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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 17 Jun 2008 13:36:39 -0400 |

--

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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