Abstract
Voter opinion surveys help frame American debate on matters of public policy and can influence legislators’ decisions. Voter surveys generally use one of two sampling methods. Voter file sampling includes information about respondents, but may lack unlisted phone numbers. Random digit dial (RDD) sampling avoids this problem, but lacks information about respondents. Consequently, RDD surveys identifying likely voters based on prior voting behavior must rely on information from respondents. However, the literature notes that over reporting of prior voting behavior is widespread. Thus, RDD likely voter surveys risk including inappropriate respondents. This thesis explores three major questions using a survey of 800 registered voters in Contra Costa County, California. First, is it feasible to predict over reporting using information generally collected during RDD surveys? Second, are over reporters different demographically than true likely voters? Third, does it matter – do the two groups differ on matters of public policy? I found that large numbers of respondents over reported voting history. Multiple regression of the survey data provided little support for the feasibility of predicting over reporting. However, statistical analysis showed that over reporters are significantly different from true likely voters, both demographically and in policy preferences. Consequently, RDD surveys are not likely to reflect the attitudes of true likely voters, and consumers of such surveys risk making policy and law with bad information.