Log-likelihood-based pseudo-R2 in logistic regression : deriving sample-sensitive benchmarks


Hemmert, Giselmar A. J. ; Edinger-Schons, Laura Marie ; Wieseke, Jan ; Schimmelpfennig, Heiko



DOI: https://doi.org/10.1177/0049124116638107
URL: http://journals.sagepub.com/doi/10.1177/0049124116...
Additional URL: https://www.researchgate.net/publication/298899419...
Document Type: Article
Year of publication: 2018
The title of a journal, publication series: Sociological Methods & Research : SMR
Volume: 47
Issue number: 3
Page range: 507-531
Place of publication: Los Angeles, CA [u.a.]
Publishing house: Sage Publications
ISSN: 0049-1241 , 1552-8294
Publication language: English
Institution: Business School > Sustainable Business (Edinger-Schons 2015-)
Subject: 330 Economics
Keywords (English): pseudo-R2 , logistic regression , goodness-of-fit , benchmarks , reporting
Abstract: The literature proposes numerous so-called pseudo-R2 measures for evaluating “goodness of fit” 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Hemmert, Giselmar A. J. ; Edinger-Schons, Laura Marie ORCID: 0000-0002-8981-3379 ; Wieseke, Jan ; Schimmelpfennig, Heiko (2018) Log-likelihood-based pseudo-R2 in logistic regression : deriving sample-sensitive benchmarks. Sociological Methods & Research : SMR Los Angeles, CA [u.a.] 47 3 507-531 [Article]


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