Principal Components/Factor Analysis
Main aspects
- Reduces a number of questions down to a few key factors
- Works by grouping together questions that are correlated
- Most commonly used on attitudinal data e.g. might reduce two dozen statements concerning smoking (agree strongly ... disagree strongly) down to half a dozen factors
- Resultant factors are usually independent of each other, measuring quite separate 'dimensions'
- Number of factors is determined by how much variation they explain and their interpretability
- Each respondent can be scored on each factor which can be shown as means in crosstabs and are sometimes used as input to a subsequent cluster analysis