Department of Statistics, UCLA

Multilevel modelling is often used in the social sciences for analyzing data that has a hiearchial structure, e.g.. students nesteded within schools. In an earlier study, we investigated the performance of various prediction rules for predicting a future observable within a hierachial data set (Afshartous & de Leeuw 2002). We apply the multilevel prediction approach to the NELS:88 educational data in order to asses the improvement in prediction; the goal is to develop model selction criteria (multilevel cross-validation and multilevel bootstrap) that assess the predictive ability of multilevel models. We also introduce two plots that 1) aid in the visualization the amount to which the multilevel model predictions are "shrunk" or translated from the OLS predictions, 2) help identify if certain groups exist for which the predictions are pariculary good or bad.