I was wondering whether you could help me with the following issue:
I am running a negative binomial model (overdispersed) on count data of sampled insects at 22 sites. Here, I would like to select the best predicting explanatory variables out of five potential variables (i.e. I am not testing a hypothesis, but rather explore the data). To do so, I used a model selection approach based on AICc (Akaike Information Criterion). Now, I learned that a model selection based on AICc (following Burnham & Anderson) is quite heavily discussed.
My question here is: since when is the model selection approach based on AICc not acceptable anymore and why? It seems that I have missed a discussion on this issue. If you could point me in any direction here, it would be very much appreciated.
Many thanks, Tanja