Linear mixed models for complex survey data: Implementing and evaluating pairwise likelihood

Abstract

As complex-survey data become more widely used in health and social science research, there is increasing interest in fitting a wider range of regression models. We describe an implementation of two-level linear mixed models in R using the pairwise composite likelihood approach of Rao and co-workers. We discuss the computational efficiency of pairwise composite likelihood and compare the estimator to the existing sequential pseudolikelihood estimator in simulations and in data from the Programme for International Student Assessment (PISA) educational survey.