%0 Journal Article
%T Log
%A Giselmar A. J. Hemmert
%A Heiko Schimmelpfennig
%A Jan Wieseke
%A Laura M. Schons
%J Sociological Methods & Research
%@ 1552-8294
%D 2018
%R 10.1177/0049124116638107
%X The literature proposes numerous so-called pseudo-R2 measures for evaluating ˇ°goodness of fitˇ± in regression models with categorical dependent variables. Unlike ordinary least square-R2, log-likelihood-based pseudo-R2s do not represent the proportion of explained variance but rather the improvement in model likelihood over a null model. The multitude of available pseudo-R2 measures and the absence of benchmarks often lead to confusing interpretations and unclear reporting. Drawing on a meta-analysis of 274 published logistic regression models as well as simulated data, this study investigates fundamental differences of distinct pseudo-R2 measures, focusing on their dependence on basic study design characteristics. Results indicate that almost all pseudo-R2s are influenced to some extent by sample size, number of predictor variables, and number of categories of the dependent variable and its distribution asymmetry. Hence, an interpretation by goodness-of-fit benchmark values must explicitly consider these characteristics. The authors derive a set of goodness-of-fit benchmark values with respect to ranges of sample size and distribution of observations for this measure. This study raises awareness of fundamental differences in characteristics of pseudo-R2s and the need for greater precision in reporting these measures
%K pseudo-R2
%K logistic regression
%K goodness-of-fit
%K benchmarks
%K reporting
%U https://journals.sagepub.com/doi/full/10.1177/0049124116638107