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Selection Bias in Linear Regression, Logit and Probit Models

Dubin, Jeffrey A. and Rivers, Douglas (1989) Selection Bias in Linear Regression, Logit and Probit Models. Sociological Methods & Research, 18 (2-3). pp. 360-390. ISSN 0049-1241. http://resolver.caltech.edu/CaltechAUTHORS:20170906-143926487

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1177/0049124189018002006DOIArticle
http://journals.sagepub.com/doi/10.1177/0049124189018002006PublisherArticle
http://resolver.caltech.edu/CaltechAUTHORS:20170905-134432262Related ItemWorking Paper
Additional Information:© 1989 SAGE Publications. First Published November 1, 1989.
Record Number:CaltechAUTHORS:20170906-143926487
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170906-143926487
Official Citation:Selection Bias in Linear Regression, Logit and Probit Models JEFFREY A. DUBIN, DOUGLAS RIVERS Sociological Methods & Research Vol 18, Issue 2-3, pp. 360 - 390 10.1177/0049124189018002006
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81207
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 Sep 2017 21:46
Last Modified:09 Nov 2017 22:24

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