Factor pattern coefficient matrix – re-expresses the variance represented in the matrix that is being analyzed and from which the factors are extracted The factors are extracted so that the first factor can reproduce the most variance in the matrix being analyzed, the second factor reproduces the second most variance, and so on. The ability of the factors to reproduce the matrix being analyzed is quantified by the reproduced matrix of associations (here covariance matrix). This is the “glass half full” perspective. We can also compute the matrix that left after the factors have been extracted. This matrix is called the residual matrix. This is the “glass half empty perspective.
Note: Chi-square and pcalculated changes as sample size changes. Chi-square statistic decreases as sample size decreases while pcalc gets closer to not being statistically significant.
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Confirmatory Factor Analysis Fit Statistics Nicola Ritter, M.Ed. EPSY 643: Multivariate Methods This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License .
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2. There are similarities and differences between all fit statistics. Table 2 NFI CFI RMSEA NFI Compares Χ² TESTED MODEL to Χ² BASELINE MODEL Assumes measured variables are uncorrelated. CFI Assumes noncentral Χ² distribution RMSEA Compares sample COV matrix and population COV matrix
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1. Researchers should consult several fit statistics when evaluating model fit.
Fit indices were developed with different rationales.
No single index will meet all our expectations for an ideal index
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