Stepwise binary logistic regression, Forward or Backward? I want to run a binary logistic regression to understanding (modeling) factors affecting nest-site selection in a bird species. I have Presence/Absence data and 13 predictors. The sample size is small. 32 Presence points and 64 Absence points.
I want to run a stepwise binary logistic regression in R manually. I have 2 questions about it.
Which method can be better? Forward or Backward?
Which statistic is more important for removing or adding variables at each step? AIC or p-value of ANOVA (Wald test) ?

NEITHER: Do not use stepwise model building
I encourage you to practice searching for answers to similar questions first. And secondly, this kind of automated model-build has been roundly and soundly thrashed. It is, to crib from Monte Python, an ex-method!
There are many similar answers on CrossValidated providing the same insight.
