Research alert-very important article: Beyond Working Memory Capacity: Attention Control as the Underlying Mechanism of Cognitive Abilities - #cognitive #intelligence #Gwm #attentionalcontrol #AC #workingmemory #WJIV #WJV #schoolpsychology #schoolpsychologists #cognition

Click on images to enlarge for better readability

Very important article (open source..click here to read/download) regarding cognitive functioning and working memory capacity and attentional control. For at least 15 years I’ve been monitoring research on the attentional-control working memory complex system (AC-Gwm)…(click here for numerous posts regarding the important of AC-Gwm).  I’m convinced that the AC-Gwm complex system is one of the core cognitive efficiency systems that helps us understand general intellectual functioning.  It has been found to be important in cognitive functioning and also in various forms of psychopathology.  

Abstract

Working memory capacity (WMC) has long served as a central indicator of individual differences in complex cognition. However, growing evidence suggests that a substantial portion of its predictive power may reflect attention control (AC)—including goal maintenance, interference management, and inhibition—rather than storage capacity alone. This review synthesizes findings across six domains: (1) perception and sensory discrimination, (2) learning and problem solving, (3) cognitive control and decision making, (4) retrieval and memory performance, (5) multitasking and real-world performance, and (6) clinical applications. Across these areas, WMC-related effects frequently align with demands on AC, though the strength and nature of this alignment vary by domain. We highlight the importance of incorporating reliable AC measures and recommend latent-variable approaches to more clearly separate storage, control, and representational processes underlying complex performance.

Keywords: attention control; working memory capacity; executive attention; fluid intelligence; interference control; individual differences; latent-variable modeling; cognitive measurement

From conclusions:

Across six domains, the evidence reviewed here suggests that the broad predictive power traditionally associated with WMC often reflects the AC operations embedded within complex-span tasks—particularly goal maintenance, interference suppression, and disengagement. This does not diminish the importance of WMC as a measurable construct; rather, it clarifies that many WMC tasks draw on AC mechanisms, which are more directly tied to performance in interference-heavy contexts.

McGrew et al. (2023) identified a similar AC-Gwm complex system in a recent WJ V psychometric network analysis study.  See the relevant research and comments  from that article below (click here to access and download the paper).  Again, a reminder—click on image to enlarge for easy reading.

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)

Journal of Intelligence “Best Paper Award” for McGrew, Schneider, Decker & Bulut (2023) Psychometric network analysis of CHC measures - #psychometric #networkanalysis #intelligence #CHC #WJIV #bestpaper #schoolpsychology #schoolpsychologist

Today I (Kevin McGrew), and colleagues Joel Schneider, Scott Decker, and Okan Bulut, were pleased to learn that our recent 2023 Journal of Intelligence article listed above (open access—click link to read or download) was selected as 1 of 2 “Best Paper Awards” for 2023.  

As stated at the journal award page, “The Journal of Intelligence Best Paper Award is granted annually to highlight publications of high quality, scientific significance, and extensive influence. The evaluation committee members choose two articles of exceptional quality that were published in the journal the previous year and announce them online by the end of June.”

Below is the abstract and two figures that may pique your interest. We thank the members of the JOI evaluation committee.

Abstract

For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.

Keywords: 

CHC; Cattell–Horn–Cattell theory; intelligence; cognitive abilities; psychometric network analysis; process overlap theory; dynamic mutualism; factor analysis; general intelligence

Click on images to enlarge for easier viewing/reading

Research Byte: Individual differences in #spatial navigation and #workingmemory - lets hear it for the new #WJV visual working memory test—#CHC #Gv #Gwm #schoolpsychology #cognition #intelligence

Individual differences in spatial navigation and working memory

Intelligence. Sorry, but not an open access downloadable article 😕

Abstract

Spatial navigation is a complex skill that relies on many aspects of cognition. Our study aims to clarify the role of working memory in spatial navigation, and particularly, the potentially separate contributions of verbal and visuospatial working memory. We leverage individual differences to understand how working memory differs among types of navigators and the predictive utility of verbal and visuospatial working memory. Data were analyzed from N = 253 healthy, young adults. Participants completed multiple measures of verbal and visuospatial working memory and a spatial navigation task called Virtual Silcton. We found that better navigators may rely more on visuospatial working memory. Additionally, using a relative weights analysis, we found that visuospatial working memory accounts for a large majority of variance in spatial navigation when compared to verbal working memory. Our results suggest individual differences in working memory are domain-specific in this context of spatial navigation, with visuospatial working memory being the primary contributor.

————————

As an FYI.  The WJ V has a new cognitive Visual Working Memory test that I created. Unfortunately, it was not included in the original WJ V launch and will be added in a later release…not sure when…no one has told me…but I think this fall.

The back story is that this test was in development for over 30 years by yours truly.  For the WJ III I developed, and we normed, a visual working memory test where examinee’s were shown a abstract line-based image on a dotted grid and were instructed to rotate the image in their mind (after the test stimuli figure was removed) and then draw the rotated image on a identical blank grid.  The idea of examinees drawing their response was to add additional clinical information about visual-motor abilities, in addition to visual working memory.  Unfortunately, after being completely normed, we learned via inter-rater reliability studies that the scoring reliability was not adequate…darn.  

The second attempt was an earlier version of the current WJ V Visual Working Memory test that had already been printed for the WJ IV norming test books.  The WJ IV version was shelved at the last minute due to cost issues as a result of the financial crises at the end of the Bush presidency.  We were instructed to reduce the cost of the WJ IV norming.  This test simply had too many printed test easel pages (was called a “page eater”) and was eliminated…double darn.  

However, this turned out to be a blessing in disguise.  With the new digital testing platform, the WJ IV version was now presented without a concern for the number of pages, and more importantly, it could have a much more complex and informative underlying scoring system since all taps on an asymetrical response grid were recorded (which was a richer set of response data than the original WJ IV version).  As stated in the WJ V technical manual (LaForte, Dailey & McGrew, 2025, p. 40):

The Visual Working Memory test requires the use of visual working memory “in the context of processing” (Maehara & Saito, 2007). For each item, the examinee briefly studies a pattern of stimulus dots inside of randomly placed squares on the screen and then must recall the specific locations of the dots. The presentation and recall screens are separated by a quick and simple visual discrimination distractor item. This test requires the examinee to maintain information in working memory while actively processing the distractor requirements. Once the distractor task is completed, it must be quickly removed from active memory to focus on recalling the locations of the stimulus dots (Burgoyne et al., 2022). Errors of both omission (i.e., erroneously recalling a dot in a box where no dot was present) and commission (i.e., failing to identify a box associated with a dot's correct location) are both factored into the test's scoring model; however, heavier emphasis is placed on visual recall through a relatively higher penalty for errors of commission.

Validity information in the WJ V TM provides evidence that the new Visual Working Memory test is a mixed measure of Gv and Gwm.  Preliminary evidence (inspection of growth curves and standard deviation distributional characteristics) was interpreted as being consistent with other measures of executive functioning.  Additional concurrent validity studies with established measures of executive functioning are needed before an evidence-based claim of executive functioning score variance can clearly be established.

I think the 30+ year wait was worth it.  I’m very proud of this test in its current form.  A “shout out” to Dr. Erica LaForte and David Dailey for creating such a response-rich stream of data for scoring…something that was not possible in the planned non-digital WJ III and WJ IV versions.

Research Byte: General Ability (#g) Level Moderates Cognitive–#Achievement Relations for #Mathematics (#WJIV)—#WJIV #WJV #schoolpsychology #mathematics #SPED #EDPSYCH

[Blogmaster comment:   First…COI info…I’m a coauthor of the WJ IV and WJ V.  Second, regular readers may have noticed that I’ve been MIA on my various social media outlets the past 2-3 months.  I needed a break after spending the last five years working on the WJ V.  I also needed to attend to some family issues.  I plan to restart my sharing of interesting new research and FYI opinion posts].

Click on image to enlarge

New pub in Journal of Intelligence.  Click here to view and download (open access).

General Ability Level Moderates Cognitive–Achievement Relations for Mathematics 

by 

Christopher R. Niileksela

  

Jacob Robbins

 

Daniel B. Hajovsky

 

Abstract

Spearman’s Law of Diminishing Returns (SLODR) suggests general intelligence would be a stronger predictor of academic skills at lower general ability levels, and broad cognitive abilities would be stronger predictors of academic skills at higher general ability levels. Few studies have examined how cognitive–mathematics relations may vary for people with different levels of general cognitive ability. Multi-group structural equation modeling tested whether cognitive–mathematics relations differed by general ability levels for school-aged children (grades 1–5 and grades 6–12) using the Woodcock-Johnson Third Edition (n = 4470) and Fourth Edition (n = 3891) standardization samples. Results suggested that relationships between cognitive abilities and mathematics varied across general ability groups. General intelligence showed a stronger relative effect on mathematics for those with lower general ability compared to those with average or high general ability, and broad cognitive abilities showed a stronger relative effect on mathematics for those with average or high general ability compared to those with lower general ability. These findings provide a more nuanced understanding of cognitive–mathematics relations.

Keywords: 

cognitive ability; academic skills; cognitive–achievement relations; Spearman’s Law of Diminishing Returns

#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  

On making individual tests in #CHC #intelligence test batteries more #cogntivelycomplex: Two approaches

The following information is from a section of the WJ IV techncial manual (McGrew, LaForte & Schrank, 2014) and will again be included in the WJ V technical manual (LaForte, Dailey, McGrew, Q1, 2025).  It was first discussed in McGrew (2012)

On making individual tests in intelligence test batteries more cogntively complex

In the applied intelligence test literature, their are typically two different approaches typically used to increase the cognitive complexity of individual tests (McGrew et al., 2014). The first approach is to deliberately design factorially complex CHC tests, or tests that deliberately include the influence of two or more narrow CHC abilities. This approach is exemplified by Kaufman and Kaufman (2004a) in the development of the Kaufman Assessment Battery for Children–Second Edition (KABC-II), where:

the authors did not strive to develop “pure” tasks for measuring the five CHC broad abilities. In theory, Gv tasks should exclude Gf or Gs, for example, and tests of other broad abilities, like Gc or Glr, should only measure that ability and no other abilities. In practice, however, the goal of comprehensive tests of cognitive abilities like the KABC-II is to measure problem solving in different contexts and under different conditions, with complexity being necessary to assess high-level functioning. (p. 16)

In this approach to test development, construct-irrelevant variance (Benson, 1998; Messick, 1995) is not deliberately minimized or eliminated. Although tests that measure more than one narrow CHC ability typically have lower validity as indicators of CHC abilities, they tend to lend support to other types of validity evidence (e.g., higher predictive validity). The WJ V has several new cognitive tests that use this approach to cognitive complexity. 

The second approach to enhancing the cognitive complexity of tests is to maintain the CHC factor purity of tests or clusters (as much as possible) while concurrently and deliberately increasing the complexity of information processing demands of the tests within the specific broad or narrow CHC domain (McGrew, 2012). As described by Lohman and Lakin (2011), the cognitive complexity of the abilities measured by tests can be increased by (a) increasing the number of cognitive component processes, (b) including differences in speed of component processing, (c) increasing the number of more important component processes (e.g., inference), (d) increasing the demands of attentional control and working memory, or (e) increasing the demands on adaptive functions (assembly, control, and monitoring). This second form of cognitive complexity, not to be confused with factorial complexity, is the inclusion of test tasks that place greater demands on cognitive information processing (i.e., cognitive load), that require greater allocation of key cognitive resources (viz., working memory or attentional control), and that invoke the involvement of more cognitive control or executive functions. Per this second form of cognitive complexity, the objective is to design a test that is more cognitively complex within a CHC domain, not to deliberately make it a mixed measure of two or more CHC abilities.

A large number of prior IQs Corner’s posts regarding the topic of cognitive complexity in intelligence testing can be found here.

Benson, J. (1998). Developing a strong program of construct validation: A test anxiety example. Educational Measurement: Issues and Practice, 17(1), 10–22.

Lohman, D. F., & Lakin, J. (2011). Reasoning and intelligence. In R. J. Sternberg & S. B. Kaufman (Eds.), The Cambridge handbook of intelligence (2nd ed., pp. 419–441). New York, NY: Cambridge University Press.

McGrew, K. S. (2012, September). Implications of 20 years of CHC cognitive-achievement research: Back-to-the-future and beyond CHC. Paper presented at the Richard Woodcock Institute, Tufts University, Medford, MA. (click here to access)

Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses in performances as scientific inquiry into score meaning. American Psychologist, 50, 741–749.

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.