Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Ole Dahl Rasmussen <odr@dca.dk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: Finite population correction with clustering of SE at a different level than the strata |

Date |
Mon, 4 Jun 2012 13:57:08 +0000 |

Dear Statalist, As part of a cluster randomized control trial, colleagues and I are doing stratified sampling and we're not sure if we're analyzing data correctly. Great if someone has suggestions. We have 46 villages. Before anything else, we went to all villages and asked them if they would be interested in participating in the project we were about to implement. We wrote down the names of the interested households on lists. We then stratified the population on village and interest: On household population lists we marked the interested households and randomly selected an absolute number, 24, of the interested and 14 on the non-interested in each village, 1750 household out of a total population of approximately 3000 households. In the end we have a total of 92 interested/village combination, which we define as our stratas in the analysis. The sampling rate inside the stratas vary from 10% to 100%. Then we randomly selected 23 of the villages and implemented a project in these 23 villages. After two years, we surveyed everybody again. Finally, following Cameron/Trivedi p 817 in Microeconometrics and others, we estimate the following: svyset vid [pweight=weights], fpc(one) || _n, strata(strataID) fpc(f) singleunit(certainty) svy: reg consumption treatXendline endline treat where - vid is and ID variable for villages, where I want clustered standard errors - weights is the inverse probability of sampling - one is a dummy that is equal to 1. - consumption is a consumption measure - treatXendline is the interaction between selection as treatment village and endline - endline is an endline dummy - treat is a treatment dummy - weights is the inverse probability of sampling - f is the total probability of sampling - strataID is an ID variable for strata which is each of the 92 village/interested combinations. So for the questions: . Are we doing it right? . In particular, is our finite population correction justified? . We want to cluster standard errors at the village level, because we think this is the relevant level, i.e. not the strata level. Is this the right way of doing it? Any suggestions and thoughts are appreciated. On behalf of the team, Ole Dahl Rasmussen University of Southern Denmark ---- Ole Dahl Rasmussen Rådgiver indenfor mikrofinans og evaluering. Ph.d.-studerende på Syddansk Universitet Adviser on Evaluation and Microfinance. PhD-student at University of Southern Denmark Nørregade 15 1165 København K / 1165 Copenhagen K www.noedhjaelp.dk / www.danchurchaid.org odr@dca.dk P +45 33152800 M +45 29699145 SKYPE odrdca VI TROR PÅ ET LIV FØR DØDEN * * 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: Finite population correction with clustering of SE at a different level than the strata***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: Finite population correction with clustering of SE at a different level than the strata***From:*Austin Nichols <austinnichols@gmail.com>

- Prev by Date:
**RE: st: Mixed model for longitudinal data: Time discrete or continuous?** - Next by Date:
**st: by prefix** - Previous by thread:
**st: Mfx after zero-inflated (ZOIB) estimator** - Next by thread:
**Re: st: Finite population correction with clustering of SE at a different level than the strata** - Index(es):