Group Heterogeneity Assessment for Multilevel Models
This work addresses a practical bottleneck for researchers using multilevel models in fields like statistics or social sciences, though it appears incremental as it builds on existing model selection methods.
The authors tackled the challenge of selecting relevant group coefficients in multilevel models due to the large number of possible configurations, proposing a framework to assess group heterogeneity, which reliably identified relevant components in simulated and real data.
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.