GLM with non-linear predictor


I am analyzing a dataset looking at procedural skill development. The data itself is binomial, whether or not a procedure was successful. When averaged over multiple participants by attempt, it forms what appears to be a growth curve (or Gompertz as I have read).

I would like to run a GLM to identify which other factors play a significant role e.g. location of the procedure, perceived difficulty, patient factors, etc. I have run a GLM in SPSS with the procedure attempt number as a cofactor (i.e. first attempt, second, third....). The output looks like what I would have predicted, but I feel like I should not be using the attempt as a co-factor since it's relationship is non-linear.

Is this fine, or is there another test I should be looking into? Or is there a way to linearize the dependent variable? I've read a few things about taylor series, but am getting way in over my head.



Omega Contributor
So you have multiple outcomes for the same person (e.g., attempt)? If so, you may be looking at multilevel logistic regression and you could control for attempt number as a predictor. The key is that you may need to control for outcomes clustered in person, otherwise there my be treated as being independent.