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 and Infographic
Here's the deal — c 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) all exceed 1, so 4 factors are retained.
Full explanation below image
Full Explanation
C 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) all exceed 1, so 4 factors are retained. Factor 5 (0.4) is below the threshold and is excluded. This is a straightforward application of the Kaiser-Guttman rule implemented in PROC FACTOR via the MINEIGEN=1 default.