Having trouble finding straightforward information this topic.
Basically, I'm trying to use the lme4 package to analyze my data, and the model looks something like (A ~ BCD) + (random effects term 1) + (random effects term 2).
'A' is a yes/no response, which, based on what I've read, indicates that I should use glmer. However, my experiment uses repeated measures - each subject undergoes many trials. It's a psychophysical experiment, so there are many subjects who essentially make yes/no judgements about many, many images. I've read that when there are many trials within a subject, you should use lmer.
What's the best way to go here? Sorry if the info given is too sparse; if anyone thinks they can help me out with this, I'll provide as much info as necessary.
TL;DR: When exactly should one use lmer vs glmer, especially in the context of psychophysical experiments where one subject will undergo many trials with binomial outcomes?
More info/part 2 of question: I initially analyzed my data using ANOVAs in SPSS. The SPSS indicated a highly significant interaction, one that is logical and predicted. When running the same data to modeled in glmer, that interaction in highly insignificant. When running through lmer, it is significant again.
If anyone can help shed some light on whether this makes sense or why it would be so, I'd appreciate it very much.