 |
 |
Dear Hans, > I have 2 groups of subjects and I would like to compare them with a > t-test. I am interested in using Randomise since it generates the > near-exact null distribution for the test statistic in a non- > parametric way. My concern is about the first level variances (the > varcopes). Are these variances accounted for in randomise or does it > just consider between subjects variance? Randomise doesn't explicitly use the varcopes, but that does _not_ mean that it only considers between subject variance. The estimates you get from the first level are precisely estimates, i.e. they represent that particular subjects response with some first level error added on to it. Hence, when you are using only the copes you will enter in the responses of the different subjects (there is your between subject variance), and each of these responses will have some error/uncertainty (there is your with subject variance). So, even when performing an ordinary least squares (OLS) analysis using only the copes the first level error is taken into account. The times when it is important to know something about the first level errors (i.e. the 1st level varcopes) is if/when the first level errors are vastly different between different subjects. This could happen e.g. of you want to look at correct responses in some memory task and subject 1 has 100 correct responses whereas subject 2 has 1 correct response. Then we can estimate subject 1's response with much higher precision and in a 2nd level analysis we would want to giver higher weight to that subject by weighting the response with the 1st level varcope. > The other option that I can use is the standard FSL mixed effects > group analysis that will account for cope, varcope and dof plus the > between subjects variance. If you are talking about 2nd level fMRI data I think either way is fine. We mainly use Randomise for the cases where we cannot assume that the 2nd level errors are normal distributed, such as e.g. VBM or TBSS. It is often also the case that Randomise is a little more sensitive when one has low degrees of freedom, in which case GRFT tends to be a little conservative. Good luck Jesper