Dong Yiran, Peng Chao-Ying Joanne
Indiana University-Bloomington, Bloomington, Indiana USA.
Springerplus. 2013 May 14;2(1):222. doi: 10.1186/2193-1801-2-222. Print 2013 Dec.
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
缺失数据对定量研究的影响可能很严重，会导致参数估计有偏差、信息丢失、统计功效降低、标准误差增加以及研究结果的可推广性减弱。在本文中，我们讨论并展示了三种有原则的缺失数据处理方法：多重填补、全信息极大似然法和期望最大化算法，并将其应用于一个真实数据集。将结果与从完整数据集和逐一删除法获得的结果进行了对比。指出了每种方法的相对优点以及它们共有的特征。本文最后强调了统计假设的重要性，并为研究人员提供了建议。如果（a）研究人员明确承认缺失数据问题及其发生的条件，（b）采用有原则的方法处理缺失数据，以及（c）将缺失数据的适当处理纳入提交发表的稿件评审标准，那么研究质量将会提高。
