I want to model data from behavioural experiment (mixed model using R's lme4) with continuous DV and two predictors: condition (binary) and block (24 subsequent blocks of experimental task). Both are my fixed effects, and (if model converges) also random effects. I'm wondering how I should handle ordinal block variable given I'm interested in both main effect of block and comparisons of conditions within blocks. Should I code block as factor or continuous (numeric)?
I want to compare conditions within blocks, so if I code block as factor I'm getting such comparisons straight away from the model's summary. But then how I can report if there is main effect of block?
If I code block as numeric (which I think can be justified as all blocks are of equal length etc), my model's output would give me main effect of block and its interaction with condition, but how can I then compare conditions within specific blocks? I have problems with conceptualising pairwise comparisons of binary and continuous variable.
It would be easiest to fit both models (with factor and numeric version of block), and report pairwise comparisons and main effects from them respectively, but I have a feeling that this wouldn't be appropriate - or would it? Any opinions/tips on how should I approach this?