From the #Cattell-Horn-Carroll (#CHC) #cognitive #intelligence theory archives: Photos of important 1999 Carroll, Horn, Woodcock, Roid et al. meeting in Chapel Hill, NC.

I was recently cleaning my office when I stumbled upon these priceless photos from a 1999 historical meeting in Chapel Hill, NC that involved John Horn, Jack Carroll, Richard Woodcock, Gale Roid, John Wasserman, Fred Schrank and myself).  The provenance (I’ve always wanted to use this word 😉) for the meeting is provided below the pictures in the form of extracted quotes from Wasserman (2019) and McGrew (2023) (links below), which I confirmed with John Wasserman via a personal email on August, 30, 2025.

The 1990 CHC-based WJ-R had already been published and the WJ III author team were nearing completion of the CHC-based WJ III (2001).  Unbeknownst to many is the fact that Woodock was originally planned to be one of the coauthors of the SB5 (along with Gale Roid), which explains his presence in the photo’s that document one of several planning meetings for the CHC-based SB5.  

I was also involved as a consultant during the early planning for the CHC-based SB5 because of my knowledge of the evolving CHC theory.  My role was to review and integrate all available published and unpublished factor analysis research on all prior editions of the different SB legacy tests. I post these pictures with the names of the people included in each photo immediately below the photo. No other comments (save for the next paragraph) are provided.  

To say the least, my presence at this meeting (as well as many other meetings with Carroll and Horn together, as well as with each alone, that occured when planning the various editions of the WJ’s) was surrealistic.  One could sense a paradigm shift in intelligence testing that was happening in real time during the meetings!  The expertise of the leading theorists regarding what became known as CHC theory, together with the expertise of the applied test developers of Woodcock and Roid, provided me with learning experiences that cannot be captured in any book or university course work. 

Click on images to enlarge.  

Be gentle, these are the best available copies of images taken with an old-school camera (not smart-phone based digital images)

(Carroll, Woodcock, McGrew, Schrank)

(Carroll, Woodcock, McGrew)

(Woodcock, Wasserman, Roid, Carroll, Horn)

(Wasserman, Roid, Carroll, Horn, McGrew)

(Carroll, Woodcock)

———————-

“It was only when I left TPC for employment with Riverside Publishing (now Houghton-Mifflin-Harcourt; HMH) in 1996 that I met Richard W. Woodcock and Kevin S. McGrew and became immersed in the extended Gf-Gc (fluid-crystallized)/ Horn-Cattell theory, beginning to appreciate how Carroll's Three-Stratum (3S) model could be operationalized in cognitive-intellectual tests. Riverside had been the home of the first Gf-Gc intelligence test, the Stanford–Binet Intelligence Scale, Fourth Edition (SB IV; R. L. Thorndike, Hagen, & Sattler, 1986), which was structured hierarchically with Spearman's g at the apex, four broad ability factors at a lower level, and individual subtests at the lowest level. After acquiring the Woodcock–Johnson (WJ-R; Woodcock & Johnson, 1989) from DLM Teaching Resources, Riverside now held a second Gf-Gc measure. The WJ-R Tests of Cognitive Ability measured seven broad ability factors from Gf-Gc theory with an eighth broad ability factor possible through two quantitative tests from theWJ-R Tests of Achievement. When I arrived, planning was underway for new test editions – the WJ III (Woodcock, McGrew, & Mather, 2001) and the SB5 (Roid, 2003) – and Woodcock was then slated to co-author both tests, although he later stepped down from the SB5. Consequently, I had the privilege of participating in meetings in 1999 with John B. Carroll and John L. Horn, both of whom had been paid expert consultants to the development of the WJ-R” (Wasserman, 2019, p. 250)

——————-

“In 1999, Woodcock brokered the CHC umbrella term with Horn and Carroll for practical reasons (McGrew 2005)—to facilitate internal and external communication regarding the theoretical model of cognitive abilities underlying the then-overlapping test development activities (and some overlapping consultants, test authors, and test publisher project directors; John Horn, Jack Carroll, Richard Woodcock, Gale Roid, Kevin McGrew, Fred Schrank, and John Wasserman) of the Woodcock–Johnson III and the Stanford Binet–Fifth Edition by Riverside Publishing” (McGrew, 2023, p. 3)

Stanford-Binet 5 post publication resources: 5-20-13

This is an update of a post made a number of years ago...with new information

Stanford Binet 5 Assessment Service Bulletins (info from Riverside Publishing web page)

• SB5 Assessment Service Bulletin #1: History of the Stanford-Binet Intelligence Scales: Content and Psychometrics
• SB5 Assessment Service Bulletin #2: Accommodations on the Stanford-Binet Intelligence Scales, Fifth Edition
• SB5 Assessment Service Bulletin #3: Use of the Stanford-Binet Intelligence Scales, Fifth Edition in the Assessment of High Abilities
• SB5 Assessment Service Bulletin #4: Special Composite Scores for the Stanford-Binet Intelligence Scales, Fifth Edition
• Quality of Performance and Change - Sensitive Assessment for Cognitive Ability by Gale H. Roid
• Technical Brief - Interpretation of SB5/Early SB5 Factor Index Scores by Gale H. Roid

IAP101 Brief #12: Use of IQ component part scores as indicators of general intelligence in SLD and MR/ID diagnosis

   
            Historically the concept of general intelligence (g), as operationalized by intelligence test battery global “full scale” IQ scores, has been central to the definition and classification of individuals with a specific learning disability (SLD) as well as individuals with an intellectual disability (ID).  More recently, contemporary definitions and operational criteria have elevated intelligence test battery composite or part scores to a more prominent role in diagnosis and classification of SLD and more recently in ID.

            In the case of SLD, third-method “consistency” definitions prominently feature component or part scores in (a) the identification of consistency between low achievement and relevant cognitive abilities or processing disorders and (b) the requirement that an individual demonstrate relative cognitive and achievement strengths (see Flanagan, Fiorello & Ortiz, 2010).  The global IQ score is de-emphasized in the third-method SLD methods.

            In contrast, the 11^th edition of the AAIDD Intellectual Disability: Definition, Classification, and Systems of Supports manual (AAIDD, 2010) placed general intelligence, and thus global composite IQ scores, as central to the definition of intellectual functioning.  This has not been without challenge.  For example, the AAIDD ID definition has been criticized for an over-reliance on the construct of general intelligence and for ignoring contemporary psychometric theoretical and empirical research that has converged on a multidimensional hierarchical model of intelligence (viz., Cattell-Horn-Carroll or CHC theory).

The potential constraints of the “ID-as-a-general-intelligence-disability” definition was anticipated by the Committee on Disability Determination for Mental Retardation, in its National Research Council report “Mental Retardation:  Determining Eligibility for Social Security Benefits” (Reschly, Meyers & Hartel, 2001).  This national committee of experts concluded that “during the next decade, even greater alignment of intelligence tests and the IQ scores derived from them and the Horn-Cattell and Carroll models is likely.  As a result, the future will almost certainly see greater reliance on part scores, such as IQ scores for Gc and Gf, in addition to the traditional composite IQ.  That is, the traditional composite IQ may not be dropped, but greater emphasis will be placed on part scores than has been the case in the past” (Reschly et al., 2002, p. 94).  The committee stated that “whenever the validity of one or more part scores (subtests, scales) is questioned, examiners must also question whether the test’s total score is appropriate for guiding diagnostic decision making.  The total test score is usually considered the best estimate of a client’s overall intellectual functioning.  However, there are instances in which, and individuals for whom, the total test score may not be the best representation of overall cognitive functioning.” (p. 106-107).

            The increased emphasis on intelligence test battery composite part scores in SLD and ID diagnosis and classification raises a number of measurement and conceptual issues (Reschly et al., 2002).  For example, what are statistically significant differences?  What is a meaningful difference?  What appropriate cognitive abilities should serve as proxies of general intelligence when the global IQ is questioned?  What should be the magnitude of the total test score? 

Appropriate cognitive abilities will only be the only issue discussed here.  This issue addresses  which component or part scores are more correlated with general intelligence (g)—that is, what component part scores are high g-loaders?  The traditional consensus has been that measures of Gc (crystallized intelligence; comprehension-knowledge) and Gf (fluid intelligence or reasoning) are the highest g-loading measures and constructs and are the most likely candidates for elevated status when diagnosing ID (Reschly et al., 2002).  Although not always stated explicitly, the third method consistency SLD definitions specify that an individual must demonstrate “at least an average level of general cognitive ability or intelligence” (Flanagan et al., 2010, p.745), a statement that implicitly suggests cognitive abilities and component scores with high g-ness.

Table 1 is intended to provide guidance when using component part scores in the diagnosis and classification of SLD and ID (click on images to enlarge and use the browser zoom feature  to view; it is recommended you click here to access a PDF copy of the table..and also zoom in on it).  Table 1 presents a summary of the comprehensive, nationally normed, individually administered intelligence batteries that possess satisfactory psychometric characteristics (i.e., national norm samples, adequate reliability and validity for the composite g-score) for use in the diagnosis of ID and SLD.

The “Composite g-score” column lists the global general intelligence score provided by each intelligence battery.  This score is the best estimate of a person’s general intellectual ability, which currently is most relevant to the diagnosis of ID as per AAIDD.  All composite g-scores listed in Table 1 meet Jensen’s (1998) psychometric sampling error criteria as valid estimates of general intelligence.  As per Jensen’s number of tests criterion, all intelligence batteries g-composites are based on a minimum of nine tests that sample at least three primary cognitive ability domains.  As per Jensen’s variety of tests criterion (i.e., information content, skills and demands for a variety of mental operations), the batteries, when viewed from the perspective of CHC theory, vary in ability domain coverage—four (CAS, SB5), five (KABC-II, WISC-IV, WAIS-IV), six (DAS-II) and seven (WJ III) (Flanagan, Ortiz & Alfonso, 2007; Keith & Reynolds, 2010).   As recommended by Jensen (1998), “the particular collection of tests used to estimate g should come as close as possible, with some limited number of tests, to being a representative sample of all types of mental tests, and the various kinds of test should be represented as equally as possible” (p. 85).  Users should consult sources such as Flanagan et al. (2007) and Keith and Reynolds, 2010) to determine how each intelligence battery approximates Jensen’s optimal design criterion, the specific CHC domains measured, and the proportional representation of the CHC domains in each batteries composite g-score.

Also included in Table 1 are the component part scales provided by each battery (e.g., WAIS-IV Verbal Comprehension Index, Perceptual Reasoning Index, Working Memory Index, and Processing Speed Index), followed by their respective within-battery g-loadings.[1]  Examination of the g-ness of composite scores from existing batteries (see last three columns in Table 1) suggests the traditional assumption that measures of Gf and Gc are the best proxies of general intelligence may not hold across all intelligence batteries.[2] 

In the case of the SB5, all five composite part scores are very similar in g-loadings (h² = .72 to .79).  No single SB5 composite part score appears better than the other SB5 scores for suggesting average general intelligence (when the global IQ score is not used for this purpose).  At the other extreme is the WJ III where the Fluid Reasoning, Comprehension-Knowledge, Long-term Storage and Retrieval cluster scores are the best g-proxies for part-score based interpretation within the WJ III.  The WJ III Visual Processing and Processing Speed clusters are not composite part scores that should be emphasized as indicators of general intelligence.  Across all batteries that include a processing speed component part score (DAS-II, WAIS-IV, WISC-IV, WJ III) the respective processing speed scale is always the weakest proxy for general intelligence and thus, would not be viewed as a good estimate of general intelligence. 

            It is also clear that one cannot assume that composites with similar sounding names of measured abilities should have similar relative g-ness status within different batteries.  For example, the Gv (visual-spatial or visual processing) clusters in the DAS-II (Spatial Ability), SB5 (Visual-Spatial Processing) are relatively strong g-measures within their respective battery, but the same cannot be said for the WJ III Visual Processing cluster.  Even more interesting are the differences in the WAIS-IV and WISC-IV relative g-loadings for similarly sounding index scores. 

For example, the Working Memory Index is the highest g-loading component part score (tied with Perceptual Reasoning Index) in the WAIS-IV but is only third (out of four) in the WISC-IV.   The Working Memory Index is comprised of the Digit Span and Arithmetic subtests in the WAIS-IV and the Digit Span and the Letter-Number Sequencing subtests in the WISC-IV.  The Arithmetic subtest has been reported to be a factorially complex test which may tap fluid intelligence (Gf-RQ—quantitative reasoning), quantitative knowledge (Gq), working memory (Gsm), and possible processing speed (Gs; Keith & Reynolds, 2010; Phelps, McGrew, Knopik & Ford, 2005).   The factorially complex characteristics of the Arithmetic subtest (which, in essence, makes it function like a mini-g proxy) would explain why the WAIS-IV Working Memory Index is a good proxy for g in the WAIS-IV but not in the WISC-IV. The WAIS-IV and WISC-IV Working Memory Index scales, although named the same, are not measuring identical constructs.

A critical caveat is that the g-loadings cannot be compared across different batteries.  g-loadings may change when the mixture of measures included in the analyses change.  Different "flavors" of g can result (Carroll, 1993; Jensen, 1998). The only way to compare the g-ness across batteries is with appropriately designed cross- or joint-battery analysis (e.g., WAIS-IV, SB5 and WJ III analyzed in a common sample).

The above within and across intelligence battery examples illustrates that those who use component part scores as an estimate of a person’s general intelligence must be aware of the composition and psychometric g-ness of the component scores within each intelligence battery.  Not all component part scores in different intelligence batteries are created equal (with regard to g-ness).  Also, not all similarly named factor-based composite scores may measure the same identical construct and may vary in degree of within battery g-ness.  This is not a new problem in the context of naming factors in factor analysis, and by extension, factor-based intelligence test composite scores, Cliff (1983) described this nominalistic fallacy in simple language—“if we name something, this does not mean we understand it” (p. 120). 

---

[1] As noted in the footnotes in Table 1, all composite score g-loadings were computed by Kevin McGrew by entering the smallest number (and largest age ranges covered) of the published correlation matrices within each intelligence batteries technical manual (note the exception for the WJ III) in order to obtain an average g-loading estimate.  It would have been possible to calculate and report these values for each age-differentiated correlation matrix for each intelligence battery.  However, the purpose of this table is to provide the best possible average value across the entire age-range of each intelligence battery.  Floyd and colleagues have published age-differentiated g-loadings for the DAS-II and WJ III.  Those values were not used as they are based on the use of the principal common factor analysis method, a method that  analyzes the reliable shared variance among tests.  Although principal factor and principal component loadings typically will order measures in the same relative position, the principal factor loadings typically will be lower.  Given that the imperfect manifest composite scale scores are those that are utilized in practice, and to also allow uniformity in the calculation of the g-loadings reported in Table 1, principal component analysis was used in this work. The same rationale was used for not using the latent factor loadings on a higher-order g-factor in SEM/CFA analysis of each test battery.  Loadings from CFA analyses represent the relations between the underlying theoretical ability constructs and g purged of measurement error.  Also, frequently the final CFA solutions reported in a batteries technical manual (or independent journal articles) allow tests to be factorially complex (load on more than one latent factor), a measurement model that does not resemble the real world reality of the manifest/observed composite scores used in practice.  Latent factor loadings on a higher-order g-factor will often differ significantly from principal component loadings based on the manifest measures, both in absolute magnitude and relative size (e.g., see high Ga loading on g in WJ III technical manual which is at variance with the manifest variable based Ga loading reported in Table 1) 

[2] The h² values are the values that should be used to compare the relative amount of g-variance present in the component part scores within each intelligence battery.

Use of CART to predict SB5 preschool IQs

Interesting application of CART methids, methods that are unfortunately underappreciated in the behaviorial sciences. More information rgarding these execellent exploratory methods can be found at links below.

http://en.wikipedia.org/wiki/Decision_tree_learning

http://www.salford-systems.com/



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Roberts and Lipnevich in "General to Multiple Intelligences": CHC model and WJ III, WISC-IV Integrated, SB5, KABC-III

This chapter is part of the above refernced book available at the following link:

http://www.apa.org/pubs/books/4311503.aspx










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Dissertation Dish: CFA of WJ III AND SB5 IQ tests in a preschool population

Joint confirmatory factor analysis of the Woodcock-Johnson Tests of Cognitive Abilities, Third Edition, and the Stanford-Binet Intelligence Scales, Fifth Edition, with a preschool population by Chang, Mei, Ph.D., Ball State University, 2011 , 126 pages; AAT 3466801

Abstract

Significant evidence from the legislation, medical/clinical, or professional practice perspective all points to the advantages and necessity of conducting comprehensive assessment of cognitive abilities, especially in young children, to identify cognitive deficits, arrive at an accurate diagnosis, and establish bases for developing interventions and recommending services. Cross-battery assessment approach provides school psychologists a useful tool to strengthen their preferred cognitive battery by adopting and comparing subtests from other batteries to build up a comprehensive and theoretically sound evaluation of an individual's cognitive profile to increase the validity of test interpretation. Using joint confirmatory factor analysis, this study explored the combined underlying construct validity of the Woodcock-Johnson Tests of Cognitive Abilities, Third Edition (WJ-III COG) and the Stanford-Binet Intelligence Scales, Fifth Edition (SB5) with an independent sample of preschool children. Seven models were examined and the results showed that relatively, the underlying construct of the two tests was best represented by a three-stratum alternative CHC model in which the Gf factor and subtests had been removed. This indicates that not all the CHC constructs shared by both tests can be reliably identified among young children. Constructs of the CHC theory may be represented differently on preschool cognitive batteries due to developmental influences. Although WJ-III COG and SB5 tests as a whole did not demonstrate good results for purposes of cross-battery assessment, certain subtests (e.g., subtests representing crystallized intelligence) from each battery offer interpretative value for individual broad ability factors, providing school psychologists an in-depth understanding of a preschooler's crystallized knowledge. Exploratory factor analyses were conducted with subtests from WJ-III COG and SB5 representing the four shared broad factors ( Gc, Gf, Gv, and Gsm ). Results revealed that a 4-factor solution is a better model fit to the data. Future research includes recruiting young children with disabilities or special needs to explore best representative underlying construct of combined WJ-III COG and SB5, allowing for cross-battery assessment



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Research bytes: Factor analysis of SB5 and a neuropsychological test battery

As per usual when I make a research byte/brief post, if anyone would like to read the original article, I can share via email---with the understanding that the article is provided in exchange for a brief guest post about it's contents. :) (contact me at iap@earthlink.net if interested). Also, if figure/images are included in the post, they can usually be made larger by clicking on the image.



Doweling, N. M., Hermann, B., LaRue, A., & Sager, M. A. (2010). Latent Structure and Factorial Invariance of a Neuropsychological Test Battery for the Study of Preclinical Alzheimer's Disease. Neuropsychology, 24(6), 742-756.

Abstract

Objective: To examine the latent structure of a test battery currently being used in a longitudinal study of asymptomatic middle-aged adults with a parental history of Alzheimer's disease (AD) and test the invariance of the factor solution across subgroups defined by selected demographic variables and known genetic risk factors for AD. Method: An exploratory factor analysis (EFA) and a sequence of confirmatory factor analyses (CFA) were conducted on 24 neuropsychological measures selected to provide a comprehensive estimate of cognitive abilities most likely to be affected in preclinical AD. Once the underlying latent model was defined and the structural validity established through model comparisons, a multigroup confirmatory factor analysis model was used to test for factorial invariance across groups. Results: The EFA solution revealed a factor structure consisting of five constructs: verbal ability, visuospatial ability, speed & executive function, working memory, and verbal learning & memory. The CFA models provided support for the hypothesized 5-factor structure. Results indicated factorial invariance of the model across all groups examined. Conclusions: Collectively, the results suggested a relatively strong psychometric basis for using the factor structure in clinical samples that match the characteristics of this cohort. This confirmed an invariant factor structure should prove useful in research aimed to detect the earliest cognitive signature of preclinical AD in similar middle aged cohorts.


Williams, T. H., McIntosh, D. E., Dixon, F., Newton, J. H., & Youman, E. (2010). A CONFIRMATORY FACTOR ANALYSIS OF THE STANFORD-BINET INTELLIGENCE SCALES, FIFTH EDITION, WITH A HIGH-ACHIEVING SAMPLE. Psychology in the Schools, 47(10), 1071-1083.

Abstract

The Stanford–Binet Intelligence Scale, Fifth Edition (SB5), is a recently published, multidimensional measure of intelligence based on Cattell–Horn–Carroll (CHC) theory. The author of the test provides results from confirmatory factor analyses in the technical manual supporting the five-factor structure of the instrument. Other authors have examined this factor structure through EFA using the standardization sample, and have not found evidence of a five-factor model. The purpose of the current study was to examine the internal construct validity of the SB5 using an independent sample of high-functioning students. Participants included 201 high-functioning, third-grade students ranging in age from 8 years, 4 months to 10 years, 11 months. Five models of the SB5 were analyzed using Analysis of Moment Structures (AMOS). Our findings indicated that a hierarchical, four-factor, post-hoc model provided the best fit to the data. Generally, implications for school psychologists include a better understanding of the factor structure of the SB5, especially as it relates to high-achieving children. Directions for future research are also discussed


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intelligence IQ tests IQ scores CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner neuroscience neurocognitive cognitive abilities cognition factor analysis confirmatory factor analysis neuropsychological test battery Stanford-Binet V intelligence battery

Dissertation Dish: Extension of CHC theory-SB5 and Bender Gestalt factor study

Structural extension of the Cattell-Horn-Carroll cross-battery approach to include measures of visual-motor integration by Brooks, Janell Hargrove, Ph.D., Georgia State University, 2009 , 117 pages; AAT 3401596

Abstract

In spite of the long-standing tradition of including measures of visual-motor integration in psychological evaluations, visual-motor abilities have not been included in the Cattell-Horn-Carroll (CHC) theory of cognitive abilities or its complementary cross-battery approach to assessment. The purpose of this research was to identify the shared constructs of a popular test of visual-motor integration and a test of intellectual functioning, and to investigate how a test of visual-motor integration would be classified within the CHC model. A large normative sample of 3,015 participants that ranged in age from 5 to 97 years completed the Bender Visual-Motor Gestalt Test, Second Edition (Bender-Gestalt II; Brannigan & Decker, 2003) and the Stanford-Binet Intelligence Scale, Fifth Edition (SB5; Roid, 2003). Correlational analyses indicated positive moderate correlations across all age ranges between the Bender-Gestalt II Copy measure and the SB5 Nonverbal Visual-Spatial Processing subscale and between the Bender-Gestalt II Recall measure and the SB5 Nonverbal Visual-Spatial Processing and Nonverbal Working Memory subscales. Exploratory factor analyses revealed a three-factor model for four age groupings and four-factor model for one age grouping, suggesting factors which represent crystallized ability, fluid reasoning, and visual-motor ability. The results of this study suggest that the Bender-Gestalt II measures abilities that are not included in the SB5. Therefore, the Bender-Gestalt II would complement an intelligence test such as the SB5 in order to form a CHC Visual Processing ( Gv ) broad ability factor. These findings also address the need for further research to validate the constructs measured by newer versions of widely-used tests of cognitive ability.

Technorati Tags: Psychology, school psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities intelligence theories, CHC theory, Cattell-Horn-Carroll, factor analysis, cross battery assessment, IQs Corner, IQ, IQ tests, IQ scores, Gv, Gc, Gf, Gp, Bender Gestalt, motor abilities, SB5, Stanford Binet

The Wechsler-like IQ subtest scaled score metric: The potential for misuse, misinterpretation and impact on critical life decisions---draft report in search of feedback

The following are the first three paragraphs (and a critical figure) of a draft of an IAP Applied Psychometrics 101 Brief Report (#5).  The complete report can be download in PDF format by clicking here.  A web-page version of the complete report can be found by clicking here (note - the web page verision may NOT display two embedded figures....viewing the PDF copy may be necessary)

I'm providing this initial draft report with the expressed intent of soliciting feedback and comments regarding the accuracy and soundness of my analyses and logic.  I'm looking for critical feedback to improve the report.  This is a draft report that will be revised if comments suggest important changes.  Please read it in the spirit of "tossing out some critical ideas" for reflective analysis and feedback.  Feedback can be sent directly to me (iap@earthlink.net) or could be provided in the form of listserv thread discussions at the NASP and/or CHC listservs.


I've recently been skimming James Flynn's new book (What is Intelligence:  Beyond the Flynn Effect) to better understand the methodology and interpretation of the Flynn effect. Of particular interest to me (as an applied measurement person) is his analysis of the individual subtest scores from the various Wechsler scales across time. As most psychologists know, Wechsler subtest scaled scores (ss) are on a scale with a mean (M) = 10 and a standard deviation (SD) = 3. The subtest ss range from 1 to 19.  In Appendix 1 of his book, Flynn states "it is customary to score subtests on a scale in which the SD is 3, as opposed to IQ scores which are scaled with SD set at 15. To convert to IQ, just multiply subtest gains by five, as was done to get the IQ gains in the last column."  At first glance, this statement makes it sound as if the transformation of subtest ss to IQ SS is an easy (“just multiply….”; emphasis added by me) and mathematically acceptable procedure without problems. However, on close inspection this transformation has the potential to introduce unknown sources of error into the precision of the transformed SS scores.  It is the goal of this brief technical post to explain the issues involved when making this ss-to- IQ SS conversion.

The ss 1-19 scale has a long history in the Wechsler batteries. For sample, in Appendix 1 of Measurement of Adult Intelligence (Wechsler, 1944), Wechsler described the steps used to translate subtest raw scores to the new ss metric. The Wechsler batteries have continued this tradition in each new revision, although the methodology and procedures to calculate the ss 1-19 values have become more sophisticated over time.   Although the methods used to develop the Wechsler ss 1-19 scale may have become more sophisticated, the resultant underlying scale for each subtest has not…scores still range from 1-19 (M=10; SD=3).  Also, the most recent Stanford-Binet—5th Edition (SB5; Roid, 2003) and Kaufman Assessment Battery for Children-2nd Edition (KABC-II) have both adopted the same ss 1-19 scale for their respective individual subtests.

Why is this relatively crude (to be defined below) scale metric still used in some intelligence batteries when other contemporary intelligence batteries provide subtest scale metrics with finer measurement resolution?  For example, the DAS-II (Elliott, 2007) places individual test scores on the T-scale (M=50; SD=10), with scores that range from 10-90.  The WJ III (McGrew & Woodcock, 2001) places all test and composite scores on the standard score (SS) metric associated with full scale and composite scores (M=100; SD=15).  The critical question to be asked is “are there advantages or disadvantages to retaining the historical ss 1-19 scale or, are their real advantages to having individual test scales with finer measurement resolution (DAS-II; WJ III)?”

......continued............
(complete report available at links in first paragraph of this post)

[Double click on image to enlarge]

Technorati Tags: psychology, Flynn Effect,school psychology, educational psychology, neuropsychology, forensice psychology, criminal psychology, criminal justice, psychometrics, Wechslers, WISC-III, WISC-IV, WAIS-III, WAIS-IV, WJ III, DAS-II, KABC-II, SB5, intelligence, IQ tests, IQ scores, scaled scores, MR, ID, mental retardation, intellectual disability, capital punishment, death penalty, IAP AP101 report

AP101 Brief #2: IQ test descriptive comparison information

AP101 # 2 Brief:  IQ test descriptive comparison information has been posted to sister blog ICDP.

Importance of following standardized IQ test directions: Another Atkins MR/IQ decision

Another Atkins MR/IQ court decision revolving around IQ score issues, this time the Stanford-Binet V.  Affidavit provided by SB5 test author.  Oral arguments available to listen to.  The decision should remind all psychologist re: the importance of following standardized testing procedures when administering an intelligence test.  More information at ICDP sister blog.

Technorati Tags: psychology, forensic psychology, criminal psychology, criminal justice, psychology and law, Atkins case, MR, mental retardation, intellectual disability, SB5, SB V, Stanford Binet Intelligence test, standardized testing, death penalty, capital punishment

Stanford Binet 5 (SB5) post-publication resources: 5-20-13

This is an update of a post made a number of years ago...with new information

Stanford Binet 5 Assessment Service Bulletins (info from Riverside Publishing web page)

• SB5 Assessment Service Bulletin #1: History of the Stanford-Binet Intelligence Scales: Content and Psychometrics
• SB5 Assessment Service Bulletin #2: Accommodations on the Stanford-Binet Intelligence Scales, Fifth Edition
• SB5 Assessment Service Bulletin #3: Use of the Stanford-Binet Intelligence Scales, Fifth Edition in the Assessment of High Abilities
• SB5 Assessment Service Bulletin #4: Special Composite Scores for the Stanford-Binet Intelligence Scales, Fifth Edition
• Quality of Performance and Change - Sensitive Assessment for Cognitive Ability by Gale H. Roid
• Technical Brief - Interpretation of SB5/Early SB5 Factor Index Scores by Gale H. Roid