| Power and sensitivity of alternative fit indices in tests of measurement invariance. | |
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MedLine Citation:
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PMID: 18457487 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Confirmatory factor analytic tests of measurement invariance (MI) based on the chi-square statistic are known to be highly sensitive to sample size. For this reason, G. W. Cheung and R. B. Rensvold (2002) recommended using alternative fit indices (AFIs) in MI investigations. In this article, the authors investigated the performance of AFIs with simulated data known to not be invariant. The results indicate that AFIs are much less sensitive to sample size and are more sensitive to a lack of invariance than chi-square-based tests of MI. The authors suggest reporting differences in comparative fit index (CFI) and R. P. McDonald's (1989) noncentrality index (NCI) to evaluate whether MI exists. Although a general value of change in CFI (.002) seemed to perform well in the analyses, condition specific change in McDonald's NCI values exhibited better performance than a single change in McDonald's NCI value. Tables of these values are provided as are recommendations for best practices in MI testing. |
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Authors:
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Adam W Meade; Emily C Johnson; Phillip W Braddy |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: The Journal of applied psychology Volume: 93 ISSN: 0021-9010 ISO Abbreviation: J Appl Psychol Publication Date: 2008 May |
Date Detail:
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Created Date: 2008-05-06 Completed Date: 2008-07-29 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0222526 Medline TA: J Appl Psychol Country: United States |
Other Details:
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Languages: eng Pagination: 568-92 Citation Subset: IM |
Copyright Information:
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PsycINFO Database Record (c) 2008 APA, all rights reserved. |
Affiliation:
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Department of Psychology, North Carolina State University, Raleigh, NC 27695-7650, USA. awmeade@ncsu.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Humans Power (Psychology)* Psychological Tests* Sensitivity and Specificity |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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