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# RE: st: Instrumental Variables approach using -xt- for three-level growth model

 From David Torres To "statalist@hsphsun2.harvard.edu" Subject RE: st: Instrumental Variables approach using -xt- for three-level growth model Date Mon, 6 May 2013 10:33:48 -0400

```Thanks for you response, Austin.

To be clear, though, I am using data from a large school district and am using as my outcome scores on the Stanford 10 math and reading assessments, so no plausible values.  My explanatory variable of interest is whether a child's parents exercised choice to send the child to a school other than the zoned school (0 = no, 1 = yes).  It's this explanatory variable that I'm focusing on as one that needs to be instrumented, given the differential likelihood of exercising choice deriving from parents of various education levels, density of neighborhood, etc.  I'm only using the 2008 kindergarten cohort and am following these children across five years.  The boundaries remain constant across the years, as far as I can tell.

As an aside, I do have addresses for all families in the district and will be geocoding these soon.  I then plan to use the -vincenty- command to calculate the distance to the nearest school (which will also be geocoded) to which a student is not zoned.  This will be included as an additional control.

--

David Diego Torres, PhD
NSF SBE Postdoctoral Research Fellow
Houston Education Research Consortium
Rice University
6100 Main Street, MS-28
Houston, TX 77005
Phone: 713-348-2984
Email: ddtorres at rice dot edu

----------------------------------------
> Date: Mon, 6 May 2013 10:18:40 -0400
> Subject: Re: st: Instrumental Variables approach using -xt- for three-level growth model
> From: austinnichols@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> David Torres <writeon4truth2@msn.com>
> You don't specify, but I assume you have 5 plausible values drawn from
> a posterior distribution on achievement from some standardized test as
> your outcome variable, and school type attended as your explanatory
> variable of interest--none of the variables you cite satisfy an
> exclusion restriction, as all would be expected to have direct impacts
> on achievement. If you do in fact have 5 plausible values as your
> outcome measure, you should not be using -xtmixed- IMHO but perhaps
> -mi- since these are 5 imputations for one observation, not 5
> observations. If you have 5 plausible values at two points in time,
> you now have 25 imputations, and so on. You should get similar results
> if you just average across the 5 imputations, but no identical of
> course. The more important challenge is finding good instruments for
> school type attended. Do you know where people live relative to the
> boundary of the "zoned school area" so you can compute that distance
> precisely? Do boundaries change over time?
>
> On Sun, May 5, 2013 at 4:57 PM, David Torres <writeon4truth2@msn.com> wrote:
> > Hey, all,
> >
> > I would like to utilize the instrumental variables approach with hierarchical data that has repeated measures (five) of students nested within schools. I'd prefer to do this in HLM7, but I'm not sure if the program allows for this more complex model setup. Anyway, I know Stata has the -xtivreg- command. Is there something similar for mixed effects models, i.e., -xtivmixed- perhaps? If not, how can I accomplish my aim using what's available in Stata. (I do have the Rabe-Hesketh & Skrondal texts.)
> >
> > I am instrumenting for the likelihood of parents exercising choice to send their children to a school outside the zoned school area. Obviously, self-selection is an issue here, but proxies that might explain the likelihood of choice are, and this list is by no means exhaustive, number of older children in the household, maternal education, or percent of persons over 18 with a bachelor's degree...or something like that. HELP.
>
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