“Be and see” the #WISC-V correlation matrix: Unpublished analyses of the WISC-V #intelligence test

 I often “play around” with data sets until I satisfy my curiosity…and never submit the results for publication.  These WISC-V analyses were completed 3+ years ago.  I stumbled upon the folder today and decided to simply post the information for assessment professionals interested in the WISC-V.  These results have not been peer-reviewed.  One must know the WISC-V subtest names to decipher the test abbreviations in some of the figures.  

This is a Gv (visual; 8 slides) summary a set of exploratory structural analyses I completed with the WISC-V summary correlation matrix (Table 5.1 in WISC-V manual). View and enjoy. 

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Research Bytes: Cognitive Clusters in Specific Learning Disorder

Cognitive Clusters in Specific Learning Disorder

1. Michele Poletti, PsyD1⇑
2. Elisa Carretta, MS2,3
3. Laura Bonvicini, MS2,3
4. Paolo Giorgi-Rossi, PhD2,3

1. ¹Child and Adolescent Neuropsychiatry Service, AUSL of Reggio Emilia, Italy
2. ²Inter-Institutional Epidemiological Unit, AUSL of Reggio Emilia, Italy
3. ³Arcispedale S. Maria Nuova, IRCCS, Reggio Emilia, Italy

1. Michele Poletti, Department of Mental Health and Pathological Addiction, Child Neuropsychiatry Service, AUSL of Reggio Emilia, Via Amendola 2, 42100, Reggio Emilia, Italy. Email: michele.poletti2@ausl.re.it

Abstract

The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual–based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children–Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

Cluster analysis of the WJ III/WISC-III intelligence tests: OBG post

This is a OBG (oldie but goodie) post that has new updated links

In a prior shameless plug, I briefly summarized the results of a recently published CHC-based confirmatory factor analysis study of a WJ-III/WISC-III cross-battery data set (Phelps, McGrew, Knopik & Ford, 2005). Following a favorite quantoid mantra ("there is more than one way to explore a data set"), I couldn't resist but conduct a more loosey-goosey (sp?) exploratory analysis of the data.

One of my favorite exploratory tools, given the Gv presentation of the multivariate structure of the data, is hierarchical cluster analysis (sometimes referred to as the "poor man's" factor analysis). Without going into detail, I subjected the data set previously described to Ward's clustering algorithm. As a word of caution, it is important to note that cluster analysis will provide neat looking cluster dendograms for random data....so one must be careful not to over-interpret the results. Yet, I find the looser constraints of cluster analysis and, in particular, the continued collapsing of clusters of tests (and lower-order clusters) into ever increasing broad higher-order clusters very thought provoking---the results often suggest different broad (stratum II) or intermediate level strata (as per Carroll's 3-stratum model).

I present the current results "as is" (click here to view or download). Blogsters will need to consult prior posts to glean the necessary pieces of information to interpret the CHC factor codes and names, the abilities measured by the WJ III tests, etc.

To say the least, some interesting hypothesis are suggested. In particular, I continue to be intrigued by the possibility of a higher-order dual cognitive processing model structure (within the CHC taxonomy) --that is, a distinction between automatic vs controlled/deliberate processing

Dissertation Dish: Gf cognitive test analysis via CFA and task analysis

A comparison of confirmatory factor analysis and task analysis of fluid intelligence cognitive subtests by Parkin, Jason R., Ph.D., University of Missouri - Columbia, 2010 , 132 pages; AAT 3488814

Abstract

Cross-battery assessment relies on the classification of cognitive subtests into the Cattell- Horn-Carroll (CHC) theory's broad and narrow ability definitions. Generally, broad ability classifications have used ability data analyzed through factor analytic methods, while narrow ability classifications have used data about subtest task demands. The purpose of this investigation is to determine whether subtest similarity judgments based on task demands data, and judgments based on ability measurement provide similar results. It includes two studies. First, middle school students (N = 63) completed six target fluid reasoning subtests that were subjected to confirmatory factor analyses to analysis subtest similarities. Second, school psychology practitioners (N = 32) sorted subtest descriptions into similarity groups. Their judgments were analyzed with multiple non-hierarchical cluster analyses. Results partially confirmed that the six target subtests were classified similarly using both data types, though need to be interpreted cautiously due to limitations. Implications for assessment practices are discussed.




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Dissertation dish: New insights on the subdomains of Gs (processing speed)

Exploring the relationships among various measures of processing speed in a sample of children referred for psychological assessments by Nelson, Megan A., Ph.D., University of Virginia, 2009 , 102 pages; AAT 3348732

Abstract

Processing speed is a robust psychometric factor in modern tests of cognitive ability (Carroll, 1993), but the common factors underlying mental speed and its contributions to individual differences in functioning are not well understood. The goal of the current study was to further explore mental speed by conducting a confirmatory factor analysis (CFA) on 11 speeded subtest scores. It was hypothesized that the 11 subtests would be best represented by a four-factor model. These four factors were then submitted to a cluster analysis to identify whether certain patterns of factor scores were related to different demographic characteristics, diagnoses, or referral questions. It was hypothesized that Learning Disorder, Attention-Deficit/Hyperactive Disorder, and comorbid LD/ADHD diagnoses would be most likely to have unique processing speed factor patterns.

Participants were 186 children (ages 6 - 18 years old) referred to a university-based clinic for a comprehensive psychological evaluation. The CFA indicated that although the 11 measures are all speeded, they are best represented as four distinct constructs, labeled perceptual speed, naming facility, academic facility, and reaction time in this study. The clusters produced in this study appeared to be most highly differentiated by level (likely influenced by intelligence level) and by pattern only in respect to reaction time factor scores. Therefore, both the CFA and cluster analyses lend support to Cattell-Horn-Carroll cognitive theory's distinction between cognitive processing speed (Gs) and decision/reaction time (Gt). Additionally, the CFA results suggest that Gs may be multifaceted, but the cluster analysis did not differentiate clusters based on the processing speed factors. Although the results of this study have important implications for both assessment clinicians and cognitive theory, further research is needed to clarify the constructs of processing speed and reaction time as well as to identify the clinical implications of different processing speed patterns

Technorati Tags: psychology, school psychology, educational psychology, neuropsychology, cognitive abilities, intelligence, IQ, IQ tests, CHC theory, Cattell-Horn-Carroll, Gs, Gt, processing speed, cognitive speed, dissertation dish, IQs Corner

Cluster analysis of the WJ III: Implications for test interpretation and CHC model extensions

IAP AP101 # 4 report is now available (click here for all AP101 reports and briefs).  "IAP AP101 Report #4: Cluster analysis of the WJ III Battery:  Implications for CHC test interpretation and possible CHC model extensions" can be downloaded or viewed by clicking here.

PPT files are also viewable and downloadable via SlideShare.

Abstract

The WJ III Battery is comprised of both cognitive (intelligence) and achievement components. As reported in the technical manual, the Cattell-Horn-Carroll (CHC) theory of cognitive abilities organizational structure of the WJ III has been validated. The current investigation analyzed the cognitive and achievement tests for all WJ III norm subjects from ages 6-18 years of age. Cluster analysis of the 50 WJ III tests provides additional validity for the CHC structure of the WJ III. More importantly, the analyses provide support for a significant number of narrow ability classifications for many WJ III tests, classifications that (to date) have largely been based on expert consensus task analysis. The results also suggest possible new interpretative clusters and intermediate CHC dimensions warranting future research regarding the CHC taxonomy of human cognitive abilities.