What are the consequences of ignoring cross-loadings in bifactor models? A simulation study assessing parameter recovery and sensitivity of goodness-of-fit indices.

ABSTRACT: Bifactor latent models have gained popularity and are widely used to model construct multidimensionality. When adopting a confirmatory approach, a common practice is to assume that all cross-loadings take zero values. This article presents the results of a simulation study exploring the impact of ignoring non-zero cross-loadings on the performance of confirmatory bifactor analysis. The present work contributes to previous research by including study conditions that had not been examined before. For instance, a wider range of values of the factor loadings both for the group factors and the cross-loadings is considered. Parameter recovery is analyzed, but the focus of the study is on assessing the sensitivity of goodness-of-fit indices to detect the model misspecification that involves ignor

SUBMITTER: Ximenez C 

PROVIDER: S-EPMC9462382 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature