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
影响因子：
7
通讯作者：
L. Collins;J. Schafer;Chi-Ming Kam
中科院分区：
文献类型：
--
作者：
L. Collins;J. Schafer;Chi-Ming Kam
两类现代缺失数据程序，最大似然（ML）和多重插补（MI），往往会产生类似的结果时，以可比的方式实施。在任何一种方法中，都可以仅为了改进缺失数据程序的目的而包括辅助变量。提出了一个模拟，以评估限制性战略的潜在成本和效益，这使得最低限度地使用辅助变量，与包容性战略，这使得这些变量的自由使用。仿真结果表明，包容性策略是非常可取的。采用包容性战略，不仅可以减少无意中遗漏一个重要的缺失原因的可能性，而且还可以在提高效率和减少偏见方面取得显著成效，而成本很小。正如在当前可用的软件中实现的那样，ML方法倾向于鼓励使用限制性策略，而MI方法使得使用包容性策略相对简单。
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.