Why Do Bi-Factor Models Outperform Higher-Order g Factor Models? A Network Perspective.

Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generating mechanism; (ii) fit indices are biased against the higher-order g factor model in favor of the bi-factor model; (iii) a network structure underlies the data. We used a Monte Carlo simulation to investigate the validity and plausibility of each of these explanations, while controlling for their rivals. To this end, we generated 1000 sample data sets according to three competing models-a bi-factor model, a (nested) higher-order factor model, and a (non-nested) network model-with 3000 data sets in total. Parameter values were based on the confirmatory analyses of the Wechsler Scale of Intelligence IV. On each simulated data set, we (1) refitted the three models, (2) obtained the fit statistics, and (3) performed a model selection procedure. We found no evidence that the fit measures themselves are biased, but conclude that biased inferences can arise when approximate or incremental fit indices are used as if they were relative fit measures. The validity of the network explanation was established while the outcomes of our network simulations were consistent with previously reported empirical findings, indicating that the network explanation is also a plausible one. The empirical findings are inconsistent with the (also validated) hypothesis that a bi-factor model is the true model. In future model selection procedures, we recommend that researchers consider network models of intelligence, especially when a higher-order g factor model is rejected in favor of a bi-factor model.

Meta-analytic structural equation modeling made easy: A tutorial and web application for one-stage MASEM.

Meta-analytic structural equation modeling (MASEM) refers to fitting structural equation models (SEMs) (such as path models or factor models) to meta-analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. This app implements the one-stage MASEM approach, and allows users to apply MASEM in a user-friendly way. The aim of this article is to provide a tutorial on one-stage MASEM and a practical guide to webMASEM. We will pay specific attention to how the data should be structured and prepared for webMASEM, because mistakes in this step may lead to faulty results without receiving an error message. The use of webMASEM is illustrated with an analysis of a meta-analytic path model in which the path coefficients are moderated by a study-level variable, a meta-analytic factor model in which the factor loadings are moderated by a study-level variable, and a meta-analytic panel model in which the effects are moderated by a study-level variable. All used datafiles and R scripts are available online.

How to Compare Psychometric Factor and Network Models.

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.