On Group Comparisons With Logistic Regression Models

By Jouni Kuha, C. Mills. 2020

101 citations recorded by the source corpus.

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the second when models are compared between groups that have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group dependent and can be compared as long as they are correctly estimated.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dedecc08e5747e974c8eaf3e8b362ab8f56fdc59

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In Economics and Econometrics

In Management Science and Operations Research

J. Scott Long

In Statistics and Probability

Citations in the corpus

Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes (2018, background): “Kuha and Mills (2018) argue that these methods are only relevant when the latent outcome is of substantive interest.”