Phoung,
I posted an identical question a few weeks back
(http://www.stata.com/statalist/archive/2006-04/msg00025.html)
for a very similar problem as you but got no response. Maybe I was not
that clear.
I tried to search the archieves and found this 3-year old message
http://www.stata.com/statalist/archive/2003-08/msg00426.html
Have tried the routine. Seems to work with few outcomes (have tried it
with 4- no grade, elem, secondary, college, and about 20th
observations) but have difficulties with finer disaggregation. Let me
warn you the program take some time to finish.
HTH,
Aniceto
On 4/21/06, Phuong Lan Nguyen <[email protected]> wrote:
> Dear Maarten,
>
> thank you very much for your email. I did use this method for my study on
> social stratification. It is very helpful way to deal with right censoring
> values. However, I plan to use sample of kids aged 15-18 years old. that
> means, they have not completed their high school. I found out
> that I may be able to use ordered probit regression if the dependent
> variable can be controlled for censored values. Do you have any hint on
> this?I only found help in creating censored values for tobit model online.
>
> Thanks.
> phuong
>
>
>
> On Thu, 20 Apr 2006, Maarten buis wrote:
>
> > Phuong:
> > The easiest way to deal with this problem is to estimate what is known in the social
> > stratification literature as a Mare model (Mare 1980 and Mare 1981). Say the educational system
> > you study has four levels, and everybody has to finish all lower levels in order to obtain a
> > certain level, than knowing someones highest eachieved level of education also implies knowing all
> > transitions that persons must have passed in order to get there. So a person who has finished
> > level two, must have passed the first and second transition. The Mare model models the probability
> > of passing a transition. You can estimate one by making a three dummie variables: one that equals
> > one if the person passed the first transition and zero if he/she fails, one that equals one if the
> > preson passed the second transition, zero if he/she fails, and missing if he/she failed the first
> > transition, and one that equals one if a person passed the third transition, zero if he/she fails,
> > and missing if he/she failed either the first or second transition. Estimate a separate -logit- or
> > -probit- on each variable. See the example below.
> >
> > Big advantage for you is that it deals with right censoring in a quite natural way, censored cases
> > can be dealt as any other as long as you know the highest achieved level of education at time of
> > the interview. Disadvantage is that now you don't get one effect for each explanatory variable but
> > as many effects as there are transitions.
> >
> > HTH,
> > Maarten
> >
> > Mare, Robert D. 1980. "Social Background and School Continuation Decisions." Journal of
> > the American Statistical Association 75(370), pp. 295-305.
> > Mare, Robert D. 1981. "Change and Stability in Educational Stratification." American Sociological
> > Review 46(1), pp. 72-87.
> >
> >
> > *------------begin example--------------
> > sysuse nlsw88, clear
> >
> > /*preliminary data prep*/
> > tab grade
> > gen ed = grade>=12
> > replace ed = 2 if grade >=13 & grade <16
> > replace ed = 3 if grade >=16
> > tab ed
> > tab race
> > gen white = race == 1
> >
> > /*generate transition dummies*/
> > gen ed01 = ed>=1
> > gen ed12 = ed>=2 if ed>=1
> > gen ed23 = ed>=3 if ed>=2
> >
> > /*estimate the Mare model*/
> > logit ed01 white south
> > logit ed12 white south
> > logit ed23 white south
> > *--------------end example--------------
> >
> > --- Phuong Lan Nguyen <[email protected]> wrote:
> >> I am working on years of schooling variable for all individuals who are in
> >> school or already completed their education. Since I plan to run ordered
> >> probit regression, I guess I need to have a special command for the
> >> censored values in the ordered probit regression. Does anyone run it
> >> before? Please give me advice on how to set up the dependent variable and
> >> the ordered probit regression.
> >
> >
> >
> > -----------------------------------------
> > Maarten L. Buis
> > Department of Social Research Methodology
> > Vrije Universiteit Amsterdam
> > Boelelaan 1081
> > 1081 HV Amsterdam
> > The Netherlands
> >
> > visiting adress:
> > Buitenveldertselaan 3 (Metropolitan), room Z214
> >
> > +31 20 5986715
> >
> > http://home.fsw.vu.nl/m.buis/
> > -----------------------------------------
> >
> >
> >
> > ___________________________________________________________
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> >
>
> ---)*(---)*(---)*(---)*(---)*(---)*(---)*(---)*(---
> Phuong Lan Nguyen
> Department of Sociology
> 202 Savery, UW
> Seattle WA 98195-3340
> Email: [email protected]
>
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