proceed with reviewing the kinds of behaviors that are typically included in leadership coding systems, before turning towards formal issues of observations based on the different schemes. In order to structure the content of the coding systems, we refer to the hierarchical taxonomy of leadership behaviors according to Yukl, Gordon, and Taber (2002), as several recent systems use this taxonomy as a theoretical framework (see Bergman, Rentsch, Small, Davenport, & Bergman, 2012; Bienefeld & Grote, 2014; Eby et al., 2003 for examples). Task-oriented behavior in leadership coding systems Similar to taxonomic approaches in descriptions of leadership behavior (Fleishman et al., 1991), most leadership coding systems categorize leadership behavior into two broad categories: task-oriented leadership and relation-oriented leadership (Yukl et al., 2002). Task-oriented behaviors include behaviors such as “assigning tasks to subordinates, maintaining definite standards of performance, asking subordinates to follow standard procedures” (Yukl, 2013, p. 64) and are included in almost all leadership coding systems. An overview and examples of task-related behavior assessed by leadership coding systems is given in Table 4.2. Yukl and colleagues (2002) divide task-related leadership behavior into the three subcategories: short-term planning, clarifying responsibilities, and monitoring operations and performance. Short-term planning includes decisions on “what to do, how to do it, who will do it, and when it will be done” (Yukl et al., 2002, p. 18). In leadership coding systems, short-term planning behaviors include making decisions on the next steps and communicating these decisions to others (e.g., Bienefeld & Grote, 2014). It can also include the proactive gathering of information (Bienefeld & Grote, 2014; Crockett, 1955), for example, to identify obstacles (Lord, 1977). Clarifying responsibilities refers to communicating the planning process and its results to others (Yukl, 2012). It includes the communication of information that is relevant for carrying out tasks (Foti & Hauenstein, 2007), assigning tasks and responsibilities to individuals (e.g., Künzle et al., 2010), and the distribution and management of resources (Künzle et al., 2010; Parker et al., 2012). Behavior descriptions in leadership coding systems often show elements of both short-term planning behaviors and clarifying responsibilities in combination. This is because planning is a cognitive process (i.e., thinking about possibilities, weighing options) and can only be observed when its results are openly communicated. Examples include describing a plan to others and proposing individual tasks for carrying out the plan (Yukl et al., 2002).
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Determining the sequence of action, coordinating the pace and timing of activities (Bienefeld & Grote, 2014; Künzle et al., 2010) Making an informed judgment based on information, situation and risk (Parker, Yule, Flin, & McKinley, 2012) Seeking to obtain information of an objective, factual or technical nature (Crockett, 1955) Presenting factual information regarding the purpose of the task (Foti & Hauenstein, 2007) Giving information, questioning about knowledge (Meinecke et al., 2016) An individual delegates tasks or roles to somebody else (Bienefeld & Grote, 2014; Künzle et al., 2010) Providing information relevant to carrying out actions, removing obstacles (Lord, 1977) Providing structure to the situation and monitoring the group’s progress towards task completion (Eby et al., 2003) Behaviors that reinforce standards (Parker, Yule, Flin, & McKinley, 2013) Validating or helping a specific person in the group; showing concern for others’ feelings and ideas, behaving politely to others (Eby et al., 2003) Indicating gratitude, general satisfaction, or positive affect; complimenting group performance or output; general courtesies; friendly behavior (Lord, 1977) Instructing others on how a task or procedure should be done or provides clarification about decisions or plans (Bienefeld & Grote, 2014) Asking others for an opinion (Bienefeld & Grote, 2014) Orienting employees, arranging for training seminars; clarifying roles, duties, job descriptions; coaching, acting as a mentor, “walking” subordinates through tasks; helping subordinates with personal development plans (Luthans & Lockwood, 1984) Stressing the importance of goals; exhorting group members to work harder; making rewards contingent upon good task performance; complimenting an individual’s task performance (Lord, 1977) Giving feedback to others (Bienefeld & Grote, 2014)
Planning and organizing
Note: Task-oriented coding categories are marked by a grey background. Sections in italics are subcategories of behavior, which are independently coded.
Recognizing others
Motivating
Consulting Training/developing
Developing a positive group atmosphere Coaching
Decision making Gathering information Giving information/ explaining Assigning work Managing resources Monitoring Maintaining standards Sensitivity
Definitions/Descriptions
Behavior Category
Table 4.2 Task- and relation-oriented behaviors and their descriptions in leadership coding systems
80 Handbook of methods in leadership research Several leadership coding systems for applied settings, such as management, surgery, and aircraft, assess monitoring of operations and performance (see Bienefeld & Grote, 2014; Luthans & Lockwood, 1984; Parker et al., 2012 for examples). The descriptions of these behaviors are similar to each other across these systems, as the authors assume that leaders control the specific action or performance of other focal individuals (Bienefeld & Grote, 2014; Luthans & Lockwood, 1984) or of the entire team (Eby et al., 2003). Relation-oriented behaviors in leadership coding systems Although every leadership coding system included in Table 4.1 assesses at least some form of task-related behavior, not all of them capture relationoriented behaviors. Relation-oriented, team-focused (Hu et al., 2015), or socio-emotional behaviors (Lord, 1977) are comparable to consideration behaviors (Fleishman, 1957) and supportive leadership (House, 1971). They include attending to followers (Judge, Piccolo, & Ilies, 2004) or the team (Lord, 1977) and have been linked to follower satisfaction and performance (see Judge et al., 2004 for a meta-analysis). The behavior coding systems vary regarding the subcategories of relation-oriented behaviors (Table 4.2). However, almost all coding systems include behaviors corresponding to the four central categories: supporting, consulting, developing, and recognizing as described by Yukl et al. (2002). Supporting behaviors imply attending to “the needs and feelings of other people” (Yukl et al., 2002, p. 20). Example categories for supporting leadership behavior include “fulfilling non-task needs to members,” “developing a positive group atmosphere” (Lord, 1977, p. 122), and the sensitivity and team-building categories of the Observational Inventory of Leader Behaviors (OILB; Eby et al., 2003). Both coding systems differentiate between supporting behavior that is directed at single individuals and supporting behavior that is directed at the entire group. Consulting behavior refers to the involvement of others in decision making (Yukl et al., 2002), for example by asking for input, allowing input, and ensuring that every member has the opportunity to give input (e.g., Bienefeld & Grote, 2014; Eby et al., 2003; Luthans & Lockwood, 1984; Parker et al., 2013). Parker and colleagues (2013) assess consulting behaviors and developing behaviors in the form of one joint behavioral category that they call guiding and supporting. In their system, developing behaviors refer to the coaching and to the teaching of others, for example by giving instructions on how to perform a task or procedure (Bienefeld & Grote, 2014) or by guiding others through tasks (Luthans & Lockwood, 1984). Recognizing behaviors involve showing appreciation for others (Yukl et
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Observational and sensor-based methods 81 al., 2002) and are described in coding systems as motivating or reinforcing behaviors, which include giving positive feedback, and complementing or rewarding performance (see Lord, 1977; Luthans & Lockwood, 1984 for examples). Leadership behavior in coding systems beyond task and relation orientation Next to task- and relation-oriented behavior, some coding systems provide additional categories (Table 4.3). The exact nature of these categories depends on the theoretical framework of the respective coding system and on the (professional) contextual focus of the given system. One of the most common of these other categories is initiative taking. Initiative refers to “overcoming inertia” (Lord, 1977, p. 122) – that is, that the individual activates the group through his or her action or through the request for an action. As inertia implies that there has been no visible group activity before, taking initiative primarily refers to the initial action (and not on the meaning or content of the action). In the OILB coding scheme, however, initiative refers to “taking action” (Eby et al., 2003, p. 1467). Here, initiative has a more task-related connotation, including that an individual makes decisions for the group and controls the group’s activities towards the team goal. As a third possibility, some coding schemes conceptualize initiative in terms of independent or autonomous working. The observed individual starts to carry out an activity or a task without being asked, told, or being instructed (e.g., Künzle et al., 2010). In sum, although taking initiative is recorded in several leadership coding systems, the meaning of the category differs substantially between coding systems. Coding systems often include additional behavior categories with a negative connotation. These range from giving negative feedback or evaluations (Luthans & Lockwood, 1984; Perkins, 2009) to disagreeing and opposing (Crockett, 1955), or even to attacking (Perkins, 2009). Positive and negative valences are sometimes combined into behavior categories, for example by distinguishing between positive relation-oriented behaviors, such as supporting others, and negative relation-oriented behaviors, such as criticizing (Meinecke et al., 2016). In two coding systems, Act4leadership (ibid.) and in the Leader Behavior Rating System (LBRS; Rice & Chemers, 1975), negative behaviors fall into the same category as self-promotion behaviors (e.g., directing attention towards oneself and pushing own ideas on others). In contrast to negative aspects of leader behavior, charismatic leadership hardly plays a role in leadership coding systems. The only coding system that includes charisma as a category is the Observational Inventory of Leader Behaviors (OILB; Eby et al., 2003). These authors define
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82
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Summarizing Opposing
Initiative
Crockett (1955)
Eby et al. (2003)
Disagreeing/attacking Summarizing Prominence seeking
Summarizing the group’s progress to date Opposition, resistance to, or disagreement with a solution, suggestion, interpretation Speaks first in the group, asks experimenter questions on the group’s behalf, takes action when the group is at an impasse Inspires the group through language and communication, generates enthusiasm Refers to his or her performance Initiating an action without being asked Requests for non-specific action, behaviors with an activational rather than motivational connotation Enforcing rules and policies; any formal organizational reprimand or notice, giving negative performance feedback Showing flexibility and changing plans if required to cope with changing circumstances to ensure that goals are met; anticipating possible complications and communicating them to staff Negative emotional responses, evaluations, or attacks Restating or enumerating content already presented Pushing own ideas on the group, interrupting others, and directing attention to own achievements
Description/Example Behaviors
Note: The non-italic text refers to broader descriptions of several behavioral codes, while the text in italics refers to specific examples of behavioral codes.
Rice & Chemers (1975)
Perkins (2009)
Disciplining/punishing
Luthans & Lockwood (1984) Parker et al. (2013)
Coping with pressure
Reference to own performance Initiate an action Initiating behavior
Komaki et al. (1986) Künzle et al. (2010) Lord (1977)
Charisma
Behavior Categories
Source
Table 4.3 Examples of additional behavior categories in leadership coding systems
Observational and sensor-based methods 83 c harisma as a behavior that “inspires the group through language and communication” (Eby et al., 2003, p. 1467) and that evokes enthusiasm within the team. For assessing charisma, the OILB relies on behaviors such as talking enthusiastically and expressing confidence and on whether an individual succeeds in convincing others of his or her ideas. Finally, there are leadership coding systems that include behavior categories that refer to everyday managerial activities. To give an example, the Leadership Observation System (LOS; Luthans & Lockwood, 1984) is based on unstructured observations of the natural activities of managers. It includes behavioral categories such as staffing (the organization of application processes, processing paperwork) and exchanging routine information (e.g., attending meetings). Rating vs sampling While the previous section addressed the content of leadership coding systems, which is important when considering the adequacy of applying a coding system to a research question, the following sections describe the different methods for capturing and recording the occurring behaviors. These methods dictate the kind of data that coding systems deliver. In general, one can distinguish between sampling and ratings methods. Sampling methods allow for the analysis of frequencies, sequences, and sometimes even behavioral patterns. Sampling methods record data in a continuous way and capture the frequencies of behaviors. Hence, each occurrence of a certain behavior is marked and recorded as it occurs. Leadership coding systems often apply event sampling, which focuses on the occurrence of behaviors as “punctual, instantaneous events without considering or recording the actual duration of the behavior” (Altmann, 1974). Researchers apply event sampling whenever the focus is on the frequency and point of time of specific behaviors during a task or interaction. As an example, Künzle and colleagues (2010) sampled how often members from different professional backgrounds in anesthesia teams show problem-solving behaviors and compared their frequencies across different stages of the task (see Bienefeld & Grote, 2014; Perkins, 2009 for further examples). State sampling provides not only information on when and how often certain behaviors occurred, but also with regard to the duration of behaviors by recording the beginning and end of each behavior (Altmann, 1974). State sampling is especially important for interaction and sequence analyses, which we describe below. Rating systems, on the other hand, provide subjective assessments of occurrences of behaviors, without providing information on the chronology or sequence of the behavior. They can be as simple as asking whether a certain behavior has occurred during the observation (yes/no) (Marks &
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84 Handbook of methods in leadership research Printy, 2003; Ray, Ray, Eckerman, Milkosky, & Gillins, 2011) or asking for the degree to which the behavior descriptions fit the observed person with Likert scales (e.g., Rice & Chemers, 1975). A more elaborate rating method is the use of the Behaviorally Anchored Rating Scale (BARS). The BARS allows the rating of a specific category of behavior; however, instead of a Likert-type scale indicating the frequency or intensity of the behavior, it provides specific behavioral examples for each rating possibility (Ohland et al., 2012). An example of a BARS that assesses the consulting behavior of managers in disabilities services is shown in Table 4.4. The behavior descriptions aim to minimize possible biases due to ambiguity, as they provide all observers with “a common frame of reference” (Bergman et al., 2012, p. 25). The different forms of sampling and rating systems affect the usability of the coding systems for the observers and the reliability of the observations. The following sections discuss issues regarding observer training that is necessary for objective, reliable and valid observations. Observer training The coding systems summarized to this point do not state how much time the coding process takes. As a comparison, interaction coding systems that are not specific to leadership such as the Discussion Coding System (DCS; Schermuly & Scholl, 2011) require a ratio of 1:8 regarding the real time captured on video and the time needed for coding. Hence, conducting an observational study is challenging, especially for the observers. In order to minimize biases and to increase reliability and accuracy, most researchers train their observers. Observers, who are often student assistants, need to be familiar with the coding system and with systematic errors and biases that may occur during the behavior coding and rating (e.g., Luthans & Lockwood, 1984). Therefore, in addition to learning the respective coding procedure, observer training usually includes information on possible distortions and biases, such as simplification, focusing on a single source, contamination from prior information, contextual errors, prejudice and stereotyping, and the halo effect (Thornton & Zorich, 1980) and how to avoid them. The amount of training depends on the number of behavior categories, the observers’ experience, and the recording methods (rating vs sampling). Training times range from two (Parker et al., 2013) to 12 (Foti & Hauenstein, 2007) to 40 hours (Komaki et al., 1986). Observer training is usually deemed successful if the raters reach agreement (e.g., Foti & Hauenstein, 2007; Komaki et al., 1986). For example, in their study on leadership behavior among sailboat skippers (Komaki, Desselles, & Bowman, 1989), observers received around 40 hours of training. In the training, they learned to use the Operation Supervisory Team Taxonomy
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85
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No evidence that the manager spends any time coaching or modeling. He/she is never out of the office and when on shift works alone. You might see not very good practice if the manager is supporting. Staff are doing things that they shouldn’t be doing and manager is not noticing or is noticing, complaining to you about but not giving feedback to staff Manager says that they spend time informally watching staff but can’t give very good examples of how he/she has used that information to try to shape up performance. Their own practice if observed is ok but they don’t correct or support other staff. They might say they talk about good practice in team meetings etc. and give some examples but there is no sense that they model or coach staff in any way, even on an ad hoc basis Manager does not spend time formally observing but can give examples of how they have spotted things while walking about or working on shift and then used that information to give feedback to staff, feed into supervision etc. No use of role play or videos in team meetings but do discuss the support for individuals. Their practice is quite good but they can’t give good examples of how they have modeled for a particular staff or activity. Or they can give some examples but these are about things other than active support or engagement – e.g., how to use a particular piece of equipment or how to give medicine etc. Manager spends some time in formal observation and giving feedback and is seen to provide modeling or coaching (or gives a good account of how he/she has done this). However, opportunities for this are left somewhat to chance and there is no system in place where manager/supervisor spends time regularly with each member of staff. So who receives modeling will depend on who the manager is on shift with etc. You observe or manager gives examples of how he/she regularly observes and works with staff. There is a system of some sort in place to ensure that all staff are observed and receive feedback on a regular basis and manager can give good examples of how he/she has done this
1
Source: Beadle-Brown, Bigby, & Bould (2015).
5
4
3
2
Example Behavior Description
Rating
Table 4.4 E xample of a Behaviorally Anchored Rating Scale item for coaching behavior taken from Beadle-Brown, Bigby, & Bould (2015)
86 Handbook of methods in leadership research and Index (OSTTI; Komaki et al., 1986), a coding system that includes “a 20-page observational code, consisting of definitions, examples and nonexamples of the taxonomic categories” (Komaki et al., 1986, p. 264) as well as decision rules and recording instructions. Furthermore, the OSTTI requires the observers to continuously sample behavior in one-minute intervals. The coders observe the behavior in the first ten seconds, code and categorize the behavior within the subsequent 40 seconds, and use the remaining ten seconds to observe behavior to understand the context of the following one-minute interval. The entire OSTTI coding system consists of seven categories containing more than 40 specific behavior descriptions (Komaki et al., 1986). Hence, the complexity of both the behavior coding itself and the recording via sampling require an intensive observation for achieving acceptable reliability. Whenever possible, scholars use video recordings for observations (Eames et al., 2010; Hu et al., 2015), often in combination with coding software as discussed below. When video recordings are not possible, for example due to privacy or security reasons, coders receive special training for real-time or live coding of behavior (see Bienefeld & Grote, 2014; Curtis, Smith, & Smoll, 1979; Parker et al., 2012 for examples), which may include field training (Komaki et al., 1986) and the use of real-time coding software (Bienefeld & Grote, 2014) in order to enhance reliability. Reliability and validity of leadership coding systems Researchers can establish the reliability of observations in multiple ways. These include the extent to which multiple observers agree on the observed behaviors (interrater agreement or reliability) and standard reliability measures such as re-test reliability and split-half reliability. However, interrater agreement or reliability are most common for observational data (Mitchell, 1979). Most studies use a subset of observations for determining interrater reliability, ranging from 7 percent (Courtright, Fairhurst, & Rogers, 1989) to 20 percent of the observations (Sims & Manz, 1984). When using video recordings for reliability analyses, different observers code some videos twice for computing the interrater reliability. When using sampling techniques, a preliminary division of the video sample into behavioral units can be carried out before the observers code the units according to the behavioral categories (see Künzle et al., 2010). If video recordings are available, interrater reliability is computed on the basis of parallel live observations by two coders (Komaki et al., 1986). Most leadership coding systems exhibit good reliabilities, regardless of the kind of reliability (see Table 4.1). However, reliability does not guarantee that a coding system delivers a valid assessment of leadership qualities (Schermuly & Scholl, 2011). Therefore, coding systems should also be valid.
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Observational and sensor-based methods 87 Unfortunately, very few coding systems report empirical validities, and if they do, validity is often determined through assessment of face validity by experts. As an example, the authors of the Surgeons’ Leadership Inventory (SLI; Parker et al., 2013) asked consultants and attending surgeons to determine whether the presented behaviors represent intraoperative leadership. Luthans and Lockwood (1984) applied a multirater approach for determining convergent and discriminant validity by comparing the results of the field observations of managers from two sources of observations: participant observers (the managers’ secretaries in this case), and outside observers (graduate students in management). In addition, they compared the results from their LOS coding system to similar categories from the Leader Behavior Description Questionnaire (LBDQ-XII; Stogdill, 1963) and to the Managerial Behavior Survey (MBS; Yukl & Nemeroff, 1979). The comparison yielded moderate convergent and discriminative validities. Perkins (2009) determined validity in a different way after observing team meetings in organizations: after the observations, the head researcher interviewed the participants and compared their answers to the meeting profiles from the coding system. As interview statements supported the findings from the observation, the coding system was deemed valid. The previous examples primarily focus on external validity. However, when deciding between using an existing system or developing a new coding system (potentially with categories and subcategories), researchers should consider the internal validity of the behaviors (DiStefano & Hess, 2005). Although leadership observations with coding schemes require extensive preparation, effort, and resources, they provide valuable information on different facets of leadership behavior and especially on the contents of communication. Therefore, they enable the assessment of leadership data beyond the limits of survey methods and are the method of choice in response to recent calls for a focus on more concrete, specific, and observable behaviors in leadership research. In the following section, we introduce sensor-based behavior assessment methods, which present a recent alternative to labor-intensive manual behavioral coding New Approaches: Sensor-based Assessment Methods in Leadership Research All observational methods up to this point assess leadership behaviors that are usually seen as typical or effective. However, whenever observers evaluate and categorize behavior shown by an individual, they
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88 Handbook of methods in leadership research a utomatically draw conclusions about the individual’s personality and intentions. These inferences, which can also be influenced by factors such as the observer’s personality (Akert & Panter, 1988), are unavoidable in interpersonal perception (Reeder & Brewer, 1979) especially when applying broadly defined behavior categories in coding systems. Hence, in order to fully understand which specific leader behaviors evoke which effects, for example on team performance, subordinate perceptions of leadership efficacy, or even observer perceptions of leadership, researchers need to analyse leadership behavior at a micro-level (Meyer et al., 2016). Microlevel behaviors, or honest signals (Pentland, 2008), are defined as “leaders’ verbal and non-verbal visible conduct and interactions with their followers that are likely to affect followers’ attitudes and behavior” (Meyer et al., 2016, p. 9). Analysing micro-level behaviors can complement traditional observation and coding systems, especially when the selection of microlevel behaviors is based on broader descriptions of leadership behavior (Meyer et al., 2016), such as in leadership coding systems. As social interactions are a key issue in leadership (Schyns & Mohr, 2004; Yukl, 2013) and in leadership coding systems, research on microlevel leadership behaviors can benefit from current developments in the field of social sensing (Meyer et al., 2016). Social sensing refers to the automated recording and analysis of micro-level social interaction behavior (Schmid Mast, Gatica-Perez, Frauendorfer, Nguyen, & Choudhury, 2015). Today, a number of computer-based methods for detecting and identifying interactional features such as body posture, eye gaze, facial expressions, and speech qualities already exist (see Schmid Mast et al., 2015 for a review). This data provides insights into the relations between micro-level behavior and interpersonal perceptions, such as expressed leadership and dominance (Sanchez-Cortes, Aran, Jayagopi, Schmid Mast, & Gatica-Perez, 2013). Sensor-based recording devices can be stationary, such as a laboratory equipped with cameras and microphones or mobile, making them applicable for field research (Schmid Mast et al., 2015). Mobile recording devices include smartphones and gaze-tracking glasses, which allow the assessment of everyday human interactions in natural settings (ibid.). In the following section, we describe two possibilities for sensor-based data capturing and their application possibilities in leadership research: sociometric badges, wearable sensors capturing multiple interaction features in field settings, and stationary motion sensors for laboratory purposes. Capturing interactional data with sociometric badges Sociometric badges are wearable social sensing devices that are equipped with microphones, accelerometers, Bluetooth receivers and transmitters,
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Observational and sensor-based methods 89 and infrared sensors. With the badges and the corresponding software, researchers can assess interaction behavior such as proximity between two individuals and speech features (e.g., speech duration, interruptions, frequency, volume). The badges are worn around the neck by the participant and can store up to 40 hours of data (Kim, McFee, Olguín Olguín, Waber, & Pentland, 2012; Olguín Olguín, 2007; Olguín Olguín & Pentland, 2010). They are therefore applicable in field studies where the observed individuals are not constantly at the same location and constant video recording or live observations are inconvenient or impossible. In order to evaluate the badges’ capability to capture leadership emergence and leadership positions in a field setting, we carried out a small pilot study in an organizational setting. We collected badge data and questionnaire data from all 29 staff members of a single research department at a German university over the course of two days. We introduced the participants to the badges prior to the assessment, so that they were aware of the sensors, the measurement variables, and the handling of the badges (e.g., how to recharge them). At the end of both days of the study, participants filled in an online questionnaire, which asked for the initials of the five co-workers with whom they had the most face-to-face communication at work. The participants also ranked these co-workers according to the estimated amount of face-to-face communication. Based on this questionnaire data, we computed social networks for each day, which we subsequently compared to the social network data that the badges supplied. In the social network that we derived from the questionnaire data, nodes represented individual participants, and edge weights represented daily communication frequency according to their rank order. For these networks, we computed the in-degree centrality, closeness centrality, and betweenness centrality for directed weighted networks with the igraph R package (Csardi & Nepusz, 2006) for each node (i.e., for each participant). In-degree centrality describes the amount and weight of received nominations (i.e., how often this person was named as one of the five persons with whom another employee had the most communication). Closeness centrality denotes the centrality of an individual on the basis of the entire network. Finally, betweenness centrality captures how often an individual is between two others within the network or rather how often the individual lies on the shortest network path between two other individuals (Prell, 2012). We compared the questionnaire-based centrality indices to those that the badges deliver based on the occurrence and duration of interactions, which are again derived from distance and proximity data from the infra-red and Bluetooth sensors (see Olguín-Olguín, 2007 for a detailed
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90 Handbook of methods in leadership research Table 4.5 D ay 1 Pearson correlations between questionnaire-based and badge-based centrality values Badge-assessed Interaction Data Degree centrality Closeness centrality Betweenness centrality
Day 1 Day 2 Total Day 1 Day 2 Total Day 1 Day 2 Total
Infrared data
Bluetooth data
Combined
0.54* 0.35 0.35 0.63* 0.63** 0.54** 0.46* 0.35 0.41*
0.43* 0.46* 0.44* 0.53* 0.56** 0.45* 0.43* 0.45* 0.54**
0.41* 0.46* 0.44* 0.53* 0.56** 0.45* 0.41* 0.42* 0.48**
Note: * p < 0.05, ** p < 0.01.
description). The software delivers the centrality values for infrared data, Bluetooth data, and a combination of both. As not all members of the staff were present on both days and because some participants did not fill out the questionnaire, n 5 25 datasets from badges (n 5 25 questionnaires) on day one and n 5 24 datasets from badges (n 5 18 questionnaires) on day two were included in the analyses. Pearson correlations of the network centrality data are summarized in Table 4.5. The results show significant positive correlations, especially regarding the first day, indicating a good validity of the badge-assessed interactions as a measure for face-to-face interactions in an organizational field setting. Hence, the badges allow an objective assessment of interactions and provide a more exhaustive picture of an individual’s face-to-face contacts as it does not depend on the individual’s memory. As the protection of privacy is an important issue in field observations, we also assessed the degree to which the participants felt distracted or monitored at the end of each day. Participants reported that they had a moderate feeling of awareness of the badges during the first day of the study, but they also indicated that they did not feel as if they were being monitored or as if they were under surveillance. This study illustrated the potentials of wearable social sensing devices for leadership research as they reflect the communication within an organization (or team) and could therefore provide information on the duration and frequency of leader–member, and team communication networks. As a case in point, a recent study on leadership emergence employed
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Observational and sensor-based methods 91 badge-elicited speaking time and found a positive relation with leadership perceptions (Chaffin et al., 2015). Additional potential of the sociometric badges and similar technologies lies in the simultaneous recording of multiple data types, such as physical activity, proximity data, and speech data. Motion sensing in leadership research Motion sensing assesses data on body movement and posture through videos, depth recording systems (Schmid Mast et al., 2015), or inertia measurement units (Feese, Arnrich, Tröster, Meyer, & Jonas, 2011). As it requires attaching sensors to the participants’ bodies, it is often conducted in laboratory settings. As the impact of posture or body language on interpersonal perception is well documented in leadership research (Darioly & Schmid Mast, 2014), automated motion sensing can contribute to leadership research by identifying motion patterns and relations, such as synchronicity, between participants. As an example, Meyer and colleagues (2016) analysed the mediating effect of non-verbal behavior on the relationship between participative leadership and peer leader evaluations, as well as participative leadership and team decision quality. The authors focused on mimicry, the mirroring of posture, movements, and gestures as an indicator of empathy. Student teams consisting of one leader and two followers worked on a simulated decision-making task. Prior to the task, the team leader received leadership training, which instilled a directive or participative leadership style. Each participant wore six motion sensors on the upper arms, wrists, head, and on the back, measuring acceleration, rate of turn and orientation (ibid.). This allowed a digital recreation of the participants’ movements. The subsequent analysis detected the averaged times a certain body posture by a follower was mirrored by the leader within 60 seconds of its initial occurrence (Feese et al., 2011). Results showed that mimicry behavior mediated the relationship between the leadership manipulation and leadership evaluations, which was assessed with the transformational leadership scale of the Multifactor Leadership Questionnaire (Bass & Avolio, 1990). However, the authors found no effect of mimicry on team decision quality. Reliability and Validity of Sensor-based Assessment Methods Determining the reliability and validity of sensor-based assessment methods is highly dependent on the methods and specific devices used. When using devices that are equipped with several different sensors, the task is more difficult as researchers need to evaluate each component’s validity and reliability independently. Regarding reliability, both
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92 Handbook of methods in leadership research easurement errors within (for example, due to fluctuations of measurem ment of one single device when repeatedly exposed to the same stimuli) and between the devices need to be evaluated for each sensor component. Different sensitivities of error variance between the two devices’ sensors, for example the built-in microphones, can reduce the reliability by causing bias if not detected and taken account of (Chaffin et al., 2015). Therefore, a starting point for researchers working with sensor-based assessment methods should always be a structured evaluation of the sensors’ variability in order to avoid systematic measurement errors. The validity of sensor-based assessment methods relies strongly on the algorithms used by the software to process the raw data (ibid.). Depending on the type of the device, the algorithms are not necessarily available or viewable. Researchers can evaluate the sensors’ validity by matching the sensor-assessed behaviors to observer-coded behaviors used as ground truth (Feese et al., 2011) or experimental set-ups (Chaffin et al., 2015). While the sensing of behavioral mimicry used in the study by Meyer and colleagues (2016) showed an average accuracy of 78.15 percent, the results regarding the sociometric badges vary. In a series of studies conducted by Chaffin and colleagues (2015), the researchers discovered limitations to the badges’ abilities when comparing actual speaking time and the speaking time assessed though the badges and the manufacturer-provided algorithm (r 5 0.15). Although the use of an optimized algorithm improved the results (r 5 0.36, p < 0.01), the authors value the speech detection capabilities of the badges as limited. However, proximity sensing had a high validity (percentage agreement 5 94.4 percent). These results highlight that it is necessary to evaluate every sensor component individually. The choice of the sensor-based assessment methods should be carefully considered regarding the environment and setting in which the actual assessment should take place and should be tested and validated in either the actual setting or a similar environment. Additionally, it is important to mention the importance of assessment duration when using wearable devices. Regarding the sociometric badges the validity increased with the length of the assessment period for all measures (Chaffin et al., 2015). The previous examples show the ways in which recent technological developments can facilitate observations in leadership research in both field and laboratory settings. However, we still need human observers and coders whenever we want to evaluate the validity of sensor-based methods, or whenever we need to analyse the content and context of interactions. In the following sections, we review means by which the manual behavior recording and the subsequent data analysis can be supported through special software and software-aided analysis techniques.
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Observational and sensor-based methods 93 Software for Creating and Analysing Observational Data The raw form of behavioral data is typically some kind of state or event sequence (Bakeman & Gottman, 1997). Such data is a time-ordered listing of behaviors or codes, where one line of data represents a given behavior, accompanied by a time stamp, denoting the time of its occurrence or its duration. On the basis of such data, researchers not only can calculate frequencies, lengths, and durations of specific behavior, but can also analyse interactions and patterns, as we describe below. Therefore, researchers need tools to create such data, which is commonly done with software for video coding and annotation, and for analysing it. In the following, we briefly describe two pieces of common software that we employ for these tasks in our research practice. Behavior coding and annotation with Mangold INTERACT Commonly, video or audio recordings are the raw material for subsequent observational assessment of (leadership) behavior. Trained coders then use behavior coding software such as INTERACT (Mangold, 2014) to create state- or event-sequence data from these recordings. In our own research, we chose this particular software for two reasons: first, it is the only software that allows opening and controlling several video files simultaneously. In our lab, we have three cameras that record digital video into three separate files (one for each camera). The INTERACT tool allows opening all three video files simultaneously and controls their playback simultaneously. In other words, if the observer presses play, all three videos play in synchrony in three separate windows. Second, INTERACT is the only coding software that we are aware of that allows coding one behavior as an occurrence of a main category, and to subsequently add subcategories to that behavior. For example, when we code group interactions with the DCS coding system (Schermuly & Scholl, 2011) the main category is the speech act. So when a person on the video starts to talk, the observer presses a key, and when the person stops talking or is interrupted, the observer presses the key again. This logs the beginning and the end of the behavior “speech act,” and coders can subsequently code whether this specific act constituted a question, a suggestion and how dominant or friendly it was. In other words, the speech act is the main category, and dominance, friendliness, and type are subcategories. Other simpler annotation software can only code main categories that are assigned to specific keys on the keyboard. With such simpler systems, coders would need to assign “speech: dominant question” and “speech: submissive question” to different buttons and would have to remember these. With INTERACT, coders simply need to remember the button for “speech start” and “speech
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94 Handbook of methods in leadership research end.” The tool can be configured in such a way that it pauses the video whenever a certain type of main category is logged (e.g., after someone on the tape finished talking) and then prompts the coder to rate the subcategories. This so-called lexical coding approach distinguishes INTERACT from other software for video coding and analysis, and has proven to be very useful in our own research. Analysis of state and event sequences with sequence analysis: TraMineR The data resulting from coding or annotating video or audio recordings is typically a sequence of events with time stamps or durations. These ordered lists denote which of the behaviors that the given coding system distinguishes happened when. Without any further treatment, researchers can calculate the frequencies or durations of specific behaviors. However, this kind of data can reveal much more interesting findings, such as patterns of reoccurring behavior and transition probabilities (i.e., the probability of a specific behavior given the previous occurrence of another specific previous behavior). For these kinds of questions, a variety of methods exist (for reviews see Chiu, 2005; Fairbairn, 2016). Of these, sequential or sequence analysis has the longest history (Bakeman & Gottman, 1997). Sequence analysis refers to a set of methods and analyses that allow comparing and classifying event sequences. These include determining the similarity between sequences, the calculation of transition probabilities, the identification of common patterns and subsequences, and the numerical description of sequence features such as entropy and turbulence. To give an example, imagine a researcher who observes and codes the following behaviors: leader smiles, follower smiles, leader asks a question, and follower shares information. The set of these behaviors constitutes the so-called alphabet of behaviors. In the dataset, these behaviors are typically abbreviated, e.g., ls, fs, lq, fi. In the terminology of sequence analysis, the observed behavior from a specific interaction constitutes an event sequence, e.g., fs-ls-fs-lq. Researchers can analyse such event sequences with the (free) TraMineR package (Gabadinho, Ritschard, Müller, & Studer, 2011) in the R environment (R Development Core Team, 2015). For the given example, a sequence analysis with TraMineR can reveal how likely it is that a follower shares information after the leader smiles (in contrast to after the leader asks a question), and whether these transition probabilities differ for different circumstances. With TraMineR, researchers can even identify the most common subsequences from a set of event sequences, and can perform cluster analyses over a set of event sequences. While INTERACT also allows performing some of these analyses, we feel that a combination of INTERACT (for annotating the videos) and R (for analysing the result-
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Observational and sensor-based methods 95 ing sequence data) is the most powerful combination for conducting observational studies.
CONTRASTING CLASSIC METHODS AND NEW DEVELOPMENTS – ADVANTAGES AND LIMITATIONS In this chapter, we reviewed both established and novel behavior observation methods. Leadership coding systems aim at observing and recording typical leadership behavior. Although many of them include similar or comparable behavior dimensions, coding systems have been designed for different applied contexts. Applying coding systems requires well-trained observers and coders who are able to interpret and classify behaviors according to the categories of the respective coding systems. Therefore, behavioral assessments of leadership behavior are somewhat costly to obtain. Sensor-based observation methods can add additional insight to leadership research by capturing behavior on the micro-level. Visible verbal and non-verbal behavior parameters can be automatically and simultaneously recorded and evaluated, and are rapidly available for further analyses. We presented two examples of how sensor-based assessment of leadership behavior can be included in organizational field settings and in laboratory settings. We further discussed and introduced new developments that facilitate the conduction of observations with behavioral coding and introduced the coding software INTERACT and sequence analysis software. In summary, a variety of methods are available for researchers who plan to include observational methods in their research. However, researchers need to carefully consider the selection of each method before conducting their study. As our review has shown, leadership coding systems allow the analysis of the behavioral content (e.g., what is communicated during the interaction). Although there is some overlap between the behavioral dimensions in the different systems or schemes, one should keep in mind that some behavior categories or descriptions were designed for specific contexts. Therefore, the behavioral descriptions should be reviewed thoroughly regarding their applicability and transferability to the planned research context. Furthermore, the respective recording technique (sampling vs rating) is important as it affects the usability and the reliability of the observations. Additionally, observers need sufficient preparation and criteria for assessing the accuracy of observers should be defined prior to the coding procedure. An important issue also pertains to establishing
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96 Handbook of methods in leadership research blindness of the observers for the hypotheses. Blindness of the observers towards the research question is necessary in order to enhance objectivity of the observers while coding and for reducing biases (Bienefeld & Grote, 2014; Eby et al., 2003). Whenever video recordings are possible, we recommend the use of coding software to facilitate the coding process. Similar planning should be carried out prior to the use of sensor-based methods. Conducting research in field settings can limit the options for social sensing options. For example, when observing employees who work in different rooms on different tasks (such as an office and a laboratory), stationary devices are often not appropriate. Even more importantly, researchers should base the selection of the captured verbal and nonverbal micro-level behaviors on prior theory as much as possible. As social sensing methods do not provide information on the behavioral content, but on the ways in which the content is communicated in interactions, we recommend deriving the specific micro-level behaviors for analyses from the broader behavior descriptions of leadership coding systems. Whenever possible, we recommend a combination of both observer-based and sensor-based methods whenever the research context and the provided resources make it possible. When conducting behavioral observations, the issue of privacy arises – especially for wearable sensors. The devices capture behavioral data as it occurs and are not able to distinguish between work and non-work activities. We therefore recommend a thorough introduction of the recording functions of social sensing devices to participants. Furthermore, researchers should seek written consent from the participants for using video and sensor data in scientific studies. Despite their advantages in comparison to questionnaire-based measures of leadership behavior, observational measures come with their own set of limitations. To state the obvious, behavioral observational methods that rely on trained observers are quite time-consuming and costly. They require intensive preparation and elaborate data analysis procedures. Observational studies also pose a challenge regarding issues of reliability and validity, as the application of other methods besides the determination of the interrater reliability, such as multitrait-multimethod (MTMM) analyses, are rare. Although methods of social sensing provide fast access to the captured data, they also require computational skills and knowledge in order to be carried out correctly. Technical difficulties and the loss of data can impact the analyses. Therefore, wearable sensors and computer-based sensing techniques are great opportunities for interdisciplinary collaborations between social scientists and scientists from IT-related disciplines.
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Observational and sensor-based methods 97
FUTURE DEVELOPMENTS In order to understand what actually happens when people lead and how leadership and leadership perceptions interact, observational methods provide an informational value that goes beyond the possibilities of survey methods. The time and effort required to conduct observational studies can be reduced by focusing on so-called thin slices of behavior (Ambady & Rosenthal, 1992). Thin slices are very short segments (e.g., 6–30 seconds; Ambady & Rosenthal, 1993) of expressive behavior, which have been shown to be able to predict various outcomes, for example in negotiations (Curhan & Pentland, 2007) and regarding perceptions of leadership (Tskhay, Xu, & Rule, 2014). Due to the capability of social sensing methods to record behavior as it occurs, these methods may facilitate the analyses of the temporal development and dynamic aspects of leadership behavior and leadership perceptions, such as changes in the quality and quantity of exerted behavior, which would be of special interest for the research areas of informal leadership development and leadership emergence in groups. Additionally, as interpersonal interactions often include different types of behavior, researchers can analyse combinations of these behaviors (Sanchez-Cortes et al., 2013) in order to gain insight into the way different behaviors are processed in order to shape interpersonal perceptions. Nevertheless, the type of social sensing method should be carefully considered regarding the study’s setting and nature of the measured behavior and pretested to secure the applicability (Chaffin et al., 2015). We recommend testing the devices both in a set-up that is as similar as possible to the planned study setting (environment and number of subjects) and by comparing the captured behaviors to manual observer recordings. Additionally, researchers should conduct a systematic evaluation of the variability between the devices (both within and between) through systematic manipulation of the environment in an experimental setting. Wearable sensors have the ability to steer sensor-based assessment methods out of the laboratory and into the field. In order to secure a congruency between the behaviors captured by technological devices and the requirements of leadership research, researchers should start including sensor-based assessment into research, or at least expose themselves to the possibilities of this technology. By actively taking part in the development processes, researchers can help developers and provide new methods for future leadership research.
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Observational and sensor-based methods 101 C.J.,. . .Schmucker, D.G. (2012). The comprehensive assessment of team member effectiveness: Development of a behaviorally anchored rating scale for self- and peer evaluation. Academy of Management Learning & Education, 11(4), 609–630. doi: 10.5465/amle.2010.0177 Olguín Olguín, D. (2007). Sociometric badges: Wearable technology for measuring human behavior. Master’s thesis. Retrieved from http://dspace.mit.edu/handle/1721.1/42169 Olguín Olguín, D., & Pentland, A. (2010). Sensor-based organisational design and engineering. International Journal of Organisational Design and Engineering, 1(1/2), 69–97. doi: 10.1504/IJODE.2010.035187 Parker, S.H., Flin, R., McKinley, A., & Yule, S. (2013). The Surgeons’ Leadership Inventory (SLI): A taxonomy and rating system for surgeons’ intraoperative leadership skills. American Journal of Surgery, 205(6), 745–751. doi: 10.1016/j.amjsurg.2012.02.020 Parker, S.H., Yule, S., Flin, R., & McKinley, A. (2011). Towards a model of surgeons’ leadership in the operating room. BMJ Quality & Safety, 20(7), 570–579. doi: 10.1136/ bmjqs.2010.040295 Parker, S.H., Yule, S., Flin, R., & McKinley, A. (2012). Surgeons’ leadership in the operating room: An observational study. American Journal of Surgery, 204(3), 347–354. doi: 10.1016/j.amjsurg.2011.03.009 Pentland, A.S. (2008). Honest signals: How they shape our world. Cambridge, MA: MIT Press. Perkins, R.D. (2009). How executive coaching can change leader behavior and improve meeting effectiveness: An exploratory study. Consulting Psychology Journal: Practice and Research, 61(4), 298–318. doi: 10.1037/a0017842 Prell, C. (2012). Social network analysis. London: Sage. Ray, R.D., Ray, J.M., Eckerman, D.A., Milkosky, L.M., & Gillins, L.J. (2011). Operations analysis of behavioral observation procedures: A taxonomy for modeling in an expert training system. Behavior Research Methods, 43(3), 616–634. doi: 10.3758/s13428-011-0140-6 R Development Core Team. (2015). R: A language and environment for statistical computing, version 3.2.3 computer software. Retrieved from http://www.r-project.org Reeder, G.D., & Brewer, M.B. (1979). A schematic model of dispositional attribution in interpersonal perception. Psychological Review, 86(1), 61–79. doi: 10.1037/0033-295X.86.1.61 Rice, R.W., & Chemers, M.M. (1975). Personality and situational determinants of leader behavior. Journal of Applied Psychology, 60(1), 20–27. doi: 10.1037/h0076362 Rush, M., Thomas, J., & Lord, R.G. (1977). Implicit leadership theory: A potential threat to the internal validity of leader behavior questionnaires. Organizational Behavior and Human Performance, 20(1), 93–110. Sanchez-Cortes, D., Aran, O., Jayagopi, D.B., Schmid Mast, M., & Gatica-Perez, D. (2013). Emergent leaders through looking and speaking: From audio-visual data to multimodal recognition. Journal on Multimodal User Interfaces, 7(1), 39–53. doi: 10.1007/ s12193-012-0101-0 Santos, J.P., Caetano, A., & Tavares, S.M. (2015). Is training leaders in functional leadership a useful tool for improving the performance of leadership functions and team effectiveness? Leadership Quarterly, 26(3), 470–484. doi: 10.1016/j.leaqua.2015.02.010 Schermuly, C.C., & Scholl, W. (2011). IKD – Instrument zur Kodierung von Diskussionen [The Discussion Coding Scheme]. Göttingen, Germany: Hogrefe. Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology: Automated interpersonal behavior assessment. Current Directions in Psychological Science, 24(2), 154–160. doi: 10.1177/0963721414560811 Schyns, B., & Mohr, G. (2004). Non-verbal elements of leadership behaviour. German Journal of Research in Human Resource Management, 18(3), 289–305. Retrieved from http://journals.sagepub.com/doi/abs/10.1177/239700220401800303 Sims, H.P., & Manz, C.C. (1984). Observing leader behavior: Toward reciprocal determinism in leadership theory. Journal of Applied Psychology, 6(2), 222–232. doi: 10.1037/0021-9010.69.2.222 Stogdill, R.M. (1963). Manual for the Leader Behavior Description Questionnaire – Form XII. Columbus, OH: Bureau of Business Research, Ohio State University.
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102 Handbook of methods in leadership research Taylor, P.J., Russ-Eft, D.F., & Chan, D.W.L. (2005). A meta-analytic review of behavior modeling training. Journal of Applied Psychology, 90(4), 692–709. doi: 10.1037/0021-9010.90.4.692 Thornton, G.C., & Zorich, S. (1980). Training to improve observer accuracy. Journal of Applied Psychology, 65(3), 351–354. doi: 10.1037/0021-9010.65.3.351 Tskhay, K.O., Xu, H., & Rule, N.O. (2014). Perceptions of leadership success from nonverbal cues communicated by orchestra conductors. The Leadership Quarterly, 25(5), 901–911. doi: 10.1016/j.leaqua.2014.07.001 Van Knippenberg, D., & Sitkin, S.B. (2013). A critical assessment of charismatic – transformational leadership research: Back to the drawing board? The Academy of Management Annals, 7(1), 1–60. doi: 10.1080/19416520.2013.759433 Yukl, G. (2012). Effective leadership behavior : What we know and what questions need more attention. Academy of Management Perspectives, 26(4), 66–85. doi: 10.5465/ amp.2012.0088 Yukl, G. (2013). Leadership in organizations. Harlow, UK: Pearson Education. Yukl, G., & Nemeroff, W. (1979). Identification and measurement of specific categories of leadership behavior: A progress report. In J.G. Hunt & L.L. Larson (Eds.), Crosscurrents in leadership (pp. 164–200). Carbondale, IL: Southern Illinois University Press. Yukl, G., Gordon, A., & Taber, T. (2002). A hierarchical taxonomy of leadership behavior: Integrating a half century of behavior research. Journal of Leadership & Organizational Behavior, 9(4), 15–32. doi: 10.1177/107179190200900102
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5. The contribution of sophisticated facial expression coding to leadership research Savvas Trichas
INTRODUCTION Previous studies have shown that facial expressions can exert substantial influence on general interpersonal perception (Zebrowitz & Montepare, 2008), and specifically on leadership perception (Stewart, 2010). Quite a few professions entail the expression and keen perception of emotion as part of their work role such as doctors, sales people, funeral directors, flight attendants, police interrogators, and bill collectors (Hochschild, 1983; Rafaeli & Sutton, 1987; Sutton & Rafaeli, 1988). A number of leadership studies have stressed the significance of facial displays (e.g., Bucy, 2000; Bucy & Bradley, 2004; Masters & Sullivan, 1989). Specifically, the importance of leaders’ emotional displays is emphasized in research on educational, organizational, and political leadership (Beatty, 2001; Bono & Ilies, 2006; Humphrey, Pollack, & Hawver, 2008; Stewart, Bucy, & Méhu, 2015). However, our knowledge regarding the impact of leaders’ facial expressions on others’ perceptions of leadership is still limited. Although there is already a considerable amount of research on leaders’ emotional displays (e.g., Bucy & Bradley, 2004; Lewis, 2000) the majority of studies ignore the benefits that can emerge from incorporating facial expression analysis techniques currently used in the field of facial expression research (Ekman, Friesen, & Hager, 2002). The few studies that have used sophisticated facial expression analysis in the context of leadership demonstrate the importance of accurately describing facial expressions when interpreting results. In such studies (Stewart, Waller, & Schubert, 2009; Trichas & Schyns, 2012) emphasis was placed on the accuracy of describing facial expressions in order to increase the depth of analysis. Research has shown that subtle differences between facial expressions can result in different perceptions (Surakka & Hietanen, 1998), highlighting the importance of accurate descriptions of facial displays. Furthermore, beyond visual coding, there are also temporal aspects that need to be considered, such as onset, offset, and apex duration, that all significantly influence observers’ evaluations of facial expressions (see Krumhuber & 103
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104 Handbook of methods in leadership research Kappas, 2005; Krumhuber, Manstead, Cosker, Marshall, & Rosin, 2009; Krumhuber, Manstead, & Kappas, 2006). The aim of the present work is to contribute to our understanding of the interaction between leadership and leaders’ facial expressions. Specifically, it is argued that the area of leadership could benefit from the incorporation of detailed facial expression coding techniques. Sample research questions that leadership scholars can investigate with the use of such methods are: (1) What are the intensity ranges of leaders’ appropriate facial behavior within specific organizational scenarios and how are these related with leadership perceptions? Exploration of such issues is interesting as awareness of appropriateness of expression intensity ranges could contribute to leadership perceptions, while being inattentive to the margins set by these ranges could mean the opposite. (2) How do facial expressions that appear on leaders’ faces for less than a second impact observers’ reactions? Leaders who are aware of the influence of such brief displays to observers’ reactions may be tuned to their own emotions and be more efficient in connecting with their followers. (3) How is the temporal development, from onset to apex, related to the perceived authenticity of a leader’s facial display? Taking into consideration that authentic expressions are linked with leadership influence (Newcombe & Ashkanasy, 2002) exploring facial expressions’ temporal aspects could provide leaders with a deeper understanding of the impact of their (and others’) facial expressions on characteristics related with leadership perceptions such as trustworthiness, thus increasing their communicational precision. The integration of such methods can eventually contribute not only to increasing research awareness but also to improving our communicative knowledge in the context of leadership. The above questions are a small sample of research potential created with the use of detailed facial coding, and each can be explored by incorporating sophisticated facial coding analysis into leadership research designs. The increased precision of analysis may ultimately help to uncover new angles that will add to our understanding of leadership processes. To support the above argument, in the following sections, literature from two areas is reviewed, namely leadership perception and facial expression. After a general introduction to facial expressions and perceptual concepts, the main literature on leaders’ facial expressions is discussed. Additionally, an argument is constructed that supports the use of sophisticated facial expression coding analysis to increasing credibility of leadership research designs. Leadership studies that included such sophisticated facial expression techniques are then presented with an extended discussion of the research of Trichas and Schyns (2012). Finally, the general discussion and conclusions follow together with implications and recommendations for leadership research and practice.
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THE COMMUNICATIVE FUNCTION OF FACIAL EXPRESSIONS Human beings begin to understand the value of facial expression early on in life. Even infants are able to perceive faces and react to the facial expressions they observe (Field, Woodson, Greenberg, & Cohen, 1982; Tronick, 1989). The way the face functions is fascinating – many muscles together transmit complex meanings by simple movement combinations. Facial expression is a non-verbal communication channel that receives a lot of research attention since it gathers together the vast majority of the sensory organs, plus the brain, in one region (Cohn & Ekman, 2008). Darwin (1872 [1962]) posits that while facial actions were originally displayed as elements of, or indications of, survival movements, such as protecting the eyes or uncovering the teeth to prepare an attack, such behaviors have evolved into complex mechanisms of transmitting emotions and interpersonal intentions. Parkinson (2005) stresses the power of facial expressions to convey not just emotion-relevant information but also social intentions and practical meanings. These practical meanings and inferences of intentions during social encounters are constructed automatically, effortlessly, and can be based on little information (Schneid, Carlston, & Skowronski, 2015; Uleman, Saribay, & Gonzalez, 2008; Wang, Xia, & Yang, 2015). This is because there is a need to make sense of information received during communication, which forces the human brain to assign justification labels (Hassin, Bargh, & Uleman, 2002). Drawing from Darwin’s seminal work, modern emotion research converges to the point that emotional facial expressions are used to communicate information relevant to the behavioral tendencies of the transmitter, thus influencing person perception and impression formation (Adams, Ambadi, Macrae, & Kleck, 2006; Cañadas, Lupiáñez, Kawakami, Niedenthal, & Rodríguez-Bailón, 2016; Hendriks & Vingerhoets, 2006). Put differently, observers use facial expressions to decode others’ emotions but also to assume intentions and permanent behavioral characteristics (Franklin & Zebrowitz, 2013; Fridlund, 1994; Hess, Blairy, & Kleck, 2000; Montepare & Dobish, 2003; Shariff & Tracy, 2011; Tiedens, 2001). In addition, ecological theory maintains that facial expressions of emotion are processed in an adaptive manner, through expectations generated from interactions with other similar facial expressions, in order to facilitate people’s adaptation to their social environment (Zebrowitz & Montepare, 2008). Facial displays signal underlying emotions but also have an approach, attack, or avoidance component that leads to meaningful impressions (Marsh, Ambady, & Kleck, 2005; McArthur &
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106 Handbook of methods in leadership research Baron, 1983; Monahan, 1998). In other words, the observation of facial expressions generates overgeneralized, biased impressions (Zebrowitz & Montepare, 2005). For example, when someone sees a person displaying anger, besides inferring the transmitter’s emotional state at the moment, the attack mode assumed by observers may be viewed as a characteristic of an aggressive, domineering, and dominant individual (Hareli & Hess, 2010; Madera & Smith, 2009; Montepare & Zebrowitz-McArthur, 1998). Facial displays influence perceivers’ reactions even when exposed for a very short amount of time. Short-lived facial displays, often referred to as micro-expressions, frequently range from 1/5th to 1/25th of a second and are often associated with concealment of emotion (Ekman, 1992, 2001, 2003b; Hurley, Anker, Frank, Matsumoto, & Hwang, 2014). Based on the premise that facial muscles are linked to brain activity, when emotions are experienced, respective muscle movements may involuntarily appear on the face for a fraction of a second (Bhushan, 2015; Frank & Ekman, 1997; Jenkins & Johnson, 1977). For instance, a person can be smiling, change to a facial expression of disgust, and back to smiling in less than a second. When micro-expressions are captured and presented in slow motion or static frames, they can have a clear emotional meaning to observers (Ekman, 2003a). However, these brief facial expressions are often difficult to detect and may affect individuals without their awareness (Winkielman & Berridge, 2004). Specifically, a considerable number of studies demonstrate that brief exposure to facial displays of basic emotions such as happiness, anger, and fear can influence observers’ reactions on a subconscious level (Channouf, 2000; Marsh & Ambady, 2007; Perron, Roy-Charland, Chamberland, Bleach, & Pelot, 2016; Winkielman, & Berridge, 2003, 2004). Moreover, prior research indicates that brief exposure to facial expressions (as much as 30 milliseconds) can significantly influence observers’ facial muscle tone, dermal reactions, cardiovascular responses, and can cause alterations in amygdala regional blood flow (Dimberg, Thunberg, & Elmehed, 2000; Wild, Erb, & Bartels, 2001; Winkielman et al., 2005). To conclude, facial displays and their impact on observers’ perceptions is an area that has been receiving increased research attention within the past decades. Based on the last few paragraphs it seems that facial expressions displayed at normal speed or in less than a second significantly impact perceivers’ reactions. In the subsequent section, the traditional research on emotions and leaders’ facial expressions is reviewed.
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TRADITIONAL RESEARCH ON EMOTIONS AND LEADERS’ FACIAL EXPRESSIONS The significance of leaders’ facial expressions has been highlighted in a variety of studies (e.g., Schyns & Morh, 2004; Stewart, 2010; Trichas, 2015). Three different types of studies are most pertinent to the perception of leadership from facial expressions: political leaders’ emotional displays (e.g., Bucy, 2000; Sullivan & Masters, 1988), leaders’ general emotional displays (e.g., Damen, Van Knippenberg, & Van Knippenberg, 2008; Glomb & Hulin, 1997), and exploration of the influence of facial displays on the perception of traits linked with leadership such as charisma, power, dominance, and trustworthiness (e.g., Awamleh & Gardner, 1999; Lau, 1982; Mazur & Mueller, 1996). In the following paragraphs, the traditional research on emotions and leaders’ facial expressions is introduced. In the research on political leaders’ displays of emotion, observers were asked to assess videos from renowned politicians’ public appearances (e.g., Bill Clinton in Bucy & Newhagen, 1999; Barack Obama in Gong & Bucy, 2016; George W. Bush in Cherulnik, Donley, Wiewel, & Miller, 2001). For instance, Bucy and Newhagen (1999) explored the influence of video-recorded presidential displays, in the context of public appearance, on recollection, thinking elaboration, and appropriateness assessments of respondents. The respondents of the aforementioned study considered negative and low-intensity leader displays more appropriate and hence evaluated them more favorably. On the contrary, positive or intense leader reactions contradicted participants’ expectations and were categorized as inappropriate (Bucy & Newhagen, 1999). Also, Gong and Bucy (2016) used television news excerpts of the 2012 US presidential debates to investigate the effect of leader appropriateness on observers’ non-verbal expectancies. Their results indicated that inappropriate facial expressions boosted viewers’ visual attention and educed negative reactions. Finally, Cherulnik et al. (2001) asked observers to evaluate both professional actors performing a fake campaign speech, and politicians (Bill Clinton and George Bush) manipulating charisma-related facial behavior factors such as frequency and valence of transmitters’ smiling, and duration and frequency of eye contact with the audience. Their results show that leaders’ charismatic behavior leads to emotional contagion, which refers to the tendency to automatically mimic and synchronize body movements, such as facial expressions with those of another individual and, therefore, to converge emotionally (Barsade, 2002; Howard & Gengler, 2001; Sy, Cȏté, & Saavedra, 2005; Wild et al., 2001). Specifically, the participants in these studies revealed higher levels of the behaviors exhibited by c harismatic leaders, thus supporting emotional contagion effects.
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108 Handbook of methods in leadership research A second line of research investigated the impact of leaders’ general emotional displays on participants’ evaluations (e.g., Damen et al., 2008; Gaddis, Connelly, & Mumford, 2004; Glomb, & Hulin, 1997), using mainly manipulated facial expressions performed by leaders. A number of studies found that observers evaluated leaders’ negative emotional expressions negatively (Gaddis et al., 2004; Glomb & Hulin, 1997; Lewis, 2000; Medvedeff, 2008) and others showed that observers evaluated leaders’ positive emotional expressions positively (Awamleh & Gardner, 1999; Medvedeff, 2008). For example, Newcombe and Ashkanasy (2002) showed their participants video recordings of leaders providing positive or negative feedback and displaying facial expressions compatible or incompatible with the message communicated. They found that positive feedback and facial expressions congruent with the verbal message communicated resulted in a more positive evaluation of the respective leader’s negotiating latitude than negative or message-incongruent expressions. In another study using an actor displaying angry or happy facial expressions, Van Kleef et al. (2009) showed that motivation to learn influenced which type of affective expressions improved team task performance: participants high in epistemic motivation performed better when the leader showed anger expressions. When motivation to learn was low, the participants showed better task performance after viewing happy expressions. Furthermore, Cherulnik et al. (2001) found that emotional variables such as intensity and frequency of smiles and eye gaze predicted perception of charismatic leadership. Melwani, Mueller, and Overbeck (2012) investigated the effect of compassion and contempt on evaluations of leadership. Specifically, participants’ social perception, leadership judgments, and discrete emotion ratings were evaluated in an interview context. Participants’ discrete emotions were evaluated taking into account facial expression, verbal tone, and body language. Their results indicate that conveying emotions of contempt and compassion is positively related to leadership perceptions. Finally, Medvedeff (2008) experimented with leaders’ negative feedback from a web camera. The manipulation of affective displays included, again: facial features, voice, and gestures for positive, negative, and neutral affective displays. The participants judged positive affective feedback more positively and negative affective feedback more negatively. Besides the research mentioned above, a third line of studies used facial expressions to investigate leadership-related traits such as dominance (Keating, Mazur, & Segall, 1981; Kraus & Chen, 2013; Mazur & Mueller, 1996; Montepare & Dobish, 2003), status (Keating, 2003; Keating, Mazur, & Segall, 1977), power (Dovidio, Heltman, Brown, Ellyson, & Keating, 1988), trustworthiness (Krumhuber, Manstead, Cosker, Marshall, & Rosin, 2007), and charisma (Awamleh & Gardner, 1999; Shea & Howell,
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Sophisticated facial expression coding 109 1999). For example, Kraus and Chen (2013) found that professional fighters during a prefight stare-down with their challenger were perceived as less dominant when they smiled than when their expression was neutral. In addition, Awamleh and Gardner (1999) linked leaders’ smiles in the context of giving a speech with the perception of charisma. Finally, Montepare and Dobish (2003) presented untrained actors posing emotions to participants and asked them to evaluate the actors in terms of emotions and trait impressions. Their findings showed that the emotion displayed in facial expressions shifted impressions of dominance and affiliation. Specifically, happy and surprised facial expressions increased perceived dominance and affiliation, angry facial expressions increased perceived dominance and decreased perceived affiliation, and sad and fear expressions decreased perceived dominance. The studies reviewed in the last few paragraphs contributed to our understanding of the perception of leaders’ emotional expressions (e.g., Bucy, 2000; Lewis, 2000; Mazur & Mueller, 1996). However, our knowledge regarding the actual impact of leaders’ facial displays is still restricted as the traditional research in the field of leadership has neglected the added contribution that can emerge from the integration of sophisticated facial action coding analysis (e.g., Hess et al., 2000; Knutson, 1996). Specifying exact facial muscle movement, intensity level, and timing can increase depth of analysis, hence expanding the range of research angles to be explored. To illustrate with an example, the use of detailed facial expression coding techniques enables scholars to determine whether a negative leader display (e.g., Bucy & Newhagen, 1999) involves eyebrows pulling together (indicator of anger) or eyebrows raising and pulling together (indicator of sadness), identifying level of intensity (from a single trace to maximum muscle activity), and determining the progress of expressions in time from onset to offset. All three factors mentioned above have been shown to significantly influence perceivers’ reactions, so by accurately specifying them and/or controlling for them could add to the precision of leadership research (e.g., Ambadar, Cohn, & Reed, 2009; Trichas & Schyns, 2012). In the following section, basic concepts in measurement of facial muscle movements are presented and the importance of integrating sophisticated facial expression analysis in leadership research is discussed.
MEASURING FACIAL EXPRESSION There are three main types of methods for measuring facial expression: facial electromyography (EMG), automatic facial image analysis, and manual facial expression coding (Cohn & Ekman, 2008). EMG involves
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110 Handbook of methods in leadership research the use of electrodes in order to measure the activity of the facial muscles. Although this method allows researchers to detect very subtle, non-visible facial muscle movements, the attachment of electrodes to the area of the face inhibits naturalistic facial expression and restricts researchers’ ability to use the instrument outside laboratory settings. Automatic facial image analysis uses computerized coding of facial expressions, which requires a lesser degree of manual processing (Littlewort et al., 2011). Automatic analysis is a promising field that can be applied with reasonable accuracy but it is still a developing methodology that needs to better address issues such as the evaluation of the full range of facial action units, intensities, and temporal aspects (Valstar, Méhu, Jiang, Pantic, & Scherer, 2012). Manual coding is the most frequent method used to measure facial behavior in facial expression research. It allows for both live observation and pre-recorded behavior (videos or photos) with high levels of validity (Cohn & Ekman, 2008) and reliability (Sayette, Cohn, Wertz, Perrott, & Parrot, 2001). The focus in this chapter is on manual coding. According to Cohn and Ekman (2008), there are three aspects of facial expressions that can and should be assessed in order to increase credibility in describing facial expressions: type, intensity, and timing. Type indicates the facial muscle that moved. When a facial expression appears, we can observe changes caused by muscle activity underneath the skin from non-static facial parts such as eyebrows, cheeks, lips, chin, the eyelids, and so on (Ekman et al., 2002). An evident facial expression is the movement observed as a result of modification of muscle positioning under the skin and the wrinkling, pouching, and bulging that result from this modification. For a smiling facial display, type would be described as: pulling of the corners of the lips back and obliquely upward, deepening of the oblique lines between the cheek and the mouth starting from the nostril corners, deepening of the line below the lower eyelid before the cheekbone, raising of the area between the oblique lines between the cheek and the mouth starting from the nostril corners and below the lower eyelid before the cheekbone (infraorbital triangle; see Ekman et al., 2002), skin bagging under the lower eyelid, production of crow’s feet wrinkles at the corners of the eyes, narrowing the eye aperture, possible nostril raising and widening, and possible chin boss skin stretching (ibid.). Besides type, a facial muscle movement might also differ in intensity. Intensity refers to the strength of muscle movement in terms of the changes to facial appearance. Sophisticated facial expression coding distinguishes five different levels of intensity: A – Trace, B – Slight, C – Marked, D – Extreme, and E – Maximum (ibid.). Consequently, the degree of the deepening of the oblique lines between the cheek and the mouth starting from the nostril corners (nasolabial furrow; ibid.) in the smiling display described above
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Smile 1: low intensity
Smile 1: high intensity
Figure 5.1 Low-intensity versus high-intensity smiling facial expressions can range from trace to maximum, indicating a lower or higher intensity level respectively (Figure 5.1). Finally, timing refers to the temporal development of the expression from onset, to apex, and back to the expression’s offset. Specifically, from one facial expression to another there might be differences in onset and/or offset speed, and general duration (Ambadar, Cohn, & Reed, 2009). The depth of analysis regarding the timing parameter increases when researchers explore individual facial action units (e.g., lip corner pull) as they can investigate if these appear simultaneously or sequentially. Sophisticated Facial Expression Description and the Facial Action Coding System There are a number of systems used for the coding of facial expression. Such examples are the Affective Expressions Scoring System (AFFEX; Izard, Dougherty, & Hembree, 1983) and the Maximally Discriminative Facial Movement Coding System (MAX; Izard, 1983). Last, there is the Facial Action Coding System (FACS; Ekman et al., 2002), considered to be the most comprehensive system for coding and decoding facial expression (Cohn, Ambadar, & Ekman, 2007). The basic methodology of these systems is to describe changes in terms of facial muscle movement by viewing recordings of facial behavior such as photographs or videos. Although the information reported below regarding FACS is fundamental in understanding the underlying philosophy of the specific manual coding technique, the next few paragraphs cannot teach the reader how to code facial behavior. Understanding that facial expressions are the result of combinations of facial muscle movements, Ekman and his colleagues constructed the Facial Action Coding System (FACS; Ekman et al., 2002) to account
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112 Handbook of methods in leadership research for every visible movement. FACS is one of the most widely used facial expression coding instruments in the behavioral sciences (Littlewort et al., 2011). It is an anatomically based instrument that is used to describe all observable muscle movement changes in the appearance of the face (Ekman et al., 2002). In order to adequately code an expression, observation of the facial behavior in slow motion and at a frame-by-frame rate is required (Cohn, Zlochoher, Lien, & Kanade, 1999). FACS objectively identifies 44 basic facial muscle movements called action units (AU), and sketches a compound system for evaluating their occurrence (Ekman & Rosenberg, 1997; Sayette et al., 2001). In other words, with the use of the above instrument, coders can mark nearly all visible changes in facial appearance as a result of facial muscle movement. The marking of these changes includes all three aspects of facial expressions mentioned earlier (type, intensity, and timing) as facial muscle movement, level of intensity, and timing of expression are assessed in FACS coding. Also, the use of a neutral frame of the face without any expression serves as a reference point or a baseline, and is considered critical in pinpointing the exact facial muscle movement and intensity (Figure 5.2). In particular, FACS coding involves identifying the muscles that moved in the frame with the facial expression and comparing the appearance to the frame with the neutral face results. For example, in Figure 5.2, a careful comparison of the frame with the facial expression to the frame depicting the actor’s neutral face reveals: severe raising of the inner brow corner and pulling together of eyebrows, extreme cheek raising and eyelid tightening, slight lifting of the upper lip at an angle, and slight deepening
Neutral face
Facial expression
Figure 5.2 T he neutral face as a reference point for identifying exact facial muscle movement and intensity
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Sophisticated facial expression coding 113 of the nasolabial furrow. Finally, the use of FACS allows for identification of AU combinations, which are argued to be the most frequent signs of basic emotions such as happiness, anger, fear, contempt, surprise, sadness, and disgust (Ekman et al., 2002). Based on the coded description of Figure 5.2, it is also suggested that the facial expression contains indicators of sadness and distress. Because of its high level of validity and reliability (Cohn & Ekman, 2008; Sayette et al., 2001), FACS has been used in several research areas including leadership, psychology, neuroscience, forensics, and computer graphics (see Sayette et al., 2001; Trichas, 2015). Importance of Sophisticated Facial Expression Description in Leadership Research As pointed out earlier in this chapter, modern FACS involves the marking of exact facial muscle movement, intensity, and timing (Ekman et al., 2002). The example below illustrates the relevance of such sophistication in coding for leadership research: as reported earlier in the section regarding traditional research on emotions and leaders’ facial expressions, Newcombe and Ashkanasy (2002) found that positive and messagecongruent facial expressions resulted in a more positive evaluation of the respective leader’s negotiating latitude than negative and messageincongruent expressions. In terms of facial muscle movement, intensity, and timing, the term “positive facial expression” is open to more than one interpretation. Concerning facial muscle movement, sample results reveal that subjects display more positive reactions to smiles with cheek raising and crow’s feet wrinkles at the corners of the eyes (also called Duchenne smiles) than they did when observing smiles without the cooccurring cheek and eye movements (e.g., Gunnery & Hall, 2014; Surakka & Hietanen, 1998). Such results are relevant for leadership research as they reveal that accurately identifying facial muscle movement may be crucial to how a leader is perceived. The significance of intensity is also supported empirically. For instance, research shows that observers’ formulate intensity expectations for facial expressions according to specific information set by communication context (Trichas & Schyns, 2012). For example, beholders might have quite different expectations of smile intensity from a leader greeting an employee from when the same leader is meeting an old friend. Leadership scholars can explore such research hypotheses, thus adding new areas of investigation to the leadership literature. Finally, with regard to the timing factor, Krumhuber et al. (2009) suggested that the temporal development of dynamic facial expression from onset to offset plays a significant role in the perceptual process. For example, Krumhuber and Kappas (2005) showed that slower onsets in smiling facial
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114 Handbook of methods in leadership research expressions are linked with more genuine impressions. Considering that leader authenticity is an area that has always been considered important to the concept of leadership (Bono & Ilies, 2006), linking temporal aspects of facial expression with leader authenticity might be another significant contribution of sophisticated facial coding analysis. To conclude, all the examples of positive facial expressions mentioned above might be labeled with a word or phrase, such as “smile.” The word smile gives the reader a main idea of what observers see in the facial action combinations involving a pull of the lips’ corners back and obliquely upward. However, it is vital to understand that when dealing with facial expressions, the perceptual impact of a smile could be different as a result of minor differences in muscle movement, intensity, or timing (e.g., Krumhuber & Kappas, 2005; Surakka & Hietanen, 1998; Trichas & Schyns, 2012). Consequently, the identification of these facial expression aspects in the coding procedure could contribute to increasing the profundity of analysis and the accuracy of results, thus adding to the credibility of leadership research on facial expressions (see Rosenberg, 2005).
LEADERSHIP STUDIES USING SOPHISTICATED FACIAL CODING ANALYSIS To the author’s knowledge, there are only a few studies that use detailed facial coding analysis to explore the influences of facial expressions in the context of leadership (Stewart et al., 2009; Stewart & Dowe, 2013; Stewart, Bucy, & Méhu, 2015; Stewart, Méhu, & Salter, 2015; Tsai et al., 2016). To begin with, Stewart et al. (2009) conducted a study on political leadership that used sophisticated facial expression coding to investigate observers’ responses. Specifically, in their study, smiling micro-momentary expressions (very briefly exposed emotional facial displays; see Ekman, 2009) were isolated and removed from former US President George W. Bush’s speech. These briefly exposed expressions depicting muscle movement with emotional meaning were identified by coding the video footage of the former president’s facial behavior in slow motion and at a frame-by-frame rate using FACS. The study showed that removing the specific smiling frames (positive micro-expressions) resulted in the respondents experiencing more feelings of anger and threat, thus indicating that viewers’ impressions can be influenced by leaders’ facial expressions of emotion even if these last less than a second. In a different study by Stewart and Dowe (2013), sophisticated facial expression coding was also employed to determine the facial actions displayed during emotional expressions exhibited by President Obama. Their
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Sophisticated facial expression coding 115 results revealed that observers were influenced by marked but also by slight muscle facial movements displayed by the president. For instance, President Obama displayed several distinct “types” of smiles. The president’s smiles that also activated the muscle causing the lower lip to move upwards received lower happiness/reassurance and higher anger/threat ratings than smiles that did not activate this lip muscle movement. Stewart, Méhu, and Salter (2015) used FACS to examine sex differences in leaders with regard to facial expression of emotion. Sample results show that facial expressions of anger in different combinations of facial muscle movement and intensity are more likely to be recognized precisely when displayed by male rather than female leaders. Furthermore, Stewart, Bucy, and Méhu (2015) explored different types of smiles of Republican presidential candidates, revealing the complexity of leader smiling behavior and the effect these different smiles have on observers’ perceptions. An important finding of the study was the significant effect subtle alterations regarding muscle movement and intensity of political leaders’ smiles had on viewers’ perceptions. For example, smiles including a contraction of the muscles surrounding the eyes received higher happiness/reassurance and low anger/threat ratings than smiles presented without the eye muscle contractions (ibid.). Other simple changes to the presidential candidates’ smiles such as tightening or pressing together the lips, pressing up the lower lip, or downward pull of the lip corners were found to result in significantly lower happiness/reassurance and to raise anger/threat ratings. In another study investigating leaders’ smiling behavior, Tsai et al. (2016) used FACS to explore cultural differences between smiles that pulled the corners of the lips back and obliquely upward with some contraction of the muscles surrounding the eyes (calm smiles) and smiles that included more intense muscle activation including jaw dropping and teeth showing (excited smiles). A significant finding of the above research is that American leaders exhibited more smiles of excitement compared to Tawainese/Chinese leaders. Extending from the above results Tsai et al. (2016) found that the display of leaders’ excitement smiles was a reflection of the extent the respective nation appreciated excitement as a positive state of high stimulation. On the other hand, the more a nation appreciated calmness as a positive state of low stimulation, the more the leader of that nation displayed calm smiles. Finally, another study that employed sophisticated facial coding methods to investigate leadership concepts is by Trichas and Schyns (2012). The research by Trichas and Schyns (2012) will be examined more thoroughly in the following section as an extended example to discuss the integration of detailed facial expression methods in leadership research.
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116 Handbook of methods in leadership research Extended Example: Trichas and Schyns (2012) Trichas and Schyns (2012), in a multi-study design, used sophisticated facial expression coding techniques to link observers’ reactions to leaders’ facial expressions with implicit leadership theories (mental concepts people have regarding behaviors, characteristics, and attitudes of leaders; see Dinh, Lord, Gardner, Meuser, Liden, & Hu, 2014; Dinh, Lord, & Hoffman, 2014; Epitropaki & Martin, 2004; and Epitropaki, Sy, Martin, Tram-Quon, & Topakas, 2013). Initially, Trichas and Schyns (2012) evaluated participants’ implicit leadership theories (ILTs). Furthermore, a business context was activated by framing photos of actors/leaders within the organization and asking the participants to assess them in terms of leadership. Specifically, the researchers asked observers to evaluate the actors/leaders exhibiting several eyebrow movements. These included eyebrow lowering and pulling together, and eyebrow raising and pulling together in different intensities. The facial expressions used in study 1 of Trichas and Schyns (2012) were coded in terms of facial muscle movement and intensity using FACS. The findings of study 1 revealed that simple brow movements such as the lowering and pulling together of the eyebrows resulted in strong but hostile leadership perceptions. In contrast, the raising and pulling together of the eyebrows was perceived as a sign of weakness, which resulted in decreasing overall ratings of leadership perception. In general, these results suggest that appearing stern might constitute a more solid foundation for leadership perception than appearing kind. In a broader view, these findings indicate that simple alterations of eyebrow movements can result in significant differences in leadership perception. This finding is consistent with previous findings highlighting the importance of subtle differences in the perceptual process (e.g., Snodgrass, 1992; Surakka & Hietanen, 1998). It is essential for leaders to understand the importance of this, as being aware of the impact of subtle details in facial expressions can help to improve accuracy of communication and ultimately shape perception. It is also vital for leadership scholars to understand that facial expression coding not only provides such awareness but also enables researchers to explore these subtle details in their designs. The use of sophisticated facial expression analysis in Trichas and Schyns (2012) was crucial in study 1 as the coding procedure allowed the researchers to isolate and use only the few action units required, thus increasing both flexibility and precision on stimuli construction. In their second study, Trichas and Schyns (2012) aimed to explore the effect of leadership perception considering additional factors such as communication context, appropriateness of expression, and authenticity.
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Sophisticated facial expression coding 117 Consequently, several pictures with facial actions/indicators of different emotions were set up in a three-stage scenario (introduction, negotiation, shaking hands before leaving) regarding a loan discussion between a bank manager and a client. The facial expressions again included eyebrows lowering and pulling together and eyebrows raising and pulling together. Additionally, smiling facial expressions with different muscle combinations and intensities were used. Specifically, the actor displayed smiles either with or without activation of the muscles around the eyes, a marker often associated with authentic smiles in static facial expressions (e.g., Gunnery & Ruben, 2016). All the facial expressions used in study 2 of Trichas and Schyns (2012) were also coded using FACS. The respondents in study 2 indicated an overall preference for leaders exhibiting smiles compared to the rest of the facial expressions. However, the results indicate that these reactions were context dependent. Specifically, during the introduction respondents considered lowintensity smiles without activation of the muscles around the eyes more appropriate. A probable reason for participants’ preferences towards pictures of non-authentic smiles might be that at the beginning of a meeting with a client, a leader is expected to simply maintain a positive tone without exaggeration. In the context of shaking hands before leaving, the participants preferred high-intensity smiles with activation of the muscles surrounding the eyes. This may be because after the negotiations, just before saying goodbye, association between the two parties is at a higher level and thus more intense, and authentic expressions are deemed appropriate. These results are important because they show that aspects of facial expression such as perceived authenticity and intensity of display may depend on the level of association between a leader and a perceiver at the time of expression. Therefore, understanding leader facial displays becomes a matter of analysing both the facial expressions and (social or situational) context. Finally, leadership first impressions from the facial expressions in study 2 were compared to observers’ ILTs. Based on Hall and Lord’s (1995) perspective arguing that people use their ILTs as a reference point when they need to categorize someone as a leader or non-leader, Trichas and Schyns (2012) anticipated that if expectations in the form of ILTs match an individual’s facial expressions, then the perception of that individual will be perceived as more leader-like than when these expectations did not match the facial expressions displayed. Indeed, Trichas and Schyns (2012) found that when ILTs are met by a person’s facial expressions, then that person is categorized as “leader,” thus implying that ILTs are used in the perception and evaluation of leaders. To put it more simply, a match between a leader’s facial expressions and participants’ ILTs led to more
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118 Handbook of methods in leadership research favorable leadership perceptions than when there was a mismatch. This finding is in accordance with a previous theory holding that people use their expectations (ILTs) as a reference point for the evaluation of good leadership (Hall & Lord, 1995) and other research demonstrating that a match between an individual’s expectations of a leader (a prototype) with the leader’s actual behaviors leads to more favorable evaluations (Nye & Forsyth, 1991). Overall, Trichas and Schyn’s (2012) research reinforced the notion that facial expressions have a commanding influence on leadership perception (Stewart et al., 2009). Specifically, different facial expressions at several intensity levels (from slight to extreme) were found to impact the perception of leadership. Consequently, the sophisticated facial expression coding Trichas and Schyns (2012) used in their studies helped increase the depth of analysis and the precision of findings, adding to the validity and reliability of research (see Rosenberg, 2005). Finally, the same research revealed that in order to understand the effect of leaders’ facial expressions one needs to consider how people both produce and perceive facial expressions. Therefore, Trichas and Schyns (2012) are in line with earlier literature supporting the idea that leadership is, at least to a degree, constructed by perceivers (Gray & Densten, 2007; Schyns, Felfe, & Blank, 2007).
IMPLICATIONS, CONTRIBUTIONS, AND FUTURE RECOMMENDATIONS The leadership research examined in this chapter reveals that facial expressions have a strong influence on leadership perception (e.g., Bucy, 2000; Masters & Sullivan, 1989). However, our knowledge regarding the actual impact of leaders’ facial displays is still restricted. The majority of relevant research does not employ a detailed approach to facial expression analysis. The present chapter used the few leadership studies that utilized sophisticated facial expression coding as reference points to draw attention to the contribution of such methods in the area of leadership research. One of the main arguments in the current chapter is that precise coding can help to uncover both pronounced and subtle perceptual impacts between displays, thus contributing to a deeper understanding of leaders’ facial expressions (Krumhuber et al., 2009). The FACS instrument, one of the most broadly used instruments of detailed facial expression analysis (Cohn & Ekman, 2008), is the method used in the contemporary leadership studies discussed in the chapter that underline the added value of sophisticated facial expression coding. The precision of FACS can facilitate the discrimination of fine perceptual influences between facial
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Sophisticated facial expression coding 119 expressions, thereby extending our understanding of facial expressions in the context of leadership (Trichas & Schyns, 2012). The identification of all observable facial movements with high reliability even in low intensities increases the credibility of research designs (see Rosenberg, 2005) and enables scholars to explore new angles in the context of leadership. The results of the research that utilized such sophisticated facial coding techniques discussed in the current chapter are of great academic value. To be more specific, a significant finding revealed from the investigation of leadership studies that used such detailed coding methods is that both marked and slight alterations in leaders’ facial displays may lead to different perceptions (Stewart, Bucy, & Méhu, 2015; Stewart, Méhu, & Salter, 2015; Stewart & Dowe, 2013; Tsai et al., 2016). This was true even when facial expressions appeared for fractions of a second (micro-expressions in Stewart et al., 2009). These findings reinforce the necessity of coding precision in facial expression research designs within the context of leadership. As is argued in the current chapter, such level of analysis allows scholars to analyse facial expressions of actual leaders (e.g., accurately identifying politicians’ micro-expressions and exploring their perceptual impact; see Stewart et al., 2009), but also enables researchers to create experimental stimuli by manipulating facial expressions (e.g., using coded facial expressions simulating business scenarios to examine the influences of certain eyebrow movements and smiles on leadership perceptions; see Trichas & Schyns, 2012). Finally, sophisticated facial expression coding can also be used to treat emotional displays as an end product. Specifically, detailed facial analysis may be used to construct dependent variables in order to investigate complex leader concepts such as authenticity of expression. Taking, for example, literature from other disciplines discriminating between authentic and non-authentic facial expressions reviewed earlier in this chapter (e.g., Gunnery & Ruben, 2016; Krumhuber & Kappas, 2005), it would be interesting to see how such knowledge applies in several leadership contexts (e.g., leader exploration of authentic and non-authentic emotional facial displays during public speaking on the basis of temporal aspects). With regard to the actual integration of sophisticated facial expression coding to leadership research, a practical issue is whether the coding in a design is done internally (the coding is conducted by one of the authors) or externally (the coding is conducted by other certified coders unrelated to the research). Either method provides high levels of credibility and reliability; Cohn & Ekman, 2008. The current chapter aims to encourage any leadership scholars investigating facial expressions to become FACS coders themselves in order to hone their awareness of the detail and variety of facial expressions from anatomy to emotional perception.
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120 Handbook of methods in leadership research Furthermore, internal coding facilitates the research process as the coder is also one of the researchers so there is continuous and immediate access to sophisticated facial action knowledge in important parts of the design (e.g., instrumentation). Of course, it should be noted that internal coding could also entail a potential disadvantage: in cases, for example, where facial expression is a dependent variable, coders’ knowledge of conditions and/or the hypotheses could be a potential threat to research credibility. In such cases, authors/coders use FACS knowledge and mindset to contribute to the design but distance themselves from the actual analyses. This can be done by involving independent FACS coders to perform the actual analysis in order to ensure both credibility and reliability of the design. Finally, another important finding revealed from the research reviewed in the current chapter was that simple muscle movements in a leader’s face resulted in context-dependent observer reactions (Trichas & Schyns, 2012). Specifically, using several FACS coded leader frames, the above scholars demonstrated that facial expressions have a strong influence on the perception of leadership but they also found that the nature of that influence is a complex situational process. When perceivers observe facial expressions, they act as “naive scientists”: they take accessible stimulus, such as facial expression, context of communication, appropriateness, and authenticity of expression and interpret them in the light of mental schemas in an attempt to understand their environment (Hassin et al., 2002; Trichas, 2015). As a result, an essential basis for exploring leadership concepts related to facial expressions is to accurately decode and consider all available information. Therefore, the focus not only falls on the expressed emotion per se, or even the aims of the leader/transmitter, but also on accurately identifying exact facial muscle movement, and how observers perceive these displays in the specific situation. The complexity of how people perceive in Trichas and Schyns (2012) implies that understanding leadership becomes an issue of creative problem solving in terms of realizing what is better under the given circumstances instead of seeking out behavioral “recipes” to apply (Meindl, 1995).
CONCLUSION The current chapter presents a set of methods for investigating facial expression, different from what has been used so far in the area of leadership perception. The studies reviewed in the previous sections integrated detailed psychological methods of facial expression coding to explore leadership perception. The findings of this chapter show that even the most subtle alterations in facial expressions matter in leadership percep-
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Sophisticated facial expression coding 121 tions, implying that awareness of the sophistication of human facial expressions can contribute to a different perspective on the leadership perceptual processes from both the transmitters’ and the perceivers’ points of view. Cohn et al. (1999) argue that obtaining accurate facial expression measurements is crucial for the credibility of research. Consequently, a main conclusion of the current chapter is that the detailed coding of facial expression can significantly contribute to our understanding of leadership perceptions.
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124 Handbook of methods in leadership research Krumhuber, E., Manstead, A.S., Cosker, D., Marshall, D., & Rosin, P.L. (2009). Effects of dynamic attributes of smiles in human and synthetic faces: A simulated job interview setting. Journal of Nonverbal Behavior, 33(1), 1–15. Krumhuber, E., Manstead, A., & Kappas, A. (2006). Temporal aspects of facial displays in person and expression perception: The effects of smile dynamics, head-tilt, and gender. Journal of Nonverbal Behaviour, 31(1), 39–56. Lau, S. (1982). The effect of smiling on person perception. The Journal of Social Psychology, 117(1), 63–67. Lewis, K.M. (2000). When leaders display emotion: How followers respond to negative emotional expression of male and female leaders. Journal of Organizational Behavior, 21(2), 221–234. Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., & Movellan, J. (2011). The Computer Expression Recognition Toolbox (CERT). Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition. Retrieved from mplab.ucsd.edu/ wp-content/uploads/2011-LittlewortEtAl-FG-CERT.pdf Madera, J.M., & Smith, B.D. (2009). The effects of leader negative emotions on evaluations of leadership in a crisis situation: The role of anger and sadness. The Leadership Quarterly, 20(2), 103–114. Marsh, A., & Ambady, N. (2007). The influence of the fear facial expression on prosocial responding. Cognition and Emotion, 21(2), 225–247. Marsh, A.A., Ambady, N., & Kleck, R.E. (2005). The effects of fear and anger facial expressions on approach and avoidance-related behaviors. Emotion, 5(1), 119–124. Masters, R.D., & Sullivan, D.G. (1989). Nonverbal displays and political leadership in France and the United States. Political Behavior, 11(2), 123–156. Mazur, A., & Mueller, U. (1996). Facial dominance. In A. Somit & S. Peterson (Eds.), Research in Biopolitics (pp. 99–111). London: JAI Press. McArthur, L.Z., & Baron, R.M. (1983). Toward an ecological theory of social perception. Psychological Review, 90(3), 215–238. Medvedeff, M.E. (2008). Leader affective displays during a negative work event: Influences on subordinate appraisals, affect, and coping strategies. Doctoral dissertation. University of Akron. Retrieved from http://etd.ohiolink.edu/send-pdf.cgi/Medvedeff%20Megan.pdf? akron1207753447 Meindl, J.R. (1995). The romance of leadership as a follower-centric theory: A social constructionist approach. Leadership Quarterly, 6(3), 329–341. Melwani, S., Mueller, J.S., & Overbeck, J.R. (2012). Looking down: The influence of contempt and compassion on emergent leadership categorizations. Journal of Applied Psychology, 97(6), 1171–1185. Monahan, J.L. (1998). I don’t know it but I like you: The influence of nonconscious affect on person perception. Human Communication Research, 24(4), 480–500. Montepare, J.M., & Dobish, H. (2003). The contribution of emotion perceptions and their overgeneralizations to trait impressions. Journal of Nonverbal Behavior, 27(4), 237–254. Montepare, J.M., & Zebrowitz-McArthur, L. (1998). Impressions of people created by age-related qualities of their gaits. Journal of Personality and Social Psychology, 55(4), 547–556. Newcombe, M.J., & Ashkanasy, N.M. (2002). The role of affect and affective congruence in perceptions of leaders: An experimental study. The Leadership Quarterly, 13(5), 601–614. Nye, J., & Forsyth, D.R. (1991). The effects of prototype biases on leadership appraisals: A test of leadership categorization theory. Small Group Research, 22(3), 360–379. Parkinson, B. (2005). Do facial movements express emotions or communicate motives? Personality and Social Psychology Review, 9(4), 278–311. Perron, M., Roy-Charland, A., Chamberland, J., Bleach, C., & Pelot, A. (2016). Differences between traces of negative emotions in smile judgment. Motivation and Emotion, 40(3), 478–488.
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Sophisticated facial expression coding 125 Rafaeli, A., & Sutton, R.I. (1987). Expression of emotion as part of the work role. Academy of Management Review, 12(1), 23–37. Rosenberg, E. (2005). The study of spontaneous facial expressions in psychology. In P. Ekman & E. Rosenberg (Eds.), What the face reveals: Basic and applied studies of spontaneous expression using the facial action coding system (2nd ed., pp. 3–17). New York: Oxford University Press. Sayette, M.A., Cohn, J.F., Wertz, J.M., Perrott, M.A., & Parrot, D.J. (2001). A psychometric evaluation of the Facial Action Coding System for assessing spontaneous expression. Journal of Nonverbal Behavior, 25(3), 167–185. Schneid, E.D., Carlston, D.E., & Skowronski, J.J. (2015). Spontaneous evaluative inferences and their relationship to spontaneous trait inferences. Journal of Personality and Social Psychology, 108(5), 681–696. Schyns, B., & Mohr, G. (2004). Nonverbal elements of leadership behaviour. German Journal of Human Resource Research, 18(3), 289–305. Schyns, B., Felfe, J., & Blank, H. (2007). Is charisma hyper-romanticism? Empirical evidence from new data and a meta-analysis. Applied Psychology: An International Review, 56(4), 505–527. Shariff, A.F., & Tracy, J.L. (2011). What are emotion expressions for? Current Directions in Psychological Science, 20(6), 395–399. Shea, C.M., & Howell, J.M. (1999). Charismatic leadership and task feedback: A laboratory study of their effects on self-efficacy and task performance. Leadership Quarterly, 10(3), 375–396. Snodgrass, J. (1992). Judgment of feeling states from facial behavior: A bottom-up approach. Unpublished doctoral dissertation. University of British Columbia. Stewart, P. (2010). Presidential laugh lines: Candidate display behavior and audience laughter in the 2008. Politics and the Life Sciences, 29(2), 55–72. Stewart, P., & Dowe, P. (2013). Interpreting president Barack Obama’s facial displays of emotion: Revisiting the Dartmouth Group. Political Psychology, 34(3), 369–385. Stewart, P.A., Bucy, E.P., & Méhu, M. (2015). Strengthening bonds and connecting with followers: A biobehavioral inventory of political smiles. Politics and the Life Sciences, 34(1), 73–92. Stewart, P.A., Méhu, M., & Salter, F.K. (2015). Sex and leadership: Interpreting competitive and affiliative facial displays based on workplace status. International Public Management Journal, 18(2), 190–208. Stewart, P., Waller, B., & Schubert, J. (2009). Presidential speechmaking style: Emotional response to micro-expressions of facial affect. Motivation and Emotion, 33(2), 125–135. Sullivan, D.G., & Masters, R.D. (1988). “Happy warriors”: Leaders’ facial displays, viewers’ emotions, and political support. American Journal of Political Science, 32(2), 345–368. Surakka, V., & Hietanen, J.K. (1998). Facial and emotional reactions to Duchenne and nonDuchenne smiles. International Journal of Psychophysiology, 29(1), 23–33. Sutton, R.I., & Rafaeli, A. (1988). Untangling the relationship between displayed emotions and organizational sales: The case of convenience stores. Academy of Management Journal, 31(3), 461–487. Sy, T., Cȏté, S., & Saavedra, R. (2005). The contagious leader: Impact of the leader’s mood on the mood of group members, group affective tone, and group processes. Journal of Applied Psychology, 90(2), 295–305. Tiedens, L.Z. (2001). Anger and advancement versus sadness and subjugation: The effect of negative emotion expressions on social status conferral. Journal of Personality and Social Psychology, 80(1), 86–94. Trichas, S. (2015). New methods of exploring facial expressions in the context of leadership perception: Implications for educational leaders. In K. Beycioglu & P. Pashiardis (Eds.), Multidimensional perspectives on principal leadership effectiveness (pp. 205–225). New York: IGI Global. Trichas, S., & Schyns, B. (2012). The face of leadership: Perceiving leaders from facial expression. The Leadership Quarterly, 23(3), 545–566.
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126 Handbook of methods in leadership research Tronick, E.Z. (1989). Emotions and emotional communication in infants. American Psychologist, 44(2), 112–119. Tsai, J., Ang, J., Blevins, E., Goernandt, J., Fung, H., Jiang, D., . . .Haddouk, L. (2016). Leaders’ smiles reflect cultural differences in ideal affect. Emotion, 16(2), 183–195. Uleman, J.S., Saribay, S.A., & Gonzalez, C. (2008). Spontaneous inferences, implicit impressions, and implicit theories. Annual Review of Psychology, 59(1), 329–360. Valstar, M.F., Méhu, M., Jiang, B., Pantic, M., & Scherer, K. (2012). Meta-analysis of the first facial expression recognition challenge. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 42(4), 966–979. Van Kleef, G., Homan, A., Beersma, B., Van Knippenberg, D., Van Knippenberg, B., & Damen, F. (2009). Searing sentiment or cold calculation? The effects of leader emotional displays on team performance depend on follower epistemic motivation. Academy of Management Journal, 52(3), 562–580. Wang, M., Xia, J., & Yang, F. (2015). Flexibility of spontaneous trait inferences: The interactive effects of mood and gender stereotypes. Social Cognition, 33(4), 345–358. Wild, B., Erb, M., & Bartels, M. (2001). Are emotions contagious? Evoked emotions while viewing emotionally expressive faces: Quality, quantity, time course and gender differences. Psychiatry Research, 102(2), 109–124. Winkielman, P., & Berridge, K. (2003). Irrational wanting and subrational liking: How rudimentary motivational and affective processes shape preferences and choices. Political Psychology, 24(4), 657–680. Winkielman, P., & Berridge, K. (2004). Unconscious emotion. Current Directions in Psychological Science, 13(3), 120–123. Winkielman, P., Berridge, K., & Wilbarger, J.L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 31(1), 121–135. Zebrowitz, L.A., & Montepare, J.M. (2005). Appearance does matter. Science, 308(5728), 1565–1566. Zebrowitz, L.A., & Montepare, J.M. (2008). Social psychological face perception: Why appearance matters. Social and Personality Psychology Compass, 2(3), 1497–1517.
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6. Behavioral genetics and leadership research
Wen-Dong Li, Remus Ilies and Wei Wang
The use of behavioral genetics in scientific inquiries may date back to the study conducted by Sir Francis Galton, Darwin’s half cousin. Galton (1869) published his study in the book titled Hereditary Genius: An Inquiry into its Laws and Consequences. Galton examined so-called “eminent men” in England, ranging from judges, musicians, to wrestlers, and found that genetic factors played a prominent role in shaping the differences in their genius. Behavioral genetics approaches are broadly defined as including twin and adoption studies and molecular genetics research that utilize DNA information (Plomin, DeFries, Knopic, & Neiderhiser, 2013). Modern behavioral genetics approaches have been widely adopted in an array of disciplines in social sciences, including psychology (e.g., Bouchard, Lykken, McGue, Segal, & Tellegen, 1990; Plomin, Owen, & McGuffin, 1994), education (e.g., Eaves et al., 1997), sociology (e.g., Freese, 2008), economics (e.g., Miller, Mulvey, & Martin, 1995), finance (e.g., Cesarini, Johannesson, Lichtenstein, Sandewall, & Wallace, 2008) and even political sciences (e.g., Fowler, Baker, & Dawes, 2008). Organizational researchers have also capitalized on the behavioral genetics approaches in their investigation of the “nature versus nurture” or “nature and nurture” issue. This issue is not a trivial one. Kurt Lewin (1935) proposed the famous equation that human behavior is jointly affected by both the person and the environment, that is, B 5 f (P, E) (B, P, and E denote behavior, person, and environment respectively). Genetic influences are typically employed to disentangle influences from the person, or nature, from situational influences that are portrayed to indicate influences from the environment, or nurture. Organizational researchers have used the behavioral genetics approaches in their examinations of job satisfaction (e.g., Arvey, Bouchard, Segal, & Abraham, 1989; Ilies & Judge, 2003), entrepreneurship (e.g., Nicolaou, Shane, Cherkas, Hunkin, & Spector, 2008; Zhang, Ilies, & Arvey, 2009), work-related personality traits (e.g., Judge, Ilies, & Zhang, 2012; Li, 2011), and work characteristics (Li, Zhang, Song, & Arvey, 2016), to name a few (for reviews, see Arvey, Li, & Wang, 2016; Ilies, Arvey, & Bouchard, 2006). The behavioral genetics approaches have also proven useful in 127
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128 Handbook of methods in leadership research a dvancing leadership research (see also Chapter 7 in this volume). In the field of leadership, researchers have long recognized the fundamental role of individual difference variables (e.g., intelligence and personality traits) in shaping leadership emergence and leadership effectiveness (e.g., DeRue, Nahrgang, Wellman, & Humphrey, 2011; Judge, Bono, Ilies, & Gerhardt, 2002; Stogdill, 1948). Researchers have adopted twin studies and molecular genetics approaches in examining critical questions in this field (e.g., Arvey, Rotundo, Johnson, Zhang, & McGue, 2006; Arvey, Zhang, Avolio, & Krueger, 2007; Li, Arvey, Zhang, & Song, 2012; Li et al., 2015). Such questions include, but are not limited to, to what extent can genetic factors influence individual differences in leadership, to what extent can relationships between different leadership constructs be accounted for by the same genetic factors, and are specific genes involved in shaping leadership, and how? Perhaps for these reasons, Bass and Bass (2008) state that “the genetic factor needs to be taken into account in any complete examination of leadership” (p. 1203). With the rapid advancement of leadership research and the behavioral genetics field, we firmly believe that behavioral genetics approaches offer leadership research useful theoretical and methodological perspectives to address crucial questions about leadership. We therefore write this chapter to partially serve this purpose. In the following sections, we first introduce the classic twin studies in behavioral genetics research. We detail univariate and bivariate biometric genetic analyses. We then discuss the use of a molecular genetics approach, which utilizes people’s specific DNA information. We hope this chapter can spur further research interest in using behavioral genetics approaches to advance our knowledge on leadership.
CLASSIC TWIN STUDIES Univariate Biometric Models Early twin studies, especially those conducted by researchers at the University of Minnesota, used a very special type of design involving identical (i.e., monozygotic) twins who are reared apart (e.g., Arvey et al., 1989; Bouchard et al., 1990). A basic premise of this approach is that because identical twins share 100 percent of their genes, when they are raised in different environments their similarities should be attributable only to their similarities in their genes, not to their environments. This approach, though well known, is not without limitations. One limitation is that, although identical twins may be reared by different families, they may still experience similar effective environments because of the similarities in their abilities, personality traits,
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Behavioral genetics and leadership research 129 values, and interests. This is neatly explained by a form of gene–environment interplay: gene–environment correlation (Plomin, DeFries, & Loehlin, 1977; Scarr & McCartney, 1983). On the one hand, identical twins may be treated similarly by others (e.g., family members, friends, and teachers) due to their similar personality traits, even though they are raised in different families. This is termed as passive gene–environment correlation, because in such situations individuals are painted as passive recipients of environmental influences. On the other hand, identical twins may also proactively seek out congruent environments (e.g., in making friends with others, looking for jobs and organizations to work in) with their individual characteristics. These associations are called active gene–environment correlations, because they underscore the agentic role of the person in selecting and building up his or her own environmental niche. Of late, more sophisticated methods have been developed using multigroup structural equation modeling (SEM) (Neale & Cardon, 1992; Plomin et al., 2013). Typically, two types of twins are used: identical twins and fraternal (i.e., dizygotic) twins. Identical twins share all of their genetic endowments, and fraternal twins, on average, share 50 percent of the genes that make them similar. This approach does not pose restrictions on whether twins are reared together or apart, because it helps to distinguish two types of environmental factors: shared environmental factors and unshared environmental factors. Shared environmental factors (C1 and C2 in Figure 6.1), also referred to as common environmental factors, represent those same environments that make twins similar (e.g., the same family and socialization experiences). Unshared environmental factors (E1 and E2), on the other hand, represent those environments that are unique λ
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Figure 6.1 Univariate biometric analyses for leadership
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130 Handbook of methods in leadership research to each twin, which make them different from each other (and potential measurement error). Figure 6.1 presents a univariate biometric model. In addition to the two types of environmental factors, this type of analysis also models influences from additive genetic factors (A1 and A2). This method has been widely used in the recent behavioral genetics literature (Arvey et al., 2016; Arvey, Wang, Song, Li, & Day, 2014; Ilies et al., 2006; Plomin et al., 2013). With this approach, a heuristic way to detect whether genetic factors may play a role in affecting a leadership variable, for instance, is that identical twins are more similar than fraternal twins. In practice, the similarity is indicated by co-twin correlation. Specifically, researchers calculate the correlations for one variable between the two co-twins within one twin pair separately for the identical twin group and the fraternal twin group. If the co-twin correlation is greater for identical twins than for fraternal twins, then it suggests likely genetic influences. Note that often, additive genetic factors are presumed to cause genetic influences, meaning that the influences of genetic factors are proportionate to the number of a specific genetic variant. However, if the co-twin correlation for identical twins is more than twice in magnitude than the correlation for fraternal twins, this often suggests the possibility for dominant genetic factors. Dominant genetic influences arise from the situation in which the presence of even one genetic variant (e.g., one genetic allele) would have substantial influences regardless of the numbers of this genetic variant. In organizational genetics research, the majority of the prior research has observed additive genetic influences (Arvey et al., 2016) and it seems rare to observe dominant genetic influences (see Zyphur, Narayanan, Arvey, & Alexander, 2009, for an exception). In such analyses, an observed variable, L (e.g., leadership role occupancy), is often modeled as the following algebraically: L 5 u + a*A + c*C + e*E
(6.1)
where A, C, and E, as discussed above, are standardized latent genetic and environmental factors (means and variance specified at 0 and 1) respectively; and a, c, and e are corresponding coefficients for them; u represents the intercept. Therefore, in SEM, the variance of a leadership variable can be modeled as: Variance 5 a2 +c2 +e2(6.2) It is important to note that as displayed in Figure 6.1, the correlation between the two genetic variables A1 and A2 (l) is specified as 1 for the
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Behavioral genetics and leadership research 131 identical twin group and 0.5 for the fraternal twin group, because identical and fraternal twins share 100 percent and 50 percent of their genes respectively. In addition, the correlation between the two shared environmental factors C1 and C2 (d) is specified as 1, because they are the same environmental factors. The correlation between the two unshared environmental factors, E1 and E2, is thus set as zero, because they are distinctive environmental factors by definition. Based on the above specifications, the covariance between a leadership variable for twin 1 and the same leadership variable for twin 2 is calculated as the following for identical twins: MZcovariance 5 a2 +c2(6.3) For fraternal twins, the covariance is modeled as the following: DZcovariance 5 0.5*a2 +c2(6.4) The extent of genetic influence, which is also termed heritability, can be calculated (5a2/[a2 +c2 +e2]), which indicates the amount of total variance in one variable that can be accounted for by individual differences in genetic factors. Environmental influences can also be determined in a similar manner. Influences from shared environmental factors are c2/ [a2 +c2 +e2] and influences from unshared environmental factors are e2/[a2 +c2 +e2]. Two examples are the unique variance analyses conducted in the studies by Arvey and colleagues (Arvey et al., 2006, 2007). In summary, in univariate biometric analyses, researchers typically capitalize on information from identical and fraternal twins. Specifically, researchers conduct multi-group (i.e., identical twin group and fraternal twin group) SEM and compare the similarities between identical and fraternal twins as detailed above. Then the influences from genetic factors as well as influences from environmental factors can be estimated. Typically, such estimates of genetic and environmental factors are used to address questions such as, to what extent does nature contribute to leadership and what is the role of nurture? It is important to point out that the classic behavioral genetics approach using the two types of twins essentially capitalizes on a quasi-natural experiment (Plomin et al., 1994). For example, researchers utilize information from two types of twins with different genetic similarities. The two different types of twins experience similar environmental influences and exhibit differentiated similarities on individual characteristics and also work outcomes (e.g., leadership). This advantage distinguishes the behavioral genetics approaches from other approaches and also
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132 Handbook of methods in leadership research rovides organizational researchers with unique advantages in investigatp ing important questions related to leadership. Like all research approaches, behavioral genetics approaches are not without limitations. One limitation of the classic twin studies pertains to the equal environments assumption, meaning that, under behavioral genetics approaches, identical twins are assumed to be exposed to roughly the same amount of shared environment as fraternal twins. Put differently, the shared environments experienced by identical twins are not more similar than those encountered by fraternal twins. Researchers have argued, however, that identical twins share more genes than fraternal twins, and thus they may be treated more similarly than fraternal twins, which may pose a challenge to the classic behavioral genetics approach of twin studies (Gottesman & Shields, 1972; Kendler, Neale, Kessler, Heath, & Eaves, 1994). If this argument holds, it may lead to inflated estimates of genetic influences. This critique prompted behavioral geneticists to conduct research to look deeper into this issue more directly. Researchers found that indeed identical twins may experience more similar environments than fraternal twins (Bouchard & Propping, 1993; Scarr & CarterSaltzman, 1979). Advocates of behavioral genetics approaches fought back and argued that, even if this was true, it may not be directly pertinent to the constructs we study and thus may have little substantial influence on the magnitudes of genetic influences (Gottesman & Shields, 1972). The counter-argument makes sense and indeed it is primarily what previous research has found (Plomin et al., 2013). Genetic influences, or heritability estimates, are often misinterpreted. Thus we would like to further clarify the interpretation of genetic influences. Most important, significant genetic influences on one variable should not be explained as indicating we cannot change this variable (Johnson, Turkheimer, Gottesman, & Bouchard, 2009). Genetic influences reflect the amount of variance in one variable that can be accounted for by individual differences in genetic make-up. Genetic influences thus pertain to inter-individual differences, while change is related to intra- individual differences. It is also important to note that, although most individual difference variables and many work-related variables are found to be substantially affected by genetic factors (Bouchard, 2004; Turkheimer, 2000), it is still important to study how much variance in one work-related variable can be accounted for by genetic variation – that is, heritability. As contended by Johnson et al. (2009), “heritability studies do continue to have some importance in areas of the social sciences in which genetic influences have not been acknowledged” (p. 218). A final point we would like to make regarding univariate biometric analyses is that the influences of shared environmental factors, C, have
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Behavioral genetics and leadership research 133 often been found to be negligible, especially for studies using adult twins (Arvey et al., 2016; Turkheimer, 2000). At first glance it seems strange, given the importance of family environments (an often-assumed shared environmental factor) on child development. There are several explanations offered by the previous research (Hoffman, 1991; Loehlin, 2007; Plomin et al., 2013). First, children in the same family do not necessarily experience environments in the same manner. For instance, one family may have 500 books for their children, but one child may read 200 of them and the other may read 100, depending on their reading abilities and interests. In this vein, family influences are captured by unshared environmental factors, the E factor. Second, the influences of family environments may gradually become less important as individuals grow up and even insubstantial in adulthood because over time individuals have more and more control over their environments. These are important reasons for most organizational behavioral genetics research to fix influences from shared environmental factors to zero in their analyses. Bivariate Biometric Models As revealed in research in psychology (Kendler & Baker, 2007), organizational behavior (Li, Zhang et al., 2016), and particularly in leadership (Arvey et al., 2007), individuals are not randomly assigned to various environments; often, the person also shapes his or her experiences, including work experiences and leadership experiences. For example, Arvey et al. (2007) reported that approximately 30 percent of the variance in one’s developmental experiences (i.e., work experience and family experience) was influenced by genetic differences. Similar findings were reported in a study on employees’ work experiences (e.g., job demands, job control, and job complexity; Li, Zhang et al., 2016). A crucial reason for such substantial genetic influences on individuals’ experiences lies in human agency: the person can select or craft his or her own environments according to his or her abilities, interests, personality traits, and values (Johnson et al., 2009). Of course, this notion is not entirely new to organizational researchers. Schneider’s (Schneider, 1987; Schneider, Goldstein, & Smith, 1995) attraction-selection-attrition model suggests that people can make an organization what it is through their own actions. The broader literature on person–organization fit also suggests that people can select their occupations and jobs according to their individual characteristics, such as personality traits and abilities (Edwards, 2008; Kristof-Brown & Guay, 2010). The burgeoning literature on proactivity has also underscored the agentic role of the person in shaping work environments (Bindl & Parker, 2010; Frese & Fay, 2001; Grant & Ashford, 2008; Li, Fay, Frese, Harms, & Gao, 2014).
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134 Handbook of methods in leadership research Such substantial genetic influences on work experiences are not trivial, considering that typically those work experience variables have been considered as purely environmental factors, meaning that these factors are not related to people’s individual characteristics (Oldham & Hackman, 2010). By extension, the traditional view assumes that the relationships between work experiences and work outcomes may have also been primarily attributed to environmental influences, and thus the person plays little role here. One such area in organizational research is work design. Classic work design theories and research have portrayed employee work characteristics as mainly influenced by organizations and managers, not the person (Grant, Fried, & Juillerat, 2010; Oldham & Hackman, 2010; Parker, Andrei, & Li, 2014). Thus a conventional wisdom in this area is that the relationships between work characteristics and work outcomes are mostly environmentally influenced. However, a recent study (Li, Zhang et al., 2016) demonstrated that genetic factors also play indispensible roles (e.g., through core self-evaluations) in explaining the relationships between work characteristics (e.g., job demands, job control, and job complexity) and well-being. Thus, the most important contribution that the behavioral genetics approaches can make to leadership research is perhaps to shed light on the causal explanation of important relationships studied in leadership research. That is, to identify to what extent such relationships can be accounted for by genetic factors through selection or environmental influences. As Johnson et al. (2009) point out, an important advantage that twin studies can bring to our scientific inquiries is that that they are able to “distinguish selection from environmental causation” (p. 218). For example, Arvey et al. (2007) reported that the majority of the relationship between family experience (a putative environmental variable) and leadership role occupancy (i.e., the extent to which people hold leadership positions) was mainly explained by genetic influences. This was a crucial reason why work experience was related to leadership role occupancy as well. Twin studies can also be utilized to address other leadership-related issues (e.g., leadership experiences and leader development) in a similar way through disentangling the distinctive roles of selection and environmental causation that underlie such relationships. One may argue, why is it so important to use genetic influences to reflect influences from the person through selection? Can’t we just use individual characteristics such as intelligence (Judge, Colbert, & Ilies, 2004), personality traits (Judge et al., 2002), and physical characteristics (Judge & Cable, 2004)? The answer is that (1) we cannot investigate influences from those variables simultaneously in one study, and (2) given that virtually all individual difference variables are under genetic influences, genetic influences thus reflect influences from all person-related variables, that is,
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Behavioral genetics and leadership research 135 λ
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Note: A 5 additive genetic factors; E 5 unique environmental factors; effects of shared environmental factors (C) were not modeled because the effects are typically not significant, which is also a consistent finding in previous research; l 5 1 for identical twins and 0.5 for fraternal twins.
Figure 6.2 B ivariate biometric analyses for leadership with a predictor based on Cholesky decomposition influence from the person as a whole (Arvey et al., 2016; Johnson et al., 2009; Li, Stanek, Zhang, Ones, & McGue, 2016). Bivariate biometric models are an extension of univariate models. One of the most widely used approaches is the one based on Cholesky decomposition (Plomin et al., 2013). Figure 6.2 represents a bivariate biometric model based on Cholesky decomposition for the relationship between a predictor (e.g., a personality trait or a measured environmental variable) and leadership. With respect to model specification, the cross-twin relationships (e.g., predictor_twin1 and predictor_twin2) are modeled similarly as in univariate analyses. The major difference between bivariate and univariate biometric models lies in the within-twin relationships (e.g., between predictor_twin1 and leadership_twin1). Chiefly, the rationale of this approach is to decompose the observed relationship between a predictor and leadership into two components: one related to the same genetic factors (A1) and the other to the same environmental factors (E1). Statistically, the genetic component of one relationship is the product of the two coefficients presenting the influences of the same genetic factors on the two observed constructs (5a11* a21). Likewise, the environmental component of the observed relationship between a predictor and a leadership variable can also be calculated (5c11* c21). Thus, researchers could further compute to what extent the observed relationship is accounted for by genetic influences through selection (5|a11* a21|/[|a11* a21|+ |c11* c21|]) and
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136 Handbook of methods in leadership research to what extent is due to environmental causation (5|c11* c21|/[|a11* a21|+ |c11* c21|]). This approach has been used previously in psychological (e.g., Plomin & Spinath, 2002) and organizational research (Li, Zhang et al., 2016; Shane, Nicolaou, Cherkas, & Spector, 2010). Biometric Growth Curve Models A natural extension of bivariate biometric models is to integrate them with growth curve models (see also Chapter 13 in this volume). Given the rapid developments of change-related issues in organizational research (e.g., Li, Song, & Arvey, 2011; Ployhart & Vandenberg, 2010; Preacher, Briggs, Wichman, & MacCallum, 2008), it seems intriguing to investigate whether change in work-related variables (e.g., leader development) can be modulated by genetic variables. Coupled with previous research on leadership development (e.g., Day, Fleenor, Atwater, Sturm, & McKee, 2014; Day, Harrison, & Halpin, 2009; DeRue, Nahrgang, Hollenbeck, & Workman, 2012; Dragoni, Tesluk, Russell, & Oh, 2009), it seems informative to probe whether genes can play a role in leadership experiences and leadership development over time. In psychology, researchers utilized this methodology and reported significant genetic influences on change in academic achievement (Johnson, McGue, & Iacono, 2006) and personality (Hopwood et al., 2011; McGue, Bacon, & Lykken, 1993). Given that relatively little empirical research in psychology has adopted this new approach and no organizational research, to our knowledge, has so far applied this approach, we only briefly discuss the model specifications here. Figure 6.3 presents such an example with a leadership variable captured three times. This model is composed of two major parts. One part is related to the latent growth curve model (on the bottom) in which a leadership variable is modeled as two latent change variables for each twin, i.e., an intercept and a slope. The intercept represents the starting point of the leadership variable and the slope is often used to indicate change in the leadership variable (given that in this example, because leadership is only measured three times, non-linear change cannot be modeled). The other part is a bivariate biometric model (on the top), predicting the latent change variables, intercept and slope. Simply put, a biometric growth curve model is a combination of a biometric model and a growth curve model. Examples of Biometric Models One good example of univariate biometric models applied in the area of leadership was the univariate model in the study by Arvey et al. (2006).
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Behavioral genetics and leadership research 137 λ
δ
AL
CL
EL
λ
δ
AS
CS
Level1 1
1
LDtwin1.time1
1
0
LDtwin1.time2
ES
AL
CL
EL
Slope1
Level2
γ
1
1
LDtwin1.time3
LDtwin2.time1
1
AS
CS
ES
Slope2 1
0
LDtwin2.time2
γ
1
LDtwin2.time3
Note: LD 5 leadership; A 5 additive genetic factors; C 5 shared environmental factors; E 5 unique environmental factors; l 5 1 for identical twins and 0.5 for fraternal twins; d 5 1 for both types of twins; g is an estimated parameter capturing the effects of time; Level1 and Level2 are initial status of leadership for twin1 and twin2 respectively, Slope1 and Slope2 are change in leadership for twin1 and twin2 respectively.
Figure 6.3 Biometric growth curve model for a leadership variable Their analyses were based on 119 pairs of identical twins and 94 pairs of fraternal twins collected through the Minnesota Twin Registry. The authors employed univariate biometric models to estimate genetic influences on leadership role occupancy (pp. 9–10). They found that there was no significant difference between a model with all the three factors, A, C, and E (i.e., the ACE model) and another model with only A and E factors (i.e., the AE model). In fact, the influence of C factors was not significant in this case. Thus, the AE model was selected as the best-fitting model and genetic factors were found to account for 30 percent of the variance in leadership role occupancy. One example of bivariate biometric models is from the study conducted by Arvey et al. (2007). They reported the results of a multivariate biometric analysis (p. 702) and here we highlight the results for work experience and leadership role occupancy. Again, the results showed that the influences of C were not significant and thus only A and E factors were modeled. As shown in Figure 2 on page 702 of Arvey et al. (2007), the same genetic factor, Aw, significantly influenced both work experience (coefficient 5 0.18) and leadership (coefficient 5 0.24). Likewise, the same environmental factor, Aw, also had significant influences on the two variables (coefficients 5 0.30 and 0.31 respectively). Thus, genetic influences accounted for 31.7 percent of the relationship between work experience
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138 Handbook of methods in leadership research and leadership role occupancy (50.18*0.24/(0.18*0.24+0.30*0.31)), and accordingly, environmental factors explaining the remaining 68.3 percent of the relationship (50.30*0.31/(0.18*0.24+0.30*0.31)).
MOLECULAR GENETICS RESEARCH Twin studies enable researchers to investigate the relative contributions of the person (through genetic influences) and the environment to the variance in one variable or to the relationships/covariance between two or among multiple variables. However, it cannot provide information regarding which specific DNA markers are responsible for the significant genetic influences (Plomin et al., 2013). Given the prevalence of selection manifested through significant genetic influences on most individual difference variables and on many work-related variables, it is intriguing to find out which gene or set of genes play a role in explaining such genetic influences. With the rapid development of DNA sequencing technology, such information on specific genes is becoming increasingly available and such research can satisfy our scientific search for knowledge. Moreover, if such genes can be identified, the next step is to examine possible multiple mechanisms related to brain functions, hormones, physical characteristics, and psychological characteristics in the relationship between those genes and work-related outcomes. Obtaining knowledge on the relationship between specific genes and leadership variables, and mediating mechanisms, as well as moderating conditions, sheds light on the biological foundation of important phenomena in organizational research (Arvey et al., 2016; Cropanzano & Becker, 2013; Heaphy & Dutton, 2008; Senior, Lee, & Butler, 2011; Waldman, Balthazard, & Peterson, 2011). So far, organizational research has lagged behind the other social sciences in molecular genetic research. Much research has been conducted, for example, in psychology (see Turkheimer, Pettersson, & Horn, 2014, for a recent review) and in political science (Fowler & Dawes, 2008, 2013). Generally, researchers have adopted the candidate gene approach or conducted genome-wide association studies (GWAS). The candidate gene approach is based on previous theories and findings on specific genetic markers (Plomin et al., 2013). The rationale is that we base our examination of specific genes on theories and previous exploratory research linking such genes to individual characteristics (e.g., personality traits) and behavioral outcomes. Then we derive a priori hypotheses and conduct our empirical investigation. On the other hand, GWAS is more data driven and its purpose is to test all possible genetic markers that may be related to a variable of interest without any theoretical or empirical grounds
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Behavioral genetics and leadership research 139 (ibid.). Thus, it seems that the candidate gene approach fits better with the theory-building and theory-testing tradition in organizational research, and leadership research is not an exception. However, due to the complexity of the inquiry into specific genetic markers and its multidisciplinary nature, a middle-ground approach that integrates theory building/testing and empirical exploration is needed (Li et al., 2015). Candidate Gene Approach The candidate gene approach was the very first approach adopted in molecular genetic research. Two seminal papers were published in 1996, both in Nature (Cloninger, Adolfsson, & Svrakic, 1996; Ebstein et al., 1996). They reported that one genetic marker on the dopamine receptor gene D4 was significantly related to personality traits such as sensation seeking and novelty seeking. The basic rationale of the candidate gene approach is to correlate genetic variation on one specific genetic marker with an observed variable of interest (e.g., leadership). The analysis often used is regression, treating genetic variation as either continuous or categorical variables. Although researchers cautioned that the candidate gene approach may produce findings that cannot be replicated in other samples (Ebstein, Israel, Chew, Zhong, & Knafo, 2010), meta-analyses have shown that for many candidate genes, their influences were indeed significant across various samples, though sometimes small in magnitude (e.g., Li, Sham, Owen, & He, 2006; Munafò, Yalcin, Willis-Owen, & Flint, 2008). Research employing the candidate gene approach often reports small effect sizes. This is typical and also understandable, given that most outcome variables we study may be influenced by multiple genes and also gene–environment interactions may play a crucial role (Turkheimer et al., 2014). Considering the large number of possible genetic variations, when such evidence on specific genes based on the candidate gene approach accumulates, more variance in a variable can be accounted for. Indeed, recently, researchers started to generate polygenetic scores based on multiple genetic markers and found polygenetic scores can indeed explain more variance than a single genetic marker (e.g., McCrae, Scally, Terracciano, Abecasis, & Costa Jr., 2010). The candidate gene approach has been adopted by organizational researchers in their search for genetic markers associated with job satisfaction (Song, Li, & Arvey, 2011), leadership (Li et al., 2015), and job changes (Chi, Li, Wang, & Song, 2016). Using the study on leadership as one example, Li et al. (2015) found that the number of 10-repeat alleles on a dopamine transporter gene, DAT1, was negatively related to proactive personality, but positively related to moderate rule-breaking behaviors;
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140 Handbook of methods in leadership research both proactive personality and rule breaking were positively related to leadership role occupancy. Thus, the DTA1 gene had both positive and negative indirect influences on leadership role occupancy, rendering the total influences non-significant. The authors stated that having this gene might be a “mixed blessing” (p. 671). This study has important implications for organizational genetics research. First, it shows that influences of specific genes on work-related outcomes may be more complex than expected. Researchers and lay people alike want to believe that there might be a gene, or some genes, for leadership or entrepreneurship. However, this research demonstrated that the influence of one gene may be both positive and negative. In evolutionary genetics research, this is called “stabilizing selection,” a natural selection process that “maintains different alleles rather than favoring one allele over another” (Plomin et al., 2013, p. 336). Second, it also has important implications for genetics researchers looking for specific genes related to personality traits or other behavioral outcomes. It underscores the importance of examining indirect mediating effects based on theories and previous empirical findings. With the decreasing cost of gene sequencing, we urge more organizational researchers to take a candidate gene approach, at least as a starting point, to examine intriguing interplays between genes and environmental factors on important leadership variables. Genome-wide Association Studies (GWAS) Although the candidate gene approach is capable of examining the genetic effects of specific genes, this approach typically requires prior knowledge or assumptions regarding which specific genes are potentially responsible for a variable of research interest. In addition, this approach only focuses on one or a very small number of genes. Recently the genome-wide association study (GWAS) method has been developed and has rapidly become widely adopted in genetic research. The GWAS method takes a bottom-up approach. With no prior hypotheses required regarding which genes may be at work, this method systematically scans thousands of genetic markers and even entire genomes to examine the genetic variations, analyse the association strength between various genetic variants and observed variables, and identify genetic variants that display statistically significant associations. The typical genetic variants focused on in GWAS are single nucleotide polymorphisms (SNPs), which are single base-pair variations with high occurring frequency in a DNA sequence in the human genome (Genomes Project Consortium, 2010). An SNP has two alleles occurring in a pair basis and can be quantitatively characterized by the frequency of the less common allele (i.e.,
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Behavioral genetics and leadership research 141 the minor allele). Although SNPs exist in a molecular form, they can have fundamental consequences for biological functions. Furthermore, the recent advancement of genotyping technology – specifically the chip-based microarray technology – has made the GWAS method easily accessible for many researchers from a wide array of disciplines (Turkheimer, 2012). This new technology provides chipbased platforms that can measure and test genetic variation for millions (recently up to five million) of SNPs with a relatively low-cost and easily readable results. We encourage leadership researchers to take advantage of such state-of-the-art approaches and technology to examine nuanced interplays between the person and the environment in their inquiries. For instance, research published in Science (Rietveld et al., 2013) found that polygenetic scores based on various single genes explained approximately 2 percent (this is a big effect size in molecular genetics research) of the variance in educational achievement. A recent study (Belsky et al., 2016) using a similar approach revealed that polygenetic scores were significantly associated with social mobility and economic success, which were mediated by individual difference variables such as intelligence and self control.
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7. Biosensor approaches to studying leadership Aurora J. Dixon, Jessica M. Webb and Chu-Hsiang (Daisy) Chang
In spite of over 70 years of research conducted on the topic of leadership, it is a field that continues to grow and generate additional interest (Day, 2012). For the purposes of this chapter, we define leadership as the process through which one individual exerts influence on one or more other individuals (Day & Antonakis, 2012). As an organizing framework in this review, we consider the applicability of biosensor techniques for research using all three of Bass’s (2008) general approaches for understanding leadership, namely: a leader-centric approach, leadership as an effect, and the interaction between leaders and their followers. First and most common is the leader-centric approach that focuses on stable individual differences of leaders (e.g., conscientiousness), the behaviors they exhibit (e.g., assigning roles; providing feedback), or on how leaders influence the external environment and the individuals who follow them (Bass, 2008; Day & Antonakis, 2012). Second, leadership has been conceptualized as an effect, such that followers’ actions (e.g., goal pursuit) and reactions (e.g., satisfaction) are considered outcomes of the leader’s influences (Day & Antonakis, 2012). Finally, Bass (2008) argues that leadership can be defined by the relations between leaders and their followers (e.g., leader–member exchange; see Graen & Uhl-Bien, 1995 for a review). These leader–member exchange (LMX) relationships can range from high quality (e.g., characterized by trust and social support) to low quality (e.g., characterized by distrust and social distance) depending on the interactions within each leader–follower dyad. Together, these three approaches have been valuable to the study of leadership because they yield unique and complementary insights for understanding leadership as both a process of influence (e.g., leader behaviors, LMX) and an outcome of that influence (e.g., leader emergence, leader effectiveness). The goal of this chapter is to review emerging biosensor methodologies for studying leadership and examine how these methods have been applied to “leader-centric”, “leadership as an effect”, and “leader–follower relations” conceptualizations of leadership. Biosensors are methods that combine biology, chemistry, and technology to measure some electrical 146
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Biosensor approaches to studying leadership 147 or chemical response in the body (Wang, 2012), thus we include methodologies used to study leadership as a process or outcome that are rooted in biology, chemistry, neuroscience, and evolutionary psychology. These methods include collecting saliva samples, drawing blood to examine chemical markers, neuroimaging techniques (e.g., functional magnetic resonance imaging – fMRI) used to examine the brain, and genetic information from DNA. The key value in using these methods is that researchers can identify physical explanations and physiological mechanisms underlying leader traits and behaviors, follower actions and reactions in response to leaders, and processes through which leader–member relationships develop. Because objective data are collected using biosensors, these methods not only provide additional explanations to elucidate leadership processes and effects, but also generate data that are free of the biases associated with self-report questionnaires. For example, self-report methods allow the potential for respondents to provide socially desirable responses, which may not reflect the true state of the construct being measured (Podsakoff & Organ, 1986). In addition, when self-report methods are used to measure multiple constructs, the relationships between those constructs are subject to common method variance, which can lead to erroneous estimates of the strength of those relationships (ibid.). Objective biosensors can provide alternative data that may be used to corroborate or revise prior findings that are based on more traditional methods such as self-reported and observational data. In this chapter, other advantages and disadvantages of biosensor approaches will also be discussed and recommendations for overcoming their limitations will be introduced.
BRIEF HISTORY OF BIOSENSOR APPROACHES IN APPLIED SETTINGS Biosensor methods are relatively recent additions to the study of workplace phenomena. A number of these methods are born out of the neuroscience movement in cognitive and social psychology, and have already been shown to further illuminate general underlying mechanisms for social and psychological phenomena. For example, Ochsner and Lieberman’s (2001) review of the emerging neuroscience discussed the linkages of various social and psychological phenomena to activation in the brain. They describe studies that used fMRI to examine the association between activation in the amygdala (i.e., the part of the brain responsible for emotions, decision-making, affective judgments, and memory) and reliance on stereotypes (i.e., cognitive representations of a social group’s attributes or behaviors that impact expectations of the social group) when perceiving
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148 Handbook of methods in leadership research facial stimuli (e.g., Hart et al., 2000; Phelps et al., 2000). By using the neuroimaging technique, the researchers expanded their understanding of the underlying mechanisms by which individuals use stereotypes to inform perceptions and judgments. Since the fMRI detected that stereotypical perceptions and amygdala activation were associated with one another, they reasoned that stereotypical perceptions were likely linked to positive or negative emotions about a specific social group. This then affected decisions about how to interact with that social group. Thus, this biosensor method enabled researchers to further develop theory to explain how stereotypes may influence perceptions, and to use objective evidence to directly test the theoretical proposition. Turning our attention more specifically to the workplace, social cognitive neuroscience research has been highlighted as a special topic and a new research stream for the study of organizational behavior. Becker and Cropanzano (2010) discussed three ways in which neuroscience techniques can be used to understand behaviors at work, such as employees’ goal selection, group climate perceptions, and resistance to organizational change. In addition, Waldman, Balthazard, and Peterson (2011) advocated for the use of neuroscientific methods to understand inspirational leadership. Scientists have also started using other biosensor approaches, such as cardiovascular health indicators (e.g., Nyberg et al., 2009), to understand the effects of the leadership process and the environmental context (e.g., a stressful workplace) on individuals. Below, we review various biosensor-based research methods that researchers have applied to investigate the mechanisms underlying leadership. We will also identify the specific biological characteristics or reactions (e.g., hormone concentration in the blood, activation in brain regions, genetic information) captured by these methods and the types of inferences that can be drawn from such indicators within the context of leadership research.
BIOSENSOR-BASED RESEARCH METHODS IN LEADERSHIP Four classes of biosensor-based methods have been prominent in leadership research. The first category involves the collection of body fluids, such as blood, saliva, and urine samples (e.g., Hansen, Larsen, Rugulies, Garde, & Knudsen, 2009; Sherman et al., 2012). These samples are used to test for specific biomarkers relevant to the research questions of interest. For example, saliva and blood samples can be collected in order to test for hormones that are associated with dominance and status relationships (e.g., Hamilton, Carré, Mehta, Olmstead, & Whitaker, 2015). The hor-
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Biosensor approaches to studying leadership 149 mones most typically studied in the leadership context are cortisol, a stress hormone, (e.g., Sherman et al., 2012) and testosterone, a hormone linked to aggression (e.g., Sellers, 2006). Alternative hormones that might be relevant to leadership include estradiol and oxytocin, which are hormones linked to nurturing (e.g., Knight & Mehta, 2014), but these have been less commonly studied. These biomarkers may be particularly relevant for understanding leader emergence, because they can elucidate the physiological explanations for why certain personality traits (e.g., extraversion) are predictive of leader emergence (e.g., Judge, Bono, Ilies, & Gerhardt, 2002). Moreover, these biomarkers may also inform the physiological basis for leader behaviors or leadership styles, such that testosterone may be associated with leaders’ likelihood to adopt an authoritarian style whereas oxytocin may be linked to leaders’ person-oriented or consideration behaviors. Also in this category is the collection of blood and urine samples in order to study biomarkers other than hormones. Research using these bodily fluids can provide information about non-hormone markers, such as cholesterol and glycated hemoglobin, which indicate responses to environmental conditions, such as the physiological effects of stressful work environments that might be triggered by leader behaviors (e.g., Hansen et al., 2009) or might be experienced by leaders in certain kinds of situations. The second biosensor-based methodology that has been used to study leadership is the monitoring of cardiovascular activity. Cardiovascular monitoring devices include galvanic skin response (GSR) measurement devices and heart rate monitors. Devices that measure GSR capture the conductivity of the skin (e.g., Seemann, 1982), which indicates variations in levels of cardiovascular activity (e.g., higher or lower heart rate). Heart rate monitors directly measure heart rate, and can do so both at specific time points and over longer periods of time (Smith & Jordan, 2015). In leadership research, cardiovascular activity is typically used to assess physiological effects of leadership on followers, such as follower wellbeing (e.g., Nyberg et al., 2009). Third, neuroimaging methodologies are used to determine patterns of brain activity. The first of these methods is electroencephalography (EEG) (e.g., Waldman et al., 2013). In EEG, multiple electrodes are placed at specific positions on a participant’s scalp to measure gross electrical activity that originates in the neurons of the brain. The placement of the electrodes indicates which areas of the brain have greater electrical activity at any given time, indicating the activation patterns of the brain at different times and in different scenarios (Mayo Clinic, 2016a). These data are analysed using calculations that look at metrics such as how powerful the EEG signal is in different areas of the brain and how similar patterns
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150 Handbook of methods in leadership research of electrical activity are across different areas of the brain (e.g., Harung, Travis, Blank, & Heaton, 2009). EEG is used in a number of different research areas, including leader performance (ibid.) and follower reactions to leader behaviors (Dinh, 2014). The second neuroimaging method used in leadership research is fMRI (e.g., Fairhurst et al., 2014). fMRI involves placing a participant into a large magnet, which is used to track blood flow in the brain over time. During this time, participants can be presented with different scenarios or stimuli if desired. Increased blood flow to an area of the brain indicates its activation, thereby mapping the brain activities of the participant (UC San Diego, 2016). These maps are used to calculate percentages of signal change in brain areas (e.g., Molenberghs, Prochilo, Steffens, Zacher, & Haslam, 2015) or changes in brain area activation over time (e.g., Boyatzis et al., 2012). fMRI has been used to study a variety of leadership outcomes, including leader performance (e.g., Gilkey, Caceda, & Kilts, 2010) and reactions of followers to different forms of leadership (e.g., Boyatzis et al., 2012). Finally, biosensor research on leadership can also be broadly defined to include research on genetics and evolutionary characteristics (see also Chapter 6 in this volume). This methodology considers leader behaviors that may have developed as a result of genetics and evolution, and focuses on investigating hereditary traits of leaders and followers (e.g., Ilies, Gerhardt, & Le, 2004). For example, Li et al. (2015) look at genetic differences among leaders and followers. Neurotransmitter levels are sometimes collected in conjunction with genetic information when studying leadership. Neurotransmitter research typically focuses on the specific neurotransmitters of serotonin (e.g., Summers & Winberg, 2006), which is linked to the inhibition of aggression, and dopamine, which is linked to personality through its relationship to positive emotionality (e.g., Depue, Luciana, Arbisi, Collins, & Leon, 1994). These neurotransmitters are often studied as the more proximal predictors for explaining how genes may influence individuals’ behaviors or characteristics. Animal studies have also provided evidence for linking neurotransmitters to various behavior patterns similar to humans’ (e.g., Schneirla, 1959). Advantages and Disadvantages of Biosensor Methods While each of the tools used to study bioindicators is unique and provides a different type of data for researchers to analyse, they share a number of advantages and disadvantages, with some variations depending on the method. First, biosensor methods share the benefits of being generally unobtrusive. Especially important, they do not require interruption
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Biosensor approaches to studying leadership 151 of the leader–follower interaction in order to collect data when repeated measurement or continuous monitoring is of interest. For instance, heart rate monitors can be worn throughout the day, and fMRI and EEG can be used to collect data during an experimental task simulating leader– follower interaction. This ability to collect data at exactly the same time the participant is experiencing a stimulus, engaging in a task, or interacting with one or more individuals gives biosensor methods a strong advantage over techniques such as self-report surveys that either require disrupting the task or retrospective reporting. A second advantage of biosensor methods is that all of them can generate objective data (Becker & Menges, 2013). Participants cannot easily consciously or unconsciously manipulate them. Biosensors minimize or eliminate concerns such as social desirability, impression management, inattention, or other human information processing biases that are common with self-reported data. Thus, biosensor data can provide additional information beyond the survey ratings. Moreover, it may be possible to verify the reliability and robustness of the raw data by reanalysing the original samples collected. Additionally, biosensor methods allow researchers to gain insight into implicit processes that people have no ability to observe themselves (ibid.). These methods allow researchers to understand participants’ physiological reactions to leadership-related stimuli that would be unobservable using self-report methods. For instance, fMRI has been used to identify whether people emerge as leaders or followers in tasks in which they have to coordinate their behaviors with other team members’ behaviors (Fairhurst et al., 2014). In such interactions, a person may not be able to identify whether they were leading or following through self-report. However, their brain activities can provide insights into the leader emergence process. Such observations are valuable in their own right, and may be even more of interest when paired with more commonly used measurement techniques. Nevertheless, there are disadvantages to overcome with biosensor methods. While biosensor approaches are more objective than self-report surveys, some data collection procedures may be perceived by participants as intrusive. Collection of biological markers may require participants to provide saliva by holding cotton swabs or rope in their mouths, collect urine in a receptacle, or draw blood samples with a needle. Similarly, individuals are required to wear potentially uncomfortable electrodes on their head with an EEG or to stay completely still in a tight space for long durations in an fMRI. These methods require much more physical involvement on the part of the participants than traditional survey research. And, while none of the techniques typically involves more than discomfort at
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152 Handbook of methods in leadership research worst, some participants might find them anxiety provoking, distasteful or experience them as invading personal privacy. As such, it is important for researchers to clearly explain what is expected of participants to institutional review boards (IRBs), which review ethical conduct of research based on human subjects to ensure that the research upholds ethical standards. IRBs have increased requirements for reporting when using biosensor methodologies. Because many of these methods (e.g., drawing blood) can be considered medical procedures, there are more constraints on this research. Medical IRBs must approve these methods, and the data must be handled especially carefully due to its sensitive nature (U.S. Food and Drug Administration, 2016). It is also important to explain these expectations directly to participants during an informed consent procedure to ensure that they understand what they are agreeing to do, and also so that they fully understand the difference between a procedure engaged in for research purposes versus something done for purposes of medical diagnosis. Researchers should be fully aware of the potential discomforts or risks to participants with the methods that were just described. Moreover, they should be knowledgeable about the practical risks and requirements for them to collect these data. For example, when biomarkers involve the collection of bodily fluids (especially for blood draws requiring venipuncture), there may be limits on the amount that can be taken over a period of time, restrictions on the personnel who are qualified to do the collection and the setting in which it can occur, emergency procedures that must be in place, and considerations related to avoiding contact with blood and other bodily fluids, such as use of rubber gloves, disinfectants and biohazard waste disposal. And, especially if biologic specimens must be collected over an appreciable length of time, there may be requirements for transporting and preserving samples prior to chemical analysis, such as special containers and low-temperature freezers. Researchers should also have clearly worked out what their responsibilities and protocols are in case a measurement taken as part of a research study is suggestive of a hidden medical condition in a participant. Because of the complications related to data collection, gaining access to biosensor data may require additional resources from the researchers. Collecting biosensor data requires financial resources, equipment and skills that may not be readily available to many leadership researchers. For example, leadership researchers may need to rely on external laboratory services (e.g., Salimetrics) to process their saliva, blood, or urine samples for hormones and genetic markers. They may need to purchase equipment (e.g., headsets) and processing software to make sense of the EEG data, since the data require additional processing above and beyond
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Biosensor approaches to studying leadership 153 its immediate output. Finally, leadership researchers may rent out MRI scanning time from local laboratories or medical centers to conduct their research, which can be quite expensive (the current hourly rate for renting an fMRI scan is approximately $600). After this rental, the processing of fMRI data can also be time intensive. Because of the requirement for additional resources, external funding may be crucial for successful leadership research involving biosensors. Moreover, researchers with expertise in the leadership literature would do well to partner with other researchers who are familiar with the relevant physiological or biochemical knowledge and biosensor techniques if they wish to conduct studies using biosensor technology. Such interdisciplinary research teams are more likely to generate high-quality research findings. There can also be limitations on the interpretation of results from biosensor methods once the researcher has them. As noted previously, biosensor approaches assess physiological indicators, which are used to indirectly infer participants’ psychological processes and reactions. While these approaches provide direct, objective comparisons of participants’ hormones, brain activities, and so on, they do not provide a direct indication of specific psychological processes experienced by the participant, nor do they test the inferred associations between bioindicators and the psychological phenomenon (e.g., Waldman et al., 2011). Lindebaum and Zundel (2013) argue that cognitive neuroscience approaches to leadership (e.g., fMRI, EEG) risk oversimplifying complex behaviors by reducing social phenomena to activation in select areas of the brain. They argue that the reduction of these phenomena to singular biological processes does not provide the whole picture of individuals’ rich experiences. By using biosensor approaches, researchers may not be able to capture the full context in which the leadership phenomenon is embedded. Additionally, researchers have noted that current biosensor approaches also lack sophistication when it comes to providing a full understanding even of underlying physiological mechanisms (Waldman et al., 2011). For example, EEG provides an overall picture of how electrical current proceeds through the brain, but it is not perfectly precise, as the spatial resolution of EEG is low (Becker & Menges, 2013). Similarly, fMRI provides information concerning where blood is flowing in the brain and gives a general idea of which areas of the brain are activated. However, it does not pinpoint exactly where the blood is flowing or why it is going there. Thus, leadership researchers will need to make inferential leaps based on a combination of leadership theory and knowledge of physiological functioning to reason why blood is flowing to an activated area based on leadership theory and what is known about the characteristics of that area of the brain.
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154 Handbook of methods in leadership research Ochsner and Lieberman’s (2001) review of a link between stereotyped perceptions and activation in the amygdala provides a good example of such inferential leaps. While there is evidence that the amygdala is linked to emotions, affective judgments, and memory, researchers cannot say definitively that the amygdala causes stereotyped perceptions. Researchers can infer that perception and the functions of the amygdala are related because the amygdala is associated with affective judgments (e.g., making a snap judgment about a social group) and blood was shown flowing to this general area when stereotypical perceptions occurred. With this in mind, researchers using biosensor approaches should consider using these methods in conjunction with traditional psychological research methods (e.g., surveys, observation) to capitalize on a multi-method approach to studying leadership. Since these biosensor methods are relatively new in the leadership domain, Becker and Cropanzano (2010) argue that they should be used to complement traditional methods of observation. In this way, the leadership field can establish the validity of biosensor methods by comparing results generated from these different methods. Finally, researchers using biosensor approaches should also take care to ensure that these methods are grounded in theory. At the present time, researchers using neuroimaging tend to look at where blood is generally flowing in the brain and use this as evidence for a psychological phenomenon. Lee and Chamberlain (2007) state that there is often little a priori theoretical reasoning as to why blood should flow to the specific areas and why the blood flow is indicative of evidence of the phenomenon in question. Thus, researchers who choose to use biosensor approaches in leadership should evaluate theories from psychology, management, and the domain from which their methodological approach is originated to develop a priori hypotheses as to why certain physiological evidence should support the existence of a leadership phenomenon. As biosensor approaches become more popular worldwide, caution should also be taken to study the equivalence of these methods across cultures (e.g., individualistic versus collectivistic countries). According to Lowe and Gardner (2000), researchers should seek to understand if leadership can be measured using the same methodology and criteria across different cultures, which would require more emic (e.g., within-culture) versus etic (e.g., cross-culture) research studies. Despite the limitations of biosensor approaches described above, the potential benefits of using these techniques can outweigh the problems with invasiveness, sophistication, and theoretical grounding, especially if leadership researchers collaborate with biosensor experts and ground their methods in theory. Below, we review the literature of biosensorbased research, providing examples of biosensor-based studies focused
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Biosensor approaches to studying leadership 155 on leader-centric approaches, understanding the effect of leadership on others, and understanding the interactions between leaders and followers.
BIOSENSOR METHODS APPLIED TO LEADERCENTRIC LEADERSHIP RESEARCH Leader-centric research assesses leaders’ individual traits, their behaviors, and their influence on others (Bass, 2008; Day & Antonakis, 2012). Leader-centered research using biosensors has investigated the topics of emergent leadership, abusive supervision, leader performance, and transformational leadership. These topics have been investigated by examining a number of different biosensors, such as including hormones and genetics. Biosensor-based Studies of Emergent Leadership Hormone assessment is often used to compare leaders and non-leaders. Sellers (2006) found that salivary testosterone is related to higher levels of status seeking for both men and women. Moreover, she found that leaders tend to have higher levels of testosterone than non-leaders. Because of the known associations between testosterone and dominance and aggression, these findings suggest that holding a leadership role is related to aggression, although the causal order of the two variables cannot be clearly established. On the one hand, it is possible that aggressive and dominant individuals are more likely to rise to leadership roles. On the other hand, occupancy of leadership roles may result in leaders exerting more dominance and influence. As such, understanding variations in levels of salivary testosterone may help identify leaders, or those who may emerge as leaders, for future research efforts. Smith and Jordan (2015) found that individuals who are concerned with status threat and acceptance threat have higher levels of salivary cortisol. Status threat can be considered a threat to one’s competence or leadership potential, whereas acceptance threat describes a threat to one’s likeability or inclusion by others. As leaders tend to engage in status-seeking behaviors, it is possible that leaders may also have higher levels of cortisol, especially when they experience threats to their power. Although individuals who are particularly concerned with their status have higher testosterone and cortisol (ibid.), research has identified a more complex pattern between these hormones and leader emergence. Multiple researchers (Hamilton et al., 2015; Knight & Mehta, 2014; Sherman et al., 2012; Sherman, Lerner, Josephs, Renshon, & Gross, 2016) have demonstrated that cortisol and testosterone interact and impact individuals’
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156 Handbook of methods in leadership research status within organizations. These two hormones interact in such a way that individuals who have high levels of testosterone and low levels of cortisol are more likely to gain power and status and emerge as leaders in organizations. While this result is inconsistent in some studies (e.g., Westendorp, 2012), the majority of research supports the association between the hormone pattern of high testosterone and low cortisol and leader status. Research on variations in levels of these hormones in leaders may be able to help inform who among incoming employees may emerge as leaders based on their hormone levels. Additionally, this research may inform understanding in how leaders and followers experience and react to status-related stressors. Genetic and evolutionary characteristic research has focused on which genes and hereditary traits are linked to leadership emergence. Zhang, Ilies, and Arvey (2009) discussed twin research on leader role occupancy, highlighting that genetic influences play a role in leadership positions, especially for individuals from poorer social environments. Using twins as their participants, De Neve, Mikhaylov, Dawes, Christakis, and Fowler (2013) estimated a 24 percent heritability of leader role occupancy, and demonstrated that leader role occupancy is linked to a specific gene, CHRNB3. Similarly, Li, Arvey, Zhang, and Song (2012) and Chaturvedi, Zyphur, Arvey, Avolio, and Larsson (2012) both studied twins, finding that leader role occupancy was predicted by the genetic makeup of the participants. Li et al. (2015) focused on DAT1, a dopamine transporter gene, collected from saliva. They found that DAT1 10-repeat allele was related to leader role occupancy, and this effect was mediated by personality trait manifestation. Specifically, DAT1 is negatively linked to proactive personality, which is positively linked to leader role occupancy. However, they also found that DAT1 is positively related to rule-breaking behavior, which in turn is positively related to leader role occupancy. While these results overall led to no effect of DAT1 on leader role occupancy, they point to the complex role of genetics in leader emergence. By understanding the link between genetics and leader role occupancy, and the intermediate trait manifestation, we may be able to better predict who will emerge as leaders based on individuals’ genetic makeups. Finally, research on neurotransmitters has indicated that dopamine levels also relate to different personality traits (Depue et al., 1994), similar to the research by Li et al. (2015). High levels of dopamine have been linked to behaviors in animals that are similar to positive emotionality (i.e., desire, incentive-reward motivation) (Schneirla, 1959), including in rats (McGinty, Lardeux, Taha, Kim, & Nicola, 2013). Based on findings from animal-based research, it is expected that behaviors associated with positive affect, and therefore positive emotionality, are related to high
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Biosensor approaches to studying leadership 157 levels of dopamine in humans. Depue et al. (1994) argue that positive emotionality is predictive of leadership emergence, and as such, dopamine may be linked to leader emergence through its positive impact on positive emotionality. Leader Behaviors In addition to examining leader role occupancy, researchers have also applied biosensor methods to examine positive and negative leader behaviors. In terms of positive leader behaviors, neuroimaging research has been applied to characterizing brain activity patterns among transformational leaders who inspire their followers with idealized influence, intellectual stimulation, inspirational motivation, and individualized consideration (Balthazard, Waldman, Thatcher, & Hannah, 2012). Balthazard et al. (2012) collected resting EEG data from civilian and military leaders, and also collected follower ratings of those leaders’ transformational leadership style. They used patterns of brain activity to classify leaders as high or low on transformational leadership. In a test sample, leaders were categorized into high (1 SD above the mean) versus low (1 SD below the mean) transformational leaders based on their followers’ ratings of their transformational leadership. Leaders high in transformational leadership consistently exhibited clearer and more distinct EEG patterns with more differentiated brain activities compared to those rated lower on transformational behaviors. These variations in the patterns of brain activities were then used to predict whether leaders were transformational versus not using a second validation sample. This classification achieved 92.5 percent accuracy in categorization. Their results suggest that transformational leaders share a common neural activation pattern, whereas non- transformational leaders do not have a consistent pattern. In addition to positive leader behaviors, biosensor approaches have also been applied to understanding the mechanisms underlying negative leader behaviors. For example, research on leaders’ abusive behaviors has typically focused on identifying hormones that are associated with these negative behaviors. In a lab study, Bendahan, Zehnder, Pralong, and Antonakis (2015) found that salivary testosterone was positively related to the level of corruption of participants placed in a simulated leadership position. In this case, corruption was operationalized by whether participants would increase their own earnings at the expense of their group’s earnings. In addition, they also found that participants’ power, measured by number of followers, interacted with their testosterone to predict corruption. Overall, like testosterone, power was positively related to antisocial behavior. However, the positive relationship between power
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158 Handbook of methods in leadership research and antisocial behavior was amplified in the high versus low testosterone participants. These findings point to the role of testosterone in explaining how power may corrupt leader behaviors, such that when power and testosterone were both at their highest, leaders were more likely to act in an antisocial way. Westendorp (2012) found that men who have high levels of salivary testosterone are often higher in psychopathy as well. In turn, psychopathy was positively related to eight factors of leadership: creative thinking, empathy, charm, agreeableness, risk taking, need for achievement, need for affiliation, and taking charge. These results suggest that testosterone and psychopathy are both positively related to leadership among males. Because psychopaths do not experience guilt or remorse, the positive association between psychopathy and leadership may explain why leaders may engage in antisocial behaviors to exploit others (ibid.). This relationship between psychopathy and antisocial behavior and exploitation points to concerns about how these leaders may treat their followers. Leader Performance Research on leader performance has primarily used neuroimaging methodologies to characterize the brain activities of effective leaders. Gilkey et al. (2010) found that individuals with high levels of emotional intelligence have different brain activity patterns from those with low levels of emotional intelligence. Using fMRI, Gilkey et al. (2010) found that leaders who self-reported high emotional intelligence tend to use areas of the brain associated with empathy and intuitive responses when reacting to fictional strategic and tactical management dilemmas. Harung and Travis (2012) found that electrical brain activity patterns captured by EEG are related to performance of leaders. More effective performers, operationalized as managers with top-level positions in their organizations, have higher integration of electrical brain activities than do average performers, or managers at lower levels in organizations. Although Harung and Travis used leaders’ level within the organization as a proxy for performance, this operationalization of leader performance may be problematic. Leaders of different levels within the organization may not only have different performance, but may also be different in other factors such as tenure, experience, and power levels. These additional differences may account for different EEG patterns observed between high- versus low-level leaders. Hannah, Balthazard, Waldman, Jennings, and Thatcher (2013) found that adaptive leaders show different EEG brain activity patterns than non-adaptive leaders when engaging in a military scenario designed to assess adaptive behavior. These EEG patterns predicted leaders’ adaptive
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Biosensor approaches to studying leadership 159 decision making above and beyond their self-reported complexity of selfconcepts. These findings point to a link between performance and brain activities, and may be used both in predicting performance and determining which leaders will perform in more effective or less effective ways. Summary Biosensor research focusing on leader-centric topics has broadly explored various facets of leadership. Research on emergent leadership has been conducted using hormone and neurotransmitter assessments and genetic methods. Leader behaviors, both positive and negative, have been studied using neuroimaging, genetics, and hormones. Finally, leader performance has been linked to different brain activity patterns using neuroimaging methods. These results indicate that testosterone is an important hormone for studying antisocial leader behaviors (e.g., Bendahan et al., 2015), and that testosterone and cortisol can provide further understanding of leadership emergence (e.g., Hamilton et al., 2015). Additionally, genes such as CHRNB3 (De Neve et al., 2013) and DAT1 (Li et al., 2015) can inform leadership emergence, as can the neurotransmitter dopamine (Depue et al., 1994). Finally, neuroimaging may be used to assess positive leader behaviors (e.g., transformational leadership: Balthazard et al., 2012) and leader effectiveness (e.g., Gilkey et al., 2010).
BIOSENSOR METHODS APPLIED TO THE IMPACT OF LEADERSHIP ON OTHERS Biosensor-based research studying the impact of leaders’ behaviors on others has focused on indicators of follower well-being, such as cardiovascular activities, in relation to leader behaviors. Earlier research on leadership styles pointed to a limited impact of leadership on follower health. For example, Seemann (1982) found similar galvanic skin responses among followers of both democratic versus autocratic leaders. This suggests that the two types of leader behaviors are associated with similar arousal levels for their followers, suggesting that there is no difference in the stressfulness elicited by the two leadership styles. However, more recent studies have painted a different picture, suggesting that leader behaviors may have a significant impact on follower well-being, as assessed through blood and urine markers (Hansen et al., 2009). Specifically, cardiovascular research indicates that certain leadership styles are better for followers’ cardiovascular health. For instance, managerial leadership is characterized by leaders’ concrete behaviors that
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160 Handbook of methods in leadership research support a healthy psychosocial work environment, such as consideration and transformational leadership behaviors (Nyberg et al., 2009). Nyberg et al. (2009) found that the subordinate-rated level of managerial leadership was negatively related to the cardiovascular problems of followers, as operationalized by hospital admissions for acute myocardial infarction or unstable angina and deaths from ischemic heart disease or cardiac arrest. In another study, Smith and Jordan (2015) found that individuals’ socialevaluative threat (i.e., concern that others will evaluate them negatively) is positively related to blood pressure and heart rate. While this study did not focus on followers specifically, social-evaluative threat may be related to followers’ concerns about leaders’ judgments. This finding suggests that followers’ heart health may suffer if their leaders are too critical, or are less skilled in delivering negative feedback in a constructive manner. Other follower-centric research uses biosensor methods to capture followers’ brain activities in response to leaders’ behaviors. Molenberghs et al. (2015) found that individuals who were listening to inspirational versus non-inspirational statements from in-group versus out-group political leaders had different patterns of brain activity. There was a main effect for statement type, such that individuals showed greater brain activities in areas associated with negative or norm-breaking information, such as the left lateral orbitofrontal cortex, the adjacent pars triangularis, and the angular gyrus, when listening to non-inspirational statements compared to when listening to inspirational statements. This main effect was qualified by an interaction between statement type and leader status. When listening to inspirational statements from in-group leaders, individuals showed activity in areas of the brain associated with semantic information processing, which indicates deeper processing and encoding of information. When listening to non-inspirational statements from in-group leaders, individuals showed activity in areas of the brain associated with reasoning about mental states. The opposite was true when listening to out-group leaders. These results identified cognitive mechanisms to explain important boundary conditions (i.e., follower identification with the leader) for the effectiveness of leaders’ inspirational messages. Beyond studying effective leader behaviors, researchers also explore the brain activity of followers in response to leaders’ unethical behaviors. Dinh (2014) measured electrical activity in the brains of followers using EEG and found that followers made implicit self-evaluations about their own ethicality in response to leaders’ dark leadership behaviors (e.g., ridiculing, invasion of privacy, and other abusive supervisory behaviors). Her results showed that participants evaluated themselves as more associated with dark attributes and acted less ethically when assigned to a supervisor who acted unethically than when assigned to a supervisor who acted sup-
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Biosensor approaches to studying leadership 161 portively toward a fictitious follower. These behaviors and evaluations were related to differences in brain activity patterns between individuals with differing self-evaluations of dark attributes, as captured by EEG (measured during interactions with the leader and during the time participants were making self-evaluations). This suggests that leaders’ impact on follower self-perceptions and their subsequent moral behaviors may be explained by different brain activities in followers. Summary Biosensor methodologies have been used in research focusing on the effects of leaders on followers. Specifically, research has examined followers’ well-being as an outcome of different leadership styles. Different leader behaviors are associated with followers’ different brain activity patterns. This research provides a more complete understanding of the effects of leaders on their followers, which complements prior research that is primarily based on self–reported measures to capture followers’ experiences.
BIOSENSOR METHODS APPLIED TO INTERACTIONBASED LEADERSHIP RESEARCH Biosensor research on interactions between leaders and followers has focused on the topics of power, status, and dominance, and leader– follower relationships. These topics have primarily been examined using neuroimaging methodologies. Neuroimaging research on social status has shown that the brain reacts to changes in status. Beasly, Sabatinelli, and Obasi (2006) argued that human brains are adjusted to navigating social systems based on status. They reviewed neuroimaging research, which has demonstrated that neural networks appear to be “wired” for navigating social situations based on status and power. Specifically, research using fMRI indicates that brain activity patterns vary depending on whether a person is interacting with a person of higher versus lower status in a hierarchy. When interacting with a person of higher status within a stable hierarchical structure, individuals have higher levels of activities in various areas of the brain (e.g., the bilateral occipital/parietal cortex, striatum, parahippocampal cortex, and the dorsolateral prefrontal cortex) than during other interactions. However, when interacting with a person of lower status within a stable hierarchical structure, no unique activation pattern is observed. In unstable hierarchies, interacting with a person of higher status involved even more areas of the brain, and demanded more cognitive resources. During these
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162 Handbook of methods in leadership research interactions, the bilateral thalamus, right amygdala, posterior cingulate, medial prefrontal cortex, premotor cortex somatosensory cortex, and supplementary motor area were activated. These results indicate that social status processing, specifically with higher-status individuals and in more complex environments, demands more processing and energy. In addition to activation patterns, there also appeared to be structural differences across individuals of chronic high versus low status as identified by magnetic resonance imaging (MRI) to assess the structure of the brain (Mayo Clinic, 2016b). Beasley et al. (2012) found that individuals who perceived themselves as low status had less gray matter (i.e., brain cells) in brain regions associated with coping with emotional and psychosocial stressors compared to those who perceived themselves as having higher status. These results point to an impact of the perception of status on individual outcomes. These results may relate to the brain structures of leaders and followers, although conclusions are not clear, as there are many environmental factors involved in brain development. Biosensor research has also assessed the neurotransmitters involved during interactions between individuals of different status. In interactions between individuals of varying social status, such as leaders and followers, serotonin and cortisol interact to reduce aggressive reactions by low-status individuals toward high-status interaction partners (Summers & Winberg, 2006). Corticosteroids increase serotonin production, and serotonin inhibits aggressive responses. Reduction in aggressive tendencies benefits both interaction partners, such that it creates a low-stress environment for both high- and low-status individuals to facilitate interaction. Moreover, it benefits the high-status individuals by reinforcing their dominance. Studies using fMRI and EEG have indicated that interactions between leaders and followers can be studied using patterns of brain activity. Jack, Boyatzis, Khawaja, Passarelli, and Leckie (2013) coached participants using positive emotional attractor (PEA) coaching given by an interviewer, which uses positive motivational factors aimed at changing a person’s behaviors. This type of coaching is designed to motivate followers to change their behaviors through inspiration. The interviewing technique used in this study simulated a coaching relationship, such that the interviewer acted as a leader and the participant as a follower. PEA coaching is contrasted with the negative emotional attractor (NEA) coaching. Rather than inspiration, NEA coaching focuses on exercising willpower and training people to monitor themselves, which is expected to deplete individuals’ executive resources. PEA coaching improved interactions between the leader (interviewer) and the follower (participant), and produced different patterns of brain activity in the participants (ibid.). Individuals coached using PEA showed greater activation of the
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Biosensor approaches to studying leadership 163 parasympathetic nervous system, which is responsible for regulating the body’s rest-and-digest functions, and areas associated with positive affect. Participants coached with the NEA technique had greater activation of the sympathetic nervous system, which is responsible for regulating the body’s fight-or-flight responses, and areas associated with negative affect. These results identify ways in which differences in brain activation are associated with different types of interactions between leaders and followers, and how different interactions may be driven by different leader behaviors. Researchers have also used neuroimaging to assess the impact of the quality of leader–follower relationships on the brain activity of followers. The impact of resonance and dissonance between followers’ preferences and leaders’ styles has been explored via fMRI. Resonance is defined as “physiological attunement and interpersonal synchrony between a leader and another individual” (Boyatzis et al., 2012, p. 261), and dissonance is defined as the absence of those characteristics. Resonance between a leader and a follower is a marker of a high-quality relationship, whereas dissonance is a marker of a low-quality relationship. Boyatzis et al. (2012) found that when followers reflected on resonant relationships with a leader, brain areas associated with positive affect and mirror neurons were activated. However, when followers reflected on dissonant relationships, areas associated with lower attention and negative affect were activated. Studies examining both leader and follower brain activities show differences in activity patterns between leaders and followers during their interactions with one another. For example, Konvalinka et al. (2014) collected EEG data from leader–follower pairs completing a joint task, and found that only the leader, but not the follower, exhibited the key pattern of brain activity associated with the task. Fairhurst et al. (2014) found that when paired with virtual partners, certain individuals emerged as leaders, and others as followers, as distinguished both by brain activity patterns captured by fMRI and task preferences of characteristics of the tapping task they were assigned to. The researchers identified leaders as the individuals who found it easier to synchronize with their virtual partner when they perceived more control over the tempo of tapping in the task. Followers, on the other hand, found it easier to synchronize their tapping with their partner’s tapping when they perceived that the virtual partner was more in control of the tempo. fMRI data found that leaders showed greater brain activity than did followers in areas associated with self-initiated actions, indicating that leaders were directing their own behaviors while followers were not. Additionally, leaders focused less on error correction and more on prioritizing maintaining tempo versus matching their partner (ibid.).
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164 Handbook of methods in leadership research Summary Research on interactions between leaders and followers has focused on two areas: status and relationships. Both of these research topics have been investigated using neuroimaging methodologies. Overall, this research points to neuroimaging as being a useful tool to identify different brain activity patterns among leaders and followers during their interactions, which is useful for understanding their mutual experiences.
DISCUSSION Biosensor approaches can be applied to understanding leadership from a leader-centric perspective, to examine leadership as an outcome, and to explore leadership as characterized by the interactions between leaders and followers. We summarized ways that biosensor approaches have informed leadership research using methods such as neuroimaging (e.g., fMRI, EEG), saliva, blood, and urine markers (e.g., cortisol, testosterone), and genetics (e.g., twin studies examining the role of specific genes). Biosensor approaches offer unique advantages, such as providing objective assessment to understand leadership processes that can be used in tandem with other data sources (e.g., survey ratings from leaders or followers). Notably, using biosensor approaches will not solve all methodological and theoretical problems within leadership research. We are not advocating that researchers abandon survey or observation methods when studying leadership. Rather, they may find that they gain by supplementing the study of leadership with other methods, including biosensor approaches. Hiller, DeChurch, Murase, and Doty (2011) have made a similar argument. Their review of leadership research from 1985 to 2010 identified that approximately 63 percent of leadership research used surveys to study the phenomenon of interest, and they recommended the use of other methods (e.g., observation, interviews, experimental manipulations) to understand the rich process of leadership. Like the other methods discussed by Hiller et al., biosensor approaches can be used in conjunction with survey research. Together, they can help isolate psychological mechanisms and their physiological roots of leadership in order to improve our understanding of the leader emergence process and factors contributing to leader effectiveness. As with much of the work conducted in 70 years of leadership research, the majority of studies discussed in this review focused on leader traits and behaviors. While these studies have been informative, other perspectives of leadership research can benefit from using biosensor approaches. For instance, applying biosensor approaches to studying leadership as an
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Biosensor approaches to studying leadership 165 effect, we may be able to better elucidate the effects leaders have on followers’ cognitive, affective, and conative processes. Similarly, relationshiporiented leadership studies can utilize biosensor approaches to further understand how high- versus low-quality relationships may develop over time among the same leader and her or his different followers. Collecting bioindicators may help discover the physiological mechanisms underlying these effects. In addition to encouraging leadership scholars to utilize a variety of research methods and designs to examine leadership, Hiller et al. (2011) also advocated for more research that focuses on the temporal dynamics of leadership. For example, they concluded that the majority of the leadership studies used cross-sectional data (59 percent of studies), with only 29 percent of the existing studies collecting longitudinal data (ibid.). As a result, we have limited understanding of how time may be a factor that can impact the leader emergence, leader–follower relationship formation, and leadership development. By studying leadership phenomena longitudinally, concerted efforts can be made to align the frequency of measurement with fluctuations in leadership phenomena as they develop and change over time. Notably, the studies using biosensor methods in this review were mostly short-term laboratory experiments or observational studies. In line with Hiller and colleagues’ (2011) recommendation, studies using biosensor approaches should also expand beyond short-term laboratory work and measure leadership longitudinally and in the field to capture changes in the phenomenon over time. An important complication when designing longitudinal biosensor research is to not only consider the temporal characteristics of the leadership phenomenon in question, but also pay attention to how the focal biomarkers of interest may change over time. For instance, both cortisol and testosterone follow a diurnal rhythm, such that typical individuals have the highest levels of cortisol and testosterone right after waking up, levels of these hormones gradually decrease over the course of the day, and are at their lowest right before bedtime (Adam & Kumari, 2009; Van Anders, Goldey, & Bell, 2014). The between-person differences in these hormones reflect both the within-person fluctuations associated with times of the day, and the effects associated with the external factors – the leadership phenomenon in this case. Moreover, it is not uncommon for different hormones to have different time-based changes in response to the same external factor. For example, when individuals experience acute stress, their cortisol level will show a clear increase about 20 minutes after the exposure to a stressor (Dickerson & Kemeny, 2004), whereas their alpha-amylase (an enzyme commonly used to mark sympathetic nervous system activation) will spike right after the
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166 Handbook of methods in leadership research exposure. Thus, when applied to leadership research, biomarker measurements need to correspond with the leadership phenomenon in question, and the timing of these measures may be critical. It is commonly recommended that researchers standardize the time of the day for data collection in order to account for the diurnal fluctuations (Van Anders et al., 2014). Moreover, researchers need to calibrate the change rates for both the leadership phenomenon and the bioindicators to guide the measurement frequency. Finally, although biosensor methods offer benefits of objective measures, their adaptation may come with a cost to the physical and psychological fidelity of the experimental setting, and even external or face validity. For example, studies utilizing fMRI require participants to stay still in isolation, and can only employ relatively simple tasks, such as tapping in sync with a partner (e.g., Fairhurst et al., 2014), or responding to agreement or disagreement with presented statements (e.g., Jack et al., 2013). These experimental tasks may have limited physical and psychological fidelity in simulating the actual leader behavior and leadership processes. Moreover, it is unclear whether results from the laboratory can generalize to the realworld contexts in which leadership is embedded, thereby creating concerns for the external validity of these findings. Finally, it is often assumed that the physiological reactions assessed by biosensor approaches are consistent across leaders in different occupational contexts (e.g., military versus religious versus business settings) and societal cultures (e.g., cultures high in individualism versus collectivism; masculinity versus femininity; House, Hanges, Javidan, Dorfman, & Gupta, 2004). To alleviate these concerns, more flexible biosensor approaches could be implemented in field settings to capture changes in biomarkers in real contexts. For instance, participants may wear GSR devices, heart rate monitors, and portable EEGs at their workplaces where the leadership process unfolds. By observing leadership phenomena in context and assessing their impact with real-time biosensor measures, the findings will likely be more generalizable than studies performed in a laboratory. Overall, there remain numerous areas of leadership research that can be further explored and clarified with biosensor approaches. As the field continues to grow, there is much promise in using biosensor methodologies to inform current leadership theories and create a more objective, multimethod based approach for answering important research questions in the leadership domain.
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Biosensor approaches to studying leadership 167
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PART III QUANTITATIVE METHODS AND ANALYTIC APPROACHES
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8. Mediation analysis in leadership studies: new developments and perspectives Rex B. Kline*
Leadership has been defined as a process of goal-directed influence that mobilizes organizations toward desired goals; that is, leaders work with people and structures within organizations to establish common meanings, goals, and outcomes (Riehl, 2012). This process is hypothetically amenable to causal analysis if (1) relevant variables are specified and measured, and (2) there is theory about direct or indirect effects among these variables. Such variables could include attitudes or behaviors of leaders, characteristics of subordinates or organizations, and outcomes. Indirect causal effects involve the distinction between distal versus proximate causes such that a distal cause is expected to affect an outcome through an intervening variable, or through a proximate cause. A related concept is that of mediation, or the causal hypothesis that changes in one variable lead to changes in another variable, or mediator, which in turn lead to changes in outcome (Little, 2013). Indirect effects do not necessarily involve changes in the variables along the chain of effects from causes to outcomes, but mediation always involves changes in these variables. The distinction between indirect effect and mediation also depends on the research design, a point elaborated later. Given a particular theory, a presumed cause may have: (a) a direct effect on an outcome but no indirect effects, (b) no direct effect but at least one indirect effect, or (c) both direct and indirect effects. Hallinger and Heck (1998) referred to the sets of hypotheses just mentioned as, respectively, direct effects models, mediated effects models, and mediated effects with antecedents models. Distal causes in leadership processes are usually described as relatively stable or enduring variables. Some examples include leader personality characteristics, such as self-efficacy, emotional temperament, and social potency, or the need for interpersonal power and desire to make an impact on others (Baker, Larson, & Surapaneni, 2015). Another example is quality of the leader–subordinate exchange relationship (e.g., LMX or vertical dyad linkage theory), which can vary across dyads with the same leader, but is generally viewed as relatively stable within a dyad (Wang, Law, Hackett, Wang, & Chen, 2005). Intervening variables should be amenable to influence by the leader, and thus are not generally understood 173
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174 Handbook of methods in leadership research as persistent qualities. Examples of potentially malleable intervening variables include organizational structures, such as human resource policies, or characteristics of subordinates, such as levels of organizational commitment or work assignments that can be modified through the actions of a leader. The expectation that leaders affect outcomes at least in part through indirect effects on other people, events, or organization factors corresponds to the hypothesis of mediation (Hallinger & Heck, 1998). There are many examples of analyses about direct or indirect effects of leadership qualities on organizational outcomes. A few recent studies are briefly described next: ● Within
a sample of employees and their respective supervisors from about 20 different companies, Neves and Story (2015) reported evidence that ethical leadership increases employees’ affective commitment to the organization, which in turn reduces deviance, or intentional behavior that violates organizational norms or policies. Also, the researchers found that the indirect effect just mentioned is stronger when the leader’s personal reputation for moral standards and effectiveness is greater. This result concerns moderation, or a conditional causal effect. Moderation, or interaction, is not the same thing as mediation, or an indirect effect, although the two are sometimes confused – see Little (2013, Ch. 9) for a clear differentiation of the two. ● Within a longitudinal sample of municipal workers, Tafvelin, Armelius, and Westerberg (2011) analysed direct effects of transformational leadership on employee affective well-being. Transformational effects occur when leaders raise awareness of moral values that encourage followers to sublimate their own personal goals for the collective good. These authors also reported evidence that transformational leadership affects well-being indirectly through an organizational climate for innovation that is encouraged by leaders. ● Within a sample of young women enrolled in university, Baker et al. (2015) reported evidence for the hypothesis that social potency indirectly affects the intention to engage in leadership activities, such as in a group setting, through the intervening variables of leadership self-efficacy and interest in leadership opportunities. Mediation analysis has become “popular” in many research areas besides the study of leadership. For example, Baron and Kenny (1986), a classical work about the estimation of mediation effects among continuous variables with no interactions, has been cited over 50 000 times
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Mediation analysis in leadership studies 175 (Kenny, 2015). It is the single most widely cited article in the Journal of Personality and Social Psychology (MacKinnon & Pirlott, 2015). There are now hundreds, if not thousands, of published reports of mediation analyses. The reasons for this interest are not hard to understand. Mediation analysis promises a look inside the “black box” of leadership or other causal variables of interest in terms of how effects of such variables operate. Pearl (2014) described mediation analysis as telling us how nature works through the analysis of direct or indirect effects on outcomes at the end of a causal chain. The directions and magnitudes of such effects may have implications for human resource practices, organizational culture, or other variables that connect leadership with outcomes. But there is also evidence that the collective enthusiasm for mediation analysis has outstripped better judgment. Specifically, the typical published mediation study may have so many flaws due to an inadequate design, improper statistical analysis, or lack of attention to assumptions that the results have little or no substantive value (Kline, 2015). Many of these problems are addressed in a recent special issue of Basic and Applied Social Psychology about problems with mediation analysis (Trafimow, 2015). These shortcomings are outlined next.
ASSUMPTIONS UNDERLYING MEDIATION MODELS Presented in Figure 8.1(a) is a basic “mediated effects with antecedents” path model in which the presumed cause, X, has a direct effect on the outcome, Y, and also an indirect effect on Y through a presumed mediator, M. The arrows at 45° angles that point to M and Y represent error (unexplained) variance and also designate these two variables as dependent (endogenous) within this particular model. The specification that the coefficient for the direct effect of X on Y equals zero (c 5 0) would change Figure 8.1(a) to a mediated effects model with the sole causal pathway: X→M→Y, which indicates that the effect of X on Y is purely indirect, and through a single mediator. For now we suppose that X, M, and Y are continuous variables. Implied in Figure 8.1(a) are several assumptions that are rarely acknowledged in the typical mediation study. Some of these assumptions are also untestable in that the data can tell us nothing about whether the assumption is plausible or not plausible (Bullock, Green, & Ha, 2010).
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a c
M
X
M
X
M
b Y
(a) Basic mediation
Y
Y (b) Reciprocal effects
(c) Correlated errors
Note: X, cause; M, mediator; Y, outcome. All three models are equivalent.
Figure 8.1 ( a) Basic mediation with antecedents model; (b) mediation model with reciprocal effects; (c) correlated errors model with no mediation An example is the assumption of modularity, which means that the causal process consists of components that are potentially isolatable, and thus can be analysed as separate entities (e.g., the trivariate model in the figure). A causal process that is organic or holistic is inseparable into parts, and thus is not amenable to standard mediation analysis (Knight & Winship, 2013). There is no empirical check for modularity, so it must be assumed, but authors of mediation studies rarely even mention the issue. Other assumptions implied in Figure 8.1(a) that generally cannot be tested with the data are listed next. It is assumed that all directionality specifications are correct, including X→M, X → Y, and M → Y in the figure. If the specifications just stated are incorrect, then the results may have no meaningful interpretation. Some researchers incorrectly believe that directionality specifications are actually tested in mediation analysis, but the truth is that such specifications are simply assumed to be true at the beginning of the empirical analysis. The reason for concern with directionality has to do with whether equivalent models with alternative causal directions (considered momentarily) are equally plausible, but directionality is generally assumed, not tested, in mediation analysis. It is also assumed for Figure 8.1(a) that there are no unmeasured common causes, or confounders, for any pair of variables among X, M, or Y. This includes the assumption of independent errors for dependent variables M and Y. It is also assumed that no omitted confounder of the association between M and Y is caused by X. These are strict requirements. Estimation of mediation using data from strong research designs that make these assumptions more tenable helps. An example of a strong design that supports directionality assumptions are longitudinal designs,
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Mediation analysis in leadership studies 177 in which presumed causes are measured before their outcomes. Another example is an experimental design where direct manipulation of causal variables helps to eliminate explanations due to confounders. But crosssectional (non-experimental) designs where all variables are concurrently measured have no inherent support for causal inference, and thus causal analysis in such designs may be intractable. There are additional assumptions accompanying the models presented in this chapter, but satisfying them is more under the control of the researcher. For example, the model in Figure 8.1(a) implies that scores on variable X are perfectly reliable, that is, rXX 5 1.0, where rXX is a reliability coefficient. This is because independent (exogenous) variables in path models have no error terms; thus, there is no allowance for imperfect reliability (i.e., rXX < 1.0) in exogenous variables. Instead, measurement error in X tends to show up “downstream” in the model, or in values of path coefficients for variables affected by X (e.g., M and Y in Figure 8.1(a)) or in their error terms. If X were the sole cause (e.g., M in the figure), then measurement error in X tends to reduce the absolute value of the coefficient. But measurement error in multiple causes of the same outcome (e.g., Y in the figure) can bias coefficients by either increasing or decreasing their absolute values. If X were an experimental variable that represented the random assignment of cases to conditions, the assumption of no measurement error would be plausible, but this is not generally true if X is a measured (non-experimental) variable, such as an attribute assessed with a self-report questionnaire. However, because measurement error in endogenous variables is manifested in their error terms, it is not assumed for M and Y in the figure that: rMM 5 rYY 5 1.0 There are ways to explicitly control for measurement error in each and every observed variable in a path model (Hayduk & Littvay, 2012), but such methods are rarely used in mediation studies. Cole and Preacher (2014) outline the negative consequences of ignoring error in path analysis.
IMPORTANCE OF RESEARCH DESIGN Most mediation studies are based on cross-sectional designs, but such designs have no formal elements that directly support casual inference. One reason is the absence of time precedence. For example, if variables X, M, and Y in Figure 8.1(a) are all measured at the same occasion, there may be no way to establish which of two variables, a presumed cause and
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178 Handbook of methods in leadership research its presumed effect, occurred first. Therefore, the only grounds for causal inference in cross-sectional designs is assumption, one supported by a clear, convincing rationale for specifying particular directions of causal effects (e.g., X → Y in Figure 8.1(a)). Making directionality specifications with any degree of confidence in cross-sectional designs depends a great deal on the researcher to rule out competing explanations of the association between X and Y. Without stating such a rationale, little confidence is warranted about interpreting path coefficients as evidence for direct causal effects in cross-sectional designs. Tate (2015) reminded us that mediation involves time-ordered relations among variables such that the mediator must always intervene in time between the cause and outcome (respectively, M, X, and Y in Figure 8.1(a)). It also requires a potentially changeable mediator; that is, not just any variable can be specified as a mediator. This explains why the specification of personality trait variables as mediators is generally inappropriate. This is because stable traits cannot mediate a cause–outcome relation. The same concern applies to other kinds of individual difference variables considered to be stable characteristics, such as self-esteem, self-efficacy, and advancement (promotion) versus security (prevention) motivational orientations, among others. But it may be reasonable to specify state variables as mediators, if such variables are hypothesized as outcomes of prior causes and thus are conceptually malleable. States are temporary behaviors, perceptions, or emotions that depend on a particular context or are affected by enduring traits at a particular time. An example is the distinction between trait anxiety as a general propensity to feel apprehensive versus state anxiety as the degree of unease at a particular moment. State anxiety is generally seen as malleable, but trait anxiety may be more enduring. The Problem of Equivalent Models With no actual or conceptual time ordering in a cross-sectional design, it is possible to generate equivalent versions of a path model that explain the data just as well as the original model. Consider Figure 8.1(a). Any rearrangement of the paths that does not result in a causal loop (i.e., the model remains recursive) would fit the same data just as well as the model of Figure 8.1(a). For instance, the alternative model with the paths listed next: X → Y, X → M, but Y → M is equivalent to Figure 8.1(a) in that both models would explain the same data equally well. Altogether there are a total of six equivalent versions of
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Mediation analysis in leadership studies 179 Figure 8.1(a) (including this figure), where each equivalent model corresponds to a different causal ordering among X, M, and Y. In this example, all of these models would have perfect fit because their degrees of freedom are zero, but the point is that analysis cannot determine which (if any) of these equivalent models is correct. The problem of equivalent models is even worse than just described, if there is no time ordering of any kind. For example, Figure 8.1(b) depicts a non-recursive model with a causal (direct feedback) loop between variables M and Y. In this model, M and Y each mediate the effects of the independent variable on each other, thus it can be described as a reciprocal effects model. In order to be identified, this model must include the assumption that the two direct effects that make up the causal loop are equal. There are two equivalent versions of Figure 8.1(b), for example, the model with the specifications: Y → M, Y → X, but M → X and X → M, where the last two direct effects are constrained to be equal. Including Figure 8.1(b), there are three equivalent versions of reciprocal effects models, and each of these variations is also equivalent to each of the six equivalent versions of Figure 8.1(a). Finally, Figure 8.1(c) is recursive but not does specify mediation because there is no direct link between M and Y. Instead, it features a correlation of the error for variable M with the error for variable Y. Nevertheless, Figure 8.1(c) and its two equivalent versions, such as the non-mediational model:
Y → M, Y → X, but M → X,
where the symbol ↔ designates correlated errors, are all equivalent to the six equivalent versions of Figures 8.1(a) and the three equivalent versions of Figure 8.1(b). Thus, there are a total of 12 equivalent versions of any model taking the form shown in Figure 8.1(a), including the original model. Without some strong evidence that can eliminate the causal directions implied by these equivalent models, they are equally plausible explanations of the data that fail to support the original conception of the mediated effect. Unfortunately, too many researchers fail even to mention the issue of equivalent models when analysing mediation in cross-sectional designs, much less garner evidence to rule out those models.
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180 Handbook of methods in leadership research Conceptual Bases for Inferring Causal Direction Sometimes alternative directionalities in cross-sectional designs can be ruled out by the nature of the variables. Suppose that variable X in Figure 8.1(a) is gender and that both M and Y are individual difference variables. It would be illogical to assume that M or Y could change a person’s gender, so the alternative specifications: M → X or Y → X would be indefensible. Another basis for specifying directionality in crosssectional designs is the Hyman–Tate conceptual timing criterion (Hyman, 1955; Tate, 2015), which requires a theoretical time-ordering of the cause, mediator, and outcome. From this perspective, variables are not required to have a strict temporal order in their measurement – cause first, mediator second, and outcome last – if there is a strong rationale about causal order. In leadership studies, for example, it may be reasonable to assume that managers have stronger effects on subordinates than the reverse. Baker et al. (2015) argued that social potency is a general personality trait that could theoretically affect leadership qualities, and thus they specified social potency as causal and leadership variables as mediators even though all variables were concurrently measured. If the rationale for a conceptual timing order is not convincing, though, causal inference is unwarranted. For instance, in some theoretical models, leadership is defined conditional on outcomes; that is, people in leadership positions who have performed well in in the past may be viewed as more leader-like because the successful performance is attributed to them (and they also may be more likely to perform well in the future). Thus, if X is a rating of leadership and Y is outcome, it would be difficult to make a strong argument for causal order in a cross-sectional design (R. Hall, personal communication, September 12, 2016). Designs that Incorporate Time Lags Other types of research designs feature actual, not conceptual, time precedence. A measurement-of-mediation design features random assignment of cases to the levels of a causal variable, for example, a two-group design in which X 5 0 for control and X 5 1 for an intervention intended to boost leadership skills. In this design, the mediator M is an individual difference variable that is measured, not manipulated, at a later time but before the outcome Y is assessed (Bullock et al., 2010). Selecting the appropriate measurement schedule, or time lags between measurement occasions, is
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Mediation analysis in leadership studies 181 critical. For example, measuring M too soon may not give the intervention enough time to have its effect, but measuring M too late can miss temporary effects that have dissipated. An advantage is that because X is an experimental variable, over replication samples it will be isolated from confounders that also affect M or Y. But because M is not manipulated, it may be plausible that M and Y share at least one unmeasured cause. In this case, the absence of randomization on M means that other causes of Y, or confounders, cannot be ruled out; thus, the measurement-ofmediation design does not completely eliminate potential spurious causal inferences. As a possible solution to this problem, Antonakis, Bendahan, Jacquart, and Lalive (2010) described the use of instrumental variables, or instruments, in leadership studies as a way to control for omitted causes of M and Y. An instrument is a variable that should have a direct effect on M, no direct effect on Y, and an indirect effect on Y only through M. The mediator M is regressed on the instrument, and the predicted mediator in this analysis replaces M in the analysis where the outcome Y is regressed on both M and X for Figure 8.1(a). This tactic removes the influence of an unmeasured common cause of M and Y from the analysis just described. MacKinnon and Pirlott (2015) describe additional statistical methods for enhancing the causal interpretation of the effect of M on Y in mediation analysis. See also Bullock et al.’s (2010) description of manipulation-ofmediation designs, where the mediator is also an experimental variable. Challenges of manipulating mediators are considerable, but successfully doing so for both variables X and M isolates confounders for any pair of variables among X, M, and Y, greatly strengthening the support for the proposed causal direction. Longitudinal designs also offer time precedence. There are specific longitudinal designs for mediation analysis, such as those described by Cole and Maxwell (2003), Maxwell and Cole (2007), Selig and Preacher (2009), Little (2013), and others. Such designs feature causes, mediators, and outcomes, each measured at multiple points in time. Also, the path models associated with such designs allow no direct effects between variables measured at the same occasion. The latter specification avoids violating the requirement for time ordering. Thus, longitudinal designs for mediation analyses guarantee that estimates of X → M relations are only made from causes that are measured before mediators and that, in turn, estimates of M → Y relations are only made from mediators that are measured before outcomes. These specifications are demonstrated next with an example. Tafvelin et al. (2011) measured transformational leadership, (X), climate for innovation (M), and affective well-being (Y) on two occasions within
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X1
X2
X
X
M
a M1
M2
M
b Y1
Y2
(a) Tafvelin et al.
W
Y
(b) Neves & Story
Y (c) De Vries et al.
Notes: a. Subscripts indicate time of measurement; X, transformational leadership; M, climate for innovation; Y, affective well-being. b. X, ethical leadership; W, reputation for performance; M, affective commitment; Y, organizational deviance. c. X, leadership support; M, need for leadership; Y, job satisfaction. Black dots on the diagram represent interactive effects.
Figure 8.2 ( a) Tafvelin et al. (2011) model;a (b) Neves and Story (2015) conceptual model with conditional mediation;b (c) example for CMA approach based on variables analysed by De Vries et al. (2002)c a 12-month interval in a large sample of municipal workers. Their model is presented in Figure 8.2(a), where subscripts represent the two measurement occasions. Because there is no direct effect from X1 to Y2, the hypothesis of a fully mediated effect of X on Y is represented in Figure 8.2(a). Adding the direct effect X1 → Y2 to the figure would represent the hypothesis of partial mediation, where X has both direct and indirect effects on Y. The model in Figure 8.2(a) features the two cross-lagged direct effects listed next: X1 → M2 and M1 → Y2, where coefficients a and b estimate these two direct effects. Coefficients a and b are proper longitudinal estimates of the two component mediating paths because there is time precedence in the measurement of each pair of variables connected with a direct effect. Also, the value of coefficient a controls for the prior effect of the mediator on itself (M1 → M2), and the value of coefficient b does the same thing for the outcome (Y1 → Y2). Figure 8.2(a) also features the four cross-sectional paths listed next:
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Mediation analysis in leadership studies 183
X1 → M1, M1 → Y1, X2 → M2, and M2 → Y2
Coefficients associated with the paths just listed are not proper estimates of mediation because there is no time precedence among variables measured on the same occasion. Figure 8.2(a) but without the four cross-sectional paths is similar to a half-longitudinal design, where the mediator and outcome (M, Y) are each measured on two occasions, but the cause (X) is measured only at time 1. At time 2, the disturbances of the mediator and outcome are assumed to covary, or M2 ↔ Y2. In a full-longitudinal design, X, M, and Y are each measured at three different occasions. Mediation in this design is estimated from the coefficients from paths of the single contiguous pathway through which the cause can affect the outcome through the mediator over time, or:
X1 → M2 → Y3,
where the subscripts indicate the measurement occasion. There are also proxy estimators of mediated effects that are available from coefficients for noncontiguous pathways, such as: X2 → M3 and M2 → Y3 See Little (2013) for more information about full-longitudinal designs for mediation analysis. To summarize, a proper design for estimating mediation offers either actual temporal precedence or conceptual temporal precedence with an ironclad rationale. This requirement is consistent with Little’s (2013) definition of mediation given at the beginning of this chapter that emphasizes the transmission of changes from cause to mediator and then to outcome. It also explains the difference between the terms “indirect effect” versus “mediation.” Specifically, mediation always involves indirect effects, but not all indirect effects automatically signal mediation. This is especially true in cross-sectional designs with no time precedence of any kind. Such designs do not allow or control for changes in any variable, cause, mediator, or outcome. Thus, without a proper research design, use of the term “mediation” may be unwarranted. The term “indirect effect” would still apply, but mediation refers to a strong causal hypothesis, and suitable designs are needed to test strong hypotheses.
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SIGNIFICANCE TESTING When all variables are continuous and there are no interaction effects, mediation is generally estimated as the product of the coefficients from the direct effects that make up the indirect causal pathway. For example, this estimator for both Figures 8.1(a) and 8.2(a) corresponds to the quantity a × b, or ab. Given hypothetical values of the unstandardized coefficients for Figure 8.1(a) listed next, a 5 1.50, b 5 0.50, and c 5 2.00, we can say that the unstandardized indirect effect of X on Y equals ab 5 1.50(0.50) 5 0.75. The unstandardized direct effect of X on Y, or 2.00, is estimated controlling for the effect of M on the Y (i.e., the other cause), so coefficient c is analogous to a partial regression coefficient. The unstandardized total causal effect of X is the sum of its direct effect and indirect effect. In other words, for every 1-point increase in the original metric of X, we expect (1) a 2-point increase in the original metric of Y through the direct effect, and (2) another 0.75 increase through the indirect effect that involves M. The sum of the direct and indirect effects is thus 2.75, which is the total effect of X on Y. (The interpretations just stated require all the assumptions for Figure 8.1(a) discussed earlier.) Unstandardized indirect effects can be tested for statistical significance. However, you should know that the outcomes of significance testing, or p values, for indirect effects may be untrustworthy due to implausible assumptions or inadequate sample sizes. Thus, statistical significance is not a scientific gold standard in mediation analysis (or in any other kind of statistical analysis). Sobel (1982) developed a hand-calculable significant test for unstandardized indirect effects that involve just three variables (i.e., there is a single mediator). In large random samples, the ratio ab/SEab, where the denominator is an approximate standard error in the Sobel method, is interpreted as a z (normal deviate) test of the unstandardized indirect effect. The Sobel test assumes normality of the sampling distribution for coefficient ab, but this is a dubious assumption as product estimators do not generally follow normal distributions. The Sobel test also assumes random sampling, but most samples in mediation studies are convenience (ad hoc) samples, so the term SEab probably does not measure solely error variation in convenience samples. In samples that are neither large nor representative, p values generated by the Sobel test can be quite inaccurate. More modern significance tests for the indirect effects from models with either single or multiple mediators are based on a technique called non-parametric bootstrapping (Preacher & Hayes, 2004, 2008). Briefly, the computer randomly samples with replacement from the researcher’s data file a large number (typically, 1000 or more) of generated data sets,
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Mediation analysis in leadership studies 185 each with the same number of cases as in the original data file. Next, the computer derives the unstandardized estimator of the indirect effect in all generated samples and then creates an empirical sampling distribution given all of these results. Finally, the computer locates within the empirical sampling distribution the values of the products estimator that correspond to the 2.5 and 97.5 percentiles. Between these two points are 95 percent of the results from the generated samples. If the value 0 does not fall with the interval bounded by the 2.5 and 97.5 percentiles, then the null hypothesis that the population product estimator equals 0 is rejected at the 0.05 level of statistical significance; otherwise, the null hypothesis is retained. Non-parametric bootstrapping makes no assumptions about the shape of the population distribution of product estimators, other than that the sample distribution matches the population distribution. Outcomes of bootstrapped significance tests of unstandardized indirect effects may be more accurate than those from the Sobel test in large and representative samples, and sample size requirements for bootstrapped tests of indirect effects are not as demanding compared with the Sobel test. But even bootstrapped results can be very inaccurate in small or unrepresentative samples. Such tests still assume random sampling, which almost never happens in mediation studies. For more information about bootstrap methods for testing the statistical significance of mediation effects, see Mallinckrodt, Abraham, Wei, and Russell (2006). What is more important than statistical significance in mediation studies is the concept of substantive significance, which involves the evaluation of results in terms of their practical, theoretical, or clinical significance in a particular research context (Kline, 2013), not just whether results are statistically significant or not significant. Reporting and interpreting effect sizes in mediation studies is one way to address substantive significance. There are ways to measure the relative sizes of direct versus indirect effects in standardized metrics that are directly comparable across different studies (Lau & Cheung, 2012; Preacher & Kelley, 2011), when it is appropriate to analyse standardized effect sizes. That increasing numbers of journals, including many in medicine, now require the reporting of effect sizes is another motivation. The International Committee of Medical Journal Editors (2015) puts it like this: ‘Avoid relying solely on statistical hypothesis testing, such as p values, which fail to convey important information about effect size and precision of estimates’ (p. 14). There are also methods in mediation analysis for interval estimation, which allow the reporting of indirect effects with confidence intervals (Cheung, 2009). Indirect effects with narrower confidence intervals are more precise than indirect effects sizes with wider intervals. Thoemmes (2015) described an indefensible use of significance testing
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186 Handbook of methods in leadership research in mediation analysis that involves equivalent models. Suppose that the product coefficient for the indirect effect of X on Y through M in Figure 8.1(a) is statistically significant. Next, the researcher reverses the arrow between M and Y and thus specifies an equivalent model where the mediator is now variable Y and the outcome variable is M. For this respecified model, the researcher finds that the product coefficient for the indirect effect of X on M through Y is not significant. The researcher concludes that these results support the original hypothesis that M mediates the effect of X on Y but does not support the alternative hypothesis that Y mediates the effect of X on M, but this conclusion is faulty. This is because the statistical significance of the indirect effect (or any other effect) can never be used to infer whether one model should be preferred over another, if the two models are equivalent. The only way to choose between two equivalent models is through assumptions that are satisfied through research design or predictions based on theory. Statistical analysis including significance testing does not distinguish equivalent models.
ANALYSING MEDIATION AND MODERATION TOGETHER The classical product method for estimating indirect effects as products of the coefficients for the direct effects among continuous variables makes the strong assumption of no interactions. In mediation analysis, this means that (1) the cause X and the mediator M do not interact; that is, the effect of X on outcome Y does not depend on the level of M just as the direct effect of M on Y does not vary across the levels of X. The assumption of no interaction also means that (2) there is no external variable with which X interacts to render its direct effect on either M or Y conditional, and (3) there is no external variable with which M interacts such that its direct effect on Y is rendered conditional. There are times when the assumption of no interactions is too restrictive. Fortunately, there are ways to represent both mediation and moderation (interaction) together in the same model. Doing so involves the estimation of conditional indirect effects. Two modern analytical methods for estimating conditional indirect effects, conditional process modeling (CPM) (Hayes, 2013) – also called conditional process analysis – and causal mediation analysis (CMA) (Pearl, 2014), are briefly described next. Of the two methods, CPM has been applied mainly to models with continuous mediators and outcomes, and when linear causal relations are assumed. The CPM approach is becoming more familiar in disciplines such as psychology and education. The CMA method is more general in
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Mediation analysis in leadership studies 187 that it can be applied to the analysis of mediators or outcomes that are categorical or continuous, and also when non-linear relations are assumed. For example, the CMA method can be extended to linear-, logistic-, loglinear, or Poisson-type regression analyses (e.g., odds ratios or risk ratios analysed for categorical outcomes). The CMA method also offers a constant definition of indirect effects across the kinds of variables and models just mentioned. It is better known in disciplines such as epidemiology, but that is changing (Vanderweele, 2015). Two key concepts in CPM are those of (1) moderated mediation, also known as a conditional indirect effect; and (2) mediated moderation, where an interaction indirectly affects an outcome through a mediator. Both types of conditional causal effects just mentioned are represented in Figure 8.2(b) where the interaction between causal variables X and W is represented by the symbol for a closed circle. This figure depicts the conceptual model described by Neves and Story (2015, p. 169). It specifies that ethical leadership (X) interacts with leader reputation for performance (W) to have a joint effect on subordinate affective commitment to the organization, the mediator (M). This interaction hypothesis says that the direct effect of X on M depends on W, and also that the direct effect of W on M depends on X. Both conditional direct effects just described for Figure 8.2(b) are the first stages of the two indirect pathways listed next:
X → M → Y and W → M → Y
where the outcome Y is organizational deviance and variables involved in conditional (i.e., moderated) direct effects are shown in bold type. Because the first stage of each indirect path just listed is conditional, each indirect effect is also conditional. This particular kind of moderated mediation is also called first-stage moderation because the first path of an indirect effect depends on an external variable (Edwards & Lambert, 2007). There are other patterns, such as second-stage moderation where just the second path of an indirect effect depends on an external variable, but all kinds of moderated mediation refer to conditional indirect effects. It is also true in Figure 8.2(b) that the joint effect of X and W on Y is specified as purely indirect through M, which corresponds to mediated moderation. That is, the interactive effect of X and W on Y is transmitted entirely through M. Although the actual model analysed by Neves and Story (2015), had additional variables, such as age, gender, and education, both mediation and moderation were analysed in their data set. The alternative approach – that is, the method of CMA – assumes that there is an interaction between the cause X and mediator M, and thus this
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188 Handbook of methods in leadership research method routinely estimates conditional effects. Direct and indirect effects in CMA are defined from a counterfactual perspective. A counterfactual does not express what has happened but what could hypothetically happen under different circumstances. The statement, “I would not have been late, if I had set my alarm earlier,” is an example of a counterfactual. In real life, counterfactuals correspond to statement such as, “If I had only. . .” or “What if. . .?” To use the CMA approach to estimate mediation, several effects must be defined. When a cause X and a mediator M interact, the controlled direct effect of X is defined as the average difference on Y, given a 1-point change in X, if M were controlled as having the same level for all cases in the population. There is a different value of the controlled direct effect for each level of the mediator, so for a continuous M there are infinitely many controlled direct effects. The natural direct effect of X is the average difference in Y, if X were to change by 1 point but M is kept to the level that it would have taken without a 1-point change in X. Unlike the case for a controlled direct effect, the level of M is not fixed to the same constant for all cases. Instead, the mediator is allowed to vary, but only over values that would be naturally observed if there was no change in X. If there is no interaction between cause and mediator, then the controlled direct effect and natural direct effect of X are equal for continuous variables in a linear model; otherwise, estimates of natural direct effects and controlled direct effects may differ. The natural indirect effect of X estimates the average change in Y as the mediator M changes from values that would be observed assuming no change in X, to the values that M would reach if X changes by 1 point. That is, the effect if the outcome is influenced by the cause operating solely through its influence on the mediator. The total causal effect of X is the sum of its natural direct effect and the natural indirect effect on Y. In contrast, the controlled direct effect does not have a simple additive relation with either the natural direct effect or natural indirect effect, so it is not part of an effect decomposition in CMA. When all variables are continuous in a linear model, calculating estimates of the controlled direct effect (CDE), natural direct effect (NDE), natural indirect effect (NDE), and the total effect (TE) is just a matter of rearranging terms in standard regression equations (Petersen, Sinisi, & Van der Laan, 2006). To illustrate this point, the following numerical example is presented, based on some of the variables analysed by De Vries, Roe, and Taillieu (2002) in a large sample of employees. The causal variable X is leadership support, the mediator M is need for leadership, and the outcome Y is job satisfaction. The hypotheses are that: (1) need for leadership mediates some of the effect of leadership support on job satisfaction, and (2) leadership support and need for leadership interact in
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Mediation analysis in leadership studies 189 their effect on job satisfaction. The hypotheses just stated are represented in Figure 8.2(c). The unstandardized regression equations for this example are listed next in symbolic form:
Mˆ 5 b0 + b1X(8.1) ˆ Y 5 q0 + q1 X + q2 M + q3 XM,
where b0 and q0 are the intercepts for, respectively, the regressions of M on X and the regression of Y on X, M, and the product term XM, which represents the interactive effect in the technique of moderated multiple regression (Cohen, Cohen, West, & Aiken, 2003, Ch. 6). The coefficient for predicting M from X is b1, and q1–q3 are the coefficients for, respectively, X, M, and XM when predicting Y. Given Equation 8.1, the CDE, NDE, and NIE of X on Y can be expressed as follows: CDE 5 q1 + q3 m(8.2) NDE 5 q1 + q3 b0 NIE 5 (q2 + q3) b1 In Equation 8.2, note that the CDE is defined for a particular constant value of the mediator (M 5 m), and the NIE is defined at the predicted level of the mediator, or b0, when X 5 MX, if X is a mean-deviated, or centered, predictor (i.e., the scores on X are centered). Also note that if there is no interaction between X and M, then q3 5 0 (see Equation 8.1). In this case: CDE 5 NDE 5 q1, which is the unconditional linear effect of X on Y (see Equation 8.2), and the NIE equals the classical product estimator of the indirect effect, or b1q2. Thus, a model with no interaction between the independent variable and the mediator can be seen as a specific instance of a more general case that includes the interaction. Based on the summary statistics for variables X, M, X, and Y reported by De Vries et al. (2002, p. 126), I conducted two linear regression analyses in SPSS using the syntax listed in the Appendix, which also contains the summary data for this example. Note that scores on variable X are centered. The two unstandardized regression equations for this example are listed next:
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Mˆ 5 2.630 + 0.072 X ˆ Y 5 4.032 + 0.290 X − 0.062 M + 0.020 XM
(8.3)
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190 Handbook of methods in leadership research In words, the predicted value of M, need for leadership, equals 2.630, if X, leadership support, equals its mean (i.e., it does not vary). For each 1-point increase in X, variable M is expected to increase by 0.072 points. The predicted score on Y, job satisfaction, is 4.032, if X equals its sample mean and M 5 0. For each 1-point increase in X when M 5 0, job satisfaction is predicted to increase by 0.290 points, controlling for M and the interaction between X and M. Each 1-point increase in M predicts a 0.062 decrease in Y when X equals the sample mean and controlling for all other effects. For every 1-point increase in X, the slope of regression line for predicting Y from M increases by 0.020 (and vice versa), controlling for all other effects. Equation 8.3 implies that:
b0 5 2.630 and b1 5 0.072 q0 5 4.032, q1 5 0.290, q2 5 −0.062, and q3 5 0.020
Continuing with the same example, the direct effect of X, leadership support, on Y, job satisfaction at a given level of need for leadership, M 5 m, is estimated as: CDE 5 0.290 + 0.020 m The researcher can select a particular value of m for the mediator and then estimate the CDE by substituting this value in the formula just listed. The direct effect of X on Y estimated at the level of M that would have been observed if no change in X had occurred is estimated as: NDE 5 0.290 + 0.020 (2.630) 5 0.343 The indirect effect of X on Y allowing M to vary as it would if no change in X had occurred is estimated as NIE 5 (−0.062 + 0.020) 0.072 5 −0.003 The total effect of X on Y is the sum of its natural direct and indirect effects, or: TE 5 0.343 − 0.003 5 0.340 Thus, the predicted score on job satisfaction (Y) is expected to increase by 0.34 points for every 1-point increase in leadership support (X) through both its natural direct effect (0.343) and also its natural indirect effect (−0.003) through need for leadership (M), assuming that X and M inter-
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Mediation analysis in leadership studies 191 act. The magnitude of the NIE for these data seems relatively small, but it is a component of the total effect of leadership support on job satisfaction and thus should be tallied. Valeri and Vanderweele (2013) describe macros for SPSS and SAS/STAT for the CMA method that allows covariates, such as variables that control for treatment history. These computer tools automatically calculate the CDE, NDE, NIE, and TE and test each effect just listed for statistical significance, but do not forget the cautions about significance testing in mediation analysis discussed earlier in this chapter. Thinking about direct or indirect effects from a counterfactual perspective is not immediately intuitive, but the CMA method offers a consistent way of defining and estimating these effects in the presence of interaction for continuous or categorical mediators or outcomes. There are also some freely available macros for CMA written for widely used computer tools for general statistical analyses, including R, SPSS, SAS/STAT, and Stata (Hicks & Tingley, 2011; Imai, Keele, & Yamamoto, 2010; Valeri & Vanderweele, 2013), so the method is becoming more and more accessible.
SUMMARY Analysing mediation is not just a matter of drawing one-way arrows between variables represented in a path diagram, calculating product estimators, testing those estimators for significance, and then describing the results as indicating mediation if those tests yield significant results. Without a strong rationale about directionality, proper attention to the many assumptions that underlie even the simplest of path diagrams with indirect effects, careful measurement and selection of variables, and collection of data in strong designs that support causal inference due to time precedence in measurement, there may be little hope that results from a mediation study will actually represent the target phenomenon. Researchers who plan to study mediation in leadership should be conversant with these issues; otherwise, the literature may wind up cluttered with many studies where mediation is claimed to be analysed, but the results have little basis for this interpretation.
NOTE * I would like to thank Dr. Rosalie Hall for her helpful comments on an earlier version of this chapter.
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REFERENCES Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. Leadership Quarterly, 21(6), 1086–1120. Baker, D.F., Larson, L.M., & Surapaneni, S. (2015). Leadership intentions of young women: The direct and indirect effects of social potency. Journal of Career Assessment, 24(4), 718–731. Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Bullock, J.G., Green, D.P., & Ha, S.E. (2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558. Cheung, M.W.L. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41(2), 425–438. Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied multiple regression/ correlation analysis for the behavioral sciences (3rd ed.). New York: Routledge. Cole, D.A., & Maxwell, S.E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. Cole, D.A., & Preacher, K.J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19(2), 300–315. De Vries, R.E., Roe, R.A., & Taillieu, T.C.B. (2002). Need for leadership as a moderator of the relationships between leadership and individual outcomes. The Leadership Quarterly, 13(2), 121–137. Edwards, J.R., & Lambert, L.S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12(1), 1–22. Hallinger, P., & Heck, R.H. (1998). Exploring the principal’s contribution to school effectiveness: 1980–1995. School Effectiveness and School Improvement, 9(2), 157–191. Hayduk, L.A., & Littvay, L. (2012). Should researchers use single indicators, best indicators, or multiple indicators in structural equation models? BMC Medical Research Methodology, 12(159). Retrieved from http://www.biomedcentral.com/1471-2288/12/159 Hayes, A.F. (2013). Introduction to mediation, moderation, and process control analysis: A regression-based approach. New York: Guilford. Hicks, R., & Tingley, D. (2011). Causal mediation analysis. Stata Journal, 11(4), 605–619. Hyman, H. (1955). Survey design and analysis: Principles, cases and procedures. Glencoe, IL: Free Press. Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. International Committee of Medical Journal Editors. (2015). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. Retrieved from http://www.icmje.org/icmje-recommendations.pdf Kenny, D.A. (2015). Mediation. Retrieved from http://davidakenny.net/cm/mediate.htm#CI Kline, R.B. (2013). Beyond significance testing: Statistics reform in the behavioral sciences (2nd ed.). Washington, DC: American Psychological Association. Kline, R.B. (2015). The mediation myth. Basic and Applied Social Psychology, 37(4), 202–213. Knight, C.R., & Winship, C. (2013). The causal implications of mechanistic thinking: Identification using directed acyclic graphs (DAGs). In S.L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 275–299). New York: Springer. Lau, R.S., & Cheung, G.W. (2012). Estimating and comparing specific mediation effects in complex latent variable models. Organizational Research Methods, 15(1), 3–16. Little, T.D. (2013). Longitudinal structural equation modeling. New York: Guilford. MacKinnon, D.P., & Pirlott, A.G. (2015). Statistical approaches for enhancing causal inter-
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Mediation analysis in leadership studies 193 pretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19(1), 30–43. Mallinckrodt, B., Abraham, W.T., Wei, M., & Russell, D.W. (2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53(3), 372–378. Maxwell, S.E., & Cole, D.A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. Neves, P. & Story, J. (2015). Ethical leadership and reputation: Combined indirect effects on organizational deviance. Journal of Business Ethics, 127(1), 165–176. Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19(4), 459–481. Petersen, M.L., Sinisi, S.E., & Van der Laan, M.J. (2006). Estimation of direct causal effects. Epidemiology, 17(3), 276–284. Preacher, K.J., & Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. Preacher, K.J., & Hayes, A.F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. Preacher, K.J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115. Riehl, C. (2012). Locating a focus on school–family–community partnerships within the scholarship of educational leadership. In S. Auerbach (Ed.), School leadership for authentic family and community partnerships: Research perspectives for transforming practice (pp. 10–28). New York: Routledge. Selig, J.P., & Preacher, K.J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6(2–3), 144–164. Sobel, M.E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco, CA: Jossey-Bass. Tafvelin, S., Armelius, K., & Westerberg, K. (2011). Toward understanding the direct and indirect effects of transformational leadership on well-being: A longitudinal study. Journal of Leadership & Organizational Studies, 18(4), 480–492. Tate, C.U. (2015). On the overuse and misuse of mediation analysis: It may be a matter of timing. Basic and Applied Social Psychology, 37(4), 235–246. Thoemmes, F. (2015). Reversing arrows in mediation models does not distinguish plausible models. Basic and Applied Social Psychology, 37(4), 226–234. Trafimow, D. (Ed.) (2015). Disadvantages of mediation analyses in basic or applied social psychology [special issue]. Basic and Applied Social Psychology, 37(4). Retrieved from http://www.tandfonline.com/toc/hbas20/37/4?nav5tocList Valeri, L., & Vanderweele, T.J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18(2), 137–150. Vanderweele. T.J. (2015). Explanation in causal inference: Methods for mediation and interaction. New York: Oxford University Press. Wang, H., Law, K., Hackett, R., Wang, D., & Chen, Z. (2005). Leader–member exchange as a mediator of the relationship between transformational leadership and followers’ performance and organizational citizenship behavior. Academy of Management Journal, 48(3), 420–432.
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APPENDIX SPSS Syntax for Linear Regression with Summary Statistics comment regression analysis of summary data. comment reported in De Vries, Roe, and Taillieu (2002, p. 126). comment x, leadership support. comment m, need for leadership. comment xm, product term. comment y, job satisfaction. matrix data variables5x m xm y /contents5mean sd n corr/format5lower nodiagonal. begin data 0 2.63 0 3.87 1.00 .800 1.00 .58 717 717 717 717 .09 –.05 0 .49 –.04 .01 end data. regression matrix5in(*)/variables5m x /dependent5m/method5enter x. regression matrix5in(*)/variables5x m xm y /dependent5y/method5enter x m xm.
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9. Person-oriented approaches to leadership: a roadmap forward
Roseanne J. Foti and Maureen E. McCusker
INTRODUCTION Organizational psychology is defined as the scientific study of people in the workplace (Landy & Conte, 2013). To study a person means to study how he or she “functions and develops as an active, intentional part of an integrated person–environment system” (Magnusson & Stattin, 2006, p. 401). Yet, with a few exceptions (e.g., Foti & Hauenstein, 2007; Meyer, Stanley & Vandenberg, 2013), research in leadership has focused almost exclusively on understanding relationships between separate dimensions of people (e.g., values, traits, beliefs, perceptions, behaviors) and workrelated outcomes. From this “variable-oriented approach” (Block, 1971), the units of analysis are the person’s individual components, or variables, and the person is treated as a summation of the components. Although the variable-oriented approach has been useful in examining dimension-level cause-and-effect relationships across people, a less common alternative approach, the “person-oriented approach,” can deepen an understanding of these psychological concepts when studying people in organizations. Person-oriented research treats the individual as an organized system of dynamically interacting variables, which form a pattern within a person over time (Magnusson, 1995). Thus, instead of addressing questions about the individual components of people, the person-oriented approach seeks to address questions related to a whole person, as a coherent, organized totality (Bergman & Magnusson, 1997). Research concerning leaders and leader development, as well as follower perceptions and dyadic relationships, is well suited for the personoriented approach because it inherently focuses on a whole person, not simply his or her components. However, like most organizational research, leadership inquiry has historically been variable-centered, resulting in a potential misalignment of research questions, methodology and analysis (Bergman & Vargha, 2013). Studying leaders and leadership as holistic and parsimonious systems is appropriate and beneficial for two reasons: (1) alignment of leader(ship) theory, methodology and analysis and (2) understanding of leadership processes beyond (a conglomerate of) dimensions, 195
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196 Handbook of methods in leadership research toward a coherent totality consisting of patterns of interacting dimensions over time. However, given the scarcity of person-oriented research in industrial-organizational psychology, organizational scientists attempting to conduct person-oriented research often find themselves faced with an array of challenging questions (Sterba & Bauer, 2010). Thus, in an attempt to advance and refine leadership research, the goals of this chapter are to generate insights into (1) the fundamental differences between variableoriented and person-oriented research, (2) the variety of methods that can be used to conduct person-oriented research studies, (3) the factors that researchers must consider when deciding whether and how to conduct a person-oriented study, and (4) how the conclusions one can draw from person-oriented research are different from (and can complement) the information gleaned from more traditional variable-focused work. We begin this chapter by first defining the person-oriented approach both theoretically and methodologically, highlighting how its theory and methods differ from the more traditional variable-oriented approach. Next, we provide a comprehensive overview of the different types of person-oriented analytical methods as well as when to use which technique. We then give one empirical example of the use of pattern-oriented methods (i.e., latent profile analysis) for leadership research using previously collected data measuring people’s leadership perceptions using an implicit leadership theory (ILT) scale (Epitropaki & Martin, 2004). We conclude by discussing how person-oriented inquiry can further current research in other areas within the organizational sciences as well as bridge connections across different disciplines.
PERSON-ORIENTED APPROACH VS THE VARIABLE-ORIENTED APPROACH The person-oriented and variable-oriented approaches each contain two facets: a theoretical facet and a methodological one (Bergman & Trost, 2006). Though interrelated, it is important to differentiate both facets for two reasons. First, past research has confused the two facets or even defined one facet by the other. For example, one may claim to use a person-oriented approach solely because the methods used are person oriented (Sterba & Bauer, 2010). Second, the true person-oriented research consists of both person-oriented theory and person-oriented methods, the latter we refer to as pattern-oriented methods (Von Eye, 2010). It is not uncommon for researchers studying organizational phenomena, including leaders, to ground research in person-oriented theory but use variableoriented methods (Bergman & Trost, 2006). In addition to the fact that
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Person-oriented approaches to leadership 197 doing so results in a problematic theory–method mismatch (Bergman & Andersson, 2010), it does not constitute the full person-oriented approach (Magnusson & Stattin, 2006). In this section we will define both facets of the person-oriented approach independently and explain how each deviates from the more familiar variable-oriented approach. The Theory Facet The emergence of variable-oriented theory in psychology dates back to the early nineteenth century, when a rapid surge of novel research methods and statistical analyses drew attention to quantitative measurement, empirical analysis, and experimental replication. The result of this detailoriented perspective was a growth in the variable as the unit of measurement, analysis and, in turn, theory (Bergman & Wångby, 2014). Since then, the variable-oriented approach has dominated most psychological research, especially within organizational sciences, and is largely responsible for today’s advanced state of knowledge discovery. According to Magnusson and Stattin (2006), the term “variable” as a psychological concept represents a particular “aspect of individual functioning” (p. 431). Thus, the theory underlying the variable-oriented approach is based in identifying and explaining causal relationships between hypothetical constructs. In this way, all the variables are each considered separate entities, which may vary both intrapersonally over time and interpersonally across people (Bergman & Magnusson, 1997). However, the relationships between the hypothetical constructs are assumed to hold across people in a specified functional form (e.g., linear, curvilinear). So, psychological variable-oriented theory is fundamentally nomothetic, as it seeks to explain behavior across all groups of people using variable-level relationships (Bergman & Andersson, 2010; MacDougall, Bauer, Novicevic & Buckley, 2014). While variable-oriented theory views a person as a summation of many variables, the theoretical facet of person-oriented theory is grounded in a “holistic-interactionistic” view of people as organized gestalts who change as “integrated totalities” with their environments. Although personoriented researchers claim that slightly different theoretical tenets underlie person-oriented research (Bergman & Magnusson, 1997; Bergman & Wångby, 2014; Von Eye & Bogat, 2006), Sterba and Bauer (2010) unify them into six principles: individual specificity, complex interaction, interindividual differences in intraindividual change, pattern summary, holism, and pattern parsimony. Next, we briefly describe each of the principles and relate them to the study of leaders and leadership. These principles and examples are consistent with a growing body of researchers
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198 Handbook of methods in leadership research who conceptualize leaders as unified totalities of dynamically interacting components (e.g., Foti & Hauenstein, 2007; Mumford et al., 2000) and leadership as a process resulting from the dynamic interactions between all the actors, their attributes, and the context within a particular social system (e.g., DeRue & Ashford, 2010; Fairhurst & Uhl-Bien, 2012). The first principle, individual specificity, states that the functioning, process, and development of one’s behavior are at least partially unique to that individual. For example, a leader’s development is influenced by many different factors in a system, and how he or she operates given the many factors is at least in part idiosyncratic (Day, Fleenor, Atwater, Sturm, & McKee, 2014). The complex interaction principle states that psychological processes and behaviors involve complex, multilevel interactions, such as Person × Person × Time × Situation × Group, which cannot be neglected. This principle can be exemplified when considering perceptions of a leader. The likelihood that one perceives another person as a leader depends on (the pattern of) the target’s characteristics and on (the pattern of) the perceiver’s characteristics, as well as the interaction of each of the totalities within a given context (DeRue & Ashford, 2010). The third principle states that there exist interindividual differences in intraindividual change. That is, given the complexity and uniqueness of interactions, some people differ in how they change and/or remain constant, but, as stated by the fourth principle, pattern summary, there is order and structure in these differences. This lawfulness can be organized into patterns or profiles of variables. For example, leaders possess different leadership styles (e.g., transformational, transactional, laissez-faire), as exhibited by different sorts of behaviors within particular contexts. Together, these intraindividual differences in behaviors can be clustered to reflect heterogeneous subgroup differences in leadership styles (Doucet, Fredette, Simard, & Tremblay, 2015). Furthermore, the fifth principle of holism reflects the person-oriented belief that since components of a process (i.e., variables) are “inextricably interwoven and believed to interact,” they cannot be interpreted outside of the system in which they operate (Bergman & Trost, 2006, p. 604). So, in the previous example, reducing the pattern of leadership behaviors to individual leader behaviors and interpreting them in isolation is meaningless because doing so neglects the complex interactions that drive order and structure in the process. Finally, the principle of pattern parsimony implies there are a finite number of patterns of profiles that can emerge, despite the fact there are often many or infinitely more potential patterns. That is, through a process of “self-organization” the patterns form a limited number of clusters of similar profiles, reflecting a certain order, or structure. O’Shea, Foti, Hauenstein, and Bycio (2009) reflected this idea in a study that examined the pattern of behaviors exhib-
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Person-oriented approaches to leadership 199 ited by effective leaders. Although there were eight possible combinations of the three dichotomized behaviors (transformational, contingent reward, and passive), people typically clustered into three different profile types. In sum, the primary difference between the variable-oriented and the person-oriented approach, from a theoretical standpoint, is that the former is a perspective that emphasizes understanding relationships between constructs at the dimension level, whereas the latter is a perspective that emphasizes understanding a dynamic totality of interacting dimensions. The Method Facet Methods are tools for analysing data to understand the processes operating within given psychological systems. For a correct application of statistical methods, however, it is crucial to recognize that they are tools for analysis of data in the same way scalpels, forceps and clamps are tools for surgery. No statistical tool has a value on its own in the research process; it is only when a statistical tool matches the phenomena under investigation that it can contribute scientifically solid answers to relevant questions (Magnusson & Stattin, 2006). As with the theory facet, the measurement model of the person-oriented approach differs from the measurement model of the variable-oriented approach. The method facet of person-oriented theory is known as the pattern-oriented method. The goal in variable-centered analysis is to explain as much variance in an outcome variable by one or more predictor variables (Meyer et al., 2013). The units of analysis are variables, and one data point, or datum, represents one person’s standing on a particular dimension (Magnusson & Stattin, 2006). The variables are each assumed to have specified, functional forms (e.g., linear, non-linear, or curvilinear) across all individuals and are regarded as distinct units. Similarly, if interactions among variables are analysed, they are also specified and modeled directly with the addition of product and polynomial terms (Bauer & Shanahan, 2007). Analysis is typically conducted using general or generalized linear models, such as analysis of variance, regression, multiple regression, or structural equation modeling (Bergman & Andersson, 2010) and results in nomothetic group generalizations about relationships between variables. Consider an example of the variable-oriented approach in studying intelligence and leadership effectiveness: the measurement of person A on the latent dimension of intelligence derives its psychological significance in relation to the positions for individuals, B, C, D and as shown in Figure 9.1. Most psychological research, including leadership, uses
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200 Handbook of methods in leadership research D
A B
C
Latent dimension: Intelligence Variable-oriented approach A Latent dimension: Intelligence Latent dimension: Agreeableness Latent dimension: Conscientiousness
Latent dimension: Extraversion Person-oriented approach Source: Permission granted by John Wiley & Sons, Inc. for this modified version of Figure 8.2(a) and (b) (p. 444) from Magnusson and Stattin (2006).
Figure 9.1 Measurement model for the variable-oriented and personoriented approach variable-oriented approaches. For example, when we are interested in the relationship between perceptions of leader intelligence and leadership effectiveness, we discuss our findings by saying that higher leader intelligence is related to greater effectiveness (Judge, Colbert, & Ilies, 2004). In other words, we are saying that two variables, a score on intelligence and a rating of leadership effectiveness, are statistically related to each other. The strengths of variable-oriented analyses are many, including objectivity and clarity of the measurement and scales, control of confounds, the ability to determine how much variance in the outcome is explained by a particular variable, and the ability to make population-level causal inferences based on statistical model testing (Bergman & Andersson, 2010; Von Eye et al., 2006). However, these methods, and the subsequent statements that result, say nothing about individuals. The expectation is that these aggregate group scores generalize to populations. In other words, we make assumptions that interindividual differences are negligible or random (Von Eye & Bogat, 2006). While the goal of variable-oriented methods is to identify relationships between variables and account for variance, the purpose of pattern-oriented methods is to identify subgroups in a population
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Person-oriented approaches to leadership 201 based on a particular configuration of variables (Meyer et al., 2013). Bergman and Trost (2006) put it simply by stating that this type of analysis entails first “identifying a subsystem relevant to the problem under study, measuring its components, and studying them all together as an undivided whole” (p. 604). Thus, the person as a whole is the unit of analysis and one datum represents a person’s standing on a pattern of dimensions (Magnusson & Stattin, 2006). These patterns, or profiles, are assumed to capture a complex process of dynamic interactions of a set of variables within a system or individual (Bergman & Andersson, 2010), as opposed to modeling each interaction individually (Bauer & Shanahan, 2007). There are a variety of person-oriented analytic methods (discussed below), but in general, these methods first identify meaningful clusters based on a pattern of dimensions, then conduct subsequent analyses with the established profiles as predictors or outcomes (e.g., Foti & Hauenstein, 2007). In this sort of analysis, the functional form of variables and interactions are not specified, as it is assumed that their reciprocal and interdependent interactions are unique to an individual or the system (Magnusson & Stattin, 2006). So, by classifying individuals into types or typologies, pattern-oriented methods allow for idiographic examinations of systems (people) in their totalities (Bergman & Trost, 2006). Recall the intelligence and leadership effectiveness example discussed above. In the person-oriented approach to this example, which is depicted in Figure 9.2, the measurement of person A on the latent construct of intelligence derives its psychological significance from its position in a configuration of data for the same individual using his or her position in the latent constructs of agreeableness, conscientiousness and extraversion. These latent constructs are assumed to represent simultaneously working dimensions in the system of leader traits. The implication of this measurement model is that statistics yield information about the individual, and generalizations refer to individuals. Moreover, as shown in Figure 9.2, each latent construct takes meaning from all the other variables in the pattern. The same position for different individuals (A and B) on the latent construct of leader intelligence may differ entirely in its significance for the rating of leadership effectiveness for the two individuals. That is, the same level of intelligence takes meaning from the different levels of agreeableness, conscientiousness and extraversion. Thus, to describe individuals adequately, the researcher has to recognize that any sample or population is rarely a homogeneous group. Rather, the researcher examines the heterogeneity of possible patterns and groupings of individuals that make sense from a theoretical perspective.
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202 Handbook of methods in leadership research AB Latent dimension: Intelligence
Latent dimension: Agreeableness Latent dimension: Conscientiousness
Latent dimension: Extraversion Source: Permission granted by John Wiley & Sons, Inc. for this modified version of Figure 8.3 (p. 445) from Magnusson and Stattin (2006).
Figure 9.2 Profiles for two individuals on three latent dimensions of leader perceptions
PATTERN-ORIENTED METHODS In the latter part of the previous section we provided a general review of pattern-oriented methods to highlight the differences from the traditional methods. Given that the primary purpose of this chapter is to provide a roadmap for conducting person-oriented leadership research, the following section provides a more extensive description of several different pattern-oriented analytical methods. In addition, we indicate which sorts of research questions are answered by each technique, which we hope will guide researchers in aligning their research methodology with research questions and theory. A summary of this guide is included in Table 9.1. The growing trend of the person-oriented approach in the behavioral sciences (Bergman & Wångby, 2014), along with recent calls for researchers to develop more advanced pattern-oriented analytical tools (e.g., Vandenberg & Stanley, 2009), has resulted in a recent growth in the number of pattern-oriented methods available. Accordingly, we have categorized the methods into three classes of analyses, adapted from Bergman and Wångby’s classification (2014): (1) cluster analysis and its extensions, (2) configural frequency analysis and its extensions, and (3) model-based classification analysis and its extensions. It should be noted that all three classes of methods include multiple different procedures, and describing all of them is beyond the scope of this chapter. So, we have limited our description to the methods most commonly employed and discussed in the literature.
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Person-oriented approaches to leadership 203 Table 9.1 Summary of pattern-oriented methods Method
Type
Indicator Data
When to Use
Cluster analysis LICUR
Exploratory
Cross-sectional
Exploratory
Longitudinal
ISOA
Exploratory
Longitudinal
CFA
Exploratory
Cross-sectional
P-CFA
Exploratory
Longitudinal
A-CFA
Exploratory
Longitudinal
Bayesian CFA
Model based
Cross-sectional (currently)
LCA
Model based
Cross-sectional
LPA
Model based
Cross-sectional
RMLCA
Model based
Longitudinal
LCGM/ LPGM
Model based
Longitudinal
LTA
Model based
Longitudinal
When expecting lack of homogeneity in the sample When examining large developmental shifts When examining patterns of stability or change of both cluster structure and individual When assuming stability in cluster classification When seeking to explore all possible combinations of patterns When assuming independence of time points within classes over time When assuming auto-associations between time points within classes over time When working with prior information When examining composites of types and antitypes When working with categorical indicators When working with continuous indicators When working with categorical indicators When working with few indicators and three or more time points When it is assumed growth follows a particular functional form When working with large sample sizes and three or more time points When examining whether profiles or classes are stable across time When examining whether people tend to remain in or transition among classes
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204 Handbook of methods in leadership research Cluster Analysis and its Extensions The simplest and possibly most straightforward pattern-oriented method is cluster analysis because it requires minimal external knowledge and information to form groups (Von Eye & Bergman, 2003). The goal of this primarily exploratory method is to disaggregate data from one entire sample into a number of smaller, non-overlapping groups, or clusters (Bergman & Wångby, 2014). This is done by identifying a collection of cases (e.g., people) in the sample that are more similar to each other than to the other cases and grouping them into their own cluster. Although there are multiple analytical techniques to do so, the most common is Ward’s (1963) procedure, which uses the squared Euclidian distance between data points to minimize variance within groups and maximize variance between them. The result is a set of groups that each contain “relatively homogeneous” cases (intragroup similarity), but that differ in a meaningful way from the other groups (intergroup difference). This idea of meaningful interpretation is crucial in cluster analysis. Von Eye and Bergman (2003) claim the most important problem associated with cluster analysis is the tendency for researchers to trust the resulting clusters as meaningfully interpretable when they may in fact be non- interpretable artifacts of the analyses. Thus, safeguarding the trustworthiness of the cluster solution, as well as using robust measurements and appropriate sample size is of paramount importance in cluster analysis. Cluster analysis can be conducted on both cross-sectional and longitudinal data. Although cross-sectional cluster analysis can be considered in part person oriented as it focuses on a pattern of the individual, it is not entirely person oriented because it neglects the process component of the person as a dynamic system (Bergman & Andersson, 2010). Rooted in the holistic-interactionistic research paradigm, the individual is seen as an organized whole with elements operating together to achieve a functioning system. This dynamic system results from interactions among its components (Bergman & Wångby, 2014). Examples of components include behaviors, biological factors, and environment factors. Dynamic systems are characterized by moments of equilibrium and moments of disequilibrium, thus these person processes can be captured only with longitudinal data. As a result, researchers have developed longitudinal approaches to cluster analysis, and there are two main ways to do so. The first method is to conduct a cluster analysis at each time point individually, and then compare the resulting clusters across time points. One example of a longitudinal extension of cluster analysis is called LICUR (linking clusters after the removal of a residue; Bergman, Magnusson, & El-Khouri, 2000), which first removes residue from each time point, runs
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Person-oriented approaches to leadership 205 a cross-sectional-like cluster analysis at each time point, then links all time points in the sequence to test for different types of cluster membership configurations (Bergman & Magnusson, 1997). This method is particularly useful for questions relating to stability and change of both the individual and the structure of the clusters (Bergman & Wångby, 2014), as well as for exploring periods characterized by “dramatic developmental shifts” (Bergman & Magnusson, 1997, p. 300). The second type of longitudinal cluster analysis is based on the assumptions that the system of cluster classifications remains across all time points (i.e., is time invariant) and that individuals belong to one cluster of this system at each point in time. An example of this method is ISOA (I-states as objects analysis; Bergman, Nurmi, & Von Eye, 2012), which first uses cluster analysis to identify a time-invariant clustering system, then each individual is assigned to a cluster at every time point. Finally each person’s sequence of cluster membership over time is examined (Bergman & Magnusson, 1997). ISOA is best suited for small samples sizes over shorter time periods. In sum, cluster analysis methods can be very useful exploratory methods to identify and describe non-homogeneous subgroups in data based on a pattern of variables. However, clustering methods provide limited practical utility in research, given their fundamentally descriptive nature (Sterba & Bauer, 2010). Thus, the following two methods describe probability and model-based pattern-oriented methods that allow researchers to make more advanced inferences about the clusters and causal relationships. Configural Frequency Analysis and its Extensions The goal of configural frequency analysis (CFA)1 is to examine all possible combinations of a pattern of variables and determine which ones occur more or less frequently than expected by a base model (Von Eye, 2002). This grants the researcher the ability to study “all possible value patterns directly” to better understand the various emergent structures in the data (Bergman & Wångby, 2014, p. 34) and to make predictions regarding the frequency of occurrence of particular patterns. The steps to conduct a standard CFA are as follows, as described by Von Eye (2002): first, to allow for a reasonable number of possible configurations, all variables are dichotomized (e.g., low, high) or trichotomized (e.g., low, medium, high), unless the variable is naturally grouped, as with nominal or ordinal data. Second, all possible configurations of variables and levels are listed and assigned to cells. Then, a base model is specified, which serves as a referent for comparison of the frequency of each of the possible configurations of variables. The frequency of each observed pattern is then compared
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206 Handbook of methods in leadership research to the base model.2 If the pattern of variables occurs significantly more frequently than is expected by the base model, it is considered a “type,” whereas if a pattern occurs significantly less frequently than is expected, it is considered an “antitype.” It should be noted that in classical CFA, the base model is one of variable independence, or of main effects, so that the emergence of types and antitypes only results when there are significant interactions between the variables (Von Eye et al., 2006). The final step includes interpretation of the resulting types and antitypes, based on the research question and purpose of the study (recall Von Eye and Bergman’s 2003 emphasis on meaningful interpretation of clusters/patterns/types). Like cluster analysis, the resulting types and antitypes from CFA are often used as predictors or outcomes in subsequent analyses. A study by O’Shea et al. (2009) exemplifies how CFA can be used to answer novel leadership questions. The purpose of their study was to test Bass’s (1985) claim that optimal leaders exhibit both transformational and transactional leadership behaviors. To do so, the researchers measured three variables of leadership behaviors: transformational behaviors (TRF), contingent reward behaviors (CR; i.e., transactional) and passive management-by-exception behaviors (P-MBE); each was dichotomized. They also measured leadership effectiveness as measured by a series of subordinate outcomes. They then conducted a CFA using the eight possible high–low combinations to examine which configurations were likely to occur more (types) or less (antitypes) frequently than chance. Three configurations emerged as types (high on TRF, CR, P-MBE; high TRF, high CR, low P-MBE; and low TRF, low CR, high P-MBE), and the remaining three configurations emerged as antitypes. Upon identification of the types, the researchers carried out planned comparisons of the effectiveness of each leadership type. In support of their hypotheses and Bass’s (1985) claim, the most effective leaders were those types who exhibited both transformational and transactional leadership behaviors. The previous example reflects both the exploratory power of CFA to answer questions related to the probability of occurrence of particular patterns as well as its predictive power to test their antecedents and outcomes. In addition, CFA is unique in that it treats both frequently occurring configurations and rare, or outlying, patterns as meaningful. However, like cross-sectional cluster analysis, classical CFA is only truly person-oriented to the extent that it incorporates longitudinal development of a person. Work by Von Eye and colleagues have developed extensions of CFA for longitudinal data, allowing for examination of configural changes over time (Von Eye, 2002; Von Eye, Mun, & Bogat, 2009). There are two longitudinal extensions, each with differing base models. The first method, P-CFA, resembles classical CFA in that the base model treats time points on one or
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Person-oriented approaches to leadership 207 multiple variables as independent, and types (antitypes) are those patterns across variables and time that occur more (less) frequently than expected given the base model. The second method, A-CFA, uses an auto-associative base model that instead of treating each time point on each variable as independent (as in P-CFA), multiple time points on each variable are treated as repeated observations of the same variable. In this way, “types and antitypes reflect longitudinal relationships among categorical variables after auto-associations are taken into account” (Von Eye et al., 2008). One final extension of CFA we will mention is one rarely used but holds potential for overcoming some of the disadvantages of standard CFA methods: Bayesian CFA. Bayesian logic is grounded in the notion that the probability of occurrence of any given value can be estimated from two sources: the observed data and prior information (Gutiérrez-Peña & Von Eye, 2000). For the purposes of this chapter, we will not discuss in depth how to conduct a Bayesian CFA (see Gutiérrez-Peña & Von Eye, 2000 for more detailed explanation), but we do note its benefits with regard to CFA. First, since Bayesian methods rely on joint probabilities of possible pattern types and antitypes, there is no need to adjust the experiment-wise alpha level. Second, this method calculates the probability of occurrence for all cross-classification patterns, allowing for the possibility of composites of types and antitypes. In view of the flexibility afforded by the Bayesian approach to analysis, it is no surprise its influence is growing rapidly in the organizational sciences in very recent years, and we anticipate this growth will be mirrored in the person-oriented domain. To summarize, the class of CFA methods and its extensions grant researchers the ability to explore and test all potential combinations of patterns in their data, as well as measure each one’s probability of occurrence. Accordingly, this class of methods is very useful for researchers interested in questions necessitating a comparison of all possible combinations of variables, those examining which combinations are most common, or those interested in studying unconventional patterns or trajectories (i.e., the antitypes). In addition, the vast number of planned comparisons results in restrictions on the experiment-wise error rate. Given that CFAs are exploratory in nature, researchers are increasingly turning to modelbased pattern-oriented methods, which use “more rigorous criteria for determining the number of clusters or classes” (Meyer et al., 2013, p. 197). We describe several different ones below. Model-based Analyses Model-based methods differ on a theoretical level from the previous methods reviewed. This class of methods is grounded in the idea that there
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208 Handbook of methods in leadership research exists unobservable heterogeneity (or latent categorical classes) in a population, which manifests as configurations of observable variables (Wang & Hanges, 2011). The goal of model-based methods is to uncover how many and what types of latent classes or profiles exist, similar to factor analysis (Vandenberg & Stanley, 2009). There are multiple different model-based methods; however, in this chapter we focus on six of the most prevalent ones: latent class analysis (LCA), latent profile analysis (LPA), repeated measures latent class analysis (RMLCA), latent class growth modeling/ latent profile growth modeling (LCGM/LPGM), growth mixture modeling (GMM) and latent transition analysis (LTA). LCA and LPA are very similar, except that the former is used for categorical predictors, while the latter is used for continuous predictors. LTA, LCGM and LPGM are all longitudinal extensions of the LCA and LPA. Simply put, in LCA and LPA, one first specifies a latent model with the fewest number of possible latent classes or profiles, and then latent classes or profiles are added to the model until it fits the data well (Von Eye & Bergman, 2003). Once the model has been specified, individuals are classified into the latent classes or profiles based on their relative probabilities. Finally, predictors and outcomes of classes or profiles are studied per traditional statistical analyses (MacDougal et al., 2014). LCA and LPA methods work in the same way, except that the former is used when the indicator variable is categorical and the latter is used when the indicator variable is continuous. There are several advantages of this method. First, the estimation of the model’s parameters produces confidence intervals as well as estimations of model fit (Bergman & Wångby, 2014). Second, individuals are classified into the groups only after the latent groups have been established. Third, it uses a “probabilistic classifying approach,” allowing for there to be uncertainty in class membership (Wang & Hanges, 2011). Finally, LPA can model curvilinear relationships between observed variables (MacDougall et al., 2014). Next we will discuss four longitudinal extensions of the cross-sectional model-based analysis discussed above. The first one is repeated measures latent class analysis (RMLCA), also known as longitudinal latent class analysis (LLCA). The goal of RMLCA is to identify subgroups of individuals based on their differing patterns of change over time. Thus, the criterion for classifying individuals into particular classes is the pattern of change of a small number of variable(s) over time. So, each latent class corresponds to a particular pattern of change in an outcome over time (Collins & Lanza, 2010). In addition, “grouping variables,” such as demographic attributes, can be included to determine if the longitudinal patterns differ based on additional characteristics (Lanza & Collins, 2006,
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Person-oriented approaches to leadership 209 p. 553). Similar to cross-sectional LCA, the indicator variables in RMLCA must be categorical, but the outcome variables and covariates may be either categorical or continuous. Although RMLCA can be conducted using many different indicators of the latent class, it is best for data with just a few indicators and three or more time points (Collins & Lanza, 2010). A similar longitudinal extension is latent class growth analysis/latent profile growth analysis (LCGA/LPGA). The difference between LCGA/ LPGA and RMLCA is that these LCGA/LPGA extensions assume that growth or development over time follows a particular functional form (Vandenberg & Stanley, 2009). That is, individuals are grouped together based on similar patterns of change in levels and slope of the indicator variable(s) over time (Sterba & Bauer, 2010). The goal in creating classes using LCGA/LPGA is to minimize the variability of individuals’ trajectories within classes, but maximize the variability in trajectories between classes. LCGA/LPGA assumes that all individuals within a particular class are homogeneous in terms of change; they all follow the same trajectory over time and any within-class deviations in trajectories are due to random noise (ibid.). As with the rest of the model-based analyses, the resulting classes characterized by particular growth trajectories can then be compared to other covariates or used as predictors or outcomes in subsequent analyses. LCGA/LPGA methods are best when working with large data sets consisting of at least three or more time points. The third longitudinal extension is growth mixture modeling (GMM; Muthén, 2004). GMM is a pattern-oriented extension of the more traditional, variable-oriented latent growth curve modeling (Wang & Bodner, 2007). (See Hall in Chapter 13 of this volume for a more detailed discussion of the application of latent growth curve modeling for leadership research.) Similar to LCGA/LPGA, GMM allows researchers to uncover classes of longitudinal change trajectories. However, GMM differs from the former in that it relaxes the assumption of intraclass homogeneity by allowing the growth parameters to vary systematically across people within classes. Thus, although there may exist qualitatively different classes of trajectories, there also may exist quantitative variation in the intercept and slope within each class; hence the word “mixture” (Sterba & Bauer, 2010). Longitudinal transition analysis (LTA) is the fifth type of longitudinal pattern-oriented analysis. Similar to GMM, LTA does not assume uniform homogeneity in class over time. However, what makes LTA unique is its ability to model changes (or “transitions”) among classes or profiles classto-class or profile-to-profile across time points (Collins & Lanza, 2010). It does so by calculating the probability of being in a particular class or
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210 Handbook of methods in leadership research profile group at a given time point, conditional on the class membership in the immediately preceding time point (Meyer et al., 2013). Thus, results of LTA estimate the probability that one will remain in his or her class or profile group over time. LTA is useful for answering questions about (1) whether particular profiles or classes are stable or variable across time and (2) whether people tend to remain in particular classes or transition among them (Wright & Hallquist, 2014).
LEADER PERCEPTIONS: MATCHING MEASUREMENT TO OUR RESEARCH QUESTION In the next section, we walk readers through an empirical example related to the measurement of implicit leadership theories (ILTs) and why use of a person-oriented approach and methods matches our research question. ILTs are schemas that guide perceptions of leaders by providing a set of assumptions and expectations about how leaders behave and how to respond to them (Foti, Bray, Thompson, & Allgood, 2012; Lord, Foti, & De Vader, 1984). Perceivers hold ILTs about leaders based on direct and indirect experiences, highlighting the importance of development and environment as essential determinants of ILTs. Over the last two decades, the conceptualization of ILTs has become increasingly complex. The classical perspective of leadership perceptions was based on a graded categorical structure defined by central or prototypical characteristics, but possessed fuzzy boundaries (Lord et al., 1984; Lord, Foti, & Phillips, 1982). More recent research suggests that ILTs are created dynamically on the basis of contextual input and that connectionist network models provide a better representation of leadership categories and how they emerge in a particular situation (Shondrick & Lord, 2010). In connectionist models of ILTs, information processing about leaders is accomplished by multiple trait units acting in parallel with the resulting pattern of processing being meaningful (Lord, Brown, Harvey, & Hall, 2001). Moreover, characteristics of leaders can be incorporated into the dynamic models, so that the prototype itself might vary depending on the gender (Foti, Knee, & Backert, 2008; Foti & Wills, 2015) or ethnicity of the leader (Sy et al., 2010) or the affective state of perceivers (Boyd & Foti, 2016). Thus, one might expect both strong individual differences in category structures among perceivers who have different experiences, and also strong contextual effects that reflect constraints from different situations. A connectionist model of ILTs matches a person-oriented approach in several key aspects. First, connectionist models of ILTs emphasize contextual constraints; similarly, the person-oriented approach is concerned with
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Person-oriented approaches to leadership 211 individuals and how individuals operate in or are influenced by the context in which they exist (Magnusson, 1988). In other words, the person in the environment is just as important as the person by environment interaction and the environment itself. Second, in connectionist models, constraints activate different meaningful prototype patterns (Lord et al., 2001). A hallmark characteristic of the person-oriented approach is that variables in and of themselves have limited meaning (Bergman & Magnusson, 1997). It is the profile of these variables as part of an indivisible pattern that takes on meaning and begins to describe individuals. Finally, each variable takes its meaning from the other variables in the pattern to form the coherent whole. Thus, when we assume that the relationships among variables are not uniform across all the values that a variable might take, we can develop profiles, patterns, or configurations that describe individuals, not scores on the variables (Bogat, 2009). Are There Distinct Profiles of Leader Perceptions? Our research question investigates whether there are distinct profiles of leader perceptions. We collected data from 709 university students (60 percent female). Using the 21-item scale developed by Epitropaki and Martin (2004), students rated how characteristic the items were of a leader, with no definition of the term provided. Responses were provided on a nine-point Likert-type scale, where 1 indicated “not at all characteristic” and 9 indicated “extremely characteristic.” The 21 characteristics appear in Table 9.2. If we were going to use a variable-oriented method, we would typically analyse the data using exploratory or confirmatory factor analysis to identify the structure of ILTs. More specifically, the objective for a variable-oriented method is to identify the number and nature of the factors or latent variables that produce the observed covariation and variation in the 21 manifest variables. Typical factor analytic dimensions for the ILT scale appear in Table 9.2. The assumption of factor analysis is that Table 9.2 21-item implicit leadership scale and associated factors Sensitivity
Intelligence
Dedication
Dynamism
Tyranny
Masculinity
Understanding Sincere Helpful
Clever Knowledgeable Educated Intelligent
Motivated Dedicated Hard-working
Energetic Strong Dynamic
Domineering Pushy Manipulative Conceited Selfish Loud
Masculine Male
Source: Epitropaki & Martin (2004).
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212 Handbook of methods in leadership research correlations between manifest variables arise because of their dependency on one or more of the same factors. Thus, Understanding and Sincere are highly correlated because both are influenced by a common underlying factor of Sensitivity. Moreover, the relationships between these variables and their way of functioning in the totality of an individual are the same for all individuals. In our factor analysis example, each variable has the same factor loading for all individuals and reflects what is characteristic for the average individual. However, does this analysis accurately reflect theorizing about ILTs? We think not. Given ILTs develop based on individuals’ direct and indirect experiences AND can vary based on the characteristics of the perceiver and the context, it follows we should investigate within-individual patterns across the 21 characteristics. We can do this with latent profile analysis (LPA). Basic ideas of LPA As previously mentioned, in LPA, individuals can be divided into subgroups based on an unobservable construct. The construct of interest is the latent variable and subgroups are called latent profiles. Latent profile analysis utilizes a categorical latent variable and continuous indicators (Collins & Lanza, 2010; Foti, Thompson, & Allgood, 2011), which differs from factor analysis where both the latent variable and the indicators, or observed variables, are continuous (Collins & Lanza, 2010). More specifically, latent profile analysis is often used to identify and describe a set of mutually exclusive and exhaustive latent profiles whose members are characterized by similar response sets of manifest indicators. The primary goal is to maximize the homogeneity within groups (i.e., individuals within a profile should look similar) and maximize the heterogeneity between groups (i.e., individuals between profile groups should look different). In LPA, the use of multiple indicators provides a basis for estimating measurement error. When measurement error is present, many individuals’ responses do not point unambiguously to membership in a particular group, thus true profile membership is unknown (Lanza, Flaherty, & Collins, 2003). Key assumptions of LPA are that the latent profile indicators are (1) continuous and normally distributed within profiles (Bauer & Curran, 2003) and (2) independent within profiles (conditional independence). The latter assumption is also present in many other latent variable modeling techniques such as item response theory. Model estimation Using a person-oriented approach, a graphical representation of our research question is seen in Figure 9.3. That is, we want to ascertain
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Person-oriented approaches to leadership 213
Leader Profiles
Understanding
Sincere
…
Male
Figure 9.3 Graphical representation of person-oriented leader perceptions
whether there are distinct profiles of leader perceptions based on the 21 manifest indicators. In latent profile models, the data are used to estimate the number of profiles in the population, the relative size of each profile and the probability of a particular response to each manifest indicator, given profile membership. The first step is model identification. This requires the specification and testing of multiple profile solutions, typically from one profile to ten profiles, using maximum likelihood estimation. Many estimation procedures require initial values for the parameters to start the estimation procedures. However, if different starting values produce very different estimates and different log-likelihoods, then the model is not well identified. Thus, the recommendation is to use many different sets of starting values, approximately 100 or more, and inspect the distribution of loglikelihood values. In a well-identified model, starting values only affect the number of iterations required for the model to converge (Lanza, Bray, & Collins, 2013). Typically, LPA models are fit without the use of any parameter restrictions, which allows the profiles to be different from each other. If there are model estimation issues, however, restricting the variances to be equal across profiles will greatly reduce the unknowns in the model and can help with model identification. Finally, most LPA software can handle missing data. If missing data on a variable depends on the variable itself, this is referred to as missing not at random (MNAR). If missing data on a variable does not depend on the variable itself, this is referred to as missing at random (MAR). Most LPA procedures use a maximum likelihood routine that adjusts for data that is MAR but not for NMAR.
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214 Handbook of methods in leadership research The second step is model selection. Various model selection information criteria have been proposed for comparing models with different numbers of profiles. From these models, the designation of the “best-fitting” model is determined using a variety of statistical indicators; including the Akaike information criterion (AIC; Akaike, 1974), the Bayesian information criterion (BIC; Schwarz, 1978) and the sample size-adjusted BIC (Sclove, 1987). All three indicators are penalized log-likelihood test statistics, which penalize models for estimating too many parameters; moreover, both versions of the BIC further penalize models by sample size. A fourth statistical indicator is the bootstrapped likelihood ratio test (BLRT; McLachlan & Peel, 2000). The BLRT in effect estimates a “difference” distribution by which different models can be compared through the use of repeated sampling methods. None of these statistical indicators can determine model fit in isolation. A determination of the best-fitting profile solution then is based on which model has lower values for these fit indicators (lower values indicates better relative fit). Finally, to help in the determination of the optimal number of profiles, the interpretability of each profile must be considered; specifically whether or not a specific profile solution is more consistent with past theory and empirical research. Returning to our example, models with one to ten profiles were fit to assess leader perceptions. Models were fit using Mplus Version 6 (Muthén & Muthén, 1998–2010). Models with more than ten profiles were not considered due to model instability (i.e., difficulty replicating the maximum likelihood solution). In all models, profile-specific means and variances were free to vary across all manifest indicators. Table 9.3 summarizes the fit criteria for the leader profile models. All of the statistical criteria considered here indicated an improvement in model fit as the number of profiles increased. Given that decrements in the BIC appeared to attenuate between the three-profile and six-profile solutions, these four solutions were substantively assessed (Lawrence & Zyphur, 2011). The four-profile solution appeared superior to the three-profile solution; each of the profiles in the four-profile solution were adequately sized, all three profiles from the three-profile solution appeared in the four-profile solution, and the additional profile in the four-profile solution was interpretable based on leadership theory. In addition, all profiles from the four-profile solution appeared in the five-profile and six-profile solutions, and the larger models appeared to differ from the smaller ones only by separating out smaller subgroups of participants from the larger ones. These smaller subgroups were increasingly difficult to interpret theoretically, as they showed patterns of means on the indicators inconsistent with the factor structure or existing leadership theory. Given the tradeoffs among model parsimony, statisti-
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Person-oriented approaches to leadership 215 Table 9.3 Model fit information for latent profile analyses of leader perceptions Leader Profiles No. of profiles 1 2 3 4 5 6 7 8*
Number of free Log-likelihood parameters 42 64 86 108 130 152 174 196
–34 273.471 –32 787.506 –31 837.777 –31 448.804 –31 145.515 –30 959.300 –30 830.608 –30 677.573
AIC
BIC
a-BIC
Entropy R2
68 630.943 65 703.011 63 847.555 63 113.607 62 551.031 62 222.600 62 009.216 61 747.147
68 830.193 66 006.631 64 255.544 63 625.966 63 167.759 62 943.697 62 834.683 62 676.982
68 696.814 65 803.386 63 982.434 63 282.990 62 754.917 62 460.990 62 282.111 62 054.545
1.00 0.88 0.91 0.90 0.91 0.89 0.89 0.90
Note: * Model was not identified. Italic font indicates selected model. AIC = Akaike information criterion; BIC = Bayesian information criterion; a-BIC = sample size adjusted BIC. Heavy emphasis was placed on the utility and theoretical interpretation of a solution. This approach to LPA model selection has been used elsewhere in the organizational literature (Lawrence & Zyphur, 2011). In all models, within-profile indicator means and variances were free to vary.
cal fit, and theory, the four-profile solution was selected as optimal to describe leader perceptions. The third step in our analysis is model interpretation. In interpreting our models, we are interested in both latent profile homogeneity and latent profile separation. Although homogeneity is not as straightforward to interpret in LPA as in LCA or factor analysis, profile separation is still a very helpful concept to consider. It is the degree to which the latent profiles can clearly be distinguished from each other. Homogeneity is analogous to the concept of saturation in factor analysis, whereas separation is analogous to the concept of simple structure in factor analysis (Lanza, Bray, & Collins, 2013). Looking at Table 9.4, we see the two estimated parameters for the four-profile solution: latent profile prevalences and profile-specific means. Prevalences are the probability of membership in a particular latent profile. Profile-specific means are analogous to factor loadings and express the relationship between manifest and latent variables. The profile-specific means form the basis for interpreting the latent structure. Profile-specific variances are the third parameter estimated in most LPA software and refer to the variance of a specific manifest indicator (e.g., understanding) given membership in a particular latent profile. For our data, the profiles were labeled Prototypical (29 percent prevalence, n 5 206), Laissez-faire (24 percent prevalence, n 5 170), Autocratic
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216
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Dedication
Intelligence
6.81 7.37
Sincere
Helpful
7.65 7.44 7.53
Knowledgeable
Educated
Intelligent
7.93 8.03
Dedicated
Hardworking
Within-profile mean across all items
8.05
Motivated
Within-profile mean across all items
7.13
Clever
Within-profile mean across all items
7.09
Understanding
8.747
8.811
8.684
8.747
8.234
8.407
8.289
8.454
7.785
8.362
8.522
8.149
8.415
Prototypical
Sensitivity
Overall item means 0.29
Item
Latent profile membership proportions
Factor
1 0.31
Autocratic
3
7.879
7.998
7.796
7.842
6.806
6.874
6.730
7.237
6.381
7.147
7.346
6.919
7.176
8.186
8.176
8.104
8.278
7.782
7.839
7.816
7.908
7.565
6.751
7.224
6.306
6.723
Within-profile item means
0.24
Laissez-faire
2
Profile
Table 9.4 Parameter estimates for leader profiles from latent profile analysis of ILT scale items
6.462
6.330
6.426
6.630
6.248
6.276
6.224
6.262
6.228
5.313
5.579
5.116
5.244
0.16
Anti-prototypical
4
217
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7.35 7.19
Strong
Dynamic
4.96 4.65 3.90 3.63 6.02
Pushy
Manipulative
Conceited
Selfish
Loud
4.25
Male
Within-profile mean across all items
4.45
Masculine
Within-profile mean across all items
5.50
Domineering
Within-profile mean across all items
7.40
Energetic
3.476
3.364
3.588
3.504
5.665
1.979
2.116
2.982
3.572
4.707
7.988
7.973
7.956
8.046
3.085
3.036
3.134
3.619
4.870
2.465
2.700
3.547
3.828
4.201
6.681
6.628
6.621
6.795
5.803
5.618
5.988
6.494
7.235
5.396
5.882
6.571
6.773
7.106
7.768
7.541
7.901
7.861
5.089
5.091
5.088
5.576
5.925
5.039
5.167
5.702
5.762
5.861
5.940
5.932
6.295
6.205
Note: Model stability and identification for all models were addressed by using multiple sets of random starting values (500 sets for initial stage optimization, 50 sets for final stage optimization), and maximum likelihood estimation with standard errors robust to non-normality was used to estimate all models (Muthén & Muthén, 1998–2010).
Masculinity
Tyranny
Dynamism
218 Handbook of methods in leadership research (31 percent prevalence, n 5 220), and Anti-prototypical (16 percent prevalence, n 5 113). Prototypical and Anti-prototypical profiles were expected based on the work of Epitropaki and Martin (2004). The Prototypical profile had the highest profile means for Sensitivity, Intelligence, Dedication, Dynamism; the lowest profile means for Tyranny; and lower than average means for Masculinity. The Anti-prototypical had the lowest profile means on Sensitivity, Intelligence, Dedication, and Dynamism and higher than average means on Tyranny and Masculinity. The Laissez-faire profile was expected based on work in the area of transformational and transactional leadership (Hinkin & Schriesheim, 2008) and was characterized by a pattern of average responses to all items. The Autocratic profile displayed a pattern of lower than average means for Sensitivity; higher than average means for Intelligence, Dedication, and Dynamism; and the highest profile means on Tyranny and Masculinity. The Autocratic profile was also expected in the literature as autocratic leaders score particularly low on the factor of consideration, as identified by the Ohio State studies (Judge, Piccolo, & Ilies, 2004). Thus for our data, Prototypical, Laissez-faire, Autocratic, and Antiprototypical profiles of leader perceptions emerged. Interestingly, there was no dominant profile of leader perceptions for university students, supporting the idea that there is variability in people’s perceptions of typical leaders. Given that transformational leadership theory dominates the leader development literature (Avolio, 2005), it is noteworthy that a majority of participants did not perceive a typical leader to be Prototypical. In contrast, the prevalence of the Anti-prototypical and Autocratic profiles reinforces the need for more research to focus on the “darker” side of leadership (Schyns & Schilling, 2013). The Laissez-faire profile is consistent with theory characterizing this type of leader as generally failing to take responsibility for managing. Recent work on the role of followers in the leadership process emphasizes both an active and a passive dimension of followership (Carsten, Uhl-Bien, West, Patera, & McGregor, 2010). Perhaps endorsing a Laissez-faire leader profile allows followers to take a more active role in the coproduction of leadership (Carsten & Uhl-Bien, 2012). Although beyond the scope of our example, structural features can be added to LPAs. For example, covariates can be added to explore whether these variables predict latent profile membership. For example, does leadership self-efficacy impact latent profile membership of leader perceptions? Alternatively, LPA with multiple groups can be performed to examine differences in the probability of profile membership as a function of gender. More interestingly, a distal outcome can be predicted from latent profile membership. For example, do profiles of leader perceptions
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Person-oriented approaches to leadership 219 predict leader–member exchange relationships? Recently, Lanza, Tan, and Bray (2013) have developed a flexible model-based approach to empirically derive and summarize the profile-dependent density functions of distal outcomes with categorical, continuous, or count distributions.
STRENGTHS AND WEAKNESSES OF THE PERSONORIENTED APPROACH We began with the variable-oriented approach, which according to Bergman and Andersson (2010), has grown to be “almost synonymous with the scientific approach” due to its dominance and flexibility (p. 155). One of the greatest strengths of the variable-oriented approach is the objectivity it affords researchers due to what are typically clear and precise measurements and reliable, valid scales. In addition, its often rigorous control of confounding variables allows for the ability to make strong causal inferences, to quantify the amount of variance in an outcome explained by a particular variable (ibid.), and to assemble a parsimonious model (Foti & Hauenstein, 2007) of relationships that can be generalized to a population. However, the variable-oriented approach carries with it several disadvantages, many of which can be addressed using the person-oriented approach. First, the variable-oriented approach assumes homogeneity of the population. In other words, it is believed that variables under study behave the same way across all individuals and systems to which results are generalized. Furthermore, it is assumed that the interactions within a system are linear. However, given the importance of context and the myriad different interactions of variables occurring within a dynamic system, this is most often not the case (Reitzle, 2013). One advantage of the person-oriented approach is that it overcomes this issue because it acknowledges that there are population subgroups characterized by patterns of variables and their non-linear interactions, resulting in something entirely different than the simple sum of the variables (Bergman & Andersson, 2010). This way of thinking is more closely aligned with the conceptualizations and research questions of processes and individuals as holistic systems. It captures the reciprocal nature of interactions between a person and his or her environment, such that the concept under study becomes a “person–environment system” (Magnusson & Stattin, 2006, p. 425). In addition, the variables forming the patterns within the system do not have to be at the same level. The pattern-oriented approach can capture characteristics and dynamics of individuals, dyads, groups, and organizations (Wiegand, Jeltsch, Hanski, & Grimm, 2013). A final advantage is that examining many different components of the process together
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220 Handbook of methods in leadership research can result in a more complete understanding of the processes as well as potential for greater predictive accuracy (MacDougall et al., 2014). Like the variable-oriented approach, the person-oriented approach is not without its shortcomings. First, given that the person-oriented approaches are underutilized in the behavioral sciences, pattern-oriented methods are not particularly well developed. However, this trend appears to be changing, as recent growth in person-oriented approaches has driven a parallel growth in advanced pattern-oriented methods. Furthermore, of the existing methods, many are descriptive in nature, which makes causal relationships harder to define. Also disadvantageous is the requirement for a large sample size (especially for the longitudinal methods) and highly reliable measures, since the methods do not account for measurement error. Additionally, interpretation of the meaning of the resulting patterns is based on the indicator/variable means within each profile (see Table 9.4). It should be noted, however, that this process is analogous to the interpretation of factors in the variable-oriented factor analysis. Our final note on disadvantages relates to replication of the pattern. The difficulty or inability of replicating patterns across different samples is often mentioned as a disadvantage in person-oriented research. However, research by Foti and colleagues suggests otherwise (e.g., Bray, Foti, Thompson, & Wills, 2014; Foti, Bray, Thompson, & Allgood, 2012; Foti & Coyle, 2015; Foti & Thompson, 2015). Findings of their research show that although the prevalence of particular patterns may vary depending on the sample, the number and type of patterns remain stable and replicable. It is important to note that although person- and variable-oriented approaches differ in both theory and method, neither is necessarily superior to the other. Rather, each should be seen as complementary and meaningful in their own regard. As Von Eye et al. (2006) explain, variable-oriented approaches are best suited for research questions aimed at understanding general trends or relationships between variables in a “well-specified population” (p. 1002). Person-oriented approaches are appropriate for answering holistic questions about a person and his or her development over time, as well as for providing explanation for individuals or groups of individuals who deviate from the aggregate average. However, we advocate for the integration of both approaches in research so as to offset their weakness and maximize their strengths. This enables gathering of information about distinct subgroups while also allowing for generalizations to be made across total samples.3 In discussing the strengths and weaknesses of both the variable- and person-oriented approaches directly above, we hope to help readers understand the value in including both approaches in their research. In particular, we hope to convince leadership researchers that doing so could
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Person-oriented approaches to leadership 221 deepen an understanding of leadership processes by addressing questions that variable-oriented methods cannot. The field of leadership has mastered the variable-oriented approach; almost all leadership research has historically taken this perspective.
THE ROADMAP FORWARD Although leadership researchers have uncovered a great deal of knowledge using the variable-oriented approach, we believe the field of leadership, in general, has not taken full advantage of the person-oriented approach to answer important and difficult questions. Specifically, there are three key areas within the leadership domain particularly suited to the person-oriented research: leadership and time, leadership and context, and leadership and non-linearity. The role of time in leadership has largely been neglected in research, as most has been primarily cross-sectional (Shamir, 2011). According to David Day (2014), there are four areas within the leader and leadership domain in need of closer temporal examination: (1) the effects of leader behavior, (2) perceptions of leaders and emergent leadership, (3) the development of dyadic leader–follower relationships, and (4) leader development. Given that the person-oriented approach is rooted in research on individual development, an inherently longitudinal process (Bergman & Magnusson, 1997), the person-oriented approach conceptually (and methodologically) incorporates time. Thus, it is fit for addressing questions related to these four temporal issues. In this section, however, we focus mainly on the fourth: leader development. Traditionally, leader development research has focused on the growth of individual leadership skills or competencies (Ruderman, Clerkin, & Connolly, 2014), and, with only a few exceptions (e.g., Day & Sin, 2011; Mumford et al., 2000), has been almost exclusively cross-sectional (Day, 2014). However, personoriented theory states that individual variables behave differently based on the environment and other interacting variables in the system (Bergman & Andersson, 2010). This is also the case with the skills and competencies involved in leader development. In addition, leader development is inherently yoked with time because development implies change, which can only happen over time (Day, 2014). Thus, conducting research using a person-oriented approach, which captures complex interactions over time, aligns leader development methodology with leader development theory. In addition, it can be used to test longitudinal models of leader development. For example, Murphy and Johnson (2011) proposed a dynamic “lifetime development” model of leader development, which
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222 Handbook of methods in leadership research claims that leadership development is driven by a complex and dynamic interaction of early developmental factors, leader identity, self-regulation, leader effectiveness and individual context, all occurring over time. The person-oriented approach can bring researchers one step closer to testing the patterns of variables and trajectories in these sorts of leader development models. Thus, one pathway on the roadmap forward for personoriented research and leadership is to address complex temporal aspects of leaders and leadership that cannot be addressed from a variable-oriented perspective. Another avenue for advancement of leadership research is a better integration of context, and the person-oriented approach provides a means to do so. The context in leadership research can refer to the environment, the situation, the task, other individuals, or other dyads in a system. The influence of context in leadership has consistently been found to be an important one (Liden, Sparrowe, & Wayne, 1997), yet for the most part, the context has largely been regarded as a separate entity, divorced from the individual. When context is included in leadership studies, it is mostly modeled using a person × interaction term, ignoring the idea that a person’s structure and dynamics are in part defined by his or her context (Bergman & Magnusson, 1997). That is, the context and situations are regarded as modifiers, not partners in the leadership process. Since context is regarded as part of a holistic unit using the pattern-oriented approach, there is no need to specify the context, as in the variable-oriented research. Furthermore, even when the context is identified, it is unlikely that it is soundly measured and properly defined (Bogat, 2009). In addition, the importance of both time and context can be highlighted with the notion of trajectories of classes over time. That is, groups may interact differently within different environments, resulting in context-dependent group trajectories. The pattern-oriented approach can also address this issue. Thus, a second pathway on the roadmap forward for person-oriented research and leadership is by focusing on the many different forms of context: situation, task, actors, and dyads. Finally, the concept of non-linearity of interactions is a very important one when studying systems. As discussed above, non-linearity refers to the idea that two variables in a system take on different forms depending on the other components in the system (Magnusson & Stattin, 2006). Bauer and Shanahan (2007) conducted the only test of non-linearity using person-oriented and variable-oriented approaches. Results from their study showed that creating a three-way interaction using variable-oriented methods was not the same as studying the non-linearity of system interactions. Although their example was related to child academic competence and dropout, not leadership, these findings underscore the importance
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Person-oriented approaches to leadership 223 of the person-oriented approach to study the complexity of interactions within social process, including leadership. Thus, our third pathway on the roadmap forward for person-oriented research and leadership is to realize the usefulness of non-linear interactions in leadership processes. Though we have provided some recommended foci for advancing leadership research with the pattern-oriented approach, we conclude this chapter with two novel examples of future research ideas using the person-oriented approach. The first example relates to leadership perceptions. Although researchers have previously used the person-oriented approach to study perceptions of leadership (e.g., Foti et al., 2012), most has been conducted exclusively at the individual level. That is, individuals’ perceptions of a leader target are typically clustered to identify subgroups of individuals. Given the growth in studying leadership processes at the dyad level (e.g., Balkundi & Kilduff, 2006) as well as recent attention to dyadic data analysis methods (see Yammarino & Gooty, Chapter 11 this volume), one interesting and novel idea is to use the person-oriented approach to study perceptions of dyad partners. For example, one might measure and subsequently identify subgroups of dyadic perceptions of both leaders and followers, then measure their relations to other interpersonal variables, such as communication, conflict, or LMX. This would facilitate a deeper understanding of the dynamic interactions underlying the process of claiming and granting, proposed by DeRue and Ashford (2010). The second example also takes a different perspective on the personoriented approach. Most of what we have discussed up to this point has focused on using the pattern-oriented methods for discovery, or to uncover/explore relationships between variables or constructs. However, pattern-oriented methods can also be used from a measurement perspective. In other words, they can be used to develop new scales of measurement. The idea behind this is to use the pattern-oriented methods to identify which manifest variables are best for measuring a particular construct. First, many different possible variables are measured, then they are classified into profile, and finally, those variables that increase the variance within a profile (homogeneity) and/or decrease variance between profiles are dropped. The result is a new set of variables useful for measuring the construct of interest. One example of where this technique could be useful is in measuring leadership perceptions. Perhaps the person-oriented approach could provide new ways to measure perceptions of leaders by, for example, incorporating goal attainment, leader emotional expressions, follower emotional expression, and events. These could, in turn, be compared to those scales used to date.
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224 Handbook of methods in leadership research
CONCLUSION We believe the person-oriented approach holds great promise for understanding leadership processes. By moving away from a primary focus on prediction of outcomes and percentage variance explained to a focus on examining how variables co-occur both relationally and over time, greater understanding in leadership can be achieved.
NOTES 1. Note: In person-oriented research, the acronym “CFA” is used to abbreviate configural frequency analysis and is not to be confused with the “CFA” of confirmatory factor analysis, a variable-oriented technique used to test the underlying factor structure of a set of observed variables. 2. Note: Given the large number of significance tests involved in CFA, it is recommended researchers make adjustments to the alpha level to control for Type I errors (e.g., Bonferroni adjustment; Von Eye, 2002). 3. Note: We advise researchers conducting variable-oriented and person-oriented analysis simultaneously to select variables with strong underlying factor structure (Foti & Thompson, 2016).
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Person-oriented approaches to leadership 227 approach. In R.M. Lerner & W. Damon (Eds.), Handbook of child psychology: Vol. 1. Theoretical models of human development (6th ed., pp. 404–464). Hoboken, NJ: Wiley. McLachlan, G.J., & Peel, D. (2000). Finite mixture models. New York: Wiley. Meyer, J.P., Stanley, L.J., & Vandenberg, R.J. (2013). A person-centered approach to the study of commitment. Human Resource Management Review, 23(2), 190–202. Mumford, M.D., Zaccaro, S.J., Johnson, J.F., Diana, M., Gilbert, J.A., & Threlfall, K.V. (2000). Patterns of leader characteristics: Implications for performance and development. The Leadership Quarterly, 11(1), 115–133. Murphy, S.E., & Johnson, S.K. (2011). The benefits of a long-lens approach to leader development: Understanding the seeds of leadership. The Leadership Quarterly, 22(3), 459–470. Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345–368). Thousand Oaks, CA: Sage. Muthén, L.K., & Muthén, B.O. (1998–2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén. O’Shea, P.G., Foti, R.J., Hauenstein, N.M.A., & Bycio, P. (2009). Are the best leaders both transformational and transactional? A pattern-oriented analysis. Leadership, 5(2), 237–259. Reitzle, M. (2013). Introduction: Doubts and insights concerning variable- and person- oriented approaches to human development. European Journal of Developmental Psychology, 10(1), 1–8. Ruderman, M.N., Clerkin, C., & Connolly, C. (2014). Leadership development beyond competencies: Moving to a holistic approach (White Paper). The Center for Creative Leadership. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. Schyns, B., & Schilling, J. (2013). How bad are the effects of bad leaders? A meta-analysis of destructive leadership and its outcomes. The Leadership Quarterly, 24(1), 138–158. Sclove, S.L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343. Shamir, B. (2011). Leadership takes time: Some implications of (not) taking time seriously in leadership research. The Leadership Quarterly, 22(2), 307–315. Shondrick, S.J., & Lord, R.G. (2010). Implicit leadership and followership theories: Dynamic structures for leadership perceptions, memory, and leader–follower processes. In G. Hodgkinson, & J. Ford (Eds.), International review of industrial and organizational psychology (Vol. 25, pp. 1–33). Chichester, UK: John Wiley & Sons. Sterba, S.K., & Bauer, D.J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22(2), 239–254. Sy, T., Shore, L.M., Strauss, J., Shore, T.H., Tram, S., Whiteley, P., & Ikeda-Muromachi, K. (2010). Leadership perceptions as a function of race–occupation fit: The case of Asian Americans. Journal of Applied Psychology, 95(5), 902–919. Vandenberg, R.J., & Stanley, L.J. (2009). Statistical and methodological challenges for commitment researchers: Issues of invariance, change across time, and profile differences. In H.J. Klein, T.E. Becker, & J.P. Meyer (Eds.), Commitment in organizations: Accumulated wisdom and new directions (pp. 383–416). Florence, KY: Routledge/Taylor and Francis Group. Von Eye, A. (2002). Configural frequency analysis – Methods, models, and applications. Mahwah, NJ: Erlbaum. Von Eye, A. (2010). Developing the person-oriented approach: Theory and methods of analysis. Development and Psychopathology, 22(2), 227–285. Von Eye, A., & Bergman, L.R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15(3), 553–580. Von Eye, A., & Bogat, G.A. (2006). Person orientation – concepts, results, and development. Merrill Palmer Quarterly, 52(3), 390–420. Von Eye, A., Bogat, G.A., & Rhodes, J.E. (2006). Variable-oriented and person-oriented
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228 Handbook of methods in leadership research perspectives of analysis: The example of alcohol consumption in adolescence. Journal of Adolescent Research, 29(6), 981–1004. Von Eye, A., Mun, E.Y., & Bogat, G.A. (2008). Temporal patterns of variable relationships in person-oriented research: Longitudinal models of configural frequency analysis. Developmental Psychology, 44(2), 437–445. Wang, M., & Bodner, T.E. (2007). Growth mixture modeling: Identifying and predicting unobserved subpopulations with longitudinal data. Organizational Research Methods, 10(4), 635–656. Wang, M., & Hanges, P.J. (2011). Latent class procedures: Applications to organizational research. Organizational Research Methods, 14(1), 24–31. Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. Wiegand, T., Jeltsch, F., Hanski, I., & Grimm, V. (2003). Using pattern-oriented modeling for revealing hidden information: A key for reconciling ecological theory and application. Oikos, 100(2), 209–222. Wright, A.G.C., & Hallquist, M.N. (2014). Mixture modeling methods for the assessment of normal and abnormal personality, part II: Longitudinal models. Journal of Personality Assessment, 96(3), 269–282.
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10. Multi-level issues and dyads in leadership research Francis J. Yammarino and Janaki Gooty
Multiple levels of analysis in theory building and theory testing are critical and have a long history in leadership research, as evidenced in works by Dansereau, Alutto, and Yammarino (1984); Dansereau and Yammarino (1998a, 1998b); DeChurch, Hiller, Murase, Doty, and Salas (2010); Dionne and Dionne (2008); Dionne et al. (2014); Gooty, Serban, Thomas, Gavin, and Yammarino (2012); Gooty and Yammarino (2011, 2016); Markham (2010, 2012); Schriesheim, Castro, Zhou, and Yammarino (2001), Yammarino and Dansereau (2009, 2011), and Yammarino, Dionne, Chun, and Dansereau (2005). These authors, among others, have noted the importance of clearly specifying the levels of analysis at which phenomena are expected theoretically, and ensuring the measurement of constructs and data analytic techniques correspond to the asserted levels of analysis, so that inference drawing is not misleading. In this chapter, we explicate a set of key multi-level issues, both theory and method related, for leadership research. We then focus on the most neglected and poorly understood level of analysis in leadership – dyads – and develop a set of key issues related to three methodological dyadic approaches in the leadership field. Finally, we provide some recommendations involving multi-level issues in theory and methods for leadership researchers.
MULTI-LEVEL ISSUES IN LEADERSHIP Levels of analysis are inherent in theoretical formulations in leadership research. They are implicit or assumed, can be explicitly incorporated, and are used to develop the boundary conditions under which a theory is expected to hold. Understanding how and if levels are specified permits an examination of the potential or degree of prevalence of theoretical misspecification. Moreover, identification of relevant levels-of-analysis issues may help account for mixed, inconsistent, and contradictory findings in prior leadership research. Without explicit incorporation of levels-ofanalysis issues, incomplete understanding of a construct or phenomenon 229
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230 Handbook of methods in leadership research may lead to faulty measures, inappropriate data analytic techniques, and erroneous conclusions. As such, levels of analysis must also be accounted for methodologically in leadership research. In brief, “[t]heory without levels of analysis is incomplete; data without levels of analysis is incomprehensible” (Yammarino et al., 2005, p. 904). Four Levels in Leadership The phrase “levels of analysis” refers to the entities, units, or objects of observation (e.g., Miller, 1978). Typically, hierarchically ordered, lowerlevel entities such as persons are nested or embedded in higher-level entities such as dyads or groups. In leadership research, we are interested in people in work organizations in terms of four key levels of analysis (e.g., Dansereau et al., 1984; Yammarino & Dansereau, 2009, 2011; Yammarino et al., 2005). First, a focus on the level of independent individuals or persons (e.g., leaders or followers) allows for the exploration of individual differences. Second, dyads, consisting of two-person groups with interpersonal relationships such as leader–follower roles, involve one-to-one interdependence between individuals. Third, groups, including workgroups and teams, are a collection of individuals who are interdependent and interact on a face-to-face or “virtual” (i.e., non-colocated) basis with one another. Fourth, collectives are clusterings of individuals that are larger than groups and whose members are interdependent based on a hierarchical structuring or a set of common or shared expectations. Collectives can include groups of groups, departments, functional areas, strategic business units, organizations, firms, and industries. These four levels of analysis constitute different lenses for the examination, both conceptually and empirically, of people in organizations. Due to the hierarchical and nested structure of levels, viewing people from increasingly higher levels of analysis necessarily means the number of entities decreases (e.g., there are fewer groups than persons in an organization) and the size of the entity increases (e.g., there are a larger number of people in collectives than in groups). Three Alternatives per Level While some prefer to assert only two views for each level (e.g., groups and group effects are viable or they are not), there are others who prefer to consider three alternatives for each level of analysis (see, for example, Klein, Dansereau, & Hall, 1994). Dansereau et al. (1984) and Yammarino and Dansereau (2009, 2011), among others, distinguish conceptually between two different (relevant) views of any level of analysis – wholes and
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Multi-level issues and dyads in leadership research 231 parts. Klein et al. (1994) call these homogeneous and heterogeneous views, respectively. The third view, called independent, indicates that the focal entities (e.g., groups) are not relevant and other entities, often lower-level ones (e.g., individuals), are plausible (see Dansereau et al., 1984; Klein et al., 1994). A wholes view is defined as a focus between entities but not within them. Differences between entities are viewed as valid, and differences within entities are viewed as random (error). This perspective can be viewed as a between-units case in which: (1) members of a unit are homogeneous (Klein et al., 1994); (2) the whole unit is of importance, entities display similarity among members; and (3) relationships among members of units are positive with respect to constructs of a theory and a function of differences between units. A parts view is defined as a focus within entities but not between them. Differences within entities are treated as valid, and differences between entities are considered to be random (error). This perspective can be viewed as a within-units case, also known as a “frog pond” effect, in which: (1) members of a unit are heterogeneous (Klein et al., 1994); (2) a member’s position relative to other members is of importance, entities display complementarity among members; and (3) relationships among members of units are negative with respect to constructs of a theory and a function of differences within units. The mechanisms that hold together parts are interdependence and behavioral integration (typically at lower levels) or functional integration and social structure (often at higher levels) (Dansereau et al., 1984; Miller, 1978; Yammarino & Dansereau, 2009, 2011). Various authors indicate that effects also may not be evidenced at a specific focal level (Dansereau et al., 1984; Klein et al., 1994; Miller, 1978; Yammarino & Dansereau, 2009, 2011). In this case of independence, two possible conclusions for a focal level are either a focus both between and within entities (sometimes called equivocal), or error between and within entities (sometimes called inexplicable or null). In both of these cases, the focal level of analysis does not clarify understanding of the constructs, variables, or phenomena of interest, and other levels must be considered. The members of a unit are (1) independent, (2) free of the unit’s influence, and (3) relationships among members of units are independent with respect to constructs of a theory and a function of differences between members (e.g., persons) independent of higher-level units (e.g., groups). In leadership research, when “person” is the focal level of analysis, the person (wholes) and the interdependent genes, properties, or behaviors over time (parts) within the person are the potential units of analysis. If the entities are independent at this level, potential higher levels are the dyad or
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232 Handbook of methods in leadership research group. For example, cognitive ability is considered relatively stable and not expected to change at multiple points in time in persons, ensuring independence between persons (a wholes view), whereas state affect, for example, for persons is transient and multiple measurements of this could change or shift over time for persons (a parts view). In leadership, wholes, a homogeneous perspective, can also imply a focus on “stability,” such as person (e.g., leader) traits that remain constant over time. Parts, a heterogeneous perspective, can also imply a focus on “change,” such as person (e.g., leader) characteristics that shift over time (see Dansereau et al., 1984; Dansereau, Yammarino, & Kohles, 1999; Yammarino & Dansereau, 2009, 2011). When “group” is the focal level of analysis, the group (wholes) and the interdependent persons or dyads (parts) within the group are the potential units of analysis. For example, the presence of a common leader across multiple dyads within the same group creates a potential lower-level (dyad-level) dependency (a parts view). If the entities are independent at this level (for example, when considering team-level diversity, we do not expect potential dyad-level dependencies), there are differences among teams (a wholes view), and potential higher levels are collectives such as the organization, strategic group, or industry. Specific, Emergent, and Cross Levels In leadership research, assuming only one level of analysis or choosing only one level without consideration of other levels can either mask effects or indicate effects when none exist (e.g., Dansereau et al., 1984; Hackman, 2003; Yammarino et al., 2005; Yammarino & Dansereau, 2009, 2011). Moreover, levels in organizational settings rarely exist independently of one another, and are typically nested, cross-classified, or somehow linked (e.g., Gooty & Yammarino, 2011, 2016). These linkages necessitate that multiple levels and their effects be considered simultaneously. In leadership, micro- versus macro-theories, processes, and concepts can be either contrasted or viewed as analogous, depending on various levels-ofanalysis formulations. Adjacent levels can interact in alignment, misalignment, or opposition to one another, and can be intertwined via complex processes (e.g., Klein & Kozlowski, 2000; Miller, 1978). The focus here for “linking” (multiple) levels of analysis is on three general types of multiplelevel formulations – level-specific, emergent, and cross-level formulations – from both wholes and parts perspectives for leadership work. Level-specific relationships First, relationships among constructs may be hypothesized to hold at a lower level (e.g., person) but not at a higher level (e.g., group). This
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Multi-level issues and dyads in leadership research 233 possibility is discussed as a discontinuity thesis (Miller, 1978), as levelspecific formulations (Dansereau et al., 1984; Miller, 1978), or empirically as disaggregated, individual, or level-specific effects (Pedhazur, 1982; Robinson, 1950). In these cases, the higher level of analysis is not relevant for understanding the theoretical constructs. Wholes at a lower level may not always emerge at a higher level (independent). This level-specific wholes formulation means that members are homogeneous with respect to the constructs of interest in all lower-level entities (e.g., groups), and higher-level entities (e.g., collectives) are not relevant. Examples of level-specific wholes formulations include various personality and leadership approaches at the individual level (see Dionne et al., 2014; Yammarino & Dansereau, 2009; Yammarino et al., 2005); many team-based and shared leadership approaches at the group level (Dionne et al., 2014; Klein & Kozlowski 2000; Yammarino & Dansereau, 2009; Yammarino et al., 2005); and organizational missions and visions and strategic leadership at the organization level (Dansereau & Yammarino, 1998a, 1998b; Yammarino et al., 2005; Yammarino & Dansereau, 2009). Parts at a lower level may not always emerge at a higher level (independent). This level-specific parts formulation means that members are heterogeneous with respect to the constructs of interest in all lower-level entities (e.g., groups), and higher-level entities (e.g., collectives) are not relevant. Examples of level-specific parts formulations include newer approaches that consider personality and leadership changes across time (see Yammarino & Dansereau, 2009) and vertical dyad linkage (VDL) and leader–member exchange (LMX in- and out-group) leadership approaches at the group level (Dansereau & Yammarino, 1998a, 1998b; Yammarino et al., 2005). Emergent relationships Second, relationships among constructs may not be asserted at a lower level but are hypothesized to manifest themselves at a higher level of analysis. This possibility is also discussed as a type of discontinuity thesis (Miller, 1978), as emergent formulations that hold at a higher level (e.g., group) after not being asserted or found to hold at a lower level (e.g., person) (Dansereau et al., 1984; Miller, 1978), empirically as higher-level effects that do not disaggregate, or as emergent effects (Miller, 1978; Robinson, 1950). In these cases, the lower level of analysis is not relevant for understanding the theoretical constructs. For an emergent wholes formulation, constructs are expected to hold at a higher (e.g., group) level where members of a higher-level entity are homogeneous with respect to the constructs after not having been expected or observed at a lower level (independent). Examples of emergent
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234 Handbook of methods in leadership research wholes formulations include concepts in relation to leadership such as group cohesion, shared mental models, as well as shared and team leadership at the team level (DeChurch et al., 2010; Klein & Kozlowski, 2000; Yammarino et al., 2005) and several of the Global Leadership and Organizational Behavior Effectiveness research program (GLOBE) cultural values dimensions at the society level (House, Hanges, Javidan, Dorfman, & Gupta 2004). For an emergent parts formulation, constructs are expected to hold at a higher (e.g., group) level where members are heterogeneous with respect to the constructs after not having been expected or observed at a lower level (independent). Examples of emergent parts formulations include concepts such as vertical dyad linkage, perhaps leader–member exchange (see Dansereau & Yammarino, 1998a, 1998b), intra-group conflict, and compatible mental models at the team level (Klein & Kozlowski, 2000), and aspects of organizational subcultures at the organization level (Katz & Kahn, 1978). Cross-level relationships Third, relationships among constructs also may be hypothesized to hold at higher (e.g., collective) and lower (e.g., group) levels of analysis. This possibility is discussed as a homology thesis (Miller, 1978), empirically as aggregated or ecological effects (Pedhazur, 1982; Robinson, 1950), and, in the traditional sense, as cross-level explanations (Behling, 1978; Dansereau et al., 1984; Miller, 1978; Yammarino & Dansereau, 2009, 2011). Note the use here is contrary to how the term “cross-level” is used in much contemporary work (e.g., Bryk & Raudenbush, 1992; Raudenbush & Bryk, 2002; Rousseau, 1985) as the effect of a higher-level variable on a lower-level association (cross-level moderation) or variable (cross-level direct effect). Our use of “cross-level” is in line with older cited traditional work as well as in the “hard” and physical sciences (e.g., Behling, 1978; Dansereau et al., 1984; Miller, 1978; Robinson, 1950), and are statements about relationships among variables that are likely to hold equally well at a number of levels of analysis (e.g., X and Y are positively related for persons and for groups). Such cross-level formulations and effects specify patterns of relationships replicated across levels of analysis. Models of this type are uniquely powerful and parsimonious because the same effect is manifested at more than one level of analysis (e.g., E 5 mc2, which holds at multiple levels of analysis, is a cross-level formulation for us and in traditional and “hard” sciences work; in contrast, most contemporary work in leadership would not term this equation as a cross-level effect). Wholes at a lower level also can aggregate or manifest themselves as wholes at a higher level. This cross-level wholes formulation means that
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Multi-level issues and dyads in leadership research 235 members are homogeneous with respect to the constructs of interest in all entities (e.g., groups and collectives) at both levels of analysis, but the entities (e.g., groups and collectives) differ from one another. Examples of cross-level wholes formulations include various GLOBE cultural dimensions that are consistent from the individual to organization to society levels (House et al., 2004), and professional and functional titles and associated expectations about them that hold from the individual to dyad to group to organization levels (see Dansereau et al., 1984; Yammarino & Dansereau, 2009). Wholes at a lower level can aggregate or manifest themselves as parts at a higher level. This cross-level parts formulation means that members are homogeneous with respect to the constructs of interest in all the lower-level entities (e.g., groups), and these differ from one another; in all higher-level entities (e.g., collectives), however, there is heterogeneity because members within the entities differ from one another. Examples of cross-level parts formulations include functional area task differences and subsystem subcultures from the individual to organization and group to organization levels (Katz & Kahn, 1978). Time and Levels Another important multi-level issue for leadership research is the theoretical specification and empirical test of potentially changing variables and phenomena (e.g., traits, properties) and shifting levels of analysis (entities) over time (see Dansereau et al., 1999; Yammarino & Dansereau, 2011). Leadership must be viewed from a longitudinal and dynamic perspective to fully understand relevant phenomena that are stable or shifting, changing, and developing over time. While much has been written about changing variables or constructs over time and strategies for analysing these changes (e.g., Chan, 1998; Yammarino et al., 2005), relatively little has been written about changing entities (levels of analysis) over time (for exceptions, see Dansereau et al., 1984, 1999; Yammarino & Dansereau, 2009, 2011). In their approach, for example, Dansereau et al. (1999) consider the plausible units of analysis (wholes, parts, independence, and null) at two points in time to account for entity changes over time. A 16-cell (4 × 4) matrix results for understanding changes or stability in levels of analysis over time. For various levels of analysis, Dansereau et al. (1999) define, describe, and illustrate the following components: four types of stable conditions (in terms of wholes, parts, lower-level independent units, and null over time); three types of changes that move the focus up from a lower to a higher level (transformation up from parts to wholes, level change up from
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236 Handbook of methods in leadership research independent units to wholes, and level change up from independent units to parts); three types of changes that move the focus down from a higher to a lower level (transformation down from wholes to parts, level change down from wholes to independent units, and level change down from parts to independent units); and six types of changes that indicate the beginning or end of a level (three “emergents” from null or nothing to wholes, parts, or independent units, and three “ends” from wholes, parts, or independent units to null or nothing). As an example, a portion of Klein and Kozlowski’s (2000) work can be cast in terms of the Dansereau et al. (1999) framework. Klein and Kozlowski (2000) offer two forms for the emergence or change of entities over time – composition and compilation. Composition, based on isomorphism, suggests that lower-level entities (e.g., persons), based on shared mental models and similar information and expertise, combine in a linear or pooled fashion with stable or uniform interactions over time, resulting in higher-level entities (e.g., teams) viewed from a homogeneous perspective. In this case, lower-level wholes shift to higher-level wholes over time. Compilation, based on discontinuity, suggests that lower-level entities (e.g., persons), based on compatible mental models and diverse information and expertise, combine in a non-linear or adaptive fashion with irregular or non-uniform interactions over time, resulting in higherlevel entities (e.g., teams) viewed from a heterogeneous perspective. In this case, lower-level wholes shift to higher-level parts over time. Fallacies, False Dichotomies, and Levels In leadership research, it is critical to avoid committing a “fallacy of the wrong level” (Dansereau, Cho, & Yammarino, 2006; Dansereau & Yammarino, 2006). The most common form of these fallacies involves inferring associations at a level other than where data are collected or analyses are conducted. An ecological fallacy occurs when lower levels of analysis are presumed to be mere disaggregations of higher-level entities. In contrast, an individual fallacy occurs when higher levels of analysis are presumed to be mere aggregations of lower-level entities. Work by Robinson (1950) and others (e.g., Dansereau et al., 1984, 2006; Miller, 1978; Rousseau, 1985) highlight the problems and issues associated with such fallacies. While Dansereau et al. (1984) and Yammarino and Dansereau (2009, 2011) note that similarities may be apparent between micro-elements at lower levels and macro-elements at higher levels, they also make clear that these elements are not identical. Persons cannot be viewed as just disaggregated groups or organizations, and groups or organizations cannot
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Multi-level issues and dyads in leadership research 237 be seen as just aggregated persons. Processes, constructs, relationships, theories, and other aspects of these entities can vary by level of analysis, such that some can emerge or hold at one level and not at other levels (e.g., Gooty & Yammarino, 2011, 2016; Klein & Kozlowski, 2000; Morgeson & Hoffman, 1999). Dansereau and Yammarino and colleagues (Dansereau et al., 1984, 2006; Dansereau & Yammarino, 2006; Markham, 2010; Yammarino & Dansereau, 2009; Yammarino et al., 2005) elaborate on various aspects of fallacies of the wrong level for leadership research. They also offer a variety of potential solutions to, and ways to avoid, such ecological and individual fallacies in theory and hypothesis formulation, measurement, data analysis, and inference-drawing procedures. Relatedly, various Aristotelian “either-or” controversies, which have existed for several millennia, can be expressed as, and often degrade to, levels-related false dichotomies and inappropriate dualisms. In terms of multi-level issues in leadership research, these dichotomies typically relate to some version of “the individual versus the environment” such as the “person–situation” and “genes–learning/development” debates. The goal is to resolve Aristotelian “either-or” controversies with a “Galilean approach” characterized by integration of the components of each duality. In leadership, there is a long history related to the “person–situation” debate (e.g., Bass, 2008; Dansereau et al., 1999; Yammarino & Dansereau, 2009, 2011). This discussion can also be framed in terms of an “individual– environment” debate (e.g., are leaders born or made?), or the micro–macro distinction (e.g., is leadership in or of the organization?), where an integration can occur to resolve the debate by viewing higher levels of analysis as the context (situation or environment) for or boundaries on lower levels of analysis (Dansereau et al., 1984, 1999; Yammarino & Dansereau, 2009). For example, rather than assuming person (lower-level) or situation (higher-level) explanations of effects, it is plausible to offer a person-bysituation or interactional explanation for leader behavior. Likewise, it is the person within situation (context) that matters for explanation of leadership phenomena; again, there is an appeal to integrate these notions in terms of lower and higher levels of analysis. Moreover, Bass (2008) develops the idea and importance of “context” as a higher level of analysis within which leadership behavior occurs and without which behaviors often become uninterpretable. For example, individual (e.g., leader) behavior occurs within a group context, individual and group behaviors (e.g., of followers) occur with an organizational (still higher-level) context, and so on. In this way, lower and higher levels of analysis together, in integration, explain the phenomena of interest and avoid the “either-or” controversy.
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238 Handbook of methods in leadership research Analytics and Levels Given the diverse multi-level issues addressed here for leadership research, a variety of multi-level methods and data analytic tools are required for assessing and testing these notions empirically. A complete review of these approaches is beyond the scope here, but some approaches will be developed more fully in the following section. At this point, suffice it to say that a core issue is the potential dependency created among observations that may not be independent due to nesting, embeddedness, and cross-classification of entities at multiple levels of analysis (see Dansereau et al., 1984; Gooty & Yammarino, 2011). As such, many common analytic techniques (e.g., OLS regression) are inappropriate for multi-level analyses. Approaches that are appropriate, however, for analysing multi-level notions in leadership work include random coefficient models (RCM) via hierarchical linear modeling (HLM) and various multi-level routines in Mplus and Stata, multi-level structural equation models (MLSEM), within and between analysis (WABA), and multi-level routines in the R software package (for details and leadership examples, see Dansereau et al., 1984, 2006; Dansereau & Yammarino, 2000, 2006; Gooty & Yammarino, 2011, 2016; Klein & Kozlowski, 2000; Yammarino, 1998; Yammarino & Markham, 1992). All these analytic tools for assessing multiple levels of analysis have procedures for testing multi-level meditation, moderation, and longitudinal (processual, developmental) notions while accounting for dependencies in the data. Another though very different analytic tool for multi-level leadership work is dynamic computational modeling and simulation (e.g., Dionne & Dionne, 2008; Dionne, Sayama, Hao, & Bush, 2010; Sayama, 2015). Dionne and Dionne (2008), for example, developed a computational model for a levels-based comparison of four types of leadership that represent three different levels – individual, dyad, and group – across a dynamic group decision-making optimization scenario. Computational modeling, including agent-based modeling and simulation, of complex non-linear relations among various traits, behaviors, properties, levels of analysis, and environmental characteristics for leadership phenomena is a rapidly growing area of work. These computational tools for modeling complex and dynamic multi-level notions can also account for potential dependencies created by multiple embedded or interrelated entities and levels of analysis in leadership research (see Sayama, 2015).
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Multi-level issues and dyads in leadership research 239
DYADS IN LEADERSHIP Having considered a variety of critical levels of analysis issues for both theory and method that apply to all levels for leadership research, we focus now on a particular level of analysis – dyads – which is the most recent in the literature. In organizational behavior, for example, there is a long history (from at least the 1960s) of considering individuals, groups and (since the 1980s or so) teams, and organizations. The mention of dyads, however, outside the realm of leadership is still rare today. But in the leadership realm, the use of the term “dyad” has been around since at least the 1970s (e.g., Dansereau, Graen, & Haga, 1975). Despite this history, dyads were often considered only as part of a group (e.g., dyads within groups, or in-group and out-group dyads). While the focus on dyads as a separate unique level of analysis has been around in leadership work since the 1980s (e.g., Dansereau et al., 1984), only very recently has this levels perspective really come to the forefront in leadership (see Gooty & Yammarino, 2011, 2016). As such, we elaborate some important multi-level theoretical and methodological issues for dyads, the most neglected and least understood level of analysis in leadership research. Foundations of Dyads As previously explained, dyadic relationships involve one-to-one (twoparty) linkages between people, and they are omnipresent in work settings and critical in leadership research. Indeed, the prevalence of dyads in work settings and yet the neglect of their examination has produced numerous calls for more theory, methods, and empirical research on this “forgotten” level of analysis (see Gooty & Yammarino, 2011; Kenny, Kashy, & Cook, 2006). The study of dyadic relationships falls in the realm of multi-level research and theory testing, but dyads are the least studied level of analysis relative to individuals, groups or organizations (see Gooty & Yammarino, 2011, 2016; Krasikova & LeBreton, 2012; Schriesheim et al., 2001). In the handful of studies where dyadic relationships are an explicit focus, considerable misalignment of theory, measurement and inferences exists. For example, LMX theory is based on dyadic relationships between a leader and each follower, whereas most empirical studies measure and analyse relationships at the individual and group levels (for reviews, see Gooty et al., 2012; Schriesheim et al., 2001). Even in studies where the dyadic level is implemented, problems still abound with the statistical methods in use, and there is a serious dearth of comprehensive conceptual and methodological treatments of the dyad as a level of analysis (see, for reviews, Gooty & Yammarino, 2011, 2016; Kenny et al., 2006; Krasikova & LeBreton, 2012).
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240 Handbook of methods in leadership research Dependencies for Dyads Within- and between-dyad dependencies Dyadic research explains and tests the relationship aspects between two members of that dyad. The dyad members might vary in their perception of relationship factors (e.g., communication, quality of exchanges) as well as demonstrate some degree of similarity. From a measurement standpoint, any observations provided by both members of a dyad describing relationship factors are “dependent”; that is, they are not independent of one another. This type of linkage creates the dyad and is also known as the within-dyad dependence (Kenny et al., 2006). In addition, dyads could also be related due to nesting within a higher level (e.g., groups, organizations) and/or due to shared partners (i.e., one dyad member, a leader for example, occurs in more than one dyad), so there is between-dyad dependence. Both types of dependencies, within and between dyads, create theoretical and methodological complexities. From a theoretical perspective, the dependence per se within each dyad and between dyads in a group might be of interest to researchers. For example, a typical LMX study seeks to understand both types of dependencies. Even in studies where these dependencies are not of theoretical interest, Gooty and Yammarino (2011, 2013), Kenny and Judd (1986), and Bliese and Hanges (2004) note that these dependencies need to be accounted for in the research design and data analysis. In particular, Gooty and Yammarino (2011) summarize the statistical errors resulting from ignoring the dyad, and its associated dependencies, as a valid level of analysis: biased standard errors, incorrect inferences due to errors in significance testing, and more Type 1 errors (for a comprehensive review of these issues, see Gooty & Yammarino, 2011; Kenny & Judd, 1986; Schriesheim et al., 2001). The dependencies in dyads could occur due to many causes such as space, time, or common fate (i.e., common external stimuli created by the work group influences). While all of these are plausible sources of dependencies in leadership research, the last one is particularly prevalent; that is, dyads are nested or embedded within groups. Dyad dependencies and groups Dyads could be cast as two-person groups, although some dyads (e.g., where each dyadic member has a specific distinguishable role such as husband and wife) might not be. Dependencies within and between dyads can arise with i observations nested in j dyads that are housed in k groups. Where these i observations are unique to the dyad they occupy, nested hierarchical data structures accommodate the interdependence in lower-
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Multi-level issues and dyads in leadership research 241 level units. For example, two-person project teams (i.e., a dyad) can be part of a larger team consisting of many such dyads. In these nested data structures, each of the i observations and j dyads at lower levels are not presumed independent unlike single-level data structures. These nested data structures, however, do presume that each lower-level unit is uniquely nested within a higher-level unit (e.g., a dyadic member belongs only to one dyad and one group). One could imagine organizational settings wherein this data structure applies to peer–peer relationships. In leadership research, however, where the leader is a key focus at the individual, dyad and group levels, this assumption of unique membership is routinely violated. The consequences of violating this assumption are similar to what we articulated above with not accounting for dependencies in data. To recap, ignoring lower-level dependencies could cause Type 1 and Type 2 errors and reduced power to detect Level 1 (i.e., lower-level) effects (see also Bliese & Hanges, 2004; Dansereau & Yammarino, 2006; Gooty & Yammarino, 2011, 2016; Kenny et al., 2006; Yammarino et al., 2005). Very simply, most studies in leadership research include leaders and followers at Level 1 (i.e., individual level) where the leader could occur in multiple dyads and in a work group. In this instance, the lower-level unit (i.e., leader) is not only nested within a higher-level unit (e.g., dyad), but also resides in multiple higher-level units (i.e., several dyads) simultaneously. This non-unique higher-level membership violates a key assumption in nested data structures. Cross-classification Following Gooty and Yammarino (2011), one way to treat this issue of non-unique dyadic membership is via cross-classification of the leader by both the dyad and the group. This design differs from traditional nested data structures in important ways. In particular, the leader and follower are both modeled at Level 1 (individual level). The dependency within and between dyads is modeled via the dyad-level membership, cross-classified by the group. This cross-classification scheme affords the advantage of modeling dyad-level variables (e.g., trust, LMX) at the appropriate dyadic level and precludes the assumption that lower-level units are uniquely nested in higher-level units. Rather, the dyads could be within the same work group (i.e., work unit or organization-level dependencies) that is modeled via the group factor. Dependence at the dyad level could exist in two forms: within each dyad and between dyads as depicted in Figures 10.1 and 10.2 (adapted from Gooty & Yammarino, 2011). There could be within-dyad dependency (indicated by double-headed arrows in Figures 10.1(a) and 10.2(a)) attributable to partner/member effects, mutual influence and/or common fate.
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242
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F2 F1
L2
L1 (b) Level 1
Level 2
Level 3
L1, F1
Dyad 1
L2, F2
Dyad 2
L3, F3
Dyad 3
Figure 10.1 Independent/unique dyads
Source: Adapted from Gooty & Yammarino (2011).
Note: Each leader and follower belongs to one dyad (a). Each leader–follower dyad is independent at the dyad level as the leader and follower are unique to that dyad. All three dyads might, however, belong to the same organization creating a higher-level dependency captured via the group factor. Nested data structure to represent independent dyads (b).
(a)
F3
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L
(b) Level 1
Level 2
L
Group
F1
Dyad 1
F2
Dyad 2
F3
Dyad 3
Figure 10.2 Dependent/non-unique dyads
Source: Adapted from Gooty & Yammarino (2011).
Note: Leader belongs to three dyads; followers belong to one dyad (a). The leader is common to all three dyads, creating between-dyad dependencies. There might be certain dependencies between all the members in the group (leader and followers) driven by organization-level (or work-unit) properties captured via the group factor. Cross-classified data structure to represent dependent dyads; solid arrows indicate dyad membership, dashed arrows indicate group membership (b).
(a)
F3
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244 Handbook of methods in leadership research Partner effects exist when the actions of one dyadic member affect the outcomes of the other (e.g., a leader’s ability to provide resources could affect follower task performance). Mutual influence refers to the reciprocal effects of dyadic members’ actions and outcomes (e.g., a leader engages in assigning tasks, a follower completes the assigned task and requests more challenging tasks or autonomy). When both members of the dyad experience common environmental stimuli, they are subject to a final source of within-dyad dependency known as common fate (e.g., a leader and follower are both bound by the same organizational policies and procedures) (see also Gooty & Yammarino, 2011; Kenny et al., 2006). Independent and dependent dyads Dependence between dyads can be conceptualized via higher-level linkages and common members/partners. In the former, dyads entail unique membership and are nested within groups and/or organizations, also known as independent/pure dyads (Dansereau et al., 1984; Gooty & Yammarino, 2011; Kenny et al., 2006), as shown in Figure 10.1(a). In such instances, each of the dyad members (i.e., L1–F1, L2–F2, and L3–F3) appears in only one dyad and thus contributes to only one interpersonal relationship. When the dyads have this structure, theory, measurement and analyses could emerge at the individual, dyadic and group levels as described further in the following paragraphs. Theory for independent dyads could suggest, for example, that LMX is positively associated with performance at the dyadic level (e.g., Gooty & Yammarino, 2011, 2016). Measurement then for this theoretical assertion is depicted in Figure 10.1(b) where leaders and followers both appear at Level 1 providing matched ratings of their LMX relationship and the performance of the follower all at the individual level. The dyadic-level measurement of LMX could be a direct consensus construct (e.g., Chan, 1998) appearing at Level 2, presuming leaders and followers agree regarding their relationship. Such dyadic LMX is presumed to vary between dyads (e.g., LMX). These dyads could be embedded in work groups subject to the same procedural justice formulations, for example. Level 3 then captures such variables that vary between work groups (e.g., procedural justice). This example of independent/pure dyads represents nested data structures in which dyadic and group membership is unique; that is, each leader and follower appears in one dyad only and each dyad appears in one work group only. In general, theory in such cases could be at the individual, dyadic and/or group levels. Measurement, analyses and inference drawing should then be aligned with such presumed theoretical suppositions. We also note that the case of independent work dyads is more easily accessible to multi-level researchers cognitively as it follows the nested
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Multi-level issues and dyads in leadership research 245 data structure configuration that is widely prevalent in leadership, teams and organizational behavior research. The next configuration, however, is much less known in the organizational sciences (for exceptions, see Gooty & Yammarino, 2011, 2016). Specifically, while multi-level data structures in the organizational sciences have largely presumed nested data, in practice this assumption is violated when the dyad level is of interest as dyadic membership is rarely unique. Such designs entail an additional level of dependency as shown in Figure 10.2. Kenny et al. (2006) have called such dyads the “one with many” design, while Dansereau et al. (1984) and Yammarino and Dansereau (2009, 2011) label such configurations “dyads within groups.” More recently, Gooty and Yammarino (2011, 2013) indicated that such dyads represent cross-classified dyads. For example, in leadership research (e.g., LMX and its predecessor, vertical dyad linkage theory; Dansereau & Yammarino, 1998a, 1998b), a typical leader is part of multiple dyads (e.g., the appearance of the same leader, L, in multiple L–F1, L–F2, L–F3 dyads in Figure 10.2). In addition to dependencies attributable to common group membership as in the case of independent dyads above, here dyads are also dependent on the common leader across multiple dyads. Just like the case of independent dyads, theory, measurement and analyses could be at the individual, dyadic and/or group levels here. Analytical strategies, however, must account for the cross-classification of the leader by dyad and group. Such analytical strategies via three different methodological approaches and associated statistical techniques are detailed in the next section. Three Dyadic Approaches The above explication about dyads and the potential dependencies within and between dyads permits a consideration of three primary methodological approaches to dyads in leadership research. These approaches, as summarized in Table 10.1, are called (1) actor–partner interaction model (APIM) which includes one with many (OWM) designs and the social relations model (SRM) (e.g., Campbell & Kashy, 2002; Kenny et al., 2006; Kenny & Livi, 2009; Krasikova & LeBreton, 2012); (2) random coefficient model (RCM) typically implemented via hierarchical linear model (HLM) and hierarchical cross-classified model (HCM) analytic software (e.g., Bryk & Raudenbush, 1992; Gooty & Yammarino, 2011, 2016; Raudenbush & Bryk, 2002; Raudenbush, Bryk, Cheong, Congden and Du Toit et al., 2004) (but also by Mplus and R software); and (3) within and between analysis (WABA) typically implemented via Data Enquiry that Tests Entity and Correlational-Causal Theories (DETECT) software (e.g.,
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246 Handbook of methods in leadership research Table 10.1 Comparison of dyadic approaches Issue
APIM/OWM/SRM
RCM/HLM/HCM
WABA/DETECT
References
Campbell & Kashy (2002); Kenny et al. (2006); Kenny & Livi (2009); Krasikova & LeBreton (2012)
Background & tradition
Psychological sciences – interpersonal & romantic relationships Do dyad- and individual-level effects affect individual-level scores? Rely on theory and partitions based on individual or higher levels
Dansereau et al. (1984); Gooty & Yammarino (2011, 2016); Schriesheim (1995); Yammarino (1998); Yammarino & Dansereau (2009, 2011) Organization sciences – behavior of individuals, groups, & organizations
Views of dyads
Two choices: homogeneity/wholes or independent/none
Bryk & Raudenbush (1992); Gooty & Yammarino (2011, 2016); Raudenbush & Bryk (2002); Raudenbush et al. (2004) Education – individual student achievement & performance Do dyad- and individual-level variables affect individual-level outcomes? Rely on theory and justify with aggregation procedures whether individual or higher levels Two choices: homogeneity/wholes or independent/none
Nature of dyads
Independent/unique or dependent/ non-unique Individual level
Independent/unique or dependent/ non-unique Individual level
Individual or higher level Dyad or higher level
Individual or higher level Dyad or higher level
Dyad or higher level
Variance partitioning and statistical significance tests using regression and SEM approaches
Inferential statistical significance tests using maximum likelihood, etc. algorithms
Tests of statistical & practical significance using ANOVA and correlation/regression approach
Key question
Dependent variables Independent variables Mediators & moderators Basis of analyses
Do variables and relationships operate at the dyad level? Tests, after assertions based on theory, whether individual, within-dyad, or between-dyad level
Three choices: homogeneity/wholes, heterogeneity/parts, or independent/none Dependent/non-unique or independent/unique Dyad level
Dyad or higher level
Note: APIM = actor–partner interdependence model; OWM = one with many; SRM = social relations model; RCM = random coefficient modeling; HLM = hierarchical linear modeling; HCM = hierarchical cross-classified models; WABA = within and between analysis; DETECT = Data Enquiry that Tests Entity and Correlational/Causal Theories.
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Multi-level issues and dyads in leadership research 247 Dansereau et al., 1984; Gooty & Yammarino, 2011, 2016; Schriesheim, 1995; Yammarino, 1998; Yammarino & Dansereau, 2009, 2011). For all three approaches, dyadic or relationship leadership research requires measurement from both parties on the constructs of interest. These measurements could be individuals reporting on their own states (e.g., follower and leader each report about their own LMX) or could focus on assessments of the relationship per se, as reported by each party (e.g., leader and follower both report about the mutual level of LMX in their relationship). This form of measurement from both parties on the same construct simultaneously is called reciprocal data/measures (by Kenny et al., 2006) or matched data/measures (by Dansereau et al., 1984; Gooty & Yammarino, 2011; Yammarino & Dansereau, 2009). Note that simply collecting a measurement from one party (e.g., a follower) on one variable (e.g., LMX), and from the other party (e.g., a leader) on another variable (e.g., performance), does not constitute dyadic research – it is individual differences research as dyadic measurement does not occur for any shared constructs. The other similarities and differences among the characteristics of these dyadic approaches are shown in the table. In particular, the three dyadic approaches arise from different disciplinary and statistical traditions that are designed to test different types of multi-level questions. APIM arises out of the psychological sciences where the focus is interpersonal and romantic relationships, and a key question is whether dyad- and individual-level effects affect individual-level scores. It relies on theory and variance partitions based on individual or higher levels. RCM arises out of education research where the focus is individual student achievement and performance, and a key question is whether higher-level variables (e.g., student–teacher relations, schools, and school districts) affect individual-level outcomes (e.g., individual student achievement). It relies on theory and justification with aggregation procedures for individual or higher levels. WABA arise out of the organization sciences where the focus is individual, group, and organizational behaviors, and a key question is whether variables and relationships operate at the dyad and higher levels. It relies on tests, after assertions based on theory, whether individual, within-dyad, or between-dyad level effects are relevant. In both the APIM and RCM approaches, when considering dyads as a level of analysis or the entities of focus, there are two plausible views: homogeneity/wholes or independent/none (as described above). Essentially, there are or are not dyads present. In contrast, in WABA there are three plausible views: homogeneity/wholes, heterogeneity/parts, or independent/none (as described above). In this case, after identifying that dyads are present, then one must decide/ask whether the dyadic partners are similar vs positioned relative to one another. All three dyadic
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248 Handbook of methods in leadership research approaches are equipped to handle independent (unique) and dependent (non-unique) dyads; the former being the original focus of the APIM and RCM via HLM, and the latter being the original focus of WABA and more recently RCM via HCM. In terms of different types of variables, the APIM and RCM dyadic approaches are similar. For these two approaches, dependent variables operate at the individual level, independent variables operate at the individual or higher levels, and mediator and moderator variables operate at the dyad or higher levels. In WABA, however, dependent variables operate at the dyad level, independent variables operate at the dyad or higher levels, and mediator and moderator variables operate at the dyad or higher levels. For data analysis, APIM relies on variance partitioning and statistical significance tests using regression and structural equation modeling approaches. In APIM, the basic SRM equation (see Kenny et al., 2006; Kenny & Livi, 2009) that applies for actor i with partner j in group k is:
Xijk 5 mk + aik +bjk +gijk (10.1)
where Xijk is the score for person i rating person j, mk is the group mean, aik is person i’s actor effect, bjk is person j’s partner effect, and gijk is the relationship effect. Variances for the random variables are: s2m, s2a, s2b, s2g. Also, at the individual level, generalized reciprocity is a person’s actor effect correlated with that person’s partner effect, sab; and, at the dyad level, dyadic reciprocity is two members’ correlated relationship effects, sgg. Last, there is an overall intercept or grand mean, uk. In total, seven parameters are estimated: one mean, four variances, and two covariances. For data analysis, RCM relies on inferential statistical significance tests using maximum likelihood and other algorithms. In RCM implemented via the HLM software, there can be three-level models with individuals nested within dyads nested within groups. The slope and intercept from these Level 1 regressions are modeled as Level 2 outcomes. A typical three-level unconditional model, say with individuals (i) nested in dyads (j) nested in groups (k), in RCM (HLM) looks as follows (for details and definitions, see Gooty & Yammarino, 2011):
Level 1: Yijk 5 π0jk + eijk Level 2: π0jk 5 b00k + r0jk Level 3: b00k 5 g000 + u00k
(10.2) (10.3) (10.4)
RCM via the use of HCM (hierarchical cross-classified random modeling, or simply cross-classification) is used when lower-level units belong to
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Multi-level issues and dyads in leadership research 249 multiple higher-level units (i.e., dependent dyads). A typical Level 1 and Level 2 unconditional model with individuals in dyads cross-classified by groups is as follows (for details and definitions, see Gooty & Yammarino, 2011):
Level 1: Yijk 5 π0jk + eijk Level 2: π0jk 5 q0 + b00j+ c00k
(10.5) (10.6)
Note that Equations 10.2 (independent dyads) and 10.5 (dependent dyads) are identical. The key difference is in the higher-level models. Equations 10.3 and 10.4 build in the dyad and group levels one step at a time as they align with the assumption that lower-level units are uniquely nested in higher-level units. From a statistical standpoint, the nested model (modeled via Equations 10.2–10.4) rests on the assumption that observations within each equation are independent. Equation 10.6 (HCM), however, builds in the column factor term {c00k} modeled simultaneously with the dyad factor term {b00k}, thus accounting for the dependency between dyads and the group simultaneously. For data analysis, WABA relies on tests of both statistical and practical significance (the latter tests are not dependent on degrees of freedom and are based in coordinate-free geometry) using ANOVA and correlation/ regression approaches. In WABA, the total correlation between two variables x and y is partitioned into within and between components, also known as the basic WABA equation (see Dansereau et al., 1984; Yammarino, 1998; Yammarino & Markham, 1992): Total Correlation 5 Between Component + Within Component rxy 5hbx hby rbxy + hwx hwy rwxy(10.7) where hbx and hby are the between etas for variables x and y, hwx and hwy are the corresponding within etas, and rbxy and rwxy are the corresponding between- and within-cell correlations. rxy is the traditional raw score (total) correlation between variables x and y. When individual and dyad levels (i 5 individuals [both sources], d 5 dyads [dependent or independent]) are considered, the equation is: rxyi 5 hbxd hbyd rbxyd + hwxd hwyd rwxyd(10.8) And when dyad and group levels (d 5 dyads [dependent], j 5 groups) are considered, the equation is: rxyd 5 hbxj hbyj rbxyj + hwxj hwyj rwxyj(10.9)
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250 Handbook of methods in leadership research
RECOMMENDATIONS FOR LEADERSHIP RESEARCH As noted above, multi-level issues and multiple levels of analysis are critical in leadership research because theory and theory-building considerations without levels of analysis are incomplete; and likewise, data and theory-testing considerations without levels of analysis are incomprehensible. As most, actually all, leadership phenomena are multi-level in nature we offer some thoughts and recommendations to summarize the importance of these issues (also see Dionne et al., 2014; Gooty & Yammarino, 2011; Yammarino et al., 2005). In particular, we hope to impress upon readers that levels of analysis are not simply an analytical issue. Levels of analysis considerations begin with theory. The following are our recommendations for theory in multi-level leadership research: 1. Define the level of analysis of the unit(s) of interest; that is, the entity (entities) to which theoretical generalizations apply. For example, some constructs in leadership research exist at the individual level (such as follower or leader perceptions of the LMX relationship). Others, such as when LMX is defined as the quality of the exchange relationship, should reside at the dyadic level. Gooty and Yammarino (2013) discuss emergence processes (e.g., shared realities) that allow for dyadic LMX (based on consensus among dyadic partners) and dyadic dispersion LMX (the degree to which dyadic partners differ) to exist at the dyadic level. Similarly, the degree to which followers differ in their perceptions of LMX with the leader in a workgroup is LMX differentiation that draws upon key compilational processes. The key point here is that constructs and the associations among them will not always emerge at higher levels. And when they do, they might change functional form. For example, dyadic LMX behaves much like individual LMX with regard to follower performance, whereas dyadic dispersion LMX attenuates the individual-level LMX – performance association (e.g., Gooty & Yammarino, 2011, 2016). 2. Define the level of analysis of the associated concepts, constructs, variables, and relationships. 3. Keeping in mind that not all constructs and relationships among them emerge at higher levels, emergence processes must be articulated that provide a theoretical justification for how and why constructs and their associations emerge at higher levels. 4. Specify the boundary conditions, including and based upon levels of analysis, for everything articulated in 1, 2, and 3 here.
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Multi-level issues and dyads in leadership research 251 The theoretical steps delineated above lead to the following recommendations regarding measurement in multi-level leadership research: 1. Construct measures at the same level of analysis depicted in the theory, models, and hypotheses. In some instances, this might be made possible because the construct only exists at the higher level (e.g., group size) and, in other cases, it might be derived from lower-level ratings as noted below. 2. When the measurement of constructs at their appropriate level is not directly possible or feasible, then lower-level ratings are combined in compositional or compilational processes to construct higher-level measures (see Chan, 1998). Compositional models assume consensus in lower-level ratings and require appropriate justification (consensus, agreement, aggregation) indices such as the rwg, ICC1, ICC2, and so on (for a review, see LeBreton & Senter, 2008). Compilational models rely on variability and do not require such consensus justification but require reliable between-unit differences to be displayed. 3. Further, validate a measure, even an established measure, if it has been modified or adapted to account for various or different levels of analysis than originally intended (e.g., for a change in referent or entities). Next, our recommendations for data analysis in multi-level leadership research are the following: 1. Permit theory (variables, relationships, and levels of analysis) to determine the multi-level technique to be used. 2. Employ appropriate multi-level techniques if the entities of interest are at a level of analysis above the individual level. Finally, below are our recommendations for drawing inferences in multi-level leadership research: 1. Include levels of analysis in both theory (i.e., as the entities) and data (i.e., as the samples and subjects). 2. State which relationships hold across different levels of analysis in terms of multi-level, cross-level, emergent, and level-specific models. In terms of dyads and the inherent dependencies both within and between dyads, a particular focus here, our recommendations are the following: 1. Conceptually, explicate why and how dyads are expected to form and operate in leadership research. This is important because leadership
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252 Handbook of methods in leadership research relationships demonstrate some form of dependency, and elucidating the nature of this dependency and how it could impact consequences is critical. 2. Methodologically, at both the research design (measurement) and the data analysis stages of leadership research, modeling dyads as a level of analysis requires a consideration of dependencies (or lack thereof). If researchers are interested in only the individual level, a rare but possible situation in leadership research, then single- or one-sided reports (measurements) from only one dyadic partner’s perspective might be appropriate. If the dyadic relationship per se is of interest, however, the more typical situation in leadership research, then matched (or reciprocal) reports (measurements) from both dyadic partners are appropriate. In these cases, the three dyadic methodological and analytic approaches reviewed here – APIM, RCM, and WABA – can be quite useful for leadership researchers. In conclusion, we have presented a variety of multi-level issues that are important in leadership research. From both theoretical and methodological perspectives, we stressed the multi-level nature of leadership research and the need to address such issues in the research process. We also considered, in some detail, dyads and dyadic dependencies relevant for leadership research. Dependencies within and between dyads give rise to cases of independent and dependent dyads, and these are treated somewhat differently in the three dyadic methodological and analytic approaches presented here. Our hope is simply that by raising awareness of these issues leadership researchers will understand better the nature of multi-level research and implement it appropriately in their studies to further faster the state of leadership science.
REFERENCES Bass, B.M. (2008). The Bass handbook of leadership. New York: Free Press. Behling, O. (1978). Some problems in the philosophy of science of organizations. Academy of Management Review, 3, 193–201. doi: 10.5465/AMR.1978.4294841 Bliese, P.D., & Hanges, P.J. (2004). Being both too liberal and too conservative: The perils of treating grouped data as though they were independent. Organizational Research Methods, 7(4), 400–417. doi: 10.1177/1094428104268542 Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical linear models. Newbury Park, CA: Sage. Campbell, L.J., & Kashy, D.A. (2002). Estimating actor, partner, and interaction effects for dyadic data using PROC MIXED and HLM5: A user-friendly guide. Personal Relationships, 9(3), 327–342. doi: 10.1111/1475-6811.00023
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Multi-level issues and dyads in leadership research 253 Chan, D. (1998). Functional relations among constructs in the same content domain at different levels of analysis: A typology of composition models. Journal of Applied Psychology, 83(2), 234–246. doi: 10.1037/0021-9010.83.2.234 Dansereau, F., & Yammarino, F.J. (Eds.) (1998a). Leadership: The multiple-level approaches (Part A: Classical and new wave) (Vol. 24 of Monographs in organizational behavior and industrial relations). Stamford, CT: JAI Press. Dansereau, F., & Yammarino, F.J. (Eds.) (1998b). Leadership: The multiple-level approaches (Part B: Contemporary and alternative) (Vol. 24 of Monographs in organizational behavior and industrial relations). Stamford, CT: JAI Press. Dansereau, F., & Yammarino, F.J. (2000). Within and between analysis: The variant paradigm as an underlying approach to theory building and testing. In K.J. Klein & S.W.J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 425–466) (SIOP Frontiers Series). San Francisco, CA: Jossey-Bass. Dansereau, F., & Yammarino, F.J. (2006). Is more discussion about levels of analysis really necessary? When is such discussion sufficient? The Leadership Quarterly, 17(5), 537–552. doi: 10.1016/j.leaqua.2006.07.002 Dansereau, F., Alutto, J.A., & Yammarino, F.J. (1984). Theory testing in organizational behavior: The variant approach. Englewood Cliffs, NJ: Prentice-Hall. Dansereau, F., Cho, J., & Yammarino, F.J. (2006). Avoiding the “fallacy of the wrong level”: A within and between analysis (WABA) approach. Group and Organization Management, 31(5), 536–577. doi: 10.1177/1059601106291131 Dansereau, F., Graen, G., & Haga, W.J. (1975). A vertical dyad linkage approach to leadership within formal organizations: A longitudinal investigation of the rolemaking process. Organizational Behavior and Human Performance, 13(1), 46–78. doi: 10.1016/0030-5073(75)90005-7 Dansereau, F., Yammarino, F.J., & Kohles, J. (1999). Multiple levels of analysis from a longitudinal perspective: Some implications for theory building. Academy of Management Review, 24(2), 346–357. doi: 10.5465/AMR.1999.1893940 DeChurch, L.A., Hiller, N.J., Murase, T., Doty, D., & Salas, E. (2010). Leadership across levels: Levels of leaders and their levels of impact. The Leadership Quarterly, 21(6), 1069– 1085. doi: 10.1016/j.leaqua.2010.10.009 Dionne, S.D., & Dionne, P.J. (2008). Levels-based leadership and hierarchical group decision optimization: A simulation. The Leadership Quarterly, 19(2), 212–234. doi: 10.1016/j. leaqua.2008.01.004 Dionne, S.D., Gupta, A., Sotak, K.L., Shirreffs, K., Serban, A., Hao, C.,. . .Yammarino, F.J. (2014). A 25-year perspective on levels of analysis in leadership research. The Leadership Quarterly, 25(1), 6–35. doi: 10.1016/j.leaqua.2013.11.002 Dionne, S.D., Sayama, H., Hao, C., & Bush, B.J. (2010). The role of leadership in shared mental model convergence and team performance improvement: An agentbased computational model. The Leadership Quarterly, 21(6), 1035–1049. doi: 10.1016/j. leaqua.2010.10.007 Gooty, J., & Yammarino, F.J. (2011). Dyads in organizational research: Conceptual issues and multi-level analyses. Organizational Research Methods, 14(3), 456–483. doi: 10.1177/1094428109358271 Gooty, J., & Yammarino, F.J. (2016). The leader–member exchange relationship: A multi-source, cross-level investigation. Journal of Management, 42(4), 915–935. doi: 10.1177/0149206313503009 Gooty, J., Serban, A., Thomas, J.S., Gavin, M.B., & Yammarino, F.J. (2012). Use and misuse of levels of analysis in leadership research: An illustrative review of leader–member exchange. The Leadership Quarterly, 23(6), 1080–1103. doi: 10.1016/j. leaqua.2012.10.002 Hackman, J.R. (2003). Learning more by crossing levels: Evidence from airplanes, hospitals, and other orchestras. Journal of Organizational Behavior, 24(8), 905–922. doi: 10.1002/ job.226
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254 Handbook of methods in leadership research House, R.J., Hanges, P.J., Javidan, M., Dorfman, P.W., & Gupta, V. (Eds.) (2004). Culture, leadership, and organizations: The GLOBE study of 62 societies. Thousand Oaks, CA: Sage Publications. Katz, D., & Kahn, R.L. (1978). The social psychology of organizations. New York: Wiley. Kenny, D.A., & Judd, C.M. (1986). Consequences of violating the independence assumption in analysis of variance. Psychological Bulletin, 99(3), 422–431. doi: 10.1037/0033-2909.99.3.422 Kenny, D.A., & Livi, S. (2009). A componential analysis of leadership: Using the social relations model. Research in Multi-Level Issues, 8 (Multi-Level Issues in Organizational Behavior and Leadership), 147–191. Kenny, D.A., Kashy, D.A., & Cook, W.L. (2006). Dyadic data analysis. New York: Guilford. Klein, K.J., & Kozlowski, S.W.J. (2000). From micro to meso: Critical steps for conceptualizing and conducting multi-level research. Organizational Research Methods, 3(3), 211–236. doi: 10.1177/109442810033001 Klein, K.J., Dansereau, F., & Hall, R.J. (1994). Levels issues in theory development, data collection, and analysis. Academy of Management Review, 19(2), 195–229. doi: 10.5465/ AMR.1994.9410210745 Krasikova, D.V., & LeBreton, J. (2012). Just the two of us: Misalignment of theory and methods in examining dyadic phenomena. Journal of Applied Psychology, 97(4), 739–757. doi: 10.1037/a0027962 LeBreton, J.M., & Senter, J.L. (2008). Answers to 20 questions about inter-rater reliability and inter-rater agreement. Organizational Research Methods, 11(4), 815–852. doi: 10.1177/1094428106296642 Markham, S.E. (2010). Leadership, levels of analysis, and déjà vu: Modest proposals for taxonomy and cladistics coupled with replications and visualization. The Leadership Quarterly, 21(6), 1121–1143. doi: 10.1016/j.leaqua.2010.10.011 Markham, S.E. (2012). The evolution of organizations and leadership from the ancient world to modernity: A multilevel approach to organizational science and leadership. The Leadership Quarterly, 23(6), 1134–1151. doi: 10.1016/j.leaqua.2012.10.011 Miller, J.G. (1978). Living systems. New York: McGraw Hill. Morgeson, F.P., & Hofmann, D.A. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24(2), 249–265. doi: 10.5465/AMR.1999.1893935 Pedhazur, E.J. (1982). Multiple regression in behavioral research. New York: Holt, Rinehart, & Winston. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage. Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., Congdon, R., & Du Toit, M. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International, Inc. Robinson, W.S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357. Retrieved from http://www.jstor.org/stable/2087176 Rousseau, D.M. (1985). Issues of level in organizational research: Multi-level and cross-level perspectives. Research in Organizational Behavior, 7, 1–37. Retrieved from digitalcollections.library.cmu.edu/awweb/awarchive?type5file&item549182 Sayama, H. (2015). Introduction to modeling and analysis of complex systems. Albany, NY: Open SUNY. Schriesheim, C.A. (1995). Multivariate and moderated within- and between-entity analysis (WABA) using hierarchical multiple linear regression. The Leadership Quarterly, 6(1), 1–18. doi: 10.1016/1048-9843(95)90002-0 Schriesheim, C.A., Castro, S.L., Zhou, X.T., & Yammarino, F.J. (2001). The folly of theorizing “A” but testing “B”: A selective level-of-analysis review of the field and a detailed leader– member exchange illustration. The Leadership Quarterly, 12(4), 515–551. doi: 10.1016/ S1048-9843(01)00095-9 Yammarino, F.J. (1998). Multivariate aspects of the varient/WABA approach: A discus-
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Multi-level issues and dyads in leadership research 255 sion and leadership illustration. The Leadership Quarterly, 9(2), 203–227. doi: 10.1016/ S1048-9843(98)90005-4 Yammarino, F.J., & Dansereau, F. (Eds.) (2009). A new kind of OB (organizational behavior). Research in Multi-Level Issues, 8, 3–60. Yammarino, F.J., & Dansereau, F. (2011). Multi-level issues in evolutionary theory, organization science, and leadership. The Leadership Quarterly, 22(6), 1042–1057. doi: 10.1016/j. leaqua.2011.09.002 Yammarino, F.J., & Markham, S.E. (1992). On the application of within and between analysis: Are absence and affect really group-based phenomena? Journal of Applied Psychology, 77(2), 168–176 (correction, p. 426). doi: 10.1037/0021-9010.77.2.168 Yammarino, F.J., Dionne, S.D., Chun, J.U., & Dansereau, F. (2005). Leadership and levels of analysis: A state-of-the-science review. The Leadership Quarterly, 16(6), 879–919. doi: 10.1016/j.leaqua.2005.09.002
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11. A social network approach to examining leadership Markku Jokisaari
For many leadership scholars, leadership is essentially a social phenomenon. For example, ‘most definitions of leadership stress social or interpersonal influence processes as key elements’ (Zaccaro & Klimoski, 2001, p. 10), and central theories of leadership focus on social exchange between leaders and their followers (Dinh et al., 2014). However, although leadership as per its definition is a relational process that includes relations with other people, leadership research has primarily focused on leaders’ attributes and behaviour – particularly others’ perceptions of their attributes and behaviour – rather than on the network of relations in which leadership is embedded. Further, research focusing on relationships has typically examined dyadic relationship characteristics, such as the quality of the working relationship between a leader and her or his followers, whereas the wider social environment around leader–follower dyads has gained much less research attention. The social network approach provides both the theory and methodology for a detailed examination of the characteristics of the social environment of leadership (for reviews, see Balkundi & Kilduff, 2005; Carter, DeChurch, Braun, & Contractor, 2015; Sparrowe, 2014; Sparrowe & Liden, 1997). Theories related to the social network approach provide concepts to help define and perceive this social context, and they further argue that the social context provides both opportunities and constraints for individual and group behaviour and related success (e.g., Kilduff et al., 2006; Tichy, Tushman, & Fombrun, 1979; Wellman, 1988). Specifically, the social network approach argues that social interaction and exchange within and between organizations includes not only formal channels but also informal social relations and interactions that affect how actors access resources and accomplish their organizational goals (e.g., Granovetter, 1985). In other words: [. . .]both formal and informal organizational elements generate a web of interactions connecting actors. These interactions, whether formally designed or informally emergent, are conduits through which organizational actors coordinate efforts, share goals, exchange information, and access resources that affect behaviours and performance outcomes. (McEvily et al., 2014, pp. 302–303)
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A social network approach to examining leadership 257 For example, opinion leaders do not necessarily have a formal leader position; rather, they are often people who are well connected between social groups through their social networks (e.g., Burt, 2005). Scholars adopting the social network approach further argue that by focusing on informal social contexts – that is, social networks – researchers can examine ‘how work really gets done in organizations’ (Cross & Parker, 2004). Similarly, the social network approach provides a tool to examine central research questions in leadership, since leadership is embedded in organizational environments that are characterized by both formal and informal relations and interactions (e.g., Balkundi & Kilduff, 2005; Fernandez, 1991). First, many leadership scholars argue that leadership is centrally related to social influence among people and the coordination of people’s efforts toward a common goal (e.g., Kaiser, Hogan, & Kraiger, 2008). In this regard, the social network approach argues that to understand social influence and the coordination of common efforts, one has to examine how people are connected to each other and to focus on the wider social environment rather than formal dyadic relations between a leader and her followers (e.g., Fernandez, 1991). Second, a central part of leadership concerns access to resources in order to enhance the performance of work groups (e.g., Kaiser et al., 2008). In this respect, the social network approach suggests that leaders’ ability to acquire resources depends on not only their formal position but also their informal relations within and outside the organization (e.g., Fernandez, 1991). For example, earlier research on social networks and leadership has shown that leaders’ position in internal and external social networks of their work groups is related to how well their work groups perform (Mehra, Dixon, Brass, & Robertson, 2006). Third, a central research question among leadership scholars is related to leadership development – that is to say, how leadership evolves over time between a leader and his or her followers (e.g., Day, Fleenore, Atwater, Sturm, & McKee, 2014). Again, the social network approach suggests that in order to understand leadership development, a researcher should focus on not only the connections between leaders and followers but also their relations outside their focal work group (Balkundi & Kilduff, 2005). For example, earlier research has shown that leaders’ position in social networks of the workplace is related to others’ perceptions of them as leaders (e.g., Venkataramani, Green, & Schleicher, 2010). In this regard, the social network approach thus provides theories and concepts to examine both formal and informal aspects of leadership. The methodology of the social network approach provides tools to measure and analyse social environments in detail, and in the social network approach specific measures have been introduced to examine the
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258 Handbook of methods in leadership research attributes of social networks and individuals’ positions in these networks. These network measures can relate to the individual, dyadic, triadic, group, and whole network levels (Wasserman & Faust, 1994), and these characteristics of social networks have important consequences for leadership. For example, earlier research has shown that leaders’ position in social networks (i.e., the individual level) is related to others’ perception of leaders’ helpfulness (Galunic, Ertuk, & Gargiulo, 2012), and the network structure of a work group (i.e., the group level) is related work group performance (Sparrowe, Liden, Wayne, & Kraimer, 2001). Furthermore, a central methodological question for a researcher engaged in social network research is whether to use a whole or personal network design. A whole network design refers to a bounded social environment, such as a department in an organization, in which all or most of the people report their relations or connections to each other (e.g., Wasserman & Faust, 1994), whereas a personal network refers to a focal individual’s direct ties and her or his perceptions of the relations among these ties in the network. These networks are often called ‘ego networks’, and the focal actor who reports his or her ties is called the ‘ego’ in the network literature. Thus, social network methods offer possibilities to examine how leadership manifests in different levels of a social environment depending on the research questions. In this chapter, I will first briefly present the central characteristics of the social network approach and discuss how they might relate to leadership research. Thereafter, I consider the methodology of the social network approach, including the study design, sampling and data collection methods, and methods to measure social networks. I further discuss central measures of networks for use in data analysis and statistical inference for network data. I also provide examples from the leadership research that has capitalized on social network methodology and theory. Finally, I present a research example in detail that includes the collection of network data and application network analysis to understand the characteristics of social networks.
SOCIAL NETWORK APPROACH AND LEADERSHIP RESEARCH The social network approach focuses on the relations between actors, such as leaders’ relations in the organization and how these relations, or lack of relations, have important consequences for their actions (e.g., Wellman, 1988). Actors may be individual persons, social groups, organizations, or other collectives. A relation can include different types of
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A social network approach to examining leadership 259 interaction or exchange between actors, such as ‘gives support to’, ‘asks advice from’ or ‘discusses with’. A relationship can be directional when an actor gives or sends something to another actor, or non-directional when actors mutually contribute to the interaction or exchange. Furthermore, the general pattern of relations between actors constitutes the structure of the network, and each actor has his or her personal relations and position within and between networks. Overall, actors and their actions are viewed as interdependent rather than independent (e.g., Wellman, 1988). The social network approach further argues that the network structure of the individuals and groups has important consequences for their goaldirected action. First, the network structure affects how actors coordinate their action toward common goals (e.g., Coleman, 1988; Oh, Labianca, & Chung, 2006). Moreover, the characteristics of network ties define how a group’s members are able to form ‘bonding ties’ (e.g., Adler & Kwon, 2002) and related trust with each other and to coordinate their action toward their goals. Second, individuals’ positions and relations affect how they are able to acquire resources through their network ties to accomplish their goals (e.g., Burt, 2005). That is, social networks are important channels for resources such as information and credentials (e.g., Lin, 2001). Third, an actor’s position in the network is related to his or her reputation in the group and how others perceive him or her (e.g., Kilduff & Krackhardt, 1994; Mehra, Dixon et al., 2006). For example, a central position in the network is often related to prestige within the group (Mehra, Dixon et al., 2006). These central consequences of network structure and position – that is, coordination and social influence, access to resources, and reputation – also play important roles in the leadership literature, as noted above. First, many leadership scholars argue that leadership involves coordinating and influencing people to attain common goals (e.g., Kaiser et al., 2008). Influencing and motivating others is in many ways a social process, and earlier research on social networks has shown that a group’s network structure can foster the leader’s efforts to motivate and coordinate group members’ behaviour to achieve group goals. For example, research shows that how group or team members are connected to each other affects team performance (Balkundi & Harrison, 2006): when more people in the team are connected to each other – that is, a high-density network – the team achieves higher performance. One argument why this group network structure benefits group performance is that when people in the network know each other, it enhances the maintenance of group norms and trust and eases the monitoring of whether group member behaviour is in accordance with group goals (e.g., Coleman, 1988).
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260 Handbook of methods in leadership research Second, leadership involves acquiring the resources needed by the group to achieve their goals – often in a competitive environment (e.g., Kaiser et al., 2008). Social capital theory in particular argues that personal and group success depends in many ways on network-based resources – social capital (e.g., Burt, 1992, 2005; Lin, 2001) such as information, advice and credentials. In addition, as mentioned above, people’s networks can enhance group norms and trust between people, which in turn enhance goal attainment (Coleman, 1988). Most of the earlier research on social networks and leadership appear to have used social capital theory as a theoretical framework (for a review, see Carter et al., 2015). Among network scholars, two primary explanations have been used to explain why the characteristics of social networks are important channels for resources – that is, network structure and social resources (e.g., Burt, 1992). First, network scholars have argued that resources such as information are more homogeneous within the group rather than between groups (e.g., Burt, 1992; Granovetter, 1973). Thus, to obtain additional resources, one must often reach outside one’s typical social circle. How one does this depends in turn on one’s network structure and related ties. Specifically, Granovetter (1973) famously argued that weak ties, such as acquaintances, often provide a bridge between different social groups and, consequently, enable access to information and opinions that are not available in one’s own social circles. Along similar lines, Burt (1992) argued that when a person’s network includes people who do not know each other, this supports access to resources. Specifically, when a network includes people who do not know each other, there are ‘structural holes’ (i.e., missing connections between persons) in the network. Furthermore, when there are structural holes in a person’s network, that person is in a brokerage position between the people who often represent different groups. Figure 11.1 presents a network diagram to illustrate weak ties and structural holes in a network. In the network diagram, actors are typically represented by dots, and lines indicate relations between actors. For example, Heather is in the middle of the diagram, and she has structural holes in her network, because she has connections, for example, to Jan and Gretel, who themselves are not connected. Furthermore, in the network literature the argument has been that these types of relations that connect different groups are often weak ties, called ‘bridging weak ties’ (Granovetter, 1973, p. 1371). Second, social resources theory (Lin, 1982, 2001) emphasizes that the resources available through social networks depend on the social contact’s position or rank. That is, a social contact’s position in the societal or organizational hierarchy is important, because it enables access to resources, such as credentials and social influence (ibid.). In addition, social resources theory postulates that actors’ own status and weak rather
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A social network approach to examining leadership 261 Ann Les Bernard
Heather
Gretel
Jan
Paul
Ed Chrissie
Teresa
Gil
Peter
Figure 11.1 An example of network diagram than strong ties in the network are related to high-status contacts (Lin, 2001). Earlier research on leadership and social networks also supports the argument that the characteristics of social networks are related to leadership and group outcomes, presumably because they enable access to resources. For example, earlier research on leaders’ positions in social networks found that the position of work group leaders in external and internal networks is related to the effectiveness of their work group (Mehra, Dixon et al., 2006). Another study showed that a focal group’s ties to other group leaders are related to group effectiveness (Oh, Chung, & Labianca, 2004). Earlier research has also established that a relationship exists between network position and power (Brass & Burkhardt, 1993). Finally, a central question in leadership research concerns how leadership evolves over time; that is, how an individual develops as a leader and how leadership evolves between leaders and followers within a social environment (e.g., Day et al., 2014). This leadership development process is inherently a social phenomenon, because the focus is on how the social exchange between leaders and followers evolves over time and how others perceive the leader’s characteristics and behaviour. The social network approach can be used to examine, for example, how leadership evolves as leaders’ connections with others change in social networks over time. Earlier research on leadership and networks has shown that
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262 Handbook of methods in leadership research leaders’ position in a social network is related to their reputation as a leader (Mehra, Dixon et al., 2006), others’ perception of their charisma (Balkundi, Kilduff, & Harrison et al., 2011), their status in the organization (Venkataramani et al., 2010), and their promotion to a leader role (Parker & Welch, 2013). For example, Balkundi and colleagues (2011) found that a leader’s central position in a social network is related to others’ perception of his or her charisma as a leader. Earlier research also indicates that perceptions of leader charisma tend to be contagious through network ties (Pastor et al., 2002); that is, employees’ perceptions of leader charisma are affected by the perceptions of their network ties. Thus, the characteristics of leaders’ social networks play an important role in how others perceive them as a leader and in their promotion to a leadership role. The social network approach has also focused on how one perceives relations between people and the antecedents and consequences of the accuracy of these perceptions – that is, cognitive networks (e.g., Krackhardt, 1990; Kilduff & Krackhardt, 1994; Krackhardt & Kilduff, 1999). For example, Kilduff and Krackhardt (1994) argued that ‘the performance reputations of people with prominent friends will tend to benefit from the public perception that they are linked to those friends’ (p. 89). Thus, they measured people’s perceptions of friendship ties in the organization: ‘Who would this person consider to be a personal friend? Please place a check next to all the names of those people who that person would consider to be a friend of theirs’ (p. 91). They also measured actual friendship ties: they asked participants to report their personal friendship ties in the workplace. A friendship tie was indicated when both parties of the reported friendship tie agreed that they were friends. Study participants rated a focal participant’s performance reputation. Friends’ prominence was indicated both by network measures (to what extent others asked him or her for advice) and formal status. The results showed that the perceived prominence of the focal person’s friend was related to the person’s own reputation. Interestingly, the actual prominence of the person’s friend was not related to performance reputation. After this brief presentation about the relevance of the social network approach in leadership research, I next focus on network methodology: network and research design; methods for sampling and collecting network data and measuring social networks; and data analysis methods (also see the Appendix for central network concepts and terms).
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A social network approach to examining leadership 263
METHODOLOGY OF THE SOCIAL NETWORK APPROACH A central question for a researcher engaged in social network research is whether to use whole or personal network design. Whole network design refers to a bounded social environment, such as a school class or a department in an organization, in which all or most of the people report their relations or connections to each other (e.g., Wasserman & Faust, 1994). For example, a researcher asks all employees to indicate their relationships with people at their department in terms of interactions, such as ‘adviceseeking’ (‘Who are the people you ask for advice?’) and social support (‘Who are the most important people to you as a source of personal support?’). Because all or most of the people in the focal unit report their ties to each other, it is possible to define who relates to whom in the focal network. The result of whole network design is a network of relationships between all study participants in a given set (e.g., Borgatti, Everett, & Johnson, 2013). A personal network refers to a focal individual’s direct ties and her or his perceptions of the relations among these ties in the network. These networks are often called ‘ego networks’, and the focal individual who reports his or her ties is called the ‘ego’ in network literature. Each named network person or tie is an ‘alter’ (e.g., Wasserman & Faust, 1994). For example, a survey could ask study participants to name persons with whom they have discussed important matters during the last six months. These named persons (alters) would then represent the participants’ personal networks. In addition, typical personal network surveys also ask participants to report their perceptions about the extent to which alters in the network are related to each other (alter–alter ties). Thus, in personal network design, the aim is to collect data on each participant’s personal social environment, and there is typically no information about how study participants are connected to each other. Whether whole or personal network design is being used has consequences for data collection, sampling, network measures and data analysis. Because whole network design aims to represent all ties among study participants, it requires a higher response rate among participants than a study based on personal network design (see, e.g., Costenbader & Valente, 2003). However, a whole network design enables a researcher to more fully use social network analysis as a statistical method, and many important network measures assume a whole network design (e.g., Wasserman & Faust, 1994).
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264 Handbook of methods in leadership research Sampling Boundary specification A sampling issue specifically related to whole network design is called ‘boundary specification’ (e.g., Wasserman & Faust, 1994). This specification defines who the actors are that can be part of a particular network; that is, network boundaries, and what types of relations between these actors are relevant to examine (Laumann, Marsden, & Prensky, 1983). The research questions somewhat indicate how to define the study sample. However, this sampling problem is highlighted in network research, because network ties may not be limited according to formal boundaries, such as by organization, department, or workplace. For example, a researcher may be interested in examining with whom people discuss important work-related matters. Bounding network ties to include participants’ work groups in the workplace, for example, may leave many network ties outside the study, because people may discuss their work with many people outside their work group. Thus, defining whom to include in the network study is important to obtain valid information about social networks and related resources. Borgatti and colleagues (2013) suggest that if the research question does not define clear boundaries regarding who could be possible network ties, a personal network design is an option. Study participants are then free to name their network ties according to the research question and related study instructions. For example, Carroll and Theo (1996) used a personal network design in their study examining managers’ social networks. They were interested in discovering how managers’ and non-managers’ social networks differ from each other. They used data based on a General Social Survey that is a representative sample of the US population. However, most of the studies on social networks and leadership have capitalized on convenience samples (for a review, see Carter et al., 2015). In general, Laumann and colleagues (1983) suggest two basic strategies for approaching boundary specification issues. The realist strategy for boundary specification argues that social units or groups have boundaries that are often recognized by all of the members. Formal social units such as an organization, workplace, or school class are examples of units with clear boundaries for their members. Thus, a researcher sets network boundaries according to the boundaries perceived by the members of a social group or unit. A researcher using a nominalist strategy sets boundaries for a network according to her or his theoretical approach and related research questions; that is, the researcher defines the network boundaries. For example, a researcher may be interested in examining communication networks within work groups in an organization, thus
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A social network approach to examining leadership 265 she or he may limit the network boundaries to include ties within work groups. Research Design A main task of the research design is often to identify supporting evidence for the causal claims between study variables; for example, the higher the quality of working relationship between a leader and a follower is, the higher the follower’s job satisfaction (e.g., Shadish, Cook, & Campbell, 2002). In other words, through the research design, the researcher tries to minimize threats to his or her claim about the argued relationships between study variables; that is, threats to internal validity. A basic classification between research designs is between cross-sectional, longitudinal, and experimental study designs. In a cross-sectional design, study variables are measured at the same time. A study based on cross-sectional design cannot make causal claims between study variables, because causal claims require that the possible cause should precede the consequence in time (ibid.). For example, Goodwin, Bowler, and Whittington (2008) used a cross-sectional design and found that both the leader’s and the follower’s network position was related to the quality of the working relationship with the leader. In longitudinal research design, study variables are measured several times over time. A longitudinal design offers the possibility to examine associations between variables over time. For example, Balkundi and colleagues (2011) found that a leader’s central position in a social network affects how others perceive their charisma as a leader. Because they used a longitudinal design, they also argued that network position has an effect on leader charisma rather than charisma explaining a central position in the network. In addition, a longitudinal design is particularly useful in modelling change over time, such as to whether the characteristics of a social network change over time (e.g., Snijders, Van de Bunt, & Steglich, 2010). However, the problem with a longitudinal design is that it cannot exclude the possibility of confounding variables that may explain the relation between two variables. Many scholars regard an experimental research design as the ‘golden standard’ (West & Thoemmes, 2010) for research, because it can rule out possible confounding variables for a causal relationship between variables (Shadish et al., 2002). A typical experimental design first includes a baseline measurement for all of the study participants, then, study participants are randomly assigned to experimental and control groups. Participants in the experimental group receive a ‘treatment’ such as leadership training, and the control group continues without treatment. Finally, a researcher compares the experimental and control groups to determine whether
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266 Handbook of methods in leadership research there are differences of interest between the groups according to the study outcome such as leadership quality. For example, Lam and Schaubroeck (2000) conducted a quasi-experimental field study in which they examined whether service-quality leadership has an effect on unit-level service effectiveness and quality. They had two experimental groups and a control group. The first experimental group included front-line employees who received service-quality training; the second experimental group consisted of employees who were perceived to be opinion leaders by their managers and who also received service-quality leadership training. For participants in the control group, no leadership training was provided. The results showed that customers rated the group led by opinion leaders who received service-quality leadership training as the most effective in service delivery. Opinion leaders who act as service-quality leaders may have more credibility when implementing new practices among employees than those who are not perceived as opinion leaders. Network scholars further argue that opinion leaders typically have a central position between network ties, which enhances their social influence (e.g., Burt, 2005). Unfortunately, the majority of research on social network and leadership is still based on cross-sectional research. Thus, causal claims about the effects of social networks on leadership, or vice versa, need more elaboration with stronger research designs. There are an increasing number of longitudinal studies that offer better support for the role of social networks in leadership and its outcomes (e.g., Balkundi et al., 2011). However, scholars in the field have rarely capitalized on experimental research design to improve the evidence regarding the causal processes between social networks and leadership. Network Methods for Data Collection There are different methods for acquiring network data: surveys (for a review, see Marsden, 2005), registers (e.g., Galunic et al., 2012), electronic sources such as email communication (Kossinets & Watts, 2006), electronic tags (Ingram & Morris, 2007), and archives (e.g., Padget & Ansell, 1993). Surveys are the most widely used method (e.g., Marsden, 2005). Below, examples of these methods are presented. I will present these methods according to whole and personal network study designs, although the same kind of network content instructions can be used in both designs. However, a whole network design typically uses name rosters of all study participants, since the researcher knows the network boundaries. Instead, in a personal network design, the researcher does not know the possible network ties beforehand.
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A social network approach to examining leadership 267 Whole network design In a whole network design, the sociometric method is the typical way to collect data. In this method, a survey often provides a name roster of all study participants, on which study participants indicate with whom they interact or communicate. Specifically, a study participant sees a roster including the names of all people in the given unit, such as an alphabetical list of all employees in the department, and she indicates, for example, with whom she ‘discusses important matters’ or whom ‘she asks for advice’. The whole network instrument can also measure ties outside the organization, such as interorganizational collaboration among organizations. For example, Figure 11.2 presents an example of a roster from a study in which the leaders indicated interorganizational collaboration and communication ties of their organization (Jokisaari & Vuori, 2010). This survey asked organizational leaders to note both their collaboration and communication with other organizations in the field. Other examples of instructions used in whole network instruments are provided in Table 11.1. There are also free-recall instruments in whole network design in which participants are asked to report their network ties with open answers – that is, no name roster is available. However, using a name roster makes reporting easier for respondents by suggesting possible network ties and reducing measurement error due to forgotten relations (Marsden, 2011). That said, respondent fatigue can be a problem if the name roster includes a high number of names. In that case, it could be wise to divide the roster, for example according to department or hierarchy. For example, in a study with 260 potential names for the name roster, the authors divided the name roster according to departments and work groups to avoid respondent fatigue (see Sparrowe & Liden, 2005). Personal network design A personal or ego network measurement survey typically involves three parts: (1) a name generator; (2) name and relationship interpreters; (3) questions related to ties between alters (network structure). First, the name generator refers to instructions in a survey by which the researcher encourages study participants to report their social relations, such as ‘With whom do you discuss important matters about work?’ There are many options for the content of the name generator; that is, what types of networks a researcher wants to examine, depending on the research questions. In addition, a survey can include one or multiple name generators (e.g., Burt, 1997). Figure 11.3 presents an example of an ego network survey, and Table 11.2 provides examples of name generators in leadership and management research. For example, Rodan and Galunic (2004) examined the role of managers’ social networks in their job performance.
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268 Handbook of methods in leadership research Instructions Below you will find a name roster of organizations in alphabetical order. Please indicate the organizations with which your organization has collaboration and/or regular communication related to employment services. In other words, please answer questions below by marking organizations that fit the questions, otherwise leave your answer blank. (A) Please indicate the employment offices with which your organization has or has had collaboration in employment services. Collaboration may include shared projects, services or development activities related to employment services. (B) Please indicate the employment offices with which your office has or has had regular communication regarding employment services. The communication may include, for example, face-to-face discussions, meetings, or e-mails.
A
B
Collaboration
Communication
1 = Formal, e.g., based
1 = Daily
on contract
2 = Weekly
2 = Informal
3 = Monthly
3 = Multiplex
4 = Seldom
Organization: Alajärvi
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Alavus
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Anjalankoski
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Enontekiö
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Figure 11.2 An example of whole network survey They used a personal network design and four name generators to obtain information about managers’ advice ties, innovative ties, buy-in ties and confidant ties (see Table 11.2). The position generator method asks directly whether a respondent
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A social network approach to examining leadership 269 Table 11.1 Examples of instructions in whole-network designs Types of relations
Instructions [in the column following each person’s name, participants were asked the following:]
Advice
‘Check the names of individuals to whom you go for help or advice about work-related matters’ (Sparrowe & Liden, 2005, p. 517) ‘Check the names of individuals you consider to be your personal friends’ (Gibbons, 2004, p. 247) ‘Check the names of individuals to whom you go to discuss confidential issues or problems at work’ (Sparrowe & Liden, 2005, p. 517) ‘Which person is influential: he/she has clout in this company?’ (Sparrowe & Liden, 2005, p. 517)
Friendship Trust Influence
knows people in certain occupations (e.g., Lin et al., 2001). For example, ‘Here is a list of jobs. Would you please tell me if you happen to know someone (on a first-name basis) having each job?’ (Lin, 1999, p. 477). This method has often been used to examine the role of social capital in career success (for reviews, see Lin, 1999, 2001). Second, a personal network measurement includes name and relationship interpreters that acquire information about the characteristics of each named network person (alter) and the nature of the relationship between the respondent (ego) and the named network person. A typical question related to the characteristics of the alter concerns his or her occupational status or position in the organizational hierarchy. In social capital theory, the status of the alter indicates resources in the network: the higher the status of the network connections, the more resources such as influence and credentials are potentially available to the focal person. Relationshipquality questions typically relate to the strength of the ties between the ego and the alters (Marsden & Campbell, 1984). A typical way to assess tie strength is to ask about ‘closeness’ of the relationship between the ego and the alter such as ‘How close do you feel to this person?’ In addition, the frequency of contact between persons has also been an indicator of tie strength, as well as the duration of the relationship, and some researchers use a combination of these measures. The interpretation is that the closer the relationship, and/or the higher the meeting frequency, the stronger the tie strength. In contrast, researchers typically operationalize the number of ‘weak ties’ by counting those ties that are rated to have low closeness and/or meeting frequency. Furthermore, researchers often ask about the content of the relationship between the ego and the alter, such as ‘What
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1 2 3
PERSON 2 1 2 3
PERSON 3 1 2 3
PERSON 4
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Figure 11.3 An example of personal network survey
4 = Other, what?
3 = Leader
2 = Supervisor
1 = Employee
4. His or her rank
3. How close you are? (1 = Distant, 5 = Very close)
3 = Other, what?
2 = supervisor
1 = co-worker
2. What is your relationship with this person (e.g., co-worker, supervisor)?
1. First name of the person?
People often discuss their important matters with others. If you think of the people in your workplace and look back over the last few months, who are the people with whom you have most often discussed important matters related to your work or workplace?
A social network approach to examining leadership 271 Table 11.2 E xamples of name-generating questions in personal network designs Name generator
Questions
Discuss important matters
‘People often discuss their important matters with others. If you think of the people in your workplace and look back over the last few months, who are the people you have most often discussed important matters related to your work or workplace?’ (Jokisaari, 2013, p. 100) ‘Getting your job done on a daily basis as a manager often requires advice and information from others. Who are the key people you regularly turn to for information and work-related advice to enhance your ability to do your daily job?’ (Rodan & Galunic 2004, p. 549) ‘Over the last six months, are there any work-related contacts from whom you regularly sought information and advice to enhance your effectiveness on the job?’ (Podolny & Baron, 1997, p. 691) ‘Most people rely on a few select others to discuss sensitive matters of personal importance, i.e., “confidants” on whom they rely for personal support. Who are the key people in your work environment that you regard as your most important people as source of personal support?’ (Rodan & Galunic, 2004, p. 549) ‘New ideas often require support from others without which you cannot proceed. Who are the key people that provide essential support to new initiatives?’ (Rodan & Galunic, 2004, p. 549) ‘List anyone that you feel is a significant part of your professional network. One way to identify these people is to go through your address book, and ask “Is this person significant in my professional network?”’ (Chua et. al., 2008, p. 442)
Advice
Support
Buy-in
‘Professional ties’
is your relation to this person (e.g., co-worker, friend)?’ Answers to this question can also be used to categorize strong (e.g., friend) and weak ties (acquaintance). Table 11.3 provides examples of questions related to the characteristics of the relationship and the alter. Finally, a personal network survey includes questions about relations between alters: participants are asked to evaluate the extent to which named network persons interact with each other or know each other. These questions about alter–alter ties enable a researcher to define the characteristics of the network structure. For example, ‘network density’ refers
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272 Handbook of methods in leadership research Table 11.3 Examples of name and relationship interpreters Name and relationship interpreters Name interpreter Relationship type Status Demographics Relationship interpreters Tie strength
‘What is your relationship with this person (e.g., co-worker, supervisor)?’ ‘Is this person’s rank (1) higher than yours; (2) equal to yours, (3) lower than yours?’ ‘Person’s occupation:’ ‘Gender of the person:’ ‘Ethnicity of the person:’ ‘How close are you to this person?’ ‘How often do you meet?’
to how well people in a focal individual’s network are connected to each other. The more they are connected to each other, the higher the network density. Figure 11.4 gives an example of questions related to alter–alter ties. It is also important to note that reporting personal networks can be time consuming for the respondent depending on how many name generators and related alter, relationship, and alter–alter ties questions a researcher uses. For example, if a researcher asks respondents to name five alters, this would mean that he or she has to evaluate ten alter–alter ties (i.e., N(N – 1)/2; N 5 number of ties); if a respondent names ten alters, there would be 45 alter–alter ties to evaluate, and with 20 alters, the number of alter–alter ties would be 190. Thus, the researcher must be careful when planning the personal network design, because the time and cognitive demands made on respondents can increase rapidly with an increasing number of network ties. Furthermore, there have been discussions about how well people remember relevant ties when asked to recall their network ties (e.g., Adams & Moody, 2007; Kogovsek & Ferligoj, 2005). In general, people tend to remember rather well their relations with those with whom they interact regularly or who represent their important network ties (for reviews, see Brewer, 2000; Marsden, 2011).
DATA MANAGEMENT AND ANALYSIS Matrices and Network Diagrams Social network analysis is an analytical technique that is used to represent relations among actors and to explore the characteristics of networks and
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1
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Person 3: Jan
Figure 11.4 An example of question how to ask alter–alter ties
Person 4: Ann
Person 3: Jan
Person 2: Gretel
Person 1: Heather
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Please indicate whether or not the persons in your network discuss important work-related matters with each other (in each pair; e.g., ‘Do Person 1 and Person 2 discuss important matters with each other?’) (1 = don’t discuss or seldom; 2 = every now and then; 3 = very often).
274 Handbook of methods in leadership research actors’ positions in the networks (e.g., Knoke & Yang, 2008; Scott, 1991; Wasserman & Faust, 1994). There are also social network analysis software programs for social network analysis, such as Multinet, NetMiner, Pajek, SIENA, and UCINET (for a review, see Huisman & Van Duijn, 2005). Network scholars represent whole network data using specific data matrices. In other words, in network analysis data are represented differently from traditional research data, in which rows represent observations such as study participants and columns represent variables such as tenure and job satisfaction for each participant. In network analysis, both rows and columns represent nodes in a network, such as people, and the cells of the matrix contain information about the ties or relations between nodes. For example, if ties are dichotomous – that is, the tie between two nodes exists or does not exist – the cells contain ‘1’ or ‘0’, respectively, depending on the presence or absence of the tie. Furthermore, a data matrix typically contains the same number of rows and columns, and the order of actors in rows and columns is identical. Thus, the data matrix, or sociomatrix, contains information about all the possible ties between nodes. It is common for a row to represent a focal actor and the column cells represent those with whom she has ties or relations. In other words, a matrix can be read as ‘who to whom’ (Monge & Contractor, 2003, p. 36) information about networks. In the case of non-directional ties, such as who discusses with whom, the matrix is symmetrical; that is, the value representing a tie from node i to node j is the same as the value representing a tie from node j to node i. As an example, in Figure 11.5, there is a symmetric data matrix between 12 nodes; thus, the value of a tie from node i to node j is the same as the value of a tie from node j to node i. In other words, the values of the cells above and below the matrix diagonal are the same. In addition, the values in the diagonal are omitted from the network analysis, because the diagonal represents a tie from a node to itself. The network diagram of these data is shown in Figure 11.1. It is quite common for a researcher to perform data transformation with network data. That is, network software programs such as UCINET (Borgatti, Everett, & Freeman, 2002) provide different procedures for working with data matrices. The common transformation procedures are symmetrizing, dichotomizing and combining data matrices (e.g., Borgatti et al., 2013). By symmetrizing, a researcher can create a data matrix in which all of the relations are reciprocated. For example, a researcher has network data on friendship ties and defines that friendship tie as existing only when both parties indicate that the other is a friend. By symmetrizing the data matrix, a researcher can create a new data matrix in which a friendship tie exists only if the friendship tie is reciprocated. Dichotomizing includes a procedure in which the valued tie is transformed
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Figure 11.5 An example of a data matrix 275
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276 Handbook of methods in leadership research to be dichotomous. For example, a researcher could ask study participants to indicate how often they seek advice from their network ties on a scale of ‘1 5 seldom, 2 5 now and then, and 3 5 often’, and he or she might want to include only those ties where advice seeking happens ‘often’. Thus, he or she might dichotomize the data matrix by including only ties that have a value greater than 2. Furthermore, researchers often ask about multiple relations, such as friendship and advice ties, and may want to combine these into one data matrix. For example, a researcher might want to explore networks based on multiplex ties; that is, a tie including different roles such as friendship and advice seeking. This is made possible by combining friendship and advice seeking data matrices into one data matrix. Network diagrams, also called graphs and sociograms, are also a common way to represent networks. A network diagram is a visualization of a network showing relations between nodes (e.g., Wasserman & Faust, 1994). Nodes are typically labelled by their name, number or another identifier. Lines represent a relation between two nodes. Arrows represent directional ties. For example, if node A asks advice from node B, then there is a line from node A with arrow pointing to node B in the graph. If node B also asks advice from node A, there is another arrow from node B to node A or a line with arrows at both ends. If a network diagram represents nondirectional ties, such as friendship ties, the line describing that tie would be without an arrowhead or arrowheads at both ends. Figure 11.1 shows a graph of 12 nodes and the relations or links between them. Because this network diagram has only lines with arrowheads at both ends, it is a nondirectional graph. A researcher typically has leeway to decide how she or he places nodes and lines in a graph. In addition, software programs for network visualization such as Gephi, NetDraw, NetMiner and Pajek offer many options that a researcher can use to visualize networks. In all, ‘the precise placement of nodes and lengths of lines in a network diagram is somewhat arbitrary, although some versions might be clearer than others. Constructing insightful sociograms is as much an artistic as a scientific activity’ (Knoke & Yang, 2008, p. 46). Measures of Networks The network approach has introduced specific measures for examining the attributes and characteristics of social networks and actors’ positions in these networks. Whole network concepts and related measures can relate to the actor, dyadic, triadic, group, and whole network levels (Wasserman & Faust, 1994). Personal network measures relate to the characteristics of the focal actor’s network and can characterize ego–alter ties, alters, and network structure.
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A social network approach to examining leadership 277 Whole network design Actor-level measures Network research on leadership has often concentrated on the actor level and measured how leaders’ positions in the networks are related to leadership outcomes and development. The concept perhaps most often used to indicate position in the network is network centrality and its indices (in-degree, out-degree, closeness, eigenvector, and betweenness centrality; Borgatti et al., 2013; Freeman, 1979; Wasserman & Faust, 1994). In-degree centrality and out-degree centrality refer to how many ties a focal actor has in the network. Specifically, in-degree centrality relates to how many others in the network name or indicate that they are related to the focal individual, such as those who ask for advice from him or her. In turn, out-degree centrality indicates from how many nodes a focal individual asks for advice, for example. Closeness centrality tells how ‘reachable’ all others in the network are to a person – in other words, how easily an individual can access resources or communicate, directly or via intermediaries, with all others in the network. Eigenvector centrality takes into account the centrality of the network nodes to whom a focal actor is connected. In other words, eigenvector centrality sums the focal actor’s ties to others by weighing them by the centrality of those others (e.g., Borgatti et al., 2013). For example, Mehra, Dixon and others (2006) found that leaders’ (eigenvector) centrality both in the friendship network of leaders and among friendship networks of their work group are related to group performance. Betweenness centrality is also an important centrality measure that indicates brokerage roles in the whole network and takes into account both direct and indirect ties; that is, the extent to which actors connect actors that themselves are not connected to each other. For example, Galunic and colleagues (2012) found that employees whose leaders showed high betweenness centrality in networks – a brokerage role – were rated as being more useful and helpful by others than employees whose leaders did not have brokerage role. In addition, young professionals whose supervisors were active in their work groups’ internal and external communication ties showed higher promotion likelihood and less turnover than others (Katz & Tushman, 1983). It is important to note that researchers often standardize the values of the indices of centrality so that they are comparable between networks of different size (e.g., Knoke & Yang, 2008). This is also an option available in network analysis software such as UCINET. As an example, Table 11.4 shows the degree, closeness, betweenness and eigenvector centrality measures (non-standardized) among persons in the network shown in Figure 11.1. These were analysed using UCINET (Borgatti et al., 2002). As seen, Heather has the highest value for degree centrality (5). As this
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278 Handbook of methods in leadership research Table 11.4 Network centrality measures of network in Figure 11.1
1 Jan 2 Gil 3 Heather 4 Chrissie 5 Gretel 6 Bernard 7 Teresa 8 Peter 9 Ed 10 Paul 11 Les 12 Ann
Degree
Closeness
Eigenvector
Betweenness
3.000 3.000 5.000 2.000 3.000 2.000 3.000 3.000 2.000 3.000 2.000 3.000
25.000 27.000 19.000 34.000 21.000 28.000 34.000 34.000 34.000 27.000 28.000 27.000
0.215 0.317 0.448 0.104 0.353 0.247 0.308 0.308 0.104 0.317 0.247 0.308
18.000 8.000 39.500 0.000 28.333 0.000 0.333 0.333 0.000 8.000 0.000 0.500
network is symmetrized, degree centrality is the same as the in-degree and out-degree centrality measures. Thus, Heather has the highest number of network ties. Heather also has the highest betweenness centrality value. As seen in Figure 11.1, Heather is in a brokerage role between three groups of people. For the closeness centrality measure, Chrissie, Teresa, Peter, and Ed show the highest values, and Heather, Gretel, and Jan show the lowest values. These values mean that Heather, Gretel, and Jan have best opportunities to access others in their network, and Chrissie, Teresa, Peter and Ed have rather peripheral positions in the network. In other words, low closeness centrality values indicate ‘high centrality’ in the network, as provided by the UCINET program. This can also be seen in Figure 11.1. However, if a researcher uses normalized values of closeness centrality, this would change the interpretation of closeness centrality values; that is, high values would indicate high closeness centrality (e.g., Borgatti et al., 2013). Dyadic level At the dyadic level of networks, structural equivalence is an often used measure to indicate the extent to which actors have ties to similar others (Wasserman & Faust, 1994). Actors are said to be structurally equivalent when they have the same relations to others in the network, and they do not have to share relations with each other. For example, in the network shown in Figure 11.1, Peter and Teresa are structurally equivalent; that is, they have ties to the same people in the network. Network theory argues that structurally equivalent actors in a network are often dependent on the same social ties and related resources
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A social network approach to examining leadership 279 and are motivated to monitor each other’s behaviour (e.g., Burt, 1987). In leadership research, Sparrowe & Liden (2005) made innovative use of the measure of structural equivalence to operationalize sponsorship in the networks: sponsorship was indicated when the leader and the follower had the same relationships in the networks of trusted relations. The trust network included people with whom they ‘discuss confidential issues or problems at work’; in other words, when a focal leader and a follower had structurally equivalent positions in the trust network that indicated sponsorship. Furthermore, for a follower, sponsorship increases legitimacy and assimilation into networks, as the leader shares his or her trusted network ties with the follower (Sparrowe & Liden, 2005). The results showed that sponsorship moderated the relation between leader–member exchange and member influence in the workplace as perceived by others. If sponsorship was high, leader–member exchange was related to member influence, but when sponsorship was low, leader–member exchange was not related to member influence. There are also other measures to indicate similar positions in the network between two actors, such as regular equivalence. The regular equivalence measure is not as strict a measure as structural equivalence when indicating a similar position in the network (e.g., Wasserman & Faust, 1994). For example, structural equivalence requires that two supervisors have relationships with the same subordinates, but regular equivalence requires only two supervisors who have subordinates. Triadic level One way to analyse whole networks is to examine triads of actors or triadic relationships. There are 16 possible ways in which three actors can form triadic relationships (e.g., Wasserman & Faust, 1994). Typically, a researcher examines the extent to which a network includes transitive triads. For example, if person A names person B as a friend, B names person C as a friend, and person A names person C as a friend, then they form a transitive triad. Particularly in research on cognitive networks, scholars have used theory based on the transitivity principle. The transitivity principle states that a perceiver will assume consistency in triadic relations; that is, assume a transitive triad. A related concept is a balance schema that argues that people tend to perceive positive relationships such as friendship or liking as reciprocated by those involved in that relationship (e.g., Krackhardt & Kilduff, 1999). If a focal person perceives that person A considers person B to be a friend, he or she also assumes that person B will recognize person A as a friend. In other words, both balance schema and transitivity state that cognitive consistency is a prime motivation in perceptions of social networks (e.g., Wasserman & Faust, 1994). For example, Krackhardt
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280 Handbook of methods in leadership research and Kilduff (1999) asked study participants from four organizations to evaluate who they thought were friends in their workplace. In other words, participants were instructed to name friendship ties between people in their workplace as they perceived them: ‘Who would this person consider to be a personal friend? Please place a check by the names of those who that person would consider to be a friend of theirs’ (perceived friendship network; Krackhardt & Kilduff, 1999, p. 773). In addition, participants were instructed to indicate their own friends in the workplace (actual friendship network). An actual friendship tie was indicated when two people both named each other as a friend. The results showed that the actual friendship network was a poor predictor of perceived network ties. The results further showed that study participants tended to perceive their close and distant ties as being balanced. (Sub)group level Often within a social network are subgroups of people who spend more time with each other or are otherwise more connected to each other compared to non-group members in the network. For example, a workplace holds subgroups of people who socialize. In the network literature a cohesive subgroup – that is, a clique – is characterized by the following attributes: mutuality or reciprocity of ties between people, reachability or closeness of all group members, high frequency of ties between people, and a higher relative density of the network within the subgroup compared to the rest of the network (Wasserman & Faust, 1994). In a cohesive group, people typically share opinions, attitudes, and views, and group norms are easy to maintain. For example, a study found that subordinates’ proximity in networks related to similarity in the perceptions of their leader’s charisma (Pastor et al., 2002). Whole network level Whole network measures capitalize on the characteristics of the complete measured network. The most common measures at the whole network level are density and centralization. Density refers to how many actual ties are present in the network compared to all possible ties in the network. For example, a meta-analysis showed that teams with high network density showed better performance and willingness to stay together than teams with low network density (Balkundi & Harrison, 2006). Network centralization concerns the extent to which the centrality of actors varies within the network. If there are few central actors in the network – that is, network relations are concentrated among a few individuals – the whole network shows high centralization. When individual centrality scores are rather evenly distributed among actors in the network, the whole network is decentralized (Wasserman & Faust, 1994). Sparrowe and colleagues (2001) examined individuals’ network central-
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A social network approach to examining leadership 281 ity and work group centralization effects on individuals’ job performance and on group performance. The results indicated that individuals’ advice network centrality related to their job performance, whereas group-level network centralization negatively related to group performance. The authors reasoned that at the group level, networks that are concentrated among a few individuals hinder cooperation among people in the group so that information is not equally shared (see also, Mehra, Smith, Dixon, & Robertson, 2006). Thus, network structure may also hinder collaboration, and group performance may then suffer. Researchers adopting the social network approach have also argued for the use of multilevel analysis to combine different levels of network research into multilevel models in order to examine how separate levels may relate to each other (e.g., Monge & Contractor, 2003). However, earlier research has rarely capitalized on multilevel network models, since: ‘most network data are either transformed to a single level of analysis (e.g., the actor or the dyadic level), which necessarily loses some of the richness in the data, or are analysed separately at different levels of analysis, thus precluding direct comparisons of theoretical influences at different levels’ (Contractor, Wasserman, & Faust, 2006, p. 684). Personal network measures Personal network measures can be divided into three categories: measures of named network persons (measures of alters), characteristics of the relationships between the ego and alters (measures of relationship), and characteristics of ties between named network persons (alter–alter ties, measures of network structure). Measures of the alters can indicate network-based resources. In social capital theory, the alter’s social status or position in the organizational or social structure indicates network-based resources that an ego can access through his or her network ties (e.g., Lin, 2001). Social status can be indicated by the alter’s educational level, socioeconomic status, or organizational rank, for example. Lin (2001) used three indicators of network-based resources based on the alter’s social status. ‘Upper reachability’ indicates the resources at the highest position or status that the ego can reach through his or her network. ‘Resource heterogeneity’ indicates the variation between highest and lowest social status or position in the network. ‘Extensity of resources’ indicates how many different social statuses or positions a person can access through his or her social ties. ‘Network range’ is also used to indicate diversity in the network in terms of different statuses or group memberships of the network ties. In addition, depending on the research question, other alter characteristics, such as ethnicity or gender, can be used. A researcher can then calculate the proportion or
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282 Handbook of methods in leadership research number of alters in each category. Alternatively, a researcher can evaluate the variation or dispersion of categories in the network. Measures of relationships typically refer to the relationship type and quality between the ego and alters. The relationship type between the ego and alter typically refers to the proportion or number of ties of a given type, such as the number of leaders, colleagues or friends in the network. Some researchers also count multiplex ties, such as whether a relation type is both a friend and a colleague. For example, Carroll and Teo (1996) examined managers’ personal networks and used a classification related to their relations with named network contacts. For example, the number of co-workers was a count of co-workers named as network ties. The proportion of co-worker ties was calculated by dividing the number of co-workers by the total number of the network ties (network size). Relationship quality between the ego and an alter is typically indicated by the strength of the tie (Granovetter, 1973). Tie strength can be indicated by counting the type of ties, such as the number of weak ties, or by averaging the tie strength between an ego and all of her alters. For example, Carroll and Teo (1996) indicated tie strength by the number of close ties, which was measured by asking respondents to indicate whether they were close to each of the people they named. The proportion of close ties was measured by dividing the number of close ties by the network size. Finally, network density and brokerage role in the network are basic measures for network structure based on personal network design. As noted above, network density indicates the number of alter–alter ties divided by the total number of possible alter–alter ties in the network. Thus, it is typically calculated by excluding ego–alter ties. For example, the relationship between a pair of named network persons can be coded 0 if participants report that these persons do not or seldom discuss things with each other, 0.5 if participants indicate that they discuss things with each other every now and then, and 1 if participants report that they discuss things with each other often (e.g., Jokisaari, 2013). Network density is then the mean of the strength of ties between all named network persons – namely, the average level of interconnection between named network ties. For example, Parker and Welch (2013) found that the density of collaboration networks was negatively related to scientists’ leadership position in academia. They argued that a low-density network indicates that network ties are from different social groups, which helps to acquire additional resources that support a promotion to a leadership role. A set of measures has been used to indicate a brokerage role in the personal network. As mentioned above, a person in a brokerage role connects people in the network who are not themselves connected to each other. In other words, a person who is in the brokerage role has structural holes in
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A social network approach to examining leadership 283 Table 11.5 N etwork constraint and density measures of network in Figure 11.1
1 Jan 2 Gil 3 Heather 4 Chrissie 5 Gretel 6 Bernard 7 Teresa 8 Peter 9 Ed 10 Paul 11 Les 12 Ann
Network constraint
Network density
0.611 0.611 0.382 1.125 0.333 1.125 0.840 0.840 1.125 0.611 1.125 0.840
0.333 0.333 0.200 1.000 0.000 1.000 0.667 0.667 1.000 0.333 1.000 0.667
his or her network (e.g., Burt, 2005). A brokerage role is assumed to offer the following benefits for a person: heterogeneous information and point of views, opportunity for control of what information one shares with others, and early access to new opportunities (Burt, 1992). Perhaps the most often used measure to indicate brokerage role – that is, structural holes, based on personal networks – is the network constraint measure (for a review, see Burt, 2005). In fact, the network constraint measure indicates network closure – in other words, a lack of brokerage opportunities or structural holes (Burt, 1992). There is a lack of brokerage opportunities when a personal network is dense (high network density) or when network ties are connected to each other through a central mutual contact (ibid.). As an example, Table 11.5 presents values for both network density and network constraints for the people in Figure 11.1. As seen, Gretel (0.33) and Heather (0.38) have the lowest value for network constraints as well as the lowest network density values (Gretel: 0.0, Heather: 0.2) and thus the best opportunities for having a brokerage role in the network. In contrast, Les, Ed, Chrissie, and Bernard have the highest values for both measures. For example, they have the highest network density value (1.0), which indicates that all of their network ties are connected to each other, as can be seen in Figure 11.1. Earlier research has also shown the benefits of brokerage roles. For example, Burt (2007) examined supply-chain managers’ brokerage opportunities using their personal networks. In the network procedure, managers were asked to name persons with whom they most often had discussed
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284 Handbook of methods in leadership research matters related to supply-chain issues. Then they were asked to indicate the perceived discussion between each pair in their personal network about supply-chain matters; that is, how often each pair discussed supplychain issues (‘often’, ‘sometimes’, or ‘rarely’). The less that the pairs of network contacts discussed with each other – that is, low network constraints – the more likely the focal manager was to play a brokerage role. The results showed that managers whose networks were characterized by high network constraint had a lower salary and lower performance evaluations than managers who had brokerage opportunities (low network constraint) in their networks. In addition, Rodan and Galunic (2004) found that structural holes in managers’ personal networks related to their performance. I have presented above measures of social networks based on both whole and personal network designs. Although network scholars typically use different measures depending on whether they used a whole or personal network design, there is a debate among network scholars regarding to what extent measures of network that were developed based on a whole network design are suitable to characterize personal networks (e.g., to what extent the measure of betweenness centrality gives similar results based on whole and personal network data, Marsden, 2002). Statistical Inference Besides providing descriptive statistics for the characteristics of social networks and actors’ positions in these networks, the social network approach offers methods for statistical inference based on social network data. These statistical procedures include quadratic assignment procedure (QAP), exponential random graph models (ERGMs, also referred to as p* models), and actor-oriented models (e.g., Borgatti et al., 2013; Wasserman & Faust, 1994). It is important to note that the assumptions of general or traditional statistical analyses, such as the independence of observations, are not valid when statistical inference is based on whole network data. The main reason is that whole network data consist of non-independent observations, and thus traditional significance tests do not apply for testing the statistical significance of estimates (e.g., Wasserman & Faust, 1994). Instead, statistical significance tests related to social networks are often based on non-parametric permutation tests – that is to say, the use of randomized samples (ibid.). The observed estimate, such as a correlation, is compared to estimates based on the distribution of simulated random samples in order to conclude whether its occurrence is more likely than one would expect to observe by chance. Statistical inference based on QAP correlation and regression analysis is based on these permutation tests –
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A social network approach to examining leadership 285 that is, randomized samples (e.g., Krackhardt, 1987). These tests provide statistical information to examine relations among networks or between networks and individual attributes. For example, a researcher may be interested in examining whether people are more likely to ask for advice from friends than one would expect by chance. He or she could examine this research question using QAP regression – that is, whether advice seeking and friendship network matrices are related to each other. QAP regression can be used when the dependent variable is a binary variable (e.g., whether people share a relation: yes/no; e.g., Borgatti et al., 2013). For example, Gibbons (2004) examined the role of friendship and advice networks in professional values. The research questions focused on, for example, the extent to which people seek advice from people whose professional values converge with their own values over time and the extent to which friends’ values converge over time. The study data included whole network data based on friendship and advice-seeking ties in workplaces, as well as information on the study participants’ professional values. She examined study hypotheses using QAP regression, and the results showed that friendship networks were related to changes in professional values and that changes in professional values were related to changes in advice-seeking ties. General programs for social network analysis such as UCINET provide procedures for QAP correlations and regression analysis. The general rationale behind using ERGMs is that ‘the observed network is seen as one particular pattern of ties out of a large set of possible patterns. In general, we do not know what stochastic process generated the observed network, and our goal in formulating a model is to propose a plausible and theoretically principled hypothesis for this process’ (Robins, Pattison, Kalish, & Lusher, 2007, p. 175). In other words, ERGMs offer tools for a researcher to examine what kind of social processes as indicated by the substructures of a network could explain the current patterns of the whole observed network. Thus, the question is often whether certain network properties or characteristics in the network are likely to occur more than one would expect by chance (e.g., Robins et al., 2007). For example, a researcher can ask whether the level of triads or reciprocity in the network is higher than one would expect by chance in a given network. For example, Lazega and Pattison (1999) examined substructures within collaboration networks in an organization and asked, among other questions, whether resource exchanges among employees show regular interaction patterns that are not limited to the dyadic level. They found, for example, that network triads are more likely to occur than one could expect based on chance. The estimation of an ERGM requires specific software, such as PNet and Statnet.
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286 Handbook of methods in leadership research Actor-driven or actor-oriented models provide tools to examine network changes and changes in covariates over time (e.g., Snijders, 2005). With these models, both the role of network characteristics and actor choices (initiation of a new tie, dissolving a tie) in network changes can be modelled. For example, a researcher can examine whether an advice network develops towards increased reciprocity over time. Furthermore, network characteristics and actor attributes are often interdependent, and it is important to model how network characteristics and actor attributes co-evolve over time. Furthermore, it is possible to analyse models on how changes in social networks and changes in actors’ attributes and behaviour are related to each other (Snijders et al., 2007). For this purpose, SIENA (Simulation Investigation for Empirical Network Analysis) software is available for statistical analyses to examine network changes over time (e.g., Snijders, 2005). For example, Emery (2012) examined whether an actor’s attributes (i.e., emotional abilities) are related to others’ perceptions of her leadership characteristics over time. In the study, participants were asked to nominate who they considered to be leaders among their group members. According to these nominations, a leadership network matrix – that is, who nominates whom as a leader – was constructed. In addition, the study participants evaluated their own emotional abilities. These measures were assessed three times during the study period. By using actor-oriented models it was possible to examine both network effects (e.g., reciprocity, transitivity) and actor effects (emotional abilities) on the emergence of others’ perceptions of leadership over time. There have also been studies on network changes in the organizational context related to how/why people have certain network positions (e.g., Kossinets & Watts, 2006; Lee, 2010), changes in work group ties (Schulte, Cohen, & Klein, 2012) and network changes during transition (Jonczyk, Lee, Galunic, & Bensaou, 2016), among others (for a review, see Tasselli et al., 2015). Case Example of Collecting and Analysing Whole Network Data1 This case example focuses on leaders’ interorganizational collaboration networks (Jokisaari & Vuori, 2010) to illustrate how to apply network measures with a whole network design. Specifically, the network measures in the case example include direct ties in the network, structural equivalence, and brokerage roles. This example also illustrates how social influence plays an important role in the decision making of organizational representatives, such as leaders. For example, when leaders have to make adoption decisions about a new organizational or work practice – that is, the adoption of an innovation – they have not yet experienced the
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A social network approach to examining leadership 287 consequences and fit of the innovation in the focal environment. The social network approach argues that in this kind of situation, leaders use information about the innovation available through their network ties, such as those with colleagues (for a review, see Burt, 2005). Specifically, the literature on social networks and the diffusion of innovations argues that the responsiveness of the representatives of the organization to a new practice is in many ways related to their social proximity to other actors in the field and their adoption behaviour. For example, earlier research has shown that exposure to the adoption behaviour of others through network ties influences the focal actor’s adoption of new practices (for a review, see, e.g., Burt, 2005). Jokisaari and Vuori (2010) examined the role of both relational (direct ties) and positional (structural equivalence, brokerage positions) characteristics of interorganizational networks in the adoption of a new practice over time. In the network questionnaire, leaders of the organizations were first asked to identify organizations with which they have (1) collaborated and (2) regularly communicated about matters related to employment services. Information on collaboration was elicited as follows: ‘Please indicate the employment offices with which your office has or has had collaboration in employment services. Collaboration may include shared projects, services or development activities related to employment services’. Regarding communication, participants were asked the following: ‘Please indicate the employment offices with which your office has or has had regular communication regarding employment services. The communication may include, for example, face-to-face discussions, meetings, or e-mails’. A roster with the names of all employment offices in Finland in alphabetical order was provided to indicate both collaborative and communication partners. This name roster is shown in part in Figure 11.2. In addition, participants were asked to indicate the time of the relationship and whether it was still ongoing. Communication ties were almost identical to collaboration ties, which is why we decided to use only collaboration ties in the analyses. Adoption among direct ties was measured by the percentage of organizations among the focal organization’s collaborative ties that had previously adopted the new innovation (group training programme: ‘The Työhön Job Search Programme’); that is, during the previous month or earlier. Adoption among structurally equivalent ties indicated the percentage of structurally equivalent organizations that had previously adopted the innovation. Structural equivalence was examined using the blockmodelling procedure (CONCOR) of the UCINET 6 program (Borgatti et al., 2002). This method identifies groups of actors with similar ties based on the correlations between the ties and divides them into blocks. The
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288 Handbook of methods in leadership research number of partitions in this hierarchical clustering method is examined. The goal is to obtain blocks that show highly correlated patterns of ties between actors within a block and low correlations with actors outside the block (Wasserman & Faust, 1994). We ran CONCOR procedures with three, four and five partitions. When three partitions were used, the procedure created seven blocks with an average within-block density of 0.18. When four partitions were used, 13 blocks were constructed, and the average within-block density was 0.75. When five partitions were used, 24 blocks were created, and the average within-block density was 0.87. However, 25 per cent of the blocks included only single or dyadic actors. The existence of blocks with a single or two actors is often an unstable solution, and partitions with such blocks should be avoided (Wasserman & Faust, 1994). Consequently, in this data set, four partitions gave the most appropriate solution, which was then used in the subsequent analyses. The brokerage position in the network at the whole network level was indicated by betweenness centrality (Freeman, 1979) and at the local level by network constraint (Burt, 1992). As noted above, betweenness centrality measures the extent to which an organization is directly connected only to the organizations that are not directly connected to each other in the network (Freeman, 1979). It takes into account both direct and indirect ties in the network and reflects the actor’s brokerage position in the whole network. The square root of betweenness centrality was used in the analyses because of the non-normality of this measure. The network constraint accounts for the immediate network of an organization, and it measures the extent to which the organization has network ties with other organizations that are connected with one another or indirectly connected via a central actor. In other words, a low network constraint means a greater likelihood of a local brokerage role. The analyses were conducted using discrete-time survival analysis to estimate ‘whether’ and ‘when’ (Singer & Willett, 2003) the adoption of a new practice occurs. This analysis takes into account the timesensitive nature of the data – in other words, whether the values of variables may vary with time. The results showed that during the early phase of the diffusion process, the adoption behaviour of collaborative organizations and local-level brokerage position were related to adoption among employment offices in the diffusion of the job search programme. Furthermore, the results showed that adoptions among structurally equivalent organizations contributed to differentiation – that is, non-adoption.
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A social network approach to examining leadership 289
CONCLUSION The social network approach argues that to understand leadership development, behaviour and outcomes, we have to see them as essentially depending on the social environment. The social network approach provides both the theory and methodology to examine the characteristics of the social environment of leadership (Balkundi & Kilduff, 2005; Carter et al., 2015; Sparrowe, 2014; Sparrowe & Liden, 1997). Specifically, theories related to the social network approach such as social capital theory argue that the social environment provides both opportunities and constraints for leadership, its outcomes, and its development. The methodology of the social network approach provides means to measure and analyse this social environment in detail in order to empirically examine the role of social networks in leadership, and there are many methods to gather network data, such as survey and registers, and different ways to define the content of networks (e.g., advice seeking, social support, friendship), depending on the research questions. Furthermore, there are many network measures to characterize social networks from the individual level, such as network centrality, to the whole network level, such as network centralization. Finally, social network analysis as a statistical method provides methods to examine the characteristics of social networks.
NOTE 1. This example is adapted from Jokisaari & Vuori (2010) with the permission of Oxford University Press
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292 Handbook of methods in leadership research Lin, N. (1999). Social networks and status attainment. Annual Review of Sociology, 25(1), 467–487. Lin, N. (2001). Social capital: A theory of social structure and action. New York: Cambridge University Press. Lin, N., Fu, Y.-C., & Hsung, R.-M. (2001). The position generator: Measurement techniques for investigations of social capital. In N. Lin, K. Cook, & R.S. Burt (Eds.), Social capital: Theory and research (pp. 57–81). Hawthorne, NY: Aldine de Gruyter. Marsden, P. (2002). Egocentric and sociocentric measures of network centrality. Social Networks, 24(4), 407–422. Marsden, P. (2005). Recent developments in network measurement. In P.J. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 8–30). New York: Cambridge University Press. Marsden, P. (2011). Survey methods for network data. In J. Scott and P.J. Carrington (Eds.), The Sage handbook of social network analysis (pp. 370–388). London: Sage Publications. Marsden, P., & Campbell, K.E. (1984). Measuring tie strength. Social Forces, 63(2), 482–501. McEvily, B., Soda, G., & Tortoriello, M. (2014). More formally: Rediscovering the missing link between formal organization and informal social structure. Academy of Management Annals, 8(1), 299–345. Mehra, A., Dixon, A.L., Brass, D.J., & Robertson, B. (2006). The social network ties of group leaders: Implications for group performance and leader reputation. Organization Science, 17(1), 64–79. Mehra, A., Smith, B.R., Dixon, A.L., & Robertson, D. (2006). Distributed leadership in teams: The network of leadership perceptions and team performance. The Leadership Quarterly, 17(3), 232–245. Monge, P., & Contractor, C. (2003). Theories of communication networks. Oxford: Oxford University Press. Oh, H., Chung, M.-H., & Labianca, G. (2004). Group social capital and group effectiveness: The role of informal socializing ties. Academy of Management Journal, 47(6), 860–875. Oh, H., Labianca, G., & Chung, M.-H. (2006). Multilevel model of group social capital. Academy of Management Review, 31(3), 569–582. Padget, J., & Ansell, C. (1993). Robust action and the rise of the Medici, 1400–1434. American Journal of Sociology, 98(6), 1259–1319. Parker, M., & Welch, E.W. (2013). Professional networks, science, ability, and gender determinants of three types of leadership in academic and engineering. The Leadership Quarterly, 24(2), 332–348. Pastor, J.C., Meindl, J.R., & Mayo, M.C. (2002). A network effects model of charisma attributions. Academy of Management Journal, 45(2), 410–420. Podolny, M., & Baron, J. (1997). Resources and relationships: Social networks and mobility in the workplace. American Sociological Review, 62, 673–693. Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29, 173–191. Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25(6), 541–562. Schulte, M., Cohen, N.A., & Klein, K.J. (2012). The coevolution of network ties and perceptions of team psychological safety. Organization Science, 23(2), 564–581. Scott, J. (1991). Social network analysis: A handbook. London: Sage. Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Belmont, CA: Wadsworth. Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press. Snijders, T.A.B. (2005). Models for longitudinal network data. In P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 215–247). New York: Cambridge University Press.
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A social network approach to examining leadership 293 Snijders, T.A., Van de Bunt, G.G., & Steglich, C.E. (2010). Introduction to stochastic actorbased models for network dynamics. Social Networks, 32(1), 44–60. Snijders, T.A.B., Steglich, C.E.G., & Schweinberger, M. (2007). Modeling the co-evolution of networks and behavior. In K. van Montfort, H. Oud, & A. Satorra (Eds.), Longitudinal models in the behavioral and related sciences (pp. 41–71). Mahwah, NJ: Erlbaum. Sparrowe, R.T. (2014). Leadership and social networks: Initiating a different dialog. In D. Day (Ed.), The Oxford handbook of leadership and organizations (pp. 434–454). New York: Oxford University Press. Sparrowe, R.T., & Liden, R.C. (1997). Process and structure in leader–member exchange. Academy of Management Review, 22(2), 522–552. Sparrowe, R.T., & Liden, R.C. (2005). Two routes to influence: Integrating leader– member exchange and social network perspectives. Administrative Science Quarterly, 50(4), 505–535. Sparrowe, R.T., Liden, R.C., Wayne, S.J., & Kraimer, M.L. (2001). Social networks and the performance of individuals and groups. Academy of Management Journal, 4(2), 316–325. Tasselli, S., Kilduff, M., & Menges, J.I. (2015). The microfoundations of organizational social networks: A review and an agenda for future research. Journal of Management, 41(5), 1361–1387. Tichy, N.M., Tushman, M.L., & Fombrun, C. (1979). Social network analysis for organizations. Academy of Management Review, 4(4), 507–519. Venkataramani, V., Green, S.G., & Schleicher, D.J. (2010). Well-connected leaders: The impact of leaders’ social network ties on LMX and members’ work attitudes. Journal of Applied Psychology, 95(6), 1071–1084. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press. Wellman, B. (1988). Structural analysis: From method and metaphor to theory and substance. In B. Wellman & S.D. Berkowitz (Eds.), Social structures: A network approach (pp. 19–61). Cambridge, UK: Cambridge University Press. West, S.G., & Thoemmes, F. (2010). Campbell’s and Rubin’s perspectives on causal inference. Psychological Methods, 15(1), 18–37. Zaccaro, S.J., & Klimoski, R.J. (2001). The nature of organizational leadership: An introduction. In S.J. Zaccaro & R.J. Klimoski (Eds.), The nature of organizational leadership (pp. 3–41). San Francisco, CA: Jossey-Bass.
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APPENDIX: CONCEPTS AND TERMS RELEVANT TO A SOCIAL NETWORK APPROACH1 Actors: Individuals, social groups, organizations or other units that are part of a network. Alter: An actor named as a member of a focal actor’s network. Brokerage: A role in a network in which an actor connects others who are not directly connected to each other (see structural hole). Centrality: A network concept that indicates different central positions in a network. Degree centrality indicates an actor’s number of network ties (with in-degree referring to the number of ties to the actor from other actors and out-degree referring to the number of ties from the actor to other actors). Betweenness centrality measures the extent to which a focal actor brokers or connects actors who are not connected to each other. Eigenvector centrality indicates the extent to which a focal actor is related to central actors in a network. Closeness centrality reveals how well an actor can reach all other actors in a network. Centralization: An indicator of the extent to which network ties are concentrated around a small number of actors. Closure: A network in which actors are connected to each other. Network density is a typical indicator of network closure. Density: A network measure that indicates the extent to which alters in a network are connected to each other. In particular, density is calculated by dividing the number of network ties between alters by the maximum number of possible ties between alters. Egocentric network: An actor’s direct ties in a network and relations between these ties – that is, the actor’s personal network (cf. whole network). Homophily: Actors’ tendency to have relations with those similar to themselves with respect to personal and social attributes, such as ethnicity, organizational rank, and/or socioeconomic status (SES). Multiplexity: A relation between two actors that includes different types of connections. For example, A and B could be both friends and co-workers. Personal network: An actor’s direct ties in a network and relations between these ties, also known as the actor’s egocentric network.
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A social network approach to examining leadership 295 Reciprocity: A relation between two actors in which both actors indicate that the relation exists. For example, A asks B for advice, and B asks A for advice. Social capital: An actor’s network-based resources, such as information, advice and recommendations that advance goal attainment. At the group level, social capital is often indicated by network closure that enhances norms and trust among group members. Strength of tie (weak or strong ties): A concept used to characterize a relation between two actors based on attributes such as emotional closeness or intimacy, meeting frequency, and reciprocity. Weak ties, such as acquaintance relations, are often characterized by low intimacy, low meeting frequency, and/or low reciprocity. Strong ties are characterized by emotional closeness, high meeting frequency, and reciprocal services; for instance, friendship relations tend to be strong ties. Structural equivalence: The extent to which two actors have similar network positions – that is, have ties to the same actors in a network. Structural hole: A missing relation between two actors; a third actor can play a broker role between these two actors. Transitivity: A concept used to describe network triads. For example, if A names B as a friend, B names C as a friend, and A names C as a friend, then a transitive triad exists. Upper reachability: An indicator of social resources in a network. Upper reachability is often indicated by the highest occupational prestige or socioeconomic status of network ties (e.g., Lin, 2001). Whole network: A network derived from reports from all or most of the actors in a given unit indicating these actors’ ties with each other (cf. personal network).
NOTE 1. See also Brass (1995) and Kilduff & Brass (2010).
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12. Diary studies in leadership
Sandra Ohly and Viktoria Gochmann
PREVIEW Diary studies have increasingly been used in different areas of management research (Figure 12.1). In this chapter we will review recent research using diary studies on leadership to illustrate the opportunities of this methodology. We will describe the adequate study design (sample size, frequency of daily entries, event-contingent vs interval-contingent responses) for diary studies on leadership. Furthermore, we will discuss additional research topics (e.g., effects of daily leader behaviors and of leadership style, dynamics of leadership) that might benefit from this research methodology. Throughout this chapter, we will provide two types of examples: studies treating leadership as a stable characteristic or as a transient state. Research on leadership has often relied on cross-sectional or longitudinal designs with two measurements. An underlying assumption in these studies is that leadership has some stability over time. Leadership at one point in time is used to predict behavior or attitudes at the same or a later point in time (see Dulebohn, Bommer, Liden, Brouer, & Ferris, 2012; Ilies, Nahrgang, & Morgeson, 2007 for details; Judge, Piccolo, & Ilies, 2004; Mackey, Frieder, Brees, & Martinko, 2017). Rarely is the development or change in leadership examined (see Nahrgang, Morgeson, & Ilies, 2009 for an exception). The assumption of stability is problematic because there are theoretical reasons to believe that the behavior, the perception of leaders, and the relationship quality change over time (Klaussner, 2014) or across situations (Dóci, Stouten, & Hofmans, 2015; see also Shamir, 2011). Several researchers have argued that a better understanding of the causal dynamics associated with leadership requires more precise theorizing and measurement (Gooty, Connelly, Griffith, & Gupta, 2010; Hoffman & Lord, 2013; Yukl, 2012). Hoffmann and Lord argued that this better measurement can be developed in diary studies. Furthermore, a one-time measurement of relationship quality might be systematically distorted by liking, previous judgments or expectations guided by implicit leadership theories, or by an overemphasis on salient performance episodes (Hoffman & Lord, 2013). For both these reasons, leadership research might benefit from conducting diary studies. Finally, diary studies allow the examination of research questions that cannot be examined otherwise, 296
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298 Handbook of methods in leadership research for example, the effects of daily leader behavior on followers’ attitudes and behaviors. In this chapter, we will (1) review research on leadership that has employed a diary design and (2) describe the general approach of diary studies and the decisions faced by researchers when conducting them. (3) We will discuss exemplary research questions in leadership research that could be tested using diary studies.
DEFINITION AND CHARACTERISTICS OF A DIARY STUDY Diary methods refer to a class of methods using one or more daily assessments (Bolger, Davis, & Rafaeli, 2003). Related terms for this methodology include experience sampling studies (Csikszentmihalyi & LeFevre, 1989), event sampling (Reis & Gable, 2000), ecological momentary assessment (Beal & Weiss, 2003), and day reconstruction method (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). Whereas experience sampling focuses on thoughts, feelings, and behavior (flow experiences, performance-related behavior) at various times across the day at fixed or variable times, event sampling is contingent on the occurrence of predefined events, such as an interaction with a supervisor. The experiences and behaviors are then reported after the event occurred. In both types of studies, leader behaviors and employee reactions could be assessed. Day reconstruction focuses on a complete assessment of daily activities and associated thoughts and feelings retrospectively, typically at the end of the day. In a typical day-reconstruction study, the activities are analysed in terms of the experienced happiness. The term ecological momentary assessment implies a higher validity of data compared to traditional methods, and includes both event sampling and experience sampling of experiences and behaviors. Data on momentary states might also be assessed using physiological sensors (heart rate or skin conductance). This type of study is called ambulatory assessment (Trull & Ebner-Priemer, 2013). Because of the similarities of these methodologies, and in line with others (Bolger et al., 2003) we prefer to use the broader term diary study. Diary studies can be either qualitative, quantitative or both. Qualitative diaries contain open reports, such as diaries in a common sense and can be used for theory building. For example, Seele (2016) used a qualitative approach to study the role of met expectations in the development of newly formed leader–subordinate dyads. Diary studies can be purely quantitative by employing short questionnaires. Miner, Glomb, and Hulin (2005), for example, asked their 41 participants twice a day how strongly they experienced an emotion on a scale ranging from 0 to 3 and if certain
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Diary studies in leadership 299 events occurred. They analysed this data using hierarchical linear modeling. Both methods can also be combined and qualitative data can be quantified by using categories rated by independent judges (e.g., Amabile, Schatzel, Moneta, & Kramer, 2004). The advantage of diary studies is the opportunity to examine research questions in naturalistic work environments and using immediate assessment (Beal, Weiss, Barros, & MacDermid, 2005) instead of relying on aggregated judgments. In questionnaire surveys evaluation can be biased because raters rely on implicit theories, schemas, or individual differences because raters cannot correctly recall the focal leader behavior (Hansbrough, Lord, & Schyns, 2015). For instance, a follower will rate his or her leader as trustworthy, charismatic, or transformational when liking this person (Brown & Keeping, 2005). Thus, the rating is distorted by the general attitude towards the leader. When asked to report specific instances of behavior, this bias is less likely to occur. Diary studies also allow the study of behavioral and emotional patterns and their interdependences. For example, when an employee comes to work and receives an inappropriate remark he or she might react emotionally and, as a consequence, might even perform worse. What effect does this inappropriate remark have on the overall performance or the relationship with the leader? How many such occurrences are needed before there is a change in the general judgment about the leader? Or is it the overall judgment that buffers the effects so that the follower of a highly charismatic leader might think: “Well, my boss got up on the wrong side of the bed” without a change in the overall evaluation? Knowledge about these patterns can be useful to fill the black box of prior established effects. Throughout this chapter, we will provide examples of how diary studies in leadership research can contribute to a better understanding of leadership and leader effectiveness. Thus daily diaries can be used to study (1) the occurrence and temporal pattern of transient states and behaviors; (2) relationships between transient states such as experiences and behaviors; and (3) relationships of stable variables (person or situation characteristics) with transient states, experiences, or behaviors (Ohly, Sonnentag, Niessen, & Zapf, 2010). Our review of previous diary studies in leadership research is organized accordingly.
PREVIOUS RESEARCH: LEADERSHIP DIARIES With regard to leadership research, these research questions refer to (1) the occurrence of leader behaviors and their temporal patterns (e.g., how
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300 Handbook of methods in leadership research much time does a leader spend in interactions with followers?); (2) leader behaviors as predictors or outcomes of transient states (e.g., do interactions with leaders lead to lower positive affect?); (3) relationships of leadership styles to employee behavior (e.g., do followers of transformational leaders show more daily creativity?). Below, we will briefly review research on each of these issues using diary studies. We will focus on methodological issues here. Daily Leadership Behavior Based on the assumption that leader behavior varies across time and situations, researchers have examined both the occurrence of leader activities, and their predictors and outcomes. Occurrence and temporal patterns of transient states and behaviors: Leader behavior In a study on daily activities in different domains, Camburn, Spillane, and Sebastian (2010) examined 48 school principals for five days and three periods over the course of three years. Their results revealed that principals spend more time on student affairs than on leadership activities such as personnel, instructional leadership, building operations, or finances. To validate the daily logs, both experience sampling and observations were used and yielded similar results. The authors conclude that daily logs were useful to study school principals’ activities in a more economical way than structured observations, that intensive contact with participants helped them achieve a high response rate, and that engagement in instructional leadership (as an important task of school principals) was lower than is desired based on professional standards. This study suggests that leaders rarely interact with their followers. Our own research stresses this issue. In a series of diary studies, employees were asked if they had an interaction with their supervisor on a particular day. If true, they were asked to shortly describe this event. These descriptions were later used to develop and test taxonomy of leader behaviors (Gochmann & Ohly, 2017). Across the four studies, 50.8 percent of the employees did not report a daily interaction with their supervisor. In a study comparing daily ratings of transformational leadership over the course of one work week and general ratings of 143 employees, Hoption (2016) found that average daily ratings are significantly lower than general ratings of transformational leadership. Hoption argues that these findings support the assumption that general ratings are not only based on the frequency of actual daily leader behavior. Leadership behavior during specific instances such as crisis or reaching milestones might
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Diary studies in leadership 301 have a disproportionately greater influence than everyday behavior. Thus, although rare, some leader–follower interactions might have a profound impact on general ratings of leadership, but also on follower motivation and behavior. To our knowledge, no study to date has examined the temporal patterns of leader behaviors. Relationships between transient states: Leader behavior as an outcome Nielsen and Cleal (2011) examined situational antecedents of transformational leadership behaviors in a sample of 58 middle managers. Participants were invited to rate their behavior on average eight times a day, at random times with a minimum of 30 minutes in between, resulting in 3072 momentary assessments in total. Twenty-six percent of the times the participants were beeped to participate in the momentary assessment, they were in contact with subordinates, and thus rated their own transformational leadership behavior using a measure adapted to the situational self-report measurement. Results revealed that participants rated their transformational leadership behavior as higher in situations when they engaged in problem-solving activities and felt in control of the situation. Transformational leadership behavior ratings were also related to perceptions of the work environment. In a study of 53 managers, the level of daily transformational, considerate, and abusive leadership behaviors were related to stable leadership identity (collective, relational, and individual; Johnson, Venus, Lanaj, Mao, & Chang, 2012). The daily leadership ratings were aggregated to an average frequency score for each manager in this study. Using a diary methodology in this case had the advantage of resulting in more accurate ratings of daily leadership behaviors (see above) than a conventional cross-sectional study, and a reduction of common source bias because leader identity and leader behavior were not assessed in a single survey at the same point in time. In a unique approach that matched leaders’ and subordinates’ ratings, 88 supervisors provided ratings of sleep quality and self-rated ego depletion over a period of two weeks; subordinates rated daily abusive supervisor behavior and work engagement (Barnes, Lucianetti, Bhave, & Christian, 2015). Daily subordinate ratings were included in further analysis when they had at least a moderate amount of contact with their supervisor on this specific day. The ratings were aggregated to the level of the work unit based on analysis of their shared variance in work unit members’ ratings. The results provide support for the theoretical model linking sleep quality, ego depletion, abusive supervision, and work unit work engagement, indicating that leader behavior is dependent on the leader’s momentary well-being.
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302 Handbook of methods in leadership research Taken together, these studies suggest that leader behaviors are dependent on internal states (depletion), identity, or contextual conditions. Relationships between transient states: Leadership behavior as predictor of employee outcomes Research indicates that employees feel lower levels of positive affect when interacting with their leader as compared to interactions with colleagues or customers (Bono, Foldes, Vinson, & Muros, 2007). In this study, 57 employees participated and gave ratings of their daily affect and their interaction partner roughly every two hours over the course of ten working days. In total, 1983 ratings were collected. Daily transformational (but not transactional) leader behavior was linked to daily work engagement in a sample of 61 naval cadets who rated their eight leaders’ behavior during 34 days on board (Breevaart et al., 2014). Daily transformational leadership ratings, provided by 42 employees on five consecutive workdays, was linked to higher levels of optimism, which in turn was linked to higher employee work engagement (Tims, Bakker, & Xanthopoulou, 2011). Daily supervisor coaching rated by 42 employees over the course of five consecutive work days of a fast food restaurant (total ratings k 5 210) was related to self-rated daily work engagement and financial returns, as indicated by the branch manager (Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). On days when supervisors provided more coaching, employees experienced higher levels of work engagement, and the fast food branch had higher levels of financial returns than on days when supervisor coaching was low. As indicated above, daily abusive supervisor behavior is linked to lower work group work engagement (Barnes et al., 2015). Aggregated daily transformational leadership and aggregated daily abusive leadership behaviors were related to leader effectiveness, as rated by subordinates and colleagues (Johnson et al., 2012). Interestingly, also the consistency of these behaviors across situations (operationalized as a low standard deviation) was related to effectiveness, indicating the usefulness of multiple assessments of leader behavior. The authors conclude that “a person who acts in a transformational manner today does not necessarily do so tomorrow” (p. 1268). Daily leader behaviors were assessed in diaries using open questions in a study by Amabile and colleagues (2004) and categorized using the managerial practice survey (Yukl, Wall, & Lepsinger, 1990). Daily behaviors including monitoring, consulting, socioemotional support, networking, and recognizing were positively related to daily perceived supervisor support for creativity, as rated by the 139 participants. Negative instances of not (competently) problem-solving, not monitoring, not motivating or
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Diary studies in leadership 303 inspiring, or not recognizing were related to lower perceived supervisor support. A total of 7194 daily observations were analysed. This study is an example of combining qualitative and quantitative analyses using diary data. Social conflicts with supervisors (assessed after work) and negative affect of employees at bedtime were linked (Volmer, 2015). In total, 98 employees provided 438 after-work surveys and 482 bedtime surveys. Analyses revealed that when employees experienced a social conflict with their supervisor during work time, they had higher levels of negative affect at bedtime, also when controlling for negative affect in the beginning of the day. This approach helps to rule out alternative explanations and provides some evidence of a causal effect. Taken together, these studies suggest that leader behaviors have an impact on their followers’ affect, motivation and behavior. Relationship of Stable Variables with Transient States: Leadership Style and Follower Reaction In studies examining leadership style (or other more stable leader characteristics), the diary is combined with an introductory questionnaire on these characteristics. For example, in a study on managers’ empathy (the tendency to react compassionately to employees), the managers rated their level of empathy before the employees filled out the diaries (Scott, Colquitt, Paddock, & Judge, 2010). Sixty employees supervised by one of 13 managers completed a daily survey for two weeks, resulting in 436 observations. Results revealed that employees with empathic managers report lower levels of somatic complaints, but not better affective wellbeing. Notably, this study also showed that manager empathy moderated the within-individual relationship between goal progress and state positive affect. Perceptions of daily goal progress were more strongly associated with positive affect for groups of employees with empathic managers. This study is an example of examining a cross-level interaction effect. Employees who rate their supervisor as being generally high on transformational leadership experience higher levels of daily positive affect (Bono et al., 2007). Again, transformational leadership (as a more stable characteristic) was rated in an introductory questionnaire, and positive affect was rated several times daily, contingent on a signal emitted by an electronic device. The advantage of using a diary methodology in this case lies in the more valid assessment of positive affect (no recall or retrospective bias in the report of affect). These studies show that leadership styles are related to employees’ affect.
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HOW TO CONDUCT A DIARY STUDY IN LEADERSHIP RESEARCH When conducting a diary study, a number of decisions have to be made. In this section, we address practical issues such as the sample size required in diary studies, compliance, design and measures, devices, and approaches to data analysis. Sample Size When planning a study, the question arises of how many participants are required. A popular rule of thumb for multilevel studies (Scherbaum & Ferreter, 2009) recommends at least 30 units on the higher level, and 30 units on the lower level. This might be hard to achieve in a diary study, and only few examples of the research cited above would meet this recommendation. In this regard, it is important to note that there are at least two sample sizes in experience sampling studies: number of participants N and total number of incidents on the lower level (days, events) k. The required sample size to achieve a certain power depends on the primary aim of the study. When researchers are interested in the relationship between daily behaviors and daily reactions, the total number of assessments k (equals N * assessments sampled per person) is relevant to determining power. Studies of this kind have employed between k 5 210 and k 5 7194 momentary/ daily assessments (Amabile et al., 2004; Xanthopoulou et al., 2009). If researchers are interested in temporal patterns of behavior, an even smaller number of participants might be sufficient, but more daily assessments are needed, for example 20 participants but up to 75 momentary/ daily assessments (see Fuller et al., 2003; Teuchmann, Totterdell, & Parker, 1999 for examples outside the leadership area). When diary data is aggregated to the person level, or when the focus is on stable predictors (leadership style), sample size requirements are the same as in conventional survey studies, and N is the relevant sample size. Diary studies have examined 53 (Johnson et al., 2012) or even 13 leaders (Scott et al., 2010), which can be considered a very small sample. Maas and Hox (2005) concluded that standard errors can be biased when N < 50. Small samples further entail the risk of being underpowered, and results might not be generalizable to the population of leaders. Researchers might be interested in interaction effects between individual differences and momentary assessments of behavior on momentary outcomes (cross-level interaction) (see Johnson et al., 2012). When testing such cross-level interactions, power issues need specific attention
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Diary studies in leadership 305 (Aguinis, Gottfredson, & Culpepper, 2013). Scherbaum and Ferreter (2009) reviewed simulation studies on this issue and concluded that an increasing number of participants often have a greater effect on power than an increasing number of daily/momentary ratings. It is important to note that there is oftentimes a dropout of participants between the introductory survey and the diary part, resulting in a smaller sample (Ohly et al., 2010). This issue was also evident in some of the studies we reviewed above. Nevertheless, the generalizability of findings also depends on the sample size, and the days sampled also need to be representative of individuals’ work life. Not all potential participants may be willing to take part in a diary study, and not all relevant events or daily ratings will be reported, resulting in selective samples on both levels (see Ohly & Fritz, 2010). In the Ohly and Fritz (2010) study, individuals under generally high levels of time pressure were less likely to provide daily ratings. Because time pressure was a focal variable in this study, we discussed how the selective dropout might have affected study findings. It is advisable to report the amount and reasons for missing data on both levels. In this context it is important to keep in mind that subordinates might not have contact with their supervisors on a daily basis. Thus, researchers might strive to oversample the number of days to have a sufficient number for the analysis. Because modern statistical analyses such as multilevel analyses are flexible in handling missing data, it is not necessary for participants to provide equal numbers of momentary assessments. It is common for researchers to limit the analysis to participants who provide a certain minimum number of momentary assessments to ensure the generalizability of findings. When only few momentary entries are provided, there is a danger that these are not representative of individuals’ everyday experience. For example, an employee might be more motivated to provide daily ratings of his or her supervisor on days when this person’s behavior was exceptionally friendly or unfriendly than on days when the behavior was like every other day. There is no definite recommendation for cut-offs because the decision when to include or exclude participants in the analysis also depends on the focal research question, and the researchers’ estimate of the likelihood that missing data occurs not completely randomly (see Schafer & Graham, 2002 for more on the issue of missing data and ways to handle it). Stone and Shiffman (2002) discuss reasons for missing data in diary studies, and recommend that researchers report the missing data rates (and dropouts and compliance rates) in detail so that others are able to assess the validity of findings.
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306 Handbook of methods in leadership research Recruitment and Compliance to Study Protocol Recruiting the appropriate number of participants and ensuring compliance is the next challenge for researchers conducting diary studies. To encourage participants’ collaboration and ensure compliance with the protocol of when to fill in the diary, experts’ recommendations include building an alliance with potential participants by explaining in detail why the often strenuous and intrusive data collection process (Uy, Foo, & Aguinis, 2010) is warranted, and what both researcher and participant can gain (Stone & Shiffman, 2002). In our own experience, some participants even enjoy taking part in diary studies because it helps them reflect on their daily behavior. Researchers can support this reflection by providing a detailed individualized feedback report at the end of the study, thereby also building an alliance. In previous research, participants were also paid contingent on their participation (Ilies, Scott, & Judge, 2006), but this procedure entails the risk that participants will produce fake data to be eligible for their remuneration (Green, Rafaeli, Bolger, Shrout, & Reis, 2006). Compliance to study protocol is important to ensure good data quality, and instructing participants accordingly is recommended (Stone & Shiffman, 2002). Study Design and Measures The frequency of assessing daily behaviors, events, or experiences also needs to be determined in advance when planning a diary study. Higher frequencies of assessments entail a number of advantages but these must be weighed against the burden on participants. First, multiple assessments provide a more accurate picture of individuals’ experiences of work. Furthermore, with multiple daily measurements, lagged effects of leadership on outcomes can be tested, which facilitates causal inferences (Judge, Simon, Hurst, & Kelley, 2014). Finally, retrospective bias can be minimized when arranging multiple daily assessments. For example, the description of a leadership event might be more accurate when p articipants are asked to describe each event as soon as it occurs. This rating scheme is called event contingent, and can be distinguished from interval-contingent and signal-contingent rating schemes. Intervalcontingent ratings are given at fixed times or in fixed intervals (e.g., at noon or every two hours), and signal-contingent ratings at random times during waking hours. Reis and Gable (2000) give recommendations for the use of different recording protocols. They also recommend that filling out the daily questionnaire should not exceed five to seven minutes, but others recommend even shorter durations (Sonnentag & Geurts, 2009;
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Diary studies in leadership 307 Table 12.1 I tems used in Zacher & Wilden (2014) to assess ambidextrous leadership Opening behaviors
Closing behaviors
Today, my supervisor allowed different ways of accomplishing a task Today, my supervisor encouraged experimentation with different ideas Today, my supervisor gave possibilities for independent thinking and action Today, my supervisor gave room for own ideas
Today, my supervisor established routines Today, my supervisor took corrective action Today, my supervisor controlled adherence to rules Today, my supervisor paid attention to uniform task accomplishments
Note: Items were rated on a five-point scale from 1 5 “not at all” to 5 – “frequently, if not always”
Uy et al., 2010) not to compromise response rates, compliance, and data quality. Therefore, abbreviated scales are used. For example, Zacher and Wilden (2014) used the four highest-loading items of two scales of leadership behavior. Specifically, the items that were used for two facets of ambidextrous leadership, opening and closing behaviors)1 are shown in Table 12.1. These items show a strong focus on daily behavior. Adapting established leadership-style instruments to the daily level requires that the respective behavior can be shown on a daily basis – for example, articulating a vision as a sample behavior of transformational leadership. Hoption (2016) used the following item: “My leader emphasized a collective sense of a mission today.” Care should be taken when adapting scales to a different time frame to ensure construct validity. For example, a daily assessment of leader–member exchange quality might not fully capture the quality of the relationship in the sense of the theoretical conception (Bono, 2013). Devices The advantage of electronic devices such as smartphones, portable or stationary computers is that compliance with the study protocol (e.g., predetermined times to answer the questions) can be tracked via an electronic time stamp. Entries that do not comply with the study protocol can be identified and eliminated. Furthermore, electronic devices can be programmed to automatically remind participants of the questionnaire (Ohly et al., 2010; Sonnentag & Geurts, 2009; Uy et al., 2010). For a detailed discussion of compliance
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308 Handbook of methods in leadership research problems with paper-and-pencil diaries see Green and colleagues (2006). With increasing smartphones and software available for different devices, we predict an increasing number of diary studies using these devices. Software is available from multiple sources.2 The available options differ in terms of support for different devices, the available rating formats, and their design. From our experience, apps will not perform equally well for users of android and iOS devices, and some apps are developed for only one type of operating system. Data Analysis Data gathered with diary methods require specific data analysis techniques. As noted above, common research hypotheses are concerned with relationships between transient variables (e.g., leader behavior and employee affect), and relationships of stable characteristics (leadership style with transient variables (employee affect). Because day-level data are nested within-person, multilevel approaches are needed to test interrelations because they take the interdependence of data into account (Raudenbush & Bryk, 2002; Snijders & Bosker, 1999; see also Chapter 10 by Yammarino and Gooty in this handbook). For example, reports of daily affect might be more similar when the same person reports them repeatedly, as compared to reports by different persons. The daily observations constitute level 1 data, and the stable person characteristics constitute level 2 data. In leadership diaries, this issue is complicated further: daily entries (level 1) might be nested in persons (level 2), who are nested in teams with one leader (level 3), resulting in a three-level data structure (Scott et al., 2010). The amount of dependence present in the data at each level can be determined by calculating ICC (intraclass correlation coefficient) values (Bliese & Ployhart, 2002). When ICC values are low, there may not be a need to conduct multilevel analysis, or a specific level (e.g., the team level) could be neglected. Relationship between two level 1 variables or between a level 2 variable and a level 1 variable can be analysed. Furthermore, the relationship between two level 1 variables (e.g., leader behavior and employee affect) might be dependent on a level 2 variable (e.g., leader’s empathy). This is called cross-level interaction. When researchers are interested in the trend of transient variables over time, additional statistical techniques are required, for example time series analysis or latent growth analysis (see Chapter 13 by Hall in this handbook). Research questions on dynamics of leadership, such as on the effect of different subsequent supervisor– employee interactions, require even more sophisticated analyses such as sequence analysis (Biemann & Datta, 2014).
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Diary studies in leadership 309 Diary data could also be aggregated to a higher level, for example daily ratings of leader behaviors could be used to determine the typical leader behavior across time (cf. Johnson et al., 2010). The disadvantage of this aggregation is that fluctuations across days are neglected in the analyses. The appropriate analysis technique must be chosen depending on the research question. A complete treatment of multilevel modeling and the alternative approaches is beyond the scope of this chapter. For an accessible treatment of multilevel analysis with a focus on analysing cross-level interactions see Aguinis and colleagues (2013). Diary data might also be analysed using qualitative methods. In our own research (Gochmann & Ohly, 2017) the daily reports of leader– employee interactions were sorted into categories of possible interaction types, for example goal-oriented behavior, positive feedback, or participation. The reliability of this categorization was examined by calculating interrater agreement. Information from the daily reports was further used to refine the categories. For more information on qualitative analysis, see Chapter 14 by Schilling in this handbook.
FUTURE RESEARCH In this part of the chapter, we will develop ideas on how to employ diary studies in future research on leadership. Leadership Style Developing Over Time A leadership style develops over time and across multiple events. Klaussner (2014) describes the emergence of abusive supervision as an escalating process of subordinate–supervisor interactions. This process starts when a subordinate perceives he or she is being treated unfairly, reacts to this perception in a dysfunctional way, which in turn stimulates supervisor behavior towards the subordinate. Klaussner argues that the “perceptions of supervisor injustice are proposed to accumulate over reoccurring trigger events when they remain unresolved” (p. 320). A diary study that examines the respective behaviors and perceptions multiple times across a longer time range would be ideally suited to studying this process. The accumulation of injustice perceptions could be studied by combining an event- and an interval-based approach in that participants are instructed to report important events as soon as they occur, but also give ratings of injustice perceptions at regular intervals. Development of LMX can be thought of as mutual reinforcements (Dienesch & Liden, 1986) in which a leader delegates important interesting
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310 Handbook of methods in leadership research tasks based on a first impression of the subordinate, and this delegation is reinforced when the subordinate shows loyalty and good performance. Previous research has examined the role of supervisor or subordinate characteristics in the development of LMX (e.g., Nahrgang, et al., 2009) but has largely neglected the role of transient states and events (see Cropanzano et al., 2017, for theoretical arguments). To study the development of a leadership style over time, the leadership style would be assessed in a pre- and a post-test questionnaire, and the relevant events or experiences would be reported in the diary (from the leader’s or the employee’s perspective, or both). Again, a combination of event- and interval-contingent methods seems ideal to capture the dynamic of mutual reinforcements or the escalating process. Because the focus of this research is on the development of a stable leadership characteristic in both cases (LMX or abusive supervision), a large number of participants is required for robust results, but less daily assessments might be adequate based on the observation that leader–subordinate interactions do not take place on a daily basis. Based on the observation that participants sometimes like to take part in diary studies, one could use the diary as part of an intervention. Diaries that provide an opportunity to reflect on past behavior and to prepare for the future, for example, by using open-ended questions, can serve as an intervention in itself (see also Burt, 1994). Diaries as an accurate picture of leader behavior could also be used to enhance leadership interventions. For example, diary reports on critical leadership incidents could serve as the basis of leadership coaching. Finally, the diaries could also serve to evaluate the effectiveness of leadership training (Hammer, Kossek, Anger, Bodner, & Zimmerman, 2011) because the daily reports of what individuals do might be a more accurate report than general leadership ratings (Hoffman & Lord, 2013). Leadership Behavior and Fluctuating States and Behaviors Temporal patterns of leadership behavior Affect has been shown to follow a distinctive temporal pattern. Some leadership behavior might be affect driven in that leaders behave more or less favorably towards their subordinates based on their own affective state. Previous research linking abusive supervision and ego depletion supports this view (Barnes et al., 2015). Furthermore, external temporal cues such as end of month or end of year can trigger specific behavior. For example, a leader in an accounting firm would show more “closing” leadership behavior shortly before the end of the month deadline. Based on these arguments, one would argue that leadership behavior follows a
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Diary studies in leadership 311 specific temporal pattern (for more on temporal lenses on behavior see Shipp & Cole, 2015). Identification of this pattern would help to develop novel interventions to enhance leadership effectiveness. Furthermore, the time of day or day of the week might be a third variable that sometimes underlies the relationship between momentary states and behaviors (Liu & West, 2015). Researchers are thus advised to carefully evaluate if there might be temporal patterns in any of the momentary states or behaviors they are interested in and to control for time if necessary (see as an example Daniels, Boocock, Glover, Hartley, & Holland, 2009). Leadership style and fluctuating states and behaviors Recent research has shown that employees feel more motivated when their leaders show inspirational leadership (Antonakis, D’Adda, Weber, & Zender, 2014). By examining the effects of specific leader behaviors in a diary study, the sustainability of this effect could be determined. For example, one could examine how specific rhetoric influence attempts are linked to daily work motivation over the course of the following work week. Gooty and colleagues (2010), after reviewing the literature on affect and leadership, give recommendations for future research in this area. For example, they identify the impact of follower behaviors on leaders’ style and leader behavior as a gap in the literature. One could study this effect using a diary approach and having leaders rate their subordinates and vice versa on a daily basis (see Barnes and colleagues’ (2015) approach on matching daily leader and subordinate ratings). Leaders could also be asked to provide ratings of their leadership style in a pre- and postdiary questionnaire to test for longer-term changes in leadership style. When the focus is on how subordinates affect leaders’ daily behavior, a large number of matching daily entries is required. When the focus is on the effect of followers’ behavior on change in leadership style, a larger number of supervisor–subordinate dyads is needed (see “Leadership Style Developing Over Time” above). Furthermore, Gooty and colleagues call for more research integrating cognitive appraisal theories with research on leadership and affect. It would be interesting to examine how the appraisal of certain events such as a conflict (both from the leader’s and from the follower’s view) affects their reaction to the event, and the subsequent behavior. The appraisal is ideally assessed directly after the event happens to reduce retrospective bias. An event-contingent scheme could be used so that participants are instructed to rate their appraisal as soon as the event has occurred. Another research area in which diaries seems ideal is the “function of emotions in instigating or suppressing moral and ethical leadership behavior” (p. 1000). Discrete emotions might be best captured using
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312 Handbook of methods in leadership research an interval-based scheme where participants are contacted at irregular intervals to report on their momentary state. A general recommendation is to better match the levels of analysis in theory, methods, and inferences. The research questions discussed above are concerned with relationships of momentary states and momentary behaviors. Johnson and colleagues (2012) argued that individual differences such as trait self-control, agreeableness and emotional stability might buffer the relationship between low sleep quality and abusive supervision. This idea calls for more research examining cross-level interactions of individual differences and daily experiences and behaviors. As described above, these studies need to sample a large number of leaders. The sampling protocol for the daily assessment depends on the focal variable. When examining the relationship between sleep quality and daily leader behaviors, an intervalcontingent rating scheme and one daily rating is appropriate. When focusing on the relationship between specific events such as conflicts with subordinates and behaviors an event-based approach might be useful. This might result in a longer study period to sample a sufficient number of events. Signal-contingent response and multiple daily assessments would be advisable when examining daily leader affect and daily leader behavior.
LIMITATIONS OF DIARY STUDIES Although diaries provide some advantages over common one-time survey research, including more precise measurements, they are not without problems. One issue with diary research is that causal conclusions are often not warranted. Although the design and the analyses eliminate concerns that stable individual differences underlie the relationships in a diary study, correlations might be due to a third state variable such as context or affect when answering the questionnaire (Bono, 2013). It might be that, due to repeated measurements, participants guess the aim of the study and provide their responses accordingly, creating spurious results. Therefore, extra measures must be undertaken to rule out alternative explanations of significant results. In the studies reviewed above, these measures include the temporal separation of predictor and outcome, the use of independent sources of data, or the use of state affect as a control variable. Researchers might also want to test for reverse causality by examining the relationship of their outcome at one point in time and the predictor at a later point in time (cf. Ilies, Scott, & Judge, 2006). It might also be that diary studies increase reflection on behavior, thus changing behavior over the time of the study. Changes in behavior might be checked by testing for time trends
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Diary studies in leadership 313 (cf. Daniels et al., 2009). These methods are also recommended for future research using diary studies in leadership research.
CONCLUSIONS This chapter reviews leadership research employing diary methodologies for a variety of research questions. By reviewing this research, discussing important decisions researchers face when conducting diary studies, and by providing examples of future research questions, we hope to inspire other researchers to employ this methodology to capture leadership “live as it is lived” (Bolger et al., 2003, p. 579).
NOTES 1. Opening behaviors encourage followers to think independently, experiment with new ways of doing things, embrace error learning, take risks, and challenge the status quo. Closing behaviors encourage followers to create routines, monitor goals, avoid errors, take corrective action, and adhere to rules and standards (Rosing et al., 2011). 2. For example, http://www.expimetrics.com/; http://ilumivu.com/; see also http://www. saa2009.org/?page_id557; all accessed July 21, 2017.
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316 Handbook of methods in leadership research Shamir, B. (2011). Leadership takes time: Some implications of (not) taking time seriously in leadership research. The Leadership Quarterly, 22(2), 307–315. Shipp, A.J., & Cole, M.S. (2015). Time in individual-level organizational studies: What is it, how is it used, and why isn’t it exploited more often? Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 237–260. Snijders, T.A.B., & Bosker, R.J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage. Sonnentag, S., & Geurts, S. (2009). Methodological issues in recovery research. In S. Sonnentag, P.L. Perrewé & D.C. Ganster (Eds.), Current perspectives in job-stress recovery. Research in occupational stress and well-being (pp. 1–36). Bingley, UK: Emerald. Stone, A.A., & Shiffman, S. (2002). Capturing momentary, self-report data: A proposal for reporting guidelines. Annals of Behavioral Medicine, 24(3), 236–243. Teuchmann, K., Totterdell, P., & Parker, S.K. (1999). Rushed, unhappy, and drained: An experience sampling study of relations between time pressure, perceived control, mood, and emotional exhaustion in a group of accountants. Journal of Occupational Health Psychology, 4(4), 37–54. Tims, M., Bakker, A.B., & Xanthopoulou, D. (2011). Do transformational leaders enhance their followers’ daily work engagement? The Leadership Quarterly, 22(1), 121–131. Trull, T.J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9(2), 151–176. Uy, M.A., Foo, M.-D., & Aguinis, H. (2010). Using experience sampling methodology to advance entrepreneurship theory and research. Organizational Research Methods, 13(1), 31–54. Volmer, J. (2015). Followers’ daily reactions to social conflicts with supervisors: The moderating role of core self-evaluations and procedural justice perceptions. The Leadership Quarterly, 26(5), 719–731. Xanthopoulou, D., Bakker, A.B., Demerouti, E., & Schaufeli, W.B. (2009). Work engagement and financial returns: A diary study on the role of job and personal resources. Journal of Occupational & Organizational Psychology, 82(1), 183–200. Yukl, G. (2012). Effective leadership behavior: What we know and what questions need more attention. Academy of Management Perspectives, 26(4), 66–85. Yukl, G.A., Wall, S., & Lepsinger, R. (1990). Preliminary report on validation of the managerial practices survey. In K.E. Clark & M.B. Clark (Eds.), Measures of leadership (pp. 223–237). Greensboro, NC: Center for Creative Leadership. Zacher, H., & Wilden, R.G. (2014). A daily diary study on ambidextrous leadership and selfreported employee innovation. Journal of Occupational and Organizational Psychology, 87(4), 813–820.
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13. Modeling leadership-related change with a growth curve approach Rosalie J. Hall
A variety of leadership and followership theories are dynamic in the sense that they imply change in the focal variables over time. In particular, one thinks of leadership theories that involve the learning and development of leadership skills and behaviors (e.g., Day, Fleenor, Atwater, Sturm, & McKee, 2014), but they also include theories about processes such as the assimilation/adaptation that occurs as a result of workplace socialization or the development of leader–member exchange relationships and trust. Indeed, recent considerations of leadership and followership from a dynamic perspective are yielding new insights. For example, Day and Sin’s (2011) theoretically based empirical study focuses on changes in ratings of perceived leadership effectiveness at four points in time as study participants took part in term-long action learning projects focused on team-building and the development of leadership. Among other results, Day and Sin demonstrated between-participant variability in ratings of perceived leadership effectiveness at the study start, indicating that first-year university students differ significantly in this leadership quality. In addition, they found differences in the shape of the individual change trajectories for leadership effectiveness over the course of the project. Specifically, the majority of the sample showed a drop in leadership effectiveness ratings from the initial measurement time, which then plateaued or showed a very slight upturn, while a smaller group showed a linear, increasing trend in ratings of leadership effectiveness across the four measurement times. This suggests differential benefits from the leadership development initiative, with one group benefiting and (at least in the short run) the other group either not benefiting or possibly even showing negative effects. In addition, Day and Sin (2011) found several important contingent relationships. One of them was that study participants who more strongly identified as a leader at a specific point in time also tended to have higher leadership effectiveness ratings at that time. They also found that different forms of goal orientation differentially related to initial ratings of leadership effectiveness and/or the pattern of change in effectiveness over time. Another example of a dynamic study related to leadership and 317
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318 Handbook of methods in leadership research followership is Jokisaari and Nurmi’s (2009) study of changes in role clarity, work mastery, and job satisfaction as a function of perceived supervisor support, using measurements collected from organizational newcomers on four occasions over about a year-and-a-half. In general, levels of perceived supervisor support for these newcomers tended to decline over time, in keeping with previous works on the “honeymoon” period of high positive evaluations that often characterizes the start of interpersonal relationships, and is typically followed by a return to more realistic levels (Fichman & Levinthal, 1991). Another interesting finding from this study was that newcomers who experienced a steeper decline over time in their perceptions of supervisor support also had steeper declines in their levels of role clarity and job satisfaction, as well as lower salary increases. Longitudinal studies such as the two just described are particularly helpful in understanding the direction and pattern of change (if any) of leadership- and followership-related variables and the rates at which processes such as leader development and follower socialization occur. To test theories of change typically requires the collection of longitudinal data consisting of three or more repeated measurements on the focal units of analysis over a time span (e.g., hours, days, weeks, months, years, etc.) that is appropriate for the research question being addressed. The data collection can be done either in a field or a laboratory context (e.g., see Rietzschel, Wisse, and Rus on laboratory studies, Chapter 3 in the current volume). And, the repeated observations may be of persons, dyads, groups, or any other type of entity. Once the data have been collected, then a suitable analytic method must be applied. There are a variety of available choices for the analysis of longitudinal data. The current chapter deals with a general approach that may be useful in many circumstances – growth curve modeling (GCM). The models are also sometimes referred to as latent curve models (e.g., McArdle & Nesselroade, 2014) or latent growth curve models. In general, such growth models are used to “estimate between-person differences in within-person change” (Curran, Obeidat, & Losardo, 2010, p. 122). This highly flexible technique can be implemented using either a multilevel modeling (MLM)/ random coefficients regression framework or a structural equation modeling (SEM) framework. This chapter provides a conceptual description of the underlying logic of growth curve modeling, an overview of multilevel and SEM approaches to specifying growth models, some tips for data collection and analysis strategy, and a discussion of considerations and limitations related to the use of the technique.
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Leadership-related change with a growth curve approach 319
OVERVIEW OF GROWTH CURVE MODELING General Considerations for Modeling Change To begin, it might be helpful to first consider in the abstract the features a useful method for analysing change should have. Suppose that you wished to model a process of leadership-related change or development over time, what characteristics of your data should you be able to demonstrate or test with an ideal analytic method? It seems that most fundamentally the analysis should allow us to separate meaningful change in leadership-related variables (in either a positive or negative direction) from random fluctuation. That is, we would wish to determine whether the observed variability that appears in the data is consistent and meaningful (i.e., implies systematic change), or does it represent something more banal such as unreliability in our measurement instrument or small and inconsistent changes due to nuisance factors such as fatigue, mood, and so on? Next, we would want to be able to characterize the pattern of changes that occur. For example, for a particular developmental process, do we see a general increase or decrease in the level of some relevant variable, such as increased self-efficacy for leadership following a training program or developmental experience (e.g., Hannah, Avolio, Walumba, & Chan, 2012; Lester, Hannah, Harms, Vogelgesang, & Avolio, 2011), or a decreased focus on an individual-level leader self-identity accompanied by an increase in relational or collective identity as leaders gain expertise in their role (e.g., Lord & Hall, 2005)? If positive or negative change is present, its rate might stay consistent over time, so that the pattern could be described as following a linear trajectory. Alternatively, there might be a non-linear pattern of change, such as a pattern of acceleration in which change occurs at a faster rate as time progresses, or a deceleration or plateauing effect in which the rate of change is initially high and then slows down. Such patterns are often referred to in the literature as growth trajectories (Singer & Willet, 2003), and can be described by parameters that capture the average direction and rate of change in the sample. Beyond this, the analytic approach should allow us to determine whether change or growth occurs rather uniformly in the sample as a whole, or whether there is individual variability in the pattern or rate of change. For example, because of a variety of experiential, personality and ability-based factors, we might expect that some persons could quickly acquire critical leadership skills while for others the process might take longer (e.g., Day & Dragoni, 2015). Thus, an ideal analytic approach would allow us to quantify the extent to which there is significant variability across leaders in the rate and pattern of change. Relatedly, this approach should also allow
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320 Handbook of methods in leadership research us to determine whether all persons are starting at the same, or different, initial levels of leadership skill or performance, and what implications this has, if any, for the pattern and rate of change. For example, we might want to investigate whether persons receiving a leadership intervention start at very different skill levels, and whether, as a result, they benefit differentially from the intervention, with some advancing rapidly and others struggling or plateaued, depending upon their starting skill level. Finally, once the pattern of change has been described, it would be advantageous to have some means to predict various aspects of that pattern via other independent variables. (The GCM literature often refers to such predictors as “covariates.”) For example, suppose we want to know whether males and females start out at the same level of leadership performance, and whether they develop at the same rate, as studied in a sample of military cadets described in Lord, Hall, and Halpin (2010). In the example just given, gender is what the growth curve literature (e.g., Singer & Willet, 2003) refers to as a time-invariant variable. That is, its value does not change over time, and we could model its effects directly onto growth curve parameters such as the intercept and slope coefficients that describe the pattern of change. The goal orientation effects found in Day and Sin (2011) are also examples of the effects of time-invariant variables. We might also want to determine the effects of time-varying variables that can change value from one measurement occasion to another, such as whether a leader’s current level of mood or self-efficacy influences his or her performance at a specific point in time. For example, Day and Sin (2011) found that the level of a participant’s leader identity at a particular point in time was associated with his or her rated leader effectiveness at that particular point in time. Thus, time-varying variables are modeled as directly influencing the value of the dependent variable at a specific point in time, and would normally be variables that we expect to vary within-person – either randomly or systematically – over the timespan of the study. We might also want to consider models that allow us to determine whether the growth trajectory of one variable relates to the growth trajectory of a second variable. For example, we might investigate whether the initial status and rate of change over time in leadership identity relate to the initial status and rate of change in leadership efficacy? Another example of this type of model, sometimes called a parallel process latent growth curve model (e.g., Wickrama, Lee, Walker O’Neal, & Lorenz, 2016), comes from the Jokisaari and Nurmi (2009) study, in which they showed relationships of the dynamic pattern of change in perceived supervisor support with rates of changes in socialization outcomes and salary
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Leadership-related change with a growth curve approach 321 increases over time. In addition, recently there has been additional work done on the development of techniques allowing the empirical identification of categories of persons with very different patterns of change in heterogeneous populations. The growth curve modeling approach offers ways to address all of these objectives. Key Characteristics of Growth Models Although foundational works were also published earlier, the most recent roots of growth curve modeling include works from the 1980s and 1990s that cut across multiple analytic approaches and scholarly disciplines. The underlying statistical approaches for manifest (i.e., observed or raw) variable longitudinal models of change over time, using maximum likelihood estimation techniques, include latent curve analysis that has most typically been implemented within a structural equation modeling framework (e.g., McArdle, 1988; McArdle & Epstein, 1987; Meredith & Tisak, 1990), random coefficients regression (Laird & Ware, 1982), and multilevel modeling (Bryk & Raudenbush, 1987; Goldstein, 1995). The different growth curve modeling literatures vary in their underlying statistical justifications for the approaches, and may also differ in some aspects such as the extent to which unbalanced or missing data can be accommodated, the variety of estimators available, the fit indices available and the choices for modeling residual terms. In addition, these somewhat different approaches may influence preferences for certain kinds of software tools. For example, those using the SEM-based approach are likely to prefer structural equation modeling software such as LISREL (Jöreskog & Sörbom, 2015), Mplus (Muthén & Muthén, 1998–2015), EQS (Bentler, 2006) or similar SEM packages. In contrast, those taking a multilevel approach are likely to prefer software such as HLM (Bryk & Raudenbush, 1992) or MLwiN (Goldstein et al., 1988; Rabash, Steele, Browne, & Goldstein, 2016), while those who use a random coefficients approach may employ general purpose statistical packages such as SAS’s PROC MIXED (SAS, n.d.) or Stata’s “xt” routines such as xtreg and xtmixed (StataCorp, 1996–2016). However, across a very broad range of GCMs (i.e., all linear models and many non-linear models), the different methods of implementing the analysis should yield essentially the same results (e.g., Curran, 2003; Ferrer, Hamagami, & McArdle, 2004). In this chapter, the emphasis will be on the multilevel modeling and the SEM approaches, as they currently seem to be most used in the leadership literature. Applications of GCM are quite popular in many research areas, including developmental, social, and personality psychology, business and
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322 Handbook of methods in leadership research anagement, and education. In part, this popularity may be because m GCMs allow one to start with a simple model describing change over time, and then incorporate additional complexities including covariates, relationships between GCMs, and identification of heterogeneous patterns of change. Our starting point in this chapter, however, will be a very simple growth curve model that has two key variables: an independent variable that reflects time (or some variable related to time, such as age), and repeated measurements of the focal dependent variable at different occasions, for example, repeated measurements of leader self-efficacy or performance. We’ll consider some particulars for each of these variables next. Considerations for the independent time variable In GCM, even though observations occur at specific points in time and may be indicated with just a few discrete values, time in general is assumed to be a continuous independent variable. As will be described in more detail a bit later, a coded time variable is typically employed in the multilevel approach to GCM, in order to indicate when a particular observation has been made. This is in contrast to some other approaches such as repeated measures ANOVA in which the independent variable indicating time is treated as categorical. The treatment of time as continuous has the advantage of also allowing the analysis of datasets in which not every person is measured at exactly the same time. (Such datasets may have a large number of different observation times, and tend to be better handled with the multilevel approach than the SEM approach.) For example, imagine that you were studying a large number of supervisors who took part in a day-long leadership training program. The training program needs to be offered at multiple times, spaced out over a four-week time period. Suppose that you wanted to collect repeated measures of a variable such as leadership effectiveness that you believe will change over time as a result of the training, using a self-report survey method and collecting data at the very start of the training program and at three later points in time. However, for practical reasons, you must distribute the post-training surveys at each time period to all of the trainees at once. If the first survey is sent out a week after the last group has received its training, the time that has elapsed since training is +1 week for the last group that was trained, but it is +5 weeks for the first group to receive training. There will be a similar kind of variability in time elapsed for the remaining two measurement periods. The training-related change in leadership effectiveness might well depend upon how much time has elapsed since training (as leadership researchers and practitioners, we hope that it is a positive change, and that it continues to increase over time!) This kind of variability can be accommodated in the growth curve
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Leadership-related change with a growth curve approach 323 modeling analysis by the coding scheme adopted to indicate the time of measurement. When data are collected from all persons at exactly the same points in time (or, in some developmental studies, at the same ages), the dataset is said to be “balanced on time” or simply, balanced. This term also applies to those datasets where the original plan was to collect observations from all persons at the same times, but some individuals are missing responses for one or more of these response times, in a pattern that is believed to be missing at random or missing completely at random. Data collections in which people are observed at different points in time, potentially even with no two people sharing the same times of observation, are called unbalanced. In general, the multilevel approach to GCM handles unbalanced datasets more readily than does the SEM approach. Considerations for the dependent variable In the models considered in this chapter, the repeated dependent variable is also assumed to be continuous, as well as normally distributed. (However, methods for similar analyses of ordered categorical dependent variable models exist; for example, see Rabe-Hesketh and Skrondal, 2012 as well as syntax examples on the Mplus website at statmodel.com.) It is also important that the dependent variable measure has the same metric or scaling across all measurement occasions, and that the construct underlying the measure maintains the same meaning (Kline, 2016; Singer & Willett, 2003). These latter two requirements are necessary so that any observed change can be attributed to processes occurring over the passage of time, and not simply to changes in the measurement instrument used or changes to what it means to participants as they develop. This is often accomplished by simply using exactly the same instrument for all measurement occasions, but in some circumstances there might be reasons to vary the content of the dependent variable measurement instrument from one time to another. For example, if implicit measures involving word fragments were used as the dependent variable (see Chong, Djurdjevic, and Johnson on implicit measures in Chapter 2 of this volume), the same set of word fragments should not be repeated from one time to the next, in order to avoid familiarity effects. In cases like this, it might be possible to identify equivalent or equated instruments in order to proceed with GCM. Finally, the SEM approach to GCM has the additional option of modeling the dependent variable as a latent factor, thus potentially increasing its reliability and construct validity. The typical maximum likelihood estimation procedure used in growth curve analysis allows for the accommodation of missing data on the dependent variable side (Curran et al., 2010). This is a very convenient
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324 Handbook of methods in leadership research feature, as in practice it can be quite difficult to get a full set of data points from every individual (or other entity) participating in a longitudinal study. However, in order for estimation to be unbiased in the presence of missing data, the missing values should be at least missing at random (i.e., missingness is not contingent upon the level of the dependent variable; see Graham, 2009 or Shafer and Graham, 2002 for a good general overview of modern methods for dealing with missing data). Parameter estimation in GCMs Both fixed and random effects are typically estimated in growth models. The fixed effects include an intercept parameter that indicates the mean population level of the dependent variable at the measurement occasion with a time code of “0” (often chosen to be the initial measurement occasion), and one or more additional parameters that describe the mean population pattern of change in the dependent variable over time (for linear models this is often a slope parameter). The key random effects parameters describe the extent of variability across persons in the coefficients that describe individual growth curve trajectories. Although in many other statistical applications we are not especially interested in values of variances, in the GCM context these random effects can be quite interesting because they tell us whether people tend to have the same growth trajectories or not. For example, if most participants in a leadership study have almost the same value of intercept for their individual-level growth trajectories for leadership effectiveness, the variance of the fixed effect intercept parameter will be small, and we might conclude that all participants have begun the study with the same level of effectiveness. However, if intercepts vary widely in value from person to person, the variance associated with the fixed effect intercept parameter will be large, suggesting that there is substantial variability in initial levels of leadership effectiveness. We can similarly look at the estimated variance in slope parameters, to determine whether the rate of change is likely to be constant or varying. For example, although we may have intuitions that some people acquire leadership skills more rapidly than others (i.e., that there is substantial variability in slope coefficients), GCM combined with a thoughtful data collection effort could help us to more precisely determine whether our intuition is correct and if so, more precisely what the actual extent of variability is. Short Overview of the Multilevel Approach to GCM The multilevel model analytic approach builds on the idea that repeated measurements of the dependent variable are clustered or nested within
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Leadership-related change with a growth curve approach 325 a higher-level entity such as a person (e.g., Bryk & Raudenbush, 1987; Rogosa & Willett, 1985). For example, a study might involve ratings of leader effectiveness, collected every three months over a period of a year. Thus, the resulting dataset has four effectiveness ratings (taken at months 1, 4, 7, and 10) for each leader included in the study. As can be inferred from this example, a key difference between the growth curve model and a more general multilevel model is that for GCM datasets the clustered observations at the lower level are ordered with respect to time. This means that time will need to be explicitly treated as a predictor variable in the multilevel GCM data analysis. The Level 1 model The most basic multilevel approach takes the form of a two-level model. The lowest level (Level 1) specifies the individual growth model – describing how an individual changes over time – as shown in the example of Equation 13.1. This model describes the value of the dependent variable as depending upon three terms: a constant intercept coefficient, a second coefficient that is multiplied by time, and a residual term. The coefficients on the right-hand side of the model can potentially be different for every person in the dataset. Thus, it captures the shape of the within-individual growth trajectory for any specific person in the dataset. Another way of saying this is that the Level 1 model captures the intra- (within-) individual effects of time on the dependent variable:
Level 1: Yij = p0i + p1iTimeij + eij(13.1)
In this model, Yij is the value of the dependent variable for a given individual (i) at a specific time (j). For example, in a study of change in leader effectiveness over time, Y13 would be the leader effectiveness rating for person 1 at the third measurement occasion. The πs of Equation 13.1 are growth parameters describing change over time at an individual level, and are estimated from the data. They can be thought of as analogous to coefficients in a standard regression model, with p0i representing an intercept term, and p1i representing a slope coefficient that captures the effect of time on the dependent variable. The values of Timeij are supplied by the researcher, to indicate the time at which, for a particular individual, a dependent variable measurement was taken. To help scale the value of the intercept estimate, one of the Timeij values is set at zero. So, for example, if there are four equally spaced measurement occasions, the values of Timeij could be coded as 0, 1, 2, and 3. In this example, we would expect only those values of time to be used, but in datasets that do not have this balanced structure, individuals could vary in their values of Timeij.
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326 Handbook of methods in leadership research Finally, eij is a residual term that reflects errors of prediction in the individual-level growth trajectory. In other words, at each relevant measurement occasion, there will likely be some difference between the actual, observed value of the dependent variable and the value that is predicted based on the intercept and slope coefficients for that individual. The residuals are assumed to be independent and normally distributed, with a mean of zero. The version of the Level 1 model shown in Equation 13.1 could be used to fit any linear pattern of change, regardless of whether it is slow or rapid, or involves an increase or a decrease in values over time. If desired, additional πs could be included in the model to introduce higherorder terms that allow testing for curvilinear effects, such as a quadratic (squared) effect of time. The effects of additional time-varying predictors could also be incorporated in this model, such as a measure of experienced stress at each point in time. Finally, alternative assumptions about residuals could be incorporated in the model, such as whether they are heteroscedastic over time (i.e., variances are unequal) in various patterns, and/ or non-independent. The Level 2 model In growth curve analysis, one or more models are also specified at a higher level. While the Level 1 model describes how an individual changes over time, Level 2 models concern potential between-persons differences (i.e., inter-individual differences) in the values of the growth parameters of the Level 1 model. These parameters are typically – at least initially – assumed to randomly vary across individuals. Continuing on with our leadership effectiveness example, we might believe that potentially both the intercept and slope parameters can vary meaningfully between individuals. In other words, the initial value of leadership effectiveness might be relatively low for some individuals, while others have moderate or high initial values of the dependent variable. And some leaders might have a relatively rapid rate of linear change in their effectiveness over time (perhaps as they benefit from developmental training or experiences), while others change slowly or not at all. These ideas are captured in the following two Level 2 models:
p0i = g00 + u0i(13.2a) p1i = g10 + u1i(13.2b)
The model in Equation 13.2a describes individual leaders’ intercept parameters (p0i) as a function of a latent mean population intercept value (g00), and (u0i), a term that reflects the deviation of the individual’s intercept value from the mean intercept value. Similarly, Equation 13.2b
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Leadership-related change with a growth curve approach 327 describes an individual slope parameter (p1i) as a function of a latent mean population slope parameter (g10) and a deviation of the individual slope parameter from the mean slope (u1i). The values of g00 and g10 are estimated as fixed effects, and describe the aggregate pattern of growth or change over time. More complex versions of Level 2 equations can also include additional terms on the right-hand side of the equation representing potential predictors of the values of individual intercepts and slopes. For example, a potential predictor for individual values of the intercept for leader effectiveness is the number of years of supervisory experience that a particular leader has. More specifically, we might expect that there is a positive relationship between years of supervisory experience and leader effectiveness. In this example, supervisory experience functions as a time-invariant predictor, as it has the same value across all measurement times. If this new predictor variable is added to the intercepts equation, it now looks like Equation 13.3 below:
p0i = g00 + g01Experiencei + u0i(13.3)
The coefficient for the supervisory experience variable (g01) can be tested to determine whether it is significantly greater than zero. Similarly, the previous equation for slopes (Equation 13.2b) could also have an added term if we believe that prior supervisory experience not only influences the intercept value but also affects the linear rate of change in leader effectiveness. Finally, although they might not at first glance look especially interesting, the values of u0i and u1i from Equations 13.2a and 13.2b can give researchers valuable information about the homogeneity or heterogeneity of the values of the individual growth parameters. These two random effects variables are typically reported on output in the form of two variances and a covariance. The two variances, t00 and t11, give an estimate of the extent to which there is variability across different individuals in the estimates of the intercept and slope growth parameters, respectively. The estimated values of these two variances can be tested to determine whether they are significantly different from zero. Suppose, for example, that in our study of changes in leadership effectiveness over time, the variance around the intercept is relatively small while the variance around the slope is relatively large. This would suggest that while most individuals were similar in their level of leadership effectiveness at the start of the study, there was substantial variability in the extent (and perhaps direction) of their changes in effectiveness over time. In addition, the covariance between the individual intercept and slope values, t01, indicates the extent to which individual intercept and slope
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328 Handbook of methods in leadership research values relate to each other, and its estimate can also be tested to determine whether it is significantly different from zero. For example, in our illustration, a positive, non-zero covariance would indicate that leaders with higher initial levels of effectiveness also tend to improve at a faster rate than those with a low initial level of effectiveness, as might be expected if a third variable such as leadership motivation or readiness to learn influenced both one’s initial level of leader effectiveness and one’s rate of increase in effectiveness. A negative covariance might occur, on the other hand, if leaders at very high initial levels of effectiveness did not have much room to improve further so had low rates of change, while leaders at low initial levels of effectiveness could make easy changes in behavior that rapidly changed their levels of effectiveness. Finally, note that the separate Level 1 and Level 2 models are sometimes combined into a single equation, by substitution (see, for example, Bryk & Raudenbush, 1992), and the interpretation of output from some analytic programs can be easier if you are familiar with this single equation expression of the GCM. Also, note that for most GCMs taking a multilevel approach, two levels such as have just been described are sufficient. But in some cases, an additional level of nesting is appropriate. For example, one might be looking at changes in followers over time, and those followers might in turn be nested in different work groups. In that case, a three-level model (with work group at the highest level) would be desirable. This type of situation is one where the multilevel modeling approach has an advantage over the SEM approach, as it is possible to specify and estimate such three-level models fairly easily. Short Overview of the SEM Approach to GCM As illustrated in the path diagram of Figure 13.1, the structural equation modeling approach to latent growth curve modeling essentially involves a specialized application of factor analysis, using means and covariance analysis (e.g., Meredith & Tisak, 1990; Willett & Sayer, 1994). (See Kline, 2016, Chapter 15, for an introduction to working with means structures in SEM.) In the GCM factor model, one or more common factors that represent change over time are specified, using the repeated measurements of the dependent variable as multiple indicators of the latent factors. Assuming that we are modeling linear growth, two latent factors would be specified: an intercept factor and a slope factor, labeled as “FI” and “FS” respectively in the path diagram of Figure 13.1. Sometimes these are referred to as chronometric factors. The latent means of these factors – illustrated in the path diagram by the paths leading from the triangle above the factors – are estimates of the population mean intercept and slope values that corre-
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Leadership-related change with a growth curve approach 329
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Note: The rectangles towards the bottom of the figure represent the repeated measurements of the dependent variable Y at four different measurement occasions. Towards the top of the figure, the circles labeled “FI” and “FS” are the intercept and slope latent factors respectively. These two latent factors have freely estimated error variances, and are allowed to freely covary. A pattern of fixed factor loadings with values of “1” is used to specify the intercept factor. A pattern of fixed factor loadings with values of 0–3 is used to specify the slope factor. The triangle near the two latent chronometric factors indicates that their means are estimated. Finally, each of the measured dependent variables has a latent residual term, e1–e4.
Figure 13.1 P ath diagram depicting an SEM model specifying a linear growth trajectory spond to the g00 and the g10 in the Level 2 equations of the multilevel model approach that was previously described. In addition, the variances of these factors provide estimates of what were termed t00 and t11 in the multilevel context, and the covariance between the two factors estimates t01.
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330 Handbook of methods in leadership research The manner in which the factor model for GCMs is specified differs from a standard CFA model in that it tends to have a larger number of fixed factor loadings. These fixed loadings help to define the chronometric factors in a pre-specified manner that describes the desired pattern of change, for example, constant, linearly increasing/decreasing, quadratic, or non-linear change. Depending upon the particular form of growth trajectory that is expected, the values of these fixed loadings will differ somewhat. However, to achieve model identification, at least one loading for each chronometric factor must be fixed to a pre-specified value, rather than freely estimated, and for each factor except the intercept factor, one loading must be fixed to zero (McArdle & Nesselroade, 2014). For example, suppose you want to fit a linear growth trajectory for balanced data with four measurement occasions, spaced a month apart for all participants in the study. In Figure 13.1, the rectangles along the bottom of the diagram represent the repeated values of the focal dependent variable (e.g., leader effectiveness), labeled as “Y,” with a subscript to denote the time of measurement. Each of the Y variables has a latent residual term (i.e., e1–e4). Note that the factor loadings for the intercept factor have all been fixed to a value of “1,” as the intercept retains a constant value across all four measurement occasions. In contrast, the factor loadings for the slope factor represent a time multiplier for the value of the slope, analogous to the values of the Timeij variable in the multilevel approach. In the illustration, at the first measurement occasion (t1), the factor loading is fixed to a value of “0.” Because we have equally spaced times of observation for all participants in this example, at Times 2, 3, and 4, respectively, the fixed values of the factor loadings are “1,” “2,” and “3.” With this set of fixed factor loadings (sometimes called basis weights), the intercept factor mean refers to the estimated population mean value of the dependent variable on the Time 1 measurement occasion (i.e., the occasion coded “0”), and the slope factor mean reflects the change in the level of the dependent variable for a one-unit change in time. Further considerations in coding for time Depending upon the specifics of one’s study, it might be useful to employ alternative weights for the slope factor loadings. For example, continuing with the example introduced in the previous paragraph, suppose that an intervention was made at the second measurement occasion, so that you wanted the estimated intercept value to reflect the mean level of the dependent variable at that point in time. In that case, you might prefer to use values of –1, 0, 1 and 2 for the fixed factor loadings on the slope factor. Alternatively, suppose that you wanted the estimated slope coefficient to be interpretable as the change from Time 1 to Time 4. To do
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Leadership-related change with a growth curve approach 331 that, you could use proportional values, making the difference between the factor loadings for the first and last measurement occasions equal to one unit, with the loadings for Times 2 and 3 falling proportionally in between, thus you could choose fixed loading values of 0, 1/3, 2/3, and 1. (Although described here in the section on the SEM approach, the same logic can be applied in choosing values for the Timeij variable if the multilevel approach is used.) Note that any of the three different sets of fixed factor loadings just described would result in the same value for the overall fit of the GCM. However, these choices will affect the values of some of the estimated model parameters. More specifically, when different values of the fixed factor loadings are used, the slope mean and variance parameters will not change, and neither will the error variances. However, the intercept mean and variance will change and so will the covariance of the slope and intercept (McArdle & Nesselroade, 2014). The previous examples of values for fixed factor loadings were for data collected at equally spaced intervals. Yet sometimes there may be good reasons to collect data at unequally spaced intervals. In such situations it may be worth considering whether a set of factor loadings that reflects the unequal spacing might be of use. For example, Boswell, Shipp, Payne, and Culbertson’s (2009) study of honeymoon and hangover effects provides a nice illustration of the application of GCM to the study of job satisfaction in the context of work socialization. In their study, they collected data on newcomer job satisfaction at four points in time, specifically: (a) Time 1, the first day on-the-job; (b) Time 2, three months after Time 1; (c) Time 3, six months after Time 1; and (d) Time 4, a year after Time 1. Notice that the time interval between Times 3 and 4 is twice as large as the interval between Times 2 and 3. Importantly, they had an a priori rationale for this data collection schedule, based on both previous socialization research and input from knowledgeable organization members. Although their published results suggest that they likely used a 0, 1, 2, 3 coding for time in their analyses, which indeed may be quite appropriate, they could also have considered values that reflect the unequal time intervals. One such fixed factor loading pattern would be 0, 1, 2, 4. (Astute readers might also notice that one way of interpreting such a loading pattern is that there is a “missing data collection occasion” halfway in between Time 3 and Time 4, for which no person in the sample has data.) To give another example of this issue that allows some more elaboration of the implications of choice of the fixed factor loading values, consider the following schedule of data collection at four points in time, with unequal intervals: Time 2 data were collected at two weeks following Time 1, Time 3 data were collected at six weeks following Time 1, and Time 4 data were
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332 Handbook of methods in leadership research collected at 12 weeks following Time 1. The choice of fixed loadings on the slope factor could accommodate this and reflect the differences in the time intervals between observations. If you used values of 0, 2, 6, and 12 as your fixed loadings, the estimated slope coefficient would reflect the mean population change in the dependent variable for a time unit of one week. Or, you could use values of 0, 1, 3, and 6, in which case the slope coefficient now provides an estimate of the change over a two-week period. Or, you might alternatively prefer to use values of 0, 0.17, 0.50, and 1. This latter coding would make the slope coefficient reflect the mean population change over the time period spanning from Time 1 to Time 4 – a period of three months. Notice that in determining these values, it does not matter whether the actual units of time are minutes, days, months, years, or any other unit. The key idea is to reflect the spacing between measurement intervals. Specification of curvilinear, non-linear and other alternative growth trajectories Two factors – specifying a linear growth trajectory – may be sufficient to describe the pattern of change over time in your data. However, it is not unusual for there to be a second- or even higher-order component to the growth trajectory. A curvilinear (i.e., polynomial) growth pattern that includes a quadratic effect can be specified by adding a third chronometric factor, and fixing the loadings from that factor to values equal to the square of the corresponding linear factor value. For example, factor loadings on the quadratic factor for Y1 to Y4 would be 0, 1, 4, and 9 (i.e., 02, 12, 22, 32), if the linear factor (FL) had loadings of 0, 1, 2, and 3. (A similar approach to specifying a quadratic term can be taken if you are using the multilevel approach, by adding another term to the Level 1 equation, consisting of p2iTimei2j. Also, a corresponding additional Level 2 equation could be added if you wish to determine variability around this component.) In addition, you might want to investigate an alternative re-parameterization of the quadratic model developed by Cudeck and Du Toit (2002), which allows for the estimation of the quadratic function’s minimum and maximum values, instead of the more familiar slope and quadratic components. A cubic effect could also be specified following a similar strategy in which the fixed factor loadings are the slope coefficients, taken to a power of three. However, although the quadratic, and sometimes the cubic, models have been used fairly extensively by researchers who want to accommodate deviations from linearity in their models, they often imply an unrealistic pattern of growth if used to predict the value of the dependent variable at time points beyond the final time of observation.
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Leadership-related change with a growth curve approach 333 For example, a quadratic function might fit a growth trajectory that rises rather quickly initially but then slows down substantially, but its extension into future time periods might imply that the level of the dependent variable at some point decreases over time, a condition that is probably not true for variables such as leadership identity, effectiveness, and so on. That drawback might make other – non-linear as opposed to polynomial – functions more attractive, even though they may be somewhat more difficult to implement. Indeed, many processes that at least partly have biological underpinnings – such as learning, or some of the biometric indicators discussed by Dixon, Webb, and Chang in Chapter 7 of this volume – are likely to change in a non-linear manner (e.g., Grimm, Ram, & Hamagami, 2011). Non-linear forms include exponential and logistic functions, as well as other possibilities. If you wish to fit growth trajectories that you believe have a nonlinear – rather than a curvilinear – form, you have some reading ahead of you as they will not be covered in detail in this chapter, but the investment of time could be very rewarding! As a starting point, you may want to see Grimm et al. (2011) for an excellent general overview. Two additional options might be considered when fitting complex growth trajectories. The first of these is the piecewise latent growth model (e.g., Bryk & Raudenbush, 1992). The piecewise model is especially useful when the nature of your sample is such that you might expect an abrupt change in slope at some point in time. Often, such changes can occur when your measurement period spans a transition of some sort. For example, perhaps you have a series of measurements of leadership identity over time from a group of managers. The first several measurement times occur before a promotion, and the remaining measurement times follow the promotion. We might expect a moderately high but flat or only slowly increasing level of leadership identity before the promotion, as these managers have been functioning in their current leader roles for a period of time. Following promotion, there may be a sudden rapid change in leader identity as the managers engage in cycles of identity claiming and granting with new subordinates and peers (e.g., DeRue & Ashford, 2010). This type of model can be fit by having two slope factors, rather than one. Pre-promotion identity measures would load on the first slope factor and post-promotion identity measures would load on the second, with the promotion as the point of inflection for the piecewise growth trajectory. For a published example of the application of this type of model, see Li, Duncan, Duncan, and Hops (2001). The second alternative model that can be helpful to consider is the fully latent curve model (McArdle, 1988; Meredith & Tisak, 1990). In this model, a subset of the fixed factor loadings is freed so that they can be
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334 Handbook of methods in leadership research estimated. The resulting estimates can be compared to the values of fixed loadings that would specify growth curves of specific forms, to indicate the extent of variability from those functional forms. Specifying such models so that they achieve identification has some subtleties, you may wish to consult Ghisletta and McArdle (2001) for an example. Finally, it should be at least briefly mentioned that one important advantage of the SEM approach to GCM is that it is relatively easy to specify models in which the dependent variable (e.g., leader effectiveness, leader identity, etc.) is latent, rather than measured. The GCMs that have been considered in this chapter so far have had manifest (measured) dependent variables, thus they fall into the category of “first order latent growth curve models.” When the dependent variable is latent, the GCM is frequently called a “second-order latent growth curve model” or a “curve of factors model” (McArdle, 1988). In such models, the dependent variables are latent factors with multiple indicators, all measured at the appropriate points in time. An advantage of using latent dependent variables is that reliability is increased because measurement error can be separated from true variance. Greater reliability might improve the ability to model the change and to find statistical significance for covariate relationships. Another advantage of latent dependent variables is that you can directly test the measurement invariance of the dependent variable across time, using a multiple group analysis. A Quick Note about Residual Structures Residual terms (e1–e4 in the SEM approach or eij in the multilevel approach) represent the variance in the Yt variables that is not explained by any of the chronometric factors (and any other variables that are modeled as having direct effects on Y at a given time, such as time-varying covariates). An advantage of using an SEM approach to estimating latent growth curves is that the residual terms can be flexibly modeled and tested. The default assumption in the specification of the GCM discussed so far in this chapter has been that the residuals are homoscedastic (i.e., of equal magnitude across time) and independent (i.e., uncorrelated with each other) once the growth component of the model has been properly specified. These assumptions are often unrealistic with longitudinal data. Researchers using the SEM approach may place additional – or relax existing – constraints on the error terms. For example, in most software packages by default the covariances among the residuals are all fixed to zero (i.e., independent/uncorrelated residuals), however, some of these restrictions might be relaxed, allowing adjacent error terms to covary, as would be implied by an autoregressive error structure. The multilevel
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Leadership-related change with a growth curve approach 335 modeling approach to GCM also allows for the investigation of autoregressive and heterogeneous error structures (e.g., Curran & Bollen, 2001), although not as flexibly as in the SEM approach. As mentioned earlier in the chapter, once the best fit to a functional form has been established, it is important to test alternative error structures. For an introduction to this issue, see Singer and Willett (2003). For further study, Wu, West, and Taylor (2009) have a good – if somewhat technical – discussion of sources of misspecification in GCMs and the variety of fit indices that may be employed to help determine the sources of misfit in one’s model. Conditional GCMs: Adding Covariates As already indicated in the section on the multilevel modeling approach, once the general form of the growth trajectory is successfully modeled, then additional predictor variables can be added to the model. These variables are typically mean-centered before being included in the analysis, to aid in the interpretation of the resulting parameter estimates. Figure 13.2 shows, in path diagram form, a latent growth curve model that includes generic time-invariant and time-varying predictor variables. The effects of time-invariant covariates can be tested for statistical significance to determine whether they influence intercepts and rates of change (i.e., a linear slope, quadratic term, etc.), while time-varying covariates can be tested to determine whether they affect the values of the dependent variable directly. For example, in the Day and Sin (2011) article, leader identity at each measurement occasion was a significant time-varying predictor of ratings of perceived leader effectiveness, while various types of goal orientation predicted intercept and slope values.
PRACTICAL CONSIDERATIONS Planning the Data Collection In general, longitudinal data collection involves substantial forethought and planning. Decisions need to be made about the optimal number of participants, as well as about how frequently and for how many occasions data should be collected. In making such decisions, you will likely want to balance requirements based on theory, a knowledge of what previous researchers have done and statistical requirements, with competing considerations of cost and accessibility. The desirable sample size depends upon several factors, including the functional form of the growth curve being estimated (more complex forms will require larger samples) and the number of
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336 Handbook of methods in leadership research Time-invariant Covariate
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Figure 13.2 P ath diagram for linear growth trajectory with freely estimated time-invariant covariate effects directly on intercept and slope parameters measurement occasions (to reach a given level of statistical power, you need more persons in the study if the number of measurement occasions is small). In general, maximum likelihood estimators require larger sample sizes, and also the statistical power to detect effects increases with a greater number of persons. Statistical power will be decreased when there are missing data.
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Leadership-related change with a growth curve approach 337 For further specifics, you might wish to consult Zhang and Wang (2009) for an example of how to implement a power analysis via SAS macros. When it comes to the issue of choosing how many measurement occasions to have, a minimum of three is advisable even for relatively simple growth models. Although technically you could fit a linear growth trajectory with only two measurement occasions, having only two measurement periods does not provide the opportunity to disconfirm a linear form, much less compare it to a more complex functional form, as a straight line will fit any two points perfectly. To demonstrate deviations from linearity, you must have at least three measurement occasions. In addition, if you expect to fit a polynomial trajectory (e.g., quadratic, cubic), you should always have at least one more measurement occasion than the highestpowered term in the equation for your functional form. For example, if you are fitting a quadratic form, the highest power would be 2, so you would need an absolute minimum of 2 + 1 5 3 measurement occasions, and it would be preferable to have more than three measurement occasions in order to help disconfirm a quadratic form if it is not the correct one. Also, as the number of measurement occasions is increased, the precision of the estimated growth parameters (e.g., intercept and slope coefficients) increases. Yet, a desire for precision might need to be balanced with practical concerns. For example, too many measurement occasions might lead to participant fatigue and dropout or careless responding, and will certainly increase the costs and effort required. How to space occasions of measurement depends upon the particular phenomenon you are trying to model. Some processes, such as the development of leader–member exchange relationships might be expected to occur fairly rapidly, and then remain relatively stable over time. The measurement occasions for such processes probably span days or weeks. In contrast, the development of certain leadership skills might take months or years, thus measurement occasions for these variables should be spaced much further apart over time. Other aspects being equal, you also might want to consider whether longer intervals could lead to greater dropout from the study or whether shorter intervals might result in too much carry over in responding from the previous measurement occasion. Finally, it is critical to be certain that you have designed your data collection procedure to allow you to link responses from the same participant across all measurement occasions. Structuring the Dataset for Analysis Depending upon your choice of statistical analysis packages, your dataset will need to be either in one of two different forms, described by Singer
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338 Handbook of methods in leadership research Table 13.1 I llustration of two dataset forms: (a) person-level dataset, one data record per individual; (b) person-period dataset, one data record per measurement occasion per individual (a) Person-level dataset Person
Repeated dependent variable Time-invariant covariate Efficacy1 Efficacy2 Efficacy3
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Note: The same values are displayed in each dataset.
and Willett (2003) as either a person-level dataset or a person-period dataset (Table 13.1). As can be seen in the table, the person-level dataset is more similar to the datasets typically used for other types of analysis, in that it has a “wide” or horizontal structure in which each person in the dataset has a single data record. The dependent data measures from the different time points are saved with different variable names. For example, you might name multiple measures of a leadership self-efficacy measure taken at different points in time as “Efficacy1,” “Efficacy2,” “Efficacy3,” and so on, to make clear which measurement occasion each one is associated with. Time-invariant covariates, such as gender or supervisory experience are indicated with a single variable for each. In contrast, time-varying covariates must be saved as multiple variables, in a manner similar to that
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Leadership-related change with a growth curve approach 339 used for the dependent variable. For example, if you wanted to treat negative affect as a time-varying covariate, you would need to have a set of variables such as “NegAff1,” “NegAff2,” “NegAff3,” and so on. Person-level datasets are more typical when a structural equation modeling approach is used to estimate the GCM. In contrast, as illustrated in Table 13.1, person-period datasets have multiple data records for each person in the dataset. Because this typically results in a dataset with many lines of data, it is sometimes called a “long form” dataset. Each line of data contains the values for one individual, for one specific measurement occasion. For example, if a balanced study had data collected from 100 persons at four points in time, there would be 400 data records in the dataset. In contrast to the person-level dataset, this data form typically has a variable that explicitly indicates time (e.g., the measurement occasion, age at which measure was taken, etc.). Ideally, you would check on whether a person-level or person-period dataset is required for your analytic package before entering your data into a dataset, and then input the data accordingly. However, if you end up with your data in the wrong form, it can generally be easily transposed. Most broad purpose data analysis packages such as SPSS, SAS, or Stata have a procedure that allows you to move from one form to the other. Indeed, even if you plan to use a more specialized software package for the GCM analyses, it is often easier to use a more general analysis package such as one of those mentioned above for those data tasks that need to take place before estimating the GCMs, such as screening for outliers, creating scale scores from survey items, and assessing reliability. Preliminary Data Steps Among the preliminary analysis steps to undertake, you should try to get a sense of the shapes of individual growth trajectories to see if the function (linear, curvilinear, non-linear, etc.) that you intend to fit is even plausible for your data. One way in which this is often done is to produce graphic displays of the individual data points, nested within individuals. An example of this is shown in Figure 13.3, which displays the pattern of data points for six different individuals who all have measurements taken at five points in time. The variability in patterns of the data points over time for different individuals shown in this figure is fairly typical. Suppose one wanted to fit a linear growth trajectory to these data. Although none of the figures shows a strictly linear pattern of change, this form would not be dismissed out of hand as several of the plots have a somewhat linear form, and all appear to show a general positive (upward) trend. A variation on this type of individual plot adds a fitted
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ID = 4 6 5 4 3 2 1 0 1
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340 Handbook of methods in leadership research ID = 10 6 5 4 3 2 1 0 1
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Figure 13.3 P lot of data over time by individual, showing varied patterns of individual growth or change (linear) regression line to the data points in each plot, rather than simply connecting adjacent values. Figure 13.4 shows another way in which this issue can be explored. It depicts a “spaghetti plot” in which all (or for large datasets, a sizeable sample
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Leadership-related change with a growth curve approach 341 6
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Figure 13.4 S paghetti plot showing fitted linear regression lines for multiple individuals on the same axes chosen at random) of the individual growth trajectories are displayed using the same set of axes. Again, there can be variations on this type of figure in whether different trajectories are fitted to the individual lines, or whether raw data points are simply connected. In addition to graphic displays, you can also simply estimate linear regression models for each individual’s data (using time as a predictor variable) and inspect the resulting coefficients, to get a sense of the range of intercept and slope values that would result from fitting a linear function. For more detail on producing and interpreting plots and other preliminary analysis procedures, see Singer and Willett (2003). Assessment of Model Fit and Parameter Estimates Interpretation of the results from the estimation of GCMs involves both an assessment of the adequacy of model fit, and significance tests of
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342 Handbook of methods in leadership research specific estimated parameters. The initial focus should be on fitting the growth trajectory, without including any of the intended covariates in the model. In the multilevel approach, the assessment of overall model fit has tended to emphasize comparisons of alternative models. SEM approaches typically also involve comparisons of alternative models, but in addition tend to consult a larger number of model fit indices, including the likelihood ratio test statistic (LRT, also commonly called the chi-square goodness of fit statistic), the RMSEA, CFI and TLI. Comparisons of nested models can be made with the LRT, and non-nested models can be compared with the AIC or BIC. An initial step in data analysis is often to fit two simple models for comparison purposes. These allow assessing: (1) whether there is sufficient variance to justify a multilevel analysis and (2) whether there is evidence for any form of growth or change. Singer and Willett (2003, Chapter 4) term these the unconditional means model and the unconditional growth model. The unconditional means model is extremely simple. It does not involve a time predictor at all, but merely partitions the total variance in the dependent variable into two portions – variability associated with differences across time points within a person, and variability associated with differences in mean levels across individuals. If variance at either of these levels is zero or very close to zero, it does not make sense to try to predict the outcome at that level. The unconditional growth model is slightly more complex – it does include a linear time predictor in the Level 1 model, but the Level 2 models do not have any additional predictors (i.e., they are simply in the form of Equations 13.2a and 13.2b). If the linear slope parameter is not statistically significant, it may mean that (on average) there is no change over time in the dependent variable, or that the change takes a complex non-linear form such as an oscillator. A comparison of results from these two models, along with results from any more complex forms of growth trajectory (such as polynomial forms) that are deemed plausible can lead to a determination of the best fitting functional form. This should be followed by some tests of alternative models to determine whether the error structure is properly specified. Estimations of models involving covariate effects should only be attempted after these preliminary models are satisfactorily specified. There is considerably more detail that should be attended to in this process, but it is beyond the scope of this overview chapter. If you plan to use GCM, you should read further in Singer and Willett (2003) or one of the other many excellent sources on the topic (see mention of some of these in the final section of this chapter).
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Leadership-related change with a growth curve approach 343
CONSIDERATIONS, LIMITATIONS, AND CONCLUDING REMARKS The goal of this chapter has been to make at least a mention of many aspects of growth curve modeling and to encourage you to apply it to your own leadership research interests. However, if you want to become proficient in this technique, you should devote some additional time to more study of the finer details of the technique. There are many excellent books with very readable descriptions, and in many cases, they provide sample syntax for various statistical packages. For example, the Singer and Willett (2003) book cited in this chapter also has a companion website with many examples that include both multilevel and SEM approaches. Other helpful books for newcomers include Duncan, Duncan, and Strycker (2006), and Wickrama et al. (2016). Persons specifically interested in the multilevel modeling approach would do well to refresh their acquaintance with relevant sections of Bryk and Raudenbush (1992). It is also helpful to carefully read other research studies that have applied GCM to see how their authors formulated their research questions and then went about addressing them analytically. Although many of the methodological and statistical issues have received emphasis in this chapter, it is critical to remember that a key ingredient for a top-quality GCM study is a thorough grounding in theory. Even if there are some exploratory aspects to your empirical investigation, your ability to interpret results depends upon your understanding not only of the analytic technique but how those results fit in with a body of literature. For example, the Day and Sin (2011) study described at the start of this chapter was firmly grounded in theories of leadership development. And, an important idea underlying the Jokisaari and Nurmi (2009) study that was described is Fichman and Levinthal’s (1991) idea of the honeymoon in interpersonal relationships. Theory may also inform decisions about how frequently measurements should be made and how many might be necessary to address your research issue. This chapter described both the multilevel and the SEM approaches to GCM. For many applications, either would be a reasonable choice, and which one is chosen might simply be based on researcher preferences and familiarity with a particular software. However, as has been discussed by various authors, including Lindenberger and Ghisletta (2004), there may be factors that make one approach preferable to the other. For example, the multilevel approach better handles datasets with unbalanced data and also those where there are a large number of patterns of missing data. The SEM approach offers a wider variety of fit indices, some of which are sensitive to sources of misfit that cannot be specifically identified with
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344 Handbook of methods in leadership research the multilevel approach (e.g., Wu et al., 2009). The SEM approach is also preferred if you want to use latent dependent variables, and when you want to look at relationships between trajectories for two different sets of dependent variables. Finally, there are a number of techniques that were not covered in this overview chapter, but that might be interesting directions for further learning. Wickrama et al. (2016) provide illustrations and guidance on a wide variety of growth curve models. In addition, when the sample contains different groups with known membership for whom it is believed the growth trajectories might differ, multiple group growth curve modeling using an SEM approach provides a fairly straightforward extension of single group models. This type of technique would allow testing for differences in leadership development trajectories for men versus women, or for groups receiving different leadership interventions. When group membership is not known in advance, but it is expected that there might be heterogeneity in growth trajectories, latent growth curve mixture modeling can be used to identify different patterns of change over time – the chapter by Pastor and Gagné (2013) on mean and covariance structure mixture modeling provides a very readable illustration of a linear growth mixture model. The Day and Sin (2011) article also provides an illustration of a similar approach, described in detail in Nagin’s (2005) book. In sum, GCM provides a flexible and useful tool for furthering our understanding of dynamic processes and relationships in the domains of leadership and followership. Finding an appropriate source of longitudinal data and learning to properly use GCM takes some investment in time and effort to achieve, but the results are likely to advance our understanding of dynamic leadership and followership processes.
REFERENCES Bentler, P.M. (2006). EQS 6 structural equations program manual. Encino, CA: Multivariate Software. Boswell, W.R., Shipp, A.J., Payne, S.C., & Culbertson, S.S. (2009). Changes in newcomer job satisfaction over time: Examining the pattern of honeymoons and hangovers. Journal of Applied Psychology, 94(4), 844–858. Bryk, A.S., & Raudenbush, S.W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101(1), 147–158. Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage. Cudeck, R., & Du Toit, S.H.C. (2002). A version of quadratic regression with interpretable parameters. Multivariate Behavioral Research, 37(4), 501–519. Curran, P.J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38(4), 529–569.
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Leadership-related change with a growth curve approach 345 Curran, P.J., & Bollen, K.A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In L.M. Collins and A.G. Sayer (Eds.), New methods for the analysis of change. Washington, DC: American Psychological Association. Curran, P.J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11(2), 121–136. Day, D.V., & Dragoni, L. (2015). Leadership development: An outcome-oriented review based on time and levels of analyses. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 133–156. Day, D.V., & Sin, H.-P. (2011). Longitudinal tests of an integrative model of leader development: Charting and understanding developmental trajectories. The Leadership Quarterly, 22(3), 545–560. Day, D.V., Fleenor, J.W., Atwater, L.E., Sturm, R.E., & McKee, R.A. (2014). Advances in leader and leadership development: A review of 25 years of research and theory. The Leadership Quarterly, 25(1), 63–82. DeRue, D.S. & Ashford, S.J. (2010). Who will lead and who will follow? A social process of leadership identity construction in organizations. Academy of Management Review, 35(4), 627–647. Duncan, T.E., Duncan, S.C., & Strycker, L.A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Ferrer, E., Hamagami, F., & McArdle, J.J. (2004). Modeling latent growth curves with incomplete data using different types of structural equation modeling and multilevel software. Structural Equation Modeling, 11(3), 452–483. Fichman, M., & Levinthal, D.A. (1991). Honeymoons and the liability of adolescence: A new perspective on duration dependence in social and organizational relationships. Academy of Management Review, 16(2), 442–468. Goldstein, H. (1995). Multilevel statistical models (2nd ed.). London: Edward Arnold. Goldstein, H., Rabash, J., Plewis, I., Draper, D., Browne, W., Yang, M.,. . .Healy, M. (1988). A user’s guide to MLwiN, version 1.0. University of London, Institute of Education. Ghisletta, P., & McArdle, J.J. (2001). Latent growth curve analyses of the development of height. Structural Equation Modeling, 8(4), 531–555. Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. Grimm, K.J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82(5), 1357–1371. Hannah, S.T., Avolio, B.J., Walumba, F.O., & Chan, A. (2012). Leader self and means efficacy: A multi-component approach. Organizational Behavior and Human Decision Processes, 118(2), 143–161. Jokisaari, M., & Nurmi, J.-E. (2009). Change in newcomers’ supervisor support and socialization outcomes after organizational entry. Academy of Management Journal, 52(3), 527–544. Jöreskog, K.G., & Sörbom, D. (2015). LISREL 9.20 for Windows. Skokie, IL: Scientific Software. Kline, R.B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Press. Laird, N.M., & Ware, J.H. (1982). Random effects models for longitudinal data. Biometrics, 38(4), 963–974. Lester, P.B., Hannah, S.T., Harms, P.D., Vogelgesang, G.R., & Avolio, B.J. (2001). Mentoring impact on leader efficacy development: A field experiment. Academy of Management Learning and Education, 10(3), 409–429. Li, F. Duncan, T.E., Duncan, S.C., & Hops, H. (2001). Piecewise growth mixture modeling of adolescent alcohol use data. Structural Equation Modeling, 8(2), 175–204. Lindenberger, U., & Ghisletta, P. (2004). Modeling longitudinal changes in old age: From covariance structures to dynamic systems. In R. Dixon, L. Backman, & L.-G. Nilsson (Eds.), New frontiers in cognitive aging (pp. 199–216). New York: Oxford University Press.
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346 Handbook of methods in leadership research Lord, R.G., & Hall, R.J. (2005). Identity, deep structure and the development of leadership skill. The Leadership Quarterly, 16(4), 591–615. Lord, R.G., Hall, R.J., & Halpin, S.M. (2010). Leadership skill development and divergence: A model for the early effects of sex and race on leadership development. In S.E. Murphy & R.J. Reichard (Eds.), Early development and leadership: Building the next generation of leaders. New York: Psychology Press/Routledge. McArdle, J.J. (1988). Dynamic but structural equation modeling of repeated measures data. In J.R. Nesselroade & R.B. Cattell (Eds.), Handbook of multivariate experimental psychology (pp. 561–614). New York: Plenum. McArdle, J.J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58(1), 110–133. McArdle, J.J., & Nesselroade, J.R. (2014). Longitudinal data analysis using structural equation models. Washington, DC: American Psychological Association. Meredith, W. & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107–122. Muthén, L.K., & Muthén, B.O. (1998–2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén. Nagin, D.S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press. Pastor, D.A., & Gagné, P. (2013). Mean and covariance structure mixture models. In G.R. Hancock and R O. Mueller (Eds.), Structural equation modeling: A second course. Charlotte, NC: Information Age Publishing. Rabash, J., Steele, F., Browne, W.J., & Goldstein, H. (2016). A user’s guide to MLwiN, version 2.36. University of Bristol, Centre for Multilevel Modelling. Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata (Vol. II, 3rd ed.). College Station, TX: Stata Press. Rogosa, D.R., & Willett, J.B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50(2), 203–228. SAS. (n.d.). The mixed procedure. SAS/STAT(R) 9.2 user’s guide (2nd ed.). Retrieved from https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm #mixed_toc.htm Shafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford: Oxford University Press. StataCorp (1996–2016). Stata statistical software. College Station, TX: StataCorp LP. Wickrama, K.A.S., Lee, T.K., Walker O’Neal, C., & Lorenz, F. (2016). Higher-order growth curves and mixture modeling with Mplus: A practical guide. New York: Routledge. Willet, J.B., & Sayer, A.G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116(2), 363–381. Wu, W., West, S.G., & Taylor, A.B. (2009). Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks. Psychological Methods, 14, 183–201. Zhang, Z., & Wang, L. (2009). Statistical power analysis for growth curve models using SAS. Behavior Research Methods, 41(4), 1083–1094.
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PART IV QUALITATIVE METHODS AND ANALYTIC APPROACHES
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14. Qualitative content analysis in leadership research: principles, process and application Jan Schilling
INTRODUCTION While the stream of leadership studies using qualitative research methods is still rather small, it has become much more common in recent years to use them. Different authors have called for more qualitative research in the leadership area (e.g., Bryman, 2004; Conger, 1998; Gordon & Yukl, 2004; Insch, Moore, & Murphy, 1997), not simply as an alternative but as a complement to quantitative methods (Stentz, Plano Clark, & Matkin, 2012). Rather than contempt for and rejection of qualitative methods by leadership researchers, the reasons for the small number of studies using qualitative methods may lie more in uncertainty about their exact application. While it is quite easy to find excellent introductions to theory, methodology, and qualitative data collection in general (e.g., Merriam & Tisdell, 2015; Neimeyer & Gemignani, 2003) and their application in leadership research specifically (e.g., Klenke, 2008), it is still sometimes difficult to gain access to “pragmatic knowledge,” especially when it comes to the process of qualitative data analysis and interpretation (Schilling, 2006). The present chapter does not strive to develop a process model for all qualitative research traditions, but to provide insight into the procedures and practices of one special approach: qualitative content analysis (Mostyn, 1985). As Parry, Mumford, Bower, and Watts (2014) point out in their summary of qualitative and historiometric methods in leadership research published in The Leadership Quarterly, content analysis is certainly (one of) the most widely used qualitative analytical methods in this area. Content analysis in general and qualitative content analysis in particular are valuable tools in leadership research, as the analysis of oral and written communications of leaders permits a deep-level examination of contextually rich leader and/or follower communication, which is highly relevant in the search for factors related to leader effectiveness (Insch et al., 1997). The aim of this chapter is to give guidance on how to design the process of qualitative content analysis, not the “one right way” (Tesch, 1990), but as stimulation for the qualitative researcher. 349
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PRINCIPLES OF QUALITATIVE CONTENT ANALYSIS While the general term content analysis is associated with a wide range of theoretical frameworks, methods, and techniques (Denzin & Lincoln, 2000), it aims at studying the contents, structures, and language of written (e.g., books, articles, newspaper headlines, historical documents) and transcribed texts (e.g., interviews, written observations, but also audio-visual material like TV segments or photography) (cf. Klenke, 2008; Krippendorff, 2004). As Mayring (2000) points out, quantitative and qualitative content analysis should not be regarded as opposites, but rather that the latter developed on the foundations of the first. Quantitative content analysis can be defined as a technique of systematically and objectively identifying specified characteristics (e.g., keywords, combinations of terms) of communication in order to make inferences about the frequency of certain variables (Holsti, 1969). Quantitative content analysis may include word frequencies, place (e.g., number of times a certain theme is presented on the front page of a newspaper) and space measurements (e.g., amount of space for articles on a certain theme), or time counts (e.g., amount of time a certain person gets on television). The strengths of quantitative content analysis (fitting the material into a model of communication, clear rules and steps of analysis, categories at the heart of the analysis, applying criteria of reliability and validity) were preserved in the development of qualitative content analysis (Mayring, 2000). Objections against quantitative content analysis were raised with regard to the risk of superficial and possibly distorting word counts without acknowledging latent contents and contexts. Based on this criticism, qualitative content analysis was developed to address this potential weakness. Qualitative content analysis in particular is defined as “an approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytic rules and step by step models, without rash quantification” (Mayring, 2000, p. 5). It tries to overcome the shortcomings of purely quantitative content analysis by providing clear guidelines for deriving and interpreting categories (Klenke, 2008). Based on this definition, four basic principles can be derived. First, qualitative content analysis is methodologically controlled in the sense of applying systematic procedures. Conger (1998) criticized that qualitative data analysis procedures (“the dirty work”; p. 114) are often missed out or described only vaguely in qualitative studies. Qualitative content analysis addresses this critique by defining clear-cut quality checks in the process of data analysis. This is necessary to counter the common
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Qualitative content analysis in leadership research 351 criticism that qualitative research is “not scientific,” “arbitrary” or purely “subjective” (cf. Silverman, 2000). Second, qualitative content analysis is contextual in the sense that it acknowledges the importance of the specific place and time of data collection, the background as well as relationships of the research subjects (in the case of interviews or observations) or objects (in the case of documents) for the interpretation of the results (cf. Lord in Chapter 16 of this volume). For example, in the investigation of the content of written corporate leadership principles, it is necessary to acknowledge that these documents (rather popular in many German companies) are often meant to be marketing instruments rather than real guidelines for corporate leaders. This background is crucial to understand that they mainly do not contain statements concerning negative or destructive leadership and very often refer to democratic leadership styles (e.g., Schilling, 2005) in terms of a social desirability effect. Third, the method is process-oriented as it tries to define distinguishable steps of data treatment, analysis and interpretation, which build on each other and thereby form a coherent sequence of decision and action. This feature of qualitative content analysis is not self-evident as some researchers reject the idea that qualitative research could be conducted in the form of a linear process of stages (e.g., Miles & Huberman, 1994). Even though qualitative research requires flexibility (Maxwell, 1998) in the sense that its process is often recursive and dynamic (Merriam & Tisdell, 2015), this does not mean that it is impossible to describe a course of action and concrete rules for handling verbal and textual data (Bachiochi & Weiner, 2002; Creswell, 1998). Fourth, even if it may seem like a contradiction in terms, qualitative content analysis is not only focused on qualitative data analysis, but could better be described as integrative as it often combines qualitative and quantitative analyses (cf. Insch et al., 1997; Parry et al., 2014; Stentz et al., 2012). As will be described in more detail below, defining the scope of and interpreting categories can be supported by different measures of category frequency. Rejecting rash quantification implies a cautious approach of unraveling the meaning of data and not prematurely comparing apples and oranges. This needs to be illustrated with an example. In an interview study on the meaning of leadership (Schilling, 2001), leaders often named delegation as an important facet of good leadership. Interestingly, when explaining their understanding of delegation, only some of the subjects stressed the importance of empowering, assigning responsibility and granting latitude to their followers. Many of the interviewees understood delegation as the process of passing certain tasks (“not suited for a supervisor”) to subordinates. Hence, it would have been a misapprehension of
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352 Handbook of methods in leadership research the true meaning of the subjects’ conceptions of leadership to just simply count the number of times the word “delegation” is mentioned. Based on the discussion above, one may question whether qualitative content analysis is actually an adequate term to describe the method, as it may seem too qualitative for quantitative researchers and too quantitative for qualitative researchers (e.g., Bryman, 2004). In fact, mixedmethods content analysis (e.g., Creamer & Ghoston, 2013; Schram, 2014) or combined content analysis (e.g., Hamad, Savundranayagam, Holmes, Kinsella, & Johnson, 2016) might be better terms, but are (at least until now) not commonly used, especially not in leadership research. However, Klenke (2008) warns that it would be overly simplistic to limit qualitative research to non-quantitative (or non-statistical) modes of data collection and analysis: “The qualitative paradigm embraces a diverse array of methodologies that can be mapped on a continuum ranging from purely qualitative to highly quantitative” (p. 6). Qualitative content analysis represents the more quantitative end of this continuum. Klenke (2008) offers five key features of qualitative research that can be used to illustrate this point. Qualitative (in contrast to quantitative) research is focused on subjective meanings and interpretations, is inductive (data-driven), uses purposive/theoretical sampling, derives data from the participants’ perspective, and implies flexible designs. While approaches like grounded theory (Glaser & Strauss, 1967) or phenomenology (Giorgi, 1997) are rather purist with regard to these characteristics, qualitative content analysis deviates from them in different ways. Following systematic procedures in a process of distinguishable steps of analysis naturally restricts the subjectivity and flexibility of the method (without completely eradicating them). Qualitative content analysis is open to both an inductive, data-driven approach of building categories and a deductive data analysis strategy with pre-defined category systems (see below). Likewise, purposive/theoretical sampling (i.e., intentionally selecting participants who can contribute an in-depth, information-rich understanding of the topic; Klenke, 2008) is very common, but not necessarily applied in qualitative content analysis (e.g., analysing the content of complete data bases like Dissertation Abstracts International in the study by Meindl, Ehrlich, and Dukerich, 1985). However, the basic feature of taking the participants’ perspective characterizes qualitative content analysis as a method basically rooted in the area of qualitative research. In summary, qualitative content analysis is a rule-based sequence of intertwined steps including both data treatment and quality checks that is sensitive to the context in which the data have been produced and offers opportunities for generating meaningful qualitative and quantitative results.
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Qualitative content analysis in leadership research 353
AREAS FOR QUALITATIVE CONTENT ANALYSIS IN LEADERSHIP RESEARCH As in all empirical studies, qualitative content analysis of course should be based on a literature review that induces the development of research questions (Insch et al., 1997). While the method is generally suitable for a wide variety of topics and research interests in leadership (Bryman, 2004), some areas may be especially interesting. Conger (1998) stresses the importance of qualitative research as the concept of leadership involves “multiple levels of phenomena, possesses a dynamic character, and has a symbolic component” (p. 109). From my point of view, qualitative content analysis is particularly helpful in investigating this symbolic component of leadership as the method allows for uncovering meanings and connotations of behavior, concepts, and relationships. Follower perceptions and sense-making of leader behavior are increasingly acknowledged as being of pivotal importance for the process of leadership (Lord & Dinh, 2014). Particularly approaches such as ethical leadership and abusive supervision imply a value-loaded and therefore necessarily subjective perspective on leadership. Interesting research questions in this area might include the comparison of different groups (e.g., leaders vs followers, lower- vs higher-level leaders) concerning their interpretation of behaviors in certain situations (e.g., under what circumstances is a certain behavior regarded as ethical, when may it be evaluated as unethical or abusive). As Martinko, Harvey, Brees, and Mackey (2013) point out, different subordinates perceive a supervisor as inspirational whereas others view the same supervisor as abusive (e.g., Steve Jobs, Lyndon B. Johnson). It would be very insightful to understand how different individuals interpret the same behavior (e.g., by showing video scenes or vignettes). Qualitative content analysis could be a useful tool with which to analyse the answers to open-ended questions on the perception and interpretation of certain behaviors in audio-visual scenes or written scenarios. A second area of research is concerned with the comparison between scientific and everyday understanding of leadership-related concepts. Schilling (2009) investigates the meaning of negative leadership by analysing the content of qualitative interviews with corporate leaders. He is able to show that leadership practitioners have quite elaborate views on different facets of destructive and ineffective leadership, their antecedents and consequences. Similar investigations on the meaning of charismatic, ethical, or authentic leadership (all of which are terms used in everyday language) could be stimulating for future research. Similarly, while the majority of research on implicit leadership theories (ILTs; e.g., Cronshaw
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354 Handbook of methods in leadership research & Lord, 1987; Offermann, Kennedy, & Wirtz, 1994) has been quantitative in nature, qualitative content analysis can complement existing evidence as it investigates what kind of characteristics people have in mind when they talk about typical or ideal leaders (cf. Schyns & Schilling, 2011). For instance, it would be interesting to analyse archival data like newspapers, business periodicals, Internet sources or corporate leadership principles (cf. Meindl et al., 1985; Schilling, 2005) to find out which characteristics and skills leaders are expected to possess. Finally, research on the relationship between leaders and followers (e.g., leader–member exchange; Liden, Sparrowe, & Wayne, 1997; Schyns & Day, 2010) could benefit from qualitative content analysis. It could, for example, be used to systematically analyse textual diary data from leaders and/or followers on the development of their relationship: which kind of evaluations, terms and emotions are used at different points of time to describe the other person and one’s relation with him or her. Contrasting the views of the leader and his or her respective followers on the quality and development of their relationship would be particularly interesting to get a deeper understanding of the dynamics and subjectivity of leader– member exchanges. These are, of course, just some examples of possible research questions to illustrate different applications of this method that can be a valuable aid to better understand the complexities and subtleties of perception and sense-making in the area of leadership.
PROCESS AND APPLICATION OF QUALITATIVE CONTENT ANALYSIS One criticism might be that it is insufficient just to look at the data analysis process (Schilling, 2006). The choice of method should always depend on the research question(s) (Silverman, 2000). While it is important to discuss each step in the data analysis process with recourse to the chosen conceptual framework (e.g., implicit leadership theories; Schyns & Schilling, 2011), it will be shown that there are concerns and problems in the course of qualitative content analysis that transcend the boundaries of different theoretical perspectives. The following description aims to give an overview on the decisions a researcher has to take at different stages of the process of qualitative content analysis. As its focus lies on the analysis of themes, it may seem strange to start an overview on content analysis with data collection, but, as Merriam and Tisdell (2015) have pointed out, qualitative research emphasizes the importance of beginning analysis early. As will be illus-
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Qualitative content analysis in leadership research 355 trated below, this is important as decisions (often implicitly) made in this phase may restrict possible data analyses at later stages. Collecting the Data It is important to distinguish between two fundamental forms of data collection as the basis for qualitative content analysis. Researchers may use qualitative content analysis with regard to generated (actively creating material, e.g., interviews; cf. Bresnen, 1995; Schyns & Schilling, 2011; Sims & Lorenzi, 1992; Waldman et al., 1998) or collected texts (searching for already existing material, e.g., articles in newspapers and business periodicals, Meindl et al., 1985; corporate leadership principles, Schilling, 2005; or presidential speeches, Bligh, Kohles, & Meindl, 2004). With regard to generated texts, most qualitative researchers (e.g., Silverman, 2000) recommend tape-recording interviews to make sure that their content is exactly retained. It should be noted though that tape recording may deter some potential interviewees from participating in the study. Also, it may be important to clearly document the context of data collection (e.g., in the form of a contact or document summary sheet; Miles & Huberman, 1994), as it may influence the data and should be considered when interpreting results. Basic questions should include (Schilling, 2006): Who are the interviewees (e.g., position in the firm, team/department)? is their relationship with the interviewer (e.g., personal contacts, unknown volunteers)? ● When (e.g., during work or leisure time, which year and month) and under which circumstances (e.g., during a process of downsizing in their company, during a crisis) were the interviews done? ● Where were the interviews done (e.g., in the office of the interviewees, at their homes, in a conference room)? ● In which context were the interviews done (e.g., as part of a management learning project, as a research project unconnected to organizational developments)? ● Were there any disturbances or outstanding reactions from (some of) the interviewees (e.g., comments after the “official” interview ended, mails before or after the interviews)? ●
● What
Likewise, if the study involves collected texts, the researcher should carefully document the following aspects: ● ●
the source (what is the origin of the document?); aim (what is the intention and target group of the document?);
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356 Handbook of methods in leadership research context (under which circumstances was the document created?); retrieval (how, where and when was it retrieved?); and ● validity (source validity: to what extent is the set of texts to be analysed actually representative of the group of interest?; Insch et al., 1997). ● ●
For example, in the case of analysing corporate documents (e.g., corporate guidelines, company reports), it may be important whether the texts focus on an internal and/or external audience (i.e., in the latter case, positive impression management may be of particular importance) or whether it was created at a time of crisis or prosperity (cf. Meindl et al., 1985). Finally, many authors (e.g., Bogdan & Knopp Biklen, 2006; Miles & Huberman, 1994) stress the importance of writing down comments and memos on what the researcher is learning in the course of the interviews or document collection (e.g., ideas for categories, interpretations). For instance, while conducting a series of interviews on implicit leadership theories (cf. Schilling, 2001, 2009), I got the impression from the statements and descriptions of the interviewees that certain leadership beha viors often occurred together. Based on this observation, I decided to code and later analyse the co-occurrence of subjective leadership concepts by the means of multidimensional scaling (see below). Thus, picking up one’s impressions and ideas during data collection is often very helpful in later stages of qualitative content analysis (Klenke, 2008). Preparing the Data Before verbal data can be analysed for their content, data preparation has to take place, which involves three major steps: data transcription, directing data analysis, and condensing data. At the beginning, the protocols of the interviews (written and/or taperecorded) have to be transferred into text files (data transcription). As Wiedemann (2013) states, different software packages like Ethnograph, MAXQDA, NVivo or ATLAS.ti (for a review see Banner & Albarran, 2009; Alexa & Zuell, 2000; Klenke, 2008) have been developed since the 1980s to specifically support manual tasks of qualitative data analysis (e.g., data preparation, condensing, and coding). Creswell (1998) states that these programs are especially useful for studies with large or diverse databases. As they require some effort to learn their handling, these programs are mainly used by researchers who plan to apply them in repeated studies. For single studies, existing spreadsheet software (e.g., Microsoft Excel, Apache OpenOffice Calc) may often be sufficient to edit and analyse the data.
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Qualitative content analysis in leadership research 357 While transferring documents (collected texts) typically involves a rather simple text transfer, the transcription of interviews necessitates a definition of explicit rules (Schilling, 2006). Before determining these rules, it is helpful to review the material (i.e., listen to some or all of the tapes and/or protocols) to obtain an idea of the overall data (Creswell, 1998; Tesch, 1990). Besides rather simple formal aspects of the transcript (program, font, size, margins), the researcher has to decide how to treat the following: ● Dialect
or slips of the tongue. Should they be preserved, ignored or respectively corrected (content-focused)? As most researchers are only interested in the content of the interview, these aspects are often ignored/corrected. If dialects are to be transcribed, it should be defined (e.g., by some examples) how the terms should be spelled in order to provide an intelligible transcript. ● Observations during the interview like sounds (like “uhs” or “ers”) as well as audible behavior (like coughing or drumming of the fingers). From a pragmatic point of view, it can be recommended to drop these aspects as long as they do not shape or alter the meaning of the content (speech focused). ● The specific questions of the interviewer (besides the main questions from the interview guideline). For example, a specific question– answer sequence (Q: “What exactly do you mean by ‘delegating’?” A: “For me, it implies passing on tasks to followers that I do not have the time to take care of myself”) could be transcribed in the form of an answer (“For me, delegating implies passing on tasks to followers that I do not have the time to take care of myself”). Schilling (2006) emphasizes the danger of only transcribing the main guiding questions. For instance, it will not be easy to control if the interviewer broke the defined rules (e.g., by posing leading questions). A careful researcher may control for these concerns by listening to a random sample from the tapes and critically searching for such incidents. If necessary, the texts are made anonymous by replacing names of people and institutions with descriptive terms (e.g., “our CEO” instead of “Mr. Smith”). A special coding scheme may be needed and applied here if the researcher is interested in comparing, for instance, the opinions of different interviewees towards a certain person or institution (Schilling, 2006). As the extent of possible analytic units may differ within the data (from single word to more than a sentence), the researcher has to direct the analysis. The literature is often rather vague in this respect. Locke (2002) states that the researcher has to use some judgment to decide what a meaningful
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358 Handbook of methods in leadership research unit of analysis is. Meaningful unit in this sense would mean a “segment of text that is comprehensible by itself and contains one idea, episode, or piece of information” (Tesch, 1990, p. 116). It is important to define at least the boundaries of unitizing. Following Mayring (1994), three kinds of units can be differentiated: 1. The smallest text component that is to be categorized has to be defined (coding unit: single word, half-sentence, full sentence, paragraph or complete text). Of course, this choice depends on the aim and topic of the study, but normally, single words or half-sentences form the basis for the smallest coding unit (Insch et al., 1997). For instance, in the case of analysing leaders’ implicit leadership theories (cf. Schilling, 2001), words like “motivating” or half-sentences like “supporting followers in case of questions” would be enough to code them as single meaningful statements. 2. The next decision concerns the biggest text component to be categorized in the study (context unit: single word, half-sentence, full sentence, paragraph or complete text). Again, while depending on the specific research interest, it can be stated that paragraphs typically form the context unit. For example, while coding corporate leadership principles (Schilling, 2005), the paragraph “Followers are supported by their leaders in their personal development. This is done by the means of daily exchange as well as regular team meetings” was categorized as one connected statement. 3. Finally, the order of the analysis has to be determined (sequencing unit: cross-paragraph or cross-text). The cross-paragraph strategy (i.e., text after text) should be chosen when the guiding questions (in case of interviews) or the paragraphs (in case of collected texts) are highly overlapping (e.g., answers to one guiding question are likely to occur in the course of another question) and aim at the same topic from different directions (e.g., the first question in an interview is focused at the characteristics of leaders in general, the second at those of good leaders; cf. Schyns & Schilling, 2011). By that, the researcher gets an idea of the full complexity of each interview or text. If the questions are rather distinct from each other and/or focus on different topics (e.g., the first question in the interview is concerned with good leadership, the second with good followership), the cross-text procedure is helpful in giving an impression of the complexity of possible answers from different interviewees towards a distinctive topic. After these initial steps, the process of condensing content analysis can begin. The next step is to reduce the material to its basic content (called
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Qualitative content analysis in leadership research 359 Table 14.1 Example of paraphrasing texts Original
Paraphrases
“A leader has to make decisions, that is, you know, leadership. Leaders decide. But, you certainly know this, you should not, not do that alone, if the decision is important, without first talking to. . . people, I mean, people that you lead. You should consult them, discuss the options with them. And then, of course, communicate the decision to your followers and communicate the reasons for it. This is especially necessary, if you don’t follow their advice. They will be motivated by this. This not common, not every leader does this, unfortunately, in this firm.”
Making decisions Talking to one’s followers before taking important decisions Consulting followers and discussing options with them Communicating the decision and the reasons for the decision to the followers especially if the leader does not follow their advice Followers will be motivated Not common practice in the firm
“paraphrasing”) by deleting all the words that are not necessary to understand the statement, and transforming the sentences into a short form (see Table 14.1 for an example). As paraphrasing can be very time-consuming, researchers often skip this step, especially if they use content-analysis software and directly categorize meaningful text segments. However, while often cumbersome, paraphrasing allows the researcher to break down the often very extensive material to a well-arranged dataset, which makes it easier to find fitting codes and compare the similarities and differences within and between the texts. If the researcher specifically aims at analysing the logical structure of the texts, remaining statements should be generalized and reduced (see Table 14.2 for an example). First of all, especially with regard to possible quantitative analyses later, it is important to make a decision on how to deal with conjunctions (e.g., “and,” “or,” “but,” “by,” “after,” “because”). Schilling (2006) recommends dissolving these relationships in order to get a realistic picture of the complexity of statements in the text. The general rule applied here should be to divide only statements, if each of them has a discrete meaning. An example in Table 14.2 (e.g., “Communicating the decision to followers” and “Communicating the reasons of the decision to followers”) underlines the difficult task for the researcher. “The complexity in the use of human language makes it unlikely that a researcher will be able to reach and apply a ‘perfect’ system of rules” (Schilling, 2006, p. 31).
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360 Handbook of methods in leadership research Table 14.2 Example of generalizing and reducing the paraphrases Paraphrases
Generalization and reduction
Making decisions
Making decisions
Talking to one’s followers before taking An important decision has to be taken Talking to followers important decisions Consulting followers and discussing options with them
Consulting followers Discussing options with followers
Communicating the decision and the reasons for the decision to the followers, especially if the leader does not follow their advice
Leader does not follow the advice of the followers Communicating the decision to followers Communicating the reasons of the decision to followers Followers will be motivated
Followers will be motivated Not common practice in the firm
Based on theoretical considerations, all statements that are not related to one’s specific research interests should be deleted at this stage. For example, Schyns and Schilling (2011) were interested in attributed traits of leaders in general and chose to delete all statements the subjects made that included leader behavior. To ensure data quality, the full material (material-related validity) should be checked against the original texts, if any relevant statement has been falsely excluded (based on theoretical considerations) by the researcher and – if possible – a second person who should be trained in regard to the process, but not involved in the previous steps. Also, if conjunctions in the text have been split up, it has to be controlled if the defined rules were kept accurately. Structuring the Data As Mostyn (1985) states, the development and application of a category system (coding) lies at the heart of qualitative content analysis. While it is very difficult to develop an approach applicable to the multitude of different research questions and aims, two main steps can be distinguished in the endeavor to code the text. First, the material may be submitted to a structuring content analysis
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Qualitative content analysis in leadership research 361 (Schilling, 2006), which means that the statements are separated in different basic dimensions. For example, in the study on implicit leadership theories (Schilling, 2001), three fundamental dimensions of implicit leadership theories (“perceived leadership behavior,” “attributed antecedents of leadership,” “attributed consequences of leadership”) were defined a priori and the text material was structured accordingly. The researcher’s basic question at t