A variety of new statistical methods from the field of machine learning have the potential to offer new impulses for research in the social, educational and behavioral sciences. In this article we focus on one of these methods: model-based recursive partitioning. This algorithmic approach is reviewed and illustrated by means of instructive examples and an application to the Mincer equation. For readers unfamiliar with algorithmic methods, the explanation starts with the introduction of the predecessor method classification and regression trees. With respect to the application and interpretation of model-based recursive partitioning, we address the principle of parsimony and illustrate that the model-based recursive partitioning approach can be employed to test whether a postulated model is in accordance with Ockham's Razor or whether relevant covariates have been omitted. Finally, an overview of available statistical software is provided to facilitate the applicability in social science research.