MLM_GOF: Stata module for computing the goodness-of-fit test after mixed-effects logistic regression

Ariel Linden

Statistical Software Components from Boston College Department of Economics

Abstract: mlm_gof performs a goodness-of-fit test for binary multilevel logistic models fitted by melogit. It extends the grouping-based test of Perera, Sooriyarachchi & Wickramasuriya (2016) and Fernando & Sooriyarachchi (2022) to models with random coefficients (Linden 2026). The test works by dividing observations within each level-2 cluster into G groups based on their conditional predicted probabilities, then testing whether group membership adds explanatory power beyond the fitted model via a joint Wald test on G-1 indicator variables. Under a well-fitting model, the group indicators should be uninformative and the Wald statistic should follow a chi-squared distribution with G-1 degrees of freedom.

Language: Stata
Requires: Stata version 11
Keywords: mixed-effect logistic regression; random intercepts; goodness of fit (search for similar items in EconPapers)
Date: 2026-04-13, Revised 2026-04-23
Note: This module should be installed from within Stata by typing "ssc install mlm_gof". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/m/mlm_gof.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mlm_gof.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mlm_gof.pdf documentation (application/pdf)

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