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

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State: Serval

Version: author

Serval ID

serval:BIB_763D9012E851

Type

**Article**: article from journal or magazin.

Collection

Publications

Fund

Title

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

Journal

Psychological Assessment

ISSN

1040-3590

Publication state

Published

Issued date

2013

Peer-reviewed

Oui

Volume

25

Number

2

Pages

496-508

Language

english

Abstract

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.

Keywords

WISC-IV, Bayesian structural equation modeling, direct hierarchical model, CHC theory,

DOI

Web of science

Create date

23/09/2013 11:29

Last modification date

03/03/2018 17:25