Exploratory factor analysis (EFA) investigates patterns among items when the structure is uncertain. Confirmatory factor analysis (CFA) evaluates a structure specified in advance. The choice follows your measurement question, not which software menu is easiest to use.
Both concern shared variation among items. Neither replaces a clear construct definition, careful wording, or evidence that respondents understand the questions as intended. Start with the questionnaire validation guide if those foundations are still unsettled.
A scale intended to measure a latent construct may benefit from factor analysis. A questionnaire collecting unrelated facts—such as attendance, transportation access, and preferred appointment times—does not automatically need a factor model or one total score.
Decide which items are expected to reflect the same construct and why. A statistical grouping that has no coherent substantive meaning is not a sufficient basis for reporting a scale.
EFA is useful for a new item pool or a substantially adapted instrument whose dimensions are uncertain. Investigate the number of factors using multiple forms of evidence, including parallel analysis, the scree pattern, interpretability, and the intended content coverage. A single eigenvalue rule is too thin a basis for the decision.
Allow factors to correlate when that is substantively plausible. Review cross-loadings and item wording together. An item that relates to two dimensions may be ambiguous, or it may capture a meaningful overlap. Deleting it mechanically can damage the concept you wanted to measure.
Principal components analysis is a different method with a different objective. If you use PCA for data reduction, label it accurately rather than presenting it as a latent factor analysis.
CFA fits a specified arrangement of items and factors. An established instrument’s documented structure can provide that model, but previous evidence does not guarantee that it works in your population or revised version.
For example, a fictional training organization might propose separate confidence and practical-readiness factors before collecting responses. CFA can evaluate whether that model is compatible with its data. A one-factor alternative may be relevant if the organization also proposes a combined score. The alternatives should answer real scoring questions, not simply compete for the best fit statistic.
Examine loadings, factor correlations, residual patterns, estimation problems, and several fit measures. Treat familiar fit cutoffs as context-dependent guides. Good global fit alone does not establish useful content coverage or a valid interpretation of the score.
Running EFA and then CFA on the same responses does not supply independent confirmation of the structure discovered from those responses. A new sample or a carefully planned holdout can provide a stronger check. Splitting a small sample may leave both analyses unstable; sometimes the honest next step is an exploratory result followed by a future confirmation study.
Describe the item pool, sample, estimator, factor-retention approach, rotation for EFA, model specification for CFA, and scoring implications. Report dropped items and data-driven changes. If modification indices suggest a revision, give a substantive reason and identify the revised model as requiring further evaluation.
After choosing a defensible structure, assess reliability for the scores you will actually report. If you intend comparisons across populations, consider measurement invariance as a separate question.
Use the measurement-review checklist to document the instrument and open decisions. DASS can help scope a factor-analysis plan before collection or review a proposed scoring structure.
Boateng and colleagues: Best Practices for Developing and Validating Scales; Knekta, Runyon, and Eddy: One Size Doesn’t Fit All
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