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Prediction in Multilevel Models

  • Author(s): Afshartous, David
  • de Leeuw, Jan
  • et al.
Abstract

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.

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