Date: Thu, 15 Oct 2009 08:53:24 -0400
From: [email protected]
Subject: Re: Re: st: Using the 2008 American National Election Study with Stata v.11
It states that "comprehensive instructions will accompany the full
release of the Panel Study Data."
I'm not familiar with ANES, but neither the paper Bob found nor the
user guide have anything about weights centered at zero. (The paper
does recommend that weights be scaled so that they sum to 1.) In
fact, weights<0 would be rejected by any survey program.
- -Steve
Date: Thu, 15 Oct 2009 09:22:30 -0400
From: Nick Winter <[email protected]>
Subject: Re: st: Using the 2008 American National Election Study with Stata v.11
The ANES weights should be used as pweights.
In recent studies (eg, the 2004 dataset), ANES supplies a variable that
indicates "strata" and "psus" that can be used for BRR or Taylor-series
approaches to variance estimation. (They don't release the complete
sampling information to prevent possible identification of individual
respondents -- this is discussed, eg, in the introductory materials for
the 2004 study documentation:
http://www.electionstudies.org/studypages/2004prepost/nes04int.txt)
So until the final,complete release of the 2008 data, you are stuck with
just using the appropriate pweight.
(As an aside, in my experience, taking proper account of the
stratification and clustering in the ANES datasets has not had dramatic
effects on standard errors. But of course my prior experience doesn't
guarantee your future performance....)
- - NW
Date: Thu, 15 Oct 2009 12:19:03 -0400
From: Austin Nichols <[email protected]>
Subject: Re: st: Using the 2008 American National Election Study with Stata v.11
Peter Wielhouwer <[email protected]> :
You can also use a geographic id as a clustering variable if PSU is
unavailable--or even if PSU is available and PSUs are nested within
geography and you think there is clustering by geography.
. u anes2008_2009panel_dataset, clear
. svyset [pw=wgtbase]
pweight: wgtbase
VCE: linearized
Single unit: missing
Strata 1: <one>
SU 1: <observations>
FPC 1: <zero>
. svy:ta w10p4, ci
(running tabulate on estimation sample)
Number of strata = 1 Number of obs = 619
Number of PSUs = 619 Population size = 610.10636
Design df = 618
- -------------------------------------------------
w10p4. |
Favor or |
oppose |
raising |
taxes on |
incomes |
over |
200k/yr | proportions lb ub
- ----------+--------------------------------------
1, Favor | .381 .3393 .4245
2, Oppos | .0682 .0496 .0931
3, Neith | .5508 .5066 .5943
|
Total | 1
- -------------------------------------------------
Key: proportions = cell proportions
lb = lower 95% confidence bounds for cell proportions
ub = upper 95% confidence bounds for cell proportions
. egen c=group(stratum cdstate), mi label
. svyset c [pw=wgtbase]
pweight: wgtbase
VCE: linearized
Single unit: missing
Strata 1: <one>
SU 1: c
FPC 1: <zero>
. svy:ta w10p4, ci
(running tabulate on estimation sample)
Number of strata = 1 Number of obs = 619
Number of PSUs = 81 Population size = 610.10636
Design df = 80
- -------------------------------------------------
w10p4. |
Favor or |
oppose |
raising |
taxes on |
incomes |
over |
200k/yr | proportions lb ub
- ----------+--------------------------------------
1, Favor | .381 .3383 .4255
2, Oppos | .0682 .0513 .0903
3, Neith | .5508 .5098 .5912
|
Total | 1
- -------------------------------------------------
Key: proportions = cell proportions
lb = lower 95% confidence bounds for cell proportions
ub = upper 95% confidence bounds for cell proportions
