583 – Advances in Multiple Imputation Methodology
Investigating the Bias of Alternative Statistical Inference Methods in Sequential Mixed-Mode Surveys
Steven G. Heeringa
University of Michigan
Tuba Suzer-Gurtekin
University of Michigan
Richard Valliant
University of Maryland
Sequential mixed-mode surveys combine different data collection modes sequentially to reduce nonresponse bias under certain cost constraints. However, as a result of nonignorable mode effects, nonrandom mixes of modes may yield unknown bias properties for population estimates such as means and totals. The assumption of ignorable mode effects governs the existing inference methods for sequential mixed-mode surveys. The objective of this paper is to describe and empirically evaluate the proposed multiple imputation estimation methods that account for both nonresponse and nonrandom mixtures of modes in a mixed-mode survey. This paper presents some empirical and simulation results for the bias of mean wage and salary income based on the public-use Current Population, 1973, Social Security Records Exact Match data.
