Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers.

ABSTRACT: Standard corrections for missing data rely on the strong and generally untestable assumption of missing at random. Heckman-type selection models relax this assumption, but have been criticized because they typically require a selection variable which predicts non-response but not the outcome of interest, and can impose bivariate normality. In this paper we illustrate an application using a copula methodology which does not rely on bivariate normality. We implement this approach in data on HIV testing at a demographic surveillance site in rural South Africa which are affected by non-response. Randomized incentives are the ideal selection variable, particularly when implemented ex ante to deal with potential missing data. However, elements of survey design may also provide a credible method

SUBMITTER: McGovern ME 

PROVIDER: S-EPMC6167756 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature