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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.

To further knowledge concerning the nature and nurture of intelligence, we scrutinized how heritability coefficients vary across specific cognitive abilities both theoretically and empirically. Data from 23 twin studies (combined N = 7,852) showed that (a) in adult samples, culture-loaded subtests tend to demonstrate greater heritability coefficients than do culture-reduced subtests; and (b) in samples of both adults and children, a subtest's proportion of variance shared with general intelligence is a function of its cultural load. These findings require an explanation because they do not follow from mainstream theories of intelligence. The findings are consistent with our hypothesis that heritability coefficients differ across cognitive abilities as a result of differences in the contribution of genotype-environment covariance. The counterintuitive finding that the most heritable abilities are the most culture-dependent abilities sheds a new light on the long-standing nature-nurture debate of intelligence.

Cronbach's (1957) famous division of scientific psychology into two disciplines is still apparent for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper, we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores-the empirical basis for the well-known g-factor-as well as more complex factorial structures. Links between network modeling, factor modeling, and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

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In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.