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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 >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > > > -- > Hitesh S. Chandwani > University of Texas at Austin > -- Hitesh S. Chandwani University of Texas at Austin * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: trying to compare means and using xi and xi3 for survey data***From:*Hitesh Chandwani <hchandwani.stata@gmail.com>

**References**:**st: trying to compare means and using xi and xi3 for survey data***From:*Hitesh Chandwani <hchandwani.stata@gmail.com>

**Re: st: trying to compare means and using xi and xi3 for survey data***From:*Steven Samuels <sjsamuels@gmail.com>

**Re: st: trying to compare means and using xi and xi3 for survey data***From:*Hitesh Chandwani <hchandwani.stata@gmail.com>

**Re: st: trying to compare means and using xi and xi3 for survey data***From:*Hitesh Chandwani <hchandwani.stata@gmail.com>

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