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
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—Friedrich Nietzsche (1844–1900)
“The playing adult steps sideward into another reality; the playing child advances forward to new stages of mastery....Child’s play is the infantile form of the human ability to deal with experience by creating model situations and to master reality by experiment and planning.”
—Erik H. Erikson (20th century)
“Little do such men know the toil, the pains,
The daily, nightly racking of the brains,
To range the thoughts, the matter to digest,
To cull fit phrases, and reject the rest.”
—Charles Churchill (1731–1764)