(1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Human cognitive abilities: A survey of factor analytic studies

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends. (PsycINFO Database Record

/pdf/a-systematic-review-of-bayesian-articles-in-psychology-the-2n71hyi83q.pdf

A systematic review of Bayesian articles in psychology: The last 25 years.

The fourth edition of the Wechsler Adult Intelligence Scale (WAIS-IV) is a revised and substantially updated version of its predecessor. The purposes of this research were to determine the construc...

WAIS-IV and Clinical Validation of the Four- and Five-Factor Interpretative Approaches

According to the most widely accepted Cattell–Horn–Carroll (CHC) model of intelligence measurement, each subtest score of the Wechsler Intelligence Scale for Adults (3rd ed.; WAIS–III) should reflect both 1st- and 2nd-order factors (i.e., 4 or 5 broad abilities and 1 general factor). To disentangle the contribution of each factor, we applied a Schmid–Leiman orthogonalization transformation (SLT) to the standardization data published in the French technical manual for the WAIS–III. Results showed that the general factor accounted for 63% of the common variance and that the specific contributions of the 1st-order factors were weak (4.7%–15.9%). We also addressed this issue by using confirmatory factor analysis. Results indicated that the bifactor model (with 1st-order group and general factors) better fit the data than did the traditional higher order structure. Models based on the CHC framework were also tested. Results indicated that a higher order CHC model showed a better fit than did the classical 4-factor model; however, the WAIS bifactor structure was the most adequate. We recommend that users do not discount the Full Scale IQ when interpreting the index scores of the WAIS–III because the general factor accounts for the bulk of the common variance in the French WAIS–III. The 4 index scores cannot be considered to reflect only broad ability because they include a strong contribution of the general factor.

Orthogonal higher order structure and confirmatory factor analysis of the French Wechsler Adult Intelligence Scale (WAIS-III).

The Wechsler Preschool and Primary Scale of Intelligence--Fourth Edition (WPPSI-IV; Wechsler, 2012) represents a substantial departure from its predecessor, including omission of 4 subtests, addition of 5 new subtests, and modification of the contents of the 5 retained subtests. Wechsler (2012) explicitly assumed a higher-order structure with general intelligence (g) as the second-order factor that explained all the covariation of several first-order factors but failed to consider a bifactor model. The WPPSI-IV normative sample contains 1,700 children aged 2 years and 6 months through 7 years and 7 months, bifurcated into 2 age groups: 2:6-3:11 year olds (n = 600) and 4:0-7:7 year olds (n = 1,100). This study applied confirmatory factor analysis to the WPPSI-IV normative sample data to test the fit of a bifactor model and to determine the reliability of the resulting factors. The bifactor model fit the WPPSI-IV normative sample data as well as or better than the higher-order models favored by Wechsler (2012). In the bifactor model, the general factor accounted for more variance in every subtest than did its corresponding domain-specific factor and the general factor accounted for more total and common variance than all domain-specific factors combined. Further, the domain-specific factors exhibited poor reliability independent of g (i.e., ωh coefficients of .05 to .33). These results suggest that only the general intelligence dimension was sufficiently robust and precise for clinical use.

http://edpsychassociates.com/Papers/WPPSIbifactor(2014).pdf

Bifactor structure of the Wechsler Preschool and Primary Scale of Intelligence—Fourth Edition.

The interpretation of the Wechsler Intelligence Scale for Children—Fourth Edition (WISC–IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell–Horn–Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8–12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC–IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

/pdf/further-insights-on-the-french-wisc-iv-factor-structure-41ct0rtudk.pdf

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

Exploratory and confirmatory factor analyses reported in the French technical manual of the WISC-IV provides evidence supporting a structure with four indices: Verbal Comprehension (VCI), Perceptual Reasoning (PRI), Working Memory (WMI), and Processing Speed (PSI). Although the WISC-IV is more attuned to contemporary theory, it is still not in total accordance with the dominant theory: the Cattell-Horn-Carroll (CHC) theory of cognitive ability. This study was designed to determine whether the French WISC-IV is better described with the four-factor solution or whether an alternative model based on the CHC theory is more appropriate. The intercorrelations matrix reported in the French technical manual was submitted to confirmatory factor analysis. A comparison of competing models suggests that a model based on the CHC theory fits the data better than the current WISC-IV structure. It appears that the French WISC-IV in fact measures six factors: crystallized intelligence (Gc), fluid intelligence (Gf), short-...

/pdf/the-four-vs-alternative-six-factor-structure-of-the-french-8ijbis99wk.pdf

The Four- vs. Alternative Six-Factor Structure of the French WISC-IV. Comparison Using Confirmatory Factor Analyses

Indice d’aptitude général pour le WISC-IV : normes francophones

L'indice d'aptitude general (IAG) a ete developpe dans le WISC-III pour estimer le niveau cognitif general. Il s'agissait de proposer une alternative au QI Total. Plus recemment, l'indice de competence cognitive (ICC) a ete propose. Jusqu'a il y a peu, seules les normes americaines de ces indices etaient disponibles ; les normes franc aises IAG et ICC pour le WISC-IV ont ete recemment developpees a partir d'une procedure d'approximation statistique. Toutefois, les normes developpees de cette maniere seraient moins appro- priees que les normes construites sur la base d'un echantillon. Cette etude vise a comparer les normes IAG et ICC developpees a partir d'une procedure d'approximation statistique a celles creees a partir d'un echantillon de 182 enfants âges de huit a 12 ans. Les correlations entre les deux types de normes sont de 0,997 pour IAG et de 0,999 pour ICC ; les differences absolues moyennes sont respectivement de 5,38 et de 2,24 points pour IAG et ICC. Enfin, pour les enfants presentant un score IAGS inferieur a 89, les normes IAGE semblent indiquer que leurs competences seraient inferieures d'environ 8-10 points a celui indique par le score IAGS. Les resultats de cette etude indiquent que les scores IAG et ICC peuvent constituer des informations utiles sur les capacites cognitives de l'enfant.

/pdf/indice-d-aptitude-general-et-indice-de-competence-cognitive-39rnsqmkh9.pdf

Isabelle Reverte

Papers

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

The Four- vs. Alternative Six-Factor Structure of the French WISC-IV. Comparison Using Confirmatory Factor Analyses

Indice d’aptitude général pour le WISC-IV : normes francophones

Indice d'aptitude général et indice de compétence cognitive pour le WISC-IV : normes empiriques versus normes statistiques

Structural Validity of the Wechsler Intelligence Scale for Children (WISC-IV) in a French-speaking Swiss sample