Re: st: estimating expenditure quartiles for subgroups of survey dat

[Steven Samuels <sjhsamuels@earthlink.net>]

--

Dan, this sounds like a useful approach, especially if there are  
additional covariates. Your analysis plan has changed

From:
1. Display and compare quantiles of your subgroups.

To:

1. Find the best fitting gamma model for each subgroup.
2. Assess how well the models fit.
3. If the models fit, compare the parameters of the best fitting  
models from each subgroup.
4. If the parameters differ, describe how the fitted distributions  
differ (e.g. with density plots).

To assess the gamma fits, you can use -qgamma- and -pgamma-,  
downloadable from SSC.  Also, -streg- will fit a generalized (3  
parameter) gamma, and this can be used as an additional check.  If  
the gamma model fits poorly in one or more of the subgroups, you can  
always go back to your original plan.

-Steve


On Jun 14, 2008, at 7:08 AM, dwaldo1@umbc.edu wrote:


> Thanks, Steve, for some valuable suggestions.
>
> Reflecting on this, it occurs to me that I could use gammafit to  
> test the
> location (and shape) parameters of the subpops' expenditures -- the  
> gamma
> appears to describe the distribution for people with expenditure --  
> and a
> logit to test the probability of use.
>
> I also dug back into the archives and came across a posting by Nick  
> Cox
> (distribution fitting curiosity, December 2006) that gets to the  
> same end
> by a different route.
>
> Dan
> <><><><><><><><><>
> Date: Fri, 13 Jun 2008 10:36:00 -0400
> From: Steven Samuels <sjhsamuels@earthlink.net>
> Subject: Re: st: estimating expenditure quartiles for subgroups of  
> survey
> data
>
> I assume that you used -pctile- to compute your weighted quartiles.
>
> I would not recommend hypothesis tests for percentiles of descriptive
> survey data with clustering and weights, even if I knew what tests to
> use (I don't). The distributions, including percentiles, of several
> finite populations will never be identical, and null hypotheses of
> equality are false a priori. (The exception is hypotheses about
> superpopulations.)  Your question appears to be: how different are
> the expenditure distributions in the subpopulations?  If so, I think
> that confidence intervals are a better approach. Download Roger
> Newson's -somsersd- package from SSC. It contains -cendif-, which
> will find confidence intervals for pairwise differences in
> percentiles and will accept probability weights and clusters.
>
> Confining yourself to a small set of quantiles could mislead. If
> sample size permits, enlarge the set of percentiles that you feed to -
> pctile- and -cendif-. You might also check weighted histograms for
> multiple modes and other anomalies.
>
> - -Steve
>
>
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