
                model{
                for(i in 1:N){
                post[i] ~ dnorm(mu[i],tau)
                mu[i] <- beta[1]+beta[2]*Prettest[i]+beta[3]*Intervention[i]+b1[School[i]] 
                }

                for(j in 1:M){
                b1[j]~dnorm(0.0,tau.b1)
                }

                tau.b1~dgamma(0.001,0.0001)
                sigma.b1<-1/tau.b1
                tau~dgamma(0.001,0.0001)
                sigma<-1/tau

                for(k in 1:p){beta[k]~dnorm(0.0,1.0E-06)}

                # ICC and TOTAL VARIANCE
                sigma.Total <-sigma + sigma.b1
                COND.icc <- sigma.b1 * pow(sigma.Total ,-1)
                UNC.ICC <- UNC.icc

                #sigmas
                COND.sigma.Total <- sigma.Total
                COND.sigma.Within <- sigma.b1
                UNC.sigma.Total <- UNC.sigma.tt
                UNC.sigma.Within <- UNC.sigma

                # EFFECT SIZE
                COND.ES.Within<-  beta[3] /sqrt(COND.sigma.Within)#conditional
                COND.ES.Total <-  beta[3] /sqrt(COND.sigma.Total)#conditional
                UNC.ES.Within<-  beta[3] /sqrt(UNC.sigma.Within)#unconditional
                UNC.ES.Total <-  beta[3] /sqrt(UNC.sigma.Total)#unconditional

                COND.g.with<- COND.ES.Within* (1- (3/(4*(N-2)-1)))#conditional
                COND.g.Total <-  COND.ES.Total* (1- (3/(4*(N-2)-1)))#conditional
                UNC.g.with<- UNC.ES.Within* (1- (3/(4*(N-2)-1)))#unconditional
                UNC.g.Total <-  UNC.ES.Total* (1- (3/(4*(N-2)-1)))#unconditional
                }
                