On 8/16/08, Doran, Harold <[EMAIL PROTECTED]> wrote: > In terms of the "design" (which is a term used loosely) the schools were > not randomly selected. They volunteered to participate in a pilot study.
Oh, that's a next level of disaster, then! You may have to work with treatment effect models, of which there are many: propensity score matching, nearest neighbor matching, instrumental variables, etc. Those methods require asymptotics in terms of number of treatment units, which would be schools -- and I would imagine those are numbered in dozens rather than thousands in your study, so straightforward application of those methods might be problematic... At least I would augment my analysis with propensity score weights: somehow estimate the (school level) probability of participating in the study (I imagine you have the school characteristics at hand for your complete universe of schools -- principal's education level, # of computers per student, fraction free/reduced price lunch, whatever... you probably know those better than I do :) ), and use inverse of that probability as the probability weight. If the selection was informative, you might see quite different results in weighted and unweighted analysis. > In Wolter (1985) he shows the variance of a cluster sample with a single > strata > and then extends that to the more general example. It turns out though in > many educational assessment studies, the single stage cluster sample is a > norm and not so rare. I can see why. Thanks, I'll keep educational statistics examples in mind for those kinds of designs! -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.