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Re: st: trying to compare means and using xi and xi3 for survey data


From   Hitesh Chandwani <hchandwani.stata@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 07:44:22 -0400

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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>>
>>
>> *
>> *   For searches and help try:
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>>
>
>
>
> --
> Hitesh S. Chandwani
> University of Texas at Austin
>



-- 
Hitesh S. Chandwani
University of Texas at Austin

*
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