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RE: st: using post stratification weights
Afif Naeem <firstname.lastname@example.org>
RE: st: using post stratification weights
Tue, 14 Feb 2012 11:37:19 -0500
> Date: Mon, 13 Feb 2012 12:52:08 -0500
> Subject: Re: st: using post stratification weights
> From: email@example.com
> To: firstname.lastname@example.org
> On Mon, Feb 13, 2012 at 11:36 AM, Afif Naeem <email@example.com> wrote:
> > The survey code-book does not provide much information with regards to design-stratification. But they do tell me that they used some combination of random digit dialing (RDD) sampling and address-based sampling (ABS) methodology. I have a feeling that they did not used design-stratification for sampling purposes.
> If they obtained a part of the sample from RDD frame, and another part
> from ABS frame, then these are two independent strata, and should be
> accounted as such. You'd have to continue clarifying this.
I guess the sampling in completely random with RDD and ABS. In that case, do I still need to define/take care of the two strata arising from RDD and ABS?
> > My main concern is the low response/completion rate of the survey i.e. 62.5% of respondents do actually complete the survey. Would using the post-stratification (i.e. Raking) weights without mentioning any post-strata correct for any bias that may arise due to low response rate? And where/how would the variable used (mentioned below) used in the Raking process would come into play? (assuming if the do come into play)
> If the response is MAR with the variables determining non-response
> used in the non-response model that led to the post-stratification
> adjustments (i.e., age, gender, etc.), then you will be fine. But this
> is a strong assumption to make.
Can you please elucidate on this point further more. Plus what does MAR stand for?
> > Moreover, the post-stratification weight variable provided in the data set ranges from a value of 0.13 to 5.6, with a mean value of 1.000075. As far as I understand, pweight is the inverse of sampling fraction and hence should be greater than (or equal to) 1. Do I need to worry about it or STATA will adjust for it?
> Stata will not make any guesses; if you specified these weights, Stata
> will use them, and does not care whether they sum up to the total
> population size (as they should) or to the sample size (which is a
> shortcut for SAS or SPSS that can't do things otherwise). It is up to
> the analyst to specify the weights appropriately and interpret the
> results. If you don't need to estimate the population totals (total
> income; total # of events; etc.), then you can get along with these
So how should I specify the weights appropriately? Do I have to modify the weights given in the data set?
> > Lastly, how precise it is to use post-stratification weights in Bivariate Logit Model. My results completely flip-over and loose statistical significance when I use weights in the model using survey commands. Signs and statistical significance can not be justified on the basis of any (economic) theory. However, when I dont use weights, the results come out as expected. I wonder if I am doing something wrong here. Can the use of weights change the parameter estimates and average marginal effects to such an extent?
> -svy postestimation- command provide design effects, i.e., the ratio
> of variances of design-based estimates vs. SRS estimates. I wouldn't
> be surprised if your poststratification has actually increased the
> variances quite a bit. Unfortunately, that's the price you have to pay
> to get design-consistent estimates.
Increase in variance of the estimates makes sense. But 6 out of 14 independent variables change sign when I use weights in my Logit model. Is hat something unheard of and am I doing something wrong here?
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
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