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

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  • Kan, Kees-Jan
  • van der Maas, Han L.J.
  • Levine, Stephen Z.

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.

Suggested Citation

  • Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    • Handle: RePEc:eee:intell:v:73:y:2019:i:c:p:52-62
    DOI: 10.1016/j.intell.2018.12.004
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    Cited by:

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    2. Caemmerer, Jacqueline M. & Keith, Timothy Z. & Reynolds, Matthew R., 2020. "Beyond individual intelligence tests: Application of Cattell-Horn-Carroll Theory," Intelligence, Elsevier, vol. 79(C).
    3. Rozgonjuk, Dmitri & Schmitz, Florian & Kannen, Christopher & Montag, Christian, 2021. "Cognitive ability and personality: Testing broad to nuanced associations with a smartphone app," Intelligence, Elsevier, vol. 88(C).
    4. Conte, Federica & Costantini, Giulio & Rinaldi, Luca & Gerosa, Tiziano & Girelli, Luisa, 2020. "Intellect is not that expensive: differential association of cultural and socio-economic factors with crystallized intelligence in a sample of Italian adolescents," Intelligence, Elsevier, vol. 81(C).
    5. Savi, Alexander O. & Marsman, Maarten & van der Maas, Han L.J., 2021. "Evolving networks of human intelligence," Intelligence, Elsevier, vol. 88(C).
    6. Sacha Epskamp, 2020. "Psychometric network models from time-series and panel data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 206-231, March.
    7. Wai, Jonathan & Lakin, Joni M. & Kell, Harrison J., 2022. "Specific cognitive aptitudes and gifted samples," Intelligence, Elsevier, vol. 92(C).
    8. Sacha Epskamp & Adela-Maria Isvoranu & Mike W.-L. Cheung, 2022. "Meta-analytic Gaussian Network Aggregation," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 12-46, March.

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