How to perform reverse score transformations? (non-normal data)


Sadly, my data are significantly non-normal, negatively and not positively skewed, so that leaves me, according to some statisticians, with only 1 available option (reverse scoring transformations; log, square root and reciprocal transformations I've heard that work wonders on positively skewed data only/data that were previously negatively skewed and now are reverse scored to be positively skewed). I've Googled the technique and all the answers that I've found refer to reverse scoring when the data-points reflect scale scores (e.g. if data-points reflect participants' answers on a 1-7 Likert scale, all you have to do is to pick the next highest number and subtract the scores from that one, e.g. 8-7, 8-6, 8-5, etc.)

My dataset, though, contains differences in RTs (i.e. judgment errors) and I find it difficult to apply the same straight-forward technique I've just described in parantheses above to my participants' mean JEs. Suppose the highest JE for a given negatively-skewed level of my IV is 207.60 - it doesn't seem sound to me to subtract each JE from the next highest number, i.e. 207.61.

I must either be confused with respect to the principle behind reverse score transformations OR use the method that I myself describe above wrongly. Could you please help?
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TS Contributor
Sadly, my data are significantly non-normal, negatively and not positively skewed,
Why do you bother? Usually, data (the dependent variable was meant here, I suppose) need not be normally distributed. Sometimes the residuals from the model (t-test, linear regression, ANOVA) are assumed to be normally distributed, but this is important only if sample size is small (n < 30 or so).

With kind regards