Hello
I am trying to "normalize" a variable which has negative values, which entails obtaining values smaller than 3 for both the skewness and the kurtosis of the resulting distribution.
If I simply use the log function I loose a lot of cases, which I would like to avoid. I also tried many other options (using the gladder command, among other options), including the most "popular" one which is adding the a constant "a" (with "a" being the absolute of the biggest negative value of the variable), and then doing the log as follows: generate ln_variable = ln(variable+a). However, when I do this, the skewness of ln_variable is a large negative number and its kurtosis is a very large positive value. Does anybody know of a transformation which I can use in order to normalise my variable (i.e. of a transformation which does not entail getting rid of negative values and which also keeps skewness and kurtosis smaller than 3)? Any help you can provide would be much appreciated.
Kind regards,
Joao
I am trying to "normalize" a variable which has negative values, which entails obtaining values smaller than 3 for both the skewness and the kurtosis of the resulting distribution.
If I simply use the log function I loose a lot of cases, which I would like to avoid. I also tried many other options (using the gladder command, among other options), including the most "popular" one which is adding the a constant "a" (with "a" being the absolute of the biggest negative value of the variable), and then doing the log as follows: generate ln_variable = ln(variable+a). However, when I do this, the skewness of ln_variable is a large negative number and its kurtosis is a very large positive value. Does anybody know of a transformation which I can use in order to normalise my variable (i.e. of a transformation which does not entail getting rid of negative values and which also keeps skewness and kurtosis smaller than 3)? Any help you can provide would be much appreciated.
Kind regards,
Joao
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