1. Analysing data from a questionnaire:
Reliability and PCA

2. Topics Coding item scores Reliability of a straightforward scale
Tests measuring more than one construct
Presenting the results

3. Recoding negative items
Masculinity questionnaire:
I agree that blue is a lovely colour
I agree that pink is a delicious colour
Recode as (max + min) – score
e.g. scale is 1—7 , recode as 8 – score
Or scale is 0 – 7, recode as 7 – score

4. Reliability of a scale measuring one construct
Split-half
Cronbach's alpha

5. Warwick sweetness scale
For example
Warwick sweetness scale

6. Scale measuring more than one construct
PCA
Correlations among items
Extraction / Rotation
PCA / varimax
Other extraction (FA) if components are correlated (e.g. anxiety & depression)

7. In action...

8. Note... Try different solutions, forcing the number of dimensions
Eigenvalues v. scree plot
Loadings; cut-off
“Simple structure” is preferred

9. Component scores
Give each person an”overall” score for Size or Smart: but how
Chess + IQ + Alevel (?)
Give more weight to the ones with
biggest loadings

10. Check that it is1-D Calculate overall scores?
PCA and scales with 1-d
Check that it is1-D
Calculate overall scores?

11. Do I need to check? -You must!
Standardised tests
Do I need to check?
-You must!

12. Presentation of reliability analysis
Method v. Results?
Give value of coefficient
p-value is generally irrelevant
PCA – report Eigenvalues or % variance explained for each component
Explain how you selected the solution you preferred
Provide a table of loadings (use a cut-off to simplify)

13. Table of loadings C1 C2 Height .93 - Weight .94 - Shoe .94 -
Chess IQ
Maths

14. Further reading
Dunbar (1998) Data analysis for psychology. London: Arnold. Ch6 pp85-88; Ch11
Klein, P. (1994) An easy guide to factor analysis. New York, Routledge.
Vowles et al. (2008) The chronic pain acceptance questionnaire.... Pain, 140,