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RE: st: Cross-classification question


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Cross-classification question
Date   Mon, 9 Jan 2012 23:10:20 -0500

Hi Sarah,
You might get more feedback on this type of question on the Multilevel discussion list: 
http://www.bristol.ac.uk/cmm/learning/support/jisc.html ;

It's true that the data you describe don't fit neatly into the usual cross-classified modeling scenarios. The closest example I have sometimes seen used is a study of intellectual growth where the students change classrooms over time... within your model the cross-classification by students and classrooms is contemporaneous but perhaps similar methods might be useful. I think you are generally on the right track w.r.t. the random effect modeling you suggest, but the following papers may give you some additional guidance:

Luo, W., & Kwok, O.-M. (2011). The Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models: A Monte Carlo Study. Journal of Educational and Behaviorial Statistics, Online First. http://jeb.sagepub.com/content/early/2011/07/30/1076998610394366.abstract

Fielding, A., & Goldstein, H. (2006). Cross-classified and Multiple Membership Structures in Multilevel Models: An Introduction and Review. Research Report RR791. Birmingham, UK: Department  for Education and Skills, University of Birmingham.http://www.socscistaff.bham.ac.uk/fielding/Cross_classified_review_RR791.pdf

Goldstein, H., Burgess, S., & McConnell, B. (2007). Modelling the effect of pupil mobility on school differences in educational achievement. Journal of the Royal Statistical Society, Series A, 170, 941-954.

Leckie, G. (2009). The complexity of school and neighbourhood effects and movements of pupils on school differences in models of educational achievement. Journal of the Royal Statistical Society, Series A, 172(3), 537-554.

Chung, H. & Beretvas, S.N. (2011). The impact of ignoring multiple membership data structures in multilevel models. British Journal of Mathematical and Statistical Psychology, Early View.http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8317.2011.02023.x/abstract

Hope this helps,

Cam

> Date: Mon, 9 Jan 2012 15:02:51 -0500
> Subject: st: Cross-classification question
> From: sfp@brandeis.edu
> To: statalist@hsphsun2.harvard.edu
> 
> Hello All,
> 
> I am working on a project that I think requires a cross-classified approach.
> 
> My data is from 1,000 students, surveyed in approximately 200
> classrooms in 1 school.  Students took the survey in at least one and
> often three different classrooms-- i.e. in Math, English, and Science.
>  The data set is organized such that each row is one students' survey
> responses in one classroom.  So, if a student took the survey three
> times, their responses would be in three different rows.  My dependent
> variable is engagement, which is continuous and specific to whatever
> classroom a student happened to to be in when they took the survey.
> My independent variables include several student characteristics as
> well as student perceptions of their teachers' classroom practices.
> 
> I believe that my data is cross-classified but I am not sure.  Most
> students belong to more than one classroom in the entire data set,
> which makes me think that it is cross-classified data.  But within
> each observation every student belongs to just one classroom and my
> dependent variable is classroom-specific, which is contrary to every
> example of cross-classification that I have found.  Can any of you
> shed some light on this dilemma for me?
> 
> Also, if my data is indeed cross-classified, it seems like I can deal
> with this issue by adding a random-effect for class subject to my
> model, since each student is only in one classroom for Math, one for
> English, etc.  If so, is this the correct stata syntax for the null
> model?
> 
> xtmixed engagement || _all.R.subject || classid:, mle
> 
> Or, is it:
> 
> xtmixed engagement || _all.R.subject || _all.R.classid: , mle
> 
> Thanks so much for your help!
> 
> Best,
> 
> Sarah
> 
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