In the analysis of test scores (e.g., in Education or Psychology), common analysis techniques often assume that data are normally distributed. However, perhaps more often than not, scores tend to deviate sometimes wildly from normal.
I am familiar with some basic normalizing transformations, like: square roots, logarithms, reciprocal transformations for reducing positive skew, reflected versions of the above for reducing negative skew, squaring for leptokurtic distributions. I have heard of arcsine transformations and power transformations, though I am not really knowledgeable about them.
So, I am curious as to what other transformations are commonly used by analysts?