*y*is ordered categorical in that:

1: very unfavorable; 2: mostly unfavorable; 3: mostly favorable; 4: very favorable

The most common way to model ordinal data like this is to postulate the existence of an underlying latent (unobserved) variable

*z*associated with each response

*k*of

*y.*In other words, we fit such data with ordered logit/probit models. Since the data is time series cross sectional, the reasonable way to model it is to fit a multilevel model (See Andrew Gelman, 2007, Data Analysis using Regression and Multilevel/Hierarchical Models).

Here is the formal expression of the model with varying intercepts (random effects) by year:

Here is the code for BUGS, mologit.txt. A trick to genereate initial values for cutpoints is to assign 0 to each cutpoints. So for our model, it is a 4*3 matrix of 0's. (4 years, 3 cutpoints for each years). If you are using R2WinBUGS, the R code for the inits should be:

inits <- function(){

list(C=matrix(0,4,3))

}

Our preliminary result suggests that there is no effect of 9/11 on American's attitudes on Muslim. The paper is going to present at this year's MPSA conference at Chicago. The presenting time is on April 5 at 10:00am. We are welcome for comments on the paper.

## 3 comments:

"Our preliminary result suggests that there is no effect of 9/11 on American's attitudes on Muslim." This is a really surprising finding, isn't it? I mean CNN calls this a "controversial photo".

BTW: Do you intend to publish your paper online?

P.S.: Nice article and thanks for the BUGS code :-)

Hi Bernd,

The paper is going to be a conference proceeding after we present it. In the meantime, we are still working on it. I will post the updated news on our paper in my blog.

Thank you!

amazing to be able to read what actually happens and try and picture the action.

http://www.suainlogistics.com/

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