统计The Analysis of Social Science Data with Missing Values

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The Analysis of Social Science Data with Missing Values

文献名称	The Analysis of Social Science Data with Missing Values
文献作者	RODERICK J. A. LITTLE;DONALD B. RUBIN
作者所在单位	University of California at Los Angeles;Harvard University
文献分类	已发表文献
学科一级分类	统计
学科二级分类	统计学
文献摘要	Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing data mechanism and multiple imputation, are applied to the bivariate problem. General properties of these methods are outlined, and applications to more complex missing-data problems are discussed. The EM algorithm, a convenient method for computing maximum likelihood estimates in missing-data problems, is described and applied to two common models, the multivariate normal model for continuous data and the multinomial model for discrete data. Multiple imputation under explicit or implicit models is recommended as a method that retains the advantages of imputation and overcomes its limitations.
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关键字	Social Science Data ; Missing Values
发表所在刊物（或来源）	SOCIOLOGICAL METHODS AND RESEARCH, Vol. 18, Nos. 2 & 3, November 1989/February 1990 292-326; ~ 1989 Sage Publications, Inc.
发表时间	November 1989/February 1990
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