Do data transformations before factor analysis need to be consistent across different variables?

(This question continues the previous one)

I am creating a questionnaire, and I have identified 3 questions which are skewed (2 positively skewed & 1 negatively skewed). I successfully transformed two of the questions using Lg10 and inverse of Lg10 on SPSS, but the second positively skewed question is still positively skewed even after the Lg10 transformation. My questions are the following:

1. Is it "okay" for the question to still be positively skewed after the transformation? Is any further action needed (any further transformation(s))?
2. Can I use a different transformation on this specific question (the remaining positively skewed one) or do I have to use the same transformation for all of the skewed questions?
3. What is the next "strongest" transformation after Lg10 on SPSS? How would it be entered on SPSS? (e.x. negatively skewed question = Lg10((Max. Score + 1) - Question))
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Regarding 1) Factor analysis is based on correlations/covariances. When a highly skewed variable is part of a correlation, the correlation can be affected by the extreme points. This will affect the factor analysis, although I do not know of literature on the extent of the effect (it's probably been studied, though).

Regarding 2) You do not need to use the same transformation on each variable. But transforming variables in different ways and then doing factor analysis can lead to factors that are somewhat hard to interpret.

Regarding 3) I don't know SPSS, sorry.

More generally, what is the nature of these questions? Are they Likert-type scales? Physical measurements? Or what? Ideally, you could tell us what they actually mean.

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Thanks for your response! The questions are Likert-type scales. Example: "I do not have a reason for living" with answer choices 1 - 6 (1 = strongly disagree/ 6 = strongly agree). –  Madeline Aug 21 '12 at 11:52
I would NOT transform Likert type items in any way. Items that are very highly skewed, with only 7 levels, are items where nearly all gave a response at the end. These items may not be useful at all; they add little information. I would first look at Cronbach alpha for items that don't correlate well with the others. I'd consider deleting the highly skewed items. However, all this depends on SUBSTANCE and what the actual questions are. It may well be that factor analysis is not the best tool. –  Peter Flom Aug 21 '12 at 12:15
I'm not to say I disagree with you, but I (a kind of) feel lack of clearness in your response. For example, you don't explain why transformation of a 6-point rating scale is unlikely to help and what you mean under "help" here. Also skewed distributions from ordinal-level items looks like farrago. Linear FA don't have ordinal input, it treats everything as interval. –  ttnphns Aug 21 '12 at 16:44