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By Timothy Teo, Liang Ting Tsai, Chih-Chien Yang (auth.), Myint Swe Khine (eds.)

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There are at least three reasons for this. First, this average difference is easy to understand by readers who are familiar with correlations but less familiar with fit indices. Hu and Bentler (1995) emphasize this, stating that the minimum difference between the observed and the model-implied variance/ covariance matrices clearly signals that the proposed model accounts for the variances/covariances very well. Second, a reason for valuing the SRMR that is probably more fundamental is that it is a precise representation of the objective of SEM, which is to reproduce, as closely as possible, the modelimplied variance/covariance matrix using the observed variance/covariance 28 SEM IN EDUCATIONAL RESEARCH: A PRIMER matrix.

Handbook of structural equation modeling (pp. 436-458). New York: Guilford Press. Marsh, H. , & Yeung, A. S. (1998). Longitudinal structural equation models of academic selfconcept and achievement: Gender differences in the development of math and English constructs. American Educational Research Journal, 35, 705-738. Martens, M. P. (2005). The use of structural equation modeling in counseling psychology research. The Counseling Psychologist, 33, 269-298. , & Ho, M. (2002). Principles and practice in reporting structural equation analyses.

2012). Handbook of structural equation modeling. New York: Guilford Press. , & Bentler, P. M. (1995). Evaluating model fit. In R. H. ), Structural equation modeling: Concepts, issues, and applications (pp. 76-99). Thousand Oaks, CA: Sage. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424-453. , & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.

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