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I have 3 groups randomised to intervention A or B or control. Participants were measured pre and post intervention - should I use repeated measures ANOVA or ANCOVA with the variable measure at time 1 (pre) as the covariate? Variables are continuous. thanks

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Sheila, What did you go with in the end? I am currently trying to decide the exact same thing (only with a 2x2 design, experiment group vs. control group at Time1 and Time2). My numbers are small (n=19 in experiment and n=17 in control) so need to bear that in mind - plus the fact that at least one of the DVs is not normally distributed... Thanks! – user13833 Sep 4 '12 at 14:00
This is not an answer to the OP's question. At best, it is a comment. However, note that CV is not intended as a bulletin board or forum; you may want to read our FAQ. – gung Sep 4 '12 at 14:05

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I would generally say that you need a repeated measures model with group, pre_post, and an interaction term. What you want to know is whether the post-test score is dependent upon the intervention, so you need to see an interaction. The meaning of that interaction would depend on actual scores.

A simpler method might be to use a pre_post subtraction score. That would make the assumption that pre-test differences (significant or otherwise), do not affect the outcome at all, or are not present at all. Check your groups, and if they are very highly similar then the subtraction score and one-way ANOVA is an easy thing to do.

The ANCOVA is much more like the difference scores or also an ANOVA on the residuals left after removing pre-test. It's not wrong, and some people prefer it. I believe I read a paper once recommending it but even they qualified it because pre-test may be correlated with group. In that case interpretation becomes difficult.

Why not run the full ANOVA and the ANCOVA? If they reach similar conclusions you're safe. If they reach different conclusions then think much more about what your data mean and maybe come back on here and ask for help at the interpretation stage.

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thnx - see my response below – Sheila Mar 25 '12 at 15:49
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Actually, the post minus pre approach is one that you should always try to avoid. The issue is not about statistics. It is about measurement. When you calculate difference scores, the reliability of the measures become very low. You don't want that.

I prefer ANCOVA if the assumptions are met (and it will in most cases), because the interpretation is easier. The readers of your study will have to look at only the post averages, 3 averages in your case, instead of all 6. If you get an interaction effect, which is expected that you do, you'd have to do a post hoc comparison of that effect. This will give you even more p-values that make reading your paper even harder.

If you want others to cite your study, make it easier to read.

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why would you say that the reliability is very low? by reliability i assume you are talking about the same reliability as referred in psychometrics. in that case, shouldn't the random error of the pre and post cancel each other out? i.e. it affects everyone randomly. – RJ- Aug 29 '12 at 3:27
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I suspect the best approach would be to get difference scores (i.e., post - pre) for each experimental unit, and then run a simple one-way ANOVA.

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It may be the easiest but I don't think characterizing it as the "best" is reasonable. At the onset it precludes seeing if any baseline*treatment interaction exists, and as John mentions it is problematic if the baseline has a causal effect on the post-treament (independent of treatment itself). Paul Allison in this article gives some examples where change scores are preferable. – Andy W Mar 25 '12 at 15:24
this is very helpful _ i have already done a repeated measure and have a sign interaction but contrasts did not reflect this . so i looked at a change score (ie i made the new variable of the difference) and put this in a simple ANOVA and it showed where the signficiant differences were but I am worried that this is poor statistical methodology and that all the testing should be within one test?? maybe this isnt right - i think on a quick look that ANCOVA may show this significance but dont want to inflate the type 1 error. i dont know if group is correlated to outcome - i'll look at this – Sheila Mar 25 '12 at 15:48

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