# regression on hierarchical data

First of all, I'm not sure whether my choice of a title and/or tags is appropriate for the questions I have. If they're not, it is also a fair chance that there is already another similar question around, which I haven't found (for the obvious reason). In this case, I'm sorry for the duplicate and willing to be redirected to it.

Following situation. While running repeated measure ANOVA (mixed effects), I decided to run also a regression in order to control for a covariate. This variable is used to approximate time, but is itself discrete (no. of repetition of irrelevant in between relevant events). At this point, I see various options of how to construct the model, which are all reasonable enough from preventing me (with only little experience with regressions) to come to a definite conclusion. What my considerations boil down to, are basically these questions:

1. How do I handle the fact that interindividual differences is a major predictor in the model, in which I am not interested, though?

Both excluding the factor "subjects" entirely from the model, and averaging can't be a solution, so my intuitive idea is to run a separate model for each subjects and unify the output together afterwards. However, browsing the web, I read that using nlme and adding subjects as random factors could also be sufficient. Unfortunately, I don't really know how.

2. Do I use the raw data (with all the repeated measures), or do I aggregate the data first and run the regression on all the means?

Using the raw data seems to be preferable; however, I wonder whether this is really true and if so, why.

3. Related to the fact that the regressor is actually discrete, I wonder whether running a regression is still a valid option. And if so, whether 5-6 data points would be sufficient to fit a model.

I don't believe it is a good idea to use a regression in this case, but I don't understand in which way this should be different from other model-fitting papers that I read, which didn't use the term "regression" but did something very similar on a comparable dataset.

Writing this, I realized, that some of my questions are not really related to the title. At any rate, I am very thankful for any advice.

Best,

E

• In what way is your data hierarchical? – StatsStudent Oct 29 '15 at 21:05
• Hierarchical in a repeated measures sense. That is, I have multiple subjects, all of which were tested on all conditions. I'm not really interested in each subject's performance, but I can't exclude them either, because interindividual differences explain quite a lot of all variance. Not sure whether "hierarchical" is the best term for that. Since I have effects on different levels I thought it is appropriate enough. – userE Oct 30 '15 at 11:23