Re: st: RE: RE: -xtmixed- and multilevel data [Was: Grouping income variables- RECODE COMMAND]

[William Buchanan <william@williambuchanan.net>]

Do you know if your depression scale is invariant across countries?  If not you may consider using country as a group and fit a multi-group SEM to first establish the measurement invariance of your depression scale.  If the scale does not meet any of the criteria for measurement invariance then any inferences that rely on adjustments of country effects will produce spurious results.

HTH,
Billy

Sent from my iPhone

> On Feb 6, 2014, at 3:54, "Antonio Rodriguez Andres" <Antonio.Andres@emu.edu.tr> wrote:
> 
> Dear Prof. Jenkins
> 
> Thank you very much for your valuable input. In my empirical application,
> what matters is to explore the effect of children on depression. That is,
> the effect of one of the individual predictors of depression. It seems to me
> that I should estimate FE models or OLS regressions for each country.
> 
> Regards
> 
> Antonio
> 
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
> S.Jenkins@lse.ac.uk
> Sent: Thursday, February 06, 2014 11:32 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: -xtmixed- and multilevel data [Was: Grouping income
> variables- RECODE COMMAND]
> 
> "Antonio Rodriguez Andres" <Antonio.Andres@emu.edu.tr>:
> 
> You are indeed stuck with 23 countries. The cited papers make 2 suggestions
> that may be relevant to you:
> 
> (1) consider whether you are really interested in getting good estimates of
> the effects of the individual-level predictors, or whether the country
> effects are integral to your research project. If the former, then you can
> exploit the large number of persons per country. And there are several
> modelling approaches at your disposal, including FE or separate regressions
> for each country.
> 
> (2) If country effects are integral, consider whether Bayesian approaches
> are feasible in your case: there is some Monte Carlo evidence that the
> estimates of country effects derived using these perform better in the small
> number of countries case. See the references cited in the paper. As a Stata
> user, you might combine, say, -runmlwin- (on SSC) with MLwiN. Or similar
> wrappers that call WinBugs.
> 
> Good luck
> Stephen
> 
> ------------------------------
> 
> Date: Wed, 5 Feb 2014 13:30:48 +0200
> From: "Antonio Rodriguez Andres" <Antonio.Andres@emu.edu.tr>
> Subject: st: RE: -xtmixed- and multilevel data [Was: Grouping income
> variables- RECODE COMMAND]
> 
> Dear Stephen,
> 
> Your feedback is much appreciated. Based on your research paper, the results
> are no longer valid with 23 countries. I am stacked with this issue and how
> to proceed. Maybe a good starting point is to replicate your table 3. My
> dependent variable is the depression score and the key variable of interest
> is having children in home and see how its effect differs across gender.
> From my read of your table, for example
> 
> xtset country idno
> regress depression  children  age  //OLS regress depression children age,
> cluster(country) //  OLS
> xi: regress depression children age i.country   //FE
> xtreg depression children age, re i(country) // Random effects
> 
> Ýs the syntax used to generate your results?
> 
> Regards
> 
> Antonio
> 
> 
> 
> Stephen
> ------------------
> Stephen P. Jenkins <s.jenkins@lse.ac.uk>
> 
> Please access the attached hyperlink for an important electronic
> communications disclaimer: http://lse.ac.uk/emailDisclaimer
> 
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