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Issues in Evaluating Model Fit With Missing Data

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Title: Issues in Evaluating Model Fit With Missing Data
Language: English
Authors: Davey, Adam
Source: Structural Equation Modeling: A Multidisciplinary Journal. 2005 12(4):578-597.
Availability: Lawrence Erlbaum Associates, Inc., Journal Subscription Department, 10 Industrial Avenue, Mahwah, NJ 07430-2262. Tel: 800-926-6579 (Toll Free); e-mail: journals@erlbaum.com.
Peer Reviewed: Y
Page Count: 20
Publication Date: 2005
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Goodness of Fit, Structural Equation Models, Data Analysis
DOI: 10.1207/s15328007sem1204_4
ISSN: 1070-5511
Abstract: Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (?[squared], root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50), factor loadings (.4 or .8), factor covariances (.4 or .8), type of missing data (missing completely at random or missing at random), and extent of missing data (0-95% on 3 of 9 indicators in increments of 5%) for correct and 2 misspecified (measurement or structural) models. Incremental and absolute fit indexes indicate better fit with higher proportions of missing data. Effects of missing data on the NFI were more varied, indicating poorer model fit as missing data increased for the correct model, and indicating better or poorer fit as an interaction of all the other factors for misspecified models. Recommendations are made for researchers and software developers.
Abstractor: Author
Entry Date: 2005
Accession Number: EJ722635
Database: ERIC
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  Data: Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (?[squared], root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50), factor loadings (.4 or .8), factor covariances (.4 or .8), type of missing data (missing completely at random or missing at random), and extent of missing data (0-95% on 3 of 9 indicators in increments of 5%) for correct and 2 misspecified (measurement or structural) models. Incremental and absolute fit indexes indicate better fit with higher proportions of missing data. Effects of missing data on the NFI were more varied, indicating poorer model fit as missing data increased for the correct model, and indicating better or poorer fit as an interaction of all the other factors for misspecified models. Recommendations are made for researchers and software developers.
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      – SubjectFull: Data Analysis
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