Hi Dr. Wicklin,

Thank you very much for this post. I have been searching for the most appropriate way to handle my data in preparation for regression analysis, and this seems like a great option. My outcome variables (and their residuals) are negatively skewed beyond what would be appropriate for analysis. I am using multiple imputation, so I don't feel comfortable doing a log-transformation because I am concerned about introducing bias in the imputation. Based on this post, I think my best bet will be to reverse code all of my predictor and outcome variables (so that interpretation will be more intuitive). I will then create generalized linear models and specify a gamma distribution, which will account for the skewness of the now positively coded variables. I am doing my analyses in R rather than SAS but I believe the principles still apply. I considered the advice of the commenter above but I don't think beta analyses will be appropriate for me given my outcome variable is not conceptualized as a proportion or percentage.

I am curious whether you have a publication I can cite that recommends this approach or if you are aware of an article that has implemented this method. I am looking for something to cite in my thesis as an example of why this approach is necessary.

Thank you again,

Kailyn Bare