An imputation approach for handling mixed-mode surveys
Seunghwan Park, Jae Kwang Kim, Sangun Park
The Annals of Applied Statistics · 2016 · Volume 10 · Issue 2
Abstract
Mixed-mode surveys are becoming more popular recently because of their convenience for users, but different mode effects can complicate the comparability of the survey results. Motivated by the Private Education Expenditure Survey (PEES) of Korea, we propose a novel application of fractional imputation to handle mixed-mode survey data. The proposed method is applied to create imputed values of the unobserved counterfactual outcome variables in the mixed-mode surveys. The proposed method is directly applicable when the choice of survey mode is self-selected. Variance estimation using Taylor linearization is developed. Results from a limited simulation study are also presented.
Keywords
Counterfactual outcome, fractional imputation, measurement error model, missing data, survey sampling
Enable JavaScript for citation tools and additional paper interactions.