model
		{
		   for(p in 1 : N) {

		      Y[p] ~ dnorm(mu[p], tau[p])
		      mu[p] <- alpha[school[p], 1] + alpha[school[p], 2] * LRT[p] 
		               + alpha[school[p], 3] * VR[p, 1] + beta[1] * LRT2[p] 
		               + beta[2] * VR[p, 2] + beta[3] * Gender[p] 
		               + beta[4] * School.gender[p, 1] + beta[5] * School.gender[p, 2]
		               + beta[6] * School.denom[p, 1] + beta[7] * School.denom[p, 2]
		               + beta[8] * School.denom[p, 3]
		      log(tau[p]) <- theta + phi * LRT[p]
		      sigma2[p] <- 1 /  tau[p]
		      LRT2[p] <- LRT[p] * LRT[p]

		   }
		   min.var <- exp(-(theta + phi * (-34.6193))) # lowest LRT score = -34.6193
		   max.var <- exp(-(theta + phi * (37.3807)))  # highest LRT score = 37.3807

		   # Priors for fixed effects:
		   for (k in 1 : 8) {  beta[k] ~ dnorm(0.0, 0.0001)   }
		   theta ~ dnorm(0.0, 0.0001); phi ~ dnorm(0.0, 0.0001)

		   # Priors for random coefficients:
		   for (j in 1 : M) {
		      alpha[j, 1:3 ] ~ dmnorm(gamma[1:3 ], T[1:3 ,1:3 ]); 
			 alpha1[j] <- alpha[j,1]
		   }
		 
		   # Hyper-priors:
		   gamma[1:3] ~ dmnorm(mn[1:3 ], prec[1:3 ,1:3 ]);
		   T[1:3 ,1:3 ] ~ dwish(R[1:3 ,1:3 ], 3)
		}