Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling: A Bifactor Perspective

TLDR: In this article, the authors consider several indices to indicate whether multidimensional data are "unidimensional enough" to fit with a unidimensional measurement model, especially when the goal is to avoid excessive bias in structural parameter estimates.

Abstract: In this study, the authors consider several indices to indicate whether multidimensional data are “unidimensional enough” to fit with a unidimensional measurement model, especially when the goal is to avoid excessive bias in structural parameter estimates. They examine two factor strength indices (the explained common variance and omega hierarchical) and several model fit indices (root mean square error of approximation, comparative fit index, and standardized root mean square residual). These statistics are compared in population correlation matrices determined by known bifactor structures that vary on the (a) relative strength of general and group factor loadings, (b) number of group factors, and (c) number of items or indicators. When fit with a unidimensional measurement model, the degree of structural coefficient bias depends strongly and inversely on explained common variance, but its effects are moderated by the percentage of correlations uncontaminated by multidimensionality, a statistic that rise...