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Psychological Test and Assessment Modeling, 2024.66:3-29

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André Beauducel, Richard Bruntsch, Martin Kersting
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Psychological Test and Assessment Modeling
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31-03-2024
DOI:
10.2440/001-0010

Disentangling fluid and crystallized intelli-gence by means of Bayesian structural equation modeling and correlation-preserving mean plausible values

Abstract:

The present study presents Bayesian confirmatory factor analyses of data from an extensive computer intelligence test battery used in the applied field of assessment in Switzerland. Bayesian confirmatory factor analysis allows to constrain the variability and distribution of model parameters according to theoretical expectations using priors. Posterior distributions of the model parameters are then obtained by means of a Bayesian estimation procedure. A large sample of 4,677 participants completed the test battery comprising 21 different tasks. Factors for crystallized intelligence, fluid intelligence, memory, and basic skills/clerical speed were obtained. The latter factor is different from speed-factors in several other tests as it encompasses speeded performance on moderately complex tasks. Three types of models were compared: for one type, only the expected salient loadings were freely estimated, and all cross-loadings were fixed to zero (i.e., independent clusters) whereas for the other two types of models normally distributed priors with a zero mean were defined. The latter two types were again altered regarding the amount of defined prior variance. Results show that defining substantial prior variances for the cross-loadings in Bayesian confirmatory factor analysis allow to overcome limitations of the independent clusters model. In order to estimate individual scores for the factors, mean plausible values were computed. However, the inter-correlations of the mean plausible-values substantially overestimated the true correlations of the factors. To improve discriminant validity of individual score estimates, it was therefore proposed to compute correlation-preserving mean plausible values. The findings can be applied to derive estimates for factorial scoring of a test battery, especially if cross loadings of subtests must be expected.

Keywords:

Fluid intelligence; crystallized intelligence; Bayesian confirmatory factor analysis, mean plausible values
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