Statistiscal Inference of Latent Structures

Statistical Inference of Latent Structures

Welcome to the assistant professorship of Statistical Inference on Latent Structures!

Many of the most critical phenomena in empirical research—such as cognitive abilities, psychological traits, social attitudes, or economic risk—cannot be observed directly. These unobserved constructs form the hidden architecture behind observable data, requiring specialized statistical methods to uncover and understand them reliably. Because measurement error and unobserved heterogeneity are omnipresent in the social and behavioral sciences, valid statistical inference is essential to bridge the gap between abstract theoretical concepts and empirical reality, ensuring that scientific conclusions are both robust and reproducible.

Our primary methodological framework to address these challenges is Structural Equation Modeling (SEM). SEM can be used to model various types of latent structures: on the one hand, latent structures as the underlying cause of observable variables on the other hand, latent structures that are formed and constituted by observable variables. By advancing the statistical methodology for both conceptualizations, our research aims to provide more accurate tools for empirical data analysis and theory testing.