IQs Corner: What is (and what is not) clinical judgment in intelligence test interpretation? - #IQ #intelligence #ID #intellectualdisability #schoolpsychologists #schoolpsychology #diagnosis

What is clinical judgment in intelligence testing?  

This term is frequently invoked when psychologists explain or defend their intelligence test interpretations.  Below is a brief explanation I’ve used to describe what it is…and what it is not, based on several sources.  Schalock and Luckasson’s AAIDD Clinical Judgment book (now in a 2014 revised version) is the best single source I have found that addresses this slippery concept in intelligence testing, particularly in the context of a potential diagnosis of intellectual disability (ID)—it is a recommended reading.

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Clinical judgment is a process based on solid scientific knowledge and is characterized as being “systematic (i.e., organized, sequential, and logical), formal (i.e., explicit and reasoned), and transparent (i.e., apparent and communicated clearly)” (Schalock & Luckasson, 2005, p.1). The application of clinical judgment in the evaluation of IQ scores in the diagnosis of intellectual disability includes consideration of multiple factors that might influence the accuracy of an assessment of general intellectual ability (APA: DSM-5, 2013).  The “unanimous professional consensus that the diagnosis of intellectual disability requires comprehensive assessment and the application of clinical judgment” (Brief of Amici Curiae American Psychological Association, American Psychiatric Association, American Academy of Psychiatry and the Law, Florida Psychological Association, National Association of Social Workers, and National Association of Social Workers Florida Chapter, in Support of Petitioner; Hall v. Florida; S.Ct., No. 12-10882; 2014; p. 8).

The misuse of clinical judgment in the interpretation of scores from intelligence test batteries should not be used as the basis for “gut instinct” or “seat-of-the-pants” impressions and conclusions of the assessment professional (Macvaugh & Cunningham, 2009), or justification for shortened evaluations, a means to convey stereotypes or prejudices, a substitute for insufficiently explored questions, or an excuse for incomplete testing and missing data (Schalock & Luckasson, 2005). Idiosyncratic methods and intuitive conclusions are not scientifically based and have unknown reliability and validity. 

If clinical judgement interpretations and opinions regarding an individual’s level of general intelligence are based on novel or emerging research-based principles, the assessment professional must document the bases for these new interpretations as well as the limitations of these principles and methods. This requirement is consistent with the Standards for Educational and Psychological Testing Standard 9.4 which states:

When a test is to be used for a purpose for which little or no validity evidence is available, the user is responsible for documenting the rationale for the selection of the test and obtaining evidence of the reliability/precision of the test scores and the validity of the interpretations supporting the use of the scores for this purpose (p. 143).

American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (2014).  Standards for educational and psychological testing.  Washington, DC:  Author. 

American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders— Fifth Edition. Washington D.C.:  Author. 

Brief of Amici Curiae American Psychological Association, American Psychiatric Association, American Academy of Psychiatry and the Law, Florida Psychological Association, National Association of Social Workers, and National Association of Social Workers Florida Chapter, in Support of Petitioner; Hall v. Florida; S.Ct., No. 12-10882; 2014; p. 8.

MacVaugh, G. S. & Cunningham, M. D. (2009). Atkins v. Virginia: Implications and recommendations for forensic practice.  The Journal of Psychiatry and Law, 37, 131-187.

Schalock, R. L. & Luckasson, R. (2005). Clinical judgment. Washington, DC: American Association on Intellectual and Developmental Disabilities. 

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Kevin S. McGrew, PhD.

Educational Psychologist

Director 

Institute for Applied Psychometrics (IAP)

www.theMindHub.com

Research Byte: Is trying hard enough? Causal analysis of the effort-IQ relationship suggests not - #intelligence #IQ #motivation #volition #CAMML #conative #noncognitive

Is Trying Harder Enough? Causal Analysis of the Effort-IQ Relationship Suggests Not.  Timothy Bates. Intelligence and Cognitive Abilities (open access—click here to locate article to read or download)

Abstract

Claims that effort increases cognitive scores are now under great doubt. What is needed is randomized controlled trials optimized for testing causal influence and avoiding confounding of self-evaluation of performance with feelings of good effort. Here we report three large studies using unconfounded measures of effort and instrumental analysis to isolate any causal effect of effort on cognitive score. An initial study (N = 393) validated an appropriate effort measure, demonstrating excellent external and convergent validity (β = .61). Study 2 (N = 500, preregistered) randomly allocated subjects to a performance incentive, using an instrumental variable analysis to detect causal effects of effort. The incentive successfully manipulated effort (𝛽 = .18, p = .001). However, the causal effect of effort on scores was near-zero and non-significant (𝛽 = .04, p = .886). Study 3 (N=1,237) replicated this null result with preregistered analysis and an externally developed measure of effort: incentive again raised reported effort (𝛽 = .17, p <.001), but effort had no significant causal effect on cognitive score (β2 = .27 [-0.07, 0.62]), p = .15). Alongside evidence of research fraud and confounding in earlier studies, the present evidence for the absence of any causal effects of effort on cognitive scores, effort research should shift its focus to goal setting – where effort is useful – rather than raising basic ability, which it appears unable to do.

Select quote from discussion: “The present results suggest a potential ‘central dogma of cognition’: that volitional effort can direct cognitive resources but cannot fundamentally alter or bypass the efficacy of the underlying cognitive systems themselves”

These findings are consistent with my proposed cognitive-affective-motivation-model-of-learning (CAMML), grounded extensively on Richard Snows concept of aptitude trait complexes, where motivational constructs are seen as driving and directing the use of cognitive abilities (via personal investment mechanisms), but not directly having a causal effect on cognitive abilities.  See first of two figures below.  Note lack of causal arrows from conative and affective domain constructs to CHC cognitive abilities.  Paper can be accessed by clicking here.

Click on images to enlarge for easier viewing

On #factoranalysis of #IQ tests—impact of software choice—plus comments about art+science of factor analysis in #intelligence test research—#schoolpsychology

Contributing to the reproducibility crisis in Psychology: The role of statistical software choice on factor analysis.  Journal of School Psychology.  Stefan C. Dombrowski.  Click here to view article source and abstract.

This is an important article for those who conduct (and also those who consume) factor analysis results of intelligence or cognitive ability tests.  

Abstract (note - bold font in abstract has been added by me)

A potentially overlooked contributor to the reproducibility crisis in psychology is the choice of statistical application software used for factor analysis. Although the open science movement promotes transparency by advocating for open access to data and statistical methods, this approach alone is insufficient to address the reproducibility crisis. It is commonly assumed that different statistical software applications produce equivalent results when conducting the same statistical analysis. However, this is not necessarily the case. Statistical programs often yield disparate outcomes, even when using identical data and factor analytic procedures, which can lead to inconsistent interpretation of results. This study examines this phenomenon by conducting exploratory factor analyses on two tests of cognitive ability—the WISC-V and the MEZURE—using four different statistical programs/applications. Factor analysis plays a critical role in determining the underlying theory of cognitive ability instruments, and guides how those instruments should be scored and interpreted. However, psychology is grappling with a reproducibility crisis in this area, as independent researchers and test publishers frequently report divergent factor analytic results. The outcome of this study revealed significant variations in structural outcomes among the statistical software programs/applications. These findings highlight the importance of using multiple statistical programs, ensuring transparency with analysis code, and recognizing the potential for varied outcomes when interpreting results from factor analytic procedures. Addressing these issues is important for advancing scientific integrity and mitigating the reproducibility crisis in psychology particularly in relation to cognitive ability structural validity.

My additional comments

The recommendation that multiple factor analysis software programs be used when analyzing the structural validity of cognitive abilities tests makes sense.  Kudos to Dr. Dombrowski for demonstrating this need.

Along these lines, it is also important to recognize that the use and interpretation of any factor analysis software is highly dependent on the statistical and substantive expertise and skills of the researcher.  I made these points (based on the writings and personal conversations with Jack Carroll) in a recent article (McGrew, 2023; open access so you can download and read) in the Journal of Intelligence.  The salient material is reproduced below.  This article can be accessed either a the journal website or via the Research and Reports section of my MindHub web page (McGrew, 2023)

(Note - Bold font in text below, extracted from McGrew (2023), is not in the original published article)

“I was fortunate to learn important tacit EFA and CFA knowledge during my 17 years of interactions with Carroll, and particularly my private one-to-one tutelage with Carroll in May 2003. Anyone who reads Chapter 3 (Survey and Analysis of Correlational and Factor-Analytic Research on Cognitive Abilities: Methodology) of Carroll's 1993 book, as well as his self-critique of his seminal work (Carroll 1998) and other select method-focused post-1993 publications (Carroll 1995, 1997), should conclude what is obvious—to Carroll, factor analyses were a blend of art and science. As articulated by some of his peers (see footnote #2), his research reflected the work of an expert with broad and deep substantive knowledge of research and theories in intelligence, cognitive psychology, and factor analysis methods. 

In 2003, after Carroll had been using CFA to augment his initial EFA analyses for at least a decade, Carroll expressed (to me during our May 2003 work week) that he was often concerned with the quality of some reported factor analyses (both EFA and CFA) of popular clinical IQ tests or other collections of cognitive ability measures (Carroll 1978, 1991, 1995, 2003). Carroll's characteristic positive skepticism regarding certain reported factor analyses was first articulated (as far as I know) in the late 1970's, when he stated “despite its many virtues, factor analysis is a very tricky technique; in some ways it depends more on art than science, that is, more on intuition and judgment than on formal rules of procedure. People who do factor analysis by uncritical use of programs in computer packages run the risk of making fools of themselves” (Carroll 1978, p. 91; emphasis added). It is my opinion that Carroll would still be dismayed by some of the EFA and CFA studies of intelligence tests published during the past two decades that often used narrow or restricted forms of factor analysis methods and rigid formal statistical rules for decision-making, with little attempt to integrate contemporary substantive research or theory to guide the analysis and interpretation of the results (e.g., see Decker 2021; Decker et al. 2021; McGrew et al. 2023). 

Carroll's unease was prescient of recently articulated concerns regarding two aspects of the theory crises in structural psychological research—the conflation of statistical (primarily factor analysis) models with theoretical models and the use of narrow forms of factor analysis methods (Fried 2020; McGrew et al. 2023). First, many intelligence test batteries only report CFA studies in their technical manuals. EFA results, which often produce findings that vary from CFA findings, are frequently omitted. This often leads to debates between independent researchers and test authors (or test publishers) regarding the validity of the interpretation of composite or cluster scores, leaving test users confused regarding the psychometric integrity of composite score interpretations. McGrew et al. (2023) recently recommended that intelligence test manuals, as well as research reports by independent researchers, include both EFA and CFA (viz., bifactor g, hierarchical g, and Horn no-g models), as well as psychometric network analysis (PNA) and possibly multidimensional scaling analyses (MDSs; McGrew et al. 2014; Meyer and Reynolds 2022). As stated by McGrew et al. (2023), “such an ecumenical approach would require researchers to present results from the major classes of IQ test structural research methods (including PNA) and clearly articulate the theoretical basis for the model(s) the author's support. Such an approach would also gently nudge IQ test structural researchers to minimize the frequent conflation of theoretical and psychometric g constructs. Such multiple-methods research in test manuals and journal publications can better inform users of the strengths and limitations of IQ test interpretations based on whatever conceptualization of psychometric general intelligence (including models with no such construct) underlies each type of dimensional analysis” (p. 24).”

#Intelligence (#IQ) #cognitive testing in perspective: An #ecological systems brief video explanation—useful for #schoolpsychology

Click on image to enlarge for easy reading

An oldie but goodie!  This is a 19+ minute narrated video (sit down with your favorite beverage and enjoy) where I explain how intelligence (IQ) or cognitive ability testing should be better understood in the context of a larger ecological systems model perspective (Bronfenbrenner).  

I first posted the video in 2015—-9 years ago! So be gentle…I’m much better with these videos now :) Thus, some of my COI statements/disclaimers/affiliations are no longer accurte (and updated version can be found a theMindHub.com—Under About IAP: The Director: Disclosures & Bio).

If all works well, just click the start arrow on the video screen…and tap the enlarge icon in the lower right corner.  This video is now hosted on YouTube, so it may be possible that you may first encounter 1-2 very brief adds that you can skip within the first 15-10 seconds.  It is possible (it seems to vary everytime) that you might be asked to “sign in” to show you are not a bot.  All you need to do is press the message, or if images of muliptle videos appear, press the first one…if you only get the message you may need to back up and try link again (no signing in….I hate having lost control of how these work by using YouTube 9 years ago…as now the starting has these mild annoyances..but it is the price for a free service).  Be aware that some of the first 4-5 slides may have minimal or no narration and you can skip ahead to the beginning…it is the first slide shown immediately below before the video. Given the caveats above, it is possible the video might not deploy exactly how I describe…the platform seems to be a bit tempormental, at least for me.  Enjoy.

#AAIDD's #IQ Part-Score Position (with reference to diagnosing #intellectual #disabiity [#ID]) Is at Variance With Other Authoritative Sources—Important for #schoolpsychologists

In a 2021 commentary regarding the most recent official AAIDD intellectual disability definition and classification manual (2021), I raised a concern regarding AAIDD’s position that only a full scale or global IQ score can be used for a Dx of ID.  No room was left for clinical judgement for n=1 unique cases.  Click here to download and read the complete article.  Below is some select text.

“AAIDD's [IQ] Part-Score Position Is at Variance With Other Authoritative Sources”

In AAIDD’s The Death Penalty and Intellectual Disability (Polloway, 2015), both McGrew (2015) and Watson (2015) suggest that [IQ] part scores can be used in special cases. (Note that these two chapters, although published in an AAIDD book, do not necessarily represent the official position of AAIDD.) The limited use of part scores is also described in the 2002 National Research Council book on ID and social security eligibility (see McGrew, 2015; Watson, 2015). The authoritative Diagnostic and Statistical Manual of Mental Disorder—Fifth Edition (DSM-5) manual implies that part scores may be necessary when it states that ‘‘highly discrepant subtest scores may make an overall IQ score invalid'' (American Psychiatric Association, 2013, p. 37). Finally, in the recent APA Handbook of Intellectual and Developmental Disabilities (Glidden, 2021), Floyd et al. (2021) state ‘‘in rare situations in which the repercussions of a false negative diagnostic decision would have undue or irreparable negative impact upon the client, a highly g-loaded part score (see McGrew, 2015a) might be selected to represent intellectual functioning'' (emphasis added; p. 412).

“ In a unique n = 1 high-stakes setting, a psychologist may be ethically obligated to proffer an expert opinion whether the full-scale score is (or is not) the best indicator of general intelligence. There must be room for the judicious use of clinical judgment-based part scores. AAIDD's purple manual complicates rather than elucidates guidance for psychologists and the courts. In high-stakes settings, a psychologist may be hard pressed to explain that their proffered expert opinions are grounded in the AAIDD purple manual, but then explain why they disagree with the ‘‘just say no to part scores'' AAIDD position.”

More on the conflation of #psychometric #g (general #intelligence): Is g the Loch Ness Monster of psycholgy?

From McGrew et al. (2023) article (click here for prior post and access to the article in Journal of Intelligence.)”  Click here for a series of slides regarding the theoretical and psychometric conflation of g.

The Problem of Conflating Theoretical and Psychometric g

“Contributing to the conflicting g-centric and mixed-g positions (regarding the interpretive value of broad CHC scores) is the largely unrecognized common practice of conflating theoretical and psychometric g. Psychometric g is the statistical extraction of a latent factor (via factor analysis) that accounts for the largest single source of common variance in a collection of cognitive abilities tests. It is an emergent property statistical index. Theoretical g refers to the underlying biological brain-based mechanism(s) that produce psychometric g. The global composite score from IQ test batteries is considered the best manifest proxy for psychometric g. The conflation of psychometric and theoretical g in IQ battery structural research ignores a simple fact—“general intelligence is not the primary fact of mainstream intelligence research; the primary fact is the positive manifold….general intelligence is but one interpretation of that primary fact” (Protzko and Colom 2021a, p. 2; italic emphasis added). As described later, contemporary intelligence and cognitive psychology research has provided reasonable and respected theories (e.g., dynamic mutualism; process overlap theory; wired cognition; attentional control), robust methods (psychometric network analysis), and supporting research (Burgoyne et al. 2022; Conway and Kovacs 2015; Kan et al. 2019; Kievit et al. 2016; Kovacs and Conway 2016, 2019; van der Maas et al. 2006, 2014, 2019) that accounts for the positive manifold of IQ test correlations in the absence of an underlying latent causal theoretical or psychometric gconstruct.” (p.3; bold font emphasis added).