Mazza Gina L, Enders Craig K, Ruehlman Linda S
a Department of Psychology Arizona State University.
b Goalistics, LLC.
Multivariate Behav Res. 2015;50(5):504-19. doi: 10.1080/00273171.2015.1068157.
Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002 ; Graham, 2009 ; Enders, 2010 ). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.
当参与者在构成一个量表的一个或多个项目上有缺失分数时，研究人员通常通过对可用项目求平均来计算比例量表分数。方法学家们警告说，比例计算可能会对构成量表的项目的均值和协方差结构做出严格假设（谢弗和格雷厄姆，2002年；格雷厄姆，2009年；恩德斯，2010年）。我们通过实证研究了比例计算，发现即使在完全随机缺失（MCAR）机制下，它也会导致偏差。为了鼓励研究人员放弃比例计算，我们描述了一种用于处理项目层面缺失数据的全信息最大似然（FIML）方法，该方法减轻了因量表分数缺失而导致的功效损失，并利用可用的项目层面数据而不改变实质性分析。具体来说，我们建议当一个或多个项目缺失时，将量表分数视为缺失，并将项目作为辅助变量纳入。我们的模拟表明，相对于量表层面的缺失数据处理，项目层面的缺失数据处理极大地提高了功效。这些结果具有重要的实际意义，特别是在招募更多参与者极其困难或昂贵的情况下。最后，我们用一个在线慢性疼痛管理项目的数据说明了所提出的方法。
