Conference Presentation
Reference
Structural Validity of the French WISC–V with the French Standardization Sample: EFA and CFA Evidence
LECERF, Thierry, CANIVEZ, Gary
LECERF, Thierry, CANIVEZ, Gary. Structural Validity of the French WISC–V with the French Standardization Sample: EFA and CFA Evidence. In: 11th International Test Comission Conference, Montréal, Canada, July 2 to 5, 2018, 2018
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http://archive-ouverte.unige.ch/unige:107040
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Structural Validity of the French WISC–V with the
French Standardization Sample: EFA and CFA Evidence Thierry Lecerf, University of Geneva
Gary L. Canivez, Eastern Illinois University Presenter: Gary L. Canivez
Lecerf, T., & Canivez, G. L. (2018). Complementary exploratory and confirmatory factor analyses of the French WISC–V: Analyses based on the standardization sample. Psychological Assessment, 30, 793–808. http://dx.doi.org/10.1037/pas0000526 Supplemental material at
http://dx.doi.org/10.1037/pas0000526.supp
Publisher proposed French WISC-V model
Same problems as identified with U.S. WISC-V: No EFA reported, Weighted Least Squares estimation in CFA, effects coding without disclosure, reduced degrees of freedom in some models suggesting modified effects coding, no examination of bifactor models, no
presentation of variance apportions, standardized measurement model also not presented
Independent French WISC-V EFA Analyses (Lecerf & Canivez, 2018)
• French WISC–V standardization sample data were requested to complete independent analyses but request was denied.
• Thus, French WISC–V subtest correlation matrix for the total
standardization sample (Table 5.1) was extracted from the Technical and Interpretive Manual (Wechsler, 2014b) and used in EFA
• Total French WISC–V standardization sample N = 1,049
• 11 age groups (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, and 16). Each group was composed of 80 to 104 participants
• Standardized scores were computed for each age group separately (M = 10; SD = 3).
Independent French WISC-V EFA Analyses (Lecerf & Canivez, 2018)
• EFA: Principal factors (axis) extraction with promax (k = 4) rotation
• Multiple criteria (Gorsuch, 1983) were examined to determine the number of factors to retain:
• Eigenvalue > 1 (Kaiser, 1960)
• Scree test (Cattell, 1966)
• Standard error of scree (SEScree; Zoski & Jurs, 1996)
• Horn’s parallel analysis (HPA; Horn, 1965) & Glorfeld's (1995) modified PA (95% CI)
• Minimum average partials (MAP; Velicer, 1976)
• Bayesian Information Criterion (BIC; Schwarz, 1978)
• Sample size adjusted BIC (SSBIC; Sclove, 1987)
• Salient factor pattern coefficients were defined as those ≥ .30 (Child, 2006)
• Factor solutions were examined for interpretability and theoretical plausibility (Fabrigar et al., 1999)
• Each factor should be marked by two or more salient loadings
• No salient cross-loadings (Gorsuch, 1983).
EFA Results (Lecerf & Canivez, 2018)
• MAP suggested one factor
• Eigenvalue > 1, Scree, SEScree, and HPA suggested 2 or 3 factors
• BIC and SSBIC suggested four factors
• Publisher/"theory" claim five factors
• Given that it is better to overextract than underextract (Wood, Tataryn, & Gorsuch, 1996), EFA began by extracting five factors to examine subtest associations based on the publisher’s suggested structure and to allow examination of the performance of smaller factors. Four, three, and two factors were subsequently extracted and examined for adequacy.
French WISC-V Five Factors Extracted:
French WISC-V Four Factors Extracted:
French WISC-V Two and Three Factors Extracted:
French WISC-V Four Factors: Schmid-Leiman Results
Independent French WISC-V CFA (Lecerf & Canivez, 2018)
• Standardization sample raw data were requested, but the publisher denied access to standardization sample without rationale
• Absent raw data, covariance matrices were produced for CFA using the correlation matrix, means, and standard deviations from the total standardization sample presented in the French WISC–V Interpretive Manual (Wechsler, 2016b, Table 5.1, p. 62)
• Structural models specified in the French WISC–V Interpretive
Manual and alternative bifactor models that were not included in analyses were examined
• R-package “Lavaan” (version 05–22 with the option “mimic MPLUS”) in Rstudio for Macintosh version 1.0.136 (R Development Core Team, 2015) was used to conduct confirmatory factor analysis (CFA) using maximum likelihood estimation
• Model-based reliabilities were estimated with coefficients omega- hierarchical (wH) and omega-hierarchical subscale (wHS), which estimate reliability of unit-weighted scores produced by the indicators (Reise, 2012; Rodriguez et al., 2015)
