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<p>A GUIDE TOASSESSMENTS THATWORK</p><p>1</p><p>A GUIDE TOASSESSMENTS</p><p>THATWORK</p><p>S e c o n d E di t i o n</p><p>EDITEDBY</p><p>John Hunsley and Eric J.Mash</p><p>1</p><p>Oxford University Press is a department of the University of Oxford. It furthers</p><p>the University’s objective of excellence in research, scholarship, and education</p><p>by publishing worldwide. Oxford is a registered trade mark of Oxford University</p><p>Press in the UK and certain other countries.</p><p>Published in the United States of America by Oxford UniversityPress</p><p>198 Madison Avenue, NewYork, NY 10016, United States of America.</p><p>© Oxford University Press2018</p><p>First Edition published in 2008</p><p>All rights reserved. No part of this publication may be reproduced, storedin</p><p>a retrieval system, or transmitted, in any form or by any means, withoutthe</p><p>prior permission in writing of Oxford University Press, or as expressly permitted</p><p>by law, by license, or under terms agreed with the appropriate reproduction</p><p>rights organization. Inquiries concerning reproduction outside the scopeofthe</p><p>above should be sent to the Rights Department, Oxford University Press,atthe</p><p>addressabove.</p><p>You must not circulate this work in any otherform</p><p>and you must impose this same condition on any acquirer.</p><p>CIP data is on file at the Library of Congress</p><p>ISBN 978– 0– 19– 049224– 3</p><p>1 3 5 7 9 8 6 4 2</p><p>Printed by Sheridan Books, Inc., United States of America</p><p>Contents</p><p>Foreword to the First Edition</p><p>by Peter E. Nathan vii</p><p>Preface xi</p><p>About the Editors xv</p><p>Contributors xvii</p><p>Part I Introduction</p><p>1. Developing Criteria for Evidence- Based</p><p>Assessment:An Introduction to Assessments</p><p>That Work 3</p><p>JOHN HUNSLEY</p><p>ERIC J.MASH</p><p>2. Dissemination and Implementation of</p><p>Evidence- Based Assessment 17</p><p>AMANDA JENSEN- DOSS</p><p>LUCIAM.WALSH</p><p>VANESA MORA RINGLE</p><p>3. Advances in Evidence- Based Assessment:</p><p>Using Assessment to Improve Clinical</p><p>Interventions and Outcomes 32</p><p>ERIC A. YOUNGSTROM</p><p>ANNA VANMETER</p><p>Part II Attention- Deficit and Disruptive</p><p>Behavior Disorders</p><p>4. Attention- Deficit/ Hyperactivity Disorder 47</p><p>CHARLOTTE JOHNSTON</p><p>SARA COLALILLO</p><p>5. Child and Adolescent Conduct Problems 71</p><p>PAUL J.FRICK</p><p>ROBERT J. McMAHON</p><p>Part III Mood Disorders and Self- Injury</p><p>6. Depression in Children and Adolescents 99</p><p>LEA R. DOUGHERTY</p><p>DANIEL N.KLEIN</p><p>THOMAS M.OLINO</p><p>7. Adult Depression 131</p><p>JACQUELINE B. PERSONS</p><p>DAVID M.FRESCO</p><p>JULIET SMALLERNST</p><p>8. Depression in Late Life 152</p><p>AMYFISKE</p><p>ALISA O’RILEY HANNUM</p><p>9. Bipolar Disorder 173</p><p>SHERI L. JOHNSON</p><p>CHRISTOPHERMILLER</p><p>LORIEISNER</p><p>vi CONTENTS</p><p>10. Self- Injurious Thoughts and Behaviors 193</p><p>ALEXANDER J. MILLNER</p><p>MATTHEW K.NOCK</p><p>Part IV Anxiety and Related Disorders</p><p>11. Anxiety Disorders in Children and</p><p>Adolescents 217</p><p>SIMON P.BYRNE</p><p>ELI R. LEBOWITZ</p><p>THOMAS H. OLLENDICK</p><p>WENDY K. SILVERMAN</p><p>12. Specific Phobia and Social Anxiety</p><p>Disorder 242</p><p>KARENROWA</p><p>RANDI E.MCCABE</p><p>MARTIN M.ANTONY</p><p>13. Panic Disorder and Agoraphobia 266</p><p>AMY R.SEWART</p><p>MICHELLE G.CRASKE</p><p>14. Generalized Anxiety Disorder 293</p><p>MICHEL J.DUGAS</p><p>CATHERINE A. CHARETTE</p><p>NICOLE J. GERVAIS</p><p>15. Obsessive– Compulsive Disorder 311</p><p>SHANNON M.BLAKEY</p><p>JONATHAN S. ABRAMOWITZ</p><p>16. Post- Traumatic Stress Disorder in Adults 329</p><p>SAMANTHA J. MOSHIER</p><p>KELLY S. PARKER- GUILBERT</p><p>BRIAN P.MARX</p><p>TERENCE M.KEANE</p><p>Part V Substance- Related and Gambling</p><p>Disorders</p><p>17. Substance Use Disorders 359</p><p>DAMARIS J. ROHSENOW</p><p>18. Alcohol Use Disorder 381</p><p>ANGELA M.HAENY</p><p>CASSANDRA L.BONESS</p><p>YOANNA E. McDOWELL</p><p>KENNETH J.SHER</p><p>19. Gambling Disorders 412</p><p>DAVID C. HODGINS</p><p>JENNIFER L.SWAN</p><p>RANDY STINCHFIELD</p><p>Part VI Schizophrenia and Personality</p><p>Disorders</p><p>20. Schizophrenia 435</p><p>SHIRLEY M.GLYNN</p><p>KIM T.MUESER</p><p>21. Personality Disorders 464</p><p>STEPHANIE L.ROJAS</p><p>THOMAS A. WIDIGER</p><p>Part VII Couple Distress and Sexual</p><p>Disorders</p><p>22. Couple Distress 489</p><p>DOUGLAS K.SNYDER</p><p>RICHARD E.HEYMAN</p><p>STEPHEN N.HAYNES</p><p>CHRISTINA BALDERRAMA- DURBIN</p><p>23. Sexual Dysfunction 515</p><p>NATALIE O.ROSEN</p><p>MARIA GLOWACKA</p><p>MARTAMEANA</p><p>YITZCHAK M.BINIK</p><p>Part VIII Health- Related Problems</p><p>24. Eating Disorders 541</p><p>ROBYNSYSKO</p><p>SARAALAVI</p><p>25. Insomnia Disorder 563</p><p>CHARLES M.MORIN</p><p>SIMON BEAULIEU- BONNEAU</p><p>KRISTINMAICH</p><p>COLLEEN E.CARNEY</p><p>26. Child and Adolescent Pain 583</p><p>C. MEGHAN McMURTRY</p><p>PATRICK J. McGRATH</p><p>27. Chronic Pain in Adults 608</p><p>THOMAS HADJISTAVROPOULOS</p><p>NATASHA L. GALLANT</p><p>MICHELLEM. GAGNON</p><p>Assessment Instrument Index 629</p><p>Author Index 639</p><p>Subject Index 721</p><p>Foreword tothe First Edition</p><p>I believe A Guide to Assessments that Work is the right</p><p>book at the right time by the right editors and authors.</p><p>The mental health professions have been intensively</p><p>engaged for a decade and a half and more in establish-</p><p>ing empirically supported treatments. This effort has led</p><p>to the publication of evidence- based treatment guidelines</p><p>by both the principal mental health professions, clinical</p><p>psychology (Chambless & Ollendick, 2001; Division 12</p><p>Task Force, 1995), and psychiatry (American Psychiatric</p><p>Association, 1993, 2006). Asubstantial number of books</p><p>and articles on evidence- based treatments have also</p><p>appeared. Notable among them is a series by Oxford</p><p>University Press, the publishers of A Guide to Assessments</p><p>that Work, which began with the first edition of A Guide</p><p>to Treatments that Work (Nathan & Gorman, 1998), now</p><p>in its third edition, and the series includes Psychotherapy</p><p>Relationships that Work (Norcross, 2002)and Principles</p><p>of Therapeutic Change that Work (Castonguay &</p><p>Beutler,2006).</p><p>Now we have an entire volume given over to evidence-</p><p>based assessment. It doesn’t appear de novo. Over the</p><p>past several years, its editors and like- minded colleagues</p><p>tested and evaluated an extensive series of guidelines for</p><p>evidence- based assessments for both adults and children</p><p>(e.g., Hunsley & Mash, 2005; Mash & Hunsley, 2005).</p><p>Many of this book’s chapter authors participated in these</p><p>efforts. It might well be said, then, that John Hunsley, Eric</p><p>Mash, and the chapter authors in A Guide to Assessments</p><p>that Work are the right editors and authors for this, the first</p><p>book to detail the assessment evidencebase.</p><p>There is also much to admire within the pages of the</p><p>volume. Each chapter follows a common format pre-</p><p>scribed by the editors and designed, as they point out,</p><p>“to enhance the accessibility of the material presented</p><p>throughout the book.” First, the chapters are syndrome-</p><p>focused, making it easy for clinicians who want help in</p><p>assessing their patients to refer to the appropriate chapter</p><p>or chapters. When they do so, they will find reviews of the</p><p>assessment literature for three distinct purposes:diagno-</p><p>sis, treatment planning, and treatment monitoring. Each</p><p>of these reviews is subjected to a rigorous rating system</p><p>that culminates in an overall evaluation of “the scientific</p><p>adequacy and clinical relevance of currently available</p><p>measures.” The chapters conclude with an overall assess-</p><p>ment of the limits of the assessments available for the syn-</p><p>drome in question, along with suggestions for future steps</p><p>to confront them. Ibelieve it can well be said, then, that</p><p>this is the right book by the right editors and authors.</p><p>But is this the right time for this book? Evidence- based</p><p>treatments have been a focus of intense professional atten-</p><p>tion for many years. Why wouldn’t the right time for this</p><p>book have been several years ago rather than now, to</p><p>coincide with the development of empirically supported</p><p>treatments? The answer, Ithink, reflects the surprisingly</p><p>brief history of the evidence- based medical practice move-</p><p>ment. Despite lengthy concern for the efficacy of treat-</p><p>ments for mental disorders that dates back more than</p><p>50years (e.g., Eysenck, 1952; Lambert & Bergin, 1994;</p><p>Luborsky, Singer, & Luborsky, 1976; Nathan, Stuart, &</p><p>Dolan, 2000), it took the appearance of a Journal of the</p><p>viii FOREWORD TO THE FIRST EDITION</p><p>American Mental Association</p><p>characteristics (Achenbach, 2001;</p><p>Wasserman & Bracken, 2013). Ideally, whether con-</p><p>ducted at the national level or the local level, this would</p><p>involve probability- sampling efforts in which data are</p><p>obtained from the majority of contacted respondents. As</p><p>those familiar with psychological instruments are aware,</p><p>such a sampling strategy is rarely used for the devel-</p><p>opment of test norms. The reliance on data collected</p><p>from convenience samples with unknown response rates</p><p>reduces the accuracy of the resultant norms. Therefore,</p><p>at a minimum, clinicians need to be provided with</p><p>an indication of the quality and likely accuracy of the</p><p>norms for a measure. Accordingly, the ratings for norms</p><p>required, at a minimum for a rating of adequate, data</p><p>from a single, large clinical sample. For a rating of good,</p><p>normative data from multiple samples, including non-</p><p>clinical samples, were required; when normative data</p><p>from large, representative samples were available, a rat-</p><p>ing of excellent was applied.</p><p>Reliability</p><p>reliability is a key psychometric element to be considered</p><p>in evaluating an instrument. It refers to the consistency of</p><p>a person’s score on a measure (Anastasi, 1988; Wasserman</p><p>& Bracken, 2013), including whether (a) all elements</p><p>of a measure contribute in a consistent way to the data</p><p>obtained (internal consistency), (b)similar results would</p><p>be obtained if the measure was used or scored by another</p><p>clinician (inter- rater reliability),1 or (c) similar results</p><p>would be obtained if the person completed the measure a</p><p>second time (test– retest reliability or test stability). Not all</p><p>reliability indices are relevant to all assessment methods</p><p>and measures, and the size of the indices may vary on the</p><p>basis of the samplesused.</p><p>Despite the long- standing recognition of the central-</p><p>ity of reliability to all forms of psychological measure-</p><p>ment, there is a persistent tendency in psychological</p><p>research to make unwarranted assumptions about reli-</p><p>ability. For example, numerous reviews have found that</p><p>almost three- fourths of research articles failed to provide</p><p>information on the reliability estimates of the measures</p><p>completed by participants in the studies (e.g., Barry,</p><p>Chaney, Piazza- Gardner, & Chavarria, 2014; Vacha-</p><p>Haase & Thompson, 2011). Inattention to reliability, or</p><p>the use of an inappropriate statistic to estimate reliability,</p><p>has the potential to undermine the validity of conclu-</p><p>sions drawn from research studies. Concerns have been</p><p>BOX 1.2 Continued</p><p>TREATMENT SENSITIVITY</p><p>Adequate = Some evidence of sensitivity to change</p><p>over the course of treatment.</p><p>Good=Preponderance of independently replicated</p><p>evidence indicates sensitivity to change over the</p><p>course of treatment.</p><p>Excellent=In addition to the criteria used for a good</p><p>rating, evidence of sensitivity to change across dif-</p><p>ferent types of treatments.</p><p>CLINICAL UTILITY</p><p>Adequate = Taking into account practical consid-</p><p>erations (e.g., costs, ease of administration, avail-</p><p>ability of administration and scoring instructions,</p><p>duration of assessment, availability of relevant</p><p>cutoff scores, and acceptability to clients), the</p><p>resulting assessment data are likely to be clini-</p><p>cally useful.</p><p>Good = In addition to the criteria used for an</p><p>adequate rating, there is some published evi-</p><p>dence that the use of the resulting assessment</p><p>data confers a demonstrable clinical benefit</p><p>(e.g., better treatment outcome, lower treatment</p><p>attrition rates, and greater client satisfaction with</p><p>services).</p><p>Excellent = In addition to the criteria used for an</p><p>adequate rating, there is independently replicated</p><p>published evidence that the use of the resulting</p><p>assessment data confers a demonstrable clinical</p><p>benefit.</p><p>10 INTroDuCTIoN</p><p>raised about the impact of these errors in a broad range of</p><p>research domains, including communication (Feng, 2015),</p><p>psychopathology (rodebaugh et al., 2016), and clinical</p><p>diagnosis (Chmielewski, Clark, Bagby, & Watson, 2015).</p><p>As emphasized throughout this volume, a careful consider-</p><p>ation of reliability values is essential when selecting assess-</p><p>ment instruments for clinical services or clinical research.</p><p>With respect to internal consistency, we focused on</p><p>coefficient alpha (α), which is the most widely used index</p><p>(Streiner, 2003). Although there have been repeated</p><p>calls to abandon the use of coefficient α in favor of more</p><p>robust and accurate alternatives (e.g., Dunn, Baguley,</p><p>& Brunsden, 2014; Kelley & Pornprasertmanit, 2016),</p><p>it is rare to find an internal consistency coefficient</p><p>other than α used in the clinical assessment literature.</p><p>recommendations in the literature for what constitutes</p><p>adequate internal consistency vary, but most authorities</p><p>seem to view .70 as the minimum acceptable value for α</p><p>(e.g., Cicchetti, 1994), and Charter (2003) reported that</p><p>the mean internal consistency value among commonly</p><p>used clinical instruments was .81. Accordingly, a rating of</p><p>adequate was given to values of .70– .79; a rating of good</p><p>required values of .80– .89; and, finally, because of cogent</p><p>arguments that an α value of at least .90 is highly desirable</p><p>in clinical assessment contexts (Nunnally & Bernstein,</p><p>1994), we required values ≥ .90 for an instrument to be</p><p>rated as having excellent internal consistency. Note that it</p><p>is possible for α to be too (artificially) high, as a value close</p><p>to unity typically indicates substantial redundancy among</p><p>items (cf. Streiner,2003).</p><p>These value ranges were also used in rating evidence</p><p>for inter- rater reliability when assessed with Pearson corre-</p><p>lations or intraclass correlations. Appropriate adjustments</p><p>were made to the value ranges when κ statistics were used,</p><p>in line with the recommendations discussed by Cicchetti</p><p>(1994; see also Charter, 2003). Note that although a num-</p><p>ber of statistics are superior to κ, it continues to be the</p><p>most commonly used inter- rater reliability statistic (Xu</p><p>& Lorber, 2014). Importantly, evidence for inter- rater</p><p>reliability could only come from data generated among</p><p>clinicians or clinical raters— estimates of cross- informant</p><p>agreement, such as between parent and teacher ratings,</p><p>are not indicators of reliability.</p><p>In establishing ratings for test– retest reliability values,</p><p>our requirement for a minimum correlation of .70 was</p><p>influenced by summary data reported on typical test–</p><p>retest reliability results found with clinical instruments</p><p>(Charter, 2003) and trait- like psychological measures</p><p>(Watson, 2004). of course, not all constructs or measures</p><p>are expected to show temporal stability (e.g., measures</p><p>of state- like variables and life stress inventories), so test–</p><p>retest reliability was only rated if it was relevant. Arating</p><p>of adequate required evidence of correlation values of .70</p><p>or greater, when reliability was assessed over a period of</p><p>several days to several weeks. We then faced a challenge</p><p>in determining appropriate criteria for good and excellent</p><p>ratings. In order to enhance its likely usefulness, the rating</p><p>system should be relatively simple. However, test– retest</p><p>reliability is a complex phenomenon that is influenced by</p><p>(a)the nature of the construct being assessed (i.e., it can be</p><p>state- like, trait- like, or influenced by situational variables),</p><p>(b)the time frame covering the reporting period instruc-</p><p>tions (i.e., whether respondents are asked to report their</p><p>current functioning, functioning over the past few days,</p><p>or functioning over an extended period, such as general</p><p>functioning in the past year), and (c)the duration of the</p><p>retest period (i.e., whether the time between two admin-</p><p>istrations of the instrument involved days, weeks, months,</p><p>or years). In the end, rather than emphasize the value of</p><p>increasingly large test– retest correlations, we decided to</p><p>maintain the requirement for .70 or greater correlation</p><p>values but require increasing retest period durations of</p><p>(a) several months and (b) at least 1 year for ratings of</p><p>good and excellent, respectively.</p><p>Validity</p><p>Validity is another central aspect to be considered when</p><p>evaluating psychometric properties. recent editions of</p><p>the Standards for Educational and Psychological Testing</p><p>(American Educational research Association, American</p><p>Psychological Association, & National Council on</p><p>Measurement in Education, 1999, 2014)explicitly state</p><p>that validity is a unitary concept and that it is not appro-</p><p>priate to consider different types of validity. Despite these</p><p>admonitions, research on validity continues to use con-</p><p>cepts such as content validity, predictive validity, and</p><p>incremental validity. Setting aside the wide range of con-</p><p>ceptual and practical issues associated with the lack of</p><p>consensus on the framing of test validity (for a detailed</p><p>discussion, see Newton & Shaw, 2013), there is a very</p><p>simple reason for incorporating several types of validity</p><p>into the rating system used in this book:The vast majority</p><p>of the literature on clinical assessment, both historically</p><p>and currently, does not treat validity as a unitary concept</p><p>(cf. Strauss & Smith, 2009). To strike a balance between</p><p>the unitary approach advocated by the Standards and</p><p>the multiplicity of validity types used in the literature,</p><p>we focused on content validity, construct validity, validity</p><p>generalization, and treatment sensitivity. In the following</p><p>DEVELoPING CrITErIA For EVIDENCE-BASED ASSESSMENT 11</p><p>paragraphs, we explain further the rationale for our use of</p><p>four types of validity. As is readily apparent by reviewing</p><p>the summary tables of instruments in the following chap-</p><p>ters, the extent and strength of research evidence across</p><p>these types of validity can vary substantially for a given</p><p>assessment instrument.</p><p>Foster and Cone (1995) drew an important distinc-</p><p>tion between “representational” validity (i.e., whether a</p><p>measure really assesses what it purports to measure) and</p><p>“elaborative” validity (i.e., whether the measure has any</p><p>utility for measuring the construct). Attending to the</p><p>content validity of a measure is a basic, but frequently</p><p>overlooked, step in evaluating representational valid-</p><p>ity (Haynes, richard, & Kubany, 1995). As discussed by</p><p>Smith, Fischer, and Fister (2003), the overall reliability</p><p>and validity of scores on an instrument is directly affected</p><p>by the extent to which items in the instrument adequately</p><p>represent the various aspects or facets of the construct the</p><p>instrument is designed to measure. Assuming that repre-</p><p>sentational validity has been established for an assessment</p><p>purpose, it is elaborative validity that is central to clini-</p><p>cians’ use of a measure. Accordingly, replicated evidence</p><p>for a measure’s concurrent, predictive, discriminative,</p><p>and, ideally, incremental validity (Hunsley & Meyer,</p><p>2003) should be available to qualify a measure for con-</p><p>sideration as evidence based. We have indicated already</p><p>that validation is a context- sensitive concept— inattention</p><p>to this fact can lead to inappropriate generalizations being</p><p>made about a measure’s validity. Thus, there should be</p><p>replicated elaborative validity evidence for each purpose</p><p>of the measure and for each population or group for which</p><p>the measure is intended to be used. This latter point is</p><p>especially relevant when considering an instrument for</p><p>clinical use, and thus it is essential to consider evidence</p><p>for validity generalization— that is, the extent to which</p><p>there is evidence for validity across a range of samples and</p><p>settings (cf. Messick, 1995; Schmidt & Hunter,1977).</p><p>In the rating system used in subsequent chapters, rat-</p><p>ings of content validity evidence required explicit consider-</p><p>ation of the construct facets to be included in the measure</p><p>and, as the ratings increased, involvement of content</p><p>validity judges to assess the measure (Haynes etal., 1995).</p><p>unlike the situation for reliability, there are no commonly</p><p>accepted summary statistics to evaluate construct validity</p><p>(but see Markon [2013] and Westen & rosenthal [2003]).</p><p>As a result, our ratings were based on the requirement of</p><p>increasing amounts of replicated evidence of elements of</p><p>construct validity such as predictive validity, concurrent</p><p>validity, convergent validity, and discriminant validity; in</p><p>addition, for a rating of excellent, evidence of incremental</p><p>validity was also required. As was the case when we intro-</p><p>duced the rating system in the first edition of this book, we</p><p>were unable to find any clearly applicable standards in the</p><p>literature to guide us in developing criteria for validity gen-</p><p>eralization or treatment sensitivity (a dimension rated only</p><p>for instruments used for the purposes of treatment moni-</p><p>toring and treatment evaluation). Therefore, adequate</p><p>ratings for these dimensions required some evidence of,</p><p>respectively, the use of the instrument with either more</p><p>than one specific group or in multiple contexts and evi-</p><p>dence of sensitivity to change over the course of treatment.</p><p>Consistent with ratings for other dimensions, good and</p><p>excellent ratings required increasingly demanding levels</p><p>of evidence in theseareas.</p><p>Utility</p><p>It is also essential to know the utility of an instrument for</p><p>a specific clinical purpose. The concept of clinical util-</p><p>ity, applied to both diagnostic systems (e.g., Keeley et al.,</p><p>2016; Kendell & Jablensky, 2003; Mullins- Sweatt, Lengel,</p><p>& DeShong, 2016)and assessment tools (e.g., di ruffano,</p><p>Hyde, McCaffery, & Bossuyt, 2012; Yates & Taub, 2003),</p><p>has received increasing attention in recent years. Although</p><p>definitions vary, they have in common an emphasis on gar-</p><p>nering evidence regarding actual improvements in both</p><p>decisions made by clinicians and service outcomes experi-</p><p>enced by clients. unfortunately, despite thousands of studies</p><p>on the reliability and validity of psychological instruments,</p><p>there is only scant attention paid to matters of utility in most</p><p>assessment research studies (McGrath, 2001). This has</p><p>directly contributed to the current state of affairs in which</p><p>there is very little replicated evidence that psychological</p><p>assessment data have a direct impact on improved provision</p><p>and outcome of clinical services. Currently, therefore, for</p><p>the majority of psychological instruments, a determination</p><p>of clinical utility must often be made on the basis of likely</p><p>clinical value rather than on empirical evidence.</p><p>Compared to the criteria for the psychometric dimen-</p><p>sions presented thus far, our standards for evidence of</p><p>clinical utility were noticeably less demanding. This was</p><p>necessary because of the paucity of information on the</p><p>extent to which assessment instruments are acceptable</p><p>to clients, enhance the quality and outcome of clinical</p><p>services, and/ or are worth the costs associated with their</p><p>use. Therefore, we relied on authors’ expert opinions to</p><p>classify an instrument as having adequate clinical utility.</p><p>The availability of any supporting evidence of utility was</p><p>sufficient for a rating of good, and replicated evidence of</p><p>utility was necessary for a rating of excellent.</p><p>12 INTroDuCTIoN</p><p>The instrument summary tables also contain one final</p><p>column, used to indicate instruments that are the best</p><p>measures currently available to clinicians for specific pur-</p><p>poses and disorders and, thus, are highly recommended</p><p>for clinical use. Given the considerable differences in</p><p>the state of the assessment literature for different disor-</p><p>ders/ conditions, chapter authors had some flexibility in</p><p>determining their own precise requirements for an instru-</p><p>ment to be rated, or not rated, as highly recommended.</p><p>However, to ensure a moderate level of consistency in</p><p>these ratings, a highly recommended rating could only be</p><p>considered for those instruments having achieved ratings</p><p>of good or excellent in the majority of its rated psycho-</p><p>metric categories. Although not required in our system, if</p><p>several instruments had comparable psychometric merits</p><p>for a given assessment purpose, some chapter authors con-</p><p>sidered the</p><p>cost and availability of an assessment instru-</p><p>ment when making this recommendation (see also Beidas</p><p>etal.,2015).</p><p>SOME FINAL THOUGHTS</p><p>We are hopeful that the rating system described in this</p><p>chapter, and applied in each of the chapters of this book,</p><p>will continue to aid in advancing the state of evidence-</p><p>based psychological assessment. We also hope that it will</p><p>serve as a stimulus for others to refine and improve upon</p><p>our efforts (cf. Jensen- Doss, 2011; Youngstrom et al., in</p><p>press). Whatever the possible merits of the rating system,</p><p>we close this chapter by drawing attention to three critical</p><p>issues related to itsuse.</p><p>First, although the rating system used for this volume</p><p>is relatively simple, the task of rating psychometric proper-</p><p>ties is not. results from many studies must be considered</p><p>in making such ratings, and precise quantitative standards</p><p>were not set for how to weight the results from studies.</p><p>Furthermore, in the spirit of evidence- based practice, it</p><p>is also important to note that we do not know whether</p><p>these ratings are, themselves, reliable. reliance on indi-</p><p>vidual expert judgment, no matter how extensive and</p><p>current the knowledge of the experts, is not as desirable</p><p>as basing evidence- based conclusions and guidance on</p><p>systematic reviews of the literature conducted according</p><p>to a consensually agreed upon rating system. However,</p><p>for all the potential limitations and biases inherent in our</p><p>approach, reliance on expert review of the scientific lit-</p><p>erature is the current standard in psychology and, thus,</p><p>was the only feasible option for the volume at this time.</p><p>For information on important developments on rating</p><p>systems used in many areas of health care research, the</p><p>interested reader can consult the website of the Grading</p><p>of recommendations Assessment, Development and</p><p>Evaluation (GrADE) working group (http:// www.grade-</p><p>workinggroup.org).</p><p>The second issue has to do with the responsible clini-</p><p>cal use of the guidance provided by the rating system.</p><p>Consistent with evaluation and grading strategies used</p><p>through evidence- based medicine and evidence- based</p><p>psychology initiatives, many of our rating criteria relied</p><p>on the consideration of the preponderance of data rel-</p><p>evant to each dimension. Such a strategy recognizes both</p><p>the importance of replication in science and the fact</p><p>that variability across studies in research design elements</p><p>(including sample composition and research setting) will</p><p>influence estimates of these psychometric dimensions.</p><p>However, we hasten to emphasize that reliance on the</p><p>preponderance of evidence for these ratings does not</p><p>imply or guarantee that an instrument is applicable for all</p><p>clients or clinical settings. our intention is to have these</p><p>ratings provide indications about scientifically strong mea-</p><p>sures that warrant consideration for clinical and research</p><p>use. As with all evidence- based efforts, the responsibility</p><p>rests with the individual professional to determine the</p><p>suitability of an instrument for the specific setting, pur-</p><p>pose, and individuals to be assessed.</p><p>Third, as emphasized throughout this volume, focus-</p><p>ing on the scientific evidence for specific assessment tools</p><p>should not overshadow the fact that the process of clinical</p><p>assessment involves much more than simply selecting and</p><p>administering the best available instruments. Choosing</p><p>the best, most relevant, instruments is unquestionably an</p><p>important step. Subsequent steps must ensure that the</p><p>instruments are administered in an appropriate manner,</p><p>accurately scored, and then individually interpreted in</p><p>accordance with the relevant body of scientific research.</p><p>However, to ensure a truly evidence- based approach to</p><p>assessment, the major challenge is to then integrate all the</p><p>data within a process that is, itself, evidence- based. This</p><p>will likely require both (a)a reframing of the assessment</p><p>process within the larger health and social system context</p><p>in which it occurs and (b) the use of new technologies</p><p>to enable complex decision- making and integration of</p><p>large amounts of assessment information in both tradi-</p><p>tional and nontraditional health service delivery settings</p><p>(Chorpita, Daleiden, & Bernstein, 2015). Much of our</p><p>focus in this chapter has been on evidence- based meth-</p><p>ods and instruments, in large part because (a) methods</p><p>http://www.gradeworkinggroup.org</p><p>http://www.gradeworkinggroup.org</p><p>DEVELoPING CrITErIA For EVIDENCE-BASED ASSESSMENT 13</p><p>and specific measures are more easily identified than are</p><p>processes and (b)the main emphasis in the assessment lit-</p><p>erature has been on psychometric properties of methods</p><p>and instruments. As we indicated early in the chapter, an</p><p>evidence- based approach to assessment should be devel-</p><p>oped in light of evidence on the accuracy and usefulness</p><p>of this complex, iterative decision- making task. 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Assessing the costs, ben-</p><p>efits, cost- effectiveness, and cost– benefit of psycho-</p><p>logical assessment:We should, we can, and here’s how.</p><p>Psychological Assessment, 15, 478– 495.</p><p>Youngstrom, E. A., Choukas- Bradley, S., Calhoun, C. D., &</p><p>Jensen- Doss, A. (2015). Clinical guide to the evidence-</p><p>based approach to diagnosis and treatment. Cognitive</p><p>and Behavioral Practice, 22,20– 35.</p><p>Youngstrom, E. A., & Van Meter, A. (2016). Empirically sup-</p><p>ported assessment of children and adolescents. Clinical</p><p>Psychology:Science and Practice, 23, 327– 347.</p><p>Youngstrom, E. A., Van Meter, A., Frazier, T. W., Hunsley,</p><p>J., Prinstein, M. J., ong, M.- L., & Youngstrom, J. K.</p><p>(in press). Evidence- based assessment as an integrative</p><p>model for applying psychological science to guide the</p><p>voyage of treatment. Clinical Psychology: Science and</p><p>Practice.</p><p>17</p><p>17</p><p>2</p><p>Dissemination and Implementation</p><p>ofEvidence- Based Assessment</p><p>Amanda Jensen- Doss</p><p>Lucia M. Walsh</p><p>Vanesa Mora Ringle</p><p>During the past two decades, there has been a major</p><p>push to increase the use of evidence- based practices in</p><p>clinical settings. The American Psychological Association</p><p>Presidential Task Force on Evidence- Based Practice</p><p>(2006) defines evidence- based practice in psychology</p><p>(EBPP) as “the integration of the best available research</p><p>with clinical expertise in the context of patient character-</p><p>istics, culture, and preferences” (p. 271) and states that</p><p>the goal of EBPP is to improve public health through</p><p>the application of research- supported assessment, case</p><p>formulation, therapeutic relationship, and treatment</p><p>approaches. Although EBPP is defined broadly, many</p><p>efforts to improve practice have focused on treatment,</p><p>with less attention paid to other aspects of practice. There</p><p>is a particular need to focus on increasing use of evidence-</p><p>based</p><p>assessment (EBA), as assessment cuts across all of</p><p>these other areas of practice. For example, assessment</p><p>results should form the foundation of case conceptualiza-</p><p>tion; inform decisions about which treatments to use; and</p><p>provide data about whether treatment is working, whether</p><p>therapy alliance is strong, and when to end treatment.</p><p>There are several reasons why a focus on EBA will</p><p>increase the likelihood that EBPP will lead to improved</p><p>public health. First, EBA can improve the accuracy of</p><p>diagnoses, which is one important component of case</p><p>conceptualization and treatment selection (Christon,</p><p>McLeod, & Jensen- Doss, 2015). Research linking diag-</p><p>nostic accuracy to improved treatment engagement and</p><p>outcomes (Jensen- Doss & Weisz, 2008; Klein, Lavigne,</p><p>& Seshadri, 2010; Kramer, Robbins, Phillips, Miller, &</p><p>Burns, 2003; Pogge et al., 2001) suggests that improved</p><p>diagnostic assessment could have a positive effect across</p><p>the treatment process. As detailed throughout this book,</p><p>evidence- based diagnostic assessment typically involves</p><p>the use of structured diagnostic interviews or rating scales.</p><p>Studies have demonstrated that when clinicians use</p><p>structured diagnostic interviews, they assign more accu-</p><p>rate diagnoses (Basco etal., 2000), better capture comor-</p><p>bidities (Matuschek et al., 2016), assign more specific</p><p>diagnoses (Matuschek et al., 2016), reduce psychiatrist</p><p>evaluation time (Hughes etal., 2005), and decrease the</p><p>likelihood that a psychiatrist will increase a patient’s medi-</p><p>cation dose (Kashner etal.,2003).</p><p>Using EBA for progress monitoring can support clinical</p><p>judgment by creating an ongoing feedback loop to support</p><p>ongoing case conceptualization (Christon etal., 2015)and,</p><p>if data suggest clients are at risk for treatment failure, revise</p><p>the treatment plan (Claiborn & Goodyear, 2005; Lambert,</p><p>Hansen, & Finch, 2001; Riemer, Rosof- Williams, & Bickman,</p><p>2005). Gold standard progress monitoring of this nature typi-</p><p>cally involves administering rating scales every session or two</p><p>and then incorporating the feedback into clinical decisions.</p><p>This differs from what we refer to here as “outcome monitor-</p><p>ing,” or administering outcome measures before and after</p><p>treatment to determine treatment effectiveness. Although use-</p><p>ful for many purposes, this type of outcome monitoring does</p><p>not support clinical decision- making during service provision.</p><p>Several monitoring and feedback systems (MFSs) have</p><p>been developed to support ongoing progress monitoring;</p><p>they typically include a battery of progress measures and</p><p>18 InTRoDUCTIon</p><p>18</p><p>generate feedback reports that often include warnings</p><p>if a client is not on track for positive outcomes (Lyon,</p><p>Lewis, Boyd, Hendrix, & Liu, 2016). Extensive research</p><p>with adult clients suggests that clinician use of MFSs can</p><p>improve outcomes, particularly for those “not on track”</p><p>for positive outcomes (Krägeloh, Czuba, Billington,</p><p>Kersten, & Siegert, 2015; Shimokawa, Lambert, & Smart,</p><p>2010); similar results have been found for youth clients</p><p>(Bickman, Kelley, Breda, De Andrade, & Riemer, 2011;</p><p>Stein, Kogan, Hutchison, Magee, & Sorbero, 2010),</p><p>although effects vary based on organizational support for</p><p>progress monitoring (Bickman etal.,2016).</p><p>The purpose of this chapter is to make the case that</p><p>significant work is needed to encourage the dissemina-</p><p>tion of information about EBA and its implementation in</p><p>clinical practice. First, we discuss “assessment as usual,”</p><p>how it differs from EBA, and reasons for these differences.</p><p>Then, we describe efforts to increase use of EBA through</p><p>dissemination and implementation efforts. Finally, we</p><p>present some ideas for future work needed to further</p><p>advance the use of EBA. Consistent with the other chap-</p><p>ters in this book, we focus on assessment of psychopathol-</p><p>ogy and its application to clinical diagnosis and progress</p><p>monitoring. Although similar issues likely exist for other</p><p>forms of assessment, such as psychoeducational assess-</p><p>ment, discussion of those is beyond the scope of thisbook.</p><p>Finally, although assessment tools to support case con-</p><p>ceptualization are described in the subsequent chapters</p><p>of this book, most of the literature studying clinician case</p><p>conceptualization practices and how to improve them has</p><p>focused on whether clinicians can apply specific theoreti-</p><p>cal models to client data and the validity of case concep-</p><p>tualizations (e.g., Abbas, Walton, Johnston, & Chikoore,</p><p>2012; Flinn, Braham, & das nair, 2015; Persons &</p><p>Bertagnolli, 1999)rather than on how to collect and inte-</p><p>grate EBA data to generate a case conceptualization. As</p><p>mentioned previously, both diagnostic assessment and</p><p>progress monitoring can support case conceptualiza-</p><p>tion; therefore, much of the literature we discuss here</p><p>has implications for case conceptualization. However, in</p><p>the Future Directions section, we address steps needed to</p><p>advance assessment- based case conceptualization.</p><p>IS THERE A RESEARCH– PRACTICE</p><p>GAPINASSESSMENT?</p><p>Despite the proliferation of excellent assessment tools,</p><p>available data suggest there are significant training and</p><p>practice gaps in both diagnostic assessment and progress</p><p>monitoring. As discussed in the following sections, these</p><p>gaps have important implications for the accuracy of</p><p>clinician- generated diagnoses and the accuracy of clini-</p><p>cian judgments about treatment progress.</p><p>Research– Practice Gaps inDiagnostic Assessment</p><p>As detailed throughout the chapters in this book,</p><p>evidence- based diagnostic assessment for most disorders</p><p>relies on standardized diagnostic interviews and/ or rat-</p><p>ing scales. Unfortunately, surveys of training programs</p><p>suggest that clinicians are not being prepared to conduct</p><p>these assessments during their graduate training. Several</p><p>surveys of psychology programs have been conducted in</p><p>the past three decades (e.g., Belter & Piotrowski, 2001;</p><p>Childs & Eyde, 2002), with the two most recent (Mihura,</p><p>Roy, & Graceffo, 2016; Ready & Veague, 2014)finding</p><p>that training in assessment has generally remained con-</p><p>stant, but there has been an increase in training focused</p><p>on assessment of treatment outcomes, psychometrics,</p><p>and neuropsychology. However, these two studies found</p><p>inconsistent results regarding the use of clinical inter-</p><p>viewing. Ready and Veague reported only half to three-</p><p>fourth of programs included clinical interviewing as a</p><p>focus of training. However, Mihura etal. found that 92%</p><p>of programs queried included clinical interviewing as a</p><p>required topic. These differences might reflect a change</p><p>during the 3 years that passed between the two studies.</p><p>However, it is also likely that the two studies also obtained</p><p>information from different programs, as Mihura and col-</p><p>leagues included more programs than Ready and Veague</p><p>and each study only obtained data from approximately</p><p>one- third of all of the American Psychological Association</p><p>(APA)- accredited programs.</p><p>Two studies have examined training in diagnostic</p><p>assessment specifically. Ponniah et al. (2011) surveyed</p><p>clinical training directors from social work, clinical psy-</p><p>chology PhD and PsyD, and psychiatric residency pro-</p><p>grams regarding training in structured assessment based</p><p>on Diagnostic and Statistical Manual of Mental Disorders</p><p>(DSM) criteria. only one- third of surveyed programs</p><p>reported providing both didactics and supervision in</p><p>diagnostic assessment, with clinical psychology PhD and</p><p>psychiatry residency programs being most likely to do so</p><p>and social work programs the least likely. These results are</p><p>concerning because master’s level clinicians represent the</p><p>majority of those providing services to those with mental</p><p>health disorders in the United States (Garland, Bickman,</p><p>& Chorpita, 2010). Focusing on clinical psychology PhD</p><p>and PsyD programs exclusively, Mihura et al. (2016)</p><p>DISSEMInATIon AnD IMPLEMEnTATIon oF EVIDEnCE-BASED ASSESSMEnT 19</p><p>19</p><p>found that less than half of the programs required a course</p><p>and</p><p>less than one- fourth required an applied practicum</p><p>on any structured diagnostic interview. Differences in</p><p>required structured diagnostic interview courses were</p><p>found between training models:73% of clinical science</p><p>and 63% of scientist- practitioner programs required a</p><p>course, whereas only 35% of practitioner- focused pro-</p><p>grams had a similar requirement.</p><p>not surprisingly based on these training gaps, available</p><p>data suggest that clinicians are not engaged in EBA for</p><p>diagnostic assessment. Existing surveys across a range of</p><p>clinicians indicate that unstructured interviews are com-</p><p>monly relied on for diagnosis (e.g., Anderson & Paulosky,</p><p>2004), that evidence- based tools are infrequently used</p><p>(e.g., Gilbody, House, & Sheldon, 2002; Whiteside,</p><p>Sattler, Hathaway, & Douglas, 2016), and diagnostic</p><p>practices often do not map on to best practice guidelines</p><p>(e.g., Demaray, Schaefer, & Delong, 2003; Lichtenstein,</p><p>Spirito, & Zimmermann,2010).</p><p>Unfortunately, these gaps between “best practice”</p><p>and “as usual” assessment practices have implications for</p><p>the accuracy of diagnoses generated in routine practice.</p><p>Studies comparing clinician- generated diagnoses to those</p><p>generated through comprehensive, research- supported</p><p>methods consistently find low rates of agreement between</p><p>the two (Rettew, Lynch, Achenbach, Dumenci, &</p><p>Ivanova, 2009; Samuel, 2015). Studies examining the</p><p>validity of clinician- generated diagnoses also suggest that</p><p>these diagnoses are less valid than the evidence- based diag-</p><p>noses (Basco etal., 2000; Jewell, Handwerk, Almquist, &</p><p>Lucas, 2004; Mojtabai, 2013; Samuel etal., 2013; Tenney,</p><p>Schotte, Denys, van Megen, & Westenberg,2003).</p><p>Research– Practice Gaps inProgress Monitoring</p><p>Most of what is known about graduate training in progress</p><p>monitoring focuses on trainee psychologists. As described</p><p>throughout this volume, most progress monitoring tools</p><p>are standardized rating scales, so many of the assessment</p><p>training gaps discussed previously also are relevant for</p><p>progress monitoring. However, other surveys have focused</p><p>on whether trainees are trained in utilizing these scales</p><p>for ongoing progress monitoring, rather than for diagnos-</p><p>tic assessment. With regard to APA accredited psychol-</p><p>ogy programs, Ready and Veague (2014) found that only</p><p>approximately half of programs offer courses focused on</p><p>progress monitoring, and Mihura etal. (2016) found only</p><p>10% of programs require their students to routinely use</p><p>outcome measures in their practical. Differing rates of</p><p>training in progress monitoring have been found between</p><p>different types of doctoral programs (e.g., counseling vs.</p><p>PsyD; overington, Fitzpatrick, Hunsley, & Drapeau,</p><p>2015) and training program models (e.g., practitioner-</p><p>scholar models vs. clinical- scientist models; overington</p><p>etal., 2015), although little is known about progress moni-</p><p>toring training in master’s programs. Similar to training</p><p>programs, fewer than half of internship directors report</p><p>having their trainees use progress monitoring (Ionita,</p><p>Fitzpatrick, Tomaro, Chen, & overington, 2016; Mours,</p><p>Campbell, Gathercoal, & Peterson, 2009), and nearly</p><p>one- third of directors have never heard of progress moni-</p><p>toring measures (Ionita etal.,2016).</p><p>Similar to diagnostic assessment, there are low rates</p><p>of progress monitoring among practicing clinicians.</p><p>Much of the research in this area is focused on outcome</p><p>monitoring (Cashel, 2002; Hatfield & ogles, 2004), with</p><p>relatively less focus given to ongoing progress monitor-</p><p>ing. Surveys suggest that many clinicians report track-</p><p>ing client progress (Anderson & Paulosky, 2004; Gans,</p><p>Falco, Schackman, & Winters, 2010). However, many</p><p>of them are not using validated measures, instead rely-</p><p>ing on tools developed within the clinic, unstructured</p><p>interviews, reports from clients, and clinical judgment</p><p>(Anderson & Paulosky, 2004; Gans etal., 2010; Johnston</p><p>& Gowers, 2005). This finding has been supported in</p><p>two recent large surveys of psychologists and master’s</p><p>level clinicians, in which fewer than 15% of clinicians</p><p>engaged in ongoing progress monitoring (Ionita &</p><p>Fitzpatrick, 2014; Jensen- Doss et al., 2016). Clinicians</p><p>who do use progress monitoring measures appear to be</p><p>using these measures to track progress internally and</p><p>for administrative purposes, but they rarely report using</p><p>them to plan treatment or monitor progress (Garland,</p><p>Kruse, & Aarons,2003).</p><p>This lack of formal progress monitoring is concern-</p><p>ing in light of data showing that it is difficult for clini-</p><p>cians to accurately judge client progress based on clinical</p><p>judgment alone. For example, when Walfish, McAlister,</p><p>o’Donnell, and Lambert (2012) asked a multidisciplinary</p><p>sample of clinicians to rate their own level of skills, none of</p><p>them ranked themselves as below average, and one- fourth</p><p>of them rated themselves at the 90th percentile of clinical</p><p>skill relative to their peers. These therapists estimated that</p><p>three- fourths of their clients improved in therapy, with</p><p>less than 5% deteriorating; nearly half of them said that</p><p>none of their clients ever deteriorated. The study authors</p><p>point out that these estimates deviated wildly from pub-</p><p>lished estimates of improvement and deterioration rates.</p><p>Interestingly, available data suggest that even when clini-</p><p>cians are trained to engage in progress monitoring, this</p><p>20 InTRoDUCTIon</p><p>20</p><p>does not improve their ability to rate progress based on</p><p>clinical judgment alone. Hannan and colleagues (2005)</p><p>removed feedback reports from a setting that had been</p><p>using an MFS and asked clinicians to predict their clients’</p><p>outcomes. Those clinicians underestimated how many</p><p>clients would deteriorate or not improve, and they over-</p><p>estimated how many would improve.</p><p>WHY IS THERE A RESEARCH– PRACTICE GAP</p><p>INASSESSMENT?</p><p>As detailed previously, lack of training is likely one factor</p><p>that contributes to clinicians’ not utilizing best assessment</p><p>practices. In addition, research has identified other clini-</p><p>cian and organizational variables that might be contribut-</p><p>ing to this research– practicegap.</p><p>Several studies have focused on clinician attitudes</p><p>that might be driving assessment practices. Jensen- Doss</p><p>and Hawley (2010, 2011) conducted a national, multi-</p><p>disciplinary survey to assess clinicians’ attitudes toward</p><p>standardized assessment tools, with a particular focus on</p><p>diagnostic assessment. on average, clinicians reported</p><p>neutral to positive attitudes toward standardized assessment</p><p>tools, although this varied by discipline, with psychologists</p><p>reporting more positive attitudes compared to psychiatrists,</p><p>marriage and family therapists, social workers, and mental</p><p>health counselors (Jensen- Doss & Hawley, 2010). Attitudes,</p><p>particularly beliefs about the practicality of standardized</p><p>assessment tools, predicted self- reported use of these tools.</p><p>other studies have found that clinicians have concerns that</p><p>structured diagnostic interviews would be unacceptable</p><p>to clients (Bruchmüller, Margraf, Suppiger, & Schneider,</p><p>2011), although data gathered directly from clients do not</p><p>support this view (Suppiger etal.,2009).</p><p>Studies have also examined clinician attitudes toward</p><p>progress monitoring. Across studies, attitudes toward these</p><p>measures have varied from neutral to positive, although</p><p>concerns regarding the validity of the measures (e.g.,</p><p>whether they accurately reflected client progress) are com-</p><p>mon (Cashel, 2002; Gilbody etal., 2002; Hatfield & ogles,</p><p>2007; Ionita et al., 2016; Johnston & Gowers, 2005). As</p><p>with diagnostic assessment, clinicians often report practi-</p><p>cal concerns about progress monitoring, including lim-</p><p>ited access to affordable measures, measures being too</p><p>long, difficulties reaching clients to fill out measures, and</p><p>little time to administer measures and keep track of when</p><p>to fill out measures (Gleacher et al., 2016; Hatfield &</p><p>ogles, 2004; Ionita etal., 2016; Johnston & Gowers, 2005;</p><p>Kotte etal., 2016; Meehan, McCombes,</p><p>Hatzipetrou, &</p><p>Catchpoole, 2006; overington etal., 2015). Clinicians also</p><p>report anxiety about progress monitoring data being used</p><p>for performance evaluation, use of these measures ruining</p><p>rapport, concern regarding how to present results correctly</p><p>to clients, and a general lack of knowledge about progress</p><p>monitoring (Ionita etal., 2016; Johnston & Gowers, 2005;</p><p>Meehan etal., 2006). In a study that separately asked about</p><p>attitudes toward the practice of progress monitoring and</p><p>attitudes toward standardized progress measures, clinicians</p><p>reported very positive attitudes toward the idea of monitor-</p><p>ing progress but more neutral attitudes toward the mea-</p><p>sures themselves (Jensen- Doss etal., 2016), suggesting that</p><p>clinicians are open to engaging in the practice if their con-</p><p>cerns about the measures themselves can be addressed.</p><p>Consistent with research on diagnostic assessment, more</p><p>positive attitudes toward progress monitoring are associ-</p><p>ated with higher rates of self- reported progress monitor-</p><p>ing practices (Hatfield & ogles, 2004; Jensen- Doss etal.,</p><p>2016; overington etal.,2015).</p><p>A number of organizational barriers and facilitators of</p><p>EBA have also been identified in the literature. Lack of</p><p>organizational support is a barrier frequently mentioned by</p><p>clinicians, including both active discouragement from super-</p><p>visors and administration regarding the use of measures and</p><p>little guidance given by organizational leaders of when and</p><p>how often to use them (Connors, Arora, Curtis, & Stephan,</p><p>2015; Gilbody etal., 2002; Ionita etal., 2016; overington</p><p>etal., 2015). Many of the practical concerns described previ-</p><p>ously also speak to organizational factors, such as the amount</p><p>of time clinicians are allowed to spend on assessment and the</p><p>budget available for purchasing assessment tools. Clinicians</p><p>also often report that administrators are more interested in</p><p>tracking administrative outcomes (e.g., length of wait list, cli-</p><p>ent turnover, and number of sessions) than outcomes such</p><p>as functioning and symptom reduction (Gilbody etal., 2002;</p><p>Johnston & Gowers, 2005). Conversely, clinicians who indi-</p><p>cate their organizations have policies or rules about assess-</p><p>ment are more likely to report using progress monitoring</p><p>(Jensen- Doss et al., 2016). Clinician assessment practices</p><p>also vary across organizational settings; providers working</p><p>in private practice settings are less likely to use standardized</p><p>diagnostic and progress monitoring tools than are those work-</p><p>ing in other settings (Jensen- Doss etal., 2016; Jensen- Doss &</p><p>Hawley,2010).</p><p>EFFORTS TOIMPROVE ASSESSMENT PRACTICES</p><p>The studies reviewed previously indicate that although</p><p>effective assessment tools exist, they often are not making</p><p>DISSEMInATIon AnD IMPLEMEnTATIon oF EVIDEnCE-BASED ASSESSMEnT 21</p><p>21</p><p>their way into practice settings. As such, several efforts</p><p>have been made to bridge this research– practice gap,</p><p>some focused on specific evidence- based measures (e.g.,</p><p>rating scales for trauma; national Child Traumatic Stress</p><p>network, 2016) and others focused on EBA processes</p><p>(e.g., using an MFS to gather data and using feedback to</p><p>make decisions about treatment; Bickman et al., 2016).</p><p>Efforts to improve clinician assessment practices can be</p><p>divided into dissemination efforts, or efforts to inform</p><p>clinicians about EBA tools, and implementation efforts</p><p>that seek to support clinicians in their use of such tools.</p><p>Implementation efforts can be subdivided into those</p><p>focused on training clinicians in EBA; those focused on</p><p>implementing EBA in individual organizations; and those</p><p>focused on integrating EBA into mental health systems,</p><p>such as state public mental health systems. Although a</p><p>comprehensive review of all of these efforts is beyond the</p><p>scope of this chapter, we highlight some illustrative exam-</p><p>ples of each approach.</p><p>Dissemination Efforts</p><p>Assessment- focused dissemination efforts have typically</p><p>created sources for clinicians to identify evidence- based</p><p>measures or guides for them to engage in EBA processes.</p><p>This volume is an example of an EBA dissemination</p><p>effort, as are publications in EBA special journal issues</p><p>(Hunsley & Mash, 2005; Jensen- Doss, 2015; Mash &</p><p>Hunsley, 2005)and review papers, such as Leffler, Riebel,</p><p>and Hughes’ (2015) review of structured diagnostic inter-</p><p>views for clinicians. The DSM board has also embarked</p><p>on efforts to improve diagnostic practices and accuracy by</p><p>outlining steps for diagnosis and creating decision trees</p><p>to support differential diagnosis (First, 2013). Although</p><p>these dissemination efforts have typically focused on</p><p>what clinicians should do, Koocher and norcross have</p><p>also published articles identifying discredited assessment</p><p>methods (Koocher, McMann, Stout, & norcross, 2015;</p><p>norcross, Koocher, & Garofalo,2006).</p><p>There are also efforts to disseminate EBA informa-</p><p>tion online. For example, there is a website dedicated</p><p>to information about measures relevant to the assess-</p><p>ment of traumatized youth (http:// www.nctsn.org/</p><p>resources/ online- research/ measures- review; national</p><p>Child Traumatic Stress network, 2016), a repository of</p><p>information about assessment tools relevant to child wel-</p><p>fare populations (http:// www.cebc4cw.org/ assessment-</p><p>tools/ measurement- tools- highlighted- on- the- cebc; The</p><p>California Evidence- Based Clearinghouse for Child</p><p>Welfare, 2017), and the PRoMIS website with measures</p><p>that assess outcomes for various health domains that pro-</p><p>mote and facilitate outcome and progress monitoring</p><p>(http:// www.healthmeasures.net/ explore- measurement-</p><p>systems/ promis; Cella et al., 2010; HealthMeasures,</p><p>2017; Pilkonis et al., 2011). In a novel approach to dis-</p><p>semination, the APA has recently funded a grant to update</p><p>assessment pages on Wikipedia, with a focus on assess-</p><p>ments that are freely available (Youngstrom, Jensen- Doss,</p><p>Beidas, Forman, & ong, 2015– 2016).</p><p>Training Efforts</p><p>Some groups are moving beyond dissemination to pro-</p><p>vide training in EBA to clinicians. Relative to the numer-</p><p>ous studies focused on training clinicians in treatments</p><p>(Herschell, Kolko, Baumann, & Davis, 2010), there are</p><p>fewer EBA training studies. Documented EBA training</p><p>efforts to date have consisted of workshops, workshops</p><p>plus ongoing consultation, and courses. Didactic training</p><p>workshops have helped improve clinician progress moni-</p><p>toring attitudes (Edbrooke- Childs, Wolpert, & Deighton,</p><p>2014; Lyon, Dorsey, Pullmann, Silbaugh- Cowdin, &</p><p>Berliner, 2015), self- efficacy (Edbrooke- Childs et al.,</p><p>2014), and use (Persons, Koerner, Eidelman, Thomas, &</p><p>Liu,2016).</p><p>Another training approach is to follow workshops with</p><p>ongoing consultation. For example, a training effort in</p><p>Washington state included 6months of expert- led phone</p><p>consultation and found that training impacted clini-</p><p>cian attitudes, skill, and implementation of standardized</p><p>assessment tools (Lyon etal.,2015).</p><p>Finally, online training has recently been applied to</p><p>EBA training. For example, Swanke and Zeman (2016)</p><p>created an online course in diagnostic assessment for mas-</p><p>ter’s level social work students. The course was based on a</p><p>problem- based learning approach wherein students were</p><p>given diagnostic problems to solve by identifying symp-</p><p>toms and matching symptoms to DSM diagnoses. At the</p><p>end of the course, the average student grade on content</p><p>quizzes was 78.7% and the class was well- received by the</p><p>students, although students’ levels of knowledge prior to</p><p>the course are not know, so it is difficult to determine</p><p>whether the course actually increased knowledge.</p><p>Organizational- Level Implementation Efforts</p><p>Another approach to increasing use of EBA is for orga-</p><p>nizations to attempt to change assessment practices</p><p>organization- wide. Several examples of such efforts have</p><p>been documented in the literature, including studies</p><p>http://www.nctsn.org/resources/online-research/measures-review</p><p>http://www.nctsn.org/resources/online-research/measures-review</p><p>http://www.cebc4cw.org/assessment-tools/measurement-tools-highlighted-on-the-cebc</p><p>http://www.cebc4cw.org/assessment-tools/measurement-tools-highlighted-on-the-cebc</p><p>http://www.healthmeasures.net/explore-measurement-systems/promis</p><p>http://www.healthmeasures.net/explore-measurement-systems/promis</p><p>22 InTRoDUCTIon</p><p>22</p><p>examining the impact of organizations incorporating</p><p>structured diagnostic interviews (e.g., Basco etal., 2000;</p><p>Lauth, Levy, Júlíusdóttir, Ferrari, & Pétursson, 2008;</p><p>Matuschek etal., 2016)and progress monitoring systems</p><p>(e.g., Bickman etal., 2011, 2016; Bohnenkamp, Glascoe,</p><p>Gracey, Epstein, & Benningfield, 2015; Strauss et al.,</p><p>2015; Veerbeek, Voshaar, & Pot,2012).</p><p>one illustrative example of organizational- level</p><p>implementation work focused on progress monitoring is</p><p>the work of Bickman and colleagues. Following an ini-</p><p>tial successful randomized effectiveness trial showing that</p><p>using an MFS called Contextualized Feedback System</p><p>(CFS) improved client outcomes (Bickman etal., 2011),</p><p>Bickman and colleagues (2016) conducted a second ran-</p><p>domized trial within two mental health organizations. All</p><p>clinicians within the agencies were required to administer</p><p>CFS, and cases were randomly assigned to receive feed-</p><p>back as soon as measures were entered into the system</p><p>(i.e., clinicians immediately received feedback reports</p><p>summarizing the CFS data) or to receiving feedback</p><p>every 6 months. Before the trial began, the investiga-</p><p>tors conducted a “pre- implementation contextualization</p><p>phase,” during which they held workgroups to understand</p><p>existing clinic procedures and brainstorm about how CFS</p><p>would fit into those procedures. Training and ongoing</p><p>consultation in CFS was provided to clinicians and to</p><p>agency administrators to ensure both clinical (i.e., using it</p><p>with individual clients) and organizational (e.g., ongoing</p><p>review of aggregated data to identify problems with CFS</p><p>implementation) use of CFS. After finding that only one</p><p>clinic demonstrated enhanced outcomes with CFS, the</p><p>authors determined that the two agencies differed in their</p><p>rates of questionnaire completion and viewing of feed-</p><p>back reports. To better understand these findings, they</p><p>then conducted qualitative interviews with the participat-</p><p>ing clinicians (Gleacher et al., 2016). Clinicians at the</p><p>clinic with better implementation and outcomes reported</p><p>more barriers to using CFS with their clients than did</p><p>clinicians at the other clinic, perhaps because they were</p><p>using it more often. However, they also reported fewer</p><p>barriers at the organizational level and more support from</p><p>their organizational leadership. The authors concluded</p><p>that organizational factors are strong drivers of implemen-</p><p>tation success.</p><p>System- Level Efforts</p><p>Another approach to implementation is for mental health</p><p>systems, such as state public mental health agencies or</p><p>agencies like the Veteran’s Administration, to enact policies</p><p>requiring evidence- based assessment. System- level imple-</p><p>mentations documented in the literature have primarily</p><p>focused on progress monitoring. An early example was</p><p>the state of Michigan’s use of the Child and Adolescent</p><p>Functional Assessment Scale (CAFAS; Hodges & Wong,</p><p>1996). As described by Hodges and Wotring (2004), clini-</p><p>cians in the public mental health system were required</p><p>to use the CAFAS to track client outcomes. Data were</p><p>then used to provide clinicians and agencies feedback on</p><p>individual client and agency- wide outcomes, including</p><p>comparison to agency and state averages.</p><p>Hawaii, which has been a pioneer in the advancement</p><p>of evidence- based treatments (EBTs) in the public sec-</p><p>tor, has supported these efforts by developing and imple-</p><p>menting an MFS that is used statewide (Higa- McMillan,</p><p>Powell, Daleiden, & Mueller, 2011; Kotte et al., 2016;</p><p>nakamura etal., 2014). To date, both clinicians and case-</p><p>workers across various agencies in the state have been</p><p>trained in and are implementing the MFS. In an effort to</p><p>encourage the use of EBA, Higa- McMillan etal. reported</p><p>the use of “Provider Feedback Data Parties” during which</p><p>client progress and clinical utilization of the data are dis-</p><p>cussed. other studies on Hawaii’s EBA efforts observed</p><p>that the fit between the MFS and case manager character-</p><p>istics facilitated MFS implementation, whereas provider</p><p>concerns about the clinical utility and scientific merit of</p><p>the MFS were reported as barriers (Kotte etal.,2016).</p><p>Internationally, system- level efforts to implement</p><p>progress monitoring have been reported in the United</p><p>Kingdom and Australia. Efforts to implement routine</p><p>monitoring throughout the United Kingdom have been</p><p>ongoing for well over a decade (Fleming, Jones, Bradley,</p><p>& Wolpert, 2016; Hall etal., 2014; Mellor- Clark, Cross,</p><p>Macdonald, & Skjulsvik, 2016). The Child outcomes</p><p>Research Consortium (CoRC; http:// www.corc.uk.net),</p><p>a learning and planning collaboration of researchers, ther-</p><p>apists, managers, and funders, has spearheaded most of</p><p>this work. CoRC has made valid, reliable, brief, and free</p><p>measures available to all clinicians working in the United</p><p>Kingdom, provided training in the measures, and created</p><p>an MFS to support their use. These measures are reported</p><p>to be widely implemented, but not at an optimal level</p><p>(Mellor- Clark etal., 2016), so efforts are now focused on</p><p>adopting more theory- driven approaches to implement-</p><p>ing the system (Mellor- Clark etal., 2016; Meyers, Durlak,</p><p>& Wandersman, 2012). In Australia, efforts to implement</p><p>progress monitoring have been ongoing since the late</p><p>1990s and include training and development of computer</p><p>systems to support data collection and analysis (Meehan</p><p>et al., 2006; Trauer, Gill, Pedwell, & Slattery, 2006).</p><p>http://www.corc.uk.net</p><p>DISSEMInATIon AnD IMPLEMEnTATIon oF EVIDEnCE-BASED ASSESSMEnT 23</p><p>23</p><p>outcome data are collected at all public clinics and are</p><p>aggregated at a national level to be used for comparison</p><p>by local clinics.</p><p>Finally, note that policies focused on other aspects of</p><p>care can also have implications for assessment. For exam-</p><p>ple, the Precision Medicine Initiative (The White House</p><p>office of the Press Secretary, 2015)focuses on increasing</p><p>personalized medical treatments that take individual dif-</p><p>ferences in genes and environment into account. Such</p><p>tailored approaches are likely going to require increased</p><p>use of psychosocial assessment in health care settings.</p><p>Similarly, the US Medicare and Medicaid system is mov-</p><p>ing increasingly toward value- based payment, where</p><p>reimbursement is based on quality, rather than quantity,</p><p>of care (Centers of Medicare & Medicaid Services, 2016).</p><p>As such, assessment of quality indicators within publicly</p><p>funded behavioral health settings is going to become</p><p>increasingly important. Finally, initiatives to implement</p><p>EBTs often lead to the development of assessment pro-</p><p>cesses to support those treatments, as evidenced by the</p><p>Hawaii initiative described previously.</p><p>FUTURE DIRECTIONS</p><p>As we hope this review has made clear, the literature</p><p>on EBA contains both good and bad news. on the one</p><p>hand, a number of excellent EBA tools exist and some</p><p>efforts are underway to encourage clinician use of those</p><p>tools. on the other hand, significant gaps continue to</p><p>exist between assessment best practices and what the</p><p>average clinician does in practice. To address these gaps,</p><p>we have several suggestions for future directions the field</p><p>shouldtake.</p><p>1. Increase graduate- level training in evidence- based</p><p>diagnostic assessment and progress monitoring. Most of the</p><p>training and implementation efforts described previously</p><p>have primarily focused on retraining clinicians whose</p><p>graduate training likely did not include in- depth training</p><p>in structured diagnostic assessment or progress monitor-</p><p>ing. Researchers focused on EBTs have called for an</p><p>increased focus on training at the graduate level because</p><p>training people well at the outset is likely easier and more</p><p>cost- effective than</p><p>trying to retrain them (e.g., Bearman,</p><p>Wadkins, Bailin, & Doctoroff,2015).</p><p>one avenue for improving graduate training is increas-</p><p>ing the specificity of accreditation guidelines for training</p><p>programs (Dozois etal., 2014; Ponniah etal., 2011). For</p><p>both psychology and psychiatry training programs, past</p><p>accreditation standards stressed the need for students</p><p>to attain competence in diagnosis of clients via mea-</p><p>surement and interviews and to assess treatment effec-</p><p>tiveness, but they gave little guidance regarding what</p><p>constitutes appropriate assessment (APA, 2006; Canadian</p><p>Psychological Association, 2011; Ponniah et al., 2011).</p><p>A similar picture exists in accreditation guidelines for</p><p>mental health counseling (American Mental Health</p><p>Counselors Association, 2011), marriage and family</p><p>therapy (Commission on Accreditation for Marriage</p><p>and Family Therapy Education, 2014), and bachelor’s</p><p>and master’s level social work programs (Commission on</p><p>Accreditation & Policy, 2015), although these guidelines</p><p>do include training in progress monitoring as a way to per-</p><p>form program evaluation.</p><p>In January 2017, a new set of accreditation guide-</p><p>lines for the APA went into effect that include EBA as</p><p>a core competency (APA Commission on Accreditation,</p><p>2015). Arecent Canadian task force focused on increas-</p><p>ing EBP use (Task Force on Evidence- Based Practice of</p><p>Psychological Treatments; Dozois et al., 2014) empha-</p><p>sized monitoring progress and outcomes throughout treat-</p><p>ment. However, the Canadian Psychological Association</p><p>accreditation guidelines for doctoral programs have not</p><p>been updated to reflect this change as of this publication.</p><p>2. Increase “best practice” training strategies in EBA</p><p>dissemination and implementation efforts. Although</p><p>exceptions exist (Lyon etal., 2015), the primary approach</p><p>that has been taken to training clinicians in EBA is</p><p>what is sometimes referred to as a “train and pray”</p><p>approach: Bring clinicians together for a workshop and</p><p>then hope they take what they have learned and apply it</p><p>in practice. The literature on training in EBTs suggests</p><p>that such an approach is unlikely to lead to sustained</p><p>practice changes (Herschell et al., 2010). Rather, train-</p><p>ing needs to involve active learning strategies, ongoing</p><p>consultation in the practice, and attention to contextual</p><p>variables such as whether clinicians have adequate orga-</p><p>nizational support to continue using the practice (Beidas</p><p>& Kendall, 2010; Herschell et al., 2010). Examples of</p><p>strategies that could be incorporated into EBA trainings</p><p>include engaging clinicians in behavioral rehearsal dur-</p><p>ing training (Beidas, Cross, & Dorsey, 2014); providing</p><p>ongoing consultation after initial training (e.g., Bickman</p><p>etal., 2016; Lyon etal., 2015); increasing sustainability of</p><p>assessment practices through “train the trainer” models</p><p>that train agency supervisors to provide ongoing supervi-</p><p>sion assessment (Connors et al., 2015); and incorporat-</p><p>ing all levels of an agency into training through learning</p><p>collaborative models that address implementation at the</p><p>clinician, supervisor, and administrator levels (e.g., Ebert,</p><p>24 InTRoDUCTIon</p><p>24</p><p>Amaya- Jackson, Markiewicz, Kisiel, & Fairbank, 2012;</p><p>nadeem, olin, Hill, Hoagwood, & Horwitz,2014).</p><p>3. Increase our focus on pragmatic assessment. Studies</p><p>conducted with clinicians consistently suggest that</p><p>perceived lack of practicality is a major barrier to clini-</p><p>cian use of EBA (e.g., Ionita etal., 2016; Jensen- Doss &</p><p>Hawley, 2010). In addition, the fact that many clinicians</p><p>who do gather assessment data do not actually incorpo-</p><p>rate that data into clinical decisions (Garland etal., 2003;</p><p>Johnston & Gowers, 2005) suggests that they may not</p><p>find the data clinically useful. Glasgow and Riley (2013)</p><p>have called for the field to focus on pragmatic measures,</p><p>which they define as measures “that [have] relevance to</p><p>stakeholders and [are] feasible to use in most real- world</p><p>settings to assess progress” (p.237). They propose criteria</p><p>for determining whether a measure is pragmatic, includ-</p><p>ing that is it important to stakeholders, such as clients,</p><p>clinicians, or administrators; that it is low burden to com-</p><p>plete; that it generates actionable information that can be</p><p>used in decision- making; and that it is sensitive to change</p><p>over time. Expanding our reviews of EBA tools to include</p><p>dimensions such as these might help identify measures</p><p>most likely to make their way into practice. one example</p><p>of such a review was conducted by Beidas and colleagues</p><p>(2015), who identified brief, free measures and rated their</p><p>psychometric support for a range of purposes, including</p><p>screening, diagnosis, and progress monitoring.</p><p>Another opportunity for increasing the practical-</p><p>ity of assessment is to take advantage of recent policies</p><p>emphasizing increased data collection and accountability</p><p>in health care settings (e.g., the “Patient Protection and</p><p>Affordable Care Act,” 2010). Lyon and Lewis (2016) point</p><p>out the opportunity that these policies provide for increas-</p><p>ing use of progress monitoring. As agencies increasingly</p><p>incorporate health information technologies, such as</p><p>electronic medical records, into their settings to meet</p><p>data reporting requirements, there is an opportunity to</p><p>integrate electronic MFSs into these systems (Lyon etal.,</p><p>2016). If progress monitoring can be built into the daily</p><p>workflow of clinicians, this greatly increases its practicality.</p><p>4. Leverage technology to increase the use of EBA.</p><p>Another avenue for increasing the practicality of assess-</p><p>ment is to incorporate technologies such as electronic</p><p>health care records platforms and smartphone applica-</p><p>tions into the assessment process. With the rise of policies</p><p>emphasizing increased data collection and accountabil-</p><p>ity in health care settings (e.g., “Patient Protection and</p><p>Affordable Care Act,” 2010), mental health settings are</p><p>increasingly relying on health information technolo-</p><p>gies, such as electronic health care records, to meet data</p><p>reporting requirements. Lyon and Lewis (2016) point out</p><p>these shifts provide an opportunity to increase the use</p><p>of progress monitoring. In a recent review, Lyon, Lewis,</p><p>Boyd, Hendrix, and Liu (2016) identified 49 digital MFSs</p><p>that could be used by clinicians with access to comput-</p><p>ers or tablets to administer progress measures and rap-</p><p>idly receive feedback. However, fewer than one- third of</p><p>those were able to be directly incorporated into electronic</p><p>health care records, and Lyon and colleagues concluded</p><p>that additional work is needed to develop digital MFSs</p><p>that can be incorporated into the daily workflow of prac-</p><p>tice in a way that is sustainable.</p><p>Another technological advance with great potential</p><p>to enhance assessment is smartphone technologies that</p><p>support data collection. Researchers have developed</p><p>applications to support real- time data collection (Trull</p><p>& Ebner- Priemer, 2009)and have begun to examine the</p><p>clinical utility of such applications for gathering informa-</p><p>tion such as mood (e.g., Schwartz, Schultz, Reider, &</p><p>Saunders, 2016) or pain ratings (Sánchez- Rodríguez, de</p><p>la Vega, Castarlenas, Roset, & Miró, 2015). Such applica-</p><p>tions could facilitate self- monitoring of symptoms between</p><p>sessions or efficient collection and scoring of progress</p><p>monitoring data in session. Many smartphone applications</p><p>to track psychological well- being are already commercially</p><p>available (e.g., a november 15, 2016, search of the Google</p><p>Play store yielded more than 50 results for “mood track-</p><p>ing”), and an important next step is to determine how</p><p>these applications can be ethically developed and incorpo-</p><p>rated into clinical practice (Jones & Moffitt,2016).</p><p>5. Develop theoretical models of organizational support</p><p>for EBA. Despite numerous studies suggesting that orga-</p><p>nizational context is critical to EBA (e.g., Gleacher etal.,</p><p>2016; Jensen- Doss etal., 2016), there is a need for concep-</p><p>tual models that can guide organizational</p><p>article in the early 1990s</p><p>advocating evidence- based medical practice over medi-</p><p>cine as an art to mobilize mental health professionals to</p><p>achieve the same goals for treatments for mental disor-</p><p>ders. The JAMA article “ignited a debate about power,</p><p>ethics, and responsibility in medicine that is now threat-</p><p>ening to radically change the experience of health care”</p><p>(Patterson, 2002). This effort resonated widely within the</p><p>mental health community, giving impetus to the efforts of</p><p>psychologists and psychiatrists to base treatment decisions</p><p>on valid empiricaldata.</p><p>Psychologists had long questioned the uncertain reli-</p><p>ability and utility of certain psychological tests, even</p><p>though psychological testing was what many psychologists</p><p>spent much of their time doing. At the same time, the</p><p>urgency of efforts to heighten the support base for valid</p><p>assessments was limited by continuing concerns over the</p><p>efficacy of psychotherapy, for which many assessments</p><p>were done. Not surprisingly, then, when empirical sup-</p><p>port for psychological treatments began to emerge in the</p><p>early and middle 1990s, professional and public support</p><p>for psychological intervention grew. In turn, as psycho-</p><p>therapy’s worth became more widely recognized, the</p><p>value of psychological assessments to help in the plan-</p><p>ning and evaluation of psychotherapy became increas-</p><p>ingly recognized. If my view of this history is on target, the</p><p>intense efforts that have culminated in this book could</p><p>not have begun until psychotherapy’s evidence base had</p><p>been established. That has happened only recently, after a</p><p>lengthy process, and that is why Iclaim that the right time</p><p>for this book isnow.</p><p>Who will use this book? Ihope it will become a favor-</p><p>ite text for graduate courses in assessment so that new</p><p>generations of graduate students and their teachers will</p><p>come to know which of the assessment procedures they</p><p>are learning and teaching have strong empirical support.</p><p>Ialso hope the book will become a resource for practitio-</p><p>ners, including those who may not be used to choosing</p><p>assessment instruments on the basis of evidence base. To</p><p>the extent that this book becomes as influential in clinical</p><p>psychology as Ihope it does, it should help precipitate a</p><p>change in assessment test use patterns, with an increase in</p><p>the utilization of tests with strong empirical support and a</p><p>corresponding decrease in the use of tests without it. Even</p><p>now, there are clinicians who use assessment instruments</p><p>because they learned them in graduate school, rather than</p><p>because there is strong evidence that they work. Now, a</p><p>different and better standard is available.</p><p>I am pleased the editors of this book foresee it provid-</p><p>ing an impetus for research on assessment instruments</p><p>that currently lack empirical support. I agree. As with</p><p>a number of psychotherapy approaches, there remain a</p><p>number of understudied assessment instruments whose</p><p>evidence base is currently too thin for them to be con-</p><p>sidered empirically supported. Like the editors, Ibelieve</p><p>we can anticipate enhanced efforts to establish the limits</p><p>of usefulness of assessment instruments that haven’t yet</p><p>been thoroughly explored. Ialso anticipate a good deal</p><p>of fruitful discussion in the professional literature— and</p><p>likely additional research— on the positions this book’s</p><p>editors and authors have taken on the assessment instru-</p><p>ments they have evaluated. I suspect their ratings for</p><p>“psychometric adequacy and clinical relevance” will be</p><p>extensively critiqued and scrutinized. While the resul-</p><p>tant dialogue might be energetic— even indecorous on</p><p>occasion— as has been the dialogue surrounding the evi-</p><p>dence base for some psychotherapies, Iam hopeful it will</p><p>also lead to more helpful evaluations of test instruments.</p><p>Perhaps the most important empirical studies we might</p><p>ultimately anticipate would be research indicating which</p><p>assessment instruments lead both to valid diagnoses and</p><p>useful treatment planning for specific syndromes. Adis-</p><p>tant goal of syndromal diagnosis for psychopathology has</p><p>always been diagnoses that bespeak effective treatments. 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NewYork:Oxford UniversityPress.</p><p>Patterson, K. (2002). What doctors don’t know (almost every-</p><p>thing). NewYork Times Magazine, May 5,74– 77.</p><p>Preface</p><p>BACKGROUND</p><p>Evidence- based practice principles in health care systems</p><p>emphasize the importance of integrating information</p><p>drawn from systematically collected data, clinical exper-</p><p>tise, and patient preferences when considering health</p><p>care service options for patients (Institute of Medicine,</p><p>2001; Sackett, Rosenberg, Gray, Haynes, & Richardson,</p><p>1996). These principles are a driving force in most health</p><p>care systems and have been endorsed as a necessary foun-</p><p>dation for the provision of professional psychological ser-</p><p>vices (American Psychological Association Presidential</p><p>Task Force on Evidence- Based Practice, 2006; Dozois</p><p>etal., 2014). As psychologists, it is difficult for us to imag-</p><p>ine how any type of health care service, including psycho-</p><p>logical services, can be provided to children, adolescents,</p><p>adults, couples, or families without using some type of</p><p>informal or formal assessment methods. Nevertheless,</p><p>until relatively recently, there was an almost exclusive</p><p>focus on issues related to developing, disseminating, and</p><p>providing evidence-</p><p>approaches to</p><p>improving assessment practices. Models of organizational</p><p>culture and climate have been developed to explain use</p><p>of EBTs (e.g., Williams & Glisson, 2014)and have been</p><p>translated into organizational interventions that improve</p><p>EBT uptake and client outcomes (e.g., Glisson, Williams,</p><p>Hemmelgarn, Proctor, & Green, 2016). Many aspects of</p><p>these models are likely applicable to the use of EBA, but</p><p>the constructs within them may need to be elaborated.</p><p>Although existing models might be helpful to guide</p><p>EBA implementation in agency settings such as clinics or</p><p>schools, these models are not as applicable to clinicians</p><p>working in private practice, who seem to be the clinicians</p><p>least likely to engage in EBA (Jensen- Doss etal., 2016).</p><p>Additional work is needed to understand the needs of this</p><p>population.</p><p>DISSEMInATIon AnD IMPLEMEnTATIon oF EVIDEnCE-BASED ASSESSMEnT 25</p><p>25</p><p>6. Conduct more work focused on EBA processes.</p><p>Although EBA consists of both psychometrically sup-</p><p>ported assessment measures and the processes by which</p><p>those measures are applied, there has historically been a</p><p>dearth of research focused on EBA processes (Hunsley</p><p>& Mash, 2007). The rise in studies about MFSs, which</p><p>consist of measures, guidelines for how often to admin-</p><p>ister them, actionable feedback about clinical results,</p><p>and, increasingly, clinical guides suggesting next steps</p><p>to take in treatment (Krägeloh et al., 2015), is a wel-</p><p>come advance on this front. However, additional work</p><p>is needed on diagnostic assessment processes and on</p><p>approaches to integrating assessment data to form a case</p><p>conceptualization.</p><p>In terms of diagnostic assessment, Youngstrom’s work</p><p>on grounding assessment decisions in probability nomo-</p><p>grams (e.g., Youngstrom, Choukas- Bradley, Calhoun,</p><p>& Jensen- Doss, 2015) is an interesting example of how</p><p>researchers can further develop and study the assessment</p><p>process. Drawing from approaches used in evidence-</p><p>based medicine (Strauss et al., 2015), Youngstrom and</p><p>colleagues have examined the diagnostic utility of various</p><p>risk factors and assessment tools (e.g., Van Meter et al.,</p><p>2014; Youngstrom, 2014; Youngstrom etal., 2004), gen-</p><p>erating data that can then be applied via a tool called a</p><p>nomogram, which helps clinicians translate assessment</p><p>information into estimated probabilities that a client meets</p><p>criteria for a disorder (for an illustration, see Youngstrom</p><p>etal., 2015). one benefit of this approach is that it can</p><p>be done sequentially, starting with lower burden assess-</p><p>ment strategies first, and only moving on to more inten-</p><p>sive assessment of diagnoses that are not ruled in or out at</p><p>earlier stages of assessment. Clinicians have been success-</p><p>fully trained to use the nomogram in two studies (Jenkins</p><p>& Youngstrom, 2016; Jenkins, Youngstrom, Washburn, &</p><p>Youngstrom, 2011), although research is needed to deter-</p><p>mine whether clinicians go on to apply the nomogram</p><p>in their work and whether use improves their diagnostic</p><p>accuracy with clients.</p><p>Another assessment process in need of additional</p><p>research is assessment- driven case conceptualization.</p><p>EBA case conceptualization models have been proposed</p><p>(Christon etal., 2015)and many theoretical conceptual-</p><p>ization models, such as the cognitive– behavioral model,</p><p>indicate that assessment should support the conceptual-</p><p>ization process (Persons & Davidson, 2010). However,</p><p>most of the research on case conceptualization has</p><p>focused on whether clinicians who review the same clini-</p><p>cal vignettes or session recordings generate the same con-</p><p>ceptualizations (Flinn etal., 2015)or whether clinicians</p><p>can be trained to apply a particular conceptualization</p><p>approach to vignettes or recordings (Abbas etal., 2012).</p><p>To our knowledge, no studies have focused on whether</p><p>clinicians can be trained to gather assessment data and</p><p>use them to generate an accurate case conceptualization,</p><p>whether such training could lead to actual changes in</p><p>clinician conceptualization practices, and whether those</p><p>practice changes might improve client outcomes. This is</p><p>clearly an area in critical need of additional research.</p><p>Finally, some chapters in this volume highlight the</p><p>utility of functional assessment for case conceptualiza-</p><p>tion and ongoing progress monitoring. However, little</p><p>is known about whether clinicians are trained in this</p><p>practice, view it favorably, utilize it in practice, or find</p><p>it feasible. In education, the requirement to conduct</p><p>functional behavioral assessment in the Individuals with</p><p>Disabilities Act Amendments of 1997 led to the need for</p><p>widespread implementation of functional assessment in</p><p>schools (Scott, nelson, & Zabala, 2003). Surveys sug-</p><p>gest that this practice is acceptable to school personnel</p><p>(Crone, Hawken, & Bergstrom, 2007; nelson, Roberts,</p><p>Rutherford, Mathur, & Aaroe, 1999), although concerns</p><p>have been raised about its feasibility (nelson et al.,</p><p>1999). However, future research is needed to understand</p><p>how this practice is viewed and utilized in other settings.</p><p>CONCLUSIONS</p><p>As this volume illustrates, decades of excellent research</p><p>has generated a rich body of clinically useful EBA tools.</p><p>Unfortunately, many of these tools have not yet made it</p><p>into practice settings, limiting their public health impact.</p><p>Fortunately, researchers and policymakers are increasingly</p><p>attending to the dissemination of these tools, as well as their</p><p>implementation in mental health organizations and systems.</p><p>Through this work, the field will progress toward a more fully</p><p>realized application of EBPP that goes beyond treatment,</p><p>hopefully improving mental health outcomes for clients.</p><p>References</p><p>Abbas, M., Walton, R., Johnston, A., & Chikoore, M. 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Washington, DC:American</p><p>Psychological Association.</p><p>http://www.jmir.org/2012/3/e76/?trendmd-shared=0</p><p>32</p><p>32</p><p>3</p><p>Advances inEvidence- Based Assessment: Using</p><p>Assessment to Improve Clinical Interventions</p><p>and Outcomes</p><p>Eric A. Youngstrom</p><p>Anna VanMeter</p><p>“Assessment” is the application of a measurement method</p><p>to support a particular goal. In the clinical enterprise,</p><p>measurement is not an end in itself. We are not trying</p><p>to simply describe our clients. They are seeking change,</p><p>and assessment should help identify problems, guide</p><p>the choice of solutions, and indicate whether things are</p><p>moving in the right direction (Hunsley & Mash, 2007).</p><p>Assessment plays a central role in psychoeducational</p><p>evaluation, custody evaluations, and forensic evaluations</p><p>as well as clinical evaluation. In each case, assessment</p><p>provides the data to guide recommendations and actions.</p><p>our discussion focuses most on assessment in the clinical</p><p>context, recognizing that many of the principles and con-</p><p>cepts apply more generally aswell.</p><p>Focusing on assessment as the application of measure-</p><p>ment to guide effective intervention distills evidence- based</p><p>assessment (EBA) to a core principle. The potential value</p><p>added by assessment changes depending on the type of</p><p>intervention and the stage of treatment. Rather than being</p><p>separate clinical activities, assessment and treatment are</p><p>transactional and linked:Treatment provides the questions</p><p>and the context for EBA (Hunsley & Mash, 2007; norcross,</p><p>Hogan, & Koocher, 2008). At the beginning of treatment,</p><p>assessment may most helpfully focus on screening, scop-</p><p>ing, and predicting diagnoses or key issues. once refined</p><p>into a formulation, assessment shifts to prescribing an inter-</p><p>vention, with potential alternatives and moderating factors</p><p>defined. As treatment gets underway, then assessment shifts</p><p>to measuring progress, including shifts in severity, move-</p><p>ment toward goals, and sometimes measurement of process</p><p>variables that play a mechanism in the treatment.</p><p>our goal is to lay out a practical model of EBA as a</p><p>transactional integration of assessment with treatment,</p><p>providing scaffolding for incorporating the different con-</p><p>tent and techniques presented in subsequent chapters. In</p><p>our model, perhaps best considered as EBA 2.0, we aug-</p><p>ment the “3 Ps” of EBA (Youngstrom, 2013)— prediction,</p><p>prescription, and process— with a preparation phase that</p><p>lays the groundwork for a successful installation of these</p><p>upgraded practices.</p><p>For concepts and principles to help anyone, they</p><p>need to be feasible. Evidence- based medicine (EBM)</p><p>often uses the metaphor of a “leaky pipeline” that con-</p><p>nects the best research evidence with the person who</p><p>would benefit (Glasziou & Haynes, 2005). The research</p><p>only helps if the clinician is aware of it, accepts that</p><p>it is valid, views it as applicable to the client, has the</p><p>necessary resources to be able to implement, acts on it,</p><p>and secures the client’s agreement and adherence. The</p><p>chapters in this volume address the first half of the poten-</p><p>tial leaks:Anthologizing the information about measures</p><p>and their psychometrics and utility directly tackles the</p><p>problems of awareness and critical appraisal and also</p><p>guides choices about applicability. EBA 2.0 pushes the</p><p>information even further down the pipeline by building</p><p>a strategic approach to assessment that makes it easier</p><p>to evaluate common issues. It also combines research</p><p>and pragmatism to sequence the order of measurements,</p><p>minimizing redundancy or unnecessary testing that</p><p>will not inform key questions guiding care. As a result,</p><p>EBA 2.0 can often choreograph assessment sequences</p><p>that deliver better results in the same time or less than</p><p>ADVAnCES In EVIDEnCE-BASED ASSESSMEnT 33</p><p>33</p><p>has been spent in traditional evaluations (cf. Camara,</p><p>nathan, & Puente,2000).</p><p>DIAGNOSIS AND TREATMENT FORMULATION</p><p>ASUSUAL</p><p>A brief review of typical practices sets a counterpoint that</p><p>highlights contrasts with EBA. Surveys indicate that most</p><p>practicing clinicians have been doing minimal assessment</p><p>beyond an unstructured interview, with the exception of</p><p>those instances in which clinicians administer, score, and</p><p>interpret a battery of assessment and write a report (and</p><p>then rarely provide treatment; Garb, 1998; Jensen- Doss</p><p>& Hawley, 2011). The multiplicity of factors involved in</p><p>each clinical scenario forces clinicians to rely on impres-</p><p>sionistic, pattern recognition approaches (Kahneman,</p><p>2011). Although our evolved cognitive strategies tended</p><p>to do well in our environment across evolutionary adapta-</p><p>tion, the complexity of modern life creates mismatches</p><p>where our fast, intuitive system often leaps to wrong clini-</p><p>cal conclusions, and we may not recover via our slower,</p><p>effortful processing strategies.</p><p>Clinical assessment appears to be a paragon of all that</p><p>can be problematic with our cognitive wiring. our efforts</p><p>at empathy focus on emotionally salient material, process-</p><p>ing it swiftly to arrive at a hypothesis that we then seek</p><p>to confirm (and fail to systematically try to disconfirm;</p><p>Croskerry, 2003). We underestimate complexity, calling</p><p>off searches when we find a plausible suspect (Jenkins &</p><p>Youngstrom, 2016; Rettew, Lynch, Achenbach, Dumenci,</p><p>& Ivanova, 2009). Cultural differences in beliefs about</p><p>causes and framing of the problem lead to errors in</p><p>hypotheses that do not get corrected easily (Carpenter-</p><p>Song, 2009; Yeh etal., 2005). As a result, studies of clini-</p><p>cal decision- making accuracy are consistently humbling.</p><p>Vignette studies show tremendous variation across clini-</p><p>cians in formulations of the same presenting problem and</p><p>assessment data (Dubicka, Carlson, Vail, & Harrington,</p><p>2008; Jenkins, Youngstrom, Washburn, & Youngstrom,</p><p>2011). Even video- recorded sessions intended as an inter-</p><p>rater reliability exercise show massive differences in scor-</p><p>ing depending on culture and training (Mackin, Targum,</p><p>Kalali, Rom, & Young,2006).</p><p>In contrast, IBM and other companies are betting</p><p>that machine learning may prove helpful in decision-</p><p>making, feeding the multivariate data to artificial intel-</p><p>ligence robots to create decision support tools (Susskind</p><p>& Susskind, 2015). They are using machine learning to</p><p>mine complex relationships from staggering numbers of</p><p>variables and honing feedback to the provider and con-</p><p>sumer in formats that can lead care. These can result in</p><p>surprisingly large gains in predictive accuracy, although</p><p>they are still not a complete solution (James, Witten,</p><p>Hastie, & Tibshirani,2013).</p><p>PREPARATIONPHASE</p><p>EBA 2.0 need not wait for the robots to fix everything.</p><p>Techniques ranging from the simple to the sophisticated</p><p>are available that would upgrade our practice. The first</p><p>step is an easy one:Take stock of the most common pre-</p><p>senting issues at our practice and make sure that we are</p><p>well prepared for them. Depression, anxiety, and atten-</p><p>tion problems are all pervasive problems that will present</p><p>to any clinical practice. other core issues may vary with</p><p>age range and practice setting. Externalizing behavior or</p><p>learning disabilities may be more common among school-</p><p>aged referrals, whereas personality disorders or substance</p><p>misuse become more likely with advancing age. The ini-</p><p>tial step in EBA 2.0 is to identify the half- dozen to dozen</p><p>most common issues. Given the sheer volume of cases</p><p>affected, even a small upgrade in assessment could pay</p><p>large dividends if it improves results for one of these fre-</p><p>quent referral issues.</p><p>A second step is to benchmark our local rates</p><p>against other clinics and settings. Benchmarking can</p><p>reveal gaps in our practice. If we see many clients with</p><p>anxiety but few with depression, that would be a sur-</p><p>prising pattern based on epidemiological studies and</p><p>clinic surveys (Rettew etal., 2009). It is possible that</p><p>our practice has become so specialized that we mostly</p><p>get referrals for a narrow set of issues, but it is worth</p><p>considering whether we unknowingly have blind-</p><p>ers that eclipse our view of common comorbidities</p><p>or competing explanations for similar behaviors. We</p><p>can formally cross- check our most common diagnoses</p><p>and case formulations against lists drawn from meta-</p><p>analyses, epidemiological studies, or billing records.</p><p>The key point is to make sure that we are not overlook-</p><p>ing a common scenario. If we are, then that becomes</p><p>a priority for continuing education, additional reading,</p><p>professional supervision and consultation, and updates</p><p>in assessment practices.</p><p>Table 3.1 lists chapters in this volume that focus</p><p>on some of the most common conditions, along with</p><p>prevalence benchmarks based on different sources.</p><p>Epidemiological studies from the general population</p><p>probably provide a lower bound for rates that would be</p><p>34 InTRoDUCTIon</p><p>34</p><p>seen at a clinic. Rettew etal.’s (2009) meta- analysis pro-</p><p>vides rates from an assortment of outpatient clinics. The</p><p>rates are a helpful starting point but are not etched in</p><p>stone. Prevalence estimates in each chapter may vary</p><p>as authors integrate different epidemiological studies or</p><p>clinical samples; for inpatient settings or specialty clinics,</p><p>it is likely that the rates of some conditions will be even</p><p>higher.</p><p>With our personal list of top referral questions in</p><p>hand, we can then organize our assessments by topic</p><p>and check whether there is a better method than the</p><p>incumbent measurement we are using for each. It need</p><p>not be a huge amount of work. This edited volume cre-</p><p>ates an easy opportunity to start at a high level:Cross-</p><p>reference this list of common issues with Table 3.1.</p><p>Review each relevant chapter to determine if there are</p><p>measures that fill gaps in our current tool kit or offer</p><p>greater utility than what we already are using. That</p><p>strategy capitalizes on the expert review of the litera-</p><p>ture that informed each chapter to create a strong foun-</p><p>dation of assessment methods for the common issues.</p><p>The book can also be helpful in updating established</p><p>and thriving clinical practices. one could pick a “topic</p><p>of the month” and spend an hour checking if there are</p><p>better assessment options available for use in the prac-</p><p>tice. At a training clinic or large practice, the topic of</p><p>the month could be the focus of a brown bag lunch</p><p>seminar; in a private practice, it could be a good use of</p><p>a cancelled appointment</p><p>slot. over the course of a year,</p><p>cycling through the different topics will update the</p><p>whole practice while keeping the focus fresh and chal-</p><p>lenging each month. Avoid perfectionism— the object</p><p>is not to find “the best” in any particular category but,</p><p>rather, to make sure that your practice is good enough</p><p>(Brighton, 2011; Hunsley, 2007)and that you ratchet it</p><p>steadily upwards.</p><p>The list of common issues also helps guide individual</p><p>assessments. At least screening or inquiring briefly about</p><p>each of the frequent topics, even if that is not what the</p><p>client first mentions, leverages the base rates. The simple</p><p>technique of asking about three to six common problems</p><p>instead of focusing on the first obvious topic avoids well-</p><p>documented pitfalls of confirmation bias, failing to seek</p><p>disconfirming evidence, and search “satisficing” (calling</p><p>off the search as soon as one hypothesis seems confirmed</p><p>rather than continuing to explore other possibilities;</p><p>Jenkins & Youngstrom, 2016). Remember that comorbid-</p><p>ity is the rule, not the exception, and undetected comor-</p><p>bid problems can undermine treatment. More systematic</p><p>approaches to assessment also help broach awkward</p><p>topics— such as substance misuse, sexual dysfunction, sui-</p><p>cidal ideation, or physical abuse— that may be difficult for</p><p>clients to spontaneously volunteer (Lucas, Gratch, King,</p><p>& Morency,2014).</p><p>Table3.1 Prevalence Benchmarks forCommon Clinical Issues Discussed inThisVolume</p><p>Clinical Rates (Rettew etal., 2009)</p><p>Condition Chapter Diagnosis as Usual</p><p>More Structured Diagnostic</p><p>Interview General Populationa</p><p>ADHD 4 23% 38% 5% in children, 2.5% in adults</p><p>Externalizing problems 5 17% CD, 37% oDD 25% CD, 38% oDD 4% CD, 3% oDD</p><p>Mood disorders 6– 9 17% MDD,</p><p>10% dysthymia</p><p>26% MDD, 8% dysthymia 7% MDD,1.5% pervasive depressive</p><p>disorder, 2.5% bipolar spectrum</p><p>Anxiety 11– 14</p><p>Child and adolescent 11 8% 18%</p><p>Social anxiety disorder/ phobia 12 6% 20% 7%</p><p>Panic 13 12% 11% 3%</p><p>Generalized anxiety disorder 14 5% 10% 3%</p><p>Post- traumatic stress disorder 16 3% 9% 3.5%</p><p>Substance use disorders 17 14% 17% –</p><p>Alcohol use disorder 18 10% 13% 5% in adolescents, 8.5% in adults</p><p>a The estimates are 12- month prevalence rates as reported in DSM- 5 (American Psychiatric Association, 2013). Epidemiological rates refer to general</p><p>population, not treatment- seeking samples, and so often represent a lower bound of what might be expected at a clinic.</p><p>ADHD, attention- deficit/ hyperactivity disorder; CD, conduct disorder; MDD, major depressive disorder; oDD, oppositional defiant disorder.</p><p>Source:Adapted from Youngstrom and Van Meter (2016) and https:// en.wikiversity.org/ wiki/ Evidence_ based_ assessment</p><p>https://en.wikiversity.org/wiki/Evidence_based_assessment</p><p>ADVAnCES In EVIDEnCE-BASED ASSESSMEnT 35</p><p>35</p><p>PREDICTIONPHASE</p><p>Considering our common issues also informs our choice</p><p>of core measures. Start with broad measures that cover the</p><p>common issues, and augment with checklists about risk</p><p>factors. In the therapeutic context, the first wave of assess-</p><p>ment is a scouting exercise to discern the areas to explore in</p><p>more depth. For adults, there are a range of broad coverage</p><p>instruments available, including checklists (e.g., Derogatis</p><p>& Lynn, 1999)and personality inventories (e.g., Minnesota</p><p>Multiphasic Personality Inventory- 2 [MMPI- 2] interpre-</p><p>tive systems, in addition to self- report options; Sellbom</p><p>& Ben- Porath, 2005). If we are working with adolescents,</p><p>then it makes sense to start with a broad assessment instru-</p><p>ment such as the Achenbach System of Empirically Based</p><p>Assessment (ASEBA; Achenbach & Rescorla, 2003)or the</p><p>Adolescent Symptom Inventory (Gadow & Sprafkin, 1997).</p><p>Scores on these measures have shown good psychometric</p><p>properties across a variety of samples, and they provide</p><p>broad coverage of most of the common issues in childhood</p><p>and adolescence. Compared to the more comprehensive</p><p>personality tests and interviews, checklists are inexpensive</p><p>and fairly quick to score, and some provide good norma-</p><p>tive data to help tease apart what is developmentally typical</p><p>from the more extreme or problematic levels of behavior.</p><p>There also are free alternatives to many of these instru-</p><p>ments (e.g., Goodman, 1999; ogles, Melendez, Davis, &</p><p>Lunnen, 2001), although the lower cost is often achieved</p><p>by reduced breadth of scales or sacrificing the quality of the</p><p>normative data (but for exceptions to this in the assessment</p><p>of adult depression, see Chapter7, this volume).</p><p>often, practitioners fall into the “rule of the tool,”</p><p>giving every client their favorite assessment instrument</p><p>without thinking much about how it matches up with</p><p>the presenting problem or the common issues. no mea-</p><p>sure is perfect. Considering strengths and shortcomings</p><p>of each measure compared to the common problems list</p><p>will help build assessment batteries that are much more</p><p>comprehensive and balanced without adding unneces-</p><p>sary components that burden the client. For example, the</p><p>ASEBA, MMPI- 2, and Symptom Checklist 90 (SCL- 90)</p><p>(Derogatis & Lynn, 1999) all omit scales that directly</p><p>assess body image or disordered eating patterns, which</p><p>could be a prevalent and serious issue in teen or adult</p><p>women (Wade, Keski- Rahkonen, & Hudson, 2011).</p><p>Alternate scoring systems that rationally select items or</p><p>use analyses with distinct criterion groups may be needed</p><p>to cover other issues, such as post- traumatic stress disor-</p><p>der (You, Youngstrom, Feeny, Youngstrom, & Findling,</p><p>2015)or substance misuse.</p><p>After the default or core assessment package is set, the</p><p>next step is to think through the interpretation of each</p><p>piece with regard to the common issues. If the goal were</p><p>an exhaustive review of the literature, then the project</p><p>would quickly become unmanageable (Youngstrom &</p><p>Van Meter, 2016). However, a comprehensive approach</p><p>is not necessary or particularly helpful; not all possible</p><p>permutations of assessment and construct are clinically</p><p>relevant: We do not need to know how an attention-</p><p>deficit/ hyperactivity disorder (ADHD) scale would do</p><p>at detecting depression, for example. We can match the</p><p>goal with the scale to focus our interpretive attention, and</p><p>we can use the “good enough” principle to keep moving</p><p>(Brighton,2011).</p><p>At the prediction phase, a major source of value for an</p><p>assessment tool would be changing the probability of the</p><p>client having a diagnosis or problem. In a detective story,</p><p>successive clues raise or lower suspicion about each suspect.</p><p>The same is true with clinical assessment:Accumulating</p><p>risk factors raise the probability, as would high scores on</p><p>a valid measure of the same construct. Low scores on tests</p><p>might also reduce the probability, as would protective fac-</p><p>tors. It is possible to integrate such information in a much</p><p>more systematic way than just intuitive, impressionistic</p><p>interpretation. Bayes’ theorem offers an algorithm for</p><p>updating a probability estimate on the basis of new infor-</p><p>mation. Although it is centuries old, and authorities such</p><p>as Meehl (1954) have advocated for its use for decades, its</p><p>time is finally arriving. Acombination of shifting winds—</p><p>with EBM, politics, and sports all incorporating it (for</p><p>popular examples, see http:// fivethirtyeight.com)— and</p><p>technology making it more accessible have made it fea-</p><p>sible to start using these methods in real time to integrate</p><p>information and guide decisions. The improvements are</p><p>profound, in terms of not just increased overall accuracy</p><p>but also improved consistency (i.e., a constructive reduc-</p><p>tion in the range of interpretations of the same data) and</p><p>reduced bias (protecting us from systematic misinterpre-</p><p>tations of the same data) (Jenkins & Youngstrom, 2016;</p><p>Jenkins et al., 2011). Tools for synthesizing assessment</p><p>information now include websites and smartphone appli-</p><p>cations (search for “Evidence- Based Medicine Calculator”</p><p>and choose from among the current best reviewed options)</p><p>as well as probability nomograms— an analog to old</p><p>slide</p><p>rules that used geometric spacing to accomplish various</p><p>computations. We include a probability nomogram as</p><p>Figure 3.1 because it helps illustrate the concepts and</p><p>represents a least common denominator in terms of tech-</p><p>nological requirements. For readers who are interested in</p><p>learning more about how to use this approach in clinical</p><p>http://fivethirtyeight.com</p><p>36 InTRoDUCTIon</p><p>36</p><p>practice, we recommend the article by Van Meter et al.</p><p>(2014), in which the authors provide extensive details on</p><p>how to integrate various types of clinical data in order to</p><p>inform the diagnostic decision- making process.</p><p>Probabilistic interpretation involves the following</p><p>series of steps: (a) Decide the starting, or prior, prob-</p><p>ability for a particular hypothesis; (b) combine it with</p><p>the information added by a specific assessment finding;</p><p>and (c)review the updated probability and decide on the</p><p>next clinical action. The information about base rates and</p><p>common issues provides a starting estimate for step (a). In</p><p>a probability nomogram, the prior probability gets plot-</p><p>ted on the left- hand column. The information from the</p><p>assessment finding gets plotted on the middle line, and</p><p>2</p><p>50</p><p>5</p><p>20</p><p>10</p><p>1</p><p>.5</p><p>.2</p><p>.1</p><p>20</p><p>30</p><p>10</p><p>5</p><p>2</p><p>100</p><p>200</p><p>500</p><p>1000</p><p>40</p><p>30</p><p>50</p><p>60</p><p>70</p><p>80</p><p>90</p><p>95</p><p>99</p><p>40</p><p>50</p><p>60</p><p>70</p><p>80</p><p>90</p><p>95</p><p>99</p><p>1</p><p>.50</p><p>.20</p><p>.10</p><p>.05</p><p>.02</p><p>.01</p><p>.005</p><p>.002</p><p>.001</p><p>20</p><p>10</p><p>5</p><p>2</p><p>1</p><p>.5</p><p>.2</p><p>.1</p><p>Pretest Probability Likelihood Ratio Posttest Probability</p><p>% %</p><p>FIGURE3.1 Probability nomogram used to combine prior probability with likelihood ratios to estimate revised, poste-</p><p>rior probability. Straus etal. (2011) provide the rationale and examples. Youngstrom (2014) and Van Meter etal. (2014,</p><p>2016)provide examples both of how to estimate diagnostic likelihood ratios from raw data and how to use a nomogram</p><p>to apply them to acase.</p><p>ADVAnCES In EVIDEnCE-BASED ASSESSMEnT 37</p><p>37</p><p>then connecting the dots to cross the right- hand line pro-</p><p>vides the graphical estimate of the revised probability.</p><p>For the probability nomogram to work, the information</p><p>from the assessment needs to be scaled using an effect size</p><p>called a diagnostic likelihood ratio (DLR). The DLR is a</p><p>ratio of how common a given finding would be in the pop-</p><p>ulation of interest divided by how common it would be in</p><p>the comparison group. For example, the DLR attached</p><p>to an implicit association task for risk of self- injury would</p><p>be a ratio of how common the result (i.e., a “positive”</p><p>test result) was among those who self- injured compared</p><p>to how common a similar result would be among those</p><p>who did not (nock & Banaji, 2007). In older terminol-</p><p>ogy, the DLR for a high risk score would be the diagnostic</p><p>sensitivity of the result (the “true positive rate”; e.g., out of</p><p>100 cases with history of self- injury, how many had a posi-</p><p>tive test result and were correctly classified as engaging</p><p>in self- injurious behavior) compared to the false- positive</p><p>rate (the complement of diagnostic specificity; e.g., out of</p><p>100 people who do not self- injure, how many had a posi-</p><p>tive test result and were incorrectly classified). ADLR can</p><p>also be estimated for low risk, or “negative” test results; for</p><p>example, how many people with a history of self- injury</p><p>had the low risk (negative) result (the false- negative rate,</p><p>or 1- sensitivity) divided by the number of people who</p><p>do not self- injure and correctly got a low risk (negative)</p><p>test result (diagnostic specificity). The algebraic relation-</p><p>ship means that it is possible to take the sensitivity and</p><p>specificity for assessments reported in the chapters of this</p><p>volume and quickly calculate the DLRs for low risk (nega-</p><p>tive) and high risk (positive) scores. Although academic</p><p>standards are starting to require greater detail, including</p><p>the sensitivity and specificity, in articles reporting on diag-</p><p>nostic tools (e.g., Bossuyt etal., 2003), finding the neces-</p><p>sary information to calculate DLRs can be challenging.</p><p>However, this only needs to be done once if we write it</p><p>down, either as marginalia or on a cheat sheet of measures</p><p>that we routinely use in our practice. It also is not neces-</p><p>sary to do this for all measures— only the ones that we are</p><p>going to use regularly.</p><p>The DLR approach is omnivorous, and it can be fed</p><p>any assessment result or data about risk or protective fac-</p><p>tors, as long as they are re- expressed as DLRs. With a little</p><p>effort, almost any effect size can be converted (Hasselbad</p><p>& Hedges, 1995; Viechtbauer, 2010), along with inputs</p><p>such as percentiles from normative data (Frazier &</p><p>Youngstrom, 2006). Another advantage of the approach</p><p>is that it can add information sequentially, in a flexible</p><p>order, and as it becomes available. To add information</p><p>about second input, take the revised probability from</p><p>the first assessment, use it as the next prior probability</p><p>(i.e., put it on the leftmost line of the nomogram or in</p><p>the starting field of a calculator), connect it with the next</p><p>DLR, and get the updated probability. If several DLRs are</p><p>available at the same time, then they can be multiplied</p><p>to get a single combined DLR. The method trades the</p><p>assumption that the correlation between inputs is mod-</p><p>est for the flexibility of input sequence. Regression- based</p><p>approaches work in the opposite way, optimally adjusting</p><p>for the degree of covariation among inputs, but at the cost</p><p>of greater complexity and an inability to work if any one of</p><p>the variables in the model is missing for a particular case</p><p>(Kraemer, 1992). More often, the ability to add new data</p><p>as they become available is a better match for the unfold-</p><p>ing process of the clinical encounter.</p><p>The third part of the EBA cycle is to consider the</p><p>updated probability of a given outcome or diagnosis and</p><p>then decide on the next clinical action. EBA 2.0 adapts</p><p>the EBM concept of two decision thresholds defining</p><p>three zones of clinical action. The low probability, inter-</p><p>mediate, and high probability zones signify watchful</p><p>waiting, assessment, and acute treatment in the EBM</p><p>formulation (Straus, Glasziou, Richardson, & Haynes,</p><p>2011). With EBA 2.0, there are distinct assessment strate-</p><p>gies and titrated interventions for each zone (Youngstrom,</p><p>2013). The low probability zone could still warrant a</p><p>surveillance or monitoring plan to detect worrisome</p><p>changes, and it could also be a place for primary pre-</p><p>ventions that are so low risk and low cost that they make</p><p>sense to deploy regardless of personal circumstances. The</p><p>intermediate zone is not just the place for more focused</p><p>assessment targeting the key hypotheses but also may be</p><p>the realm for using broad- spectrum, low- risk interventions</p><p>such as many forms of therapy. This is the arena in which</p><p>targeted prevention, peer counseling, bibliotherapy, and</p><p>generic supportive counseling all could be appropriate,</p><p>along with changes in sleep hygiene, diet, and other life-</p><p>style factors. The high probability zone may be the place</p><p>where treatment shifts to specialist interventions, acute</p><p>pharmacotherapy, and other tertiary interventions. At this</p><p>stage, assessment shifts to monitoring treatment response,</p><p>searching for cues of progress (and using failure to prog-</p><p>ress as a sign that the case formulation should be revisited;</p><p>Lambert, Harmon, Slade, Whipple, & Hawkins,2005).</p><p>neither threshold— between low probability and inter-</p><p>mediate or between intermediate and high— has a rigid</p><p>location on the probability scale. This is by design. The</p><p>threshold should shift depending on the relative risks and</p><p>benefits attached to the treatment, or the costs associated</p><p>with a false negative (i.e., missing a case that truly has the</p><p>38 InTRoDUCTIon</p><p>38</p><p>target problem) or false positive (i.e., overdiagnosis). With</p><p>very low- risk, low- cost interventions, the treatment threshold</p><p>could drop so low that everyone gets the intervention:This is</p><p>the primary prevention model, with inoculation and iodized</p><p>salt to prevent thyroid problems as widespread public health</p><p>examples. Although there are models to algebraically weight</p><p>costs and benefits and precisely shift the threshold (for four</p><p>different but conceptually related models, see Kraemer,</p><p>1992; Straus etal., 2011; Swets, Dawes, & Monahan, 2000;</p><p>Yates & Taub, 2003), these are complicated to implement</p><p>without computer support. They also probably are not suf-</p><p>ficient in themselves. Ultimately, the decisions about when</p><p>and how to treat are informed by clinical expertise and</p><p>patient values, and the decision- making should be shared</p><p>with the client (Harter & Simon,2011).</p><p>PRESCRIPTIONPHASE</p><p>Returning to the flow through the EBA process, the com-</p><p>bination of risk factors and screening or initial assessments</p><p>will probably be enough to move hypotheses into the mid-</p><p>range “assessment zone” or demote them from further</p><p>consideration, but they will not suffice to confirm hypoth-</p><p>eses on their own. nor will they push revised probabili-</p><p>ties high enough to guide treatment in isolation. If the</p><p>EBA system is working, then the initial test results serve to</p><p>revise the list of hypotheses that are candidates for further</p><p>intensive evaluation.</p><p>Assess More Focused Constructs</p><p>and Add Collateral Informants</p><p>The next stages involve gathering more focused measures</p><p>and collateral perspectives, as well as perhaps selecting a</p><p>semi- structured approach for confirming diagnoses. The</p><p>more focused measures include not just self- report scales</p><p>and checklists, of which there are an abundance reviewed</p><p>in the following chapters, but also in many cases perfor-</p><p>mance measures such as neurocognitive tests. Collateral</p><p>informants are a routine part of evaluations for youths,</p><p>where parents or teachers may be initiating the referral.</p><p>Although less commonly used, they can play a valuable</p><p>role not just in couples counseling but also in assessing</p><p>behaviors when individuals may lack insight (e.g., mania,</p><p>psychosis, or adult autism; Dell’osso etal., 2002)or when</p><p>they may not be motivated to provide accurate reports (as</p><p>might be the case with substance misuse, antisocial behav-</p><p>ior, or food intake with eating disorders). Treat each chap-</p><p>ter topic as a portfolio of options for a particular diagnostic</p><p>hypothesis, and then select an assessment instrument that</p><p>is “highly recommended” for evaluating each. If there</p><p>is information about collateral report options as well, it</p><p>is worth picking one of the top- tier ones and having it</p><p>available, too. Although collaterals provide converging</p><p>perspectives, the correlations tend to be low to moderate</p><p>(r=.2 to .4 in adults, based on an extensive meta- analysis;</p><p>Achenbach, Krukowski, Dumenci, & Ivanova, 2005).</p><p>Disagreements also are informative in terms of gauging</p><p>insight, motivation for treatment, and other valuable con-</p><p>textual information (for a detailed review and suggestions,</p><p>see De Los Reyes etal.,2015).</p><p>Semi- Structured Diagnostic Interviews</p><p>If the goal is to establish a formal diagnosis, then a semi-</p><p>structured diagnostic interview is the next step indicated</p><p>in the process. In contrast, the standard of practice for</p><p>decades has been an unstructured interview, where the</p><p>clinician listens to the presenting problem, generates a</p><p>hypothesis, and seeks confirming evidence. Clinicians</p><p>like this approach because it should employ our training</p><p>and expertise to be able to recognize complex patterns</p><p>of information and to sniff out key moderating variables.</p><p>Unfortunately, studies repeatedly show that rather than a</p><p>set of virtuoso diagnostic performances, what we accom-</p><p>plish with unstructured interviews are formulations with</p><p>near- chance inter- rater agreement. That state of affairs</p><p>guided the decision of the third and subsequent revi-</p><p>sions of the Diagnostic and Statistical Manual (DSM;</p><p>American Psychiatric Association, 2013) to emphasize</p><p>improving reliability, and it also was the impetus for</p><p>developing structured diagnostic interviews. Fully struc-</p><p>tured interviews are highly scripted, to the point that they</p><p>could be delivered via computer. The scripting and auto-</p><p>mation push inter- rater reliability to nigh perfection, at</p><p>the expense of sacrificing clinical judgment.</p><p>Semi- structured interviews offer a middle way. They</p><p>are structured in the sense that they include the same set</p><p>of topics regardless of presenting problem or clinical intu-</p><p>ition, and they also embed the algorithms to satisfy spe-</p><p>cific diagnostic criteria. Asemi- structured interview about</p><p>depression, for instance, should ask about at least the nine</p><p>symptoms in the criteria for a major depressive episode,</p><p>as well as include questions checking that the symptoms</p><p>are part of an episodic change in functioning lasting at</p><p>least 2 weeks and causing impairment in at least one set-</p><p>ting. The “semi” aspect means that the interviewer need</p><p>not stick exactly to a script but instead can paraphrase,</p><p>or reword using the patient’s own terms. The clinician</p><p>ADVAnCES In EVIDEnCE-BASED ASSESSMEnT 39</p><p>39</p><p>also can re- inject clinical judgment to the process, but</p><p>now at the level of leaves and roots, rather than starting</p><p>with sweeping decisions about choice of branch in the</p><p>decision- making tree. In practice, compared to fully struc-</p><p>tured interviews, semi- structured approaches tend to take</p><p>longer to learn to administer reliably, and they may yield</p><p>lower reliability estimates. If that price affords better clini-</p><p>cal validity and more uptake, it is well worth paying.</p><p>Clinicians cling to unstructured interviews. We offer a</p><p>set of rationalizations:The more structured interviews will</p><p>take too long; they will damage rapport with our clients;</p><p>clients will not like the interview. Surveys decisively rebut</p><p>the issues of patient preference. Patients prefer the more</p><p>thorough approaches, believing that clinicians have a more</p><p>comprehensive and accurate understanding of the situation</p><p>afterwards (Bruchmuller, Margraf, Suppiger, & Schneider,</p><p>2011; Suppiger etal., 2009). The issue of time could be</p><p>handled in any of at least three ways. First, use the previ-</p><p>ous information from the EBA 2.0 process to select specific</p><p>modules. Rather than grinding through an entire interview,</p><p>choose semi- structured interview components focused on</p><p>the hypotheses still in contention. This method uses the</p><p>prior assessment data to accomplish what many interviews</p><p>implement with gating logic and skip out questions. The</p><p>selective approach also offers the possibility of choosing</p><p>modules from different interviews that are optimized for</p><p>particular conditions. The interviews reviewed in subse-</p><p>quent chapters provide the list of options, and a practitioner</p><p>could build an eclectic and modular follow- up interview,</p><p>taking the best from each category. Second, spend longer</p><p>on the interview. Data show that clients do not mind, and</p><p>insurance companies are willing to reimburse for the more</p><p>focused follow- up interview because the prior EBA steps</p><p>have documented medical necessity. Third, technology</p><p>is now making it possible to offload the structured inter-</p><p>view as an activity that the client does before meeting the</p><p>practitioner (Susskind & Susskind, 2015). Completely</p><p>computer- administered interviews are decreasing in cost</p><p>and increasing in sophistication. The structured interview</p><p>could become another input in the assessment process,</p><p>leading to a set of most likely diagnoses, which the clinician</p><p>then probes before deciding on a final formulation.</p><p>Other More Intensive Testing</p><p>An EBA approach would deploy other assessments with</p><p>incremental or confirmatory value at this stage. These are</p><p>methods that are more burdensome or expensive, preclud-</p><p>ing use in a universal screening or core battery approach.</p><p>In the diagnostic arena, they may also put more of a</p><p>premium on specificity, even at the expense of lower sen-</p><p>sitivity, because now the goal is confirmation of a hypoth-</p><p>esis that has already passed through the earlier stages of</p><p>detection (a high- sensitivity filter) and evaluation (Straus</p><p>et al.,</p><p>2011). This is the realm of systematic behavioral</p><p>observation with targeted hypotheses, of neurocognitive</p><p>testing, of drug testing kits, and of polysomnography to</p><p>evaluate the potential presence of a formal sleep disorder.</p><p>This could become the province of wearable consumer</p><p>devices and health- related smartphone applications that</p><p>measure sleep, activity, heart rate, and other physiological</p><p>and behavioral parameters.</p><p>Treatment Planning and Goal Setting</p><p>The assessments should serve to identify treatment targets</p><p>by pushing the probability high enough to warrant cor-</p><p>responding intervention, by direct confirmation using a</p><p>sufficiently structured interview, or by a combination of</p><p>these. EBA should not only establish a treatment target</p><p>but also detect secondary targets, such as comorbidities</p><p>or areas of impaired functioning. It should also provide</p><p>alerts to factors that would change the choice of interven-</p><p>tion. Comorbid substance misuse, low verbal ability, or</p><p>a personality disorder all could significantly complicate</p><p>treatment and lead to poorer prognosis if not addressed.</p><p>Having arrived at a case formulation, the next step is</p><p>to negotiate a treatment plan and set measurable goals.</p><p>We view this as a negotiation because collaborative</p><p>approaches to care are desirable on ethical and utilitarian</p><p>grounds. When clients buy into the plan, they are more</p><p>invested in treatment and more likely to follow through on</p><p>recommendations and achieve better outcomes. Client</p><p>beliefs and preferences should be considered throughout</p><p>the assessment and treatment process, but they deserve</p><p>extra attention here. Many areas of medicine have devel-</p><p>oped decision aids to help the patient understand the risks</p><p>and benefits of different treatment options. This is an area</p><p>for growth in clinical psychology. At a minimum, a direct</p><p>and culturally sensitive discussion should occur, and the</p><p>provider should explicitly link elements of treatment to</p><p>the stated preferences and provide a meaningful rationale</p><p>for how treatment would promote attaining the goals. The</p><p>client may not be ready or motivated to work on every-</p><p>thing that the assessment process reveals. When it is pos-</p><p>sible to focus on shared goals, engagement and rapport</p><p>will be at a substantial advantage.</p><p>With targets agreed upon, assessments also establish</p><p>a baseline measure of severity, and many can add nomo-</p><p>thetic benchmarks against which to measure progress.</p><p>40 InTRoDUCTIon</p><p>40</p><p>Tools such as behavior checklists that have standardiza-</p><p>tion data offer normative comparisons in the form of per-</p><p>centiles, T scores, and the like. Interestingly, the scores</p><p>that are the most elevated are not always the most impair-</p><p>ing or distressing (Weisz etal., 2011), and so yet again it</p><p>is valuable to get the client’s input. Selecting one or more</p><p>scales as an operational definition of a treatment outcome</p><p>will provide a more quantifiable and perhaps objective</p><p>indication of progress.</p><p>PROCESS:TREATMENT MONITORING</p><p>AND TREATMENT OUTCOME</p><p>Therapy, like going on a diet, is a challenging form of</p><p>behavior change. The chances of success increase with</p><p>explicit goals and regular brief measures of progress— like</p><p>weighing in on a bathroom scale— and process. The psy-</p><p>chometric qualities and practical parameters are quite dif-</p><p>ferent for a progress or process measure compared to a</p><p>diagnostic assessment (Youngstrom et al., 2017). Brevity</p><p>is a major consideration. Although loss of diagnosis may</p><p>be a goal of treatment, few practitioners or clients would</p><p>want to repeat a full structured interview several times</p><p>over the course of treatment. Sensitivity to treatment</p><p>effects is another key function; in part for this reason, per-</p><p>sonality or general cognitive ability tests are not used as</p><p>outcome measures. Treatment sensitivity requires a blend</p><p>of enough retest stability to indicate when problems per-</p><p>sist, yetalso malleability that can indicate if the interven-</p><p>tion has the desired effect. Indices of retest reliability are</p><p>not adequate in isolation to judge suitability for measur-</p><p>ing outcome. Conceptually, generalizability coefficients</p><p>or intraclass correlations quantifying the amount of vari-</p><p>ance attributable to treatment would be ideal, although</p><p>they are rarely reported in the literature.</p><p>Nomothetic Goal Setting</p><p>norm- referenced measures create an opportunity for</p><p>nomothetic definitions of treatment milestones. Jacobson</p><p>and colleagues developed an influential model for this,</p><p>framing clinically significant change as requiring psy-</p><p>chometrically reliable improvement along with transit-</p><p>ing an a priori benchmark (Jacobson, Roberts, Berns, &</p><p>McGlinchey, 1999). Jacobson and colleagues used a reli-</p><p>able change index (RCI) as a way of showing that individual</p><p>treatment response was unlikely to be due to measurement</p><p>error or instability. The RCI converts raw change scores</p><p>into a z- score- type metric, using the standard error of the</p><p>difference as the scale. Values greater than 1.65 would</p><p>connote 90% confidence that the change was reliable, and</p><p>1.96 would demarcate 95% confidence. In practice, retest</p><p>stabilities are rarely reported, and even less likely to match</p><p>the naturalistic length of treatment, so people often use the</p><p>internal consistency reliability as the basis for estimating</p><p>the standard error of the measure and then the standard</p><p>error of the difference (ogles, 1996). Research reports</p><p>and reviews tend to focus on group statistics and not the</p><p>standard errors, so it may be necessary to calculate these</p><p>for the outcome measures we use regularly. For each com-</p><p>mon treatment target, select one assessment instrument</p><p>that will be feasible to use, and make a cheat sheet with</p><p>the standard error of the difference score; or, even more</p><p>conveniently, jot down the number of points required for</p><p>90% or 95% confidence in the change. A more recent</p><p>alternative to the RCI is the minimally important differ-</p><p>ence (MID) method, which uses patient preferences to</p><p>define the smallest increment of change that they would</p><p>find meaningful (Thissen et al., 2016). MID milestones</p><p>tend to be smaller than RCI ones, making them easier to</p><p>achieve and also indicating that more subtle changes can</p><p>still be important to the individual.</p><p>The second part of Jacobson and colleagues’ (1999)</p><p>definition involves passing a benchmark defined by nor-</p><p>mative data. There are three operational definitions:mov-</p><p>ing Away from the clinical range, moving Back into the</p><p>nonclinical range, and moving Closer to the nonclini-</p><p>cal than clinical average. The Back definition requires</p><p>normative data in a nonclinical sample, and the Away</p><p>definition needs a relevant clinical sample to generate</p><p>the benchmark; the Closer definition needs both the</p><p>nonclinical and the clinical samples for estimation. The</p><p>requirements create a practical barrier to implementa-</p><p>tion:Many assessments lack the requisite normative data</p><p>(Youngstrom etal., 2017). The thresholds are also rarely</p><p>reported, although they are relatively simple to calculate</p><p>if the data are accessible. Jacobson and colleagues recom-</p><p>mended using two standard deviations (SDs) as the rule</p><p>of thumb for defining the Away and Back thresholds (e.g.,</p><p>moving beyond 2 SDs from the clinical mean or back</p><p>within 2 SDs of the nonclinical mean), and the Closer</p><p>threshold is the weighted average of the clinical and non-</p><p>clinical means. Again, these are worth calculating for the</p><p>primary outcome measure we select for each common</p><p>treatment target. Writing them down leverages the few</p><p>minutes of work involved, providing a resource for treat-</p><p>ment across manycases.</p><p>From a psychometric perspective, measures best suited</p><p>for the nomothetic definitions of clinically significant</p><p>ADVAnCES In EVIDEnCE-BASED ASSESSMEnT 41</p><p>41</p><p>change will have high reliability— translating into precise</p><p>estimates of the client’s true score in classical test theory—</p><p>coupled with large separation between the clinical and</p><p>nonclinical distributions,</p><p>based interventions, with only cursory</p><p>acknowledgment of the role that evidence- based assess-</p><p>ment (EBA) activities play in the promotion of evidence-</p><p>based services.</p><p>Fortunately, much has changed with respect to EBA</p><p>since the publication of the first edition of this volume in</p><p>2008. Agrowing number of publications are now available</p><p>in the scientific literature that address the importance of</p><p>solid assessment instruments and methods. Special sec-</p><p>tions on EBA have been published in recent issues of top</p><p>clinical psychology journals (e.g., Arbisi & Beck, 2016;</p><p>Jensen- Doss, 2015). The evidence base for the value of</p><p>monitoring treatment progress has increased substantially,</p><p>as have calls for the assessment of treatment progress to</p><p>become standard practice (e.g., Lambert, 2017). There</p><p>is also mounting evidence for assessment as a key com-</p><p>ponent for engaging clients in effective mental health</p><p>services (Becker, Boustani, Gellatly, & Chorpita, 2017).</p><p>Unfortunately, some long- standing problems evident</p><p>in the realm of psychological assessment remain. Many</p><p>researchers continue to ignore the importance of evaluat-</p><p>ing the reliability of the assessment data obtained from</p><p>their study participants (e.g., Vacha- Haase & Thompson,</p><p>2011). Despite the demonstrated impact of treatment</p><p>monitoring, relatively few clinicians systematically and</p><p>routinely assess the treatment progress of their clients</p><p>(Ionita & Fitzpatrick, 2014), although it appears that stu-</p><p>dents in professional psychology programs are receiving</p><p>more training in these assessment procedures than was the</p><p>case in the past (e.g., Overington, Fitzpatrick, Hunsley,</p><p>& Drapeau, 2015). All in all, though, when viewed from</p><p>the vantage point of the early years of the 21st century, it</p><p>does seem that steady progress is being made with respect</p><p>toEBA.</p><p>As was the case with the first edition, the present vol-</p><p>ume was designed to complement the books published</p><p>by Oxford University Press that focus on bringing the best</p><p>of psychological science to bear on questions of clini-</p><p>cal importance. These volumes, A Guide to Treatments</p><p>that Work (Nathan & Gorman, 2015)and Psychotherapy</p><p>xii PREFACE</p><p>Relationships that Work (Norcross, 2011), address inter-</p><p>vention issues; the present volume specifically addresses</p><p>the role of assessment in providing evidence- based ser-</p><p>vices. Our primary goal for the book was to have it address</p><p>the needs of professionals providing psychological services</p><p>and those training to provide such services. Asecondary</p><p>goal was to provide guidance to researchers on scientifi-</p><p>cally supported assessment tools that could be used for</p><p>both psychopathology research and treatment research</p><p>purposes. Relatedly, we hope that the summary tables pro-</p><p>vided in each chapter will provide some inspiration for</p><p>assessment researchers to try to (a) develop instruments</p><p>for specific assessment purposes and disorders for which,</p><p>currently, few good options exist and (b)expand our lim-</p><p>ited knowledge base on the clinical utility of our assess-</p><p>ment instruments.</p><p>ORGANIZATION</p><p>All chapters and tables in the second edition have been</p><p>revised and updated by our expert authors to reflect recent</p><p>developments in the field, including the publication of</p><p>the fifth edition of the Diagnostic and Statistical Manual</p><p>of Mental Disorders (DSM- 5; American Psychiatric</p><p>Association, 2013). For the most part, the general cover-</p><p>age and organization of the first edition, which our read-</p><p>ers found useful, has been retained in the second edition.</p><p>Consistent with a growing developmental psychopathol-</p><p>ogy perspective in the field, the scope of some chapters</p><p>has expanded in order to provide more coverage of assess-</p><p>ment issues across the lifespan (e.g., attention- deficit/</p><p>hyperactivity disorder in adults). The most important</p><p>changes in organization involve the addition of two new</p><p>chapters, one dealing with the dissemination and imple-</p><p>mentation of EBA (Chapter2) and the other dealing with</p><p>new developments in EBA (Chapter3). The contents of</p><p>these chapters highlight both the important contributions</p><p>that assessment can make to the provision of psychological</p><p>services and the challenges that mental health profession-</p><p>als face in implementing cost- effective and scientifically</p><p>sound assessment strategies.</p><p>Consistent with evidence- based psychology and</p><p>evidence- based medicine, the majority of the chapters</p><p>in this volume are organized around specific disorders</p><p>or conditions. Although we recognize that some clients</p><p>do not have clearly defined or diagnosable problems, the</p><p>vast majority of people seeking psychological services</p><p>do have identifiable diagnoses or conditions. Accurately</p><p>assessing these disorders and conditions is a prerequisite</p><p>to (a) understanding the patient’s or client’s needs and</p><p>(b) accessing the scientific literature on evidence- based</p><p>treatment options. We also recognize that many patients</p><p>or clients will present with multiple problems; to that end,</p><p>the reader will find frequent references within a chapter</p><p>to the assessment of common co- occurring problems that</p><p>are addressed in other chapters in the volume. To be opti-</p><p>mally useful to potential readers, we have included chap-</p><p>ters that deal with the assessment of the most commonly</p><p>encountered disorders or conditions among children,</p><p>adolescents, adults, older adults, and couples.</p><p>Ideally, we want readers to come away from each chap-</p><p>ter with a sense of the best scientific assessment options</p><p>that are clinically feasible and useful. To help accomplish</p><p>this, we were extremely fortunate to be able to assemble a</p><p>stellar group of contributors for this volume. The authors</p><p>are all active contributors to the scientific literature on</p><p>assessment and share a commitment to the provision of</p><p>EBA and treatment services.</p><p>To enhance the accessibility of the material presented</p><p>throughout the book, we asked the authors, as much as pos-</p><p>sible, to follow a common structure in writing their chap-</p><p>ters. Without being a straitjacket, we expected the authors</p><p>to use these guidelines in a flexible manner that allowed for</p><p>the best possible presentation of assessment work relevant</p><p>to each disorder or clinical condition. The chapter format</p><p>generally used throughout the volume is as follows:</p><p>Introduction:A brief overview of the chapter content.</p><p>Nature of the Disorder/ Condition: This section</p><p>includes information on (a) general diagnostic consid-</p><p>erations, such as prevalence, incidence, prognosis, and</p><p>common comorbid conditions; (b)evidence on etiology;</p><p>and (c) contextual information such as relational and</p><p>social functioning and other associated features.</p><p>Purposes of Assessment:To make the book as clinically</p><p>relevant as possible, authors were asked to focus their</p><p>review of the assessment literature to three specific assess-</p><p>ment purposes:(a) diagnosis, (b)case conceptualization</p><p>and treatment planning, and (c) treatment monitoring</p><p>and evaluation. We fully realize the clinical and research</p><p>importance of other assessment purposes but, rather than</p><p>attempting to provide a compendium of assessment mea-</p><p>sures and strategies, we wanted authors to target these</p><p>three key clinical assessment purposes. We also asked</p><p>authors to consider ways in which age, gender, ethnicity,</p><p>and other relevant characteristics may influence both the</p><p>assessment measures and the process of assessment for the</p><p>disorder/ condition.</p><p>For each of the three main sections devoted to spe-</p><p>cific assessment purposes, authors were asked to focus on</p><p>PREFACE xiii</p><p>assessment measures and strategies that either have demon-</p><p>strated their utility in clinical settings or have a substantial</p><p>likelihood of being clinically useful. Authors were encour-</p><p>aged to consider the full range of relevant assessment meth-</p><p>ods (interviews, self- report, observation, performance tasks,</p><p>computer- based methods, physiological, etc.), but both sci-</p><p>entific evidence and clinical feasibility were to be used</p><p>most often indexed as Cohen’s</p><p>d effect size. The high reliability is often achieved via</p><p>increasing scale length, as the number of items is part of</p><p>the internal consistency reliability formula. As a result,</p><p>the tools precise enough to measure change well may</p><p>be too long to repeat frequently. The nomothetic bench-</p><p>marks may work best as midterm and final exams— panels</p><p>of evaluation that are used less often but that provide fairly</p><p>deep evaluation of progress (Youngstrom etal.,2017).</p><p>Idiographic Goal Setting</p><p>A complementary approach to goal setting and track-</p><p>ing is an idiographic approach, in which the client</p><p>defines targets of interest and uses a simple way of scal-</p><p>ing and recording them to provide frequent feedback.</p><p>often, these are single- item scales, with simple Likert-</p><p>type scoring. The Youth Top Problems approach asks</p><p>the youth and the caregiver to each pick three things</p><p>that they want therapy to improve and then report on</p><p>them at every session using a 0– 10 scale (Weisz et al.,</p><p>2011). The reliability of the approach derives from the</p><p>repeated measurement. one could think of the num-</p><p>ber of repetitions as the functional length of the scale.</p><p>The brevity and the salience of the content (because the</p><p>client chose it) make the approach feasible. It can be</p><p>remarkably sensitive to treatment effects. It also is likely</p><p>to enhance treatment effects, much as stepping regularly</p><p>on a bathroom scale increases the effectiveness of the</p><p>diet. Measurement- based care advocates using these</p><p>sorts of short, focused evaluations. These also can pro-</p><p>vide feedback in real time, allowing for course correc-</p><p>tions during treatment if there is failure to progress or if</p><p>there are iatrogenic effects.</p><p>Process Measurement</p><p>Many interventions are skill based, and it is possible to</p><p>track the behaviors that are components of the thera-</p><p>peutic process. The possibilities are broad and include</p><p>examples such as daily report cards when evaluating</p><p>interventions for impulsive or externalizing behaviors</p><p>(see Chapter 4, this volume), completion of three- and</p><p>five- column charts in cognitive– behavioral therapy, use</p><p>of coping or diary cards in dialectical behavioral therapy,</p><p>or counting the number of core conflictual relational</p><p>themes surfaced during a session of psychodynamic</p><p>therapy (Luborsky, 1984). Tracking the number of cancel-</p><p>lations or no- shows also provides a behavioral measure of</p><p>engagement, and other measures of adherence are pos-</p><p>sible. Process variables can include mediational variables</p><p>in treatment models, and some may be worth measuring</p><p>during the course of therapy to ensure that the interven-</p><p>tion is starting to produce the desired changes, even if the</p><p>more global outcomes may take some weeks to achieve.</p><p>The burgeoning number of mental health applications</p><p>for smartphones and other devices will create ways of trac-</p><p>ing utilization without requiring additional work on the</p><p>part of the client. These variables are more tied to the</p><p>particular intervention used, and so they are less likely to</p><p>be covered in a chapter devoted to assessment. They are</p><p>important, nonetheless, and will repay any investment in</p><p>planning and gatheringthem.</p><p>Maintenance Monitoring</p><p>When treatment goes well, termination planning should</p><p>celebrate the success, and also a plan should be devel-</p><p>oped for maintenance and for relapse prevention (Ward,</p><p>1984). The reality is that many conditions are recurrent</p><p>(e.g., mood disorders), chronic (e.g., ADHD and person-</p><p>ality disorders), or prone to relapse (e.g., substance mis-</p><p>use). There also may be predictable triggers and stressful</p><p>events, such as moving or separating from a partner,</p><p>that create opportunities to plan ahead and promote the</p><p>generalization of successful behaviors. As we conclude a</p><p>course of therapy, it makes sense to have an assessment</p><p>strategy that will monitor gains and provide early warning</p><p>of things worsening. Kazdin and Weisz (1998) discussed</p><p>a “dental model” of care, in which routine check- ups</p><p>are scheduled without waiting for a crisis. These pro-</p><p>mote prevention as well as early intervention. For cli-</p><p>ents to use the monitoring strategies, the strategies need</p><p>to be low friction, convenient, and focused on things</p><p>that the clients care about (Youngstrom et al., 2017).</p><p>Here, too, phone applications and wearable technology</p><p>are making innovations possible. Daily items tracking</p><p>substance use or stress, or wearables tracking exercise</p><p>and sleep, create new opportunities for monitoring</p><p>long- term health.</p><p>UTILITY:HOW MUCH WILL ITCOST?</p><p>Psychological assessment looks different viewed through</p><p>the lens of EBA 2.0, with different techniques woven</p><p>through the intervention process from before the</p><p>42 InTRoDUCTIon</p><p>42</p><p>start of treatment to after its conclusion. Although full</p><p>implementation of EBA adds several new techniques,</p><p>and moves assessment out of its traditional box at the</p><p>beginning of treatment (or even separated entirely from</p><p>treatment, as often happens with the assessment report</p><p>model), it does not usually demand extra time from the</p><p>client. Sequencing is key. If everyone were screened for</p><p>all conditions, regardless of prevalence, or if all com-</p><p>pleted comprehensive neurocognitive batteries along</p><p>with structured interviews, then time and cost would</p><p>balloon (Kraemer, 1992; Youngstrom & Van Meter,</p><p>2016). Using knowledge of base rates lets us configure</p><p>our assessment sequence to cover common scenarios</p><p>first. Then we selectively add an assessment only when</p><p>it has the potential to answer questions about prediction,</p><p>prescription, or process. Use of short forms, hybrid mod-</p><p>els that blend rating scales with modular semi- structured</p><p>interviews, and brief idiographic items all promote feasi-</p><p>bility. Gathering the nuts and bolts in advance— making</p><p>the cheat sheet with the diagnostic likelihood ratios, reli-</p><p>able change indices, and normative benchmarks— is a</p><p>one- time investment in enhancing the assessment and</p><p>treatment for all subsequent clients.</p><p>Fiscal costs are in flux, as test publishers are now</p><p>experimenting with subscription or fee- for- scoring mod-</p><p>els. There also are a plethora of public domain and free</p><p>options, many of which have accumulated evidence</p><p>of reliability and validity across a range of populations</p><p>and settings (Beidas etal., 2015; see also Chapter7, this</p><p>volume). Professional societies are currently reviewing</p><p>and anthologizing many of these (e.g., the American</p><p>Academy of Child & Adolescent Psychiatry’s Practice</p><p>Toolbox [http:// www.aacap.org/ aaCaP/ Clinical_ Practice_</p><p>Center/ Home.aspx] pages) and the Society of Clinical</p><p>Psychology’s assessment pages [http:// www.div12.org]),</p><p>making them more convenient to find, and a growing</p><p>number are now available on Wikipedia and Wikiversity</p><p>(Youngstrom etal., 2017). The proliferating mental health-</p><p>related software applications also are low cost orfree.</p><p>As a result, neither time nor cost is a serious obstacle</p><p>to implementing EBA 2.0. The gains in accuracy of</p><p>diagnosis are profound. Inasmuch as diagnosis and for-</p><p>mulation guide the effective choice of treatment, bet-</p><p>ter outcomes should follow. 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Clinical</p><p>Psychology:Science and Practice, 23, 327– 347.</p><p>Youngstrom, E. A., Van Meter, A., Frazier, T. W., Hunsley, J.,</p><p>Prinstein, M., ong, M.- L., & Youngstrom, J. K. (2017).</p><p>Evidence- based assessment as an integrative model for</p><p>applying psychological science to guide the voyage of</p><p>treatment. Clinical Psychology: Science and Practice,</p><p>24, 331–363.</p><p>45</p><p>PartII</p><p>Attention- Deficit and Disruptive</p><p>Behavior Disorders</p><p>46</p><p>47</p><p>47</p><p>4</p><p>Attention- Deficit/ Hyperactivity Disorder</p><p>Charlotte Johnston</p><p>Sara Colalillo</p><p>This chapter focuses on the assessment of attention-</p><p>deficit/ hyperactivity disorder (ADHD) in clinical settings</p><p>and on measures appropriate for youth. Six- to 12- year- old</p><p>children are the group most frequently referred for assess-</p><p>ment and treatment of ADHD, and therefore literatures</p><p>regarding assessment at other ages are not as well devel-</p><p>oped and not reviewed in this chapter. However, consis-</p><p>tent with the recent adoption of a lifespan perspective on</p><p>ADHD (American Psychiatric Association [APA], 2013),</p><p>in this chapter we do include brief information pertain-</p><p>ing to the assessment of ADHD in adulthood. Research</p><p>focused on the assessment of ADHD earlier in life, partic-</p><p>ularly in the preschool years, is mounting (e.g., Ghuman</p><p>& Ghuman, 2014; Harvey, Lugo- Candeals, & Breaux,</p><p>2015; Rabinovitz, o’neill, Rajendran, & Halperin, 2016).</p><p>There are a number of challenges to the identification of</p><p>ADHD in this younger age range, including less consis-</p><p>tency in the contexts in which children are assessed (e.g.,</p><p>preschool, day care, and home care) and less distinctive-</p><p>ness of ADHD symptoms and other problem behaviors.</p><p>However, the potential benefits to early identification of</p><p>the disorder make this area of work an important frontier.</p><p>Similarly, although most youth are diagnosed with ADHD</p><p>prior to adolescence, some symptom presentations (e.g.,</p><p>primary problems with inattention) or some circum-</p><p>stances may result in ADHD escaping earlier detec-</p><p>tion. In addition, the increased autonomy or academic</p><p>demands associated with adolescence often necessitate</p><p>a renewed focus on ADHD assessment as a precursor to</p><p>developing or modifying treatment plans. Readers are</p><p>referred to Barkley (2006) for an overview of issues related</p><p>to assessment of ADHD in adolescents.</p><p>The relatively high prevalence of ADHD, combined</p><p>with the pernicious nature of the problems associated with</p><p>it and the persistence of the disorder over time (APA, 2013),</p><p>make comprehensive and accurate clinical assessment an</p><p>imperative for guiding clinical care in this population. In</p><p>addition, perhaps more than many diagnoses, the ADHD</p><p>diagnosis has been the subject of considerable contro-</p><p>versy. Much of this controversy is fueled by frequent, and</p><p>at times sensationalistic, media reports. Many individuals,</p><p>including parents of children who undergo assessments</p><p>for ADHD, express fear that this is an overused diagnos-</p><p>tic label designed merely to control children’s naturally</p><p>rambunctious or extroverted nature and to justify the use</p><p>of psychotropic medications. Contrary to these concerns,</p><p>the scientific community has provided ample evidence</p><p>to support the validity of the disorder and its associated</p><p>treatments (Barkley, 2002; Kooij et al., 2010; national</p><p>Institutes of Health, 2000). Furthermore, evidence sug-</p><p>gests that although the diagnosis may sometimes be over-</p><p>used, it is just as frequently missed (e.g., Angold, Erkanli,</p><p>Egger, & Costello, 2000; Levy, 2015; Sayal, Goodman,</p><p>& Ford, 2006). However, for each individual child there</p><p>is no substitute for careful, evidence- based assessment to</p><p>provide the best possible clinical service and to assist par-</p><p>ents and children in understanding the meaning of the</p><p>diagnostic label, the link between assessment and treat-</p><p>ment recommendations, and the need to monitor impair-</p><p>ments and treatment</p><p>effects overtime.</p><p>We begin the chapter with an overview of ADHD, pro-</p><p>viding a sense of the core characteristics of the disorder</p><p>that need to be assessed. We then review assessment mea-</p><p>sures for children that serve three purposes, along with</p><p>the unique challenges that may accompany each purpose:</p><p>(a) measures used for diagnostic purposes, (b)measures</p><p>useful for case formulation and treatment planning, and</p><p>(c)assessments for monitoring the course and outcome of</p><p>interventions. For each purpose, we have constructed a</p><p>table indicating measures that meet psychometric criteria</p><p>48 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>48</p><p>set out by the editors in Chapter1 of this volume. In the</p><p>text, we offer brief descriptions of these measures and</p><p>occasionally mention other promising assessment tools</p><p>that do not, as yet, meet the criteria used for including</p><p>measures in the tables. Following the review of assessment</p><p>tools appropriate for children, we consider the best tools</p><p>available for the assessment of ADHD in adults. Finally,</p><p>we conclude with an overview of the state- of- the- art with</p><p>regard to the assessment of ADHD, with a focus on the</p><p>challenges that remain for research and clinical practice.</p><p>THE NATURE OFADHD</p><p>The study of ADHD is one of the largest empirical lit-</p><p>eratures in child psychopathology and encompasses evi-</p><p>dence regarding the genetic, biological, neurological,</p><p>psychological, social, and cultural characteristics of the</p><p>disorder. Significant advances are being made in our</p><p>understanding of ADHD, including exciting theoretical</p><p>and empirical works probing the core causes and nature</p><p>of the disorder (e.g., Gallo & Posner, 2016; Karalunas</p><p>etal., 2014; Musser, Galloway- Long, Frick, & nigg, 2013;</p><p>nigg, Willcutt, & Doyle, 2005; Sonuga- Barke, Cortese,</p><p>Fairchild, & Stringaris, 2016). The vibrant nature of</p><p>research on ADHD bodes well for advancing our ability</p><p>to clinically assess, treat, and potentially even prevent this</p><p>disorder. However, the rapidly expanding and dynamic</p><p>nature of the research also means that evidence- based</p><p>assessment of ADHD must continually change as it incor-</p><p>porates new evidence. Thus, one challenge to the assess-</p><p>ment of ADHD is the need for clinicians to constantly</p><p>update their knowledge about the disorder and to revise</p><p>assessment tools and methods accordingly. The first and</p><p>perhaps most critical recommendation we offer for the</p><p>assessment of ADHD is that the information in this chap-</p><p>ter has an expiry date, and only by keeping abreast of the</p><p>science of ADHD can clinical practice in this area remain</p><p>appropriate.</p><p>ADHD is defined in the most recent edition of the</p><p>Diagnostic and Statistical Manual of Mental Disorders</p><p>(DSM- 5; APA, 2013) as a neurodevelopmental disorder</p><p>characterized by developmentally inappropriate and mal-</p><p>adaptive levels of inattention, impulsivity, and hyperactiv-</p><p>ity occurring in multiple settings with an onset prior to</p><p>age 12 years. So defined, ADHD has a prevalence rate</p><p>among school- aged children of approximately 5%, with</p><p>more boys than girls affected. ADHD symptoms are per-</p><p>sistent over time, and at least two- thirds of children who</p><p>meet diagnostic criteria will continue either to meet</p><p>diagnostic criteria or to suffer impairment due to symp-</p><p>toms into adolescence and adulthood (e.g., Kooij etal.,</p><p>2010). Beyond the core symptoms of the disorder, indi-</p><p>viduals with ADHD frequently experience difficulties in</p><p>areas such as academic or job performance, interpersonal</p><p>relations, oppositional and conduct problems, and inter-</p><p>nalizing problems (anxiety and mood disorders).</p><p>Depending on the type of symptoms that an individual</p><p>displays at the time of assessment, ADHD diagnoses are</p><p>assigned as predominantly inattentive, predominantly</p><p>hyperactive– impulsive, or combined presentations.</p><p>Individuals with the predominantly inattentive presenta-</p><p>tion have problems such as difficulties in paying close</p><p>attention to details or sustaining attention. The predomi-</p><p>nantly hyperactive– impulsive presentation is character-</p><p>ized by behaviors such as motor overactivity or restlessness</p><p>and also difficulties inhibiting behavior. The combined</p><p>presentation includes both types of problems. The two</p><p>symptom dimensions, inattention and hyperactivity–</p><p>impulsivity, are highly related (e.g., Martel, von Eye, &</p><p>nigg, 2012; Toplak et al., 2009), and most individuals</p><p>with the diagnosis show elevations in both types of symp-</p><p>toms. The predominantly hyperactive– impulsive presen-</p><p>tation appears most common in younger children and</p><p>may reflect a developmental stage of the disorder (e.g.,</p><p>Hart et al., 1995). The overlap between the predomi-</p><p>nantly inattentive presentation and what has been called</p><p>sluggish cognitive tempo or concentration deficit disorder</p><p>remains somewhat unclear, although recent evidence</p><p>suggests these may be distinct disorders (e.g., Becker etal.,</p><p>2016). Although some research shows differential links</p><p>between the type of ADHD symptom presentation and</p><p>patterns of comorbidity or elements of treatment response</p><p>(e.g., MTA Cooperative Group, 1999; Pliszka, 2015),</p><p>other work suggests poor stability and specificity related to</p><p>which type of symptom is most prevalent in an individual</p><p>(e.g., Willcutt etal., 2012), and DSM- 5 has moved away</p><p>from subtyping ADHD to the more descriptive focus on</p><p>symptom presentation.</p><p>ASSESSMENT OFADHD INCHILDREN</p><p>The assessment of ADHD in childhood shares the conun-</p><p>drum assessors face with many childhood disorders, where</p><p>multiple sources of information must be considered. As</p><p>defined by DSM, ADHD is characterized by symptoms</p><p>and impairment that occur cross- situationally. In the prac-</p><p>ticalities of assessment, this means that information from</p><p>both home and school contexts is considered essential</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 49</p><p>49</p><p>to the assessment process. Given the limitations of child</p><p>self- report (e.g., Loeber, Green, Lahey, & Stouthamer-</p><p>Loeber, 1991), the assessment of childhood ADHD</p><p>places a heavy reliance on parent and teacher reports of</p><p>the child’s behavior. Although information from multiple</p><p>informants and contexts is viewed as critical to the assess-</p><p>ment of ADHD, there is abundant evidence that these</p><p>sources frequently show only minimal convergence (e.g.,</p><p>Achenbach, McConaughy, & Howell, 1987). In addition,</p><p>evidence is meager with respect to the best methods for</p><p>combining this information (for exceptions, see Gadow,</p><p>Drabick, et al., 2004; Martel, Schimmack, nikolas, &</p><p>nigg, 2015) or specifying which combinations of infor-</p><p>mation offer the best incremental validity in the assess-</p><p>ment process (Johnston & Murray, 2003). The influence</p><p>of rater (e.g., depressed mood or ADHD symptoms in</p><p>the parent) or situational (e.g., classroom structure and</p><p>home routines) characteristics must also be considered</p><p>in evaluating the information provided by the multiple</p><p>sources (e.g., De Los Reyes, 2013; Dirks, De Los Reyes,</p><p>Briggs- Gowan, Cella, Wakschlag, 2012). Thus, the puzzle</p><p>of how to best combine multiple, often discrepant, pieces</p><p>of information remains a challenge for assessment.</p><p>PURPOSES OFADHD ASSESSMENT</p><p>Clinical assessments of childhood ADHD serve a variety</p><p>of purposes, ranging from confirming an ADHD diag-</p><p>nosis to ruling out differential diagnoses such as anxiety</p><p>disorders or learning problems to assessing the response</p><p>of a child’s ADHD symptoms and functioning to a psy-</p><p>chosocial treatment or change in medication regimen.</p><p>Varied assessment approaches and tools may be needed</p><p>for addressing each of these different purposes. In this</p><p>chapter, we focus on assessments for the purpose of diag-</p><p>nosis, treatment planning, and treatment monitoring. In</p><p>selecting and evaluating assessment tools for each of these</p><p>purposes, we employed the rating system used throughout</p><p>the chapters of this volume, as described in Chapter1.</p><p>At this point, we offer a caveat regarding our selection</p><p>and evaluation of the assessment</p><p>measures included in</p><p>our tables. We searched broadly for measures and infor-</p><p>mation supporting their use. However, we used practical</p><p>criteria that limited this search. To meet the dual goals of</p><p>accessibility and independent research validation of the</p><p>measures, we prioritized measures that are currently com-</p><p>mercially or publicly available but that also have evidence</p><p>of reliability, validity, or both reported by independent</p><p>investigators in published studies. Given the breadth of</p><p>the assessment literature, we acknowledge that we may</p><p>have missed a small number of measures or information</p><p>that would allow measures to meet the psychometric cri-</p><p>teria required for inclusion in the tables. Within the text</p><p>of the chapter, we occasionally describe other measures</p><p>that do not meet the psychometric criteria required for</p><p>table entry but that hold promise in the assessment of</p><p>ADHD. For such measures, although we continue in an</p><p>attempt to be comprehensive, the sheer number of mea-</p><p>sures with limited psychometric information requires a</p><p>selective approach to inclusion.</p><p>ASSESSMENT FORDIAGNOSIS</p><p>Although most evidence supports a dimensional view of</p><p>ADHD symptoms (e.g., Marcus & Barry, 2011), assess-</p><p>ment for diagnosis requires a categorical decision. There</p><p>are no objective neurological, biological, or other diagnos-</p><p>tic markers for ADHD, and the diagnostic decision rests</p><p>on perceptions of the child, typically offered by parents</p><p>and teachers. These reports of whether or not the child</p><p>shows particular symptoms will be influenced by variables</p><p>such as the context in which the child is observed (e.g.,</p><p>home vs. school), characteristics of the rater (e.g., expecta-</p><p>tions and mood), and clarity of the assessment questions.</p><p>In making diagnostic decisions, the clinician must remain</p><p>aware of the assumptions underlying not only diagnostic</p><p>categories but also the use of informant perceptions and</p><p>the multiple possible explanations for discrepancies across</p><p>informants. Research remains sorely needed to guide and</p><p>improve the diagnostic validity of such decisions, and cli-</p><p>nicians are best advised to resist unwarranted adherence</p><p>to the use of arbitrary cut- offs or algorithms for combining</p><p>information.</p><p>According to DSM- 5 (APA, 2013), an ADHD diag-</p><p>nosis in childhood requires not only that at least six of</p><p>the nine symptoms of either inattention or hyperactivity–</p><p>impulsivity be present but also that these symptoms have</p><p>existed for at least 6months, at a level that is maladaptive</p><p>and inconsistent with developmental level. The symp-</p><p>toms must have presented before the age of 12years and</p><p>lead to clinically significant impairment in social and/</p><p>or academic functioning evidenced in two or more set-</p><p>tings. In addition, the symptoms should not be better</p><p>explained by other conditions such as oppositional defi-</p><p>ant disorder or anxiety disorders. Thus, the assessment of</p><p>ADHD requires not only measuring symptoms but also</p><p>their onsets and their associated impairments in multiple</p><p>settings and gathering information regarding co- occurring</p><p>50 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>50</p><p>problems. Each of these requirements presents an assess-</p><p>ment challenge.</p><p>Defining symptoms as developmentally inappropri-</p><p>ate requires that assessment tools permit comparisons to</p><p>a same- aged normative group. In addition, consideration</p><p>should be given to the gender and ethnic composition of</p><p>the normative sample. DSM- 5 criteria do not specify gen-</p><p>der or ethnic differences in how the disorder is displayed</p><p>and would suggest the use of norms combined across child</p><p>gender and based on samples with representative numbers</p><p>of ethnic- minority children (as well as population charac-</p><p>teristics). However, studies have revealed differences in</p><p>the rates and severity of ADHD symptoms across genders</p><p>and ethnic groups (e.g., Arnett, Pennington, Willcutt,</p><p>Defries, & olson, 2015; DuPaul et al., 2016; Morgan,</p><p>Staff, Hillemeier, Farkas, & Maczuga, 2013). Although</p><p>such evidence would encourage the use of gender- or</p><p>ethnicity- specific norms, such use carries a strong caveat</p><p>given that the DSM diagnostic criteria are specified with-</p><p>out regard to such child characteristics. Where possible,</p><p>clinicians would be wise to consider comparisons to both</p><p>specific and general norms; where specific norms do not</p><p>exist, clinicians should at least acknowledge the possible</p><p>role of culture, gender, or other characteristics in inter-</p><p>preting assessment information regarding the relative</p><p>level of ADHD symptoms presented by thechild.</p><p>Assessing the diagnostic criteria related to the age of</p><p>symptom onset and duration of symptoms also can be</p><p>challenging. Few established measures tap these aspects</p><p>of the diagnosis, and clinicians typically rely on more</p><p>informal parent interviews to provide this information.</p><p>This reliance on unstandardized retrospective recall</p><p>carries an obvious psychometric liability (e.g., Angold,</p><p>Erkanli, Costello, & Rutter, 1996; Russell, Miller, Ford,</p><p>& Golding,2014).</p><p>Given that ADHD is defined by its presence in mul-</p><p>tiple situations, strategies are needed for combining</p><p>assessment information from parent and teacher reports</p><p>into a single diagnostic decision. The most common</p><p>methods employ either an “or” rule, counting symptoms</p><p>as present if they are reported by either the parent or the</p><p>teacher, or alternately an “and” rule, counting symptoms</p><p>as present only if endorsed by both parent and teacher.</p><p>Evidence suggests that of these two options, the “or” rule</p><p>for combining information may have the greatest valid-</p><p>ity, but either method of combination of informants</p><p>generally outperforms the reliance on a single reporter</p><p>(e.g., Shemmassian, & Lee, 2016). other combinatorial</p><p>methods, including averaging across raters to reduce the</p><p>influence of any one informant, also show promise (e.g.,</p><p>Martel et al., 2015). In addition, studies from our lab</p><p>(Johnston, Weiss, Murray, & Miller, 2011, 2014)demon-</p><p>strate that the convergence between parent and teacher</p><p>reports of child ADHD symptoms can be improved by</p><p>providing parents with instructional materials that clarify</p><p>the nature of ADHD behaviors and how to rate them</p><p>(e.g., distinguishing between behaviors that occur only</p><p>when the child is tired versus those that are more per-</p><p>vasive and distinguishing between age- appropriate and</p><p>age- inappropriate behaviors). Still, we know that rater</p><p>or source variance is substantial and often accounts for</p><p>more variance in rating scale scores than the inattentive</p><p>and hyperactive– impulsive dimensions of behavior (e.g.,</p><p>Gadow, Drabick, etal., 2004; Gomez, Burns, Walsh, &</p><p>De Moura, 2003). Until further evidence is available,</p><p>clinicians must rely on clinical judgment, grounded in a</p><p>solid knowledge of the empirical literature, in combining</p><p>information from multiple sources and methods to arrive</p><p>at a final diagnostic decision in childhoodADHD.</p><p>Finally, in assessments intended to offer a diagnosis of</p><p>ADHD, the clinician must have a working knowledge of</p><p>other childhood disorders in order to make informed dif-</p><p>ferential and comorbid diagnoses. The process of teasing</p><p>apart whether inattentive or impulsive behaviors are best</p><p>accounted for by ADHD or by problems such as fetal alco-</p><p>hol effects, autism, learning problems, or anxiety remains</p><p>a challenge. Given the space limitations of this chapter,</p><p>we do not cover measures useful for assessing these other</p><p>childhood disorders and instead refer the reader to other</p><p>child assessment resources (Frick, Barry, & Kamphaus,</p><p>2010; Mash & Barkley, 2007)and the relevant chapters in</p><p>this volume. However, we note that the limitations of our</p><p>current knowledge and diagnostic systems often contrib-</p><p>ute to the difficulties of discriminating among disorders,</p><p>and the clinician may need to assign an ADHD diagnosis</p><p>as a “working hypothesis” rather than as a confirmed deci-</p><p>sion. To the extent that the core nature of ADHD remains</p><p>under debate, best practices</p><p>for discriminating this condi-</p><p>tion from other related conditions will remain somewhat</p><p>elusive.</p><p>A related problem of discriminating among disorders</p><p>arises in the use of assessment measures, especially older</p><p>measures, in which conceptualizations of ADHD are con-</p><p>founded with symptoms of other disorders. For example,</p><p>the hyperactivity scales of earlier versions of the Conners</p><p>Parent and Teacher Rating Scales (Goyette, Conners,</p><p>& Ulrich, 1978) included items more characteristic of</p><p>oppositional problems. Similarly, the hyperactivity sub-</p><p>scale of the 1982 version of the Personality Inventory for</p><p>Children- Revised (Lachar, 1982)assesses behaviors such</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 51</p><p>51</p><p>as cheating and peer relations, which are not core ADHD</p><p>symptoms. Clinicians are reminded to not judge the</p><p>appropriateness of measures on the basis of titles or scale</p><p>names but, rather, to give careful consideration to actual</p><p>item content and whether this content is congruent with</p><p>current conceptualizations ofADHD.</p><p>Overview ofMeasures forDiagnosis</p><p>Narrowband ADHD Checklists</p><p>Among measures designed to assess ADHD symptoms,</p><p>we include only those that map onto the symptoms as</p><p>described in DSM. Anumber of rating scales have been</p><p>produced that are tied, more or less directly, to DSM</p><p>symptoms of ADHD, either those contained in DSM-</p><p>IV or the essentially unchanged symptom list in DSM-</p><p>5. one of the most widely used of these is the ADHD</p><p>Rating Scale- 5 (DuPaul, Power, Anastopoulos, & Reid,</p><p>2016; DuPaul, Reid, etal., 2016). This recently updated</p><p>brief rating scale, which can be completed by parents or</p><p>teachers, lists the 18 DSM- 5 symptoms of ADHD, along</p><p>with a six- item scale assessing the impairment associated</p><p>with these symptoms. The ADHD Rating Scale- 5 pro-</p><p>vides a total score and has inattentive and hyperactivity–</p><p>impulsivity subscales, supported by factor analysis, that</p><p>are useful in determining ADHD presentation type. The</p><p>impairment scale is an addition to this most recent ver-</p><p>sion of the ADHD Rating Scale, and it is advantageous</p><p>given that impairment due to symptoms is a diagnostic</p><p>requirement for ADHD. For both parent and teacher</p><p>ratings, age- and gender- specific norms are available for</p><p>large representative samples. Limited information on</p><p>norms combined across genders is available. The man-</p><p>ual outlines evidence of small, but potentially meaning-</p><p>ful, differences in scores across ethnic groups, and these</p><p>demand attention when using the measure with minority</p><p>group children. The reliability and validity of scores on</p><p>the measure, either in the current DSM- 5 or in earlier</p><p>DSM- IV versions, are generally good (Table 4.1). The</p><p>ADHD Rating Scale- 5 is the only measure in Table 4.1</p><p>with evidence of test– retest reliability over a period of</p><p>months, in contrast to the shorter test– retest intervals for</p><p>other measures. Scores on the ADHD Rating Scale- 5 cor-</p><p>relate with other ADHD measures and discriminate chil-</p><p>dren with ADHD from nonproblem controls and from</p><p>clinical controls. Sensitivity and specificity information is</p><p>available, with some evidence that teacher ratings on the</p><p>ADHD Rating Scale provide greater specificity and par-</p><p>ent ratings provide greater sensitivity in making ADHD</p><p>diagnoses (e.g., DuPaul, Power, etal.,2016).</p><p>In addition to the ADHD Rating Scale- 5, a number</p><p>of very similar questionnaires exist, all with items listing</p><p>the DSM symptoms of ADHD, and in some cases associ-</p><p>ated problems such as sluggish cognitive tempo (e.g., the</p><p>Disruptive Behavior Scale [Gomez,2012] and the Child</p><p>and Adolescent Disruptive Behavior Inventory [Lee,</p><p>Burns, Snell, & McBurnett, 2014]). These measures</p><p>range in the extent of psychometric and normative infor-</p><p>mation available to support their use. other measures</p><p>Table4.1 Ratings ofInstruments Used forDiagnosis</p><p>Instrument Norms</p><p>Internal</p><p>Consistency</p><p>Inter- Rater</p><p>Reliabilitya</p><p>Test– Retest</p><p>Reliability</p><p>Content</p><p>Validity</p><p>Construct</p><p>Validity</p><p>Validity</p><p>Generalization</p><p>Clinical</p><p>Utility</p><p>Highly</p><p>Recommended</p><p>narrowband ADHD Rating Scales</p><p>ADHD Rating Scale- 5</p><p>Parent E E nA G A G E A ✓</p><p>Teacher E E nA G A G E A ✓</p><p>Conners 3 DSM- IV- TR Symptom Scales</p><p>Parent E E nA A G G E A ✓</p><p>Teacher E E nA A G G E A ✓</p><p>ADDES- 4</p><p>Parent E E nA A G A E A</p><p>Teacher E E nA A G A E A</p><p>Structured Interviews</p><p>DISC- IV nA nR A A G G G A</p><p>a This column reflects inter- rater agreement between clinical judges, and this information is not available for most measures where, instead, parent and</p><p>teacher agreement is more commonly assessed.</p><p>Note: ADDES- 4=Attention- Deficit Disorder Evaluation Scales; DISC- IV=Diagnostic Interview Schedule for Children- IV; A=Adequate; G=Good;</p><p>E=Excellent; nR=not Reported; nA=not Applicable.</p><p>52 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>52</p><p>that are described for assessing ADHD offer content that</p><p>is not entirely consistent with DSM criteria and are not</p><p>recommended for diagnostic purposes. For example, the</p><p>Brown Attention- Deficit Disorder Scales for Children and</p><p>Adolescents (Brown, 2001)is a parent and teacher report</p><p>measure of the deficits in executive functioning that are</p><p>thought to be associated withADHD.</p><p>The DSM- IV- TR Inattentive and Hyperactive/</p><p>Impulsive Symptom Scales of the Conners 3rd Edition</p><p>(Conners 3; Conners, 2008)are derived from the longer</p><p>parent (110 items) and teacher (115 items) forms and map</p><p>onto the DSM symptoms of ADHD. Although not all of</p><p>these items are worded exactly as the DSM symptoms,</p><p>they appear synonymous. The third edition of this mea-</p><p>sure also includes validity scales to assess the accuracy and</p><p>integrity of responses, as well as brief yes/ no items assess-</p><p>ing impairment due to symptoms. The normative sample</p><p>is large and representative, and information regarding the</p><p>scores of a large clinical group of children with ADHD is</p><p>available. normative percentiles for the symptom scales</p><p>are available for the genders separately and combined.</p><p>The scales have good psychometric properties (Conners,</p><p>2008; see Table 4.1) and are well validated. The long his-</p><p>tory of the Conners Rating Scales in the study of ADHD</p><p>provides an extensive research background for this</p><p>measure.</p><p>The Attention- Deficit Disorder Evaluation Scales</p><p>(ADDES- 4; McCarney & Arthaud, 2013a, 2013b) are</p><p>updated versions of parent (46 items) and teacher (60</p><p>items) forms that yield inattention and hyperactive–</p><p>impulsive subscale scores reflecting DSM symptoms of</p><p>ADHD. Items were developed with input from diagnos-</p><p>tic and educational experts. The normative samples are</p><p>quite large and generally representative. Information</p><p>from a sample of children with ADHD (although method</p><p>of diagnosis is not clearly specified) also is available for</p><p>the parent and teacher versions. Separate age and gender</p><p>scores are calculated. The reliability and validity informa-</p><p>tion for the measure as reported in the manual is gener-</p><p>ally good (McCarney & Arthaud, 2013a, 2013b; see Table</p><p>4.1); however, a limited number of independent valida-</p><p>tion studies are available, particularly for the most recent</p><p>fourth edition of the measure.</p><p>We note that several briefer measures of ADHD symp-</p><p>toms also exist and are used primarily for screening pur-</p><p>poses (e.g., Conners 3 ADHD Index). one prominent</p><p>example is the Strengths and Difficulties Questionnaire</p><p>(SDQ) Hyperactivity/ Inattention Subscale (Goodman,</p><p>1997). This five- item scale has reasonable psychomet-</p><p>ric properties and normative data and appears useful</p><p>in detecting possible cases of ADHD (Algorta, Dodd,</p><p>Stringaris, & Youngstrom, 2016; Goodman, 2001). The</p><p>SDQ is available free of charge, is easy to score, and</p><p>includes subscales reflecting other problems— clear</p><p>advantages in a screening measure.</p><p>Structured Interviews</p><p>We included one structured interview, the Diagnostic</p><p>Interview Schedule for Children- IV (DISC- IV; Shaffer,</p><p>Fisher, Lucas, Dulcan, & Schwab- Stone, 2000), in</p><p>Table 4.1. It is</p><p>recognized that structured interviews often</p><p>have limited psychometric information. In particular,</p><p>the categorical model underlying these measures means</p><p>that normative information is considered unnecessary.</p><p>However, given the heavy reliance on structured inter-</p><p>views in many research and medical settings, we opted</p><p>to include at least one such measure. We caution the cli-</p><p>nician to consider carefully the costs of such interviews</p><p>(e.g., heavy investment of clinician and family time) in</p><p>contrast to the relatively low incremental validity offered</p><p>by these measures compared to parent and teacher ratings</p><p>of ADHD symptoms (e.g., Pelham, Fabiano, & Massetti,</p><p>2005; Vaughn & Hoza,2013).</p><p>The DISC- IV (Shaffer et al., 2000) maps directly</p><p>onto DSM- IV diagnostic criteria for a range of child dis-</p><p>orders, including ADHD, and it includes both symptom</p><p>and impairment questions. Given that DSM- 5 criteria for</p><p>ADHD are essentially unchanged from DSM- IV criteria,</p><p>the interview remains appropriate for assessment. The</p><p>DISC- IV is available in multiple languages and in parent</p><p>and youth versions. The child version has limited psycho-</p><p>metric properties, although some studies support the use of</p><p>combined responses across parents and children (Shaffer</p><p>etal., 2000). The highly structured nature of the DISC- IV</p><p>diminishes the importance of estimating inter- rater reliabil-</p><p>ity or inter- judge agreement for this measure. Psychometric</p><p>information for the fourth version of the DISC is somewhat</p><p>limited; however, combined with information on earlier</p><p>versions, support is generally adequate for the reliability of</p><p>the measure for making ADHD diagnoses (Shaffer etal.,</p><p>2000). Similarly, evidence supports the convergent valid-</p><p>ity of ADHD diagnoses made using the DISC- IV (e.g., de</p><p>nijs et al., 2004; Derks, Hudziak, Dolan, Ferdinand, &</p><p>Boomsma, 2006; McGrath, Handwerk, Armstrong, Lucas,</p><p>& Friman, 2004; Sciberras et al., 2013). It is noteworthy</p><p>that there is heavy reliance on this measure in many large</p><p>research studies.</p><p>other structured and semi- structured interviews used</p><p>in the assessment of ADHD include the Kiddie Schedule</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 53</p><p>53</p><p>for Affective Disorders and Schizophrenia (K- SADS;</p><p>Kaufman et al., 1997) and the Child and Adolescent</p><p>Psychiatric Assessment (CAPA; Angold & Costello, 2000).</p><p>As with the DISC- IV, these interviews typically have not</p><p>been subjected to extensive psychometricstudy.</p><p>Measures Not Useful inthe Assessment</p><p>ofADHD Diagnoses</p><p>The current diagnostic criteria for ADHD remain rela-</p><p>tively subjective, and the drive to develop and access</p><p>more objective indicators of the disorder has been strong.</p><p>Anumber of cognitive performance measures have been</p><p>proposed as useful in this regard, many of which are ver-</p><p>sions of continuous performance tests. Some of these</p><p>measures have come considerable distances in providing</p><p>normative information, evidence of stability over time,</p><p>and sensitivity to the effects of medication treatments</p><p>(e.g., the Conners CPT II [Conners & MHS Staff,2000]</p><p>and the objective QbTest [Ramtvedt, Røinås, Aabech, &</p><p>Sundet,2013]), yet they remain limited in their clinical</p><p>utility (Hall etal., 2016). Although these measures offer</p><p>the promise of objective measurement of ADHD symp-</p><p>toms (in contrast to the subjectivity inherent in parent</p><p>and teacher reports), their relations to other measures of</p><p>ADHD symptoms often are modest, and there is limited</p><p>evidence to support their predictive or discriminant valid-</p><p>ity. In particular, scores on these measures produce high</p><p>rates of false- negative diagnoses such that normal range</p><p>scores are often found in children who meet diagnostic</p><p>criteria for ADHD according to other measures. Again,</p><p>none of these measures are, as yet, sufficiently developed</p><p>to meet the designated psychometric criteria for this vol-</p><p>ume or to be useful in making diagnostic decisions for</p><p>individual children (Duff & Sulla, 2015). Similarly, pat-</p><p>terns of subscale scores on intelligence tests, biological</p><p>markers such as blood tests or brain imaging have not</p><p>been of demonstrated use in the clinical assessment of</p><p>ADHD (e.g., Kasper, Alderson, & Hudec, 2012; Koocher,</p><p>McMann, Stout, & norcross,2015).</p><p>Overall Evaluation</p><p>Based on ease of use and predictive power, combining</p><p>information from teacher and parent versions of brief</p><p>DSM- 5- based rating scales appears to offer the best avail-</p><p>able option in the diagnosis of ADHD. Although child</p><p>self- report versions exist for several of the measures</p><p>reviewed, the validity of child report is typically lower</p><p>than that of parent or teacher reports, and for this reason</p><p>we have not included these versions. Structured and semi-</p><p>structured diagnostic interviews are a mainstay in research</p><p>on ADHD; however, evidence suggests that they may not</p><p>add incrementally to the diagnostic information gath-</p><p>ered more efficiently with rating scales (e.g., ostrander,</p><p>Weinfurt, Yarnold, & August, 1998; Pelham etal., 2005;</p><p>Vaughn & Hoza, 2013; Wolraich etal., 2003). We do note,</p><p>however, consistent with recommended pediatric and</p><p>psychiatric assessment guidelines (American Academy</p><p>of Child and Adolescent Psychiatry, 2007; American</p><p>Academy of Pediatrics, 2011), that there is a definite need</p><p>for additional information, perhaps gathered through par-</p><p>ent interviews or child self- report, to supplement rating</p><p>scales in order to fully assess for possible comorbid or dif-</p><p>ferential diagnoses, age of onset and history of symptoms,</p><p>and other important clinical information relevant to the</p><p>diagnosis ofADHD.</p><p>ASSESSMENT FORCASE CONCEPTUALIZATION</p><p>AND TREATMENT PLANNING</p><p>Three treatments have received empirical support</p><p>for childhood ADHD (Evans, owens, & Bunford,</p><p>2014):pharmacotherapy, behavioral treatment, and their</p><p>combination. In assessments for treatment planning, the</p><p>clinician is seeking information to assist with (a)develop-</p><p>ing a conceptualization of the factors contributing to the</p><p>child’s difficulties and prioritizing treatment targets or</p><p>goals (e.g., Which ADHD symptoms are most impairing</p><p>or most likely to respond quickly to treatment?), (b)match-</p><p>ing difficulties to recommended treatments (e.g., Do this</p><p>child’s primary difficulties match the ADHD problems</p><p>that have been targeted with behavioral or medication</p><p>treatments?), or (c) identifying environmental elements</p><p>that may be used in treatment (e.g., Does the teacher offer</p><p>rewards for academic work completed?). Information</p><p>regarding factors that may interfere with treatment suc-</p><p>cess (e.g., Does this child have a physical condition that</p><p>may limit the utility of medication?) or the child’s inter-</p><p>ests and strengths (e.g., sports interests or skills) also will</p><p>be useful.</p><p>In this section, we review measures that provide</p><p>information relevant to conceptualizing the nature of</p><p>the problems experienced by children with ADHD and</p><p>the planning of treatments specifically targeting ADHD</p><p>symptoms, symptom- related impairment, or possible</p><p>comorbid conditions. However, we caution the reader that</p><p>this focus is narrow and that much case conceptualiza-</p><p>tion and treatment planning for ADHD involves broader</p><p>54 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>54</p><p>consideration of co- occurring difficulties in child, family,</p><p>academic, or peer functioning. Pelham and colleagues</p><p>(2005), in their excellent review of evidence- based assess-</p><p>ments for ADHD, offer a cogent and convincing argu-</p><p>ment that adaptations and impairments in functioning,</p><p>rather than ADHD symptoms per se, should form the</p><p>basis for treatment planning in ADHD. Thus, adequate</p><p>treatment planning for ADHD necessitates gathering and</p><p>integrating information far beyond symptom or diagnostic</p><p>status. Information from a variety of sources, regarding a</p><p>wide range of child and family functioning, is necessary</p><p>to inform treatments that match the needs and resources</p><p>of each child and family. For example, the</p><p>clinician must</p><p>consider the child’s family, social and cultural context,</p><p>relevant medical and educational history and concerns,</p><p>the child’s and family’s goals for treatment, and available</p><p>treatment options. Although difficulties in domains such</p><p>as academics and social relationships are often closely</p><p>linked to ADHD (and may even be the result of ADHD</p><p>symptoms), assessment methodologies in these areas</p><p>are only briefly considered here. The parent– child rela-</p><p>tionship or parenting style, the parent’s psychological or</p><p>marital functioning, and the child’s peer relationships or</p><p>self- esteem are among the areas that might be considered</p><p>in a more comprehensive definition of treatment plan-</p><p>ning forADHD.</p><p>We refer the reader to chapters within this volume</p><p>and to other excellent child assessment resources (Frick</p><p>etal., 2010; Mash & Barkley, 2007)for detailed informa-</p><p>tion regarding assessment of the problems and conditions</p><p>that are frequently associated with ADHD and that often</p><p>figure prominently in conceptualizing the problems and</p><p>planning treatment for children with this disorder. We</p><p>cannot state strongly enough how important these other</p><p>domains of assessment are in planning treatments for</p><p>children with ADHD that will be maximally sensitive</p><p>to the child’s and the family’s needs and concerns and</p><p>that will also hold the greatest potential for altering not</p><p>only the child’s current functioning but also long- term</p><p>outcomes.</p><p>Overview ofMeasures forCase Conceptualization</p><p>and Treatment Planning</p><p>Broadband Checklists</p><p>Parent and teacher reports on broadband measures of</p><p>child psychopathology provide useful information in plan-</p><p>ning treatments for children with ADHD (see Table 4.2).</p><p>These measures provide insight into a range of difficulties,</p><p>in addition to ADHD, and may direct the clinician to</p><p>more in- depth assessments of coexisting disorders or disor-</p><p>ders that may account for ADHD- like symptoms. Scores</p><p>on these broadband measures also allow the clinician to</p><p>incorporate knowledge of potential comorbidities into</p><p>treatment planning as appropriate. For example, some</p><p>evidence suggests that behavioral treatments for ADHD</p><p>may have better outcomes among children with comor-</p><p>bid anxiety disorders (MTA Cooperative Group, 1999),</p><p>and behavioral treatments are empirically supported for</p><p>addressing the oppositional or conduct disorder problems</p><p>or both that are frequently comorbid with ADHD (e.g.,</p><p>Powell etal.,2014).</p><p>We include only broadband rating scales with sub-</p><p>scales specifically targeting ADHD symptoms or behav-</p><p>iors. These measures vary in the extent to which their</p><p>subscales map directly onto DSM ADHD criteria or</p><p>symptom dimensions. For example, both the Attention</p><p>Problems subscale of the Child Behavior Checklist and</p><p>the ADHD Index of the Conners 3 include a mixture</p><p>of inattention and impulsivity/ hyperactivity items and</p><p>are not comprehensive in covering DSM symptoms.</p><p>Thus, these subscale measures typically cannot be sub-</p><p>stituted for the narrowband checklists described previ-</p><p>ously. However, the subscales relevant to attention or</p><p>hyperactivity– impulsivity found on many broadband</p><p>checklists will offer supplemental information that may</p><p>be useful in arriving at diagnostic decisions, particularly</p><p>in complex cases. Because the role of these broadband</p><p>measures in treatment planning is to provide a screening-</p><p>level assessment of a range of behavior problems, we</p><p>require satisfactory psychometric properties at the level of</p><p>subscale scores (as well as total scores).</p><p>The parent (Children Behavior Checklist [CBCL])</p><p>and teacher (Teacher Report Form [TRF]) versions from</p><p>the Achenbach System of Empirically Based Assessment</p><p>(ASEBA; Achenbach & Rescorla, 2001)are well- known and</p><p>widely used measures, available in several languages, that</p><p>have lengthy clinical and research traditions. AYouth Self-</p><p>Report form is available for children aged 11 to 18years,</p><p>but it is not described here. The parent and teacher check-</p><p>lists are used for children 6 to 18years of age (a version for</p><p>younger children also is available), and norms are based</p><p>on large representative normative samples, as well as sam-</p><p>ples of clinic- referred children (although norms specific</p><p>to different clinical diagnoses are not generally available).</p><p>There are 118 items, requiring 15 to 20 minutes to com-</p><p>plete, as well as subscales assessing competence (although</p><p>the psychometric properties of the competence subscales</p><p>are generally not as strong as the behavior problem scales).</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 55</p><p>55</p><p>The ASEBA provides empirically derived subscales that</p><p>are similar across the multiple informant versions of the</p><p>measure and assess a variety of emotional and behavior</p><p>problems, such as attention, rule breaking, and aggres-</p><p>sion. The measures also yield overall Internalizing and</p><p>Externalizing scores, as well as rationally derived subscales</p><p>that map onto DSM diagnostic categories. The similarity</p><p>in item content across informants allows for the calcula-</p><p>tion of inter- rater agreements, and information is available</p><p>to compare levels of agreement to those in the normative</p><p>sample. Considerable validity evidence is presented in the</p><p>ASEBA manual, and numerous reviews provide additional</p><p>evidence of the convergent, discriminant, and content</p><p>validity of the measures (e.g., Frick etal., 2010; Gladman</p><p>& Lancaster, 2003; McConaughy, 2001; Pelham et al.,</p><p>2005). As indicated in Table 4.2, both parent and teacher</p><p>versions have solid psychometric properties, and evidence</p><p>supports the incremental validity of gathering information</p><p>from both sources (e.g., Ang etal., 2012; Hanssen- Bauer,</p><p>Langsrud, Kvernmo, & Heyerdahl, 2010). However, as</p><p>with many of the measures reviewed in this chapter, few</p><p>studies have examined the incremental validity or clinical</p><p>utility of ASEBA scores.</p><p>The Behavior Assessment System for Children, 3rd</p><p>Edition (BASC- 3; Reynolds & Kamphaus, 2015) is a</p><p>multidimensional measure of adaptive and problem</p><p>behaviors that has teacher and parent versions for chil-</p><p>dren aged 6 to 11 years (as well as preschool and ado-</p><p>lescent versions not considered here). The measure takes</p><p>approximately 10 to 20 minutes to complete and has mul-</p><p>tiple language versions. The BASC- 3 provides rationally</p><p>derived clinical subscales including Hyperactivity and</p><p>Attention Problems, as well as a number of other problem</p><p>dimensions and composite scores for Adaptive Behavior,</p><p>Externalizing and Internalizing Problems, and a total</p><p>Behavioral Symptoms Index. The teacher version also has</p><p>scales related to School Problems. one advantage of the</p><p>BASC- 3 is that it offers validity checks to assist the clinician</p><p>Table4.2 Ratings ofInstruments Used forCase Conceptualization and Treatment Planning</p><p>Instrument Norms</p><p>Internal</p><p>Consistency</p><p>Inter- Rater</p><p>Reliabilitya</p><p>Test– Retest</p><p>Reliability</p><p>Content</p><p>Validity</p><p>Construct</p><p>Validity</p><p>Validity</p><p>Generalization</p><p>Clinical</p><p>Utility</p><p>Highly</p><p>Recommended</p><p>Broadband Rating Scales</p><p>ASEBA</p><p>Parent:CBCL E G nA G G G E A ✓</p><p>Teacher:TRF E E nA G G G E A ✓</p><p>BASC- 3</p><p>Parent E G nA A G G E A ✓</p><p>Teacher E E nA A G G E A ✓</p><p>Conners 3</p><p>Parent E E nA G G G E A ✓</p><p>Teacher E E nA G G G E A ✓</p><p>Vanderbilt</p><p>Parent G E nA A A G E A</p><p>Teacher G E nA nR A G E A</p><p>Measures of Impairment</p><p>VABS- II</p><p>Parent E E nA A G G G A ✓</p><p>Teacher E E nA nR G G G A ✓</p><p>CAFAS nR A E nR A G G A</p><p>IRS</p><p>Parent nR nR nA G A G A A</p><p>Teacher G nR nA G A G G A ✓</p><p>CoSS</p><p>Parent E E nA A G A G A ✓</p><p>Teacher E E nA A G A G A ✓</p><p>a This column reflects inter- rater agreement between clinical judges, and this information in not available for most measures where, instead, parent and</p><p>teacher agreement is more commonly assessed.</p><p>Note: ASEBA=Achenbach System of Empirically Based Assessment; CBCL=Child Behavior Checklist; TRF=Teacher Report Form; BASC- 3=Behavior</p><p>Assessment System for Children- 3; Vanderbilt=Vanderbilt ADHD Diagnostic Parent and Teacher</p><p>Rating Scales; VABS- II=Vineland Adaptive Behavior</p><p>Scales, 2nd Edition; CAFAS=Child and Adolescent Functional Assessment Scale; IRS=Impairment Rating Scale; CoSS=Children’s organizational</p><p>Skills Scale; A=Adequate; G=Good; E=Excellent; nR=not Reported; nA=not Applicable.</p><p>56 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>56</p><p>in detecting careless or untruthful responding, misunder-</p><p>standing, or other threats to validity. BASC- 3 norms are</p><p>based on a large representative sample and are available</p><p>both in aggregate form and differentiated according to</p><p>the age, gender, and clinical status of the child. As noted</p><p>previously, not only does this measure evaluate behavioral</p><p>and emotional problems but also it identifies the child’s</p><p>positive attributes, an aspect with obvious use in planning</p><p>treatment. Current psychometric information is available</p><p>in the measure’s manual (Reynolds & Kamphaus, 2015).</p><p>Given the relative recency of this measure, in some cases</p><p>in Table 4.2 we have relied on the psychometric infor-</p><p>mation available for earlier parent and teacher versions,</p><p>specifically the BASC- 2 (e.g., Kamphaus, Reynolds,</p><p>Hatcher, & Kim, 2004; Pelham etal., 2005; Sandoval &</p><p>Echandia,1994).</p><p>The Conners 3 (Conners, 2008) is the most recent</p><p>revision to a set of scales that have been closely allied with</p><p>research and clinical work in ADHD for many years. The</p><p>Conners 3 has multiple language versions and there are</p><p>parent and teacher versions (as well as a youth self- report</p><p>not described here), each with both short (5 to 10 minutes)</p><p>and long (15 to 20 minutes) forms available. The short</p><p>forms focus on a range of behavior problems, whereas</p><p>the longer forms also include subscales assessing DSM</p><p>symptom criteria for ADHD and oppositional defiant and</p><p>conduct disorders, as well as screening and impairment</p><p>scales. norms are based on a large representative sample</p><p>of 6- to 18- year- old children and are also available for a</p><p>clinical sample. norms are available for the genders com-</p><p>bined, with some scales also having gender- specific infor-</p><p>mation. The Conners 3 manual and published reviews</p><p>of the measure outline the strong psychometric proper-</p><p>ties of both the current and earlier versions of the mea-</p><p>sure (e.g., Conners, 2008; Kao & Thomas, 2010; Pelham</p><p>etal.,2005).</p><p>Finally, the Vanderbilt ADHD Diagnostic Parent and</p><p>Teacher Rating Scales (Bard, Wolraich, neas, Doffing, &</p><p>Beck, 2013; Wolraich etal., 1998, 2003; Wolraich, Bard,</p><p>neas, Doffing, & Beck, 2013)are another DSM- based set</p><p>of symptom rating scales that include ADHD symptoms,</p><p>oppositional and conduct problems, as well as anxiety and</p><p>depression items. norms are based on a relatively large</p><p>sample, but of limited representativeness. Preliminary</p><p>psychometric evidence is available, although further vali-</p><p>dation is needed.</p><p>other broadband questionnaires have been developed</p><p>that may prove useful in treatment planning for ADHD,</p><p>although these measures require further research. For</p><p>example, the Child Symptom Inventory- 4 (CSI- 4; Gadow</p><p>& Sprafkin, 2002)assesses a variety of DSM- IV emotional</p><p>and behavioral disorders in children between ages 5 and</p><p>12years. Although a DSM- 5 version of this scale is listed</p><p>on the authors’ webpage, this version has not been fully</p><p>evaluated.</p><p>Measures ofImpairment</p><p>As noted previously, there is a growing and appropri-</p><p>ate focus on adaptive functioning as central to under-</p><p>standing and treating ADHD, with efforts underway to</p><p>develop a core set of ability and disability concepts rel-</p><p>evant to ADHD within the International Classification</p><p>of Functional Disability and Health (Schipper et al.,</p><p>2015). Global and multidimensional measures of impair-</p><p>ment are valuable in a comprehensive assessment of the</p><p>functioning of children with ADHD. In particular, these</p><p>measures are likely to be useful in decisions regarding the</p><p>need for treatment and in identifying appropriate treat-</p><p>ment foci. We concur with arguments made by others</p><p>(e.g., Pelham etal., 2005) that impairments in adaptive</p><p>behavior must figure prominently in treatment planning</p><p>and monitoring for children with ADHD, more so than</p><p>absolute levels of ADHD symptoms. As noted in our</p><p>description of measures useful for diagnosis of ADHD,</p><p>several of these measures now include items tapping</p><p>impairment, although these are typically brief ratings.</p><p>Thus, currently, the clinician must choose between brief</p><p>or promising measures specific to ADHD (e.g., ADHD</p><p>Rating Scale- 5 impairment items) and well- established</p><p>measures of adaptive behavior that are broad and may not</p><p>be particularly appropriate to ADHD- related difficulties</p><p>(e.g., the Vineland Adaptive Behavior Scales; Sparrow,</p><p>Cicchetti, & Bala,2005).</p><p>The Vineland Adaptive Behavior Scales, Second</p><p>Edition (VABS- II; Sparrow etal., 2005)has been a lead-</p><p>ing measure of the personal and social skills needed for</p><p>everyday living. A 2016 revision of the measure (VABS- 3;</p><p>Sparrow, Cicchetti, & Saulnier, 2016) includes updated</p><p>items, forms, and norms. However, the revision is not yet</p><p>widely available nor used extensively in research; there-</p><p>fore, we focus our comments on the VABS- II. Although</p><p>typically used to identify individuals with developmental</p><p>problems, some evidence supports the use the VABS in</p><p>groups of children with ADHD (e.g., Craig etal., 2015;</p><p>Ware etal., 2014). Consisting of a Survey Interview Form,</p><p>Parent/ Caregiver Rating Form, Expanded Interview</p><p>Form, and a Teacher Rating Form, the VABS- II requires</p><p>20 to 60 minutes to complete. It is organized around four</p><p>behavior domains (communication, daily living skills,</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 57</p><p>57</p><p>socialization, and motor skills) and has demonstrated</p><p>strong psychometric properties. norms for the parent and</p><p>teacher rating scale forms are based on large representa-</p><p>tive groups, including a variety of clinical groups, and the</p><p>reliability and validity of scores on the measure range from</p><p>adequate to excellent, as reported in the manual (Sparrow</p><p>etal., 2005; see Table4.2).</p><p>The Child and Adolescent Functional Assessment</p><p>Scale (CAFAS; Hodges & Wong, 1996) is an additional</p><p>multidimensional measure of impairment that may</p><p>serve as an aid in case conceptualization and treatment</p><p>planning for children with ADHD. The CAFAS uses</p><p>interviewer ratings to assess a child’s (ages 7 to 17years)</p><p>degree of impairment due to emotional, behavioral, or</p><p>psychiatric problems. Consisting of 315 items and mea-</p><p>suring functioning in areas such as school, home, and</p><p>community and behaviors such as emotional regulation,</p><p>self- harm, and substance use, the CAFAS requires only</p><p>10 minutes to complete. Although normative data are not</p><p>available, reliability and validity information for this mea-</p><p>sure are generally satisfactory, as indicated in Table4.2.</p><p>The Impairment Rating Scale (IRS; Fabiano et al.,</p><p>2006) was developed specifically to assess the areas of</p><p>functioning that are frequently problematic for children</p><p>with ADHD. Parent and teacher versions are available in</p><p>the public domain, with questions pertaining to areas such</p><p>as academic progress, self- esteem, peer relations, problem</p><p>behavior, impact on the family, and overall function-</p><p>ing. Preliminary norms are available only for the teacher</p><p>version. Test– retest reliability has been established over</p><p>periods up to 1year. Within samples of ADHD and con-</p><p>trol children, convergent and discriminant validity have</p><p>been demonstrated, and evidence suggests that parent</p><p>and teacher IRS ratings accounted for unique variance</p><p>in predicting child outcomes beyond ADHD symptoms</p><p>(Fabiano etal., 2006; see Table4.2).</p><p>A recent, useful addition to measures assessing dif-</p><p>ficulties related to ADHD, particularly in the academic</p><p>domain, is the Children’s organizational Skills Scales</p><p>(CoSS; Abikoff & Gallagher, 2009). With parent,</p><p>teacher, and child (not reported here) versions, this</p><p>measure taps children’s difficulties with task</p><p>planning,</p><p>organized actions, and memory and materials manage-</p><p>ment, and also includes questions specifically measuring</p><p>the impairment caused by these organizational difficul-</p><p>ties. The measure has good psychometric properties,</p><p>and norm information is available based on a large,</p><p>representative sample. Thus, it will offer useful informa-</p><p>tion, particularly for assessing and planning for school</p><p>interventions.</p><p>Observational Measures</p><p>Informal observations of children in clinical settings have</p><p>little clinical utility in detecting ADHD or planning for</p><p>its treatment (e.g., Edwards etal., 2005). However, more</p><p>structured observational measures do have potential</p><p>utility in treatment planning. Using such measures can</p><p>clearly identify a child’s ADHD symptoms and the impair-</p><p>ments that ensue from these symptoms, which should be</p><p>targeted in treatment plans. Unfortunately, despite vari-</p><p>ability in the psychometric information available, all the</p><p>measures located failed to demonstrate adequate levels of</p><p>the criteria used for table inclusion. For example, these</p><p>observational measures seldom have norms or report the</p><p>temporal stability of scores. These limitations preclude</p><p>the inclusion of these measures in the tables; however, we</p><p>do offer some suggestions for available observational mea-</p><p>sures designed for classroom use or for assessing parent–</p><p>child interactions.</p><p>The Direct observation Form (DoF) is an observa-</p><p>tional component of the ASEBA (Achenbach & Rescorla,</p><p>2001) and uses a 10- minute observation of the child’s</p><p>behavior in a classroom context, recommended to be</p><p>repeated on three to six occasions. Although the measure</p><p>includes a narrative and ratings of the child’s behavior,</p><p>psychometric information is reported primarily for the</p><p>time sampling of 96 behaviors (the behaviors overlap with</p><p>items on the CBCL and TRF). For normative compari-</p><p>sons, the DoF recommends that two nonproblem chil-</p><p>dren be observed simultaneously with the target child in</p><p>order to provide individualized norms. Although the man-</p><p>ual also presents norms based on moderate- size samples</p><p>of clinic- referred and nonproblem children, the value of</p><p>these norms is likely to be limited by the variability across</p><p>classroom contexts (e.g., variables such as classroom rules,</p><p>physical structure, and ratio of problem to nonproblem</p><p>children will undoubtedly influence the rates of problem</p><p>behaviors displayed by children). The manual reports</p><p>moderate to high levels of inter- rater reliability using the</p><p>DoF, and DoF scores correlate in expected ways with</p><p>other measures and with clinical status (Achenbach &</p><p>Rescorla, 2001). In combination with an ASEBA form</p><p>used to facilitate observations of child behavior in psy-</p><p>chological test situations (the Test observation Form),</p><p>some evidence points to the ability of these observations</p><p>to assess unique variance in child behavior beyond parent</p><p>or teacher ratings (McConaughy etal.,2010).</p><p>Another potential measure useful in tapping the</p><p>classroom difficulties of children with ADHD is the</p><p>Behavioral observation of Students in Schools (BoSS;</p><p>58 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>58</p><p>Shapiro, 2011). This measure, with many computerized</p><p>and interactive features, taps task engagement and off-</p><p>task behaviors (both inattentive and hyperactive) during</p><p>classroom activities. Evidence of inter- rater reliability is</p><p>provided (although several hours of training are required),</p><p>and the observations have been shown to discriminate</p><p>between children with ADHD and typically developing</p><p>classmates (DuPaul etal.,2004).</p><p>To assess aspects of ADHD that are problematic</p><p>within parent– child interactions, a number of observa-</p><p>tional systems developed in research contexts are avail-</p><p>able, although most are too complex to provide reliable</p><p>estimates in clinical practice. Perhaps one exception to</p><p>this is the Behavioral Coding System (BCS; McMahon</p><p>& Forehand, 2003). Using the BCS, the clinician codes</p><p>parent and child behaviors in two 5- minute interactions:a</p><p>free- play situation and a situation in which the parent</p><p>directs the interaction. The presence of six parent behav-</p><p>iors (rewards, commands, time out, etc.) and three child</p><p>behaviors (compliance, noncompliance, etc.) is recorded</p><p>every 30 seconds, and the sequence of behaviors speci-</p><p>fying parental antecedents, child responses, and paren-</p><p>tal consequences can be analyzed. Such information is</p><p>readily translated into treatment goals, particularly for</p><p>behavioral treatments. Interobserver agreement and test–</p><p>retest reliability of the BCS are adequate, and the system</p><p>is sensitive to differences in compliance between clinic-</p><p>referred and nonreferred children (evidence reviewed in</p><p>McMahon & Forehand,2003).</p><p>Finally, we highlight that observations of individual-</p><p>ized behavioral targets, by parents or teachers, are likely to</p><p>be useful in conceptualizing and planning for treatment</p><p>of each child’s difficulties. For example, with clear and</p><p>simple behavioral definitions, frequency counts of prob-</p><p>lematic behaviors that are relevant for each particular</p><p>child (e.g., times out of seat in the classroom and failure</p><p>to complete assigned household chores) can be made and</p><p>may serve as an integral part of treatment planning.</p><p>Overall Evaluation</p><p>Broadband parent and teacher checklists provide essen-</p><p>tial information regarding behavior problems that may</p><p>accompany or result from ADHD and which may inform</p><p>treatment planning. These measures are typically well</p><p>developed, possess solid psychometric properties, and</p><p>the clinician can feel confident in the information they</p><p>provide. However, even more relevant information for</p><p>treatment planning is likely to be derived from assess-</p><p>ment of the child’s functioning and impairments in daily</p><p>home and classroom situations. Emerging measures</p><p>of impairment, particularly those designed to be sensi-</p><p>tive to the aspects of functioning most closely linked to</p><p>ADHD, have clear potential in identifying appropriate</p><p>treatment targets and assisting the clinician in prioritiz-</p><p>ing these targets. In a similar fashion, the context- specific</p><p>and objective nature of observational assessments of the</p><p>child’s behavior, both in school and at home, have great</p><p>potential for treatment planning. These measures may</p><p>also assess environmental antecedents and consequences</p><p>of the child’s behaviors, yielding information of immedi-</p><p>ate relevance to the planning of behavioral interventions.</p><p>An important future direction in the development of any</p><p>of these assessment measures will be to work to estab-</p><p>lish their incremental validity and clinical utility within</p><p>the context of multiple sources and types of assessment</p><p>information.</p><p>ASSESSMENT FORTREATMENT MONITORING</p><p>AND TREATMENT EVALUATION</p><p>In conducting assessments to monitor and evaluate</p><p>treatment implementation or progress in children with</p><p>ADHD, there is a need for measures that are reliable</p><p>over time, sensitive to relatively small changes in behav-</p><p>ior or symptoms, and practical to use on a frequent basis</p><p>(e.g., brief and inexpensive). In monitoring medication</p><p>treatments, measurement of side effects also is recom-</p><p>mended (e.g., Barkley Side Effects Rating Scale; Barkley</p><p>& Murphy, 2006), although standardized measures for</p><p>this purpose are not available. one prominent issue in</p><p>considering assessment measures to be used in treat-</p><p>ment monitoring is the stability of scores over time and</p><p>the vulnerability of measures to the effects of repeated</p><p>assessments (Solanto & Alvir, 2009). For example, does</p><p>a decrease in symptom severity on a measure over time</p><p>reflect the benefits of treatment, or could the change</p><p>be predicted solely on the basis of regression of scores</p><p>to the mean? If treatment effects are to be assessed over</p><p>a longer period, the availability of age norms also will</p><p>be important in order to place score changes within the</p><p>appropriate context of developmental changes in the</p><p>behavior. As with disagreements in diagnostic informa-</p><p>to</p><p>guide decisions about methods to include.</p><p>Assessment for Diagnosis: This section deals with</p><p>assessment measures and strategies used specifically for</p><p>formulating a diagnosis. Authors were asked to focus</p><p>on best practices and were encouraged to comment on</p><p>important conceptual and practical issues in diagnosis</p><p>and differential diagnosis.</p><p>Assessment for Case Conceptualization and Treatment</p><p>Planning: This section presents assessment measures</p><p>and strategies used to augment diagnostic information</p><p>to yield a full psychological case conceptualization that</p><p>can be used to guide decisions on treatment planning.</p><p>Specifically, this section addresses the domains that the</p><p>research literature indicates should be covered in an EBA</p><p>to develop (a)a clinically meaningful and useful case con-</p><p>ceptualization and (b) a clinically sensitive and feasible</p><p>service/ treatment plan (which may or may not include</p><p>the involvement of other professionals).</p><p>Assessment for Treatment Monitoring and Treatment</p><p>Outcome:In this third section, assessment measures and</p><p>strategies were reviewed that can be used to (a)track the</p><p>progress of treatment and (b)evaluate the overall effect of</p><p>treatment on symptoms, diagnosis, and general function-</p><p>ing. Consistent with the underlying thrust of the volume,</p><p>the emphasis is on assessment options that have support-</p><p>ing empirical evidence.</p><p>Within each of the three assessment sections, standard</p><p>tables are used to provide summary information about</p><p>the psychometric characteristics of relevant instruments.</p><p>Rather than provide extensive psychometric details in</p><p>the text, authors were asked to use these rating tables to</p><p>convey information on the psychometric adequacy of</p><p>instruments. To enhance the utility of these tables, rather</p><p>than presenting lists of specific psychometric values for</p><p>each assessment tool, authors were asked to make global</p><p>ratings of the quality of the various psychometric indices</p><p>(e.g., norms, internal reliability, and construct validity)</p><p>as indicated by extant research. Details on the rating sys-</p><p>tem used by the authors are presented in the introductory</p><p>chapter. Our goal is to have these tables serve as valuable</p><p>summaries for readers. In addition, by using the tables to</p><p>present psychometric information, the authors were able</p><p>to focus their chapters on both conceptual and practical</p><p>issues without having to make frequent detours to discuss</p><p>psychometrics.</p><p>At the conclusion of each of these three main sec-</p><p>tions there is a subsection titled Overall Evaluation that</p><p>includes concise summary statements about the scientific</p><p>adequacy and clinical relevance of currently available</p><p>measures. This is where authors comment on the avail-</p><p>ability (if any) of demonstrated scientific value of follow-</p><p>ing the assessment guidance they have provided.</p><p>Conclusions and Future Directions:This final section</p><p>in each chapter provides an overall sense of the scope</p><p>and adequacy of the assessment options available for the</p><p>disorder/ condition, the limitations associated with these</p><p>options, and possible future steps that could be taken to</p><p>remedy these limitations. Some authors also used this sec-</p><p>tion to raise issues related to the challenges involved in</p><p>trying to ensure that clinical decision- making processes</p><p>underlying the assessment process (and not just the assess-</p><p>ment measures themselves) are scientificallysound.</p><p>ACKNOWLEDGMENTS</p><p>To begin with, we express our gratitude to the authors. They</p><p>diligently reviewed and summarized often- voluminous</p><p>assessment literatures and then presented this informa-</p><p>tion in a clinically informed and accessible manner. The</p><p>authors also worked hard to implement the guidelines we</p><p>provided for both chapter structure and the ratings of vari-</p><p>ous psychometric characteristics. Their efforts in construct-</p><p>ing their chapters are admirable, and the resulting chapters</p><p>consistently provide invaluable clinical guidance.</p><p>We also thank Sarah Harrington, Senior Editor for clini-</p><p>cal psychology at Oxford University Press, for her continued</p><p>interest in the topic and her ongoing support for the book.</p><p>We greatly appreciate her enthusiasm and her efficiency</p><p>throughout the process of developing and producing this</p><p>second edition. We are also indebted to Andrea Zekus,</p><p>Editor at Oxford University Press, who helped us with the</p><p>process of assembling the book from start to finish. Her assis-</p><p>tance with the myriad issues associated with the publication</p><p>process and her rapid response to queries was invaluable.</p><p>Finally, we thank all the colleagues and contributors</p><p>to the psychological assessment and measurement litera-</p><p>tures who, over the years, have shaped our thinking about</p><p>assessment issues. We are especially appreciative of the</p><p>input from those colleagues who have discussed with us</p><p>the host of problems, concerns, challenges, and promises</p><p>associated with efforts to promote greater awareness of the</p><p>need for EBA within professional psychology.</p><p>xiv PREFACE</p><p>References</p><p>American Psychiatric Association. (2013). Diagnostic and sta-</p><p>tistical manual of mental disorders (5th ed.). Arlington,</p><p>VA:American Psychiatric Publishing.</p><p>American Psychological Association Presidential Task Force on</p><p>Evidence- Based Practice. (2006). Evidence- based prac-</p><p>tice in psychology. American Psychologist, 61, 271– 285.</p><p>Arbisi, P. A., & Beck, J. G. (2016). Introduction to the special</p><p>series “Empirically Supported Assessment.” Clinical</p><p>Psychology:Science and Practice, 23, 323– 326.</p><p>Becker, K. D., Boustani, M., Gellatly, R., & Chorpita, B. F.</p><p>(2017). Forty years of engagement research in children’s</p><p>mental health services: Multidimensional measure-</p><p>ment and practice elements. Journal of Clinical Child</p><p>& Adolescent Psychology. Advance online publication.</p><p>Dozois, D. J. A., Mikail, S., Alden, L. E., Bieling, P. J.,</p><p>Bourgon, G., Clark, D. A., . . . Johnston, C. (2014).</p><p>The CPA Presidential Task Force on Evidence- Based</p><p>Practice of Psychological Treatments. Canadian</p><p>Psychology, 55, 153– 160.</p><p>Institute of Medicine. (2001). Crossing the quality chasm:A</p><p>new health system for the 21st century. Washington,</p><p>DC:National AcademiesPress.</p><p>Ionita, F., & Fitzpatrick, M. (2014). Bringing science to</p><p>clinical practice: A Canadian survey of psychological</p><p>practice and usage of progress monitoring measures.</p><p>Canadian Psychology, 55, 187– 196.</p><p>Jensen- Doss, A. (2015). Practical, evidence- based clinical</p><p>decision making: Introduction to the special series.</p><p>Cognitive and Behavioral Practice, 22,1– 4.</p><p>Lambert, M. J. (2017). Maximizing psychotherapy outcome</p><p>beyond evidence- based medicine. Psychotherapy and</p><p>Psychosomatics, 86,80– 89.</p><p>Nathan, P. E., & Gorman, J. M. (Eds.). (2015). A guide to</p><p>treatments that work (4th ed.). NewYork, NY:Oxford</p><p>UniversityPress.</p><p>Norcross, J. C. (Ed.). (2011). Psychotherapy relationships</p><p>that work: Evidence- based responsiveness (2nd ed.).</p><p>NewYork, NY:Oxford UniversityPress.</p><p>Overington, L., Fitzpatrick, M., Hunsley, J., & Drapeau, M.</p><p>(2015). Trainees’ experiences using progress monitor-</p><p>ing measures. Training and Education in Professional</p><p>Psychology, 9, 202– 209.</p><p>Sackett, D. L., Rosenberg, W. M.C., Gray, J. A.M., Haynes,</p><p>R. B., & Richardson, W. S. (1996). Evidence based</p><p>medicine:What it is and what it is not. British Medical</p><p>Journal, 312,71– 72.</p><p>Vacha- Haase, T., & Thompson, B. (2011). Score reliabil-</p><p>ity: A retrospective look back at 12 years of reliability</p><p>generalization studies. Measurement and Evaluation in</p><p>Counseling and Development, 44, 159– 168.</p><p>About theEditors</p><p>John Hunsley, PhD, is Professor of Psychology in the</p><p>School of Psychology at the University of Ottawa and</p><p>is a Fellow of the Association of State and Provincial</p><p>Psychology Boards and the Canadian Psychological</p><p>Association. He has served as a journal editor, an edito-</p><p>rial board member for several journals, and an editorial</p><p>consultant for many journals in psychology. He has pub-</p><p>lished more than 130 articles,</p><p>tion gathered from multiple sources, discrepancies in</p><p>reports of treatment- related changes in child behaviors</p><p>are expected across informants and settings. Again, cli-</p><p>nicians must struggle with how to combine or prioritize</p><p>the multiple bits of information in reaching an overall</p><p>conclusion regarding the progress of treatment.</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 59</p><p>59</p><p>In this section, we consider measures that have dem-</p><p>onstrated not only basic psychometric properties but also</p><p>sensitivity to change due to medication, psychosocial</p><p>interventions, or both. Although several measures meet</p><p>these criteria, almost all of the evidence of this sensitiv-</p><p>ity is derived from studies aggregating across groups of</p><p>children, and information regarding performance of</p><p>the measures in individual cases awaits investigation.</p><p>Furthermore, it is common in research studies to amal-</p><p>gamate multiple measures into composite scores to cre-</p><p>ate more reliable scores for use in treatment comparisons</p><p>(e.g., MTA Cooperative Group, 2004). Although advan-</p><p>tageous from a research perspective, this approach limits</p><p>the ability of such studies to inform us regarding the sensi-</p><p>tivity to treatment of any of the measures used in isolation</p><p>or with individual children.</p><p>Overview ofMeasures forTreatment Monitoring</p><p>and Treatment Evaluation</p><p>Narrowband ADHD Checklists</p><p>no evidence of treatment sensitivity has yet been pub-</p><p>lished based on either the ADHD Rating Scale- 5 or the</p><p>Conners 3 DSM- IV- TR Symptom Scales. However, for</p><p>the ADHD Rating Scale- 5, evidence from the ADHD-</p><p>IV Rating Scale version (relatively unchanged) indicates</p><p>sensitivity to medication treatment, at a group level, in</p><p>numerous studies (e.g., Huss et al., 2016; Kollins et al.,</p><p>2011). other symptom- level measures, although lacking</p><p>in some psychometric characteristics, may bear consid-</p><p>eration for treatment monitoring depending on the spe-</p><p>cific clinical needs of each case. For example, the IoWA</p><p>(Loney & Milich, 1982) is a 10- item measure derived</p><p>from an older version of the Conners’ Teacher Rating</p><p>Scale that assesses inattentive– overactive and aggressive</p><p>symptoms. Considerable evidence supports the construct</p><p>validity, internal consistency, and stability of scores on the</p><p>measure (Johnston & Pelham, 1986; Loney & Milich,</p><p>1982; nolan & Gadow, 1994; Waschbusch & Willoughby,</p><p>2008). At a group level, the measure has been proven use-</p><p>ful in multiple studies assessing the effectiveness of medi-</p><p>cation treatments for ADHD (e.g., Maneeton, Maneeton,</p><p>Intaprasert, & Woottiluk,2014).</p><p>The BASC- 3 Flex Monitor (Reynolds & Kamphaus,</p><p>2016), which includes items tapping behaviors associated</p><p>with ADHD (as well as other problems), was designed</p><p>to allow frequent and individually tailored assessment to</p><p>monitor effectiveness of treatments for ADHD. Teacher,</p><p>parent, and child forms are available, with digital versions</p><p>and graphical depiction of change in a child’s scores over</p><p>time. normative performance on the Monitor can be</p><p>estimated from the BASC norms. Unfortunately, despite</p><p>being developed with the explicit purpose of treatment</p><p>monitoring, there is little published evidence of the valid-</p><p>ity of the scale for this purpose. A similar measure, the</p><p>SKAMP (Swanson, 1992), is a brief 10- item scale assess-</p><p>ing academic impairment related to inattention and dis-</p><p>ruptive behavior. Murray and colleagues (2009) reported</p><p>means and standard deviations for the measure from a</p><p>large sample, divided by gender, ethnicity, and grade level,</p><p>and documented good internal consistency. Satisfactory</p><p>single- day stability also has been demonstrated (e.g.,</p><p>Wigal, Gupta, Guinta, & Swanson, 1998). The SKAMP</p><p>has repeatedly demonstrated sensitivity to the effects of</p><p>medication or combined medication and psychosocial</p><p>treatment (e.g., Greenhill etal., 2001; Manos etal., 2015;</p><p>Wigal et al., 2014). Unfortunately, the SKAMP is not</p><p>widely or easily accessible.</p><p>Broadband Checklists</p><p>As indicated in Table 4.3, the parent and teacher versions</p><p>of the ASEBA have demonstrated sensitivity to behavioral,</p><p>medication, and combined interventions for children with</p><p>ADHD or disruptive behaviors (e.g., Ialongo etal., 1993;</p><p>Kazdin, 2003; Masi etal., 2016; Wang, Wu, Lee, & Tsai,</p><p>2014). Earlier versions of the Conners 3, both parent and</p><p>teacher forms, have consistently demonstrated sensitivity</p><p>to medication treatments for children with ADHD (e.g.,</p><p>Gadow, Sverd, Sprafkin, nolan, & Grossman, 1999; Weiss</p><p>etal., 2005), and some evidence supports their sensitivity</p><p>to behavioral interventions as well (e.g., Horn, Ialongo,</p><p>Popovich, & Peradotto, 1987; Pisterman etal.,1989).</p><p>Measures ofImpairment</p><p>Among the measures of impairment, the CAFAS has</p><p>demonstrated sensitivity to behavioral or mental health</p><p>interventions, in both general and ADHD samples, with</p><p>generally adequate psychometric properties as indicated</p><p>in Table 4.3 (e.g., Puddy, Roberts, Vernberg, & Hambrick,</p><p>2012; Timmons- Mitchell, Bender, Kishna, & Mitchell,</p><p>2006). However, this sensitivity has not been examined</p><p>specifically within ADHD samples. Both the IRS (Fabiano</p><p>etal., 2006)and the Weiss Functional Impairment Rating</p><p>scale (Weiss etal., 2005; available online at http:// naceon-</p><p>line.com/ AdultADHDtoolkit/ assessmenttools/ wfirs.pdf)</p><p>have demonstrated evidence of treatment sensitivity, for</p><p>both behavioral and medication treatments, specifically</p><p>http://naceonline.com/AdultADHDtoolkit/assessmenttools/wfirs.pdf</p><p>http://naceonline.com/AdultADHDtoolkit/assessmenttools/wfirs.pdf</p><p>60 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>60</p><p>in samples of children with ADHD (Hantson etal., 2012;</p><p>owens, Johannes, & Karpenko, 2009; Stein etal., 2015;</p><p>Waxmonsky et al., 2010). Both measures have a parent</p><p>version, and the IRS also has a teacher version, which is</p><p>useful in assessing intervention effects within the class-</p><p>room. The CoSS does appear to be sensitive to classroom</p><p>interventions (Abikoff et al., 2013); however, as yet, no</p><p>independent replications of this sensitivity are available.</p><p>Observational Measures</p><p>As noted previously, observational measures may be use-</p><p>ful in treatment planning, and similar to the procedures</p><p>of a daily report card, such observations can yield ongo-</p><p>ing assessment of treatment progress and documentation</p><p>of treatment outcome. For example, frequency counts of</p><p>problematic behavior in either home or school contexts</p><p>that are individualized for each child have an obvious util-</p><p>ity in monitoring treatment and guiding decisions regard-</p><p>ing needed changes in regimens. Such observations have</p><p>proven sensitive to the effects of both medication and</p><p>behavior management strategies (Pelham et al., 2005),</p><p>and evidence suggests that functional assessments with</p><p>observable targets improve treatment effectiveness (Miller</p><p>& Lee, 2013). Structured parent– child interaction obser-</p><p>vational measures, such as the BCS, have demonstrated</p><p>sensitivity to the effects of behavioral parent training (evi-</p><p>dence reviewed in McMahon & Forehand, 2003). Despite</p><p>the clear relevance of these observational measures for</p><p>assessing treatment- related change, the advantages of these</p><p>measures are combined with a lack of information regard-</p><p>ing expected normative changes in scores over time and a</p><p>lack of traditional validity evidence (Kollins,2004).</p><p>Overall Evaluation</p><p>As with measures useful for treatment planning, the</p><p>measures with the strongest psychometric properties</p><p>(i.e., ADHD symptom scales and broadband checklists),</p><p>although potentially useful in monitoring treatment out-</p><p>comes, are more limited in their ability to assess details</p><p>of each child’s impairments or to be sensitive to the rela-</p><p>tively rapid changes in child behavior that are common</p><p>in medication and behavioral interventions. In addition,</p><p>Table4.3 Ratings ofInstruments Used forTreatment Monitoring and Treatment outcome Evaluation</p><p>Instrument Norms</p><p>Internal</p><p>Consistency</p><p>Inter- Rater</p><p>Reliabilitya</p><p>Test– Retest</p><p>Reliability</p><p>Content</p><p>Validity</p><p>Construct</p><p>Validity</p><p>Validity</p><p>Generalization</p><p>Treatment</p><p>Sensitivity</p><p>Clinical</p><p>Utility</p><p>Highly</p><p>Recommended</p><p>narrowband ADHD Rating Scales</p><p>ADHD Rating Scale- 5</p><p>Parent E E nA G A G E E A ✓</p><p>Teacher E E nA G A G E E A ✓</p><p>IoWA</p><p>Parent A G nA A A G G G A</p><p>Teacher A G nA A A G G G A</p><p>Broadband Rating Scales</p><p>ASEBA</p><p>Parent:CBCL E G nA E G G E E A ✓</p><p>Teacher:TRF E E nA G G G E E A ✓</p><p>Conners 3</p><p>Parent E E nA G G G E E A ✓</p><p>Teacher E E nA G G G E E A ✓</p><p>Measures of Impairment</p><p>CAFAS nR A E nR A G G G A</p><p>IRS</p><p>Parent nR nR nA G A G A E A</p><p>Teacher G nR nA G A G G E A ✓</p><p>Weiss A G nA A nR A A E A ✓</p><p>a This column reflects inter- rater agreement between clinical judges, and this information in not available for most measures where, instead, parent and</p><p>teacher agreement is more commonly assessed.</p><p>Note: ASEBA=Achenbach System of Empirically Based Assessment; CBCL=Child Behavior Checklist; TRF=Teacher Report Form; CAFAS=Child</p><p>and Adolescent Functional Assessment Scale; IRS= Impairment Rating Scale; Weiss=Weiss Functional Impairment Rating Scale; A=Adequate;</p><p>G=Good; E=Excellent; nR=not Reported; nA=not Applicable.</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 61</p><p>61</p><p>the length of the broadband checklists is often prohibitive</p><p>for repeated assessments. Clinicians are advised to give</p><p>careful consideration to supplementing these measures</p><p>with others that may more directly assess the child’s daily</p><p>functioning (e.g., impairment scales or observational</p><p>measures), with appropriate caution in the use of these</p><p>measures due to their psychometric limitations. Clinical</p><p>research is urgently needed to expand the evidence of the</p><p>reliability and validity of scores on these measures and,</p><p>most important, to provide empirical support for the clini-</p><p>cal utility they are assumed to possess.</p><p>ASSESSMENT OFADHD INADULTHOOD</p><p>Evidence supporting the lifespan persistence of ADHD</p><p>symptoms is strong (e.g., Turgay et al., 2012), and the</p><p>DSM- 5 made revisions explicitly designed to address</p><p>assessment issues within the adult population. Specifically,</p><p>symptom examples were provided that are more appro-</p><p>priate for adults (e.g., feelings of restlessness rather than</p><p>overt motor activity and forgetful in paying bills or keep-</p><p>ing appointments) and, reflecting the normative decrease</p><p>in symptoms across age, only five symptoms of either</p><p>inattention or hyperactivity– impulsivity are required for a</p><p>diagnosis in adulthood.</p><p>Assessment of ADHD in adulthood presents some</p><p>challenges that overlap with those present in child assess-</p><p>ments, but also some that are unique to the adult stage.</p><p>As in childhood, it is important that multiple sources of</p><p>information be considered in the assessment of symp-</p><p>toms. In contrast to childhood, in adulthood there is a</p><p>reliance on self- reports as one source of information, and</p><p>these are considered alongside the perceptions of others</p><p>who know the individual well (e.g., a spouse). However,</p><p>as in childhood, the reports from these different sources</p><p>seldom converge completely (e.g., Barkley, Knouse, &</p><p>Murphy, 2011). Moreover, not only are there few guide-</p><p>lines for how to reconcile these reports in adulthood com-</p><p>pared to childhood, but also there are greater obstacles</p><p>to obtaining useful perceptions from other informants</p><p>(e.g., there is no close other available or the client may be</p><p>reluctant to consent to the gathering of this information).</p><p>ADHD in adults, as in children, is highly comorbid with</p><p>a range of other disorders (Kooij etal., 2012), and form-</p><p>ing clear, differential diagnoses is often a challenge. More</p><p>so than in childhood, the possibility of adults overreport-</p><p>ing symptoms, perhaps in order to receive special services</p><p>or dispensations, also must be considered (e.g., Sollman,</p><p>Ranseen, & Berry, 2010). Finally, although emerging</p><p>evidence suggests the possibility that ADHD can arise in</p><p>adults who were not so diagnosed in childhood (Moffitt</p><p>et al., 2015), the prevailing view continues to be con-</p><p>sistent with that of DSM- 5, which requires evidence of</p><p>an onset of symptoms and impairment prior to the age</p><p>of 12years to substantiate an ADHD diagnosis. Thus, in</p><p>assessing ADHD in adults, evidence must be gathered</p><p>regarding the childhood occurrence of symptoms/ impair-</p><p>ment, and again, multiple sources of information (e.g.,</p><p>self- reports, reports from parents or siblings, and school</p><p>records) are expected to provide the best approximation</p><p>of this information.</p><p>Several measures are available to assess current and ret-</p><p>rospective reports of ADHD symptoms in adults, although</p><p>few are well developed or, as yet, widely used. We have</p><p>focused our comments on the most recent, most widely</p><p>studied, and most easily accessible of these. one set of</p><p>measures, useful for diagnosis, case conceptualization,</p><p>and treatment monitoring, has been developed by Russell</p><p>Barkley. The set includes both self- and other- reports, for</p><p>both symptoms and impairment, in both adulthood and</p><p>retrospectively for childhood. The Barkley Adult ADHD</p><p>Rating Scale- IV (BAARS; Barkley, 2011a) contains both</p><p>self- and other- reports of adult and childhood symptoms</p><p>as well as single- item measures of age of symptom onset</p><p>and yes/ no assessments of impairment in four domains.</p><p>The items were developed to map onto DSM criteria, and</p><p>an additional nine items were added to tap the newer con-</p><p>struct of sluggish cognitive tempo (concentration deficit</p><p>disorder). norms, based on a large sample representative</p><p>of the US population, exist for the self- report versions of</p><p>the scale (allowing calculation of age- referenced percen-</p><p>tile scores). norms for the other- report versions are not</p><p>available. The BAARS- IV yields scores for Inattention,</p><p>Hyperactivity, Impulsivity, as well as sluggish cognitive</p><p>tempo, and a screener version using the items that best</p><p>discriminate clinic- referred adults with ADHD from com-</p><p>munity and psychiatric controls also is available. The sub-</p><p>scale and total scores demonstrate internal consistencies</p><p>in the .78 to .90 range and 2- to 3- week test– retest reliabili-</p><p>ties in the .66 to .88 range. Across a number of studies,</p><p>scores on the BAARS- IV have demonstrated convergent</p><p>validity with other measures of adult ADHD symptoms</p><p>(Kooij etal., 2008)and with a range of occupational and</p><p>relationship outcomes (Barkley, 2011a). Versions of the</p><p>BAARS- IV for use in non- US populations also have been</p><p>presented (e.g., Vélez- Pastrana etal., 2016). Finally, the</p><p>BAARS- IV has been used successfully to monitor out-</p><p>comes of both psychosocial (Safren etal., 2010)and medi-</p><p>cation (Spencer etal., 2007)treatments for adult ADHD.</p><p>62 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>62</p><p>The clinical utility of the measure is enhanced by a pub-</p><p>lisher policy that grants limited permission to make copies</p><p>of the measure from the manual.</p><p>Similar ADHD symptom checklists for adults include</p><p>the Adult ADHD Rating Scale, developed in conjunc-</p><p>tion with the World Health organization (Kessler etal.,</p><p>2005) and available online (https:// www.hcp.med.har-</p><p>vard.edu/ ncs/ asrs.php), and the Conners Adult ADHD</p><p>Rating Scale (Conners etal., 1999), which includes long,</p><p>short, and screener forms and is normed with satisfactory</p><p>psychometric information.</p><p>Beyond self- and other- reported rating scales, clinical</p><p>interviews specific to adult ADHD also have been devel-</p><p>oped and may be useful for diagnostic purposes. These</p><p>include the Conners Adult ADHD Diagnostic Interview</p><p>for DSM- IV (CAADID- IV; Epstein, Johnson, & Conners,</p><p>2001)and the Diagnostic Interview for ADHD in Adults</p><p>(DIVA 2.0; Kooij, 2013), which is available online (http://</p><p>www.divacenter.eu/ DIVA.aspx?id=499). Both measures</p><p>assess DSM symptoms of ADHD and, as is typical of</p><p>diagnostic interviews, neither is normed. Preliminary</p><p>evidence of inter- rater reliability and convergent/ predic-</p><p>tive validity is available for</p><p>both measures (e.g., Kooij,</p><p>2013; Solanto, Wasserstein, Marks, & Mitchell, 2012).</p><p>The DIVA 2.0 is available in several languages, free of</p><p>charge, and includes a computer application to facilitate</p><p>ease of administration and scoring. The CAADID- IV is</p><p>composed of two parts. The first portion covers develop-</p><p>mental and demographic history, including comorbidities</p><p>and psychosocial stressors, and can be completed as a self-</p><p>report measure prior to review with the clinician. The sec-</p><p>ond part covers both adult and childhood symptoms, with</p><p>useful prompts and adult- appropriate symptom examples</p><p>provided to guide the assessment. Impairment, pervasive-</p><p>ness, and age of onset are assessed.</p><p>Assessment of the impairments associated with ADHD</p><p>symptoms is critical, particularly for case conceptualiza-</p><p>tion, and sometimes for treatment monitoring. Several of</p><p>the rating scales and interview measures described pre-</p><p>viously incorporate the assessment of impairment, given</p><p>its role in diagnostic criteria, and, as for children, efforts</p><p>are underway to develop a core set of concepts relevant</p><p>to adult ADHD for the International Classification of</p><p>Functioning, Disability and Health (Schipper etal., 2015).</p><p>Currently, assessment of impairment associated with</p><p>ADHD can be undertaken with the Barkley Functional</p><p>Impairment Scale (BFIS; Barkley, 2011b). This mea-</p><p>sure, developed to reflect a clearly defined construct of</p><p>psychosocial impairment, has both self- and other- report</p><p>forms. The self- report version has norms derived from</p><p>the same representative normative sample used for the</p><p>BAARS- IV. The BFIS items cover 15 domains of func-</p><p>tioning (e.g., home, community, occupational, and daily</p><p>responsibilities), and ratings load on a single factor with</p><p>strong internal consistency (alpha= .97) and test– retest</p><p>reliability (r=.72). Evidence for convergent and discrimi-</p><p>nant validity is presented (e.g., correlations with symptom</p><p>severity, disability status, and clinical group membership).</p><p>of course, in addition to impairment, as with children,</p><p>assessment of a range of possible comorbid conditions</p><p>and other aspects of functioning is critical in forming a</p><p>comprehensive case formulation of ADHD in adults,</p><p>and these constructs also may be important in monitor-</p><p>ing treatment progress. Given the nascent nature of the</p><p>adult ADHD assessment literature, we do not review such</p><p>measures here, but we encourage clinicians to follow</p><p>sound clinical practice guidelines (e.g., those provided by</p><p>the European Consensus on Adult ADHD, the national</p><p>Institute for Health and Care Excellence [nICE] from</p><p>the United Kingdom, or the Canadian ADHD Resource</p><p>Alliance).</p><p>CONCLUSIONS AND FUTURE DIRECTIONS</p><p>A multitude of tools for assessing ADHD across the lifes-</p><p>pan are available, both commercially and in the public</p><p>domain, and new additions emerge regularly. In contrast</p><p>to this abundant quantity of measures, few measures are</p><p>available that possess substantial research on their psy-</p><p>chometric qualities or that have been validated for uses</p><p>beyond diagnostic questions. In this final section of the</p><p>chapter, we draw attention to prominent unanswered</p><p>questions regarding assessments for ADHD diagnoses</p><p>and for treatment planning and monitoring. We again</p><p>note that our focus on assessment measures should not</p><p>overshadow the fact that the process of assessing an indi-</p><p>vidual with ADHD involves much more than simple</p><p>administration of a standard set of measures. Clinicians</p><p>must make client- specific decisions regarding which mea-</p><p>sures are best suited for each individual client and family</p><p>(e.g., Is this child represented in the measure’s normative</p><p>group?), at which point in the assessment process (e.g., Is</p><p>the measure needed primarily for assigning a diagnosis or</p><p>for monitoring the child’s response to a new medication?),</p><p>and how information from multiple sources and measures</p><p>is best combined to answer the assessment question (e.g.,</p><p>Is a sibling an adequate reporter of childhood symptoms</p><p>in an adult client?). In addition, information derived from</p><p>the measures presented here must be supplemented with</p><p>https://www.hcp.med.harvard.edu/ncs/asrs.php</p><p>https://www.hcp.med.harvard.edu/ncs/asrs.php</p><p>http://www.divacenter.eu/DIVA.aspx?id=499</p><p>http://www.divacenter.eu/DIVA.aspx?id=499</p><p>ATTEnTIon-DEFICIT/HYPERACTIVITY DISoRDER 63</p><p>63</p><p>clinical judgments regarding each individual’s situation</p><p>and context (e.g., cultural factors) and must be employed</p><p>within the context of a caring and supportive therapeutic</p><p>relationship between clinician and client.</p><p>In diagnosing ADHD, the use of unstructured inter-</p><p>views as a guide for identifying general areas of con-</p><p>cern (in terms of both ADHD and comorbid disorders),</p><p>developmental and treatment history, and information</p><p>specific to the client’s circumstances remains common,</p><p>despite the known limitations of this assessment method.</p><p>Further efforts to develop and evaluate more structured</p><p>and semi- structured tools that could couple the gather-</p><p>ing of this information in a systematic manner with a</p><p>sensitivity to individual client differences and the need</p><p>to establish a strong working relationship between clini-</p><p>cian and client would be clinically valuable. Similarly,</p><p>although a few standardized measures with adequate</p><p>psychometric properties have proven their value in plan-</p><p>ning and monitoring treatment progress in children, the</p><p>most promising measures in this area originate from a</p><p>behavioral perspective but lack standardization, norm</p><p>development, and broad psychometric evaluation. We</p><p>believe that these measures have the greatest potential</p><p>for enhancing the selection of appropriate treatment tar-</p><p>gets for children with ADHD and for providing careful,</p><p>continuous, and objective feedback regarding treatment</p><p>progress. However, one cannot ignore the inadequacies</p><p>of these measures in terms of traditional psychomet-</p><p>ric properties. Continued research is much needed to</p><p>address these limitations and to develop and test clini-</p><p>cally useful measures appropriate to assessing and moni-</p><p>toring change in the functional impairments that form</p><p>the core of ADHD treatment planning. Technological</p><p>advances, such as online data collection platforms, com-</p><p>puterized scoring and reporting templates, and portable</p><p>recording options, offer exciting possibilities in moving</p><p>forward with the development of assessment tools, but</p><p>they are perhaps particularly applicable within the realm</p><p>of treatment monitoring.</p><p>Turning to the more common and psychometrically</p><p>tested assessment methods commonly used in diagnosis,</p><p>particularly rating scales, consensus appears to be that</p><p>for both children and adults, information from multiple</p><p>informants and contexts is necessary (e.g., Barkley, 2011a;</p><p>Pelham etal., 2005). What is now needed is greater con-</p><p>centration on evaluating methods for combining this</p><p>information and establishing the relative incremental</p><p>validity of different informants and contexts. Similarly,</p><p>much further research is needed to clarify the relative mer-</p><p>its of different assessment methods (e.g., symptom- specific</p><p>rating scales, structured interviews, and observations) for</p><p>arriving at diagnostic or treatment decisions. We know</p><p>exceptionally little about which types of information are</p><p>the most crucial in determining which types of assessment</p><p>and treatment to administer. To maximize the extent to</p><p>which our assessments can boast of being both evidence-</p><p>based and cost- effective, research with a clear focus on</p><p>the clinical utility or incremental validity of how each</p><p>piece of assessment information fits (or does not) within</p><p>the puzzle of an optimally designed assessment process</p><p>for ADHD is urgently needed.</p><p>Beyond the need to refine the measures and process</p><p>of assessing ADHD, we have been struck by two signifi-</p><p>cant gaps that exist in this area. First, there often appears</p><p>to be a disconnect between assessments of ADHD diag-</p><p>noses and assessments with greater relevance to the</p><p>treatment of the disorder. As we have repeatedly noted,</p><p>among individuals referred with ADHD, it is often the</p><p>case that the most pressing clinical problems are those</p><p>related to functional impairments (e.g., in interpersonal</p><p>relationships or academic/ vocational functioning) or to</p><p>comorbid conditions (e.g., learning problems or depres-</p><p>sion). Symptom severity, the target of diagnostic assess-</p><p>ment, is clearly related to these impairments but not</p><p>synonymous with them. Knowledge of an individual’s</p><p>level of ADHD symptoms offers little treatment guidance</p><p>because changes in these symptom levels may not mir-</p><p>ror changes in the functional problems that instigated</p><p>help- seeking. Second, as in many areas, there remains a</p><p>significant gap between research on ADHD assessment</p><p>and treatment and the delivery of these services outside</p><p>of research settings. The dissemination and uptake of the</p><p>most evidence- based assessment tools (and treatments)</p><p>lags woefully behind the advancing scientific knowledge.</p><p>Recent work in the development and evaluation of clini-</p><p>cal care pathways for ADHD offers an important bridge</p><p>over this gap (e.g., Carroll etal., 2013; Coghill & Seth,</p><p>2015; Vander Stoep etal., 2017)and holds promise as a</p><p>future direction in improving the assessment (and subse-</p><p>quent treatment) ofADHD.</p><p>In closing, we acknowledge a number of resources rel-</p><p>evant to the assessment of ADHD and refer clinicians to</p><p>these resources for additional guidelines and information</p><p>useful in this endeavor. Recent books by Barkley (2015)</p><p>and Anastopoulos and Shelton (2001) provide excel-</p><p>lent coverage of assessment issues in ADHD. Clinical</p><p>guidelines for assessing ADHD have been provided</p><p>by the American Academy of Pediatrics (2011) and the</p><p>American Academy of Child and Adolescent Psychiatry</p><p>(2007). Pelham and colleagues’ (2005) contribution on</p><p>64 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>64</p><p>evidence- based assessment for ADHD continues to be an</p><p>excellent resource. We trust that this chapter, along with</p><p>these additional resources, provides the clinician with</p><p>an overview of the issues prominent in the assessment of</p><p>ADHD and with a guide to currently available and useful</p><p>measures.</p><p>References</p><p>Abikoff, H., & Gallagher, R. (2009). Children’s organizational</p><p>skills scale. Tonawanda, nY:Multi- Health Systems.</p><p>Abikoff, H., Gallagher, R., Wells, K. C., Murray, D. W.,</p><p>Huang, L., Lu, F., & Petkova, E. (2013). 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McMahon</p><p>Conduct problems (CP) in youth are one of the most com-</p><p>mon reasons that children and adolescents are referred</p><p>to mental health clinics (Kimonis, Frick, & McMahon,</p><p>2014). This is not surprising given that CP often causes</p><p>significant disruptions for the child at home and school,</p><p>and it is the form of psychopathology that has been most</p><p>strongly associated with delinquency and violence (odgers</p><p>etal., 2007). An extensive body of research has led to an</p><p>increased understanding of the many processes that may</p><p>be involved in the development of severe CP (Frick &</p><p>Viding, 2009). This research has many important implica-</p><p>tions for designing more effective interventions to prevent</p><p>or treat these problems (Conduct Problems Prevention</p><p>Research Group, 2000; Frick, 2012) and for improving</p><p>the methods for assessing children and adolescents with</p><p>severe CP (McMahon & Frick, 2005). The focus of this</p><p>chapter is on the implications for assessment.</p><p>In the next section, we provide a brief overview of sev-</p><p>eral key findings from research on CP in children and</p><p>adolescents and highlight several findings that we believe</p><p>have the most direct relevance to the assessment process.</p><p>Specifically, we focus on research illustrating the great</p><p>heterogeneity in the types, severity, and course of CP in</p><p>youth, as well as the frequent co- occurring problems in</p><p>adjustment that often accompany CP. We also summarize</p><p>research showing important dispositional and contextual</p><p>risk factors that have been related to CP and that could play</p><p>an important role in the development or maintenance of</p><p>CP. We then review some recent causal models that have</p><p>been proposed to explain how these many risk factors could</p><p>affect the development of the child and leadtoCP.</p><p>After the brief overview of these select but critical areas</p><p>of research, we then focus on the implications of this</p><p>research for three types of assessments that are often con-</p><p>ducted for children with CP. First, we focus on methods</p><p>for determining whether the level of CP is severe, impair-</p><p>ing, and developmentally inappropriate enough to be con-</p><p>sidered “disordered” and in need of treatment. Second, we</p><p>focus on assessments that can be used for developing case</p><p>conceptualizations, which can guide comprehensive and</p><p>individualized treatment plans for children with CP. Using</p><p>comprehensive interventions that rely on multiple compo-</p><p>nents tailored to the child’s individual needs has proven to</p><p>be most effective for treating children and adolescents with</p><p>CP (Conduct Problems Prevention Research Group, 2000;</p><p>Frick, 2012). Third, we focus on measures that can be used</p><p>to monitor and evaluate treatment progress and outcomes.</p><p>Unfortunately, the availability of measures for this crucial</p><p>assessment purpose is quite limited.</p><p>After summarizing research on CP and its implica-</p><p>tions for assessment, we conclude this chapter with a</p><p>section highlighting some overriding issues related to</p><p>assessing children with CP, such as the need to assess chil-</p><p>dren with multiple measures that provide information on</p><p>their adjustment in multiple contexts. We also provide a</p><p>summary of some of the major limitations in the existing</p><p>assessment technology and make recommendations for</p><p>future work to overcome these limitations.</p><p>THE NATUREOFCP</p><p>Types and Severity ofCP and Common</p><p>Co- Occurring Conditions</p><p>CP constitutes a broad spectrum of “acting- out” behaviors,</p><p>ranging from relatively minor oppositional behaviors such</p><p>as yelling and temper tantrums to more serious forms of</p><p>antisocial behavior such as physical destructiveness, steal-</p><p>ing, and physical violence. There have been numerous</p><p>72 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>72</p><p>methods used to divide CP into more discrete and homo-</p><p>geneous types of behaviors (for comprehensive reviews,</p><p>see Frick & Marsee, 2006; Kimonis, Frick, & McMahon,</p><p>2014). For example, the fifth edition of the Diagnostic</p><p>and Statistical Manual of Mental Disorders (DSM- 5;</p><p>American Psychiatric Association [APA], 2013) includes</p><p>CP in the category of disruptive, impulse control, and</p><p>conduct disorders. The DSM- 5 makes a distinction</p><p>between the categories of oppositional defiant disorder</p><p>(oDD) and conduct disorder (CD). oDD is a pattern of</p><p>angry/ irritable (e.g., often loses temper), argumentative/</p><p>defiant (e.g., defying or not complying with grown- ups’</p><p>rules or requests), and vindictive (e.g., has been spite-</p><p>ful or vindictive) behaviors. CD consists of more severe</p><p>antisocial and aggressive behavior that involves serious</p><p>violations of others’ rights or deviations from major age-</p><p>appropriate norms. The behaviors are categorized into</p><p>four groups: aggressiveness to people and animals (e.g.,</p><p>bullying and fighting), property destruction (e.g., fire- set-</p><p>ting and other destruction of property), deceptiveness or</p><p>theft (e.g., breaking and entering, stealing without con-</p><p>fronting victim), and serious rule violations (e.g., running</p><p>away from home or being truant from school before age</p><p>13years).</p><p>In addition to this division in the DSM- 5, factor analy-</p><p>ses have resulted in another method for differentiating</p><p>among types of CP. In a meta- analysis of more than 60</p><p>published factor analyses, Frick et al. (1993) found that</p><p>CP could be described by two bipolar dimensions. The</p><p>first dimension was an overt– covert dimension. The overt</p><p>pole consisted of directly confrontational behaviors such</p><p>as oppositional defiant behaviors and aggression. In con-</p><p>trast, the covert pole consisted of behaviors that were</p><p>nonconfrontational in nature (e.g., stealing and lying; see</p><p>also Tiet, Wasserman, Loeber, Larken, & Miller, 2001;</p><p>Willoughby, Kupersmidt, & Bryant, 2001). The second</p><p>dimension divided the overt behaviors into those that were</p><p>overt- destructive (aggression) and those that were overt-</p><p>nondestructive (oppositional), and it divided the covert</p><p>behaviors into those that were covert- destructive (property</p><p>violations) and those that were covert- nondestructive (sta-</p><p>tus offenses; i.e., those behaviors that are illegal because</p><p>of the child’s or adolescent’s age). one way in which this</p><p>clustering of CP is useful is that the four symptom patterns</p><p>are fairly consistent with the distinctions made in many</p><p>legal systems for differentiating types of delinquent behav-</p><p>iors, which generally distinguish between violent offenses</p><p>(overt- destructive), status offenses (covert- nondestructive),</p><p>and property offenses (covert- destructive; e.g., office of</p><p>Juvenile Justice and Delinquency Prevention,1995).</p><p>Two specific forms of CP— noncompliance and</p><p>aggression— deserve additional attention. noncompliance</p><p>(i.e., excessive disobedience to adults) appears to be</p><p>important as one of the earliest predictors of the devel-</p><p>opment of CP, and it seems to play an important role</p><p>in many of the subsequent academic and social prob-</p><p>lems exhibited by children with CP (Chamberlain &</p><p>Patterson, 1995; McMahon & Forehand, 2003). Most</p><p>important, however, research has shown that when child</p><p>noncompliance is improved as a result of intervention,</p><p>there is often concomitant improvement in other CP</p><p>behaviors and a subsequent reduction in later risk for CP</p><p>(e.g., Russo, Cataldo, & Cushing, 1981; Wells, Forehand,</p><p>& Griest,1980).</p><p>There is also evidence that aggression is an impor-</p><p>tant dimension of CP (Burt, 2013). By its very nature,</p><p>aggression results in harm to another child (Crick &</p><p>Dodge, 1996). Furthermore, research has consistently</p><p>shown that aggressive behavior in children and ado-</p><p>lescents is often quite stable after the preschool years</p><p>(Broidy et al., 2003). Importantly, research has found</p><p>that there appears to be several different forms of aggres-</p><p>sive behavior (Crick & Dodge, 1996; Poulin & Boivin,</p><p>2000). The first type of aggression is often referred to</p><p>as retaliatory aggression, hostile aggression, or reactive</p><p>aggression, in which aggression is viewed as a defensive</p><p>reaction to a perceived threat and is characterized by</p><p>anger and hostility (Little, Jones, Henrich, & Hawley,</p><p>2003). The second type of aggressive behavior is gener-</p><p>ally unprovoked and is used for personal gain (instru-</p><p>mental) or to influence and coerce others (bullying and</p><p>dominance). This type of aggressive behavior is referred</p><p>to as instrumental aggression, premeditated aggression,</p><p>or proactive aggression (Poulin & Boivin,2000).</p><p>Importantly, although these different types of aggres-</p><p>sion are often correlated (e.g., correlations ranging from</p><p>r=.40 to .70 in school- aged samples; Little etal., 2003),</p><p>studies have consistently documented different correlates</p><p>to the two forms of aggression (for reviews, see Dodge &</p><p>Pettit, 2003; Marsee & Frick, 2010). For example, reac-</p><p>tive but not proactive aggression has been consistently</p><p>linked to a tendency to misinterpret ambiguous behaviors</p><p>as hostile provocation (Crick & Dodge, 1996; Hubbard,</p><p>Dodge, Cillessen, Coie, & Schwartz, 2001)and to poorly</p><p>regulated responses to emotional stimuli (Marsee & Frick,</p><p>2007; Vitaro, Brengden, & Tremblay, 2002). In contrast,</p><p>proactive but not reactive aggression has been associated</p><p>with the tendency to view aggression as an effective means</p><p>to reach goals (Crick & Dodge, 1996)and with reduced</p><p>levels of emotional reactivity (i.e., skin conductance and</p><p>CHILD AnD ADoLESCEnT ConDUCT PRoBLEMS 73</p><p>73</p><p>heart rate acceleration; Hubbard et al., 2002;</p><p>Muñoz,</p><p>Frick, Kimonis, & Aucoin,2008).</p><p>In addition to proactive and reactive forms of aggres-</p><p>sion, both of which are overt in nature, several research-</p><p>ers have identified a form of indirect aggression, called</p><p>relational aggression, that involves strategies that attempt</p><p>to harm another child through harming his or her social</p><p>relationships (Marsee & Frick, 2010). These behaviors</p><p>include excluding a child from groups, rumor spreading,</p><p>and friendship manipulation. Several studies have shown</p><p>that when girls behave aggressively, they are more likely</p><p>to use relational aggression than overt aggression (e.g.,</p><p>Crapanzano, Frick, & Terranova, 2010; Marsee et al.,</p><p>2014). Furthermore, research has suggested that it may</p><p>be possible to divide relational aggression into instrumen-</p><p>tal and reactive forms, similar to overt aggression (Little</p><p>et al., 2003; Marsee et al., 2014). Importantly, children</p><p>who show relational aggression show many of the same</p><p>social (e.g., peer rejection) and dispositional (e.g., impul-</p><p>sivity and callousness) risk factors as physically aggressive</p><p>youth (Marsee etal.,2014).</p><p>EpidemiologyofCP</p><p>A meta- analysis of epidemiological studies estimated that</p><p>the worldwide prevalence of oDD among children and</p><p>adolescents ages 6 to 18years is 3.3% and the prevalence</p><p>of CD is 3.2% (Canino, Polanczyk, Bauermeister, Rohde,</p><p>& Frick, 2010). These prevalence estimates did not vary</p><p>significantly across countries or continents, although the</p><p>vast majority of studies included in the meta- analysis</p><p>were conducted in north America and Europe. There</p><p>is, however, evidence for differences in prevalence</p><p>rates of CP for children of different ages. The level of</p><p>CP tends to decrease from the preschool to school- age</p><p>years (Maughan, Rowe, Messer, Goodman, & Meltzer,</p><p>2004)and increase again in adolescence (Loeber, Burke,</p><p>Lahey, Winters, & Zera, 2000). For example, Loeber</p><p>et al. reported prevalence rates for CD of 5.6%, 5.4%,</p><p>and 8.3% for boys aged 7, 11, and 13years, respectively,</p><p>and prevalence rates for oDD of 2.2%, 4.8%, and 5.0%</p><p>for boys of the same age in a sample of 1,517 youth in</p><p>a large urban area. However, the increase in the preva-</p><p>lence of CP from childhood to adolescence may not</p><p>be consistent for all types of CP. Specifically, there is</p><p>evidence that mild forms of physical aggression (e.g.,</p><p>fighting) show a decrease in prevalence rates across</p><p>development, whereas nonaggressive and covert forms of</p><p>antisocial behavior (e.g., lying and stealing) and serious</p><p>aggression (e.g., armed robbery and sexual assault) show</p><p>an increase in prevalence rates from childhood to adoles-</p><p>cence (Loeber & Hay,1997).</p><p>There also appear to be sex differences in the preva-</p><p>lence of CP. overall estimates of the sex ratio for boys</p><p>and girls with CP range from 2:1 to 4:1 (Loeber et al.,</p><p>2000). However, this overall ratio hides several important</p><p>developmental differences. Specifically, there are few sex</p><p>differences between boys and girls in the prevalence rates</p><p>of most types of CP prior to age 5years (Maughan etal.,</p><p>2004). However, after age 4 years the rate of girls’ CP</p><p>decreases, whereas the rate of CP for boys either increases</p><p>or stays at the same rate, leading to a male predominance</p><p>of CP throughout much of childhood (Loeber et al.,</p><p>2000). numerous studies have also noted that the sex</p><p>ratio between girls and boys with CP narrows dramatically</p><p>from approximately 4:1 in childhood to approximately</p><p>2:1 in adolescence due to an increase in the number of</p><p>girls engaging in CP in adolescence (for a review, see</p><p>Silverthorn & Frick,1999).</p><p>CP and Co- Occurring Problems inAdjustment</p><p>A consistent finding in research with children who</p><p>show CP is that they often have a number of problems</p><p>in adjustment, in addition to their CP, and these prob-</p><p>lems are critical to address in assessment and interven-</p><p>tion. Attention- deficit/ hyperactivity disorder (ADHD) is</p><p>one of the most common comorbid conditions associated</p><p>with CP. In a meta- analytic study, Waschbusch (2002)</p><p>reported that 36% of boys and 57% of girls with CP had</p><p>comorbid ADHD. Importantly, this review also suggested</p><p>that the presence of ADHD often signals the presence of</p><p>a more severe and more chronic form of CP in children.</p><p>Internalizing disorders, such as depression and anxiety,</p><p>also co- occur with CP at rates higher than expected by</p><p>chance (Zoccolillo, 1992). In most cases, CP precedes</p><p>the onset of depressive and anxiety symptoms, and these</p><p>symptoms are often viewed as consequences of the many</p><p>adjustment problems experienced by a child with CP</p><p>(Frick, Lilienfeld, Ellis, Loney, & Silverthorn, 1999;</p><p>Loeber & Keenan, 1994). In addition, children who pres-</p><p>ent with the angry/ irritable mood symptoms of oDD are</p><p>more likely to develop internalizing types of difficulties</p><p>(e.g., Burke, Hipwell, & Loeber, 2010; Rowe, Costello,</p><p>Angold, Copeland, & Maughan, 2010; Stringaris &</p><p>Goodman, 2009). CP is also related to substance use (e.g.,</p><p>Hawkins, Catalano, & Miller, 1992). The comorbidity</p><p>between CP and substance abuse is important because</p><p>when youths with CP also abuse substances, they tend to</p><p>show an early onset of substance use and they are more</p><p>74 ATTEnTIon-DEFICIT AnD DISRUPTIVE BEHAVIoR DISoRDERS</p><p>74</p><p>likely to abuse multiple substances (Lynskey & Fergusson,</p><p>1995). With preschool- aged children, language impair-</p><p>ment may be associated with CP (Wakschlag & Danis,</p><p>2004), and in older children, CP is often associated with</p><p>academic achievement below a level predicted by their</p><p>intellectual level (Hinshaw,1992).</p><p>Multiple Risks Associated withCP</p><p>Most researchers agree that CP is the result of a complex</p><p>interaction of multiple causal factors (Kimonis, Frick, &</p><p>McMahon, 2014). These factors can be summarized in</p><p>five categories: biological factors, cognitive correlates,</p><p>family context, peer context, and the broader social ecol-</p><p>ogy (e.g., neighborhood and community). Although a</p><p>number of biological correlates (e.g., neurochemical and</p><p>autonomic irregularities) of CP have been identified and</p><p>are likely important for causal theories (Frick & Viding,</p><p>2009), they are not reviewed here because the current</p><p>state of knowledge is not sufficiently developed to have</p><p>clear implications for assessment.</p><p>In contrast, there are several aspects of the youth’s</p><p>cognitive and learning styles that have been associated</p><p>with CP that may be important to the assessment process.</p><p>First, compared to others, youths with CP tend to score</p><p>lower on intelligence tests, especially in the area of verbal</p><p>intelligence (Loney, Frick, Ellis, & McCoy, 1998; Moffitt,</p><p>2006). Furthermore, these scores are predictive of the per-</p><p>sistence of CP and engagement in delinquent behaviors</p><p>during adolescence (Frick & Loney, 1999). Second, many</p><p>children and adolescents with CP tend to show a learning</p><p>style that is more sensitive to rewards than punishments.</p><p>This has been labeled as a reward- dominant response</p><p>style, and it could explain why many of these youths persist</p><p>in their maladaptive behaviors, despite the threat of seri-</p><p>ous potential consequences (Frick etal., 2003; o’Brien &</p><p>Frick, 1996). Third, many youths with CP show a variety</p><p>of deficits in their social cognition— that is, the way they</p><p>interpret social cues and use them to respond in social</p><p>situations (Crick & Dodge, 1994; Webster- Stratton &</p><p>Lindsay, 1999). For example, children and adolescents</p><p>with CP have been shown to have deficits in encoding</p><p>social cues (e.g., lack of attention to relevant social cues),</p><p>to make more hostile attributional biases and errors in the</p><p>interpretation of social cues, to have deficient quantity</p><p>and quality of generated solutions to social conflict, and</p><p>to evaluate aggressive solutions more positively (Dodge &</p><p>Pettit,2003).</p><p>The critical role of parenting practices in the</p><p>development and maintenance of CP has been well</p><p>established (e.g., Chamberlain & Patterson, 1995; Loeber</p><p>& Stouthamer- Loeber, 1986). Types</p><p>chapters, and books related</p><p>to evidence- based psychological practice, psychological</p><p>assessment, and professional issues.</p><p>Eric J. Mash, PhD, is Professor Emeritus in the</p><p>Department of Psychology at the University of Calgary. He</p><p>is a Fellow of the American Psychological Association, the</p><p>Canadian Psychological Association, and the American</p><p>Psychological Society. He has served as an editor, edito-</p><p>rial board member, and consultant for many scientific</p><p>and professional journals and has written and edited many</p><p>books and journal articles related to child and adolescent</p><p>mental health, assessment, and treatment.</p><p>Contributors</p><p>Jonathan S. Abramowitz, PhD: Department of</p><p>Psychology and Neuroscience, University of North</p><p>Carolina at Chapel Hill, Chapel Hill, North Carolina</p><p>SaraAlavi: Eating and Weight Disorders Program, Icahn</p><p>School of Medicine at Mt. Sinai, NewYork, NewYork</p><p>Martin M. Antony, PhD: Department of Psychology,</p><p>Ryerson University, Toronto, Ontario,Canada</p><p>Christina Balderrama- Durbin, PhD: Department of</p><p>Psychology, Binghamton University—State University of</p><p>NewYork, Binghamton, NewYork</p><p>Simon Beaulieu- Bonneau,PhD: École de psychologie,</p><p>Université Laval, Quebec City, Quebec,Canada</p><p>Yitzchak M. Binik, PhD: Department of Psychology,</p><p>McGill University, Montreal, Quebec,Canada</p><p>Shannon M.Blakey, MS: Department of Psychology and</p><p>Neuroscience, University of North Carolina at Chapel</p><p>Hill, Chapel Hill, North Carolina</p><p>Cassandra L. Boness,MA: Department of Psychological</p><p>Sciences, University of Missouri, Columbia, Missouri</p><p>Simon P. Byrne, PhD: Yale Child Study Center, Yale</p><p>School of Medicine, New Haven, Connecticut</p><p>Colleen E. Carney, PhD: Department of Psychology,</p><p>Ryerson University, Toronto, Ontario,Canada</p><p>Catherine A. Charette: Département de psychoé-</p><p>ducation et de psychologie, Université du Québec en</p><p>Outaouais, Gatineau, Quebec,Canada</p><p>Sara Colalillo, MA: Department of Psychology,</p><p>University of British Columbia, Vancouver, British</p><p>Columbia, Canada</p><p>Michelle G. Craske, PhD: Department of Psychology,</p><p>University of California at Los Angeles, Los Angeles,</p><p>California</p><p>Lea R. Dougherty, PhD: Department of Psychology,</p><p>University of Maryland, College Park, Maryland</p><p>Michel J. Dugas,PhD: Département de psychoéducation</p><p>et de psychologie, Université du Québec en Outaouais,</p><p>Gatineau, Québec,Canada</p><p>LoriEisner, PhD: Needham Psychotherapy Associates,</p><p>LLC</p><p>xviii CONTRIBUTORS</p><p>Juliet Small Ernst: Cognitive Behavior Therapy and</p><p>Science Center, Oakland, California</p><p>Amy Fiske, PhD: Department of Psychology, West</p><p>Virginia University, Morgantown, West Virginia</p><p>David M. Fresco, PhD: Department of Psychological</p><p>Sciences, Kent State University, Kent, Ohio; Department</p><p>of Psychiatry, Case Western Reserve University School of</p><p>Medicine, Cleveland, Ohio</p><p>Paul J. Frick,PhD: Department of Psychology, Louisiana</p><p>State University, Baton Rouge, Louisiana; Learning</p><p>Sciences Institute of Australia; Australian Catholic</p><p>University; Brisbane, Australia</p><p>Michelle M. Gagnon, PhD: Department of</p><p>Psychology, University of Saskatchewan, Saskatoon,</p><p>Saskatchewan,Canada</p><p>Natasha L. Gallant, MA: Department of Psychology,</p><p>University of Regina, Regina, Saskatchewan,Canada</p><p>Nicole J. Gervais, PhD: Department of Psychology,</p><p>University of Toronto, Toronto, Ontario, Canada</p><p>Maria Glowacka: Department of Psychology and</p><p>Neuroscience, Dalhousie University, Halifax, Nova</p><p>Scotia,Canada</p><p>Shirley M. Glynn, PhD: VA Greater Los Angeles</p><p>Healthcare System and UCLA Department of Psychiatry</p><p>and Biobehavioral Sciences, David Geffen School of</p><p>Medicine, Los Angeles, California</p><p>Thomas Hadjistavropoulos, PhD: Department of</p><p>Psychology, University of Regina, Regina, Saskatchewan,</p><p>Canada</p><p>Angela M. Haeny, MA: Department of Psychological</p><p>Sciences, University of Missouri, Columbia, Missouri</p><p>Alisa O’Riley Hannum, PhD, ABPP: VA Eastern</p><p>Colorado Healthcare System, Denver, Colorado</p><p>Stephen N. Haynes, PhD: Department of Psychology,</p><p>University of Hawai’i at Mānoa, Honolulu,Hawaii</p><p>Richard E. Heyman,PhD: Family Translational Research</p><p>Group, NewYork University, NewYork, NewYork</p><p>David C. Hodgins, PhD: Department of Psychology,</p><p>University of Calgary, Calgary, Alberta,Canada</p><p>John Hunsley,PhD: School of Psychology, University of</p><p>Ottawa, Ottawa, Ontario,Canada</p><p>Amanda Jensen- Doss,PhD: Department of Psychology,</p><p>University of Miami, Coral Gables, Florida</p><p>Sheri L. Johnson, PhD: Department of Psychology,</p><p>University of California Berkeley, Berkeley, California</p><p>Charlotte Johnston, PhD: Department of Psychology,</p><p>University of British Columbia, Vancouver, British</p><p>Columbia, Canada</p><p>Terence M. Keane,PhD: VA Boston Healthcare System,</p><p>National Center for Posttraumatic Stress Disorder, and</p><p>Boston University School of Medicine, Boston, Massachusetts</p><p>Daniel N. Klein,PhD: Department of Psychology, Stony</p><p>Brook University, Stony Brook, NewYork</p><p>Eli R. Lebowitz, PhD: Yale Child Study Center, Yale</p><p>School of Medicine, New Haven, Connecticut</p><p>Kristin Maich,MA: Department of Psychology, Ryerson</p><p>University, Toronto, Ontario,Canada</p><p>Brian P. Marx, PhD: VA Boston Healthcare System,</p><p>National Center for Posttraumatic Stress Disorder,</p><p>and Boston University School of Medicine, Boston,</p><p>Massachusetts</p><p>Eric J. Mash,PhD: Department of Psychology, University</p><p>of Calgary, Calgary, Alberta,Canada</p><p>Randi E. McCabe, PhD: Anxiety Treatment and</p><p>Research Clinic, St. Joseph’s Healthcare, Hamilton, and</p><p>Department of Psychiatry and Behavioral Neurosciences,</p><p>McMaster University, Hamilton, Ontario,Canada</p><p>Yoanna E. McDowell,MA: Department of Psychological</p><p>Sciences, University of Missouri, Columbia, Missouri</p><p>CONTRIBUTORS xix</p><p>Patrick J. McGrath, PhD: Centre for Pediatric</p><p>Pain Research, IWK Health Centre; Departments</p><p>of Psychiatry, Pediatrics and Community Health &</p><p>Epidemiology, Dalhousie University; Halifax, Nova</p><p>Scotia, Canada</p><p>Robert J. McMahon, PhD: Department of</p><p>Psychology, Simon Fraser University, Burnaby, British</p><p>Columbia, Canada; BC Children’s Hospital Research</p><p>Institute, Vancouver, British Columbia, Canada</p><p>C. Meghan McMurtry,PhD: Department of Psychology,</p><p>University of Guelph, Guelph; Pediatric Chronic Pain</p><p>Program, McMaster Children’s Hospital, Hamilton;</p><p>Department of Paediatrics, Schulich School of Medicine</p><p>& Dentistry, Western University, London; Ontario,</p><p>Canada</p><p>Marta Meana, PhD: Department of Psychology,</p><p>University of Nevada Las Vegas, Las Vegas,Nevada</p><p>ChristopherMiller, PhD:VA Boston Healthcare System,</p><p>Center for Healthcare Organization and Implementation</p><p>Research, and Harvard Medical School Department of</p><p>Psychiatry, Boston, Massachusetts</p><p>Alexander J. Millner,PhD: Department of Psychology,</p><p>Harvard University, Cambridge, Massachusetts</p><p>Charles M. Morin, PhD: École de psychologie,</p><p>Université Laval, Quebec City, Quebec,Canada</p><p>Samantha J. Moshier, PhD: VA Boston Healthcare</p><p>System and Boston University School of Medicine,</p><p>Boston, Massachusetts</p><p>Kim T. Mueser, PhD: Center for Psychiatric</p><p>Rehabilitation and Departments of Occupational</p><p>Therapy, Psychological and Brain Sciences, and</p><p>Psychiatry, Boston University, Boston, Massachusetts</p><p>Matthew K. Nock, PhD: Department of Psychology,</p><p>Harvard University, Cambridge, Massachusetts</p><p>Thomas M. Olino, PhD: Department of Psychology,</p><p>Temple University, Philadelphia, Pennsylvania</p><p>Thomas H. Ollendick,PhD: Department of Psychology,</p><p>Virginia Polytechnic Institute and State University,</p><p>Blacksburg, Virginia</p><p>Kelly S. Parker- Guilbert,PhD: Psychology Department,</p><p>Bowdoin College, Brunswick, ME and VA Boston</p><p>Healthcare System, Boston, Massachusetts</p><p>Jacqueline B. Persons, PhD: Cognitive Behavior</p><p>Therapy and Science Center, Oakland, California and</p><p>Department of Psychology, University of California</p><p>atBerkeley, Berkeley, California</p><p>Vanesa Mora Ringle: Department of Psychology,</p><p>University of Miami, Coral Gables, Florida</p><p>Damaris J. Rohsenow, PhD: Center for Alcohol and</p><p>Addiction Studies,</p><p>of parenting practices</p><p>that have been closely associated with the development</p><p>of CP include inconsistent discipline, irritable explosive</p><p>discipline, poor supervision, lack of parental involvement,</p><p>and rigid discipline (Chamberlain, Reid, Ray, Capaldi, &</p><p>Fisher, 1997). In addition to parenting practices, various</p><p>other risk factors that may have an impact on the family</p><p>and may serve to precipitate or maintain CP have been</p><p>identified. These familial factors include parental social</p><p>cognitions (e.g., perceptions of the child), parental per-</p><p>sonal and marital adjustment (e.g., depression, ADHD,</p><p>antisocial behavior, substance abuse), and parental stress</p><p>(McMahon & Estes, 1997; McMahon & Frick,2005).</p><p>Research suggests that the child’s relationship with</p><p>peers can also play a significant role in the develop-</p><p>ment, maintenance, and escalation of CP. Research</p><p>has documented a relationship between peer rejection</p><p>in elementary school and the later development of CP</p><p>(Chen, Drabick, & Burgers, 2015). In addition, peer</p><p>rejection in elementary school is predictive of an asso-</p><p>ciation with a deviant peer group (i.e., one that shows</p><p>a high rate of antisocial behavior and substance abuse)</p><p>in early adolescence (Chen etal., 2015). This relation-</p><p>ship is important because association with a deviant peer</p><p>group leads to an increase in the frequency and severity</p><p>of CP (Patterson & Dishion, 1985), and it has proven</p><p>to be a strong predictor of later delinquency (Monahan,</p><p>Steinberg, Cauffman, & Mulvey, 2009) and substance</p><p>abuse (Dishion, Capaldi, Spracklen, & Li, 1995;</p><p>Fergusson, Swain, & Horwood,2002).</p><p>Finally, there are factors within the youth’s larger social</p><p>ecology that have been associated with CP. one of the</p><p>most consistently documented of these correlates has been</p><p>low socioeconomic status (SES; Frick, Lahey, Hartdagen,</p><p>& Hynd, 1989). However, several other ecological fac-</p><p>tors, many of which are related to low SES, such as poor</p><p>housing, poor schools, and disadvantaged neighborhoods,</p><p>have also been linked to the development of CP (Ray,</p><p>Thornton, Frick, Steinberg, & Cauffman, 2016). In addi-</p><p>tion, the high rate of violence witnessed by youths who</p><p>live in impoverished inner- city neighborhoods has also</p><p>been associated with CP (Howard, Kimonis, Munoz, &</p><p>Frick, 2012; oberth, Zheng, & McMahon,2017).</p><p>Causal TheoriesofCP</p><p>Although there is general agreement that CP in children</p><p>and adolescents is associated with multiple risk factors,</p><p>there is less agreement as to how these risk factors play</p><p>CHILD AnD ADoLESCEnT ConDUCT PRoBLEMS 75</p><p>75</p><p>a role in the development of CP. Also, in addition to</p><p>accounting for the large number of risk factors, causal the-</p><p>ories of CP need to consider research suggesting that there</p><p>may be many different causal pathways through which</p><p>youth develop these behaviors, each involving a different</p><p>constellation of risk factors and each involving somewhat</p><p>different causal processes (Frick & Viding,2009).</p><p>The most widely accepted model for delineating dis-</p><p>tinct pathways in the development of CP distinguishes</p><p>between childhood- onset and adolescent- onset subtypes</p><p>of CP. That is, the DSM- 5 (APA, 2013)makes the distinc-</p><p>tion between youths who begin showing CP before age</p><p>10years (i.e., childhood onset) and those who do not show</p><p>CP before age 10years (i.e., adolescent onset). This dis-</p><p>tinction is supported by a substantial amount of research</p><p>documenting important differences between these two</p><p>groups of youths with CP (for reviews, see Fairchild, van</p><p>Goozen, Calder, & Goodyer, 2013; Frick & Viding, 2009;</p><p>Moffitt, 2006). Specifically, youths in the childhood-</p><p>onset group show more serious aggression in childhood</p><p>and adolescence and are more likely to continue to show</p><p>antisocial and criminal behavior into adulthood (odgers</p><p>etal., 2007). More relevant to causal theory, many of the</p><p>dispositional (e.g., temperamental risk and low intelli-</p><p>gence) and contextual (e.g., family dysfunction) correlates</p><p>that have been associated with CP are more strongly asso-</p><p>ciated with the childhood- onset subtype. In contrast, the</p><p>youths in the adolescent- onset subtype show lower rates of</p><p>these same risk factors. If they do differ from other youths,</p><p>it seems primarily to be in showing greater affiliation</p><p>with delinquent peers and scoring higher on measures of</p><p>rebelliousness and authority conflict (Dandreaux & Frick,</p><p>2009; Moffitt & Caspi, 2001; Moffitt, Caspi, Dickson,</p><p>Silva, & Stanton,1996).</p><p>The different characteristics of youths in the two sub-</p><p>types of CP have led to theoretical models that propose</p><p>very different causal mechanisms operating across the two</p><p>groups. For example, Moffitt (2006) has proposed that</p><p>youths in the childhood- onset group develop CP behav-</p><p>ior through a transactional process involving a difficult</p><p>and vulnerable child (e.g., impulsive, with verbal defi-</p><p>cits, and with a difficult temperament) who experiences</p><p>an inadequate rearing environment (e.g., poor parental</p><p>supervision and poor- quality schools). This dysfunctional</p><p>transactional process disrupts the child’s socialization,</p><p>leading to poor social relations with persons both inside</p><p>(i.e., parents and siblings) and outside (i.e., peers and</p><p>teachers) the family, which further disrupts the child’s</p><p>socialization. These disruptions lead to enduring vulner-</p><p>abilities that can negatively affect the child’s psychosocial</p><p>adjustment across multiple developmental stages. In con-</p><p>trast, Moffitt views youths in the adolescent- onset pathway</p><p>as showing an exaggeration of the normative developmen-</p><p>tal process of identity formation that takes place in ado-</p><p>lescence. Their engagement in antisocial and delinquent</p><p>behaviors is conceptualized as a misguided attempt to</p><p>obtain a subjective sense of maturity and adult status in</p><p>a way that is maladaptive (e.g., breaking societal norms)</p><p>but encouraged by an antisocial peer group. Given that</p><p>their behavior is viewed as an exaggeration of a process</p><p>specific to the adolescent developmental stage and not</p><p>due to enduring vulnerabilities, their CP is less likely to</p><p>persist beyond adolescence. However, they may still have</p><p>impairments that persist into adulthood due to the conse-</p><p>quences of their CP (e.g., a criminal record, dropping out</p><p>of school, and substance abuse; Moffitt & Caspi,2001).</p><p>This distinction between childhood- onset and</p><p>adolescent- onset trajectories to severe CP has been very</p><p>influential for delineating different pathways through</p><p>which youths may develop CP, although it is important</p><p>to note that the applicability of this model to girls requires</p><p>further testing (Fairchild etal., 2013; Silverthorn & Frick,</p><p>1999). Furthermore, several authors have argued that the</p><p>distinction should be considered more quantitative than</p><p>qualitative (Fairchild etal., 2013; Lahey etal., 2000). That</p><p>is, a review by Fairchild etal. (2013) supports the conten-</p><p>tion that dispositional factors play a greater role in CP</p><p>when the onset is earlier. However, their review suggested</p><p>that this effect continues into adolescence. Furthermore,</p><p>this review noted that although the childhood- onset path-</p><p>way tended to show a more chronic course across the lifes-</p><p>pan, there was still substantial variability in the outcomes</p><p>within each pathway. The authors concluded that the tim-</p><p>ing and severity of exposure to environmental adversity in</p><p>vulnerable individuals seem to account for the differences</p><p>in age of onset and differences in outcome.</p><p>Researchers have also begun extending this concep-</p><p>tualization in a number of important ways. For example,</p><p>research has identified a subgroup of youths (approxi-</p><p>mately 25%– 30%) within the childhood- onset pathway</p><p>who show high rates of callous and unemotional (CU)</p><p>traits (e.g., lacking empathy and guilt) (Kahn, Frick,</p><p>Youngstrom, Findling, & Youngstrom, 2012). Despite</p><p>designating only a minority of children in the childhood-</p><p>onset pathway, the subgroup is important for a number of</p><p>reasons.</p><p>Brown University, Providence, Rhode</p><p>Island</p><p>Stephanie L. Rojas, MA: Department of Psychology,</p><p>University of Kentucky, Lexington, Kentucky</p><p>Natalie O. Rosen,PhD: Department of Psychology and</p><p>Neuroscience, Dalhousie University, Halifax, Nova Scotia,</p><p>Canada</p><p>Karen Rowa, PhD: Anxiety Treatment and Research</p><p>Clinic, St. Joseph’s Healthcare, Hamilton, and</p><p>Department of Psychiatry and Behavioral Neurosciences,</p><p>McMaster University, Hamilton, Ontario,Canada</p><p>Amy R. Sewart,MA: Department of Psychology, University</p><p>of California Los Angeles, Los Angeles, California</p><p>Kenneth J. Sher, PhD: Department of Psychological</p><p>Sciences, University of Missouri, Columbia, Missouri</p><p>Wendy K. Silverman, PhD: Yale Child Study Center,</p><p>Yale School of Medicine, New Haven, Connecticut</p><p>Douglas K. Snyder, PhD: Department of Psychology,</p><p>Texas A&M University, College Station,Texas</p><p>Randy Stinchfield, PhD: Department of Psychiatry,</p><p>University of Minnesota, Minneapolis, Minnesota</p><p>xx CONTRIBUTORS</p><p>Jennifer L.Swan: Department of Psychology, University</p><p>of Calgary, Calgary, Alberta,Canada</p><p>Robyn Sysko, PhD: Eating and Weight Disorders</p><p>Program, Icahn School of Medicine at Mt. Sinai,</p><p>NewYork, NewYork</p><p>Anna Van Meter, PhD: Ferkauf Graduate School of</p><p>Psychology, Yeshiva University, NewYork, NewYork</p><p>LuciaM.Walsh: Department of Psychology, University</p><p>of Miami, Coral Gables, Florida</p><p>Thomas A. Widiger, PhD: Department of Psychology,</p><p>University of Kentucky, Lexington, Kentucky</p><p>Eric A. Youngstrom, PhD: Department of Psychology</p><p>and Neuroscience, University of North Carolina at</p><p>Chapel Hill, Chapel Hill, North Carolina</p><p>PartI</p><p>Introduction</p><p>3</p><p>1</p><p>Developing Criteria for</p><p>Evidence- Based Assessment:</p><p>An Introduction to Assessments ThatWork</p><p>John Hunsley</p><p>Eric J.Mash</p><p>For many professional psychologists, assessment is</p><p>viewed as a unique and defining feature of their expertise</p><p>(Krishnamurthy et al., 2004). Historically, careful atten-</p><p>tion to both conceptual and pragmatic issues related to</p><p>measurement has served as the cornerstone of psychologi-</p><p>cal science. Within the realm of professional psychology,</p><p>the ability to provide assessment and evaluation services</p><p>is typically seen as a required core competency. Indeed,</p><p>assessment services are such an integral component of</p><p>psychological practice that their value is rarely questioned</p><p>but, rather, is typically assumed. However, solid evidence</p><p>to support the usefulness of psychological assessment is</p><p>lacking, and many commonly used clinical assessment</p><p>methods and instruments are not supported by scientific</p><p>evidence (e.g., Hunsley, Lee, Wood, & Taylor, 2015;</p><p>Hunsley & Mash, 2007; Norcross, Koocher, & Garofalo,</p><p>2006). Indeed, Peterson’s (2004) conclusion from more</p><p>than a decade ago is, unfortunately, still frequently</p><p>true:“For many of the most important inferences profes-</p><p>sional psychologists have to make, practitioners appear</p><p>to be forever dependent on incorrigibly fallible inter-</p><p>views and unavoidably selective, reactive observations as</p><p>primary sources of data” (p. 202). Furthermore, despite</p><p>the current emphasis on evidence- based practice, profes-</p><p>sional psychologists report that the least common purpose</p><p>for which they use assessment is to monitor treatment</p><p>progress (Wright etal.,2017).</p><p>In this era of evidence- based health care practices,</p><p>the need for scientifically sound assessment methods</p><p>and instruments is greater than ever (Barlow, 2005).</p><p>Assessment is the key to the accurate identification of</p><p>clients’ problems and strengths. Whether construed as</p><p>individual client monitoring, ongoing quality assurance</p><p>efforts, or program evaluation, assessment is central to</p><p>efforts to gauge the impact of health care services pro-</p><p>vided to ameliorate these problems (Brown, Scholle, &</p><p>Azur, 2014; Hermann, Chan, Zazzali, & Lerner, 2006).</p><p>Furthermore, the increasing availability of research-</p><p>derived treatment benchmarks holds out great promise</p><p>for providing clinicians with meaningful and attainable</p><p>targets for their intervention services (Lee, Horvath, &</p><p>Hunsley, 2013; Spilka & Dobson, 2015). Importantly,</p><p>statements about evidence- based practice and best-</p><p>practice guidelines have begun to specifically incor-</p><p>porate the critical role of assessment in the provision</p><p>of evidence- based services (e.g., Dozois et al., 2014).</p><p>Indeed, because the identification and implementation</p><p>of evidence- based treatments rests entirely on the data</p><p>provided by assessment tools, ignoring the quality of</p><p>these tools places the whole evidence- based enterprise in</p><p>jeopardy.</p><p>DEFINING EVIDENCE- BASED ASSESSMENT</p><p>There are three critical aspects that should define</p><p>evidence- based assessment (EBA; Hunsley & Mash,</p><p>2007; Mash & Hunsley, 2005). First, research findings</p><p>and scientifically supported theories on both psycho-</p><p>pathology and normal human development should be</p><p>used to guide the selection of constructs to be assessed</p><p>and the assessment process. As Barlow (2005) suggested,</p><p>4 INTroDuCTIoN</p><p>EBA measures and strategies should also be designed to</p><p>be integrated into interventions that have been shown to</p><p>work with the disorders or conditions that are targeted</p><p>in the assessment. Therefore, while recognizing that</p><p>most disorders do not come in clearly delineated neat</p><p>packages, and that comorbidity is often the rule rather</p><p>than the exception, we view EBAs as being disorder- or</p><p>problem- specific. Aproblem- specific approach is consis-</p><p>tent with how most assessment and treatment research is</p><p>conducted and would facilitate the integration of EBA</p><p>into evidence- based treatments (cf. Mash & Barkley,</p><p>2007; Mash & Hunsley, 2007; Weisz & Kazdin, 2017).</p><p>This approach is also congruent with the emerging</p><p>trend toward personalized assessment and treatment</p><p>(e.g., Fisher, 2015; Ng & Weisz, 2016; Sales & Alves,</p><p>2016; Seidman etal., 2010; Thompson- Hollands, Sauer-</p><p>Zavala, & Barlow, 2014). Although formal diagnostic sys-</p><p>tems provide a frequently used alternative for framing the</p><p>range of disorders and problems to be considered, com-</p><p>monly experienced emotional and relational problems,</p><p>such as excessive anger, loneliness, conflictual relation-</p><p>ships, and other specific impairments that may occur in</p><p>the absence of a diagnosable disorder, may also be the</p><p>focus of EBAs. Even when diagnostic systems are used</p><p>as the framework for the assessment, clinicians need to</p><p>consider both (a)the potential value of emerging trans-</p><p>diagnostic approaches to treatment (Newby, McKinnon,</p><p>Kuyken, Gilbody, & Dalgleish, 2015)and (b)that a nar-</p><p>row focus on assessing symptoms and symptom reduction</p><p>is insufficient for treatment planning and treatment eval-</p><p>uation purposes (cf. Kazdin, 2003). Many assessments are</p><p>conducted to identify the precise nature of the person’s</p><p>problem(s). It is, therefore, necessary to conceptualize</p><p>multiple, interdependent stages in the assessment pro-</p><p>cess, with each iteration of the process becoming less</p><p>general in nature and increasingly problem- specific with</p><p>further assessment (Mash & Terdal, 1997). In addition,</p><p>for some generic assessment strategies, there may be</p><p>research to indicate that the strategy is evidence- based</p><p>without being problem- specific. Examples of this include</p><p>functional assessments (Hurl, Wightman, Haynes, &</p><p>Virues- ortega, 2016)and treatment progress monitoring</p><p>systems (e.g., Lambert,2015).</p><p>A second requirement is that, whenever pos-</p><p>sible, psychometrically strong measures should be</p><p>used to assess the constructs targeted in the assess-</p><p>ment. The measures should have evidence of reli-</p><p>ability, validity, and clinical utility. They should also</p><p>possess appropriate norms for norm- referenced inter-</p><p>pretation and/ or replicated supporting evidence for the</p><p>accuracy (sensitivity, specificity, predictive power, etc.)</p><p>of cut- scores for criterion- referenced interpretation (cf.</p><p>Achenbach, 2005). Furthermore, there should be sup-</p><p>porting evidence to indicate that the EBAs are sensitive</p><p>to key characteristics of the individual(s) being assessed,</p><p>including characteristics such as age, gender, ethnic-</p><p>ity, and culture (e.g., Ivanova et al., 2015). Given the</p><p>range of purposes for which assessment instruments</p><p>can be used (i.e., screening, diagnosis, prognosis, case</p><p>conceptualization, treatment formulation, treatment</p><p>monitoring, and treatment evaluation) and the fact that</p><p>psychometric evidence is always conditional (based on</p><p>sample characteristics and assessment purpose), support-</p><p>ing psychometric evidence must be considered for each</p><p>purpose for which an instrument or assessment strategy is</p><p>used. Thus, general discussions concerning the relative</p><p>merits of information obtained via different assessment</p><p>methods have little meaning outside of the assessment</p><p>purpose and context. Similarly, not all psychometric ele-</p><p>ments are relevant to all assessment purposes. The group</p><p>of validity statistics that includes specificity, sensitivity,</p><p>positive predictive power, and negative predictive power</p><p>is particularly relevant for diagnostic and prognostic</p><p>assessment purposes and contains essential information</p><p>for any measure that is intended to be used for screening</p><p>purposes (Hsu, 2002). Such validity statistics may have</p><p>little relevance, however, for many methods intended to</p><p>be used for treatment monitoring and/ or evaluation pur-</p><p>poses; for these purposes, sensitivity to change is a much</p><p>more salient psychometric feature (e.g., Vermeersch,</p><p>Lambert, & Burlingame,2000).</p><p>Finally, even with data from psychometrically</p><p>strong measures, the assessment process is inherently</p><p>a decision- making task in which the clinician must</p><p>iteratively formulate and test hypotheses by integrating</p><p>data that are often incomplete or inconsistent. Thus,</p><p>a truly evidence- based approach to assessment would</p><p>involve an evaluation of the accuracy and usefulness</p><p>of this complex decision- making task in light of poten-</p><p>tial errors in data synthesis and interpretation, the costs</p><p>associated with the assessment process, and, ultimately,</p><p>the impact that the assessment had on clinical out-</p><p>comes. There are an increasing number of illustrations</p><p>of how assessments can be conducted in an evidence-</p><p>based manner (e.g., Christon, McLeod, & Jensen- Doss,</p><p>2015; Youngstrom, Choukas- Bradley, Calhoun, &</p><p>Jensen- Doss, 2015). These provide invaluable guides</p><p>for clinicians and provide a preliminary framework that</p><p>could lead to the eventual empirical evaluation of EBA</p><p>processes.</p><p>DEVELoPING CrITErIA For EVIDENCE-BASED ASSESSMENT 5</p><p>FROM RESEARCH TOPRACTICE:USING</p><p>A “GOOD- ENOUGH” PRINCIPLE</p><p>Perhaps the greatest single challenge facing efforts to</p><p>develop and implement EBAs is determining how to</p><p>start the process of operationalizing the criteria we just</p><p>outlined. The assessment literature provides a veritable</p><p>wealth of information that is potentially relevant to EBA;</p><p>this very strength, however, is also a considerable liability,</p><p>for the size of the literature is beyond voluminous. Not</p><p>only is the literature vast in scope but also the scientific</p><p>evaluation of assessment methods and instruments can be</p><p>without end because there is no finite set of studies that</p><p>can establish, once and for all, the psychometric proper-</p><p>ties of an instrument (Kazdin, 2005; Sechrest, 2005). on</p><p>the other hand, every single day, clinicians must make</p><p>decisions about what assessment tools to use in their prac-</p><p>tices, how best to use and combine the various forms of</p><p>information they obtain in their assessment, and how to</p><p>integrate assessment activities into other necessary aspects</p><p>of clinical service. Moreover, the limited time available</p><p>for service provision in clinical settings places an onus</p><p>on using assessment options that are maximally accurate,</p><p>efficient, and cost- effective. Thus, above and beyond the</p><p>scientific support that has been amassed for an instru-</p><p>ment, clinicians require tools that are brief, clear, clini-</p><p>cally feasible, and user- friendly. In other words, they need</p><p>instruments that have clinical utility and that are good</p><p>enough to get the job done (Barlow, 2005; Lambert &</p><p>Hawkins, 2004; Weisz, Krumholz, Santucci, Thomassin,</p><p>& Ng, 2015; Youngstrom & Van Meter,2016).</p><p>As has been noted in the assessment literature, there</p><p>are no clear, commonly accepted guidelines to aid clini-</p><p>cians or researchers in determining when an instrument</p><p>has sufficient scientific evidence to warrant its use (Kazdin,</p><p>2005; Sechrest, 2005). The Standards for Educational and</p><p>Psychological Testing (American Educational research</p><p>Association, American Psychological Association, &</p><p>National Council on Measurement in Education,</p><p>2014)sets out generic standards to be followed in devel-</p><p>oping and using psychological instruments but is silent</p><p>on the question of specific psychometric values that an</p><p>instrument should have. The basic reason for this is that</p><p>psychometric characteristics are not properties of an</p><p>instrument per se but, rather, are properties of an instru-</p><p>ment when used for a specific purpose with a specific</p><p>sample. Quite understandably, therefore, assessment</p><p>scholars, psychometricians, and test developers have been</p><p>reluctant to explicitly indicate the minimum psycho-</p><p>metric values or evidence necessary to indicate that an</p><p>instrument is scientifically sound (cf. Streiner, Norman,</p><p>& Cairney, 2015). unfortunately, this is of little aid to the</p><p>clinicians and researchers who are constantly faced with</p><p>the decision of whether an instrument is good enough,</p><p>scientifically speaking, for the assessment task athand.</p><p>Prior to the psychometric criteria we set out in the</p><p>first edition of this volume, there had been attempts to</p><p>establish criteria for the selection and use of measures for</p><p>research purposes. robinson, Shaver, and Wrightsman</p><p>(1991), for example, developed evaluative criteria for</p><p>the adequacy of attitude and personality measures, cov-</p><p>ering the domains of theoretical development, item</p><p>development, norms, inter- item correlations, internal</p><p>consistency, test– retest reliability, factor analytic results,</p><p>known groups validity, convergent validity, discriminant</p><p>validity, and freedom from response sets. robinson and</p><p>colleagues also used specific psychometric criteria for</p><p>many of these domains, such as describing a coefficient</p><p>α of .80 as exemplary. Adifferent approach was taken by</p><p>the Measurement and Treatment research to Improve</p><p>Cognition in Schizophrenia Group to develop a consen-</p><p>sus battery of cognitive tests to be used in clinical trials</p><p>in schizophrenia (Green etal., 2004). rather than setting</p><p>precise psychometric criteria for use in rating potential</p><p>instruments, expert panelists were asked to rate, on a nine-</p><p>point scale, each proposed tool’s characteristics, includ-</p><p>ing test– retest reliability, utility as a repeated measure,</p><p>relation to functional outcome, responsiveness to treat-</p><p>ment change, and practicality/ tolerability. An American</p><p>Psychological Association Society of Pediatric Psychology</p><p>task force used a fairly similar strategy. The task force</p><p>efforts, published at approximately the same time as the</p><p>first edition of this volume, focused on evaluating psycho-</p><p>social assessment instruments that could be used in health</p><p>care settings (Cohen etal., 2008). Instrument character-</p><p>istics were reviewed by experts and, depending on the</p><p>available empirical support, were evaluated as promising,</p><p>approaching well- established, or well- established. These</p><p>descriptors closely resembled those that had been used to</p><p>identify empirically supported treatments.</p><p>Clearly, any attempt to develop a method for deter-</p><p>mining the scientific adequacy of assessment instruments</p><p>is fraught with the potential for error. The application</p><p>of criteria that are too stringent could result in a solid</p><p>set of assessment options, but one that is so limited in</p><p>number or scope as to render the whole effort clinically</p><p>worthless. Alternatively, using excessively lenient criteria</p><p>could undermine the</p><p>whole notion of an instrument or</p><p>process being evidence based. So, with a clear awareness</p><p>of this assessment equivalent of Scylla and Charybdis, a</p><p>6 INTroDuCTIoN</p><p>decade ago we sought to construct a framework for the</p><p>chapters included in the first edition of this volume that</p><p>would employ good- enough criteria for rating psycho-</p><p>logical instruments. In other words, rather than focus-</p><p>ing on standards that define ideal criteria for a measure,</p><p>our intent was to provide criteria that would indicate the</p><p>minimum evidence that would be sufficient to warrant</p><p>the use of a measure for specific clinical purposes. We</p><p>assumed, from the outset, that although our framework</p><p>is intended to be scientifically sound and defensible, it is</p><p>a first step rather than the definitive effort in designing a</p><p>rating system for evaluating psychometric adequacy. our</p><p>framework, described later, is unchanged from the first</p><p>edition because there have been no developments in the</p><p>measurement and assessment literatures that have caused</p><p>us to reconsider our earlier position. Indeed, as we indi-</p><p>cate in the following sections of this chapter, several criti-</p><p>cal developments have served to reinforce our views on</p><p>the value of the framework.</p><p>In brief, to operationalize the good- enough principle,</p><p>specific rating criteria are used across categories of psycho-</p><p>metric properties that have clear clinical relevance; each</p><p>category has rating options of adequate, good, and excel-</p><p>lent. In the following sections, we describe the assessment</p><p>purposes covered by our rating system, the psychometric</p><p>properties included in the system, and the rationales for</p><p>the rating options. The actual rating system, used in this</p><p>volume by all authors of disorder/ problem- oriented chap-</p><p>ters to construct their summary tables of instruments, is</p><p>presented in two boxes later in this chapter.</p><p>ASSESSMENT PURPOSES</p><p>Although psychological assessments are conducted for</p><p>many reasons, it is possible to identify a small set of inter-</p><p>related purposes that form the basis for most assessments.</p><p>These include (a)diagnosis (i.e., determining the nature</p><p>and/ or cause[s] of the presenting problems, which may or</p><p>may not involve the use of a formal diagnostic or catego-</p><p>rization system), (b)screening (i.e., identifying those who</p><p>have or who are at risk for a particular problem and who</p><p>might be helped by further assessment or intervention),</p><p>(c)prognosis and other predictions (i.e., generating pre-</p><p>dictions about the course of the problems if left untreated,</p><p>recommendations for possible courses of action to be</p><p>considered, and their likely impact on the course of</p><p>the problems), (d) case conceptualization/ formulation</p><p>(i.e., developing a comprehensive and clinically rele-</p><p>vant understanding of the client, generating hypotheses</p><p>regarding critical aspects of the client’s biopsychosocial</p><p>functioning and context that are likely to influence the</p><p>client’s adjustment), (e) treatment design/ planning (i.e.,</p><p>selecting/ developing and implementing interventions</p><p>designed to address the client’s problems by focusing on</p><p>elements identified in the diagnostic evaluation and the</p><p>case conceptualization), (f) treatment monitoring (i.e.,</p><p>tracking changes in symptoms, functioning, psychologi-</p><p>cal characteristics, intermediate treatment goals, and/ or</p><p>variables determined to cause or maintain the problems),</p><p>and (g)treatment evaluation (i.e., determining the effec-</p><p>tiveness, social validity, consumer satisfaction, and/ or cost-</p><p>effectiveness of the intervention).</p><p>The chapters in this volume provide summaries of</p><p>the best assessment methods and instruments available</p><p>for commonly encountered clinical assessment purposes.</p><p>While recognizing the importance of other possible</p><p>assessment purposes, chapters in this volume focus on</p><p>(a) diagnosis, (b) case conceptualization and treatment</p><p>planning, and (c) treatment monitoring and treatment</p><p>evaluation. Although separable in principle, the purposes</p><p>of case conceptualization and treatment planning were</p><p>combined because they tend to rely on the same assess-</p><p>ment data. Similarly, the purposes of treatment monitor-</p><p>ing and evaluation were combined because they often,</p><p>but not exclusively, use the same assessment methods</p><p>and instruments. Clearly, there are some overlapping</p><p>elements, even in this set of purposes; for example, it is</p><p>relatively common for the question of diagnosis to be</p><p>revisited as part of evaluating the outcome of treatment.</p><p>In the instrument summary tables that accompany each</p><p>chapter, the psychometric strength of instruments used</p><p>for these three main purposes are presented and rated.</p><p>Within a chapter, the same instrument may be rated for</p><p>more than one assessment purpose and thus appear in</p><p>more than one table. Because an instrument may possess</p><p>more empirical support for some purposes than for others,</p><p>the ratings given for the instrument may not be the same</p><p>in each of the tables.</p><p>The chapters in this volume present information on</p><p>the best available instruments for diagnosis, case con-</p><p>ceptualization and treatment planning, and treatment</p><p>monitoring and evaluation. They also provide details on</p><p>clinically appropriate options for the range of data to col-</p><p>lect, suggestions on how to address some of the challenges</p><p>commonly encountered in conducting assessments, and</p><p>suggestions for the assessment process. Consistent with</p><p>the problem- specific focus within EBA outlined previ-</p><p>ously, most chapters in this volume focus on one or more</p><p>specific disorders or conditions. However, many clients</p><p>DEVELoPING CrITErIA For EVIDENCE-BASED ASSESSMENT 7</p><p>present with multiple problems and, therefore, there</p><p>are frequent references within a given chapter to the</p><p>assessment of common co- occurring problems that are</p><p>addressed in other chapters in the volume. To be opti-</p><p>mally useful to potential readers, the chapters are focused</p><p>on the most commonly encountered disorders or condi-</p><p>tions among children, adolescents, adults, older adults,</p><p>and couples. With the specific focus on the three critical</p><p>assessment purposes of diagnosis, case conceptualization</p><p>and treatment planning, and treatment monitoring and</p><p>treatment, within each disorder or condition, the chapters</p><p>in this volume provide readers with essential information</p><p>for conducting the best EBAs currently possible.</p><p>PSYCHOMETRIC PROPERTIES</p><p>AND RATING CRITERIA</p><p>Clinical assessment typically entails the use of both idio-</p><p>graphic and nomothetic instruments. Idiographic mea-</p><p>sures are designed to assess unique aspects of a person’s</p><p>experience and, therefore, to be useful in evaluating</p><p>changes in these individually defined and constructed</p><p>variables. In contrast, nomothetic measures are designed</p><p>to assess constructs assumed to be relevant to all indi-</p><p>viduals and to facilitate comparisons, on these constructs,</p><p>across people. Most chapters include information on</p><p>idiographic measures such as self- monitoring forms and</p><p>individualized scales for measuring treatment goals. For</p><p>such idiographic measures, psychometric characteristics</p><p>such as reliability and validity may, at times, not be easily</p><p>evaluated or even relevant (but see Weisz etal., 2011). It</p><p>is crucial, however, that the same items and instructions</p><p>are used across assessment occasions— without this level</p><p>of standardization it is impossible to accurately determine</p><p>changes that may be due to treatment (Kazdin,1993).</p><p>The nine psychometric categories rated for the instru-</p><p>ments in this volume are norms, internal consistency,</p><p>inter- rater reliability, test– retest reliability, content valid-</p><p>ity, construct validity, validity generalization, sensitivity</p><p>to treatment change, and clinical utility. Each of these</p><p>categories is applied in relation to a specific assessment</p><p>purpose (e.g., case conceptualization and treatment plan-</p><p>ning) in the context of a specific disorder or clinical con-</p><p>dition (e.g., eating disorders, self- injurious behavior, and</p><p>relationship conflict).</p><p>Consistent with our previous com-</p><p>ments, factors such as gender, ethnicity, and age must be</p><p>considered in making ratings within these categories. For</p><p>each category, a rating of less than adequate, adequate,</p><p>good, excellent, not reported, or not applicable was</p><p>possible. The precise nature of what constituted adequate,</p><p>good, and excellent varied, of course, from category to cat-</p><p>egory. In general, however, a rating of adequate indicated</p><p>that the instrument meets a minimal level of scientific</p><p>rigor; good indicated that the instrument would generally</p><p>be viewed as possessing solid scientific support; and excel-</p><p>lent indicated there was extensive, high- quality support-</p><p>ing evidence. Accordingly, a rating of less than adequate</p><p>indicated that the instrument did not meet the minimum</p><p>level set out in the criteria. Arating of not reported indi-</p><p>cated that research on the psychometric property under</p><p>consideration had not yet been conducted or published.</p><p>Arating of not applicable indicated that the psychomet-</p><p>ric property under consideration was not relevant to the</p><p>instrument (e.g., inter- rater reliability for a self- report</p><p>symptom rating scale).</p><p>When considering the clinical use of a measure, it</p><p>would be desirable to only use those measures that would</p><p>meet, at a minimum, the criteria for good. However,</p><p>because measure development is an ongoing process, the</p><p>rating system provides the option of the adequate rating</p><p>in order to fairly evaluate (a)relatively newly developed</p><p>measures and (b)measures for which comparable levels</p><p>of research evidence are not available across all psycho-</p><p>metric categories in the rating system. In several chapters,</p><p>authors explicitly commented on the status of some newly</p><p>developed measures, but by and large, the only instru-</p><p>ments included in chapter summary tables were those</p><p>that had adequate or better ratings on the majority of the</p><p>psychometric dimensions. Thus, the instruments pre-</p><p>sented in these tables represent only a subset of available</p><p>assessmenttools.</p><p>Despite the difficulty inherent in promulgating sci-</p><p>entific criteria for psychometric properties, we believe</p><p>that the potential benefits of fair and attainable criteria</p><p>far outweigh the potential drawbacks (cf. Sechrest, 2005).</p><p>Accordingly, reasoned arguments from respected psycho-</p><p>metricians and assessment scholars, along with summaries</p><p>of various assessment literatures, guided the selection of</p><p>criteria for rating the psychometric properties associated</p><p>with an instrument. Box 1.1 presents the criteria used in</p><p>rating norms and reliability indices; Box 1.2 presents the</p><p>criteria used in rating validity indices and clinical utility.</p><p>Norms</p><p>When using a standardized, nomothetically based</p><p>instrument, it is essential that norms, specific criterion-</p><p>related cutoff scores, or both are available to aid in</p><p>the accurate interpretation of a client’s test score</p><p>8 INTroDuCTIoN</p><p>BOX 1.1 Criteria ata Glance:Norms and</p><p>Reliability</p><p>NORMS</p><p>Adequate=Measures of central tendency and distribu-</p><p>tion for the total score (and subscores if relevant) based</p><p>on a large, relevant, clinical sample are available.</p><p>Good=Measures of central tendency and distribution</p><p>for the total score (and subscores if relevant) based</p><p>on several large, relevant samples (must include</p><p>data from both clinical and nonclinical samples) are</p><p>available.</p><p>Excellent=Measures of central tendency and distribu-</p><p>tion for the total score (and subscores if relevant)</p><p>based on one or more large, representative samples</p><p>(must include data from both clinical and nonclini-</p><p>cal samples) are available.</p><p>INTERNAL CONSISTENCY</p><p>Adequate = Preponderance of evidence indicates</p><p>α values of .70– .79.</p><p>Good=Preponderance of evidence indicates α values</p><p>of .80– .89.</p><p>Excellent = Preponderance of evidence indicates</p><p>α values≥.90.</p><p>INTER- RATER RELIABILITY</p><p>Adequate = Preponderance of evidence indicates κ</p><p>values of .60– .74; the preponderance of evidence</p><p>indicates Pearson correlation or intraclass correla-</p><p>tion values of .70– .79.</p><p>Good=Preponderance of evidence indicates κ val-</p><p>ues of .75– .84; the preponderance of evidence</p><p>indicates Pearson correlation or intraclass correla-</p><p>tion values of .80– .89.</p><p>Excellent = Preponderance of evidence indicates κ</p><p>values ≥ .85; the preponderance of evidence indi-</p><p>cates Pearson correlation or intraclass correlation</p><p>values≥.90.</p><p>TEST– RETEST RELIABILITY</p><p>Adequate = Preponderance of evidence indicates</p><p>test– retest correlations of at least .70 over a period</p><p>of several days to severalweeks.</p><p>Good = Preponderance of evidence indicates test–</p><p>retest correlations of at least .70 over a period of</p><p>several months.</p><p>Excellent = Preponderance of evidence indicates</p><p>test– retest correlations of at least .70 over a period</p><p>of a year or longer.</p><p>BOX 1.2 Criteria ata Glance:Validity and Utility</p><p>CONTENT VALIDITY</p><p>Adequate = The test developers clearly defined</p><p>the domain of the construct being assessed and</p><p>ensured that selected items were representative of</p><p>the entire set of facets included in the domain.</p><p>Good = In addition to the criteria used for an</p><p>adequate rating, all elements of the instrument</p><p>(e.g., instructions and items) were evaluated</p><p>by judges (e.g., by experts or by pilot research</p><p>participants).</p><p>Excellent = In addition to the criteria used for a</p><p>good rating, multiple groups of judges were</p><p>employed and quantitative ratings were used by</p><p>the judges.</p><p>CONSTRUCT VALIDITY</p><p>Adequate=Some independently replicated evidence</p><p>of construct validity (e.g., predictive validity, con-</p><p>current validity, and convergent and discriminant</p><p>validity).</p><p>Good = Preponderance of independently repli-</p><p>cated evidence, across multiple types of validity</p><p>(e.g., predictive validity, concurrent validity, and</p><p>convergent and discriminant validity), is indica-</p><p>tive of construct validity.</p><p>Excellent=In addition to the criteria used for a good</p><p>rating, there is evidence of incremental validity</p><p>with respect to other clinicaldata.</p><p>VALIDITY GENERALIZATION</p><p>Adequate=Some evidence supports the use of this</p><p>instrument with either (a)more than one specific</p><p>group (based on sociodemographic characteristics</p><p>such as age, gender, and ethnicity) or (b)in mul-</p><p>tiple contexts (e.g., home, school, primary care set-</p><p>ting, and inpatient setting).</p><p>Good = Preponderance of evidence supports the</p><p>use of this instrument with either (a)more than</p><p>one specific group (based on sociodemographic</p><p>characteristics such as age, gender, and eth-</p><p>nicity) or (b) in multiple settings (e.g., home,</p><p>school, primary care setting, and inpatient</p><p>setting).</p><p>Excellent=Preponderance of evidence supports the</p><p>use of this instrument with more than one specific</p><p>group (based on sociodemographic characteristics</p><p>such as age, gender, and ethnicity) and across mul-</p><p>tiple contexts (e.g., home, school, primary care set-</p><p>ting, and inpatient setting).</p><p>DEVELoPING CrITErIA For EVIDENCE-BASED ASSESSMENT 9</p><p>(American Educational research Association, American</p><p>Psychological Association, & National Council on</p><p>Measurement in Education, 2014). For example,</p><p>norms can be used to determine the client’s pre- and</p><p>post- treatment levels of functioning and to evaluate</p><p>whether any change in functioning is clinically mean-</p><p>ingful (Achenbach, 2001; Kendall, Marrs- Garcia, Nath,</p><p>& Sheldrick, 1999). Selecting the target population(s)</p><p>for the norms and then ensuring that the norms are</p><p>adequate can be difficult tasks, and several sets of norms</p><p>may be required for a measure. one set of norms may be</p><p>needed to determine the meaning of the obtained score</p><p>relative to the general population, whereas a different</p><p>set of norms could be used to compare the score to spe-</p><p>cific subgroups within the population (Cicchetti, 1994).</p><p>regardless of the population to which comparisons are</p><p>to be made, a normative sample must be truly represen-</p><p>tative of the population with respect to demographics</p><p>and other important</p>
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