 |
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: Question about svyset command
Thomas,
You are dealing with two misconceptions. The first is one that Steven
didn't mention, and the second is one that Steven mentioned but did not
relate as directly to your situation as he could have.
1. A multi-stage sampling design is a design in which sampling takes
place multiple times. E.g., if you sample 20% of the districts in a
state, sample 10% of the schools in each district, that's a two-stage
sample. Let's say we conduct interviews of all of the students in all of
the classrooms within each selected school. Children are nested within
classrooms within schools within districts--that's four levels of
nesting. But the design is still two-stage, despite the four levels of
nesting.
*You* have a *single-stage* stratified cluster design. The fact that
cases are nested within the clusters you select is what makes it a
cluster design. Your telling Stata that the cases were selected at the
"second stage" with 100% probability is the same as telling Stata that
there is no second stage. That's why the estimates look the same whether
you tell Stata about the "second stage" or not.
2. Also, Steven is definitely correct if your goal is not to generalize
to the 10,500 trials you mentioned, but rather to this type of trial in
general, wherever and whenever it takes place. In fact, what he says is
more obviously applicable to your study then it is to most sample
surveys of people. If the 10,500 trials are taken to be representative
of some larger population of trials, then you are dealing with
superpopulation parameters, like he said. From my limited reading,
however, I think that the consensus on this topic among statisticians is
less widespread than he suggests, and the consensus about what to do
about it is almost nonexistent (except for what he said about the FPC).
Korn and Graubard (1999) say, for example, that aside from ignoring the
FPC there is no agreement on what to do in order to reduce the bias in
estimates of superpopulation parameters from complex sample designs (p.
228).
On the other hand, if what you really want to know about is those 10,500
trials because there is something special about this specific population
of trials then Steven is wrong and you should use the FPC. With regard
to the value of FPC1, it should either be the size of the stratum -- and
you didn't say anything about what your strata were -- or the number of
cases selected from the stratum divided by the size of the stratum. I
suggest the former because you're less likely to make an error in its
calculation.
Michael
Steven Samuels wrote:
Thomas,
1. The finite population corrections should affect only standard
errors and confidence intervals, not estimates of means, proportions,
or confidence intervals.
2. fpc's should be employed only for descriptive analyses
(proportions, means). These analyses describe the specific finite
population that you sampled: tort, contract, and real property trials
in the 75 counties.
If the purpose of your model is analystic: to develop predictions,
estimate odds ratios, compare proportions, or otherwise test
hypotheses, you should *omit* the finite population corrections. The
reasoning is interesting (Cochran, 1977, p.39): It is seldom of
scientific interest to ask if a null hypothesis (e.g. that two
proportions are equal) is exactly true in a finite population . Except
by a very rare chance, a null hypothesis will never be true. You would
discover this by enumerating the entire population. This leads to the
adoption of a "superpopulation" viewpoint, which is taken by almost
all statisticians these days. See also Deming(1966) pp 247-261
"Distinction between enumerative and analystic studies"; Korn and
Graubard (1999), p. 227.
In other words, you should use one -svyset- for describing the target
population and another for the logistic regression.
Two questions came to mind:
1. If a trial had >1 plaintiff or >1 defendant, would that not
increase the probability of a post trial motion? How are you going to
account for that?
2. For descriptive analyses, counties selected with certainty need
special treatment. Look up the "singleunit" option for -svyset-.
Good luck!
-Steve
References
Cochran, W. G. (1977). Sampling techniques (3ded.). New York: Wiley.
Deming, W. E. (1966). Some theory of sampling. New York: Dover
Publications.
Korn, E. L., & Graubard, B. I. (1999). Analysis of health surveys
(Wiley series in probability and statistics). New York: Wiley.
On Feb 19, 2009, at 12:04 AM, [email protected] 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 t 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
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
--
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: [email protected]
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/