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Determining Sufficient Number of Imputations Using Variance ofImputation Variances: Data from 2012 NAMCS Physician Workflow MailSurvey

机译:使用方差确定足够的插补数插补差异:2012年NAMCS医师工作流邮件中的数据调查

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摘要

How many imputations are sufficient in multiple imputations? The answer given by different researchers varies from as few as 2 - 3 to as many as hundreds. Perhaps no single number of imputations would fit all situations. In this study, η, the minimally sufficient number of imputations, was determined based on the relationship between m, the number of imputations, and ω, the standard error of imputation variances using the 2012 National Ambulatory Medical Care Survey (NAMCS) Physician Workflow mail survey. Five variables of various value ranges, variances, and missing data percentages were tested. For all variables tested, ω decreased as m increased. The m value above which the cost of further increase in m would outweigh the benefit of reducing ω was recognized as the η. This method has a potential to be used by anyone to determine η that fits his or her own data situation.
机译:多个插补中有多少插补就足够了?不同研究人员给出的答案范围从2至3到数百不等。也许没有一个单一的推论能适合所有情况。在这项研究中,使用2012年国家门诊医疗调查(NAMCS)医师工作流邮件,根据m,归因数和ω(归因差异的标准误)之间的关系,确定了最小的归因数η。调查。测试了各种值范围,方差和丢失数据百分比的五个变量。对于所有测试变量,ω随着m的增加而减小。将m值(在m值之上进一步增加m的成本将超过减小ω的收益)视为η。任何人都可以使用此方法来确定适合他或她自己的数据情况的η。

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