I have a five condition dataset where all conditions share variables
However condition 2 additionally has
reading.time.2 and conditions 3-5 additionally have
score (but not
These differences in variables derive from the purpose of the conditions
- Condition 1: Control
- Condition 2: Stronger Control (extra reading time equivalent to treatment)
- Condition 3: Treatment 1
- Condition 4: Treatment 2
- Condition 5: Treatment 3
All conditions have approximately 60 participants each (N=300).
So far I have analyzed the data 2 ways
- Model A:
postscore ~ condition * prescore * reading.time.1(using data for all five conditions). I found significant differences here b/w two conditions.
- Model B:
postscore ~ condition * prescore * reading.time.1 * trials * score(using data from just conditions 3-5). I found significant differences here b/w all three conditions.
My question: I'm wondering if there's a way to combine all predictors into one model across all five conditions using some kind of regression. If not, should I be looking at multiple sample/group SEM