Is it OK to fit random effects before fixed effects? I'm using lmer to analyse my data, building nested models and using anova() to compare them against each other in an incremental way. Now, I know enough to only test a single term at a time (i.e. only one term changes between model 1 and model 2, so that when I compare them with anova I know what question I am answering), my question rather relates to the order in which you should test your terms of interest.
Is there a preference or a rule that states whether you should fit your fixed effect terms before your random effect terms? If so, what is the reason? 
Thanks all very much
 A: I found Field et al. 'Discovering statistics using R' had a helpful, simple walkthrough for running Multilevel Model (MLM) analyses in R. 
1) Run a model with dv and just the intercept e.g. dv~1 (you can use gls for this). 
2) Run a model as above including Subject as the random factor (using lme or lmer).
3) Compare the intercept only model with the model with 'Subject' as the random factor (you can use anova() for this) - this will tell you if there is a need to conduct MLM, that is if there is a benefit of including 'Subject' as a random factor. If so, MLM might be worthwhile and so you can proceed, if not just do a plain regression/ ANOVA. 
4) Start adding predictors to the model one by one (keeping 'Subject' as a random factor) and comparing the model with each addition. 
Not sure whether this answers your question as I am relatively new to Multilevel modelling, but thought I would post anyway. Hope this helps a little. 
A: There is a paper by Prof Judith Singer which describes by example using a similar approach. She uses SAS instead of R but the output is the same and you will be able to get a feel for how she built a model up, sequentially including random effects and then fixed effects. If I recall correctly, some of the random effects at the higher level became less important after including group level covariates in her model. 
http://www.biostat.jhsph.edu/~fdominic/teaching/ML/sasprocmixed.pdf
