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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.1
- 수록면
- 1 - 9 (9page)
- DOI
- 10.4178/epih.e2022096
이용수
초록· 키워드
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OBJECTIVES: The Korea National Health and Nutrition Examination Survey (KNHANES) is a public health survey that as sesses individuals’ health and nutritional status and monitors the prevalence of major chronic diseases. In general, sampling weights are adjusted for unit non-responses and imputation is conducted for item non-responses. In this study, we proposed strategies for imputing item non-responses in the KNHANES in order to improve the usefulness of data, minimize bias, and in crease statistical power.
METHODS: After applying logical imputation, we adopted 2 separate imputation methods for each variable type: unweighted sequential hot-deck imputation for categorical variables and sequential regression imputation for continuous variables. For variance estimation, multiple imputations were applied to the continuous variables. To evaluate the performance of the pro posed strategies, we compared the marginal distributions of imputed variables and the results of multivariable regression analy sis for the complete-case data and the expanded data with imputed values, respectively.
RESULTS: When comparing the marginal distributions, most non-responses were imputed. The multivariable regression coeffi cients presented similar estimates; however, the standard errors decreased, resulting in statistically significant p-values. The pro posed imputation strategies may cope with the loss of precision due to missing data, thus enhancing statistical power in analyses of the KNHANES by providing expanded data with imputed values.
CONCLUSIONS: The proposed imputation strategy may enhance the utility of data by increasing the number of complete cases and reducing the bias in the analysis, thus laying a foundation to cope with the occurrence of item non-responses in further sur veys
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