mlmeval: Complementary tools for an integrated approach to multilevel model selection

Model evaluation is an unavoidable facet of multilevel modeling (MLM). Current guidance encourages researchers to focus on two overarching model-selection factors: model fit and model adequacy (McCoach et al. 2022). Researchers routinely use information criteria to select from a set of competing models and assess the relative fit of each candidate model to their data. However, researchers must also consider the ability of their models and their various constituent parts to explain variance in the outcomes of interest (i.e., model adequacy). Prior methods for assessing model adequacy in MLM are limited. Therefore, Rights and Sterba (2019) proposed a new framework for decomposing variance in MLM to estimate R2 measures. Yet there is no Stata package that implements this framework. Thus, we propose a new Stata package that computes both (1) a variety of model fit criteria and (2) the model adequacy measures described by Rights and Sterba to facilitate multilevel model selection for Stata users. The goal of this package is to provide researchers with an easy way to utilize a variety of complementary methods to evaluate their multilevel models.