I'm separately analyzing 5 subsets of my data and running multiple linear regressions with the same outcome variable in each. (I am also running a single regression with all the subsets and using the subset variable as a predictor, but it is useful to run separate regressions for each subset because the results are quite different for each subset, in ways you would not be able to tell from the overall regression.)
The outcome variable is continuous and, prior to the log-transformation, I was modeling it in its original form. It is usually positive but in some cases zero (for that reason, I add a constant when log-transforming it.)
The variable is highly skewed in 4 subsets so I'm log-transforming it with a constant, chosen separately for each subset to lower the skewness of the variable in that subset to acceptable levels.
The thing is, the variable in the 5th subset is only slightly skewed (.28), well within the acceptable range. However, if I have a single table illustrating all 5 regressions, it's going to be hard for any reader to compare the magnitude of coefficients across the different subsets if 4 of the subsets have a logged y variable and one of them doesn't.
So my question is, does it make any sense to log-transform the y variable for the slightly skewed subset too? Would this make the results more accurate?
I tried doing so, adding a constant to reduce the skewness all the way to .006. When I run the regression with the log-transformed y variable, the results are mainly the same, but now two additional x variables have statistically significant effects. So it really does change the overall results.
On the one hand, presenting my results this way would be easier to interpret for the reader, and now the outcome variable is utterly devoid of skewness. But on the other hand, it wasn't necessary to log-transform that y variable for that subset. Is there a risk I actually made my results less accurate? Is there any other reason why this is a bad idea?