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:


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*Is it "okay" for the question to still be positively skewed after the transformation? Is any further action needed (any further transformation(s))?

*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? 

*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))

 A: 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. 
A: I agree with Peter that scores on 1-6-range items should not be transformed - not because it's theoretically wrong but because it's unlikely to help.  One option is, as I think Peter implies,  to find sound ways to combine such items into scales, which may have more interval-level properties than do the items themselves.  These scales could then be inputs for further procedures that address your research questions.  
A second option is actually to use the original items in a factor analysis.  This goes against common wisdom, and it won't help you if you are using maximum likelihood factor extraction, which depends on normally distributed items.  But I am often surprised at how useful principal axis factor analytic results turn out even when based on skewed distributions from what were originally considered ordinal-level items.  Commonalities, variances explained, and factor loadings often come out satisfyingly high despite this apparent violation of best practices. If you try this, you may find it's helpful, but don't take the loadings and other results too literally:  they'll be approximations.  E.g., I wouldn't report loadings to more than 1 decimal place.
A: In my experience, transforming data using any combination of techniques (Square-root, Log, Inverse, etc.) is entirely valid from a statistical point-of-view. The only issue is that interpretation becomes increasingly difficult. For example, if two items load on the same factor and one has a log transformation, then you must interpret the two as related in some nonlinear fashion. 
Moreover, Norris & Aroian (2004) indicate transformations may not even be necessary: http://www.ncbi.nlm.nih.gov/pubmed/14726780
