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

[Hitesh Chandwani <hchandwani.stata@gmail.com>]

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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-- 
Hitesh S. Chandwani
University of Texas at Austin

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