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Factoring Methods in Exploratory Factor Analysis (EFA)

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.
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