The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems 0128157550, 9780128157558

Table of contents :
Cover
The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems
Copyright
Contents
List of contributors
Preface
Section 1: Assessment guidelines
1 Assessment and identification of learning disabilities
Models of learning disability identification
School-based identification
Identification in clinical settings
Learning disabilities: achievement, capability, unexpectedness, and cause
Achievement
Capability
Unexpectedness
Causation
Where to focus time and resources
Identifying learning disabilities: a hybrid model
Inadequate response to appropriate instruction
Poor achievement in reading, mathematics, and/or written expression
Evidence that other factors are not the primary cause
Take-home messages and future directions
References
2 Assessment and diagnosis of attention-deficit/hyperactivity disorder
Historical context
Epidemiology
Etiology
Developmental course
Clinical presentation
Situational variability of primary symptoms
Cooccurring features
Functional impairment
Guidelines for diagnosing attention-deficit/hyperactivity disorder
Diagnostic criteria and classification
Multiinformant, multimethod assessment strategy
Assessment procedures
Interpreting diagnostic evaluation data
Diagnostic feedback and treatment planning
References
3 Assessment of attention-deficit/hyperactivity disorder and comorbid reading disorder with consideration of executive func...
Introduction and overview
The importance of attention-deficit/hyperactivity disorder comorbidity with learning disorders
Overview of the chapter
Prevalence and clinical implications of comorbidity between attention-deficit/hyperactivity disorder and reading disorder
Frequency of comorbidity between attention-deficit/hyperactivity disorder and reading disorder
Conclusion
Functional implications of comorbidity
Cross-sectional analyses
Developmental outcomes
Conclusion
Competing explanations for comorbidity
Artifactual models
Clinic sampling bias
Shared method variance and symptom overlap
Rater bias
Phenocopy model
Conclusion
Common etiology and causal models as explanations for comorbidity between attention-deficit/hyperactivity disorder and read...
Family studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity
Conclusion
Twin studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity
Conclusion
Molecular genetic studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity
Neurocognitive models of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity
Conclusion
Conclusion and future directions
Clinical implications
Diagnostic formulation
Clinical assessment procedures
Intervention
Future directions for studies of comorbidity between attention-deficit/hyperactivity disorder and reading disorder
Early shared risk factors
Attention-deficit/hyperactivity disorder and reading disorder in adults
Understanding shared cognitive weaknesses in attention-deficit/hyperactivity disorder and reading disorder
Conclusion
References
Further reading
Section 2: Recommendations for intervention and treatment
4 Response to intervention framework: an application to school settings
Historical overview of response to intervention and rationale for its use
Response to intervention as three levels of increasingly intensive services
General description of response to intervention levels of instruction
Primary prevention
Secondary prevention
Tertiary prevention
Response to intervention scenario with case example: Norma
Primary prevention
Secondary prevention
Tertiary prevention
Questions raised about the response to intervention model
Overall model
Primary prevention
Secondary prevention
Tertiary prevention/intervention
Response to intervention evaluation
National Research Center on Learning Disabilities studies
National evaluation of response to intervention
Implications for future research and practice
References
5 Educational therapy
Important component abilities in achievement at the elementary level
Important components of oral language and reading
Important components of math
Important components of written expression
Developmental shifts and interrelationships
Common profiles of academic difficulties
Three common profiles of poor reading
Implications of the profiles for math and written expression
Effective educational therapy
Characteristics of explicit, systematic teaching
The benefits of visual aids and manipulatives
Appropriate curricula and materials
Application to children with different poor reader profiles
Communicating with parents and finding appropriate therapy
References
6 Academic accommodations and modifications
Introduction
Legal classifications for specific learning disability and attention-deficit/hyperactivity disorder
Special education
Special education: history and law
Clashing classification system: schools versus clinics
Common eligibility categories
Individualized education program process
Reevaluation and review of goals
Section 504 of the Rehabilitation Act of 1973
Background and history of law
Eligibility and documentation needed for services
504 Plan process
Accommodations and modifications
Definition and differentiation
Accommodations and modifications for specific learning disability and attention-deficit/hyperactivity disorder
Extended time
Read aloud
Technological supports
Setting
Modifications
Making accommodations and modifications effective
Single-subject design
Conclusion
References
Further reading
7 Behavioral interventions
Theoretical underpinnings of behavioral interventions
Behavioral parent training
Empirical support
School-based interventions
Empirical support
Child organizational skill interventions
Empirical support
Multicomponent behavioral interventions
Empirical support
Summary and future directions
Resources for clinicians
Resources for parents
References
8 Executive function training for children with attention-deficit/hyperactivity disorder
Introduction
Why alternative treatments are needed for children with attention-deficit/hyperactivity disorder
Implications derived from clinical outcome studies
Implications derived from neuroimaging studies
Strengthening basic cognitive processes associated with core foundational learning
Implications derived from cognitive/experimental investigations
The functional working memory model of attention-deficit/hyperactivity disorder and transfer effects
The functional working memory model
Desirable training outcomes: near- and far-transfer effects
Executive function training programs
Conceptual rationale and currently available programs
Executive function training efficacy
Ready, fire, misaim approach of executive function training programs and methodological considerations
Neurotherapies
Neurofeedback
Electroencephalogram
fMRI/Functional near-infrared spectroscopy
Brain stimulation
Repetitive transcranial magnetic stimulation
Transcranial direct current stimulation
Summary
Practitioner considerations and recommendations
Organizational strategies to improve executive function–dependent academic activities
Organizational Skills Training
Homework, Organization, and Planning Skills
Supporting Teen’s Autonomy Daily
Summary
Memory strategies to improve learning
Managing information encoding difficulties
Information input channel
Practitioner recommendations to accommodate and strengthen orthographic conversion
Information retention and retrieval
Practitioner recommendations to accommodate and strengthen short-term storage
Summary and future directions
References
Further reading
9 Tying it all together
Learning disorder and attention-deficit/hyperactivity disorder often cooccur
Complexities: severity of risk, individual variability, and comorbidity
Importance of integrating learning disorder–attention-deficit/hyperactivity disorder care
Case examples
Attention-deficit/hyperactivity disorder+learning disorder
Evaluation procedures
Reason for referral
Background information and symptom history
Behavioral observations
Test results and interpretation
Intellectual functioning
Academic functioning
Behavioral and emotional functioning
Summary
Diagnoses
Recommendations
Learning disorder+comorbidity
Evaluation procedures
Reason for referral
Background information and symptom history
Behavioral observations
Test results and interpretation
Intellectual functioning
Adaptive behavior
Academic functioning
Attention, anxiety, and depression
Summary
Diagnoses
Recommendations
Attention-deficit/hyperactivity disorder
Evaluation procedures
Reason for referral
Background information and symptom history
Behavioral observations
Test results and interpretation
Intellectual functioning
Academic functioning
Attention and executive function
Summary
Diagnosis
Recommendations
Attention-deficit/hyperactivity disorder+comorbidity
Evaluation procedures
Reason for referral
Background information and symptom history
Behavioral observations
Test results and interpretation
Broad-band screening
Attention problems
Anxiety
Depression
Intellectual functioning
Academic functioning
Summary
Diagnoses
Recommendations
Important future directions for learning disorder–attention-deficit/hyperactivity disorder research
Summary
Index
Back Cover

Citation preview

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems Edited by

Michelle M. Martel Psychology Department, University of Kentucky, Lexington, KY, United States

Academic Press is an imprint of Elsevier 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 © 2020 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. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-815755-8 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Acquisition Editor: Nikki Levy Editorial Project Manager: Pat Gonzalez Production Project Manager: Sujatha Thirugnana Sambandam Cover Designer: Matthew Limbert Typeset by MPS Limited, Chennai, India

Contents List of contributors Preface

xi xiii

Section 1 Assessment guidelines 1.

Assessment and identification of learning disabilities

3

Emily A. Farris, Erin E. Alexander and Timothy N. Odegard

2.

4 6 8

Models of learning disability identification School-based identification Identification in clinical settings Learning disabilities: achievement, capability, unexpectedness, and cause Achievement Capability Unexpectedness Causation Where to focus time and resources Identifying learning disabilities: a hybrid model Inadequate response to appropriate instruction Poor achievement in reading, mathematics, and/or written expression Evidence that other factors are not the primary cause Take-home messages and future directions References

19 24 25 28

Assessment and diagnosis of attention-deficit/ hyperactivity disorder

33

9 9 10 12 14 16 17 18

Arthur D. Anastopoulos and Kaicee K. Beal Historical context Epidemiology Etiology Developmental course

34 34 35 36

v

vi

3.

Contents

36 37 37 38

Clinical presentation Situational variability of primary symptoms Cooccurring features Functional impairment Guidelines for diagnosing attention-deficit/hyperactivity disorder Diagnostic criteria and classification Multiinformant, multimethod assessment strategy Assessment procedures Interpreting diagnostic evaluation data Diagnostic feedback and treatment planning References

39 39 42 43 46 49 50

Assessment of attention-deficit/hyperactivity disorder and comorbid reading disorder with consideration of executive functioning

55

Erik G. Willcutt Introduction and overview The importance of attention-deficit/hyperactivity disorder comorbidity with learning disorders Overview of the chapter Prevalence and clinical implications of comorbidity between attention-deficit/hyperactivity disorder and reading disorder Functional implications of comorbidity Competing explanations for comorbidity Artifactual models Common etiology and causal models as explanations for comorbidity between attention-deficit/hyperactivity disorder and reading disorder Family studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity Twin studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity Molecular genetic studies of reading disorder, attention-deficit/ hyperactivity disorder, and their comorbidity Neurocognitive models of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity Conclusion and future directions Clinical implications Future directions for studies of comorbidity between attention-deficit/hyperactivity disorder and reading disorder Conclusion References Further reading

55 55 56 57 58 61 61

62 63 64 65 66 67 67 68 69 69 73

Contents

vii

Section 2 Recommendations for intervention and treatment 4.

Response to intervention framework: an application to school settings

77

Pamela M. Stecker, Janie Hodge and Catherine A. Griffith

5.

Historical overview of response to intervention and rationale for its use Response to intervention as three levels of increasingly intensive services General description of response to intervention levels of instruction Response to intervention scenario with case example: Norma Questions raised about the response to intervention model Overall model Primary prevention Secondary prevention Tertiary prevention/intervention Response to intervention evaluation National Research Center on Learning Disabilities studies National evaluation of response to intervention Implications for future research and practice References

80 82 84 84 86 87 88 88 89 90 92 94

Educational therapy

99

78 79

Louise Spear-Swerling Important component abilities in achievement at the elementary level Important components of oral language and reading Important components of math Important components of written expression Developmental shifts and interrelationships Common profiles of academic difficulties Three common profiles of poor reading Implications of the profiles for math and written expression Effective educational therapy Characteristics of explicit, systematic teaching The benefits of visual aids and manipulatives Appropriate curricula and materials Application to children with different poor reader profiles Communicating with parents and finding appropriate therapy References

100 100 104 105 105 106 106 109 110 110 114 115 115 119 121

viii

Contents

6.

Academic accommodations and modifications

125

Dan Florell and Andrea Strait Introduction Legal classifications for specific learning disability and attention-deficit/hyperactivity disorder Special education Section 504 of the Rehabilitation Act of 1973 Accommodations and modifications Definition and differentiation Accommodations and modifications for specific learning disability and attention-deficit/hyperactivity disorder Extended time Read aloud Technological supports Setting Modifications Making accommodations and modifications effective Single-subject design Conclusion References Further reading

7.

Behavioral interventions

125 126 126 133 135 135 136 136 138 138 139 140 141 142 143 144 147 149

Lauren M. Friedman and Linda J. Pfiffner

8.

Theoretical underpinnings of behavioral interventions Behavioral parent training Empirical support School-based interventions Empirical support Child organizational skill interventions Empirical support Multicomponent behavioral interventions Empirical support Summary and future directions Resources for clinicians Resources for parents References

150 151 152 157 159 160 160 161 161 163 164 165 165

Executive function training for children with attention-deficit/hyperactivity disorder

171

Mark D. Rapport, Samuel J. Eckrich, Catrina Calub and Lauren M. Friedman Introduction 171 Why alternative treatments are needed for children with attentiondeficit/hyperactivity disorder 171

Contents

Implications derived from clinical outcome studies Implications derived from neuroimaging studies Strengthening basic cognitive processes associated with core foundational learning Implications derived from cognitive/experimental investigations The functional working memory model of attention-deficit/ hyperactivity disorder and transfer effects Executive function training programs Conceptual rationale and currently available programs Executive function training efficacy Ready, fire, misaim approach of executive function training programs and methodological considerations Neurotherapies Neurofeedback Brain stimulation Repetitive transcranial magnetic stimulation Transcranial direct current stimulation Summary Practitioner considerations and recommendations Organizational strategies to improve executive function dependent academic activities Organizational Skills Training Homework, Organization, and Planning Skills Supporting Teen’s Autonomy Daily Summary Memory strategies to improve learning Managing information encoding difficulties Information input channel Information retention and retrieval Summary and future directions References Further reading

9.

Tying it all together

ix 171 173 173 173 175 177 178 178 180 180 181 182 183 183 183 183 184 184 185 186 186 187 187 188 189 191 192 196 197

Michelle M. Martel Learning disorder and attention-deficit/hyperactivity disorder often cooccur Complexities: severity of risk, individual variability, and comorbidity Importance of integrating learning disorder attentiondeficit/hyperactivity disorder care Case examples Attention-deficit/hyperactivity disorder 1 learning disorder Learning disorder 1 comorbidity

197 198 199 199 200 204

x

Contents

Attention-deficit/hyperactivity disorder Attention-deficit/hyperactivity disorder 1 comorbidity Important future directions for learning disorder attentiondeficit/hyperactivity disorder research Summary Index

209 214 220 221 223

List of contributors Erin E. Alexander Tennessee Center for the Study and Treatment of Dyslexia, Middle Tennessee State University, Murfreesboro, TN, United States Arthur D. Anastopoulos Department of Human Development & Family Studies, University of North Carolina Greensboro, NC, United States Kaicee K. Beal Department of Human Development & Family Studies, University of North Carolina Greensboro, NC, United States Catrina Calub Department of Psychology, University of Central Florida, FL, United States Samuel J. Eckrich Department of Psychology, University of Central Florida, FL, United States Emily A. Farris Tennessee Center for the Study and Treatment of Dyslexia, Middle Tennessee State University, Murfreesboro, TN, United States Dan Florell Department of Psychology, Eastern Kentucky University, KY, United States Lauren M. Friedman Department of Psychiatry, University of California, San Francisco, CA, United States Catherine A. Griffith Department of Education and Human Development, Clemson University, Clemson, SC, United States Janie Hodge Department of Education and Human Development, Clemson University, Clemson, SC, United States Michelle M. Martel Psychology Department, University of Kentucky, Lexington, KY, United States Timothy N. Odegard Tennessee Center for the Study and Treatment of Dyslexia, Middle Tennessee State University, Murfreesboro, TN, United States; Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, United States Linda J. Pfiffner Department of Psychiatry, University of California, San Francisco, CA, United States Mark D. Rapport Department of Psychology, University of Central Florida, FL, United States Louise Spear-Swerling Department of Special Education, Southern Connecticut State University, New Haven, CT, United States

xi

xii

List of contributors

Pamela M. Stecker Department of Education and Human Development, Clemson University, Clemson, SC, United States Andrea Strait Department of Psychology, Eastern Kentucky University, KY, United States Erik G. Willcutt University of Colorado Boulder, Boulder, CO, United States

Preface Learning disorders and attention-deficit/hyperactivity disorder (ADHD) are among the most common presenting problems confronted by clinicians conducting assessments in children. Learning disorders occur in 10% 20% of children, and ADHD occurs in 5% 10% of children; yet, these issues are part of the presenting problem in child clinical assessments roughly half of the time. Further, many—if not most—of the requested child assessments are focused on comprehensive assessment of both learning and attention. Yet, no definitive resources for assessing, diagnosing, and recommending treatment for these two common problems currently exist for clinicians, particularly in readily accessible form. The current book will provide this much-needed guidance to clinicians conducting assessments of learning and attention problems in children. There are a number of books available on learning disorders and ADHD in childhood, but typically these topics are tackled separately rather than together, despite their common cooccurrence. Further, there are several books available on academic success, or intervention, strategies for learning (and attention) problems, targeted toward educators and/or parents. Yet, these books are targeted to educators or parents and are focused solely on intervention. Finally, there are a few books available which tackle learning and attention problems together in adulthood. The current book will differ from other books by providing an integrated discussion of childhood learning disorders and ADHD assessment and treatment recommendations geared toward assessment practitioners. This book is needed given the current lack of recommendations for psychologists about how to conduct these common assessments, which is particularly timely given recent research advances and legislative changes. Therefore this book provides the much-needed guidance for clinicians conducting assessments and recommending treatments and interventions for learning disorders and/or ADHD. The first part of the book provides detailed information about assessment of learning disorders, ADHD, and their comorbidity and associated characteristics. The second part of the book details intervention and treatment recommendations, including response to intervention, educational therapy, academic accommodations and modifications, behavioral therapy, and executive function training. Finally, the book

xiii

xiv

Preface

concludes with directions for future work, as well as sample clinical reports with assessment and intervention recommendations as a guide for clinicians. Overall, it is hoped that this information will provide evidence-based guidance to clinicians seeking to assess and/or treat these common problems.

Chapter 1

Assessment and identification of learning disabilities Emily A. Farris1, Erin E. Alexander1 and Timothy N. Odegard1,2 1

Tennessee Center for the Study and Treatment of Dyslexia, Middle Tennessee State University, Murfreesboro, TN, United States, 2Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, United States

It is not uncommon for children to struggle academically at one time or another. However, some children experience severe and persistent learning difficulties that are confined to specific academic areas. For example, there are children who struggle with mathematics but read without difficulty, whereas other children struggle profoundly with reading but not with mathematics. These children are capable of learning in spite of experiencing severe difficulties in doing so. Their rate of learning, however, is much slower than their peers, and there are no obvious explanations as to why. When such observations were starting to be made in the middle of the last century, efforts to identify and categorize children as learning disabled began to emerge in hopes of guaranteeing them access to educational opportunities that would allow them to reach their potential, which at that time were not guaranteed (Kirk & Gallagher, 1979). Often, but not always, these educational opportunities come through special education services in the child’s school under the eligibility category of specific learning disability (SLD). As of the fall of 2015, of all US students ages 6 through 21 years receiving special education services, 38.8% were identified under the eligibility category of SLD, representing the largest group of students receiving special education services out of 13 disability categories (Office of Special Education & Rehabilitative Services, 2017). Furthermore, a summary provided by the National Center for Learning Disabilities notes that individuals with learning disabilities (LDs) have higher rates of failure based on course grades and high stakes tests, higher rates of high school dropout, are more likely to have some type of involvement with the criminal justice system and live in poverty (Cortiella & Horowitz, 2014). Thus there is a great need to continue to identify and support these children.

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00001-0 © 2020 Elsevier Inc. All rights reserved.

3

4

The Clinical Guide to Assessment and Treatment

A determination of an LD can be made based on different models and specific procedures in compliance with guidelines set forth by government agencies and diagnostic manuals. However, who is said to have an LD is determined by which of these identification procedures are adopted (Fletcher & Miciak, 2017; Siegel, 1999; Stanovich, 1999), and efforts to categorize children as learning disabled have been further complicated by there not being a single causal factor to explain the learning difficulties of these children. The construct of LDs is based on the premise that children labeled as learning disabled struggle to learn as the result of intrinsic factors. Their learning struggles are not the result of obvious differences inherent to the child (e.g., vision impairment, hearing impairment) or lack of instruction due to other factors such as poor attendance or the child not having access to instructional opportunities. Without an obvious set of factors to account for the learning difficulties, the basis of these learning struggles was thought to be neurobiological. Support for this position was provided by appealing to older concepts, such as minimal brain dysfunction and neurobiological causes offered for other conditions such as dyslexia (Hinshelwood, 1917; McCandliss & Noble, 2003; Orton, 1928; Pugh et al., 2000; Richardson, 1992; Turkeltaub, Gareau, Flowers, Zeffiro, & Eden, 2003; Wender, 1975). Yet it was not possible at the time when LD was first conceptualized to demonstrate a neurobiological difference at the individual level to drive identification efforts, and it is still not possible to do so. Thus there was a need for guiding principles to operationalize who does and does not qualify as learning disabled, and from this need arose the notion that children with LDs were unified by their “developmental discrepancies in abilities and achievement” (Kirk & Gallagher, 1979, p. 281). This idea came to be conceptualized as “unexpected underachievement” in policies and procedures used to guide the identification and classification of children based on the presence of an LD (Education of All Handicapped Children Act, 1975; U.S. Department of Education, 2006). Variations of these initial policies and procedures continue to determine how resources are allocated and who receives additional educational opportunities through special education (U.S. Department of Education, 2004).

Models of learning disability identification There are similarities as well as differences in identification procedures used in the schools versus clinics or private practice settings. Whereas within the schools, SLD is the required terminology as outlined by the Individuals with Disabilities Education Act (IDEA; U.S. Department of Education, 2006), outside of the school setting, LDs are currently encompassed under the term “Specific Learning Disorder” in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5; American Psychiatric Association, 2013). (See Table 1.1 for a comparison of the criteria specified in these

Assessment and identification of learning disabilities Chapter | 1

TABLE 1.1 Comparison of identification procedures in clinic (DSM-5) or school (IDEA) settings. DSM-5

IDEA

Specific Learning Disorder

Specific Learning Disability

Umbrella term

Additional specifications With impairment in ______ with specified area(s) as indicated below.

Name area(s) as indicated below

Achievement areas (can be identified in one or more of these areas) Reading

Reading. Specify area(s): word reading accuracy; reading rate or fluency; reading comprehension

Basic reading skill; reading fluency skills; reading comprehension

Written expression

Written expression. Specify area (s): spelling accuracy; grammar and punctuation accuracy; clarity or organization of written expression

Written expression

Mathematics

Mathematics. Specify area(s): number sense; memorization of arithmetic facts; accurate or fluent calculation; accurate math reasoning

Math calculation skills; math problem-solving

Other

N/A (included under language disorder)

Oral expression; listening comprehension (also included under language impairment)

Exclusionary criteria Capability

Intellectual disabilities

Intellectual disability

Sensory modalities

Uncorrected vision or auditory acuity

Vision, hearing, or motor disabilities

Mental/ emotional

Other mental or neurological disorders

Emotional disturbance

Environment/ social

Psychosocial adversity

Environmental, cultural, or economic disadvantage

Language

Lack of proficiency in the language of academic instruction

Exposure to instruction

Inadequate educational instruction

Inadequate educational instruction (considered but not listed under exclusionary criteria) (Continued )

5

6

The Clinical Guide to Assessment and Treatment

TABLE 1.1 (Continued) DSM-5

IDEA

How instructional response is acknowledged Duration

Symptoms persist for at least 6 months, despite targeted interventions

N/A

Evidence may include

School reports; work samples; rating scales

Direct observations in classroom

Curriculum-based measures

Curriculum-based measures and curriculum-embedded measures

Clinical interview; rating scales

Parent and teacher interviews

Previous psychological or educational assessments Progress monitoring data is used to show if student’s rate of improvement will close the gap between student and grade-level peers

documents guiding the identification processes, which are outlined in the following sections.)

School-based identification The IDEA is a federal law whose purpose is to ensure that students with disabilities receive a free appropriate public education (i.e., FAPE) through the provision of special education and related services (U.S. Department of Education, 2006). The current federal regulations needed to implement IDEA provide the following definition of SLD in 34 CFR 300.8(c)(10): 1. General. Specific learning disability means a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in the imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. 2. Disorders not included. Specific learning disability does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of intellectual disability, of emotional disturbance, or of environmental, cultural, or economic disadvantage (U.S. Department of Education, 2006, p. 46757).

Assessment and identification of learning disabilities Chapter | 1

7

Federal regulations guide the decision-making process schools must use when determining the presence of an SLD. As stated in 34 CFR 300.309(a) (1), first, school teams must determine that the child does not achieve adequately for the child’s age or to meet State-approved grade-level standards in one or more of the following areas, when provided with learning experiences and instruction appropriate for the child’s age or State-approved grade-level standards: oral expression, listening comprehension, written expression, basic reading skill, reading fluency skills, reading comprehension, mathematics calculation, and mathematics problem solving (U. S. Department of Education, 2006, p. 46786).

Therefore a student could have an SLD in more than one academic area. Interestingly, under the IDEA, the first two areas are also encompassed under speech or language impairment. In the DSM-5, which is used for evaluations outside the schools, these two areas are encompassed under language disorder and are not listed under areas of LD (American Psychiatric Association, 2013). Although the IDEA definition of SLD has changed very little since the statute’s inception in 1975, the guidance regarding how to identify it has evolved. For example, the 1997 reauthorization of IDEA continued to rely heavily on the determination of a discrepancy between an individual’s achievement and intellectual disability, thereby promoting the use of an intelligence quotient (IQ) achievement discrepancy model. With the most current reauthorization of IDEA in 2004, federal regulations no longer emphasized the use of the severe discrepancy model due to scientific evidence (Fletcher, Lyon, Fuchs, & Barnes, 2007), which is discussed further below. Rather, states were required to adopt criteria for identifying specific LDs that met the following guidelines as specified in 34 CFR 300.307(a): G

G

G

Must not require the use of a severe discrepancy between intellectual ability and achievement for determining whether a child has a specific learning disability, as defined in 34 CFR 300.8(c)(10); Must permit the use of a process based on the child’s response to scientific, research-based intervention; and May permit the use of other alternative research-based procedures for determining whether a child has a specific learning disability, as defined in 34 CFR 300.8(c)(10) (U.S. Department of Education, 2006, p. 46786).

Thus states cannot mandate the use of an IQ achievement discrepancy approach in the identification of an LD as was the norm prior to the reauthorization of IDEA (Mercer, Jordan, Allsopp, & Mercer, 1996), but the reauthorization did not go as far as to preclude schools from choosing to use an IQ achievement discrepancy model. As such, states have been given some choice regarding which of the following three identification models may be used, as long as they do not require the discrepancy model: the

8

The Clinical Guide to Assessment and Treatment

IQ achievement discrepancy model, a response to intervention (RTI) model, or alternative research-based procedures, which are generally interpreted as a pattern of strengths and weaknesses (PSW) model, of which there are several variations. These models are briefly described later in this chapter. For example, the State of Washington’s regulations allow districts to use the discrepancy model, an RTI model, or a combination of the two (Bresko, Gill, Dorn, Kanikeberg, & Mendoza, 2014). A consequence of giving states this choice is that a student could potentially meet criteria for SLD in one state but not in another. Additionally, there is some acknowledgment that LDs occur on a continuum of severity as evidenced by attempts to provide accommodations within the general education setting to children with milder difficulties who may not meet eligibility criteria under IDEA through other legislation, such as provisions of Section 504 of the Rehabilitation Act of 1973, as described by Schraven and Jolly (2010).

Identification in clinical settings The DSM-5 is used to make diagnostic decisions outside of the school setting. In describing the diagnostic features of specific learning disorder, the DSM-5 defines it as: a neurodevelopmental disorder with a biological origin that is the basis for abnormalities at a cognitive level that are associated with the behavioral signs of the disorder. The biological origin includes an interaction of genetic, epigenetic, and environmental factors, which affect the brain’s ability to perceive or process verbal or nonverbal information efficiently and accurately (American Psychiatric Association, 2013, p. 68).

Whereas the previous edition of the DSM, the DSM-IV-TR, listed each disorder separately by achievement area (i.e., Reading Disorder, Mathematics Disorder, and Disorder of Written Expression), the current addition includes these under the umbrella term of Specific Learning Disorder (American Psychiatric Association, 2000). Within this category, clinicians are to specify whether the individual manifests impairment in reading, written expression, or mathematics. These categories are further divided by skill deficit. For impairments in reading, skill deficits can be identified in one or more of the following areas: word reading accuracy, reading rate or fluency, and reading comprehension. For impairments in written expression, skill deficits can be identified in spelling accuracy, grammar and punctuation accuracy, and/or clarity or organization of written expression. Subskills of mathematics impairments include the following: number sense, memorization of arithmetic facts, accurate or fluent calculation, and/or accurate mathematics reasoning. Clinicians are to record the severity of the impact of the learning disorder (i.e., mild, moderate, or severe) based on the degree of functional impairment. Thus those with mild learning disorders may need

Assessment and identification of learning disabilities Chapter | 1

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fewer accommodations and supports than those whose learning disorder is more severe. This is similar to the additional federal regulations used in school settings to impact the provision of accommodations to children in general education settings. The terms “dyslexia” and “dyscalculia” are listed as alternate terms that clinicians could use to describe particular patterns of difficulties. These terms, however, do not represent the primary label used. Note that the description of difficulties encompassed by dyslexia is consistent with the research-based definition used by the International Dyslexia Association (Lyon, Shaywitz, & Shaywitz, 2003). Also, note that based on this definition of dyslexia, it would encompass a learning disorder in word reading accuracy or reading rate or fluency as well as written expression impacting spelling. When the DSM-5 was updated in 2013, the guidelines for identifying LDs shifted from a focus on IQ achievement discrepancy to identifying academic skill deficits that have persisted despite targeted interventions. No longer is an intellectual assessment required. This resembles the RTI model that schools are now permitted to use for identification. In both settings, the focus is now more on the instruction and less on intrinsic factors. To appreciate why these shifts came about, it is important to understand the theoretical models that undergird and help motivate the descriptions of LDs provided in the legislation for identification procedures or manuals with diagnostic guidelines for clinicians.

Learning disabilities: achievement, capability, unexpectedness, and cause Regardless of the conceptual model or definition referenced, LDs are marked by four interrelated components: achievement, capability, unexpectedness, and cause. Aspects of these factors can be found in all definitions and models of LDs (e.g., Fletcher, Lyon, Fuchs, & Barnes, 2018; Hammill, 1990; Mercer, et al., 1996). How these components manifest in practice depends on assumptions inherent to how each model conceptualizes and measures a child’s capability, determines if academic underachievement is unexpected, and weights the importance of and method used for ascertaining the cause of a documented area of academic underachievement.

Achievement Most methods used for the identification of students with LDs include low achievement as a defining characteristic, which is necessary but insufficient for the identification of LDs. Identification procedures based on any model used for the identification of LDs will include measures of a child’s level of achievement. Such information is crucial because it provides an observation of the exact behaviors of interest, the child’s ability to read, write, spell, and

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solve mathematical problems (Fletcher & Miciak, 2017; Siegel, 1999; Stanovich, 1999). Furthermore, empirical data suggest that there are five different forms of LDs that each impact a different area of achievement: word recognition and spelling, reading comprehension, mathematical computations, mathematical problem-solving, and written expression (Fletcher et al., 2018). Standardized norm-referenced measures of achievement are used to assess a child’s level of performance in these areas. Common examples of such measures include the Woodcock Johnson Test of Achievement, currently in the fourth edition (Schrank, Mather, & McGrew, 2014), the Kaufman Test of Educational Achievement, currently in the third edition (Kaufman & Kaufman, 2014), and the Wechsler Individual Achievement Test, currently in the third edition (Psychological Corporation, 2009). Each of these test batteries measures the specific domains impacted by LDs and includes norms for age and grade that allow for standard scores to be computed. The provision of standard scores supports efforts to determine if a child is achieving at a level comparable to his or her peers. This is often operationalized as a cut point. For example, a cut point of the 25th percentile could be adopted. Those children for whom 75% of their age group outperformed them on a given measure of academic achievement (e.g., untimed isolated word reading) would be deemed as exhibiting low achievement in this area. Yet, difficulties arise in determining the exact placement of cut points that impact the provision of services to individual children (Francis et al., 2005). These difficulties emphasize that if they are to be used, the creation of cut points should be made with great care.

Capability The concept of capability has been foundational to LDs since their inception as a construct. It was central to the logic inherent to policies establishing LDs with US public policy in 1968 and the adoption of Public Law 94 142 (Education of All Handicapped Children Act) in 1975 as eloquently captured by Samuel Kirk in his summary paraphrasing the intent of the legislation: We hold these truths to be self-evident, that all children, handicapped and nonhandicapped, are created equal; that they are endowed by their creator with certain inalienable rights, among these are the right to equal education to the maximum of each child’s capability. To secure these rights, Public Law 94 142 was established. We, the people of these United States, solemnly declare that all exceptional children shall be educated at public expense, and that their education will be in the least restrictive environment (Kirk & Gallagher, 1979, p. xi).

There is much to appreciate about the framing of educational needs within the context of equality and civil rights. Yet, inherent to this public

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law and the policies that would follow are two presumptions: first, that children have limits on their capability to benefit from instruction and second, that their capability (i.e., potential) to learn can be objectively measured. Capability to learn is often operationalized as a score on an IQ test. A child’s score on an IQ test can serve as a gatekeeper that determines eligibility for services based on LD status, when it is assumed that an IQ score is a true measure of a person’s potential. The use of IQ measures in identification models of LD has received considerable criticism (e.g., Siegel, 1989, 1999; Stanovich, 1999). One of the largest concerns with adopting an IQ score is that it is a measure of past learning and not the potential for future learning. It measures a child’s amassed vocabulary and background knowledge based on prior experiences and educational opportunities (Siegel, 1989). Furthermore, the pattern of performance across subtests or factor scores obtained from IQ tests do not differ between children with LD and typically achieving children (e.g., D’Angiulli & Siegel, 2003). Other criticisms of the use of IQ measures to determine potential to learn is that these measures are not reliable predictors of how much a child will benefit from educational opportunities and do not mediate the effectiveness of instruction, with children benefitting equally in response to instruction regardless of their IQ (Stuebing et al., 2002; Vellutino et al., 1996; Vellutino, Scanlon, & Lyon, 2000). As such, an IQ score does not flag a child as needing a qualitatively different form of intervention in an academic area. The idea that one should have to first measure a child’s capacity to learn with a measure of IQ prior to intervening to address academic underachievement is indicative of a “test-then-teach” approach. This approach focuses energies and resources on extensive testing at a single point in time at the expense of allocating the finite resources to first try to intervene and gather data to measure a child’s rate of learning in response to initial interventionfocused instructional efforts. Instead of relying on a single measure of a child’s IQ, which is not a reliable predictor of how well a child will learn in response to instruction, approaches to the identification of individuals with LDs should ensure that they are demonstrably difficult to teach (Fletcher & Miciak, 2017; Siegel, 1999). This is because no person can be defined with an LD in the absence of evidence of a lack of adequate response to instruction that is effective with most students (Fletcher, Coulter, Reschly, & Vaughn, 2004; Lyon et al., 2003). Such ideas are now incorporated into federal regulations from the Office of Special Education and Rehabilitative Services within the U.S. Department of Education following the reauthorization of IDEA in 2004 (U.S. Department of Education, 2006). Academic deficits are necessary, but insufficient in isolation for a classification of an LD. This motivates the use of a child’s failure to respond to instruction as an inclusionary factor that marks a child experiencing unexpected underachievement.

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The Clinical Guide to Assessment and Treatment

Unexpectedness Instead of being the result of a set of readily apparent primary causes, the observed underachievement associated with LDs is unexpected. The individuals do not have uncorrected deficits in seeing or hearing. The individuals do not exhibit behavioral issues that interfere with their ability to receive standard forms of instruction. They do not present with a documented intellectual disability that would be comprised of not only a low score on an IQ test, but also a determination of the individual’s difficulty functioning adaptively within their environment. Additionally, they are receiving standard forms of instruction proven to be effective with most individuals. Operationalizing unexpected underachievement represents a very real challenge that comes with very real dangers, especially in conjunction with the assumption that it is possible to measure a child’s true potential. There has been, and in some instances continues to be, a reliance on using a discrepancy between a person’s level of achievement and their potential to demarcate unexpected underachievement. This came to be operationalized as the discrepancy between a child’s performance on an IQ test and their level of achievement in an academic domain, having been mandated in procedures adopted in the majority of states across the United States in the 1990s (Mercer et al., 1996). This discrepancy model of identification has several limitations that results in the underidentification of LDs due to the restriction of who can qualify for services. First, they rely on the use of a judgment of whether the difference between achievement and IQ scores is of a sufficient magnitude to be considered discrepant or unexpected (e.g., a difference of 22.5 standard scores reflecting 1.5 standard deviations), and this magnitude judgment may vary across states. Such decisions fall prey to the statistical and practical limitations associated with cut points as thresholds placed on single achievement domain scores described above, and the associated lack of stability of group membership over time for a given individual, even when the shared variance across intelligence and achievement measures is accounted for in the calculations of the discrepancy (Francis et al., 2005; Stuebing et al., 2002). A discrepancy based on a difference in standard scores can also lead to more individuals with relatively high as opposed to low IQs being identified as LD (Stanovich, 1999). Second, due in part to the interrelationship of the underlying latent constructs represented by scores on IQ and achievement tests, and especially the understanding that reading skills can foster the development of cognitive skills assessed on intelligence tests, it can become increasingly difficult for individuals to exhibit a discrepancy of sufficient magnitude to warrant identification with LD (e.g., Fletcher et al., 2004; Siegel & Hurford, 2019; Stanovich, 1999). Such joint development of IQ and skills in different achievement domains can be described as situations where the rich get richer

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and the poor get poorer. This finding makes it more difficult for a child to be identified with a LD, especially as the child gets older and is unable to experience the mutual benefit of typical development in both areas of achievement and so-called capability. While underidentifying LDs may meet a need to limit the amount of resources allocated to address instructional needs of struggling learners, it does not meet the motivation for an equitable allocation of educational opportunities that motivated the establishment of the construct of LDs and the public law that codified them. Third, there is a lack of evidence for differences in achievement abilities between groups identified as poor achieving with or without the constraint of an IQ discrepancy. For example, children with low achievement scores in word recognition who differ in IQ scores do not significantly differ in their word recognition, pseudoword reading, real and exception word reading, spelling, and syntactic abilities (Siegel, 1989). Additionally, children identified as having reading difficulties with or without the use of an IQ discrepancy only exhibited small to negligible differences in performance on reading and cognitive measures, although both groups performed more poorly than children whose reading scores were within the average range (Fletcher et al., 1994). Nor are there reliable differences in brain activation during word rhyming tasks engaging phonological processing in children with low word recognition achievement scores who did or did not have an IQ discrepancy (Tanaka et al., 2011). In addition, as confirmed by meta-analysis, individuals with poor reading achievement with an IQ achievement discrepancy do not substantially differ from individuals with poor reading achievement who do not present with an IQ achievement discrepancy on measures of reading, phonological processing, and rapid naming skills (Hoskyn & Swanson, 2000; Stuebing et al., 2002). Moreover, a separate meta-analysis of 22 studies examining improvements in reading skills following intervention in groups identified with or without using an IQ discrepancy observed IQ to only account for at most up to 3% of the variance in intervention response regardless of the specific reading outcome measure used, type of IQ score, or age of the participants (Stuebing, Barth, Molfese, Weiss, & Fletcher, 2009). Thus empirical evidence suggests that using an IQ achievement discrepancy neither differentiates the severity of underachievement nor aids in the prediction of RTI. Although our examples of empirical studies are primarily in the reading domain, there is evidence for similar findings of the lack of usefulness of IQ achievement discrepancy models for identifying individuals with LDs in other domains as summarized in Fletcher et al. (2018). As such, the use of IQ tests as a representation of a child’s capability to learn or determine whether struggles with underachievement are unexpected is unwarranted. This leads to investigations of questions related to causal factors in efforts to help determine how to ascertain the unexpectedness aspect of the identification of a child with an LD.

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The Clinical Guide to Assessment and Treatment

Causation Individuals affected by LDs historically have been thought to represent a distinct group whose difficulties in academic achievement are not primarily caused by limitations in sensation (i.e., hearing, seeing), intelligence, emotional difficulties that interfere with learning, or an inadequate opportunity to learn (Kirk, 1963). LDs are the product of a complex set of relationships between neurobiological causes that result in differences in how individuals process information that is constrained within the context of their environment. Individuals with LDs also commonly experience alterations to their mood and affect (e.g., depression, anxiety) that can impact their engagement in academic activities (Klassen, Tze, & Hannok, 2011; Mugnaini, Lassi, La Malfa, & Albertini, 2009; Willcutt & Pennington, 2000b). Additionally, they may exhibit conduct issues in classroom settings and elsewhere (Hinshaw, 1992; Willcutt & Pennington, 2000b). Any and all of these additional factors can result in the manifestation of behaviors that look like attention issues. At the same time, attention problems can also cooccur as a comorbid condition, and the high rate of comorbidity between LDs and attention problems are likely due in part to shared vulnerabilities (i.e., underlying causes) that give rise to both (Willcutt & Pennington, 2000a; Willcutt et al., 2010). These additional factors complicate the identification of individuals with LDs and can also hinder efforts to remediate their academic skills. At times, this can result in children struggling to learn as the result of neurobiological differences being labeled with conduct issues and attention problems that are the result as opposed to the cause of their challenges with learning. Although the primary causes are neurobiological in nature, the observed manifestation of LDs is deficit in a specific set of academic skills. Observed processing differences often intervene between the neurobiological causes and deficits in academic skills characteristic of LDs. This has led to the development of models designed to understand a child’s PSW within cognitive processing domains, such as executive function, processing speed, and visual spatial processing (e.g., Flanagan, Fiorello, & Ortiz, 2010; Hale, Fiorello, Bertin, & Sherman, 2003; Hale, Kaufman, Naglieri, & Kavale, 2006). However, the supposition that psychological/cognitive factors cause the observed deficits in academic skills is misleading. The conceptualization of psychological constructs, especially those that are cognitive, is transient. They are based on metaphors that were adopted to serve as placeholders until more exacting representations of how the brain represents itself in the mind could be achieved. This reality places limitations on the utility of models of LDs predicated on the need to identify a psychological or cognitive cause of identified underachievement in academic skills. The difficulties inherent in attempts to measure these psychological constructs are highlighted by examining differences in LD identification rates

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across profiles obtained from different test instruments. In this regard, Miciak, Taylor, Denton, and Fletcher (2015) had second grade students complete a series of assessments that were subdivided to create two comparable batteries (i.e., the same set of constructs were captured using scores from different tests). They observed agreement in whether a student’s profile revealed a PSW sufficient to meet LD criteria within a specific academic domain (e.g., basic reading or reading comprehension) using both batteries for only 23% of their participants, whereas an additional 76% of participants met criteria for LD in a specific academic domain relying on score profiles from either one of the two batteries, but not both. These results suggest that the choice of the specific instruments used during the assessment, and the associated variability in shared variance for different achievement measures with a measure of a given cognitive processing domain may result in different decisions regarding whether a child is eligible for intervention services. Such conclusions are also supported by empirical data from other studies with older students including fourth, sixth, and seventh graders (e.g., Miciak et al., 2014, 2016). Furthermore, the statistical and practical concerns related to establishing thresholds or cut points also apply to these patterns of strengths and weaknesses models (McGill, Styck, Palomares, & Hass, 2016). Moreover, although it may be convenient to adopt models reliant on measures of latent cognitive constructs to drive the identification of LDs, there is growing evidence that their utility in doing so is limited by the underlying reality of what marks LDs, a lack of response to instruction. In one study, the only cognitive measure that accounted for unique variance to the prediction of a child’s response to instruction was nonverbal reasoning, which only explained an additional 1.4% of the variance, suggesting that cognitive measures do not increase the ability to determine whether a child continues to exhibit underachievement following specialized direct instruction (Miciak et al., 2014). In short, the cognitive measures used to create a profile that may demonstrate a PSW do not add value to the prediction of response to instruction (McGill et al., 2016; for a meta-analysis see Stuebing et al., 2015). Furthermore, they do not add additional information to aid in treatment decisions as evidenced by empirical data revealing that when significant effects are observed using cognitive measures, the effects are small (Burns et al., 2016; Miciak et al., 2016). Some authors have even gone so far as to suggest that given the lack of empirical evidence for specific cognitive processing and achievement interactions reliance on these patterns of strengths and weaknesses reflects a sort of illusory correlation and may lead to perceptual fallacies associated with hindsight bias, as reported in McGill and Busse (2017). Additionally, Williams and Miciak (2018) estimated that the expense associated with completing only the testing needed to assess a student’s cognitive profile as it relates to LD identification in terms of labor, including training of already licensed personnel, and materials would be in the range

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The Clinical Guide to Assessment and Treatment

of $200,000 to $250,000 per year for a medium-sized school district serving 17,400 K 12 students planning to conduct 108 evaluations (a number determined based on reported figures in a recent year). The combination of the monetary expense, time, and minimal payoff in terms of increased sensitivity for identification and instructional planning leads to serious ethical questions about whether these cognitive profiles should be assessed as a standard part of an LD evaluation. These findings led to changes that are embodied in the RTI framework that is intended to provide children with empirically validated reading instruction differentiated to meet their needs (Fuchs & Fuchs, 2006; Fuchs & Vaughn, 2012). It begins with effective classroom instruction, which is referred to as Tier I. Students in the general education classroom receive universal screenings, at which time those at risk are identified and are placed into small group instruction known as Tier II. In Tier II, students ideally receive interventions daily, and their progress is monitored on a more regular basis than Tier I (i.e., on a weekly or biweekly basis). As the individual student’s data are reviewed, it may be determined that he or she requires a Tier III intervention. Tier III requires more frequent monitoring and intensive intervention (Fletcher & Vaughn, 2009). This RTI framework is structured to run in schools; however, evidence is building to suggest that schools struggle to implement RTI (Fuchs & Vaughn, 2012). For example, Gilbert et al. (2012) observed 47% of students who received Tier II instruction in first grade to still have below-average-level achievement in word reading in third grade. Although the RTI framework holds considerable promise, it has not been demonstrated to be the solution to the conundrum of how to appropriately conduct assessments to identify children with LD.

Where to focus time and resources Before outlining our recommendations that could or should be used to aid in the determination of whether a child meets criteria for or has characteristics associated with an LD, it is important to take a step back and gain a better appreciation of the purposes and functions of assessment. In other words, we should ask ourselves, why are we conducting these extensive and expensive assessments with children? Assessments may be conducted for several reasons. From an individual standpoint, regardless of the setting in which a child is being assessed (i.e., school, hospital, clinic, or private practice), the ultimate goal is not categorical diagnosis, but the development of a clear plan and recommendations to help the child to succeed. Any assessments that are conducted to gain a better understanding of the extent to which a child is experiencing learning difficulties should focus on gathering information that can inform instruction. Determining that a child can be labeled and fit into a particular diagnostic category leaves the child, parents, and educators with the important question

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of what to do next. A diagnosis of a learning disorder, even when specifiers are included to indicate the areas of weakness (e.g., word reading, mathematical problem-solving, etc.), does not automatically dictate the specific skillbased instruction that should be pursued to enable the child to achieve better performance in an academic area, to have the requisite skills to benefit from instruction in other academic materials (e.g., history, science), and ultimately succeed in life. As such, assessments should not focus on categorical diagnostic determinations, but rather on creating a focused—yet comprehensive—description of the child’s skills profile. Thus the purpose of testing is to inform instructional decisions. The categorical distinction of LD is driven by policies stemming from IDEA and not the reality of LDs as documented by systematic empirical research. Many of the existing classification models rely on establishing some sort of discrepancy—often between ability, measured broadly (i.e., IQ achievement discrepancy models), or as strengths and weaknesses within cognitive processing areas (i.e., patterns of strengths and weaknesses models), and achievement in particular academic domains in attempts to determine if the child in question is exhibiting unexpected underachievement. As we summarized, previous research demonstrates that the reliability of categorical distinctions and the usefulness of such groupings in informing instructional interventions impacting treatment outcomes is severely lacking (e.g., Siegel & Hurford, 2019). Using set cut points as a way to determine whether an individual meets a criterion for a learning disorder also fails to appropriately consider the statistical reality of dealing with measurement error and continuous measures with shared correlations across attributes (Fletcher et al., 2018). Thus researchers have begun to promote hybrid approaches that use multiple criteria for learning disorder identification (e.g., Erbeli, Hart, Wagner, & Taylor, 2018; Fletcher & Miciak, 2017; Spencer et al., 2014). Next, we provide examples of the types of information to be gathered, and how such information can be used within a hybrid model for identification of characteristics of LDs.

Identifying learning disabilities: a hybrid model What defines an LD is (1) the severity of underachievement; (2) that the level of underachievement is unexpected because the individual has not responded adequately to instruction that is effective for most individuals; and (3) evidence of adaptive impairment, such as poor school achievement. In light of the limitations inherent to the discrepancy and PSW models, we advocate for a hybrid model of identification similar to recommendations proposed by a consensus group of researchers (Bradley, Danielson, & Hallahan, 2002) that has recently been advanced by Fletcher et al. (2018). This model can be used whether one is relying on the definition of LDs

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outlined in the reauthorization of IDEA (U.S. Department of Education, 2006) or the DSM-5 (American Psychiatric Association, 2013). This variation of a hybrid model requires three types of information to be gathered and synthesized in order to make recommendations for instructional plans: 1. Document inadequate response to appropriate instruction. 2. Document poor achievement in reading, mathematics, and/or written expression. 3. Ensure that other factors (e.g., sensory disorders, intellectual, limited proficiency in the language of instruction) are not the primary cause of low achievement.

Inadequate response to appropriate instruction RTI is a multitiered framework for early identification and support of students who may be struggling to learn. Key components of RTI include universal screening and progress monitoring. Universal screening allows schools to quickly ascertain which students are at risk of not meeting grade-level expectations in basic skill areas, such as reading and mathematics. Students deemed at risk would be immediately provided with intervention. Research suggests that screeners should measure skills that support the different areas of academic achievement implicated in LDs (e.g., letter knowledge, word recognition, vocabulary, mathematical operations, number sense). Progress monitoring allows educators to determine if a student is making progress in the intervention and to make instructional decisions based on this information. Research continues to suggest that the changes made should be to increase the explicitness of instruction and amount of instruction. Research does not support changing to a qualitatively different form of instruction. For example, the form of reading instruction does not change from general education to special education classrooms. What changes is the context and resources that can be allocated to deliver instruction, how direct the skill-based instruction that support the area of academic achievement is, and the number of opportunities to practice concepts and skills to consolidate learning, which in the case of reading includes opportunities to apply these skills to read and comprehend text (Fletcher et al., 2018). RTI is based on the premise that the student’s core (i.e., Tier I) instruction and any intervention are based on scientific evidence. This is crucial to ensure that all students receive appropriate research-based instruction, and for students suspected of LDs, this helps rule out lack of appropriate instruction as the primary cause of the learning difficulties. This increases the likelihood of correctly identifying students with a true LD. Multiple sources of information should be used when deciding if a student needs intervention. Along with parent and teacher observations,

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curriculum-embedded and curriculum-based measures (CBM) should be considered. Curriculum-embedded measurements are informal or formal tests teachers give students to check their understanding and mastery of skills that were recently taught. Information obtained from these assessments is invaluable because it is directly linked to the specific instruction being provided to a child. As such, they are an important way to gauge how well a child is learning concepts and skills being taught as part of instruction and/or intervention. CBM are brief, skill-based assessments often used to document skill development. These measures differ from curriculum-embedded measures in that they are not directly linked to the instruction a child is receiving. They measure a child’s ability to apply the skills that he or she should be learning. Curriculum-based measurements are commonly administered to all students within a grade three times a year as part of a universal screening process. Collecting these measurements helps educators to get a snapshot of their students and to identify which students are struggling and are at risk of continued and future academic underachievement. Students who are receiving interventions may be administered these measures more frequently as part of progress monitoring. Curriculum-based measurements allow teachers to plan instruction and to determine how a student is performing in relation to specified performance standards (i.e., benchmarks). Curriculum-based measurements can be normed based on a national or a local sample, which allows comparison of a student’s performance to the average student in his or her grade.

Poor achievement in reading, mathematics, and/or written expression Evidence of underachievement is typically determined through the use of a standardized, norm-referenced measure individually administered to the student. This allows for the comparison of a student’s basic skills to that of a representative national sample of others of the same age or grade. Examples of commonly used measures in their current edition include the following: the Woodcock Johnson Tests of Achievement, 4th Edition (WJ-IV ACH; Schrank et al., 2014), the Wechsler Individual Achievement Test, 3rd Edition (WIAT-III; Psychological Corporation, 2009), and the Kaufman Test of Educational Achievement, 3rd Edition (KTEA-3; Kaufman & Kaufman, 2014). It is important to note that these tests do not measure a student’s attainment of grade-level standards (i.e., goals for what students should be able to know and do at a particular grade level). Rather, they measure the student’s ability to perform basic skills (e.g., word reading, math calculation skills). These types of tests allow the examiner to determine a student’s relative standing in relation to peers (i.e., whether or not the student is performing similar to, below, or above the average student in the normative sample), and age-based norms are preferred due to having consistent intervals.

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Table 1.2 presents a nonexhaustive list of subtests from these batteries that may be used when conducting an assessment in which LD is suspected. There are three subcategories of reading disability: basic reading skills or word reading accuracy, reading fluency, and reading comprehension. When looking for basic reading skills deficits, examiners should measure both word reading skills and decoding (i.e., word attack skills), such as by using the Word Reading and Pseudoword Decoding subtests from the WIAT-III, or analogous offerings from another battery. Both word reading and decoding tests require the student to read a list of words of increasing difficulty. The words are presented in a list so that he or she cannot use the additional context provided by a passage of connected text to guess at words. Decoding skill is measured by having the student read a list of pseudowords, which are a proxy for real words, having the same regular spelling patterns of other real words. Pseudoword (i.e., nonsense word) reading removes the possibility that a student may have memorized a word by forcing them to apply decoding skills to identify the word. Reading fluency is typically measured by having the student read passages aloud while the examiner times him or her and marks any word reading errors, additions, or omissions, such as on the Oral Reading Fluency subtest on the WIAT-III. Rate of reading and reading accuracy are calculated. Reading comprehension can be measured in a few ways. A student may read passages and then answer questions about what was read (e.g., Reading Comprehension subtest from the WIAT-III) or a cloze procedure may be used, where the student reads a sentence or paragraph with a word missing and then supplies the missing word (e.g., Passage Comprehension subtest from the WJ-IV). A cloze type test presents a respondent with text with a missing word, and the goal is to provide an appropriate word to complete the text. Measures of reading comprehension vary greatly on the specific skills they measure and on the extent to which a respondent must read the text to correctly answer the question, which can impact if a child is identified with a reading-comprehension-based LD (for a discussion, see Keenan & Meenan, 2012). The first type of mathematics LD, math calculation skills, is typically measured by asking a student to complete a series of computations of increasing difficulty (e.g., addition, subtraction, multiplication, division, fractions). For example, on the Numerical Operations subtest from the WIATIII, a student would complete a problem such as 67 3 2 5 ___. Some achievement tests also include measures of computational fluency by determining how many addition, subtraction, or multiplication computations a student can complete within a time limit (e.g., Math Fact Fluency on the WJIV). The second type of math disability, math problem-solving (i.e., math reasoning), requires the examinee to listen to the problem, determine what information is relevant, determine what type of operation would be used, and then solve the problem. For example, “If Mrs. Smith baked four pies and

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TABLE 1.2 Nonexhaustive list of sample norm-referenced tests of achievement to aid in the identification of learning disabilities. Construct

Test/Subtest(s)

Task

WIAT-III Word Reading

Student is asked to read a list of real words of increasing difficulty.

WJ-IV ACH Letter Word Identification; KTEA-3 Letter and Word Recognition

Student is asked to identify letters and read a list of real words of increasing difficulty.

Phonological Decoding Accuracy

WIAT-III Pseudoword Decoding; WJ-IV ACH Word Attack; KTEA-3 Nonsense Word Decoding

Student is asked to read a list of pseudowords (i.e., nonsense words) of increasing difficulty.

Reading Fluency (Text Level)

WIAT-III Oral Reading Fluency; WJ-IV ACH Oral Reading

Student is asked to read aloud short passages. Each passage is timed and reading errors are recorded. Depending on subtest used passages are chosen based on current grade level of child.

WJ-IV ACH Sentence Reading Fluency

Student is asked to read aloud simple sentences. Each sentence is timed.

KTEA-3 Silent Reading Fluency

Student is asked to silently read simple sentences and mark yes or no to indicate if the sentence is true or false. Student has 2 min to complete as many items as possible.

WIAT-III Reading Comprehension

Student is asked to read passages (aloud or silently) and answer open-ended questions about what was read.

WJ-IV ACH Passage Comprehension

Student is asked to silently read a passage with a word missing and then orally provide the missing word.

WJ-IV ACH Reading Recall

Student is asked to silently read a story and then freely recalls as much of the story as he or she can.

Reading Word Reading Accuracy

Reading Comprehension

(Continued )

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The Clinical Guide to Assessment and Treatment

TABLE 1.2 (Continued) Construct

Test/Subtest(s)

Task

KTEA-3 Reading Comprehension

On early items, student is asked to match a symbol or word(s) with a picture. Student is then asked to read and follow simple instructions. For later items, student is asked to read passages of increasing difficulty and answer questions about them. For the most difficult items, student is asked to rearrange five sentences into a paragraph and then answer questions about the paragraph. This test of silent reading comprehension is untimed.

Math Calculations

WIAT-III Numerical Operations; WJ-IV Calculations; KTEA-3 Math Computation

Student is asked to complete math problems of increasing difficulty (e.g., addition, subtraction, multiplication, division, fractions, decimals, square roots, exponents, and algebraic expressions, etc.) in a response booklet.

Math Computational Fluency

WJ-IV ACH Math Fact Fluency; KTEA-3 Math Fluency

Student is asked to complete addition, subtraction, and multiplication problems within a time limit.

Math Problem Solving

WIAT-III Math Problem Solving; WJ-IV ACH Applied Problems; KTEA-3 Math Concepts and Applications

Student is asked to listen to problems, determine what information is relevant, determine what type of operation would be used, and solve the problem. Some problems may involve applying mathematical concepts to real world situations.

WIAT-III Spelling; WJ-IV ACH Spelling

Student is asked to write letters that represent a sound and then to spell real words of increasing difficulty that are dictated by the examiner.

Mathematics

Written expression Spelling (may also be relevant when assessing reading abilities)

(Continued )

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TABLE 1.2 (Continued) Construct

Test/Subtest(s)

Task

Written Expression

WIAT-III Sentence Composition

Student is provided with two sentences and asked to write one “good” sentence that combines them. Later, student is given a word and asked to write a complete sentence using that word. Errors in semantics, grammar, and mechanics are recorded.

WJ-IV Writing Samples

Student is asked to produce meaningful written sentences in response to a variety of tasks.

KTEA-3 Written Expression

On early items, student is asked to trace and copy letters, and to write dictated letters, words, and a sentence. Older students are asked to complete a variety of writing tasks in the context of a story, including writing dictated sentences, adding punctuation and capitalization, filling in missing words, completing and combining sentences, writing compound and complex sentences, and writing an essay based on the story.

WIAT-III Essay Composition

Student is given 10 min to write an essay on a given topic. Essay is scored based on theme development and text organization.

WJ-IV ACH Sentence Writing Fluency

Student is asked to compose and write legible, simple sentences with acceptable English syntax.

KTEA-3 Writing Fluency

Student is asked to write one sentence for each picture presented in a response booklet. Student has 5 min to complete as many sentences as possible.

Writing Fluency

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The Clinical Guide to Assessment and Treatment

gave one to her neighbor, how many pies would she have left?” The Math Problem Solving subtest from the WIAT-III can be administered to assess this skill. Written expression is measured in a variety of ways. Writing fluency measures require a student to write a certain amount within a certain timeframe. Untimed writing measures may require a child to combine multiple sentences into one or to respond to a writing prompt by writing an essay. Spelling may also be assessed within these measures or with a separate spelling subtest, in which a student hears a word used in a sentence and then writes the word.

Evidence that other factors are not the primary cause Federal regulations such as IDEA (U.S. Department of Education, 2006) require schools to consider exclusionary factors before identifying a student with an LD and similar language about exclusionary factors is used in the DSM-5 (American Psychiatric Association, 2013). (See Table 1.1 for a comparison of the specific wording differences across these sets of guidelines.) Regardless of the setting in which an assessment of a potential LD is occurring, screening children for vision or hearing problems is important. Children who do not pass school-based screenings may be referred for additional evaluation by medical personnel. Additionally, medical personnel typically identify potential motor disabilities. Children with fine motor impairments, for example, may have difficulty with the motor components of writing. Thus their difficulties with written expression may be a direct result of their motor disability rather than a true LD. Although intellectual assessment is not required or recommended for specific LD identification under either IDEA or within the DSM-5, when this is suspected, intelligence tests may need to be used. It is important to choose measures that are culturally and linguistically appropriate. These, along with measures of adaptive behavior (i.e., measures of conceptual, social, and practical skills), are necessary for the identification of an intellectual disability. To be identified with an intellectual disability, the child must perform significantly below the mean on both the intelligence test and the adaptive behavior measure. These students also have significant struggles with achievement, but their academic achievement skills are typically far below that of the typical child who is the same age, and multiple areas of achievement are affected (thus not “specific” to certain areas). An explicitly named exclusionary factor that is unique to the DSM-5 is a lack of proficiency in the language of academic instruction. While this factor is not specified in the list of exclusionary criteria in the federal regulations, states have chosen to include it in their guidelines. For example, in the Tennessee Standards for Special Education Evaluation and Eligibility from the Tennessee Department of Education (2017), the team is required to

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determine that “underachievement is not primarily the result of Limited English Proficiency” (among other factors). Determining that a child lacks proficiency in the language of instruction, and that deficiency is the primary cause of the child’s learning difficulties has its own unique set of challenges. Students who are just learning to speak a new language will find it even more difficult to read or write in that new language. While testing students’ academic skills in their native language would be ideal, it is often not feasible. Comparing the student’s progress to others who have been instructed in the new language for a similar amount of time and who have similar learning experiences can be helpful. Parents can also provide insight regarding family history of learning problems and by providing information on how the child is performing in relation to siblings. Some students have significant emotional and/or behavioral difficulties that interfere with their learning. In clinic settings, this may involve additional diagnoses of mood and anxiety disorders, as specified in the DSM-5 (American Psychiatric Association, 2013). Under IDEA, the analogous exclusionary factors are subsumed under the disability category of emotional disturbance (U.S. Department of Education, 2006). Identification of this disability is beyond the scope of this chapter, but in general, it is typically identified through multiple measures that may include direct and indirect observations in multiple settings, a comprehensive social history, and parent, teacher, and/or self-report ratings, and it does not necessarily require a formal psychiatric diagnosis under DSM-5 criteria. In IDEA, school teams must rule out the above exclusionary criteria as primary causes of a student’s underachievement. However, this can be a challenge for multiple reasons, including the fact that LD has been shown to be comorbid with other conditions, especially if it is not identified or addressed with appropriate intervention (e.g., Willcutt & Pennington, 2000b). Because learning problems that are unidentified or improperly treated can lead to frustration, anxiety, and stress, when these are present to a significant degree, determining which issue came first (and is thus primary) is difficult.

Take-home messages and future directions The original premise behind the conceptualization of LDs was to guarantee access to educational opportunities for children exhibiting unexpected underachievement. Such ideas still stand today and have arguably been extended to a focus on providing educational opportunities for all children as all children are capable of learning. This is a fundamental shift in thinking about potential. As we have illustrated, the construct of LD was based on the notion that some children are capable of learning while others are not. What has been clearly demonstrated is that all children are capable of learning and benefit from the same form of instruction. Some children respond to this

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instruction at a slower rate and require the instruction to be even more direct and sustained. As such, there is a move away from conceptual models that rely on demonstrating a discrepancy between a child’s capability and achievement. There is also a push for conceptualizing LDs not as a discontinuous and categorical entity, but as a term to describe individuals along a continuum of achievement. In this regard, LDs reflect a potentially severe level of underachievement. Subsequently, the focus is on how to provide all students with direct instruction in reading, mathematics, and writing utilizing empirically validated evidence-based methods because all children can benefit from such practices. This is part of the idea behind the RTI models, as well as continued and growing interest in determining what and how best to teach children basic skills in the general education classroom. Likewise, the components used to define and describe LDs (i.e., achievement, capability, unexpectedness, and cause), upon their initial conceptualization, are still the same components highlighted in legislation and diagnostic manuals today, but the operationalization of these constructs has morphed with an increased understanding facilitated by the continually growing body of empirical evidence. Currently, in order to adhere to state and federal regulations and fully utilize knowledge obtained from extant empirical research, best practices for LD identification should focus on a hybrid model that incorporates the three procedural steps we described above regardless of the setting in which the assessment is occurring. A hybrid model motivates the need to collect data to demonstrate that a child is responding inadequately to appropriate instruction, has poor achievement in one or multiple academic domains, and other factors such as sensory disorders are not the primary contributing factor potentially explaining the child’s low level of achievement. Then the data that have been gathered should be synthesized, not only to allow for a child to potentially be identified as having an LD, but to provide crucial recommendations for instructional plans. Thus the hybrid model blends the RTI framework with additional data obtained through individualized testing. However, collecting cognitive measures and conducting IQ testing is neither warranted nor needed. This focus is shared across approaches prescribed in different settings (schools with IDEA regulations and clinicians referring to DSM-5). However, what differentiates the operationalization of this hybrid model is that the content of these individually administered tests are focused to provide a deeper understanding of a child’s skills in the domain of interest. This deep dive into specific skills is done in a purposeful way in order to obtain information about the child’s current standing with regard to content that the child has or will be taught through both traditional norm-referenced measures and CBM. Such data allow for instruction to be tailored to the child’s skill deficits with the understanding that doing so may increase the likelihood that the child is able to master the concepts being taught and develop

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proficiency as skills are continually practiced. Again, whether a child is being assessed in a school or clinic setting, the primary goal of assessment should be to develop treatment recommendations. A deep dive into the child’s skills within the domain of interest allows for such treatment recommendations to be more precisely matched to the individual’s skill deficits. Therefore it is our hope that clinicians and students walk away from reading this chapter with a broader understanding of the conceptualization of LDs and the interrelationship between conceptualization, assessment protocols, and intervention. Regardless of the setting individuals conducting assessments are in, there is value to be gained from using the techniques proposed within the hybrid model. The hybrid model and its enhanced ability to inform instruction continues to meet the mandate implied by the first iteration of legislation regarding LDs, which is to provide children with access to educational opportunities. The hybrid model and the substantial amount of research that has contributed to its development over time hopefully increase the successes that a child experiences when provided with these educational opportunities. As always, there is still more to learn, and models can and should continue to be refined as more evidence becomes available. In this regard, there are some open questions that one should consider when working with children with LDs. One component of the hybrid model relies on the same data and infrastructure as RTI models in order to ascertain the instruction that is provided to children and their response to it. This need for response to instruction data carries with it the implication that LDs must be identified within schools. However, emerging research suggests that all schools may not yet be running the RTI framework effectively and efficiently within lower grades, and even less data are available about the efficacy of RTI approaches for upper grades (Fuchs & Fuchs, 2017). A further discussion on these concerns is outside the scope of this chapter. The concerns are mentioned to indicate that, at least at the current time, the identification of LDs may be hindered if left entirely to schools. On one hand, this leads to questions about schools having the resources to fully embrace and implement a hybrid approach like we propose on their own. It also leads to a discussion on how to remediate core instruction programs within Tier I and leverage empirically validated approaches to the differentiation of core instruction (e.g., Connor & Morrison, 2016). On the other hand, this also leads to a discussion on how other groups, such as parents or clinicians, can and should contribute to the process. The hybrid model relies on both RTI data and that deep dive into a child’s skill profile. As described above, the deep dive can involve administering normreferenced tests of achievement, as well as curriculum-based measurements and curriculum-embedded measures to inform intervention plans. Consequently, school teams and private clinicians need to have access to and a full understanding of how to interpret RTI data. Additional training in RTI

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implementation, the associated measurements that are directly and indirectly tied to the instruction the child receives, and the science of the development of academic skills impacted by LDs would likely be beneficial to professionals across all settings in order to be able to fully utilize the rich data that we propose collecting. Additional research likely also needs to be conducted to further refine the measures that are available to make them better calibrated for the purpose of tailoring instruction to a child’s needs. Parents who have the resources to do so may consult trained clinicians to assist in acquiring this information from their children if resources are lacking in the child’s school itself. Yet these individuals also need training in and access to measures from the school in order to fully embrace the hybrid model as a way to guide their recommendations. However, consulting practitioners in settings outside of the school undermines the intent of equity that motivated the initial laws and guidelines surrounding LD if, as a society, we do not strive to improve school-based identification and intervention systems for all children. The content and quality of assessment and instruction depends on the training of those doing it and the resources available to them, while working within the imperfect systems that are in place and which they may have little control over. It is necessary for the different stakeholder groups, including school psychologists, classroom teachers and interventionists, school administrators, parents, psychologists in private practice or clinic settings, and legislative officials to work together to utilize their shared knowledge bases to maximize the potential of a hybrid model to improve the identification and intervention systems for all children.

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Hammill, D. D. (1990). On defining learning disabilities: An emerging consensus. Journal of Learning Disabilities, 23(2), 74 84. Available from https://doi.org/10.1177/ 002221949002300201. Hinshaw, S. P. (1992). Externalizing behavior problems and academic underachievement in childhood and adolescence: Causal relationships and underlying mechanisms. Psychological Bulletin, 111(1), 127 155. Hinshelwood, J. (1917). Congenital word-blindness. The Lancet, 190(4922), 980. Hoskyn, M., & Swanson, H. L. (2000). Cognitive processing of low achievers and children with reading disabilities: A selective meta-analytic review of the published literature. School Psychology Review, 29(1), 102 119. Kaufman, A. S., & Kaufman, N. L. (2014). Kaufman test of educational achievement (third). San Antonio, TX: Pearson. Keenan, J. M., & Meenan, C. E. (2012). Test differences in diagnosing reading comprehension deficits. Journal of Learning Disabilities, 47(2), 125 135. Available from https://doi.org/ 10.1177/0022219412439326. Kirk, S. A. (1963). Behavioral diagnosis and remediation of learning disabilities. Conference on Exploring Problems of the Perceptually Handicapped Children, 1, 1 23. Kirk, S. A., & Gallagher, J. J. (1979). Educating exceptional children (3rd ed). Boston, MA: Houghton Mifflin. Klassen, R., Tze, V., & Hannok, W. (2011). Internalizing problems of adults with learning disabilities: A meta-analysis. Journal of Learning Disabilities. Available from https://doi.org/ 10.1177/0022219411422260. Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A definition of dyslexia. Annals of Dyslexia, 53(1), 1 14. Available from https://doi.org/10.1007/s11881-003-0001-9. McCandliss, B. D., & Noble, K. G. (2003). The development of reading impairment: A cognitive neuroscience model. Mental Retardation and Developmental Disabilities Research Reviews, 9(3), 196 204. Available from https://doi.org/10.1002/mrdd.10080. McGill, R. J., & Busse, R. T. (2017). When theory trumps science: A critique of the PSW model for SLD identification. Contemporary School Psychology, 21(1), 10 18. Available from https://doi.org/10.1007/s40688-016-0094-x. McGill, R. J., Styck, K. M., Palomares, R. S., & Hass, M. R. (2016). Critical issues in specific learning disability identification: What we need to know about the PSW model. Learning Disability Quarterly, 39(3), 159 170. Available from https://doi.org/10.1177/ 0731948715618504. Mercer, C. D., Jordan, L., Allsopp, D. H., & Mercer, A. R. (1996). Learning disabilities definitions and criteria used by state education departments. Learning Disability Quarterly, 19, 217 232. Miciak, J., Stuebing, K. K., Vaughn, S., Roberts, G., Barth, A. E., & Fletcher, J. M. (2014). Cognitive attributes of adequate and inadequate responders to reading intervention in middle school. School Psychology Review, 43(4), 407 427. Available from https://doi.org/ 10.17105/SPR-13-0052.1. Miciak, J., Taylor, P., Denton, C. A., & Fletcher, J. M. (2015). The effect of achievement test selection on identification of learning disabilities within a patterns of strengths and weaknesses framework. School Psychology Quarterly, 30(3), 321 334. Available from https:// doi.org/10.1037/spq0000091. Miciak, J., Williams, J. L., Taylor, W. P., Cirino, P. T., Fletcher, J. M., & Vaughn, S. (2016). Do processing patterns of strengths and weaknesses predict differential treatment response?

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Journal of Educational Psychology, 108(6), 898 909. Available from https://doi.org/ 10.1037/edu0000096. Mugnaini, D., Lassi, S., La Malfa, G., & Albertini, G. (2009). Internalizing correlates of dyslexia. World Journal of Pediatrics: WJP, 5(4), 255 264. Available from https://doi.org/ 10.1007/s12519-009-0049-7. Office of Special Education and Rehabilitative Services, U. S. D. of E. (2017). 39th Annual report to congress on the implementation of the individuals with disabilities education act, 2017. Washington, DC. Orton, S. T. (1928). Specific reading disability - Strephosymbolia. Journal of the American Medical Association, 90, 1095 1099. Psychological Corporation. (2009). WIAT III: Wechsler Individual Achievement Test (Third). San Antonio, TX: Author. Pugh, K. R., Mencl, W. E., Jenner, A. R., Katz, L., Frost, S. J., Lee, J. R., . . . Shaywitz, B. A. (2000). Functional neuroimaging studies of reading and reading disability (developmental dyslexia). Mental Retardation and Developmental Disabilities Research Reviews, 6(3), 207 213. Available from https://doi.org/10.1002/1098-2779(2000) 6:3 , 207::AID-MRDD8 . 3.0.CO;2-P. Richardson, S. O. (1992). Historical perspectives on dyslexia. Journal of Learning Disabilities, 25(1), 40 47. Available from https://doi.org/10.1177/002221949202500107. Schrank, F. A., Mather, N., & McGrew, K. S. (2014). Woodcock-Johnson IV Tests of Achievement (IV). Rolling Meadows, IL: Riverside. Schraven, J., & Jolly, J. L. (2010). Section 504 in American public schools: An ongoing response to change. American Educational History Journal, 37(2), 419 436. Siegel, L. S. (1989). IQ is irrelevant to the definition of learning disabilities. Journal of Learning Disabilities, 22(8), 469 478. Available from https://doi.org/10.1177/002221948902200803. Siegel, L. S. (1999). Issues in the definition and diagnosis of learning disabilities: A perspective on Guckenberger v. Boston University. Journal of Learning Disabilities, 32(4), 304 319. Available from https://doi.org/10.1177/002221949903299405. Siegel, L. S., & Hurford, D. P. (2019). The case against discrepancy models in the evaluation of dyslexia. Perspectives on Language and Literacy, 45, 23 28. Spencer, M., Wagner, R. K., Schatschneider, C., Quinn, J. M., Lopez, D., & Petscher, Y. (2014). Incorporating RTI in a hybrid model of reading disability. Learning Disability Quarterly, 37 (3), 161 171. Available from https://doi.org/10.1177/0731948714530967. Stanovich, K. E. (1999). The sociopsychometrics of learning disabilities. Journal of Learning Disabilities, 32(4), 350 361. Available from https://doi.org/10.1177/002221949903200408. Stuebing, K. K., Barth, A. E., Molfese, P. J., Weiss, B., & Fletcher, J. M. (2009). IQ is not strongly related to response to reading instruction: A meta-analytic interpretation. Exceptional Children, 76(1), 31 51. Stuebing, K. K., Barth, A. E., Traham, L. H., Reddy, R. R., Miciak, J., & Fletcher, J. M. (2015). Are child cognitive characteristics strong predictors of responses to intervention? A metaanalysis. Review of Educational Research, 85(3), 395 429. Available from https://doi.org/ 10/3102/0034654314555996. Stuebing, K. K., Fletcher, J. M., LeDoux, J. M., Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2002). Validity of IQ-Discrepancy classifications of reading disabilities: A meta-analysis. American Educational Research Journal, 39(2), 469 518. Tanaka, H., Black, J. M., Hulme, C., Stanley, L. M., Kesler, S. R., Whitfield-Gabrieli, S., . . . Hoeft, F. (2011). The brain basis of the phonological deficit in dyslexia is independent of

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IQ. Psychological Science, 22(11), 1442 1451. Available from https://doi.org/10.1177/ 0956797611419521. Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(7), 767 773. Available from https://doi.org/10.1038/nn1065. U.S. Department of Education. (2004). Individuals with disabilities education improvement Act (No. 20 U.S.C. y 1400). Washington, DC: Author. U.S. Department of Education. (2006). 34 CFR Parts 300 and 301: Assistance to states for the education of children with disabilities and preschool grants for children with disabilities. Final Rules. Federal Register, 71, 46540 46845. Vellutino, F. R., Scanlon, D. M., & Lyon, G. R. (2000). Differentiating between difficult-toremediate and readily remediated poor readers: More evidence against the IQ-achievement discrepancy definition of reading disability. Journal of Learning Disabilities, 33(3), 223 238. Vellutino, F. R., Scanlon, D. M., Sipay, E. R., Small, S. G., Pratt, A., Chen, R., & Denckla, M. B. (1996). Cognitive profiles of difficult-to-remediate and readily remediated poor readers: Early intervention as a vehicle for distinguishing between cognitive and experiential deficits as basic causes of specific reading disability. Journal of Educational Psychology, 88 (4), 601 638. Wender, P. H. (1975). The minimal brain dysfunction syndrome. Annual Review of Medicine, 26, 45 62. Willcutt, E. G., & Pennington, B. F. (2000a). Comorbidity of reading disability and attentiondeficit/hyperactivity disorder: Differences by gender and subtype. Journal of Learning Disabilities, 33(2), 179 191. Willcutt, E. G., & Pennington, B. F. (2000b). Psychiatric comorbidity in children and adolescents with reading disability. Journal of Child Psychology and Psychiatry, 41(8), 1039 1048. Willcutt, Erik G., Betjemann, R. S., McGrath, L. M., Chhabildas, N. A., Olson, R. K., DeFries, J. C., & Pennington, B. F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46, 1345 1361. Available from https://doi.org/10.1016/j.cortex.2010.06.009. Williams, J., & Miciak, J. (2018). Adoption costs associated with processing strengths and weaknesses methods for learning disabilities identification. School Psychology Forum: Research in Practice, 12(1), 17 29.

Chapter 2

Assessment and diagnosis of attention-deficit/hyperactivity disorder Arthur D. Anastopoulos and Kaicee K. Beal Department of Human Development & Family Studies, University of North Carolina Greensboro, NC, United States

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by developmentally deviant levels of inattention and hyperactivity-impulsivity that first appear in childhood, persist across the life span, and cause clinically significant impairment(s) in multiple domains of daily life functioning (American Psychiatric Association, 2013). Approximately 6.5% of the general child population exhibits ADHD (Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014). Given its prevalence, it should come as no surprise that ADHD is one of the most common reasons that children are referred to mental health and medical health care professionals for evaluation and treatment (Barkley, 2015). Because accurate identification of ADHD is a necessary first step to guide the selection and implementation of evidence-based treatments, this chapter will focus on the assessment and diagnosis of ADHD in children. It will begin with a brief review of the historical unfolding of this disorder, from its earliest formal beginnings to its current appearance in the health care field as ADHD. The epidemiology, etiology, and developmental course will be presented next. This will be followed by a detailed description of the way in which ADHD presents itself clinically, in terms of the variability in its primary symptom presentation, its cooccurring features, and its impact on daily functioning. Against this background, evidence-based guidelines for diagnosing ADHD will be discussed, including suggestions for making differential diagnoses and for handling discrepancies across different sources of evaluation data.

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00002-2 © 2020 Elsevier Inc. All rights reserved.

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Historical context ADHD is not a new clinical phenomenon. It is, however, the most recent in a long line of diagnostic labels that have been used to describe children who display developmentally deviant and impairing symptoms of inattention, impulsivity, and/or hyperactivity. From the early 1900s until the 1970s, most of the diagnostic labels applied to this pattern of behavior emphasized a presumed etiological basis. This included diagnostic classifications such as postencephalitic behavior disorder, organic drivenness, minimal brain damage, and minimal brain dysfunction. In the absence of empirical evidence to support the validity of such terms, the field ultimately shifted away from etiologically based diagnostic labels in favor of symptom-based descriptions. This transition began during the 1950s and 1960s with the arrival of hyperkinetic-impulse disorder, hyperactive child syndrome, and hyperkinetic reaction of childhood. Influenced by the landmark research of Virginia Douglas (1972), the field subsequently shifted away from diagnoses emphasizing hyperactive behavior to symptom-based labels highlighting inattentive features. Hence, the terms attention-deficit disorder with hyperactivity, attention deficit disorder without hyperactivity (ADD), and attention-deficit hyperactivity disorder (ADHD) came into use. One point of historical clarification is in order regarding the confusion that sometimes exists with respect to using ADD versus ADHD. The term ADD made its entrance into the field when the third edition of the Diagnostic and Statistical Manual of Mental Disorders was published (DSMIII; American Psychiatric Association, 1980). ADHD was first introduced in the revised DSM-III (American Psychiatric Association, 1987), reappeared in the fourth edition of the DSM (American Psychiatric Association, 1994), and continues to be the diagnostic term of choice in the current fifth edition of the DSM (American Psychiatric Association, 2013). Although ADHD replaced ADD, some individuals continue to use ADD, sometimes out of habit, other times due to a common misunderstanding that ADHD does not accurately capture children primarily displaying inattentive features in the absence of hyperactivity. Because the DSM-5 version of this disorder is based on contemporary theory and empirical evidence that allows for specifying an inattentive clinical presentation, and for historical reasons as well, use of the term ADHD is clearly preferred.

Epidemiology As is true for any other mental health disorder, there are many factors that can impact estimates of the prevalence of ADHD among children. This includes, for example, whether DSM-5 or other diagnostic criteria were employed, whether full or partial diagnostic criteria were met, the source of the sample (clinic-referred vs community), the demographic characteristics

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of the sample (age, gender, race/ethnicity, urban/rural), the informants (parents, teachers, child), and the methods used to collect epidemiological data (interviews, rating scales, psychological testing). The results of single studies can vary a great deal due to cross-study methodological differences, thereby creating confusion about the prevalence of ADHD. Because meta-analytic investigations simultaneously examine large numbers of studies and take cross-study variability into account, prevalence estimates drawn from such investigations are likely to be more accurate. One of the first such metaanalytic efforts that focused its examination on studies using DSM-IV criteria revealed childhood ADHD prevalence rates ranging from 5.9% to 7.1% (Willcutt et al., 2012). A subsequent meta-analysis reported results consistent with these findings, further suggesting that the overall rate of ADHD within the general population is relatively stable over time (Polanczyk et al., 2014). In addition to showing temporal stability, ADHD prevalence rates have been reported to be quite similar across many different cultures based upon analyses of epidemiological research findings obtained from children in North America, Europe, South America, and Asia (Barkley, 2015). Many studies show that rates of ADHD differ according to gender, with male children outnumbering female children by a factor of 2:1 in community samples (Willcutt et al., 2012) and as high as 6:1 or 10:1 in clinic-referred samples (Busch et al., 2002). Although such gender differences have been consistently reported, questions remain as to whether these represent true developmental differences between boys and girls versus an alternative explanation postulating that these differences instead arise from unchecked methodological bias (Maniadaki & Kakouros, 2018). To this latter point, recent findings have suggested that informant gender may come into play, such that boys may be more likely to be identified as being at risk for ADHD when rated by female teachers and mothers than by male teachers and fathers (Anastopoulos et al., 2018).

Etiology Although several lines of evidence point toward biological factors being involved in the etiology of ADHD, our understanding of what causes ADHD is far from complete. More than likely, there are many biological pathways that can lead to ADHD, interacting with various environmental factors along the way (Nigg, Nicholas, & Burt, 2010). Among these biological factors, a genetic pathway has been identified as being intricately involved in ADHD (Faraone et al., 2005; Willcutt et al., 2006). To a lesser degree, prenatal and birth complications may also be involved. Together, such findings suggest that for most children, ADHD initially stems from an inborn biological predisposition that alters neurochemical and neurophysiological functioning in ways that lead to the phenotypic expression of this disorder (Barkley, 2015).

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Despite the preponderance of evidence supporting the role played by genetics and neurobiological factors, other theories of what causes ADHD periodically make their way into the public’s eye. This includes, for example, theories suggesting that the clinical presentation of ADHD can best be explained by an underlying sensory processing disorder, excessive consumption of sugar, poor parenting, chaotic home environments, or watching too much television. Although many of these circumstances are intuitively appealing and can be associated with ADHD, there is insufficient evidence from well-controlled studies to suggest that they play a causal role (Barkley, 2015). They may, however, interact with genetic and neurobiological factors to intensify inattention and hyperactivity-impulsivity symptoms in a child with a preexisting ADHD condition.

Developmental course ADHD first appears during childhood, operationally defined by DSM-5 as prior to 12 years of age (American Psychiatric Association, 2013). For many children, ADHD symptoms are first noticed around 3 4 years, but can occur as early as infancy or as late as middle school, depending on the severity of ADHD, as well as the presence or absence of various protective factors (e.g., high intelligence) and risk factors (e.g., economic disadvantage) that may moderate the timing of its appearance. Although once thought of as a childhood-only disorder, there is ample research evidence from longitudinal studies documenting that ADHD is a chronic condition that persists throughout the life span (Barkley, Murphy, & Fischer, 2008; Weiss & Hechtman, 1986). Hyperactive-impulsive symptoms typically arise in early childhood, whereas inattentive symptoms tend to be first noticed upon entrance into formal schooling. Across development, inattention difficulties typically remain relatively stable, in contrast with hyperactive-impulsive symptoms that often diminish in frequency and severity, particularly during the transition from childhood to adolescence.

Clinical presentation The way in which ADHD presents itself clinically is by no means the same for every child. From one child to the next, ADHD can vary in terms of not only the relative distribution of its inattentive and hyperactive-impulsive symptoms but also the frequency and severity of such features. Even within the same child, the way in which ADHD expresses itself can change over time, as was noted in the previous discussion of its developmental course. Another important clinical consideration is that ADHD is also frequently accompanied by cooccurring or comorbid diagnostic conditions. Moreover, its impact may be felt in any one or combination of various daily life domains, including educational functioning, family interactions, and social

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functioning with peers. Being aware of this variability in clinical presentation is critically important to the process of conducting diagnostic assessments and planning treatment.

Situational variability of primary symptoms One of the most interesting and, at the same time, confusing aspects of ADHD is its situational variability; that is, the fact that its inattentive and hyperactive-impulsive symptoms can fluctuate a great deal from day to day, and sometimes even from moment to moment, in response to changes in situational demands (Zentall & Leib, 1985). As one parent once stated, “Yes, I know my child has trouble focusing in schoolwork, but how can he have ADHD if he can pay attention to video games for hours on end?” Implicit in this question is an all-or-nothing line of thinking—that is, one cannot be diagnosed with ADHD unless its symptoms are present all the time. Nothing could be further from the truth. Children with ADHD can pay attention, can sit still, and can think before they act. Unlike other typically developing children, however, they cannot do these things consistently across time and settings, due to their diminished capacity for regulating their behavior under differing situational demands. To this point, research has shown that ADHD symptoms are much less likely to occur in situations characterized by novelty, one-on-one supervision, and/or feedback that is immediate, frequent, and salient (Barkley, 2015). Under opposite conditions, ADHD symptoms are much more likely to appear. In this way, ADHD can be thought of as a disorder of performance variability rather than inability. Awareness of this phenomenon makes it easier to understand the inconsistent performance often displayed by children with ADHD.

Cooccurring features Another factor contributing to the variability in clinical presentation is the fact that ADHD is often accompanied by cooccurring or comorbid diagnostic conditions and other clinical features. Up to 60% of youth with ADHD have at least one cooccurring condition (Pliszka, 2014). Oppositional defiant disorder (ODD) is the most prevalent of these, with roughly 40% of children and adolescents with ADHD meeting full diagnostic criteria for ODD. Of this number, another 25% may develop more serious antisocial behavior in the form of conduct disorder (CD). Approximately 25% 35% of children and adolescents with ADHD may also meet criteria for a diagnosable anxiety disorder, the most common of which is generalized anxiety disorder (Elia, Ambrosini, & Berrettini, 2008). Rates of depression in children and adolescents with ADHD have also consistently been reported to be high, falling between 25% and 30% (Angold, Costello, & Erkanli, 1999; Pliszka, 2014). Although not formally diagnosed, many children with ADHD can also

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display clinically significant executive functioning deficits (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005) and emotion regulation difficulties (Anastopoulos et al., 2011) requiring therapeutic attention. In combination with ADHD, cooccurring disorders and features can greatly increase the severity of a child’s overall psychosocial impairment, thereby making the prognosis for such individuals less favorable. In view of such circumstances, evaluations of children referred for ADHD concerns should routinely include assessment procedures directly addressing cooccurring diagnostic conditions and other features.

Functional impairment The combined impact of ADHD and its comorbid features increases the risk for a myriad of psychosocial difficulties across the life span (Barkley et al., 2008). For example, preschoolers with ADHD place enormous caretaking demands on their parents and frequently display aggressive behavior when interacting with siblings or peers. Difficulties acquiring academic readiness skills may be evident as well, but these tend to be of less clinical concern than the family or social problems that preschoolers present. As children with ADHD move into the elementary school years, academic problems take on increasing importance. Most clinic-referred children with ADHD exhibit academic underachievement relative to their aptitude, including lower test scores, higher rates of course failure, more suspensions and expulsions, greater use of special education services, and lower grade point averages (DuPaul & Stoner, 2015). School-aged children with ADHD may also experience impairment at home, particularly in terms of relationships with family members. Frequently, parents find themselves involved in resolving various school, peer, and sibling difficulties, which occur throughout childhood and in adolescence. Thus many parents experience considerable stress in their parenting roles, especially when comorbid ODD features are present (Mash & Johnston, 1990). Families of adolescents with ADHD are also characterized by greater intrafamily conflict, negativity, and parenting stress than control families (Barkley, Guevremont, Anastopoulos, & Fletcher, 1992; Johnston & Mash, 2001). Compounding difficulties between parents and their children with ADHD are the findings that biological mothers and fathers of children with ADHD may also have ADHD themselves and are more likely to experience other psychological conditions, including depression, anxiety, learning disabilities, conduct problems, and antisocial behavior (Barkley, 2015). In terms of social functioning, children and teens with ADHD may experience a pattern of rejection in social situations, as they tend to exhibit poor social and communication skills and have fewer close friends (Bagwell, Molina, Pelham, & Hoza, 2001; Mikami, 2010). Adolescents with ADHD are also at risk for impairment in terms of driving behavior; use and abuse of

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alcohol, nicotine, and drugs; and risky sexual behavior (Flory, Molina, Pelham, Gnagy, & Smith, 2006; Molina, Marshal, Pelham, & Wirth, 2005).

Guidelines for diagnosing attention-deficit/hyperactivity disorder The clinical presentation of ADHD can be quite variable from one child to the next, as well as over time within the same child. Such differences in ADHD symptom presentation, cooccurring features, and patterns of impairment necessitate a broader approach to the assessment of children referred for ADHD concerns. ADHD evaluations should seldom if ever be limited to the assessment of just ADHD. Instead, evaluations of children referred for ADHD concerns would seem better conceptualized as comprehensive psychological evaluations, within which (1) ADHD is the primary but not exclusive focus, and (2) the multiple systems within which the identified child functions (e.g., family, school, peers) are taken into consideration to better capture the variability in clinical presentation that may occur.

Diagnostic criteria and classification Within North America, the various editions of the DSM published by the American Psychiatric Association have served the purpose of facilitating communication among practitioners, educators, researchers, insurance companies, and governmental agencies. Outside of North America, this same purpose has been served by the various versions of the International Classification of Diseases (ICD) written by the World Health Organization (WHO). As mentioned earlier, the diagnostic criteria for ADHD and various other mental disorders are now contained in the DSM-5 (American Psychiatric Association, 2013). After many years of differing with the North American perspective on children who display developmentally deviant levels of inattention and hyperactivity-impulsivity, the most recently published revision of the ICD system, ICD-11 (WHO, 2018) now includes ADHD as one of its categories and presents diagnostic criteria in line with those used for ADHD in DSM-5. For the purposes of this chapter and its intended audience, the DSM-5 criteria will now be described and used to illustrate the diagnostic decision-making process for the remainder of this chapter. As described in greater detail in DSM-5, there are five major criteria that must be met to diagnose ADHD in individuals of any age. The first of these, Criterion A, stipulates that there be evidence of a “persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development.” Operationally for children under 17 years of age, this means that there needs to be evidence of at least six out of nine inattention symptoms and/or six out of nine hyperactive-impulsive symptoms occurring

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frequently for at least the past 6 months; moreover, the frequency and severity of these inattention and hyperactive-impulsive symptoms must be developmentally deviant (i.e., in excess of what is expected for children of the same age and gender). Criterion B requires that several of these inattention and hyperactive-impulsive symptoms should have emerged before 12 years of age. Criterion C addresses the pervasiveness of ADHD, requiring that several inattention or hyperactive-impulsive symptoms be evident in two or more settings. There must also be clear evidence that these symptoms interfere with, or reduce the quality of, daily functioning (Criterion D). Finally, it is also necessary to consider whether symptoms and their interference with functioning might better be explained by another mental disorder (Criterion E). All five criteria must be met to establish an ADHD diagnosis. Although it is commonly the case that these criteria are addressed in the order in which DSM-5 presents them, such an approach lends itself to an overemphasis on counting symptoms (Criterion A), often at the expense of appropriate attention to Criteria B E. Adhering to this ordering of the criteria also runs counter to the realities of clinical practice in which impairments in school, home, and social functioning, more so than the symptoms themselves, are what prompts parents and teachers to request professional consultation and evaluation. For these and many other reasons, a previously proposed reordering of Criteria A E is a clinically more logical way to assess ADHD and should be considered for use (Anastopoulos & Shelton, 2001). The starting point for this reordering (Fig. 2.1) is Criterion D, especially based on evidence that impairment should be given greater weight than symptom counts in making ADHD diagnostic decisions in adolescents (Sibley, Pelham, Molina, et al., 2012). Fundamentally, this is a two-part criterion that examines whether there is (1) any evidence of interference with, or reduction in the quality of, daily functioning; and (2) reason to believe that some symptoms of inattention and/or hyperactive-impulsive are directly causing or contributing to (1). Assuming this is to be the case, it then becomes necessary to determine whether these symptoms are of sufficient clinical significance to rise to the level of ADHD. First to be considered among the remaining criteria is Criterion C. This is primarily because in the process of evaluating Criterion D, information often becomes available that allows for an assessment of the setting contexts in which symptoms are occurring, thereby addressing the pervasiveness requirement of Criterion C. With Criteria D and C met, the next check point in the diagnostic analysis is Criterion A, addressing three questions: (1) is the frequency of inattention and hyperactive-impulsive symptoms above threshold (e.g., six or more symptoms from either the nine-item inattention list or the nine-item hyperactive-impulsive list)? (2) Are the frequency and severity of these symptoms atypical or developmentally deviant, well beyond what would be expected of others of the same age and gender? (3) Have these symptoms been present

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Criterion D Evidence of functional impairment

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FIGURE 2.1 Reordering of the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for attention-deficit/hyperactivity disorder.

Criterion C Symptoms occur in 2+ settings

Criterion A Frequency and developmental deviance of symptoms

Criterion B Age of onset

Criterion E Exclusionary conditions

for the past 6 months or longer? If the answer to all three questions is yes, one begins examining Criterion B, which addresses yet another temporal characteristic of ADHD, requiring that symptoms of inattention and hyperactive-impulsive first emerged prior to 12 years of age. When met, Criteria D, C, A, and B together represent DSM-5’s inclusionary criteria for ADHD. Before concluding that ADHD is present, however, one final hurdle must be cleared. More specifically, it remains necessary to consider the possibility that this clinical picture might better be accounted for by another mental disorder. Assuming such exclusionary conditions can be ruled out, one can then conclude that ADHD is likely the best explanation for the reported symptoms and functional impairment. Depending on the relative distribution of primary symptoms, an ADHD diagnosis can be subclassified in terms of being either a combined, predominantly inattentive, or predominantly hyperactive-impulsive clinical presentation. To receive an ADHD combined classification, children must display at least six out of nine symptoms from the inattention list, along with six or more out of nine symptoms from the hyperactive-impulsive list. For children with an ADHD predominantly inattentive clinical presentation, there must be evidence of at least six out of nine symptoms from the inattention list, but less than six out of nine symptoms from the hyperactive-impulsive list.

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Conversely, the ADHD predominantly hyperactive-impulsive clinical presentation requires less than six out of nine symptoms from the inattention list, but six or more from the nine symptoms hyperactive-impulsive list. A word of clinical caution is in order. It is sometimes the case that people think about the predominantly inattentive and the predominantly hyperactiveimpulsive clinical presentations as pure inattention and hyperactiveimpulsive classifications, respectively. Although they can be at times, more often they are not. To illustrate, a child can receive a diagnosis of ADHD predominantly inattentive clinical presentation by displaying six inattention symptoms along with five hyperactive-impulsive symptoms. Clearly, this child has hyperactive-impulsive difficulties of a magnitude warranting clinical attention, regardless of the formal diagnostic label that may imply otherwise. Practitioners should be sure to mention this qualification when discussing predominantly inattentive and predominantly hyperactiveimpulsive clinical presentations in their summary reports and in their feedback to parents and teachers, given its implications for planning and implementing treatment. Practitioners should also keep this same qualification in mind when receiving reports from other health care professionals. More to the point, if a practitioner receives a report for a child diagnosed as ADHD predominantly inattentive clinical presentation, it would behoove the practitioner to dig deeper to determine whether this represents a purer form of this diagnosis (e.g., eight inattention symptoms and zero hyperactiveimpulsive symptoms) versus one in which there are subthreshold levels of hyperactive-impulsive symptoms that warrant therapeutic attention (e.g., eight inattention symptoms and five hyperactive-impulsive symptoms).

Multiinformant, multimethod assessment strategy Although the DSM-5 offers general guidance to practitioners about the kinds of evaluation data that must be collected to determine whether a child displays ADHD, it does not specify how this information should be gathered. Awareness of (1) the situational variability of primary ADHD symptoms, (2) the increased risk for cooccurring diagnostic features and clinical features, and (3) the many different domains in which daily functioning can be impaired can serve to inform the assessment process, above and beyond that suggested by the DSM-5 criteria. For example, because ADHD symptoms are subject to situational variability, practitioners should aspire to collect information from the multiple primary settings in which a child functions. At the very least this should include information about the child’s behavior and performance in the home and school settings. An even more complete picture of the child can be obtained by gathering information from multiple informants within each setting, each of whom may interact with the child in different ways that are clinically meaningful. Ideally, this would include mothers and fathers, as well as two or more teachers who know the child

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well and can provide insight about the child’s current and past functioning. As a way of cross-checking the accuracy of collected data, it is also useful for practitioners to gather information from the same informant using different assessment methodologies. For example, parents may provide information about their child by responding to interview questioning, by filling out behavior questionnaires, and by supplying copies of past records pertinent to the child’s evaluation (e.g., report cards from prior grades). Whenever possible, teachers can be asked to provide similar types of interview and rating scale information. Such a comprehensive approach to the assessment of ADHD, known as a multiinformant, multimethod approach, is essential to ensure that all DSM-5 criteria for ADHD are met. Comprehensive assessment also allows practitioners to (1) identify disorders other than ADHD that may be present, (2) determine whether these disorders are cooccurring with ADHD or serve as a basis for ruling out an ADHD diagnosis, and (3) identify the domains in which daily functioning may be impaired to guide treatment planning. In addition to clinical interviews and behavior rating scales completed by parents and teachers, it can be helpful at times to have the child respond to interview and self-report rating scale questions, especially when the assessment involves an adolescent or when concerns exist with respect to the possibility of depression or anxiety difficulties being present. In some cases, intelligence and educational testing, continuous performance tests, direct observations of the child in naturalistic settings, and medical examinations may also be useful (for further discussion, see Anastopoulos & Shelton, 2001). For practitioners working in private self-employment settings, the decision to gather many different types of evaluations must be weighed against the financial and time costs associated with doing so.

Assessment procedures Clinical interviews are useful in gathering relevant historical information and assessing whether the child meets diagnostic criteria for ADHD and other disorders. An unstructured interview with parents may be conducted to gather background data, including the presenting concerns, developmental and health history, educational history, family history, and social history. An unstructured interview with the child can also shed light on the child’s perceptions of the presenting concerns and history. To ensure diagnostic accuracy, it is recommended that a structured or semistructured interview be given to assess whether the child meets diagnostic criteria for ADHD and/or other specific conditions. Commonly used structured and semistructured interviews include the Child and Adolescent Psychiatric Assessment (Angold, Prendergrast, Cox, Harrington, Simonoff, & Rutter, 1999), the Diagnostic Interview for Children and Adolescents-IV (Reich, Welner, Herjanic, & MHS staff, 1996), the Diagnostic Interview Schedule for

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Children-IV (DISC-IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000), and the Schedule for Affective Disorders and Schizophrenia for School-Age Children (Kaufman et al., 1997). None of these interviews has yet been updated for DSM-5. Given that there were few changes in the diagnostic criteria for ADHD from DSM-IV to DSM-5, these interviews are likely still appropriate for assessing ADHD. However, DSM-5 contains more substantial changes to the diagnostic criteria for common cooccurring conditions and added new diagnoses, such as disruptive mood dysregulation disorder, that are not covered at all in these existing structured interviews. Practitioners may therefore need to supplement these older structured and semistructured interviews with additional questions to ensure that DSM-5 diagnostic criteria are applied. Practitioners are also encouraged to rapidly adopt updated versions of these interviews as they become available. Narrow-band rating scales focus specifically on ADHD symptoms and are useful in establishing frequency counts and whether symptoms of inattention and hyperactive-impulsive are developmentally deviant. Narrow-band ADHD measures include the ADHD Rating Scale-5 (DuPaul, Power, Anastopoulos, & Reid, 2016), the Conners Third Edition (Conners 3; Conners, 2008), the SNAP-IV (Swanson, 1992), and the Vanderbilt ADHD Rating Scales (Wolraich, Feurer, Hannah, Pinnock, & Baumgaertel, 1998). Of these, both the ADHD Rating Scale-5 and the Conners 3 are updated for DSM-5. The ADHD Rating Scale-5 was written and standardized on a nationally representative sample based on DSM-5 criteria for ADHD. Parent and teacher versions are available and both versions include the 18 symptoms of ADHD, along with 6 items that directly address impairment stemming from these symptoms. The Conners 3 was published before the release of the DSM-5 but has since released a DSM-5 scoring update. The Conners 3 assesses not only symptoms of ADHD but also symptoms of ODD and CD and has parent, teacher, and self-report versions. Broad-band rating scales assess a wide range of child behaviors. In the context of ADHD assessment, they may be particularly useful in screening for conditions that cooccur with ADHD or that provide a better explanation for the presenting concerns. Commonly used broad-band scales include the Achenbach System of Empirically Based Assessment (Achenbach, 2009), the Behavior Assessment System for Children Third Edition (Reynolds & Kamphaus, 2015), the Conners Comprehensive Behavior Rating Scales (Conners, 2008), and Conners Early Childhood (Conners EC; Conners, 2009). Each of these measures has been made available or updated since the release of DSM-5, includes parent-, teacher-, and self-rated versions (except for the Conners EC, which includes only parent- and teacher-rated versions), and provides adequate coverage of internalizing, externalizing, and socialadaptive functioning. In addition, the DSM-5 includes a “DSM-5 Parent Guardian-Rated Level 1 Cross-Cutting Symptom Measure—Child Age 6 17” and “DSM-5 Self-Rated Level 1 Cross-Cutting Symptom

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Measure—Child Age 11 to 17.” These cross-cutting measures screen for a range of emotional and behavioral concerns and guide users to areas where further inquiry is indicated. In addition to their flexibility, low cost, and convenience, most narrowband and broad-band rating scales are standardized on large nationally representative samples and provide normative tables that are arranged according to a child’s age and gender, and type of informant (i.e., parent or teacher). The availability of such normative tables makes rating scales especially useful in addressing the DSM-5 requirement for developmental deviance—that is, in determining whether the severity of reported ADHD symptoms is significantly deviant from developmental expectations for children of the same age and gender. Although there is no rigid rule for where the cut-point between typical and atypical functioning should lie, there is a consensus that scores placing a child’s level of ADHD symptomatology above the 93rd percentile are significant and suggestive of a diagnosis. Stated somewhat differently, scores in this range indicate that less than 7% of same-age and same-sex children would display a similar level of behavior, which is in line with the reported overall prevalence of this disorder (American Psychiatric Association, 2013; Willcutt et al., 2012). Because many children with ADHD display executive functioning difficulties (Barkley, 2015), executive functioning should be assessed. Although neuropsychological testing and other clinic-based tests of executive function such as Conners Continuous Performance Test are available (Conners, 2000), their administration and scoring can be time consuming and expensive. Alternatively, behavior rating scales, which are cost- and time-efficient, can be used as an indirect measure of a child’s executive functioning. For example, parents and teachers can complete the Behavior Rating Inventory of Executive Function, Second Edition (BRIEF 2; Gioia, Isquith, Guy, & Kenworthy, 2015; Roth, Isquith, & Gioia, 2005). The BRIEF 2 parent- and teacher-report is composed of nine clinical scales, along with composite scores that can be derived for emotion regulation, behavioral regulation, and cognitive regulation, in addition to a measure of overall global executive functioning deficits. Yet another option is the Children’s Organizational Skills Scale (Abikoff & Gallagher, 2009), which assesses parent and teacher perceptions of the child’s task planning, organized actions, and memory and materials management. Regardless of which executive functioning measure is used, it is important to keep in mind that not all children with ADHD have executive functioning difficulties. Thus the mere presence or absence of such difficulties should not be used to rule in or rule out an ADHD diagnosis. Emotion regulation is also important to assess in youth suspected of having ADHD. One such measure for assessing this is the Emotion Regulation Index for Children and Adolescents (Macdermott, Gullone, Allen, King, & Tonge, 2010), which includes three subscales—emotional control, emotional

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self-awareness, and situation responsiveness—to identify areas of emotion regulation difficulty that can be targeted for intervention. School records and teacher-reported information about a child’s current academic performance in the classroom are invaluable and represent the most accurate and comprehensive way of determining if the child is displaying impairment in educational functioning. Naturalistic observations of the child in school and home settings can also provide important clinical information. The advantages of conducting such observations, however, are often offset by the amount of time they require and by the fact that practitioner reimbursement for performing this type of service can be expensive and is seldom covered by insurance. Yet another way to assess academic functioning is through individually administered educational achievement testing using measures such as the Wechsler Individual Achievement Test-III (Wechsler, 2016) and the Woodcock Johnson IV Tests of Achievement (Schrank, Mather, & McGrew, 2014). Because impairment can also occur in the home setting, it is important to collect information regarding current family structure and functioning. In addition to painting a picture of how family experiences may affect the child’s functioning, such information can also shed important clinical light on how the child’s behavior is impacting parent child relations and other aspects of family functioning, which should also be targeted for therapeutic intervention as needed. One of the most widely used rating scale instruments for collecting this type of information is the fourth edition of the Parenting Stress Index (PSI; Abidin, 2012). Designed for use with children up to 12 years of age, the PSI contains 101 items and an optional 19-item Life Stress Scale. Parents complete these items, and multiple scores are generated that provide insight into what characteristics of the child and parents may be contributing to parenting stress. For children older than 12 years, parents can complete the Stress Index for Parents of Adolescents (Sheras, Abidin, & Konold, 1998) to evaluate parenting stress across three domains, including adolescent (i.e., stress as a function of adolescent characteristics), parent (i.e., effect of parenting on other life roles), and adolescent parent relationship (i.e., quality of parent teen relationship).

Interpreting diagnostic evaluation data To establish an ADHD diagnosis, it is incumbent upon evaluators to ensure that all five of the DSM-5 criteria have been met. Failure to do so increases the likelihood of false-positive diagnoses—that is, identifying a child who does not really have ADHD as having ADHD. To illustrate how this might happen, consider the common practice of using only 18-item ADHD rating scales completed by parents/teachers as the basis for making a diagnosis. Assume for a moment that at least six of nine inattention symptoms have

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been endorsed, along with three of nine hyperactive-impulsive symptoms. Such results are certainly consistent with the possibility of an ADHD predominantly inattentive presentation because they meet the frequency requirement of Criterion A. Although necessary for arriving at this conclusion, this information is not sufficient for finalizing an ADHD diagnosis because there are many possible alternative explanations for why a child might display excessively high levels of inattention. For example, such symptoms could reasonably be explained based upon a consideration of an underlying anxiety or depressive disorder or perhaps even a learning disability. Recent maltreatment, family discord, and sleep difficulties represent other possible explanations. The point to be made is that inattention, in and of itself, is not the exclusive domain of ADHD. Therefore using ADHD rating scales alone, while potentially useful in a screening sense, is an assessment strategy that is inadequate for formally diagnosing ADHD. Although more time consuming, addressing all five DSM-5 criteria leads to greater diagnostic accuracy. As mentioned earlier, a reordering of the five criteria allows for a smoother processing of the vast amounts of clinical information collected in the context of a multiinformant, multimethod assessment approach. All sources of evaluation data should be used to address each of the five criteria. This process begins by addressing the question of whether there is clear evidence of clinically significant impairment in school, home, or peer functioning, and ends by determining whether other psychiatric, learning, or health conditions provide a better explanation for observed symptom and impairment patterns. Ideally, information collected from all informants is synchronous, thereby allowing for a clear diagnostic determination. Unfortunately, discrepancies in the evaluation data collected from multiple informants is more often the case in clinical practice. A particularly common discrepancy arises among parents, with mothers often reporting clinically significant ADHD levels, and fathers’ ratings placing the child’s behavior within the normal range. Discrepancies may also occur when teacher ratings suggest the presence of ADHD and parent ratings do not. In cases where input from multiple teachers is obtained, there can even be discrepancies from one teacher to the next. Do such discrepancies automatically render a decision that there is no basis for making an ADHD diagnosis? Not necessarily. Unlike a courtroom in which a unanimous opinion is required, it remains entirely possible to conclude that an ADHD diagnosis is present even when there are discrepancies across multiple informants. This possibility is best understood from a consideration of the situational variability of ADHD symptoms that was discussed earlier. More to the point, by examining the contexts in which each informant interacts with the child, a practitioner can usually make sense of the informant discrepancies that have emerged. In the first example above, it may be the case that one parent has greater responsibility for imposing home

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demands that the child finds boring or unpleasant. This might include getting ready for school, doing homework, and completing household chores. To the extent that the mother in this example has greater child care responsibilities, it would be reasonable to expect that her interactions with the child would have a greater likelihood of eliciting ADHD symptoms, and therefore her ADHD ratings would reflect this more than the father’s ratings. In the second example, there may only be one child in the family and therefore the main context in which the parents interact with the child is in one-on-one situations. In contrast, the teacher may interact with the child in a classroom with 25 students. Because the demands for self-regulation are much greater in a large group situation in the classroom versus the one-on-one situation at home, it is much more likely that the child would exhibit ADHD symptoms in school versus at home, thereby accounting for the informant discrepancy. In the final example, it may be the case that one teacher interacts with a child in a regular education classroom with 25 students versus an educational specialist who spends time with the child in one-on-one tutoring sessions. As was true for the parent teacher discrepancy, the group versus one-on-one contextual difference may help to explain why the classroom teacher observes ADHD symptoms and the educational specialist does not. Along with informant discrepancies, another common challenge facing practitioners is the need for explaining the presence of other diagnostic conditions that emerge from the assessment. More specifically, the question that must be addressed is: Do such conditions better explain the child’s clinical presentation and therefore necessitate ruling out an ADHD diagnosis? Or might it instead be the case that that another diagnostic condition exists in addition to an ADHD diagnosis? For there to be another diagnosed condition, there first needs to be clear evidence that the additional behaviors and emotions that the child is displaying are distinct and developmentally deviant—that is, outside the boundaries of what might be considered normal or typical behavioral or emotional experiences. Thus it is not just that the child is feeling very sad, for example, it must also be at a level of sadness that goes beyond what many typically developing children experience. Having extensive knowledge of typical child development is immensely helpful in making this determination. Even more helpful, primarily because they are more objective, are comparisons of the child’s clinical presentation with that of children of the same age and gender. Such comparative information is readily available from standardized rating scales and questionnaires for which normative data, grouped by age, gender, and informant, are available. In addition to considering the developmental deviance of cooccurring behavioral or emotional symptoms, it is necessary to address the impact of these same symptoms on academic, home, and social functioning. The degree to which a child’s daily functioning is impaired must be clinically significant, exceeding that which would be expected for most children. Thus

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for a child who is experiencing anxiety but still earning a grade of B 1 in an advanced course, it would be a difficult case to make to label this type of performance as “impaired” functioning. Assuming for a moment that there is ample evidence to support the existence of another diagnosable condition, such as major depressive disorder (MDD), how does one determine whether it is exclusionary, thereby ruling out ADHD, or cooccurring and thus in addition to ADHD? One strategy for disentangling this issue is to examine both conditions in terms of their onset and duration across a developmental timeline representing the child’s lifetime. For example, if there is clear evidence that ADHD concerns began when the child was 7 years of age, whereas the first MDD episode did not surface until 11 years of age, this would strengthen the argument in favor of diagnosing both conditions and considering the MDD to be cooccurring or comorbid with ADHD. On the other hand, if both conditions first appeared around the age of 11 years, or if the ADHD did not arise until the age of 12 years, it would be necessary to consider the possibility that what looked like an ADHD clinical presentation might be better explained by the MDD diagnosis. Another strategy that can be used to address this differential diagnosis situation is to examine the degree to which the presence of ADHD symptoms is affected by the presence of MDD symptoms. Using the above timeline example once again, assume for a moment that ADHD and MDD both started around the age of 11 years. If the MDD episode clearly ended by the age of 12 years but the ADHD issues persisted well beyond that, there would be little reason to rule out an ADHD diagnosis based on the MDD. In short, the task facing the practitioner is to determine whether ADHD symptoms occur independently versus only in the presence of MDD.

Diagnostic feedback and treatment planning Upon completing a diagnostic assessment, practitioners must document their findings in a written summary report that can and should be shared with the parents of the identified child. This is best accomplished in a face-to-face meeting with the parents. Depending on the age of the child, a separate feedback session can also be conducted with the child. A good place to start the feedback session is by restating the referral question posed by the parents at the time of initial referral. For most referred children, this question will be: Does my child have ADHD? Because most parents are not familiar with the formal diagnostic criteria for ADHD, it is often helpful to provide an overview of the reordered DSM-5 criteria that must be met in order to arrive at an ADHD diagnosis. To facilitate parental comprehension of this portion of the feedback session, a visual depiction of the clinical decision-making flowchart can be presented. The practitioner can then reference the collected multiinformant, multimethod data that address whether there is strong enough evidence to claim that the child is displaying

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clinically significant impairment in multiple domains of daily functioning. Assuming there is, the practitioner can then address the remaining diagnostic criteria in a similar fashion. In so doing, the practitioner has essentially walked the parents through the same decision-making process that he/she used to conceptualize the case. Generally speaking, parents immensely appreciate having their child’s ADHD status explained in this way. Before moving on to other aspects of the evaluation, it is critically important for practitioners to check in with parents about their reaction to learning that their child does have ADHD. Most are not at all surprised because they typically have suspected this might be the case for quite some time. Some parents may feel sadness because they were hoping this would not be the case. Most parents, however, are relieved to finally have a clearer understanding of their child’s difficulties, for which they know treatments are available. Whatever the reaction, it is important for practitioners to process this with parents before providing additional feedback. At this point in the feedback session, practitioners should mention that a high percentage of children with ADHD have additional diagnoses. This sets the stage for discussing whether their child is experiencing any additional difficulties requiring clinical attention. As was done for the ADHD diagnosis, practitioners should briefly describe the diagnostic criteria for any cooccurring disorder that may be present, and then explain how the collected evaluation data address each of these criteria. To prevent parents from potentially feeling like their child is being piled on with one diagnosis after another, it is critical for practitioners to shift gears and to discuss in detail their child’s many strengths. In the context of these strengths, the practitioner can then transition into a detailed description of the treatment approach recommended to address the child’s needs. Emphasis should be placed on the idea that, to address the multiple needs of the child in the multiple settings in which he/she functions, multiple evidence-based treatments must be used in combination. This may include, for example, a trial of stimulant medication, classroom modifications, and parent training, to name a few. Ideally, at the conclusion of the feedback session, parents are better informed about their child’s difficulties and more hopeful that improvements in their child’s life can take place.

References Abidin, R. R. (2012). Parenting stress index (4th ed.). Lutz, FL: Psych Assessment Resources. Abikoff, H., & Gallagher, R. (2009). Children’s organizational skills scales (COSS). North Tonawanda, NY: Multi-Health Systems. Achenbach, T. M. (2009). The Achenbach System of Empirically Based Assessment (AESBA): Development, findings, theory, and applications. Burlington, VT: University of Vermont Research Center for Children, Youth, & Families. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.

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American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed. revised). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed). Washington, DC: Author. Anastopoulos, A. D., Beal, K. K., Reid, R. J., Reid, R., Power, T. J., & DuPaul, G. J. (2018). Impact of child and informant gender on parent and teacher ratings of attention-deficit/ hyperactivity disorder. Psychological Assessment. Available from https://doi.org/10.1037/ pas0000627, Advance online publication. Anastopoulos, A. D., & Shelton, T. L. (2001). Assessing attention-deficit/hyperactivity disorder. New York: Kluwer Academic/Plenum Publishers. Anastopoulos, A. D., Smith, T., Garrett, M., Morrissey-Kane, E., Schatz, N., Sommer, J., . . . Ashley-Koch, A. (2011). Self-regulation of emotion, functional impairment, and comorbidity among children with AD/HD. Journal of Attention Disorders, 15(7), 583 592. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57 87. Angold, A., Prendergrast, M., Cox, A., Harrington, R., Simonoff, E., & Rutter, M. (1999). The Child & Adolescent Psychiatric Assessment (CAPA). Psychological Medicine, 25, 739 753. Bagwell, C. L., Molina, B., Pelham, W. E., & Hoza, B. (2001). Attention-deficit hyperactivity disorder and problems in peer relations: Predictions from childhood to adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1285 1292. Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: a handbook for diagnosis and treatment (4th ed.). New York: Guilford Press. Barkley, R. A., Guevremont, D. C., Anastopoulos, A. D., & Fletcher, K. E. (1992). A comparison of three family therapy programs for treating family conflicts in adolescents with attention-deficit hyperactivity disorder. Journal of Consulting and Clinical Psychology, 60 (3), 450 462. Barkley, R. A., Murphy, K. R., & Fischer, M. (2008). ADHD in adults: What the science says. New York: The Guilford Press. Busch, B., Biederman, J., Cohen, L., Sayer, J., Monuteaux, M., Mick, E., . . . Faraone, S. (2002). Correlates of ADHD among children in pediatric and psychiatric clinics. Psychiatric Services, 53(9), 1103 1111. Conners, C. K. (2000). Conners continuous performance test. North Tonawanda NY: MultiHealth Systems. Conners, C. K. (2008). Conners (3rd ed.). North Tonawanda, NY: Multi-Health Systems. Conners, C. K. (2009). Conners early childhood. North Tonawanda, NY: Multi-Health Systems. Douglas, V. (1972). Stop, look and listen: The problem of sustained attention and impulse control in hyperactive and normal children. Canadian Journal of Behavioural Science, 4(4), 259 282. Available from https://doi.org/10.1037/h0082313. DuPaul, G. J., Power, T. J., Anastopoulos, A. D., & Reid, R. (2016). ADHD rating scale 5 for children and adolescents. New York: Guilford Press. DuPaul, G. J., & Stoner, G. (2015). ADHD in the schools: Assessment and intervention strategies (3rd ed.). New York: Guilford Press. Elia, J., Ambrosini, P., & Berrettini, W. (2008). ADHD characteristics: I. Concurrent comorbidity patterns in children & adolescents. Child and Adolescent Psychiatry and Mental Health, 2.

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Faraone, S., Perlis, R., Doyle, A., Smoller, J., Goralnick, J., Holmgren, M., & Sklar, P. (2005). Molecular genetics of attention-deficit/hyperactivity disorder. Biological Psychiatry, 57(11), 1313 1323. Available from https://doi.org/10.1016/j.biopsych.2004.11.024. Flory, K., Molina, B. S. G., Pelham, W. E., Jr., Gnagy, E., & Smith, D. (2006). Childhood ADHD predicts risky sexual behavior in young adulthood. Journal of Clinical Child and Adolescent Psychology, 35, 571 577. Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2015). Behavior rating inventory of executive function (2nd ed.). Lutz, FL: Psychological Assessment Resources. Johnston, C., & Mash, E. J. (2001). Families of children with attention-deficit/hyperactivity disorder: Review and recommendations for future research. Clinical Child and Family Psychology Review, 4, 183 207. Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., & Ryan, N. (1997). Schedule for Affective Disorders and Schizophrenia in School-Age Children Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 980 988. Macdermott, S., Gullone, E., Allen, J., King, N., & Tonge, B. (2010). The emotion regulation index for children and adolescents (ERICA): A psychometric investigation. Journal of Psychopathology and Behavioral Assessment, 32(3), 301 314. Available from https://doi. org/10.1007/s10862-009-9154-0. Maniadaki, K., & Kakouros, E. (2018). The complete guide to ADHD: Nature, diagnosis, and treatment (1st ed.). New York: Routledge. Mash, E., & Johnston, C. (1990). Determinants of parenting stress: Illustrations from families of hyperactive children and families of physically abused children. Journal of Clinical Child and Adolescent Psychology, 19(4), 313 328. Mikami, A. (2010). The importance of friendship for youth with attention-deficit/hyperactivity disorder. Clinical Child and Family Psychology Review, 13(2), 181 198. Available from https://doi.org/10.1007/s10567-010-0067-y. Molina, B. S., Marshal, M. P., Pelham, W. E., & Wirth, R. J. (2005). Coping skills and parent support mediate the association between childhood attention-deficit/hyperactivity disorder and adolescent cigarette use. Journal of Pediatric Psychology, 30(4), 345 357. Nigg, J., Nicholas, M., & Burt, S. A. (2010). Measured gene-by-environment interaction in relation to attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 863 873. Pliszka, S. R. (2014). Comorbid psychiatric disorders in children. In R. A. Barkley (Ed.), Attention-deficit/hyperactivity disorder (4th ed., pp. 140 168). New York: Guilford. Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalance estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 434 442. Reich, W., Welner, Z., Herjanic, B., & MHS staff. (1996). Diagnostic Interview for Children and Adolescents-IV Computer Program (DICA-IV). New York: MultiHealth Systems. Reynolds, C. R., & Kamphaus, R. W. (2015). Behavior assessment system for children (3rd ed.). Bloomington, MN: PsychCorp. Roth, R. M., Isquith, P. K., & Gioia, G. A. (2005). Behavior rating inventory of executive function-adult version (BRIEF-A). Lutz, FL: Psychological Assessment Resources. Schrank, F. A., Mather, N., & McGrew, K. S. (2014). Woodcock-Johnson IV tests of achievement. Rolling Meadows, IL: Riverside. Shaffer, D., Fisher, P., Lucas, C., Dulcan, M., & Schwab-Stone, M. (2000). NIMH diagnostic interview schedule for children, version IV (NIMH DISC-IV): Description, differences from

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previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28 38. Sheras, P. L., Abidin, R. R., & Konold, T. R. (1998). Stress Index for Parents of Adolescents. Lutz, FL: PAR, Inc. Sibley, M. H., Pelham, W. E., Molina, B. S. G., Gnagy, E. M., Waxmonsky, J. G., Waschbusch, D. A., . . . Kuriyan, A. B. (2012). When diagnosing ADHD in young adults emphasize informant reports, DSM items, and impairment. Journal of Consulting and Clinical Psychology, 80, 1052 1061. Swanson, J. M. (1992). School-based assessments and interventions for ADD students. Irvine, CA: KC Publishing. Wechsler, D. (2016). WIAT-III A&NZ: Wechsler individual achievement test. Pearson Australia Group Pty Limited. Weiss, G., & Hechtman, L. (1986). Hyperactive children grown up: Empirical findings and theoretical considerations. New York: Guilford Press. Willcutt, E., Betjemann, R., Wadsworth, S., Samuelsson, S., Corley, R., DeFries, J., . . . Olson, R. (2006). Preschool twin study of the relation between attention-deficit/hyperactivity disorder and prereading skills. Reading and Writing, 20(1 2), 103 125. Available from https:// doi.org/10.1007/s11145-006-9020-3. Willcutt, E., Doyle, A., Nigg, J., Faraone, S., & Pennington, B. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57(11), 1336 1346. Available from https://doi.org/10.1016/j. biopsych.2005.02.006. Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohde, L. A., Tannock, R., . . . Lahey, B. B. (2012). Validity of DSM IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991 1010. Wolraich, M. L., Feurer, I., Hannah, J. N., Pinnock, T. Y., & Baumgaertel, A. (1998). Obtaining systematic teacher reports of disruptive behavior disorders utilizing DSM-IV. Journal of Abnormal Child Psychology, 26, 141 152. World Health Organization. (2018). The international classification of diseases: The global standard for diagnostic health information (11th ed.). World Health Organization. Zentall, S., & Leib, S. (1985). Structured tasks: Effects on activity and performance of hyperactive and comparison children. The Journal of Educational Research, 79(2), 91 95.

Chapter 3

Assessment of attention-deficit/ hyperactivity disorder and comorbid reading disorder with consideration of executive functioning Erik G. Willcutt University of Colorado Boulder, Boulder, CO, United States

Introduction and overview The importance of attention-deficit/hyperactivity disorder comorbidity with learning disorders Many previous studies have focused on comorbidity between attention-deficit/hyperactivity disorder (ADHD) and internalizing and externalizing disorders, whereas ADHD comorbidity with learning disorder (LD) has been particularly understudied. In our meta-analysis of nearly 600 studies of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSMIV) ADHD (Willcutt et al., 2012), less than 10% of the studies reported any information about measures of academic achievement. Despite this lack of research attention, recent results have consistently underscored the important implications of concurrent reading difficulties for many aspects of ADHD, including etiological influences on ADHD symptoms, associations between ADHD symptoms and a range of aspects of adaptive and neuropsychological functioning, and the long-term developmental outcomes of individuals with ADHD (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Rucklidge & Tannock, 2002; Willcutt, Betjemann, et al., 2010; Willcutt, Betjemann, Pennington, et al., 2007; Willcutt, Pennington, Olson, & DeFries, 2007). On a more practical level, one of the only treatment studies to test the influence of comorbidity found that reading tutoring led to improvements in reading achievement in groups with inattention without reading problems and reading The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00003-4 © 2020 Elsevier Inc. All rights reserved.

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difficulties in the absence of inattention, but had no impact for the group with both reading and attentional difficulties (Rabiner & Malone, 2004). Taken together, these results underscore the need for a more comprehensive understanding of the nature and implications of the relation between ADHD and difficulties in reading.

Overview of the chapter The overarching goal of the current chapter is to provide a succinct overview of current knowledge regarding comorbidity between ADHD and reading disorder (RD), a common childhood disorder that is defined by significant underachievement in reading that is unexpected based on an individual’s age and development (e.g., American Psychiatric Association, 2013). This chapter focuses on RD rather than mathematics or writing difficulties because RD is the most common learning disability, and less is known about the association between ADHD and writing or mathematics difficulties. The first section of the chapter summarizes data regarding the frequency of comorbidity between ADHD and RD and then reviews studies that examined the impact of comorbid RD on the functioning of individuals with ADHD. The second section summarizes studies that tested several competing explanations for comorbidity between RD and ADHD, including recent results from our neuropsychological studies of twins that have begun to identify the shared and unique etiological influences and neurocognitive weaknesses that lead to the development of ADHD, RD, and their comorbidity. Finally, the chapter concludes by discussing the clinical implications of these results and highlighting several key directions for future research. Most of the conclusions in this chapter are based on a systematic summary of published studies of RD and ADHD. In addition, to address several key questions that have not been fully addressed in the existing literature, new analyses were completed in several ongoing studies in our laboratory. As part of their participation in the Colorado Learning Disabilities Research Center (CLDRC) twin study, groups of 8- to15-year-old twins with ADHD but not RD (N 5 405), both ADHD and RD (N 5 205), and neither ADHD nor RD (N 5 1050) completed an extensive battery that includes measures of ADHD symptoms, academic achievement, functional impairment, and neuropsychological functioning (for a more detailed description of the CLDRC, see McGrath et al., 2011; Peterson et al., 2017; Willcutt, 2014, 2015; Willcutt, Betjemann, et al., 2010; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). Similar measures were administered in the International Longitudinal Twin Study of Early Reading Development, a longitudinal study of an unselected sample of 928 twins that was assessed seven times between preschool and the end of ninth grade (e.g., Christopher et al., 2013, 2015; Willcutt, Betjemann, Wadsworth, et al., 2007). Finally, data from a large sample of undergraduate students with ADHD (N 5 353) and

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without ADHD (N 5 3647) were used to examine the association between ADHD and reading difficulties in emerging adulthood (e.g., Willcutt & Bidwell, 2011), a developmental period that has received little attention in previous studies of ADHD and RD.

Prevalence and clinical implications of comorbidity between attention-deficit/hyperactivity disorder and reading disorder Frequency of comorbidity between attention-deficit/hyperactivity disorder and reading disorder Our comprehensive meta-analysis of studies of DSM-IV ADHD found that 15% 50% of children and adolescents with ADHD also met criteria for RD, with an overall weighted estimate of 28% (Willcutt et al., 2012). Further, comorbidity between ADHD and RD is significant in both community samples (Bauermeister et al., 2005; Willcutt, Betjemann, et al., 2010) and samples recruited from clinics (Molina & Pelham, 2001; Semrud-Clikeman et al., 1992; Willcutt et al., 2011), in both males and females (Willcutt & Pennington, 2000), and in samples of both children and young adults (Willcutt et al., 2012). Taken together, these results indicate clearly that comorbidity between ADHD and RD is a robust phenomenon. However, while all measures of ADHD and RD are significantly associated, important nuances emerged when ADHD symptom dimensions and subtypes were examined separately. Results of the meta-analysis indicated that reading difficulties are more strongly associated with inattention symptoms than with symptoms of hyperactivity and impulsivity in both children and adolescents (Willcutt et al., 2012), and similar results have been reported in initial studies of young adults (Willcutt & Bidwell, 2011). Similarly comparisons of groups with and without ADHD indicate that rates of RD are primarily elevated in ADHD subgroups that are characterized by significant inattention (e.g., the DSM-5 combined presentation and primarily inattentive presentation). A similarly nuanced pattern emerged when different aspects of reading were examined separately. The vast majority of previous studies of ADHD and RD have defined reading difficulties based on measures of untimed single word reading. To test whether results may differ for other measures of reading, new analyses were completed for this chapter in the samples described earlier. As expected, all aspects of reading were significantly correlated with both inattention symptoms (r 5 0.28 0.41) and hyperactivity impulsivity symptoms (r 5 0.15 0.28), but these associations were significantly stronger for inattention (mean correlation 5 0.35 vs 0.21). Correlations were also significantly higher between inattention and measures of timed reading fluency and complex reading comprehension than untimed single word reading. Preliminary neuropsychological analyses suggest that

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the strong association between inattention and reading fluency may be explained by a shared weakness in cognitive processing speed, whereas the association between ADHD and difficulties in reading comprehension may be due to weaknesses in executive functions and the higher-order language skills that facilitate the conceptual understanding of the meaning of words as they are read.

Conclusion The existing literature provides robust evidence of an association between ADHD and RD. Although this effect is significant for all measures of reading and ADHD, it is most pronounced for the inattention dimension of ADHD symptoms and for measures of reading that require fluent reading of connected text and application of higher-order cognitive skills to understand the content of complex written passages.

Functional implications of comorbidity Cross-sectional analyses Comorbidity between ADHD and RD or any other disorder is especially important to understand if the presence of the second disorder is associated with different or greater impairment in individuals with ADHD. New crosssectional analyses completed for this chapter clearly indicate that the presence of comorbid RD is associated with greater difficulties in a range of important domains of functioning (Fig. 3.1). Specifically, while groups with ADHD with and without RD were both more likely to exhibit impairment than a comparison group without ADHD or RD, the group with comorbid RD and ADHD was more likely than the group with ADHD alone to exhibit

FIGURE 3.1 Proportion of individuals with significant functional impairment in groups with attention-deficit/hyperactivity disorder (ADHD) 1 reading disorder (RD), ADHD only, and neither RD or ADHD in the Colorado Learning Disabilities Research Center twin study.

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significant global, social, and academic impairment, and were more likely to be retained in school.

Developmental outcomes Initial cross-sectional analyses were extended for this chapter by completing new analyses of developmental outcomes in a subset of twins that returned for a follow-up assessment 5 years after they completed the initial CLDRC twin study (N 5 125 twins with ADHD alone, 60 twins with ADHD 1 RD, and 230 twins with neither disorder). In comparison to participants with ADHD alone, the group with both ADHD and RD exhibited significantly worse outcomes across a range of important academic domains in late adolescence, including lower overall academic grades, specific weaknesses in reading, mathematics, and writing, and pronounced difficulties with academic tasks such as taking notes during lectures and completing long-term projects such as term papers and classroom presentations (Fig. 3.2). These results underscore the important impact of comorbid RD. On the other hand, it is also important to underscore that the group with ADHD alone also exhibited more difficulty than individuals without ADHD in all domains with the exception of class presentations. These results suggest that academic outcomes are less positive for students with ADHD whether or not they also

FIGURE 3.2 Rates of comorbid psychopathology and substance use in a 5-year longitudinal outcome study of groups with attention-deficit/hyperactivity disorder (ADHD) 1 reading disorder (RD), ADHD only, and neither RD or ADHD drawn from the Colorado Learning Disabilities Research Center twin study.

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have RD, but the greatest difficulties are experienced by individuals with both disorders. It is perhaps not surprising that individuals with comorbid ADHD and RD exhibit specific academic weaknesses. However, in comparison to individuals with ADHD alone, individuals with ADHD and RD also exhibit greater impairment across a range of functional outcomes that go well beyond academic difficulties. For example, our earlier results demonstrated that the group with both ADHD and RD was more impaired than the group with ADHD alone on measures of relationships with peers and parents, delinquent and rule breaking behaviors, and adaptive behaviors such as completion of chores (e.g., Willcutt, Betjemann, Pennington, et al., 2007). Similarly the new analyses completed for this chapter indicate that the group with comorbid ADHD and RD is more likely to meet criteria for generalized anxiety disorder, as well as to misuse alcohol and use cannabis (Fig. 3.3).

Conclusion Both cross-sectional and longitudinal analyses indicated that the presence of comorbid RD is associated with greater impairment in a range of domains among individuals with ADHD. The next section reviews studies that attempted to understand the etiology of this comorbidity by testing competing hypotheses that have been proposed to explain why ADHD and RD cooccur so frequently.

FIGURE 3.3 Academic impairment in a 5-year longitudinal outcome study of groups with attention-deficit/hyperactivity disorder (ADHD) 1 reading disorder (RD), ADHD only, and neither RD or ADHD drawn from the Colorado Learning Disabilities Research Center twin study.

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Competing explanations for comorbidity Artifactual models As a first step toward understanding the etiology of comorbidity between ADHD and RD, it is essential to test whether this comorbidity is observed in all samples, or if it may simply be an artifact of a biased sampling procedure or measurement problem in some studies. Therefore this first section summarizes the results of studies that tested several different artifactual explanations for comorbidity between RD and ADHD.

Clinic sampling bias Many early studies that reported comorbidity between RD and ADHD were conducted in samples ascertained from clinics (e.g., Semrud-Clikeman et al., 1992), leaving open the possibility that comorbidity might only occur in more severely affected samples that are referred for clinical assessment and intervention. However, analyses of community samples by our group and others reported rates of comorbidity between ADHD and RD that are similar to the rates reported in samples recruited from clinics (e.g., 25% 50%), providing strong evidence that comorbidity between ADHD and RD does not simply reflect clinic sampling bias (e.g., Levy, Hay, Bennett, & McStephen, 2005; McGrath et al., 2011; Willcutt, Betjemann, et al., 2010; Willcutt & Pennington, 2000). Shared method variance and symptom overlap Another way that artifactual comorbidity could occur is if the same method is used to assess both disorders or if the same symptoms are included in the definitions of both disorders (Angold, Costello, & Erkanli, 1999). Because RD is assessed by psychometric achievement tests whereas ADHD is assessed by behavioral ratings, the relation between RD and ADHD cannot be explained by shared method variance. Similarly, the symptoms of RD and ADHD do not overlap, indicating that these hypotheses can be clearly rejected. Rater bias The rater bias hypothesis suggests that raters may be biased to endorse ADHD symptoms if they know that an individual has significant reading difficulties, even when the individual is not exhibiting atypical levels of inattention. In unselected samples of twins attending preschool in the United States, Australia, and Scandinavia, parent and teacher ratings of ADHD symptoms were significantly correlated with prereading skills prior to the initiation of formal reading instruction (Willcutt, Betjemann, Pennington, et al., 2007). Therefore these ratings were not biased by any overt reading difficulties

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exhibited by the child, suggesting that these sorts of rater biases are not likely to explain most cases of comorbidity between RD and ADHD.

Phenocopy model The phenocopy (secondary symptom) model suggests that one disorder leads raters to endorse symptoms of a second disorder in the absence of the underlying pathophysiological dysfunction that leads to the second disorder when it occurs in isolation. For example, a child with severe reading difficulties might appear inattentive or off task during reading assignments in the classroom due to their inability to read, rather than as a consequence of the neuropsychological weaknesses in domains such as executive functions or processing speed that are typically associated with ADHD. An initial paper based on a small sample of children with RD and ADHD provided some support for a phenocopy model in which RD leads to secondary symptoms of ADHD (Pennington, Groisser, & Welsh, 1993). However, subsequent studies based on much larger samples have provided little additional support for this hypothesis, suggesting that most cases of comorbidity between ADHD and RD are not explained by phenocopy effects (Kibby & Cohen, 2008; Martinussen & Tannock, 2006; Purvis & Tannock, 2000; Rucklidge & Tannock, 2002; Seidman, Biederman, Monuteaux, Doyle, & Faraone, 2001; Willcutt, Betjemann, et al., 2010; Willcutt, Pennington, et al., 2005). Conclusion Taken together, existing data provide little support for explanations that suggest that comorbidity between ADHD and RD is an artifact. Therefore the next section reviews studies that tested three competing causal models that have been proposed to explain the significant comorbidity between ADHD and RD that is consistently observed in both community and clinic samples. Common etiology and causal models as explanations for comorbidity between attention-deficit/hyperactivity disorder and reading disorder Over a dozen different explanations have been proposed to account for true comorbidity between complex disorders (Angold et al., 1999; Neale & Kendler, 1995), and a number of these hypotheses have been tested as explanations for comorbidity of RD and ADHD. Due to space constraints, this section focuses on three plausible models that have been examined most frequently in studies of RD and ADHD. The common etiology model suggests that the two disorders cooccur more often than expected by chance due to shared genetic or environmental influences that increase risk for both disorders. This model is often called the correlated liabilities model because the liability to the two disorders is

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partially shared, whereas the disorders are distinguished by other etiological influences that are specific to each disorder. The direct causation model suggests that one disorder directly causes the underlying pathophysiological weaknesses and symptoms of the second disorder. For example, if severe ADHD symptoms interfere with a child’s ability to attend to early classroom instruction on the specific phonological processing skills that underlie the development of reading, these attentional difficulties could directly cause weaknesses in both phonological processing and reading development in the absence of the genetic or environmental risk factors that lead to RD in isolation. Finally, the three independent disorders model suggests that the comorbid group is best understood as a third independent disorder with an etiology that is distinct from the etiology of RD or ADHD alone. The optimal methods to test these three competing explanations are etiologically informative designs such as family, twin, and molecular genetic studies, along with studies that examine the shared and unique neurocognitive correlates of ADHD, RD, and comorbid RD 1 ADHD. In the remainder of this section we summarize the latest results of studies that applied these methods.

Family studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity If genetic or family environmental influences increase susceptibility to a disorder, the disorder should occur more frequently in relatives of individuals with the disorder than in relatives of individuals without the disorder. Consistent with this expectation, family members of children with RD or ADHD are 4 8 times more likely to meet criteria for the disorder than the families of individuals without RD or ADHD (Faraone et al., 2000; Willcutt, 2014; Willcutt, Pennington, et al., 2010). Because RD and ADHD are both significantly familial, the common etiology hypothesis would be supported if RD and ADHD also tend to cooccur in the same families. Although not all studies reported evidence of shared familial influences on general learning disabilities and ADHD (Doyle, Faraone, DuPre, & Biederman, 2001), the most recent results from the CLDRC suggest that family members of probands with RD or ADHD are approximately three times more likely to meet criteria for the other disorder than family members of comparison probands without either disorder (Willcutt, 2014). Family data also provide a critical test of the three disorders model. This model makes the strong prediction that the familial risk factors that lead to comorbid RD 1 ADHD are distinct from the familial influences associated with each disorder when it occurs alone. Therefore the three disorders model would be supported if family members of comorbid probands exhibited higher rates of comorbid RD 1 ADHD, but did not exhibit higher rates of either disorder alone. Contrary to this hypothesis, the latest analyses of the CLDRC

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indicated that siblings of probands with both RD and ADHD exhibited higher rates of RD alone (28%), ADHD alone (20%), and RD 1 ADHD (14%) than siblings of comparison probands (all ,5%). These results argue against the three disorders model and instead provide additional support for the hypothesis that RD and ADHD are distinct but related disorders that sometimes cooccur due to shared familial risk factors (e.g., Willcutt, 2014).

Conclusion Significant shared familial influences play an important role in comorbidity between RD and ADHD and may potentially reflect common genetic influences on both disorders. However, family studies cannot provide conclusive evidence regarding genetic effects because members of biological families living in the same home share both genetic and family environmental influences. Instead, other behavioral genetic designs such as twin studies are necessary to disentangle the relative contributions of genetic and environmental influences to ADHD, RD, and their comorbidity. Twin studies of reading disorder, attention-deficit/hyperactivity disorder, and their comorbidity By comparing the similarity of monozygotic twins, who share all of their genes, to dizygotic twins, who share half of their segregating genes on an average, twin analyses provide estimates of the extent to which individual differences or extreme scores are due to genetic or environmental influences (Plomin, DeFries, Knopik, & Neiderhiser, 2013). Heritability is defined as the proportion of the total phenotypic variance in a trait that is attributable to genetic influences. The proportion of variance due to environmental factors is subdivided to distinguish two types of environmental influences. Shared environmental influences are environmental factors that increase the similarity of individuals within a family in comparison to unrelated individuals in the population. These effects may potentially include environmental influences within the home or any other shared experiences such as mutual friends or shared teachers. In contrast, nonshared environmental influences specifically affect only one member of a twin pair or affect the two twins differently. Examples of nonshared environmental risk factors could include a head injury or other accident, a traumatic event, or exposure to physical or sexual abuse (if the other twin was not similarly exposed). Our review of all published twin studies on RD and ADHD indicated that heritability estimates were moderate to high for both RD and ADHD, with 60% 70% of the risk for RD and 75% 80% of the risk for ADHD explained by genetic influences (e.g., Willcutt, 2014; Willcutt, Pennington, et al., 2010). Shared environmental influences account for an additional 10% 15% of the variance in reading, but play a minimal role in the etiology

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of ADHD symptoms. Nonshared environmental influences and measurement error typically explain 20% 25% of the variance in both reading and ADHD symptoms. Several large cross-sectional studies of twins by our group and others then extended these univariate results to examine the etiology of covariance between ADHD and reading (Ebejer et al., 2010; Greven, Harlaar, Dale, & Plomin, 2011; Greven, Rijsdisjk, Asherson, & Plomin, 2012; Hart et al., 2010; Paloyelis, Rijsdijk, Wood, Asherson, & Kuntsi, 2010; Willcutt, 2014; Willcutt, Betjemann, et al., 2010; Willcutt, Betjemann, Wadsworth, et al., 2007). These studies indicate that common genetic influences explain the majority of the covariance between individual differences in reading and both ADHD symptom dimensions, but shared genetic influences are significantly stronger for reading and inattention symptoms than for reading and hyperactive-impulsive ADHD symptoms. Similarly, shared genetic influences with ADHD are stronger for measures of reading fluency and reading comprehension than for single word reading, consistent with the phenotypic results described earlier in this chapter.

Conclusion Analyses of twins consistently indicate that comorbidity between RD and ADHD is primarily explained by shared genetic influences, whereas other genetic and environmental influences are uniquely associated with ADHD and RD. The next section describes initial studies that have attempted to identify the specific genetic loci that account for this shared genetic variance. Molecular genetic studies of reading disorder, attention-deficit/ hyperactivity disorder, and their comorbidity Molecular genetic methods can now be used to screen the entire human genome for genes that increase risk for individual disorders or shared risk across disorders. The high heritability of RD and ADHD led to initial optimism that specific genes would be identified that explained a large proportion of the risk for each disorder and their comorbidity. However, results of molecular genetic studies have turned out to be much more complicated than expected. Rather than finding specific genes with large effects on each disorder, genome-wide scans consistently indicate that each genetic risk factor has a very small effect on RD or ADHD (Gialluisi et al., 2014; Neale et al., 2008). These findings suggest that RD and ADHD arise from the combined effects of dozens, or even hundreds, of genetic and environmental risk factors, each of which leads to a small increase in risk for the disorder in isolation. Only a handful of studies have tested whether a subset of these specific genetic risk

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factors may contribute to comorbidity between RD and ADHD (Gayan et al., 2005; Willcutt et al., 2002). All reported effects are small, but initial results are at least consistent with this possibility.

Neurocognitive models of reading disorder, attention-deficit/ hyperactivity disorder, and their comorbidity Similar to the initial optimism regarding the potential identification of specific genetic loci with large effects, early neurocognitive theories of ADHD and RD typically proposed simple models in which a single specific neurocognitive deficit was necessary and sufficient to account for all symptoms of each disorder. However, it is now clear that these single deficit cognitive models do not provide a satisfactory explanation for ADHD, RD, or other developmental disorders (Pennington, 2006; Sonuga-Barke, Sergeant, Nigg, & Willcutt, 2008; Willcutt, Sonuga-Barke, Nigg, & Sergeant, 2008). For example, while a robust body of evidence supports the theory that ADHD is due in part to weak inhibitory control (Barkley, 1997), the magnitude of the observed effect sizes suggest that weak inhibition explains a relatively small proportion of the overall risk for ADHD (Willcutt, 2015; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). In addition to inhibitory difficulties, neuropsychological studies suggest that ADHD may also be associated with more general difficulties with executive control (Pennington & Ozonoff, 1996; Willcutt et al., 2014), aversion to the experience of delay (SonugaBarke et al., 2008), and slower and more variable cognitive processing speed (McGrath et al., 2011; Shanahan et al., 2006; Sonuga-Barke & Castellanos, 2007). Each of these weaknesses accounts for a relatively small amount of the total variance in ADHD symptoms in the population, and multivariate analyses of different combinations of these variables suggest that these constructs may independently increase susceptibilty to ADHD (e.g., Peterson et al., 2017). Further, similar patterns have been reported for RD and nearly all other developmental disorders (e.g., Peterson et al., 2017; Willcutt et al., 2008, 2013). These complex results precipitated a major reconceptualization of theoretical models of ADHD, RD, and other related disorders. Rather than attempting to identify a single necessary and sufficient cause that is specific to each disorder, more recent theoretical models explicitly hypothesize that ADHD and other developmental disorders are heterogeneous conditions that arise from the additive and interactive effects of multiple genetic and environmental risk factors that lead to weaknesses in multiple cognitive domains (Pennington, 2006; Peterson et al., 2017; Sonuga-Barke et al., 2008; Willcutt, 2015; Willcutt et al., 2008). Further, these models suggest that a subset of neurocognitive weaknesses are likely to be shared across disorders, leading to the frequent comorbidity that is consistently observed.

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To test this possibility, we included composite measures of six key neuropsychological constructs related to RD and ADHD in a single structural equation model predicting RD and ADHD symptoms (McGrath et al., 2011; Peterson et al., 2017; Willcutt, Betjemann, et al., 2010). Results indicated that only ADHD was associated with weak inhibitory control, but both RD and ADHD were associated with slow and variable cognitive processing speed and other aspects of executive functions such as verbal working memory. These results provide key evidence of shared neuropsychological underpinnings of RD and ADHD, and multivariate twin analyses indicated that these shared neurocognitive weaknesses are associated with the common genetic influences that increase risk for both RD and ADHD (Willcutt, Betjemann, et al., 2010).

Conclusion Taken together, results of multivariate twin analyses suggest that comorbidity between RD and ADHD is due to shared genetic influences that lead to slower and less consistent cognitive processing.

Conclusion and future directions Clinical implications Diagnostic formulation The existing literature strongly supports a model in which RD and ADHD are distinct disorders that cooccur due to a subset of shared genetic risk factors. Consistent with this overarching framework, RD and ADHD are independently associated with important aspects of functional impairment in a range of domains. Therefore rather than attempting to determine which disorder is “primary” when ADHD and RD cooccur in the same individual, clinicians should conceptualize both conditions as primary disorders that may each require targeted intervention. Clinical assessment procedures Comprehensive assessments of ADHD should also assess reading and other academic difficulties, and current learning difficulties should be considered carefully when clinical recommendations are developed. However, a key barrier to the systematic assessment of learning difficulties is the time required to complete a full battery of individually administered measures of academic achievement. Therefore while a comprehensive battery of academic achievement measures is the optimal approach to assess reading and other dimensions of academic functioning, it is also important to recognize that this may not be feasible in some research or clinical settings. If time constraints prevent a full assessment of reading, it is still likely to be helpful to administer

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a brief screening measure to identify individuals who may need a referral for a more comprehensive evaluation of academic functioning. Nearly all standardized achievement tests include a very brief (3 5 minutes) measure of single word reading that can be used effectively to screen for reading difficulties. Alternatively, we also developed and validated the Colorado Learning Difficulties Questionnaire (CLDQ), a 20-item parent-report rating scale that was specifically designed as a screening measure to identify individuals who may require additional academic assessment (Willcutt et al., 2011). The CLDQ is freely available from the author.

Intervention Despite the voluminous literatures describing treatment of ADHD and RD in isolation, surprisingly few intervention studies of either disorder have examined the impact of comorbidity between ADHD and RD. One initial study suggested that treatment of reading difficulties may not be as effective for children with concurrent attentional difficulties (Rabiner & Malone, 2004), but much more work is needed to fill this important gap in the literature. Future directions for studies of comorbidity between attentiondeficit/hyperactivity disorder and reading disorder Early shared risk factors Longitudinal twin studies of preschool samples provide important verification that early reading weaknesses and inattention symptoms are significantly correlated prior to the beginning of formal reading instruction (Willcutt, Betjemann, Wadsworth, et al., 2007). However, no longitudinal studies to date have prospectively examined early risk factors prior to the age of 4 years that may play a role in the development of comorbidity between ADHD and RD. Attention-deficit/hyperactivity disorder and reading disorder in adults The studies reviewed in this chapter indicate that the presence of comorbid RD is associated with more severe initial impairment and less positive developmental outcomes 5 years after an initial assessment was completed in late childhood. However, little is known about the longer-term adult outcomes of individuals with comorbid ADHD and RD. Understanding shared cognitive weaknesses in attention-deficit/ hyperactivity disorder and reading disorder The neuropsychological studies reviewed in this chapter clearly suggest that RD and ADHD are associated with slow processing speed on standard psychometric measures. However, interpretation of these results is not always

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straightforward, as slow processing speed could potentially be associated with RD and ADHD for different reasons. For example, this pattern could reflect general slow processing across all trials in groups with RD or other learning disabilities, but intermittent lapses in attention on a subset of trials in groups with ADHD. Because the coarse measures of processing speed used in previous studies are not sensitive to these different possibilities, future research is needed using more sensitive measures of trial-level processing efficiency or alternative methodologies such as event-related potentials or functional neuroimaging.

Conclusion This chapter provided an overview of current knowledge regarding the frequency, implications, and etiology of comorbidity between ADHD and RD. In comparison to individuals having ADHD without RD, individuals with both ADHD and RD exhibit increased functional impairment and have less positive developmental outcomes. Etiologically informative analyses indicate that nearly all of the phenotypic covariance between RD and ADHD is due to shared genetic influences that are associated with slower and more variable cognitive processing speed. Future research is needed to examine the treatment implications of comorbidity between ADHD and RD, and clinical assessment should assess for both disorders given their high cooccurrence.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry and Allied Disciplines, 40, 57 87. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65 94. Bauermeister, J. J., Matos, M., Reina, G., Salas, C. C., Martinez, J. V., Cumba, E., & Barkley, R. A. (2005). Comparison of the DSM-IV combined and inattentive types of ADHD in a school-based sample of Latino/Hispanic children. Journal of Child Psychology and Psychiatry, 46, 166 179. Christopher, M. E., Hulsander, J., Keenan, J., DeFries, J., Pennington, B. F., Byrne, B., . . . Wadsworth, S. (2015). The genetic and environmental etiologies of the longitudinal relations between pre-reading skills and reading at the end of first and fourth grades. Child Development, 86, 342 361. Christopher, M. E., Hulslander, J., Byrne, B., Samuelsson, S., Keenan, J. M., Pennington, B. F., . . . Olson, R. K. (2013). Modeling the etiology of individual differences in early reading development: Evidence for strong genetic influences. Scientific Studies of Reading, 17, 350 358.

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Doyle, A. E., Faraone, S. V., DuPre, E. P., & Biederman, J. (2001). Separating attention deficit hyperactivity disorder and learning disabilities in girls: A familial risk analysis. American Journal of Psychiatry, 158, 1666 1672. Ebejer, J. L., Coventry, W. L., Byrne, B., Willcutt, E. G., Olson, R. K., Corley, R., & Samuelsson, S. (2010). Genetic and environmental influences on inattention, hyperactivityimpulsivity, and reading: Kindergarten to grade 2. Scientific Studies of Reading, 14, 293 316. Faraone, S. V., Biederman, J., Mick, E., Williamson, S., Wilens, T., Spencer, T., . . . Zallen, B. (2000). Family study of girls with attention deficit hyperactivity disorder. American Journal of Psychiatry, 157, 1077 1083. Gayan, J., Willcutt, E. G., Fisher, S. E., Francks, C., Cardon, L. R., Olson, R. K., . . . DeFries, J. C. (2005). Bivariate linkage scan for reading disability and attention-deficit/hyperactivity disorder localizes pleiotropic loci. Journal of Child Psychology and Psychiatry, 46, 1045 1056. Gialluisi, A., Newbury, D. F., Willcutt, E. G., Olson, R. K., DeFries, J. C., Brandler, W. M., . . . Fisher, S. E. (2014). Genome-wide screening for DNA variants associated with reading and language traits. Genes, Brain, and Behavior, 13(7), 686 701. Greven, C. U., Harlaar, N., Dale, P., & Plomin, R. (2011). Genetic overlap between attentiondeficit hyperactivity disorder and reading is largely driven by inattentiveness rather than by hyperactivity-impulsivity. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20, 6 14. Greven, C. U., Rijsdisjk, F. V., Asherson, P., & Plomin, R. (2012). A longitudinal twin study of associations between ADHD symptoms and reading. Journal of Child Psychology and Psychiatry, 53, 234 242. Hart, S. A., Petrill, S. A., Willcutt, E. G., Thompson, L. A., Schatschneider, C., Deater-Deckard, K., & Cutting, L. E. (2010). Exploring how ADHD symptoms are related to reading and mathematics performance: General genes, general environments. Psychological Science, 21, 1708 1715. Kibby, M. Y., & Cohen, M. J. (2008). Memory functioning in children with reading disabilities and/or attention deficit/hyperactivity disorder: A clinical investigation of their working memory and long-term memory functioning. Child Neuropsychology, 14, 525 546. Levy, F., Hay, D. A., Bennett, K. S., & McStephen, M. (2005). Gender differences in ADHD subtype comorbidity. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 368 376. Martinussen, R., Hayden, J., Hogg-Johnson, S., & Tannock, R. (2005). A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 377 384. Martinussen, R., & Tannock, R. (2006). Working memory impairments in children with attention-deficit hyperactivity disorder with and without comorbid language learning disorders. Journal of Clinical and Experimental Neuropsychology, 28, 1073 1094. McGrath, L. M., Pennington, B. F., Shanahan, M. A., Santerre-Lemmon, L. E., Barnard, H. D., Willcutt, E. G., . . . Olson, R. K. (2011). A multiple deficit model of reading disability and attention-deficit/hyperactivity disorder: Searching for shared cognitive deficits. Journal of Child Psychology and Psychiatry, 52, 547 557. Molina, B. S., & Pelham, W. E. (2001). Substance use, substance abuse, and LD among adolescents with a childhood history of ADHD. Journal of Learning Disabilities, 34, 333 342, 351.

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Neale, B. M., Lasky-Su, J., Anney, R., Franke, B., Zhou, K., Maller, J. B., . . . Faraone, S. V. (2008). Genome-wide association scan of attention deficit hyperactivity disorder. American Journal of Medical Genetics Part B (Neuropsychiatric Genetics), 147B, 1337 1344. Neale, M. C., & Kendler, K. S. (1995). Models of comorbidity for multifactorial disorders. American Journal of Human Genetics, 57, 935 953. Paloyelis, Y., Rijsdijk, F., Wood, A. C., Asherson, P., & Kuntsi, J. (2010). The genetic association between ADHD symptoms and reading difficulties: The role of inattentiveness and IQ. Journal of Abnormal Child Psychology, 38, 1083 1095. Pennington, B. F. (2006). From single to multiple deficit models of developmental disorders. Cognition, 101, 385 413. Pennington, B. F., Groisser, D., & Welsh, M. C. (1993). Contrasting cognitive deficits in attention-deficit hyperactivity disorder versus reading disability. Developmental Psychology, 29, 511 523. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37, 51 87. Peterson, R. L., Boada, R., McGrath, L., Willcutt, E. G., Olson, R. K., & Pennington, B. F. (2017). Cognitive prediction of reading, math, and attention: Shared and unique influences. Journal of Learning Disabilities, 50, 408 421. Plomin, P., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2013). Behavioral genetics (6th ed.). New York: Worth Publishers. Purvis, K. L., & Tannock, R. (2000). Phonological processing, not inhibitory control, differentiates ADHD and reading disability. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 485 494. Rabiner, D. L., & Malone, P. S. (2004). The impact of tutoring on early reading achievement for children with and without attention problems. Journal of Abnormal Child Psychology, 32, 273 284. Rucklidge, J. J., & Tannock, R. (2002). Neuropsychological profiles of adolescents with ADHD: Effects of reading difficulties and gender. Journal of Child Psychology and Psychiatry, 43, 988 1003. Seidman, L. J., Biederman, J., Monuteaux, M. C., Doyle, A. E., & Faraone, S. V. (2001). Learning disabilities and executive dysfunction in boys with attention-deficit/hyperactivity disorder. Neuropsychology, 15, 544 556. Semrud-Clikeman, M., Biederman, J., Sprich-Buckminster, S., Lehman, B. K., Faraone, S. V., & Norman, D. (1992). Comorbidity between ADDH and learning disability: A review and report in a clinically referred sample. Journal of the American Academy of Child and Adolescent Psychiatry, 31, 439 448. Shanahan, M. A., Pennington, B. F., Yerys, B. E., Scott, A., Boada, R., Willcutt, E. G., . . . DeFries, J. C. (2006). Processing speed deficits in attention deficit/hyperactivity disorder and reading disability. Journal of Abnormal Child Psychology, 34, 585 602. Sonuga-Barke, E. J., & Castellanos, F. X. (2007). Spontaneous attentional fluctuations in impaired states and pathological conditions: A neurobiological hypothesis. Neuroscience and Biobehavioral Reviews, 31, 977 986. Sonuga-Barke, E. J., Sergeant, J. A., Nigg, J., & Willcutt, E. (2008). Executive dysfunction and delay aversion in attention deficit hyperactivity disorder: Nosologic and diagnostic implications. Child and Adolescent Psychiatric Clinics of North America, 17, 367 384. Willcutt, E. G. (2014). Using behavior genetic methods to understand the etiology of comorbidity. In S. Rhee, & A. Ronald (Eds.), Behavior genetics of psychopathology (pp. 231 252). New York: Springer.

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Willcutt, E. G. (2015). Theories of ADHD. In R. Barkley (Ed.), Attention deficit hyperactivity disorder: A clinical handbook (4th ed., pp. 391 404). New York: Guilford. Willcutt, E. G., Betjemann, R. S., McGrath, L. M., Chhabildas, N. A., Olson, R. K., DeFries, J. C., & Pennington, B. F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46, 1345 1361. Willcutt, E. G., Betjemann, R. S., Pennington, B. F., Olson, R. K., DeFries, J. C., & Wadsworth, S. J. (2007). Longitudinal study of reading disability and attention-deficit/hyperactivity disorder: Implications for education. Mind, Brain, and Education, 4, 181 192. Willcutt, E. G., Betjemann, R. S., Wadsworth, S. J., Samuelsson, S., Corley, R., DeFries, J. C., . . . Olson, R. K. (2007). Preschool twin study of the relation between attention-deficit/hyperactivity disorder and prereading skills. Reading and Writing, 20, 103 125. Willcutt, E. G., & Bidwell, L. C. (2011). Etiology of ADHD: Implications for assessment and treatment. In B. Hoza, & S. W. Evans (Eds.), Treating attention deficit hyperactivity disorder. Kingston, NJ: Civic Research Institute. (pp. 6-2 6-18). Willcutt, E. G., Boada, R., Riddle, M. W., Chhabildas, N., DeFries, J. C., & Pennington, B. F. (2011). Colorado Learning Difficulties Questionnaire: Validation of a parent-report screening measure. Psychological Assessment, 23, 778 791. Willcutt, E. G., Chhabildas, N., Kinnear, M., Defries, J. C., Olson, R. K., Leopold, D. R., . . . Pennington, B. F. (2014). The internal and external validity of sluggish cognitive tempo and its relation with DSM-IV ADHD. Journal of Abnormal Child Psychology, 42, 21 35. Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57, 1336 1346. Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohde, L. A., Tannock, R., . . . Lahey, B. B. (2012). Validity of DSM-IV attention-deficit/hyperactivity disorder dimensions and subtypes. Journal of Abnormal Psychology, 121, 991 1010. Willcutt, E. G., & Pennington, B. F. (2000). Comorbidity of reading disability and attention-deficit/hyperactivity disorder: Differences by gender and subtype. Journal of Learning Disabilities, 33, 179 191. Willcutt, E. G., Pennington, B. F., Duncan, L., Smith, S. D., Keenan, J. M., Wadsworth, S. J., & DeFries, J. C. (2010). Understanding the complex etiology of developmental disorders: Behavioral and molecular genetic approaches. Journal of Developmental and Behavioral Pediatrics, 31, 533 544. Willcutt, E. G., Pennington, B. F., Olson, R. K., Chhabildas, N., & Hulslander, J. (2005). Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: In search of the common deficit. Developmental Neuropsychology, 27, 35 78. Willcutt, E. G., Pennington, B. F., Olson, R. K., & DeFries, J. C. (2007). Understanding comorbidity: A twin study of reading disability and attention-deficit/hyperactivity disorder. American Journal of Medical Genetics Part B (Neuropsychiatric Genetics), 144B, 709 714. Willcutt, E. G., Pennington, B. F., Smith, S. D., Cardon, L. R., Gayan, J., Knopik, V. S., . . . DeFries, J. C. (2002). Quantitative trait locus for reading disability on chromosome 6p is pleiotropic for attention-deficit/hyperactivity disorder. American Journal of Medical Genetics, 114, 260 268. Willcutt, E. G., Petrill, S. A., Wu, S., Boada, R., DeFries, J. C., Olson, R. K., & Pennington, B. F. (2013). Implications of comorbidity between reading and math disability: Neuropsychological and functional impairment. Journal of Learning Disabilities, 46, 500 516.

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Willcutt, E. G., Sonuga-Barke, E. J. S., Nigg, J. T., & Sergeant, J. A. (2008). Recent developments in neuropsychological models of childhood disorders. Advances in Biological Psychiatry, 24, 195 226.

Further reading Faraone, S. V., Biederman, J., Weber, W., & Russell, R. L. (1998). Psychiatric, neuropsychological, and psychosocial features of DSM-IV subtypes of attention-deficit/hyperactivity disorder: Results from a clinically referred sample. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 185 193. Willcutt, E. G., Pennington, B. F., Chhabildas, N. A., Friedman, M. C., & Alexander, J. (1999). Psychiatric comorbidity associated with DSM-IV ADHD in a nonreferred sample of twins. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1355 1362.

Chapter 4

Response to intervention framework: an application to school settings Pamela M. Stecker, Janie Hodge and Catherine A. Griffith Department of Education and Human Development, Clemson University, Clemson, SC, United States

A multitiered system of support generally may be described as a framework for providing appropriate services to all students, wherein academic and behavioral outcomes are assessed and monitored, and progressive levels (or tiers) of instructional support are provided to students designated as at-risk for failure and who are not responding as expected to high-quality instruction. We use response to intervention (RTI), though, as the term in this chapter to discuss the general framework for providing increasingly more intensive academic intervention, especially for students with learning difficulties and attention-related issues. Emanating from a system of triage, such as in the provision of medical services, support is provided where needed, but the most intensive intervention is reserved for those who require it the most. RTI first was included in federal law with the Reauthorization of Individuals With Disabilities Education Improvement Act of 2004 (IDEIA, P.L. 108 446), which allowed use of a student’s poor academic response to research-based interventions as a part of the eligibility criteria for specific learning disabilities (SLDs). Our discussion includes a brief historical perspective of RTI and a general description of an RTI model that imbeds three levels of increasingly more concentrated academic support. We discuss several instructional and assessment-related issues at each level and trace these practices using a hypothetical student with an SLD in the area of reading; we pose some questions to be addressed at each level of service. Finally, we provide an overview of RTI evaluation and discuss implications for research and practice.

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00004-6 © 2020 Elsevier Inc. All rights reserved.

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Historical overview of response to intervention and rationale for its use When the IDEIA was first passed in 1975 (P.L. 94 142), students with SLD were assured a free, appropriate public education in the least restrictive environment. The most common method for operationalizing eligibility for special education services for students with SLD was the use of an ability achievement (IQ achievement) discrepancy in which assessment of the student’s intelligence or cognitive aptitude was compared with actual achievement on standardized, norm-referenced measures, although many state education agencies used variations of this method (Mercer, Jordan, Allsopp, & Mercer, 1996). This discrepancy approach to classification relied on the notion that students with SLD demonstrated low achievement that was not commensurate with their average or high intelligence. However, dissatisfaction with the discrepancy method ensued. Arguments included wide variation in prevalence rates of SLD across states due to differences in application of the discrepancy approach as well as inclusion of other components, and contradictions were noted in whether the same individuals would be classified as having an SLD by criteria used in other states (Ysseldyke, Algozzine, & Epps, 1983). Some researchers had difficulties detecting differences on reading or reading-related tasks between students with or without discrepancies (e.g., poor readers with low achievement vs poor readers exhibiting discrepancies; Fletcher et al., 1994; Ysseldyke et al., 1983), and others cited psychometric issues with discrepancy formulae that focused on the use of arbitrary cutoff scores or on standard scores without taking into account statistical regression (Stanovich, 1999). Additional criticisms were noted (see Fuchs, Mock, Morgan, & Young, 2003; Preston, Wood, & Stecker, 2016), but perhaps one of the most serious problems with the discrepancy approach was that many youngsters experienced years of poor achievement before their discrepancies were large enough to qualify them for special education services (Bradley, Danielson, & Doolittle, 2007). In further reauthorization of the law in 1997 (P.L. 105 117), emphasis was placed on results and accountability, so early and accurate identification of students with disabilities was seen as key for improving outcomes (Bradley & Danielson, 2004). During this period, the National Joint Committee on Learning Disabilities (NJCLD) wrote a letter to the Office of Special Education Programs (OSEP) requesting that more attention be focused on SLD identification. Bradley and Danielson outlined the subsequent response and activities planned by OSEP that resulted from the NJCLD letter, known as the LD Initiative, which included commissioned papers, roundtable discussions, and an LD Summit. They described how these discussions became the impetus for OSEP to fund a National Research Center on Learning Disabilities (NRCLD) to continue research on important issues raised; to explore alternative methods for SLD identification, including RTI; and to provide technical assistance to states based on research

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findings. Some of this work influenced changes in the next reauthorization. With IDEIA (2004), state education agencies could not require their local districts to use the IQ achievement discrepancy approach for classification. Instead, law permitted alternate ways to identify students with SLD, including response to research-based intervention as a part of the evaluation process (Bradley et al., 2007). Thus RTI was applied as an educational framework for evaluating effects of evidence-based instructional practices on student achievement. Included in law as an alternate component for evidencing poor academic response to generally otherwise effective instruction in SLD evaluation, the RTI model became a part of larger school reform, designed to address the needs of all students. Simply described, the focus of RTI was on students being identified early when they failed to respond adequately to the provision of high-quality instruction; then more strategic supplemental instruction would be delivered, typically in small groups. If a student continued to respond inadequately despite this more targeted instruction, intensive intervention would be implemented that more closely addressed individual student needs. With some RTI models, this level of intensity was considered special education. This process centered on prevention: prevent development of an enlarging discrepancy between the student and peers and the development of a disability by reducing the impact of the student’s adverse learning characteristics, including attention-related problems, on outcomes while emphasizing student strengths to accelerate academic growth (Lembke, McMaster, & Stecker, 2010). At first glance, this approach to providing educational services is highly appealing. Research-based programs are in place in general education instruction. All students are included in the ongoing evaluation of achievement, and assessment data are used to inform decision-making within and across levels of increasingly more intensive instruction. Student response is evaluated, and more intensive instruction is provided to students targeted as at risk. Students receive help when they need it regardless of having an identified disability, and they receive help early, before multiple years of low achievement contribute to progressively more difficult problems to remediate. Although IDEIA (2004) does not endorse a particular RTI model, Fuchs and Fuchs (2007) provide a general model upon which we also base our description. We provide recommendations for the structure of RTI practices at each level and summarize issues that researchers and practitioners have identified as challenges in RTI implementation, including some aspects for which empirical evidence may be lacking to guide decision-making.

Response to intervention as three levels of increasingly intensive services Although features of RTI vary across models (for more description, see Fuchs et al., 2003), Bradley et al. (2007) summarized NCRLD’s analysis of

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potential RTI model sites and identified specific features that appeared to be associated with successful implementation. These features included research-based general education instruction delivered by highly qualified teachers, which included student assessment in the curriculum; universal screening; targeted research-based interventions provided at the secondary level with ongoing progress monitoring; student response determined by progress data and application of decision rules; evaluation of fidelity of interventions; and provision of referral for comprehensive evaluation of students with suspected learning disabilities at any point in the RTI process, as assured by law. We draw from the Fuchs and Fuchs (2007) example and describe three levels of support, focusing specifically on assessment and instructional components. Although we do not recommend specific instructional programs or assessment tools, we describe general features that should be considered at each level.

General description of response to intervention levels of instruction Primary prevention The primary level of prevention is general education classroom instruction based on a comprehensive program that relies on research-based practices for meeting the needs of the large majority of learners (e.g., Coyne, Oldham, Leonard, Burns, & Gage, 2016; Taylor, 2008). Early in the year, a universal screening measure is administered, and typically a cut score is used to classify a student’s risk status. If students are designated as at risk for reading difficulties, the teacher is alerted and may begin to use some instructional and behavioral adaptations during instruction to help engage these students more fully. In addition to attending closely to student behaviors and academic responses, the teacher administers a progress monitoring measure for 6 8 weeks and graphs student data as a second part of the screening process (Fuchs, Fuchs, & Compton, 2012). These data enable a school assistance team to determine whether the targeted students responded favorably to primary prevention or whether their poor response triggers the need for additional assistance. When students’ low screening scores are corroborated by a pattern of inadequate progress as their response to otherwise generally effective classroom instruction, a secondary level of prevention is initiated. Secondary prevention Not intended to supplant core instruction, secondary prevention is provided along with core instruction and involves implementation of a researchvalidated program delivered frequently throughout the week related to targeted foundational skills (Gersten et al., 2009; Hall & Burns, 2018). This level of prevention is delivered to students with similar needs in small-group settings by someone who has been trained in the validated program.

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Implementation occurs for a specified length of time (perhaps the same length of time as used in the validated research protocol), and progress monitoring occurs regularly (e.g., weekly) to help gauge student response. Success may be gauged by the student’s level of performance on assessments given at the end of the intervention period and by positive trend on progress data. Students who are successful may exit and return to only the first level of service. For students who fail to show adequate progress and who score below a criterion, special education evaluation may be initiated. For students who show some improvement on progress monitoring data but have failed to reach a criterion score, the next step is less clear. Additional assistance at the second level may be tried with more intensive intervention (see Austin, Vaughn, & McClelland, 2017), or special education evaluation may be considered. When the student is evaluated for a suspected learning disability, progress monitoring and performance data collected through the first two levels of RTI would contribute to the diagnosis. That is, repeated poor response to strong classroom and targeted supplemental instruction helps to eliminate a competing hypothesis that the student performed inadequately due to poorquality instructional practices. It is assumed that the student’s learning characteristics prevent or interfere with attainment of academic skills. Although other tests of achievement may be given, along with measures to exclude other disabilities as the cause of the student’s difficulties, use of progress monitoring data would be considered an important aspect of SLDs identification through demonstration of student nonresponsiveness to what would otherwise be considered high-quality instruction.

Tertiary prevention Although some RTI models implement tertiary prevention as a more intensive level of prevention prior to special education eligibility, we view this level of instruction as one that is intensified to focus on individual student needs. Thus it meets the intent of special education service. This level makes use of data-based individualization (DBI; Fuchs, Fuchs, & Vaughn, 2014), that is, simultaneously using (1) progress monitoring data to determine when to make alterations in instructional programs to better meet individual needs, and (2) principles of effective instruction and intensity of design and delivery for addressing the nature of program changes. Special educators then use the DBI process to systematically modify student programs to bring about better student growth. They try to accelerate academic growth to help close the performance gap between the student with a disability and peers without disabilities. Progress monitoring data are used at entry, during, and at exit of this level for DBI decision-making. For example, reliable and valid progress monitoring data are used for developing statements of present level of academic achievement, determining long-term goals, and deciding when to alter

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program components. Data may be compared with norms for deciding about exit. Similarly progress monitoring data may used to monitor the continued success of individual student programs when returning to a less intensive level(s) of service delivery.

Response to intervention scenario with case example: Norma Primary prevention Norma is a second grader who is new to the school. She scored below the cut score on the district’s screening measure in reading. Her teacher, Mr. Conger, made a concerted effort to observe more closely Norma and four other students who scored poorly on the screening tool. He used oral reading fluency (ORF; Deno, 1985; Fuchs, Fuchs, Hosp, & Jenkins, 2001) for weekly progress monitoring by calculating the number of words read correctly in 1 minute by each of the five students at risk. He kept these students close in proximity and used questioning and choral responding strategically to engage them more actively during instruction. Mr. Conger provided additional practice opportunities to this group and closely monitored their responses and daily work. He met with the school’s assistance team to describe his efforts with the group, provided samples of Norma’s class work, documentation of her off-task and distractible behavior, and a graph of 8 weeks of progress monitoring data that showed minimal progress on ORF. Secondary prevention services were added to Norma’s program. Secondary prevention Norma received 30 minutes of instruction in a strategic reading program 5 days per week within a small group of four other students from her grade. An interventionist trained in the use of the district’s standard reading treatment for second graders at risk implemented the program and monitored weekly progress with ORF. A reading specialist observed instruction twice a month to document program fidelity and to help with collecting and graphing progress monitoring data. At the end of the 12-week standard treatment, Norma’s reading achievement was assessed with the designated subtests of a technically sound reading assessment, and she scored at the 10th percentile. Her progress monitoring data showed a rate of improvement of 0.5 words growth per week, which was minimal progress for young readers despite daily explicit and systematic instruction and repeated practice on decoding skills, sight words, oral language, and fluency building. The team recommended special education evaluation, and parental permission was obtained. The school psychologist observed Norma during instruction and gave assessments that included reading achievement as well as tools to eliminate sensory, intellectual, and/or behavioral causes for her difficulties. After considering Norma’s evaluation, including her progress monitoring data that

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showed little improvement in spite of targeted assistance, the multidisciplinary team deemed Norma eligible for special education under the SLD label. Norma’s special education teacher, Ms. Able, administered several ORF measures to describe Norma’s present level of academic functioning. Norma currently was reading 14 words correctly in 1 minute. Ms. Able used this information to guide the team in determining an appropriately ambitious long-term goal for Norma’s individualized education program (IEP). The team examined typical ORF growth rates and year-end benchmarks for first and second graders and recognized that Norma’s level of performance of 14 words correct per minute was significantly discrepant from peers. The team targeted a 2.5-words per week growth rate across the year as an ambitious rate of improvement for Norma. If her teachers could accelerate her academic growth by delivering intensive and specialized reading intervention, her performance at her annual goal date should show a decrease between the discrepancy in Norma’s and her typical peers’ performance. To determine the long-term goal planned for 40 instructional weeks later (i.e., her annual goal date), the team multiplied 2.5 words correct per minute per week by 40 weeks and added this product to the starting level of 14 words correct per minute to establish 114 words read correctly in 1 minute in grade-level text as her annual reading goal for her IEP ([2.5 3 40] 1 14 5 114).

Tertiary prevention Although Norma remained in Mr. Conger’s second grade class, she also received daily 60 minutes of intensive, pull-out instruction in Ms. Able’s resource class. Ms. Able worked with Norma and two other students with similar needs during this time and implemented DBI. She used a validated reading intervention as her starting point but consulted the National Center on Intensive Intervention website (https://intensiveintervention.org) to review principles for intensifying her instruction. She implemented several changes to the instructional program to make it more intensive for her students and monitored progress twice a week with ORF. Ms. Able connected Norma’s current level of performance (14 words correct per minute) to her long-term goal (86 words correct per minute) on her graph. This goal line showed the rate of progress that Norma needed to sustain through the year in order to meet her long-term IEP goal (Yell & Stecker, 2003). Ms. Able applied standard data-based decision rules every month to compare Norma’s rate of improvement and level of performance to the projected goal line. When the decision rules indicated Norma was not progressing as quickly as needed to reach her long-term goal, Ms. Able adjusted instruction once again for Norma and continued to monitor her progress. She continued this cycle of intensifying instruction and monitoring the effect of that change using Norma’s progress monitoring data. Ms. Able systematically modified Norma’s reading program over time to better individualize her instruction by

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focusing on Norma’s needs and her response to the intervention. In addition, Ms. Able consulted with Mr. Conger about practices he could use during his classroom instruction to help Norma.

Questions raised about the response to intervention model Anticipating questions that practitioners may have about the RTI model we described, we identify several issues concerning the overall model and levels. Although we base our responses on recommendations of researchers examining RTI, citing some sources for more detailed explanations, our brief answers do not represent all views. Nonetheless, these questions may serve as a useful framework to guide decision-making during planning stages for RTI.

Overall model Must students advance through these levels of increasingly more intensive intervention prior to proceeding with special education evaluation? RTI provides the framework for schools to include practices for assessing and responding instructionally to all students’ needs. Although IDEIA (2004) allows data from student response to research-based methods to be used in the eligibility process, schools must honor appropriate referrals for special education evaluation at any point in time, even without any data from the RTI process. Why are only three levels of prevention described in this model? Three levels of support, including special education services, are recommended for several reasons. Students who truly have learning disabilities should be afforded special education service if they need it. The model we described provides at least two opportunities for students at risk to receive instruction to support their varying learning needs. If students failed to respond adequately to otherwise strong instruction, they may require—and deserve—the more intensive intervention that can be provided through special education service. Just as RTI data cannot be required for a special education evaluation, similarly RTI cannot be used as a way to avoid special education identification. Providing multiple levels of support prior to special education evaluation delays when students with disabilities receive more specialized help, ultimately denying their rights and requiring them to wait to fail in RTI, similar to an earlier criticism of the IQ achievement discrepancy approach. Fuchs et al. (2012) describe the use of multistage screening both to reduce numbers of potential candidates for secondary prevention and to indicate which students likely should bypass secondary intervention in favor of intensive intervention. Other concerns include whether general education has the capacity for providing multiple levels of intervention and whether these interventions are reliably and substantively different from each other.

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How should students move in and out of levels? Ideally, in this RTI model, reliable, valid, and sensitive assessments are used to make determinations about the success of the intervention for particular students. This databased decision-making process emanates largely from several decades of research with curriculum-based measurement (CBM; Deno, 1985) as a technically sound method for examining student response to instructional programs. Special educators using CBM as the basis for instructional decision-making have been able to effect greater achievement among their students with disabilities than special educators using other methods for decision-making with comparably performing peers (Jung, McMaster, Kunkel, Shin, & Stecker, 2018; Stecker, Fuchs, & Fuchs, 2005). This generalization is supported by research in which teachers used data-based decision-making for students requiring the most intensive intervention, such as those students within the tertiary level. Although progress monitoring data may be used within the first two levels as a way to evaluate student response, it is less clear what specific decision rules should be in place. To move out of the first level, both universal screening data and progress monitoring data have been used to more accurately identify the students who need supplemental instructional assistance than universal screening alone (Fuchs et al., 2012). For the secondary level of support, having progress monitoring data, such as CBM, may facilitate diagnosis of SLDs, the overarching legislative use for RTI. With secondary interventions using a standard treatment, however, the intervention is implemented for a limited period of time, and progress monitoring data are used to document student response but may not necessarily be used solely for determining when the student exits. Rather, another assessment is often used at the conclusion of the standard treatment to evaluate where the student falls with regard to a criterion score or percentile rank, similar to the use of the initial universal screener. However, the dual discrepancy (Fuchs & Fuchs, 2007) of both low level of performance and inadequate slope of improvement from CBM data provide strong justification for determining whether students need intensive intervention. The question may be more complex for movement in and out of the tertiary level. When students with disabilities receive special education, school practices may need to be more flexible in moving students with disabilities to secondary or primary levels, with or without tertiary intervention. The use of CBM data has a long history for monitoring and evaluating the success of students moving from special education to inclusive programs of instruction (Mathes, Fuchs, Roberts, & Fuchs, 1998). Although RTI practices in schools should be made transparent to school staff, parents, and other stakeholders, parental involvement is crucial with tertiary levels that include special education. Movement out of the tertiary level, though, may not necessarily result in release from special education; the special educator may continue to use progress data to determine the effects of changes in instructional level on student growth.

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Primary prevention Why use a comprehensive core program? When using student achievement data as response to instruction, IDEIA requires that research-based programs be used. A validated program has research (e.g., experimental or quasiexperimental research) to document its success in significantly improving student achievement. Research-based programs are designed by principles and practices supported by research, even if the particular program itself has not been studied. On a practical level, a core program alleviates teacher responsibility, time, and effort in developing a comprehensive program that addresses knowledge and skills in a systematic and integrated way. Without a welldesigned core program, the quality of student instruction is dependent entirely on the particular expertise of the teacher. Instead teachers should expect to use research-based tools and practices and validated programs, when available. What practices should be followed when schools identify large numbers of students as at risk? The core program should be well designed and comprehensive enough to meet the varying needs of the large majority of learners using it (e.g., at least 80%). Even if using a validated core program, though, lack of implementation fidelity or lack of teacher instructional expertise may limit its overall effectiveness. Sometimes student populations with particular characteristics may require use of a different or more explicit program. Thus when large numbers of students are identified as at risk in a school, the selection and implementation of a validated program and delivery of teacher professional development (Fuchs & Vaughn, 2012) must be addressed. Large numbers of students in secondary or tertiary instruction may overtax the capacity of school resources for delivering effective supplementary and tertiary instruction to those who require it the most. Strengthening primary instruction would be a critical first or concurrent step to better meet the needs of the school population and to reduce the number of students designated as at risk. Why use progress monitoring in addition to a universal screening measure for decision-making about students at risk? The purpose of the universal screening is to target all students who may be at risk; thus a criterion may be used to overselect students. Error is preferred in selecting more students, some who initially score low but may not truly need supplemental instruction (i.e., false positives), over failing to target a student who needs more assistance but happened to score at criterion (i.e., false negatives). The problem with overselection is that the school may not have the resources necessary to provide secondary prevention to large numbers of students. Researchers (Compton et al., 2010), however, have been able follow students at risk with as few as 5 weeks of progress monitoring data to more accurately determine which students truly are in need of supplemental instruction, thereby reducing the number of students incorrectly identified by the initial screener and

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the load placed on school resources allocated for secondary services (for more discussion on false positives and false negatives, see Fuchs et al., 2012).

Secondary prevention Why use a standard treatment rather than an approach that attempts to match targeted students’ needs to different treatments? Although some schools devise secondary programs presumably by identifying similar student needs and relying on interventionist expertise to tailor the instruction to the group (e.g., problem-solving approach), we recommend the use of standard treatments for supplemental instruction. A standard treatment that has been validated with other similar peers who are low performing is preferable for both empirical and practical reasons. For example, when using RTI data as a part of the diagnosis of learning disabilities, having both a sound core program and evidence-based supplementary intervention accompanied by poor student response to these practices aid in the elimination of the lack of or poor-quality instruction as potential causes of the low achievement and probable disability. When an interventionist or school team devises the secondary program, the quality of the supplementary instruction is largely unknown, and maintaining fidelity may be of concern when trying to deal with multiple versions of secondary practices in the school. Although professional development should be provided for implementers of the standard treatment, having fewer treatments makes it easier to ensure fidelity and to provide coaching. Fewer levels are preferable when considering SLD evaluation (Fuchs, Fuchs, & Stecker, 2010). Who should deliver this instruction? Typically someone who is trained in delivering the designated treatment provides this level of support. Specialists, such as reading specialists or school psychologists, may serve to teach or support interventionists in the intervention, document fidelity of implementation, or collect and interpret assessment data. Special educators, however, would be best suited to deliver intensive intervention to students who present some of the school’s most difficult instructional challenges (Fuchs & Fuchs, 2016). How long should secondary prevention last? Length of treatment varies in validation studies of secondary programs. When implementing a program, if one expects similar achievement results to those obtained in research, fidelity of treatment is a critical factor. Thus one way to answer this question is to use the same treatment length as the corresponding validation study. The treatment needs to be long enough to effect meaningful and sustained change in student learning, but length also needs to be balanced against student need. For example, delaying special education evaluation for a student who responds inadequately to secondary prevention is a disservice to the student. Some students exhibit such serious learning difficulties initially that they would be better

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served by skipping supplemental instruction altogether and should be referred directly for special education evaluation (Fuchs et al., 2012).

Tertiary prevention/intervention Why should special educators try to intensify or make changes to an already validated program? Although many secondary programs are validated, fewer programs have been validated specifically for students with disabilities. Special education is designed to meet individual student needs and should enable students to improve measurably (Endrew F. v. Douglas County School District, 2017). Consequently special educators may begin with validated programs and research-based practices associated with improved student achievement, but they are expected to systematically alter instructional components to better meet individual needs to accelerate student growth. In this way, teachers use DBI to build formatively more effective instruction over time for individuals with disabilities by (1) regularly and frequently collecting progress monitoring data to judge student response to the intervention; (2) using data-based decision rules for determining when to alter instructional programs; and (3) using principles of intensive intervention to formulate the content, procedures, and format of instructional changes (Fuchs et al., 2014). Fuchs, Fuchs, and Malone (2017) provide a structure for designing intensive intervention practices, based on program components supported by research. The National Center on Intensive Intervention (https://intensiveintervention.org) provides multiple resources for learning about DBI, both progress monitoring and intensive intervention. Online instructional modules with accompanying slides and ancillary materials may be used for self-instruction or as a part of professional development study groups in schools. Why use CBM progress monitoring measures and standard decision rules? Research supports the use of CBM for informing instructional decision-making to improve achievement among students with intensive learning needs (Jung et al., 2018; Stecker et al., 2005). The experimental studies included in these reviews had used specific data-based rules relying primarily on trend of student improvement and/or level of adjacent data points across several weeks in comparison to the student’s projected goal line. Although applying these same decision rules to other levels in RTI or to performance of students without disabilities may not be supported by the experimental studies reviewed, these data-based procedures provide the basis for decision-making in the DBI process at the tertiary level.

Response to intervention evaluation Because federal legislation preceded much of the research needed to fully guide the process, numerous investigators have since expended great time

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and effort in developing and testing many components and outcomes of RTI. Mixed results have been reported, but we summarize several investigations and include identified limitations important for interpretation.

National Research Center on Learning Disabilities studies As an outgrowth from the LD Initiative, researchers from the NRCLD, which was funded by the OSEP, conducted large-scale longitudinal studies in reading and mathematics to examine issues related to identification for secondary or tertiary intervention services for first-grade students within an RTI framework, implementation of secondary intervention to prevent disabilities, and operationalization of responsiveness (or nonresponsiveness) to intervention (Fuchs, Compton, Fuchs, Bryant, & Davis, 2008). In the longitudinal reading investigation, Compton, Fuchs, Fuchs, and Bryant (2006) sought to identify procedures that yielded a high percentage of true positives and reduced the number of false positives in the risk pool among students not receiving secondary prevention. On the basis of test results, researchers identified the six lowest performing readers in each of 42 classes. They monitored student progress for 5 weeks using word identification fluency (WIF). At the end of second grade, another battery of tests was administered to the 206 students remaining in the study. A total of 20 children were classified with reading disabilities based upon either of two methods: a standard score below 85 on a composite measure of reading or below 85 on any of three main components of the battery (i.e., untimed word identification and decoding, timed word identification and decoding, and comprehension). Logistic regression and classification tree analysis were applied to predict classification of students as having a reading disability or not. Classification tree analysis improved diagnostic accuracy in both composite and component approaches. The use of initial score on WIF did not contribute to accuracy of the prediction, but adding both slope and level at end of 5 weeks on WIF did improve the classification accuracy of the prediction model. Another aspect of this study (Fuchs et al., 2008) focused on effects of a secondary intervention that involved small group tutoring for first graders on reading performance at the end of second grade. Researchers administered weekly WIF to identify students who were not responsive to reading instruction in fall of first grade for small-group tutoring during the spring. A comparison group of matched students did not receive tutoring but were followed with WIF and the same battery of standardized measures as the tutored group. Results indicated that students in the tutored group demonstrated greater WIF growth than students who were not tutored during the spring semester of first grade and that the standardized measures corroborated this growth. Data collected at the end of second grade suggested that effects of the tutoring intervention were maintained but that the difference between the two groups decreased.

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A third component of the longitudinal study (Fuchs et al., 2008) involved application of varying methods and measures to identify technically appropriate definitions of student responsiveness and nonresponsiveness to interventions. Using the same sample of first graders previously described, researchers used student beginning and ending test battery information and progress monitoring data across the fall and spring for considering a variety of classification methods for reading disability. They examined initial low performance, slope discrepancy, dual discrepancy (i.e., discrepancy of slope and final level), final normalized score, final benchmark, and IQ achievement discrepancy at end of year and used different measures for each method. Methods and measures varied in hit rate, prevalence, specificity, and sensitivity. For example, the IQ achievement discrepancy at the end of first grade provided good hit rates and specificity yet resulted in large number of students who needed intervention (as designated by poor performance at end of second grade) and were not identified. These findings seemed to support the argument for moving away from the discrepancy method for SLD identification. Additionally, they examined stability over time by examining method and classification at the end of second grade using specific decision rules. Although four approaches appeared promising, none fully satisfied desired tenets for prevalence, hit rate, specificity, and sensitivity. This study, then, illustrated how different students were identified based on specific method and measures used, which sounds similar to earlier criticisms of the IQ achievement discrepancy approach. Nonetheless, these studies demonstrated the need for continued research to develop empirically based operationalization of nonresponsiveness and disability.

National evaluation of response to intervention Subsequent to the longitudinal studies conducted by NRCLD, the prevalence of RTI as a method of LD identification and intensification of instruction to support a range of learners across grade levels increased, and guidance documents on effective practices for making RTI work in the schools became readily available (e.g., National Center on Response to Intervention). Some researchers (Fuchs & Deshler, 2007), however, argued for systematic research to identify factors critical to RTI implementation. In 2010 the National Center for Education Evaluation and Regional Assistance commissioned a national evaluation of RTI (Balu et al., 2015). Researchers identified a purposive (impact) sample of 146 schools from 13 states reporting use of RTI practices for at least 3 years and a reference sample of 100 schools representative of elementary schools from the same 13 states. Impact schools were selected for including the following practices: three or more tiers (levels) of support for reading, universal screening at least two times per year, decision-making for movement among Tiers 2 and 3 based on data, and progress monitoring for students performing below grade level to

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determine intervention effectiveness for Tiers 2 and 3. Researchers compared prevalence of RTI practices between the impact and reference samples and, for impact schools, examined intensity of services provided to students at different reading levels and effect of intervention for a targeted sample of students. For the first comparison, impact schools reported a greater level of full implementation of RTI than did the reference schools, although half of the reference schools reported use of RTI. Impact schools reported greater frequency of instruction at Tiers 2 and 3 and greater likelihood of allocating more staff for intervention, using two or more universal screening measures and prescribed procedures for decision-making. Within impact schools, reading teachers and interventionists completed survey questions about reading services provided to different groups of students based on their reading levels (Balu et al., 2015). Placement in tiers remained stable across the year for the majority of students, but survey responses indicated that practitioners used data for decision-making about placing some students in tiers. Differing from common descriptions in the literature, nearly half of the schools reported provision of intervention to all students, that is, those reading at or above grade level, rather than to only those reading below grade level. Also, two-thirds of the schools reported some type of intervention during Tier 1 rather than providing it only as a supplement to Tier 1 services. Schools reported intensification of services through reduced group size, additional time in small group instruction, and inclusion of phonics instruction in small-group instruction for students below grade level than for those at or above grade level. Finally Balu et al. (2015) examined information related to the effects of RTI in impact schools on reading achievement for a group of targeted students who fell just below cut points for eligibility for intervention services. The authors argue that a randomized control trial would have been preferred to determine whether RTI is effective; however, the ability to randomly assign schools to RTI or non-RTI was not a possibility with the widespread implementation in existence. Therefore researchers used a regression discontinuity design to estimate the impact of students just below or just above the cut points for services. Results indicated that intervention services in impact schools had a negative effect on a comprehensive reading measure for first graders just below the Tier 1 cut point on a screening measure, while outcomes for second and third graders just below the cut point were not statistically significant. Their findings suggested that impact of intervention services was not effective for students selected close to the cut points. An important consideration is that this evaluation did not address overall effectiveness of RTI; rather, it compared estimated outcomes for students just below and just above the cut point of eligibility for intervention. In part due to possible misinterpretations of findings, editors of Exceptional Children (Lloyd & Therrien, 2017) invited several researchers to respond to the RTI evaluation report (Balu et al., 2015). Gersten, Jayanthi,

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and Dimino (2017) provided commentary clarifying the results of the evaluation. Specifically, authors emphasized that the research question was only about whether the cut scores and particular interventions used among the impact schools helped a small proportion of students. They contended that the national evaluation did not, and was not intended to, answer the question of whether early intervention in reading works for all students who receive reading intervention. These researchers suggested a variety of reasons that students in the study falling just below the cut points (40th percentile) did not benefit from intervention: large intervention groups, lack of effective training and support for interventionists, students missing core instruction, inadequate screening tools that produce false positives, or problems with intervention and core curricula. They suggested that more field evaluations are needed and that examination of fidelity of implementation at the school level might help answer some of the lingering questions not answered by the national evaluation. Fuchs and Fuchs (2017) identified several features of the national evaluation (Balu et al., 2015) that are important for interpreting these results, including quasiexperimental RD design, use of impact schools that reported full implementation of RTI while only about 60% actually met the criterion for having students in interventions at each of three tiers, and use of selfreport data. The authors ascertained that, due to the narrow focus, findings from the national evaluation should have little impact on how one evaluates RTI as a service delivery model. Regardless of concerns with methodology, they suggested that findings from the RTI evaluation support experiences and reports from others—that schools and practitioners experience significant challenges in implementing RTI effectively. They recommended that further research is needed to determine what is effective and what is doable at the school level.

Implications for future research and practice The complexities addressed in the NRCLD studies (Fuchs et al., 2008) and controversial results of the national evaluation effort (Balu et al., 2015) provided impetus for continued research concerning RTI implementation and effectiveness. This chapter describes early reading practices primarily. However, for RTI to be used successfully as an alternate approach for SLD identification, consideration must be given to application to other content areas, age levels, and populations. The NRCLD had included longitudinal studies in mathematics (e.g., Fuchs et al., 2007), but, like the longitudinal work in reading, researchers addressed only elementary-aged students. Researchers following lines of investigation with grant-supported activities have focused on factors related to assessments and decision- making, targeted skills, and effective instructional practices at primary, secondary, and tertiary levels (Fletcher, Lyon, Fuchs, & Barnes, 2019; Jimerson, Burns, &

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VanDerHeyden, 2015), but it appears that relatively little work has addressed RTI in mathematics with secondary students. The dearth of research addressing RTI in secondary school subjects certainly hampers its implementation. Challenges include development or identification of appropriate screening and progress monitoring tools as well as evidence of only minimal gains in achievement despite strong secondary and tertiary programs (Vaughn et al., 2010, 2012). Researchers (Fuchs, Fuchs, & Compton, 2010; Vaughn & Fletcher, 2012) posited that RTI may need to be reconfigured in secondary settings to better address the needs of learners who already perform significantly below peers. They explained that middle and high school students already are functioning several years below grade level and need more intensive intervention early. Rather than delaying intensive intervention, they recommended skipping the secondary level for students whose low achievement predicts they likely would not benefit. Research also needs to guide what constitutes effective tertiary intervention across content areas for older students whose learning problems are persistent and serious. Other questions relate to implementation of RTI for English language learners (ELLs). Relatively few peer-reviewed studies address needs of ELLs within the RTI framework. One study (Linan-Thompson, Vaughn, Prater, & Cirino, 2006) provided a secondary level of explicit, systematic reading instruction in either English or Spanish to first-grade ELLs at risk. Compared to a control group receiving the school’s business-as-usual program for struggling readers, positive effects of the explicit reading intervention were observed at the end of first grade and were maintained through second grade. Many questions linger, however, regarding length of treatment, appropriate assessment measures, instructional strategies for older ELLs, and practices for students whose first language is not Spanish. Although our discussion of RTI practices is relatively simplistic, it highlights the need for continued research. If a school’s RTI practices include (1) assessments that target students early, (2) validated programs implemented by trained interventionists, and (3) decision-making frameworks based on data, improved student achievement is more likely to be exhibited. Although researchers have addressed these components over the past couple of decades, much work still remains for determining (1) which assessment practices target the “right” students, (2) what components of intervention programs produce the best results for students with varying skill profiles, and (3) which progress monitoring measures and decision rules should be used to determine nonresponsiveness and movement among levels of instruction. Although RTI practices are intended to better meet the needs of all students, a basic premise is that students who need intensive intervention get it. Government statistics (U.S. Department of Education, 2018) document that over half of students with disabilities spend at least 80% of their school day in general education classrooms and that this percentage increased over the

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previous decade. It is doubtful, though, whether general educators, in their role to serve large numbers of students with varying needs, have the training or capacity to deliver the intensity of intervention required by students with pervasive and serious learning problems. Consequently a paradox is produced when nonresponsive students finally reach the tertiary level of special education services only to be placed back in the primary prevention level, perhaps with supports, where they originally were deemed as nonresponsive to instruction. Although students may receive some special education support within general education, Fuchs et al. (2012) call this service “special education lite” and argued that students with intensive needs rightfully deserve intensive intervention in order to accelerate their growth. Without such intensity in service, the achievement gap between students with and without disabilities is not likely to decrease. DBI is a validated approach for improving student achievement (Jung et al., 2018) that teachers use to formulate this intensity of intervention based on individual student need. One of the biggest questions remaining, then, is whether the field currently is preparing (Sayeski, Bateman, & Yell, 2019) and supporting special educators to learn and to use DBI effectively. Without development of this expertise, RTI will not fulfill its promise.

References Austin, C. R., Vaughn, S., & McClelland, A. M. (2017). Intensive reading interventions for inadequate responders in grades K-3: A synthesis. Learning Disability Quarterly, 40(4), 191 210. Available from https://doi.org/10.1177/0731948717714446. Balu, R., Zhu, P., Doolittle, F., Schiller, E., Jenkins, J., & Gersten, R. (2015). Evaluation of response to intervention practices for elementary school reading (NCEE 2016-4000). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Bradley, R., & Danielson, L. (2004). The office of special education program’s LD initiative: A context for inquiry and consensus. Learning Disability Quarterly, 27, 186 188. Available from https://doi.org/10.2307/1593671. Bradley, R., Danielson, L., & Doolittle, J. (2007). Responsiveness to intervention: 1997 2007. TEACHING Exceptional Children, 39(5), 8 12. Available from https://doi.org/10.1177/ 004005990703900502. Compton, D. L., Fuchs, D., Fuchs, L. S., Bouton, B., Gilbert, J., Barquero, L. A., . . . Crouch, R. C. (2010). Selecting at-risk first-grade readers for early intervention: Eliminating false positives and exploring the promise of a two-stage gated screening process. Journal of Educational Psychology, 102, 327 341. Available from https://doi.org/10.1037/a0018448. Compton, D. L., Fuchs, D., Fuchs, L. S., & Bryant, J. D. (2006). Selecting at-risk readers in first grade for early intervention: A two-year longitudinal study of decision rules and procedures. Journal of Educational Psychology, 98(2), 394 409. Available from https://doi.org/10.1037/ 0022-0663.98.2.394. Coyne, M.D., Oldham, A., Leonard, K., Burns, D., & Gage, N. (2016). Delving into the details: Implementing multitiered K 3 reading supports in high-priority schools. In B. Foorman (Ed.), Challenges to implementing effective reading intervention in schools. New directions

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for child and adolescent development, 154, 67 85. Available from https://doi.org/10.1002/ cad.20175. Deno, S. L. (1985). Curriculum-based measurement: The emerging alternative. Exceptional Children, 42(3), 219 232. Available from https://doi.org/10.1177/001440298505200303. Endrew, F. v. (2017). Douglas County School District, 137 S. Ct. 988. Fletcher, J. M., Lyon, G. R., Fuchs, L. S., & Barnes, M. A. (2019). Learning disabilities: from identification to intervention (2nd ed.). New York: Guilford. Fletcher, J. M., Shaywitz, S. E., Shankweiler, D. P., Katz, L., Liberman, I. Y., Fowler, A., . . . Shaywitz, B. A. (1994). Cognitive profiles of reading disability: Comparisons of discrepancy and low achievement definitions. Journal of Educational Psychology, 86(1), 6 23. Available from https://doi.org/10.1037/0022-0663.86.1.6. Fuchs, D., Compton, D. L., Fuchs, L. S., Bryant, J., & Davis, G. N. (2008). Making “secondary intervention” work in a three-tier responsiveness-to-intervention model: Findings from the first-grade longitudinal reading study of the National Research Center on Learning Disabilities. Reading and Writing, 21(4), 413 436. Available from https://doi.org/10.1007/ s11145-007-9083-9. Fuchs, D., & Fuchs, L. S. (2016). Responsiveness-to-intervention: A “systems” approach to instructional adaptation. Theory into Practice, 55, 225 233. Available from https://doi.org/ 10.1080/00405841.2016.1184536. Fuchs, D., & Fuchs, L. S. (2017). Critique of the national evaluation of response to intervention: A case for simpler frameworks. Exceptional Children, 83(3), 255 268. Available from https://doi.org/10.1177/0014402917693580. Fuchs, D., Fuchs, L. S., & Compton, D. L. (2012). Smart RTI: A next-generation approach to multilevel prevention. Exceptional Children, 78(3), 263 279. Available from https://doi. org/10.1177/001440291207800301. Fuchs, D., Fuchs, L. S., & Stecker, P. M. (2010). The “blurring” of special education in a new continuum of general education placements and services. Exceptional Children, 76(3), 301 323. Available from https://doi.org/10.1177/001440291007600304. Fuchs, D., Fuchs, L. S., & Vaughn, S. (2014). What is intensive intervention and why is it important? TEACHING Exceptional Children, 46(4), 13 18. Available from https://doi.org/ 10.1177/0040059914522966. Fuchs, D., Mock, D., Morgan, P. L., & Young, C. L. (2003). Responsiveness-to-intervention: Definitions, evidence, and implications for the learning disabilities construct. Learning Disabilities Research & Practice, 18(3), 157 171. Available from https://doi.org/10.1111/ 1540-5826.00072. Fuchs, L. S., & Fuchs, D. (2007). A model for implementing responsiveness to intervention. TEACHING Exceptional Children, 39(5), 14 20. Available from https://doi.org/10.1177/ 004005990703900503. Fuchs, L. S., Fuchs, D., & Compton, D. L. (2010). Rethinking response to intervention at middle and high school. School Psychology Review, 39(1), 22 28. Fuchs, L. S., Fuchs, D., Compton, D. L., Bryant, J. D., Hamlett, C. L., & Seethaler, P. M. (2007). Mathematics screening and progress monitoring at first grade: Implications for responsiveness to intervention. Exceptional Children, 73(3), 311 330. Available from https://doi.org/10.1177/001440290707300303. Fuchs, L. S., Fuchs, D., Hosp, M. K., & Jenkins, J. R. (2001). Oral reading fluency as an indicator of reading competence: A theoretical, empirical, and historical analysis. Scientific Studies of Reading, 5(3), 239 256. Available from https://doi.org/10.1207/ s1532799xssr0503_3.

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Fuchs, L. S., Fuchs, D., & Malone, A. S. (2017). The taxonomy of treatment intensity. Teaching Exceptional Children, 50(1), 35 43. Available from https://doi.org/10.1177/ 0040059917703962. Fuchs, L. S., & Vaughn, S. (2012). Responsiveness-to-intervention: A decade later. Journal of Learning Disabilities, 45(3), 195 203. Available from https://doi.org/10.1177/ 0022219412442150. Gersten, R., Compton, D., Connor, C. M., Dimino, J., Santoro, L., Linan-Thompson, S., & Tilly, W. D. (2009). Assisting students struggling with reading: Response to Intervention and multi-tier intervention for reading in the primary grades. A practice guide (NCEE 2009 4045). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from ,https://ies.ed.gov/ncee/wwc/practiceguides/.. Gersten, R., Jayanthi, M., & Dimino, J. (2017). Too much? Too soon? Unanswered questions from national response to intervention evaluation. Exceptional Children, 83(3), 244 254. Available from https://doi.org/10.1177/0014402917692847. Hall, M. S., & Burns, M. K. (2018). Meta-analysis of targeted small-group reading interventions. Journal of School Psychology, 66, 54 66. Available from https://doi.org/10.1016/j. jsp.2017.11.002. Individuals with Disabilities Education Improvement Act of 2004, 20 U.S.C. y 1400 et seq. Jimerson, S. R., Burns, M. K., & VanDerHeyden, A. (Eds.), (2015). Handbook of response to intervention: The science and practice of multi-tiered systems of support (2nd ed.). New York: Springer. Jung, P.-G., McMaster, K. L., Kunkel, A., Shin, J., & Stecker, P. M. (2018). Effects of databased individualization for students with intensive learning needs: A meta-analysis. Learning Disabilities Research & Practice, 33(3), 144 155. Available from https://doi.org/ 10.1111/ldrp.12172. Lembke, E. S., McMaster, K. L., & Stecker, P. M. (2010). The prevention science of reading Research within a response-to-intervention model. Psychology in the Schools, 47(1), 22 35. Available from https://doi.org/10.1002/pits.20449. Linan-Thompson, S., Vaughn, S., Prater, K., & Cirino, P. T. (2006). The response to intervention of English language learners at risk for reading problems. Journal of Learning Disabilities, 39(5), 390 398. Available from https://doi.org/10.1177/00222194060390050201. Lloyd, J. W., & Therrien, W. J. (2017). Preview. Exceptional Children, 83(3), 242 243. Available from https://doi.org/10.1177/0014402917695083. Mathes, P. G., Fuchs, D., Roberts, P. H., & Fuchs, L. S. (1998). Preparing students with special needs for reintegration: Curriculum-based measurement’s impact on transenvironmental programming. Journal of Learning Disabilities, 31(6), 615 624. Available from https://doi.org/ 10.1177/002221949803100613. Mercer, C. D., Jordan, L., Allsopp, D. H., & Mercer, A. R. (1996). Learning disabilities definitions and criteria used by state education departments. Learning Disability Quarterly, 19(4), 217 232. Available from https://doi.org/10.2307/1511208. Preston, A. I., Wood, C. L., & Stecker, P. M. (2016). Response to intervention: Where it came from and where it’s going. Preventing School Failure: Alternative Education for Children and Youth, 60(3), 173 182. Available from https://doi.org/10.1080/1045988X.2015.1065399. Sayeski, K. L., Bateman, D. F., & Yell, M. L. (2019). Re-envisioning teacher preparation in an Era of Endrew F.: Instruction over access. Intervention in School and Clinic, 1 8. Available from https://doi.org/10.1177/1053451218819157.

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Stanovich, K. E. (1999). The sociopsychometrics of learning disabilities. Journal of Learning Disabilities, 32(4), 350 361. Available from https://doi.org/10.1177/002221949903200408. Stecker, P. M., Fuchs, L. S., & Fuchs, D. (2005). Using curriculum-based measurement to improve student achievement: Review of research. Psychology in the Schools, 42(8), 795 819. Available from https://doi.org/10.1002/pits.20113. Taylor, B. M. (2008). Tier 1: Effective classroom reading instruction in the elementary grades. In D. Fuchs, L. S. Fuchs, & S. Vaughn (Eds.), Response to intervention: A framework for reading educators (pp. 5 25). Newark, DE: International Reading Association. U.S. Department of Education, Office of Special Education and Rehabilitative Services, Office of Special Education Programs. (2018). 40th Annual report to congress on the Implementation of the Individuals With Disabilities Education Act, 2018. Washington, DC. Retrieved from ,https://www2.ed.gov/about/reports/annual/osep/index.html.. Vaughn, S., Cirino, P. T., Wanzek, J., Wexler, J., Fletcher, J. M., Denton, C. D., . . . Francis, D. J. (2010). Response to intervention for middle school students with reading difficulties: Effects of a primary and secondary intervention. School Psychology Review, 39(1), 3 21. Vaughn, S., & Fletcher, J. M. (2012). Response to intervention with secondary school students with reading difficulties. Journal of Learning Disabilities, 45(3), 244 256. Available from https://doi.org/10.1177/0022219412442157. Vaughn, S., Wexler, J., Leroux, A., Roberts, G., Denton, C., Barth, A., & Fletcher, J. (2012). Effects of intensive reading intervention for eighth-grade students with persistently inadequate response to intervention. Journal of Learning Disabilities, 45(6), 515 525. Available from https://doi.org/10.1177/0022219411402692. Yell, M. L., & Stecker, P. M. (2003). Developing legally correct and educationally meaningful IEPs using curriculum-based measurement. Assessment for Effective Intervention, 28(3 4), 23 88. Available from https://doi.org/10.1177/073724770302800308. Ysseldyke, J. E., Algozzine, B., & Epps, S. (1983). A logical and empirical analysis of current practices in classifying students as learning disabled. Exceptional Children, 50, 160 166. Available from https://doi.org/10.1177/001440298305000207.

Chapter 5

Educational therapy Louise Spear-Swerling Department of Special Education, Southern Connecticut State University, New Haven, CT, United States

Ellen Garvey is a clinical psychologist at an agency that does independent evaluations of children with learning problems. Recently Dr. Garvey evaluated two boys, Jamie and Eli, who both happened to be fourth graders although they were from different school districts. Each had been referred based on concerns about his reading comprehension. Dr. Garvey noticed that the boys had almost opposite patterns of performance in their evaluations. Jamie had serious difficulties with word decoding and spelling, but on tests of oral language comprehension, he had strengths in all areas except for his weak phonological skills. Jamie’s reading comprehension was indeed poor, but that weakness was clearly due to his poor decoding. In contrast, Eli had strong decoding skills and grade-appropriate spelling, as well as good performance on phonological-processing measures. However, his poor reading comprehension was accompanied by multiple oral language weaknesses in areas such as vocabulary and syntax. Furthermore, Dr. Garvey noticed that each boy also had nearly opposite patterns of performance in mathematics and written expression. Whereas Jamie had difficulties with calculation skills, he had a strong understanding of mathematics word problems that were read aloud to him. He knew which information was relevant, which operation to use, and so on, even though frequent calculation errors sometimes caused him to come up with the wrong answer to the problem. Eli had the opposite pattern in mathematics; his calculation skills were grade appropriate, but he had weak problem-solving skills. In written expression, although Jamie was a poor speller, he had good ideas for writing and knew what he wanted to say, although he could not always get this information down on paper without adult help because of his poor spelling. Eli’s spelling was a strength, but his ability to put his thoughts into writing—for example, his word choice and sentence structure—was a significant weakness. Dr. Garvey was intrigued by these patterns, in part because she had seen similar patterns in other work with children during her training. The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00005-8 © 2020 Elsevier Inc. All rights reserved.

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This chapter describes the educational usefulness of reading profiles in understanding a variety of learning problems—including some difficulties in mathematics as well as literacy. An understanding of the different reading profiles underlying Jamie’s and Eli’s difficulties could help Dr. Garvey integrate a wide array of assessment data and provide valuable implications for making educational recommendations. I begin with a description of important component abilities in reading, mathematics, and written expression, and how different profiles manifest as different patterns of strengths and weaknesses across these component abilities. The next section of the chapter focuses on features of effective educational therapy, in mathematics and writing as well as reading, for students with different profiles. The concluding section of the chapter presents ways to communicate with parents and help them find appropriate treatment for their children.

Important component abilities in achievement at the elementary level In reading, a component ability has been defined as one that can have an independent influence on performance and development in reading comprehension (Hoover & Gough, 1990). Components of writing and mathematics can be conceptualized in a similar manner, as abilities that can have an independent influence on performance and growth in broad written expression or overall math achievement. Components of oral language also play key roles in academic achievement and are fundamental to understanding the different reading profiles that will be discussed later. I focus on component abilities important to achievement at the elementary level, because most research has centered upon this level. However, the componential approach outlined here can be helpful in understanding struggling learners of all ages, including secondary students, because these learners often have weaknesses in foundational components of reading, writing, or mathematics. Table 5.1 summarizes these component abilities, with a brief explanation of each one, including sample assessment tasks and a few examples of standardized tests for assessing each component.

Important components of oral language and reading Many research studies, scholarly reviews, and meta-analyses (e.g., Foorman et al., 2016; National Reading Panel [NRP], 2000; National Research Council, 1998; Stanovich, 2000) have emphasized the importance of five components of reading: phonemic awareness, phonics (decoding), fluency, vocabulary, and comprehension. Three of these components involve oral language abilities rather than actual reading. Phonemic awareness involves sensitivity to and the ability to manipulate sounds in spoken words—for example, the ability to segment a spoken word into its constituent sounds

TABLE 5.1 Important components of reading, written expression, and mathematics. Domain/components

Description (example of assessment task)

Examples of standardized tests/subtests

Reading and oral language

Phonemic awareness

Awareness of, sensitivity to, and the ability to manipulate, individual phonemes (sounds) in spoken words (e.g., in phoneme deletion tasks, child is asked to say “wrote.” Now say wrote without the /t/,” i.e., row)

CTOPP-2 Phonological Awareness; WJ-IV Segmentation

Phonics (also called word decoding or word attack)

Knowledge of letter sounds and the ability to apply that knowledge in reading unfamiliar printed words (e.g., child is asked to decode nonsense words such as streck)

WJ-IV Word Attack; WIAT-III Pseudoword Decoding

Fluency

The ability to read text accurately, with ease, and with appropriate speed; fluent oral reading also includes good phrasing and intonation (prosody) (e.g., child is asked to read a grade-appropriate passage orally under timed conditions)

GORT-5 Rate; WJ-IV Sentence Reading Fluency; WIAT-III Oral Reading Fluency

Vocabulary

Knowledge of the meanings of individual words, usually assessed orally (e.g., child is asked to point to the correct picture when the examiner says a word)

WIAT-III Receptive Vocabulary; PPVT-5

Comprehension

The ability to understand sentences or passages read aloud (listening comprehension) (e.g., child listens to a passage read aloud by the examiner and then answers questions about it)

WJ-IV Oral Comprehension; WIAT-III Oral Discourse Comprehension (Continued )

TABLE 5.1 (Continued) Domain/components

Description (example of assessment task)

Examples of standardized tests/subtests

Mathematics

Math concepts

Grasp of key concepts in math such as place value, meaning of operations, or fractions (e.g., child is asked to point to a picture that represents a specific fraction)

KeyMath 3—Basic Concepts subtests

Fact fluency (automatic recall)

Ability to recall from memory the answers to basic facts in addition, subtraction, multiplication, division—such as 4 1 4, 16 2 9, 5 3 5 (e.g., child is given a series of facts to solve in writing under timed conditions)

WJ-IV Math Facts Fluency; WIATIII Math Fluency subtests

Procedural knowledge

Knowledge of the series of written steps needed for solving a multidigit calculation problem such as addition with regrouping (e.g., child is given a series of multidigit calculations to solve in writing, usually untimed)

WJ-IV Calculation; WIAT-III Numerical Operations

Problem-solving

Ability to solve word problems appropriate to the child’s grade (e.g., child is given a series of word problems to solve, with problems read aloud so that reading is not required, and with consideration of possible impact of calculation weaknesses on answers)

WJ-IV Applied Problems; KeyMath 3—Applications Subtests

Written expression

Basic writing skills

Foundational skills such as spelling, handwriting (keyboarding), capitalization, punctuation, and sentence structure (e.g., child is given a series of written sentences and must identify the spelling, grammar, or capitalization error, then tell how to correct it)

WJ-IV Spelling; WJ-IV Editing; WIAT-III Spelling

Text composition

The ability to translate one’s thoughts into language as well as organize and sequence one’s ideas in writing (e.g., child is asked to respond to a prompt in writing on a particular topic, such as his/her favorite game)

WIAT-III Essay Composition; TOWL-4 Story Composition

Writing processes

The ability to use planning, revision, editing, and other processes involved in producing a lengthy piece of writing (e.g., a checklist or rubric might be employed to evaluate the child’s use of these processes)

Usually assessed informally

Note: CTOPP-2, Comprehensive Test of Phonological Processing, 2nd edition; GORT-5, Gray Oral Reading Test, 5th edition; PPVT-5, Peabody Picture Vocabulary Test, 5th edition; TOWL-4, Test of Written Language, 4th edition; WIAT-III, Wechsler Individual Achievement Test, 3rd edition; WJ-IV, Woodcock Johnson Tests of Achievement/Oral Language.

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(e.g., fish 5 /f/, /i/, /sh/). Phonemic awareness and other phonologicalprocessing abilities, such as phonological memory, have a strong relationship to the development of decoding skills. Vocabulary involves knowledge of word meanings and is usually assessed orally in order to avoid confounding vocabulary knowledge with decoding skill. Comprehension involves broad listening comprehension and, like vocabulary, is important to assess orally as well as through conventional reading comprehension measures. The other two components of reading involve print. Phonics or decoding is the ability to read unfamiliar words using knowledge of letter sounds and the alphabetic code. Decoding skills generally are assessed through the use of nonsense words (e.g., plake), which unlike real words, a child cannot have memorized. Fluency is the ability to read grade-appropriate passages not only with accuracy, but also with reasonable ease and speed. Prosody, or the ability to read aloud with appropriate intonation and phrasing, is an important characteristic of fluent oral reading. In addition to the components of language just mentioned—phonological abilities, oral vocabulary, and broad listening comprehension—further evaluation of individual students’ oral language abilities may sometimes be warranted. In particular, for students at risk of significant oral language problems, evaluation in specific areas of language (e.g., pragmatic language or syntax) may provide important insights into the student’s academic difficulties.

Important components of math At the elementary level, important components of mathematics consist of basic math concepts, automatic recall of facts (fact fluency), procedural knowledge, and the ability to solve word problems (National Math Advisory Panel [NMAP], 2008). Examples of essential math concepts in these grades include an understanding of place value and base ten; the meaning of operations such as addition, subtraction, multiplication, and division; and fraction concepts. Facts are basic whole-number equations in the four operations— for instance, 2 1 3, 10 2 4, 6 3 5, 1243—whose answers ideally should be memorized in order for children to fluently perform more advanced computations. To perform multidigit calculations easily, children need not only fluent fact recall, but also procedural knowledge, such as how to regroup to solve an item like 500 2 124, or how to perform the series of steps needed to solve a long division problem. Word problems involve applying calculation skills, but they also require reasoning about a problem. These reasoning skills include recognizing the correct operation needed to solve a problem, the relevant information for solving the problem, and the series of steps for solving a multistep problem, such as adding several numbers then subtracting from that total.

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Important components of written expression Research on written expression (e.g., Abbott, Berninger, & Fayol, 2010; Berninger, 2009; Graham, MaCarthur, & Fitzgerald, 2007) has highlighted the importance of at least three component abilities in written expression achievement: basic writing skills, text composition, and writing processes. Basic writing skills include not only spelling and handwriting (or keyboarding), but also conventions such as capitalization, punctuation, and sentence structure (e.g., writing complete sentences as opposed to fragments or runons). Text composition requires translating one’s thoughts into language as well as organizing those thoughts—for example, choosing the appropriate word to convey one’s meaning or sequencing one’s ideas in a way that will make sense to the reader. Writing processes such as planning, revising content, and editing for mechanics are also critical to good written expression. Except in the earliest grades, few students produce a strong piece of writing in a single draft or without any advance planning.

Developmental shifts and interrelationships In each academic domain, although all components are ultimately important to children’s achievement, each component is not equally important at all stages of typical children’s development. Rather, the importance of a given component often shifts with development. In reading, phonemic awareness and decoding skills are especially central to the early stages of learning to read, when children are learning the alphabetic code (Foorman et al., 2016; NRP, 2000). In the later grades, when typical readers have well-developed decoding skills and the comprehension demands of the texts used in school escalate, vocabulary and comprehension abilities become increasingly important to further growth in reading (Chall, 1983). Similarly, the ability to use written expression processes such as planning and revision is somewhat less central to success in writing in the earliest grades, but becomes especially important in adolescence (Graham & Perin, 2007), when students are expected to produce lengthier, more complex pieces of writing. At any stage of development, components also interact with each other in important ways. A particularly common dynamic across all academic domains involves the way that difficulties in a lower-level foundational skill can impair performance in a higher-level component area. For example, labored decoding may drain a child’s attentional resources for understanding a text, leading to poor reading comprehension even though the child’s oral language comprehension abilities are strong. Similarly in mathematics, poor fact fluency can create a drain on the student’s ability to perform more complex calculation procedures or solve word problems (Namkung & Fuchs, 2016); in written expression, weaknesses in basic writing skills can negatively affect a student’s text composition and motivation to write

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(Abbott et al., 2010). If clinicians recognize this dynamic and identify component weaknesses correctly, then accommodations or assistive technology can often help students function in higher-level areas while they continue to work on their foundational weaknesses in intervention. Various components also have important interrelationships across domains. Decoding and spelling abilities correlate strongly with each other; children who are poor decoders are almost always poor spellers as well (Ehri, 2005). Broad language comprehension plays a key role not only in reading comprehension but also in math problem-solving (Fuchs, Gilbert, Fuchs, Seethaler, & Martin, 2018). Children with limitations in vocabulary will likely have difficulties with word choice in writing, as well as with reading comprehension and perhaps also mathematics problem-solving. These interrelationships help explain why, as Dr. Garvey found, information about individual poor readers’ profiles also has implications for understanding and successfully addressing difficulties they may have in writing or mathematics.

Common profiles of academic difficulties Interdisciplinary research on language and reading (e.g., Catts, Compton, Tomblin, & Bridges, 2012; Catts, Adlof, & Weismer, 2006; Cutting et al., 2013; Kieffer, 2010; Norbury & Nation, 2011; Spear-Swerling, 2004, 2015; Valencia, 2011) suggests that at least three profiles of reading difficulties are common. These profiles involve specific word recognition difficulties (SWRDs), specific reading comprehension difficulties (SRCDs), and mixed reading difficulties (MRDs). Children can certainly be identified with other kinds of learning disorders, such as mathematics or writing disabilities that are not associated with poor reading. Nevertheless, reading problems are very common among students with learning disorders, and poor reader profiles are quite useful for understanding these students’ academic difficulties. The profiles are summarized in Table 5.2 and discussed further below.

Three common profiles of poor reading SWRDs involve core weaknesses in decoding, usually related to phonological weaknesses, coupled with at least average vocabulary knowledge and broad language comprehension (Catts, Adlof, & Weismer, 2006; Leach, Scarborough, & Rescorla, 2003). In addition to their difficulties reading individual words, children with SWRDs also frequently have problems with reading fluency and reading comprehension. However, their difficulties in these latter areas are associated entirely with problems in word reading, not language comprehension. Poor fluency may be due either to inaccurate or nonautomatic reading of individual words. Likewise, for these children, poor reading comprehension is caused entirely by problems in word reading; if children can decode a text accurately and with reasonable fluency, then they

TABLE 5.2 Common profiles of reading difficulties with frequently associated difficulties in math and writing. Reading profile

Word recognition

Phonemic awareness

Reading comprehension

Reading fluency

Oral comprehension

Mathematics

Written expression

Specific word recognition difficulties (SWRDs)

Below average, because of inaccurate or nonautomatic decoding

Usually below average; other phonologicalprocessing abilities (e.g., phonological memory) may also be weak

Usually below average, due to problems with word reading, not broad language comprehension; some students can compensate and score in average range in reading comprehension

Usually below average, due to problems with inaccurate or nonautomatic word reading, not broad language comprehension

Average or better broad oral comprehension and vocabulary

May be unimpaired, but any math difficulties tend to involve fact fluency and calculation, not core problemsolving abilities

Basic writing skills, especially spelling, below average; other components of writing may be unimpaired

Specific reading comprehension difficulties (SRCDs)

Average or better

Average or better

Below average, but not because of decoding; often due to language comprehension weaknesses

May be unimpaired, but any difficulties are based in language, not word reading

Usually below average in one or more areas such as vocabulary, syntax, pragmatics

May be unimpaired, but difficulties in problem-solving most common

Text composition often below average; basic writing skills such as spelling often unimpaired

Mixed reading difficulties (MRDs)

Below average, because of inaccurate or nonautomatic decoding

Usually below average; other phonologicalprocessing abilities may also be weak

Below average, due to a combination of poor word reading (decoding) and language comprehension weaknesses

Usually below average, due to a combination of poor decoding and language comprehension weaknesses

Usually below average in one or more areas such as vocabulary, syntax, pragmatics

May have generalized difficulties in both calculation and problemsolving

Usually, generalized difficulties in both basic writing skills and text composition

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can comprehend it. Jamie, the first child described at the outset of this chapter, had a profile of SWRD. SWRD is a common profile in students with dyslexia, which was Jamie’s diagnosis. SRCDs involve the opposite profile, one that is distinct from dyslexia (Cutting et al., 2013). Poor readers with SRCDs have at least average decoding and phonological skills, but nevertheless, their reading comprehension is impaired (Leach et al., 2003). Usually students’ reading comprehension difficulties are associated with oral language weaknesses in areas such as vocabulary, broad listening comprehension, or higher-level language abilities such as pragmatics (Catts et al., 2006; Norbury & Nation, 2011). Therefore these students tend to display comprehension weaknesses not only during reading but also in listening activities, such as during teacher read-alouds or class discussions. If students with SRCDs have problems with reading fluency, those problems are based in language, not decoding. For example, students with this profile might read text slowly because they are having trouble comprehending it (Valencia, 2011). Eli’s profile exemplified SRCDs. He had a reading disability, but one different from dyslexia. Children with a mixed profile, or MRDs, have a combination of the above types of difficulties (Catts et al., 2006; Leach et al., 2003). They not only have weaknesses in decoding and phonological skills but also have a core comprehension component to their reading problems. Clinicians might encounter a profile of MRD in students with broad language disabilities or in some students with autism spectrum disorders (Norbury & Nation, 2011). Although students with MRDs often have weaknesses in listening comprehension, their reading comprehension typically is more impaired than their listening due to the additional influence of poor decoding. Fluency problems in students with MRDs may relate to both factors, poor decoding and language weaknesses. Knowledge about individual students’ profiles can help clinicians understand and integrate a wide array of assessment data, with important implications for instruction, assessment, and accommodations (Catts, Kamhi, & Adlof, 2012; Spear-Swerling, 2015). To be effective, interventions must properly target a child’s component weaknesses. Children with SWRDs and MRDs generally benefit from phonics interventions, whereas those with SRCDs do not, because their difficulties lie outside the domain of decoding (Aaron, Joshi, Gooden, & Bentum, 2008). Students with MRDs need more than phonics intervention to be successful; they also require intervention targeting the source(s) of their comprehension weakness, such as vocabulary, background knowledge, syntax, or comprehension monitoring (Oakhill, Cain, & Elbro, 2015). In assessment, oral reading fluency CBMs may be very useful for monitoring progress in children with SWRDs and MRDs but probably not for students with SRCDs, who will require assessments more focused on comprehension. Students with SWRDs often benefit from accommodations that involve listening to text that is too difficult for them to read, whereas for those with SRCDs, merely hearing a text read aloud is less helpful (Erickson, 2013).

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Implications of the profiles for math and written expression Reading profiles involve constellations of strengths and weaknesses in important cognitive-linguistic abilities that underlie literacy and math achievement. Therefore the profiles have implications for children’s functioning in math and written expression as well as reading. These implications are summarized in the two right-hand columns of Table 5.2. Children with SWRDs such as Jamie usually have weaknesses in basic writing skills, especially spelling. However, with remediation of or assistive technology for basic writing skills, their text composition can often be good. In math, the phonological weaknesses characteristic of many children with SWRDs may sometimes affect fact recall and calculation skills (Peng et al., 2016; Simmons & Singleton, 2008); if the child has co-occurring ADHD, calculation seems especially likely to be affected (Fuchs et al., 2008). However, because these children’s broad language abilities are unimpaired, core problem-solving abilities often are a strength, especially with remediation of their calculation weaknesses or a calculator to compensate for them. Conversely, children with SRCDs such as Eli have good phonological spelling skills, but their underlying language weaknesses—such as poor vocabulary knowledge or syntactic weaknesses—can be expected to affect their writing as well as their reading. Vocabulary and other language problems may also affect problem-solving in mathematics (Fuchs et al., 2018). For example, if children do not understand the meaning of words such as increase and decrease, or if they have trouble understanding the syntax of word problems, then they may have difficulty choosing the correct operation or filtering out irrelevant information. Children with a mixed profile may tend to display relatively broad types of achievement weaknesses, with difficulties in many component areas. As is true for the other profiles, these difficulties can range from very mild to severe. Neither Jamie nor Eli had any co-occurring conditions such as ADHD or executive function difficulties, which could also affect a child’s profile. For instance, some students with co-occurring dyslexia and ADHD might display a mixed profile, with word reading problems due to dyslexia, and with ADHD contributing to problems in both word reading and comprehension. As another example, a student with dyslexia who comes from a low-income background might have experientially based vocabulary limitations; this kind of student might also display an MRD profile rather than the usual SWRD profile because of his or her weakness in vocabulary. Moreover, whatever their profile, students with serious reading difficulties may sometimes receive instruction in math or writing that is less than ideal. For example, the intensity of instruction needed to improve the student’s reading may allow for limited time to address other academic areas, or poor reading may interfere with the student’s ability to benefit from general education math or writing instruction. All of these factors—co-occurring disabilities, experience, and instruction— may affect a child’s profile of difficulties and should be taken into account.

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Finally, the purview of this chapter involves children with learning problems, so the examples provided emphasize various disabilities. However, reading profiles also yield important insights about more experientially based reading problems. Such problems include those characteristic of English learners or children from poverty backgrounds (e.g., Kieffer, 2010). Profiles thus have broad utility for many types of practitioners (Spear-Swerling, 2015).

Effective educational therapy Effective educational interventions for students with different poor reader profiles involve a range of explicit, systematic teaching approaches, with greater levels of intensity for students with greater levels of difficulty. As part of explicit teaching, visual aids or manipulatives benefit many children’s learning, in both literacy and mathematics.

Characteristics of explicit, systematic teaching Explicit, systematic teaching approaches have been found especially valuable to at-risk and struggling readers (Archer & Hughes, 2011; Brady, 2011; Hooper et al., 2013; Kilpatrick, 2015; NRP, 2000) and mathematics learners (Gersten et al., 2009; NMAP, 2008). Characteristics of this kind of instruction are displayed in Table 5.3, with examples for some specific components of reading, mathematics, and written expression. In explicit instruction, important concepts and skills are taught directly by the teacher through clear explanation and modeling; children are not expected to develop these skills solely through exposure or incidental teaching. For instance, for written expression, the teacher explicitly models and explains important skills such as proper letter formation in handwriting or planning strategies for text composition, rather than expecting children to largely infer these skills from exposure. Systematic means that skills are taught according to a logical sequence, one that also takes into account research on children’s development. A related characteristic is that instruction ensures mastery of prerequisite skills before children move on to a more complex skill. For example, in reading, children are not expected to decode difficult short-vowel words such as splint before they can decode simpler words such as sit or spin. In mathematics, they are not expected to perform long division when they have not yet mastered the multiplication or subtraction skills required for long division. To ensure mastery of prerequisite skills, ongoing formative assessment and a criterion for mastery are essential. Without formative assessment, teachers often cannot reliably determine if an individual child has mastered a particular skill, and without a criterion for mastery, it will be difficult to know when it is appropriate to move on to a more complex skill. Mastery criteria vary somewhat depending on the skill being evaluated and the

TABLE 5.3 Characteristics and examples of explicit instruction in different academic domains. Characteristic

Examples in reading

Important skills and concepts are taught directly by the teacher

G

G

Instruction is systematic, with important prerequisite skills taught before more complex skills

G

G

Examples in mathematics

Vocabulary: Teacher explains the meaning of essential words that children need to comprehend a text Comprehension: Teacher explains usefulness of a narrative graphic organizer and models how to use it to help understand a specific text

G

Decoding: Children are taught sounds for the letter patterns oa and ea before being expected to decode words (e.g., float, steam) with these patterns Fluency: Accurate text reading at a given level is developed before children practice building speed of text reading at that level

G

G

G

Examples in writing

Procedural knowledge: Teacher explains and models the procedure for multiplying by a two-digit multiplier (e.g., 32 3 45) Concepts: Teacher models how to represent a three-digit number with base ten blocks, explaining how the representation links to the written numeral

G

Procedural knowledge: Children are taught procedures for twodigit addition without regrouping before they learn procedures for regrouping Problem-solving: Children are taught easier problem types (e.g., one-step, no irrelevant information) before harder problem types (e.g., two-step, with irrelevant information)

G

G

G

Basic writing skills: Teacher explains a spelling rule (e.g., for adding endings to a base word) and models how to apply it to appropriate words Writing processes: Teacher explains why planning processes are important and models a planning strategy such as outlining Basic writing skills: Children are taught the most common conventions such as ending punctuation before less common ones such as using quotation marks Text composition: Children are taught to write correct sentences before more lengthy, complex pieces of writing (Continued )

TABLE 5.3 (Continued) Characteristic

Examples in reading

Appropriate examples are used in instruction

G

G

Prompt corrective feedback is provided to children’s errors

G

G

Examples in mathematics

Decoding: Teacher provides examples of silent e words that avoid phonetically irregular words such as have or done Vocabulary: Teacher provides appropriate examples and nonexamples of a new vocabulary word such as vehicle

G

Fluency: If a child misreads a word when reading text aloud, teacher points to the word and provides cues for correction as needed; child then rereads the sentence to establish fluency Comprehension: Teacher asks frequent comprehension questions during children’s reading and provides feedback, or cues children to reread a relevant part of the text, if they have misunderstood something in it

G

G

G

Examples in writing

Procedural knowledge: Teacher avoids examples with zeroes in introducing a new skill, because zeroes tend to be confusing to children Problem-solving: If the teacher is teaching a new problem type such as subtraction comparison, problems fit this structure; they may later be mixed with previously mastered problem types for cumulative review

G

Fact fluency: Teacher monitors whether a child is applying the counting-down strategy correctly, and provides feedback if the child is getting the wrong answer (e.g., because s/he is starting with the wrong number) Concepts: Teacher assesses whether students understand that the denominator of a fraction shows how many equal parts into which a whole is divided; appropriate feedback is provided to those who do not

G

G

G

Text composition: Teacher provides multiple examples of good text models (e.g., for a good concluding paragraph) and explains why they are effective Writing processes: Teacher provides and explains an example of an effective strategy for editing one’s work

Basic writing skills: Teacher provides targeted feedback to individual students about specific errors in spelling, punctuation, or sentence structure in written compositions Text composition: Teacher provides targeted feedback to individual students about specific problems in clarity of writing, word choice, or organization

Children perform at an appropriate criterion level of mastery before moving on to the next skill

G

G

Decoding: Children demonstrate mastery of consonant-vowelconsonant words (e.g., lap, fit, men) before being expected to decode more difficult short-vowel words with consonant clusters (e.g., strap, thump) Fluency: Children meet benchmarks for fluent reading of easier texts (e.g., Grade 2) before practicing fluent reading of more difficult ones (e.g., Grade 3)

G

G

Fact fluency: Children demonstrate a high level of accuracy solving subtraction facts under untimed conditions before they are expected to solve subtraction facts quickly under timed conditions Problem-solving: Children demonstrate mastery of basic one-step word problems before they are expected to solve twostep problems

G

G

Basic writing skills: In spelling, children master spelling onesyllable base words before learning to spell those words with endings Writing processes: Children master specific conventions (such as specific capitalization rules, e.g., capitalizing names) before being expected to edit their compositions for those conventions

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instructional program, but at a minimum, are usually set at 80% or higher. For a very basic foundational skill such as letter or numeral recognition, a mastery criterion close to 100% may be desirable, but if the criterion is set this high for an area such as reading comprehension, math problem-solving, or text composition, making progress will be difficult. If a teacher is using an explicit, systematic program, it will usually provide mastery criteria for moving on to the next step. Example choice is also very important in explicit, systematic teaching. For instance, continuous-sound phonemes such as /s/ or /f/ are easier to blend than stop consonants such as /g/, /b/, or /t/; therefore particularly for children with phoneme blending difficulties, decoding instruction begins with words such as sun or fan before introducing words such as tap or big. In mathematics, children often find problems containing zeroes more confusing than other examples, even ones that involve the same procedure. Therefore a teacher who is introducing children to two-digit subtraction with regrouping would start with an item such as 52 2 26 rather than 50 2 26. Another characteristic of explicit, systematic teaching involves the provision of prompt corrective feedback to errors. If a child trying to decode fun misread the word as “fan,” a teacher might point to the vowel to draw the child’s attention to it, asking, “What sound does short u make?” If a young math student failed to regroup on an item like 52 2 26, yielding the answer 34, the teacher might draw the child’s attention to the ones column, asking, “Can you subtract 6 from 2?” For written expression, the teacher would provide targeted feedback about students’ compositions that would help individual students improve their writing. Intensity of intervention is an important consideration. A child with mild learning needs might make good progress with a small-group intervention that meets two or three times a week, whereas one who is further behind or who has more severe difficulties might make little or no progress under these conditions. Intensity of instruction is most often manipulated by decreasing group size, increasing children’s amount of intervention time, or increasing explicitness of intervention (e.g., providing more scaffolding by the teacher). Intensity is an important variable for clinicians to consider not only when first making recommendations, but also when a child’s current educational therapy does not appear to be effective. In the latter case, sometimes the problem is not an inappropriate intervention, but rather insufficient intensity of intervention.

The benefits of visual aids and manipulatives Visual aids and manipulatives are useful for many components of literacy and mathematics. Graphic organizers—such as those for narrative texts or different types of informational texts—are known to improve comprehension (NRP, 2000), and manipulatives such as the Story Grammar Marker (Moreau

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& Fidrych, 1994) can be beneficial for teaching comprehension as well. Graphic organizers are also very useful in writing instruction (Troia, 2014). If structured using appropriately patterned words, word-building activities with letter tiles can be quite effective for decoding and spelling instruction (McCandliss, Beck, Sandak, & Perfetti, 2003). Visual aids or manipulatives also are vital in math, particularly for teaching basic concepts and problemsolving (NMAP, 2008). For example, children who are learning fraction concepts can be taught how to draw simple pictures to represent different fractions such as half, one-thirds, two-thirds, and so on; in problem-solving, pictures can be useful for reasoning about a problem, such as determining the correct operation to use to solve it. These kinds of aids are quite compatible with explicit, systematic teaching and should be integrated with it as suitable for an individual student’s needs.

Appropriate curricula and materials Even for a very accomplished teacher, it is quite difficult to teach skills in a highly explicit, systematic manner without using a structured curriculum and set of materials. Multiple commercial programs in reading and mathematics—and to a lesser extent, in writing—incorporate the features of explicit, systematic teaching. There is not a single best program, although clinical judgment may suggest that a particular program is a better fit to an individual student. For example, a child whose decoding problems are accompanied by severe weaknesses in phonemic awareness may benefit from a decoding program emphasizing that area, whereas for a poor decoder with good phonemic awareness and problems centering upon multisyllabic words, a different decoding program might be more appropriate. Clinicians should speak with knowledgeable educators about the features of different programs, examine such programs themselves, and consult helpful web resources for evidence-based information about effective interventions. Good web resources for this purpose include the Institute for Education Sciences (IES) practice guides (http://ies.ed.gov/ncee/wwc/publications_reviews.aspx), the Center on Instruction (www.centeroninstruction.org), the Iris Center at Vanderbilt University (https://iris.peabody.vanderbilt.edu/), the VaughnGross Center and Meadows Center at University of Texas at Austin (https:// www.meadowscenter.org/vgc/), and the Florida Center for Reading Research (www.fcrr.org).

Application to children with different poor reader profiles Children with different reading profiles need interventions focused on their specific component weaknesses in reading, writing, and (sometimes) math. In addition, like all children, those with learning disorders require continued instruction in and opportunities to develop their strengths. However, learning

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in children’s unimpaired component areas can often continue primarily in the general education setting, with appropriate accommodations and assistive technology to ensure access. Specific word recognition difficulties. Children with SWRDs require highly explicit, systematic phonics instruction targeted to their current skill levels. A beginning decoder may need to start phonics instruction with very basic consonant-vowel-consonant (CVC) words, whereas an older poor decoder may have mastered decoding one-syllable words and may require a focus on two-syllable or multisyllabic words. Criterion-referenced phonics assessments should be used to help target instruction appropriately. For Jamie, these assessments showed that although he had mastered decoding of CVC and other short-vowel words, he still needed work on many other types of one-syllable words such as those with silent e and vowel team patterns; therefore his intervention had to start with these types of words. It is important to note that, while research reviewed by the National Reading Panel (2000) did not find significant differences in efficacy among different types of systematic phonics approaches, post-NRP research (e.g., Brady, 2011; Christensen & Bowey, 2005) favors synthetic-phonics approaches that teach letter sounds and phoneme blending over larger-unit approaches such as onset-rime or word families. In these latter approaches, phonics instruction focuses on larger units such as onsets and rimes, which are intrasyllabic linguistic units intermediate between phonemes and syllables, or on whole words. For example, to decode an unfamiliar vowel team word such as float, in an onset-rime approach Jamie would learn the sound for the onset fl- and the rime oat, as well as how to blend those two parts; in a word families approach, he would learn highly patterned rhyming words (e.g., boat, coat, goat), with an expectation that he would then be able to infer how to decode float by analogy. However, in a phoneme-level synthetic-phonics approach, intervention would emphasize learning sounds for common letters and letter patterns, as well as phoneme-level blending skills, from the start. For a word such as float, Jamie would learn the sound for the letter pattern oa, as well as how to blend it with previously learned single consonant sounds (i.e., f, l, t) to produce the whole word float. In addition to learning the important skill of phoneme blending, Jamie would also apply his decoding skills to many other words with oa such as throat, soap, road, groan, oak, soak, and so on. Eventually, as he progressed to more advanced stages of decoding, Jamie also would learn to recognize and decode larger units in words, such as morphemes involving common roots, prefixes and suffixes. This type of initial phoneme-level synthetic-phonics approach is the one most likely to benefit Jamie (Brady, 2011). As part of phonics instruction, many children with SWRDs require phonemic awareness instruction. Furthermore, even older children beyond the initial stages of learning the alphabetic code may benefit from advanced phonemic awareness intervention to build orthographic mapping and automatic

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word recognition (Kilpatrick, 2015). Applications to text reading, including oral reading of text with a knowledgeable teacher who provides appropriate corrective feedback, are essential to include in lessons in order to build fluency. Both repeated reading of the same text and continuous wide reading (e.g., of a range of texts) may help improve students’ reading rate (NRP, 2000; Wexler, Vaughn, Roberts, & Denton, 2010). Children with SWRDs typically have problems in spelling and basic writing skills, although sometimes children’s writing problems are overlooked until they reach the later elementary grades, and writing receives more emphasis in schooling. Explicit teaching of spelling should be integrated with children’s phonics interventions, so that as children are learning to decode a particular word pattern, they also learn how to spell that pattern. For common phonetically irregular words (e.g., the, have, was, were), multisensory techniques such as repeatedly tracing and saying the letters in the word can be helpful. Once children have mastered basic letter-sound correspondences and spellings of simple words, they should learn common spelling generalizations, such as rules for adding endings to a base word (e.g., stop 1 -ing 5 stopping). Children’s writing instruction should also include explicit, systematic teaching of other basic writing skills such as handwriting, keyboarding, punctuation, and capitalization. If a child’s difficulties are accompanied by calculation weaknesses, explicit teaching of basic numerical skills should be helpful (Peng et al., 2016). Depending on the child’s specific needs, these skills may include basic number concepts (e.g., quantity comparisons) and fact strategies such as counting up in addition, counting down in subtraction, and counting by series for multiplication and division (e.g., counting by 5s, 10s, 2s, and 3s). For basic number concepts and fact accuracy, visuals and manipulatives are important. Once children can solve facts accurately, timed practice activities to build automatic recall, starting with an easier subset of facts and building to additional facts as a child demonstrates fluency on easier subsets, can be beneficial. Teachers should also provide explicit, systematic instruction on procedures, such as those for regrouping (see, e.g., Stein, Kinder, Silbert, Carnine, & Rolf, 2018). Specific reading comprehension difficulties. Children with this profile need instruction that is targeted to their specific comprehension weaknesses, which may vary for different children with SRCDs. Evaluation of areas such as vocabulary, syntax, and pragmatics should be used to help target the specific comprehension weaknesses of individual students. Eli’s difficulties revolved around vocabulary knowledge and understanding complex syntax, so he required explicit instruction in those areas. A child with a different underlying pattern of comprehension weaknesses—for example, age-appropriate vocabulary knowledge and syntactic competence but weaknesses in pragmatic language—needs a somewhat different focus.

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Oral language interventions are especially important to integrate in therapy for students with SRCDs. These students appear to do better with intervention that combines oral language with reading comprehension intervention, as opposed to interventions focused on reading comprehension alone (Clarke, Snowling, Truelove, & Hulme, 2010). Furthermore, with proper coordination across academic domains, addressing individual children’s underlying language weaknesses can potentially benefit not only their reading comprehension, but also their problem-solving in math (Fuchs et al., 2018). An example suggested by Fuchs et al. (2018) involves connecting teaching of comparison word problems in math, in which children have to compare two quantities, with compare-and-contrast informational text structure in reading. For students with SRCDs who have poor reading fluency, therapy focused on language comprehension and vocabulary are likely to benefit fluency much more than interventions aimed at accuracy or rate of word reading. Vocabulary interventions should include both general and domainspecific types of words. For instance, in mathematics, teachers should provide explicit teaching of important math vocabulary, such as words that signal different operations (e.g., total, difference) as well as fundamental math terminology (e.g., for children learning fractions, numerator and denominator). Another important area to address for vocabulary development involves morphological awareness, that is, teaching of common roots, prefixes, and suffixes, and how to use them to infer meanings of unfamiliar words. If a student is taught that the root geo means “earth,” that knowledge can be used to help infer the meanings of words such as geology, geologist, or geode. Morphological interventions improve vocabulary with transfer to reading comprehension and appear especially beneficial for struggling learners (Goodwin & Ahn, 2013). Integration of reading comprehension and written expression activities can further develop each area. Teaching students how to write a summary of a text they have read or to use new vocabulary words in writing can help improve the students’ reading, as well as their writing (Graham & Hebert, 2010). As another example, teaching students about important cohesive words in text can improve not only students’ reading comprehension (Oakhill et al., 2015), but also their written expression. For instance, words such as because, therefore, and consequently tend to signal a cause-andeffect relationship. Understanding the relevance of these words can help students comprehend a text better; learning to use the words effectively can benefit the clarity and organization of their writing. With regard to organization of writing, graphic organizers that facilitate reading comprehension may be equally useful for text composition and can also facilitate planning processes in writing (Troia, 2014). Finally children with SRCDs benefit from explicit comprehension strategy instruction, such as explicit teaching about text structure, summarization

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strategies, and comprehension monitoring (Gersten, Fuchs, Williams, & Baker, 2001; NRP, 2000). Again, some of these strategies, such as knowledge about text structure, can potentially improve their writing as well. Recent research suggests that it is not the specific strategies themselves that foster comprehension development, but rather that learning strategies may be a vehicle to promote a more strategic approach to reading in general and more active engagement with the text (Compton, Miller, Elleman, & Steacy, 2014). Educational therapy should therefore not overemphasize comprehension strategy instruction (Willingham, 2006/2007) and should also address other important aspects of comprehension development such as background knowledge, as appropriate to individual students’ needs. Mixed reading difficulties. Students with MRDs require a combination of the types of therapy benefiting students with SWRDs and SRCDs. Again, individual students’ treatment should be targeted to their specific weaknesses in different components of reading, writing, and if relevant, mathematics. Multicomponent interventions that address multiple areas of difficulty, such as basic writing skills and text composition (e.g., Hooper et al., 2013), or word reading and comprehension (e.g., Gelzheiser, Scanlon, Vellutino, Hallgren-Flynn, & Schatschneider, 2011), may be especially helpful for students with MRDs. In addition, because students with MRDs can have many needs across multiple academic areas, setting priorities for therapy is especially important. Consider a sixth grade student whose decoding skills are extremely low, but who also has mild vocabulary limitations. This type of student would benefit from a strong initial treatment emphasis on decoding, but still with some attention to oral vocabulary development that should facilitate the student’s reading comprehension in more advanced text as the student’s decoding improves. Priorities can be revised as the student progresses, and his or her most important needs change. For students with MRDs it is especially important to seek ways to address multiple areas efficiently. One good example involves the use of morphological interventions. These interventions can promote students’ word reading and spelling development, as well as their vocabulary development; for example, as they learn the meaning of roots such as geo or psych, they also learn how to read and spell the roots. Other examples of ways to integrate instruction across multiple areas include integration of reading comprehension and text composition or reading comprehension and math problemsolving, using the kinds of activities mentioned previously in this section.

Communicating with parents and finding appropriate therapy Clinicians like Dr. Garvey can use poor reader profiles to help communicate children’s needs to their parents, which can enable parents to better advocate for their children. For instance, if a child with dyslexia has a profile of SWRDs, it is important for parents to understand that although the child may

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perform poorly on measures of reading comprehension, the true problem is decoding, not comprehension. It is also helpful for parents to know if a child’s difficulties in fact fluency and spelling are likely connected to dyslexia, but if other components of math (e.g., problem-solving) and writing (e.g., core text composition abilities) are unimpaired. To enable parents to find appropriate therapy, clinicians should be aware of basic legal provisions for students with disabilities, such as a student’s right to a free appropriate public education and the parents’ right to refer the child for comprehensive evaluation at any time. In the case of independent evaluations such as those done by Dr. Garvey, educators in public school districts are not necessarily obligated to implement evaluators’ recommendations. However, if clinicians frame recommendations in specific, educationally useful ways, this increases the likelihood that educators will pay attention to them. If a child appears to have a disability, but the school district does not agree that he or she is eligible for services, parents can either pursue due process or seek other ways to provide treatment, such as private tutoring. An option for parents who cannot afford private tutoring is to look for services through a good university assessment clinic or reading clinic, where children work with master’s candidates under supervision, and services are provided for free. However, if a child’s difficulties are severe, clinicians should ensure that parents understand the need for appropriate intensity of treatment. For instance, a student with severe dyslexia may need one-to-one or very small-group intervention almost every day, something that is difficult to provide entirely via private tutoring given the expense and after-school time involved. Parents seeking a good tutor should look for specific expertise, training, or program certification on a prospective tutor’s re´sume´. The tutor’s expertise should match the child’s needs. For example, for a child with dyslexia like Jamie, a tutor with training in the Orton-Gillingham approach (see www.ortonacademy.org), the Wilson Reading System (Wilson, 2017), or another systematic phonics program could be an excellent choice, whereas for a child with a specific reading comprehension disability like Eli, parents would want to seek expertise in teaching broad language and comprehension skills. An effective tutor should provide explicit, systematic teaching in the child’s weak areas, with appropriate progress-monitoring assessments to monitor growth and to adjust instruction as needed. Readministration of standardized tests every year or two is useful for more long-term monitoring. Effective therapy should result in meaningful gains being demonstrated on these kinds of measures. Lastly, progress in therapy may be influenced by a wide range of variables, including the nature of the child’s disabilities and his or her weak component areas. Students with problems that are relatively severe or that involve complex comorbidities will tend to progress more slowly than those with milder disorders. Likewise, it is often easier to achieve good progress in

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certain component areas, such as building accuracy in decoding and calculation procedures, than in others, such as fluency in reading or math. Nevertheless, information about common poor reader profiles provides a valuable starting point for clinicians to determine appropriate recommendations for treatment and progress monitoring in children with a variety of learning disorders.

References Aaron, P. G., Joshi, M., Gooden, R., & Bentum, K. (2008). Diagnosis and treatment of reading disabilities based on the component model of reading: An alternative to the discrepancy model of LD. Journal of Learning Disabilities, 41, 67 84. Abbott, R. D., Berninger, V. W., & Fayol, M. (2010). Longitudinal relationships of levels of language in writing and between writing and reading in grades 1 to 7. Journal of Educational Psychology, 102, 281 298. Archer, A., & Hughes, C. (2011). Explicit instruction: Effective and efficient teaching. New York: Guilford. Berninger, V. W. (2009). Highlights of programmatic, interdisciplinary research on writing. Learning Disabilities Research & Practice, 24, 69 80. Brady, S. (2011). Efficacy of phonics teaching for reading outcomes: Indications from post-NRP research. In S. Brady, D. Braze, & C. Fowler (Eds.), Explaining individual differences in reading: Theory and evidence (pp. 69 96). New York: Psychology Press. Catts, H. W., Adlof, S. M., & Weismer, S. E. (2006). Language deficits in poor comprehenders: A case for the simple view of reading. Journal of Speech, Language, and Hearing Research, 49(2), 278 293. Catts, H. W., Compton, D. L., Tomblin, J. B., & Bridges, M. S. (2012). Prevalence and nature of late-emerging poor readers. Journal of Educational Psychology, 104(2), 166 181. Catts, H. W., Kamhi, A. G., & Adlof, S. M. (2012). Defining and classifying reading disabilities. In A. G. Kamhi, & H. W. Catts (Eds.), Language and Reading Disabilities (3rd ed., pp. 47 78). New York: Pearson Education. Chall, J. (1983). Stages of reading development. New York: McGraw-Hill. Christensen, C. A., & Bowey, J. A. (2005). The efficacy of orthographic rime, graphemephoneme correspondence, and implicit phonics approaches to teaching decoding skills. Scientific Studies of Reading, 9, 327 349. Clarke, P. J., Snowling, M. J., Truelove, E., & Hulme, C. (2010). Ameliorating children’s reading-comprehension difficulties: A randomized controlled trial. Psychological Science, 21, 1106 1116. Compton, D. L., Miller, A. C., Elleman, A. M., & Steacy, L. M. (2014). Have we forsaken reading theory in the name of “quick fix” interventions for children with reading disability? Scientific Studies of Reading, 18, 55 73. Cutting, L. E., Clements-Stephens, A., Pugh, K. R., Burns, S., Cao, A., Pekar, J. J., et al. (2013). Not all reading disabilities are dyslexia: Distinct neurobiology of specific comprehension deficits. Brain Connectivity, 3, 199 211. Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9, 167 188. Erickson, K. (2013). Reading and assistive technology: Why the reader’s profile matters. Perspectives on Language and Literacy, 39, 11 14.

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Foorman, B., Beyler, N., Borradaile, K., Coyne, M., Denton, C. A., Dimino, J., et al. (2016). Foundational skills to support reading for understanding in kindergarten through 3rd grade (NCEE 2016-4008). Washington, DC: U.S. Department of Education. Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Government Printing Office. Fuchs, L. S., Fuchs, D., Stuebing, K., Fletcher, J. M., Hamlett, C., & Lambert, W. (2008). Problem solving and computational skill: Are they shared or distinct aspects of mathematical cognition? Journal of Educational Psychology, 100, 30 47. Fuchs, L. S., Gilbert, J. K., Fuchs, D., Seethaler, P. M., & Martin, B. (2018). Text comprehension and oral language as predictors of word-problem solving: Insights into word-problem solving as a form of text comprehension. Scientific Studies of Reading, 22, 152 166. Gelzheiser, L. M., Scanlon, D., Vellutino, F., Hallgren-Flynn, L., & Schatschneider, C. (2011). Effects of the interactive strategies approach extended: A response and comprehensive intervention for intermediate-grade struggling readers. The Elementary School Journal, 112, 280 306. Gersten, R., Beckmann, S., Clarke, B., Foegen, A., Marsh, L., Star, J. R., . . . Scott, L. (2009). Assisting students struggling with mathematics: Response to intervention (RTI) for middle and secondary schools. Washington, DC: Institute of Education Sciences, US Department of Education. ,http://ies.ed.gov/ncee/wwc/publications/practiceguides/.. Gersten, R., Fuchs, L. S., Williams, J. P., & Baker, S. (2001). Teaching reading-comprehension strategies to students with learning disabilities: A review of research. Review of Educational Research, 71, 279 320. Goodwin, A. P., & Ahn, S. (2013). A meta-analysis of morphological interventions in English: Effects on literacy outcomes for school-age children. Scientific Studies of Reading, 17, 257 285. Graham, S., & Hebert, M. A. (2010). Writing to read: Evidence for how writing can improve reading. A Carnegie Corporation time to act report. Washington, DC: Alliance for Excellent Education. Graham, S., MaCarthur, C. A., & Fitzgerald, J. (Eds.), (2007). Best practices in writing instruction. New York: Guilford. Graham, S., & Perin, D. (2007). Writing next: Effective strategies to improve writing of adolescents in middle and high schools A report to Carnegie Corporation of New York. Washington, DC: Alliance for Excellent Education. Hooper, S., Costa, L., McBee, M., Anderson, K., Yerby, D., Childress, A., & Knuth, S. (2013). A written language intervention for at-risk second grade students: A randomized controlled trial of the process assessment of the learner lesson plans in a tier 2 response-to-intervention model. Annals of Dyslexia, 63, 44 64. Hoover, W. A., & Gough, P. B. (1990). The simple view of reading. Reading and Writing: An Interdisciplinary Journal, 2, 127 160. Kieffer, M. J. (2010). Socioeconomic status, English proficiency, and late-emerging reading difficulties. Educational Researcher, 39, 484 486. Kilpatrick, D. A. (2015). Essentials of assessing, preventing, and overcoming reading difficulties. Hoboken NJ: Wiley & Sons. Leach, J. M., Scarborough, H. S., & Rescorla, L. (2003). Late-emerging reading disabilities. Journal of Educational Psychology, 95, 211 224. McCandliss, B., Beck, I. L., Sandak, R., & Perfetti, C. (2003). Focusing attention on decoding for children with poor reading skills: Design and preliminary tests of the word building intervention. Scientific Studies of Reading, 7, 75 104.

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Moreau, M. R., & Fidrych, H. (1994). The story grammar marker: Teacher’s manual. Springfield, MA: MindWing Concepts. Namkung, J. M., & Fuchs, L. S. (2016). Cognitive predictors of calculations and number line estimation with whole numbers and fractions among at-risk students. Journal of Educational Psychology, 108, 214 228. National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education. National Reading Panel. (2000). Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. Washington, DC: National Institutes of Health. National Research Council. (1998). Preventing reading difficulties in young children. Washington, DC: National Academies Press. Norbury, C., & Nation, K. (2011). Understanding variability in reading comprehension in adolescents with autism spectrum disorders: Interactions with language status and decoding skill. Scientific Studies of Reading, 15, 191 210. Oakhill, J., Cain, K., & Elbro, C. (2015). Understanding and teaching reading comprehension: A handbook. New York: Routledge. Peng, P., Namkung, J., Fuchs, D., Fuchs, L., Patton, S., Yen, L., . . . Hamlett, C. (2016). A longitudinal study on predictors of early calculation development among young children at risk for learning difficulties. Journal of Experimental Child Psychology, 152, 221 241. Simmons, F. R., & Singleton, C. (2008). Do weak phonological representations impact on arithmetic development? A review of research into arithmetic and dyslexia. Dyslexia, 14, 77 94. Spear-Swerling, L. (2004). Fourth-graders’ performance on a state-mandated assessment involving two different measures of reading comprehension. Reading Psychology, 25, 121 148. Spear-Swerling, L. (2015). The power of RTI and reading profiles: A blueprint for solving reading problems. Baltimore, MD: Brookes Publishing Co. Stanovich, K. E. (2000). Progress in understanding reading: Scientific foundations and new frontiers. New York: Guilford Press. Stein, M., Kinder, D., Silbert, J., Carnine, D., & Rolf, K. (2018). Designing effective mathematics instruction: A direct instruction approach (5th ed.). New York: Pearson. Troia, G. (2014). Evidence-based practices for writing instruction (Document No. IC-5). Retrieved from University of Florida, Collaboration for Effective Educator Development, Accountability, and Reform Center website ,http://ceedar.education.ufl.edu/tools/innovation-configuration/.. Valencia, S. W. (2011). Reader profiles and reading disabilities. In R. Allington, & A. McGillFranzen (Eds.), Handbook of research on reading disabilities (pp. 25 35). New York: Routledge. Wexler, J., Vaughn, S., Roberts, G., & Denton, C. (2010). The efficacy of repeated reading and wide reading practice for high school students with severe reading disabilities. Learning Disabilities Research & Practice, 25, 2 10. Willingham, D. T. (2006/2007). The usefulness of brief instruction in reading comprehension strategies. American Educator, Winter, 39 45, 50. Wilson, B. A. (2017). Wilson reading system (4th ed.). Oxford, MA: Wilson Language Training Corporation.

Chapter 6

Academic accommodations and modifications Dan Florell and Andrea Strait Department of Psychology, Eastern Kentucky University, KY, United States

Introduction Once students have been assessed and diagnosed with a specific learning disability (SLD) or attention-deficit/hyperactivity disorder (ADHD), they will likely need various accommodations and modifications made to their academic curriculum in order to succeed. In this chapter, we first provide a brief overview of the laws that schools must follow, which mandate providing services for students with disabilities. More specifically, we will review the history and process upon which special education services are offered to students with disabilities through the Individuals with Disabilities Education Improvement Act of 2004 (2004) (IDEIA). This will include highlighting specific disability areas that students with SLD or ADHD frequently qualify for services. As part of this, the chapter will highlight the classification differences between disorders diagnosed within the schools and those in the medical/clinical world. Another important law reviewed is Section 504 of the Rehabilitation Act of 1973. This law allows for academic accommodations and modifications within a general education classroom. This law has a different definition of disabilities and can serve as an outlet for students whose disabilities do not qualify for special education services under IDEIA. Once this legal background has been established, we will define the difference between accommodations and modifications. Next, we review various academic accommodations and modifications that are commonly used and review whether there is any empirical basis supporting their usage. Finally, we explain how to decide what accommodations and modifications to implement with particular students, including how to set up a monitoring system and make sure skills are in place so that students with SLD or ADHD are able to benefit from their accommodations and modifications.

The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00006-X © 2020 Elsevier Inc. All rights reserved.

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Legal classifications for specific learning disability and attention-deficit/hyperactivity disorder Special education Special education: history and law There was a time in the public school system when students with disabilities were not provided any services aimed at alleviating the effects of their disabilities. In fact, some students who had more severe disabilities were denied access to the public schools. This began to change when a series of court cases in the 1960s and early 1970s expanded the focus of discrimination within the public school system to include students with disabilities. The court decisions increasingly held schools accountable for educating all students regardless of their disabilities. Two landmark cases during this time were the Pennsylvania Association for Retarded Children (P.A.R.C.) versus Commonwealth of Pennsylvania (1971, 1972) and Mills versus Board of Education of District of Columbia (1972). The momentum started in the courts continued into federal legislation with the enactment of the Education for All Handicapped Children Act of 1975 (Pub. L. No. 94 142). This law required that all schools that receive federal funds were required to serve students with disabilities and had to make efforts to identify these students and provide services. The law used the term “free and appropriate education,” which is frequently referred to as FAPE and can come up in discussion of special education services (Yell, Katsiyannis, Ryan, McDuffie, & Mattocks, 2008). This special education law has been updated and reauthorized on an irregular basis by the Congress ever since. The law was first reauthorized with the Education of the Handicapped Act Amendments of 1986 (Pub. L. No. 99 457). This update expanded coverage of students with disabilities from birth to 3 years and specified early intervention for infants with disabilities. In 1990 the law was again reauthorized, and its name was changed to Individuals with Disabilities Education Act (Pub. L. No. 101 476). Seven years later, the Individuals with Disabilities Education Act Amendments of 1997 updated the law. This revision focused on improving the educational outcomes of students with disabilities and encouraged more active interventions prior to referral for special education services (Jacob, Decker, & Lugg, 2016). The last revision of the law was in 2004 with the Individuals with Disabilities Education Improvement Act (IDEIA; Pub. L. No. 108 446). There were several significant changes to the law including the implementation of Response to Intervention (RTI), which is described in Chapter 5, Educational therapy. In addition, the law set out higher achievement expectations for students with disabilities and encouraged schools to include students with disabilities in the regular education classrooms (Russo, Osborne, &

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Borreca, 2005). The view of special education changed to that of a provided service rather than a specific place. This meant that special education teachers would become integrated into classrooms and provide assistance rather than pulling out students with disabilities to provide services in a separate location. The change of students being serviced within the regular education classroom setting may be an unfamiliar one for some parents or grandparents who are reluctant to have their child receive special education services. Sometimes it can help educate them about the change in approach so they can see that special education services are more seamless in the education process and not the social stigma it may have been when they attended school. While IDEIA applies to all schools in the United States, it is important to understand that at its heart, it is a funding law. This is due to the fact that states control education within their own boundaries, and the federal government cannot tell a state how to educate its populace. Since the federal government cannot dictate education standards, it has to influence states through giving and withholding additional funding for the schools (Jacob et al., 2016). This means that implementation of IDEIA is left up to each individual state in regard to how it interprets the regulations of the law. This has realworld impact such that students with disabilities can qualify for services in one jurisdiction and not qualify in another just because they moved. In addition, schools that do not take federal funding are not obligated to offer disability services under IDEIA. This means that private schools can choose not to provide disability services. Both the inconsistency of disability regulations between states, and private schools not offering special education services, can cause considerable confusion and frustration for parents. This potential confusion and frustration for parents needs to be anticipated by psychologists and mentioned in feedback sessions and while discussing recommendations.

Clashing classification system: schools versus clinics All diagnoses are not created equal in regard to receiving special education services under IDEIA. Before getting into the particular eligibility areas of IDEIA, it is important to note that there are two separate systems that are commonly used to diagnose SLDs and ADHD. While these two systems overlap considerably, there are differences, which can mean that a student qualifies for a particular disorder in one system and not in another. The two systems come from medicine and education. In the medical world, various mental disorders are diagnosed using the Diagnostic and Statistical Manual of Mental Disorder—Fifth Edition (DSM-5) or the International Statistical Classification of Diseases and Related Health Problems—Tenth Edition (ICD-10). The DSM-5 was created by the American Psychiatric Association while the ICD-10 was created by the

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United Nations. Each diagnostic system identifies specific symptoms of a disorder and has criteria that need to be met before a student can be diagnosed with a disorder. If a student has been diagnosed with a disorder outside of the public schools, it is likely the DSM-5 was the classification system used as it is what most medical professionals use including pediatricians, psychologists, and psychiatrists (Tobin & House, 2016). It is in the DSM-5 that a student can be diagnosed with SLD and/or ADHD. The DSM-5 also allows for a student to be diagnosed with more than one disorder, which is referred to as comorbidity. For example, a student with ADHD can also have generalized anxiety disorder. The issue of comorbidity can influence the type of services a student can qualify for in school (Parritz & Troy, 2014). In the education world, disabilities fit under 13 areas, which are defined in IDEIA. Unlike the DSM-5, these categories were created by the Congress and can be broader in scope for some disabilities and narrower in others. This is where disagreement between the two systems can occur (Tobin & House, 2016). However, before discussing the differences in the two systems, it is helpful to review the most pertinent IDEIA categories for students with SLDs and/or ADHD.

Common eligibility categories While IDEIA specifies 13 areas of disability, there are only three that are likely to apply to students with SLD and/or ADHD. These categories include SLD, other health impairments (OHIs), and emotional disturbance (ED). SLD. In IDEIA, SLDs are more narrowly defined than in the DSM-5. There are eight areas of SLDs in IDEIA. These are oral expression, listening comprehension, written expression, basic reading, reading fluency, reading comprehension, mathematics calculation, and mathematics problem-solving. Students can qualify in one or more of these areas. The areas that students qualify in will dictate the type of accommodations and modifications that are available to them (Cottrell & Barrrett, 2017). For example, a student who qualifies for SLD in mathematics computation will not receive accommodations in her reading class as it does not pertain to her disability. There are various criteria that an evaluation needs to cover for a student to qualify for an SLD in the schools. As mentioned earlier, each state has its own criteria for qualification. However, according to IDEIA, all evaluations must include the following: G

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The student was not making sufficient progress in meeting age or stateapproved grade-level standards. The student has not been able to make sufficient progress despite having exposure to research-based interventions. This is typically documented through a school’s RTI or multitier systems of support, which is outlined in further detail in Chapter 5, Educational therapy.

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The student has been observed in the classroom in the area of suspected disability, and any relevant behavior is noted (Lichenstein, 2014). This means a student with a suspected disability in reading needs to be observed in English class where reading is required. While not always required, schools will frequently include cognitive and academic testing outside of the classroom as a part of a school evaluation.

Evaluations conducted outside of the school using the medical model often do not include data regarding academic progress over time and evidence of exposure to research-based interventions. Typically, the evaluations focus on overall cognitive functioning and compare it to current academic achievement (Tobin & House, 2016). While that can be adequate for a diagnosis using DSM-5 criteria, it may not be enough to meet qualification using IDEIA criteria. This can result in considerable frustration for parents who believe an outside diagnosis will automatically qualify their child for services. While schools can take outside SLD evaluations into consideration, they will likely need to supplement or do their own evaluation before eligibility decisions can be made. For psychologists working outside of the school, there are some general areas that an SLD evaluation should address to make it more consistent with the IDEIA criteria schools use. Such an evaluation should include the following: G

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Cognitive assessment battery—while this is not mandated, it can give a good sense of a student’s likelihood of academic success and rule out an intellectual disability. In addition, some states require it as part of eligibility. Academic achievement battery—this should address the primary areas of academic concern. Most achievement batteries have broad academic clusters and ones that are specific to IDEIA categories. Check the achievement battery and give the IDEIA-related tests. For example, the Woodcock Johnson Test of Achievement—Fifth Edition has the option of testing for a broad reading cluster or clusters in basic reading, reading fluency, and reading comprehension. Schools prefer the multiple reading clusters for eligibility. Grades and group-standardized test results—psychologists will need to request that parents bring in this information. This provides evidence of a history of academic difficulties the student has experienced over time. It can also be helpful if parents can get the student’s teachers to write a short summary about observed academic difficulties in the classroom. Interview questions—Psychologists should include interview questions regarding prior intervention attempts at school or home to address the academic deficits. This reinforces that the academic deficits are unlikely due to environmental influences. Other questions should rule out any

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vision, hearing, or OHIs, which could be negatively impacting the student’s academic achievement. OHIs. OHI is a very broad disability category that covers a variety of physical and mental disabilities. This category includes any issue that results in “limited strength, vitality, or alertness, including a heightened alertness to environmental stimuli, that results in limited alertness with respect to the educational environment. . .” (34 C.F.R. y 300.8). Additional criteria indicate that this must be due to chronic or acute health problems, which include ADHD, and that it adversely affects a student’s educational performance. Unlike SLD, the OHI category allows for medical professionals to provide an ADHD diagnosis and qualify for special education services with one caveat. The ADHD must “adversely” impact educational performance and not just “substantially limit” it. While there may not seem to be a difference between the terms, it can mean the difference between a student qualifying for OHI or receiving accommodations and modifications under a 504 plan (discussed later in this section). Schools will often conduct a follow-up assessment after a student has received a medical diagnosis of ADHD. This evaluation can include behavior rating scales, observations, and a review of the students’ academic record prior to deciding on whether the level of academic impairment is adversely impacting students to a significant enough degree so as to qualify for services under OHI (Schutte et al., 2017). Psychologists can mitigate this situation to a degree by employing behavior rating scales from the student’s teachers and gathering a history of past grades and/or behavioral issues in the school. By doing this, psychologists are providing the school evidence of the negative impact on a student’s functioning at school due to ADHD. This makes it easier for schools in making the case that a student with ADHD is being “adversely” impacted versus “substantially limited.” If there is no evidence of a negative impact on a student’s school functioning, then a student is unlikely to qualify for special education services under OHI and would rather receive accommodations and modifications under a 504 plan. ED. ED is another broad disability area when compared to DSM-5 categories and encompasses many common disorders such as anxiety, depression, oppositional defiance, and conduct disorders. In order to qualify for ED, students have to display one or more of five characteristics to “a marked degree over a long period of time” (34 C.F.R. y 300.8). These five characteristics include inability to learn that cannot be explained by intellectual, sensory, or health factors; inability to build or maintain satisfactory interpersonal relationships with peers and teachers;

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inappropriate types of behavior or feelings under normal circumstances; general pervasive mood of unhappiness or depression; and tendency to develop physical problems or fears associated with personal or school problems.

Due to issues with comorbidity, students with ADHD may qualify for ED rather than OHI. For example, a student who displays oppositional defiant behaviors along with having ADHD could qualify as ED if the defiant behavior is the primary driver of the student’s behavior. The ED qualification typically is better for students who need more intensive and structured behavioral supports. While an outside medical diagnosis of a mental disorder will lend support for an ED classification, many schools will want to conduct their own evaluations that will typically include the use of behavior rating scales, observations, interviews, and record reviews. Other components that might be utilized are adaptive rating scales, cognitive testing, and academic testing to address the five characteristics outlined in IDEIA (Allen & Hanchon, 2013). Psychologists in settings outside the school can try to collect similar data that is previously listed to demonstrate a student meets one of the five characteristics. Interviews and behavior rating scales would be essential techniques to employ to make an evaluation school-friendly.

Individualized education program process There is an organized process outlined in IDEIA for referring, evaluating, and reaching a decision regarding a student’s eligibility for special education services. This process can vary to some degree from state to state depending on the way the state’s regulations are written in order to comply with the federal law. The following referral process is focused on students with suspected SLD or ADHD. In general, the referral process is started when a teacher or parent, verbally or in writing, requests evaluation of a student due to concerns regarding their behavior or lack of academic progress in a particular area. A referral meeting is called where parents and various school staff meet to establish the need for an evaluation. Typically, there has to be evidence that prior interventions have been attempted in the regular education setting, and they have not been effective. Once the decision has been made to evaluate the student, and parent permission has been received, the school has 60 days to complete the evaluation and hold an individualized education program (IEP) meeting according to IDEIA. Since there are differing state regulations that states created to be in compliance with IDEIA, some states will use school days, while others will use calendar days when calculating their timeline for completing an evaluation (Cortellia, 2006). Also, IEP meetings sometimes go by other names such as admission review and dismissal meeting in Texas.

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After completion of the evaluation, the IEP meeting occurs. At this meeting the IEP meeting team must consider several factors when making eligibility decisions. These include results of the evaluation of the student, strengths of the student, parent concerns, and the functional, academic, and developmental needs of the student. In addition, if the student has behavioral issues, such as with ADHD, then the team should contemplate strategies to address them such as the use of academic accommodations and modifications. The team should also reflect on whether the student will need assistive technology devices and services (34 C.F.R. y 300.324). The IEP has several required sections. Highlights of these requirements will be reviewed with an eye toward students who have SLD or ADHD. The IEP should start with a statement of the student’s current levels of academic achievement and how their disability impacts the student’s involvement in the general education curriculum. Based on the disability impact, annual goals need to be created, which will address the student’s ability to make progress in the general education curriculum. It also needs to be specified how progress toward meeting those annual goals will be measured (34 C.F. R. y 300.320). Another portion of the IEP will describe the special education and related services provided. This is the section where any program modifications or supports that are provided will ensure that the student makes appropriate progress toward meeting their annual goals. In addition, individual accommodations that are necessary to measure academic achievement and functional performance of the student on statewide testing and district-wide assessment will be described (34 C.F.R. y 300.320). After the goals and various interventions and accommodations have been decided upon, the IEP needs to state when these services and modifications will go into effect, including their anticipated location, duration, and frequency. After completion of the IEP, the school needs to make the program accessible to all of the student’s teachers and various service providers. Each teacher and provider needs to be made aware of their responsibilities under the IEP including the specific accommodations, modifications, and supports that are required to be provided by the IEP (34 C.F.R. y 300.323). In addition, parents should always be provided a copy of the IEP.

Reevaluation and review of goals Once a student is receiving special education services, their progress toward meeting their annual goals needs to be examined at least once a year. In addition, a reevaluation of the student should occur every 3 years or earlier if decided upon by the IEP team (34 C.F.R. y 300.303). The annual review is an opportunity for parents to come together with various teachers and service providers who have been providing services to their child. The aim is to get

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parents active in the special education process and to have a venue in which to discuss their concerns about their child’s progress. At the annual IEP meeting, the team must determine whether the annual goals that were created have been achieved. If they have, new goals will be created to reflect the student’s increasing academic or behavioral competence. If the goals have not been met, the team will spend time identifying likely factors that are contributing to the lack of progress and modify the goals to more accurately reflect the student’s skill development. It also can be the time where the team reviews the results of any reevaluations that were conducted (34 C.F.R. y 300.324). While many students with SLD or ADHD qualify for special education services under IDEIA, there are others who have these disabilities but do not qualify as the disability is not adversely impacting academic progress to a significant degree. When students have a disability, but do not qualify for services under IDEIA, they can still receive modifications and accommodations in the general education setting under Section 504.

Section 504 of the Rehabilitation Act of 1973 Background and history of law Section 504 of the Rehabilitation Act of 1973 (Section 504) was civil rights legislation that prohibited discrimination against students with disabilities in public schools who receive federal funds (Cortiella & Kaloi, 2010). Section 504 was the beginning of other federal legislations, such as PL 94 142, which allowed for students with disabilities to receive accommodations and modifications of their curriculum due to a disability. This law has been further clarified under the Americans with Disabilities Act of 1990 (ADA; Pub. L. No. 101 336) and the American with Disabilities Amendment Act of 2008 (ADAA; Pub. L. No. 110 325). There are three areas of potential discrimination that Section 504 addresses. It prohibits public schools from excluding students from participating in school programs or activities only due to their disability. Schools have to take reasonable steps to prevent harassment based on the student’s disability, and schools have to provide accommodations to ensure students with disabilities have equal opportunities to benefit from programs and activities (Jacob et al., 2016). In order to qualify for Section 504 accommodations, a student needs to have a physical or mental impairment that substantially limits one or more major life activities. Examples of major life activities include taking care of oneself, walking, eating, learning, reading, concentrating, and communicating (Zirkel, 2011). Qualification for a disability in Section 504 is much broader than what is defined in IDEIA (Brady, 2004; Cortiella & Kaloi, 2010). Therefore all students who qualify for disabilities under IDEIA will

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qualify for Section 504 accommodations. In addition, students who have disabilities that substantially limit one or more major life activities are also eligible for accommodations including students with SLDs or ADHD (Martin & Zirkel, 2011; Schnoes, Reid, Wagner, & Marder, 2006).

Eligibility and documentation needed for services Parents of students with disabilities should be made aware of their rights and the school’s duties under Section 504 (Hardcastle & Zirkel, 2012). Schools are required to designate a person in the district to coordinate efforts in complying with Section 504 (Reid & Katsiyannis, 1995). Unlike the specific requirements laid out in IDEIA and subsequent regulations, Section 504 has a broader view of disability than IDEIA with no specific disability categories or defined evaluation process (Brady, 2004; Cortiella & Kaloi, 2010). There are three areas that need to be addressed as part of a Section 504 evaluation to determine whether a student has a qualifying disability (Brady, 2004). The first is whether there is a physical or mental impairment. Second, the impairment needs to substantially limit a major life activity. Third, the kind of accommodation that the student needs in order to enjoy the benefits of a school program must be decided (Jacob et al., 2016). 504 Plan process Unlike IDEIA, Section 504 does not specify a process by which to create an accommodation plan. As such, there can be considerable variability in the process when one is created. A 504 plan needs to be developed by a group that includes someone who knows the child well. The group looks at evaluation data that has been collected. This data does not have to include a medical diagnosis as alternative assessment methods can be used for the purpose of decision-making (O’Conner, Yasik, & Horner, 2016). The alternate assessments that can be used are quite varied as Section 504 only requires that there is the presence of a disability that substantially limits any major life activity. Section 504 does not operationally define what makes a “substantial” limitation (Reid & Katsiyannis, 1995). The group then decides what type of accommodations needs to be implemented (Hardcastle & Zirkel, 2012). More specifically, there are several components that are required as part of a 504 plan. The plan should include a description of the nature of the concern and the basis for the determination of the impairment. It needs to describe how the impairment substantially affects a major life activity and what reasonable accommodations are necessary for the student to receive. Finally, the plan needs to specify when it will be reviewed or reassessed and who was part of the 504 meeting (Brady, 2004). When compared to a teacher’s knowledge of IDEIA, Section 504 is less understood. Many teachers are unaware of the requirements in Section 504

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(O’Conner et al., 2016). This means that parents may need to be more proactive in making teachers aware of child accommodations that are provided in a 504 plan. The beginning of a school year is a good time for parents to make teachers aware of students coming into their classes with existing 504 plans and their relevant accommodations.

Accommodations and modifications Definition and differentiation A majority of students with disabilities receive most of their education within the general education classroom for a significant portion of the day (IDEIA, 2004). These students have been placed in the special education program, in part, because of academic deficits. Yet it is important for these students to be able to access the curriculum within the general education environment. Therefore educators and parents need to understand different options that may benefit particular students, grasp the distinction between accommodations and modifications, and choose and facilitate chosen accommodations and modifications within the student’s IEP or 504 plan. The terms “accommodation” and “modification” have often been used interchangeably within the literature even though they are different concepts (Harrison, Bundord, Evans, & Sarno, 2013). An accommodation holds the student to the same standard as other students, but the student is provided some type of support to help achieve that standard. Accommodations can be grouped into the following categories: presentation, response, timing/scheduling, and setting. Presentation accommodations involve changes in how grade-level academic material (e.g., instruction, assessments, and assignments) are provided to the student. Response accommodations involve changes in how the student is allowed to respond to instruction (e.g., respond through a technological device, use of a scribe, and being able to use specially designed tools/materials to assist with responding). Timing/Scheduling accommodations allow for changes in the organization of the time allotted for a test (i.e., overall time could be broken down and presented with breaks throughout the time period). Timing/Scheduling accommodations can also provide for a difference in the amount of time given for the presentation of a lesson and extended time to complete an assignment or test. Setting accommodations involves a change in the location for the student who receives the instruction and/or completes the assignments or assessment (Harrison et al., 2013). Modifications were defined as changes to practices in the school that alter the expectations of a student to compensate for a disability. This could involve lowering or reducing expectations. If the work product the student is expected to turn in is different than what is expected of a typical student, then the change in expectation is a modification. For example, shortened assignments would be a modification. There is a change in the expectation

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of the work product. A change in work product is not always the case, as there are other types of modifications that are discussed later, but if there is a change in work product that would be considered a modification. Turnbell, Turnbull, and Wehmeyer (2007) provide guidance in regard to how curriculum modifications can be conceptualized. Their framework suggests that there should be two categories of curriculum modifications: curriculum adaptations and curriculum augmentations. Curriculum adaptations involve a change in the teaching pedagogy used to present general education curriculum to a student (Soukup, Wehmeyer, Bashinski, & Bovaird, 2007). This may involve the use of technology. Examples of using technology to change how information is presented include videos, digital media instead of print versions, audiobooks, and recording a lesson for a student. Curriculum augmentations can also include adding something to the content of the general education curriculum that allows the student to learn different strategies and techniques to enhance their learning and better access the general education curriculum. Examples of curriculum modifications include teaching student-study strategies, cognitive strategies, and studentdirected learning strategies (Soukup et al., 2007).

Accommodations and modifications for specific learning disability and attention-deficit/hyperactivity disorder Selecting specific accommodations and modifications for a student is an important task with much consideration provided to the individual students’ skills, skill deficits, and the skills targeted for improvement. Unfortunately, the research on the effectiveness of specific accommodations and modifications is relatively sparse (Fuchs & Fuchs, 2001; Witmer, Cook, Schmitt, & Clinton, 2015). As a result, many accommodations are being recommended and used without any evidence to support their effectiveness (Harrison et al., 2013). When looking at the impact of accommodations on students, researchers recommend considering the differential boost. Differential boost is identified when scores of students with disabilities are improved more than nondisabled students provided with the same accommodation. The theory is that if differential boost is present, then the accommodation is serving its purpose in providing a “leveling of the playing field” instead of providing an advantage to students diagnosed with a disability, which would reduce the validity of the scores obtained (Fuchs & Fuchs, 2001). A review of research available for a select group of accommodations and modifications is provided later.

Extended time Extended time is the most commonly used accommodation for students with a learning disability or ADHD (Schnoes et al, 2006). This accommodation

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provides extra time to complete assignments and tests. Extended time is often presented as time and a half. For example, if a typical student receives 30 minutes to complete the assignment, a student receiving extended time (defined as time and a half) would receive 45 minutes to complete the same assignment. The amount of time given to the student should be decided based on what amount of time provides them with a level playing field as their peers. This accommodation would be appropriate for students who can do the work, but more slowly than their peers. Students with attention problems may benefit from this accommodation because of the on-task to offtask ratio that may be different for them than a typical peer. The time spent off-task, and the time it takes to get back on task, for a student who can do the work, can decrease their performance simply because they cannot get through the same amount of work as a typical student without attention concerns. Students with learning disabilities may take longer to complete assignments because they may not be as fluent as a typical peer in regard to producing the information needed to do well on an assignment. Fuchs et al. (2000) note that valid test accommodations result in scores for students with disabilities that would be equivalent to those of students without disabilities who do not receive accommodations. The focus is on obtaining valid scores, not optimal scores. Care should be taken to ensure that students with disabilities are not prevented from using accommodations that allow them to demonstrate what they know; however, care should also be taken not to provide students with disabilities overly permissive accommodations that would inflate scores, which would reduce the validity of the scores. There have been mixed results in regard to the effectiveness of extended time (Fuchs & Fuchs, 2001; Pritchard et al., 2016). Pariseau, Fablano, Massetti, Hart, and Pelham (2010) found that students diagnosed with ADHD completed more problems correctly per minute within the standard condition compared to an extended time condition. The authors hypothesized that the students used their time more effectively and efficiently when given a shorter amount of time to complete the work. Children in the extended time condition completed a greater percentage of problems with accuracy, but again, the rate of correctly completed problems was higher in the standard time condition. Overall, the authors concluded that the rate of accurate work completion of students with ADHD was not meaningfully improved when given more time to complete their work. Fuchs et al. (2000) found that students with SLDs did not benefit from extended time more than students without a disability. The overall recommendation is that extended time should be used for students who need help overcoming functional impairments in processing speed or fluency (when the purpose of the test is not to measure processing speed or fluency; Kettler, 2012). Students that may experience functional impairments in processing speed or fluency include, but are not limited to, students with anxiety, ADHD, cognitive impairments, autism, traumatic brain injuries, and learning disabilities. If the symptoms from the

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disability result in impairments in processing speed or fluency, then extended time as a modification could be warranted.

Read aloud The read-aloud accommodation provides test takers with the opportunity to have items read aloud to them, or the opportunity to read the test items aloud to themselves before providing the answer on the assessment. There is controversy regarding this accommodation when the primary goal of the assessment is to measure reading ability (Randall & Engelhard, 2010). The results of the effectiveness of the read-aloud accommodation on test scores have been mixed (Elbaum, 2007; Fuchs, Fuchs, & Capizzi, 2005; Witmer et al., 2015). Fuchs and Fuchs (2001) found that students diagnosed with SLDs, who were provided the accommodation of reading aloud to themselves on a test, performed better. Students without a learning disability actually performed worse when allowed to read the test aloud. The authors note that this is an example of “leveling the playing field.” Another study found that students with disabilities did not benefit from having the text read aloud to them on a math application test but did benefit from the read-aloud accommodation when the assessment was a measure of math problem-solving (Fuchs & Fuchs, 2001). Tindal, Heath, Hollenbeck, Almond, and Harniss (1998) also found that students with disabilities performed better under a read-aloud condition when their teacher read out the assessment to them. Students without a disability did not show the same positive impact from the read-aloud condition. Another study found no benefit of the read-aloud testing accommodation for students with ADHD on assessments of reading and math (Pritchard, et al., 2016). Elbaum (2007) investigated the read-aloud testing accommodation on a mathematics assessment for 6th-to 10th-grade students identified as having an SLD. Both students identified as having a disability and without a disability benefited from the read-aloud condition in this study. The results indicated that the greater benefit was to students without an identified disability; therefore the results of this study do not support the read-aloud accommodation for all students identified as having SLD. While there is some research support for the read-aloud accommodation for students with disabilities, there is also research that does not support the effectiveness of this practice. Overall, the read-aloud accommodation may be a valid one but should be used with care, especially if the academic area to be assessed is reading as it may skew the scores of students when applied to reading assessments.

Technological supports A common accommodation is providing a student with a disability a calculator to use on math assignments and assessments. Overall, the literature on

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the impact of using a calculator for students with disabilities is mixed (Pritchard et al., 2016). Research studies have found that both students with and without disabilities performed better on assessments when they were able to use a calculator (Bouck, 2009). Bouck, Bouck, and Hunley (2015) found that calculators can benefit a student with disabilities only if the student understood mathematics. There had to be a level of understanding in regard to the math before the calculator could be of benefit. Bone and Bouck (2018) had similar results as students with disabilities presented with fewer errors when allowed to use a calculator; but overall, their scores did not substantially improve. The authors concluded that calculators could be beneficial to students with disabilities to use as long as the students had the needed understanding of the mathematical concepts to use the calculator effectively. Pritchard et al. (2016) found that when elementary and middle school students identified with ADHD were allowed to use a calculator, their performance did not significantly improve on the academic assessment. Another commonly used technological accommodation is text-to-speech (TTS) read-aloud. This accommodation is based on the idea of listeningwhile-reading. The student listens to someone reading the text (with TTS it involves a computer orally reading a digital text; Anderson-Inman & Horney, 2007), while the student is instructed to read along silently looking at the printed text. The research on the effectiveness of listening-whilereading is unclear (Schmitt, Hale, McCallum, & Mauck, 2011). The research on the effectiveness of the TTS read-aloud accommodation is also mixed. Some studies find a positive impact, and some find that students show no improvement or even a negative result (Meyer & Bouck, 2017).

Setting Another common accommodation for students with SLDs and ADHD is providing the assessment in an alternative environment that has fewer distractions than the typical testing environment (Pritchard et al., 2016). In practice, this accommodation often does not stand alone (i.e., students are often provided a reduced distraction environment so that other accommodations can be implemented). Therefore the efficacy of the reduced distraction settings may be difficult to determine (Fuchs et al., 2005). Despite this challenge, researchers have attempted to study the impact of a reduced distraction environment on students with disabilities. Pritchard et al. (2016) found that a reduced distraction environment was not associated with better performance for elementary- and middle-school students diagnosed with ADHD. Fuchs et al. (2000) found a differential boost to fourth- and fifth-grade students with disabilities on a measure of math problem-solving. However, a differential boost was not found for these same students on measures of math concepts and applications. Overall, the literature on the impact of a reduced distraction environment for students with disabilities is mixed

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(Pritchard et al., 2016). Implementation of this particular accommodation should be done with care and consideration for the impact it has on each specific student. Overall, the research on all the accommodations mentioned earlier is mixed. Some studies point to a benefit while others do not. All of the accommodations previously discussed appear to be supported by at least some research. These accommodations include extended time, read-aloud, technological supports (use of calculator and TTS), and a reduced distraction environment.

Modifications Lists of possible academic modifications are long and include, but are not limited to, shortened assignments, graphic organizers, use of vocabulary word banks, word bank of choices for answers to test questions, audiobooks in place of reading text, and modified workload or length of assignments/ tests. While it is relatively simple to find a long list of suggestions for modifications, it becomes increasingly difficult to find research support for these recommendations. When looking at only research for modifications for students with SLDs or ADHD, the literature becomes even more sparse. Graphic organizers stood out as a modification use for students with ADHD and SLDs, which has a research base of support. Graphic organizers are a common tool used to help students in visually organizing information in a way that is more meaningful for them. Examples of graphic organizers are plentiful within a basic internet search. Graphic organizers can be implemented as modifications for students across all grade levels and many subjects including math, reading, social studies, science, and language arts (Kim, Vaughn, Wanzek, & Wei, 2004). Graphic organizers allow for a visual demonstration of relationships between important pieces of information. A student uses the graphic organizer as a way to organize and identify information into a visual map or diagram. Examples of graphic organizers include advanced organizers, knowledge maps, concept maps, story maps, and Venn diagrams. Graphic organizers have been reported to be an effective strategy to use for the planning and prewriting stage of written expression tasks (Boon, Barbetta, & Paal, 2018). There is also research support for the use of graphic organizers to help facilitate improved reading comprehension for students with learning disabilities (Alturki, 2017; Jitendra & Gajria, 2011). In addition, research support is present for the effectiveness of graphic organizers when used in social studies (Hall, Kent, McCulley, Davis, & Wanzek, 2013) and math (Ives, 2007). Ives (2007) concluded that the use of advanced organizers to teach higher level mathematics to students with language and attention problems can facilitate an improved conceptual understanding of mathematical concepts. Many students with SLDs and ADHD

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demonstrate academic challenges for which graphic organizers may be appropriate. Students with ADHD may benefit from the structure and stepby-step format of graphic organizers. These characteristics may also benefit students with SLDs in the same way. While the research base for accommodations and modifications is limited compared to the long lists of recommended accommodations and modifications, one should not be discouraged. A critical future direction is a growing evidence base for specific accommodations and modifications with attention to personalization based on individual learning profiles (e.g., comorbid anxiety). In addition, monitoring the impact of the accommodation and/or modification will be crucial.

Making accommodations and modifications effective When choosing accommodations and/or modifications for students, it is important to encourage student success, provide these students with access to the general education curriculum, and allow them an opportunity to provide valid scores on assessments. It is essential to be aware of research support, or lack thereof, for specific accommodations and modifications. As the research base in this area is relatively sparse, it becomes important to focus attention on what information can be used to make the best decisions possible in regard to choosing specific accommodations and modifications for each individual student. No accommodation has been found to be beneficial to all students with a specific identified disability; therefore the emphasis continues to be on making accommodation and modification decisions based on each specific individual’s learning profile (Fuchs et al., 2005; Witmer et al., 2015). Fuchs and Fuchs (2001) provide direction in regard to using a data-based approach for determining what test accommodations should be provided to students with disabilities. The authors confirm that there is not much depth in the research literature in this area. It was also noted that teachers experience difficulty in making good decisions regarding test accommodations when relying only on informal judgments. In order to rectify these issues, it is important to objectively examine the student’s strengths and needs. For example, providing a student with extended time on an assignment that they do not have the skill to complete would not be a helpful accommodation. Likewise, having more time to be exposed to something a student cannot do is not likely to result in anything positive. Providing a student with extended time who has difficulty maintaining attention to task may be hurtful to some students as more time may make it more difficult to stay on task. With some students, having more concentrated time might be helpful to reduce distraction and some students might perform better with more time (as long as their rate of distractibility does not increase with increased time). So who might extended time be

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useful for? Potentially, a student who can do the work but is not able to complete the work in the typically allotted time. This could be a student who reads slowly but can comprehend the text if given time to do so. Another example of an accommodation would be to provide a student with two sets of classroom curriculum materials, one for home and the other for school. This would not be helpful if the student is unable to use the materials well at home independently. Sending a child home with materials, if they do not have the ability to use them and/or the support at home to use them, would not likely generate any positive results. There are many cases where the accommodation and/or modification do not match the need of the specific student. While it is helpful to understand potential challenges students with SLDs or ADHD may experience, it is critical to evaluate the need for specific accommodations and/or modifications not only in light of any disability, but also based on a student’s specific behavioral, academic, and cognitive skill profile. Using research-supported accommodations and modifications is important, but knowing how to select the most appropriate accommodations and modifications for specific students is critical. If there is not a strong research basis for an accommodation and/or modification (i.e., there has not been quality-controlled research studies conducted), but the IEP team feels that the student could benefit from the accommodation and/or modification, one must consider how to ensure the modification and/or accommodation is having the desired impact.

Single-subject design Single-subject design research methodology could be used to address this concern. In single-subject research design a target behavior is first operationally defined. For example, a 12-year-old student named Emily receives special education services for an SLD in reading. Her reading skill deficits negatively impact her ability to demonstrate her knowledge on assignments and tests. She is unable to read fluently enough to comprehend the text well. Emily is also unable to get through the text in the allotted time. She is motivated and maintains good attention to task. What accommodations and/or modifications might provide Emily with an avenue to demonstrate what she really knows? One could argue that Emily would benefit from extended time and having someone read the text aloud to her. If these accommodations are written on her IEP and subsequently implemented, how will someone really know if these accommodations are providing her with the desired benefit? The reality is that, without collecting data specifically on the effectiveness of these accommodations, there would be no evidence to guide the team making subsequent decisions. Yet, the use of a single-subject design could provide valuable insight into the utility of these accommodations for Emily and help the

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IEP team determine whether this accommodation is useful to Emily or whether other interventions should be used instead. The scope of this chapter is not to go into lengthy detail regarding single-subject design, so further exploration of this is recommended. However, a brief overview for how this approach could be implemented in Emily’s case will be provided: 1. Operationally define the behavior (the “behavior” in academic terms would be something such as reading fluency, reading comprehension, reading accuracy, and math calculation). An example of an operational definition could be that reading fluency is the target behavior for Emily. Reading fluency is operationally defined as the speed in which Emily accurately reads text and is measured by words correct per minute. 2. Develop a data sheet to measure that behavior. 3. Collect baseline data on the target behavior (baseline data is taken before the accommodation or modification is implemented). 4. Collect data after the implementation of the modification or accommodation (ideally a teacher would only want to implement one at a time to truly measure the impact of each accommodation or modification). 5. Graph both sets of data (baseline and after implementation of the modification or accommodation). 6. Visually analyze data. 7. Calculate effect size or percentage of nonoverlapping data (Daly, Neugebauer, Chafouleas, & Skinner, 2015). Overall, using the data about a specific child to make an individualized decision, and collecting data to determine the impact of the decision made is recommended. Considering the research for the accommodations and modifications is also important, but even with strong research support, this does not necessarily mean that a specific accommodation or modification is the right choice for a specific student. Using all meaningful and available data on a student to guide decisions, and then monitoring the effectiveness of those decisions, may lead to improved student outcomes as well as a better use of resources. Further, collection of such information on multiple students will eventually lead to a better evidence base by which to evaluate the efficacy of specific accommodations and modifications across groups of children, as well as within individual children, a critically important future direction and current research gap in the field.

Conclusion Students receiving special education services due to negative academic impact as a result of diagnoses of SLDs and/or ADHD are likely to need accommodations and/or modifications to facilitate a higher level of academic achievement. The symptoms of SLDs and ADHD may prevent students from learning in a traditional format and may inhibit their ability to complete test

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and/or assignments accurately. Legislation is in place to protect students receiving special education services or assess who qualify for accommodations under Section 504. Students in special education should have as much access to the general education curriculum as possible. To make this possible for many students, accommodations and/or modifications may be needed. While there is research on various accommodations and/or modifications, there are many recommended accommodations and/or modifications that do not have a research base. In addition, there are many that have a research base, but the results are mixed in regard to the effectiveness for specific populations of students. There are also accommodations and modifications that are strongly backed by research support. Those involved on teams making decisions for students are strongly encouraged to always consider the research base; but beyond that, it is critical to use objective and accurate data to guide decisions in regard to the selection of accommodations and/or modifications for specific students. It is then important to monitor that student’s progress and use the progress monitoring data to further guide decision-making. Accommodations and modifications that take up time and energy, but are not effective, should be removed and replaced with more effective choices. Student learning should be the top priority, so that educators are also encouraged to consider the impact on learning. If a student is provided with the accommodation of having assignments and tests read to them, how is that student also being worked with to encourage independent reading? It is recommended that when these strategies are used, an intervention be in place to work on remediating the need for that accommodation and/or modification when appropriate. The desire is to “level the playing field” for students who need it based on the impact a disability has on their ability to be successful academically. To do this, decision makers must be well-informed, knowledgeable, and willing to look carefully at each individual student’s needs and ongoing progress.

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Witmer, S. E., Cook, E., Schmitt, H., & Clinton, M. (2015). The read-aloud accommodation during instruction: Exploring effects on student self-perceptions and academic growth. Learning Disabilities: A Contemporary Journal, 13(1), 95 109. Yell, M. L., Katsiyannis, A., Ryan, J. B., McDuffie, K. A., & Mattocks, L. (2008). Ensure compliance with the Individuals with Disabilities Education Improvement Act of 2004. Interventions in School and Clinic, 44(1), 45 51. Zirkel, P. (2011). The “new” section 504. Principal, 91(2), 54 56.

Further reading Agran, M., Alper, S., & Wehmeyer, M. (2002). Access to the general curriculum for students with significant disabilities: What it means to teachers. Education & Training in Mental Retardation & Developmental Disabilities, 37(2), 123 133. Baker, S. K., Gersten, R., & Scanlon, D. (2002). Procedural facilitators and cognitive strategies: Tools for unraveling the mysteries of comprehension and the writing process, and for providing meaningful access to the general curriculum. Learning Disabilities Research & Practice, 17(1), 65 77. Bouck, E. C. (2016). A national snapshot of assistive technology for students with disabilities. Journal of Special Education Technology, 31(1), 4 13. Available from https://doi.org/ 10.1177/0162643416633330. Bouck, E. C., Joshi, G. S., & Johnson, L. (2013). Examining calculator use among students with and without disabilities educated with different mathematical curricula. Educational Studies in Mathematics, 83, 369 385. Available from https://doi.org/10.1007/s10649-012-9461-3. Bulgren, J. A., & Carta, J. J. (1993). Examining the instructional contexts of students with learning disabilities. Exceptional Children, 59(3), 182 191. Cobb Morocco, C. (2001). Teaching for understanding with students with disabilities: New directions for research on access to the general education curriculum. Learning Disability Quarterly, 24(1), 5 13. Deshler, D., Shumaker, J., Bulgren, J., Lenz, K., Jantzen, J., Adams, G., et al. (2001). Making learning easier: Connecting new knowledge to things students already know. Teaching Exceptional Children, 33(4), 82 85. Greenwood, C. R., Carta, J. J., Arreaga-Mayer, C., & Rager, A. (1991). The behavior analyst consulting model: Identifying and validating naturally effective instructional models. Journal of Behavioral Education, 1(2), 165 191. Jitendra, A. K., Edwards, L. L., Choutka, C. M., & Treadway, P. S. (2002). A collaborative approach to planning in the content areas for students with learning disabilities: Accessing the general curriculum. Learning Disabilities Research & Practice, 17(4), 252 267. Swanson, H. L., & Deshler, D. (2003). Instructing adolescents with learning disabilities: Converting a meta-analysis to practice. Journal of Learning Disabilities, 36(2), 124 135. Swanson, H. L., & Hoskyn, M. (2001). Instructing adolescents with learning disabilities: A component and composite analysis. Learning Disabilities Research & Practice, 16(2), 109 119.

Chapter 7

Behavioral interventions Lauren M. Friedman and Linda J. Pfiffner Department of Psychiatry, University of California, San Francisco, CA, United States

Attention-deficit/hyperactivity disorder (ADHD) and specific learning disabilities (SLDs) are two of the most prevalent disorders in childhood affecting approximately 6% and 9% of children worldwide, respectively (Altarac & Saroha, 2007; Willcutt, 2012). The disorders are also commonly cooccurring, with comorbidity rates ranging from 38% to 45% (DuPaul, Gormley, & Laracy, 2013), suggesting that children with ADHD are almost five times more likely to be diagnosed with SLD relative to the general population (American Psychiatric Association, 2013). Even in the absence of comorbid diagnoses, children with ADHD evince significant academic challenges relative to those without ADHD as evidenced by lower scores on standardized testing (Frazier, Youngstrom, Glutting, & Watkins, 2007), report card grades (Loe & Feldman, 2007), reduced academic productivity (Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006), and lower high school and college matriculation and graduation rates (Barkley, Fischer, Smallish, & Fletcher, 2006; Kuriyan et al., 2013). In a complementary fashion, children with SLD experience attentional difficulties in the classroom and while completing academic-related tasks. ADHD and SLD are also associated with significant adverse outcomes, and children comorbid for ADHD and SLD exhibit worse educational, neurocognitive, social, and occupational impairments including more severe executive functioning deficits, higher rates of grade retention, increased likelihood of placement in special education classes, greater use of in-school tutoring services, and poorer social skills relative to those with ADHD or SLD alone (Bental & Tirosh, 2007; Seidman, Biederman, Monuteaux, Doyle, & Faraone, 2001; Wei, Yu, & Shaver, 2014; Willcutt et al., 2007, 2010; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). Complicating the clinical picture, however, is that academic skill-related challenges may lead to performance-related decrements, and vice-a-versa. That is, children with learning disabilities may appear inattentive phenotypically because they lose focus, engage in off-task behaviors, and become The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00007-1 © 2020 Elsevier Inc. All rights reserved.

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frustrated due to the arduous nature of learning-related tasks. Children with ADHD may acquire academic deficits due to difficulties concentrating during classroom instruction, sustaining attention while completing seat work, and engaging impulsively in off-task behaviors in school settings. That is, it is important to disentangle whether the observed attentional and academic challenges are secondary to impairments in academic skill (i.e., related to SLD) or performance (i.e., related to ADHD) to directly address the underlying contributors. However, for children with true ADHD/SLD comorbidity—that is, following best-practice assessment procedures (see Chapter 3: Assessment of attention-deficit/hyperactivity disorder and comorbid reading disorder with consideration of executive functioning), diagnoses of both ADHD and SLD are warranted—intervention recommendations are less clear. Despite the significant diagnostic and symptom overlap, few studies have examined interventions specifically for children with comorbid ADHD/SLD. Current intervention recommendations for children with ADHD and SLD comorbidity suggest that children benefit from specific treatment of each disorder (DuPaul et al., 2013; DuPaul, Eckert, & Vilardo, 2012; Tamm et al., 2017). In these cases, deficits in academic skill or knowledge can be addressed via direct instruction (see Chapter 6: Academic accommodations and modifications), whereas current best-practice recommendations (AAP Subcommittee on ADHD, 2011) suggest that performance-related deficits (i.e., those secondary to ADHD) are best addressed through a combination of medication and behavioral interventions (DuPaul et al., 2012). The ensuing chapter will review common elements of and empirical support for behavioral interventions for ADHD and learning problems, including organizational skills training, as well as provide guidance on accessing relevant information and services.

Theoretical underpinnings of behavioral interventions For over half a century, behavioral treatments have been the most widely used and researched nonpharmacological interventions for children with ADHD and learning problems. Such interventions are predicated on the principles of social learning, or “contingency theory” (Patterson, 1982), and underscore the centrality of environmental and social contingencies on fostering and maintaining problematic behaviors while simultaneously considering modeling/imitation of behaviors and various cognitive factors (e.g., attributions and cognitive appraisals). Behavioral interventions typically begin with a “functional behavior analysis,” which involves specifying the antecedents (variables preceding the behavior), behaviors (positive behaviors to increase or negative behaviors to decrease), and consequences (variables that serve as rewards or punishers for the behavior), often termed the Antecedents, Behavior, Consequences

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(ABCs) of behavioral interventions. Following this analysis, specific strategies are identified to modify the antecedents and consequences to promote desired behaviors or reduce unwanted behaviors, while closely considering the function the behavior serves. Functional behavior analyses are critical for children comorbid for ADHD and SLD, as the causes of and contributors to attentional and academic challenges can be multifactorial. For example, poor grades in math may reflect lack of motivation (i.e., deficient positive consequences or rewards in the child’s environment), situational factors that impede focused attention during math lessons (e.g., antecedents such as sitting near a distracting peer, instructions that are too long and complex for the child to follow), and/or math skill/knowledge deficits. It is interesting to note that behavioral interventions typically select target behaviors that cause impairment in daily functioning (e.g., academic, social, and organizational targets) rather than core ADHD symptoms per se. However, empirical evidence indicates that behavioral treatments often have powerful direct and indirect effects on the core diagnostic symptoms of inattention, hyperactivity, and impulsivity, in addition to the well-documented improvements on academic enablers such as study skills, organization, engagement, and motivation that are often deficient in children comorbid for ADHD and SLD.

Behavioral parent training Behavioral parent training (BPT) is the predominant nonpharmacological intervention paradigm for children with ADHD. Treatment typically consists of 8 12 sessions that focus on (1) providing parents psychoeducation about ADHD and SLD, (2) teaching proven parenting skills to improve desired behaviors and decrease problematic behaviors by altering the antecedents and consequences, as discussed earlier, and (3) practicing and troubleshooting skill implementation. Parents learn strategies that are helpful in a wide variety of contexts that children with ADHD and SLD often find challenging such as homework completion, morning and evening routines, organization, and independence in other home-based tasks. Although models for delivering BPT vary widely—treatment can be administered in a group format, individually, or with families (parents and children together)—not all families benefit equally from the treatment modalities. For example, a group format is helpful for families who share similar challenges and may benefit from receiving support or sharing ideas with other group members. Group sessions are also beneficial for families who may be reluctant to adopt new strategies, as parents often become more open after seeing other families implement skills successfully. However, groups are not indicated for all children and families. An individual modality may be warranted if more intensive and tailored interventions are necessary due to the severity of the child’s challenges, if family structural and

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interactional patterns impede skill implementation, for parents who have interpersonal styles that may derail group cohesion and function, or if a slower pace is desired. Regardless of the treatment modality, most BPT interventions employ a common curricula, as outlined in Table 7.1, to (1) enhance parent child relationships by improving quality time together, (2) increase wanted behaviors by providing effective instructions and positive reinforcement such as rewards and specific praise, and (3) decrease unwanted behaviors by providing specific corrective statements, using planned ignoring to reduce attention-seeking behaviors, or employing time-outs or a response cost (e.g., loss of privileges). Often, BPT curricula emphasize positive consequences, such as rewards and praise, over negative consequences. Not only does this serve to enhance the parent/child relationship, which can become eroded by consistent negative and corrective feedback as well as harsh parenting practices including as yelling and arguing, but it is also consistent with research suggesting that children with ADHD have reward-dominant styles. Recent evidence indicates that children with ADHD display a lack of sensitivity to partial reinforcement, elevated rewards thresholds, aversion to delayed reinforcement, and decreased responsiveness to cues of punishment or nonreward (Parry & Douglas, 1983; Pfiffner, 2008; Quay, 1997; Sonuga-Barke, 2005; Sonuga-Barke, 2011). Therefore, the emphasis on positive, immediate, and salient reinforcement, in conjunction with clear rules and directions for obtaining rewards, is more likely to improve problematic behaviors relative to negative consequences.

Empirical support Over the past 50 years, numerous outcome studies, systematic reviews (Evans, Owens, Wymbs, & Ray, 2018; Massetti et al., 2008; Pelham & Fabiano, 2008; Pelham, Wheeler, & Chronis, 1998), and metaanalytic investigations (Daley et al., 2014; Fabiano et al., 2009; Sonuga-Barke et al., 2013) have shown empirical support for BPT programs for children with ADHD. One recent metaanalytic review found large-magnitude effect size improvements across a wide array of outcomes including decreased parent ratings of ADHD symptomatology, externalizing symptoms, and impulsivity, and increased positive behaviors/decreased unwanted behaviors during behavioral observations and using individualized target behavior checklists (Fabiano et al., 2009). Improvements in academic productivity were also observed using several methods of assessment including direct observations of off-task behavior and percent of work completed. Treatment-related improvements were found regardless of methodology (i.e., between-group, pre post, within group, and single-subject studies) and study-participant characteristics (i.e., IQ, age, race, gender, comorbid externalizing disorders, percentage of twoparent families, and number of treatment sessions). A second metaanalytic

TABLE 7.1 Common elements of behavioral parent training. Topic

Description

Psychoeducation

Describe behavioral parent training and why it is a proven treatment for children with ADHD and SLD Discuss core symptoms, diagnostic criteria, and empirically supported treatments for both ADHD and SLD Review parent child coercive interaction cycle—how negative behaviors from both parent and child are accidentally perpetuated Explain theoretical rationale behind behavior therapy, such as the ABC model—changing antecedents (i.e., situations) and consequences (i.e., rewards and punishers) to modify problematic behavior

Quality time/Attending

Children with ADHD and SLD often receive more frequent corrective feedback and have more negative interactions with their parents. To improve parent/child relationships, increased quality time is recommended Parents are instructed to use attending skills: Child directs the activity G Parent attends actively by narrating in a nondirective, neutral manner without interrupting or suggesting G Attend to and praise positive behavior G Ignore minor negative behaviors G

Labeled praise

Because positive behaviors are often ignored by parents of children with ADHD and SLD as negative behaviors tend to be more salient, parents are instructed to differentially reinforce positive behaviors by providing effective praise. Effective praise statements are as follows: G Specific—label the precise behavior. For example, “I like the way you got started right away on your homework assignment” rather than “good job” G Immediate—given directly after the behavior occurs G Consistent—occurs every time the behavior occurs G Unspoiled—avoids linking praise with negative or discouraging statements. For example, “You did a great job staying on task while finishing your book report. I wish you did that with all of your homework”

Positive reinforcement/ token economy

Develop a “rewards menu” containing daily and weekly rewards with child’s assistance Select observable behaviors (e.g., gets started right away) rather than qualities (e.g., stop being lazy). Behaviors should be only slightly challenging for the child so they can obtain rewards and remain motivated Assign points or tokens to each target behavior Assign “values” for each reward Provide reinforcement/tokens consistently and immediately following positive behaviors (Continued )

TABLE 7.1 (Continued) Topic

Description

Effective instructions

Effective instructions should: G get the child’s attention; G be specific, brief, and state what the child should do rather than what he/she should not do (e.g., take your math homework out of your backpack); G use command statements (e.g., read over your spelling words) rather than phrasing instructions as a question (e.g., do you want to start learning your spelling words) or “let’s” statements (e.g., let’s get started on your spelling words); G use a neutral tone of voice; G pause for 10 s to allow child to process instructions and respond; and G praise child for complying or giving negative consequence if he/she does not comply

“When Then”/ Premack’s principle

Use desirable behaviors as reinforcers to increase compliance with less desirable behaviors Often in the format of “When/Then statements” (e.g., “when you finish your math homework, then you may have 10 min of screen time”)

Planned ignoring

Negative behaviors often serve to get attention from parents and other adults Minor misbehaviors such as whining, negotiating, arguing are selectively ignored Planned ignoring is a negative consequence, as you are taking away something that the child wants—your attention To practice planned ignoring successfully, parents should: G give the child a warning (e.g., “I will help you with your math homework once you use a calm, clear voice. I am not going to talk to you while you are whining”); G not talk to the child; G not give child physical attention; and G attend to the child as soon as the behavior stops

Time out

Time out from positive reinforcement or enjoyable activities following misbehavior Behaviors that result in time out should be discussed with the child ahead of time When implementing time out, parents should: issue a warning (e.g., “I have asked you to put away the tablet. If you do not put it away now, you will earn a 5-min time out”); G designate a “time out area” away from positive activities; and G be time limited and have the child show appropriate behavior for at least the last portion of the time out G

(Continued )

TABLE 7.1 (Continued) Topic

Description

Response cost

Removal of tokens, points, privileges, or toys when the child shows misbehavior Can be implemented within a token economy system. When using this method, parents should continue to frame token economy in positive manner G Provide prespecified number of “bonus tokens” and assess a fine for each misbehavior G Directly fine and remove tokens or points the child already earned. If using this method, be sure to avoid “bankruptcy” so that children are still motivated to participate in the token economy and earn rewards

Developing a homework plan

Increase structure during homework completion by making rules explicit and planning ahead for potential problems by G determining “homework spot” ahead of time; G setting “homework time” and completing homework at the same time every day (e.g., after snack time); G creating an assignment system so that parents can know what needs to be completed and modeling this organization system for kids G extending token economy to include homework routine with target behaviors such as gets started right away, has all materials out, stays on task with fewer than one reminder

Parent stress management

Identify and use coping strategies (e.g., mindfulness, deep breathing, and preferred activities) to use when parents feel stressed Recognize antecedents or activities that tend to cause stress so parents may preemptively use stress management tools Identify and replace unhelpful thoughts (e.g., I shouldn’t have to monitor homework so closely!) with helpful thoughts (e.g., Math is particularly challenging for my child. It makes sense that he would need extra help following through and organizing himself. I’m glad that I am available to help him)

Note: For additional information, please see Barkley (2013); Bloomquist (1996); Forehand and McMahon (1981); Kazdin (2008), Pfiffner and Kaiser (2010). ADHD, Attention-deficit/hyperactivity disorder; SLDs, specific learning disabilities.

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review (Sonuga-Barke et al., 2013) identified smaller effects for behavioral interventions. However, the inclusion criteria for the latter review was considerably limited (e.g., only randomized controlled trials with ADHD symptomatology as an outcome measure), exclusively examined blinded ADHD symptomatology ratings, and ignored important secondary and functional outcomes such as academic achievement and productivity, organization, and homework problems. A follow-up metaanalysis correcting for some of these limitations corroborated earlier findings including small-to-moderatemagnitude improvements on measures of academic skills and performance (Daley et al., 2014). While empirical support for BPT and other behavioral interventions is well documented for children with ADHD, similar evidence is lacking for children with SLD. Our team could not identify any study examining BPT effectiveness for children where SLD served as the primary inclusionary criteria or target of interest. Moreover, the evidence for the effect of BPT programs on academic achievement or performance outcomes is similarly limited, with most studies reporting behavioral outcomes such as ADHD symptomatology, externalizing behaviors, organization and social skills deficits, and academic productivity such as direct observations of on-task behaviors or rates of completed assignments. Metaanalytic reviews of the few studies that report academic achievement outcomes show nonsignificant (Van der Oord, Prins, Oosterlaan, & Emmelkamp, 2008) or small-magnitude (Fabiano et al., 2009) effect size improvements. It is important to note, however, that academic achievement is rarely a target of BPT, which often focuses on secondary features of ADHD and SLD such as task completion, compliance, morning and evening routines, organization, and independence. Only one study to date has examined the effect of BPT on children comorbid for ADHD and SLD. Tamm et al. (2017) conducted a randomized, controlled trial examining the effectiveness of intensive reading instruction, ADHD treatment (BPT and medication management), and combined reading instruction, BPT, and medication for children with comorbid ADHD and reading disability. Domain-specific treatment effects were observed, wherein children assigned to the BPT/medication and combined conditions improved in parent- and teacher-reported ADHD symptoms, while those receiving reading instruction did not. There was also no added benefit from combined treatment, as BPT/medication and combined groups reported similar improvements. Children assigned to the reading instruction and combined conditions showed improvement on standardized reading measures, whereas children receiving BPT/medication therapy did not evince any reading gains. Again, there was no added benefit to combined versus mono-domain therapy. These results suggest that comorbid ADHD/SLD should be treated with a two-pronged approach—using combined behavioral and medication interventions to target ADHD-related impairments and direct instruction to augment academic underperformance.

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School-based interventions Like BPT, school-based behavioral interventions have a long history of success and empirical support for children with ADHD. Such programs may be delivered as stand-alone interventions or adjunctive to BPT interventions to address a wide range of problem behaviors and enhance generalization across treatment settings. School-based behavioral interventions are predicated on the same concepts as BPT programs and improve classroom behavior by structuring antecedents, providing positive reinforcement of desired behaviors through labeled praise and specialized contingency-management programs, and/or implementing negative consequences within the classroom. Indeed, many of the abovementioned behavioral strategies taught in BPT curricula, such as labeled praise, effective instructions, and planned ignoring, can be used in school settings and often yield powerful effects on observed behavior, such as on-task performance, academic productivity, and rule compliance for children with ADHD. One empirically supported classroom-based intervention for children with ADHD is the daily report card (DRC; see Fig. 7.1). DRCs are specialized contingency-management programs that ask teachers to rate predetermined behaviors targeted for improvement. Target behaviors are personalized for students based on intraindividual challenges and can address a wide variety of academic, behavioral, or social domains (see Table 7.2 for sample DRC targets). Teachers provide a rating for each behavior, ideally at several time points throughout the day to not only provide immediate feedback to children on their classroom behavior but also to provide the child an opportunity to earn points throughout the day. Home-based rewards are provided based

FIGURE 7.1 Sample daily report card (DRC) (this example used in Pfiffner et al., 2016).

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TABLE 7.2 Sample daily report card goals. Classroom domain

Sample behavior goal

Academics and study habits

G

G

G

G G G G G G G

Peers

G G G G G G G G G G

Rules and consequences

G G G G G G G G

Gets started right away (Qualifiers: with fewer than __ reminders) Completes assigned work (Qualifiers: accurately, neatly, quickly) Has necessary materials (Qualifiers: out and ready in __ minutes, on desk) Copies down assignments accurately Completes and returns homework Stays on task Keeps desk organized Double-checks work to look for errors Asks for help when needed Reads all instructions before asking for help Treats others with respect Shows good sportsmanship Makes good eye contact Waits turn without interrupting Uses appropriate tone of voice Keeps hands and feet to self Ignores peers’ negative behavior Talks in a brief manner Handles frustrations with peers well Asks peers to play Follows classroom rules Follows teacher instructions Waits patiently to be called on Raises a quiet hand before speaking Shows good accepting Follows seat rules Follows line rules Participates in class (Qualifiers: appropriately, by raising a quiet hand, at least twice a day)

Clinicians and teachers usually select no more than three target behaviors at a time. These may include 1 2 behavior goals that are challenging for the student, and one behavior goal that is somewhat less challenging. The list above provides sample target behaviors that have been shown to be useful in randomized, controlled trials (e.g., Pfiffner et al., 2016).

on DRC ratings to (1) facilitate regular communication between parents and teachers, (2) provide consistency and accountability across settings, and (3) enhance children’s motivation to perform the target behaviors. Clinicians often work with teachers and schools directly to create, implement, and troubleshoot DRCs or indirectly by coaching parents on how to establish and troubleshoot DRCs at their child’s school.

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Empirical support Extant literature consistently supports the use of classroom-based behavioral interventions for children with ADHD (DuPaul et al., 2012; Evans et al., 2018; Pelham & Fabiano, 2008; Pelham et al., 1998). In a metaanalytic review of 60 studies conducted between 1996 and 2010 (DuPaul et al., 2012), school-based interventions for children with ADHD were found to produce moderate-to-large-magnitude improvements across several behavioral functioning outcomes including ADHD symptoms, frequency of rule violations, on-task behaviors, percentage of completed seat work, accuracy of completed work, academic grades, progress of targeted behaviors, and teacher ratings of academic skills. School-based contingency-management programs produced greater improvements on behavioral outcomes such as ADHD symptoms and on-task behaviors relative to academic outcomes such as grades and work completion/accuracy. A second review (Trout, Ortiz Lienemann, Reid, & Epstein, 2007) also found large-magnitude effect size improvements for classroom-based behavioral interventions, particularly for interventions that include token economies and response-cost strategies. The efficacy of the DRC, in particular, has also received significant empirical scrutiny in recent years and has been found efficacious in a variety of school-based settings, including in the context of a general education classroom (Owens et al., 2012)and special education services (Fabiano et al., 2010). Compared to treatment-as-usual control groups, the DRC produced significant improvement in academic productivity, parent- and teacher-rated oppositional defiant and conduct disorder symptomatology, and blinded observations of classroom comportment. Collectively, the current literature supports the use of behavioral interventions within the classroom as an evidence-based treatment for children with ADHD for improving on-task performance and decreasing rule violations. Similar to BPT interventions, however, the evidence base for classroombased behavioral interventions for children with SLD is lacking. No study to date has examined behavioral interventions for children where SLD is either the primary focus or inclusion criteria. Further, only a handful of studies examine improvements on standardized academic achievement measures among children with ADHD following classroom-based behavioral interventions, such as the DRC. These studies typically find small-magnitude or nonsignificant improvements on academic achievement measures. For example, following 7 months of DRC intervention among elementary school aged students with ADHD, Fabiano et al. (2010) demonstrated significant improvements on blinded observations of disruptive behavior, as well as teacher ratings of academic productivity, disruptive behavior, and IEP goal attainment. However, these improvements were unaccompanied by complementary improvements on standardized academic achievement measures of reading and math skills and suggest that concomitant treatment for specific

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learning challenges is necessary to ameliorate SLD-related deficits among children with comorbid ADHD and SLD.

Child organizational skill interventions In contrast to BPT and school-based interventions which train parents and teachers to deliver the intervention to children, organizational skills treatments directly train children in skills they apply themselves. This approach is relatively new to the treatment armamentarium for ADHD and has become an increasing focus in ADHD treatment since (1) youth with ADHD often lack skills for organizing tasks and materials, managing their time, and prioritizing and planning effectively (Langberg, Epstein, et al., 2011; Langberg, Epstein, Urbanowicz, Simon, & Graham, 2008; Langberg, Vaughn, et al., 2011) and (2) these deficits have been shown to underlie poor academic outcomes, including low productivity, test performance, and grades (Power, Werba, Watkins, Angelucci, & Eiraldi, 2006; Schultz, Evans, & Serpell, 2009). Therefore training skills to ameliorate these deficits would be expected to directly benefit academic outcomes and activities of daily living more generally. Organizational skills training typically includes teaching children skills for organizing their belongings (e.g., planners, backpacks, lockers, and study areas at home), using an assignment book to track assignments, and utilizing checklists to plan ahead and manage multiple tasks. Behavioral strategies such as didactic instruction, modeling, rehearsal, and reinforcement are used to teach the skills. Parents, and less often teachers, are taught to deliver incentives to reward successful completion of organizational tasks and/or tasks that require organizational skills (e.g., homework). Organizational skills training programs have been developed for delivery in clinic and/or school settings, and for youth in elementary (Abikoff et al., 2013), middle (Langberg, Epstein, Becker, Girio-Herrera, & Vaughn, 2012), and high school (Evans et al., 2016). Organizational skills training has been studied as a stand-alone treatment, or, more often, as part of a multicomponent program (Evans et al., 2016; Pfiffner et al., 2014, 2016; Power et al., 2012; Sibley et al., 2014).

Empirical support Bikic et al. (2017) recently conducted a metaanalytic review of existing randomized controlled trials of organizational skills training for ADHD (12 trials, N 5 1054 children), which evaluated the training as either a standalone treatment or embedded in a multicomponent program. Metaanalytic findings revealed that these treatments lead to large-range improvements in children’s organizational skills at home per parent report and moderate-range improvements at school per teacher report. Modest benefits were observed on ratings of inattention and on academic performance measures, including

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GPA. Differences existed in the magnitude of effects across the individual studies. The pattern of these differences suggested that more intensive programs and those with more extensive teacher components may be associated with greater gains for youth across outcome domains. Few studies have examined whether learning problems moderate or predict outcomes from organizational skills interventions. In one investigation related to this topic, Breaux et al. (2018) examined predictors of treatment response among middle-school adolescents with ADHD who received either a contingency-management or skill-based intervention for homework problems. Both GPA and math and reading achievement scores obtained prior to delivery of the treatment were the most consistent predictors of parentand teacher-rated treatment response. Those with low GPAs (i.e., ,2.0) and low- to below-average academic achievement (i.e., reading or math achievement standard scores ,95) were less likely to have reductions in homework problems and improved homework completion following treatment. Another study that embedded organizational skills training within a multicomponent behavioral intervention found similar results (see next). As was the case with BPT and school-based interventions, these studies suggest that benefits from organizational skills training may require more intensive treatment for youth with ADHD and comorbid learning problems.

Multicomponent behavioral interventions Driven by the cross-setting impairments associated with ADHD and SLD diagnoses, coupled with limited generalizability of treatment gains across settings, several multicomponent behavioral interventions have been developed recently for the treatment of ADHD. These interventions include a combination of behavioral intervention modalities including BPT, classroom behavioral management, DRCs, and/or child skills training. In addition to directly targeting problems across settings, multicomponent interventions can produce a beneficial synergistic effect on the individual components wherein their combination is greater than the sum of their parts. This is especially true for child skills where didactic learning is likely insufficient to produce maximal benefits, and ongoing monitoring and positive incentives from parents and teachers (e.g., within a token economy) are necessary to promote generalization of learned skills. Treatment programs have been developed for all age-groups—preschool to high school—and can be implemented within the clinic setting by mental health professionals or within the school setting by school mental health providers.

Empirical support As expected, multicomponent behavioral interventions produce significant and often sustained beneficial effects across various domains including

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ADHD symptoms, defiant behavior, social skills, organizational skills, and global impairments. Importantly, studies of multicomponent treatments find significant improvement in educational outcomes such as teacher ratings of academic skills, report card grades, student engagement, and standardized measures of academic achievement (Pfiffner, Villodas, Kaiser, Rooney, & McBurnett, 2013). When compared to single component interventions, improvements from multicomponent interventions are often greater than the individual behavioral interventions they comprise (Pfiffner et al., 2014). To date, only two studies have examined response to multicomponent interventions among children with comorbid ADHD/SLD. Friedman et al. (2019) examined whether the presence of an SLD in reading or math predicts response to a multicomponent behavioral intervention for elementary school aged students with ADHD-Predominantly Inattentive Presentation (ADHD-I). The Child Life and Attention Skills Program (CLAS, Pfiffner et al., 2014) is a multicomponent intervention utilizing strategies tailored to address the cross-setting challenges specific to children with ADHD-I such as academic work completion (e.g., completing and turning in assigned work, accuracy of school- and homework), work behavior/study skills (e.g., following directions, having necessary materials for work, getting started on work), and social situations (e.g., joining and maintaining conversations, showing good sportsmanship). CLAS uses a three-pronged treatment approach consisting of the three empirically supported behavioral intervention domains outlined earlier—(1) 10 weekly parent training group sessions along with up to 6 individual family meetings with the parent, child, and therapist to troubleshoot applying and generalizing the skills taught, (2) 10 weekly child skills group sessions that run concurrently with parent sessions, are reinforced by parents and teachers using a token economy, and focus on building independence, organization, emotion regulation, assertiveness, and social skills, and (3) a classroom component consisting of evidence-based classroom management strategies, a customized school home DRC, and up to five classroom meetings with the parent, child, and therapist to discuss DRC goals, classroom accommodations, and the skills taught within the child component. In a randomized, controlled trial (Pfiffner et al., 2014), CLAS was associated with significant improvements in teacher-rated attention, social skills, organization, and global functioning, as well as parent-rated organizational skills, relative to parent training alone and to treatment as usual. CLAS also demonstrated superior results relative to treatment as usual on parent-rated attention, social skills, and global functioning. SLD status in reading and math was examined as a predictor of positive treatment response to CLAS (Friedman et al., 2019) and was found to significantly predict teacher-rated attention, organizational skills, and study skills. A similar pattern emerged across all teacher-rated outcomes wherein all children improved within the domains assessed, irrespective of SLD status, but children without a comorbid SLD showed greater treatment-related improvement relative to those

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with a comorbid learning disability. Reading or math disability status did not affect treatment-related response within the home setting on parent-rated inattention, organizational skills, and homework problems. These findings suggest that children with ADHD/SLD comorbidity benefit significantly from multimodal behavioral interventions; however, such interventions are not sufficient to address the cross-domain challenges associated with comorbidity, and supplemental intervention for learning challenges may be necessary to produce maximal improvements. In a second study, the Multimodal Treatment Study for ADHD (MTA, 1999) examined whether SLD comorbidity was associated with differential treatment response (Langberg et al., 2010). The MTA study was one of the largest multisite, randomized controlled studies for children with ADHD. A total of 597 children aged 7 9 were randomly assigned to 14 months of intensive behavioral treatment (35 BPT sessions, child skill instruction, school and summer intervention program including DRC), optimally titrated psychostimulant management, the combination of behavioral intervention and medication management, or usual community care. Combined behavioral and medication intervention was superior over both monotherapies and treatment as usual in reducing ADHD and oppositional defiant disorder symptoms, internalizing symptoms, social skills, and parent child relationships (Jensen et al., 2001). When examined as a moderator of treatment-related improvement in parent-rated homework problems, such as homework avoidance and poor productivity (Langberg et al., 2010), the presence of an SLD neither predicted which children benefited most from therapy modalities on parent-rated homework problems nor affected the magnitude of treatmentrelated improvements on homework problems, a finding consistent with those of Friedman et al. (2019). However, SLD-related effects were only examined for homework problems, and whether SLD moderates or predicts treatment-related improvements in ADHD symptoms, academic outcomes, or other related deficits were not examined in this sample.

Summary and future directions Collectively, decades of empirical investigations and multiple systematic and metaanalytic reviews of randomized, controlled studies support the use of behavioral interventions for children with ADHD. The significant empirical support has prompted leading oversight and treatment evaluation committees, such as the Society of Clinical Child and Adolescent Psychology (American Psychological Association, Division 53), Institute of Education Science’s What Works Clearinghouse, and the Oxford Center for Evidence-Based Medicine Guidelines, to classify BPT, behavioral classroom management strategies such as the DRC, and child-focused skills alone and combined as proven effective evidence-based treatments for children with ADHD. Despite the significant diagnostic overlap between ADHD and SLD, as well as high prevalence rates of SLD among school-aged children,

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behavioral interventions among those with ADHD/SLD comorbidity are critically understudied. To date, only one study (Friedman et al.,2019) has examined relative treatment effectiveness among children with ADHD/SLD comorbidity compared to ADHD alone across multiple outcomes and found that children with ADHD/SLD improve following behavioral interventions, although improvements in the school setting are attenuated significantly. This finding, coupled with evidence that behavioral interventions produce either small-magnitude or nonsignificant improvements on standardized measures of academic achievement, suggests that behavioral treatments are necessary but not sufficient to address the cross-domain and unique challenges evinced among children with dual ADHD/SLD diagnoses. That is, multimodal treatment targeting ADHD (i.e., behavioral interventions, medication) and SLD (i.e., direct instruction, tutoring) is necessary to address the crossdomain deficits associated with ADHD/SLD comorbidity. However, further study would be needed to evaluate the temporal sequencing of interventions to determine whether (1) ADHD and SLD intervention should occur concomitantly or (2) the symptoms and impairment related to one disorder require amelioration prior to initiating intervention for the comorbid condition.

Resources for clinicians Behavioral parent training resources: G

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Defiant Children: A Clinician’s Manual for Assessment and Parent Training, 3rd Edition (2013) by Russell A. Barkley; Guilford Press. Helping the Noncompliant Child, Second Edition: Family-Based Treatment for Oppositional Behavior, 2nd Edition (2005) by Robert McMahon and Rex Forehand; Guilford Press. Parent Management Training: Treatment for Oppositional, Aggressive, and Antisocial Behavior in Children and Adolescents, 1st Edition (2008) by Alan E. Kazdin; Oxford University Press. School-based behavioral intervention resources:

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ADHD in the Schools: Assessment and Intervention Strategies, 3rd Edition (2015) by George J. DuPaul and Gary Stoner; Guilford Press. All About ADHD: The Complete Practical Guide for Classroom Teachers, 2nd Edition (2011) by Linda J. Pfiffner; Scholastic Teaching Resources. Child skills training resources:

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Organizational Skills Training for Children with ADHD: An Empirically Supported Treatment (2014) by Richard Gallagher, Howard B Abikoff, and Elana G. Spira; Guilford Press.

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Homework, Organizational, and Planning Skills (HOPS) Interventions: A Treatment Manual (2011) by Joshua M. Langberg; National Association of School Psychologists (NASP).

Resources for parents Books: G

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Getting Ahead of ADHD: What Next-Generation Science Says about Treatments That Work and How You Can Make Them Work for Your Child (2017) by Joel Nigg; Guilford Press. Taking Charge of ADHD: The Complete, Authoritative Guide for Parents, 3rd Edition (2015) by Russell Barkley; Guilford Press. Journal of an ADHD Kid: The Good, the Bad, and the Useful (2014) by Tobias Stumpf and Dawn Schaefer Stumpf; Woodbine House. Annie’s Plan: Taking Charge of Schoolwork and Homework (2006) by Jeanne Kraus; Magination Press. Improving Children’s Homework, Organization, and Planning Skills (HOPS): A Parent’s Guide (2011) by Joshua M. Langberg; NASP.

Websites for general information, workshops, information sessions, and educational materials about learning and attention challenges are as follows: G

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National Institute of Mental Health: http://www.nimh.nih.gov/health/publications/attention-deficit-hyperactivity-disorder/complete-index.shtml Understood: https://www.understood.org/en Addwarehouse.com Chadd.org http://www.parentseducationnetwork.org/ To find a behavioral therapist in your area, http://www.findcbt.org/xFAT/ For comprehensive ADHD centers: https://chadd.org/organizationdirectory/ For individual service providers: https://chadd.org/professional-directory/

References Abikoff, H., Gallagher, R., Wells, K. C., Murray, D. W., Huang, L., Lu, F., & Petkova, E. (2013). Remediating organizational functioning in children with ADHD: Immediate and long-term effects from a randomized controlled trial. Journal of Consulting and Clinical Psychology, 81(1), 113 128. Altarac, M., & Saroha, E. (2007). Lifetime prevalence of learning disability among US children. Pediatrics, 119(Suppl. 1), S77 S83. American Academy of Pediatrics Subcommittee on Attention-Deficit/Hyperactivity Disorder. (2011). ADHD: Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 128, 1007 1022, peds-2011.

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American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5s). American Psychiatric Pub. Barkley, R. A. (2013). Defiant children: A clinician’s manual for assessment and parent training. Guilford Press. Barkley, R. A., Fischer, M., Smallish, L., & Fletcher, K. (2006). Young adult outcome of hyperactive children: Adaptive functioning in major life activities. Journal of the American Academy of Child & Adolescent Psychiatry, 45(2), 192 202. Bental, B., & Tirosh, E. (2007). The relationship between attention, executive functions and reading domain abilities in attention deficit hyperactivity disorder and reading disorder: A comparative study. Journal of Child Psychology and Psychiatry, 48(5), 455 463. Bikic, A., Reichow, B., McCauley, S. A., Ibrahim, K., & Sukhodolsky, D. G. (2017). Metaanalysis of organizational skills interventions for children and adolescents with attention-deficit/hyperactivity disorder. Clinical Psychology Review, 52, 108 123. Bloomquist, M. L. (1996). Skills training for children with behavior disorders: A parent and therapist guidebook. Guilford Press. Breaux, R. P., Langberg, J. M., Bourchtein, E., Eadeh, H. M., Molitor, S. J., & Smith, Z. R. (2018). Brief homework intervention for adolescents with ADHD: Trajectories and predictors of response. School Psychology Quarterly. Advanced Online Publication. Daley, D., Van der Oord, S., Ferrin, M., Danckaerts, M., Doepfner, M., Cortese, S., & SonugaBarke, E. J. S. (2014). Behavioral interventions in attention-deficit/hyperactivity disorder: A meta-analysis of randomized controlled trials across multiple outcome domains. Journal of the American Academy of Child & Adolescent Psychiatry, 53(8), 835 847. DuPaul, G. J., Eckert, T. L., & Vilardo, B. (2012). The effects of school-based interventions for attention deficit hyperactivity disorder: A meta-analysis 1996-2010. School Psychology Review, 41(4), 387 412. DuPaul, G. J., Gormley, M. J., & Laracy, S. D. (2013). Comorbidity of LD and ADHD: Implications of DSM-5 for assessment and treatment. Journal of Learning Disabilities, 46 (1), 43 51. Evans, S. W., Langberg, J. M., Schultz, B. K., Vaughn, A., Altaye, M., Marshall, S. A., & Zoromski, A. K. (2016). Evaluation of a school-based treatment program for young adolescents with ADHD. Journal of Consulting and Clinical Psychology, 84(1), 15 30. Evans, S. W., Owens, J. S., Wymbs, B. T., & Ray, A. R. (2018). Evidence-based psychosocial treatments for children and adolescents with attention deficit/hyperactivity disorder. Journal of Clinical Child & Adolescent Psychology, 47(2), 157 198. Fabiano, G. A., Pelham, W. E., Jr, Coles, E. K., Gnagy, E. M., Chronis-Tuscano, A., & O’Connor, B. C. (2009). A meta-analysis of behavioral treatments for attention-deficit/ hyperactivity disorder. Clinical Psychology Review, 29(2), 129 140. Fabiano, G. A., Vujnovic, R. K., Pelham, W. E., Waschbusch, D. A., Massetti, G. M., Pariseau, M. E., . . . Carnefix, T. (2010). Enhancing the effectiveness of special education programming for children with attention deficit hyperactivity disorder using a daily report card. School Psychology Review, 39(2), 219 239. Forehand, R. L., & McMahon, R. J. (1981). Helping the noncompliant child: A clinician’s guide to parent training. New York: Guilford Press. Frazier, T. W., Youngstrom, E. A., Glutting, J. J., & Watkins, M. W. (2007). ADHD and achievement: Meta-analysis of the child, adolescent, and adult literatures and a concomitant study with college students. Journal of Learning Disabilities, 40(1), 49 65. Friedman, L. M., McBurnett, K., Dvorsky, M. R., Hinshaw, S. P., & Pfiffner, L. J. (2019). Learning Disorder confers setting-specific treatment resistance for children with ADHD,

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Predominantly Inattentive Presentation. Journal of Clinical Child and Adolescent Psychology, Advanced online publication. Jensen, P. S., Hinshaw, S. P., Kraemer, H. C., Lenora, N., Newcorn, J. H., Abikoff, H. B., . . . Conners, C. K. (2001). ADHD comorbidity findings from the MTA study: Comparing comorbid subgroups. Journal of the American Academy of Child & Adolescent Psychiatry, 40(2), 147 158. Junod, R. E. V., DuPaul, G. J., Jitendra, A. K., Volpe, R. J., & Cleary, K. S. (2006). Classroom observations of students with and without ADHD: Differences across types of engagement. Journal of School Psychology, 44(2), 87 104. Kazdin, A. E. (2008). Parent management training: Treatment for oppositional, aggressive, and antisocial behavior in children and adolescents. Oxford University Press. Kuriyan, A. B., Pelham, W. E., Molina, B. S. G., Waschbusch, D. A., Gnagy, E. M., Sibley, M. H., . . . Yu, J. (2013). Young adult educational and vocational outcomes of children diagnosed with ADHD. Journal of Abnormal Child Psychology, 41(1), 27 41. Langberg, J. M., Arnold, L. E., Flowers, A. M., Epstein, J. N., Altaye, M., Hinshaw, S. P., . . . Molina, B. S. G. (2010). Parent-reported homework problems in the MTA study: Evidence for sustained improvement with behavioral treatment. Journal of Clinical Child & Adolescent Psychology, 39(2), 220 233. Langberg, J. M., Epstein, J. N., Becker, S. P., Girio-Herrera, E., & Vaughn, A. J. (2012). Evaluation of the homework, organization, and planning skills (HOPS) intervention for middle school students with ADHD as implemented by school mental health providers. School Psychology Review, 41(3), 342 364. Langberg, J. M., Epstein, J. N., Girio-Herrera, E., Becker, S. P., Vaughn, A. J., & Altaye, M. (2011). Materials organization, planning, and homework completion in middle-school students with ADHD: Impact on academic performance. School Mental Health, 3(2), 93 101. Langberg, J. M., Epstein, J. N., Urbanowicz, C. M., Simon, J. O., & Graham, A. J. (2008). Efficacy of an organization skills intervention to improve the academic functioning of students with attention-deficit/hyperactivity disorder. School Psychology Quarterly, 23(3), 407 417. Langberg, J. M., Vaughn, A. J., Williamson, P., Epstein, J. N., Girio-Herrera, E., & Becker, S. P. (2011). Refinement of an organizational skills intervention for adolescents with ADHD for implementation by school mental health providers. School Mental Health, 3(3), 143 155. Loe, I. M., & Feldman, H. M. (2007). Academic and educational outcomes of children with ADHD. Journal of Pediatric Psychology, 32(6), 643 654. Massetti, G. M., Lahey, B. B., Pelham, W. E., Loney, J., Ehrhardt, A., Lee, S. S., & Kipp, H. (2008). Academic achievement over 8 years among children who met modified criteria for attention-deficit/hyperactivity disorder at 4 6 years of age. Journal of Abnormal Child Psychology, 36(3), 399 410. MTA Cooperative Group. (1999). A 14-month randomized clinical trial of treatment strategies for attention-deficit/hyperactivity disorder. Archives of General Psychiatry, 56(12), 1073 1086. Van der Oord, S., Prins, P. J. M., Oosterlaan, J., & Emmelkamp, P. M. G. (2008). Efficacy of methylphenidate, psychosocial treatments and their combination in school-aged children with ADHD: A meta-analysis. Clinical Psychology Review, 28(5), 783 800. Owens, J. S., Holdaway, A. S., Zoromski, A. K., Evans, S. W., Himawan, L. K., Girio-Herrera, E., & Murphy, C. E. (2012). Incremental benefits of a daily report card intervention over time for youth with disruptive behavior. Behavior Therapy, 43(4), 848 861.

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Parry, P. A., & Douglas, V. I. (1983). Effects of reinforcement on concept identification in hyperactive children. Journal of Abnormal Child Psychology, 11(2), 327 340. Patterson, G. R. (1982). Coercive family process (Vol. 3). Castalia Publishing Company. Pelham, W. E., Jr, & Fabiano, G. A. (2008). Evidence-based psychosocial treatments for attention-deficit/hyperactivity disorder. Journal of Clinical Child & Adolescent Psychology, 37 (1), 184 214. Pelham, W. E., Jr, Wheeler, T., & Chronis, A. (1998). Empirically supported psychosocial treatments for attention deficit hyperactivity disorder. Journal of Clinical Child Psychology, 27 (2), 190 205. Pfiffner, L., & Kaiser, N. (2010). Behavioral parent training. Dulcan’s textbook of child and adolescent psychiatry. Am Psychiatric Assoc. Pfiffner, L. J. (2008). More rewards or punishment? In K. McBurnett, & L. J. Pfiffner (Eds.), Attention deficit hyperactivity disorder: Concepts, controversies, new directions—Series 37 (pp. 291 300). NY: Informa Healthcare. Pfiffner, L. J., Hinshaw, S. P., Owens, E., Zalecki, C., Kaiser, N. M., Villodas, M., & McBurnett, K. (2014). A two-site randomized clinical trial of integrated psychosocial treatment for ADHD-inattentive type. Journal of Consulting and Clinical Psychology, 82(6), 1115 1127. Pfiffner, L. J., Rooney, M., Haack, L., Villodas, M., Delucchi, K., & McBurnett, K. (2016). A randomized controlled trial of a school-implemented school home intervention for attention-deficit/hyperactivity disorder symptoms and impairment. Journal of the American Academy of Child & Adolescent Psychiatry, 55(9), 762 770. Pfiffner, L. J., Villodas, M., Kaiser, N., Rooney, M., & McBurnett, K. (2013). Educational outcomes of a collaborative school home behavioral intervention for ADHD. School Psychology Quarterly, 28(1), 25 36. Power, T. J., Mautone, J. A., Soffer, S. L., Clarke, A. T., Marshall, S. A., Sharman, J., . . . Jawad, A. F. (2012). A family school intervention for children with ADHD: Results of a randomized clinical trial. Journal of Consulting and Clinical Psychology, 80(4), 611 623. Power, T. J., Werba, B. E., Watkins, M. W., Angelucci, J. G., & Eiraldi, R. B. (2006). Patterns of parent-reported homework problems among ADHD-referred and non-referred children. School Psychology Quarterly, 21(1), 13 33. Quay, H. C. (1997). Inhibition and attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 25(1), 7 13. Schultz, B. K., Evans, S. W., & Serpell, Z. N. (2009). Preventing failure among middle school students with attention deficit hyperactivity disorder: A survival analysis. School Psychology Review, 38(1), 14 27. Seidman, L. J., Biederman, J., Monuteaux, M. C., Doyle, A. E., & Faraone, S. V. (2001). Learning disabilities and executive dysfunction in boys with attention-deficit/hyperactivity disorder. Neuropsychology, 15(4), 544 556. Sibley, M. H., Altszuler, A. R., Ross, J. M., Sanchez, F., Pelham, W. E., Jr, & Gnagy, E. M. (2014). A parent-teen collaborative treatment model for academically impaired high school students with ADHD. Cognitive and Behavioral Practice, 21(1), 32 42. Sonuga-Barke, E. J. S. (2005). Causal models of attention-deficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57(11), 1231 1238. Sonuga-Barke, E. J. S., Brandeis, D., Cortese, S., Daley, D., Ferrin, M., Holtmann, M., . . . Do¨pfner, M. (2013). Nonpharmacological interventions for ADHD: Systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. American Journal of Psychiatry, 170(3), 275 289.

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Sonuga-Barke, E. J. S. (2011). ADHD as a reinforcement disorder moving from general effects to identifying (six) specific models to test. Journal of Child Psychology and Psychiatry, 52 (9), 917 918. Tamm, L., Denton, C. A., Epstein, J. N., Schatschneider, C., Taylor, H., Arnold, L. E., . . . Newman, N. C. (2017). Comparing treatments for children with ADHD and word reading difficulties: A randomized clinical trial. Journal of Consulting and Clinical Psychology, 85 (5), 434. Trout, A. L., Ortiz Lienemann, T., Reid, R., & Epstein, M. H. (2007). A review of nonmedication interventions to improve the academic performance of children and youth with ADHD. Remedial and Special Education, 28(4), 207 226. Wei, X., Yu, J. W., & Shaver, D. (2014). Longitudinal effects of ADHD in children with learning disabilities or emotional disturbances. Exceptional Children, 80(2), 205 219. Willcutt, E. G. (2012). The prevalence of DSM-IV attention-deficit/hyperactivity disorder: A meta-analytic review. Neurotherapeutics, 9(3), 490 499. Willcutt, E. G., Betjemann, R. S., McGrath, L. M., Chhabildas, N. A., Olson, R. K., DeFries, J. C., & Pennington, B. F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46(10), 1345 1361. Willcutt, E. G., Betjemann, R. S., Pennington, B. F., Olson, R. K., DeFries, J. C., & Wadsworth, S. J. (2007). Longitudinal study of reading disability and attention-deficit/hyperactivity disorder: Implications for education. Mind, Brain, and Education, 1(4), 181 192. Willcutt, E. G., Pennington, B. F., Olson, R. K., Chhabildas, N., & Hulslander, J. (2005). Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: In search of the common deficit. Developmental Neuropsychology, 27(1), 35 78.

Chapter 8

Executive function training for children with attentiondeficit/hyperactivity disorder Mark D. Rapport1, Samuel J. Eckrich1, Catrina Calub1 and Lauren M. Friedman2 1

Department of Psychology, University of Central Florida, FL, United States, 2Department of Psychiatry, University of California, San Francisco, CA, United States

Introduction Emerging interest in computer-based executive function (EF) (cognitive) training for children with attention-deficit/hyperactivity disorder (ADHD) has evolved based on an amalgamation of clinical outcome, experimental, and neuroimaging findings over the past decade. We present a summary and integration of the findings initially to illuminate the underlying rationale for developing alternative treatments for children with ADHD and why most emphasize strengthening basic cognitive processes associated with core foundational learning. Afterward, we review extant computer-administered cognitive training programs and corresponding empirical evidence concerning their effectiveness for improving learning and educational outcomes in children with ADHD. Nascent, potentially promising interventions such as neurofeedback (NF) and noninvasive brain stimulation are discussed in the ensuing section. We conclude the chapter with a summary of recently developed organizational and memory strategies, the focus of which is to strengthen EF-dependent academic skills in youth with ADHD.

Why alternative treatments are needed for children with attention-deficit/hyperactivity disorder Implications derived from clinical outcome studies Most readers of this book are familiar with the largest multisite, clinical treatment outcome study involving children with ADHD conducted in the The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00008-3 © 2020 Elsevier Inc. All rights reserved.

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world, that is, the Multimodal Treatment Study of Children with ADHD (MTA). As an aide-memoire, 579 children were assigned randomly to one of four treatment groups among which one group received individually titrated psychostimulant medication; the second group received rigorous behavioral intervention (parent/teacher child management training coupled with contingency management procedures); the third group received both interventions concurrently; and the fourth group received community care: parents were provided with a list of community mental health services to pursue for their child. A typically developing (non-ADHD) group of children was also included to determine how well children assigned to the four treatment groups fared across multiple domains relative to same-age peers. The results of the MTA study have been reported in numerous publications, and follow-up studies continue to be published. For purposes of this chapter, we limit our discussion of treatment outcome to two domains that underscore the acute need for alternative treatments for ADHD: core behavioral symptoms and learning. Briefly, psychostimulants alone and combined with behavioral intervention were associated with largemagnitude reductions in core ADHD symptoms (i.e., inattention, hyperactivity, and impulsivity) for up to 24 months, whereas behavioral treatment alone was associated with more moderate decreases. Reductions in core symptoms, however, were unaccompanied by significant improvement in academic and learning outcomes, including standardized achievement test scores (DuPaul, Morgan, Farkas, Hillemeier, & Maczuga, 2018; Molina et al., 2009). The MTA results highlight the acute need to develop alternative treatments for the disorder for two reasons. One is that they contradict a central assumption of the DSM-5 clinical model: core ADHD symptoms, particularly inattentiveness, underlie academic achievement deficiencies by interfering with basic learning processes such as attending to, comprehending, and following classroom instructions. The finding that amelioration of core symptoms does not result in improved learning and achievement poses serious challenges to the model’s validity and highlights the misguided certitude of targeting behavioral improvement rather than learning outcomes in children with ADHD. And the second, more compelling reason for developing alternative treatments is that a majority of children with ADHD evidence significant learning-related difficulties in core foundational areas beginning in kindergarten (DuPaul, Morgan, Farkas, Hillemeier, & Maczuga, 2016), and these difficulties are relatively immutable to current gold-standard treatments. As a result, these disparities continue throughout childhood and are potent predictors of multiple outcomes in late adolescence and early adulthood including educational obtainment, occupational rank/stability/ functioning, socioeconomic status, homelessness, and use of public assistance programs (Hechtman et al., 2016; Ramos-Olazagasti, Castellanos, Mannuzza, & Klein, 2018).

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Implications derived from neuroimaging studies The relative impotence of psychostimulant and behavioral treatments to improve academic and learning outcomes in children with ADHD was anticipated based on what is known about the two treatments relative to findings derived from neuroimaging studies (cf. Rapport, Orban, Kofler, & Friedman, 2013, for a review). For example, widely distributed hypoactivity in frontal/ prefrontal cortical regions implicated in executive functioning is well documented in children with ADHD (cf. Dickstein, Bannon, Castellanos, & Milham, 2006, for a metaanalytic review), and the relations among central nervous system (CNS) arousal, increased activity level, and task performance are well established (Barry, Clarke, McCarthy, Selikowitz, & Rushby, 2005; Rapport et al., 2008). The near-normalization of attention and gross motor activity observed with psychostimulants and behavioral interventions likely reflects their impact on arousal-regulating mechanisms needed to activate EF-supporting structures within these brain regions (Cortese, 2012). Repeated resonance scans acquired prospectively from 5 to 15 years of age, however, reveal a 2.5 3-year delay in attaining peak cortical thickness in these same prefrontal/frontal regions in a majority of children with ADHD relative to typically developing children (Shaw et al., 2007). Activating these regions is thus unlikely to translate into improved cognitive functioning or learning outcomes due to the ontogenetically underdeveloped structures themselves and EFs these structures support.

Strengthening basic cognitive processes associated with core foundational learning Implications derived from cognitive/experimental investigations Appreciating the centrality of EF deficits is critical for understanding the wide range of behavioral, socio-emotional, and particularly the learning/academic achievement difficulties experienced by children with ADHD. Briefly, EFs refer to a family of higher order, interrelated cognitive processes that enable a wide range of activities such as goal directed behavior, reasoning, planning, problem-solving, learning, and creative thinking (i.e., cool EFs). They also are related integrally to socio-emotional control, delayed reward tolerance, and interpersonal interactions (i.e., hot EFs). Metaanalytic reviews, factor analytic studies, and neuroimaging investigations consistently identify three primary EFs: behavioral inhibition, working memory (WM), and cognitive flexibility. Two of these EFs, WM and cognitive flexibility, show developmental continuity and are associated with a strong, independent genetic basis (Friedman et al., 2008; Huizinga, Dolan, & van der Molen, 2006). A majority (89%) of children with ADHD evidence impairment in at least one EF, and WM is likely to be impaired more than twice as often

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relative to behavioral inhibition and cognitive flexibility (Kofler et al., 2018). This latter finding takes on additional significance because WM is the only EF that bears a strong integral relation with foundational knowledge and predicts academic achievement in children between kindergarten and fifth grade more accurately than IQ after controlling for their shared variance (Alloway & Alloway, 2010). Most contemporary models view WM as a multicomponent, limitedcapacity cognitive system responsible for the temporary storage and processing of information required for reasoning, planning, problem-solving, and other goal directed behaviors. It consists of a domain-general, supervisory attentional controller termed the central executive (CE), and two subsidiary, domain-specific short-term memory (STM) stores that are used to hold/maintain verbal (PH: phonological) and nonverbal (VS: visuospatial) information for brief time intervals unless refreshed continually. The CE is a flexible system responsible for the control and regulation of multiple cognitive processes and is considered the working component of WM. It handles the mental processing of information held internally in STM by means of (1) selective attention and oversight of the storage subsystems; (2) updating (i.e., replacing memory content in STM with newer, more relevant information); (3) manipulation/dual processing/serial reordering (i.e., mentally manipulating, reordering, and/or processing information in STM while simultaneously replacing/storing new information); and (4) interference control (i.e., preserving information being processed in STM by inhibiting irrelevant internal and external information from accessing WM). The CE is localized primarily in the prefrontal cortex, whereas the PH and VS STM stores are localized in the left temporoparietal region and Broca’s area, posterior parietal, and superior occipital cortices, respectively (Baddeley, 2007). Differentiating between the working (CE) and memory (PH/VS STM) components is important because of their distinct neuroanatomical locations and unique contributions to core ADHD symptoms and adverse functional outcomes such as learning deficiencies. Extant evidence indicates that children with ADHD evince large-magnitude deficits in the CE component of WM (Rapport et al., 2008), which are related functionally to the three core clinical symptoms: inattention (Kofler, Rapport, Bolden, Sarver, & Raiker, 2010), hyperactivity (Rapport et al., 2009), and impulsivity (Raiker, Rapport, Kofler, & Sarver, 2012). Conversely, children with ADHD exhibit small-tomoderate magnitude deficits in PH and VS STM (Kasper, Alderson, & Hudec, 2012), which are either minimally involved or unrelated to core diagnostic symptoms. Both upper level CE and lower level STM processes play an important role in ADHD-related reading difficulties (Friedman, Rapport, Raiker, Orban, & Eckrich, 2017), math deficits (Friedman, Rapport, Orban, Eckrich, & Calub, 2018), and written expression deficiencies (Eckrich, Rapport, Calub, & Friedman, 2018) and often work in concert with

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learning-related processes such as encoding/decoding, orthographic conversion, processing speed, and oral expression.

The functional working memory model of attention-deficit/ hyperactivity disorder and transfer effects Evaluating the numerous cognitive training interventions for ADHD in the following section requires two complementary sets of information. The first summarizes a contemporary model that elucidates why particular neurocognitive weaknesses in children with ADHD contribute to core clinical symptoms and foundational learning deficits and represent ideal targets for intervention. The second highlights the two primary types of training effects that can occur following cognitive training, their relative importance, and how to evaluate these effects.

The functional working memory model The functional WM model of ADHD conceptualizes WM as a core deficit and endophenotype and provides a framework for investigating ADHDrelated WM deficits (see Fig. 8.1). A central tenet of the model is that

FIGURE 8.1 An updated schematic diagram of the functional WM model of ADHD depicting genotypic, endophenotypic, phenotypic influences. Biological influences give rise to individual differences in neurocognitive substrates that are etiologically responsible for executive function deficits (primarily WM) that contribute to core behavioral symptoms and impaired functioning across multiple areas. ADHD, Attention-deficit/hyperactivity disorder; WM, working memory. Adapted with permission from the author, Rapport et al., (2018).

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underlying heritable etiological factors such as slowed nerve growth factors and correspondingly reduced neurotransmitter functioning result in neural structure and functional deficits, respectively. Evidence for this can be seen in the 2.5 3-year delay in cortical maturation observed in children with ADHD via neuroimaging (Shaw et al., 2007), as well as the excess slow wave and decreased fast wave activity in frontal/prefrontal regions that implicate cortical underarousal (El-Sayed, Larsson, Persson, & Rydelius, 2002). Two interrelated phenomena result from these deficits: (1) slowed cortical maturation results in an underdeveloped WM system, which is requisite for attention demanding activities such as reasoning, problem-solving, behavioral/interpersonal discourse regulation, and developing foundational knowledge competencies (e.g., reading, mathematics); and (2) frontal/prefrontal underarousal results in excessive gross motor activity to maintain alertness when children are faced with environmental presses that place clear demands on the CE and its multiple processes. Discernable implications can be derived from the functional WM model of ADHD. The most obvious is that interventions aimed at improving suspected underlying neurological substrate(s), and core endophenotypic features of ADHD should produce the greatest level and breadth of therapeutic change (Rapport, Chung, Shore, & Issacs, 2001; Rapport, Chung, Shore, Denney, & Isaacs, 2000; Rapport, Orban, Kofler, Friedman, & Bolden, 2015). Conversely, interventions aimed at phenotypical (e.g., attention, hyperactivity, and impulsivity) and peripheral symptoms (social skill deficits) should result in limited success and bear no impact on foundational learning deficits that rely heavily on WM. Novel interventions are thus more likely to be successful if they target EFs such as WM that are not only deficient in ADHD, but also related to the primary behavioral and learning functional impairments associated with the disorder. Finally, past investigations indicate that other brain-based mechanisms and processes (e.g., encoding/decoding, orthographic conversion, and oral expression) must also be considered and included in training exercises for children with ADHD who present with learning disabilities in core foundational areas such as reading and math (Friedman et al., 2017, 2018).

Desirable training outcomes: near- and far-transfer effects Transfer of learning is the most important goal of computerized cognitive training. In practice, it reflects the ability to transfer what is learned in one context or situation to another. Sometimes it occurs at a subconscious level and requires minimal conscious effort if the learning to be transferred has achieved automaticity and is sufficiently similar to the learning in the new situation. This type of learning is referred to as a near-transfer effect and reflects the extent to which cognitive training improves performance on untrained tasks measuring the identical EF-related processes targeted during

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training. Several computerized cognitive training programs reviewed in the following section, for example, use simple span tasks (e.g., digits) to train children’s short-term verbal memory. If a child recalls 3 digits pretraining and 6 digits posttraining, and a comparable degree of improvement is demonstrated on a simple span word list task, the near-transfer effect would be quite considerable (i.e., 100%). Near-transfer training effects are typically estimated in the literature by calculating effect sizes (ESs) based on pre posttraining score differences. The true efficacy of computerized EF training programs lies in their ability to improve important, ecologically valid outcomes such as academic performance, ADHD symptomatology, and behavior, which depend on trained abilities, as well as cognitive functions that are different from those used during training but rely on overlapping brain regions (i.e., far-transfer effects). Far-transfer presents a significantly greater challenge for children because the similarity and pragmatic relevance between what is trained initially and the abilities required by the transfer task are greatly reduced (i.e., the transfer tasks usually require additional abilities, processes, and knowledge over and above those included during the training). Training children’s WM and demonstrating that training improves math achievement following the intervention represents an example of a far-transfer effect. The expected level of far-transfer can be estimated a priori by determining the shared variance (R2) between the training and transfer tasks in the literature. For example, if measures of WM and applied mathematical problem are correlated 50% based on extant literature, a 100% improvement in WM following training can theoretically translate into a 25% (0.50 3 0.50) performance improvement in a child’s math problem-solving ability. The most critical far-transfer effects in need of improvement in children with ADHD involve core areas of foundation learning such as reading and math, as reviewed previously. Achieving far-transfer effects in these areas are particularly difficult because foundational learning involves the simultaneous use of multiple WM upper (CE) and lower level (STM) processes in conjunction with core-specific processes such as automatized decoding abilities and basic math knowledge among others.

Executive function training programs The failure of current, gold-standard treatments to normalize academic achievement among children with ADHD—coupled with mounting empirical evidence corroborating the centrality and contribution of EF deficits to the symptoms and impairments associated with the disorder—has enthused the development of novel treatment programs. Among these, computerized cognitive training programs that focus on strengthening WM and other EFrelated processes have garnered the greatest interest and are reviewed next;

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others, in a more nascent stage of development and requiring validation, are reviewed in the ensuing section.

Conceptual rationale and currently available programs Introduced in the early 2000s, EF training programs are computer-based interventions designed to foster the growth of deficient and/or delayed neural structures, as well as the communications among regions, through repeated practice. Their design is predicated on the concept of neuroplasticity, that is, that neural pathways can be modified (synaptogenesis) or newly developed (neurogenesis) through repeated training exercises that gradually increase in difficulty using adaptive training.1 The underlying conceptual basis is analogous to engaging in weekly physical exercises such as lifting weights to promote muscle development and gradually increasing the reps and/or weight hefted to maximize the desired outcome. For EF training programs the frontal/prefrontal brain region is targeted, and improved EF ability is expected to generalize to improvements in areas such as general cognitive functioning, academic performance/achievement, ADHD symptomatology, social relationships, and behavioral functioning to the extent that they rely on the targeted brain regions. Many commercially available treatment programs exist currently. Cogmed and Captain’s Log by BrainTrain are two of the more widely used EF training programs marketed to families with children with ADHD. Traditionally, these training programs are delivered through an approved service provider (e.g., clinician, school mental health provider) who provides remote supervision and weekly performance feedback. Training sessions are completed on the child’s home computer. Modal treatment length is 5 6 weeks, and training sessions occur approximately five times weekly for 30 minutes per session.

Executive function training efficacy A recent comprehensive metaanalytic review of extant cognitive training programs (n 5 25) revealed that a wide array of EF-related processes are targeted for training. These include working/STM (n 5 17), behavioral inhibition (n 5 6), set shifting (n 5 2), and attention (n 5 11). The higher number of EF processes targeted relative to the total number of studies reflects the targeting of multiple EFs in several studies (Rapport et al., 2013). Collectively, a total of 913 children with ADHD were included in the 25 reviewed studies. 1. Adaptive training refers to the practice of gradually increasing the difficulty of an on-going training exercise based on a child’s performance to maximize therapeutic gain.

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Examining near-transfer effects revealed that studies training STM (often inappropriately described as WM) resulted in moderate-magnitude improvements on unrelated STM measures, and these benefits appear to last up to 6 months in the small subset of studies that examined treatment maintenance effects. In contrast, studies attempting to improve attention, behavioral inhibition, set shifting, or multiple EFs did not result in significant near-transfer effects. Far-transfer effects were assessed across three outcome categories and included children’s academic achievement, performance on cognitive measures, and parent teacher ratings of core ADHD symptoms. Examining fartransfer effects uncovered no evidence of treatment-related improvements in academic achievement. Nearly three-fourths of the studies documented enhanced cognitive performance; however, these investigations either failed to incorporate near-transfer measures (which must be demonstrated antecedent to testing for far-transfer effects) or reported far-transfer effects that were similar to or greater than near-transfer effects. For the former studies, it is unknown whether the reported cognitive improvements resulted from nonspecific treatment effects such as practice effects, expectancy biases, or the normative developmental trajectory of cognitive abilities in children. Investigations reporting far-transfer effects equal to or exceeding neartransfer effects were particularly suspect in light of cognitive transfer theory, wherein the expected magnitude of far-transfer improvement is limited to the multiplicative relation between near- and far-transfer effects. As an example, children’s VS STM and fluid intellectual abilities as measured by the Raven’s Progressive Matrices overlap 31% based on extant research (Kytta¨la¨ & Lehto, 2008), yet Klingberg et al. (2002) reported larger fluid intelligence gains using the Raven’s (ES 5 1.05) relative to VS STM performance (ES 5 0.86) following a VS STM training program, a finding that defies cognitive transfer theory predictions. Parent teacher ratings of ADHD core symptoms were used in a majority of investigations included in the metaanalysis to determine whether fartransfer effects occurred as a function of cognitive training. Most studies reported positive effects of EF training on core ADHD symptoms; however, a moderator analysis revealed that treatment-related effects were limited to studies in which the primary raters (parent and/or teacher) were unblinded and involved with and/or were knowledgeable of the child’s training. That is, when appropriate experimental methodology was used to control for expectancy biases, there were no discernable EF training-related changes in ADHD-related core symptoms. Collectively, the metaanalytic results indicate that moderate improvements may occur for some near-transfer measures following some forms of EF training; however, none of the training programs resulted in significant far-transfer effects on academic achievement or ADHD-related core symptoms, and a small number of studies reporting improved cognitive

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performance were highly suspect. As a result, parents and practitioners alike should be wary of web-based mountebanks touting these EF training programs as effective treatments for ADHD.

Ready, fire, misaim approach of executive function training programs and methodological considerations Despite the disappointing outcomes associated with current EF training programs, it would be premature to conclude that the development of successful cognitive training programs for children with ADHD is unobtainable. For example, one of the most glaring methodological shortcomings plaguing current EF training programs is the conspicuous lack of congruence between the cognitive abilities targeted by the programs and extant empirical literature on ADHD-related WM deficits. As reviewed earlier, the CE component of WM is the only EF to (1) show large-magnitude deficits among children with ADHD; (2) underlie the core ADHD symptoms of inattention, hyperactivity, and impulsivity; and (3) demonstrate strong relations with important functional outcomes such as academic performance/achievement and social functioning. As a result, strengthening WM CE processes should be considered a cornerstone of computer-based EF training programs for ADHD. This would appear to be a no-brainer, and the most popular training programs available currently claim to train and improve WM, which implies that upper level CE processes are targeted. Scrutiny of these programs, however, reveals that a majority of their modules target lower level verbal and/or VS short-term storage components (memory) rather than upper level CE-related WM processes. Targeting these subsidiary components of the WM system rather than the large-magnitude upper level CE deficiencies and expecting corresponding improvement in ADHD symptoms and academic/cognitive performance is akin to training your glutes and biceps and expecting six-pack abs; it is highly unlikely that you will obtain the desired outcome. An additional methodological limitation inherent to extant computerbased EF training programs worth consideration is their modal treatment duration, which is 5 6 weeks. It is unknown currently whether 5 6 weeks of treatment (typically 30 minutes per day, 5 days per week) is sufficient to induce neuroplastic change in light of the 2- to 3-year cortical maturational delay in a majority of children with ADHD (Shaw et al., 2007).

Neurotherapies The recategorization of ADHD as a neurodevelopmental disorder in the DSM-5 reflects accumulating evidence that brain maturational delays underlie and contribute to many of the behavioral symptoms and cognitive deficits in affected children (Shaw et al., 2007, 2012). Both structural and functional abnormalities in frontal, basal ganglia, parietal, and cerebellar regions have

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been identified (Cortese et al., 2016), and targeting these regions represents attractive nonpharmacological targets for EF training programs based on the concept of neuroplasticity (Jancke, 2009; Rapoport & Gogtay, 2008). Neurotherapies aim to improve the functioning of these aberrant brain regions, either by teaching self-upregulation via neurofeed back (NF) or applying noninvasive external electrical currents (brain stimulation). While some neurotherapies appear to diminish particular behavioral ADHD symptoms, additional research is needed to demonstrate far-transfer and long-term effects.

Neurofeedback NF has been investigated for nearly 50 years and is considered an evidencebased treatment for childhood ADHD according to the American Academy of Pediatrics (AAP, 2010). The central premise of NF training is that particular behaviors and cognitive functions will improve as children learn to self-regulate underlying neural activation patterns via operant reinforcement procedures. The treatment involves measuring brain activity while providing visual or auditory feedback to the child regarding the quality and/or pattern of neural activity in real time, then training children to alter their brain activity (e.g., increase theta versus beta frequency in the frontal lobes) using this feedback. For example, children may be shown a rocket on a computer screen that blasts off and heads toward a space station incrementally based on successful increases in activation of the targeted region.

Electroencephalogram To date, metaanalytic reviews evaluating the efficacy of NF via electroencephalogram (EEG) for children with ADHD have revealed mixed results. For example, sustained (6-month follow-up; Van Doren et al., 2018) medium to large ES improvements are reported for core clinical symptoms (Arns, Heinrich, & Strehl, 2014); however, these effects are no longer significant after controlling for expectancy effects (Cortese et al., 2015). In a related vein, recent metaanalyses revealed that NF was not more efficacious than psychostimulant medication for reducing ADHD symptoms and improving global functioning (Catal´a-Lo´pez et al., 2017) and did not improve children’s performance on laboratory measures of inhibition and attention (Cortese et al., 2015). While small ES improvements are reported on some neurocognitive measures (e.g., Digit Span, Stroop, Tower of London) following EEG-NF treatment, they do not differ significantly from the negligible neurocognitive performance increases observed in children receiving treatment as usual at 1-year follow-up (Bink, Bongers, Popma, Janssen, & van Nieuwenhuizen, 2016).

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NF has also been used to target academic performance in children with ADHD. Preliminary evidence appeared promising and revealed large ES improvements in children’s academic performance (Meisel, Servera, GarciaBanda, Cardo, & Moreno, 2013); however, no significant improvements were reported at 6-month follow-up (Duric, Assmus, Gundersen, Duric Golos, & Elgen, 2017), and the lack of a no-treatment group to control for expected academic improvement during the course of an academic year limits the interpretability of the findings. Collectively, the use of EEG-related NF as a treatment to improve core symptoms, neurocognitive performance, and academic functioning in children with ADHD remains unsupported based on extant controlled empirical investigations but warrants additional study.

fMRI/Functional near-infrared spectroscopy In recent years, NF investigators have begun incorporating neuroimaging techniques such as functional near-infrared spectroscopy (NIRS) and fMRI, which have higher spatial resolution into their armamentarium to target and strengthen specific brain regions. These interventions involve teaching children to self-regulate blood-oxygen level dependent (BOLD2) responses in specific brain regions. Higher spatial resolution is considered beneficial to NF because BOLD signals result in a quicker response than NF-EEG (Thibault, MacPherson, Lifshitz, Roth, & Raz, 2018), and specific subcortical regions can be targeted. This may be particularly beneficial given that children with ADHD have been shown to have abnormal activation patterns in the basal ganglia and the interior frontal gyrus (Norman et al., 2016). Although findings of NF with fMRI and NIRS have demonstrated feasibility (Marx et al., 2015; Zilverstand et al., 2017), their potential usefulness as effective interventions to remediate core symptoms and functional outcomes in children with ADHD remains untested.

Brain stimulation Another novel neurotherapeutic intervention that has received scrutiny is noninvasive brain stimulation, that is, repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). These interventions involve improving activation of targeted brain regions, either by activating underaroused regions or inhibiting over-aroused regions of the brain via noninvasive electric stimulation. Brain stimulation interventions also have the capacity to modulate cortical excitability and increase synaptic plasticity (Demirtas-Tatlidede, Vahabzadeh-Hagh, & Pascual-Leone, 2013). 2. BOLD signals reflect changes in blood flow, which occur in response to neural activity. Neural activation is represented by increased blood flow to the region, an initial increase in oxygenated hemoglobin (oxyHb) and a decrease in deoxygenated hemoglobin (deoxyHb).

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Repetitive transcranial magnetic stimulation rTMS is a technique that involves short electromagnetic pulses administered through an electromagnetic coil near the targeted brain area. In general, high-frequency ( . 5 Hz) rTMS promotes excitability of neurons, whereas low-frequency (1 Hz) rTMS inhibits the excitability of neurons (Lefaucheur et al., 2014). While rTMS is shown to be a feasible and safe option for children with ADHD (Go´mez et al., 2014), it has not been shown to be superior to sham rTMS on ADHD symptoms or cognition (Paz et al., 2017).

Transcranial direct current stimulation tDCS is an alternative noninvasive technique, which involves the delivery of a low electric current to the scalp. To facilitate the depolarization or hyperpolarization of neurons, a positive (anodal) or negative (cathodal) current, respectively, is applied via electrodes (Ashkan, Shotbolt, David, & Samuel, 2013). Recent findings reveal some reductions in clinical symptoms and improved cognitive functions in individuals with ADHD (Breitling et al., 2016; Soff, Sotnikova, Christiansen, Becker, & Siniatchkin, 2017); however, larger sham-controlled studies are needed to establish the efficacy of tDCS and identify optimal stimulation protocols for different age and patient subpopulations.

Summary The current field of neurotherapy in ADHD has provided some promising, albeit limited results. A growing number of investigations have reported preliminary findings indicating that NF may reduce ADHD core behavioral symptoms to a moderate degree; however, far transfer to important functional outcomes such as academic functioning and sustained maintenance of effects remain unsubstantiated.

Practitioner considerations and recommendations Given the relative impotence of current treatment approaches to directly ameliorate the neurocognitive deficits associated with ADHD and the important functional outcomes that rely on them, current best-practice recommendations do not support using EF training or neurotherapies such as neurofeedback (NF) and brain stimulation for children and adolescents with ADHD. Although these interventions are not harmful, the considerable time and financial resources required to engage in these treatments are better spent on interventions with greater empirical support. Currently, psychostimulant medication home school-based contingency management remain the most viable and effective treatments for children with ADHD and are

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associated with improvements in ADHD symptomatology, on-task behaviors, compliance, and externalizing symptoms. Both treatments, however, are prohibitively expensive, difficult to maintain, fail to impact foundational learning and related academic skills experienced by a majority of children with ADHD, and do not address the underlying etiological neurocognitive deficits associated with the disorder. In recognition of these shortcomings, auxiliary interventions teaching compensatory strategies to remediate EF deficits have garnered widespread interest in recent years. In the final section, we summarize two categories of auxiliary, compensatory interventions that target EFdependent academic skills, namely organizational and memory strategies.

Organizational strategies to improve executive function dependent academic activities Underdeveloped EFs such as WM, cognitive flexibility, and behavioral inhibition are often associated with disorganization, poor time management, and inchoate planning abilities, which in turn, impair academic functioning in a majority of youth with ADHD. The inability of gold-standard treatments to ameliorate EF-related deficits and downstream abilities such as organization and planning has resulted in a resurgence of contingency management based interventions that focus on implementing and/or explicitly teaching compensational strategies to directly address organizational planning. Although many organizational skills programs have been created in recent years or are incorporated into larger contingency management programs, we review three relatively well-known interventions that focus exclusively on organization and planning compensatory approaches: (1) Organizational Skills Training (OST) program for children in elementary school; (2) Homework, Organization, and Planning Skills (HOPS) program for youth in middle school; and (3) Supporting Teen’s Autonomy Daily (STAND) program for youth in high school.

Organizational Skills Training OST is a module-based program, the primary focus of which is to help students learn how to track assignments, manage materials, plan near- and longterm academic assignments, and engage in efficient time management. Conventional behavioral techniques are included such as modeling, monitoring, praising, prompting, and providing positive incentives, and complemented by managerial materials and procedures such as daily planners and specialized folders for organizing lockers/desks/backpacks and managing homework. More detailed information and a step-by-step guide to OST is available in the published manual, Organizational Skills Training for Children with ADHD: An Empirically Supported Treatment (Gallagher, Abikoff, & Spira, 2014).

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A recent metaanalysis of organizational skills programs for children and adolescents with ADHD, such as OST, found moderate-to-large magnitude improvements in teacher-rated (d 5 0.54) and parent-rated (d 5 0.83) measures of organizational skills (Bikic, Reichow, McCauley, Ibrahim, & Sukhodolsky, 2017). Although parent-rated measures of inattention decreased moderately, training-related effects for teacher-rated inattention, academic performance, and student GPA were small and based on Cohen’s (1988) interpretation, unobservable to the untrained eye (d 5 0.26, d 5 0.33, and d 5 0.29, respectively). These findings suggest that although OST may improve inattention and organization, improvements are unaccompanied by complementary large-scale remediation of academic achievement and performance, the very outcome they are intended to address.

Homework, Organization, and Planning Skills Recent evidence that homework management is a robust predictor of functional improvements in children with ADHD relative to other organizational skills has spurred interest in designing contingency management interventions of homework-related organizational skills. A recent example, the HOPS intervention, uses a contingency management system of homeworkrelated organizational behaviors to compensate for ADHD-related EF deficits (Langberg, Epstein, Becker, Girio-Herrera, & Vaughn, 2012). School mental health providers follow a published manual, meet with students individually over the course of 16 sessions, and review three primary skill areas: the organization of school-related materials, recording and management of homework assignments, and planning/time management. Similar to the OST program previously described, the materials’ organizational modules include a variety of activities such as creating and using a book bag and school binder and developing a viable plan to transfer homework to and from school. The recording/management of homework modules train students to use a planner to record assignments and important dates. Finally, the planning/time-management modules inform students how to structure assignments, projects, and test preparation into manageable chunks. Consistent with the contingency management systems developed in the 1960s, incentives are employed to reinforce compliance for completing program goals based on a weekly checklist. More detailed information and a step-by-step guide to the HOPS program is available in the published manual, Homework, Organization, and Planning Skills (HOPS) Interventions (Langberg, 2011). A recent study examining the HOPS program revealed significant improvements in nearly all areas of trained organizational skills (e.g., materials management and homework completion) relative to a waitlist control group based on parent report (Langberg et al., 2012); however, no significant improvements were observed by classroom teachers. These findings suggest that parent-rated improvements may be secondary to parental bias as a result

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of their knowledge of and active involvement in the intervention. Albeit promising, interpretation of the results requires caution due to the lack of a credible control group and need for independent replication by researchers with no monetary incentives related to the program’s success.

Supporting Teen’s Autonomy Daily As EFs mature during adolescence and young adulthood, skill-based training interventions need to match the increased organizational demands of middle and high schools. Programs such as STAND combine skill-based behavioral/ organizational training with motivational training to enhance family engagement using 10-, 50-minute manualized family therapy sessions attended by the parent and teen. Families are offered the flexibility of selecting four from a menu of seven possible sessions depending on their interests and circumstances: recording homework daily, creating a homework contract, organizing school materials, prioritizing/managing time out of school, note taking, preparing for tests/quizzes, and troubleshooting home problems. A skill and plan for applying the skill is introduced during each session, and a contract outlining agreed upon contingencies for utilizing the skill during the upcoming week is created. More detailed information and a step-by-step guide to the STAND program is available in the published manual, Parent-Teen Therapy for Executive Function Deficits and ADHD: Building Skills and Motivation (Sibley, 2016). In a recent study, adolescents with ADHD were randomly assigned to STAND (n 5 67) or a treatment as usual group (n 5 61) and compared on multiple outcome measures (Sibley, 2016). Results revealed significant improvement in parent measures of organization, time, and management skills, as well as ADHD symptoms, use of contingent home privileges, and homework recording; however, relatively few of the parent ratings remained significant at 6-month follow-up. In contrast, classroom teacher ratings of organization, time management, and planning problems, ADHD symptoms, work completion, and GPA were not improved significantly following treatment and raise the possibility that the positive parent ratings may be secondary to illusory biases or placebo effects (e.g., Hawthorne effects).

Summary Skill-based behavioral interventions such OST, HOPS, and STAND teach children with ADHD strategies to compensate for underdeveloped EFs rather than targeting underlying neurocognitive mechanisms of EFs explicitly. Such organizational skills interventions are associated with improvements in time management, managing of materials, and planning in the near term; however, complementary improvements in school performance and academic achievement are either smaller or absent. A two-pronged intervention approach that

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provides external supports (compensatory organizational strategies) while changing the neurobiological foundations of ADHD (cognitive training interventions) will likely be necessary to induce potent and long-term improvements in real-world outcomes for children and teens with ADHD.

Memory strategies to improve learning As reviewed earlier, children with ADHD evince significant deficits in WM. In this final subsection, we review routines and memory strategies that may prove beneficial to many children and adolescents with the disorder.

Managing information encoding difficulties For information to access the WM system, it must be perceived accurately via visual and/or auditory channels. Consequently, a first step for addressing memory challenges involves assessing for vision and hearing problems via early developmental/medical history information obtained from the parent, teacher report, and direct observation/interactions with the child. Environment influences can also interfere with receiving and registering information accurately, which in turn, affects what information gains access to the WM system. For example, distractions from external (e.g., cell phone, text messages, television, and noisy surroundings) and internal stimuli (e.g., thoughts and worries) gain immediate access to the limited capacity PH short-term store (STM) and can disrupt the encoding process. That is, STM can only hold a limited amount of information, and if the available space is already taken up by nonrelevant information, material related to homework or schoolwork will be unable to access the WM system for further processing (see Fig. 8.1). A quiet environment devoid of distracting music, TV, cell phones, electronic devices, and computer-based messaging/alerts/apps is recommended to minimize interference and optimize information processing. A designated computer with restricted or no internet access and with no loaded apps or games should be available in the child’s homework workspace to minimize distraction and optimize stimulus control on occasions when a computer is necessary to complete assignments. Another factor that can adversely affect children’s ability to learn new information is the potentially interfering effects of other recently learned information, a phenomenon referred to as proactive interference. For example, when a child is attempting to remember the battles of the civil war but is confusing them with battles of the revolutionary war learned earlier, the child’s learning is disrupted by proactive interference. To minimize proactive interference, information should be rehearsed in small, manageable chunks rather than crammed into a prolonged study session, and children should be given short, active breaks during which they are able to engage in a preferred activity between studying different topics. To address issues related to

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retroactive interference (i.e., new information interfering with the ability to remember previously learned information), multistep instructions should be written down and readily accessible. White erase boards and note cards placed in conspicuous places represent inexpensive means to accomplish this goal.

Information input channel The interplay between stimulus-receiving modalities has important implications for learning. Verbally presented information (e.g., classroom instruction and audiobook) gains direct access to the auditory cortex of the brain, whereas reading or pictorial information must be visually inspected and orthographically converted to PH code before it can enter the auditory cortex and be held temporarily in PH STM for additional processing by the CE. This is why some parents report that their children are better able to comprehend and recall information when it is read to them orally rather than having to read and orthographically convert the information themselves. These children are unable to convert the read information efficiently and lose significant information before it becomes available in the PH STM store to be used for passage comprehension-related tasks (i.e., the system becomes bottlenecked). A recent study of the bottleneck phenomenon involving children with ADHD revealed that two interleaved processes contributed to reading comprehension deficits in ADHD: slowed orthographic conversion and underdeveloped CE processes (Friedman et al., 2017). Slowed orthographic conversion can occur for several reasons; however, one of the most common causes is that automaticity has not been developed for a large number of basic words, math facts (addition, subtraction, and multiplication), and relevant rules (literacy and numeracy) when attempting to activate this information in long-term memory (LTM). The bottleneck occurs because the pursuit of the needed words or math facts slows the speed by which it reaches the limited capacity PH STM store, which can only maintain the information for several seconds unless it is constantly refreshed via covert rehearsal. The resulting consequence is that many children will forget the task instructions—particularly multistep instructions—prior to fully retrieving the needed information from LTM.

Practitioner recommendations to accommodate and strengthen orthographic conversion G Use flash cards with printed words and associated pictures beginning with easily recognized two- and three-letter words to increase automaticity of word recognition. Begin at a slow speed and use a game-like format (points or check marks that can be traded in for preferred activities

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or other incentives) to gradually increase the speed by which the words can be recognized and stated orally. Once words can be recognized quickly, gradually introduce unknown words of the same length prior to introducing longer words. Have the child continue this exercise a minimum of 5 days per week for approximately 15-minutes per day (e.g., during breakfast) throughout the year until a 400-word fast-recognition vocabulary has developed. For children devoid of a strong knowledge of phonics, adopt a similar flash card approach beginning with easily recognized sound combinations (e.g., “th”), and eventually use two cards at a time to combine (blend) sounds to make a whole word (e.g., bi 1 rd). Use flash cards of simple number combinations (begin with addition), which can be solved mentally without the benefit of a pencil/paper or calculator. Begin with the 1’s (1 1 3 5 4 with the answer shown on the reverse side), then introduce the 2’s, then the 3’s, and so on until all combinations up to the 9’s have been mastered and can be answered quickly and accurately. Use a game-like format similar to the one recommended immediately above and limit training sessions to 15 minutes.

Information retention and retrieval Once information has been registered in the STM stores (e.g., PH loop and VS sketchpad), several processes work to maintain this information in the stores so that it can be used to solve novel problems or answer basic questions. Metaanalytic data demonstrate that certain cognitive training techniques for children with ADHD can enhance STM with sustained benefits over (relatively) short durations (i.e., 6 months; Rapport et al., 2013). These techniques often focus on expanding the capacity of STM storage.

Practitioner recommendations to accommodate and strengthen short-term storage G Chunking: more information can be remembered if it is organized into smaller pieces or chunks. Chunking can be accomplished by several complementary techniques; however, they all follow a similar format—break down spelling lists and other tasks into meaningful “chunks” (e.g., three to four words at a time). G Many children with low WM spans are unable to maintain the necessary information in the PH STM needed to guide their behavior during mental activities such as completing homework and classroom activities; they end up losing focus and become inattentive, the latter of which often reflects an overloaded WM system. Use the following recommendations to address these difficulties: G Reduce and simplify multistep instructions. Write down all assignments on white erase boards or other media to allow children to

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reference the instructions rather than overwhelming their PH STM system by trying to hold the information and simultaneously complete an academic activity. G Alternatively, use a recorder or cell phone to record written assignments or instructions, and then play them back, stopping as needed. Classroom and homework assignments that contain multiple steps should also be recorded and/or written down and placed in close proximity to the child so that they can be referenced readily. G For longer written assignments such as class reports, journal writing, and other lengthier assignments, train the child to record his/her thoughts using a recorder or cell phone. Then play the recorded information back, stopping frequently, to enable the information to be keyboarded into a word processing document (note: efficient keyboarding can be developed using an inexpensive training app). Following this procedure allows children to consciously express their ideas without stopping to writing them down concurrently, the latter of which causes them to lose their train of thought (i.e., impedes STM). Alternatively, dictation software may be helpful to enable children and teens to quickly and efficiently record their thoughts on paper before dropping out of their STM. Recommended techniques to aid the retrieval of information. G Consolidation is a process by which the hippocampus begins to turn certain short-term memories into longer term memories. In general, the more that information is rehearsed, the more likely it will be remembered; however, consolidation is enhanced when new material is related to information already stored in LTM. Using the example of numbers, one might make an association with autobiographical information such as a family member’s birthday or other patterns that carry meaning. Other strategies such as mnemonics (e.g., Memory Never Ever Misses Over Night If Consolidated) and spelling cues (e.g., “she screamed ‘EEE’ as she passed the cemetery”) aid consolidation and allow for easier retrieval. In general, retention of information is maximized when it is held in memory in an organized manner and is attached to other meaningful information already in LTM. G Other consolidation recommendations: break down long lists of items to be remembered, such as spelling words, into shorter lists (e.g., three to five words). Have the child practice recalling this list several times concurrently, and then take a break for a minimum of 15 20 minutes before checking for recall accuracy. Once the list is mastered, go back over it once shortly before the child goes to sleep that evening. Recheck accuracy the following morning. The spelling of some words will likely be forgotten but will be relearned more quickly than required during the initial learning phase. Continue this procedure until all words on the list are learned and recalled accurately.

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For reading comprehension, it is helpful to understand the context of the story and read with a purpose. Specific techniques include reviewing the topic headings in the text before reading, developing a priori questions that can be answered at the end of the reading, and outlining information covered in each paragraph. Studying and homework habits can be improved by practicing effective stimulus control procedures. Over time, simply sitting at the same distraction-free workstation triggers a response to complete homework, or if already working on a writing project, significantly speedier access to previously recalled information from LTM. Sleep and exercise: the contribution of adequate sleep and exercise to memory processes cannot be overstated. The rapid eye movement stage during sleep is known to facilitate consolidation and overnight learning. In contrast, sleep deprivation contributes to inattention and poor encoding of newly learned information. Finally, exercise promotes neuronal excitability and promotes enhanced overall human cognition with particular benefits for memory consolidation.

Summary and future directions EF training for youth with ADHD has slowly evolved from an inchoate to a nascent stage of development. Cognitively oriented training programs have been hampered by a lack of recognition that training must focus primarily on strengthening upper level CE processes rather than lower level STM storage/ rehearsal processes to achieve far-transfer effects. Moreover, results of empirically based mediation models exploring the role of WM and foundational learning in children with ADHD provide compelling evidence that critical complementary processes requisite for learning (e.g., encoding/ decoding, orthographic conversion speed, phoneme recognition/fluency, and oral language ability) must also be integrated into training modules. Neurotherapies represent a potentially promising approach for addressing underdeveloped EFs in ADHD by improving the functioning of underdeveloped brain regions by teaching self-upregulation (NF) or applying noninvasive external electrical currents (brain stimulation). The efficacy of these novel approaches remains inconclusive and warrant continued investigation; however, strengthening select brain regions alone is unlikely to result in improved foundational learning without complementary training of the multiple processes inherent to foundational learning. Finally, skills-based organizational strategies are largely repackaged parent contingency management programs ballyhooed in the 1970s, but they are more focused on training organizational skills inherent to completing homework consistently compared to broad-based behavioral management therapies such as those used in the MTA study. These intervention strategies can prove helpful in the near term but show no evidence of sustainability without a

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high level of continued parental involvement. More critically, skills-based organizational interventions have not resulted in far-transfer effects such as improved achievement test scores and are unlikely to without corresponding growth in key EF processes such as WM and cognitive flexibility. Given the limited empirical support associated with current iterations of cognitive training and NF, these interventions are not recommended for children and adolescents with ADHD. Psychostimulant medication and parent/ teacher contingency management remain the best-practice interventions currently. Incorporating one of the more structured, age-appropriate homework organizational interventions, coupled with memory enhancement strategies, is likely to provide additional assistance for youth to address the increasing organizational burden common to late elementary, middle and high school years. Practitioners can play an important role by training parents how to implement these treatments with the recognition that they represent maintenance interventions and require on-going monitoring to maintain effectiveness. Novel interventions that integrate strengthening high-order CE WM processes and requisite core foundational learning processes, however, are needed to address the chronic academic achievement deficits experienced by a majority of youth with ADHD, and future iterations of EF training programs may prove more fruitful should they adhere more closely to the empirically supported neurobiological deficits that underlie the disorder.

References Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106(1), 20 29. American Academy of Pediatrics. (2010). Appendix S2: Evidence-based child and adolescent psychosocial interventions. Pediatrics, 125(Suppl. 3), S128. Arns, M., Heinrich, H., & Strehl, U. (2014). Evaluation of neurofeedback in ADHD: The long and winding road. Biological Psychology, 95, 108 115. Ashkan, K., Shotbolt, P., David, A. S., & Samuel, M. (2013). Deep brain stimulation: A return journey from psychiatry to neurology. Postgraduate Medical Journal, 89(1052), 323 328. Baddeley, A. (2007). Working memory, thought, and action (Vol. 45). Oxford: OUP. Barry, R. J., Clarke, A. R., McCarthy, R., Selikowitz, M., & Rushby, J. A. (2005). Arousal and activation in a continuous performance task. Journal of Psychophysiology, 19(2), 91 99. Bikic, A., Reichow, B., McCauley, S. A., Ibrahim, K., & Sukhodolsky, D. G. (2017). Metaanalysis of organizational skills interventions for children and adolescents with attention-deficit/hyperactivity disorder. Clinical Psychology Review, 52, 108 123. Bink, M., Bongers, I. L., Popma, A., Janssen, T. W., & van Nieuwenhuizen, C. (2016). 1-year follow-up of neurofeedback treatment in adolescents with attention-deficit hyperactivity disorder: Randomised controlled trial. BJPsych Open, 2(2), 107 115. Breitling, C., Zaehle, T., Dannhauer, M., Bonath, B., Tegelbeckers, J., Flechtner, H. H., & Krauel, K. (2016). Improving interference control in ADHD patients with transcranial direct current stimulation (tDCS). Frontiers in Cellular Neuroscience, 10(72), 1 10. Catal´a-Lo´pez, F., Hutton, B., Nu´n˜ez-Beltr´an, A., Page, M. J., Ridao, M., Mac´ıas Saint-Gerons, D., . . . Moher, D. (2017). The pharmacological and non-pharmacological treatment of

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attention deficit hyperactivity disorder in children and adolescents: A systematic review with network meta-analyses of randomised trials. Systematic Reviews, 4(19), 1 12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Hillsdale, NJ: Lawrence Earlbaum Associates. Cortese, S. (2012). The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): What every clinician should know. European Journal of Paediatric Neurology, 16 (5), 422 433. Cortese, S., Ferrin, M., Brandeis, D., Buitelaar, J., Daley, D., Dittmann, R. W., . . . Zuddas, A. (2015). Cognitive training for attention-deficit/hyperactivity disorder: Meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. Journal of the American Academy of Child & Adolescent Psychiatry, 54(3), 164 174. Cortese, S., Ferrin, M., Brandeis, D., Holtmann, M., Aggensteiner, P., Daley, D., . . . Zuddas, A. (2016). Neurofeedback for attention-deficit/hyperactivity disorder: Meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. Journal of the American Academy of Child and Adolescent Psychiatry, 55(6), 444 455. Demirtas-Tatlidede, A., Vahabzadeh-Hagh, A. M., & Pascual-Leone, A. (2013). Can noninvasive brain stimulation enhance cognition in neuropsychiatric disorders? Neuropharmacology, 64, 566 578. Dickstein, S. G., Bannon, K., Castellanos, F. X., & Milham, M. P. (2006). The neural correlates of attention deficit hyperactivity disorder: An ALE meta-analysis. Journal of Child Psychology and Psychiatry and Allied Disciplines, 47(10), 1051 1062. DuPaul, G. J., Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Academic and social functioning associated with attention-deficit/hyperactivity disorder: Latent class analyses of trajectories from kindergarten to fifth grade. Journal of Abnormal Child Psychology, 44(7), 1425 1438. DuPaul, G. J., Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2018). Eight-year latent class trajectories of academic and social functioning in children with attention-deficit/ hyperactivity disorder. Journal of Abnormal Child Psychology, 46(5), 979 992. Duric, N. S., Assmus, J., Gundersen, D., Duric Golos, A., & Elgen, I. B. (2017). Multimodal treatment in children and adolescents with attention-deficit/hyperactivity disorder: A 6month follow-up. Nordic Journal of Psychiatry, 71(5), 386 394. Eckrich, S. J., Rapport, M. D., Calub, C. A., & Friedman, L. M. (2018). Written expression in boys with ADHD: The mediating roles of working memory and oral expression. Child Neuropsychology. Available from https://doi.org/10.1080/09297049.2018.1531982. El-Sayed, E., Larsson, J. O., Persson, H. E., & Rydelius, P. A. (2002). Altered cortical activity in children with attention-deficit/hyperactivity disorder during attentional load task. Journal of the American Academy of Child and Adolescent Psychiatry, 41(7), 811 819. Friedman, L. M., Rapport, M. D., Orban, S. A., Eckrich, S. J., & Calub, C. A. (2018). Applied problem solving in children with ADHD: The mediating roles of working memory and mathematical calculation. Journal of Abnormal Child Psychology, 46(3), 491 504. Friedman, L. M., Rapport, M. D., Raiker, J. S., Orban, S. A., & Eckrich, S. J. (2017). Reading comprehension in boys with ADHD: The mediating roles of working memory and orthographic conversion. Journal of Abnormal Child Psychology, 45(2), 273 287. Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in EFs are almost entirely genetic in origin. Journal of Experimental Psychology General, 137(2), 201 225. Gallagher, R., Abikoff, H. B., & Spira, E. G. (2014). Organizational skills training for children with ADHD: An empirically supported treatment. New York: Guilford Publications.

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Go´mez, L., Vidal, B., Morales, L., B´aez, M., Maragoto, C., Galvizu, R., . . . S´anchez, A. (2014). Low frequency repetitive transcranial magnetic stimulation in children with attention deficit/ hyperactivity disorder. Preliminary results. Brain Stimulation, 7(5), 760 762. Hechtman, L., Swanson, J. M., Sibley, M. H., Stehli, A., Owens, E. B., Mitchell, J. T., . . . Group, M. T. A. C. (2016). Functional adult outcomes 16 years after childhood diagnosis of attention-deficit/hyperactivity disorder: MTA results. Journal of the American Academy of Child and Adolescent Psychiatry, 55(11), 945 952. Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in EF: Developmental trends and a latent variable analysis. Neuropsychologia, 44(11), 2017 2036. Jancke, L. (2009). The plastic human brain. Restorative Neurology and Neuroscience, 27(5), 521 538. Kasper, L. J., Alderson, R. M., & Hudec, K. L. (2012). Moderators of working memory deficits in children with attention-deficit/hyperactivity disorder (ADHD): A meta-analytic review. Clinical Psychology Review, 32(7), 605 617. Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of Clinical and Experimental Neuropsychology, 24(6), 781 791. Kofler, M. J., Irwin, L. N., Soto, E. F., Groves, N. B., Harmon, S. L., & Sarver, D. E. (2018). Executive functioning heterogeneity in pediatric ADHD. Journal of Abnormal Child Psychology. Available from https://doi.org/10.1007/s10802-018-0438-2, Advance online publication. Kofler, M. J., Rapport, M. D., Bolden, J., Sarver, D. E., & Raiker, J. S. (2010). ADHD and working memory: The impact of central executive deficits and exceeding storage/rehearsal capacity on observed inattentive behavior. Journal of Abnormal Child Psychology, 38(2), 149 161. Kytta¨la¨, M., & Lehto, J. E. (2008). Some factors underlying mathematical performance: The role of visuospatial working memory and non-verbal intelligence. European Journal of Psychology of Education, 23(1), 77. Langberg, J. M. (2011). Homework, organization, and planning skills (HOPS) interventions: A treatment manual. Bethesda, MD: National Association of School Psychologists. Langberg, J. M., Epstein, J. N., Becker, S. P., Girio-Herrera, E., & Vaughn, A. J. (2012). Evaluation of the Homework, Organization, and Planning Skills (HOPS) intervention for middle school students with ADHD as implemented by school mental health providers. School Psychology Review, 41(3), 342 364. Lefaucheur, J. P., Andre-Obadia, N., Antal, A., Ayache, S. S., Baeken, C., Benninger, D. H., . . . Garcia-Larrea, L. (2014). Evidence based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Clinical Neurophysiology, 125(11), 2150 2206. Marx, A.-M., Ehlis, A.-C., Furdea, A., Holtmann, M., Banaschewski, T., Brandeis, D., . . . Strehl, U. (2015). Near-infrared spectroscopy (NIRS) neurofeedback as a treatment for children with attention deficit hyperactivity disorder (ADHD): A pilot study. Frontiers in Human Neuroscience, 8. Meisel, V., Servera, M., Garcia-Banda, G., Cardo, E., & Moreno, I. (2013). Neurofeedback and standard pharmacological intervention in ADHD: A randomized controlled trial with sixmonth follow-up. Biological Psychology, 94(1), 12 21. Molina, B. S., Hinshaw, S. P., Swanson, H. L., Arnold, L. E., Vitello, B., Jensen, P. S., . . . Hoza, B. (2009). The MTA at 8 years: Prospective follow-up of children treated for combined-type ADHD in a multisite study. Journal of American Academy of Child & Adolescent Psychiatry, 48(5), 484 500.

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Norman, L., Carlisi, C. O., Lukito, S., Hart, H., Mataix-Cols, D., Radua, J., & Rubia, K. (2016). Comparative meta-analysis of functional and structural deficits in ADHD and OCD. JAMA Psychiatry, 73(8), 815 825. Paz, Y., Friedwald, K., Levkovitz, Y., Zangen, A., Alyagon, U., Nitzan, U., & Bloch, Y. (2017). Randomised sham-controlled study of high-frequency bilateral deep transcranial magnetic stimulation (dTMS) to treat adult attention hyperactive disorder (ADHD): Negative results. World Journal of Biological Psychiatry, 31, 1 6. Raiker, J. S., Rapport, M. D., Kofler, M. J., & Sarver, D. E. (2012). Objectively-measured impulsivity and attention-deficit/hyperactivity disorder (ADHD): Testing competing predictions from the working memory and behavioral inhibition models of ADHD. Journal of Abnormal Child Psychology, 40(5), 699 713. Ramos-Olazagasti, M. A., Castellanos, F. X., Mannuzza, S., & Klein, R. G. (2018). Predicting the adult functional outcomes of boys with ADHD 33 years later. Journal of the American Academy of Child and Adolescent Psychiatry, 57(8), 571 582. Rapoport, J. L., & Gogtay, N. (2008). Brain neuroplasticity in healthy, hyperactive and psychotic children: Insights from neuroimaging. Neuropsychopharmacology, 33, 181 197. Rapport, M. D., Alderson, R. M., Kofler, M. J., Sarver, D. E., Bolden, J., & Sims, V. (2008). Working memory deficits in boys with attention-deficit/hyperactivity disorder (ADHD): The contribution of central executive and subsystem processes. Journal of Abnormal Child Psychology, 36(6), 825 837. Rapport, M. D., Bolden, J., Kofler, M. J., Sarver, D. E., Raiker, J. S., & Alderson, R. M. (2009). Hyperactivity in boys with attention-deficit/hyperactivity disorder (ADHD): A ubiquitous core symptom or manifestation of working memory deficits? Journal of Abnormal Child Psychology, 37(4), 521 534. Rapport, M. D., Chung, K. M., Shore, G., Denney, C. B., & Isaacs, P. (2000). Upgrading the science and technology of assessment and diagnosis: Laboratory and clinic-based assessment of children with ADHD. Journal of Clinical Child Psychology, 29(4), 555 568. Rapport, M. D., Chung, K. M., Shore, G., & Issacs, P. (2001). A conceptual model of child psychopathology: Implications for understanding attention deficit hyperactivity disorder and treatment efficacy. Journal of Clinical Child Psychology, 30(1), 48 58. Rapport, M. D., Friedman, L. M., Eckrich, S. J., & Calub, C. (2018). Working memory and attention-deficit/hyperactivity disorder. In T. P. Alloway (Ed.), Working Memory & Clinical Neurodevelopmental Disorders: Theories, Debates and Interventions. NY: Taylor & Francis. Rapport, M. D., Orban, S. A., Kofler, M. J., & Friedman, L. M. (2013). Do programs designed to train working memory, other EFs, and attention benefit children with ADHD? A metaanalytic review of cognitive, academic, and behavioral outcomes. Clinical Psychology Review, 33(8), 1237 1252. Rapport, M. D., Orban, S. A., Kofler, M. J., Friedman, L. M., & Bolden, J. (2015). Executive function training for children with ADHD. In R. Barkley (Ed.), Attention Deficit Hyperactivity Disorder (4th ed., pp. 641 665). New York: Guildford Press. Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., . . . Rapoport, J. L. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences of the United States of America, 104(49), 19649 19654. Shaw, P., Malek, M., Watson, B., Sharp, W., Evans, A., & Greenstein, D. (2012). Development of cortical surface area and gyrification in attention-deficit/hyperactivity disorder. Biological Psychiatry, 72(3), 191 197.

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Sibley, M. H. (2016). Parent-teen therapy for EF deficits and ADHD: Building skills and motivation. New York: Guilford Publications. Soff, C., Sotnikova, A., Christiansen, H., Becker, K., & Siniatchkin, M. (2017). Transcranial direct current stimulation improves clinical symptoms in adolescents with attention deficit hyperactivity disorder. Journal of Neural Transmission, 124(1), 133144. Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R., & Raz, A. (2018). Neurofeedback with fMRI: A critical systematic review. NeuroImage, 172, 786 807. Van Doren, J., Arns, M., Heinrich, H., Vollebregt, M. A., Strehl, U., & K Loo, S. (2018). Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. European Child and Adolescent Psychiatry. Available from https://doi.org/10.1007/s00787018-1121-4, Advance online publication. Zilverstand, A., Sorger, B., Slaats-Willemse, D., Kan, C. C., Goebel, R., & Buitelaar, J. K. (2017). fMRI neurofeedback training for increasing anterior cingulate cortex activation in adult attention deficit hyperactivity disorder. An exploratory randomized, single-blinded study. PLoS One, 12(1), e0170795. Available from https://doi.org/10.1371/journal. pone.0170795.

Further reading Weaver, L., Rostain, A. L., Mace, W., Akhtar, U., Moss, E., & O’reardon, J. P. (2012). Transcranial magnetic stimulation (TMS) in the treatment of attention-deficit/hyperactivity disorder in adolescents and young adults: A pilot study. The Journal of ECT, 28(2), 98 103.

Chapter 9

Tying it all together Michelle M. Martel Psychology Department, University of Kentucky, Lexington, KY, United States

Learning and attention problems affect 10% of children or more. Yet, until recently, approaches for remediating such learning problems have been slow, cursory, and remediation based (vs preventive). With the advent of response to intervention (RTI), educational therapy, and behavioral training, everything is changing. Early identification of children with learning problems and attention-deficit/hyperactivity disorder (ADHD) is now considered of foremost importance, as is immediate, staged, and intensive services with frequent individually oriented monitoring of progress over time. Such approaches, although they have been slow to be adopted at the level of individual schools, will doubtless provide information critical to rapidly improving such services and, most importantly, child-learning problems. In this way, more and more children can begin to enjoy and feel that they are good at school and academics, providing them with infinite possibilities for their future.

Learning disorder and attention-deficit/hyperactivity disorder often cooccur The chapters in this book highlight the idea that learing disorders (LDs) and ADHD often overlap and have high comorbidity rates of approximately 30%. Therefore, unsurprisingly, learning and attention problems dramatically affect one another. A child who has problems paying attention will have difficulty learning, and a child who struggles to learn will have trouble paying attention to academic subjects. This can be particularly problematic during early development when learning to read, write, and perform math calculation requires intensive practice, repetition, and concentration. In fact, etiological work suggests that these overlapping problems are due primarily to genetic, or familial, risk factors with slow or variable cognitive processing as an underlying explanatory mechanism. Further, such cooccurrence can be detected as early as age 4. Thus it is critical for clinicians to be open to the The Clinical Guide to Assessment and Treatment of Childhood Learning and Attention Problems. DOI: https://doi.org/10.1016/B978-0-12-815755-8.00009-5 © 2020 Elsevier Inc. All rights reserved.

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possibility that both learning and attention problems coexist and—when they cooccur—to assess for them so as to be able to treat them conjointly.

Complexities: severity of risk, individual variability, and comorbidity LD and ADHD both exist on a continuum of risk ranging from mild to severe. Children with learning and attention problems can move around on that continuum, moving in and out of diagnostic categories over time. Therefore it is increasingly important to note even mild problems in these areas for monitoring and preventive or immediate intervention strategies to be able to be applied as necessary over time. Children with learning and attention problems also exhibit substantial heterogeneity in their learning and attention profiles. For example, children who struggle with reading problems can struggle for a number of reasons. One child might struggle with slow, effortful reading that leaves them struggling to complete sentences in a timely fashion and causes problems with comprehension. Another student may be able to read quickly but finds himself/herself at the end of a chapter having no recall or comprehension of what they read. Such students may or may not have trouble with math computation, completion of math word problems, or handwriting or story construction. Some students may have problems only with math computation, completion of math word problems, and handwriting or story construction. Such problems must be assessed at the level of the individual so that personalized intervention can be tailored to their personal profile. Attention problems manifestation can also look very different from one child to the next. One child might present as hyperactive and disruptive, being unable to sit still, impulsively interrupting, and disrupting class. However, another child may be very quiet, sitting at the back of the classroom and daydreaming rather than paying attention. Yet, both of these types of children will have difficulty learning class material and would benefit from treatment of their attentional difficulties so as to facilitate learning. Further, such profiles can and do change over time at the level of the individual child. A child that starts out having difficulty with automatic reading may well develop problems with comprehension of longer texts and writing paragraphs and stories. A child that has problems with reading comprehension may struggle in math to understand word problems. A child that starts out being hyperactive may well develop problems with attention as class material becomes more difficult and requires higher levels of concentration. Therefore it is important to quickly begin intervention for initial difficulties and carefully monitor and assess any subsequent problems as they arise. Children with learning problems may have other issues that are negatively impacting their ability to learn. Children who struggle to pay attention

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and have ADHD are likely to have difficulties learning, perhaps across the board or differentially in reading, but their underlying, core issue may be attention (not learning). In addition, a child with high anxiety may have problems concentrating while learning new skills and/or during tests, a problem, that can masquerade as ADHD, cooccur with ADHD, or cooccur or cause learning problems. Therefore if initial school interventions are not helpful, it is important to obtain more comprehensive assessment to rule out issues that can cooccur with or cause learning problems. Anxiety and mood problems, not dealt with comprehensively in the current book, can be quickly and easily ruled in or out during psychological evaluation and successfully treated in therapy. Speech and language and coordination problems can also be impactful on learning and can be easily be assessed, particularly in young children and those who are struggling with verbal tasks and handwriting, respectively. These problems can be very effectively treated by trained speech and language and occupational therapists, respectively, perhaps even at their school, but certainly in private settings.

Importance of integrating learning disorder attention-deficit/ hyperactivity disorder care As the chapters in this book highlight, learning problems are complex and must be carefully assessed and parsed from other related difficulties such as ADHD and other psychological problems (e.g., anxiety). If there is a question of ADHD negatively impacting learning, ADHD should be rapidly assessed, along with its frequently cooccurring conditions, and ruled out. If ADHD is diagnosed, it should be quickly treated so that learning interventions can be maximally and quickly effective. Given interventions for LD and ADHD are rapidly becoming highly and quickly effective, it is important to get them both in place as rapidly as possible. RTI and educational therapy are highly effective for treating LD. For ADHD, behavioral treatment and medication are highly efficacious at remediating attention problems with either one being effective (for parents who do not want to do both interventions or who are uncomfortable doing one or the other). For children who struggle with both LD and ADHD, a combination of all services will be most effective and should be immediately applied so that all can be maximally effective for facilitating school outcomes.

Case examples Several illustrative case examples are provided below in order to illustrate current best practice guidelines for assessment strategies, interpretation, and recommendations for comorbid LD 1 ADHD and LD and ADHD alone and with complicating factors. The testing battery used should be considered as just one example of tests that could be used. For more detailed information

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about the best tests to use to assess and diagnosis learning and attention problems, see the LD and ADHD assessment chapters earlier in the book.

Attention-deficit/hyperactivity disorder 1 learning disorder Psychological Evaluation Report Name: Chase Smith Date of Birth: 02/14/2005 Age: 10 years Grade: Fourth Sex: Male Dates of Evaluation: 04/07/2015 and 04/14/2015 Date of Report: 04/15/2015

Evaluation procedures Written informed consent Clinical interview Personal history form Kaufman Brief Intelligence Test, Second Edition (KBIT-2) Kaufman Test of Educational Achievement, Third Edition (KTEA-3) Behavior Assessment System for Children, Second Edition (BASC-2): Parent and Teacher Scales ADHD Rating Scale: Parent and Teacher Report Reason for referral Chase is a 10-year-old boy who was brought for evaluation by his parents due to concerns about possible attention problems. His parents requested an evaluation for possible ADHD. Background information and symptom history Chase is a 10-year-old boy who currently attends fourth grade. He reportedly started receiving intervention for a possible LD at school in the first grade. He has since experienced a pattern of pull-outs in reading, writing, and mathematics, as well as after-school tutoring in mathematics. He currently receives services in writing and mathematics, and he will be retested in reading soon. However, Chase reportedly experiences a pattern whereby, after he is pulled out for special help in these areas, he will be reintegrated and, once again, start to fall behind until he again has to be pulled back for services once more. Chase reportedly receives accommodations such as modified tests, extra time to take tests, and separate testing accommodations, as well as special help in the classroom. With these accommodations, he receives all As. His parents are concerned that Chase might have attention problems without hyperactivity given the fact that he has a difficult time focusing, is

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forgetful, has problems with sustained attention, is disorganized, and often loses things. No hyperactivity or impulsivity or behavior problems were reported. No concerns related to anxiety or depression were endorsed. Chase lives with his biological parents and his young brother. No recent stressors were reported. Chase’s developmental history is unremarkable. He was the product of a 36-week pregnancy. He has allergies and asthma, but is otherwise healthy. No developmental delays were reported, although fine motor skills were reported to be slightly behind in prekindergarten. Family history is significant for dyslexia. Chase reportedly sleeps and eats well. Chase has at least four close friends. He likes to collect war artifacts and attend reenactments. He also enjoys drawing and videogames. During Chase’s personal interview, he reported being concerned that he might have ADHD. He reported having difficulties in concentrating and feels like he is often “out in space.” He denied any issues related to anxiety or depression.

Behavioral observations Chase was quiet, but likeable. A man of few words, he seemed genuine, open, and honest. He completed academic tasks relatively slowly. Test results and interpretation Intellectual functioning Kaufman Brief Intelligence Test, Second Edition The Kaufman Brief Intelligence Test (KBIT-2) is a short test of general intellectual ability and verbal and nonverbal reasoning. On the KBIT-2, Chase performed in the average range, earning an IQ composite score of 90, ranking him at the 25th percentile when compared with other children of his age. Chances are 90 out of 100 that his IQ falls within a range from 84 to 97. There were no significant differences between his verbal and nonverbal reasoning. He performed in the average range (SS 5 96; 39th percentile) on verbal tasks assessing verbal knowledge and ability to solve riddles. He performed slightly, but not significantly, worse in nonverbal reasoning, again performing in the average range (SS 5 87; 19th percentile) on a task assessing his ability to complete incomplete patterns in a series of pictures. Overall, Chase’s intellectual functioning fell in the average range compared to other individuals his age. Academic functioning Kaufman Test of Educational Achievement, Third Edition The Kaufman Test of Educational Achievement, Second Edition (KTEA3) is a collection of tasks designed to measure academic performance across the areas of reading, mathematics, and writing. On the KTEA-3, compared to same-age peers, Chase performed in the low average range. Specifically, Chase performed average in reading with a standard score of 95, ranking at

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or above 37th percentile of same-age peers. He performed in the low average to impaired range in mathematics with a standard score of 79, scoring at or above 8% of his peers. He performed in the below average range in writing with a standard score of 81 (10th percentile). Overall, Chase’s academic performance ranges from impaired to average. While his reading skills are a relative strength, falling in the average range, his writing and particularly mathematics were his relative weaknesses, falling in the low average to impaired range, consistent with a diagnostic and statistical manual of mental disorders (DSM)-5-defined specific LD with impairment in mathematics and written expression. Behavioral and emotional functioning Behavior Assessment System for Children, Second Edition (BASC-2): Parent and Teacher Scales ADHD Rating Scale: Parent and Teacher Report Chase’s mother, father, special education teacher, and regular classroom teacher completed the Behavior Assessment System for Children, Second Edition (BASC-2). His mother reported marginally clinically significant attention problems (T score 5 63, 88th percentile), compared to same-sex and same-age peers. His father reported marginally clinically significant attention problems (T score 5 60, 83rd percentile) and atypicality (T score 5 64, 90th percentile). His special education teacher reported marginally significant problems with anxiety (T score 5 67, 94th percentile) and learning (T score 5 59, 81st percentile). His regular classroom teacher reported clinically significant problems with anxiety (T score 5 99, 99th percentile), depression (T score 5 83, 98th percentile), learning (T score 5 74, 97th percentile), and withdrawal (T score 5 80, 99th percentile), as well as marginally significant problems with attention (T score 5 60, 81st percentile). On the ADHD Rating Scale, Chase’s mother reported eight clinically significant symptoms of inattention and 0 of hyperactivity impulsivity; his father reported five symptoms of inattention and one symptom of hyperactivity impulsivity. Chase’s special education teacher reported six symptoms of inattention and one of hyperactivity impulsivity, and his regular education teacher similarly reported six symptoms of inattention and zero symptoms of hyperactivity impulsivity. Overall, all reporters agreed on the presence of clinically significant inattention, consistent with ADHD, predominantly inattentive presentation.

Summary Chase is a 10-year-old boy who was brought in for psychological evaluation by his parents due to concerns about possible attention problems. His parents requested an evaluation for ADHD. Overall, self-report during an interview suggested difficulty focusing, and Chase’s self-report was consistent with

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parent and two teachers’ reports of attention problems in the classroom. All agreed that Chase is currently experiencing problems with inattention, but not hyperactivity impulsivity, consistent with ADHD, predominantly inattentive presentation. Further, although Chase’s intellectual ability is average, based on a screening test, his writing and particularly mathematics were relative weaknesses, falling in the low average to impaired range, consistent with a DSM-5-defined specific LD with impairment in mathematics and written expression. His reading skills were a relative strength, falling in the average range. Intervention for attention will likely positively impact Chase’s academic performance and make school easier and more enjoyable for him. A combination of intervention for attention and learning problems will likely improve his mathematics and writing skills.

Diagnoses Attention-Deficit/Hyperactivity Disorder, predominantly inattentive presentation Specific learning disorder with impairment in mathematics Specific learning disorder with impairment in written expression Recommendations 1. Intervention for ADHD. Research suggests that medication or behavioral treatment for ADHD are both effective with a combination of the two most effective. a. Medication. Psychostimulant medication can be helpful for managing inattention, particularly during the school day. Consulting a pediatrician or a child psychiatrist is recommended. b. Behavioral intervention. Behavioral intervention will also be helpful for teaching Chase the necessary coping and organization skills necessary for addressing attention problems day-to-day. These services will focus on how to best parent and work with Chase in the classroom in regard to teaching Chase strategies for managing inattention (e.g., how to break up large tasks into small ones, how to organize materials, how to develop metacognition skills, how to control behavior, how to use reinforcement systems such as videogames to facilitate concentrating for longer and longer periods). c. Academic accommodations. With a diagnosis of ADHD, Chase will likely be eligible to receive academic accommodations, such as preferential seating near the front of the classroom, extra time for tests, and/or more frequent redirection or breaks, varying based on the school. Further, special study strategies are likely to be helpful for Chase. That is, he may find it easier to get started on homework if he breaks assignments down into smaller steps. He is also encouraged to use a simple organization strategy (e.g., one folder) and use frequent

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rewards for short on-task periods. Finally, frequent communication with Chase’s school is suggested. 2. Continued school-based intervention in mathematics and writing. Chase continues to lag behind same-age peers in mathematics and writing; so continued intervention in these areas is recommended with periodic reevaluation. a. Continued intervention and accommodations in mathematics and writing is recommended in the short-term, although it is hoped that treatment for ADHD will lead to more rapid and more lasting gains in these areas; therefore reevaluation of mathematics and writing is recommended in another 1 2 years.

Learning disorder 1 comorbidity Psychoeducational Evaluation Report Name: Janice Thompson Date of Birth: 05/14/2004 Age: 11 years Grade: Fifth Sex: Female Dates of Evaluation: 03/09/2016, 03/24/2016, 04/07/2016 Date of Report: 04/12/2016

Evaluation procedures Written informed consent Clinical interview Personal history form Kaufman Assessment Battery for Children (KABC-2) Kaufman Test of Educational Achievement—Second Edition (KTEA-3) Adaptive Behavior Assessment System—Third Edition (ABAS-3): Parent Form Behavior Assessment System for Children, Second Edition (BASC-2): Parent and Teacher Rating Scales ADHD Rating Scale-IV: Home and School Versions Screen for Child Anxiety-Related Disorders (SCARED): Self-Report Child Depression Inventory-Second Edition (CDI-2): Self-Report Reason for referral Janice is an 11-year-old girl who was brought in for evaluation by her mother, father, and maternal grandmother due to concerns about learning. The current evaluation was requested in order to clarify Janice’s psychoeducation profile with attention to learning problems, cognitive function, speech and language problems, and possible attention problems to facilitate her

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upcoming transition to middle school. Janice’s parents want to be sure they are doing everything they can to help her with school.

Background information and symptom history Janice is an 11-year-old girl who is currently a student in fifth grade. She has a long-standing history of learning problems. She first began prekindergarten at a Montessori school, but it was reportedly not a good fit. She subsequently attended the local elementary school and then, when her area was redistricted, a new local elementary school. Janice reportedly had her first individualized education plan (IEP) in kindergarten due to auditory processing problems and mild cognitive delay. She had speech therapy from the time she was 4 years old until she was 10 years old, as per maternal report. Current IEP paperwork dated February 2016 indicates that Janice currently receives special education services for a specific learning disability in reading, mathematics, and written expression due to lack of RTI. These services commenced in 2013. She also receives services for speech-language impairment beginning in January 2011, as well as developmental delay in the areas of cognition, communication, and self-help in June 2011. She historically received audiology services for auditory processing disorder, but these have been discontinued as they have not been helpful for her. Recent language testing, conducted in January 2016, indicated continued impairment in speech and language with standardized scores ranging from 61 to 75 on the CELF-5 and PPVT-4. Cognitive testing conducted in 2013 indicated below average scores of 80 84, based on the KABC-2. This was higher than her KABC-2 scores in 2011 which ranged from 69 to 73. Recent adaptive behavior ratings by her teacher on the Adaptive Behavior Inventory—Short Form indicated average adaptive behavior (SS 5 101, 53rd percentile). Current accommodations include special education services via RTI and speech and language therapy (30 minutes once a week), as well as use of readers, paraphrasing, manipulatives, scribes, calculator, extended time, testing in area with minimal distractions, and extended time. With these services, Janice currently receives As and Bs. Janice’s parents are reportedly concerned about attention problems due to fidgeting, troubles focusing on reading, and being easily distracted. Her parents approached their pediatrician for an evaluation of attention problems, and he referred them to the current psychological agency. No concerns were noted about mood. Some mild anxiety related to schoolwork, tests, and her visitation schedule between her parents was noted. No behavior problems were endorsed. Janice’s parents have reportedly been divorced for about 10 years. They reportedly have a good relationship and split visitation. Currently, due to schooling preferences, Janice stays with her biological father, stepmother, and younger stepbrother (age 7) during the weekdays. On the weekend, she

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stays with her biological mother and younger half sister (age 8). Janice reported problems transitioning between the households because the rules are different. She also reported that her father often yells, and she wishes she had some alone time with her mother, like her sister does. Janice’s developmental history is unremarkable except for speech delays. She was the product of a normal pregnancy. Her speech was delayed and she could not speak until she was 3.5 years old. She has a history of severe ear infections and tubes, as well as allergies and tonsil and adenoid removal. A recent hearing evaluation in November 2015 was normal. Janice reported few friends and problems with one “mean girl” at school who is mean to her and other children in special education. Family history is significant for learning problems. Janice likes Barbies, puppies, drawing, design, and computer games. During her personal interview, Janice reported that she would like making more friends and would like her visitation schedule switched. She also would like help with schoolwork and tests. Finally, she does not want to have to do schoolwork (i.e., write a paragraph) as a punishment.

Behavioral observations Janice was very likeable. She was shy and slow to speak but was open and genuine, crying about some issues she discussed. Her speech seemed slightly behind her age level, and she seemed sometimes to take awhile processing questions. However, she was very polite and thoughtful. Test results and interpretation Intellectual functioning The Kaufman Assessment Battery for Children—II was administered as a measure of mental and cognitive processing. Compared to same-age peers, Janice’s overall mental processing composite was 80, which falls at the 9th percentile compared to same-age peers and is low average to borderline impaired; there is a 90% chance that her true score falls between 75 and 85. Janice exhibited relative strengths in her learning and planning abilities which fell in the average range with scores of 92 (30th percentile) and 93 (32nd percentile), respectively. She exhibited low average simultaneous, or holistic, processing ability with a score of 82 (12th percentile). Her sequential processing, or ability to attend and concentrate, was a weakness, falling in the borderline impaired range with a score of 74 (fourth percentile). Likewise, her crystalized knowledge was a weakness, falling in the mildly impaired range with a score of 66 (first percentile), and this weakness is relatively infrequent in the population, occurring less than 10% of the time. Overall, Janice’s intellectual functioning ranged from impaired to average with relative strengths in learning and planning and weaknesses in sequential processing and crystallized knowledge.

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Adaptive behavior Janice’s mother completed the Adaptive Behavior Assessment System— Third Edition (ABAS-3), a measure of adaptive, or functional, behaviors needed for day-to-day living. Based on Janice’s mother responses in comparison to parental responses about children of the same age, Janice exhibits adaptive behavior falling in the average range based on a standard score of 98 which falls at the 45th percentile. Specifically, she exhibits average practical and social skills with scores of 108 (70th percentile) and 99 (47th percentile), respectively, with low average conceptual skills (SS 5 87, 19th percentile). Overall, Janice’s adaptive behavior is average and not a concern at present. Therefore she does not meet criteria for intellectual disability which requires both low intelligence and low adaptive behavior. Academic functioning The Kaufman Test of Educational Achievement—Third Edition (KTEA-3) is a collection of tasks designed to measure academic performance across the areas of reading, mathematics, and writing. On the KTEA-3 compared to same-age peers, Janice performed in the mildly impaired range (SS 5 68, second percentile). Specifically, she performed in the borderline impaired range in reading with a standard score of 70, performing the same or better than only 2% of same-age peers. She also performed in the borderline impaired range in mathematics, with a standard score of 73 which falls at the 4th percentile. She exhibited particular difficulty with writing, performing in the profoundly impaired range with a standard score of 60 (, 1st percentile). Overall, Janice exhibits impaired academic performance with impaired performance across reading, mathematics, and writing with particularly weak skills in writing. These scores are roughly comparable with her intelligence score and consistent with difficulties with language. Although these scores are not low enough to meet the outdated intelligence-achievement discrepancy criteria as put forward in the last version on the DSM-IV-TR and utilized by the Department of Education, Janice’s achievement scores are low enough to meet DSM-5 criteria for LD with impairment in reading, mathematics, and written expression which specifies scores below 75 based on age-based norms. Attention, anxiety, and depression Janice’s parents, grandmother, and teachers completed the Behavior Assessment System for Children, Second Edition (BASC-2) and the ADHD Rating Scale to assess behavioral and emotional problems including attention. Janice’s mother, father, and grandmother were in agreement about being concerned about withdrawal (T score 5 69 92, 95 99th percentile) and problems with functional communication (T score 5 27 39, 2 15th percentile), based on their ratings compared to ratings by parents of same-age, same-sex children. Janice’s mother and grandmother were also concerned about anxiety

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(T score 5 58 72, 80 98th percentile), depression (T score 5 68 77, 94 98th percentile), and attention problems (T score 5 60 63, 83 88th percentile). Sylvia, Janice’s special education teacher, reported marginally clinically significant concerns about learning problems (T score 5 64, 89th percentile) and functional communication problems (T score 5 36, 11th percentile). Janice’s other teacher Jennifer did not endorse any problems. However, all caregivers and teachers furthermore agreed on the relative absence of clinically significant ADHD symptoms of inattention and hyperactivity on the ADHD rating scale, noting that three or fewer symptoms were present out of the six needed for diagnosis. Janice completed the Child Depression Inventory (CDI), a measure of depression. She did not endorse clinically significant total mood problems (T scores 5 62, 88th percentile), although she did endorse elevated levels of functional problems (T score 5 71, 98th percentile) and ineffectiveness (T score 5 72, 99th percentile). She endorsed clinically significant levels of anxiety on the Screen for Anxiety-Related Disorders (SCARED) with a total score of 39 with particular concerns noted in the generalized and social anxiety domains. Overall, parent, teacher, and self-report suggest anxiety, specifically social anxiety, but not depression or attention problems. Related problems, with withdrawal and functional problems, were also noted.

Summary Janice is an 11-year-old girl in the fifth grade who was brought in for evaluation by her mother, father, and paternal grandmother due to concerns about learning. The current evaluation was requested in order to clarify Janice’s psychoeducation profile with attention to learning problems, cognitive function, speech and language problems, and possible attention problems to facilitate her upcoming transition to middle school. Janice’s parents want to be sure they are doing everything they can to help her with school. Janice has a long-standing history of learning problems, and recent testing by the school in January 2016 suggests continued language problems falling in the impaired range. Results of the current evaluation indicate that Janice’s intellectual functioning ranged from impaired to average. Yet, Janice’s adaptive behavior is average. Therefore, she does not meet the criteria for intellectual disability at present. Janice exhibits impaired academic performance with impaired performance across reading, mathematics, and writing with particularly weak skills in writing. These scores are roughly comparable with her intelligence score and are consistent with difficulties with language. Although these scores are not low enough to meet the outdated intelligence-achievement discrepancy criteria as put forward in the last version on the DSM-IV-TR and utilized by the state’s Department of Education, Janice’s achievement scores are low enough to meet DSM-5 criteria for LD with impairment in reading, mathematics, and written expression which specifies scores below 75 based on age-based norms. In addition, parent, teacher,

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and self-report suggest anxiety, specifically social anxiety, as well as withdrawal and functional problems, but not depression or attention problems. Based on testing results and examiner observations, the following recommendations are provided with the goal of improving Janice’s academic performance and attention.

Diagnoses Language disorder Learning disorder with impairment in reading, mathematics, and written expression Social anxiety disorder Recommendations 1. Intervention for language disorder. Intensive speech and language therapist through school and through a private organization after school is strongly recommended. It is expected that ongoing improvements in language will improve academic skills, as well as self-confidence and will additionally decrease anxiety. 2. Intervention for LD. Continued intensive RTI services and tutoring through Janice’s school with close monitoring of her performance, and continued special education services are recommended. Continued modifications and accommodations for language-intensive tasks such as writing and note-taking are highly recommended. Extra time for tasks and tests is also highly recommended, as currently practiced. 3. Intervention for social anxiety disorder. Cognitive-behavioral therapy, or CBT, is a highly efficacious treatment for anxiety. Such services can be provided in the community. Further, given Janice’s likely ongoing difficulties with school, it would be helpful to play up her unique skill sets and talents (e.g., social skills and creativity). Finally, consistency in across-house routines and transitions will help Janice’s anxiety. 4. Future development of applied skills. Janice’s language difficulties and low average cognitive function make school difficult for her. As she advances through school, it will likely be important for her to focus on applied skills and vocational interests. Attention-deficit/hyperactivity disorder ADHD Assessment Report Name: Nathan Brown Date of Birth: 03/09/2007 Age: 8 years Grade: Second Sex: Male Dates of Evaluation: 05/20/2015; 05/27/2015 Date of Report: 05/27/2015

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Evaluation procedures Written informed consent Clinical interview Personal history form Kaufman Brief Intelligence Test, Second Edition Kaufman Test of Educational Achievement, Second Edition Behavior Assessment System for Children, Second Edition: Parent and Teacher Rating Scales—Child ADHD Rating Scale-IV: Home and School Versions Behavioral Rating Inventory of Executive Function: Parent Forms Reason for referral Nathan was brought in for evaluation by his mother for an evaluation for ADHD due to difficulties concentrating. Background information and symptom history Nathan is an 8-year-old Caucasian male who currently attends second grade. The presenting concern is difficulties concentrating and doing well in school. Per maternal report, Nathan has difficulty staying on task and following through and is forgetful. He will reportedly often skip longer reading tasks and math problems and often rushes through his work. When probed, Nathan’s mother also indicated that he often makes careless mistakes, leaves seat when he should remain seated, fidgets, and climbs on things. These issues have reportedly been present since birth. Nathan corroborated many of these problems, stating that he is worried about how he is doing at school, and he has problems concentrating. He described his room as messy, looking like an area where a “bomb with tornados” went off. He reported that he often forgets when his teacher tells him to stop doing something and will continue to do it anyway; he also indicated that he often gets in trouble at school for talking. Nathan reportedly makes A’s and B’s at school currently and scores between the 50th and 70th percentile on standardized testing. Nathan currently lives with his mother, father, and older brother. He has a number of close friends with whom he enjoys playing. He enjoys sports, particularly baseball, soccer, and hockey. He indicated that he is mostly happy (vs sad, worried, mad). Developmental and medical history is unremarkable. Nathan went to term and was born via cesarean section. No notable developmental issues or delays were reported aside from a slight lisp with the letter “s.” No notable medical or history of mental health problems were reported. No other problems were reported at present with the exception of difficulty in concentrating and doing schoolwork.

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Behavioral observations During the testing session, Nathan seemed happy and enthusiastic and was cooperative. He exerted good effort on all tests, although he had a tendency to answer quickly and then change his response later. During the reading subtest, he seemed to become bored toward the end of testing when passages were longer and more difficult. At the very end of testing, he seemed to skip reading the longest passages (as indicated by his speed of completion and skipped items); at these times, he was responsive to being reoriented. Nathan fidgeted with a toy throughout testing and was often out of his seat, although he was still able to attend to task instructions. Nathan’s mood was bright, and he had a high activity level. During the clinical interview and discussions with his mother, he played actively with toys in the playroom. However, he was good-natured and cooperative. Test results and interpretation Intellectual functioning Kaufman Brief Intelligence Test, Second Edition The Kaufman Brief Intelligence Test (KBIT-2) is a short test of general intellectual ability and verbal and nonverbal reasoning. On the KBIT-2, Nathan performed in the average range, earning an IQ composite score of 107, ranking him in the 68th percentile when compared with other children of his age. Chances are 90 out of 100 that his IQ falls within a range from 100 to 114. Notably, there were no significant differences between Nathan’s verbal and nonverbal reasoning scores within the general composite. Nathan performed in the average range (SS 5 100; 50th percentile) on verbal tasks assessing verbal knowledge and ability to solve riddles. He performed slightly, but not significantly, better in nonverbal reasoning, again performing in the average range (SS 5 112; 79th percentile) on a task assessing his ability to complete incomplete patterns in a series of pictures. Overall, Nathan’s intellectual functioning fell in the average range, compared to other individuals his age. Academic functioning Kaufman Test of Educational Achievement, Second Edition The Kaufman Test of Educational Achievement, Second Edition (KTEA2) are a collection of tasks designed to measure academic performance across the areas of reading, mathematics, and writing. On the KTEA-2, compared to same-age peers, Nathan performed in the average to above average range. Specifically, Nathan performed average to above average in reading with a standard score of 116, ranking at or above 86th percentile of sameage peers. He exhibited a strength in mathematics, performing in the above average range with a standard score of 121, scoring at or above 92% of his

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peers. He performed in the average range in writing with a standard score of 99 (47th percentile). It should be noted that writing was the last subtest completed, during the second hour of testing, and Nathan seemed to be rushing through this last subtest as indicated by quick responses and frequent fidgeting and leaving of his seat. Overall, Nathan’s academic performance is average to high average with a strength in mathematics and a relative weakness in writing, the latter seemingly due to poor attention and perseverance. Attention and executive function Behavior Assessment System for Children, Second Edition: Parent and Teacher Rating Scales—Child; ADHD Rating Scale-IV: Home and School Versions; Behavioral Rating Inventory of Executive Function: Parent Forms Nathan’s parents completed the Behavior Assessment System for Children, Second Edition (BASC-2) and the ADHD Rating Scale-IV, as measures of attention and other behavioral and psychological problems. Nathan’s teacher and both parents agreed that Nathan is currently exhibiting clinically significant levels of hyperactivity (T score 5 74 91), scoring as high or higher than 97% 99% of his same-age peers. Likewise, Nathan’s teacher and parents agreed that Nathan is exhibiting clinically significant levels of attention problems (T score 5 67 74), scoring as high or higher than 93% 99% of his same-age peers. Likewise, Nathan’s teacher and parents endorsed a clinically significant number of both inattentive and hyperactive-impulsive ADHD symptoms on the ADHD Rating Scale-IV, endorsing at least six symptoms within both inattentive and hyperactive-impulsive domains, consistent with ADHD— combined presentation. Finally, Nathan’s parents completed the Behavioral Rating Inventory of Executive Function (BRIEF), a measure of Nathan’s ability to exercise behavioral and mental control in the service of obtaining a goal. Nathan’s parents agreed that Nathan is exhibiting clinically significant problems with inhibition of behavior (T score 5 73 82, 97 99th percentile), as well as problems thinking about his thinking (T score 5 68 80, 95 99th percentile), specifically in his ability to hold and manipulate information in short-term memory, organize his materials, and self-monitor (T score 5 66 or above; 96th percentile or above). Overall, Nathan’s teacher and parents agree that Nathan is currently experiencing clinically significant inattention and hyperactivity impulsivity, consistent with ADHD—combined presentation (314.01). These problems were corroborated by Nathan’s mother during her interview, Nathan during his short clinical interview, by the examiner’s behavioral observations during testing, and by Nathan’s related problems in behavioral and mental control. However, no other behavioral or psychological problems were reported at present.

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Summary Nathan is an 8-year-old Caucasian male in the second grade who was brought in for an ADHD assessment due to concerns about concentration, reported by his mother and himself. During testing, Nathan also seemed to struggle with concentration during longer tasks as indicated by a tendency to skip items. He frequently fidgeted and was out of his seat during testing. Results of the current evaluation indicate that Nathan demonstrates average intellectual functioning. Nathan’s academic performance is average to high average with a strength in mathematics and a relative weakness in writing, the latter likely due to poor attention and perseverance. Nathan’s teacher and parents agree that Nathan is currently experiencing clinically significant inattention and hyperactivity impulsivity, consistent with ADHD—combined presentation (314.01). These problems were corroborated by Nathan’s mother during her interview, Nathan during his short clinical interview, by the examiner’s behavioral observations during testing, and by Nathan’s related problems in behavioral and mental control. However, no other behavioral or psychological problems were reported at present. The following recommendations are provided with the goal of improving Nathan’s current functioning and academic performance. Diagnosis ADHD—combined presentation (314.01). Recommendations 1. Intervention for ADHD. Research suggests that medication or behavioral treatment for ADHD are both effective with a combination of the two being most effective. a. Medication. Psychostimulant medication can be helpful for managing inattention, particularly during the school day. A child psychiatrist or your pediatrician can work on finding the best medication and dosage for Nathan. b. Behavioral intervention. Behavioral intervention can be a useful adjunct for teaching Nathan the necessary coping and organization skills necessary for addressing attention problems day-to-day. These services will focus on how to best parent and work with Nathan in the classroom regarding teaching Nathan strategies for managing inattention (e.g., how to break up large tasks into small ones, how to organize materials, how to develop metacognition skills, how to control behavior, how to use reinforcement systems to facilitate concentrating for longer and longer periods). c. Academic accommodations. With a diagnosis of ADHD, Nathan will likely be eligible to receive academic accommodations, such as preferential seating near the front of the classroom, extra time for tests,

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and/or more frequent redirection or breaks, varying based on school. Further, special study strategies are likely to be helpful for Nathan. That is, he may find it easier to get started on homework if he breaks assignments down into smaller steps. He is also encouraged to use a simple organization strategy (e.g., one folder) and use frequent rewards for short on-task periods. Finally, frequent communication with Nathan’s school is suggested.

Attention-deficit/hyperactivity disorder 1 comorbidity Name: Date of Birth: Age: Grade: Sex: Dates of Evaluation: Date of Report:

Chance Hampton 02/02/2009 8 years Second Male 06/08/2017, 8/04/2017 09/08/2017

Evaluation procedures Written informed consent Clinical interview Personal history form Behavior Assessment System for Children, Second Edition (BASC-2): Parent and Teacher Rating Scales—Child ADHD Rating Scale-IV: Home Version: Parent and Teacher Ratings Screen for Child Anxiety Related Disorders (SCARED) Child Depression Inventory—Second Edition (CDI-2) Kaufman Brief Intelligence Test (KBIT), Second Edition Kaufman Test of Educational Achievement (KTEA), Third Edition Reason for referral Chance was brought in by his mother due to concerns about his academic performance and attention, as well as possible mood and anxiety problems. His mother requested a comprehensive evaluation of his learning with consideration of possible attention, anxiety, and mood problems in order to find a way to help Chance do better in school and feel better about himself. Background information and symptom history Chance is an 8-year-old male currently going into third grade next year. As per maternal report, his school has been concerned about his reading and mathematics performance, as well as his attention for several years. His mother reported being concerned about these issues at present because they are causing Chance a lot of problems in school and with his peers who made

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fun of him and make him feel bad about himself. Chance’s mother reported that he initially had behavioral issues (i.e., problems keeping his hands to himself) in first grade, which in subsequent years morphed into attention problems and being withdrawn when he had difficulty following class material. Chance’s mother indicated that he is often overwhelmed with schoolwork in both reading and mathematics. He also reportedly has problems following instructions, being organized, losing his things, and paying attention to details. He was not described as overactive or hyperactive, but his mother did indicate that he often interrupts and has difficulty waiting his turn. He was reported to be very anxious, worrying about people dying. Finally, for the last 7 8 months, he has cried almost every day over minor provocations (e.g., not getting to eat at a particular restaurant), an issue his mother thinks might have something to do with his difficulties at school. Chance reportedly had a school psychological evaluation last year. As per maternal report, no diagnosis was made, but his memory was reported to be impaired. Chance reportedly lives with his mother, and they are moving into a new home soon. His parents were never married, but he sees his father every other weekend. His grandfather recently passed away. No problems during pregnancy or developmental delays were reported, although Chance’s mother could not remember when he spoke his first word. He used complete sentences at 3.5 years of age. He reportedly saw a speech pathologist for annunciation problems from a young age, and this service through the school is ongoing. No other special services at school were reported, although someone does work one-on-one with Chance occasionally. Chance has reportedly indicated that he wishes he could follow directions better and he wishes he could read better.

Behavioral observations Chance frequently interrupted the clinician and his mother during the session and would repeat questions multiple times. He also had a tendency to repeat himself. During the testing session, Chance appeared engaged and motivated during assessment measures. He was pleasant to work with and spoke openly and personably. During the assessment session, Chance frequently appeared distracted and had to be reminded a few times to focus on the testing material. His speech indicated stream of consciousness and was tangential. He had a difficult time sitting still and consistently fidgeted during the session. However, it appears that results are an accurate presentation of his abilities, and he seemed to provide his full effort during the assessments. Test results and interpretation Broad-band screening Behavior Assessment System for Children (BASC), Third Edition: Parent, and Teacher Rating Scales

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Chance’s mother and his teacher competed the BASC as a broad-band screen of clinical problems. Chance’s mother reported clinically significant concerns in the areas of internalizing problems (T score 5 85, 99th percentile). This score was driven by clinically significant concerns in anxiety (T score 5 79, 99th percentile) and depression (T score 5 94, 99th percentile), as well as marginally significant concerns related to somatization (T score 5 62, 86th percentile). Chance’s mother also endorsed marginally significant concerns related to externalizing problems (T score 5 69, 95th percentile). This score was driven by clinically significant concerns related to conduct problems (T score 5 70, 95th percentile), as well as marginally significant concerns related to hyperactivity (T score 5 65, 92nd percentile) and aggression (T score 5 63, 90th percentile). Chance’s mother endorsed clinically significant concerns related to attention problems (T score 5 72, 99th percentile), atypicality (T score 5 77, 97th percentile), and withdrawal (T score 5 82, 99th percentile). Chance’s teacher reported clinically significant concerns related to school problems (T score 5 85, 99th percentile). This score was driven by clinically significant concerns related to attention problems (T score 5 77, 99th percentile) and learning problems (T score 5 86, 99th percentile). Chance’s teacher indicated clinically significant concerns related to atypicality (T score 5 83, 98th percentile) and marginally significant concerns related to withdrawal (T score 5 64, 90th percentile), anxiety (T score 5 60, 85th percentile), and depression (T score 5 63, 89th percentile). In sum, Chance’s mother and teacher indicated concerns related to internalizing problems (e.g., anxiety, depression, withdrawal), as well as attention and learning problems and atypicality. His mother also reported some concerns regarding conduct problems and other externalizing issues. Attention problems ADHD Rating Scale-IV: Home and School Versions Chance’s mother and teachers completed the ADHD Rating Scale. Chance’s mother reported clinically significant levels of both inattentive and hyperactive/impulsive ADHD symptoms, reporting eight clinically significant symptoms of inattention and six clinically significant symptoms of hyperactivity impulsivity. Chance’s teacher endorsed nine clinically significant symptoms of inattention and two clinically significant symptoms of hyperactivity impulsivity. Thus it appears that Chance is exhibiting elevated levels of ADHD symptoms, particularly in the inattentive symptom domain. However, Chance’s mother also endorsed elevated symptoms of hyperactivity impulsivity. Anxiety Screen for Child Anxiety Related Disorders (SCARED): Parent Version

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Chance’s mother completed the SCARED as a measure of anxiety. Chance’s mother endorsed a large number of symptoms indicative of an anxiety disorder, specifically generalized anxiety disorder, separation anxiety disorder, and school avoidance. Overall, it seems that Chance is experiencing symptoms consistent with a several different forms of anxiety, particularly school-related avoidance and separation anxiety disorder. Depression Child Depression Inventory (CDI): Parent and Child Versions Chance and his mother completed the CDI as a measure of depression. Chance’s mother indicated that Chance is experiencing very elevated levels of total depressive problems (T score 5 74), emotional problems (T score 5 72), and functional problems (T score 5 71). Chance also indicated very elevated levels of total depressive problems (T score 5 85), emotional problems (T score 5 76), negative mood and physical symptoms (T score 5 82), functional problems (T score $ 90), ineffectiveness (T score $ 90), and interpersonal problems (T score 5 76). Thus Chance and his mother both endorse significant issues related to depression, both in terms of emotional and functional domains. Intellectual functioning Kaufman Brief Intelligence Test (KBIT), Second Edition Chance was administered the KBIT as a measure of mental and cognitive processing. Compared to the same-age peers, Chance’s overall mental processing composite was 83, which fell in the below average range. There is a 90% change that his true score falls between 77 and 91. Chance’s verbal composite score was 89 (23rd percentile), which falls in the below average range. Chance’s nonverbal composite score was 82 (12th percentile), which also falls in the below average range. Thus Chance exhibits below average verbal and nonverbal processing. Academic functioning Kaufman Test of Educational Achievement (KTEA), Third Edition Chance was administered the KTEA as a measure of his reading and mathematics abilities. Chance’s reading composite score was 77, which falls in the below average range and the 6th percentile. There is a 90% chance that his true score falls between 72 and 82. Chance’s math composite score was 82, which falls in the below average range and at the 12th percentile. There is a 90% chance that his true score falls between 77 and 87. Therefore Chance’s reading and mathematics abilities both fell within the below average range.

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Summary Chance is an 8-year-old Caucasian male who will be attending third grade. Chance’s mother reported concerns regarding his academic performance (particularly reading and math), difficulties with attention, and problems with depression and anxiety. According to his mother, Chance previously received a psychological evaluation at school, and results suggested some memory impairments. Results indicated that Chance experiences significant problems with attention and learning. Specifically, Chance’s mother and his teacher endorsed concerns regarding his ability to sustain his attention and concentration. In addition, both Chance’s mother and teacher endorsed clinically significant symptoms of ADHD within the inattentive symptom domain, and his mother endorsed symptoms within the hyperactive-impulsive symptom domain as well (e.g., interrupting others and difficulty waiting his turn). These issues may have contributed to his memory impairments, given that one of the primary symptoms of ADHD involves forgetfulness. In addition, his attention problems and difficulties in concentrating may have also contributed to difficulties with his current below-average verbal and nonverbal abilities, as well as his low academic achievement. Chance’s mother and teacher endorsed significant concerns related to withdrawal, atypicality, anxiety, and depression. It is likely that his problems, related to inattention, have contributed to his social problems and ability to communicate with his peers (as stated by his mother), leading to his tendency to withdraw from social settings and thus appear “atypical.” In addition, Chance reportedly has cried almost every day for the past 7 8 months for various reasons. These symptoms are likely contributing to Chance’s feelings of ineffectiveness, low self-worth, and interpersonal problems. Additional depressive symptoms include a diminished ability to concentrate, irritability, trouble sleeping, and recurrent thoughts of other people dying. In terms of anxiety, Chance and his mother reported excessive anxiety regarding school and peer interactions. Chance’s teacher and mother endorsed clinically significant concerns related to anxiety on the BASC, and Chance’s mother also reported clinically significant concerns on the SCARED. Thus Chance appears to be exhibiting significant symptoms of major depressive disorder and generalized anxiety disorder. Intervention for ADHD and depression and anxiety will likely improve Chance’s academic problems.

Diagnoses Attention-deficit/hyperactivity disorder; inattentive presentation (F90.0) Major depressive disorder; recurrent with moderate severity (F33.1) Generalized anxiety disorder (F41.1)

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Recommendations 1. Intervention for ADHD. Research suggested medication or behavioral treatment for ADHD are both effective with a combination of the two being most effective. a. Medication. Psychostimulant medication can be helpful for managing symptoms of ADHD, particularly during the school day and when attempting to complete homework. A child psychiatrist or your pediatrician can work with you on finding the best medication and dosage for your child. Child psychiatrists recommended in the area include Dr. Paul Brown. b. Behavioral intervention. Given that Chance experiences symptoms of frustration, aggression, anxiety, and low self-esteem, he would likely benefit from CBT for these difficulties. Such therapy would allow Chance to develop coping skills, social problem-solving strategies, and other skills to recognize and change negative thoughts about himself and gain confidence in social situations. In terms of low selfesteem, cognitive restructuring, mindfulness training, and problemsolving skills may aid in changing Chance’s dysfunctional thinking patterns and alter ineffective behavioral patterns. Such therapy can be provided here or in the community. 2. Cognitive behavior therapy for anxiety and depression. Cognitive behavior therapy can also be useful for helping children reduce their symptoms of anxiety and depression. Children learn to identify and replace negative thinking patterns and behaviors with positive ones. They also learn to separate realistic from unrealistic thoughts and are given home activities to practice what they learn in therapy. Given that Chance experiences symptoms of major depression and generalized anxiety disorder, such therapy might be useful for helping Chance to deal with these issues when they occur. 3. School accommodations. Given Chance’s symptoms of ADHD and depression/anxiety, he should be eligible for accommodations at school, including extra time on tests, testing in a separate room, and direct communication between his mother and teacher regarding assignments. In addition, his reading and math performance should be carefully monitored after treatment for ADHD and depression/anxiety so that RTI can be utilized to immediately remediate any remaining difficulties. 4. Reevaluation in 2 years. Chance’s learning difficulties may stem from his ADHD and anxiety/depression-related impairments, so it is likely that treating these symptoms will facilitate improvements in other areas as well. Therefore it is recommended that Chance be reevaluated in 2 years to formally assess improvement in skills and evaluate whether any problem areas remain. As these case examples illustrate, children with LD and/or ADHD can look very different from one another. LD and ADHD can coexist or exist

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The Clinical Guide to Assessment and Treatment

independently with or without other complicating factors, such as anxiety, depression, or language problems. Comprehensive and at least somewhat flexible testing batteries are helpful depending on the initial referral question, as well as any other issues that need to be assessed and ruled out. Parents may mention such issues during the initial interview, but clinicians should always ask about and screen for commonly comorbid problems. Further, such issues (e.g., anxiety and language problems) may come to the forefront during evaluation, whether or not the parents mentioned them and should therefore be readily able to be added to assessment batteries. Further, information from the school is often critical to determining the presence of learning problems and the problems that can impact learning, particularly attention problems, language problems, and adaptive and intellectual functioning. Information from the school, whether obtained through report cards, IEP evaluation reports, telephone consultations with the teacher, or anything else, should be included in the report whenever possible. Consultation with other professionals (e.g., pediatricians, psychiatrists) can also be crucial for assessing and treating attention problems and commonly comorbid problems (e.g., anxiety and depression).

Important future directions for learning disorder attentiondeficit/hyperactivity disorder research Despite the tremendous amount of progress that has been made in treating LD and ADHD, a number of vitally important future directions remain. RTI is finally being implemented in many schools around the nation. Yet, many schools lack the resources to provide intensive RTI efforts that require substantial trained staff support. More support for RTI should be provided at the national, state, and district levels in order to ensure more even application of RTI, especially in those districts and schools that have the most need of it due to having fewer resources. In addition, more work is needed on the efficacy and effectiveness of current innovative RTI programs. Given the relative new implementation of RTI, many protocols are currently relatively unvetted. Yet, the ongoing nature of RTI evaluations provides unique opportunities for thorough vetting and testing. Research funding should be provided for the collection and analysis of this data in order to facilitate evidence-based guidelines for RTI implementation and progress monitoring. Attention should be paid to individual student profiles and needs, and tailored intervention at both individual and group levels should be tested. Likewise, in tandem with work on efficacy of RTI, more research is needed on the evidence base for accommodations and modifications. It is currently unclear whether most commonly-given accommodations and modifications are truly effective, and such information is particularly lacking in the most important populations: students with the special needs such as those with learning and/or attention difficulties. This kind of research could be

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fairly easily tied in with RTI and IEP progress monitoring, but again, such efforts require support at the staff level. Such work needs to address which accommodations and modifications are most important at the individual student level, based on unique profiles and learning, attention, and emotional issues. In addition, research needs to establish how such accommodations and modifications can be best weaned versus when they need to be continued for longer term. Finally, work needs to address the generalizability of such efforts and/or evaluate whether there are times when they are counter indicated. Finally, more work in needed on the efficacy of executive function training and neurotherapies, and such work should be better integrated with work on etiology. Current work suggests such executive function training and neurotherapies are not particularly useful, especially given their cost. Continued research is needed to allow for clear-cut recommendations regarding these approaches which are currently often touted to parents as potentially helpful options. If they are not currently recommended, official statements should be provided to encourage parents to direct their time and energy in more useful directions, such as educational therapies, behavioral therapies, medication options, or even cognitive processing speed. Finally, it is critical that more work provide best practice guidelines for intervention on comorbid ADHD and LD, given that most work on evidence-based treatment to date has been conducted on only one or the other disorders in isolation without attention to their substantial comorbidity.

Summary Learning and attention problems commonly occur in children and require early assessment and treatment in schools and the clinic. Such effective treatments as RTI and educational therapy for learning problems and behavioral treatment and medication for ADHD can be tremendously helpful in preventing negative school outcomes and facilitating academic success. Yet, a personalized approach to assessment and treatment with ongoing monitoring over time requires extensive resources by school, parents, and medical and mental health professionals. For this reason, ongoing support for and research on the efficacy and effectiveness of preventive and interventional strategies are recommended.

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A

Ability 2 achievement (IQ 2 achievement) discrepancy, 78 Academic accommodations and modifications, 203 204, 213 214 definition and differentiation, 135 136 effective, 141 143 graphic organizers, 140 141 Section 504 of the Rehabilitation Act of 1973, 133 135 specific learning disability and ADHD, 126 140 extended time, 136 138 read aloud, 138 setting, 139 140 technological supports, 138 139 Academic achievement battery, 129 Academic activities and learning disabilities (LDs), 14 Academic difficulties profiles, 106 110 math and written expression, 109 110 poor reading, 106 108, 107t Academic functioning, 201 202, 207, 211 212 Academic productivity, 159 Academic skills, 14, 149 150 Achenbach System of Empirically Based Assessment, 44 45 Achievement, 9 10 discrepancy model, 7 Adaptive Behavior Assessment System—Third Edition (ABAS-3), 207 Adaptive behaviors, 60, 206 ADHD-Predominantly Inattentive Presentation (ADHD-I), 162 163 ADHD Rating Scale-5, 44, 216 ADHD-related reading difficulties, 174 175 ADHD symptomatology, 152 156, 177, 183 184

Aggressive behavior, 38 American Psychiatric Association, 127 128 Antecedents, 150 151 Anxiety, 207 208, 216 217 Assessment cognitive assessment battery, 129 criterion-referenced phonics, 116 early, 221 formative, 110 114 multiinformant, multimethod assessment strategy, 42 43 skill-based, 18 19 Attention, 207 208 and gross motor activity, 173 Attention-deficit disorder with hyperactivity, 34 Attention deficit disorder without hyperactivity (ADD), 34 Attention-deficit/hyperactivity disorder (ADHD), 209 214 assessment procedures, 43 46 comorbidity with. See Comorbidity, ADHD cooccurring features, 37 38 developmental course, 36 diagnostic criteria and classification, 39 42 diagnostic feedback and treatment planning, 49 50 DSM-5 diagnostic criteria, 34 35 epidemiology, 34 35 etiology, 35 36 evaluation data, 46 49 functional impairment, 38 39 historical context, 33 34 and learning disorder (LD), 200 204 multiinformant, multimethod assessment strategy, 42 43 phenotypic expression, 35 prevalence rates, 34 35 primary symptoms, situational variability, 37

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224

Index

Attention problems, 216 Automatized decoding abilities, 177

B Basic reading, 14 15 Basic writing skills, 105 Behavioral classroom management strategies, 163 Behavioral inhibition, 173 174 Behavioral interventions, 149, 203, 213, 219 behavioral parent training (BPT), 151 156 child organizational skill interventions, 160 161 future directions, 163 164 multicomponent behavioral interventions, 161 163 resources for clinicians, 164 165 for parents, 165 school-based interventions, 157 160 theoretical underpinnings of, 150 151 Behavioral parent training (BPT), 151 156 elements of, 153t empirical support, 152 156 Behavioral parent training resources, 164 Behavioral treatments, 150 151, 173 Behavior Assessment System for Children, Second Edition (BASC-2), 202, 207 208, 212 Behavior Assessment System for Children Third Edition, 44 45 Behavior Rating Inventory of Executive Function Second Edition (BRIEF 2), 45 Behaviors, 150 151 Blood-oxygen level 2 dependent (BOLD) responses, 182 BPT. See Behavioral parent training (BPT) Brain stimulation, 182 183 repetitive transcranial magnetic stimulation, 183 transcranial direct current stimulation, 183 Broad-band rating scales, 44 45 Broad-band screening, 215 216 Broad-based behavioral management therapies, 191 192 Broad listening comprehension, 100 104, 108

C Capability, 10 11 Case examples, 199 220

Causation, 14 16 CD. See Conduct disorder (CD) Central executive (CE), 174 Child and Adolescent Psychiatric Assessment, 43 44 Child-focused skills, 163 Child Life and Attention Skills Program (CLAS), 162 163 Child organizational skill interventions, 160 161 Children’s Organizational Skills Scale, 45 Children with different reading profiles, 115 119 Children with SWRDs, 117 Child skills training resources, 164 Child’s skills profile, 16 17 Choice, 114 Clashing classification system, 127 128 Classroom-based behavioral interventions, 159 Clinical decision-making flowchart, 49 50 Clinical settings, identification in, 8 9 Cloze type test, 20 Cognitive aptitude, 78 Cognitive assessment battery, 129 Cognitive-behavioral therapy, 209 for anxiety and depression, 219 Cognitive flexibility, 173 174 Cognitive strategies, 136 Cognitive testing, 205 Colorado Learning Difficulties Questionnaire (CLDQ), 67 68 Colorado Learning Disabilities Research Center (CLDRC) twin study, 56 57, 58f Combined behavioral and medication intervention, 163 Common eligibility categories, 128 131 Commonwealth of Pennsylvania (1971, 1972), 126 Communicating with parents and finding appropriate therapy, 119 121 Comorbidity, 198 199 ADHD, 214 220 clinical assessment procedures, 67 68 clinic sampling bias, 61 common etiology and causal models as, 62 63 competing explanations for, 61 67 diagnostic formulation, 67 functional implications of, 58 60 future directions for studies, 68 69 intervention, 68

Index with learning disorder (LD), 55 56 phenocopy model, 62 rater bias, 61 62 reading disorder (RD), common etiology and causal models, 57 58 family studies of, 63 64 molecular genetic studies of, 65 66 neurocognitive models of, 66 67 twin studies of, 64 65 shared method variance and symptom overlap, 61 learning disabilities (LDs), 204 209 Complexities, 198 199 Component abilities in achievement, 100 106 developmental shifts and interrelationships, 105 106 math, 104 oral language and reading, 100 104 reading, written expression and mathematics, 101t written expression, 105 Comprehension, 100 104 monitoring, 108 Conduct disorder (CD), 37 38 Conners Comprehensive Behavior Rating Scales, 44 45 Conners Continuous Performance Test, 45 Conners Early Childhood, 44 45 Conners Third Edition, 44 Consequences, 150 152 Consolidation, 190 Consonant-vowel-consonant (CVC) words, 116 Continued school-based intervention in mathematics and writing, 204 Correlated liabilities model, 62 63 Criminal justice system, 3 Criterion-referenced phonics assessments, 116 Cross-cutting measures screen, 44 45 Curriculum adaptations, 136 Curriculum augmentations, 136 Curriculum-based measurement (CBM), 18 19, 85 Curriculum-embedded measurements, 18 19

D Daily report card (DRC), 157 158, 157f, 158t goals, 158t Data-based individualization (DBI), 81 82, 93 94 DBI. See Data-based individualization (DBI)

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Decision-making process schools, 7 Deficient positive consequences, 150 151 Degree of functional impairment, 8 9 Depression, 207 208, 217 Diagnostic and Statistical Manual of Mental Disorder—Fifth Edition (DSM-5), 127 128 Diagnostic and Statistical Manual of Mental Disorders, 4th edition, 55 56 Diagnostic Interview for Children and Adolescents-IV, 43 44 Diagnostic Interview Schedule for ChildrenIV, 43 44 Differential boost, 136 Direct instruction, 15 Disability categories, 3 Discrepancy model of identification, 12 Domain-specific short-term memory (STM) stores, 174 Domain-specific treatment effects, 156 Dual discrepancy, 89 Dyscalculia, 9 Dyslexia, 4, 9, 109

E Early assessment, 221 Educational therapy academic difficulties profiles, 106 110 math and written expression, 109 110 poor reading, 106 108, 107t appropriate curricula and materials, 115 children with different reading profiles, 115 119 communicating with parents and finding appropriate therapy, 119 121 component abilities in achievement, 100 106 developmental shifts and interrelationships, 105 106 math, 104 oral language and reading, 100 104 reading, written expression and mathematics, 101t written expression, 105 systematic teaching, characteristics of explicit, 110 114, 111t visual aids and manipulatives, benefits, 114 115 Educational usefulness, 100 Education of All Handicapped Children Act, 10

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Index

Education of the Handicapped Act Amendments of 1986, 126 Effective instructions, 153t Electroencephalogram, 181 182 Eligibility from the Tennessee Department of Education, 24 25 ELLs. See English language learners (ELLs) Emotional control, 45 46 Emotional disturbance (ED), 6, 128, 130 131 Emotional self-awareness, 45 46 Emotion regulation, 45 46 Emotion Regulation Index for Children and Adolescents, 45 46 Encoding/decoding, 174 175 English language learners (ELLs), 93 Etiology attention-deficit/hyperactivity disorder (ADHD), 35 36 and causal models, 62 63 reading disorder (RD), 57 58 Evidence-based supplementary intervention, 87 Exceptional Children, 91 92 Executive function (EF) training for children with ADHD, 45 brain stimulation, 182 183 repetitive transcranial magnetic stimulation, 183 transcranial direct current stimulation, 183 clinical outcome studies, 171 172 cognitive/experimental investigations, 173 175 functional working memory model, 175 176 information retention and retrieval, 189 191 memory strategies to improve learning, 187 191 near- and far-transfer effects, 176 177 neuroimaging studies, 173 neurotherapies, 180 182 organizational strategies, 184 186 Homework, Organization, and Planning Skills (HOPS) program, 185 186 Organizational Skills Training (OST) program, 184 185 Supporting Teen’s Autonomy Daily (STAND) program, 186 practitioner considerations and recommendations, 183 184 programs, 177 180

conceptual rationale and currently available programs, 178 efficacy, 178 180 methodological considerations, 180 Executive function training programs, 177 180 conceptual rationale and currently available programs, 178 efficacy, 178 180 methodological considerations, 180 Explicit instruction, 110 114 in different academic domains, 111t Extended time, 136 138

F Family structure and functioning, ADHD, 46 Fluency, 100 104, 106 108 Formative assessment, 110 114 Free appropriate public education (FAPE), 6 7 Functional near-infrared spectroscopy (NIRS), 182 Functional working memory model, 175 176, 175f Future directions, 220 221

G Generalized anxiety disorder, 60 Grades and group-standardized test results, 129 Graphic organizers, 114 115, 140 141 Group sessions, 151 152

H Heritability, 64 Higher-level language abilities, 108 Home-based rewards, 157 158 Home 2 school-based contingency management, 183 184 Homework, Organization, and Planning Skills (HOPS) program, 185 186 Hybrid model, 17 25 exclusionary factors, 24 25 inadequate response to appropriate instruction, 18 19 poor achievement in reading, mathematics, and/or written expression, 19 24 Hyperactive child syndrome, 34 Hyperactive-impulsive symptoms, 36, 39 40 Hyperactivity, 174 175

Index Hyperkinetic-impulse disorder, 34 Hyperkinetic reaction of childhood, 34

I Impulsivity, 174 175 Inattention, 39 42, 174 175 Individualized education program (IEP), 83, 131 132, 205 Individuals with Disabilities Education Act (IDEA), 4 6, 25 Amendments of 1997, 126 Individuals with Disabilities Education Improvement Act of 2004 (2004) (IDEIA), 125 127 Individual variability, 198 199 Information retention and retrieval, 189 191 short-term storage, practitioner recommendations, 189 191 Institute for Education Sciences (IES), 115 Institute of Education Science’s What Works Clearinghouse, 163 Instruction intensity, 114 Intellectual ability, 201 Intellectual disability, 7, 24 Intellectual functioning, 201, 206, 211, 217 Intensity, 114 Intensity of intervention, 114 Intensive speech and language therapy, 209 International Dyslexia Association, 9 International Longitudinal Twin Study of Early Reading Development, 56 57 International Statistical Classification of Diseases and Related Health Problems—Tenth Edition (ICD-10), 127 128 Intervention, 150 151, 203, 213, 219 learning disabilities (LDs), 209 multicomponent behavioral, 161 163 oral language, 118 Intrasyllabic linguistic units, 116 IQ 2 achievement discrepancy, 9, 13 IQ test, 10 13

K Kaufman Assessment Battery for Children— II, 206 Kaufman Brief Intelligence Test (KBIT-2), 201, 217 Kaufman Test of Educational Achievement (KTEA), Third Edition, 10, 19 20, 201 202, 207, 217

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L Labeled praise, 153t Language weaknesses, 109 LDs. See Learning disabilities (LDs) Learning deficiencies, 174 175 Learning disabilities (LDs), 1 achievement, 9 10 capability, 10 11 causation, 14 16 comorbidity, 204 209 hybrid model, 17 25 exclusionary factors, 24 25 inadequate response to appropriate instruction, 18 19 poor achievement in reading, mathematics, and/or written expression, 19 24 identification, 4 9, 5t in clinical settings, 8 9 school-based identification, 6 8 intervention, 209 take-home messages and future directions, 25 28 time and resources, 16 17 unexpectedness, 12 13 Learning disorder (LD) comorbidity, ADHD with, 55 56, 197 198 integrating care, 199 research, future direction, 220 221 Learning struggles, 4 Letter or numeral recognition, 110 114 Letter sounds, 116 Literacy, 100, 109 110, 114 115 Long-term memory (LTM), 188 Low achievement scores, 13

M Major depressive disorder (MDD), 49 Mastered decoding one-syllable words, 116 Mathematics components, 101t, 104 deficits, 174 175 impairments, 8 9 learners, 110 learning disorders, 106 literacy and, 110 multiple commercial programs, 115 problem-solving, 106, 109 visual aids and manipulatives, 114 115 Math Fact Fluency, 20 24

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Index

Memory strategies to improve learning, 187 191 information encoding difficulties, 187 188 information input channel, 188 189 Mills versus Board of Education of District of Columbia (1972), 126 Minimal brain dysfunction, 4 Mixed reading difficulties (MRDs), 106, 119 Models of identification, 17 25 Molecular genetic methods of comorbidity, ADHD, 65 66 Mood and anxiety disorders, 25 MRDs. See Mixed reading difficulties (MRDs) Multicomponent behavioral interventions, 161 163 Multiinformant, multimethod assessment strategy, 42 43 Multimodal Treatment Study for ADHD, 163 Multimodal Treatment Study of Children with ADHD (MTA), 171 172 Multiple oral language weaknesses, 99 Multitiered system of support, 77

N Narrow-band rating scales, ADHD, 44 National Center for Education Evaluation, 90 91 National Center for Learning Disabilities, 3 National Center on Intensive Intervention, 88 National Center on Response to Intervention, 90 92 National Joint Committee on Learning Disabilities (NJCLD), 78 79 National Reading Panel (2000), 116 National Research Center on Learning Disabilities (NRCLD) studies, 78 80 Near- and far-transfer effects, 176 177 Neurocognitive theories of ADHD and RD, 65 66 Neurodevelopmental disorder, 8 Neurofeedback, 181 182 electroencephalogram, 181 182 functional near-infrared spectroscopy (NIRS), 182 Neurotherapies, 180 182, 191 Nonshared environmental influences, 64 Norm-referenced measure, 19 20 Numeral recognition, 110 114 Numerical Operations subtest, 20 24

O

ODD. See Oppositional defiant disorder (ODD) Office of Special Education and Rehabilitative Services, 11 Office of Special Education Programs (OSEP), 78 79 Onset-rime, 116 Oppositional defiant disorder (ODD), 37 38 Oppositional defiant disorder symptoms, 163 Oral expression, 174 175 Oral language components, 100 104 interventions, 118 Oral Reading Fluency subtest, 20 Organizational Skills Training (OST) program, 160, 184 185 Organizational strategies, executive function training, 184 186 homework, organization, and planning skills, 185 186 organizational skills training, 184 185 supporting teen’s autonomy daily, 186 Organization and planning compensatory approaches, 184 Orthographic conversion, 174 175 Other health impairments (OHIs), 128 130 Oxford Center for Evidence-Based Medicine Guidelines, 163

P Parenting Stress Index (PSI), 46 Parent-rated homework problems, 163 Parents learn strategies, 151 Pattern of strengths and weaknesses (PSW) model, 7 8 Patterns of strengths, 17 Pennsylvania Association for Retarded Children (P.A.R.C.), 126 Phoneme blending, 116 Phoneme-level blending skills, 116 Phoneme-level synthetic-phonics approach, 116 Phonemic awareness, 100 104 Phonics (decoding), 100 104 Phonological-processing measures, 99 Planned ignoring, 153t Positive reinforcement/token economy, 153t Pragmatics, 108 Presentation accommodations, 135 Prevention, 198, 221

Index primary, 80, 82 secondary, 80 83 tertiary, 81 84 Proactive interference, 187 188 Problem-solving approach, 87 Processing speed, 174 175 Progress monitoring, 79 83, 88, 93 data, 81 82 Prompt corrective feedback to errors, 114 Pseudoword reading, 13, 20 Psychoeducation, 153t Psychological constructs, 14 Psychostimulant medication, 183 184, 192, 203, 213, 219 Psychostimulants, 172 173

Q Quality time/attending, 153t

R Rate of learning, 3 Raven’s Progressive Matrices overlap, 179 Read aloud accommodation, 138 Reading accuracy, 9 and cognitive measures, 13 components, 100 104 disability, subcategories, 20 fluency, 20, 65 profiles, 100, 109, 115 116 rate/fluency, 9 real and exception word, 13 Reading comprehension, 14 15, 65, 118, 191 -based LD, 20 difficulties, 117 Reading Comprehension subtest, 20 Reading disorder (RD), 56 57 family studies of, 63 64 frequency of comorbidity between ADHD and, 57 58 molecular genetic studies of, 65 66 neurocognitive models of, 66 67 twin studies of, 64 65 Rehabilitation Act of 1973, 8, 133 135 Repetitive transcranial magnetic stimulation (rTMS), 183 Response accommodations, 135 Response to intervention (RTI), 18 19, 126 127 English language learners (ELLs), 93 framework, 16

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future research and practice, 92 94 intensive services, 79 84 primary prevention, 80, 82 secondary prevention, 80 83 tertiary prevention, 81 84 model, 7 8 and rationale for its use, 78 79 research-based programs, 79 response to intervention evaluation, 88 92 National evaluation of response to intervention, 90 92 National Research Center on Learning Disabilities studies, 89 90 Retroactive interference, 187 188 RTI. See Response to intervention (RTI) Rule breaking behaviors, 60

S Schedule for Affective Disorders and Schizophrenia for School-Age Children, 43 44 School accommodations, 219 School-aged children, ADHD, 38 School-based behavioral intervention resources, 164 School-based contingency-management programs, 159 School-based identification, 6 8 School-based interventions, 157 160 academic productivity, 159 classroom-based behavioral interventions, 159 daily report card (DRC), 157 158, 157f, 158t empirical support, 159 160 home-based rewards, 157 158 school-based contingency-management programs, 159 School records and teacher-reported information, 46 School settings, application to. See Response to intervention (RTI) Section 504 of the Rehabilitation Act of 1973, 133 135 Americans with Disabilities Act of 1990, 133 American with Disabilities Amendment Act of 2008, 133 background and history, 133 134 eligibility and documentation, 134 physical or mental impairment, 133 134 504 plan process, 134 135

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Index

Self-employment settings, 43 Setting accommodations, 135 Severity of risk, 198 199 Short-term storage, practitioner recommendations, 189 191 Short-term verbal memory, 176 177 Single-subject design research methodology, 142 143 Situation responsiveness, 45 46 Skill-based assessments, 18 19 Slope discrepancy, 89 SNAP-IV, 44 Social and communication skills, ADHD, 38 39 Social anxiety disorder intervention, 209 Social functioning, ADHD, 38 39 Society of Clinical Child and Adolescent Psychology, 163 Special education, 126 133 clashing classification system, 127 128 common eligibility categories, 128 131 goals, reevaluation and review, 132 133 history and law, 126 127 individualized education program process, 131 132 “Special education lite”, 93 94 Specific learning disabilities (SLDs) and ADHD, 3 6, 77, 126 140 academic achievement battery, 129 cognitive assessment battery, 129 DSM-5 definition, 8 evaluations criteria, 128 129 extended time, 136 138 grades and group-standardized test results, 129 interview questions, 129 130 read aloud, 138 setting, 139 140 special education, 126 133 clashing classification system, 127 128 common eligibility categories, 128 131 goals, reevaluation and review, 132 133 history and law, 126 127 individualized education program process, 131 132 technological supports, 138 139 Specific reading comprehension difficulties (SRCDs), 106, 108, 117 Specific word recognition difficulties (SWRDs), 106 108, 116

Spelling, 13 SRCDs. See Specific reading comprehension difficulties (SRCDs) Standardized achievement test scores, 172 Story Grammar Marker, 114 115 Stress Index for Parents of Adolescents, 46 Student-directed learning strategies, 136 Students at risk, 82, 84, 86 87 Student’s intelligence, 78 Supporting Teen’s Autonomy Daily (STAND) program, 186 SWRDs. See Specific word recognition difficulties (SWRDs) Syntactic abilities, 13 Syntactic weaknesses, 109 Systematic teaching, 110 114, 111t

T Teaching student-study strategies, 136 Technological supports accommodation, 138 139 Tennessee Standards for Special Education Evaluation, 24 25 “Test-then-teach” approach, 11 Text composition, 105 Text-to-speech (TTS) read-aloud, 139 Timing/scheduling accommodations, 135 Total phenotypic variance, 64 Transcranial direct current stimulation (tDCS), 183

U Untimed writing measures, 24

V Vanderbilt ADHD Rating Scales, 44 Verbal and nonverbal reasoning, 201 Visual aids and manipulatives, 114 115 Visual spatial processing, 14 Vocabulary, 100 104, 108 interventions, 118 knowledge, 109

W Weaknesses models, 17 Wechsler Individual Achievement Test, 19 20, 46 “When 2 Then”/Premack’s principle, 153t

Index Woodcock 2 Johnson Test of Achievement, 10, 19 20 Word achievement scores, 13 attack skills, 20 families, 116 problems, 104 reading skills and decoding, 20 recognition, 13 Word identification fluency (WIF), 89 Word Reading and Pseudoword Decoding subtests, 20

231

Writing fluency, 24 Writing processes, 105 Written expression disabilities, 19 24, 101t, 105, 109 110 components, 101t, 105 deficiencies, 174 175 impacting spelling, 9 implications of profiles, 109 110 performance and growth in, 100 processes, 105 reading difficulties associated with, 107t teacher explicitly models, 110