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:
“Evaluation is creation: hear it, you creators! Evaluating is itself the most valuable treasure of all that we value. It is only through evaluation that value exists: and without evaluation the nut of existence would be hollow. Hear it, you creators!”
—Friedrich Nietzsche (1844–1900)
“Socrates, who was a perfect model in all great qualities, ... hit on a body and face so ugly and so incongruous with the beauty of his soul, he who was so madly in love with beauty.”
—Michel de Montaigne (1533–1592)
“Joshua fit de battle ob Jerico, Jerico, Jerico,
Joshua fit de battle ob Jerico,
An’ de walls come tumblin’ down.”
—Unknown. Joshua Fit De Battle of Jericho (l. 1–3)