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.
