  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.
