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.

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.

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Interpretation of the French Wechsler Intelligence Scale for Children-Fifth Edition (French WISC-V; Wechsler, 2016a) is based on a 5-factor model including Verbal Comprehension (VC), Visual Spatial (VS), Fluid Reasoning (FR), Working Memory (WM), and Processing Speed (PS). Evidence for the French WISC-V factorial structure was established exclusively through confirmatory factor analyses (CFAs). However, as recommended by Carroll (1995); Reise (2012), and Brown (2015), factorial structure should derive from both exploratory factor analysis (EFA) and CFA. The first goal of this study was to examine the factorial structure of the French WISC-V using EFA. The 15 French WISC-V primary and secondary subtest scaled scores intercorrelation matrix was used and factor extraction criteria suggested from 1 to 4 factors. To disentangle the contribution of first- and second-order factors, the Schmid and Leiman (1957) orthogonalization transformation (SLT) was applied. Overall, no EFA evidence for 5 factors was found. Results indicated that the g factor accounted for about 67% of the common variance and that the contributions of the first-order factors were weak (3.6 to 11.9%). CFA was used to test numerous alternative models. Results indicated that bifactor models produced better fit to these data than higher-order models. Consistent with previous studies, findings suggested dominance of the general intelligence factor and that users should thus emphasize the Full Scale IQ (FSIQ) when interpreting the French WISC-V. (PsycINFO Database Record

The aim of this study was to examine to what extent inhibitory control and working memory capacity are related across the life span. Intrusion errors committed by children and younger and older adults were investigated in two versions of the Reading Span Test. In Experiment 1, a mixed Reading Span Test with items of various list lengths was administered. Older adults and children recalled fewer correct words and produced more intrusions than did young adults. Also, age-related differences were found in the type of intrusions committed. In Experiment 2, an adaptive Reading Span Test was administered, in which the list length of items was adapted to each individual's working memory capacity. Age groups differed neither on correct recall nor on the rate of intrusions, but they differed on the type of intrusions. Altogether, these findings indicate that the availability of attentional resources influences the efficiency of inhibition across the life span