Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach

Abstract Although one often has detailed information about participants in a program, the lack of comparable information on non-participants precludes standard qualitative choice estimation. This challenge can be overcome by incorporating a supplementary sample of covariate values from the general population. This paper presents new estimators based on this sampling strategy, which perform comparably to the best existing supplementary sampling estimators. The key advantage of the new estimators is that they readily incorporate sample weights, so that they can be applied to Census surveys and other supplementary data sources that have been generated using complex sample designs. This substantially widens the range of problems that can be addressed under a supplementary sampling estimation framework. The potential for improving precision by incorporating imperfect knowledge of the population prevalence rate is also explored.