Shravan writes,

Here is a typical problem I keep running into. I’m analyzing eyetracking data of the sort you have already seen in the polarity paper. Specifically, I am analyzing re-reading times at a particular word as a function of some experimental conditions that I will call c1 and c2. I expect an effect of c1 and c2, and an interaction. I get it when I analyze on raw reading times (milliseconds) but get only the interaction when I analyze on the log RTs. The logs’ residuals are normally distributed and the raw RTs’ are not. I am inclined to trust the log RTs more because of the normal residuals (theory, however, is more in line with raw RT-based results). But reviewers keep insisting I analyze on untransformed (raw) reading times, and your book also advises the reader to ignore residuals.

My reply:

1. The log scale makes more sense to me. On the other hand, the last time I analyzed eye-tracking data was 17 years ago, and I didn’t know anything about the experimental setup even then!

2. If an interaction might be important, I’d include it in the model. Then if its coefficient isn’t statistically significant, you can say that.

3. Hey, we do look at residuals in our book! Take a look at Chapter 5.

4. I wouldn’t pick the model based on normality of the residuals. As we discuss in the book, the distribution of the residuals is the least important aspect of the model.