Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach

The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PA_PCA) or principal axis, and exploratory graph analysis with Louvain clustering (EGA_LV). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PA_PCA (PAP_CA-FS) and EGA_LV (EGA_LV-FS). Additionally, we examined the direct multilevel solution provided by EGA_LV. All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGA_LV and PA_PCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAP_CA-FS and EGA_LV-FS showed a close to perfect accuracy across all the conditions, while EGA_LV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGA_LV (group factors) and EGA_LV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2025 APA, all rights reserved).