In my experiment participants had to make a binary (yes-no) decision about various stimuli. I have two categorical (stimulus characteristics coded as -1 0 and 1 and treatment group coded as 0 1) and three continuous (questionnaire scores) variables.
After useful feedback on a previous question of mine, Difference between generalized linear models & generalized linear mixed models in SPSS, I decided to analyse this dataset with Generalized Estimating Equations (in SPSS). There are several options for selecting link functions that are suitable for binary data. I ran the model with binary logistic binomial identity, binomial Logit, binomial probit and some other ones. If I compare the goodness of fit values (Quasi Likelihood under Independence Model Criterion and Corrected Quasi Likelihood under Independence Model Criterion), these are smallest when I select the link function binomial log complement. Does this mean that this is my best option?
The results actually change a little bit in terms of significance.
In another post I read that I should plot my data. But I don't really know what condition I should put on the x-axis, I don't have a time variable or anything like that. Is looking at the goodness of fit a good idea to do instead?