Donald Burrill wrote:
> Probably the best approach is the multilevel (aka hierarchical) modelling
> advocated by previous respondents. Possible problems with that approach:
> (1) you'll need purpose-built software, which may not be conveniently
> available at USD; (2) the user is usually required (as I rather vaguely
Very good point.
> recall from a brush with Goldstein's ML3 a decade ago) to specify which
> (co)variances are to be estimated in the model, both within and between
> levels, and if your student isn't up to this degree of technical skill,
> (s)he may not have a clue as to what the output will be trying to say.
MlWiN is much easier to use (though does require good knowledge of standard
GLM regression equations). The default is just to model the variance at each
level, though adding in variance parameters is very easy. I'd love to have a
standard GLM program with the same interface (adding, deleting terms from a
visual representation of the regression equation).
I agree that in lots of cases multilevel modeling may be the "ideal" choice
but not sensible in practice (sample size considerations or for some teaching
contexts).
For some problems, a multilevel model is not required at all. By treating
repeated obs as independent N is inflated. It may be sufficient (depending on
what effects you want to estimate) just to correct N to reflect this design
effect. Snijders and Bosker's book is pretty lucid on this (pp16-24).
Thom
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