Table 1 Comparison of EFA, CFA and ESEM

Theory/Data

Data driven approach, (exploratory)

Theory-driven, (confirmatory)

Fundamentally, confirmatory technique that integrates exploratory elements

Item-Factor Loadings

Cross-loadings are allowed and not fixed. loadings are freely estimated

Cross-loadings are not allowed

Unique specification of items onto respective latent factors applies

Non-target cross-loadings are constrained to be as close to zero as possible, but are still allowed

Factor Structure & Parsimony

Complex factorial structures may emerge. Issues with parsimony may be present

Parsimonious models. Simple/clear factorial structures (sometimes criticised as overly simplistic)

Complex factorial structures, especially, in large datasets. However, more control applies compared to EFA

Interpretability Risks

Extracted factors may not always be meaningful

Despite the increased insight into scale-scoring, adequate item loadings and high levels of reliability it provides, positively biased factor correlations and lower goodness of fit may be present

With non-zero cross-loadings, the bias and inflated statistics are reduced