Mixture multigroup SEM voor het vergelijken van structurele relaties over vele groepen

Social scientists often examine the relationships between two or more latent variables or constructs, and Structural Equation Modeling (SEM) is the state-of-the-art for doing so. When comparing these structural relations among many groups, they likely differ across the groups. However, it is equally likely that some groups share the same relations and that clusters of groups emerge in terms of the relations between the latent variables. For validly comparing the latent variables' relations among groups, the measurement of the latent variables should be invariant across the groups (i.e., measurement invariance), whereas often at least some measurement parameters differ across the many groups. Restricting these measurement parameters to be equal across groups, when they are not, causes the structural relations to be estimated incorrectly and thus invalidates their comparison. Therefore, this PhD project aims to develop, evaluate, and apply a novel method to capture differences and similarities in structural relations while accounting for the reality of measurement non-invariance. We propose mixture multigroup SEM (MMG-SEM) to cluster groups focused entirely on the structural relations by making them cluster-specific, while allowing for the measurement parameters to be (partially) group-specific. In this way, MMG-SEM disentangles differences in structural relations from differences in measurement parameters.