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Selection Bias in Linear Regression, Logit and Probit Models

Author

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  • JEFFREY A. DUBIN

    (California Institute of Technology)

  • DOUGLAS RIVERS

    (Stanford University)

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.

Suggested Citation

  • Jeffrey A. Dubin & Douglas Rivers, 1989. "Selection Bias in Linear Regression, Logit and Probit Models," Sociological Methods & Research, , vol. 18(2-3), pages 360-390, November.
    • Handle: RePEc:sae:somere:v:18:y:1989:i:2-3:p:360-390
    DOI: 10.1177/0049124189018002006
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    References listed on IDEAS

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    3. Fieger, Peter, 2013. "Using Student Outcome Survey Data for institutional performance measurement," MPRA Paper 76839, University Library of Munich, Germany.
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    5. Becker, Rolf, 2000. "Determinanten der Studierbereitschaft in Ostdeutschland : eine empirische Anwendung der Humankapital- und Werterwartungstheorie am Beispiel sächsicher Abiturienten in den Jahren 1996 und 1998 (Determi," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 33(2), pages 261-276.
    6. Birhanu, Mulugeta Yitayih & Girma, Anteneh & Puskur, Ranjitha, 2017. "Determinants of success and intensity of livestock feed technologies use in Ethiopia: Evidence from a positive deviance perspective," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 15-25.
    7. Joshua Clinton & John Lapinski, 2004. "Targeted advertising and voter turnout: An experimental study of the 2000 presidential election," Natural Field Experiments 00226, The Field Experiments Website.
    8. Maria Felice Arezzo & Giuseppina Guagnano, 2018. "Response-Based Sampling for Binary Choice Models With Sample Selection," Econometrics, MDPI, vol. 6(1), pages 1-17, March.
    9. Cheng, Tyrone C. & Lo, Celia C., 2018. "Racial disparities in the proportion of needed services maltreated children received," Children and Youth Services Review, Elsevier, vol. 94(C), pages 72-81.
    10. Jessica Pearlman & Lisa D. Pearce & Dirgha J. Ghimire & Prem Bhandari & Taylor Hargrove, 2017. "Postmarital Living Arrangements in Historically Patrilocal Settings: Integrating Household Fission and Migration Perspectives," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1425-1449, August.
    11. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.
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    13. Valérie Revest & Alessandro Sapio, 2016. "Graduation and sell-out strategies in the Alternative Investment Market," Discussion Papers 4_2016, CRISEI, University of Naples "Parthenope", Italy.

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