Multilevel Linear Models Analysis using Generalized Maximum Entropy

The aimed of this study was to introduce the general multilevel models and discusses the Generalized Maximum Entropy (GME) estimation method that may be used to fit such models. The proposed procedure is applied to the two-level data model. The GME estimates were compared with Goldstein’s generalized least squares estimates. The comparisons are made by two criteria; the bias and the efficiency. We find that the estimates of two level’s model were substantially and significantly biased using Goldstein’s generalized least squares approach. However, the GME estimates are unbiased and consistent, we conclude that the GME approach is a recommended procedure to fit multilevel models. An application to a real data in education is also discussed.