This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by DUBIN, J. A. Articles by RIVERS, D. Search for Related Content Social Bookmarking                         What's this?

Selection Bias in Linear Regression, Logit and Probit Models

California Institute of Technology

DOUGLAS RIVERS

Stanford University

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.

Sociological Methods & Research, Vol. 18, No. 2-3, 360-390 (1989)
DOI: 10.1177/0049124189018002006


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				J.-P. Schrapler Respondent Behavior in Panel Studies: A Case Study for Income Nonresponse by Means of the German Socio-Economic Panel (SOEP) Sociological Methods Research, August 1, 2004; 33(1): 118 - 156. [Abstract] [PDF]