SRM: PCA depends on scale and is not a supervised predictor ranking
Principal components summarise variation in the predictors. They do not automatically select the directions best associated with an outcome. Interpret loadings with the preprocessing convention and the units in mind.
Worked example or practice scenario
A data matrix contains annual income in dollars and a satisfaction score from 1 to 5. Without scaling, income may dominate variance solely because of units. A correlation-based PCA answers a different question from covariance-based PCA on the raw measurements.
Try this next
Run a small example under both scale conventions and explain the difference. A component explaining 80% of predictor variance need not explain 80% of an outcome’s variation. Keep that distinction visible in your written answer.
Reading sources
ActNet editorial guide · October 1, 2026 · Original illustrative examples.