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ERIC Number: EJ1126571
Record Type: Journal
Publication Date: 2017
Pages: 28
Abstractor: As Provided
ISSN: ISSN-0022-0973
Effects of Missing Data Methods in SEM under Conditions of Incomplete and Nonnormal Data
Li, Jian; Lomax, Richard G.
Journal of Experimental Education, v85 n2 p231-258 2017
Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate, parameter estimate bias, MSE of parameter estimates, standard error coverage, model rejection rate, and model goodness of fit--RMSEA. A three-factor CFA model was used. Findings indicated that FIML outperformed the other methods in MCAR, and MI should be used to increase the plausibility of MAR. SRPI was not comparable to the other three methods in either MCAR or MAR.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A