Factor Analysis

Also known as: Factor Analysis, PCA, Principal Component Analysis, Dimensionality Reduction

Statistical technique reducing multiple correlated variables to a smaller number of underlying latent dimensions or factors.

Factor Analysis is a dimensionality reduction technique that identifies the latent factors or underlying constructs that explain the correlation between multiple observed variables. Instead of analyzing 30 brand image attributes individually, factor analysis can reduce them to 4-5 more interpretable dimensions.

Two main types: Exploratory Factor Analysis (EFA)—discovers factor structure without prior hypotheses, ideal for exploratory research; Confirmatory Factor Analysis (CFA)—verifies whether data fits a predefined factor structure, used for scale validation.

In market research, factor analysis is fundamental for: building brand equity scales, identifying customer satisfaction dimensions, and reducing variables for segmentation and predictive models.