That’s very reassuring. Thanks so much for taking the time to look at it for me Steve. I will be sure to try different covariance structures now that I know the model is set up properly Regarding how it affects things to put in a type= option, this is my current understanding (from a non-statistician perspective) which I hope I have right! random intercept week/subject=student ; or equivalently random intercept week/subject=student type=VC ; This models the trend over weeks by student. That is, whether the effect of week on grade depends on which student we are talking about, or, put another way, whether there is a different change in grade over the weeks for each different student. This accounts for the correlation between repeated measurements on a student, but assumes the correlation is the same no matter how close together or far apart the weeks are. random intercept week/subject=student type=ARH(1) ; This models the trend over weeks by student and allows the correlation between repeated measurements on a student to decline as weeks get further apart. Exact pattern of decline depends on which type= we are talking about. ARH(1) for example, can accommodate unequal spacing and change over the weeks whereas AR(1) assumes that all measurements made the same number of weeks apart share the same correlation as each other. There are other covariance structures with other patterns and rates of change in variance.
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