Exploratory Factor Analysis (EFA) Online
Find the underlying factors behind a set of items. Includes sampling adequacy tests, principal axis factoring, varimax rotation and a sorted loading table.
Updated · By Bibin XavierData 180 × 12
| Respondenttext · 180 levels | TR1numeric · coded | TR2numeric · coded | TR3numeric · coded | TR4numeric · coded | SAT1numeric · coded | SAT2numeric · coded | SAT3numeric · coded | SAT4numeric · coded | LOY1numeric · coded | LOY2numeric · coded | LOY3numeric · coded |
|---|---|---|---|---|---|---|---|---|---|---|---|
| R001 | 2 | 2 | 1 | 2 | 3 | 1 | 3 | 3 | 4 | 2 | 3 |
| R002 | 5 | 4 | 5 | 4 | 3 | 4 | 3 | 4 | 4 | 3 | 4 |
| R003 | 4 | 4 | 3 | 4 | 1 | 2 | 2 | 4 | 2 | 3 | 1 |
| R004 | 1 | 2 | 3 | 2 | 4 | 4 | 4 | 4 | 3 | 4 | 4 |
| R005 | 3 | 4 | 2 | 3 | 3 | 3 | 3 | 4 | 3 | 2 | 2 |
| R006 | 5 | 4 | 5 | 4 | 5 | 5 | 4 | 4 | 4 | 3 | 4 |
| R007 | 3 | 4 | 4 | 4 | 3 | 3 | 2 | 4 | 1 | 3 | 3 |
| R008 | 3 | 3 | 4 | 4 | 5 | 4 | 5 | 5 | 5 | 5 | 4 |
Variables and options
Results
180 rows · example dataTotal variance explained
| Factor | Initial eigenvalue | % of variance | Cumulative % | Rotated SS loadings | % of variance |
|---|---|---|---|---|---|
| 1 | 4.976 | 45.24 | 45.24 | 2.286 | 20.79 |
| 2 | 1.515 | 13.77 | 59.01 | 2.097 | 19.06 |
| 3 | 1.127 | 10.24 | 69.26 | 1.997 | 18.15 |
| 4 | 0.598 | 5.43 | 74.69 | ||
| 5 | 0.564 | 5.13 | 79.82 | ||
| 6 | 0.473 | 4.30 | 84.12 | ||
| 7 | 0.431 | 3.92 | 88.04 | ||
| 8 | 0.404 | 3.67 | 91.71 | ||
| 9 | 0.346 | 3.14 | 94.85 | ||
| 10 | 0.301 | 2.74 | 97.59 | ||
| 11 | 0.265 | 2.41 | 100.00 |
Scree plot
Rotated factor loadings
| Item | Factor 1 | Factor 2 | Factor 3 | Communality |
|---|---|---|---|---|
| TR3 | .760 | .613 | ||
| TR2 | .698 | .561 | ||
| TR1 | .692 | .534 | ||
| TR4 | .673 | .578 | ||
| SAT1 | .711 | .571 | ||
| SAT3 | .689 | .589 | ||
| SAT2 | .661 | .315 | .574 | |
| SAT4 | .563 | .417 | ||
| LOY1 | .755 | .654 | ||
| LOY3 | .737 | .672 | ||
| LOY2 | .726 | .618 |
Note. Extraction: principal axis factoring. Rotation: varimax with Kaiser normalization. Loadings below 0.3 hidden; bold ≥ .50. Items sorted by their main factor.
Interpretation
Sampling adequacy is sufficient (KMO = .869) and Bartlett’s test is statistically significant, so the correlations are suitable for factoring. 3 factors explain 58.0% of the common variance.
How to cite this tool
Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.
APA 7
Xavier, B. (2026). Exploratory Factor Analysis (EFA) Online [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/exploratory-factor-analysis/
BibTeX
@software{xavier_factoranalysis_2026,
author = {Xavier, Bibin},
title = {Exploratory Factor Analysis (EFA) Online},
publisher = {FreeStatisticalTools.com},
year = {2026},
url = {https://freestatisticaltools.com/exploratory-factor-analysis/},
note = {Web application}
}Suggested sentence for your methods section: “The analysis was carried out with the free Exploratory Factor Analysis (EFA) Online tool on FreeStatisticalTools.com (Xavier, 2026).”
What is exploratory factor analysis?
Exploratory factor analysis finds the latent factors behind a set of observed items, such as questionnaire questions. This tool runs principal axis factoring with varimax rotation and reports the adequacy tests reviewers expect.
When to use exploratory factor analysis
Use EFA to discover how many constructs a set of items measures and which items belong together, typically when developing or adapting a questionnaire.
Assumptions
- At least 5–10 cases per item, and ideally 150+ cases.
- KMO of .60 or more and a significant Bartlett’s test.
- Linear relationships among items.
How to use this tool
- Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
- Choose the variables. Select the items.
- Set the options. Fill in number of factors, rotation and hide loadings below.
- Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.
How to report exploratory factor analysis in APA style
Report KMO, Bartlett’s test, the extraction and rotation method, the number of factors with the rule used, variance explained, and the rotated loading table.
Frequently asked questions
What KMO value is acceptable?
KMO of .60 or more is the minimum; .80 and above is meritorious (Kaiser, 1974).
How many factors should I keep?
Common rules are eigenvalues above 1 (Kaiser) and the elbow in the scree plot. Theory and interpretability should guide the final choice.
What factor loading is significant?
Loadings of .40 or more are commonly treated as meaningful, and .50 or more as strong. Items that load on two factors may need to be removed.
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