Investigating the Bias of Alternative Statistical Inference Methods in Sequential Mixed-Mode Surveys

• Activity Number: 583
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Tuba Suzer-Gurtekin*+ and Steven G. Heeringa and Richard Valliant
• Companies: University of Michigan and University of Michigan and University of Maryland
• Keywords: mixed-mode surveys ; multiple imputation ; mode effects ; selection model

Abstract

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.