Together, these results thus provide (further) support for a network approach towards general intelligence. Furthermore, the confirmatory networks provided considerably better fits to the data than those factor models considered. For those who are interested in the subject, intelligence, we have found the following results: Network models of intelligence replicated over (WAIS standardization) samples, like well-established factor models do. Our paper therefore included such comparison.
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This shows, first of all, the enormous flexibility of psychonetrics. This flexibility comes with another advantage: researchers can now compare the fit statistics of psychometric network models and latent variables models without worrying if differences in results might stem from the use of different software packages.Ī comparison between the network and the latent variable models is especially valuable (‘fair’) when both types of models are truly confirmatory. In a recent publication and its associated tutorial website, we (Kan, de Jonge, van der Maas, Levine & Epskamp, 2020) illustrate how psychonetrics can be used for both Gaussian graphical modeling (GGM) and latent variable analysis. The first publications using R package psychonetrics are popping up! A table with fit measures computed by the psychonetrics package. This blog post was written by Kees-Jan Kan.
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