Published August 2, 2017
| Submitted
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Correcting for Survey Misreports using Auxiliary Information with an Application to Estimating Turnout
- Creators
- Katz, Jonathan N.
- Katz, Gabriel
Abstract
Misreporting is a problem that plagues researchers that use survey data. In this paper, 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.
Additional Information
Revised edition. Original date: August 2008Attached Files
Submitted - sswp1294_-_revised.pdf
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sswp1294_-_revised.pdf
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Additional details
- Eprint ID
- 79510
- Resolver ID
- CaltechAUTHORS:20170727-155558097
- Created
-
2017-08-02Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 1294R