A Review of Missing Data Handling Methods in Education Research

被引：122
|
作者：
Cheema, Jehanzeb R. [1 ]
机构：
[1] Univ Illinois, Coll Educ, Champaign, IL 61820 USA
来源：
REVIEW OF EDUCATIONAL RESEARCH | 2014年 / 84卷 / 04期
关键词：
missing data; imputation; education research; listwise deletion; missing value analysis; REPORTING PRACTICES; MAXIMUM-LIKELIHOOD; MULTIVARIATE DATA; INCOMPLETE DATA; REGRESSION; VALUES; PSYCHOLOGY; IMPUTATION; VARIABLES; SELECTION;
D O I：
10.3102/0034654314532697
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those methods as a quick fix. This study reviews the current literature on missing data handling methods within the special context of education research to summarize the pros and cons of various methods and provides guidelines for future research in this area.
引用
收藏
页码：487 / 508
页数：22
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