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From |
"Ángel Rodríguez Laso" <angelrlaso@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Trend analysis - independent surveys |

Date |
Wed, 30 Jul 2008 09:41:20 +0200 |

I have a related question so I take advantage of the thread. I want to compare answers to identical questions (in more or less the same positions in the questionnaire) in two waves (2001 and 2007) of a survey (not panel) with the same target population. In both waves, a multistage selection procedure was followed: In the first stage, PSUs were selected at random after stratification, but strata were defined differently in each wave (seven geographical areas in the first wave and eleven health areas in the second, not overlapping). PSUs in both waves were defined identically and many of them appear in both samples. In the second stage, individuals were selected at random within PSUs. A finite population correction had to be used because around one third of the PSUs in each stratum were sampled. Sample weights were used in both waves because of differing selection probabilities and poststratification adjustments. If I merge both waves, would it be correct to merge also strata and PSU variables from both waves? Notice that PSUs names are identical and some individuals from both waves will belong to the same PSU, but the same PSU will belong to a different stratum in each wave, because strata names differ in both waves. Many thanks. Ángel 2008/7/25, Stas Kolenikov <skolenik@gmail.com>: > If the rounds are independent, then > > t=(avergage[round t]-average[round s])/sqrt(variance_t [average in > round t]+variance_s [average in round s]) > > is normal / t with sum of design degrees of freedom / t with > Satterthwaite corrected degrees of freedom, depending on how you want > to think about them. The quick and dirty solution is to save the four > above quantities as locals or scalars and form this t-statistic. If > you had strata and cluster IDs with some sort of insider access, you > could put those together with > > use dataset1 > append dataset2 > ********* > * make sure PSU labels are different in two years > ********* > svy: sum whatever , over[year] > lincom [whatever]year2 - [whatever]year1 > > With separate bootstrap weights, that would not necessarily work, I am > afraid. If you have the same number of replicate weights in both > periods, it might. > > On Fri, Jul 25, 2008 at 9:41 AM, Mark Latendresse > <mark_latendresse@hc-sc.gc.ca> wrote: > > Hello, > > I have data on 7 independent cross-sectional surveys 1999-2007 (not panels) > > for which the target populations were identical. We would like to test for > > trends among several sub-groups on average number of cigarettes smoked per > > day (continuous variable). How can I do a trend analysis in Stata that will > > take into account sampling weights and stratification? We have bootstrap > > weights for each survey, however, we can also obtain the design effects for > > each survey if necessary. > > > -- > 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/

**References**:**st: Trend analysis - independent surveys***From:*Mark Latendresse <mark_latendresse@hc-sc.gc.ca>

**Re: st: Trend analysis - independent surveys***From:*"Stas Kolenikov" <skolenik@gmail.com>

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