- The general rule of thumb of the minimum sample size are not valid and useful.
- What I did with the data I have:
- Repeat the method Garson (http://www2.chass.ncsu.edu/garson/pa765/factor.htm#kmo) proposed until the KMO overall is over .60.
- Check the communality of each variable. Drop the variables that has the smallest communality using the above Garson method, until the communalities of all variables are above .60.
- Check the mean value of all communalities to ensure that the mean value is over .07. If not, repeat step 2.
- Use Kaiser strategy (dropping all components with eigenvalues under 1.0) and Scree plot to determine the number of factors.
- Set the loading size cut-off value as .60, and drop the factors that has less than 3 variables.