Abstract: Survey data often include missing values due to nonresponse. Especially, sensitive questions such as questions about income or assets tend to show higher percentage of missing values. When missing values occur, complete-case analysis may lead to biased estimates of parameters. Korean Longitudinal Study of Aging(KLoSA) is a longitudinal study to evaluate aging trends in the Korean population and apply the results to the social welfare and labor policy. KLoSA collected baseline data in 2006 and first follow-up data in 2008. We conducted multiple imputation based on hotdeck to handle missing values in KLoSA baseline and first follow-up data. In this study, we explain the imputation strategy adopted for filling in missing values of major outcome variables in KLoSA.