A data scientist runs PROC FACTOR with METHOD=PRINCIPAL ROTATE=VARIMAX on a 20-variable dataset. What is the purpose of ROTATE=VARIMAX?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because Varimax rotation is an orthogonal rotation that maximizes the variance of squared loadings within each factor column, driving loadings toward 0 or 1 and making each variable load strongly on as few factors as possible, which aids interpretability. A is wrong because eigenvalue sorting is not what rotation does.
Full explanation below image
Full Explanation
B is correct because Varimax rotation is an orthogonal rotation that maximizes the variance of squared loadings within each factor column, driving loadings toward 0 or 1 and making each variable load strongly on as few factors as possible, which aids interpretability. A is wrong because eigenvalue sorting is not what rotation does. C is wrong because Varimax operates on the loading matrix, not the raw data. D (Kaiser criterion) is implemented via MINEIGEN= option, not ROTATE=VARIMAX.