In this previous question @adhesh asked about the benefits of coding a binary variable zero-one rather than one-two. I realised when I wrote this answer that I had quite a lot to say about the topic of coding and scaling variables in order to maximise interpretation of regression coefficients.
Nonetheless, I also routinely read papers and theses in psychology that present regression models, where it seems that little thought has gone into the scaling of variables to maximise ease of interpretation of model coefficients. Often, the presentation of standardised betas is the extent of it. I also see this problem come up when presenting means and standard deviations of psychological variables where the scale chosen makes it difficult to interpret where the sample sits on the variable in absolute terms.
- What are the principles of scaling variables to maximise interpretation?
- Or alternatively, what is a good resource that succinctly outlines the principles of scaling variables for maximising interpretation?