Exploratory Factor Analysis Extracting Signal from Noise in Survey Data

The presentation will be a brief introduction to Exploratory Factor Analysis techniques used for analyzing survey data. The presentation will cover Principal Components Analysis (PCA) as a technique for reducing data as distinct from Maximum Likelihood (ML) or Principal Axis Factor (PAF) analyses used for exploring and discriminating shared and unique variance in latent factors for normally and non-normally distributed survey data, respectively.