Removing incomplete cases is so much easier than multiple imputation; why can't I just do that?

The shortcomings of various case-deletion strategies have been well documented (e.g. Little & Rubin, 1987). If the discarded cases form a representative and relatively small portion of the entire dataset, then case deletion may indeed be a reasonable approach. However, case deletion leads to valid inferences in general only when missing data are missing completely at random in the sense that the probabilities of response do not depend on any data values observed or missing. In other words, case deletion implicitly assumes that the discarded cases are like a random subsample. When the discarded cases differ systematically from the rest, estimates may be seriously biased. Moreover, in multivariate problems, case deletion often results in a large portion of the data being discarded and an unacceptable loss of power.


see this,


http://sites.stat.psu.edu/~jls/mifaq.html#cc

mi是指？ 建议楼主有时还是把英文全名列上。【虽说 您应该是指multiple imputation】

1.当然是mi值得用时，就会用上阿！ 【还是看您资料的状况而定】
2. Stata手册的MI值得参考 【这是Stata11版相当重要的特色】
    Cameron 与 Trivedi 那本微观经济计量学 最后一章 值得参考。

主要还是看遗漏机制，在某些遗漏机制下，不理会也是不错的方法，
另外，插补的方法相当多，mi只不过是其中一个，除Stata外，我见过SAS也可执行，
即便插补很重要，但遗漏的比率不高，譬如5%及以下【Schafer, 1996】，该删的就删，也无伤。

paper是很专业的，但容易无法一一且有系统的介绍，
我建议还是读书和Stata手册才比较有感觉。
【当然，如果您喜欢速成，找个新点的paper，读读文献发展也是不错的，但要小心，切莫迷失焦点】

谢谢楼上 专业！

量化数据分析:通过社会研究检验想法   Donald J.Treiman (作者)

第8章 缺失数据的多重填补法
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引言
一个具体例子：在俄罗斯，文化资本对教育获得的影响
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