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


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Non-parametric tests for survey data? (e.g., Kruskal-Wallace)
Date   Tue, 10 Feb 2009 21:38:07 -0000

I think you're basically right on the explanations. Given (a) and (b),
(c) is not puzzling. You can add (d): StataCorp prefers models. Most
user-programmers prefer models, for that matter.  

Nick 
n.j.cox@durham.ac.uk 

Michael I. Lichter

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.)


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