Bayesian Distributional Latent Variable Models

In psychology and related sciences, a lot of research is concerned with studying latent variables, that is, constructs which are not directly observable. Statistical methods for modeling latent variables based on manifest (observable) indicators are thus crucial to the scientific progress in those fields. Two major interconnected statistical areas dealing with latent variables exist, namely, Item Response Theory (IRT) and Structural Equation Modeling (SEM). Although the two fields are closely connected, the frontiers of IRT and SEM have developed in somewhat different directions.

A combination of these two major frontiers would enable researchers to tackle a lot of advanced psychological research questions at the intersection of psychometrics, personnel psychology, cognitive psychology, and applied psychology. In order for us to gain better insights into behavioral and cognitive processes, their mathematical approximations should match the processes’ complexity in both overall distributional form and its components that are expressed as complex functions of predicting variables.

This project aims to develop a framework for Bayesian distributional latent variable models that combines the principles of IRT and SEM with the flexibility of distributional regression powered by modern Bayesian estimation methods.

Project Members: Luna Fazio

Funders: German Research Foundation (DFG), TU Dortmund University