It is a statistical method used to uncover the hidden structure in a set of variables. It helps researchers identify clusters of related items (called factors) and reduce complex data into simpler, interpretable dimensions.
"Factor analysis identifies latent variables that explain patterns of correlations within observed variables."
Discover underlying factor structure without prior assumptions.
Test if data fit a hypothesized model you already have in mind.
Correlation between a variable and a factor (-1 to +1).
Indicate variance explained by each factor.
How much of a variable's variance is explained by the extracted factors.
Optimizes structure for interpretation.
The "elbow" point helps determine how many factors to retain. Factors above the break point are usually retained, while those in the rubble are discarded.
Interact with the graph points to see eigenvalues.
Choose Extraction (Principal Axis) and set Rotation to Varimax.
Check the Rotated Component Matrix.
| Item | Factor 1 | Factor 2 |
|---|---|---|
| Q1 | .74 | .10 |
| Q2 | .70 | .12 |
| Q3 | .13 | .81 |
| Q4 | .18 | .77 |