I first learned about quasi-logistic regression and the "emprical logit" from Dale Barr's (2008) paper, which just happened to be right next to the growth curve analysis paper that Jim Magnuson, J. Dixon, and I wrote. I came to understand and like this approach in 2010 when Dale and I co-taught a workshop on analyzing eye-tracking data at Northwestern. I give that background by way of establishing that I'm positively disposed to the empirical logit method. So I was interested to read a new paper by Seamus Donnelly and Jay Verkuilen (2017) in which they point out some weaknesses of this approach and offer an alternative solution.
