An introduction to modern missing data analyses
An introduction to modern missing data analyses
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DOI：
10.1016/j.jsp.2009.10.001
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发表时间：
2010-02-01
影响因子：
5
通讯作者：
Enders, Craig K.
中科院分区：
文献类型：
--
作者：
Baraldi, Amanda N.;Enders, Craig K.
大量近期的方法学研究聚焦于两种现代缺失数据分析方法：极大似然法和多重填补法。这些方法相较于传统技术（例如删除法和均值填补法）具有优势，因为它们所需的假设不那么严格，并且减少了传统技术的缺陷。本文解释了缺失数据分析的理论基础，概述了传统的缺失数据技术，并对极大似然法和多重填补法进行了通俗易懂的描述。特别是，本文着重介绍极大似然估计，并呈现了来自美国青年纵向研究数据的两个分析实例。其中一个实例包含了对辅助变量使用的描述。最后，本文阐述了研究人员如何利用有意的或计划好的缺失数据来优化其研究设计。（C）2009学校心理学研究学会。由爱思唯尔有限公司出版。保留所有权利。
A great deal of recent methodological research has focused on two modem missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs. (C) 2009 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.