My questions deal with transforming/normalizing data. I am dealing with non-normal distributions, and I understand skewness and kurtosis, as well as the desirability to transform and “normalize“the dataset to approximate the normal curve. I am aware as well of the different types of transformations one can do on data of various directions and degrees of skewness in order to affect normalization (Square Root, Logarithm, Inversion, Reflection).

Here is my question:

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I’m using SPSS 13. None of the guide books, the helps, the tutorials or the websites I’ve found provide decent instructions on transforming data to create normal distributions. I believe you perform the required transformations in the Transform--->Compute function, but after that, I simply don’t understand how to proceed. Could someone tell me how to perform Square Root, Logarithmic, Inverse, and Reflection or other appropriate transformations on a variable?

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I understand in advance that many of the professionals on here might find my basic questions somewhat tedious to answer – In such a case, a reference to a book or website dealing with the issue in question would be just as helpful. In all cases, any advice, or insights are greatly and deeply appreciated.