Measuring and Modeling Persons and Situations 0128192003, 9780128192009

Table of contents :
Cover
Front-Matter
Copyright
Contents
Contributors
1. A role for information theory in personality modeling, assessment, and judgment
2. What falls outside of the Big Five? Darkness, derailers, and beyond
3. Semantic and ontological structures of psychological attributes
4. Ubiquitous computing for person-environment research: Opportunities, considerations, and future directions
5. Modeling the mind: Assessment of if … then … profiles as a window to shared psychological processes and individual differences
6. Psychological targeting in the age of Big Data
7. Virtual environments for the representative assessment of personality: VE-RAP
8. Improving measurement of individual differences using social networks
9. Situational judgment tests: From low-fidelity simulations to alternative measures of personality and the person-situation interplay
10. Intra-individual variability in personality: A methodological review
11. Modeling the dynamics of action
12. Conceptualizing and measuring the implicit personality: The state of the science
13. Conceptualizing and measuring the psychological situation
14. Network approaches to representing and understanding personality dynamics
15. Neural network models of personality structure and dynamics
16. Interdependence approaches to the person and the situation
17. Formally representing how psychological processes shape actions and one another using functional fields
18. Integrating Cybernetic Big Five Theory with the free energy principle: A new strategy for modeling personalities as complex systems
19. Computational models of appraisal to understand the person-situation relation
20. An economic approach to modeling personality
Index

Citation preview

Measuring and Modeling Persons and Situations

Measuring and Modeling Persons and Situations

Edited by

Dustin Wood | Stephen J. Read P.D. Harms | Andrew Slaughter

Academic Press 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-819200-9 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Acquisitions Editor: Joslyn Chaiprasert-Paguio Editorial Project Manager: Barbara Makinster Production Project Manager: Kiruthika Govindaraju Cover Designer: Christian J. Bilbow Typeset by SPi Global, India

Contents Contributors

1.

xvii

A role for information theory in personality modeling, assessment, and judgment David M. Condon and Ren
e Mo˜ttus Background on Shannon’s information theory Analog applications Digital applications Invoking information-theoretic approaches in personality assessment Emphasizing the needs of researchers over test administrators Next steps Towards a digital representation of the analog experience References

2.

2 5 8 17 17 20 23 24

What falls outside of the Big Five? Darkness, derailers, and beyond P.D. Harms and Ryne A. Sherman Dark personality Dark Triad Hogan personality derailers Alternative models of dark traits Mapping the psychological drivers of dark traits Prediction of work outcomes Positive effects of dark traits Nonlinear relationships Moderators of dark personality Gender Age and time Culture and context Dark personality assessment issues Self-awareness and honest reporting Specificity of measures Highly abbreviated measures Five-factor based models Emerging trends in assessment

34 35 38 40 42 43 44 45 46 46 47 48 48 48 49 50 51 52

v

vi Contents Future directions and concluding thoughts References

3.

54 57

Semantic and ontological structures of psychological attributes Jan Ketil Arnulf and Kai Rune Larsen Introduction Like all other people: Language is a strong situation Digital text analysis Background A practical guide to conducting LSA Comparing texts to Likert scale data The semantic trap called the jingle-jangle fallacy Semantic overlap in research: How most people will react Where semantics failed: The case of personality testing Like no other people: Individual differences in STSR Like some other people: Group-level differences identified in STSR Conclusion: Applications and developments in semantics References

4.

69 71 75 75 77 78 80 81 85 87 90 93 95

Ubiquitous computing for person-environment research: Opportunities, considerations, and future directions Sumer S. Vaid, Saeed Abdullah, Edison Thomaz, and Gabriella M. Harari Assessing behaviors, persons, and environments with ubicomp devices Measuring and modeling behaviors Measuring and modeling persons Measuring and modeling environments Outlook Practical considerations Ethical considerations Future directions Conclusion References

5.

104 106 118 123 125 126 131 132 134 135

Modeling the mind: Assessment of if … then … profiles as a window to shared psychological processes and individual differences Vivian Zayas, Randy T. Lee, and Yuichi Shoda Introduction Mental inference in daily life: An anecdotal illustration Cognitive-Affective Processing System theory: Basic principles

145 147 148

Contents vii

The CAPS network Original conceptualization and measurement of if … then … profiles A contemporary approach to conceptualizing if … then … profiles: The Highly-Repeated Within-Person (HRWP) approach The HRWP approach: Assessing features present in a situation A primer to the HRWP approach Data-driven, “bottom-up” approach Theory-driven, “top-down” approach How does studying if … then … profiles shed light on the personality system? Illustrating the HRWP “top-down” approach: Reanalysis of a transference study Studying transference The HRWP approach to studying transference Reanalysis of the transference study using the HRWP approach Exploring unexplained variability: Identifying systematic variability Implications of using the HRWP approach A more direct approach of assessing intraindividual psychological processes by measuring intraindividual dynamics Bolstering causal inference and assessing the heterogeneity (vs homogeneity) of effects A unified framework for assessing shared psychological processes and individual differences What do if … then … profiles reveal about a person? What is an if? Looking forward Applications of the HRWP approach Concluding remarks Appendix R Code References

6.

148 151 153 154 157 159 160 160 162 163 165 166 173 177 178 179 180 180 181 182 183 183 183 184

Psychological targeting in the age of Big Data Ruth E. Appel and Sandra C. Matz Introduction Psychological targeting: A two-stage framework Drivers of psychological targeting: Big Data and Machine Learning New and “bigger” data New analytical techniques The science of psychological targeting Stage 1: Psychological profiling Psychologically-informed interventions Research opportunities Research challenges

193 194 196 196 197 199 199 202 203 206

viii Contents Practical advice Ethical considerations Ethical opportunities Ethical challenges Suggestions for addressing the ethical challenges Conclusion References

7.

208 209 210 211 214 216 217

Virtual environments for the representative assessment of personality: VE-RAP Lynn Carol Miller, David C. Jeong, and John L. Christensen What is a virtual representative digital environment: Relevance? Commercial virtual environments and personality correlates What are we trying to assess dynamically in virtual environments? Virtual environment affordances to leverage for personality assessment Virtual representative environments for correlational and experimental studies Generalizability to everyday life Challenges abound: Why representative virtual environments? Shifting granularity and time scale: Challenges Measuring the “unit”  situation dynamics Virtual environments for representative assessments: How? Overview of formative research needed Target audience Behavioral and script options for the POI: Formative research needed Personality conceptualization Representative locations Affordances Measuring goal-affordance dynamics Scripts, situational-GPRB, and behavioral options Interoceptive states and affect Ultimate BOI Discussion Conclusions and future directions References

8.

224 224 225 228 235 235 236 236 237 238 238 239 240 240 241 242 242 242 243 244 245 245 246

Improving measurement of individual differences using social networks Andrew Slaughter and Janie Yu Introduction Analyzing social structure Descriptive statistics for social structure

253 254 255

Contents

Integrating models for SNA and IRT Item response theory models for individual differences Measurement models for independent dyads and egocentric networks IRT-ERGM: Integrating models for individual measurement and network structure Example analysis Discussion References

9.

ix 261 261 264 268 275 279 281

Situational judgment tests: From low-fidelity simulations to alternative measures of personality and the person-situation interplay Filip Lievens, Philipp Sch€ apers, and Christoph N. Herde Introduction SJTs: Definition and brief history Definition Brief history The traditional view: SJTs as low-fidelity simulations Underlying theory and rationale SJT development Construct-related validity evidence Criterion-related validity evidence SJTs in the training context SJTs as alternative measures of personality Characteristics and development Underlying theory Construct-related and criterion-related validity evidence SJTs and the personality-situation interplay Assessment of situation construal Assessment of within-person variability across situations Assessment of situation-trait contingencies Assessment of proactive transactions Assessment of behavioral responses Assessment of narratives and goals Assessment of personality disorders Epilogue References

285 287 287 289 289 289 290 291 292 292 293 293 295 296 298 298 299 300 301 302 302 303 303 304

10. Intra-individual variability in personality: A methodological review Alisha M. Ness, Kira O. Foley, and Eric Heggestad Introduction Foundational issues and approaches underlying IIVP Key contemporary theoretical perspectives on IIVP Examining the current state of the literature and future recommendations

313 314 318 323

x Contents Theories of IIVP Research designs for intensive longitudinal data Personality state measurement Statistical techniques for examining IIVP Key findings from the literature Describing the nature of IIVP Antecedents and outcomes of IIVP Examining relationships at the state-level Conclusion References

325 329 336 339 342 342 344 345 345 346

11. Modeling the dynamics of action Ashley D. Brown and William Revelle Personality is multilayered and spatiotemporally coherent A glimpse of dynamics’ distant past Observing and explaining behavioral dynamics Cattell and the data box Dynamics in the marginally more modern era Time and change Descriptive models Dynamic processes and stochastic variation aren’t the same Dynamic cybernetic models Part 1: Cognitive maps and TOTE units Part 2: Set points and ponderostats Part 3: Human models The dynamics of action model The Cues-Tendencies-Actions model CTA + Reinforcement Sensitivity Theory = CTARST Reinforcement sensitivity theory Building CTARST CTARST models real data from three real studies CTARST output and its relation to personality Conclusions and future directions References

355 357 357 358 360 360 362 363 364 364 366 367 368 370 372 372 373 373 376 378 380

12. Conceptualizing and measuring the implicit personality: The state of the science Amanda N. Moeller, Benjamin N. Johnson, Kenneth N. Levy, and James M. LeBreton Projective measures Exemplary projective measures: Thematic apperception test and picture story exercise State of the field of projective measures Major advantages of projective measures Critiques of projective measures

393 393 394 395 395

Contents

Predictive validity of projective measures Response latency measures Exemplary response latency measure: The implicit association test State of the field of response latency measures Major advantages of response latency measures Critiques of response latency measures Predictive validity of response latency measures Conditional reasoning measures Exemplary conditional reasoning measure: Conditional reasoning test for aggression State of the field of conditional reasoning measures Major advantages of conditional reasoning measures Critiques of conditional reasoning measures Predictive validity of conditional reasoning measures Application of various measurement systems for understanding the implicit personality Projective measures Response latency measures Conditional reasoning measures Implications and future directions Summary Declaration of conflicting interests Funding References

xi 396 397 398 399 401 402 403 406 407 408 409 409 410 411 411 412 413 413 416 417 417 417

13. Conceptualizing and measuring the psychological situation John F. Rauthmann and Ryne A. Sherman Conceptualization Terminology Situational Cs Other situational concepts Characteristics contra cues and classes Reality Principles Measurement Rater types Instruction sets Toward an integration: The componential agreement Lens model of situations (CALMS) Basic concepts and variables Quantifying agreement Explaining agreement Contextualizing agreement Treating agreements as correlates

427 427 430 433 435 438 442 445 446 447 448 449 452 452 453 456

xii Contents Modeling Some future directions References

456 456 457

14. Network approaches to representing and understanding personality dynamics Emorie D. Beck and Joshua J. Jackson Early thinking on dynamics Allport: Theorizing on dynamic organization Cattell: Formalizing dynamic organization Structure vs dynamics: The person-situation debate Social cognitive approaches to personality Recent thinking on dynamics Within-person variability The future of dynamics A dynamic systems theory of personality Network tools Redefining processes and dynamics Systems: It’s about equilibria Conclusion References

466 466 467 468 469 473 473 475 475 478 487 488 493 494

15. Neural network models of personality structure and dynamics Stephen J. Read and Lynn Carol Miller A neural network model of personality Theoretical background: Components and structure of the VIP model Underlying neurobiology Neural network implementation Simulations Implications for learning and change Implications for measurement of personality Implications for predictions of behavior Summary/Discussion References

502 503 506 507 513 530 532 533 534 535

16. Interdependence approaches to the person and the situation Fabiola H. Gerpott, Isabel Thielmann, and Daniel Balliet Dimensions of interdependence Mutual dependence Power Conflict

540 540 541 541

Contents

Coordination Future interdependence Information certainty How personality shapes behavior in (objective) interdependent situations Affordances for the expression of personality in (objective) interdependent situations Assessing subjective interdependence The influence of personality on subjective representations of interdependent situations Objective vs subjective interdependent situations and behavior: A research agenda on the role of the person The role of interdependence perceptions in linking personality and behavior The influence of the interaction partner on an actor’s situation perception Interaction effects between the interdependence dimensions Conclusion References

xiii 542 542 543 543 544 548 551 553 554 555 557 558 559

17. Formally representing how psychological processes shape actions and one another using functional fields Dustin Wood Basics of functional field models The functionality assumption Formally representing situations within field models The mediation principle: All forces have mediating processes, and all actions have reasons The sources of forces: How people use information to construct the psychological situation Functional field models as underlying our model of the “Field at a given time” Identifying the field effects (i.e., traits) of a particular factor Model-based estimation of field effects Experiment-based estimation of field effects Representing verbal descriptions of actions, people, and situations within field models Verbal statements as the outcomes of tests on the person’s mental model of the world The value of trait information Using functional fields to represent arguments about how traits relate to one another Representing semantically redundant relationships Functional relationships between traits Representing sources of trait covariation

566 567 568 570 572 572 577 578 579 587 587 595 596 597 598 601

xiv Contents Some broader implications of functional field models for understanding behavior Action selection is situation selection Q: Why did they do it? A: Why don’t you just ask? Conclusion References

606 606 607 608 609

18. Integrating Cybernetic Big Five Theory with the free energy principle: A new strategy for modeling personalities as complex systems Adam Safron and Colin G. DeYoung A cybernetic approach to personality The free energy principle and CB5T Predictive processing and personality neuroscience Active inference (AI) Generative models and personality modeling Personality traits from the perspectives of CB5T and FEP-AI The metatraits The Big Five Conclusion: Levels of analysis in personality modeling References

617 620 623 625 628 633 635 637 640 642

19. Computational models of appraisal to understand the person-situation relation Nutchanon Yongsatianchot and Stacy Marsella Introduction Emotion as a window on types of situation Appraisal theories of emotion Lazarus’ appraisal theory The component process model (Scherer, 1984, 2009, 2013) The Ortony, Clore, Collins model (Clore & Ortony, 2013; Ortony, Clore, & Collins, 1990) Computational models of appraisal theory of emotion EMA (Gratch & Marsella, 2004; Marsella & Gratch, 2009) Appraisals in EMA Emotion derivation in EMA Coping in EMA EMA and situations: Implications of model Concluding remarks References

651 652 654 655 656 660 663 667 667 668 669 670 671 672

20. An economic approach to modeling personality Lex Borghans and Trudie Schils Introduction Economic literature about personality

675 677

Contents xv

The basics of an economic model Personality and situation Introducing personality to the model Selection into situation Choosing a situation Introducing sorting into the situation in an economic model of personality Ability versus preferences Personality: a preference and/or an ability? Model Implications The role of incentives Conclusion References Index

681 683 684 687 687 688 690 690 691 691 692 694 695 699

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Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Saeed Abdullah (103), College of Information Sciences and Technology, Pennsylvania State University, State College, PA, United States Ruth E. Appel (193), Department of Communication, Stanford University, Stanford, CA, United States Jan Ketil Arnulf (69), Department of Leadership and Organizational Behaviour, BI Norwegian Business School, Oslo, Norway Daniel Balliet (539), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Emorie D. Beck (465), Northwestern University Feinberg School of Medicine, Chicago, IL, United States Lex Borghans (675), Maastricht University School of Business and Economics, Maastricht, The Netherlands Ashley D. Brown (355), Johnson O’Connor Research Foundation, Chicago, IL, United States John L. Christensen (223), Department of Communication, University of Connecticut, Storrs, CT, United States David M. Condon (1), Department of Psychology, University of Oregon, Eugene, OR, United States Colin G. DeYoung (617), University of Minnesota, Minneapolis, MN, United States Kira O. Foley (313), The Consortium of Universities of the Washington Metropolitan Area and George Washington University, Washington, DC, United States Fabiola H. Gerpott (539), WHU—Otto Beisheim School of Management, D€ usseldorf, Germany; Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Gabriella M. Harari (103), Department of Communication, Stanford University, Stanford, CA, United States P.D. Harms (33), Management, University of Alabama, Tuscaloosa, AL, United States Eric Heggestad (313), Department of Psychological Science, University of North Carolina at Charlotte, Charlotte, NC, United States Christoph N. Herde (285), Lee Kong Chian School of Business, Singapore Management University, Singapore, Singapore Joshua J. Jackson (465), Washington University in St. Louis, St. Louis, MO, United States xvii

xviii Contributors

David C. Jeong (223), Department of Communication, Santa Clara University, Santa Clara, CA, United States Benjamin N. Johnson (389), The Pennsylvania State University, University Park, PA, United States Kai Rune Larsen (69), Leeds School of Business, University of Colorado Boulder, Boulder, CO, United States James M. LeBreton (389), The Pennsylvania State University, University Park, PA, United States Randy T. Lee (145), Department of Psychology, Cornell University, Ithaca, NY, United States Kenneth N. Levy (389), The Pennsylvania State University, University Park, PA, United States Filip Lievens (285), Lee Kong Chian School of Business, Singapore Management University, Singapore, Singapore Stacy Marsella (651), Northeastern University, Boston, MA, United States; University of Glasgow, Glasgow, United Kingdom Sandra C. Matz (193), Columbia Business School, Columbia University, New York, NY, United States Lynn Carol Miller (223, 499), Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, United States Amanda N. Moeller (389), The Pennsylvania State University, University Park, PA, United States Rene Mo˜ttus (1), Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Institute of Psychology, University of Tartu, Tartu, Estonia Alisha M. Ness (313), Foundational Science Research Unit, U.S. Army Research Institute for the Behavioral and Social Sciences, Ft. Belvoir, VA, United States John F. Rauthmann (427), Bielefeld University, Bielefeld, NW, Germany Stephen J. Read (499), Department of Psychology, University of Southern California, Los Angeles, CA, United States William Revelle (355), Department of Psychology, Northwestern University, Evanston, IL, United States Adam Safron (617), Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD; Kinsey Institute, Indiana University, Bloomington, IN; Cognitive Science, Indiana University, Bloomington, IN, United States Philipp Sch€apers (285), Free University of Berlin, Berlin, Germany Trudie Schils (675), Maastricht University School of Business and Economics, Maastricht, The Netherlands Ryne A. Sherman (33, 427), Hogan Assessment Systems, Tulsa, OK, United States

Contributors

xix

Yuichi Shoda (145), Department of Psychology, University of Washington, Seattle, WA, United States Andrew Slaughter (253), Predictive Analytics and Modeling Research Unit, US Army Research Institute for the Behavioral and Social Sciences Ft., Belvoir, VA, United States Isabel Thielmann (539), University of Koblenz-Landau, Landau, Germany Edison Thomaz (103), Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States Sumer S. Vaid (103), Department of Communication, Stanford University, Stanford, CA, United States Dustin Wood (565), University of Alabama, Tuscaloosa, AL, United States Nutchanon Yongsatianchot (651), Northeastern University, Boston, MA, United States Janie Yu (253), Consultant, United States Vivian Zayas (145), Department of Psychology, Cornell University, Ithaca, NY, United States

Chapter 1

A role for information theory in personality modeling, assessment, and judgment☆ David M. Condona and Ren
e Mo˜ttusb,c a

Department of Psychology, University of Oregon, Eugene, OR, United States, bDepartment of Psychology, University of Edinburgh, Edinburgh, United Kingdom, cInstitute of Psychology, University of Tartu, Tartu, Estonia

The field of information theory wrestles with the question of how to convey a signal efficiently—quickly and without much loss—from a source to a target. We begin with the assertion that the fundamental goal of personality assessment is to address this same question: to capture and convey the essential psychological individual differences. In this work, we discuss this similarity in detail, arguing that the two fields are linked by more than a superficial analogy, even though the technical details of each discipline seem to have little in common. These ideas are developed through consideration of the briefly overlapping histories of the two fields and the subsequently divergent ways that each has proceeded to address this same challenge. With examples, we advocate for the broader adoption of more information-theoretic approaches in personality assessment as a means of advancing basic personality research. Importantly, we emphasize from the outset that this work aims to open a line of inquiry that can and hopefully will be continued through more extensive, specific, and empirical evaluation. It does not aim to thoroughly map the more technical aspects of probability theory and statistics used in information theory into personality assessment, largely because the basic precepts of information theory are unfamiliar to those who focus on measuring and modeling persons and situations. It is intended as a high-level overview of the most relevant topics.

☆

Author Notes: The authors would like to thank Colin DeYoung, William Revelle, and Dustin Wood for their feedback during various stages of this work. Correspondence should be addressed to David M. Condon at: [email protected].

Measuring and Modeling Persons and Situations. https://doi.org/10.1016/B978-0-12-819200-9.00018-1 Copyright © 2021 Elsevier Inc. All rights reserved.

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Measuring and modeling persons and situations

Background on Shannon’s information theory Our references to information theory in this chapter are rooted in the two-part article by Claude E. Shannon (1948a, 1948b) titled “A Mathematical Theory of Communication.” Setting forth the conceptual basis for modern telecommunication systems, including both the system as a whole and its component parts, this landmark work has become one of the most highly cited papers published to date and is often credited with ushering in the information age. The essence of the article is encapsulated by Shannon’s primary observation that: The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Shannon (1948a, p. 379)

This simple statement was a departure from prior work in communications at the time and its novelty stemmed from the call to focus on the transmission of messages rather than their nature (e.g., lists of numbers, letters, words, still, or moving images). Shannon disregarded the fact that messages “refer to or are correlated according to some system with certain physical or conceptual entities” (Shannon, 1948a, p. 379). He proposed instead that each message can be abstractly represented with a small number of symbols (0s and 1s) as one of a finite set of possible messages (given any fixed length), and in so doing, underscored the utility of focusing on the elemental properties of the messages rather than their interpreted “semantic” meaning. In communications theory, later labeled more broadly as information theory (IT), this utility stemmed from consideration of the underlying units of the messages and message length (i.e., the quantity of information conveyed) rather than more intractable concerns about differential methods for encoding and decoding different types of content (i.e., theories of abstraction). As Shannon states, the “semantic aspects of communication are irrelevant to the engineering problem” (Shannon, 1948a, p. 380). At the lowest level, one approach to the communication of messages could suit all types. Drawing a parallel with psychological assessment, this would mean defocusing from the structure of the hypothetical phenomena (e.g., traits) being assessed and prioritizing the efficacy of obtaining information about how individuals vary (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). Shannon built on this essential idea by identifying several properties of the low-level representations of the content to be conveyed and the process of conveyance itself—the communication system. Here, we primarily focus on the latter (the conveyance of underlying signals), for our primary aim is to argue that the features of this process are broadly applicable to personality assessment in ways that differ from the approaches traditionally taken. Some, possibly many, of the more technical properties of the low-level representations described by Shannon are also relevant for the assessment of individual differences in

Information theory in personality Chapter

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psychological characteristics (e.g., bandwidth-fidelity, the probabilistic nature of information signals, entropy, signal-to-noise ratios), although a full treatment of these topics is beyond the scope of this introductory work, which is about the broad associations between Shannon’s ideas and personality assessment. For more information, consider discussions by Ones and Viswesvaran (1996) and Hogan and Roberts (1996) regarding bandwidth-fidelity; Brunswik (1955), Jackson and Paunonen (1980), and Uher (2013) regarding the probabilistic nature of informative signals in personality; Golino et al. (2020) and Del Giudice (2020) regarding applications of entropy; and Cronbach and Gleser (1964), Nicewander (1993), and Revelle and Condon (2019) regarding signal-to-noise ratios in psychological assessment. However, there is one important technical point in Shannon’s work (1948a) that is especially relevant to our ideas for advancing assessment. That is, in the absence of knowledge regarding the nature of a given “message” being conveyed (e.g., its content or size), Shannon proposed that estimation of its features should be based on the presumption that all messages in the possible settings are equally probable, even though the possible set is, in practical terms, often very large (Shannon, 1948a, p. 393). In the absence of information about incoming messages, they are drawn stochastically from a very large set—effectively random and entirely unpredictable from the perspective of the receiver (aka the destination). This parallels the intuition in personality assessment that the set of possible expressions of behavior is also very large (Saucier, 1997), and—in the absence of a priori information about behavioral manifestation (including the individual, situation, and social and cultural norms to which the individual ascribes)—all behaviors are equally possible and therefore stochastic from the perspective of the observer. To be clear, behaviors are rarely perceived as random by the individual responsible for carrying out the behavior or by most observers; expectations typically reflect prior knowledge of density distributions of behavior ( Jones, Brown, Serfass, & Sherman, 2017). We return to this topic later because it is fundamental to our call for a more information-theoretic approach to psychological assessment, but we raise it here to emphasize the difference between the diversity of human behavior within and across cultures and the small number of specific attributes that are typically evaluated with modern “omnibus” personality assessments (e.g., the Ten-Item Personality Inventory [Gosling, Rentfrow, & Swann Jr, 2003]; the Big Five Inventory 10 [Rammstedt & John, 2007]). In Shannon’s terms, this is the difference between a telegraph operator who accepts all messages from points unknown regardless of content or length and another that only accepts messages relating to a very small handful of prespecified topics. Returning to the process of message conveyance, Shannon’s work provided a simple but elegant model for communication systems, and we find it useful for the conveyance of psychological signals as well. Fig. 1 is a reproduction of this model from Shannon’s original work (Kopp, Korb, & Mills, 2018; p. 381;

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Measuring and modeling persons and situations

FIG. 1 Shannon’s schematic diagram of a general communication system.

Shannon, 1948a), illustrating the communication system components and their relation to one another. These include 1. An information source that produces the sequence of messages to be transmitted, ranging in complexity from a simple vector (e.g., telegraphic code) to a multidimensional array of values that includes a time dimension (e.g., color video transmission with an associated audio channel). 2. A transmitter that converts the signal to a form that is suitable for transmission. This conversion involves the sampling of the information source, quantification, encoding, and possibly compression (depending on the complexity of the message relative to the bandwidth of the communication system). 3. A channel through which the message is transmitted (e.g., a piece of paper, coaxial cable, radiofrequency, a beam of light). During transmission through the channel, messages are sometimes perturbed by the noise of two types (stochastic noise and distortion), leading to uncertainty about the fidelity of the received message relative to the original encoding. 4. A receiver that converts the signal using the inverse of operations performed by the transmitter, specifically decompression (when needed) and decoding. 5. A destination that serves as the target or recipient of the message. Important characteristics of the object representing the destination will have a meaningful effect on the utility of the message once delivered and should be taken into account with respect to its conversion and transmission at all prior stages. Following Shannon’s initial elaboration of these preliminary concepts, the field evolved rapidly and, more or less, continuously over the ensuing 50 years. Without describing the technical details of these subsequent advancements in detail, it should be noted that they—and the technological innovations that they enabled—are instrumental to the information-theoretic applications of personality

Information theory in personality Chapter

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assessment described at the end of this chapter, to say nothing of the extent to which they have revolutionized many other aspects of modern life. One detail that should be noted here, however, is the technical distinction made by Shannon and others with respect to discrete vs continuous systems: discrete systems are those where digitization of the signal occurs, whereas analog signals are passed through continuous systems. Both the digital and analog applications have proven relevant for personality psychology, in different ways. Digital applications seek to optimize the process of encoding and communicating high-fidelity representations of psychological phenomena (i.e., assessment as an end unto itself). The transmission itself is the focus of these applications, and while the messages are used to represent or recreate analog phenomena, digitization is inherent to the manner of conveyance. In contrast, analog applications of information-theoretic approaches are suitable for testing and refining models that aim to understand the analog processes of human behavior. The focus of these applications is the behavior itself, and while empirical or simulated studies may involve some degree of digitization, the underlying processes are analog by nature. As we will emphasize, these are very different aspects of psychological research, especially in the domain of personality science.

Analog applications Analog applications of Shannon’s ideas gained traction almost immediately in psychology despite misgivings by Shannon himself (Shannon, 1956), for many psychologists readily recognized that humans are (analog) information processors in the sense that phenomenological experience results from the perception of signals from the physical properties of encountered objects (light, sound, temperature, etc.; Campbell, 1958). These signals, and to a lesser extent the human systems for processing them, are deemed effectively continuous rather than discrete and are transmitted and stored in “lossy” formats (i.e., human communication and memory systems) that are prone to reductions in the signal/ noise ratio over time. Recognition of these underlying similarities led, in broad terms, to an information-processing revolution in cognitive psychology from roughly 1950 to 1970 (Miller, 2003; Xiong & Proctor, 2018), after which most of the cognitive psychology applications had fallen out of favor or been replaced with newer ideas (Laming, 2001; Luce, 2003). Still, several lasting contributions followed from the efforts of cognitive psychologists to incorporate concepts from information theory, the most notable being the suggestion of the 7  2 rule to approximate the bandwidth limits of information processing (Miller, 1953, 1956). Others included contributions to research on different aspects of memory (Aborn & Rubenstein, 1952), hearing (Meyer, 1957), perception (Attneave, 1959), and attention (Broadbent, 1957).

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FIG. 2 Funder’s schematic diagram of accurate personality judgment.

A more familiar embodiment of the analog path for personality psychologists relates to the processing of information about personality specifically— person perception. Funder’s (1995) Realistic Accuracy Model (RAM) of the process for arriving at accurate personality judgments is, to a considerable extent, an elaboration of Brunswik’s (1952) lens model, which itself owes a debt to Shannon’s model (Cooksey, 2001). Subsequent work in this line has pointed directly to the relevance of information theory for personality judgments including the effect on the judgment of information quantity and information quality (Letzring, Wells, & Funder, 2006). At a broad level, it is reasonably straight-forward to describe the process of person perception using the terminology of Shannon’s system. To illustrate this point, see Fig. 2 for reproduction of Funder’s (1995) RAM. The target personality attribute is the information source in Shannon’s case (see Fig. 1).a In order to be “transmitted,” features of the attribute must be relevant and available to the perceiver. In other words, there must be motivation and potential for the message to be transmitted from the source to the destination. Attributes that are not sufficiently relevant to the perceiver will not be transmitted because the channel (i.e., the focus of attention) will be occupied with the processing of other information. Similarly, signals of sufficient relevance will be transmitted but not received/perceived if they lack sufficient availability to the perceiver. In this case, the signal is not strong enough, as may occur if the attribute is weakly transmitted by the target or is made inaccessible by other competing signals in the environment. If the message is relevant and available, it remains possible that it will not be detected—either it is not received or it is inaccurately interpreted—due to attributes of the perceiver. The availability and detection steps in Funder’s model both suffer from the potential for noise to be introduced in the transmission channel, as is also indicated between transmission and receipt in Shannon’s model. For example, evidence of the attribute may only be intermittently available (as with stochastic noise) or availability/ detection may be directly “distorted” by the target or perceiver (e.g., caused by conflicting goals of the target or misinterpretation by the receiver). The final step of Funder’s model extends beyond Shannon’s channel somewhat, as it a. Also, the distal object for Brunswik (1952).

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allows for the possibility that some signals may be received without being put to use. Just as inboxes often overflow with mail, there are sometimes more signals received about a target’s personality than can be taken into consideration at one time. In fact, the experience of being overwhelmed by personality-relevant signals is likely common and warrants further consideration. Humans are highly social animals, consistently exposed to large samples of information-generating behaviors, often from multiple individuals at once, leading to an overload in our ability to process them all in real-time. Even under these demanding circumstances, humans typically perceive information produced by these behaviors and shape it into judgments of personality without much effort, or even awareness. In this sense, we are constantly “assessing” the personalities of our close friends and family, more-distant contacts, new acquaintances, strangers, celebrities, and even ourselves. Though sometimes overwhelmed, the communication channel of personality assessment is nearly always open, and individual differences in the processing of these incoming personality-relevant signals even contribute to many important features of personality. Consider, for example, differences in the extent to which people seek out social interaction of various types, and differences in various cognitive and affective reactions to ambiguous signals from others. Similarly, the personality judgment literature has sought to improve our understanding of various aspects of the analog process connecting the “emissions” of these information-generating behaviors to a huge range of personality outcomes. This includes work that (1) distinguishes personality from species-typical characteristics of human behavior (McAdams & Pals, 2006); (2) attempts to identify moderating factors in the perception of personality (Edwards & Von Hippel, 1995; Todorov, Olivola, Dotsch, & Mende-Siedlecki, 2015); (3) acknowledges the interplay between these information-generating behaviors, manifestations of personality, and features of the situations in which they are expressed (Fleeson, 2004; Funder, 2006); and (4) seeks to classify the salient features of each broad category of the components (the behaviors, situations, and personality) involved in information signal emission. The extent of progress on this last front varies considerably, from decades-long efforts to specify parsimonious models of personality (Goldberg, 1993a) to relatively recent attempts to specify the generalizable features of situations (Rauthmann et al., 2014; Chapters 13, 16). We provide a more thorough discussion of the relationship between everyday judgments of personality and formal personality assessment after reviewing the history of digital applications of IT and personality. For now, as an interim summary, we emphasize that the analog applications of IT were most rapidly embraced in cognitive psychology, with a more indirect but long-lasting effect in personality psychology via the subdiscipline of person perception. Advancements in our understanding of person perception, as we will argue, are relevant for personality assessment in the sense that these informal, analog, and often

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unconscious judgments are the reality we should be aiming to capture with novel digital approaches to personality assessment.

Digital applications The prospect of applying Shannon’s ideas about the most efficient means of conveying digitally encoded messages was only considered for a few years before Cronbach (1955a) declared it incompatible with the aims of psychological assessment. This outcome likely caught Cronbach’s closest colleagues by surprise, for his initial views about the potential utility of information theory were enthusiastic. Shortly after the release of Shannon’s 1948 publications, Cronbach successfully garnered funding from the Office of Naval Research “to examine testing problems in terms of Shannon’s information theory” (Cronbach, 1952), focusing in particular on the relationship between bandwidth and fidelity. His seminal work on coefficient alpha (Cronbach, 1951) during this era also includes an extended discussion of “Shannon information” as a coefficient for quantifying the amount of information in a set of messages/signals. Over time, however, his enthusiasm gave way to concern about a perceived incompatibility between the application of information theory ideas and the nascent standards of psychological assessment (Cronbach, 1953). He articulated this concern at a conference on information theory applications in psychology in 1954—one of three conferences on the topic that year (Quastler, 1955)— and in a subsequent chapter summarizing his views (Cronbach, 1955a). The case in favor of an information theory approach was closed before anyone else had chimed in.b

The nature of Cronbach’s concern Cronbach’s issues with the application of information theory in psychological assessment include both a major concern and several minor complaints. These lesser points are presented in a hasty and confusing manner (Cronbach, 1953) so we do not take them up in this review, though we feel it is necessary to acknowledge them for the sake of transparency. His primary issue is that the formulae presented by Shannon (1948a): … apply precisely only when patterns of inputs can be encoded simultaneously, a condition not fulfilled in our testing problem. As approximations, the formulas express the number of standard independent items required per person to classify b. Ironically, Funder has noted elsewhere (Funder & West, 1993) that research on accurate personality judgments was brought to a “screeching halt” by Cronbach during this same era. Though Cronbach’s harsh critiques of prior research on the topic (Cronbach, 1955b; Gage & Cronbach, 1955) were not well-understood at the time (nor subsequently; Funder & West, 1993), his main point (Cronbach, 1955b; Gage & Cronbach, 1955) was that the field suffered from inadequate theoretical specification of sufficiently broad latent variables; a point that indirectly relates to his dismissal of digital applications of information theory (Cronbach, 1955a).

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him, before and after testing. But this has meaning only if tests are used sequentially, with different persons given different numbers of items. Hence, there will be few occasions to treat data by the formulas [of Shannon]. Cronbach (1953, p. 41)

In essence, Cronbach is saying that Shannon’s model for the digital transmission of informative signals shown in Fig. 1 (and the formulae that follow from it) is incompatible with the standard model of psychological testing in use at that time. He argues that Shannon’s ideas would only be relevant for psychological testing if it were possible to capture the full psychological signal in a single message—in his words, the simultaneous encoding of patterns of inputs. This contrasts with the standard approach of psychological testing, where data are digitally encoded one item at a time in a structured order. In Cronbach’s era (and even still today), a relatively small number of constructs were assessed in a given testing session, with several items for each construct. In other words, the incompatibility stems, in his view, from the fact that it is not possible to exhaustively assess the individual systematically and comprehensively, across many constructs, in a single assessment (i.e., “message”). To address this concern, it is useful to revisit the components in Shannon’s system, especially with respect to the encoding and decoding steps that are more relevant for digital applications than analog cases. The information source (Step 1) produces a sequence of messages that are encoded by the transmitter (2). In other words, features of the messages to be relayed are atomized into some essential elements. Shannon (1948a) elaborates on the atomization process in great detail though we can effectively summarize this idea by pointing to examples such as the atomization of sentence-length messages being sent via telegram as a string of letters/words or video images being sent through the web as a string of data packets that are reassembled by the receiver (e.g., a router or video player). Regardless of their form, the elements are transmitted through the channel (3) to the receiver (4) where they are more-or-less faithfully reassembled, depending on the limitations of the channel components and the introduction of noise during transmission. With primitive (low-bandwidth) communication technology of the mid-20th century, faithful transmission and reassembling of detailed content were often slow. Allowances were thus made, in practical applications, to keep transmissions short by minimizing the information to be transmitted. Importantly, information scientists working in the tradition of Shannon focused their research on improving the system of signal transmission to allow for more efficient transmission of ever-increasing amounts of information rather than focusing on the minimum amount of information needed to retain a reliable signal. Shannon’ approach was also remarkably successful: after several decades of progress, it is now possible to faithfully reproduce (i.e., “model”) life-like visual and auditory experiences virtually instantly across vast distances.

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The latter approach—the path not taken by information scientists—focused on parsimonious data capture and was pursued with great success by Cronbach and colleagues in the development of psychological testing methods. A (too) short summary of the most durable tenets of Cronbach’s legacy would be that psychological testing and measurement necessarily involve the modeling of latent constructs with few composite variables (Cronbach, 1960) that can (and should) be evaluated in terms of reliability (Cronbach, 1947, 1951) and multiple forms of validity (Cronbach & Meehl, 1955). In conjunction with the constraints of limited testing time (noted by Cronbach, 1953), the use of composites and the emphasis on internal consistency, in particular, all contribute to the prioritization of parsimonious modeling in psychological testing. To meet this priority, the amount and content of data to be collected is nearly always prescribed in advance for each person tested. This is in contrast to the information theory approach where the goal is to identify prominent (i.e., salient or unexpected) features of the individual, without prescribing the amount or even the content of the data to be collected in advance. The fact that this idea may seem radical to many psychologists today is a testament to the success of psychometricians such as Cronbach in the mid-20th century. The two approaches are more compatible than Cronbach suggested, as both seek to encode, transmit, and interpret (i.e., decode) signals about psychological content. The traditional psychometric testing approach encodes only specific features of the signal using testing instruments that are familiar to most psychologists—brief, fixed-form, full-information, survey-based measures administered in controlled environments. Widely-used, modern examples include the Ten-Item Personality Inventory (Gosling et al., 2003) or the Big-Five Inventory ( John & Srivastava, 1999). But many other types are possible, including those which are not ideally suited for the psychological testing administrations that Cronbach had in mind in his time. Personality attributes lead to the production of signals that can be digitally encoded through any number of technologies. These include a diverse range of survey methodologies (e.g., longer forms, randomized item administrations, planned missingness designs, observer reports, diverse situations and settings of administration) and nonsurvey-based approaches (e.g., passive data collection via electronic audio and video recording, digital footprints). Once encoded, the transmission of the signal through the channel is uneventful in assessment contexts (data are stored electronically on a server), though it typically does need to travel from the encoding equipment to the decoder. Noise is introduced at both the encoding and decoding steps, and possibly through the channel itself. Once decoded, it has arrived at the destination where the results can be interpreted (typically, by the psychologist and/or data analyst). With the benefit of hindsight, Cronbach’s dismissal seems an understandable but unfortunate symptom of his role in the psychological testing milieu around 1950. We feel that the circumstances surrounding Cronbach’s claims are worth reviewing, as they help to explain why information-theoretic

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approaches have not been invoked over the last 70 years and point to ways in which they might be. This is done in the following three subsections on (1) the role of psychological testing in the advancement of personality assessment, (2) the primacy of latent variable theory in personality testing, and (3) the meaning of “information” for personality assessment. The role of psychological testing in the advancement of personality assessment In retrospect, it seems clear that one of the factors contributing to Cronbach’s dismissal was the gestalt view of psychological testing during this era. This view—a general understanding of best practices for psychological testing— was beginning to coalesce by the early 1950s, and Cronbach was one of the most visible leaders of this burgeoning industry with promising growth prospects. To explain the gestalt view, it helps to briefly summarize the history of testing before the postwar era. One of the most critically important innovations in testing was the use of questionnaires for data collection, and this development is often overlooked by virtue of having such primacy in personality psychology, especially today. Cronbach did not take this innovation for granted and frequently called attention to the ingenuity of questionnaires (Cronbach, 1960, 1970).c He explicitly defends the use of questionnaires over direct observation because surveys “avoided the labor and delay involved in direct observation of behavior” in the many “situation[s] where individual interviewing of every man was totally out of the question” (Cronbach, 1960, p. 465). For further evidence of this consequential idea, see Cronbach and Gleser (1965) and Cronbach (1970). Many “best practices” in psychological testing evolved from this fundamental idea that questionnaires are a workaround for behavioral observation, and the effects were relevant for the evolution of theory as much as methods. The first formal personality inventory — the Woodworth Personality Data Sheet (WPDS, Woodworth, 1917) — was commissioned to use the questionnaire approach to evaluate military recruits for emotional stability (Cronbach, 1960; Gibby & Zickar, 2008; Goldberg, 1971). The questions were derived c. Cronbach credits Galton for development of the self-report questionnaire (Cronbach, 1960; Galton, 1884, 1869). It seems worth noting that Galton’s The Measurement of Character (1884) is commonly cited in the personality literature as the origin of the Lexical Hypothesis (Allport & Odbert, 1936; Goldberg, 1990, 1993a) on account of a passing comment regarding the author’s consultation of the dictionary in an attempt to “gain an idea of the number of the more conspicuous aspects of the character” (Galton, 1884, p. 181). The next two paragraphs of this same reference make more explicit suggestions for the advancement of personality assessment, though he never directly advocates for the lexical approach. Instead, he proposes the empirical mapping of character through two paths: (1) assessment via high-stakes testing (specifically, interviews or tests that “elicit some manifestation of character,” presumably including surveys) and (2) more ordinary sustained observation of small frequent behaviors (something akin to the information theory approach we elaborate below).

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from the list of symptoms covered in psychiatric interviews (Poffenberger, 1962). The WPDS was never copyrighted and served as the basis for several subsequent tests (Gibby & Zickar, 2008), each of which borrowed heavily from earlier tools. Thurstone, for example, acknowledged the recycling of content explicitly in stating that his Personality Schedule (Thurstone & Thurstone, 1930) “was compiled mostly from several shorter lists already published, including Woodworth’s Psychoneurotic Inventory [an earlier name for the WPDS (Woodworth, 1917)], House’s monograph on this subject [the Mental Hygiene Inventory (House, 1927)], Laird’s schedules of questions [the Colgate Tests of Emotional Outlets (Laird, 1925)], Freyd’s list of questions [about introversion and extroversion (Freyd, 1924)], and Allports’list of questions [about ascendance-submission (Allport, 1928)]” (Thurstone & Thurstone, 1930, p. 1). All of the questions in the WPDS were included in Thurstone’s measure without revision! This pattern continued into the postwar era, overlapping with Cronbach’s own ascendancy. The most widely used measure of that period was the Bernreuter Personality Inventory (BPI, Bernreuter, 1931); the content of this inventory was acknowledged to be a subset of Thurstone’s aggregation of others’ items (Bernreuter, 1933; Gibby & Zickar, 2008).d Though rarely referenced today, the BPI was administered to millions of test-takers in the 1930s and 1940s, peaking at more than 1 million in 1953 (Whyte, 1956). Several competing measures were close behind, including the business-focused HummWadsworth Temperament Scale (Wadsworth, 1936, 1937) and several newer entrants that remain familiar today: the Minnesota Multiphasic Personality Inventory (Hathaway & McKinley, 1940), the Myers-Briggs Type Indicator (Myers, 1945), the Guilford-Zimmerman Temperament Survey (Guilford & Zimmerman, 1949), and the 16 Personality Factor Questionnaire (Cattell & Eber, 1950). This review is more than just a history lesson; it provides a sense of the landscape of psychological testing in Cronbach’s era and supports more specific claims about perceived best practices in methods. As noted, one was the strategy of using behavioral questionnaires (mainly, self-report) rather than more taxing and time-consuming approaches to behavioral observation. Following from this, methods for digitized encoding (scoring and coding) gained consensus—in some ways, it was the urge to digitize that drove the use of checklists and Likert-like response scales—as did the development and utility of increasingly sophisticated statistical techniques. It is no coincidence that several statistical methods were developed or vastly advanced by psychologists working to develop better testing methods—psychometricians. This includes d. Allport threatened legal action against the owners of the BPI, while also acknowledging that Bernreuter’s measure was an improvement upon his own (Gibby & Zickar, 2008). The stark contrast between modern-day concerns about copyright infringement and the liberal sharing of content during the early years of test development is discussed in greater detail by Deary and Bedford (2011).

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the correlation coefficient (Rodgers & Nicewander, 1988; Spearman, 1904a), factor analysis (Cattell, 1946a, 1946b; Mulaik, 1987; Spearman, 1904b; Thurstone, 1931), and evaluations of reliability (Cronbach, 1947, 1951) and validity (Anastasi, 1950; Cronbach & Meehl, 1955).e The point is not that Cronbach should be faulted for a perspective that was influenced by the milieu of his era, but rather that his perspective shaped his dismissal of an information-theoretic approach that did not obviously fit into the features of personality assessment that were relevant for psychological testing. Before and during the 1950s, the psychological testing industry was finally beginning to enjoy the benefits of industrial, military, and educational applications of psychometric theory. Cronbach was a key player in the informal alliance—the latent variable theory-psychological testing complex—that had evolved during the decades of the mid-20th century between psychometricians and the organizations that made use of psychological testing. Indeed, the tasks of developing and administering psychological assessments were often carried out by the same individuals. Like many such complexes (i.e., the militaryindustrial complex), the benefits of this alliance were (and continue to be) mutually reinforcing for both sides but also serve to reduce the incentives for developing divergent approaches. Indeed, the informal codification of procedures for psychological test development unfolded rather quickly under the guidance of a small group of quantitatively oriented psychologists. Many of these individuals were funded by governmental agencies and similar organizations to develop psychological tests for use in specific applications, and their influence has had a long-lasting impact on personality assessment. The primacy of latent variable theory in psychological testing Building on recognition of the latent variable theory-psychological testing (LVT-PT) complex, it is also necessary to consider the role of latent variable theory in Cronbach’s dismissal of information-theoretic approaches. Are these topics incompatible? In short, no. But, to answer this question more thoroughly, it is first necessary to clarify that our use of the label “latent variable theory” draws upon the framing of Borsboom (2008). That is, we use this term in reference to the broad framework of ideas and practices that relate to the use of latent variable modeling as a procedure for mapping the relation between theoretical constructs and empirical data. While there are many types of latent variable models (mostly differentiated by the involvement of discrete vs continuous variables), they share the same goal of using a regression function to relate the manifest (observed) variables to those which are inferred (unobservable). In this way, latent variable theory enables the testing, comparison, and refinement of psychological theories by modeling observed data. For a e. Academic and intellectual lineage is relevant to this point, for Cattell was a student of Spearman and Cronbach a student of Thurstone (who was still very active in the early 1950s).

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thoughtful treatment of the assumptions, implications, and applications of latent variable theory, Borsboom’s (2008) contribution is recommended. The relevance of latent variable theory to the current discussion stems from its use in test development, most familiar with techniques like factor analysis and principal components analysis. These tools are invoked to reduce large amounts of information down to a manageable (i.e., parsimonious) number of broad concepts that are central to the topic(s) under study. The utility of these data reduction techniques is hard to contest, for they have advanced our understanding of many psychological domains, including several that are central to personality such as the structure of individual differences in temperament (Cattell, 1947; Goldberg, 1993b; Norman, 1963), cognitive abilities (Carroll, 1993; Cattell, 1987), interests (Holland, 1959; Prediger, 1982), and much more. For these domains, in particular, assessment tools intending to capture broad ranges of content (e.g., personality traits) have typically been derived from the reduced, factor analytic models of latent constructs that make use of composite variables and prioritize parsimony. Problems creep in, however, when these techniques are used to develop tests that are subsequently widely adopted as personality assessments in research contexts with too little recognition of the information that gets ignored. This includes content that was omitted from the outset of test development as well as content that was originally included but later dropped because it did not explain sufficient variance in the validation process (Saucier, 1997). It also includes information that is collected as part of the test but ignored as allegedly unreliable noise (McCrae, 2015), as is prescribed by the latent variable approach advocated by Cronbach. To be clear, the exclusion of this content by test developers is intentional, reasonable, and typically well-acknowledged. Yet, the benefits are diminished when these tools are used in research contexts that differ considerably from the testing situation and/or the validation setting. Consider, again, the small number of prestructured attributes evaluated with very brief, omnibus personality assessments [e.g., the Ten-Item Personality Inventory (Gosling et al., 2003); the Big Five Inventory 10 (Rammstedt & John, 2007)]; these measures are among the most widely-used for personality assessments in research contexts. The question is not whether the measures are technically consistent with the latent variable model in which they are rooted, but rather their ability to reasonably serve as assessments of personality as a broad phenomenon. Conscientiousness, for example, is measured by the extent to which one is “dependable and self-disciplined” and “disorganized and careless” (Gosling et al., 2003). Many features of this trait family are missing entirely such as “the propensity to be self-controlled, responsible to others, hardworking, orderly, and rule abiding” (Roberts, Lejuez, Krueger, Richards, & Hill, 2014). Many more might be included from outside the boundaries of the familiar Big Five traits, and each of these features could be measured independently rather than indexed. An information-theoretic approach might include

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behavioral markers and/or contextually specific variables that are not even prespecified in the same manner as standard survey instruments. This difference in the use of prespecified variables reflects a deeper distinction between the two approaches. Many, perhaps even most, of the psychological scales developed over the history of psychology have been created with a heavy reliance on theory from the outset. Consider the Myers-Briggs Type Indicator (Myers, 1945), for example, which is based on one team’s interpretation of Jung’s previously untested theory about personality ( Jung, 2016). The utility of this measure is severely impaired by this theory-heavy approach to design. On the other hand, the joint application of the latent variable and informationtheoretic approaches can be uniquely powerful, as exemplified (somewhat imperfectly) by psycholexical models of personality. The Big Five framework is vastly more valid than the MBTI because it began with a broad sampling of the universe of personality-relevant human attributes. A more optimal (if less pragmatic) implementation of the two approaches might have used even more than the 500–600 trait-descriptors (reported in Goldberg, 1992) and these would have been assessed relative to a wide range of situational contexts and a maximally diverse sample of respondents. Note that we do not mean to argue that the information-theoretic approach precludes the use of data reduction techniques or subsequent invocation of latent variable modeling to evaluate the mechanisms underlying psychological phenomena. On the contrary, the utility of both theories may be improved when used in conjunction: information-theoretic approaches that seek to broadly characterize phenomena and latent variable approaches for positing, testing, comparing, and refining causal models. The problem only lies with the exclusive use of latent variable theory approaches for psychological assessment. The meaning of information for personality assessment In Shannon’s framing (1948a), the information-theoretic measure of information has subsequently been called Shannon information or, more frequently, entropy. This name is due, in part, to its rough equivalence to the probability equation for thermodynamic entropy. This poses a confusing problem, in retrospect, for the terminology of information theory approaches is easily conflated with a large body of testing-related research conducted during the same era (roughly 1948 to 1955; see Lord, 1953). The aim of this and subsequent research on “item response theory” (IRT) was to specify the amount of psychometric information provided about a specific trait level based on the pattern of responses to a scale or single item. The functions used to model the psychometric information in IRT are generally known as Fisher information functions (Markon, 2013), causing further difficulty for these were first described (Fisher, 1922) more than 25 years ahead of Shannon’s work, and perhaps earlier in other forms and applications (Aldrich, 1997).

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Put simply, “information” is an unfortunately vague term, and one with a history of use in psychological test theory relating to the estimation of a latent trait level on the basis of a given response to a measure of that trait. Consistent with the prespecification of variables in traditional psychological testing, this usage of the term information depends on knowing what one seeks to measure a priori, and it further assumes that the test is given to assess the targeted construct is both valid and specific to a particular dimension (e.g., a dimension of a latent construct). The basic logic is to situate a test-taker’s responses relative to the underlying dimension on the basis of prior knowledge about the distribution of responses. It is a comparison of two possible states (probability distributions) and is, therefore, sometimes referred to as “relative entropy” (Rioul, 2018). In short, the approach being advocated by Cronbach and his contemporaries (whether classical test theorists or those laying the foundation for IRT in the early 1950s) was to focus psychological tests on a small number of prespecified latent constructs. In so doing, they focused only on the extent to which test-takers compared to others on each of these constructs. This was often done independently; that is, without comparison across multiple constructs simultaneously. Shannon’s entropy, by contrast, can be thought of as an absolute measure; a measure of the amount of information gained by each message. Alternatively, this is sometimes framed in terms of informational complexity or surprisal value; that is, the probability of the message given the context. These ideas point to a focus on the most uniquely identifying features of the individual—and these may often not be captured by the prespecified latent constructs. Of course, this poses a problem in cases like those described by Cronbach where the amount of information that can reasonably be captured is limited relative to the full range of possibilities. In these cases, it is unlikely that the most identifying features for any given individual would be collected by chance. Fortunately, as we discuss in the next section, the confluence of several developments since 1955 has begun to feasibly address this concern by allowing for much less limited data collection that Cronbach could have anticipated, including some types that are entirely passive. In summary, our point is not that Cronbach’s dismissal of information theory principles was inappropriate at the time—it probably was not—but rather that it is time for his rationale to be reconsidered. The boundaries of personality science extend, inherently, beyond the scope of existing psychological testing tools (Mo˜ttus et al., 2020). Exploratory approaches should pursue methodological advances that are not based on existing assessment paradigms, and these innovations should not be limited to behavioral questionnaires. Personality psychologists who embrace the spirit of information theory should seek to maximize the information (Shannon information or entropy) captured across a large number of narrow psychological individual differences (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). With some irony, we recognize that this has only become possible thanks to the most recent technological achievements of the information age.

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Invoking information-theoretic approaches in personality assessment Our advocacy for a more information-theoretic approach involves a shift of focus away from priori-imposed parsimony in personality assessment, moving instead towards a more precise representation of the diversity of individual differences expressed in the real world. Progress in this direction is likely to occur as a by-product of the increasing ability to collect increasingly more data, and these technological advances can also bring about the opportunity for theoretical advancement beyond traditional approaches. This shift can be about more than data collection, extending to include new analytic methods and personality descriptions across more than the familiar parsimonious dimensions. In other words, we are not just calling for more data; we also need and can develop new and more flexible frameworks for representing and thinking of the data (Mo˜ttus et al., 2020). Importantly, we are not advocating against further development of the traditional psychometric approach advanced by Cronbach and others. Rather, we are arguing that Cronbach and others focused only on one specific coding system for personality (latent variable modeling), and dismissed the informationtheoretic approach for reasons that are not always relevant in modern research contexts. As we have already discussed, the synergistic nature of the latent variable theory-psychological testing complex was no coincidence, for the emphasis on parsimony and improved signal-to-noise ratios grew out of the practical demands of testing (limited time and strong motivation to maximize accuracy at the level of the individual). An information-theoretic approach would be much more flexible. It could allow for a broader range of assessment, including both greater breadth of content (higher bandwidth) and a greater diversity of information types, including a wide range of behavioral and situational variables across multiple occasions (a la Brunswik, 1955).

Emphasizing the needs of researchers over test administrators A specific starting point stems from one of Cronbach’s claims about information theory being relevant only to conditions that are not met by his “testing problem” (Cronbach, 1953, p. 41). The primary challenge with testing is to efficiently classify large numbers of individuals according to attributes that have been deemed diagnostic, whether clinically or for predicting performance in military, occupational, and educational settings (Meehl, 1954; Revelle, Wilt, & Condon, 2011). In other words, create a test that balances the needs of scalability across test-takers, efficiency in administration and interpretation, and utility for each individual (this requires retest reliability and validity; Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). As previously mentioned, an early and consequential decision was made in the testing arena to use fixed-length questionnaire forms (with each questionnaire prompt

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coming to be known as an item). When wrestling with the prospect of using an information-theoretic approach, Cronbach offers the following example: Suppose we have 50 binary test items and 50 people. I get 50 bits of information if I give 50 items to one person; or I give one item to 50 persons. … Now hypothetically, the configuration of 50 responses might help me decide if one person was schizophrenic; I could use the information altogether in making one decision. But with one item per person, there are only 50 facts to be considered separately, each for a decision about one person. There is no problem with which these data as a configuration or single message bear. The message about one person and the message about 50 people occupy equal message space, but only a small part of the information about the configuration of people is functional. Cronbach (1953)

This false dichotomy is well-suited to his viewpoint. The data collector is limited to a small amount of data collection (here, 50 binary digits or “bits”) and only two all-or-nothing options; one captures a little information about many people, the other a somewhat larger amount about only one person. For test administrators, the latter is preferred for it does a better job of balancing the needs of the testing context—for schizophrenia, in this example, but this could be generalized to other psychological phenomena, including those with higher base rates or even Gaussian-like distributions in broad populations (i.e., personality constructs). However, it is not at all certain that the latter option is better for personality researchers, especially when considering more realistic data collection parameters. Consider, for example, how the situation changes when it becomes possible to collect many thousands of bits of information. Today, sample sizes in personality research will often contain 150,000 bits and sometimes much more. This is equivalent to three bits per item (five to eight response choices) for 100 items and 500 participants. Data from the Eugene-Springfield Community Sample (Goldberg & Saucier, 2016) contains more than 10 million bits and the data reported in Johnson (2014) contains approximately 500 million bits or 0.625 gigabytes.f The scientific literature provides strong evidence that many researchers prefer to collect data from large samples of participants rather than use a case study approach that seeks to characterize a single participant across all imaginable questionnaire items. A more important observation is that most researchers choose to use a blended approach, one that strives for high analytic power (based on large sample sizes) but uses the shortest and most parsimonious assessments possible. The blend adapts the LVT-PT approach to better suit f. Though very large relative to the data sets collected by personality researchers historically, it is important to keep in mind—especially when considering the prospects for information theoretic approaches—that these are trivial relative to data sets collected in other scientific disciplines. For perspective, 0.5 GB is equivalent to about 3 min of high-quality digital video (DV/AVI format). Approximately 4.0  1011 GB are collected each year at CERN.

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the needs of researchers, though the benefit of collecting much more information (bigger samples) accrues to higher levels of measurement precision rather than breadth. The prospect of broader adoption of information-theoretic approaches among personality researchers points to a wide range of possibilities, including those suggested by Cronbach’s contemporaries. In Brunswik’s work on Representative Design (1955)—a component of his broader Conceptual Framework for Psychology (1952)—he called for much closer scrutiny of the effects of the environment on personality phenomena studied under testing (and experimental) conditions. Brunswik meant this quite broadly, calling for sweeping improvements in methods including data collection on the environmental features at the time of assessment, multiple assessments across heterogeneous environments, and greater sampling of items across assessments (Brunswik, 1955). Brunswik distinguished these changes from more familiar notions of simply moving assessments into “real-life circumstances” (i.e., ecologically valid conditions); he sought to emphasize the mechanistic effects of situational variables. Perhaps unsurprisingly, Brunswik’s suggestions were not readily embraced by his contemporaries (Leary, 1987), though many did acknowledge being influenced by his ideas, despite their intractability at the time. Cattell elaborated similar ideas with his “data box” (1966), a metaphor he had first proposed 20 years earlier (Cattell, 1946b; see also Chapters 11 & 14). The data box was used to illustrate the need for descriptive research in personality on the associations among persons, variables, and occasions. Fig. 3 (occasion 1) depicts the essence of Cattell’s suggestion, with Cronbach’s earlier example included (his dichotomy indicated with arrows A and B). Brunswik’s suggestions were only partially captured in Cattell’s original 3D box metaphor (persons by variables by occasions), in that he called for multivariate timeseries data collection within persons that also measured features of the

FIG. 3 A revision of Cattell’s data box, with Cronbach’s dichotomy of options (indicated with arrows A and B).

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environment at each occasion, as shown in Fig. 3. Truly representative designs, according to Brunswik (1955), would further account for multiple perspectives, including the target, multiple informants, and objective behavioral markers. The fifth dimension of perspective is not shown in Fig. 3, though it is represented, in some sense, by its consideration across multiple raters (of targets, across variables, and multiple situations, over time). It is important to acknowledge that these methodological suggestions by Cattell and Brunswik do little by themselves to invoke informationtheoretic approaches. Yet, they provide a means of framing the diversity of personality-relevant signals to be encoded, one that more realistically suggests the nature of the “problem” from the perspective of personality researchers rather than psychological testing administrators.

Next steps To push these ideas more obviously into an information-theoretic perspective, personality science will need to undertake investment in several “slow science” projects (Reischer & Cowan, 2020). Though the prospect of achieving representations like those shown in Fig. 3 has been greatly improved by recent technological innovations, they remain daunting and beyond the reach of small, independent research teams. Given this, there is a great need for collaborative approaches across multiple labs and involving personality scientists with diverse training, theoretical perspectives, and backgrounds. Several of the required projects should be expected to unfold over many years, given their scope and the hope of collecting data across multiple occasions. A model for these collaborations is provided by the aforementioned Eugene-Springfield Community Sample (Goldberg & Saucier, 2016), the data collection project associated with the International Personality Item Pool “collaboratory” (Goldberg, 1999). What are the aims of these projects? The most formidable (and possibly the most disputatious) is to atomize the various types of signals about how individuals can differ into their essential elements. This does not need to be as complicated as it sounds. Some progress has already been achieved along these lines in the historical work of identifying the “atoms” of personality descriptors in the language (i.e., Allport & Odbert, 1936; Ashton, Lee, & Goldberg, 2004; Norman, 1967). A prospective example of this might include collaborative “bottom-up” development of assessment taxonomies across the range of domains relevant for personality-contexts, as suggested by Condon, Chapman, et al. (2020) and Condon, Wood, et al. (2020). It remains an open question whether it is feasible to pursue the development of a singular, exhaustive taxonomy that integrates the many domains of psychological individual differences (temperament/character, interests, values, worldviews, strivings, motivations, abilities, etc.; Condon, 2014) or if the preliminary development of many such taxonomies is more reasonable in the near term.

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A full account of the steps needed for a bottom-up approach to assessment remains beyond the scope of the current work. In any case, these likely vary across domains of content. Efforts are currently underway to begin such development in the domain of content relating to personality items like those in the International Personality Item Pool (though not limited to these exclusively). This initiative—involving both authors of this work—has not yet progressed beyond the stage of developing iterative procedures for consideration by the field more generally (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020; Mo˜ttus et al., 2020). These are iterative in that the goal—identifying an essential set of assessment content that is defensibly exhaustive without being excessively redundant—can only be achieved with multiple rounds of empirically-informed refinement. These rounds would optimally involve data collection similar to the methods suggested in Fig. 3. An obvious challenge to initiatives such as these relates to the difficulty of large-scale data collection. Recruiting and retaining large numbers of participants is only the first hurdle (though see our comments below regarding the potential to combine samples across research groups and paradigms as in a mega-analysis). An equal challenge relates to the need to collect more data than participants are willing to give in a single setting. One option is to spread the data collection across many waves (as was done in the Eugene-Springfield Community Sample; Goldberg & Saucier, 2016), but this introduces new problems. A more efficient alternative would incorporate a planned missingness design (Revelle et al., 2016). For example, one might collect data from 50 variables randomly sampled from a much larger pool, perhaps many thousands (Condon, 2019). This approach would be fruitless if used with sample sizes common in the 1950s, or if research teams are dealing with inconsistent groupings of variables. Yet, the power of this technique is magnified when scaled to many thousands of participants within a study and/or across multiple research teams. Additional aims for these projects follow from the benefits of consensus around more detailed assessment frameworks such as moving from the Big Five-centric assessments to those that account for the hierarchical organization of personality, from few broad traits to numerous more specific ones (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). Several suggestions follow from information-theoretic techniques including (1) evaluations of the informational content that follows from the administration of variable subsets to independent (and demographically distinct) samples of participants, (2) across situations, (3) occasions, and (4) perspectives. The details of this suggestion—based on entropy (aka Shannon information in this case)—are left for subsequent work, but the basic notion is to evaluate differences in the overall amount of information across studies by comparing the entropy of individual items or item subsets. Evidence for either would indicate differences in the expected probabilities of signal magnitudes, as might be expected given differing circumstances across studies. Further studies of the properties of

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transmission of personality-relevant information signals might also make use of more traditional psychometric properties. For example, evaluations of the extent to which signals are consistently encoded/decoded over time will make use of (5) signal-to-noise statistics, especially test-retest coefficients. Similarly, qualitative methods for evaluating the introduction of noise would consider the consistency of signal detection and utility based on the (6) translatability, (7) social desirability, and (8) literacy levels of item content. Most of the next steps described in this section have focused on applications with survey items, but this need not be the case in practice. In our view, one of the most consequential results of Cronbach’s failure to recognize the potential of an information-theoretic approach has been the long-standing primacy of questionnaire-based methods over behavioral observation. Surveys will continue to be the dominant method of data collection in personality in the near term, but the increasing adoption of more passive assessment techniques suggests this dominance may not last. Examples of these new methods include digital footprint data collected from social media, electronic health records, and passive sensor data (e.g., Cooper et al., 2020; Hall & Matz, 2020; Harari et al., 2020; Stachl et al., 2020; Tay, Woo, Hickman, & Saef, 2020; Wiernik et al., 2020). These methods may enable the evaluation of new considerations—such as the transmission of covert signals (i.e., information signals that can be accurately transmitted and received by intended targets but not by unintended targets; Smaldino, Flamson, & McElreath, 2018)—and new challenges [thorough discussions of these are provided by Girard and Cohn (2016) and Harari, M€ uller, Aung, and Rentfrow (2017)]. Data privacy concerns loom large among these challenges, but new decentralized approaches to digital identity management via blockchain and similar technologies offer some promise for the eventual possibility of securely sharing massive amounts of personality-relevant data (Wang & De Filippi, 2020). Importantly, evaluations across studies using methods like those described above would not necessarily be limited to individual research groups, as is the norm currently. Consensus regarding the atomization of personality signals would hasten (and arguably, enable) the standardization of variables. In turn, this would increase the pace of personality research (Goldberg, 1999), in part by increasing the motivation to pursue information-theoretic approaches. Prior attempts at standardization in personality have mainly focused on the important aspect of pushing survey content into the public-domain (Armstrong, Allison, & Rounds, 2008; Condon, 2019; Goldberg, 1999), but this is not sufficient. Agreement about the lowest-level units would lead to much more detailed standardization. In the case of variables, for example, this might include standardized variable labels, response choices, item stems, and recall periods (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). Similar examples might be given for environmental criteria (in the situations dimension) or variablespecific time intervals between administrations (for occasions).

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In sum, the atomization of personality signals is not an end unto itself, but a requisite step in the more efficient assessment (i.e., transmission) of psychological individual differences. It is with some irony that the next step after achieving tentative consensus around the boundaries and operationalizations for these essential elements likely involves the need to fill in the “nomological network” often attributed to Cronbach and Meehl (1955), though updated framings of this idea (Campbell & Fiske, 1959; Messick, 1995) are likely to be more useful. It is expected that the benefits of nomological networks will be dramatically improved with more circumscribed boundaries, not only with respect to their traditional use in making claims about construct validation but more importantly in the empirical testing of theories that are qualitatively distinct from those that have previously been common in personality psychology. In other words, the advancement of strong theories that “explicate a precise set of assumptions and axioms about a phenomenon non-ambiguously” (Fried, 2020, p. 4), rather than the more familiar class of weak theories in psychology that merely specify relations among variables and are effectively incapable of being disconfirmed.

Towards a digital representation of the analog experience In our view, an important but imperceptible fracture has developed in personality science: that the aims of personality science research are increasingly less aligned with the goals of person-level personality assessment. The basis for this fracture has likely been present for decades, as the aims of basic personality research have consistently differed from the aims of personality testing. Realization of the fracture, however, has occurred as the potential to capture much more informative and diverse data has increased over time. The demands of testing—scalability across test-takers, efficiency in administration and interpretation, and utility for each individual—encourage a myopically nomothetic, conservative, and opaque approach to assessment. By contrast, research would often benefit from assessment techniques that are more flexible, inclusive, and transparent. The person perception literature provides a ready example of this situation because it is evident that the Big Five is a poor match for the analog experience of assessing the personality of others, so we shall revisit the analog/digital distinction as we conclude. This subdiscipline of personality science has generally invoked broad operationalizations of personality when exploring the procedural components of personality judgment. Mainly, this has meant the use of traits like the Big Five, even when considering judgments of very narrow behaviors that are typically briefly expressed, and studied with stimuli like photographs (Borkenau, Brecke, M€ ottig, & Paelecke, 2009; Gosling, Ko, Mannarelli, & Morris, 2002). This need not be the default, for the ever-increasing bandwidth of data collection provides an opportunity for researchers to more fully capture the rich complexity of psychological individual differences. Yet, this cannot be

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achieved without some detailed alternative framework (Condon, Chapman, et al., 2020; Condon, Wood, et al., 2020). With concerted effort and time, the personality assessment subfield can make this happen. It requires the development of personality assessment tools—based on information-theoretic aims rather than data reduction via latent variable models—that invoke a much more complex framing of personality than is necessary for psychological testing applications. Consider, for example, the benefit gained from an understanding of the relationship between the accuracy of judgments for smiling behavior as a function of “Extraversion” vs “variance in emotional expressiveness using items X, Y, and Z across situations involving 3 or more non-familial others.” The former allows for much less formal theory testing and refinement than the latter. Our call for information-theoretic approaches is not intended as a reversal of Cronbach’s dismissal. We acknowledge that tremendous benefit has come from the identification of broad, omnibus assessment tools, and that there will continue to be many use cases where these remain the best recommendation. We further acknowledge that the steps we have outlined herein require substantial field-wide investment beyond the scope of what can be achieved by individual research teams. Yet, the time is ripe for looking beyond the broad and intentionally underinformed methods of assessment that are predominantly used by researchers today. Our aspiration for personality assessment is to begin assembling an alternative to these dominant tools that can digitally reproduce the complex and diverse analog experience of personality in everyday life.

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Chapter 2

What falls outside of the Big Five? Darkness, derailers, and beyond P.D. Harmsa and Ryne A. Shermanb a

Management, University of Alabama, Tuscaloosa, AL, United States, bHogan Assessment Systems, Tulsa, OK, United States

From its beginning, personality psychology has been driven by investigations of not only what is seen but also what is unseen. For many scholars, it is enough to know how individuals typically behave under ordinary circumstances. For others, having a more complete view of the individual requires them to dig deeper, to uncover their motives and desires, their hidden thoughts and attitudes, their unexpressed emotions and their fears, and their unconscious aspects hidden even to themselves. The study of dark personality straddles these two perspectives as a way of explaining antisocial, aggressive, and often self-defeating behaviors that can derail the careers and relationships of otherwise normal individuals. It allows us to gain a more complete understanding of the individual by seeing what happens when the psychological filters and restraints fail, and the hidden darkness seeps to the surface. Personality is often defined as internal or psychological characteristics which are relatively stable and drive regularities or patterns in behavior, cognition, and emotion (see Allport, 1931; Funder, 1991; Hogan, Hogan, & Roberts, 1996). From a psychological perspective, personality is essential for explaining the “why” behind the human experience (Harms, Wood, & Spain, 2016), the lens through which situations are interpreted and acted on (Lewin, 1936; Tett & Burnett, 2003; Tett & Guterman, 2000). From an applied perspective, personality is essential to understand because it is so consequential—for individuals, for organizations, and for nations (Hogan & Sherman, 2020). Personality traits have been linked with a number of important outcomes including income, education, relationship success, well-being, and job performance (e.g., Ozer & Benet-Martinez, 2006; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). The preponderance of the research in the domain of personality has been limited to or focused on a set of characteristics known as the Five-Factor Model Measuring and Modeling Persons and Situations. https://doi.org/10.1016/B978-0-12-819200-9.00016-8 Copyright © 2021 Elsevier Inc. All rights reserved.

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or “Big Five” (see Goldberg, 1993; John & Srivastava, 1999). However, there have been increasing calls for personality psychologists to take into account other dimensions or domains of psychology in order to more fully account for personality structure and processes (e.g., Block, 1995; Feher & Vernon, 2020; McClelland, 1985; Mo˜ttus, Kandler, Bleidorn, Riemann, & McCrae, 2017; Paunonen & Jackson, 2000; Wood, Harms, & Vazire, 2010; Wood, Nye, & Saucier, 2010). One of the alternative frameworks that have developed in response to dominance and deficits of the Big Five has been that of dark personality traits, which have become increasingly popular in both the applied (Guenole, 2014; Harms & Spain, 2015; Spain, Harms, & LeBreton, 2014) and basic personality literature (Furnham, Richards, & Paulhus, 2013; Miller, Vize, Crowe, & Lynam, 2019) as well as popular press books targeted at lay audiences (e.g., Babiak & Hare, 2006; Dutton, 2012; Ghaemi, 2011; Maccoby, 2003). Further, as a consequence of both the popularity of the dark personality framework and the accumulating evidence of the importance of such traits for predicting life outcomes, it has been argued that the inclusion of such traits alongside the Big Five is necessary to adequately capture the full breadth of personality traits (Veselka, Schermer, & Vernon, 2012).

Dark personality It can be argued that the earliest models of dark personality go back more than 100 years to Emil Kraepelin (Kraepelin & Diefendorf, 1907) who described individuals with a variety of disturbed personalities such as the “morally insane” (individuals who lacked sympathy and had a tendency toward cruelty), the “unstable” (individuals who were irrational and moody), and the “morbid liars and swindlers” (individuals who tended to be intelligent, but who derived joy from deceiving others). However, most scholars would agree that current theorizing and measurement of dark personality can be traced back to a paper by Hogan and Hogan (2001) which proposed that there existed a set of interpersonally-aversive traits which shared characteristics of Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) Axis-2 personality disorders (American Psychiatric Association, 1994), but which were neither debilitating nor chronic enough to warrant clinical attention or interventions. Instead, these so-called dark traits would function as personality quirks that could emerge depending on circumstances and occasionally cause interpersonal problems for what were otherwise normal individuals. Specifically, stress or other high-pressure situations have often been thought to be a trigger for the expression of many dark traits (see Spain, Harms, & Wood, 2016 for a review); however, this is not necessarily true for all dark traits. For this reason, a dark personality can be argued to represent the aspects of personality that matter most, when it matters most. Hogan and Hogan (2001) argued that these characteristics were necessary to account for aberrant

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behaviors that could potentially ruin careers and relationships in nonclinical, working populations. For this reason, they opted to describe these traits as “personality derailers.” Within these derailers, a distinction can be made between traits where there may be the intention to harm others, which can be thought of as destructive in nature, and those where harm to the self and others is inadvertent, which are simply dysfunctional (Spain et al., 2016).

Dark Triad Shortly after Hogan and Hogan (2001) introduced the idea that important personality characteristics fit in the space between normal-range personality traits such as the Big Five (which describe and predict how normal people behave under normal circumstances) and abnormal personality disorders, Paulhus and Williams (2002) proposed that a set of what they called subclinical personality traits and which they labeled the “Dark Triad” which they believed were particularly predictive of behavioral deviance and interpersonal problems (see also Paulhus, 2014). The three traits that make up the Dark Triad are narcissism, psychopathy, and Machiavellianism. Although this initial grouping of traits was selected for their historical popularity in basic personality research and was never intended to serve as a taxonomy of dark traits, many subsequent treatments of Dark Triad have treated them as such.a In the past two decades, the Dark Triad has also become the dominant framework among research psychologists for assessing dark personality and its consequences.

Narcissism Of the Dark Triad characteristics, narcissism has received the most attention from researchers investigating nonclinical populations (Cain, Pincus, & Ansell, 2008; Campbell, Hoffman, Campbell, & Marchiso, 2011; Grijalva & Harms, 2014; Miller & Campbell, 2008). Loosely based on the ancient myth of Narcissus, a young man so vain that he fell in love with his own reflection in a pool of water and wasted away rather than cease gazing at himself, narcissism is characterized by feelings of superiority over others, an excessive need for attention, a tendency toward feeling entitled, and a willingness to exploit others (Morf & Rhodewalt, 2001; Paulhus & Williams, 2002; Raskin & Hall, 1979). For narcissists, the need to feed their egos is so strong that they do not limit themselves to simply solicit praise (or praise themselves), but often feel compelled to put down others as well (Morf & Rhodewalt, 2001). In essence, it becomes a form of toxic self-esteem. a. Paulhus has subsequently argued that the Dark Triad traits could form the basis of a taxonomy of a particular type of dark personality characteristics which share common features such callousness and aggression (Paulhus, 2014). On the basis of this logic, Paulhus and his coauthors have suggested that sadism be added as a fourth dark trait and relabeled the grouping as the “Dark Tetrad” (Paulhus, Curtis, & Jones, 2018).

36 Measuring and modeling persons and situations

Although narcissism has a rich history of research and many researchers have well-developed theoretical models, most debates as to the nature of narcissism tend to be driven by factor analyses of the original subclinical measure of narcissism, the widely-used Narcissistic Personality Inventory (Raskin & Hall, 1979). These have produced a variety of models as to the number of factors within narcissism, but there is some agreement that it contains at least three subdimensions reflecting dominance (believing oneself to be a leader), exhibitionism (wanting to show off), and entitlement (or a willingness to exploit others) (Ackerman et al., 2011). There is also an ongoing debate as to whether a more neurotic form of narcissism (Ackerman, Hands, Donnellan, Hopwood, & Witt, 2017; Miller et al., 2018), sometimes called hypersensitive or vulnerable narcissism (Hendin & Cheek, 1997; Pincus & Lukowitsky, 2010; Wink, 1991) and characterized by low levels of self-esteem and strong reactions to ego threats, should be integrated into models of narcissism. These debates, along with issues surrounding the measurement of narcissism, where the choice of measure can play a huge role in determining the direction and size of relationships between narcissism and criteria (e.g., Grijalva, Newman, et al., 2015) have resulted in oftentimes inconsistent and incoherent literature (Miller, Lynam, Hyatt, & Campbell, 2017). A recent advance in the narcissism literature has been the introduction of the Narcissistic Admiration and Rivalry Concept model (NARC; Back et al., 2013), which allows for a deeper understanding of the psychological drivers of narcissism. In this framework, narcissism can be seen as being motivated by either desire to secure praise from others or to punish and diminish others. Narcissists are believed to be more inclined to attempt to solicit praise (admiration), a less toxic or interpersonally aversive form of self-enhancement, but may resort to more competitive and hostile behaviors (rivalry) when their egos are threatened (see also Huang, Krasikova, & Harms, 2020). Thus, the model accounts for much of the complexity seen in prior narcissism research as well as provides a coherent framework for integrating prior research on grandiose and vulnerable narcissism, and describes a potential process to explain how some of the more toxic aspects of narcissism may arise.

Psychopathy Psychopathy is often characterized as high levels of impulsivity paired with low levels of anxiety and empathy (Babiak & Hare, 2006; Hare, 1985; Skeem, Polaschek, Patrick, & Lilienfeld, 2011). This combination of traits means that psychopaths tend to put little or no thought into the consequences of their actions and to feel little remorse when they hurt others. Further, some research has suggested that psychopaths have a diminished capacity for understanding social exchange norms and are impaired in terms of precautionary reasoning (Emrer & Kiehl, 2010). Consequently, individuals high in psychopathy are also known for their tendency to be antagonistic toward others and their tendency to

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seek immediate gratification (Cleckley, 1976; Hare, 1993). Psychopathy is often considered the “darkest” of the three traits in the Dark Triad and is typically assumed to have the most severe negative outcomes (Furnham et al., 2013; but see also O’Boyle, Forsyth, Banks, & McDaniel, 2012). Although much of the traditional work on psychopathy takes a twodimensional approach, based on low impulse control and low empathy, to understanding and assessing psychopathy (LeBreton, Shiverdecker, & Grimaldi, 2018), other recently introduced models have proposed models with additional dimensions. One popular model (Williams, Paulhus, & Hare, 2007) proposes that psychopathy consists of four dimensions: interpersonal manipulation (superficial charm and deceit), erratic lifestyle (impulsivity and thrillseeking), callous effect (lack of empathy or remorse), and criminal tendencies. However, one issue with this model and measure is that it is not clear if the fourth dimension represents an actual aspect of personality or is simply a self-report of the behaviors that would typically result from higher levels of psychopathy. A promising alternative model, the triarchic model of psychopathy (Patrick, Fowles, & Krueger, 2009), posits that psychopathy consists of three distinct, but overlapping traits: boldness (e.g., interpersonal dominance), disinhibition (e.g., impulsivity), and meanness (e.g., lack of empathy). According to this model, other conceptualizations of psychopathy can be mapped onto its structure depending on which aspects of psychopathy they tend to emphasize. Moreover, the model allows that some combinations or dimensions of psychopathy may actually be associated with varying degrees of personal and professional success (Lilienfeld, Watts, & Smith, 2015; Patrick et al., 2009). That said, critics have argued that these three dimensions do not correlate very highly with one another nor do they interact in such a way as to make them more predictive of aggression and other aversive behaviors (Miller, Maples-Keller, & Lynam, 2016). Consequently, the subject to the structure and components of psychopathy remains a topic of considerable debate (Miller et al., 2016).

Machiavellianism Machiavellianism is characterized by a cynical perspective of humans combined with a willingness, bordering on enthusiasm, to lie to and manipulate others (Christie & Geis, 1970). It is the only component of the Dark Triad to not be derived from an existing clinical construct. Individuals with high levels of Machiavellianism tend to be characterized by a lack of concern for or caring for relationships, ideology, and concerns of conventional morality while at the same time lacking any other gross psychopathology or cognitive deficits (Christie & Geis, 1970; Jones & Paulhus, 2009). The most widely used measure is the Mach-IV (Christie & Geis, 1970), which consists of 20 attitude statements conveying broad cynicism and comfort with deceit or manipulative behaviors. The nature of these items, such as their dated language and the inability to

38 Measuring and modeling persons and situations

generate appropriate other-report items from personal attitudes, has resulted in ongoing demands for improved measures (LeBreton et al., 2018; Miller et al., 2019). Some alternative measures and models have been developed that have attempted to introduce new aspects to traditional conceptions of Machiavellianism such as the desire for status (Dahling, Whitaker, & Levy, 2009; Kesseler et al., 2010), but these new scales show poor convergence with other Machiavellianism measures or between their own subscales (DeShong, Helle, Lengel, Meyer, & Mullins, 2017; Kesseler et al., 2010). There has been some debate in recent years as to whether or not psychopathy and Machiavellianism are really distinct constructs (McHoskey, Worzel, & Szyarto, 1998; Miller, Hyatt, Maples-Keller, Carter, & Lynam, 2017). Although some meta-analytic evidence suggests that there is substantial overlap between measures of each (e.g., Vize, Lynam, Collison, & Miller, 2018), it has been noted that there are important conceptual differences between the two constructs. For example, psychopathy is characterized by thrill-seeking and recklessness, whereas Machiavellians are thought to be highly planful and mindful of the consequences of their actions ( Jones & Paulhus, 2014; Paulhus, 2014). Moreover, it has been noted that much of the research used as the basis for drawing the conclusion that these two traits are not meaningfully distinct has tended to utilize poorly-designed instruments that may not be accurately reflecting the intended constructs at all (Miller et al., 2012; Rauthmann & Kolar, 2012). Consequently, a reasonable conclusion is that there is a need for the development of new instruments that better represent the theoretical conceptualization of these traits. One recent example of such an effort (Grosz, Harms, Dufner, Kraft, & Wetzel, 2020) has managed to develop two short measures, the M7 and P7, that come very close to addressing the problems common in other measures. That is, while the P7 psychopathy scale is highly related to impulsivity and higher levels of deviance, the M7 Machiavellianism scale is more highly associated with low levels of honesty. That said, even well-designed self-report measures such as the M7 and P7 are always going to be limited by the difficulties inherent in the task of accurately assessing individuals who readily and skillfully lie and engage in impression management.

Hogan personality derailers Although the Dark Triad represents one of the more popular sets of traits for investigating dark traits, a broader and more comprehensive approach to assessing dark personality can be found in the personality derailer scales of the Hogan Development Survey (HDS; Hogan & Hogan, 2001) and its corresponding model. The HDS assesses a set of 11 traits originally based on reviews of the management derailer literature (Bentz, 1985; McCall & Lombardo, 1983; Van Velsor & Leslie, 1995) as well as Axis-2 disorders from the DSM IV (American Psychiatric Association, 1994). For the purposes of making users more receptive to feedback, the 11 HDS dimensions have been given euphemistic labels (see Table 1 for descriptions). For example, a person exhibiting high levels

TABLE 1 Descriptions and conceptual overlap of HDS dimensions with Dark Triad. Subclinical trait

DSM-IV construct

Dark Triad construct

Description of high scorers

Moving away Excitable

Borderline

Moody and inconsistent concerns; being enthusiastic about persons, ideas, and projects and then becoming disappointed in them

Skeptical

Paranoid

Cautious

Avoidant

Resistant to change and reluctant to take even reasonable chances for fear of being evaluated negatively

Reserved

Schizoid

Socially withdrawn and lacking interest in or awareness of the feelings of others

Leisurely

Passiveaggressive

Autonomous, indifferent to the requests of others, and often irritable when others persist

Machiavellianism

Cynical, distrustful, overly sensitive to criticism, and skeptical of others’ true intentions

Moving against Bold

Narcissistic

Narcissism

Unusually self-confident, unwilling to admit mistakes or listen to advice, and unable to learn from experience

Mischievous

Antisocial

Psychopathy

Enjoys taking risks and testing the limits

Colorful

Histrionic

Expressive, dramatic, and desires to be noticed

Imaginative

Schizotypal

Acts and thinks in creative and unusual ways

Diligent

Obsessivecompulsive

Careful, precise, and critical of the performance of others

Dutiful

Dependent

Eager to please, reliant on others for support, and reluctant to take independent action

Moving toward

Note: Original descriptions were taken from the HDS manual (Hogan & Hogan, 1997). Adapted from Spain, S. M., Harms, P. D., & LeBreton, J. M. (2014). Dark personality at work. Journal of Organizational Behavior, 35, 41–60.

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of cynicism, distrust, and paranoia is called “skeptical.” It should also be noted that in spite of the dimensional structure, the HDS scales are not assessments of personality disorders, but rather tendencies to engage in self-defeating behaviors that can come and go depending on the social context, stress levels, and other ambient factors. In their original conceptualization, it was argued that these factors represented strategies that could potentially lead to short-term advantages, but with the expectation of having long-term detrimental effects for relationships, job performance, and leadership (Hogan & Hogan, 1997; Hogan & Kaiser, 2005; Paulhus, 1998). This broader set of traits also tends to cluster into three sets of behavioral themes which Hogan and Hogan (2001) labeled Moving Away, Moving Against, and Moving Towards. Individuals high on traits that fall into the Moving Away theme (Excitable, Skeptical, Cautious, Reserved, and Leisurely) manage stress and their own insecurities through emotional outbursts, avoiding others, and freezing up. They are sensitive to criticism, signs of betrayal, and tend to perceive hostile intent in others. Individuals high on traits within the Moving Against theme (Bold, Mischievous, Colorful, and Imaginative) tend to believe that they are entitled to special treatment or acclaim, resistant to feedback, and have a propensity to blame others for mistakes. They can be openly hostile or bullying toward others but are especially likely to do so if they feel they have been shortchanged. The Moving Against theme also represents the section of the Hogan model which most closely resembles the Dark Triad. Traits in the Moving Against domain are most closely aligned with those found in the Dark Triad and its variants. Individuals high on the Moving Towards theme (Diligent and Dutiful) are characterized by a desire to please authority figures and will engage in extraordinary efforts to secure their approval. Consequently, they demand of themselves and others extreme levels of dedication, precision, and compliance. Individuals scoring high on the Moving Towards theme are often liked by their supervisors but disliked by their peers and subordinates because of their obsequious nature and demanding attitude toward others.

Alternative models of dark traits Honesty/Humility Although dark personality traits are often thought to exist in a theoretical space separate from normal traits, the HEXACO model of personality suggests that a sixth factor, which they label Honesty-Humility, should be added to the conventional Five-Factor Model (Ashton et al., 2004; Ashton & Lee, 2007, 2009). The Honesty-Humility dimension includes content related to feelings of entitlement, willingness to deceive others, and greed and has been shown to be substantially correlated to each of the Dark Triad traits (Hodson et al., 2018; Lee & Ashton, 2005). Consequently, the evidence suggests that Honesty-Humility could be positioned as a generalized dark

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personality trait in personality models (albeit one that covers a narrow range of dark traits).

Big D and other “core” models Taking a different approach to get to a similar destination, several authors have attempted to determine the “core” of the dark traits, and the Dark Triad in particular (e.g., Book, Visser, & Volk, 2015; Jacobwitz & Egan, 2006; Jones & Figueredo, 2012; Tran, Bertl, Kossmeier, Pietschnig, & Stieger, 2018), by seeking common elements across dark personality dimensions or through factor analysis. Work in this line of thinking has suggested that all Dark Triad traits seem to share low agreeableness (Paulhus & Williams, 2002), lack of empathy ( Jones & Paulhus, 2010), duplicity ( Jones & Paulhus, 2017), aggression (Paulhus et al., 2018), and callousness (Paulhus, 2014), but there is yet no agreement on what or even if there is a psychological core driving dark traits.b This is largely owing to the fact that the results obtained are necessarily influenced, or even determined, by the measures selected for analysis and the statistical approach used (Watts, Waldman, Smith, Poore, & Lilienfeld, 2017). For example, in contrast to factor-analytic approaches, network analyses of dark triad traits have not found support for a common factor so much as support for the similarity between particular facets of dark traits (Trahair, Baran, Flakus, Kowalski, & Rogoza, 2020). One recent attempt to break through this impasse has been a proposed super factor of dark personality called the “D factor” (Moshagen, Hilbig, & Zettler, 2018; Moshagen, Zettler, & Hilbig, 2020) which is posited to exist in all dark personality measures to some degree. Applying this logic, the authors factoranalyzed 9–12 measures of dark personality and other related traits and found a substantial first factor which they suggested reflected a tendency toward utility maximization for the self. However, because the analyses relied almost exclusively on convenience online samples, self-report measures for both dark traits and criteria, and utilized a very limited set of measures, it remains to be seen how well received the D factor approach will be, or whether it will be dismissed as an atheoretical methodological artifact, and another dead end. Although the research literature is extremely limited at this point, it should be noted that D is extremely highly negatively correlated with Honesty-Humility (r ¼  .80) and fails to predict nonself-report criteria incrementally beyond Honesty-Humility (Moshagen et al., 2018). Consequently, the evidence to date suggests that the D factor represents a step backward in terms of both theory and assessment. b. Interestingly, research comparing the Dark Triad traits to the general factor of personality, essentially a general positive self-descriptive tendency, showed no relationship for narcissism, but moderately negative relationships with Machiavellianism and psychopathy (Kowalski, Vernon, & Schermer, 2016). This suggests that one common feature of each of these traits is a willingness to rate oneself using negative terms.

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Mapping the psychological drivers of dark traits Beyond understanding simply what types of behaviors are associated with various personality traits, there have been calls for investigations into the psychological underpinnings of traits with the understanding that even if some traits may share similar outcomes, they may not necessarily be motivated by similar agendas (Wood, Gardner, & Harms, 2015). Specifically, there is a utility in understanding an individual’s motives, abilities, and perceptual tendencies (MAPs; Harms, Spain, & Wood, 2014) and how they interact in order to drive patterns of behavior. For instance, the NARC model (Back et al., 2013) represents an excellent example of this where narcissistic behaviors can be understood as the product of two different motivational patterns. Within the HDS framework, there is evidence that the various HDS traits are characterized by different patterns of motives and values (Furnham, Hyde, & Trickey, 2014). It is possible that taking the motivational antecedents of each trait into account may allow for a better understanding of the unique nature of each characteristic, even if they are highly correlated or share similar behavioral patterns. For example, Bold (which broadly aligns with narcissism) is characterized by a high need for recognition or status while Mischievous (which broadly aligns with psychopathy) does not (Furnham et al., 2014; Furnham & Pendleton, 2016). Likewise, Diligent and Dutiful, the two facets of the Moving Towards theme, tend to correlate positively and share a high need for security but are differentiated by Diligent being positively associated with a need for power, and Dutiful being negatively associated with a need for power (Furnham et al., 2014). Similar distinctions have been made for other motive and value profiles of dark traits. For example, in the Dark Triad literature, there is evidence that narcissists positively value adhering to social norms, whereas individuals high in psychopathy value breaking social norms ( Jonason, Foster, Kavanagh, Gouveia, & Brikas, 2018). Likewise, assessments of vocational interests show that narcissists tend to desire people-oriented occupations while Machiavellians actively seem to avoid those professions ( Jonason, Wee, Li, & Jackson, 2014). Other assessments of basic motives report individuals high in narcissism and psychopathy both reporting a high need for power while those in Machiavellianism being characterized instead by negative relationships with the need for affiliation and intimacy ( Jonason & Ferrell, 2016). This is reflected in other research showing that individuals with dark personality characteristics tend to perceive less value of acting in an appropriate manner than others (Wood, Harms, Spain, & Lowman, 2018). In terms of abilities, it has been argued that key deficits in emotional capacities, such as perceiving emotions in others and regulating one’s own emotions, maybe at the root of some dark traits (Spain, Harms, & Wood, 2016). Although psychopaths are often conceived of as not being able to experience authentic emotions, and in particular anxiety or fear, there is evidence that they can learn

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to mimic emotional displays in order to project genuineness (Book et al., 2015). Machiavellians appear to be particularly attuned to detecting negative emotions in others, but not positive emotions (Bagozzi et al., 2013). Individuals higher on aggression are more sensitized to detect aggression in others or to interpret ambiguous information as being aggressive (Brennan & BaskinSommers, 2020). Other capabilities such as the capacity to trust also seem to play an important role in determining how individuals with dark personalities interact with others and form and maintain a relationship. Individuals with antisocial dispositions have lower levels of trust in other individuals (Engelman, Schmid, De Dreu, Chumbley, & Fehr, 2019; see also Monaghan, Bizumic, Williams, & Sellborn, 2020). This is because they project their own untrustworthy motivations on others. These individuals are less likely to reward others who display trust in them and are far more likely to punish those who betray their trust (Engelman et al., 2019).

Prediction of work outcomes In general, theoretical models and empirical research linking dark personality traits with workplace behaviors and outcomes has been somewhat predictable. Specifically, the vast majority of studies of dark traits have predicted and established that highly aversive personality characteristics such as the Dark Triad tend to have negative relationships with desirable behaviors and outcomes such as job performance (O’Boyle et al., 2012) and effective leadership (Grijalva, Harms, Newman, Gaddis, & Fraley, 2015; Landay, Harms, & Cred
e, 2019), but are positively related to undesirable behaviors and outcomes such as counterproductive workplace behaviors (Grijalva & Newman, 2015; O’Boyle et al., 2012) or toxic leadership (Harms, Wood, Landay, Lester, & Vogelgesang-Lester, 2018; Krasikova, Green, & LeBreton, 2013). More often than not, these studies also suggest that dark personality traits add incremental validity above and beyond traditional Big Five measures (e.g., O’Boyle et al., 2012; Pletzer, Bentvelzen, Oostrom, & de Vries, 2019). When more comprehensive measures of dark personality such as the HDS are used, these gains can be substantial, sometimes contributing two to three times as much variance explained as the Big Five traits (Harms, Spain, Hannah, Hogan, & Foster, 2011). Further, because acting in accordance with the impulses or desires associated with dark traits tends to be suppressed by the individual under ordinary circumstances, the degree to which dark traits add to the prediction of outcomes is assumed to be a function of the degree to which the individual has discretion or autonomy in their job or task (Kaiser & Hogan, 2007). For example, we would expect leaders to be more likely to display dark traits, and therefore show larger effects for them, because they face fewer social sanctions such as the threat of being fired for indiscretions

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(Keltner, Gruenfeld, & Anderson, 2003; Wood & Harms, 2017). This dark side of discretion can be particularly pernicious in the workplace as negative or abusive forms of leadership have been shown to be particularly predictive of employee well-being (Harms, Cred
e, Tynan, Leon, & Jeung, 2017). Beyond broad job criteria, there are some outcomes where specific dark traits have been linked to theoretically-relevant criteria (see Hogan & Holland, 2003) and these tend to provide more insight into how these characteristics function. For example, studies have demonstrated that narcissists are particularly effective at self-presentation in interview settings (Paulhus, Westlake, Calvez, & Harms, 2013) or when pitching new products to potential investors (Goncalo, Flynn, & Kim, 2010). Following this trend, more recent work on dark personality in the workplace has taken to investigating interactive effects between the dark traits of leaders and followers with the idea that some combinations are likely to be particularly compatible or combustible (Grijalva & Harms, 2014; Harms, Bai, & Han, 2016). For example, some research has shown that pairings of Machiavellian followers and Machiavellian leaders are particularly prone to feelings of distrust and stress (Belschak, Muhammad, & Den Hartog, 2018).

Positive effects of dark traits As noted earlier, Hogan and Hogan (2001) originally conceived dark personality as representing a set of characteristics that may have positive outcomes under some circumstances or in the short-term, but that would be detrimental or cause derailment in the long-term. More recently, Jones introduced mimicry-deception theory (MDT; Jones, 2014; Jones & de Roos, 2016), a variant of evolutionary theory, as a framework for understanding how and why dark traits persist in the population. Jones postulated that dark personality traits could be understood through comparisons to the different strategies utilized by predators in the animal kingdom. Specifically, MDT suggests that dark personality can be differentiated into different dimensions depending on the complexity of deception used, the rate of resource extraction, the degree of host integration, and the efforts made to avoid detection. But the overall theme of MDT closely aligns with the original conceptualization by Hogan and Hogan (2001). That is, that these traits exist and persist because they can suit a function and may sometimes allow individuals to advance their agendas. It is when things are taken too far or persist for too long that they become toxic. To that end, there have been calls in the literature for examining circumstances when dark traits can lead to positive outcomes (Harms & Spain, 2015; Judge, Piccolo, & Kosalka, 2009; Smith, Hill, Wallace, Recendes, & Judge, 2018). For example, in a large-scale, longitudinal study of military cadets using the HDS (Hogan & Hogan, 2001), it was shown that different sets of dark personality traits both positively and negatively predicted different aspects of leader performance and training development (Harms, Spain, &

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Hannah, 2011). Higher levels of officership were associated with high Excitable, Colorful, and Diligent levels, but lower levels of Skeptical and Leisurely. In contrast, displaying good judgment was associated with high Cautious, Bold, and Diligent levels, but lower levels of Skeptical and Imaginative. Following a similar logic, some entrepreneurship scholars have advanced the idea that various dark personality traits and related characteristics may exhibit both positive and negative effects of the process of starting and developing new ventures depending on the trait, the level of human interaction and relationships needed, and other socio-emotional capacities (Grijalva & Harms, 2014; Klotz & Neubaum, 2016; Wiklund, Hatak, Patzell, & Shepherd, 2018). That is, that starting new ventures often requires a good deal of willingness to take risks, self-confidence, and perhaps even a willingness to exaggerate in order to bring in investors. These characteristics are frequently featured as aspects of dark traits such as narcissism and psychopathy. However, this literature also acknowledges that high levels of these traits can be associated with rule-breaking, deceit, interpersonal problems, and potentially reckless decision-making that can also lead to the failure of new ventures. Finally, it must be remembered that there are jobs and tasks that are necessary for society to function smoothly which require having people available who are willing to do unpleasant things. These activities can range from exceptional circumstances such as interjecting oneself to stop a fight or potential abuse, to workplace actions such as terminating problem employees, to entire vocations such as those who must deal with dead bodies, punish criminals, or risk their own personal safety in combat. To the extent that these activities are viewed as being essential to societal functioning, it could be argued that individuals who are tolerant of or even attracted to, such activities could be seen as evidence for a positive side of dark personality traits.

Nonlinear relationships Beyond simply finding circumstances that can flip the typical relationships between dark traits and social outcomes, other research has suggested that simple linear trends may sometimes be inappropriate, given their original conceptualization as a characteristic becomes maladaptive when it is taken to the extreme or is displayed too often (Spain et al., 2014). It has been noted that the extremities of both ends of normal personality traits are frequently associated with negative outcomes in both work and life (Carter, Miller, & Widiger, 2018). Similarly, there is accumulating evidence that dark traits, even malevolent ones, can have nonlinear relationships with outcomes such as leadership (Benson & Campbell, 2007; Grijalva, Harms, et al., 2015; Landay et al., 2019), sales performance (Titze, Blickle, & Wihler, 2017), and counterproductive work behaviors (Davison, LeBreton, Stewart, & Bing, 2020). Specifically, many of these studies seem to demonstrate that for many dark traits, there is some moderate level of the traits where the behaviors

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displayed are functional, and it is only at the extremely high levels where the toxicity of the trait emerges (see Grijalva, Harms, et al., 2015; Landay et al., 2019; Vergauwe, Hofmans, Wille, Kaiser, & De Fruyt, 2018). The threshold of inflection for where a particular dark trait changes from positive (or neutral) to negative is likely to be dependent on the nature of the job, the characteristics of the individuals who witness those behaviors, and the prevailing cultures at both the organizational and national levels (Padilla, Hogan, & Kaiser, 2007).

Moderators of dark personality But it is not just the level of a trait that leads to various outcomes. As indicated earlier, the relationship between dark personality traits and important life outcomes may itself depend on a number of other factors, such as social context or individual-level factors.

Gender Although seldom addressed directly in the subclinical literature, there is a considerable amount of evidence for gender differences in dark traits. Narrative reviews of the Dark Triad literature have asserted that men are consistently higher on all three traits ( Jones & Paulhus, 2014). However, meta-analytic evidence reveals that these differences tend to vary greatly depending on the instrument used and even the subfacets within the dark traits (Grijalva, Newman, et al., 2015; see also Collison, South, Vize, Miller, & Lynam, 2011; Szabo & Jones, 2019). For example, narcissism measures most frequently used by academic researchers such as the Narcissistic Personality Inventory (Raskin & Terry, 1988), the Dirty Dozen (DD; Jonason & Webster, 2010), the Short Dark Triad (SD3; Jones & Paulhus, 2014) and the Narcissistic Admiration and Rivalry Questionnaire (NARQ; Back et al., 2013) all suggest that men are significantly more narcissistic than women (Grijalva, Newman, et al., 2015). However, popular commercial instruments such as the HDS Bold scale show only a small elevation for narcissism in men (Grijalva, Newman, et al., 2015). At a broader level, analyses using the more comprehensive HDS model suggest a nuanced pattern, where women actually score higher on some derailers that reflect the Moving Towards and Moving Away themes, while men tend to be higher on Moving Against theme traits (Furnham et al., 2014; Harms, 2016). Adding to this complexity is that men and women may also differ in the behavioral manifestations of dark personality traits (e.g., Verona & Vitale, 2006). A classic example of this is that men may be more likely to display physical aggression while women may be more inclined toward subtle, indirect, or verbal aggression toward others (Bj€ orkqvist, 1994). Similarly, research has suggested that psychopathy in males manifests itself as antisocial or even criminal behaviors, whereas in females psychopathy tends to manifest itself through

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attention-seeking behaviors, sexual provocativeness, and overreactions to minor events (Hamburger, Lilienfeld, & Hogben, 1996). Further, there is also the issue that behaviors associated with dark personality may be perceived or reacted to differently depending on whether gender norms are violated or not (De Hoogh, Den Hartog, & Nevicka, 2015; Williams & Tiedens, 2016). For example, in a meta-analytic review of the relationship between psychopathy and leadership, Landay et al. (2019) found that men appeared to benefit from high levels of psychopathy in terms of promotions, while women did not and those female leaders who displayed psychopathic characteristics were rated as being poorer leaders while men suffered no similar sanctions. It has been argued that both the manifestations of dark traits and how they are viewed by others tend to correspond to social norms for gender roles and behaviors. Consequently, the differences that have been documented in prior research may ultimately diminish or change over time and may also vary across cultures.

Age and time In general, there appears to be a trend for mean levels of dark personality traits to decrease with age (Bartlett & Bartlett, 2015). This roughly aligns with literature suggesting that as individuals successfully navigate life’s challenges and become more socially invested in work and relationships, their personalities tend to develop in a prosocial manner (Lodi-Smith & Roberts, 2007; see also Harms, Roberts, & Winter, 2006; Li, Fay, Frese, Harms, & Gao, 2014; Roberts & Mroczek, 2008). However, there is also evidence from longitudinal studies that individuals who engage in workplace deviance tend not to experience these same positive developmental trends (Roberts, Walton, Bogg, & Caspi, 2006). On the whole though, because very few studies of dark personality have been conducted longitudinally, we know strikingly little about developmental trends in this domain. One other aspect of the relationship between time and dark traits that has received considerable attention has been whether or not there are generational differences in narcissism over time. Work by Twenge and colleagues (e.g., Twenge & Foster, 2010; Twenge, Konrath, Foster, Campbell, & Bushman, 2008) have argued that narcissism (as measured by the Narcissistic Personality Inventory (NPI)) has increased in more recent generations. Other researchers have suggested that these effects are the result of selective sampling of evidence and methodological artifacts (e.g., Grijalva, Newman, et al., 2015; Roberts, Edmonds, & Grijalva, 2010; Wetzel et al., 2017). Regardless of how this debate ultimately resolves itself, it is interesting to note that more recent analyses have begun to focus on socio-political and economic conditions that may drive generational differences in dark personality traits (e.g., Fletcher, 2015; Leckelt et al., 2016). For example, some

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research has suggested that individuals who transition into adulthood during recessions are less likely to be narcissistic (Bianchi, 2014), possibly because of the challenges, failures, and limited ability to pursue their own agendas and dreams as a result of economic restrictions means that they learn humility rather than entitlement. Although the evidence for the impact of these macrolevel factors on personality development is still quite limited, these approaches appear promising.

Culture and context As noted elsewhere in this chapter, there is a general acceptance that dark traits can be triggered by events and maybe magnified or mitigated by context (Grijalva & Harms, 2014; Padilla et al., 2007). Although the number of studies is limited, there is evidence that national culture can influence the expression of dark traits. For example, meta-analytic evidence shows that the link between narcissism and workplace deviance is weaker in collectivistic cultures, presumably because there are more likely to be social sanctions for narcissistic behaviors (Grijalva & Newman, 2014). Another interesting feature of individuals with dark personalities is that they tend to prefer to be around other individuals with similar tendencies (Blanchard, Lyons, & Centifanti, 2016). This raises the possibility that behavioral manifestations of dark personality, particularly interpersonally aversive traits like the Dark Triad, may be more likely to be expressed in environments where they are tolerated (Landay et al., 2019). This leads to an interesting hypothesis that organizations or national cultures where corruption or abuse is prevalent may be more fertile grounds for the open expression of dark traits. Similarly, simply the perception that deviant behaviors are more likely to be tolerated may ultimately prove to be a major contributor to explaining why and when antisocial behaviors are more likely to be exhibited (Wood et al., 2018). To the best of our knowledge, these ideas have not yet been tested in a systematic way, but they do represent a potentially actionable avenue for reducing deviance in the workplace via changes in culture.

Dark personality assessment issues Self-awareness and honest reporting One of the inherent problems with assessing dark personality is that there is almost always a threat to assessment validity because the subject may be unable or unwilling to provide accurate information (Spain et al., 2014). Many dark traits are characterized by hostility toward others and a tendency toward deceptive behavior. Consequently, friction-free compliance with researchers may be unlikely and, when given the opportunity, individuals high on dark traits may be unlikely to provide responses that align with their actual behaviors and cognitions. Specifically, they may intentionally fake or mispresent

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themselves in such a way as to make them look good or to further their agenda. Some scales, such as the Mach-IV (Christie & Geis, 1970) and the NPI (Raskin & Terry, 1988) take this into account by designing the scale responses with the understanding that responses themselves are behaviors (see Hogan & Nicholson, 1988). For example, items on Machiavellianism inventories tend to ask respondents to endorse cynical opinions that an individual high in that trait would perceive as reflecting higher intelligence or understanding of the way the world worked as opposed to simply asking if they thought that the people around them were unintelligent or beneath them. Similarly, narcissism scales may require that respondents choose between two desirable options, but ask the individual to pick the one that fits them best. By offering paired contrasts between communal (e.g., “I am a nice person”) and agentic (e.g., “I am a smart person”) options, they can detect tendencies toward narcissism by determining whether there is a tendency to prioritize personality factors or characteristics associated with high levels of narcissism. However, scales that have been developed with a nuanced understanding of the character of dark individuals in mind are rare and most inventories simply rely on more straightforward self-reports of behaviors and attitudes and, thus, leaving themselves more vulnerable to faking or deceit. Another related potential issue with assessing dark personality is that the individuals in question, those with elevated scores on dark personality traits, may not be aware of their actual standing on these characteristics or that they are problematic (Lilienfeld & Andrews, 1996). Many dark traits are characterized by deficits in perceptions of social norms that may make it unlikely that they would readily identify a problem. Similarly, the issue of dark traits being expressed primarily in crisis or high-stress situations may make it less likely that the individual in question would internalize specific patterns of behavior as being true of them rather than simply temporary reactions to a particular circumstance. It is also possible that some individuals may not have been exposed to circumstances that may have elicited or allowed certain behaviors to manifest yet. For example, few individuals are ever put in a position where they have the opportunity or reason to kill another person, and it would be impossible to accurately reflect how one would feel in such a scenario. All of these issues combine to make it a challenge to assess dark traits and the fact that the vast majority of the research to date has relied almost exclusively on self-reports and crosssectional data (see Muris, Merckelbach, Otgaar, & Meijer, 2017) has only exacerbated the problem.

Specificity of measures A further concern is whether measures of dark personality actually match the specific conceptualizations of the traits they intend to measure and whether or not they can meaningfully distinguish between different constructs or are just generic indicators of bad or inappropriate behavior (see Dini
c, Wertag,

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Tomasˇevi
c, & Sokolovska, 2020; Glenn & Sellbom, 2015). These problems become exaggerated when scales are modified without concern for their construct validity. As noted earlier with regards to efforts to differentiate Machiavellianism from psychopathy, the construct drift associated with careless scale design and attempts to generate hyper-abbreviated measures has become so severe that the essence of the target constructs has been all but lost.

Highly abbreviated measures Measures that have been greatly shortened in order to reduce assessment time often sacrifice predictive power in order to maximize reliability (see Cred
e, Harms, Nierhorster, & Gaye-Valentine, 2012). For example, in an attempt to meet an arbitrary standard of internal reliability, scale designers may select items with the most homogenous content, as opposed to choosing items that correlate with one another less highly, but would have better coverage of the content of multidimensional constructs (McCrae, Kurtz, Yamagata, & Terracciano, 2011; Smith, McCarthy, & Anderson, 2000). Such measures may lead to overor underemphasizing certain scale content, with the result that the shortened scales and original scales may no longer be assessing exactly the same construct. This has implications for comparing research findings using these scales to findings using the original scales. For example, in an assessment of the relationship between gender and narcissism, the hyper-abbreviated NPI-16 scale (Ames, Rose, & Anderson, 2006) showed a significantly larger gender effect than did the original scale on which it is was based (Grijalva et al., 2015), the 40-item NPI (Raskin & Terry, 1988). An added complication is that meta-analysts frequently correct effect size estimates based on reliability under the assumption that shorter, less reliable measures will tend to have smaller effects. When these abbreviated measures actually generate larger effects, the result of the correction is to magnify the error rather than to reduce it. Within the dark personality literature, the popularity of hyper-shortened measures increasingly threatens to undermine the ability of the field to build a firm foundation of research results. Chief among these shortened measures is the Dirty Dozen (DD; Jonason & Webster, 2010), an extremely short measure of the Dark Triad. The measure itself exhibits poor reliability across scales, while the scales themselves fail to capture the multidimensional aspects of the Dark Triad constructs. Moreover, the Dirty Dozen has been criticized on the grounds that its scales have poor convergent and discriminant validity with other personality measures, which was true even in the paper where it was originally introduced (Carter, Campbell, Muncer, & Carter, 2015; Kajonius, Persson, Rosenberg, & Garcia, 2016; Miller et al., 2012; Rauthmann & Kolar, 2012). The problems with the Dirty Dozen are severe enough that it tends to generate inaccurate results in a systematic way such that the DD Machiavellianism scale appears to (poorly) measure psychopathy, whereas the DD psychopathy scale appears to (poorly) measure Machiavellianism (Rauthmann & Kolar, 2013).

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A slightly longer alternative measure, the Short Dark Triad (SD3; Jones & Paulhus, 2014), attempted to bridge the need for appropriately reflecting the multidimensional aspects of the dark traits while at the same time taking care to maintain a reasonable fidelity with the original instruments and acceptable levels of reliability. Even so, the SD3, like the Dirty Dozen, still ended up with Machiavellianism and psychopathy scales that failed to sufficiently differentiate between themselves ( Jones & Paulhus, 2014; Persson, Kajonius, & Garcia, 2019). Although neither of these options for assessment is ideal, based on the available evidence, the SD3 (as opposed to the DD) appears to be a more reliable measure of the Dark Triad and displays greater fidelity to the theoretical constructs (Maples, Lamkin, & Miller, 2014). New measures are being developed to refine and expand the SD3 into the SD4 (to include sadism; Paulhus, Buckels, Trapnell, & Jones, in press), and it appears to show greater differentiation between traits. However, efforts are already underway to generate a shortened version of this scale that once again eschews construct validity for brevity and a catchy label (i.e., the H8; Webster & Wongsomboon, 2020).c

Five-factor based models Meta-analytic work indicates that the Dark Triad traits do not substantially correlate with Big Five personality traits (O’Boyle, Forsyth, Banks, Story, & White, 2014). All three show moderately negative relationships with agreeableness while narcissism positively correlates with extraversion and psychopathy negatively correlates with conscientiousness (O’Boyle et al., 2014). Machiavellianism shows the least content, conceptual, and empirical overlap with Big Five (Kesseler et al., 2010). Even so, simple linear regressions of Big Five traits capture between 30% and 60% of the variance in Dark Triad measures while more nuanced models using facets from the Neuroticism-ExtraversionOpenness Personality Inventory (NEO-PI-R) (Costa & McCrae, 1985, 1992) can capture upwards of 88% of the variance in psychopathy after statistical corrections are made (O’Boyle et al., 2014). Given the ubiquity of the Five-Factor Model in personality psychology, it is not surprising that there have been several attempts to create proxy measures for dark traits using the Big Five. These include studies looking to measure psychopathy (Miller & Lynam, 2015; Miller, Lynam, Widiger, & Leukefeld, 2001), narcissism (Glover, Miller, Lynam, Crego, & Widiger, 2012), Machiavellianism (Collison, Vize, Miller, & Lynam, 2018), and a number of other aberrant personality dimensions (Wille, De Fruyt, & De Clerq, 2013). These efforts have been made using both the broad factor scores of the Big Five as well as with subfacet scores. c. We would suggest that the same criticisms could and should be applied to other hyper-short measures of dark personality such as the Single Item Narcissism Scale (SINS; Konrath, Meier, & Bushman, 2014).

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Despite these interesting, and even promising attempts to reduce dark measures to specific combinations of the Big Five, serious concerns have been raised about whether well-defined psychological traits can be accurately recovered from combinations of the Big Five (see Cred
e, Harms, Blacksmith, & Wood, 2016). Beyond the leap of faith that must be taken to use statistical abstractions to represent cognitions, emotions, and behaviors, there is also the problem of content coverage and, for historical reasons, Big Five measures nearly always fail to assess the elements of personality which are most defining of dark personality traits. Simply put, evaluative terms such as “evil” or “dangerous” were removed from further consideration and analysis in the early stages of the psycholexical research that led to the Big Five (Allport & Odbert, 1936; Goldberg, 1981; Saucier, 1997; Saucier & Goldberg, 1998). Although this was done to try to limit the content of the models to more behaviorally-based items, this decision may have ultimately and inadvertently resulted in the exclusion of some of the most socially consequential personality descriptors. In addition, the absence of such content has meant that many of the defining elements of dark traits such as manipulativeness, egoism, and the absence of moral or ethical behavior considerations are “missing” from the Big Five (Paunonen & Jackson, 2000). It is, therefore, not surprising that some of these dark personality proxy measures fail to correlate very highly with other indices of dark traits, fail to replicate wellestablished patterns of external correlates (e.g., Collison et al., 2018; K€ uckelhaus, Blickle, Kranefeld, K€ ornig, & Genau, 2020), and can be used to generate dangerously misleading results, such as a recent paper (Murphy, 2019) that used crude Big Five profiles to assert that certain regions of the United States were characterized by high levels of psychopathy.

Emerging trends in assessment Given the problems with collecting reliable, accurate self-reports of dark personality traits, it is no surprise that many researchers have sought other means of assessing dark personality traits at a distance, through behavioral indicators, or through other means. One obvious avenue for many personality scholars is to utilize personality from peers or others (Thomas et al., 2003). Evidence suggests that individuals who exhibit dark personality traits are at least somewhat aware of their reputations with both self and metaperceptions correlating with informant reports at a moderate level and similar in size to the relationships seen for Big Five traits (Maples-Keller & Miller, 2018). However, one problem with getting other reports of dark personality is that they tend to be highly-valenced and to discriminate very poorly. Specifically, because the item content tends to reflect moral, ethical, or deviant behaviors, there is a tendency for raters to produce ratings that reflect a halo (or horns) effect where all items are endorsed in a similar way depending on the quality of the relationship one has with the target. For example, the corporate

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psychopathy measure (Boddy, 2010), which is used by subordinates to rate their leaders, has been highly criticized for lacking both sound psychometric structure and construct validity ( Jones & Hare, 2016). Specifically, the measure appears to be little more than a rating of disliking, but because nearly all studies using this measure utilize same-source informants for assessing the consequences of leader psychopathy, it tends to generate highly inflated estimates of the effects of this construct (see Landay et al., 2019). Within the domain of strategic management, one popular technique for assessing dark personality in CEOs has been to code the content of letters to the shareholders, speeches, website profiles, and media releases (e.g., Chatterjee & Hambrick, 2007; Cragun, Olsen, & Wright, 2020; Van Scotter & De D
ea Roglio, 2020). However, these studies have tended to produce inconsistent results (Grijalva, Harms, et al., 2015), and the validity and reliability of the techniques used have been seriously called into question (Van Scotter, 2020). Several other similar approaches have been taken whereby social media account information has been coded to create proxies of dark traits. For example, subjective evaluations made from social media have provided evidence for small relationships between self-reported and perceived personality for narcissism (r ¼ .22) and psychopathy (r ¼ .18), but not Machiavellian (r ¼ .10; Molen, Kaplan, Choi, & Montoya, 2018). These low levels of convergence were attributed to poor cue utilization on the part of raters. More intensive, data-driven accounts of social media posting on Twitter has demonstrated that narcissists tend to be characterized by more playful language and more extreme positive and negative language, whereas individuals high in psychopathy tend to utilize more violent, angry, and crude (i.e., swear words) language in their postings (Preotiuc-Pietro, Carpenter, Giorgi, et al., 2016). Interestingly, these datadriven approaches have also been able to document that the relationships between dark traits and behaviors such as social media posts are more accurately characterized as an absence of certain kinds of language (e.g., tentative language such as “maybe” or “perhaps” for narcissism) rather than the presence of signaling words or expressions (e.g., heavy usage of personal pronouns to signal narcissism; Holtzman et al., 2019; see also Akstlnalte, Robinson, & Sadler-Smith, 2020). Because this negative information or absence of signals is inherently for humans to detect, it calls into question research attempting to use human coders to generate valid inferences (e.g., Grijalva, Maynes, Badura, & Whiting, 2020; see Kluemper, Rosen, & Mossholder, 2012).d Other emerging approaches include the use of technologies designed for accessing unconscious or biological indicators of dark traits. For example, there has been some recent work investigating wearable devices for assessing behavioral and emotional patterns and how those might be linked to dark personality d. Though it can be argued that human raters may be able to utilize context cues more effectively than machine-learning approaches or frameworks based simply on word counts.

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traits (e.g., Cabrera-Quiros, Gedik, & Hung, 2020; see also Chaffin et al., 2017; Ihsan & Furnham, 2018). Similarly, there is developing literature concerned with investigating neurological patterns and their correspondence to dark traits (e.g., Bowyer et al., 2020; Perkins, Latsman, & Patrick, 2020). A less invasive approach has been the utilization of gamification techniques to investigate whether dark traits can be linked with malicious behaviors and decisions in competitive contexts (e.g., Harilal et al., 2018). Though these approaches are only in their infancy in terms of validation, they nonetheless represent potentially viable future approaches for assessing dark traits and other psychological phenomena that are inherently difficult to assess accurately via self-report (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017).

Future directions and concluding thoughts Given the short history of dark personality, the advances that have been made in terms of measurement, theory development, and documenting the important role such traits play as a determinant of work behavior and outcomes have been remarkable. Even so, concerns remain, and there is a vital need for more robust and reliable research. Arguments have been made for a more inclusive and comprehensive set of dark traits including spitefulness, greed, dependency, and perfectionism (Marcus & Zeigler-Hill, 2015). We would add to that list other older domains of personality such as insecure attachment styles (Harms, 2011), authoritarian personality (Harms et al., 2018), and also suggest that there is a critical need to investigate the similarities and differences between traditional dark personality measures and more clinically-oriented assessments (e.g., Guenole, 2014, 2015) based on the recently introduced DSM-5. Other less-noxious dark traits are also worth examination. For example, recent work has shown that a general risk-propensity trait is an effective predictor of both workplace deviance and safety behaviors above and beyond the Big Five (Zhang, Highhouse, & Nye, 2018). Such measures may prove useful because they eliminate the content related to social hostility common to most assessments of dark personality. In doing so, they provide an opportunity for more precise measurement, better prediction, and will not necessarily produce the same types of negative reactions often seen when assessing subclinical traits. More importantly though, is that there needs to be the development respect for theoretical models that more fully account for the origins, circumstances, and expected outcomes of particular traits. Recent work on trait paranoia (Chan & McAllister, 2013) serves as an excellent example of what dark personality researchers should be doing. Too often, empirical studies in this literature take an overly simplistic view of dark traits. For example, assuming a common motivational or theoretical framework such as social exchange theory (e.g., O’Boyle et al., 2012) or reinforcement sensitivity theory (e.g., Jonason & Jackson, 2016) for explaining all dark traits means that it is unlikely that

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researchers will be able to fully and accurately explore the ways in which dark traits differ from one another and the contexts where they might be activated. Newer models such as MDT ( Jones, 2014) will help advance theorizing, but they are not sufficiently fleshed out to provide coverage for the breadth of dark personality traits. We remain convinced that embracing a psychodynamic functionalist perspective for understanding dark personality could potentially be fruitful in this regard (see Harms et al., 2014; Harms, Wood, & Spain, 2016; Hogan & Sherman, 2020; Spain et al., 2016; Wood et al., 2018). Such an approach would seek not only to understand the motives behind dark traits but would also take into account abilities or capacities for emotion regulation or social skills that might inhibit or enable dark personality factors. Further, an accounting of dark personality from a functionalist perspective would necessarily need to account for perceptual tendencies that may lead individuals to believe that dark or destructive behaviors are effective, will be tolerated, or are normative. Finally, functionalist accounts of dark personality could also investigate circumstances under which dark traits are triggered by stress or by having autonomy or power over others. The ultimate goal of such research would not only be to get a more complete account of the psychological processes underlying dark traits but to find critical leverage points in such processes where organizations can initiate developmental interventions or changes to job designs in order to inhibit or prevent dark behaviors in the workforce from happening. Beyond the need for better theorizing is the need for better research design in the field of dark personality. The preponderance of empirical research on dark personality simply pairs sets of self-report measures together with the expectation that undesirable qualities will correlate with undesirable behaviors and outcomes. These types of studies fail to add a deeper understanding of the cognitive and behavioral processes associated with dark traits, or the larger causal network in which they are embedded. Rather, it could be argued that they simply reflect that people who will say negative things about themselves in one domain will also be more inclined to say negative things about themselves in another. As noted earlier, there is increasing evidence (e.g., Grijalva, Harms, et al., 2015; Landay et al., 2019) of nonlinear effects associated with dark personality traits. However, until measures are developed that fully capture the range and breadth of dark traits, the robustness of such effects remains unclear. Relatedly, there is still much to be done to fully represent the original conceptualizations of dark traits in the way that we analyze our data. One promising avenue is the use of necessary condition analysis (NCA; Dul, 2016). This approach would allow us to better reflect a lack of abilities or the presence of a deviant impulse that may contribute to deviant behaviors and to more precisely estimate thresholds of traits associated with deviance. A final area of concern is still the measurement of dark traits itself. Metaanalytic reviews are only useful insofar as the constituent studies use valid, reliable measures. To the extent that short measures with weaker psychometric

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properties and construct coverage are being used for the sake of convenience, findings from the resulting meta-analyses will be more suspect. Measures designed by trying to make good-enough versions of already problematic scales are inherently flawed in the same way as photocopies of blurry or cropped photographs. In practice, the constant push to reduce scale length means that increasingly less of the original construct space is captured in each successive round. It is truly the “garbage-in garbage-out” approach to science. Such approaches will frequently result in both Type I and Type II errors in terms of estimating even simple relationships (Cred
e et al., 2012), and research has demonstrated that declines in measurement validity over time can eventually lead to false conclusions that previously robust relationships are no longer present (e.g., Cred
e & Kuncel, 2008). Unfortunately, we believe that the study of dark personality faces the same fate unless researchers again make assessment validity a priority. It is difficult to see a coherent understanding of the field emerging, even with the use of meta-analytic integration, when the results of studies largely depend on which measures are used (Watts et al., 2017). We see promise in some of the behavior-scraping techniques mentioned previously, but we would argue that the goal of such research should not be to try to replicate or confirm existing models and theories, but rather to find out what can emerge from such sources, how it might differ, and whether it offers new insights and predictive validity. Similarity, we would strongly urge personality scholars should embrace our rich history of assessment by developing implicit, indirect, and projective instruments of dark traits (see Harms & Luthans, 2012; James & LeBreton, 2010; Moeller, Johnson, Levy, & LeBreton, this volume; Sokolowski, Schmalt, Langens, & Puca, 2000). At some point in our history, our discipline (personality psychology), seems to have lost touch with our clinical roots and forgotten the value of trying to understand the hidden drivers of behavior. For researchers investigating dark personality, an area which is fundamentally characterized by undesirable behaviors and outcomes, having insights into the irrational or malevolent aspects of the human psyche is absolutely essential. The study of personality is consequential because humans are consequential. But we are fundamentally flawed. A full understanding of human nature means not only assessing or evaluating what people do on average or under ordinary conditions. But it also means plumbing the depths of their hidden motives and desires, of seeing how they behave when under stress or in a crisis, of seeing how they respond to being in positions of power over others and trying to figure out who they are when they think no one is watching. This is why the study of dark personality is so critical. Without it, we only have half the picture. For this reason, we would argue that the study of dark personality is simply too interesting, too important, and too fundamental to understanding who we are to ignore or to not take seriously in our research and our theorizing. We must do more and we must do better.

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Chapter 3

Semantic and ontological structures of psychological attributes Jan Ketil Arnulfa and Kai Rune Larsenb a

Department of Leadership and Organizational Behaviour, BI Norwegian Business School, Oslo, Norway, bLeeds School of Business, University of Colorado Boulder, Boulder, CO, United States

Introduction Our research uses digital tools to analyze the questions used in Likert-scale surveys. In many cases, we can predict the correlations between independent, mediating, and dependent variables before obtaining human responses. The predictions are made based solely on the item texts. In extreme cases, this means that the empirical data add no new information to what could be known when the scales were constructed. To illustrate the problem, we offer an example from a doctor’s office: On examining a patient, the doctor collects three points of information: fever, headache, and a rash. The three are not perfectly or uniquely related, but each observation adds information. Their co-occurrence may indicate measles and exclude some other diagnoses. If the doctor skipped the headache and the rash but instead measured the fever on three different scales—Fahrenheit, Celsius, and Kelvin—the measures would be wonderfully coherent but not tell us anything new. In what follows, we want to distinguish between what we call empirical correlations and semantically driven correlations. An empirical correlation is a numerical expression of how two independent measures covary, such as between the symptoms mentioned above. A correlation between them is a useful clue for further investigations. However, a semantically driven correlation is where the score on one measure strongly implies the score on the other, given the shared structure of concepts, as with the different temperature scales. Someone who finds it likely that

Measuring and Modeling Persons and Situations. https://doi.org/10.1016/B978-0-12-819200-9.00013-2 Copyright © 2021 Elsevier Inc. All rights reserved.

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today is Thursday will find it equally likely that tomorrow is Friday. Two separate questions linking Thursdays to Fridays will not tell us anything new because their relationship is given in language. While this example is obvious and easy to avoid, STSR assumes that such situations frequently arise in survey research because semantic influences take more partial and fuzzy forms that are harder to detect. For example, people who score as highly motivated at work are less likely to score that they are looking for a new job. This is because liking a job and not wanting to quit is implied by what some, but not all people mean by being motivated. Our aim in developing STSR is to show that assessment of semantic structures in survey research points at both problems and possibilities. The biggest problem with semantically driven correlations is their undetected redundancy. On the bright side, semantic assessments open up new ways to look at individual and group characteristics in semantic processing and the way these influences measurement levels. We will start with showing how the semantic algorithms can find patterns that will be similar to all people, predicting 86% of the variation in respondent correlation matrices (Arnulf, Larsen, Martinsen, & Bong, 2014). We argue that this happens because the semantic relationships in language are not “out there” in the world but rather aspects of semantic processing by the brain. When this happens, the data do not reflect the topic of the survey (Arnulf, Larsen, Martinsen, & Egeland, 2018). Instead, the survey statistics sample behavior of an unexpected kind: The data reflect language processing in the brain, which to most speakers is a fairly uniform process. This is why such surveys may fail to detect cultural differences (Arnulf & Larsen, 2020). With a brief overview of latent semantic analysis (LSA), we will illustrate how the technology helps identify semantic overlap (Nimon, Shuck, & Zigarmi, 2016). Describing how we can assess individual differences in semantic item processing (Arnulf, Larsen, Martinsen, et al., 2018), we can finally identify group differences through semantics, showing that comparisons of score levels between groups may depend on substantial differences in how the groups interpret the items (Arnulf, Nimon, Larsen, Hovland, & Arnesen, 2020). We have three aims with this presentation: First to show how well-accepted measurement instruments may suffer from reduced predictive validity despite good or even excellent psychometric properties (Sharma, Yetton, & Crawford, 2009), to the extent that their reliability is founded on semantic instead of empirical relationships (Arnulf, Larsen, Martinsen, et al., 2018; Gefen & Larsen, 2017). Semantic similarity in items across different scales usually improves psychometric indices, a possible reason why disciplines like organizational behavior (OB) tend to suffer from inflated statistical relationships (Podsakoff, MacKenzie, & Podsakoff, 2012). The threat to predictive validity comes when the constructs are semantically related, but the measures of underlying real-world phenomena are less clear-cut, as theoretically outlined by van Knippenberg and Sitkin (2013).

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Second, this offers a new opportunity and new tools to look for more genuine sources of variance in individuals, hopefully improving the prediction of subsequent behaviors (Gollwitzer & Sheeran, 2006) and reduce the proliferation of redundant constructs (Arnulf et al., 2020; Larsen & Bong, 2016). And third, we believe that STSR offers important conceptual insights in the measurement field, to help overcome some of the theoretical blind spots that have allowed confusion between semantic and empirical problems to exist in the field of measurement (Arnulf, 2020).

Like all other people: Language is a strong situation The theoretical distinction between person and situation in psychology is central to most theories about individual differences and practical attempts at predicting behavior. Its significance is succinctly put by Walter Mitchel’s observation that situations, like people, can vary in their “strength” or the potential to elicit certain types of behavior (Meyer, Dalal, & Hermida, 2010; Mischel, 1973; Mischel & Shoda, 1998). Mischel’s point was that the stronger the situation, the more it alone explains behavior and, hence, the less we know about the individual. One question that has rarely been asked is if language itself can be seen as a “situation.” An important assumption for using Likert-scale responses as comparable measures between people is that they read and understand the items in the same way (Schwarz, 2007). While this is almost taken for granted, the language parsing process is itself a complex cognitive process that has been neglected in psychometrics (Borsboom, 2008). The point of departure for STSR is that reading and responding to items is itself a behavior (Baumeister, Vohs, & Funder, 2007; Schwarz, 1999). What we will try to show is that language has a strong propensity in itself to elicit behaviors—to the extent that language production is itself behavior. This propensity is so strong that we tend to take it for granted; therefore, it is out of sight in our theoretical considerations. When semantics determine the survey statistics, the numbers may look interesting, but they are no longer “about” the topic of the survey—they now reflect the semantic processing of the respondents (Arnulf, Larsen, Martinsen, et al., 2018). Most speakers will adhere perfectly to the linguistic rule that a Thursday precedes a Friday, which in turn is succeeded by Saturday. In this sense, language production is a “strong” situation because speakers are not free to determine the meaning of words and sentences. The strength of semantic relationships of this sort in the language is in fact so imperative that it is a sign of serious mental disorder if someone adopts personal definitions of the weekdays (Elvevag et al., 2017). Like most situations, semantic relationships can vary in strength. Words can be exchanged with other words with similar but not identical meanings, something we call synonyms. Synonyms may be more or less accurate and introduce

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some types of semantic “slack.” Assume that our speaker does not claim that it is “Friday,” but instead says “Weekend is here.” The nationality or religious faith of the speaker may have different implications for the weekdays, such that a “weekend” may be different for groups of speakers, be they Jews, Muslims, or Christians. In this case, the precise meaning of “weekend” is a group-level feature of the language, and not merely an individual characteristic. Language usage is a community of practice with shared assumptions about what can be taken for granted (Gumperz, 1996; Sidnell & Enfield, 2012). To the extent that language is not universally precise, it still elicits predictable behaviors on a group level. The term “weed” refers to very different things for a group of professional gardeners and a group of students planning a trip to Colorado. At this point, we need to bring up the problematic aspect that semantics raise for measurement instruments in psychology. In 1931, Rensis Likert decided to measure people’s attitudes towards any kind of topic using phrases from which he obtained numerical responses (Likert, 1932). While Likert and his followers were confident that the ensuing numbers would reflect the strength of the attitudes (“To which extent do you agree that…”), others were not so sure. A contemporary social psychologist, Richard LaPiere, famously demonstrated that the verbal behavior in surveys did not predict actual behavior—in LaPiere’s case, prejudice against Chinese immigrants in California (LaPiere, 1934). Skeptics from the psychometric tradition, such as Thurstone, did not accept Likert’s straightforward assumptions about what his numbers meant (Andrich, 1996; Drasgow, Chernyshenko, & Stark, 2015). Organizational behavior, social psychology, and individual differences have largely shared a common methodological practice with strong roots in psychometrics and statistical modeling of latent variables based on data obtained from verbal scales (Borsboom, 2008, 2009; Cascio, 2012; Nunnally & Bernstein, 2010). One reason for the prevalence of such measurement methods and techniques of analysis may be the ease of access to computer software, and not because the researchers truly understand the nature of the numbers they deal with (van Schuur & Kiers, 1994). We think semantic relationships may have spread inadvertently in research instruments because they seemingly improve statistics but with dubious value as a source of information. Strong semantic similarities will create internal coherence in scales, emerging as high alpha reliabilities, while weaker but still notable semantic relationships will link the scales. The stronger semantic relationships will cluster as factors and the weaker relationships will appear as correlations between factors (Arnulf et al., 2014; Arnulf, Larsen, & Dysvik, 2018). To construct an example from the ones previously used: The likelihood that someone will agree that “tomorrow is Good Friday” will be 1:365, which means that this is an unlikely event. However, if we know that someone agrees that “tomorrow is Friday,” the odds that tomorrow is Good Friday are down to 1:52. If we now also know that the person agrees that “it is now

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springtime,” chances are dramatically increased that tomorrow will also be Good Friday (in this case, around 1:13). What we see in this example is the semantic relationships between three items (one asking about a religious holiday, the second about a weekday, and the third about the season of the year). What one needs to understand is that all statements are tied to other statements through semantic relationships, and these lend themselves to statistical calculations even when there are uncertainties involved. Together, such statements produce a “semantic network” of statements where their mutual relationships are interlocked. This is something akin to Cronbach and Meehl’s (1955) idea of a “nomological network,” but it is important to see that semantic relationships are not subject to “discovery”—they are parts of definitions. Semantic networks make extremely strong situations because they cannot be negated by individuals at will. No one is free—or at least accurate—to assume that Good Friday this year will happen on a Tuesday in September. The strength of the semantic networks explains two thought-provoking phenomena in psychological measurements. The first is how subjects have been found to calculate their responses to topics to which they held no previous opinions, based on how they rated previous and similar issues (Feldman & Lynch, 1988). People even display this sort of calculative responses towards topics that are invented and introduced in surveys simply to see how compliant the respondents will be (Schwarz, 1999). In extreme cases, it is possible to elicit responses with good psychometric properties even in items with partly or even completely meaningless content (Maul, 2017)! The second is Michell’s demonstration (1994) that respondents have a very acute understanding of how response options to survey scales relate to each other. Building on unfolding theory (Coombs, 1964; Coombs & Kao, 1960), Michell posits that once a respondent chooses a preferred response to a series of survey items, the other possible options are “folding” around the chosen response value. The distances between chosen and other possible responses depend on their proximity to the preferred response. To quote Michell (p. 298), “the meaning of such distances must reside in the semantic structure of the attitude statements.” To prove this mathematically, he transformed Likert scales into various types of binary yes-no answers (Guttman scales), where each alternative was a semantic spelling out of the response option. For example, an item concerning war and human nature had the options “Human nature being what it is, nuclear war is a certainty-… a nuclear war in the future is extremely likely, …it is quite likely that the future will bring nuclear war, …it is fairly unlikely that there will be a nuclear war, …there will almost certainly not be a nuclear war, …nuclear war in the future is an impossibility.” In his study, he let all respondents consider each and every one option separately and calculated how systematically people were able to choose their “folding” pattern along with the scales. Michell found that respondents chose the “right” text option—defined as the response considered logically coherent with their other

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responses (e.g., not agreeing that “nuclear war is a certainty” after disagreeing that “nuclear war in the future is extremely likely”)—in about 94% of all choice points. As he observed, if we regard responses to survey items as cognitive behavior—parsing the item and choosing the correct unfolding option—not many psychological theories come close to predicting behavior with this rate of accuracy. Again, the behavior in question is the cognitive text processing, not the behavior predicted by the questionnaire (Baumeister et al., 2007). In other words, respondents seem very well equipped to respond as systematically to text options as they would to numerical scales (Kjell, Kjell, Garcia, & Sikstrom, 2019). Michell’s observations are rooted in what we think is the main condition of STSR: Language production is in itself an extremely strong situation, as in the obvious case of Fridays and Thursdays. When people choose behaviors within the language domain, their responses are produced by a “linguistic” or semantic situation. As has been pointed out with strong and weak situations, we cannot make inferences about individual characteristics where people simply choose response options that everyone else in that situation would choose. It is another psychological puzzle why humans tend to overlook the importance of semantic relationships in survey research. If the relationships are given through semantics—why do we not see that immediately? We are certainly sometimes aware of the possibility, as when we allow similarly worded items within scales, but try to take precautions that these relationships should not apply between scales (Abdi, 2003). One reason for this is probably our lack of metalinguistic capabilities, the main driver behind research on language in cognitive psychology (Pinker, 1994, 2008). When it comes to language, humans are “competent without comprehension,” an expression coined by Dennett (2018) to describe our tendency to get lost and disagree on language. We all speak but cannot really explain how we do it. A simple example is how grown-ups who learn a second language can cite rules of grammar that completely baffle native speakers of the same language. They just speak correctly with no conscious ideas about the rules that apply. Even the philosopher Bertrand Russell complained that it is hard to judge a sound line of argumentation from a faulty one (Russell, 1922). The text algorithms are ways to simulate human understanding of language, but where we are in complete control and can replicate each step of the process. While the algorithms are still inferior to the human language parsing, they come close enough to work with the survey statistics. We can use the algorithms as an impartial judge on the semantics of surveys, simply because they are open for scrutiny from all interested parties. The algorithms can be tweaked, but the tweaks themselves are also a transparent part of the method. We could say that a language algorithm is an operationalization of language as a “strong” situation. Before presenting some of the applications, we will, therefore, make a short presentation of the technology.

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Digital text analysis Background There are tight links between natural language, formal logic, and computer programming. Formal logics can be seen as a way to distill the absolute essence of semantic precision—creating unequivocal propositions with minimal room for interpretations or misunderstandings. This motivated logicians such as Frege, Wittgenstein, or Boole, whose legacies remain in programming languages (Arnulf, 2020). Frege (1884, 1918) explicitly wanted to “clean language of its unhealthy psychological fat.” Wittgenstein wanted to outline conditions for how language could be a precise, unequivocal tool for expressing empirical truths. One may think of these efforts, as well as programming languages, as extreme cases of “language as a strong situation” where all statements would have exactly the same meaning to every speaker or listener. With no room for relativity, all computers carry out code identically. Programming languages, therefore, feels annoyingly rigid and inhuman. The process of making computers understand true human language falls broadly into the category “Natural Language Processing” or NLP. The human language contains a mixture of semantic precision (Fridays always follow Thursdays) and a more open-ended indeterminacy where almost no terms have fixed, unalterable meanings, opening up for poetry (Enfield, 2007; Gumperz, 1996; Lucy, 1996; Sidnell & Enfield, 2012). Human language understanding keeps eluding linguists and cognitive psychologists alike—we seem to be applying a multitude of processes simultaneously when understanding speech, in ways that make us “competent without comprehension” (Dennett, 2018). While the multitude of approaches to NLP must be omitted here, we choose to focus on the two algorithms we have been using in our own research. The first algorithm, termed “MI” after its inventor Mihalcea (Mihalcea, Corley, & Strapparava, 2006; Mohler & Mihalcea, 2009), follows loosely the approach of a human looking up a phrase in a dictionary. The algorithm splits any sentence into its parts of speech or tokenized words, then looks these words up in a digital dictionary called “WordNet” (Miller, 1995; Poli, Healy, & Kameas, 2010). WordNet is a database created by human linguists who determined the relationships among words according to their proximities, organized in hierarchical categories of concepts similar to human understanding (Maki, McKinley, & Thompson, 2004). For example, “dog” is related to “wolf” by being “canine.” This makes MI a sort of standard approach, where sentences derive their meaning based on lexical information. By calculating the distances between words in the sentences—as given in WordNet—the MI algorithm can establish how close or distant the meanings of two different phrases may be. The proximity in meaning is indicated in a number between 0 and 1, where 0 means completely unrelated, and 1.0 would indicate that the phrases mean the same.

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This approach is crude, however, being relatively insensitive to the fact that the same word may have different meanings depending on the context. Meanings derived from WordNet are also abstract commonalities from all speakers of English and do not pick up any expertise or group-determined usage of words. Another approach is latent semantic analysis (LSA) (Dennis, Landauer, Kintsch, & Quesada, 2013; Dumais, Furnas, Landauer, Deerwester, & Harshman, 1988). This algorithm belongs to an approach called “bag of words” because it does not concern itself with word position or classifications of the words it meets. Instead, it will determine the meaning of a text by comparing it to a huge text mass called a “semantic space,” created out of a large number of texts or documents. The documents are turned into a sparse matrix where each document is a row and each column is a word. Each cell is then a count of how many times each word appears in each document. With this document-term matrix, one may then perform a singular value decomposition (SVD), a statistical procedure akin to factor analysis. The SVD renders three matrices—a term matrix, a document matrix, and a matrix containing only the singular values. These three matrices allow a number of statistical procedures, for example, to sort the texts according to their contents. In our projects, we have been using LSA to estimate the similarity of two or more texts similar to the procedure described for the MI algorithm above. Two item texts may be projected into the semantic space, returning a measure of their similarity usually expressed as cosines (the difference between their vectors when projected into the semantic space). One may say that LSA estimates the degree to which words or text may replace each other in the same context without changing the meaning of the text. The output is again a number between 0 and 1, where 1 expresses identical meaning and 0 no overlap in meaning. The LSA algorithm can find similar meaning in sentences that do not share words and can differ between sentences that do share words, but where these words have different meanings. One example is how LSA will differentiate between the phrases “the radius of spheres” and “the music of spheres” (Dennis et al., 2013), but at the same time indicate that the two phrases “physicians operate on patients” and “doctors perform surgery” are similar. As LSA extracts its knowledge from the semantic space it has been trained on, it has been suggested that LSA comes close to how children learn speech and, therefore, offers a mathematical theory of meaning (Landauer & Dumais, 1997). It also means that LSA is sensitive to the semantic space that is applied. In our work, we have used semantic spaces from a variety of document texts, for example, ordinary newspaper texts, business press texts, or texts from scientific journals in various disciplines such as psychology or information theory. The output of LSA will differ depending on the semantic space, which must be taken into account for interpretations. This offers both opportunities and liabilities because it brings a certain element of expertise to the algorithm— say, what certain terms will mean in a business context or for psychologists

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(Arnulf, Larsen, & Martinsen, 2018b). At the same time, the algorithm can only deal with one semantic space at a time, as different from a human speaker who can be both a business person and a psychologist and, therefore, will be more flexible while interpreting a specific text by understanding its context.

A practical guide to conducting LSA Latent semantic analysis is available in many commonly used programming languages such as R or Python. The article “A guide to text analysis with latent semantic analysis in R” (Gefen, Endicott, Miller, Fresneda, & Larsen, 2017) offers an easy introductory description of LSA, using two R packages called “lsa” (Wild, 2015) and “LSAfun” (Guenther, 2015). These packages contain most of the functions needed to read and clean data from e.g., a computer directory or csv-file, turn the data first into a sparse document-text matrix and then perform the singular value decomposition that creates a semantic space. They also contain tools to project words or phrases into the semantic space and compute their relationships. Here is one simple script that only needs input in the form of an Excel sheet with a list of documents in a column. Any column of text strings will do but one may, for example, try article abstracts collected in a column. Organize the sheet with a column that identifies the texts (e.g., called “title”), and the text in a column titled “text”: #Make sure to install the necessary packages “lsa”, “LSAfun”, “quanteda”, and “readxl”. #load necessary packages library(lsa) library(LSAfun) library(quanteda) library(readxl) #set a working directory setwd("C:/mydirectory...") TextData