Free tool that turns exploratory factor analysis (EFA) output into a report that meets the APA 7th edition results reporting standards: paste the pattern matrix from SPSS ("Pattern Matrix"), R (psych::fa) or jamovi — it understands decimal commas and the blank cells of suppressed loadings — and get the APA 7 factor-loading table with a configurable suppression threshold (|λ| < .30 or your own), communalities (h²), flagged cross-loadings and eigenvalues and % of variance at the foot; the scree plot with the Kaiser line at eigenvalue 1, downloadable as a high-resolution PNG with no watermark; the results paragraph in English or Spanish (KMO with the Kaiser label, the Bartlett test, extraction and rotation method, retention criterion, variance explained and which items load on which factor); and a checklist of what reviewers expect (participants per item, KMO ≥ .70, minimum loadings, 3 items per factor).
Worked example: EFA of a 12-item academic anxiety scale in 412 university students, principal axis factoring with oblimin rotation. KMO = .88 (meritorious per Kaiser), Bartlett χ²(66) = 1834.20, p < .001; three factors were retained under the Kaiser criterion (eigenvalues 4.62, 2.10 and 1.45), explaining 68.1% of the variance; item AA12 shows a cross-loading (.35 on Factor 1 on top of its .58 on Factor 3).
Can I compute the communality by summing squared loadings? Only under an orthogonal rotation (varimax, quartimax); with oblimin or promax the factors correlate and h² must come from your software (the h2 column in psych, 1 − Uniqueness in jamovi). What KMO is acceptable? At least .70; Kaiser (1974) labels values in the .90s marvelous, .80s meritorious, .70s middling, .60s mediocre, .50s miserable and below .50 unacceptable.
Paste your pattern matrix from SPSS, R or jamovi and get EFA results to APA 7th edition standards: loadings table, scree plot PNG and the written paragraph.
| KMO | Kaiser (1974) label |
|---|---|
| ≥ .90 | Marvelous |
| .80 – .90 | Meritorious |
| .70 – .80 | Middling |
| .60 – .70 | Mediocre |
| .50 – .60 | Miserable |
| < .50 | Unacceptable |
The working minimum is .70. Below .50 the matrix is not factorable and there is no point going on.
The communality h² can only be obtained by summing squared loadings under an orthogonal rotation (varimax, quartimax). With oblimin or promax the factors correlate and h² must come from your software: the h2 column in R’s psych, or 1 − Uniqueness in jamovi.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. Bartlett, M. S. (1954). A note on the multiplying factors for various chi-square approximations. Journal of the Royal Statistical Society, Series B, 16(2), 296–298.
If you use it in your thesis or in a paper, cite it so that readers of your Methods section know how the calculation was done.
Ciudad-Fernández, V. (2026). Exploratory factor analysis APA report generator [Web application]. Ciudad Science. Retrieved October 14, 2026, from https://analisisdedatospsicologia.com/en/tools/efa-apa-report-generator
For your Methods section: Sampling adequacy was judged with the KMO (Kaiser, 1974) obtained in the statistical software and the exploratory factor analysis decisions were reported following Fabrigar et al. (1999); the APA-style write-up of the results was prepared with the Ciudad Science exploratory factor analysis APA report generator (Ciudad-Fernández, 2026).
@misc{ciudadfernandez2026reporteafe,
author = {Ciudad-Fern{\'a}ndez, V{\'i}ctor},
title = {{Exploratory factor analysis APA report generator}},
year = {2026},
publisher = {Ciudad Science},
howpublished = {Web application},
url = {https://analisisdedatospsicologia.com/en/tools/efa-apa-report-generator},
urldate = {2026-10-14},
note = {Accessed 2026-10-14},
}