지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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The analytical focus of this study centers on how to treat the missing values in public administration research. Examining the mechanism of missing values in data gathering, the paper constructs a regression model for ordinal categorical data of the survey by the KOSSDA numbered A1-205-0040. Applying four solutions for missing values such as complete case analysis method, EM technique, regression methodology, and multiple imputation, this paper tries to approach the focus of the research.
Results confirm that the complete case analysis method can not be an appropriate solution for missing values in reality. The higher the percentage of missing values in data set, the riskier the application of this solution. On the contrary, the other three solutions turn out to be the appropriate remedies for missing values in general. Especially, the usefulness of EM technique and regression methodology can be highlighted. In short, future research should accommodate the necessity/accessibility of scientific techniques as well as investigate the mechanism of missing values in a given data set.
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참고문헌 (21)
참고문헌 신청
Allison, P. D / 2002 / Missing Data / Sage. Thousand Oaks: CA
Berk, Richard A / 1983 / An Introduction to Sample Selection Bias in Sociological Data / American Sociological Review 48 : 389 ~ 398
Dempster, A. P. / 1977 / Maximum Likelihood Estimation form Incomplete Data vis the EM Algorithm (with Discussion) / Journal of Royal Statistical Association B39 : 1 ~ 38
Diggle, P. / 1994 / Informative dropout in longitudinal data analysis / Applied Stat 43 : 49 ~ 94
Gibson, N. / 2003 / Treatment of Missing Data at the Second Level of Hierarchical Linear Models / Educational and Psychological Measurement 63 (2) : 204 ~ 238
Berk, Richard A / 1983 / An Introduction to Sample Selection Bias in Sociological Data / American Sociological Review 48 : 389 ~ 398
Dempster, A. P. / 1977 / Maximum Likelihood Estimation form Incomplete Data vis the EM Algorithm (with Discussion) / Journal of Royal Statistical Association B39 : 1 ~ 38
Diggle, P. / 1994 / Informative dropout in longitudinal data analysis / Applied Stat 43 : 49 ~ 94
Gibson, N. / 2003 / Treatment of Missing Data at the Second Level of Hierarchical Linear Models / Educational and Psychological Measurement 63 (2) : 204 ~ 238
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