Evaluating Model Fit
Most statistical methods only require one statistical test to determine the significance of the analyses. However, in CFA, several statistical tests are used to determine how well the model fits to the data. Note that a good fit between the model and the data does not mean that the model is “correct”, or even that it explains a large proportion of the covariance. A “good model fit” only indicates that the model is plausible. When reporting the results of a confirmatory factor analysis, one is urged to report: a) the proposed models, b) any modifications made, c) which measures identify each latent variable, d) correlations between latent variables, d) any other pertinent information, such as whether constraints are used. With regard to selecting model fit statistics to report, one should not simply report the statistics that estimate the best fit, though this may be tempting. Though several varying opinions exist, Kline (2010) recommends reporting the Chi-squared test, the RMSEA, the CFI, and the SRMR.
Read more about this topic: Confirmatory Factor Analysis
Famous quotes containing the words evaluating, model and/or fit:
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—Friedrich Nietzsche (1844–1900)
“Research shows clearly that parents who have modeled nurturant, reassuring responses to infants’ fears and distress by soothing words and stroking gentleness have toddlers who already can stroke a crying child’s hair. Toddlers whose special adults model kindliness will even pick up a cookie dropped from a peer’s high chair and return it to the crying peer rather than eat it themselves!”
—Alice Sterling Honig (20th century)
“It is bad enough that our geniuses cannot do anything useful, but it is worse that no man is fit for society who has fine traits. He is admired at a distance, but he cannot come near without appearing a cripple.”
—Ralph Waldo Emerson (1803–1882)