Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

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

From |
Afif Naeem <afeef745@hotmail.com> |

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

Subject |
RE: st: using post stratification weights |

Date |
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: skolenik@gmail.com > To: statalist@hsphsun2.harvard.edu > > On Mon, Feb 13, 2012 at 11:36 AM, Afif Naeem <afeef745@hotmail.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 > weights. 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? Afif > -- > 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/

**Follow-Ups**:**Re: st: using post stratification weights***From:*Stas Kolenikov <skolenik@gmail.com>

**References**:**st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**RE: st: using post stratification weights***From:*Cameron McIntosh <cnm100@hotmail.com>

**Re: st: using post stratification weights***From:*Stas Kolenikov <skolenik@gmail.com>

**RE: st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**Re: st: using post stratification weights***From:*Steve Samuels <sjsamuels@gmail.com>

**RE: st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**Re: st: using post stratification weights***From:*Steve Samuels <sjsamuels@gmail.com>

**RE: st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**Re: st: using post stratification weights***From:*Stas Kolenikov <skolenik@gmail.com>

- Prev by Date:
**Re: st: mixed** - Next by Date:
**re: st: RDD robustness check: Controlling for covariates?** - Previous by thread:
**Re: st: IRF interpretation** - Next by thread:
**Re: st: using post stratification weights** - Index(es):