I want to do principal component analysis (factor analysis) on SPSS based on 22 variables. However, some of my variables are very skewed (skewness calculated from SPSS ranges from 2-80!!!).

So here are my questions:

1. Should I keep the skewed variables like that or Could I transform the variables on principal component analysis? If yes, how would i interpret factor scores?

2. What type of transformation should I do? log10 or ln?

3. Originally, my KMO (Kaiser-Meyer-Olkin) is 0.413. A lot of literature recommended the minimum of 0.5. Can I still do factor analysis or do I need to remove variables to raise my KMO to 0.5?

I want to do principal component analysis (factor analysis) on SPSS based on 22 variables. However, some of my variables are very skewed (skewness calculated from SPSS ranges from 2–80!).

So here are my questions:

1. Should I keep the skewed variables like that or could I transform the variables on principal component analysis? If yes, how would I interpret factor scores?

2. What type of transformation should I do? log10 or ln?

3. Originally, my KMO (Kaiser–Meyer–Olkin) is 0.413. Much literature recommends a minimum of 0.5. Can I still do factor analysis, or do I need to remove variables to raise my KMO to 0.5?