计划缺失设计——通过有意缺失让研究更高效  

Planned Missing Data Design: Through Intended Missing Data Make Research More Effective

作　　者：王孟成[1,2] 叶浩生[1,2] 

机构地区：[1]广州大学心理与脑科学研究中心 [2]广州大学教育学院心理学系,广州510006

出　　处：《心理科学进展》2014年第6期1025-1035,共11页Advances in Psychological Science

基　　金：国家社会科学基金教育学青年项目:"心理健康教育的循证实践模式及本土化研究"(CBA130124)

摘　　要：缺失值是社会科学研究中非常普遍的现象。全息极大似然估计和多重插补是目前处理缺失值最有效的方法。计划缺失设计利用特殊的实验设计有意产生缺失值,再用现代的缺失值处理方法来完成统计分析,获得无偏的统计结果。计划缺失设计可用于横断面调查减少(或增加)问卷长度和纵向调查减少测量次数,也可用于提高测量有效性。常用的计划缺失设计有三式设计和两种方法测量。Missing data is a very common phenomenon in social science research. Amongmost existing statistical analysis about missing data, Full-information maximum likelihood estimation and multiple imputation are recommended as most common approach at present for handling missing data. Planned missing data designs use special research design which create completely random missing data, and then employ modem missing data estimation techniques to get unbiased parameters and maximize statistical power in the process. Planned missing data designs can be used in cross-section survey to increase questionnaire items or reduce respondent burden. In longitudinal survey to shorten measure oceasion, moreover, enrich validities. There are two types of planned missing data designs： 3-form design and two-method measurement design.

关 键 词：缺失值 计划缺失设计 全息极大似然估计 多重插补 三式设计 两种方法测量 

分 类 号：B841[哲学宗教—基础心理学]