Multiple Imputation for Missing Data in the KLoSA Study

• 발행기관: 한국자료분석학회
• 저자명: 송주원(Juwon Song) 이수영(Soo Young Lee) 윤초롱(Chorong Yoon) 윤라헬(Rahyel Yoon) 송경화(Kyung-hwa Song) 김병원(Byungwon Kim) 이혜정(HyeJung Lee) 장지연(Jiyeun Chang)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.9 No.5, 2085~2095쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2007.10.30

영문 초록

Most survey data include missing values due to nonresponse. Especially, sensitive questions such as income or assets tend to show higher percentage of missing values. When missing values occur, complete-case analysis may lead to biased estimates in 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. In 2006, KLoSA collected baseline data. We conduct multiple imputation based on hotdeck to handle missing values in the KLoSA baseline data. In this study, we explain the imputation method for filling in missing values and discuss the results of imputation.

목차

1. Introduction

2. KLoSA Study

3. Multiple Imputation for the KLoSA study

4. Results

5. Discussion

References

키워드

Multiple Imputation Missing Data Hotdeck Unfolding Brackets