Patrician Patricia A
Walter Reed Army Medical Center, Washington, DC 20012, USA.
Res Nurs Health. 2002 Feb;25(1):76-84. doi: 10.1002/nur.10015.
Missing data occur frequently in survey and longitudinal research. Incomplete data are problematic, particularly in the presence of substantial absent information or systematic nonresponse patterns. Listwise deletion and mean imputation are the most common techniques to reconcile missing data. However, more recent techniques may improve parameter estimates, standard errors, and test statistics. The purpose of this article is to review the problems associated with missing data, options for handling missing data, and recent multiple imputation methods. It informs researchers' decisions about whether to delete or impute missing responses and the method best suited to doing so. An empirical investigation of AIDS care data outcomes illustrates the process of multiple imputation.
缺失数据在调查研究和纵向研究中经常出现。不完整的数据存在问题，尤其是当存在大量缺失信息或系统性无应答模式时。逐一删除法和均值插补法是处理缺失数据最常用的技术。然而，更新的技术可能会改善参数估计、标准误差和检验统计量。本文的目的是回顾与缺失数据相关的问题、处理缺失数据的方法以及最新的多重插补方法。它为研究人员在是否删除或插补缺失回答以及最适合这样做的方法方面提供决策依据。一项关于艾滋病护理数据结果的实证研究说明了多重插补的过程。
