Principal Component Analysis (PCA) Online
Summarize many correlated variables with a smaller set of components that keep most of the variance.
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
| Component | Initial eigenvalue | % of variance | Cumulative % | Rotated SS loadings | % of variance |
|---|---|---|---|---|---|
| 1 | 4.976 | 45.24 | 45.24 | 2.699 | 24.54 |
| 2 | 1.515 | 13.77 | 59.01 | 2.550 | 23.19 |
| 3 | 1.127 | 10.24 | 69.26 | 2.369 | 21.53 |
| 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 component loadings
| Item | Component 1 | Component 2 | Component 3 | Communality |
|---|---|---|---|---|
| TR3 | .827 | .714 | ||
| TR1 | .787 | .663 | ||
| TR2 | .780 | .673 | ||
| TR4 | .738 | .664 | ||
| SAT1 | .804 | .695 | ||
| SAT3 | .752 | .674 | ||
| SAT2 | .727 | .327 | .663 | |
| SAT4 | .717 | .592 | ||
| LOY1 | .836 | .767 | ||
| LOY2 | .826 | .752 | ||
| LOY3 | .806 | .761 |
Note. Extraction: principal components. Rotation: varimax with Kaiser normalization. Loadings below 0.3 hidden; bold ≥ .50. Items sorted by their main component.
Interpretation
The first 3 components capture 69.3% of the total variance in 11 variables.
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). Principal Component Analysis (PCA) Online [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/principal-component-analysis/
BibTeX
@software{xavier_principalcomponentanalysis_2026,
author = {Xavier, Bibin},
title = {Principal Component Analysis (PCA) Online},
publisher = {FreeStatisticalTools.com},
year = {2026},
url = {https://freestatisticaltools.com/principal-component-analysis/},
note = {Web application}
}Suggested sentence for your methods section: “The analysis was carried out with the free Principal Component Analysis (PCA) Online tool on FreeStatisticalTools.com (Xavier, 2026).”
What is principal component analysis?
Principal component analysis reduces many correlated variables to a few components that keep most of the variance. Variables are standardized automatically by using the correlation matrix.
When to use principal component analysis
Use PCA to reduce data before further analysis, or to build composite scores. Use EFA instead when you want to model latent constructs.
Assumptions
- Correlated, continuous variables.
- Variables are standardized automatically (correlation matrix).
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 variables.
- Set the options. Fill in number of components, 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.
Frequently asked questions
What is the difference between PCA and factor analysis?
PCA summarizes total variance into components. Factor analysis models only the shared variance to estimate latent constructs.
How many components should I retain?
Keep components with eigenvalues above 1, or the number before the scree plot levels off.
Should I rotate the components?
Rotation such as varimax makes loadings easier to interpret, which helps when you want to name the components.
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