On Group Comparisons With Logistic Regression Models

Jouni Kuha and Colin Mills

Sociological Methods & Research, 2020, vol. 49, issue 2, 498-525

Abstract: 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.

Keywords: logit models; probit models; regression modeling; latent variables; average treatment effects (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:49:y:2020:i:2:p:498-525

DOI: 10.1177/0049124117747306

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