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ERIC Number: EJ866532
Record Type: Journal
Publication Date: 2010-Feb
Pages: 33
Abstractor: As Provided
Reference Count: 0
ISBN: N/A
ISSN: ISSN-0022-4405
An Introduction to Modern Missing Data Analyses
Baraldi, Amanda N.; Enders, Craig K.
Journal of School Psychology, v48 n1 p5-37 Feb 2010
A great deal of recent methodological research has focused on two modern 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. (Contains 7 figures and 6 tables.)
Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: Researchers
Language: English
Sponsor: N/A
Authoring Institution: N/A