Sophia Rabe-Hesketh

The project team developed improved tools for fitting statistical models in complex settings, specifically hierarchical Bayesian models for handling multilevel data including nested and non-nested groupings, latent variables, and large numbers of parameters.

Some statistical models used in education research are complex. This complexity arises in part because the data structures that underlie these statistical models involve multiple nested (i.e., cross-classified multilevel models) and non-nested groupings (i.e., partially-nested designs).

In this project, the researchers developed and tested new approaches to support applied researchers' use of Bayesian modal estimation (also called maximum a posteriori estimation) for multilevel models. The goal of this methodological innovation was to make it easier for applied researchers to avoid nonsensical results when they use multilevel models.