JP Azevedo <[email protected]> want to use -test- after -svy: tabulate-:
> I'm trying to implement a t test with sampling weights using the svy:
> tabulate option.
>
> My understand from the help file is that it is possible to do this by using
> the svy postestimation commands. However, I've looked the help and could not
> figure out how to use those commands after svy: tabulate oneway.
>
> I would like to confirm if my understand is correct and would appreciate any
> examples.
Yes, you can use the -test- command to perform tests of hypotheses between the
proportions estimated by -svy: tabulate-. Unfortunately, the column names of
the e(b) are not standard, making this difficult. Let's try with one of the NHANES II datasets mentioned in [SVY] manual.
. webuse nhanes2b
. svy: tabulate sex race
(running tabulate on estimation sample)
Number of strata = 31 Number of obs = 10351
Number of PSUs = 62 Population size = 1.172e+08
Design df = 31
--------------------------------------
1=male, | 1=white, 2=black, 3=other
2=female | White Black Other Total
----------+---------------------------
Male | .4225 .0435 .0133 .4794
Female | .4566 .052 .012 .5206
|
Total | .8792 .0955 .0253 1
--------------------------------------
Key: cell proportions
Pearson:
Uncorrected chi2(2) = 4.5159
Design-based F(1.93, 59.72) = 1.2442 P = 0.2946
Suppose we want to test that the population proportion of white males is the
same as the population proportion of white females. First, we must find out
how to identify these two proportions by looking at the column names in
e(b).
. mat li e(b)
e(b)[1,6]
p11 p12 p13 p21 p22 p23
y1 .42254909 .04349737 .01330376 .45660537 .05200855 .01203586
Now we can use these identifiers in our call to -test-.
. test _b[p11] = _b[p21]
Adjusted Wald test
( 1) p11 - p21 = 0
F( 1, 31) = 14.10
Prob > F = 0.0007
This method can be very cumbersome for large tables. In that case, I would
recommend using -svy: mean- with indicator variables that identify the cells
you want to compare or -svy: proportion- with a categorical variable. An
example of the latter follows:
. egen cell = group(sex race), label
. svy: proportion cell
(running proportion on estimation sample)
Survey: Proportion estimation
Number of strata = 31 Number of obs = 10351
Number of PSUs = 62 Population size = 1.2e+08
Design df = 31
_prop_1: cell = Male White
_prop_2: cell = Male Black
_prop_3: cell = Male Other
_prop_4: cell = Female White
_prop_5: cell = Female Black
_prop_6: cell = Female Other
--------------------------------------------------------------
| Linearized Binomial Wald
| Proportion Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
cell |
_prop_1 | .4225491 .008073 .406084 .4390142
_prop_2 | .0434974 .0064771 .0302872 .0567075
_prop_3 | .0133038 .0065997 -.0001564 .026764
_prop_4 | .4566054 .0107333 .4347146 .4784962
_prop_5 | .0520085 .0068534 .038031 .0659861
_prop_6 | .0120359 .004008 .0038614 .0202103
--------------------------------------------------------------
. test _b[_prop_1] = _b[_prop_4]
Adjusted Wald test
( 1) [cell]_prop_1 - [cell]_prop_4 = 0
F( 1, 31) = 14.10
Prob > F = 0.0007
--Jeff
[email protected]
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