Direct comparison
CFA vs EFA: Which Fits Your Research Stage
EFA discovers an unknown factor structure; CFA tests one you specify in advance. The deciding question: are you developing a scale, or validating one.
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How do Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) compare side by side?
The table below compares Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) across 10 procurement-relevant dimensions, from what it answers through software.
Side-by-side comparison
| Dimension | Exploratory Factor Analysis (EFA) | Confirmatory Factor Analysis (CFA) |
|---|---|---|
| What it answers | How many factors underlie these items, and which items load on which factor? | Does this specific, pre-specified factor structure fit the data? |
| Research stage | Scale development -- no confirmed structure yet | Scale validation -- a structure already exists and needs testing |
| Factor structure | Estimated from the data (number of factors, item loadings) | Specified by the researcher before estimation |
| Cross-loadings | Estimated for every item on every factor, then interpreted | Fixed to zero for items not hypothesized to load on a factor |
| Can the model be rejected by the data? | No -- it always returns a solution for the number of factors requested | Yes -- poor fit indices are a real, informative result |
| Evaluated with | Eigenvalues, parallel analysis, scree plot, rotated loadings | CFI, TLI, RMSEA, SRMR, chi-square (see SEM fit indices guide) |
| Statistical framework | Principal axis / ML extraction plus rotation | Structural equation modeling (measurement model only) |
| Typical next step | A candidate structure to test via CFA on independent data | Average variance extracted, composite reliability, measurement invariance testing |
| Same-sample rule | Fine to run alone on a full sample | Should not be run on the same sample used for the EFA that produced the structure being tested |
| Software | SPSS (Dimension Reduction), R (psych, EFAtools), Stata (factor) | R (lavaan), Mplus, AMOS, Stata (sem) |
Common questions
Common questions about Exploratory Factor Analysis (EFA) vs Confirmatory Factor Analysis (CFA)
Can I run EFA and CFA on the same sample?
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Not as a validation claim. Running EFA to discover a structure and then CFA on that same data to "confirm" it is circular -- the CFA is close to guaranteed to fit well because the structure was extracted from that exact data. Split the sample before analysis (EFA on one half, CFA on the other) or use genuinely separate samples.
Which comes first, EFA or CFA?
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EFA, if no structure exists yet for what you're measuring -- that is what scale development is. CFA comes after, on new or held-out data, to test whether the structure EFA suggested actually holds. If a validated structure already exists from prior published work, you can start directly with CFA and skip EFA.
Do I need to run EFA if my scale is already published and validated?
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No. Re-running EFA on a previously validated instrument re-opens a question that's already been answered and risks arbitrarily finding a different structure by chance. Run CFA instead, to test whether the published structure holds in your sample, population, or context.
My CFA shows poor fit -- should I switch to EFA on the same data?
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Not on the same data you just used for the CFA -- that reintroduces the same-sample circularity problem in reverse. If you need to explore why the pre-specified structure doesn't fit, do it on a new or independently held-out sample, and treat whatever EFA finds there as provisional until it's tested with a fresh CFA on yet another sample.
Going deeper
