Group Heterogeneity Assessment for Multilevel Models

TL;DR

This paper introduces a flexible framework to efficiently assess group heterogeneity in multilevel data, aiding in model selection and improving analysis accuracy, demonstrated through simulations and real data.

Contribution

It proposes a novel, adaptable method for evaluating differences between groups in multilevel models, facilitating better model specification.

Findings

Framework reliably identifies relevant multilevel components

Effective in both simulated and real datasets

Enhances model accuracy and interpretability

Abstract

Many data sets contain an inherent multilevel structure, for example, because of repeated measurements of the same observational units. Taking this structure into account is critical for the accuracy and calibration of any statistical analysis performed on such data. However, the large number of possible model configurations hinders the use of multilevel models in practice. In this work, we propose a flexible framework for efficiently assessing differences between the levels of given grouping variables in the data. The assessed group heterogeneity is valuable in choosing the relevant group coefficients to consider in a multilevel model. Our empirical evaluations demonstrate that the framework can reliably identify relevant multilevel components in both simulated and real data sets.