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Steve,
Sorry for overlooking the obvious. Here are the commands and output. (I note as usual the wonderful output efficiency of Stata over the others.)
arnold
_____________________
*Stata*
svyset skulid [pw=w2f2f3], strata(strat) fpc(fpc) || classid
pweight: w2f2f3
VCE: linearized
Strata 1: strat
SU 1: skulid
FPC 1: fpc
Strata 2: <one>
SU 2: classid
FPC 2: <zero>
. svy, subpop(if year==2008 & skulid==80001): mean smkskul
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 1 Number of obs = 131
Number of PSUs = 9 Population size = 783.698
Subpop. no. obs = 16
Subpop. size = 120.542
Design df = 8
--------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
smkskul | .5806258 .014649 .5468452 .6144064
--------------------------------------------------------------
Note: 45 strata omitted because they contain no subpopulation members
___________________
SAS:
PROC SURVEYMEANS DATA = ytabstest RATE = FPC;
VAR SMKSKUL;
STRATA STRAT;
CLUSTER SKULID CLASSID;
WEIGHT SKULWT;
DOMAIN skulstrat;
RUN;
The SAS System 08:13 Tuesday, June 30, 2009 315
The SURVEYMEANS Procedure
Data Summary
Number of Strata 27
Number of Clusters 1282
Number of Observations 21212
Sum of Weights 98864
Statistics
Std Error
Variable Label N Mean of Mean 95% CL for Mean
???????????????????????????????????????????????????????????????????????????? SMKSKUL SMKSKUL 1706 0.488438 0.015833 0.45735470 0.51952078
????????????????????????????????????????????????????????????????????????????
Domain Analysis: skulstrat
Std Error
skulstrat Variable Label N Mean of Mean 95% CL for Mean
???????????????????????????????????????????????????????????????????????????? 0 SMKSKUL SMKSKUL 1690 0.487015 0.016001 0.45560287 0.51842627
1 SMKSKUL SMKSKUL 16 0.580626 0.104178 0.37624423 0.78500743
????????????????????????????????????????????????????????????????????????????
The SAS System 08:13 Tuesday, June 30, 2009 316
PROC DESCRIPT DATA = ytabstest DESIGN = WOR;
NEST STRAT SKULID CLASSID / MISSUNIT;
TOTCNT TOTSAMP _MINUS1_ _MINUS1_;
VAR SMKSKUL;
CLASS SMKSKUL;
WEIGHT SKULWT;
SUBPOPN skulstrat = 1;
RUN;
S U D A A N
Software for the Statistical Analysis of Correlated Data
Copyright Research Triangle Institute August 2008
Release 10.0
DESIGN SUMMARY: Variances will be computed using the Taylor Linearization Method, Assuming a
Without Replacement (WOR) Design
Sample Weight: SKULWT
Stage 1 Stratification Variable: STRAT
Stage 1 Population Count Variable: TOTSAMP
Stage 2 NEST Variable: SKULID (stage type is data dependent)
Stage 2 Population Count Variable: _MINUS1_
Stage 3 With Replacement Sampling Variable: CLASSID
Stage 3 Population Count Variable: _MINUS1_
Number of observations read : 20434 Weighted count : 97843
Observations in subpopulation : 226 Weighted count : 1650
Denominator degrees of freedom : 128
Date: 06-30-2009 SUDAAN Page: 1
Time: 13:38:12 Table: 1
Frequencies and Values for CLASS Variables
by: SMKSKUL.
----------------------------------
SMKSKUL Frequency Value
----------------------------------
Ordered
Position:
1 6 0
Ordered
Position:
2 10 1
----------------------------------
Date: 06-30-2009 SUDAAN Page: 2
Time: 13:38:12 Table: 1
Variance Estimation Method: Taylor Series (WOR)
For Subpopulation: SKULSTRAT = 1
by: Variable, SUDAAN Reserved Variable One.
--------------------------------------------------------------------
| | | SUDAAN Reserved Variable |
| Variable | | One |
| | |-----------------------------|
| | | Total | 1 |
--------------------------------------------------------------------
| | | | |
| SMKSKUL | Sample Size | 16 | 16 |
| | Weighted Size | 120.54 | 120.54 |
| | Total | 69.99 | 69.99 |
| | Lower 95% Limit | | |
| | Total | -39.85 | -39.85 |
| | Upper 95% Limit | | |
| | Total | 179.83 | 179.83 |
| | Mean | 0.58063 | 0.58063 |
| | SE Mean | 0.09 | 0.09 |
| | Lower 95% Limit | | |
| | Mean | 0.39690 | 0.39690 |
| | Upper 95% Limit | | |
| | Mean | 0.76435 | 0.76435 |
--------------------------------------------------------------------
On Tue, Jun 30, 2009 at 1:52 PM, <[email protected]> wrote:
> ==
> Arnold,
>
> We cannot judge the cause of the discrepancy since you do not show the
> Stata -svyset- output, nor the Stata
> estimation commands and output, nor the equivalent SAS commands and
> output. Please do so. If there are so many variables as to make
> scanning the output difficult, please run the commands with as few
> variables as needed to demonstrate the issue. Best would be to use one
> of the downloadable example data sets from the Stata survey manual.
>
> -Steve
>
>
> On Tue, Jun 30, 2009 at 12:41 PM, Levinson,
> Arnold<[email protected]> wrote:
>> Survey analysis experts:
>> I have data from a stratified two-stage school survey. The first stage sampled schools within strata, the second sampled classrooms within selected schools.
>>
>> When estimating variables of interest at the school level, I get hugely different variance estimates running Stata vs. SAS or SUDAAN. Stata's estimates are generally a lot smaller than SAS's or SUDAAN's, and the latter to are similar or identical to each other.
>>
>> My Stata svyset statement is:
>>
>> svyset skulid [pw=skulwt], strata(strat) fpc(fpc) || classid
>>
>> My SAS/SUDAAN design statements are the equivalent.
>>
> Steven Samuels
> [email protected]
> 18 Cantine's Island
> Saugerties NY 12477
> USA
> 845-246-0774
>
Arnold H. Levinson, PhD
Assistant Professor of Community and Behavioral Health
Colorado School of Public Health
Director, Tobacco & Amendment 35 Program Evaluation Group (TPEG, APEG)
13001 E. 17th Place, Mail Stop F542
P.O. Box 6508, Aurora CO 80045
[email protected]
voice: 303-724-3541
fax: 303-724-3544
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