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| From | Steven Samuels <sjsamuels@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: trying to compare means and using xi and xi3 for survey data |
| Date | Tue, 5 Jul 2011 09:21:32 -0500 |
"Is this interpretation accurate?"
Yes
Steve
sjsamuels@gmail.com
On Jul 5, 2011, at 6:44 AM, Hitesh Chandwani wrote:
Steven,
I used the following commands:
. char insured_pub_pvt_un[omit]2
. xi: svy: regress totchg_num i.insured_pub_pvt_un
And got the following output:
i.insured_pub~n _Iinsured_p_0-4 (naturally coded; _Iinsured_p_2 omitted)
(running regress on estimation sample)
Survey: Linear regression
Number of strata = 75 Number of obs = 103817
Number of PSUs = 966 Population size = 469088.57
Design df = 891
F( 3, 889) = .
Prob > F = .
R-squared = 0.0106
------------------------------------------------------------------------------
| Linearized
totchg_num | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iinsured_~0 | (dropped)
_Iinsured_~1 | 6504.334 915.0348 7.11 0.000 4708.46 8300.209
_Iinsured_~3 | -3015.988 705.0121 -4.28 0.000 -4399.666 -1632.31
_Iinsured_~4 | 1070.352 1961.327 0.55 0.585 -2779.007 4919.711
_cons | 13894.47 837.4082 16.59 0.000 12250.95 15538
------------------------------------------------------------------------------
I think the fact that the "0" group was dropped again has something to
do with the fact that all observations in this group have pweights set
to zero. The way I interpret the output is that the coefficients are
the differences in mean between the omitted group (group 2) and the
other groups (1, 3, and 4, respectively) with the corresponding
t-statistic values being a comparison of means with the omitted group.
Is this interpretation accurate?
Regards,
Hitesh
On Tue, Jul 5, 2011 at 7:30 AM, Hitesh Chandwani
<hchandwani.stata@gmail.com> wrote:
> Hi Steven,
>
> There is no evident coding error that I can see. If I use the
> -,noomit- option, how do I interpret the results? The coefficients are
> clearly the means, but what do the t-values indicate?
>
> xi, noomit: svy: reg totchg_num i.insured_pub_pvt_un , nocons
> (running regress on estimation sample)
>
> Survey: Linear regression
>
> Number of strata = 75 Number of obs = 103817
> Number of PSUs = 966 Population size = 469088.57
> Design df = 891
> F( 4, 888) = .
> Prob > F = .
> R-squared = 0.1513
>
> ------------------------------------------------------------------------------
> | Linearized
> totchg_num | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> _Iinsured_~0 | (dropped)
> _Iinsured_~1 | 20398.81 1171.304 17.42 0.000 18099.97 22697.64
> _Iinsured_~2 | 13894.47 837.4082 16.59 0.000 12250.95 15538
> _Iinsured_~3 | 10878.49 844.9702 12.87 0.000 9220.121 12536.85
> _Iinsured_~4 | 14964.83 1801.761 8.31 0.000 11428.64 18501.02
> ------------------------------------------------------------------------------
>
> Regards,
> Hitesh
>
>
> On Tue, Jul 5, 2011 at 12:34 AM, Steven Samuels <sjsamuels@gmail.com> wrote:
>>
>> I suspect a coding error.
>>
>> Suppose insure_cat is your original insurance variable. Have you looked at
>>
>> *******************************
>> bys insure_cat: sum totchg_num
>>
>> *****************************
>> Have you tabulated each insurance indicator against insure_cat?
>>
>> In any case, direct survey approaches are:
>> ************************
>> svy: mean totchg_num, over(insure_cat)
>> xi, noomit: svy: reg totch_num i.insure_cat, nocons //pre-Stata 11
>> svy: reg totch_num ibn.insure_cat, nocons //Stata 11 +
>> ************************
>>
>>
>> Steve
>>
>>
>> Steven J. Samuels
>> Consultant in Statistics
>> 18 Cantine's Island
>> Saugerties, NY 12477 USA
>> Voice: 845-246-0774
>> Fax: 206-202-4783
>> sjsamuels@gmail.com
>>
>> On Jul 4, 2011, at 5:02 PM, Hitesh Chandwani wrote:
>>
>> Hello Statalisters,
>>
>> I am using cost survey data and have 2 questions:
>>
>> 1) Comparison of means
>>
>> Using the svy: mean procedure, I can get means of cost for all
>> categories of a particular variable. But since this variable is not
>> dichotomous, using -test- or -lincom- as a postestimation command to
>> compare the means, doesn't yield any results. What I thought of was
>> dummy coding the categories and then running a regression. Instead of
>> manually creating dummy variables, I decided to use -xi-; which brings
>> me to my next question,
>>
>> 2) -xi- and -xi3- will both omit one category as a reference
>> category..which is fine. But, in my output, after omitting the first
>> category, another category is indicated as (dropped). Moreover, there
>> is still no value for the F-statistic.
>>
>> Firstly, is my approach correct? And secondly, why are 2 categories
>> being dropped?
>>
>> (One explanation that I could come up with for the 2 dropped
>> categories is that the pweight for the observations in the omitted
>> category " _Iinsured_p_0" is set to zero and hence Stata needs to use
>> another category as reference)
>>
>> The following is my syntax as well as output:
>>
>>
>> xi: svy: regress totchg_num i.insured_pub_pvt_un
>> i.insured_pub~n _Iinsured_p_0-4 (naturally coded; _Iinsured_p_0 omitted)
>> (running regress on estimation sample)
>>
>> Survey: Linear regression
>>
>> Number of strata = 75 Number of obs = 103817
>> Number of PSUs = 966 Population size = 469088.57
>> Design df = 891
>> F( 3, 889) = .
>> Prob > F = .
>> R-squared = 0.0106
>>
>> ------------------------------------------------------------------------------
>> | Linearized
>> totchg_num | Coef. Std. Err. t P>|t| [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> _Iinsured_~1 | 6504.334 915.0348 7.11 0.000 4708.46 8300.209
>> _Iinsured_~2 | (dropped)
>> _Iinsured_~3 | -3015.988 705.0121 -4.28 0.000 -4399.666 -1632.31
>> _Iinsured_~4 | 1070.352 1961.327 0.55 0.585 -2779.007 4919.711
>> _cons | 13894.47 837.4082 16.59 0.000 12250.95 15538
>> ------------------------------------------------------------------------------
>>
>> . test _Iinsured_p_1 _Iinsured_p_2 _Iinsured_p_3 _Iinsured_p_4
>>
>> Adjusted Wald test
>>
>> ( 1) _Iinsured_p_1 = 0
>> ( 2) _Iinsured_p_2 = 0
>> ( 3) _Iinsured_p_3 = 0
>> ( 4) _Iinsured_p_4 = 0
>> Constraint 2 dropped
>>
>> F( 3, 889) = 23.78
>> Prob > F = 0.0000
>>
>> Any help in understanding this issue will be greatly appreciated.
>>
>> Regards,
>> --
>> Hitesh S. Chandwani
>> University of Texas at Austin
>> *
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>>
>>
>> *
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>>
>
>
>
> --
> Hitesh S. Chandwani
> University of Texas at Austin
>
--
Hitesh S. Chandwani
University of Texas at Austin
*
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