Journal of Electoral Studies

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Selection Bias Models on Election Prediction

Ying-Lung Chou and Shing-Yuan Sheng
2005 / 11
Volume 12, No.2

pp.1-44
10.6612/tjes.2005.12.02.01-44
Paper

Abstract

When predicting elections, researchers always face the difficulty that some respondents do not answer the question of vote choice. However, including only those who give a clear answer on vote choice, researchers might have the problem of selection bias. This article tries to assess the effects of selection bias on vote choice model, correct the wrong estimates resulting from the bias, and predict elections accurately. In this article, we apply a bivariate selection bias model-- developed by Dubin and Rivers-- to five different elections. The research findings show that if researchers include only those respondents who give a clear answer on vote choice question, they might take a risk of overestimating the effects of independent variables on vote choice. This is because respondents who give a clear answer on the question of vote choice may also have definite and strong political preferences, and they are quite different from those who do not give a clear answer. After correcting the estimating errors resulting from selection bias, we might predict election outcome accurately. The largest predicting error in four elections is 1.16%. It is less than sampling error. The model cannot do a better job in only one election. Fortunately, it does not do a worse job, either. The overall result shows that the selection bias model is a reliable tool in predicting elections.

Keywords

election prediction

selection bias model

nonresponse in survey