More “the CHC cognitive taxonomy is the consensus taxonomy of human cognitive ability” quotes

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Another AI evaluation framework informed by the human CHC cognitive abilities taxonomy

According to my tracking of the literature (of course, most likely not complete), here is a third article (open access from the Journal of Intelligence) that proposes using the CHC cognitive ability taxonomy (or parts of it) to evaluate an aspect of AI performance—in this case AI-assisted organizational decision-making.  The other two proposed AI evaluation framework articles using the CHC taxonomy (alone or as part of a much grander framework) can be found here and here. 

In a related development, Kameron Green—the developer behind an ambitious and grand AI evaluation framework (in second to last link above)—today posted a link to an AI generated video summary overview of his HCQM taxonomic model—which would help most readers understand his lengthy HCQM paper.

Cognitive Performance Under AI Advice: Development and Initial Validation of a CHC-Informed Assessment for Organizational Decision-Making

Abstract

Artificial intelligence (AI) is increasingly embedded in organizational decision-making, requiring employees not only to use AI-generated recommendations but also to evaluate their quality and determine when reliance is appropriate. Although established research examines behavioral reliance on algorithmic and AI advice, fewer studies have approached performance under AI advice as an individual-differences assessment problem integrating psychometric structure, cognitive correlates, process indicators, and criterion-related evidence. This study developed and initially validated a CHC-informed, performance-based assessment of cognitive performance under AI advice using 24 organizational decision scenarios. The assessment was designed around three closely related content/performance dimensions—AI error detection, evidence integration, and cognitive control and adaptive reliance—while also capturing confidence, response time, and reliance behavior. The validation sample comprised 780 employed adults in Türkiye. Psychometric analyses included confirmatory factor analysis, multidimensional item response theory, response-time analyses, scenario-level logistic regression, measurement invariance, differential item functioning, and internal cross-validation. Results indicated a dominant general cognitive-performance component together with additional structure corresponding to the three theoretically specified dimensions. Assessment performance was positively associated with established cognitive measures, including ICAR-16 reasoning performance, working memory, processing speed, and attentional control, whereas associations with AI-related self-reports were generally weaker. Dimension-aligned analyses supported the expected associations of ICAR-16 with AI error detection and working memory with evidence integration. Performance was also moderately associated with concurrently assessed organizational decision quality (r = 0.408) and explained additional variance in this criterion beyond demographic and work characteristics, AI experience, conventional cognitive-performance measures, and AI-related self-reports (ΔR2 = 0.103, p < .001). In contrast, several hypothesized scenario-specific associations involving AI confidence, time pressure, interruptions, and resistance to confidently inaccurate advice were not supported. Overall, the findings provide initial evidence for a performance-based approach to assessing how employees evaluate and respond to AI advice, while indicating that the proposed scenario-specific mechanisms and group-comparability findings require further replication before consequential applications are considered.

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)

#WJV and #CHC theory of #cognitive abilities: An animated video overview of CHC theory model used in WJ V revision - attention #schoolpsychology #SLD #SPED #psychology #intelligence

An oldie but goodie.  I originally posted this CHC cognitive theory 2.5 update video 6 years ago, after Joel Schneider and I published the latest update to the CHC theory of cognitive abilities (Schneider & McGrew, 2018).  

I ran across it the other day.  It is a “silent animated movie” presentation with cool animations and morphing slide transitions.  See the second image below (second slide in video) for an important instruction….the slides automatically transition every 4 seconds—-so be ready to push the “pause” button if you need time to read and study any slide in detail.  

Warning.  If I access this video directly (outside of blogger platform) from my browser, it runs just fine.  But, if you click on the third image below (that should start the video), you may likely get a  message that you “need to sign in” to view the video…and I can’t figure it out…perhaps you can.  So, if that happens, either click on the raw URL link that follows or in blue font or cut and paste that lin (just the link info between the “ ” marks) into your web browser… “ https://www.youtube.com/watch?v=6FVEyaBT2R4”.  I hope one option works.  If not…”it is what it is”..it is worth the extra small effort. The YouTube video is the third image down.  I can’t control if YouTube inserts brief ads, it is what they do these days with many uploaded videos.

This 2018 version of the CHC theory is the theoretical blueprint for the forthcoming WJ V revision, scheduled for launch in Feb. 2025.  Additional free information about the WJ’s and CHC theory can be found at my MindHub web page.  COI disclosure—I am the senior author of the WJ V and a coauthor of the current WJ V

Enjoy  

Stay tunned!!!! #WJV g and non-g multiple #CHC theoretical models to be presented in the forthcoming (2025) technical manual: Senior author’s (McGrew) position re the #pscyhometric #g factor and #bifactorg models.

(c) Copyright, Dr. Kevin S. McGrew, Institute for Applied Psychometrics (11-14-24)

Warning, may be TLDR for many. :).  Also, I will be rereading again multiple times and may tweak minor (not substantive) errors and post updates….hey….blogging has an earthy quality to it:)

        In a recent publication, Scott Decker, Joel Schneider, Okan Bulut and I (McGrew, 2023; click here to download and read) presented structural analysis of the WJ IV norm data using contemporary psychometric network analysis (PNA) methods.  As noted in a clip from the article below, we recommended that intelligence test researchers, and particularly authors and publishers of the respective technical manuals for cognitive test batteries, needed to broaden the psychometric structural analysis of a test battery beyond the traditional (and almost exclusive) relieance on “common cause” factor analysis (EFA and CFA) methods to include PNA analysis…to compliment, not supplant factor based analyses.

(Click on image to enlarge for easier reading)

         Our (McGrew et al., 2023) recommendation is consistent with some critics of intelligence test structural research (e.g., see Dombrowski et al., 2018, 2019; Farmer et al., 2020) who have cogently argued that most intelligence test technical manuals typically present only one of the major classes of possible structural models of cognitive ability test batteries.  Interestingly, many school psychology scholars who conduct and report independent structural analysis of a test battery also do something similar…they often only present one form of structural analysis—-namely, bifactor g analyses.  

        In McGrew et al. (2023) we recommended future cognitive ability test technical manuals embrace a more ecumenical multiple method approach and include, when possible, most all major classes of factor analysis models, as well as PNA. A multiple-methods research approach in test manuals (and journal publications by independent researchers) 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. Leaving PNA methods aside for now, the figure below presents the four major families of traditional CHC theoretical structural models.  These figures are conceptual and are not intended to represent all nuances of factor models. 

(Click on image for a larger image to view)

         Briefly, the four major families of traditional “common cause” CHC CFA structural models (Carroll, 2003; McGrew et al., 2023) vary primarily in the specification (or lack thereof) of a psychometric g factor. The different families of CHC models are conceptually represented in the figure above. In these conceptual representations the rectangles represent individual (sub)tests, the circles latent ability factors at different levels of breadth or generality (stratum levels as per Carroll, 1993), the path arrows the direction of influence (the effect) of the latent CHC ability factors on the tests or lower-order factors, and the single double headed arrow all possible correlations between all CHC broad CHC factors (in the Horn no-g model in panel D).  
        The classic hierarchical g model “places a psychometric g stratum III ability at the apex over multiple broad stratum II CHC abilities” (McGrew et al., 2023, p. 2). This model is most often associated with Carroll (1993; 2003) and is called (in panel A in the above figure) the Carroll hierarchical g broad CHC model. In this model the shared variance of subsets of moderately to highly correlated tests are first specified as 10 CHC broad ability factors (i.e., the measurement model; Gf, Gc, Gv, etc.). Next the covariances (latent factor correlations) among the broad CHC factors are specified as being the direct result of a higher-order psychometric g factor (i.e., the structural model). 

        A sub-model under the Carroll hierarchical g broad CHC model includes three levels of factors—several first-order narrow (stratum I) factors, 10 second-order broad (stratum II) CHC factors, and the psychometric g factor (stratum III). This is called the Carroll hierarchical g broad+narrow CHC model in panel B in the figure above. In the above example, two first-order narrow CHC factors (auditory short-term storage-Wa; and auditory working memory capacity-Wc, which, in simple terms, is a factor defining auditory short-term memory tasks that also include heavy attentional control-based (AC as per Schneider & McGrew, 2018) active manipulation of stimuli—the essence of Gwm or working memory).  For illustrative purposes, a narrow naming facility (NA) first-order factor, which has higher-order effects or influences from broad Gs and Gr is specified for evaluation.  Wouldn’t you like to see the results of this hierarchical broad+narrow CHC model?  Well……..stay tunned for the forthcoming WJ V technical manual (Q1 2025; LaForte, Dailey, & McGrew, 2025, in preparation) and your dream will come true.

        The third model is the Horn no-g model (McGrew, et al., 2023).  John Horn long argued that psychometric g was nothing more than a statistical abstraction or artifact (Horn, 1998; Horn & Noll, 1997; McArdle, 2007; McArdle & Hofner, 2014; Ortiz, 2015) and did not represent a brain or biologically based real cognitive ability. This is represented by the Horn no-g broad CHC model in panel D. The Horn no-g broad CHC model is like the Carroll hierarchical g broad CHC model, but the 10 broad CHC factor intercorrelations are retained instead of specifying a higher- or second-order psychometric g factor. In other words, the measurement models are the same but the structural models are different. In some respects the Horn no-g broad CHC model is like contemporary no-g psychometric network analysis models (see McGrew, 2023) that eschew the notion of a higher-order latent psychometric g factor to explain the positive definite correlation variance between individual tests (or first-order latent factors in the case of the Horn no-g model) in an intelligence battery (Burgoyne et al. 2022; Conway &Kovacs, 2015; Euler et al., 2023; Fried, 2020; Kan et al. 2019; Kievit et al. 2016; Kovacs & Conway, 2016, 2019; McGrew, 2023; McGrew et al., 2023; Protzko & Colom 2021a, 2021b, van der Maas et al. 2006, 2014, 2019).  Over the past decade I’ve become more aligned with no-g psychometric network CHC models (e.g, process overlap theory or POT) or Horn’s no-g CHC model, and have, tongue-in-check, referred to the elusive psychometric g ability (not the psychometric g factor)  as the “Loch Ness Monster of Psychology” (McGrew, 2021, 2022).

        Three of these common cause CHC structural models (viz., Carroll hierarchical g broad CHC model, Carroll hierarchical g broad+narrow CHC, and Horn no-g broad CHC), as well as Dr. Hudson Golino and colleagues hierarchical exploratory graph analysis psychometric network analysis models (that topic is saved for another day), are to be presented in the structural analysis section of the forthcoming WJ V technical manual validity chapter.  Stay tunned for some interesting analysis and interpretations in the “must read” WJ V technical manual. Yes….assessment professionals, a well written and thourough technical manual can be your BFF!

        Finally, the fourth family of models, which McGrew et al. (2023) called g-centric models, are commonly known as bifactor g models. In the bifactor g broad CHC model (panel C in figure) the variance associated with a dominant psychometric g factor is first extracted from all individual tests. The residual (remaining) variance is modeled as 10 uncorrelated (orthogonal) CHC broad factors. The bifactor g model was excluded from the WJ V structural analysis. Why…..after I (McGrew et al., 2023) recommended that all four classes of traditional CHC structural analysis models should be presented in a test batteries technical manual????
        Because…the complexity involved in specifying and evaluating bi-factor g models with 60 cognitive and achievement tests was found to be extremely complex and fraught with statistical convergence issues.  Trust me…I tried hard and long to run bifactor g models for the WJ V norm data.  It was possible to run bifactor g models separately on the cognitive and achievement sets of WJ V tests, but that does not allow for the direct comparison to the other three structural models that utilized all 60 cognitive and achievement tests in single CFA models.  Instead, at of the time the WJ V technical manual analyses were being completed and are now being summarized, the Riverside Insights (RI) internal psychometric research team was tackling the complex issues involved in completing WJ V bifactor g models, first in the separate sets of cognitive and achievement tests.  Stay tunned for future professional conference paper presentations, white papers, or journal article submissions by the RI research team.

        Furthermore, the decision to not include bifactor g models does not suggest that the evaluation of WJ V bifactor g-centric CHC models is not important. As noted by Reynolds and Keith (2017), “bifactor models may serve as a useful mathematical convenience for partitioning variance in test scores” (p. 45; emphasis added). The bifactor g model pre-ordains “that the statistically significant lion’s share of IQ battery test variance must be of the form of a dominant psychometric g factor (Decker et al., 2021)” (McGrew, et al., 2023, p. 3). Of the four families of CHC structural models, the bifactor g model is the conceptual and statistical model that supports the importance of general intelligence (psychometric g) and the preeminence of the full-scale or global IQ score over broad CHC test scores (e.g., see Dobrowski et al., 2021; Farmer et al., 2021a, 2021b; McGrew et al., 2023)—a theoretical position inconsistent with the position of the WJ V senior author (yours truly) and with Dr. Richard Woodcock’s legacy (see additional footnote comments at the end). It is important to note that there is a growing body of research that has questioned the preference for bifactor g cognitive models based only on statistical fit indices, as structural model fit statistics frequently are biased in favor of bifactor solutions. Per Bonifay et al. (2017),“the superior performance of the bifactor model may be a symptom of ‘overfitting’—that is, modeling not only the important trends in data but also capturing unwanted noise” p. 184–185). For more on this, see Decker (2021), Dueber and Toland (2021), Eid et al., (2018), Greene et al. (2022), and Murray and Johnson(2013). See Dombroski et al. (2020) for a defense of some of the bifactor g criticisms.

        Recognizing the wisdom of Box’s (1976) well known axiom that “all models are wrong, but some are useful” the WJ V technical manual authors (LaForte, Dailey, McGrew, 2025, in preparation) encourage independent researchers to use the WJ V norm data to evaluate and compare bifactor g CHC models with the models presented in forthcoming WJ V technical, as well as  alternative models (e.g., PASS, process overlap theory, Cattell’s triadic Gf-Gc theory, etc.) suggested in the technical manual.

Footnote:  Woodcock’s original (and enduring) position (Woodcock, 1978, 1997, 2002) regarding the validity and purpose of a composite IQ-type g score is at odds with the bifactor g CHC model. With the publication of the original WJ battery, Woodcock (1978) acknowledged the pragmatic predictive value of statistically partitioning cognitive ability test score variance into a single psychometric g factor, with the manifest total IQ score serving as a proxy for psychometric g. Woodcock stated “it is frequently convenient to use some single index of cognitive ability that will predict the quality of cognitive behavior, on the average, across a wide variety of real-life situations. This is the [pragmatic] rationale for using a single score from a broad-based test of intelligence” (p.126). However, Woodcock further stated that “one of the most common misconceptions about the nature of cognitive ability (particularly in discussions characterized by such labels as ‘IQ’ and ‘intelligence’) is that it is a single quality or trait held in varying degrees by individuals, something like [mental] height” (p. 126). In several publications Woodcock’s position regarding the importance of an overall general intelligence or IQ score was clear—“The primary purpose for cognitive testing should be to find out more about the problem, not to obtain an IQ” (Woodcock, 2002, p.6; also see Woodcock, 1997, p. 235). Two of the primary WJ III, WJ IV, and WJ V authors have conducted research or published articles (see Mather & Schneider, 2023; McGrew, 2023; McGrew et al., 2023) consistent with Woodcock’s position and have advocated for a Horn no-g or emergent property no-g CHC network model. Additionally, based on the failure to identify a brain-based biological g (i.e., neuro-g; Haier et al., 2024) in well over a century of research since Spearman first proposed g in the early 1900’s, McGrew (2020, 2021) has suggested that g may be the “Loch Ness Monster of psychology.” This does not imply that psychometric g is unrelated to combinations of different neurocognitive mechanisms, such as brain-wide neural efficiency and the ability of the whole-brain network, which is comprised of various brain subnetworks and connections via white matter tracts, to efficiently adaptively reconfigure the global network in response to changing cognitive demands (see Ng et al., 2024 for recent compelling research linking psychometric g to multiple brain network mechanisms and various contemporary neurocognitive theories of intelligence; NOTE…click link to download PDF of article and read sufficiently to impress your psychologist friends!!!!).

On the origin and evolution (from 1997 chapter to 2025 #WJV) of the #CHC #intelligence theories definitions: The missing CHC definition’s birth certificate

This is an updated version of an OBG (oldie but goodie) post originally made in 2017.  

The historical development of the CHC model of intelligence has been documented by McGrew (2005) and Schneider and McGrew (2012) and summarized by Kaufman and colleagues (Kaufman, 2009; Kaufman, Raiford & Coalson, 2016). Additional extensions and historical anecdotes were rececntly presented by McGrew (2023) in an article included in a special issue of the Journal of Intelligence focused on Jack Carroll’s tri-stratum theory @ 30 years. McGrew (2023) recommended that CHC theory should now be referred to as a group of CHC theories (i.e., a family of orthogonally correlated models) that recognizes the similarities and differences between the theoretical models of Cattell, Horn and Carroll.

An unexplained crucial, yet missing piece of the CHC story, is the origin of the original CHC broad and narrow ability definitions.  The CHC ability definition birth certificate, until recently, had not been revealed.  To fend off possible CHC “birther” controversies, I will now set the record straight again (as was first done in 2017) regarding the heritage of the past and current CHC definitions.

Given the involvement of both John Horn and Jack Carroll in revisions of the WJ-R and WJ III, which was the impetus for the combined CHC theory, it is not surprising that the relations between the “official” CHC ability definitions and the WJ tests were “reciprocal in nature, with changes in one driving changes in the other” (Kaufman et al., 2016, p. 253).  Furthermore, “the WJ IV represented the first revision in which none of the original CHC theorists was alive at the time of publication, producing and imbalance in this reciprocal relationship—-“the WJ IV manuals now often served as the official source for the latest CHC theory and model of cognitive abilities (J. Schneider, personal communication, March 15, 2015)” (Kaufman et al., 2016; p. 253).  Kaufman et al. noted that with the development of subsequent non-WJ CHC assessment and interpretation frameworks (e.g., Flanagan and colleagues CHC cross-battery assessment; Miller’s integrated school neuropsychology/CHC assessment model), some confusion has crept into what represents the authoritative “official” and “unofficial” definitions and sources.  

In Schneider & McGrew (2012) and Schneider & McGrew (2018), the incestuous nature of the evolution of the CHC definitions continued by building primarily on the McGrew (2005) definitions, which in turn were reflected in the 2001 WJ III manuals, which in turn drew from McGrew (1997).  In my original 2017 post regarding this topic, it was judged time to divorce the official CHC definitions from the WJ series and authors (particularly myself, Kevin McGrew). 

However, the CHC birth certificate is still often questioned.  Did the CHC definitions magically appear?  Did they come down in tablet form from a mountain top?  After the Cattell-Horn and Carroll models were first married by McGrew (1997), were the definitions the result of some form of immaculate conception?  Did  McGrew (1997) develop them unilaterally?  

Here is….the “rest of the story.”  

The original CHC definitions were first presented in McGrew’s (1997) chapter where the individual tests from all major intelligence batteries where classified as per the first integration of the Cattell-Horn and Carroll models of cognitive abilities (then called a “proposed synthesized Carroll and Horn-Cattell Gf-Gc framework”).  In order to complete this analysis, I (Kevin McGrew) needed standard CHC broad and narrow definitions—but none existed.  I consulted the Bible…Carroll’s Human Cognitive Abilities (1993).

I developed the original definitions (primarily the narrow ability definitions) by abstracting definitions from Carroll’s (1993) book.  After completing the first draft of the definitions, I sent them to Carroll. He graciously took time to comment and edit the first draft. I subsequently revised the definitions and sent them back. Jack and I engaged in several iterations until he was comfortable with the working definitions. As a result, the original narrow ability definitions published in McGrew (1997) had the informal stamp of approval of Carroll, but not of Horn. The official CHC definition birth certificate should list Carroll and McGrew as the parents.  

Since then the broad and narrow CHC ability definitions have been parented by McGrew (McGrew & Woodcock, 2001; McGrew, 2005; McGrew et al., 2014) and more recently, uncle Joel Schneider (Schneider & McGrew, 2012; Schneider & McGrew, 2018). The other WJ III and WJ IV authors (Mather, Schrank, and Woodcock) served as aunts and uncles at various points in the evolution of the definitions, resulting in the current “unofficial” definitions being in the WJ IV technical manual (McGrew et al., 2014) and the Schneider & McGew (2018) chapter. 

With new data-based insights from the the validity analysis of the norm data from the forthcoming WJ V (LaForte, Dailey & McGrew, 2025, in preparation), the WJ V technical manual will provide, yet again, a slightly new and improved set of CHC definitions.  Stay tunned.

No doubt the WJ V 2025 updated CHC definitions will still have a clear Carroll/McGrew, WJ III /WJ IV/WJ V and Joel Schneider genetic lineage (McGrew, 1997—>McGrew & Woodcock, 2001—>McGrew, 2005—>Schneider & McGrew, 2012—>McGrew et al., 2014—>Schneider & McGrew, 2012, 2018).  We (Schneider and McGrew) are reasonably comfortable with this fact.  However, we hope that the WJ—>WJ V set of CHC definitions will eventually move out of the influence of the WJ/CHC house and establish a separate residence, identity, and process for future growth.  I am aware that Dr. Dawn Flanagan and colleagues are working on a new revision of their CHC cross-battery book and related software and will most likely include a new set of revised defintions.  Perhaps a melding with the WJ V technical manual definition appendix with the work of Flanagan et al. would be a good starting point.  Perhaps some group or consortium of interested professionals could be established to nurture, revise, and grow the CHC defintions.

On the conflation of #psychometric and theoretical #g (general #intelligence): Is g the Loch Ness Monster of psychology?

Click on slide images to enlarge for better viewing. Parts of a recent presenation in 2023.  When I mention SP, I am referring to school psychology.

 

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores "Beyond g"

(Note.  I’ve made several similar posts with a similar message on several social media outlets over the last 1.5 years)

Yes.  This may be seen as a brag post (I plead the fifth). But, I really want (need?) to share this recent publication (January 2023).  Why? Because, after 40 years of scholarship, I consider this article (which is open access and can be downloaded and read freely) to be one of my 5 top peer-reviewed research publications. The article is part of a special issue (Assessment of Human Intelligence-State of the Art in the 2020s) of the Journal of Intelligence, edited by Alan Kaufman et al. Warning—it is a long article. The article is the result of collaboration with Joel Schneider, Scott Decker and Okan Bulut. 

The content of the article pushes the “edge of the envelop” regarding intelligence theories and testing via the use of exploratory psychometric network analysis (PNA) within the context of network non-g (i.e., psychometric g) models of intelligence. This approach represents an emerging paradigm shift for thinking about intelligence theories and testing. As stated by Savi et al. (2021) "factor analysis models dominated the 20th century of intelligence research, but network models will dominate the 21st."  I believe Savi et al. are more-or-less correct. I believe PNA and non-g network models can move intelligence theories and testing forward—as they have become stagnant via the repeated use of "common cause" descriptive and taxonomic-generating factor analysis methods.  Used in isolation, factor analysis-based intelligence test and theory models constrain school psychologists and other assessment professionals from moving forward (as described in the paper).  For far too long, especially in school psychology, we have been "stuck on g" factor analysis based models of test interpretation.

As stated in our article, "newer non-g emergent property theories of intelligence might lead to better intervention research for individuals who have been marginalized by society. Holden and Hart (2021) suggest that network-based non-g theories, particularly those that feature Gwm-AC mechanisms [the working memory-attentional control complex] (process overlap theory in particular) may hold promise as a vehicle for improving, and not harming, social justice and equity practices and valued outcomes for individuals in marginalized groups" (McGrew et al., 2023).  Read the original Holden and Hart article if you are interested in the social justice implications of a new way of thinking about intelligence grounded in modern network non-g conceptualizations of intelligence.

Even if the methodological material is not your cup of tea, much of the McGrew et al. (2023) introduction is relevant to assessment practitioners. Also, several sections in the discussion deal with practical implications for understanding new insights into intelligence theories, broad cluster test interpretation in general, and some strengths and weaknesses of the WJ IV CHC test and cluster scores. 

If you are not familiar with the Journal of Intelligence (JOI), I would suggest SPs take a look. It is not the Intelligence journal from ISIR. It is the "new kid on the block" and has quickly become a prestigious open access publication outlet with a top notch editorial board. Since it is open access, all articles can be downloaded, read, and shared freely—an awesome free source of emerging thinking in the field of intelligence. JOI is publishing interesting articles from a wide variety of perspectives by a diversity of scholars interested in intelligence, cognition, and related topics. It has become one of my favorite journals the past few years. 

Finally, exploratory hierarchical psychometric network analysis methods (along with traditional structural analysis methods) were applied to the WJ V norm data—these results will be in the WJ V Technical Manual (LaForte, Dailey, McGrew, 2025).

 My WJ IV conflict of interest (COI) is included in the linked PDF article.  My WJ V COI and additional COI information can be found at the MindHub web portal.