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Survey Participation, Nonresponse Bias, Measurement Error Bias, and Total Bias
KRISTEN OLSON is a graduate student in the program of survey methodology at the University of Michigan.
Address correspondence to the author; e-mail: olsok{at}isr.umich.edu.
A common hypothesis about practices to reduce survey nonresponse is that those persons brought into the respondent pool through persuasive efforts may provide data filled with measurement error. Two questions flow from this hypothesis. First, does the mean square error of a statistic increase when sample persons who are less likely to be contacted or cooperate are incorporated into the respondent pool? Second, do nonresponse bias estimates made on the respondents, using survey reports instead of records, provide accurate information about nonresponse bias? Using a unique data set, the Wisconsin Divorce Study, with divorce records as the frame and questions about the frame information included in the questionnaire, this article takes a first look into these two issues. We find that the relationship between nonresponse bias, measurement error bias, and response propensity is statistic-specific and specific to the type of nonresponse. Total bias tends to be lower on estimates calculated using all respondents, compared with those with only the highest contact and cooperation propensities, and nonresponse bias analyses based on respondents yield conclusions similar to those based on records. Finally, we find that error properties of statistics may differ from error properties of the individual variables used to calculate the statistics.