Re: st: multinomial multilevel analysis in Stata

[Rosie Chen <jiarongchen2002@yahoo.com>]

Thanks a lot to Cameron and Hind. I am reading the manual and articles suggested below. Very helpful. 

I have a couple of more questions below and would appreciate advice from this listserv. 


(1) For example, suppose the outcome variable measures the percentage of budget purchasing luxurious items within a person's monthly salary, and it is on a continuous scale. I can recode it into a three-category variable, 1 indicating no purchase at all, 2 indicating larger than zero but smaller than 20%, and 3 indicating 20% or more. Should I treat it as an ordinal outcome or multinomial outcome?

(2) If I treat it as an ordinal outcome and run an ordinal multilevel analysis, how to test the assumption of constant effects of covariates for different categories in the ordinal multilevel analysis? 

(3) Do I need to include weighting variable for each level of data, if there are 3 levels in the model? It seems to me to be common to use level 1 weights, but I haven't seen a lot of studies using weights for all levels. 


Thank you! 


Rosie




----- Original Message -----
From: Cameron McIntosh <cnm100@hotmail.com>
To: STATA LIST <statalist@hsphsun2.harvard.edu>
Cc: 
Sent: Friday, October 14, 2011 1:21 AM
Subject: RE: st: multinomial multilevel analysis in Stata

Rosie,



You should also see: 

Kuss, O., & McLerran, D. (2007). A note on the estimation of the multinomial logistic model with correlated responses in SAS. Computer Methods and Programs in Biomedicine, 87(3), 262-269. 




Malchow-Møller, N., & Svarer, M. (2003). Estimation of the multinomial logit model with random effects. Applied Economics Letters, 10(7), 389-392.




Hedecker, D. (2003). A mixed-effects multinomial logistic regression model. Statistics in Medicine, 22(9), 1433–1446.




Hartzel, J., Agresti, A., & Caffo, B. (2001). Multinomial logit random effects models. Statistical Modelling, 1(2), 81-102.




Skrondal, A., & Rabe-Hesketh, S. (2003). Multilevel logistic regression for polytomous data and rankings. Psychometrika, 68, 267-287.




Chen, Z., & Kuo, L. (2000). A Note on the Estimation of the Multinomial Logit Model With Random Effects. The American Statistician, 55, 89-95.




Wang, S., & Tsodikov, A. (2010). A Self-consistency Approach to Multinomial Logit Model with Random Effects. Journal of Statistical Planning and Inference, 140(7), 1939-1947.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851106/pdf/nihms-191118.pdf



Hope this helps,



Cam



----------------------------------------




> Date: Thu, 13 Oct 2011 21:00:45 -0700
> Subject: Re: st: multinomial multilevel analysis in Stata
> From: hindstata@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> Hi Rosie
>
> I would recommend looking GLLAMM because of the breadth of modeling
> you can do within this modeling framework - most of the information
> you need is provided here
> http://www.bepress.com/ucbbiostat/paper160/
>
> Hope this helps
> Hind
>
> On Thu, Oct 13, 2011 at 8:30 PM, Rosie Chen <jiarongchen2002@yahoo.com> wrote:
> > Hello, I am not familiar with multilevel analysis using Stata, especially when the outcome is a categorical variable. Could someone give me advice and/or provide resources that can allow me to have a quick start of such analysis? Thank you very much!
> >
> >
> > Rosie
> >
> > *
> > * 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:
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> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/                           
*
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*
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