Issues in Evaluating Model Fit With Missing Data
Saved in:
| 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 |
| 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. |
|---|---|
| ISSN: | 1070-5511 |
| DOI: | 10.1207/s15328007sem1204_4 |
