Hello,
I�d like to compare two means of two different groups (dummy
variables), where each one is affected by a grouping variable. I was
looking in the Stata FAQS and found that this was posible with the -
suest - command, but when I use it, it doesn�t work and I don�t know
what�s wrong. I�appreciate if you could give me some advice.
My dummy variables are like this:
act_m = (participating == 1 & male == 1) ; 0 otherwise
act_f = (participating == 1 & female == 1) ; 0 otherwise
pension = 1 if recieves a pension; 0 otherwise
And I need to compare the 'act_m' mean when 'pension' == 1 with the
mean of 'act_f' when 'pension' == 1.
What I was doing was (and didn�t work):
svyset folio [pw = factor]
mean act_m if (hm3_hym == 1 & menor15h == 1) [fw = factorex], over(pension)
est store act_m1
mean act_f if (hm3_hym == 1 & menor15h == 1) [fw = factorex], over(pension)
est sto act_f1
suest act_m1 act_f1,svy
test [act_m1]pensionh [act_h1]pensionh
The error that Stata brings is:
suest act_m1 act_h1,svy
unable to generate scores for model act_m1
suest requires that predict allow the score option
r(322);
Well, I�d appreciate very much your help! :)
Leonor
Thank you so much Austin!..... This has been very helpful... I appreciate it
Regards
Renato
On Feb 24 2008, Austin Nichols wrote:
Renato <[email protected]>:
Try -suest- e.g.
sysuse auto
svyset [pw=wei]
qui svy: reg len for
est sto length
qui svy: reg turn for
est sto turn
qui svy: reg tru for
est sto trunk
suest length turn trunk, svy
test [length]foreign [turn]foreign [trunk]foreign
On 23 Feb 2008 16:34:29 -0600, <[email protected]> wrote:
I have a data base with 2 different test scores and two groups, a treatment
group and a control group. I have managed to test whether the mean of each
test scores is different among the two groups using svymean and then lincom
in this way:
svy: mean testA, over(treatment)
lincom [testA]1-[testA]0
svy: mean testB, over(treatment)
lincom [testB]1-[testB]0
What I want to do is jointly test whether both variables (testA and testB)
are jointly statistically different among the two groups, but incorporating
the sample design in my estimation.
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