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Re: st: Finite population correction with clustering of SE at a different level than the strata
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re: st: Finite population correction with clustering of SE at a different level than the strata
Date
Sun, 10 Jun 2012 22:18:58 -0400
I should have added: In Stata, you can do the randomization version of any test with -permute-. Among the tests to consider are the two-sample t-test or a rank test like -kwallis-. You can compute the CI for a parameter by inverting the test as discussed (briefly) at www.epibiostat.ucsf.edu/biostat/sen/statgen/permutation.html. With 23 villages per group, you will probably need to simulate the p-values for each parameter, and that web page recommends 10,000 replicates for high precision. Also Roger Newson's -somersd- (from SSC) will compute CIs for Hodges-Lehmean median difference (which is not the same as the difference in medians).
Steve
[email protected]
Inference can also be based on the treatment randomization (e.g.http://www.ncbi.nlm.nih.gov/pubmed/8804140), no superpopulation needed. Randomization-based hypothesis tests would be done on village-level estimates, and confidence intervals calculated by inverting the tests.
There is, of course, extra information about individual households that can be mined. The village-to-village contamination that Ole reports is a problem for which I have no advice.
Steve
[email protected]
On Jun 6, 2012, at 7:00 AM, Ole Dahl Rasmussen wrote:
Dear Austin
Thanks for your response. Let me try to clarify.
I'm running a randomized trial. 23 villages do not get treatment. So there's not complete correlation between interested and treatment. 23 villages do get treatment, which is an offer to participate in a microfinance intervention. Interest was elicited in all village prior to randomization. The regression is supposed to give the intent-to-treat estimate and I could IV for the effect of treatment on the treated, except that I have contamination.
Best,
Ole