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In psychology and the social sciences, it is often of interest how complex structural relations among variables are moderated by profiles or combinations of persons’ attributes. Some state-of-the-art methods, such as latent class analysis, are well-suited for this purpose. However, they can lead to methodological problems (e.g., convergence issues) or interpretative difficulties (e.g., due to nondistinctive profiles). For these cases, two other approaches combining structural equation modeling with machine learning have been proposed, namely structural equation model (SEM) trees and SubgroupSEM. These approaches allow for exploration of how parameters of a SEM differ depending on combinations of a person's attributes. This can be useful for generating hypotheses for future research. In this paper, we provide an empirical illustration of SubgroupSEM using an example from research on the development of commitment in interdisciplinary study programs in German higher education and identify combinations of vocational interests related to exceptional development.

In this paper, we propose a classification scheme for study programs. The classification scheme makes it possible to classify study programs in respect of their interdisciplinary design. Interdisciplinary study programs do not fit particularly well into existing classification systems. The proposed classification scheme differentiates between interdisciplinary, multidisciplinary and monodisciplinary programs. It has been applied to the open responses of first-year students of starting cohort 5. The resulting classification is available in NEPS Scientific-Use-Files 13-0-0. We present the classification scheme, the coding process, and an overview of the results., NEPS Survey Papers