Friday, October 31, 2008

Happy Halloween

Reynolds Unwrapped by Dan Reynolds

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Lifetime of IQ research by Deary book

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A Lifetime of Intelligence
Follow-Up Studies of the Scottish Mental Surveys of 1932 and 1947
Ian J. Deary, PhD; Lawrence J. Whalley, PhD; and John M. Starr, PhD

285 pages. List price: $69.95. APA member/Affiliate price: $49.95
ISBN 978-1-4338-0400-7 Item # 4318049

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Friday, October 24, 2008

WJ test reliability over time

"A test does not change from one time to another: people do. There may be considerable change on some traits, but relatively little on others. Test-retest studies evaluate the tendency for change in people, not some aspect of test quality. A test that does not reflect such changes in human traits would be an insensitive measure of those traits" (McGrew, Werder, & Woodcock, 199, p. 99).

Over on the NASP Listserv Dr. Gary Canivez asked the following question, in response to a post regarding changes in scores on the K-ABC and WJ---"Does anyone have references for long-term stability of WJ or KABC-2 scores? I'd be interested in references for such studies."

There was a very sophisticated test-retest study reported in the WJ-R Technical Manual (McGrew, Werder & Woodcock, 1991) (click here to view/download). Unfortunately it is in a test technical manual...a document that is too often ignored once a test is purchased. Additonal information can be found in the following article.

  • McArdle, J. J., FerrerCaja, E., Hamagami, F., & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115-142. (click to view)
I urge practicing psychologists to read these reports. Yes..they are hard to digest. They are not reporting simple test-retest correlations. Instead, they are complex research designs intended to identify and partition the sources of test score variance that change over time. Scores for some tests will change over time...and this is what you it is reflecting trait variance....some traits do change over time (just as our weight changes over time...unfortunately, using in an upward direction). Other traits measured by tests are more stable across time...and thus the scores will likely change less.

And then, of course, a person's state (concentration, anxiety, fatigue, etc.) at any testing moment can impact test performance...and test's sensitive to these states (e.g., Gsm, Gs) will likely reflect these temporary state fluctuations. This reflects state variance...which is NOT a problem with the measure...the measure is accurately reflecting how the person is doing at that time. School psychologists (and others who do psychological testing), unfortunately, typically receive measurement training that only talks about simple test-retest reliability studies and results....a diservice to our profession. We need to understand our instruments better. Properly designed test-retest studies, where the retest interval is varied, can help identify and portion the difference sources of test score variance to the appropriate sources of stable or unstable change score variance.

Read the WJ-R technical manual excerpt in particular. It is the most readable and understandable of the two sources I've included in this post.

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Thursday, October 23, 2008

WISC-III/WJ III cross-battery Guttman 2-D Radex analysis

One more WISC-III/WJ III cross-battery analysis--this time a 2-D Guttman Radex MDS model (click here).  As readers have noted, I've been on a bit of a data analysis binge this past week (in preparation for writing a manuscript---and after being refreshed by an actual 2+ week vacation) and have reported:  (a) WISC-III/WJ III cross-battery g+specific cog-ach relations SEM, (b) WJ III 2-D Guttman Radex MDS of WJ III norm sample ages 6-8, and (c) WJ III 3-D Guttman Radex MDS of ages 9-13 of norm sample.  It is hoped these analyses provide useful information in understanding the characteristics of the tests in the WJ III and Wechsler intelligence batteries.

Unfortunately this analysis is based on the WISC-III and not the more recent WISC-IV.  Nevertheless, the results still provide useful information on the WISC-III tests that are still present in the WISC-IV.

Given all I've written regarding the various MDS models, I'm going to only make a few comments and hope others take the presentation of these data to engage in additional discussion, interpretion, etc.----have some fun.

A few observations/comments:
  • Gv tests (both WISC-III and WJ III) continue to surface on the more outer rings of the MDS models---suggesting that they are more lower-level perceptual/processing measures and do not capture complex Gv cognitive processing.  See my Gv comments on this the other day.  The same can be said for Ga tests.
  • WJ III Understanding Directions is consistently one of the more cognitive complex tests.  And, it is largely a language-based measure of working memory (Gsm-MW).  Remember that as per the Radex model, cognitive complexity deals with the amount of elements/components that are processed.....and is not the same as abstract thinking (Gf-ish stuff).  WJ III Numbers Reversed also shows up close to the center, with WISC-III Digit Span not far behind.  Does this support the popular working memory=Gf/g research hypothesis?
  • The Gc tests from both batteries appear similar in placement.
  • As would be expected, the WJ III Gf tests (Concept Formation, Numerical Reasoning [which is a combo of Number Series and Number Matrices], and Analysis-Synthesis are within the center "cognitive complexity" circle.
I'm sure there is much more that can be gleaned, but I'll leave that to the readers to discover, debate, and discuss.  I actually think a 3-D MDS model is necessary to capture the characteristics of the measures...but I've run out of time and steam on these analysis.  Maybe at a later date.

A couple caveats I provided the other day are also relevant here--(a) I'm a coauthor of the WJ III (conflict of interest disclosure) and (b) these results have not been peer-reviewed

Wednesday, October 22, 2008

Wikipedia article 'Raymond Cattell'

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Wikipedia article 'John B. Carroll'

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Wikipedia article 'Chc theory'

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WJ III EWOK (Evolving Web of Knowledge) v3.1 is now available

v3.1 of the WJ III EWOK (Evolving Web of Knowledge) is now available.

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Braille Adaptation of WJ III Tests of Achievement

Thanks to Lynne Jaffe for providing an update on the Braille Adaptation of the WJ III Tests of Achievement. A description can be found at v3.1 of the WJ III EWOK If you want to skip the "big picture" of the WJ III EWOK and go directly past go, and go directly to the relevant information, click here.

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Tuesday, October 21, 2008

Computerized cognitive assessments

Interesting post from the most visible brain fitness company on the

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PS caveat to prior WISCIII/W III study post

I failed to mention one additional important caveat in the prior
post. The study results I posted have not been peer-reviewed.

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WISC-III/WJ III cross-battery g+specific cog-ach abilities findings

WISC-III/WJ III cross-battery g+specific abilities research reinforces "just say maybe" program of CHC g+specific abilities research.

I'm just starting the process of drafting a manuscript to summarize the results of the IAP CHC COG-ACH Correlates Meta-Analysis project (click here).  In that on-line EWOK (Evolving Web of Knowledge) I list a McGrew (2007a) study in the reading and math summary tables.  That reference reflects unpublished re-analysis I completed (last fall) with the Phelps et al. (2007) joint (cross-battery) WISC-III/WJ III dataset.  In order to include the findings in the synthesis manuscript, I felt it appropriate to at least informally publish the final models for reference. 

Two important caveats.  I'm a coauthor of the WJ III (conflict of interest disclosure).  The second caveat is outlined in the brief report of the results that I have posted.  When I have time I will update the synthesis EWOK. I will change the reference to McGrew (2008) reflecting posting.

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Monday, October 20, 2008

IQs Corner Recent Literature of Interest 10-19-08

This weeks "recent literature" of interest is now available. Click here to access.

WJ III: 2-D MDS analysis ages 6-18

As promised, this is a follow-up to my posting of a 3-D Guttman Radex MDS model of WJ III tests. I'm now presenting a 2-D Radex model based on the analysis of all WJ III norm subjects from ages 6-18 (using the WJ III NU norms). You can view/download the pdf file by clicking here.

I could write an entire chapter on implications, hypotheses, etc. Instead, I'm going to make just a few comments and post a few questions in hopes that this approach to examining the characteristics of tests generates some interest. IMHO, MDS is an excellent analytic tool that provides a unique lens by which to augment our factor-analytic based understanding of cognitive ability tests. I wish more of us would complete these analyses with all major intelligence batteries.

A few thoughts/comments/questions:
  • Note that Concept Formation is near the middle of the circle. This whole round of MDS analyses I've been posting is based on a concern (see J. Schneider comments) whether the CF test was a good measure of Gf....and if it was strongly related to g. As per MDS interpretation, the proximity of CF to the middle cross-hairs suggests it is one of the more "cognitively complex" tests in the entire WJ III battery. This would support its interpretation as a strong indicator of Gf and g.
  • Notice that Sound Awareness (Ga/Gsm), Understanding Directions (Gsm/Gc), Applied Problems (Gq/Gf), Quantiative Concepts (Gq/Gf), and Verbal Comprehension (Gc) are also close to the middle - suggesting that they are all cognitively demanding measures in terms of the concept of cogntive complexity. And...interestingly they come from different CHC broad factors. I'm convinced that the reason Sound Awareness and Understanding Directions are cognitively complex is the major working memory load placed on subjects during these tasks. This should serve to remind us that cognitive complexity does NOT necessarily need to be associated with abstract "thinking" (Gf-ish) type tasks. Further notice that Auditory Working Memory is not that far away either. Do these findings support the research that suggest a strong relation between working memory (Gsm-MW) and Gf or g?
  • Notice how "tight" or "cohesive" some of the respective CHC factor tests are. Clearly the Grw, Gq, Gc, Gf, and Gsm (except for MS) tests all tend to hang in the same proximity. In contrast notice the wide degree of distance between the WJ III Gv and Ga tests. Does this suggest that some broad CHC domains are more tight/cohesive while other domains are much broader (lower domain cohesion). What does this mean for test interpretation? What does this mean for understanding the theoretical nature of the different CHC factor domains?
  • Notice the cool cognitive efficency (CE) quandrant. Isn't it sweet how most all the Gs and Gsm tests can be circumscribed in one area. Yet...there is distance between the CE tsts which probably is very informative in understanding differences in the characteristic process/content demands placed on subjects. Isn't this exciting?
  • Is the fact that most Gv tests are far from the cognitive complexity center (as were most of the Wechsler Gv tests in the enclosed slide in the file) helping us understand why traditonal Gv tests are repeatedly found to be unrelated (statistically) to school achievement (esp. mathematics), when we know that considerable research indicates that Gv is important for mathematics. Does this tell us that we have yet, in the world of applied test development, failed to develop sufficiency complex and cognitively demanding Gv tests that would relate more to school achievement (e.g., visual-spatial working memory tests). Curious minds want to know.
  • Like Gv, notice the distances between the Ga tests (which do form a nice psychometric factor when using traditonal factor methods). Incomplete Words is quite far away from Sound Blending, which in turn is closer to the acquired knowledge tests. Does this suggest that IW may be the more "pure" phonetic coding measure while SB is potentially influenced by training and education? Further note the location of Auditory Attention --- I've included it among the cognitive efficiency area. Is this telling us that the sound discrimination (Ga component) of the AA test is minimal while the selective attention (under distraction--ability to resist distractions) component is greater?
  • I'm not comfortable with the interpretation of quandrant 4 in the model. Can others suggest ideas? I think part of the problem is that a 3-D model (like the one I posted the other day) may required to better account for the dimensionality of the complete set of WJ III tests.
I could stare at this forever and generate more thoughts, hypotheses, questions, etc. I'd like to leave that to others. Please feel free to start a thread discussing the potential benefits of examing cognitive and achievement tests via the lens of MDS analysis. It clearly is an under-utilized methodology that can help us better understand our measures. A problem is that most quantoids (myself include) have become seduced by the more sexy contemporary SEM (CFA) methods. Maybe it is time we go "back to the future."

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Friday, October 17, 2008

WJ III: Guttman Radex MDS analysis

More on "Gf=g revisited" thread (click here for original post) that produced some excellent discussion (click here) on the NASP listserv.

In response to the request for the application of Guttman's Radex MDS model to the Woodcock Johnson III (in age 9-13 norm sample), I looked through my old files and found a 3D MDS WJ III model that I completed a number of years ago. The slides have been posted in a pdf file for viewing. It would take a manuscript to explain and interpret everything....I hope the broad stroke hypotheses (esp. regarding the nature of three dimensions) stimulate some thought and discussion.

Yesterday I completed a new 2D model across all school-age subjects (6-18 years). I hope to post those findings within the week. Stay tunned.

[Conflict of interest disclosure - I'm a coauthor of the WJ III]

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Thursday, October 16, 2008

IQ Research bytes #5: Working memory and motviation orientation

The most recent issue of the Journal of Educational Psychology has a number of interesting articles which are featured below.

Working memory and intelligence in children: What develops? By Swanson, H. Lee

  • This study explored the contribution of the phonological and executive working memory (WM) systems to 205 (102 girls, 103 boys, 6 to 9 years old) elementary school children's fluid and crystallized intelligence. The results show that (a) a 3-factor structure (phonological short-term memory [STM], visual-spatial WM, and verbal WM) was comparable between age groups, (b) controlled attention and STM storage accounted for 67% of the age-related variance in WM, (c) effect sizes for direct paths from WM were substantially larger when predicting fluid intelligence than crystallized intelligence, and (d) the contribution of STM to intelligence was isolated to reading. The results suggest that the development of WM is distinct from STM, controlled attention plus storage accounted for age-related WM changes, and WM underlies age-related changes in both fluid and crystallized intelligence.
The following articles all deal with some important conative variables related to academic and intellectual performance, as I've summarized in a Model of Academic Competence and Motivation (MACM) in my Beyond IQ Project. (esp. note section on Motivation Orientation)

On the measurement of achievement goals: Critique, illustration, and application. By Elliot, Andrew J.; Murayama, Kou

  • The authors identified several specific problems with the measurement of achievement goals in the current literature and illustrated these problems, focusing primarily on A. J. Elliot and H. A. McGregor's (2001) Achievement Goal Questionnaire (AGQ). They attended to these problems by creating the AGQ-Revised and conducting a study that examined the measure's structural validity and predictive utility with 229 (76 male, 150 female, 3 unspecified) undergraduates. The hypothesized factor and dimensional structures of the measure were confirmed and shown to be superior to a host of alternatives. The predictions were nearly uniformly supported with regard to both the antecedents (need for achievement and fear of failure) and consequences (intrinsic motivation and exam performance) of the 4 achievement goals. In discussing their work, the authors highlight the importance and value of additional precision in the area of achievement goal measurement

The relationships among students' future-oriented goals and subgoals, perceived task instrumentality, and task-oriented self-regulation strategies in an academic environment. By Tabachnick, Sharon E.; Miller, Raymond B.; Relyea, George E.
  • The authors performed path analysis, followed by a bootstrap procedure, to test the predictions of a model explaining the relationships among students' distal future goals (both extrinsic and intrinsic), their adoption of a middle-range subgoal, their perceptions of task instrumentality, and their proximal task-oriented self-regulation strategies. The model was based on R. B. Miller and S. J. Brickman's (2004) conceptualization of future-oriented motivation and self-regulation, which draws primarily from social-cognitive and self-determination theories. Participants were 421 college students who completed a questionnaire that included scales measuring the 5 variables of interest. Data supported the model, suggesting that students' distal future goals (intrinsic future goals in particular) may be related to their middle-range college graduation subgoal, to their perceptions of task instrumentality, and to their adoption of proximal task-oriented self-regulation strategies.

Addressees of performance goals. By Ziegler, Albert; Dresel, Markus; Stoeger, Heidrun

  • As performance goals aim to both procure acknowledgment of one's abilities and to avoid revealing a lack of one's abilities, the authors hypothesized that students hold specific performance goals for different addressees and that there are specific correlational patterns with other motivational constructs. They analyzed a data set of 2,675 pupils (1,248 boys and 1,426 girls) attending Grades 8 and 9 (mean age=15.0, SD=0.97). The students completed a questionnaire consisting of 12 items measuring performance approach goals and 12 items measuring performance avoidance goals. In each subset, 4 groups of addressees were differentiated: parents, teachers, peers, and the acting individual him/herself. Additionally, several external criteria were measured. The authors concurrently tested theory-driven, structural equation models. Incorporating all 24 items, the best-fitting model was a multitrait-multimethod model, which posited 2 factors for approach and avoidance goals and 4 addressee factors. While performance goals addressing parents showed relationships to maladaptive motivational and learning patterns, performance goals addressing classmates and self showed relationships to adaptive motivational and learning patterns. The relationships between performance goals addressing teachers and external criteria were rather weak and unsystematic.

Achievement goals and achievement during early adolescence: Examining time-varying predictor and outcome variables in growth-curve analysis. By Shim, S. Serena; Ryan, Allison M.; Anderson, Carolyn J.
  • The present study advances understanding of (a) the development of achievement goals, (b) the changing association of achievement goals and achievement over time, and (c) the implications of changes in achievement goals for changes in achievement over time. African American and European American adolescents' (N=588) achievement goals and subsequent achievement were assessed at 4 time points (fall and spring of 6th and 7th grades) and modeled using growth-curve analytic techniques. There was an overall decline in all 3 types of achievement goals (mastery, performance-approach, and performance-avoidance goals), because of within-year rather than between-year decreases. The association between mastery goals and achievement was null at Time 1 and then positive at the following 3 time points. The association between performance-approach goals and achievement went from negative to null across time. Changes in students' goals, as well as their initial levels of goals, were particularly important in understanding how mastery goals foreshadow achievement. The implications of the findings for both theory and practice are discussed.
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Encephalon brain blog carnival 56

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Gf=g revisited: Schneider, Lopez and Fiorello comment

Joel Schneider provided some excellent thoughts and questions related to my recent "Gf=g: Maybe not" post over on the NASP listserv. His comments were then augmented by Ruben Lopez and Cathy Fiorello. I thought the quality of the comments were so good that they should be preserved for others to read. They are produced below - "as is" from the NASP list.

Joel Schneider comment:

Kevin's recent blog post about the Gf=g hypothesis is interesting and worth reading.

For most hypotheses about the structure of cognitive abilities, I can think of no better dataset on which to test them than on the WJ-III standardization sample. However, in this particular case, I've always had my doubts about WJ-III Gf tests. I am confident that both the primary WJ-III Gf tests are excellent markers of Gf. However, I've always thought that they contained a hint of common variance that was non-Gf related. What that is, I can't quite put my finger on it but it has something to do with executive control of attention. Both involve a need to generate hypotheses and test them in working memory in ways that seem more involved than the traditional matrix Gf tests. Both of them also seem to require math-like thought processes, especially in the more difficult items.

Suppose that the Gf=g hypothesis were true. Let's say that Concept Formation and Analysis-Synthesis both consist of the following sources of variance:

CF = Gf + Something Extra + error
AS = Gf + Something Extra + error

The latent variable that would be constructed to represent Gf in a CFA would thus be: WJ-III Gf = Gf + Something Extra

The chi square test to see if constraining the Gf to g path to 1.0 would be significant, not because the Gf=g hypothesis is wrong, but because the 2 WJ-III Gf subtests were not pure enough markers of Gf. It would only take a little something extra for the chi square test to be significant.

I would think that adding one Raven-like matrix in the Gf mix would reduce the problem (if there actually is a problem). These tests seem less-mathy and more visual-spatialish and thus might reduce the non-Gf common variance.

The tables Kevin links to include a Gf latent variable that consists of:

Concept Formation
Numerical Reasoning (Number Matrices + Number Series?)
Applied Problems
Quantitative Concepts

If I am right about CF and AS being mathy and if mathiness is not exactly the same as Gf, then this WJ-III Gf is likely to be WJ-III Gf = Gf + Mathiness

I was very surprised to see how strong an indicator of Gf Quantitative Concepts is, given its Gc-like question format. Perhaps it is glomming onto Gf not because it has a lot of Gf in it but because it is attracted to the math-like elements of the other indicators. Even so, I am very much at a loss to understand why Quantitative Concepts has a higher loading on Gf than does Applied Problems.

Ruben Lopez responds:

Hi Joel,

Maybe the messiness of Gf's measurability even in an exceptional measure like the WJ-III--may have more to do with abstraction and its relationship to g than with a separate Gf.

Consider Dr. David Lohman's discussion of Gf's relationship to Gq in "The Woodcock-Johnson III and the Cognitive Abilities Test (Form 6): A concurrent validity study" (March 2003):

"Recent discussions of the nature of general ability have emphasized the importance of physiological processes (Jensen, 1998), the role of working memory (Kyllonen, 1996), or the congruence between aprimary Inductive Reasoning factor, the stratum II Fluid Ability factor (Gf), and g (Gustafsson, 2002). However, the present study supports Keith and Witta's (1997) hypothesis that quantitative reasoning may be an even better indicator of g. Quantitative reasoning has always been represented in some form in achievement test batteries, and in aptitude tests (such as the SAT) designed to predict academic success. But a broad quantitative knowledge factor (Gq) was not added to Gf-Gc theory until the late 1980s (Horn, 1989). Carroll's (1993) three-stratum theory, on the other hand, considers quantitative reasoning to be part of a broad fluid reasoning (Gf) factor. Confirmatory factor analyses of different ability test batteries mirror this ambivalence. Some studies find g and Gq indistinguishable [as in Keith & Bickley's (1992) factor analysis of the Stanford-Binet IV or Lohman & Hagen's (2002) factor analyses of the CogAT Primary Battery], other studies find Gq to be the best indicator of g (as in Keith & Witta's (1997) factor analyses of the WISC-III or Lohman & Hagen's (2002) factor analyses of the CogAT Multilevel Battery], and yet other studies find distinguishable g and Gq factors [as in Bickley, Keith, & Wolfe's (1995) factor analysis of the Woodcock-Johnson Psychoeducational Battery-Revised].

Paradoxically quantitative reasoning has not been much studied because it is difficult to separate from g unless combined with tests of more specific mathematical knowledge and skill (as in the Gq factor). But it is this overlap with g that makes quantitative reasoning particularly interesting as a vehicle for understanding the nature of g. Perhaps the most salient characteristic of quantitative concepts is abstraction. Even elementary operations like counting require abstraction: two cats are in some way the same as two dogs or two anything. The number line itself is an abstraction, especially when it includes negative numbers. Abstraction is most obvious in understanding concepts such as variable or, later, imaginary number.

Several early definitions of g emphasized abstract thinking or reasoning abilities. And the transition from concrete to abstract thinking figured prominently in Piaget's theory of intelligence. Modern definitions of g emphasize the importance of working memory resources or even of reasoning, but do not have much to say about the role of abstract thinking. These analyses suggest a closer study of quantitative reasoning might be a good place to begin in exploring this possibility.
" (p. 16).

And don't forget Keith and colleagues recommendation that the Arithmetic subtest should be added to the Perceptual Reasoning scale to assess Gf.

Cathy Fiorello chimes in:

Folks may be interested in looking at Guttman's model of intelligence in this context. Some colleagues and I had an article in Intelligence a couple of years ago (Cohen, Fiorello, & Farley, maybe 2006?) with a Smallest Space Analysis of the WISC-IV. Guttman's model was supported, which considers level of abstraction as one dimension of a three-dimensional model.

Wednesday, October 15, 2008

Gf=g revisted: Maybe not

Does Gf=g?

The possibility that Gf is isomorphic with general intelligence (g - if you believe g exists) has been discussed/debated in many research articles during the past few decades.  Kvist and Gustafsson (2008) recently took a new approach to investigating the viability of the Gf=g hypothesis [The reference and the journal abstract are included at the bottom of this post.]  These researchers use Cattell's investment theory to test the hypothesis. They argue, as per an extension of Catell's Investment hypothesis, that in populations with homogeneous learning experiences the Gf=g relationship would hold, while in more heterogenous populations the relationship between Gf and g would not approach unity.  As noted in their abstract and article, their research confirmed their hypothesis. 

They then attempt to explalin the lack of the Gf=g relation in other studies as per population/sample differences (viz., the failure to find this relation is possibly due to samples that are heterogeneous with regard to differential opportunities to develop knowledge and skills).  They attempt to explain Carroll's (2003) failure to find Gf=g in an analysis of the WJ-R norm data.  According to Kvist and Gustafsoon:
  •  "in the study by Carroll (2003) previously referred to, which failed to find the perfect relation between Gf and g, the matrices analyzed were pooled across the ages from kindergarten to adulthood, and this may have caused a population heterogeneity which prevented the perfect relation to appear.  These data could be reanalyzed with the data organized into homogeneous age groups to test this hypothesis." (p. 433).
As coauthor of the more current Woodcock-Johnson III (where the broad CHC constructs [Gf, Gc, Glr, etc.] have even better construct validity than the WJ-R), I thought I'd take a peak at the Gf-->g loadings in the age-differentiated analyses reported in the WJ III Technical Manual (McGrew & Woodcock, 2001).  Below are the Gf loadings on g (by five age-differentiated groups), as well as other broad factor loadings that were of similar magnitude or higher in each respective group (click here for more complete summary tables).

  • 6 to 8 years --   Gf (.96), Ga (.98)
  • 9 to 13 years -- Gf (.89), Gsm (.91)
  • 14-19 years  --  Gf (.92), .Gc (.90)
  • 20-39 years --   Gf (.92), Ga (.96), Glr (.95)
  • 40 and above -- Gf (.94), Ga (.97)
What to conclude? First, if I can find the time, I could re-run these models and constrain the Gf-->g loading to 1.0 and do a chi-square difference test ( much little time).  However, it is my experience that latent factor loadings in the high .80's and low .90's typically fail this test.  More importantly, notice the fact that other broad CHC abilities show latent factor g loadings equal to (and sometimes a bit higher) than Gf.  The above results, which follow Kvist and Gustafsson's recommendation to analyze the data by different age levels (and not pool into a single grand wide-age span sample), in my judgement, failsto support their hypotheses for the failure to find the Gf=g relation.  So....the Kvist and Gustafsson findings need to be tempered with the possible alternative hypothesis that studies may or may not replicate the Gf=g relation due to study differences in the type and breadth of markers used to operationalize ability constructs (that are then modeled to load on g).

Kvist, A. & Gustafsson, J-E. (2008) The relation between fluid intelligence and the general factor as a function of cultural background: A test of Cattell's Investment theory.  Intelligence, 36, 422-436
(click to view)

  • Abstract:  According to Cattell's [Cattell, R.B. (1987). Intelligence: Its structure, growth and action. New York: North-Holland.]Investment theory individual differences in acquisition of knowledge and skills are partly the result of investment of FluidIntelligence (Gf) in learning situations demanding insights in complex relations. If this theory holds true Gf will be a factor of General Intelligence (g) because it is involved in all domains of learning. The purpose of the current study was to test the Investment theory, through investigating the effects on the relation between Gf and g of differential learning opportunities for different subsets of a population. A second-order model was fitted with confirmatory factor analysis to a battery of 17 tests hypothesized to measure four broad cognitive abilities The model was estimated for three groups with different learning opportunities (N=2358 Swedes, N=620 European immigrants, N=591 non-European immigrants), as well as for the total group. For this group the g–Gf relationship was .83, while it was close to unity within each of the three subgroups. These results support the Investment theory.

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Tuesday, October 14, 2008

IQs Corner APA book nook: PsycCRITIQUES - Volume 53, Issue 42 is now available online

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A new issue of PsycCRITIQUES is available online.

October 15, 2008
Volume 53, Issue 42

Book Reviews
1. Social Development, Social Inequalities, and Social Justice
Authors: Cecilia Wainryb, Judith G. Smetana, and Elliot Turiel (Eds.)
Reviewer: Robert W. Howard

2. Dignity for All: How to Create a World Without Rankism
Authors: Robert W. Fuller and Pamela A. Gerloff
Reviewer: Thomas Scheff

3. Love, Sex & Long-Term Relationships: What People with Asperger Syndrome Really Really Want
Author: Sarah Hendrickx
Reviewer: Mardi Allen

4. The Attachment Connection: Parenting a Secure and Confident Child Using the Science of Attachment Theory
Author: Ruth P. Newton
Reviewer: F. Richard Ferraro

5. Windows to the Brain: Insights From Neuroimaging
Authors: Robin A. Hurley and Katherine H. Taber (Eds.)
Reviewer: Simon M. McCrea

6. Helping Couples Cope with Women's Cancers: An Evidence-Based Approach for Practitioners
Authors: Karen Kayser and Jennifer L. Scott
Reviewer: Leslie B. Rosen

7. The Cure Within: A History of Mind–Body Medicine
Author: Anne Harrington
Reviewer: Mary Louise Allen-Huffman

8. Dyslogic Syndrome: Why Millions of Kids Are "Hyper," Attention-Disordered, Learning Disabled, Depressed, Aggressive, Defiant, or Violent—and What We Can Do About It
Author: Bernard Rimland
Reviewer: Sheila O'Brien Quinn

Film Review
9. Indiana Jones and the Kingdom of the Crystal Skull
Director: Steven Spielberg
Reviewers: George M. Zinkhan and Jenna M. Drenten

Sharp Brains news

I have an FYI feed on my two major blogs that provides constant
updates from their blog. Occassionally I like to feature a post or
two. The link below should take you to an important post regarding a
new research sponsorship program they have just announced.

Sent from KMcGrew iPhone (IQMobile)

Monday, October 13, 2008

Dissertation dish: K-ABC II, SB5, WJ III CHC factor analysis studies

Two new CHC-based dissertations I stumbled across this weekend.

A joint-confirmatory factor analysis using the Woodcock-Johnson III Tests of Cognitive Ability and the Stanford-Binet Intelligence Scales: Fifth Edition with high-achieving children by Williams, Tasha H., Ph.D., Ball State University, 2005, 206 pages; AAT 3176652

  • Abstract: A considerable about of research has concentrated on studying the performance of high achieving children on measures of intellectual functioning. Findings have indicated high achieving children display differences in performance patterns as well as in the cognitive constructs measured when compared to their average peers. The conceptualization of intelligence has evolved over time and contemporary theories of intelligence have described cognitive ability as consisting of multiple constructs which are often interrelated. Currently, one of the most comprehensive and empirically supported theories of intelligence is the Cattell-Horn-Carroll (CHC) theory (Cattell, 1941; Horn, 1968; Carroll, 1993). The multidimensional and hierarchical CHC theory has served as the foundation for the development and recent revisions of cognitive ability measures such as the Woodcock-Johnson Tests of Cognitive Ability - Third Edition (WJ-III COG; McGrew & Woodcock, 2001) and the Stanford-Binet Intelligence Scales - Fifth Edition (SBS; Roid, 2003b). The purpose of this study was to explore the construct validity of the WJ-III COG and SBS with a sample of high achieving children. Individual confirmatory factor analyses were conducted using the WJ-III COG and SBS. Additionally, a joint confirmatory factor analysis was conducted using both the WJ-III COG and SBS. The results indicated an alternative six-factor WJ-III COG and four-factor SBS models provided the best fit to the data of a high achieving sample, supporting previous research suggesting high achieving children display differences in cognitive constructs when compared with their average counterparts. The joint-confirmatory factor analysis indicated the best measures for the CHC factors measured by both the WJ-III COG and SB5 to help guide cross-battery assessments with high functioning children. Clinical applications of the findings are discussed.

The validity of intelligence tests using the Cattell-Horn-Carroll Model of intelligence with a preschool population by Morgan, Kimberly Elaine, Ph.D., Ball State University, 2008, 219 pages; AAT 3303357

  • Abstract: Individual differences in human intellectual abilities and the measurement of those differences have been of great interest to the field of school psychology. As such, different theoretical perspectives and corresponding test batteries have evolved over the years as a way to explain and measure these abilities. A growing interest in the field of school psychology has been to use more than one intelligence test in a "cross-battery" assessment in hopes of measuring a wider range (or a more in-depth but selective range) of cognitive abilities. Additionally, interest in assessing intelligence began to focus on preschool-aged children because of initiatives to intervene early with at-risk children. The purpose of this study was to examine the Stanford-Binet Intelligence Scales, Fifth Edition (SB-V) and Kaufman Assessment Battery for Children, Second Edition (KABC-II) in relation to the Cattell-Hom-Carroll (CHC) theory of intelligence using a population of 200 preschool children. Confirmatory factor analyses (CFAs) were conducted with these two tests individually as well as in conjunction with one another. Different variations of the CHC model were examined to determine which provided the best representation of the underlying CHC constructs measured by these tests. Results of the CFAs with the SB-V revealed that it was best interpreted from a two-stratum model, although results with the KABC-II indicated that the three-stratum CHC model was the best overall design. Finally, results from the joint CFA did not provide support for a cross-battery assessment with these two particular tests.

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Friday, October 10, 2008

IQs Corner Recent Literature of Interest- 10-11-08

This weeks "recent literature" of interest is now available. Click here to access.

IQs Corner "Followers" option

I just added the Blogger "followers" gadget option to this blog. Check the left hand column and join as a follower.

Wednesday, October 08, 2008

Does working memory belong in the CHC taxonomy?

Working memory---does it belong in the CHC taxonomy?

Recently, on either the CHC or NASP listservs (I can't recall which...poor Glr this morning), there was a brief thread re: the CHC classification of a test (I believe it was a Wechsler subtest) as measuring working memory (or not). I chimed in to remind people that working memory (Gsm-MW) is NOT like most other narrow abilities in the individual differences trait-type CHC taxonomy. At this time, I'd like to again reinforce this point with reference to more exteneded comments I made in McGrew (2005). Also, last evening I reread a very good article where similar comments where articulated by a leading group of working memory researchers (this information is presented below).

First, immediately below is how working memory was defined in my last web-based listing of the CHC broad and narrow abilities (and in my 2005 CHC: Past, Present, Future chapter in Flanagan and Harrison's CIA2 book). I've added italics/bold to reinforce the point I'm trying to articulate in the current post.

  • Working Memory (MW): Ability to temporarily store and perform a set of cognitive operations on information that requires divided attention and the management of the limited capacity resources of short-term memory. Is largely recognized to be the mind's "scratchpad" and consists of up to four subcomponents. The phonological or articulatory loop processes auditory-linguistic information while the visuo-spatial sketch/scratchpad is the temporary buffer for visually processed information. The central executive mechanism coordinates and manages the activities and processes in working memory. The most recent component added to the model is the episodic buffer. Recent research (see chapter text) suggests that MW is not of the same nature as the other 60+ narrow factor-based trait-like individual difference constructs included in this table. MW is a theoretically developed construct (proposed to explain memory findings from experimental research) and not a label for an individual-differences type factor. MW is retained in the current CHC taxonomy table as a reminder of the importance of this construct in understanding new learning and performance of complex cognitive tasks (see chapter text).
In the body of the McGrew (2005) chapter I explained this point further (again--I've added italic and bold to emphasize my current points)

  • "Although Flanagan and I (McGrew & Flanagan, 1998; Flanagan et al. 2000) previously argued for MW’s preliminary “membership” status in the CHC taxonomy, this recommendation was based primarily on logical and rational considerations. Our recommendation was tempered by Carroll’s (1993) skepticism toward the working memory construct. Carroll (1993) stated that 'although some evidence supports such a speculation, one must be cautious in accepting it because as yet there has not been sufficient work on measuring working memory, and the validity and generality of the concept have not yet been well established in the individual differences research' (p. 647)."
  • "Although MW is undeniably a valid and important psychological construct, this does not necessarily mean MW is a factor analytic, latent trait, individual differences type construct similar to the 60+ narrow cognitive abilities that are the cornerstone of the CHC taxonomy (see Table 3). According to Carroll (1993), 'evidence for the existence of a latent trait derives from a demonstration that a number of similar task sets are highly correlated, or in factor- analytic terms, have weights on the same factor. A factor, if it is well established in a number of empirical investigations, is in essence a latent trait reflecting differences over individuals in ability characteristics or potentials' (p. 22). According to Carroll’s definition, the trait-factor evidence for MW is still questionable."
In the following article, which was in response to a meta-analytic article by Ackerman et al. (2005), Oberauer et al. (2005) make the point much better than I do. I very much like the main essence of their comments--namely, working memory is an explantory theoretical construct that is attempting to explain intelligence. Again, italics/bold in the text below are added by the blogmaster (IQ McGrew)

Oberauer, K., Schulze, R., Wilhelm, O. & Suß, H-B. (2005). Working Memory and Intelligence—Their Correlation and Their Relation: Comment on Ackerman, Beier, and Boyle. Psychological Bulletin, 131(1), 61-65. (click to view/download)

  • Abstract: On the basis of a meta-analysis of pairwise correlations between working memory tasks and cognitive ability measures, P. L. Ackerman, M. E. Beier, and M. O. Boyle (2005) claimed that working memory capacity (WMC) shares less than 25% of its variance with general intelligence (g) and with reasoning ability. In this comment, the authors argue that this is an underestimation because of several methodological shortcomings and biases. A reanalysis of the data reported in Ackerman et al. using the correct statistical procedures demonstrates that g and WMC are very highly correlated. On a conceptual level, the authors point out that WMC should be regarded as an explanatory construct for intellectual abilities. Theories of working memory do not claim that WMC is isomorphic with intelligence factors but that it is a very strong predictor of reasoning ability and also predicts general fluid intelligence and g.
  • "Ackerman et al. (2005) treated WMC as one more beast in the zoo of ability constructs. They were content with giving it its place in the three-stratum theory of Carroll—with an inclination toward relegating it into the rank and file, together with lower level constructs such as psychometric speed. We think that this reflects a misunderstanding of why most researchers are interested in the correlation between WMC and intelligence. The aim of that research is to validate WMC as an explanatory construct for intellectual abilities. The psychometric ability constructs have been derived largely inductively, reflecting the common variance among tests that have been constructed as diagnostic tools for aspects of mental abilities as described in everyday language. In contrast, WMC is a construct that derives deductively from theories of the cognitive architecture in which a limited-capacity WM plays a central role, although not always under the same name, (Anderson & Lebiere, 1998; Atkinson & Shiffrin, 1968, to cite just the most prominent ones). These theories assign short-term memory or WM a crucial role for complex tasks such as reasoning and text comprehension."
  • "By treating WMC as another primary factor in the ability hierarchy, Ackerman et al. (2005) ignore its theoretical background. WMC is a construct that bridges the gap between research on individual differences in abilities and cognitive science, including experimental cognitive psychology and formal modeling of cognitive processes. The tasks used to measure WMC have been constructed to operationalize processes postulated in theories of WM, and although these theories are admittedly still in their infancy, they provide some guidance as to what features a good WM task should have."
  • "Among the theoretical constructs within current theories of information processing, WMC is the one parameter that correlates best with measures of reasoning ability, and even with gf and g. Therefore, investigating WMC, and its relationship with intelligence, is psychology’s best hope to date to understand intelligence. Stopping short at searching for the place of WMC among the factor hierarchy of ability constructs is like being satisfied with a Linne´an taxonomy of creatures and refusing to proceed toward explaining the origin of species."
Kudos to Obereaur et al. Well stated.

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Dissertation Dish: Processing Speed (Gs) Working Memory (Gsm-M) and math LD

Yet another study supporting the critical importance of cognitive efficiency variables (Gs-processing speed; Gsm-MW - working memory) and academic achievement (math), this time in young adults.

Working memory and processing speed as correlates of math skills in adults with learning disabilities by Zimmermann, Ute, Ph.D., Alliant International University, Los Angeles, 2008, 122 pages; AAT 3317770


Math skills are required in every walk of life throughout a lifespan. The foundation for mathematical performance is innate and there appears to be a natural tendency for humans to build upon this foundation from simple arithmetic to more complex algorithms. Until recently the underlying mechanisms for math functioning have only been sparsely studied, especially in adults. The purpose of this correlational study was to identify the relationship between specific neuropsychological abilities and math skills in adults identified with a learning disability through the use of well established, rigorously standardized, and widely used test batteries, namely the WAIS-III and the Woodcock Johnson-III Tests of Achievement. Furthermore the Intake Self Report and the Academic Attribute Survey II (AAS-II) were utilized to investigate if students tend to over-report their difficulties in math performance when compared to their actual ability level.

This study utilized archival data from a community college in an suburban area of southern California. One hundred fifty participants met eligibility criteria for inclusion in the study. The participant sample consisted of 76 male and 74 females, with ethnic diverse backgrounds. All participants met eligibility criteria for learning disabilities as described by Title 5 regulations that govern the California Community Colleges in accordance with the State Education Code and Federal legislative guidelines. Analysis of data revealed that students tend to over-estimate their perceived difficulties in math when compared to their actual ability level. Correlational analysis indicated significant correlations between the WAIS-III Processing Speed Index and each of the three subtests of the WJ-III Broad Math Cluster. Significant correlations were also revealed between WAIS-III Working Memory Index and each of the three subtests of the WJ-III Broad Math Cluster. A comparison in magnitude of correlations revealed that working memory was the dominant factor for math performance in adults, with processing speed secondary to working memory. Exploratory analyses were performed to investigate the role of gender. No significant correlations emerged between the Processing Speed Index and any of the three Broad Math Cluster subtests for the female participants. Findings are discussed with recommendations for future research.

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New neuropsychology books

I just received an e-newsletter from the Neuropsychology Arena listing some new books in the area of neuropsychology. Click here to view.

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Tuesday, October 07, 2008

IQs Corner APA book nook: PsycCRITIQUES - Volume 53, Issue 41 is now available online

Sent from KMcGrew iPhone (IQMobile)

October 8, 2008
Volume 53, Issue 41

Book Reviews
1. Children and the Dark Side of Human Experience: Confronting Global Realities and Rethinking Child Development
Author: James Garbarino
Reviewer: Judith L. Gibbons

2. The Bounds of Cognition
Authors: Frederick Adams and Kenneth Aizawa
Reviewer: Susan E. F. Chipman

3. The Levity Effect: Why It Pays to Lighten Up
Authors: Adrian Gostick and Scott Christopher
Reviewer: Richard D. Harvey

4. A Guide to Assessments That Work
Authors: John Hunsley and Eric J. Mash (Eds.)
Reviewer: Steven Taylor

5. Civil Juries and Civil Justice: Psychological and Legal Perspectives
Authors: Brian H. Bornstein, Richard L. Wiener, Robert F. Schopp, and Steven L. Willborn (Eds.)
Reviewer: Amy Bacharach

6. Women's Reflections on the Complexities of Forgiveness
Authors: Wanda Malcolm, Nancy DeCourville, and Kathryn Belicki (Eds.)
Reviewer: Patricia M. Berliner

7. The Bullies: Understanding Bullies and Bullying
Author: Dennis Lines
Reviewer: Selda Ozdemir

8. Afterschool Matters: Creative Programs That Connect Youth Development and Student Achievement
Author: Sara Hill (Ed.)
Reviewer: Joseph A. Durlak

Film Review
9. The Incredible Hulk
Director: Louis Leterrier
Reviewer: Thomas Scheff

Friday, October 03, 2008

Writing can make you feel good

The BRAIN BLOGGER has a nice post on the therapeutic impact of keeping
a diary or journal. This is not new news to folks who have been doing
this for years-but it is nice to have some research comfirmarion. I
often wonder if my blogging provides a similar benefit. Hmmmm.

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Braille Assessment Inventory (BAI) available for free download

In 1996 I coauthored a small instrument called the Sharpe-McNear-McGrew Braille Assessment Inventory (BAI).  It was then published by Hawthorne Press.  A Buros Review is available.  It is now out of print---but I occasionally receive inquires regarding the instrument. After consulting with the first author (Mike Sharpe), we decided to make a PDF copy of this instrument available for download.  The manual can be downloaded by clicking here.  The test record can be downloaded by clicking here

As stated in the manual, "The BAI was designed to assit educators and others in determining whether Braille instruction is an appropriate intervention for students ages 6-18 who are blind or visually impaired."

Hopefully the instrument will be of use to certain professionals.

Thursday, October 02, 2008

IQs Corner Recent Literature of Interest 10-3-08

This weeks (actually, the last three weeks---I just returned from Australia) recent literature of interest can be found by clicking here and here.

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IQs Corner APA book nook 10-2-08

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Subject: PsycCRITIQUES - Volume 53, Issue 40 is now available online

October 1, 2008
Volume 53, Issue 40

Book and Video Review
1. Ethics in Mental Health Research: Principles, Guidance, and Cases
with James M. DuBois
Reviewer: Joseph G. Ponterotto

Dialogues in Behavioral Health Research Ethics: A DVD Series to Facilitate Ethical Action
with James M. DuBois and Jean Campbell
Reviewer: Joseph G. Ponterotto

Book Reviews
2. Bad Men Do What Good Men Dream: A Forensic Psychiatrist Illuminates the Darker Side of Human Behavior
Author: Robert I. Simon
Reviewer: Kimberly Kirkland

3. Executive Functions and the Frontal Lobes: A Lifespan Perspective
Authors: Vicki Anderson, Rani Jacobs, and Peter J. Anderson (Eds.)
Reviewer: Michael Hogan

4. Mating Intelligence: Sex, Relationships, and the Mind's Reproductive System
Authors: Glenn Geher and Geoffrey Miller (Eds.)
Reviewer: Jonathan D. Springer

5. Inside Intuition
Author: Eugene Sadler-Smith
Reviewer: Michael Hogan

6. Clinical Psychology: Science, Practice, and Culture
Author: Andrew M. Pomerantz
Reviewer: Jeanne M. Slattery

7. Family Therapy With Suicidal Adolescents
Author: Anthony P. Jurich
Reviewer: William L. Hathaway

8. Organization Cognition and Learning: Building Systems for the Learning Organization
Authors: Luca Iandoli and Giuseppe Zollo
Reviewer: Richard W. Ackley

Film Review
9. The Great Debaters
Director: Denzel Washington
Reviewer: Alejandra Suarez