국민건강조사에서 무응답 대체에 관한 연구 (A Study On Imputation In Multipurpose Sample Survey : Focus On National Hearth Survey)

서지정보

· 발행기관 : 한국자료분석학회
· 수록지 정보 : Journal of The Korean Data Analysis Society / 10권 / 4호 / 2099 ~ 2111페이지
· 저자명 : 도세록, 엄정국, 이관제

초록

무응답 조정층을 만드는 방법은 여러 가지가 있다. 비모수적으로 층을 만드는 의사결정나무 방법에서 다지분리(multiway split)가 가능한 CHAID(chi-squared automatic interaction detection)에 의하여 분류된 분할층을 무응답 대체 보조변수로 만들어 다목적 표본조사인 "2001년도 국민건강영양조사" 자료를 바탕으로 효과적인 통계적 무응답 처리 방법을 제시하였다. CHAID 수행을 위하여 최대 나무깊이(tree depth) 수, 한 가지에서 분리 node 수, 카이제곱 통계량의 유의확률을 지정하여 나무구조에 의한 분할층을 만들었다. 분산의 과소추정문제를 해결하기 위하여 일반화회귀 추정량(GREG: generalized regression estimator)을 적용하였고, 일반화회귀 추정 효과를 파악하기 위한 모의실험을 통하여 일반화회귀 추정 후의 결과를 비교하였다.

영어초록

Nonresponses are unavoidable in sample survey. In multipurpose survey, nonresponses of survey data prevent doing a integrated data analysis. The primary object of this study is to assess the effective ways to impute nonresponses in multipurpose sample survey. We consider the construction of segmentation classes by decision trees algorithm to choose auxiliary variables for multiple imputation. Because decision trees can combine many kinds of variables and classify them into homogeneous classes. In addition to imputation we propose the post weighting adjustment by Generalized regregssion to protect under estimation of variance.
A simulation study is done based on the data of 2001 Korean National Health Survey, which consists of Family Interview Survey, Health Behavior survey, and Examination Survey. From the Family Interview Survey, we select 12 variables and input them into CHAID(chi-cquare automatic interaction detection). From CHAID, we obtain 10 nonresponse segmentation classes which describing nonresponse propensities for Health Behavior Survey and 15 segmentations for Examination Survey. We use the segmentation classes as the covariates of multiple imputation for the missing values of the two surveys.
In order to evaluate the effects of imputation method, we conduct a simulation and compare the relative biases of the outcomes which use segmentation classes as auxiliarly variables with those of using real variables. We find that segmentation method reduce relative biases a little in both means and variances. Using segmentation classes as covariates in multiple imputation seems to appropriate and overcome the difficulties in building imputation models and choosing auxiliarly variables.
In order to estimate unbiased population total, we use GREG(generalized regregssion estimator) calibration estimator, the effects of GREG calibration are stable increases on variance estimation in Examination Survey, but abrupt in Health Behavior Survey.

참고자료

· 없음