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Hi all,

I have a IRT model as followings:
model
{
for (i in 1:NE){
   theta[i]~dnorm(0,1)
   for (j in 1:NI){
       p[i,j]<- phi(a[j]*theta[i]-b[j])
       y[i,j]~dbern(p[i,j])
   }
}
for (i in 1:NI){
  a[i]~dnorm(mua,siga) I(0,)
  b[i]~dnorm(mub,sigb)
}

mua~dnorm(0,.0001)
mub~dnorm(0,.0001)
siga~~dchisqr(.5)
sigb~~dchisqr(.5)
}

The thetas' distribution is distributed as N(0,1).
My question is why the variance of the point estimates of thetas is not 
close to 1?
The variance of the estimates of thetas is like 0.3.
How can I make the estimates of thetas to have a variance of 1 as I 
speficifed in the model?
Thanks very much for any hint.

Lily

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