Specification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contribute

Abstract

Although structural equation model (SEM) is a powerful and widely applied tool particularly in social sciences, few studies have explored how SEM and statistical learning methods can be combined. The purpose of this paper is to explore how gradient component-wise boosting (GCB) can contribute to item selection. We ran 200 regressions with different farmer psychological variables collected to explain variation in an animal welfare indicator (AWI). The most frequently selected variables from the regressions were selected to build a SEM to explain variation in the AWI. The results show that boosting selects relevant items for a SEM.