[R] mixed effects or fixed effects?

From: dan kumpik <daniel.kumpik_at_physiol.ox.ac.uk>
Date: Wed 24 Jan 2007 - 15:25:39 GMT


Hi,

I am running a learning experiment in which both training subjects and controls complete a pretest and posttest. All analyses are being conducted in R. We are looking to compare two training methodologies, and so have run this experiment twice, once with each methodology. Methodology is a between-subjects factor. Trying to run this analysis with every factor included (ie, subject as a random factor, session nested within group nested within experiment) seems to me (after having tried) to be clumsy and probably uninterpretable.

        My favoured model for the analysis is a linear mixed-effects model, and to combine the data meaningfully, I have collated all the pretest data for controls and trained subjects from each experiment, and assumed this data to represent a population sample for naive subjects for each experiment. I have also ditched the posttest data for the controls, and assumed the posttest training data to represent a population sample for trained subjects for each experiment. I have confirmed the validity of these assumptions by ascertaining that a) controls and trained listeners did not differ significantly at pretest for either experiment; and b) control listeners did not learn significantly between pretest and posttest (and therefore their posttest data are not relevant). This was done using a linear mixed-effects model for each experiment, with subject as a random factor and session (pretest vs posttest) nested within Group (trained vs control).

        Therefore, the model I want to use to analyse the data would ideally be a linear mixed-effects model, with subject as a random factor, and session (pre vs post) nested within experiment. Note that my removal of the Group (Trained vs Control) factor simplifies the model somewhat, and makes it more interpretable in terms of evaluating the relative effects of each experiment.

        What I would like to know is- a) would people agree that this is a meaningful way to combine my data? I believe the logic is sound, but am slightly concerned that I am ignoring a whole block of posttest data for the controls (even though this does not account for a significant amount of the variance); and b) given that each of my trained subjects appear twice- one in the pretest and once in the posttest, and the controls only appear once- in the pretest sample, is there any problem with making subject a random factor? Conceptually, I see no problem with this, but I would like to be sure before I finish writing up.

Many thanks for your time

Dan



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