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Re: st: --svy & --pweights: problems for median, graphs & regression


From   Steven Samuels <sjhsamuels@earthlink.net>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: --svy & --pweights: problems for median, graphs & regression
Date   Thu, 11 Sep 2008 07:16:20 -0400



You are welcome, hafida. Looking over your post again, I saw that you were concerned about Normality. A Normal distribution for the error terms is not a needed assumption of survey regression commands, (nor of OLS, either, except to create prediction intervals). I have often run -svy: reg- on discrete outcomes. What you do need is a correct model for the mean.

-Steve


On Sep 11, 2008, at 5:10 AM, Nur.Hikmayani@studentmail.newcastle.edu.au wrote:


Thanks heaps, Steve!
Also for correcting the way I wrote commands...

hafida--




Nur Hafidha Hikmayani
School of Medicine & Public Health
The University of Newcastle
Australia




----- Original Message -----
From: Steven Samuels <sjhsamuels@earthlink.net>
Date: Thursday, September 11, 2008 2:07 pm
Subject: Re: st: --svy & --pweights: problems for median, graphs & regression


Hafida



a. Look at -help-   for -pctile- and -_pctile-.  These take pweights

b. -histogram- will take fweights, but not pweights: See http://
www.stata.com/statalist/archive/2007-10/msg00327.html and preceding

messages in the thread.

c. -svy: reg- automatically computes standard errors that are
robust
to heteroskedasticity. Homogeneity of variance is not an assumption

for survey tests of means or regression coefficients.

d.  See: http://www.stata.com/statalist/archive/2007-
12/msg00621.html
to compute an adjusted R-square with survey data.  You should
consider other measures of fit, such as -linktest-.

e.  No--not if you want to believe the standard errors and tests.
However you might be able to test hypotheses about single-outcome
survey regressions with -suest-.

To use the survey-enabled programs like -svy: reg-  for inference
(hypothesis, confidence intervals, standard errors) you must first -

svyset- your data.  -svyset- will allow you to account for the
entire
sampling design, not just weighting.


For future reference the appropriate way to refer to Stata commands

in the list is with hyphens around them"  "-manova-" , not "--manova"


-Steve

On Sep 11, 2008, at 12:59 AM,
Nur.Hikmayani@studentmail.newcastle.edu.au wrote:

Hi all,

I have 4 continuous DVs (quality of life domains of SF-36: GH,
PF,
MH, SF) which are moderately inter-correlated (0.5) and two of
them
have skewed distributions. I originally intended to use --manova
&
--mvreg until lately when I realised that I'm using a dataset
from
a survey with over-sampling for participants living in remote
areas. The dataset had had a weighted variable already so have to

take this into account. To some extent, this had affected the
statistical method I'd like to use previously. So far, I've
performed a separate analysis for each DV as I have no idea on
how
to apply --svy nor --pw to --manova and --mvreg.

Some of my concerns are:
a. I need descriptives other than mean & proportion, particularly

for skewed DVs which I think median or percentiles is more
appropriate. While --svy does not support this, is there a way to

get the estimates?
b. How to create histogram for weighted mean DV as this will help

get a sense if assumption for normality for an OLS regression is
met?> c. When using --regress with --svy to get ANOVA, how is
homogeneity
of variance assessed?
d. I noticed that there is no adjusted R-squared when using --
svy,
so is it appropriate to build a model using R-squared instead?
e. Lastly, if it's not impractical at all, is it still possible
to
run --mvreg and take weights into account?

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