A data scientist applies PROC FACTOR with NFACTORS=4 and uses the SCREE plot output to decide the final number of factors. The eigenvalues are: Factor 1: 5.2, Factor 2: 3.1, Factor 3: 1.8, Factor 4: 1.1, Factor 5: 0.4. Using the Kaiser criterion (eigenvalue > 1), how many factors should be retained?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you keep factors with eigenvalues greater than 1. Factors 1, 2, 3, and 4 all clear that bar, so you retain 4 factors.
Full Explanation
The answer of 4 factors is correct because the Kaiser criterion retains factors with eigenvalues greater than 1. Factors 1 (5.2), 2 (3.1), 3 (1.8), and 4 (1.1) exceed 1, while factor 5 (0.4) does not. This is the Kaiser-Guttman rule implemented in PROC FACTOR via the MINEIGEN=1 default. Retaining 2 factors discards factors 3 and 4; retaining 3 factors discards factor 4; retaining 5 factors keeps factor 5.