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Re: st: Question about svyset command


From   "Michael I. Lichter" <mlichter@buffalo.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Question about svyset command
Date   Thu, 19 Feb 2009 15:43:05 -0500

I agree with Stas about the vital importance of defining the target population.

Steven, however, is making me more confused about the difference between inferences about finite populations vs. those about superpopulations. I'll use my own study as an example.

I'm analyzing results from a survey of physicians regarding health information technology (HIT) adoption. The survey was stratified and a couple of the strata had large sampling fractions (like 1/3 and 1/8). My target population is all primary care physicians delivering patient care during a specific interval in time--and the interval in time is meaningful, because I expect HIT adoption levels to be different (higher) today than they were back when the data were collected. The target population and the (list) frame population are undoubtedly different for a variety of reasons, including the inherent near-impossibility of maintaining a complete and accurate list of any large population. Still, I think I'm interested in a finite population of actual physicians practicing at a specific point in time, not a theoretical, infinite superpopulation. Am I right?

I want to know about (a) current adoption rates by stratum (estimating proportion & variance), (b) differences in adoption rates across strata at this particular point in time (e.g., using chi-square), (c) the general relationship between various predictors (or covariates) and adoption (e.g., using logistic regression). Are the first two finite population objectives and the last a superpopulation-related objective, so that variances should be estimated one way for the first two and a different way for the third?

Thanks.

Michael

Steven Samuels wrote:
--

Thomas could generalize to the entire US in 2005. According to http://www.icpsr.umich.edu/cocoon/NACJD/STUDY/23862.xml he is omitting from his data 45 strata that covered the rest of the country.

I actually agree with Stas. I do think that there are uses for regression and comparisons with fpc's in descriptive studies. I once analyzed Behavioral Risk Factor Surveillance System (BRFSS) data in California, and characterized historical changes smoking prevalence with a regression line. It fit pretty well.

I also would favor logistic regression and log-linear modeling as smoothing techniques to economically describe a population. Confidence intervals (with fpc's) for differences between two proportions can also be informative; one might want to know "How different were the proportions in that population at that time?".

In my experience, though, most investigators who do regressions do not intend their analyses to be descriptive only. Until Thomas tells us the purpose of his study, we will not really know what to advise.

-Steve


On Feb 19, 2009, at 1:24 PM, Stas Kolenikov wrote:

Adding to the previous comments:

In all likelihood, your results are only generalizable to those most
populous counties, as they are probably large metropolitan areas. You
would need to think very carefully about what the population is to
which the results are generalizable. Your superpopulation, if you can
think of one, would be all potential trials in these and similar large
counties. I would imagine that in a 3000 people county in Idaho,
people won't be suing each other as furiously as somewhere in New
Jersey or California, as there is plenty of land to live on... but
that's something for you to clarify.

Hence, just like Michael, I would disagree with Steven about ignoring
fpc so happily. They would affect your standard errors, correctly
showing that you got more than half of your total finie population. If
you had all of your population, you would have a census logistic
regression, which would be just some sort of the line saying where
your 0s and 1s are. Now, if you had a census regression, what would
standard errors stand for? On one hand, you've got all possible
observations, so there is no uncertainty left -- the
sampling/randomization/design variance is zero. But if you are
thinking about the social process that has created those observations
(trials), then you can still think about model variances that should
be on the scale of 1/N -- and to get these, you would need to ignore
fpc.

Your design specification thus depends on which variance you want to
estimate. With census regression, your are saying, "There is a line of
best fit, and I am prepared to find out it does not fit the data
perfectly, but if my goal is to get as close to that line of best fit
as possible, then my sample logistic regression is the answer". That
line of best fit is a well defined population concept; whether it
makes a substantive sense or not -- that's certainly open to
interpretation. With a superpopulation model, you are saying, "I know
perfectly well that these and only these factors affect the
probability of observing that post-trial motion, and they enter the
logistic equation linearly, and all that." Your results will only be
as good as your model, and you are putting a lot of trust in correct
specification there.

On Wed, Feb 18, 2009 at 11:04 PM,  <thomashcohen@aol.com> wrote:
Iâm a beginner Stata user and have a question about the svyset command in
Stata that I hope someone can help me with.

For some background, I'm engaged in a logistic regression model that
examines the likelihood of either a plaintiff or defendant filing a post trial motion. The database I'm working with is the Civil Justice Survey of
State Courts (CJSSC). The CJSSC provides case level data for all tort,
contract, and real property trials conclude in a sample of 46 of the
nation's 75 most populous counties in 2005. Data are collected on about
8,000 trials in these 46 counties which are weighted to represent about
10,500 trials concluded in the nation's 75 most populous counties. I
understand that one of the nice features of Stata is that it allows you to take into account the sampling structure of a dataset when doing logistic regression modeling. Here is the Stata code that I used to take in account
the sampling structure of these civil trial data:

svyset sitecode [pweight=bwgt0], strata(strata) fpc(fpc1) || su2, fpc(fpc2)

Where
Sitecode = County where the civil trial took place
Bwgt0 = Weights to weight the data from 46 to the 75 most populous counties Strata = Strata where the counties are located. The dataset has 5 strata fpc1 = The probability of a county appearing in the sample. For example, a county with a weight of 2 would have a 50% probability of appearing in the
sampl
e
su2 = Unique identifier that identifies the trials that occurred in each of
the 46 counties
Fpc2 = 1 for all 8,000 trials disposed in the 46 counties. I gave fpc2 a
value of 1 because I wanted to tell Stata that the trials had a 100%
probability of showing up in these 46 counties.
I think that I got the part of this programming that deals with the first level of the sample design correct. It's the second level that I'm having some problems with At the second level of the sample design, I'm trying to correct for the fact that I have data for every civil trial concluded in the 46 counties. Basically, I want to tell Stata that part of this sample is
actually a census of all trials concluded in the 46 counties in 2005. I
understand Stata has a finite population correction command that takes into
account the census like format of these data. The logistic regression
results were the same irrespective of whether I used the 1st or 2nd stages
in the sample design. I think this is telling me that Stata is not
correcting for the census like aspect of this sample. Can anyone give me some guidance as to whether I'm correctly taking into account the sampling structure of these data. In particular, I would like to know whether I'm
using the fpc2 factor correctly. Any assistance you could give on this
matter would be very much appreciated.
Thanks
Thomas Cohen


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--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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--
Michael I. Lichter, Ph.D.
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / E-Mail: mlichter@buffalo.edu

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