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st: Non-parametric tests for survey data? (e.g., Kruskal-Wallace)


From   "Michael I. Lichter" <mlichter@buffalo.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Non-parametric tests for survey data? (e.g., Kruskal-Wallace)
Date   Tue, 10 Feb 2009 16:24:46 -0500

I don't see any procedures for doing non-parametric tests (aside from chi-square in svy: tab) with complex survey data (stratified, unequal probabilities of selection). I am particularly looking for tests of difference in ordinal dependent variables across k groups (k > 2). Kruskall-Wallace is the most obvious test, but only available for non-survey data. I assume that these procedures are not available because (a) it's not clear what to do with weights in nonparametric analyses anyway (which I infer partly from the fact that none of Stata's nonparametric procedures take weights), (b) because there's no theory about whether/how they should work, and/or (c) because nobody has gotten around to it yet.

I'm looking for suggestions.

One possibility that comes to mind is to generate ranks using -egen- and analyze using -svy: mean- or -svy: reg- (I'd use one-way ANOVA if somebody could explain how to do it with -svy- commands). I could also do -svy: intreg- for the variables that represent ranges underlying continuous variables (since most of my ordinal variables do represent well-defined but unequal-sized ranges of underlying continuous variables, e.g., 1 = "> 1", 2 = "2-4" 3 = "5 or more"), but that would require -intreg- to be robust to floor effects, and I doubt that it is (since the method assumes an underlying Normal distribution). (I guess -mlogit-, -ologit- and -gologit2- are also possibilities.)

Thanks.

--
Michael I. Lichter, Ph.D.
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / E-Mail: mlichter@buffalo.edu

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