Solved – Transformations not correcting significant skews

I am running an experiment which measured using Likert Scales and I have 6 variables out of 24 which either have significant skewness or kurtosis. These are mixed with some positive and some negative values.

I transformed my data using log transformations, Square root transformations, reciprocal transformations and reverse score transformations, but this did not solve the problem.
In fact, all this did was give me a greater number of significantly skewed variables (for example square root transformations left me with 11 variables significantly skewed or suffering from kurtosis).

Is there any other type of data transformations that anyone can recommend that may help, especially when some skews are positive and others are negative? If so, does anyone know the SPSS syntax involved?

A few points

  1. If your variables are individual Likert items on (say) a 7 point scale, then it is arguable whether they should be treated as continuous at all.

  2. This sounds like you are using these variables as independent variables. If so, why do you want them to be normally distributed?

  3. It also sounds like you applied all the transformations willy-nilly to all the variables. This is not right. Positive skew can be corrected (at least sometimes) by logs. Negative skew would be made worse; negative skew could be dealt with (perhaps) by squaring or reciprocal. But, again, applying these transformations to a Likert scale makes relatively little sense, usually.

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