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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 |

Thanks. Michael Steven Samuels wrote:

--Thomas could generalize to the entire US in 2005. According tohttp://www.icpsr.umich.edu/cocoon/NACJD/STUDY/23862.xml he is omittingfrom his data 45 strata that covered the rest of the country.I actually agree with Stas. I do think that there are uses forregression and comparisons with fpc's in descriptive studies. I onceanalyzed Behavioral Risk Factor Surveillance System (BRFSS) data inCalifornia, and characterized historical changes smoking prevalencewith a regression line. It fit pretty well.I also would favor logistic regression and log-linear modeling assmoothing techniques to economically describe a population. Confidenceintervals (with fpc's) for differences between two proportions canalso be informative; one might want to know "How different were theproportions in that population at that time?".In my experience, though, most investigators who do regressions do notintend their analyses to be descriptive only. Until Thomas tells usthe 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 svysetcommand inStata that I hope someone can help me with. For some background, I'm engaged in a logistic regression model thatexamines the likelihood of either a plaintiff or defendant filing aposttrial motion. The database I'm working with is the Civil JusticeSurvey ofState 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. Iunderstand that one of the nice features of Stata is that it allowsyou totake into account the sampling structure of a dataset when doinglogisticregression modeling. Here is the Stata code that I used to take inaccountthe 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 placeBwgt0 = Weights to weight the data from 46 to the 75 most populouscountiesStrata = Strata where the counties are located. The dataset has 5stratafpc1 = The probability of a county appearing in the sample. Forexample, acounty with a weight of 2 would have a 50% probability of appearingin thesampl esu2 = Unique identifier that identifies the trials that occurred ineach ofthe 46 countiesFpc2 = 1 for all 8,000 trials disposed in the 46 counties. I gavefpc2 avalue 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 thefirstlevel of the sample design correct. It's the second level that I'mhavingsome problems with At the second level of the sample design, I'mtrying tocorrect for the fact that I have data for every civil trialconcluded in the46 counties. Basically, I want to tell Stata that part of thissample isactually a census of all trials concluded in the 46 counties in 2005. Iunderstand Stata has a finite population correction command thattakes intoaccount the census like format of these data. The logistic regressionresults were the same irrespective of whether I used the 1st or 2ndstagesin the sample design. I think this is telling me that Stata is notcorrecting for the census like aspect of this sample. Can anyonegive mesome guidance as to whether I'm correctly taking into account thesamplingstructure of these data. In particular, I would like to know whetherI'musing 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/-- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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: mlichter@buffalo.edu * * 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/

**Follow-Ups**:**Re: st: Question about svyset command***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**st: Question about svyset command***From:*thomashcohen@aol.com

**Re: st: Question about svyset command***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: Question about svyset command***From:*Steven Samuels <sjhsamuels@earthlink.net>

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