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

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 fr...

Full description

Saved in:
Bibliographic Details
Published in:American journal of political science 2010-07, Vol.54 (3), p.815-835
Main Authors: Katz, Jonathan N., Katz, Gabriel
Format: Article
Language:English
Subjects:
AJPS WORKSHOPS
Bayesian analysis
Bayesian method
Data collection
Elections
Error correction models
Estimating techniques
Estimation
Estimation methods
Information analysis
Markov analysis
Markov Process
Measurement
Missing data
Modeling
Monte Carlo simulation
Musical intervals
Parametric models
Political science
Presidential elections
Self reports
Survey data
U.S.A
Values
Voter behavior
Voter Turnout
Voting
Voting turnout
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0092-5853
1540-5907
DOI:10.1111/j.1540-5907.2010.00462.x