Log-likelihood-based Pseudo- R 2 in Logistic Regression: Deriving Sample-sensitive Benchmarks
The literature proposes numerous so-called pseudo- R 2 measures for evaluating “goodness of fit” in regression models with categorical dependent variables. Unlike ordinary least square- R 2 , log-likelihood-based pseudo- R 2 s do not represent the proportion of explained variance but rather the impr...
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Veröffentlicht in: | Sociological methods & research 2018-08, Vol.47 (3), p.507-531 |
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Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The literature proposes numerous so-called pseudo- R 2 measures for evaluating “goodness of fit” in regression models with categorical dependent variables. Unlike ordinary least square- R 2 , log-likelihood-based pseudo- R 2 s do not represent the proportion of explained variance but rather the improvement in model likelihood over a null model. The multitude of available pseudo- R 2 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- R 2 measures, focusing on their dependence on basic study design characteristics. Results indicate that almost all pseudo- R 2 s 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- R 2 s and the need for greater precision in reporting these measures. |
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ISSN: | 0049-1241 1552-8294 |
DOI: | 10.1177/0049124116638107 |