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The examples below use Stata 9. If you are using Stata versions 7 or 8, please see this page.NOTE: If you want to see the design effect or the misspecification effect, use estat effects after the command.
The data files used for the examples in this text can be downloaded in a .zip file from the Wiley Publications website. You can then use a program such as WinZip to unzip the data files. If you need assistance getting data into Stata, please see our Stata Class Notes, especially the unit on Entering Data. (NOTE: The *.dat files are the data files, and the *.txt files contain the codebook information.)
Table 6.2, page 216.
NOTE: You need to increase the amount of memory available to Stata before opening this data file because it is so large.
set mem 5m (5120k) use nhanes3.dta, clear
svyset SDPPSU6 [pweight = WTPFHX6], strata(SDPSTRA6)
pweight: WTPFHX6
VCE: linearized
Strata 1: SDPSTRA6
SU 1: SDPPSU6
FPC 1: <zero>
xi: svy: logit HBP HSAGEIR HSSEX I.DMARACER BMPWTLBS BMPHTIN I.SMOKE
I.DMARACER _IDMARACER_1-3 (naturally coded; _IDMARACER_1 omitted)
I.SMOKE _ISMOKE_1-3 (naturally coded; _ISMOKE_1 omitted)
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16963
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 8, 42) = 193.50
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0807254 .0024847 32.49 0.000 .0757323 .0857185
HSSEX | .2040417 .0754752 2.70 0.009 .0523686 .3557149
_IDMARACER_2 | .558488 .0743918 7.51 0.000 .4089921 .7079839
_IDMARACER_3 | .0436902 .3004571 0.15 0.885 -.5601009 .6474814
BMPWTLBS | .0116062 .0008349 13.90 0.000 .0099284 .013284
BMPHTIN | -.0592606 .0126097 -4.70 0.000 -.0846008 -.0339204
_ISMOKE_2 | -.0764019 .0949624 -0.80 0.425 -.2672361 .1144323
_ISMOKE_3 | .0610105 .1050502 0.58 0.564 -.1500959 .2721169
_cons | -4.257218 .8040119 -5.29 0.000 -5.87294 -2.641496
------------------------------------------------------------------------------
Table 6.3 , page 218.
svy: logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16964
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 6, 44) = 205.76
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0799522 .0026616 30.04 0.000 .0746036 .0853008
HSSEX | .1938372 .0790581 2.45 0.018 .0349641 .3527104
_IDMARACER_2 | .5715161 .0709902 8.05 0.000 .4288559 .7141762
_IDMARACER_3 | .0519777 .3006959 0.17 0.863 -.5522933 .6562486
BMPWTLBS | .0114421 .0008405 13.61 0.000 .0097531 .0131311
BMPHTIN | -.0589891 .0126859 -4.65 0.000 -.0844824 -.0334958
_cons | -4.211455 .7940002 -5.30 0.000 -5.807058 -2.615852
------------------------------------------------------------------------------
Table 6.43, page 219.
logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN
Iteration 0: log likelihood = -8602.8989
Iteration 1: log likelihood = -6870.2255
Iteration 2: log likelihood = -6671.2868
Iteration 3: log likelihood = -6663.7359
Iteration 4: log likelihood = -6663.7081
Logit estimates Number of obs = 16964
LR chi2(6) = 3878.38
Prob > chi2 = 0.0000
Log likelihood = -6663.7081 Pseudo R2 = 0.2254
------------------------------------------------------------------------------
HBP | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0696379 .0013966 49.86 0.000 .0669007 .0723751
HSSEX | .0904745 .0613365 1.48 0.140 -.0297429 .2106919
_IDMARACER_2 | .4765584 .0509377 9.36 0.000 .3767225 .5763944
_IDMARACER_3 | .0916109 .1430926 0.64 0.522 -.1888453 .3720672
BMPWTLBS | .0083715 .0006091 13.74 0.000 .0071776 .0095654
BMPHTIN | -.0451575 .0085049 -5.31 0.000 -.0618268 -.0284882
_cons | -3.871509 .529282 -7.31 0.000 -4.908883 -2.834135
------------------------------------------------------------------------------
Table 6.5, page 221.
Design-based analysis:
svy: logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN, or
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16964
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 6, 44) = 205.76
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | 1.083235 .0028831 30.04 0.000 1.077457 1.089045
HSSEX | 1.213899 .0959685 2.45 0.018 1.035583 1.422919
_IDMARACER_2 | 1.77095 .1257201 8.05 0.000 1.5355 2.042503
_IDMARACER_3 | 1.053352 .3167386 0.17 0.863 .5756282 1.927548
BMPWTLBS | 1.011508 .0008502 13.61 0.000 1.009801 1.013218
BMPHTIN | .942717 .0119592 -4.65 0.000 .9189878 .9670589
------------------------------------------------------------------------------
Model-based analysis:
logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN, or
Iteration 0: log likelihood = -8602.8989
Iteration 1: log likelihood = -6870.2255
Iteration 2: log likelihood = -6671.2868
Iteration 3: log likelihood = -6663.7359
Iteration 4: log likelihood = -6663.7081
Logit estimates Number of obs = 16964
LR chi2(6) = 3878.38
Prob > chi2 = 0.0000
Log likelihood = -6663.7081 Pseudo R2 = 0.2254
------------------------------------------------------------------------------
HBP | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | 1.07212 .0014973 49.86 0.000 1.069189 1.075059
HSSEX | 1.094694 .0671447 1.48 0.140 .9706951 1.234532
_IDMARACER_2 | 1.610522 .0820362 9.36 0.000 1.4575 1.77961
_IDMARACER_3 | 1.095938 .1568206 0.64 0.522 .8279146 1.45073
BMPWTLBS | 1.008407 .0006142 13.74 0.000 1.007203 1.009611
BMPHTIN | .9558469 .0081294 -5.31 0.000 .9400457 .9719138
------------------------------------------------------------------------------
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