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Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling
Abstract
In this study, the authors investigated incorporating adjusted model fit information into the root mean square error of approximation (RMSEA) fit index. Through Monte Carlo simulation, the usefulness of this adjusted index was evaluated for assessing model adequacy in structural equation modeling when the multivariate normality assumption underlying maximum likelihood estimation is violated. Adjustment to the RMSEA was considered in 2 forms: a rescaling adjustment via the Satorra-Bentler rescaled goodness-of-fit statistic and a bootstrap adjustment via the Bollen and Stine adjusted model p value. Both properly specified and misspecifed models were examined. The adjusted RMSEA was evaluated in terms of the average index value across study conditions and with respect to model rejection rates under tests of exact fit, close fit, and not-close fit.
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Curran, P. J., West, S. G., & Finch, J. F. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.16 · Cited by 5,512
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Chicco, D., Warrens, M. J., & Jurman, G. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.623 · Cited by 5,380
The Performance of RMSEA in Models With Small Degrees of Freedom
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015) · Sociological Methods & Research · doi:10.1177/0049124114543236 · Cited by 2,841
Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008) · Sociological Methods & Research · doi:10.1177/0049124108314720 · Cited by 1,538
Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness?
DiStefano, C., Liu, J., Jiang, N., & Shi, D. (2018) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2017.1390394 · Cited by 372
A new strategy for testing structural equation models
Moshagen, M., & Erdfelder, E. (2016) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2014.950896 · Cited by 321
Metadata
- Authors
- Nevitt, J., & Hancock, G. R.
- Venue
- The Journal of Experimental Education
- Publisher
- Informa UK Limited
- Published
- 2000-01
- Volume
- 68
- Issue
- 3
- Pages
- 251-268
- ISSN
- 0022-0973 (ISSN-L)
- 1940-0683
- OpenAlex
- W2050881663
- Cited by
- 160
- References
- 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nevitt, J., & Hancock, G. R. (2000). Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling. The Journal of Experimental Education. https://doi.org/10.1080/00220970009600095
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
