A comparison of inclusive and restrictive strategies in modern missing data procedures
A comparison of inclusive and restrictive strategies in modern missing data procedures
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DOI：
10.1037//1082-989x.6.4.330
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发表时间：
2001-12-01
影响因子：
7
通讯作者：
Kam, CM
中科院分区：
文献类型：
--
作者：
Collins, LM;Schafer, JL;Kam, CM
两类现代缺失数据处理程序，最大似然法(ML)和多重填补法(MI)，在以类似的方式实施时，往往会产生类似的结果。在这两种方法中，都有可能仅出于改进丢失数据程序的目的而包括辅助变量。模拟评估了限制性策略与包容性策略的潜在成本和收益。限制性策略使用的辅助变量最少，而包容性策略则自由使用这些变量。模拟结果表明，包容性战略是最受青睐的战略。有了包容性战略，不仅可以减少疏忽遗漏一个重要原因的机会，而且还有可能在提高效率和减少偏见方面取得显著收益，而只需付出很小的代价。正如在现有软件中实现的那样，ML方法倾向于鼓励使用限制性策略，而MI方法使使用包容性策略变得相对简单。
Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.