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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

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

Subject |
re:Re: st: Regression Discontinuity Design |

Date |
Sun, 9 Oct 2011 12:23:55 -0400 |

First off, I apologize that my initial response was more-or-less the same as Austin's. I get the digest, so my responses are always a day "after the fact". On the other hand, Austin and I both had identical concerns, so that should give you some comfort (at least it does for me) :-) A couple of additional points: Austin is correct in suggesting that if your reviewers are concerned with the impact of "unobservables", you may want to consider the IV approach. However, the main problem with the IV approach (at least in my opinion), is actually finding a good IV. Austin suggested the "distance to clinic", which may be a good IV if you have it. Under this approach, you may have sufficient sample size if the unit of measure is the individual. You'd have to test other potential IVs accordingly... My initial suggested approach using a multi-level approach was to allow for clustering at the various levels. However, this will not adjust for unobservables, and so the reviewers' concerns are not addressed. Propensity score matching (or weighting) would only ensure balance on observables, and so the reviewers' concerns about the confounding trend in income, etc. remains a viable threat to validity. Perhaps a viable alternative approach would be to model the data longitudinally as either a time series (at the community level), or longitudinally using individual level data. I am not clear what your data look like, but if you have multiple time points, you could perhaps account for the differing "trends" you spoke of. Good luck! Ariel Date: Fri, 7 Oct 2011 16:57:54 +0200 From: Nyasha Tirivayi <ntirivayi@gmail.com> Subject: Re: st: Regression Discontinuity Design Dear Ariel Quick answers to your questions: a) X variable for assignment is HIV prevalence rate at community level b) cut off is 22% c) yes I am also worried n=8 at community level is not sufficient My outcome is labour supply for individuals in 200 treated households (residing in 4 chosen communities) and 200 control households (residing in 4 control communities). However does this still seem as randomization at community level if program placement was non-random i.e. they specifically targeted communities with higher HIV rates (above 22%). Household recruitment was not randomized either. In that case can I use multilevel modelling? I had done propensity score matching, but reviewers feel there are unobservables I am overlooking. So with cross sectional data, what other methods can I plausibly use? Kindly advise Regards Nyasha Tirivayi Maastricht University Netherlands On Fri, Oct 7, 2011 at 4:33 PM, Ariel Linden, DrPH <ariel.linden@gmail.com> wrote: > Hi Nyasha, > > It seems like you've got several different things going on here at once. The > RD design can be thought of as an observational study equivalent of an RCT > (where the cutoff represents the randomization). If we think about it in > those simple terms, then I'd ask you this: (a) what is the X variable that > you'd be using for assignment, (b) what would be the cutoff, and (c) do you > think that a N=8 is reasonable? > > It is not clear from your description what either (a) or (b) is, but I can > certainly say without any hesitation that N=8 is not sufficient. > > An excellent recent article for you to read on the RD design is: Lee, D.S., > Lemieux, T. (2010) Regression discontinuity designs in econometrics. Journal > of Economic Literature 48, 281-355. > > Without getting into too deep of a methodological discussion, it seems to me > that if you already have randomization at the community level, you should > consider hierarchical/multi-level modeling to tease out whatever effect you > are looking for. > > Ariel > > > > Date: Fri, 7 Oct 2011 00:39:17 +0200 > From: Nyasha Tirivayi <ntirivayi@gmail.com> > Subject: st: Regression Discontinuity Design > > Hello > > I have questions about implementing a regression discontinuity > approach. I have cross sectional data from 200 households on a social > program and 200 control households. The program was targeted at two > levels- geographically and at household level. > > The geographic placement of the social program in communities appears > to have been done based on HIV prevalence rates of more than 20.5% for > 3 "treated" communities and less than 20.5% for 3 "control > communities". Two clinics do not follow this cutoff making it a fuzzy > discontinuity design at community level. After geographic placement, > households were then selected based on a means tested score. However > we do not have access to this data. We have data from 200 randomly > sampled households who are actually in the social program and residing > in the treated communities and from 200 control households with > similar household characteristics to the treated households but > residing in the control communities. > > My questions are as follows: > 1. Would it be valid to use the community level discontinuity for > impact evaluation? What software can I use in Stata? > 2. If so would an RD approach based on 8 communities be valid? Is the > sample of communities too small? > 3. If RD is no appropriate what other methods besides propensity score > matching can I use, that can also take care of unobservables even with > cross sectional data? > > Kindly advise > > Regards > > N.Tirivayi > Maastricht University > Netherlands * * 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: Re: st: Regression Discontinuity Design***From:*Nyasha Tirivayi <ntirivayi@gmail.com>

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