Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?

By K. Kan, H.L.J. van der Maas, S. Levine. Intelligence, 2019

86 citations recorded by the source corpus.

Abstract The current study implements psychometric network analysis within the framework of confirmatory (structural equation) modeling. Utility is demonstrated by three applications on independent data sets. The first application uses WAIS data and shows that the same kind of fit statistics can be produced for network models as for traditional confirmatory factor models. This can assist deciding between factor analytical and network theories of intelligence, e.g. g theory versus mutualism theory. The second application uses the ‘Holzinger and Swineford data’ and illustrates how to cross-validate a network. The third application concerns a multigroup analysis on scores on the Brief Test of Adult Cognition by Telephone (BCATC). It exemplifies how to test if network parameters have the same values across groups. Of theoretical interest is that in all applications psychometric network models outperformed previously established (g) factor models. Simulations showed that this was unlikely due to overparameterization. Thus the overall results were more consistent with mutualism theory than with mainstream g theory. The presence of common (e.g. genetic) influences is not excluded, however.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4e9f21028b311d7fbed2063801b35b45e4ad1ee0

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires (2019, background): “Kan, van der Maas, and Levine (2019), for example, show how fit indices can be applied to network models so that they can be compared to confirmatory factor analysis models.”