Combining EFA and CFA tests both exploratory structure and confirmatory fit of CN dimensions.

Evidence

We propose a different method to deal with heterogeneity of CN that combines exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA employs a semi-supervised statistic learning method to detect possible latent factor structures, while CFA employs a supervised method. CFA also allows scholars to make testable statistic decisions based on statistical significance testing (Likelihood-Ratio test) to choose a modeling structure that best fits the data structure. — Ruiqian Li and Paul Froese, The Duality of American Christian…, 93

Categorization

• XREF: Offers the reader a methodological template for adjudicating whether an ideology scale is truly multidimensional vs collinear. — connects to: A high Cronbach’s alpha does not prove a scale is unidimensional.

Related

• A high Cronbach’s alpha does not prove a scale is unidimensional. (similarity 0.93)
• Combining exploratory and confirmatory factor analysis produces theory-driven results robust across samples (similarity 0.84)
• Scales with high Cronbach-α may still conceal several distinct latent factors. (similarity 0.79)