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Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating Turnout

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  • Jonathan N. Katz
  • Gabriel Katz

Abstract

Misreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies.

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  • Jonathan N. Katz & Gabriel Katz, 2010. "Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating Turnout," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 815-835, July.
    • Handle: RePEc:wly:amposc:v:54:y:2010:i:3:p:815-835
    DOI: 10.1111/j.1540-5907.2010.00462.x
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    1. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
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    3. Carlos Daniel Paulino & Paulo Soares & John Neuhaus, 2003. "Binomial Regression with Misclassification," Biometrics, The International Biometric Society, vol. 59(3), pages 670-675, September.
    4. Gordon J. Prescott & Paul H. Garthwaite, 2002. "A Simple Bayesian Analysis of Misclassified Binary Data with a Validation Substudy," Biometrics, The International Biometric Society, vol. 58(2), pages 454-458, June.
    5. Burden, Barry C., 2000. "Voter Turnout and the National Election Studies," Political Analysis, Cambridge University Press, vol. 8(4), pages 389-398, July.
    6. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    7. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    8. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
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    Cited by:

    1. Vincent Mahler & David Jesuit & Piotr Paradowski, 2015. "Electoral Turnout and State Redistribution: A Cross-National Study of 14 Developed Countries," LIS Working papers 633, LIS Cross-National Data Center in Luxembourg.
    2. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Details and Extensions," Sociological Methods & Research, , vol. 46(3), pages 342-369, August.
    3. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    4. Jeffrey Naecker, 2015. "The Lives of Others: Predicting Donations with Non-Choice Responses," Discussion Papers 15-021, Stanford Institute for Economic Policy Research.
    5. Maria Felice Arezzo & Giuseppina Guagnano, 2019. "Misclassification in Binary Choice Models with Sample Selection," Econometrics, MDPI, vol. 7(3), pages 1-19, July.
    6. B. Douglas Bernheim & Daniel Bjorkegren & Jeffrey Naecker & Antonio Rangel, 2013. "Non-Choice Evaluations Predict Behavioral Responses to Changes in Economic Conditions," NBER Working Papers 19269, National Bureau of Economic Research, Inc.
    7. Peter Z. Schochet, 2013. "A Statistical Model for Misreported Binary Outcomes in Clustered RCTs of Education Interventions," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 470-498, October.

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