PUB - Publikationen an der Universität Bielefeld

Structural equation modeling (SEM) is one of the most popular statistical frameworks in the social and behavioural sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In this paper, we present a new approach combining subgroup discovery – a well-established toolkit of supervised learning algorithms and techniques from the field of computer science – with structural equation models. We provide an introduction how subgroup discovery can be applied to detect subgroups with exceptional parameter constellations in structural equation models based on user-defined interestingness measures. Furthermore, technical details on the algorithmic components, efficiency, and further computational aspects are presented. Then, our approach is illustrated with two real-world data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by a short introduction in the R package subgroupsem, which is a viable implementation of our approach for applied researchers.