Solutions for Missing Data in Structural Equation Modeling

Rufus Lynn Carter   |    Volume One  |   

Abstract

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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