How to model the effect of time in a balanced repeated measures design with 2 measures each at baseline, during instruction and post instruction? I want to examine growth for 38 participants. I have 2 scores at each of  3 separate phases of instruction - baseline, during instruction, and post instruction. Restated, I have 2 baseline scores, 2 instruction scores, and 2 post scores for each participant. All participants are in one group. 


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*How should I test growth? 

*A one-way repeated measures ANOVA, or a two-way repeated measures ANOVA? 

*Should I just put this in as 6 consecutive scores (e.g. Time 1-6) and look at the factor of time, or should I look at an instruction phase by time interaction. Does that even make sense?

 A: There seems to be many possible ways to do this, depending on what you want exactly.
The idea of ANOVA or repeated measure ANOVA only makes sense (to me) if you have different treatment groups (say, half of the 38 received different instructions etc.). Since all participants belong to 1 group, it seems to me all you need is a good old paired t(z)-test. 
But firstly, you need to define growth. E.g. if you define growth to be the difference between the last measurement and the first, then you can run:
t.test(Y6,Y1,paired=TRUE) (where Y6 and Y1 are the measurements at the corresponding time).
If you define growth to the the difference between the last 2 and the first 2, then you can first derive that variable, and reduce the problem to the previous case.
Ypost=(Y6+Y5)/2;Ybase=(Y1+Y2)/2 and then t.test(Ypost,Ybase,paired=TRUE)
This is of course the simplest way to do the analyses, there is arguably more sophisticated ways to do things, like a linear mixed model with random participant effect and temporally correlated error structure. But without knowing what exactly you want to do, it seems best to stick with the simpler way (i.e. t.test).
