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Cited by 306

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Selection Bias in Linear Regression, Logit and Probit Models

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DUBIN, JEFFREY A., RIVERS, DOUGLAS·1989·Sociological Methods & Research

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.

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Metadata

Authors
DUBIN, J. A., & RIVERS, D.
Venue
Sociological Methods & Research
Published
1989-11
Volume
18
Issue
2-3
Pages
360-390
ISSN
  • 0049-1241 (ISSN-L)
  • 1552-8294
OpenAlex
W2168741590
Cited by
306
References
2
FWCI
1.84
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Selection Bias in Linear Regression, Logit and Probit Models. https://app.cochat.ai/papers/10.1177/0049124189018002006
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DUBIN, J. A., & RIVERS, D. (1989). Selection Bias in Linear Regression, Logit and Probit Models. Sociological Methods & Research. https://doi.org/10.1177/0049124189018002006

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.