Whereby no interaction terms or main effects are significant in a model, should model simplification be carried out and the interaction terms removed sequentially until the remaining variables are either significant/insignificant?
Info on the model I am running: I am currently carrying out a GLMM with negative binomial distribution looking at the effects of different factors on the number of farmland birds along the edges of fields.
The main fixed effects included in the model are: Crop type (3 level factor, which was experimentally manipulated) Hedgerow structure (continuous) Percentage gaps (continuous) Month (2 level factor) with all 2 way interactions and 3 way interactions with month included. Field is included as a random factor to account for non-independence and offset is field length.
This is the first time i have deal with complex models and as i am partly hypothesis testing (whether crop type has an effect) I am unsure what the correct protocol is. The model output is included below. Thanks