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

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