Principal components analysis is appropriate when the primary concern is to identify the underlying dimensions and the common variance is of interest.

Question

Quizplus · Accepted Answer

Principal components analysis (PCA) is used to reduce dimensionality by transforming variables into a set of uncorrelated components, focusing on total variance, not just common variance. Factor analysis, on the other hand, is more appropriate for identifying underlying dimensions and common variance.

Principal Components Analysis Is Appropriate When the Primary Concern Is