Sanders, ElizabethZheng, Zixie2025-08-012025-08-012025-08-012025Zheng_washington_0250O_28265.pdfhttps://hdl.handle.net/1773/53532Thesis (Master's)--University of Washington, 2025Reporting effect sizes is an important staple of modern research, as they facilitate the quantification of variable relations for meta-analyses irrespective of operational scales used, enable policymakers to understand practical returns on a program investment, help alleviate reliance on null hypothesis testing, and are an essential ingredient for planning research sample sizes. In single-level logistic regression, it is well-known that traditional variance-explained metrics like those in linear regression are not possible; instead, pseudo-R^2 measures that capture how much better a model with regressors fits compared to a null model are in wide use and are available in popular statistical computing packages. While Nakagawa et al. (2017) have proposed a McKelvey-Zavoina-type metric, there have been no systematic studies of effect sizes for multilevel (random effects) logistic regression models. Further, our systematic review of educational and psychological applied literature over the past five years indicates that most research employing multilevel logistic models does not report any effect sizes – likely due to the gap in the methodological literature. The present study therefore uses simulation to compare the performance of pseudo-R^2 metrics for multilevel logistic regression models, including the McKelvey-Zavoina proposed by Nakagawa, as well as a new adjusted McKelvey-Zavoina metric, for 2-level random intercept models with varying ICCs, numbers of clusters, and cluster sizes. Those results indicate that the adjusted-McKelvey-Zavoina metric was best at reproducing the underlying data-generating R^2 value. Limitations and future research directions are discussed.application/pdfen-USCC BY-NC-NDEffect sizeGeneralized linear modelMultilevel logistic regressionPseudo-R-squareRandom effects modelsStatisticsEducational tests & measurementsEducation - SeattleThesis