Factoring Methods in Exploratory Factor Analysis (EFA)

There are three main methods for factor extraction.

Principal Component Analysis (PCA)

Use for a softer solution

• Considers all of the available variance (common + unique) (places 1’s on diagonal of correlation matrix).
• Seeks a linear combination of variables such that maximum variance is extracted—repeats this step.
• Use when there is concern with prediction, parsimony and you know the specific and error variance are small.
• Results in orthogonal (uncorrelated factors).

Principal Axis Factoring (PAF)

• Considers only common variance (places communality estimates on diagonal of correlation matrix).
• Seeks least number of factors that can account for the common variance (correlation) of a set of variables.
• PAF is only analyzing common factor variability; removing the uniqueness or unexplained variability from the model.
• PAF is preferred because it accounts for co-variation, whereas PCA accounts for total variance.

Maximum Likelihood (ML)

Use this method if you are unsure

• Maximizes differences between factors. Provides Model Fit estimate.
• This is the approach used in AMOS, so if you are going to use AMOS for CFA and structural modeling, you should use this one during the EFA.