Revisiting the Bi-Factor Model: Can Mixture Modeling Help Assess Its Applicability?. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Raykov, Tenko; Marcoulides, George A.; Menold, Natalja; Harrison, Michael
• Is Part Of: Structural equation modeling. Volume 26:Issue 1(2019)
• Page Start: 110
• Page End: 118
• Publication Date: 2019-01-02
• Subjects: bi-factor model -- global factor -- latent class -- local factor -- mixture -- unobserved heterogeneity; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1436441
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : This article revisits from the perspective of finite mixture modeling the increasingly popular bi-factor model applied in contemporary behavioral and social research. It is pointed out that in a population with substantial unobserved heterogeneity resulting from a mixture of latent classes, and where the unidimensional model holds along with models that markedly differ from the bi-factor model, the latter may turn out to be spuriously plausible. To raise caution about this possibility, an example of a 3-class setting is provided, where correspondingly (a) the single (global) factor model, (b) a model with a global factor and a single local factor, and (c) a model with a global factor and two local factors hold, while the bi-factor model with a global factor and three local factors is also plausible for the analyzed data overall. Examination of population heterogeneity prior to testing the bi-factor model is therefore recommendable in empirical research, in order to avoid spurious findings of its plausibility when ignoring substantial unobserved heterogeneity in studied populations.