Dealing With Missing Data in Developmental Research
Dealing With Missing Data in Developmental Research
复制标题
DOI：
10.1111/cdep.12008
复制
发表时间：
2013-03-01
影响因子：
6.4
通讯作者：
Enders, Craig K.
中科院分区：
文献类型：
--
作者：
Enders, Craig K.
近年来，处理缺失数据的方法有了很大的改进，研究人员现在可以从各种复杂的分析选项中进行选择。方法学文献支持最大似然法和多重插补法，因为这些方法相对于旧方法有了实质性的改进，包括强大的理论基础、限制性较少的假设、偏倚减少的可能性和更大的功效。这些好处是特别重要的发展研究，其中磨损是一个普遍存在的问题。本文简要介绍了处理缺失数据的现代方法及其在发展研究中的应用。
Approaches to handling missing data have improved dramatically in recent years and researchers can now choose from a variety of sophisticated analysis options. The methodological literature favors maximum likelihood and multiple imputation because these approaches offer substantial improvements over older approaches, including a strong theoretical foundation, less restrictive assumptions, and the potential for bias reduction and greater power. These benefits are especially important for developmental research where attrition is a pervasive problem. This article provides a brief introduction to modern methods for handling missing data and their application to developmental research.