Hi Jason,

I think that Thierry's suggestion might be just what you need and it has the
advantage of being likely to work.

Firstly, modeling change in each marker's severity as a slope is a
compromise that doesn't fit well with your hypothesis. It assumes that if a
marker's severity changes over time then it changes linearly - whereas your
hypothesis is more general, a marker may just change day to day in a random
fashion.

Nesting a day random effect inside a marker effect, (1|marker/day), models
marker severity as changing randomly day to day around an overall random
effect for marker. The standard deviation of the nested day effect will tell
you something about its importance and you can also compare two models, one
with and one without the nested day effect. I believe likelihood ratio test
are valid between models with different random effects? (whereas there are
problems when models have differing fixed effects).

I would compare, for example, these two models:

m1 <- lmer(score ~ 1+day+gender+school+(1|marker)
      +(1|candidate)+(1|batch), mg2007_sub)
m2 <- lmer(score ~
1+day+gender+school+(1|marker/day)+(1|candidate)+(1|batch), mg2007_sub)


This is assuming that there is typically >5 levels of day per marker. I'm
assuming from your first post that markers usually marked scripts on most of
the 15 days.


Andy.


andydolman@gmail.com

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