Selection Bias in Linear Regression, Logit and Probit Models

Jeffrey A. Dubin and Douglas Rivers
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Jeffrey A. Dubin: California Institute of Technology
Douglas Rivers: Stanford University

Sociological Methods & Research, 1989, vol. 18, issue 2-3, 360-390

Abstract: Missing data are common in observational studies due to self-selection of subjects. Missing data can bias estimates of linear regression and related models. The nature of selection bias and econometric methods for correcting it are described. The econometric approach relies upon a specification of the selection mechanism. We extend this approach to binary logit and probit models and provide a simple test for selection bias in these models. An analysis of candidate preference in the 1984 U.S. presidential election illustrates the technique.

Date: 1989
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:18:y:1989:i:2-3:p:360-390

DOI: 10.1177/0049124189018002006

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