The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Sensitivity of Fit Indexes to Misspecified Structural or Measurement Model Components: Rationale of Two-Index Strategy Revisited
Open accessAbstract
In previous research (Hu & Bentler, 1998, 1999), 2 conclusions were drawn: standardized root mean squared residual (SRMR) was the most sensitive to misspecified factor covariances, and a group of other fit indexes were most sensitive to misspecified factor loadings. Based on these findings, a 2-index strategy-that is, SRMR coupled with another index-was proposed in model fit assessment to detect potential misspecification in both the structural and measurement model parameters. Based on our reasoning and empirical work presented in this article, we conclude that SRMR is not necessarily most sensitive to misspecified factor covariances (structural model misspecification), the group of indexes (TLI, BL89, RNI, CFI, Gamma hat, Mc, or RMSEA) are not necessarily more sensitive to misspecified factor loadings (measurement model misspecification), and the rationale for the 2-index presentation strategy appears to have questionable validity.
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Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669
Testing Structural Equation Models.
Clogg, C. C., Bollen, K. A., & Long, J. S. (1995) · Social Forces · doi:10.2307/2580595 · Cited by 8,211
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem1103_2 · Cited by 6,691
Evaluation of goodness-of-fit indices for structural equation models.
Mulaik, S. A., James, L. R., Van Alstine, J., & Bennett, N. (1989) · Psychological Bulletin · doi:10.1037//0033-2909.105.3.430 · Cited by 2,923
Fan, X., Thompson, B., & Wang, L. (1999) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705519909540119 · Cited by 1,663
Self-citationMonte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models
GERBING, D. W., & ANDERSON, J. C. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002002 · Cited by 1,046
An evaluation of incremental fit indices: A clarification of mathematical and empirical properties.
Marsh, H. W., Balla, J. R., & Hau, K. (1996) · Dialnet (Universidad de la Rioja) · Cited by 558
Sample Size and Bentler and Bonett's Nonnormed Fit Index
Bollen, K. A. (1986) · Psychometrika · doi:10.1007/bf02294061 · Cited by 386
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On making causal claims: A review and recommendations
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010) · The Leadership Quarterly · doi:10.1016/j.leaqua.2010.10.010 · Cited by 2,441
Structural equations modeling: Fit Indices, sample size, and advanced topics
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Clarifying conditions and decision points for mediational type inferences in Organizational Behavior
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Metadata
- Authors
- Fan, X., & Sivo, S. A.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2005-07
- Volume
- 12
- Issue
- 3
- Pages
- 343-367
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2145675764
- Cited by
- 409
- References
- 18
- FWCI
- 19.36
- Type
- journal article
- Language
- en
- Open access
- green
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- Fan, X., & Sivo, S. A. (2005). Sensitivity of Fit Indexes to Misspecified Structural or Measurement Model Components: Rationale of Two-Index Strategy Revisited. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1207/s15328007sem1203_1
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