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Mastering Factor Analysis in SPSS | Reduce data complexity and identify key factors | Prof. Kelly Fan

Learn factor analysis in SPSS from Prof. Kelly Fan with a focus on data reduction and summarization, using communalities and rotations to extract essential information. Understand how to analyze ordinal categorical factors and create new factors. Walk through example questions to measure depression and happiness. Visualize results with scree plot and component plot. Discover the importance of Varimax and Promax rotations for accurate interpretation. Ideal for students and researchers in statistics and psychology.

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Mastering Factor Analysis in SPSS | Reduce data complexity and identify key factors | Prof. Kelly Fan

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  1. Applied Statistics Using SPSS Topic: Factor Analysis By Prof Kelly Fan, Cal State Univ, East Bay

  2. Outline • Introduction • Principal component analysis • Rotations • Using communalities other than one

  3. Introduction • Reduce data • Summarize many ordinal categorical factors by a few combinations of them (new factors)

  4. Example. 6 Questions • Goal: a measure of depression and a measure of happiness (how pleasant) • 6 questions with response using number 1 to 7. The smaller the number is, the stronger the subject agrees. 4: no opinion

  5. Example. 6 Questions • I usually feel blue. • People often stare at me. • I think that people are following me. • I am usually happy. • Someone is trying to hurt me. • I enjoy going to parties. Q. Which questions will a depressed person likely agree with? A happy person?

  6. Data Set:

  7. Data Set:

  8. Principal Component Analysis Analyze >> Data Reduction >> Factor… The bigger the eigenvalue is, the more information this factor (component) carries.

  9. A Visual Tool: Scree Plot

  10. Communalities • Communalities represent how much variance in the original variables is explained by all of the factors kept in the analysis.

  11. SPSS Output

  12. SPSS Output

  13. A Visual Tool: Component Plot (Loading plots in SPSS)

  14. Discussion • Q4 & Q6 are highly and positively correlated and so should be at the same direction of any factor (here component 1 & 2) • Similarly, the other questions should be at the same direction of factor 1 & 2 (component 1 & 2) • Need a rotation!!

  15. Rotation: Varimax Method

  16. Varimax rotation

  17. Before and After Rotation

  18. Rotation: Promax Method (optional) • Used when factors (depression/happiness) are allowed to be correlated (non-orthogonal)

  19. Using Communalities Other Than One • When the original questions are not equally important • Different methods of “extraction”

  20. Un-weighted Least Squares • Initial communality of a question is the R^2 (squared multiple correlation) of regressing all others against this question

  21. Before and After Varimax Rotation

  22. Varimax rotation

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