Chain graph reduction into power chain graphs

Franco, Víthor Rosa; Barros, Guilherme Wang; Wiberg, Marie; Laros, Jacob Arie

doi:10.5964/qcmb.8383

Cited by 3 publications

(1 citation statement)

References 57 publications

(60 reference statements)

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“…Thus, looking at this pattern, it would not be surprising that PA PCA errors by more than 10 group factors in structures with five or six general factors. A last reason that may impact the performance of PA PCA is the presence of causal relations between the group factors (Franco et al, 2022).…”

Section: The Hexaco-100 Inventorymentioning

confidence: 99%

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

Jiménez¹,

Abad²,

García-Garzón³

et al. 2023

Psychological Methods

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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 (PA PCA-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 crossloadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PA PCA-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.

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Section: The Hexaco-100 Inventorymentioning

confidence: 99%