Savalei Victoria, Rhemtulla Mijke
University of British Columbia.
University of California, Davis.
J Educ Behav Stat. 2017 Aug;42(4):405-431. doi: 10.3102/1076998617694880. Epub 2017 Mar 9.
In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data-that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.
在许多建模情境中，模型中的变量是为每个参与者测量的原始项目的线性组合；例如，回归模型和路径分析模型依赖量表分数，而结构方程模型通常使用组块作为潜在结构的指标。目前，不存在能够妥善处理项目层面缺失数据的分析估计方法。然而，项目层面多重填补（MI）能够直接处理此类缺失数据。在本文中，我们开发了一种处理项目层面缺失数据的分析方法——即一种直接从不完整数据集获得唯一一组参数估计值且无需填补的方法。所提出的方法是两阶段最大似然（TSML）方法的一种变体，并且它在分析上等同于项目层面MI。我们将新的TSML方法与处理项目层面缺失数据的三种现有替代方法进行比较：量表层面的完全信息最大似然法、有效案例最大似然法和项目层面MI。我们发现TSML方法是最佳的分析方法，并且其性能与项目层面MI相似。我们建议在流行软件中实施该方法并对其进行进一步研究。
