NOTE:
If you want to see the design effect or the misspecification effect, use
estat effects after the command.This example is taken from Levy and Lemeshow’s
Sampling of Populations.
page 247 simple one-stage cluster sampling
use https://stats.idre.ucla.edu/stat/books/sop/tab9_1a.dta, clear
svyset devlpmnt [pweight=wt1], fpc(M)
pweight: wt1
VCE: linearized
Strata 1: <one>
SU 1: devlpmnt
FPC 1: M
svy: total NVSTNRS NGE65
(running total on estimation sample)
Survey: Total estimation
Number of strata = 1 Number of obs = 40
Number of PSUs = 2 Population size = 100
Design df = 1
--------------------------------------------------------------
| Linearized
| Total Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
NVSTNRS | 57.5 1.936492 32.89454 82.10546
NGE65 | 167.5 1.936492 142.8945 192.1055
--------------------------------------------------------------
svy: mean NVSTNRS hhneedvn
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 1 Number of obs = 40
Number of PSUs = 2 Population size = 100
Design df = 1
--------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
NVSTNRS | .575 .0193649 .3289454 .8210546
hhneedvn | .525 .0193649 .2789454 .7710546
--------------------------------------------------------------
svy: ratio NVSTNRS NGE65
(running ratio on estimation sample)
Survey: Ratio estimation
Number of strata = 1 Number of obs = 40
Number of PSUs = 2 Population size = 100
Design df = 1
_ratio_1: NVSTNRS/NGE65
--------------------------------------------------------------
| Linearized
| Ratio Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
_ratio_1 | .3432836 .0075924 .2468131 .4397541
--------------------------------------------------------------
svy: mean nge65dv
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 1 Number of obs = 40
Number of PSUs = 2 Population size = 100
Design df = 1
--------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
nge65dv | 33.5 .3872983 28.57891 38.42109
--------------------------------------------------------------
This example is taken from Lehtonen and Pahkinen’s
Practical Methods for Design and Analysis of Complex Surveys.
page 83 Table 3.6 Estimates from a one-stage
CLU sample (n = 8); the Province’91 population.
input id str clu wt ue91 lab91 1 1 2 4 666 6016 2 1 2 4 528 3818 3 1 2 4 760 5919 4 1 2 4 187 1448 5 1 8 4 129 927 6 1 8 4 128 819 7 1 8 4 331 2543 8 1 8 4 568 4011 end
gen fpc = 32
svyset clu [pweight=wt], strata(str)
pweight: wt
VCE: linearized
Strata 1: str
SU 1: clu
FPC 1: <zero>
svy: total ue91
(running total on estimation sample)
Survey: Total estimation
Number of strata = 1 Number of obs = 8
Number of PSUs = 2 Population size = 32
Design df = 1
--------------------------------------------------------------
| Linearized
| Total Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
ue91 | 13188 3940 -36874.45 63250.45
--------------------------------------------------------------
svy: ratio ue91 lab91
(running ratio on estimation sample)
Survey: Ratio estimation
Number of strata = 1 Number of obs = 8
Number of PSUs = 2 Population size = 32
Design df = 1
_ratio_1: ue91/lab91
--------------------------------------------------------------
| Linearized
| Ratio Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
_ratio_1 | .129289 .0065018 .0466761 .211902
--------------------------------------------------------------