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					                            Interpretation of
                  Confirmatory Factor Analysis Results
Fit Indices – Brown (2006) recommends reporting at least one index from each
       category below:

A. Absolute Fit – indices that evaluate how close the observed variance-covariance
   matrix is to the estimated matrix

   1. Chi-Square (χ2)
       Desire a result that is not statistically significant (i.e., observed covariance
         matrix equal to estimated matrix)
       Sensitive to sample size (large samples may result in significant χ2)
       Sensitive to multivariate nonnormality of the data

   2. Standardized root mean square residual (SRMR)
       Ranges from 0 – 1.0 (1.0 indicates perfect fit)
       Recommend SRMR≤.08 (Hu & Bentler, 1999)

   3. Standardized Residuals
       Not technically a fit index, but can provide information about closely the
         estimated matrix corresponds to the observed matrix (i.e., how well the data
         fits the model)
       Desire standardized residuals closer to 0 (i.e., little or no difference between
         observed covariance matrix and estimated matrix)

B. Parsimony Correction – “similar to absolute fit indices, but incorporate a penalty
   function for poor model parsimony” (Kalinowski, 2006, p. 13)

   1. Root Mean Square Error of Approximation (RMSEA)
       Ranges from 0 - +∞
       Recommend RMSEA≤.06 (Hu & Bentler, 1999; Thompson, 2004)

C. Comparative Fit – evaluate the fit of the hypothesized model to null model (i.e.,
   covariances = 0)

   1. Comparative Fit Index (CFI)
       Ranges from 0 – 1 (1.0 indicates perfect fit)
       Recommend CFI≥.95 (Hu & Bentler, 1999; Thompson, 2004)

   2. Tucker-Lewis Index (TLI)
       Usually interpreted within the range of 0 – 1.0
       Recommend TLI≥.95 (Hu & Bentler, 1999)

   3. Normed Fit Index (NFI)
       Ranges from 0 – 1.0
       Recommend NFI≥.95 (Thompson, 2004)
                                       References

Brown, T.A. (2006). Confirmatory factor analysis for applied research. New York, NY:

       The Guildford Press.

Bryant, F.B., & Yarnold, P.R. (1995). Principal-components analysis and exploratory and

       confirmatory factor analysis. In L. Grimm & P. Yarnold (Eds.), Reading and

       understanding multivariate statistics (pp. 99-136). Washington, D.C.: American

       Psychological Association.

Gorsuch, R.L. (1983). Factor analysis (2nd ed.) Hillsdale, NY: Erlbaum.

Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indices in covariance structure

       analysis: Conventional criteria versus new alternatives. Structural Equation

       Modeling, 6, 1-55.

Kalinowski, K.E. (2006). Using structural equation modeling to conduct confirmatory

       factor analysis.

Schumacker, R.E., & Lomax, R.G. (2004). A beginner’s guide to structural equation

       modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding

       concepts and applications. Washington, D.C.: American Psychological

       Association.

				
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