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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   st: Trying to compare means and using xi and xi3 for survey data
Date   Mon, 4 Jul 2011 23:08:01 -0400

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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