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Article: Sensitivity of goodness of fit indexes to lack of measurement invariance

TitleSensitivity of goodness of fit indexes to lack of measurement invariance
Authors
Issue Date2007
Citation
Structural Equation Modeling, 2007, v. 14, n. 3, p. 464-504 How to Cite?
AbstractTwo Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for testing invariance at different levels are recommended. Copyright © 2007, Lawrence Erlbaum Associates, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/202135
ISSN
2015 Impact Factor: 3.225
2015 SCImago Journal Rankings: 2.324

 

DC FieldValueLanguage
dc.contributor.authorChen, Fangfang-
dc.date.accessioned2014-08-22T02:57:42Z-
dc.date.available2014-08-22T02:57:42Z-
dc.date.issued2007-
dc.identifier.citationStructural Equation Modeling, 2007, v. 14, n. 3, p. 464-504-
dc.identifier.issn1070-5511-
dc.identifier.urihttp://hdl.handle.net/10722/202135-
dc.description.abstractTwo Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for testing invariance at different levels are recommended. Copyright © 2007, Lawrence Erlbaum Associates, Inc.-
dc.languageeng-
dc.relation.ispartofStructural Equation Modeling-
dc.titleSensitivity of goodness of fit indexes to lack of measurement invariance-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-34548107817-
dc.identifier.volume14-
dc.identifier.issue3-
dc.identifier.spage464-
dc.identifier.epage504-

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