I have the following situation: - ~300 participants, for each of them I have ~30 participant-specific data (from questionnaire) - For each participant I have ~200 points of data consisted of 1 independent variable (reaction time) and 1 dependant/predicted variable (attention).
The goal is, when given a new participant with his ~200 points of data - we would make a prediction for each of his points.
So I clearly want a case-by case prediction, but take specific participant traits into account.
I'm used to working with data "case-wise", where each case has 1 dependent(predicted) variable and a lot of indepndent (predictors). But here I need to predict variation within participant, but also take participant into account.
- I have tried normalizing all response times within participants, and then train model on each case, ignoring variation between participants. No good.
- Another option I considered: just adding all participant-related data to every point within participant, making data overabundant, but flat. Didn't try it yet, but seems more reasonable.
But is there a good or conventional way to treat this kind of data? I believe for some people it is a common case. I mean to somehow explain to the model, that "these 200 data points are for one participants", not just "these 200 data points have the same value for 30 traits"?
Tool-wise: I plan to try Random Forest and Neural Network to see which does better. Doing it all in R.