A Pseudo Maximum Likelihood Approach to Multilevel Modelling of Survey Data

Abstract

Abstract An application of the pseudo maximum likelihood method to estimation of a multilevel linear model fitted to the dependent observations coming from a finite population is demonstrated. The proposed approach provides a closed form solution for estimating of the model parameters. It is computationally simpler than the iterative procedures suggested in the literature (e.g., the iterative probability weighted least squares method of Pfeffermann et al. (Pfeffermann, D., Skinner, C.J., Holmes, D.J., Goldstein, H., Rasbash, J. (1998 Pfeffermann, D., Skinner, C. J., Holmes, D. J., Goldstein, H. and Rasbash, J. 1998. Weighting for unequal selection probabilities in multilevel models. Journal of Royal Statistical Society B, 60: 23–40. [Crossref] , [Google Scholar]). Weighting for unequal selection probabilities in multilevel models. Journal of Royal Statistical Society B 60:23–40)). Issues related to model and sample design hierarchies and their impact on estimation are discussed. A problem of weighting at different levels is addressed. A small simulation study showed that the proposed procedure is efficient even for small within group sample sizes.