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ERIC Number: EJ1062693
Record Type: Journal
Publication Date: 2006
Pages: 4
Abstractor: As Provided
Reference Count: 13
ISSN: EISSN-2161-4210
Solutions for Missing Data in Structural Equation Modeling
Carter, Rufus Lynn
Research & Practice in Assessment, v1 p4-7 Win 2006
Many times in both educational and social science research it is impossible to collect data that is complete. When administering a survey, for example, people may answer some questions and not others. This missing data causes a problem for researchers using structural equation modeling (SEM) techniques for data analyses. Because SEM and multivariate methods require complete data, several methods have been proposed for dealing with these missing data. What follows is a review of several methods currently used, a description of strengths and weaknesses of each method, and a proposal for future research.
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A