Department of Statistics, UCLA

Multilevel modeling is an increasingly popular technique for analyzing hierarchial data. We consider the problem of predicting a future observable y,j in the Jth group of a hierarchial dataset. Three prediction rules are presented and assessed via a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of level-1 and level-2 sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, Prior, and Multilevel estimators for the level-1 coefficients Bj. The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups J increases.