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. 2022 Feb 24;75(11):2129–2148. doi: 10.1177/17470218221078858

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© Experimental Psychology Society 2022

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

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Figure 3. — Model comparison: confirmatory versus exploratory factor.

Left panel: the large BIC difference suggests a much better fit for Exploratory Factor Analysis (EFA) than Confirmatory Factor Analysis (CFA). Right panel: distributions of bootstrapped (10,000 samples each) total variances explained by the CFA and EFA models, respectively. Note that for the CFA, 250 bootstrapped variances explained (out of 10,000) were greater than one due to the bootstrapping process, so were excluded for this figure. The overlapping distributions suggest both the CFA and EFA models explain comparable total variances, with the EFA explaining marginally more. BIC: Bayes Information Criterion.