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st: RE: polytomous logistic regression and logistic regression using weights


From   "CJ Lan" <CJ@jupiter.fl.us>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: polytomous logistic regression and logistic regression using weights
Date   Wed, 9 Nov 2011 16:01:20 -0500

For the multinomial logit, disregard univariate or multivariate, the
covariate could be category dependent initially.  After parameter
estimation, the covariate could be eliminated if it is tested not
statistically significant.  

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Miranda
Sent: Tuesday, November 08, 2011 11:05 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: polytomous logistic regression and logistic regression
using weights

Hello,

I have a statistical question, which may or may not be related using
Stata.

I have a categorical outcome variable which has three categories. I will
use the multinominal logistic regression for both the univariate and
multivariate analysis. However, with regarding to the univariate
analysis, their might be such circumstances that certain covariate is
associated with one category of the outcome while not with the other
category. Then how should I make the decision of whether or not to
include this covariate in multivariate analysis? Any other techniques I
could use for the bivariate analysis?

Another question: I am using the respondent driven sampling (RDS)
methods for recruit participants in the study and thus using the related
analytical tool (RDSAT) to do the data analysis. For those of you who
are familiar with this technique, individual or population weights of
the outcome variable are usually exported from the RDSAT and imported
with original data. When doing univariate and/or multivariate analysis,
these weights are usually applied to the outcome variable. When using
Stata, it does have a tab for weights selection in the logistic
regression model. I am not sure whether I need to choose frequency
weights, sampling weights.

Best,
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