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# Re: st: Question about svyset command

 From Steven Samuels To statalist@hsphsun2.harvard.edu Subject Re: st: Question about svyset command Date Thu, 19 Feb 2009 08:17:07 -0500

```Thomas,

```
1. The finite population corrections should affect only standard errors and confidence intervals, not estimates of means, proportions, or confidence intervals.
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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.
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```
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.
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In other words, you should use one -svyset- for describing the target population and another for the logistic regression.
```
Two questions came to mind:
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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.
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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, 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.
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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:
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```
svyset sitecode [pweight=bwgt0], strata(strata) fpc(fpc1) || su2, fpc(fpc2)
```
Where
Sitecode = County where the civil trial took place
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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
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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. Itâ??s the second level that Iâ??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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```

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```

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