Multilevel multinomial logit regression model with random effects: application to flash EuroBarometer euro survey data

A multilevel model for nominal data in the framework of generalized linear mixed models (GLMM) is developed to account for the inherent dependencies among observations within clusters. Motivated by a dataset from the Flash EuroBarometer survey (FEBS), the random region and country effects are incorporated into the linear predictor of a GLMM to accommodate the nested clusterings. The fixed effects are estimated by maximizing the penalized likelihood function, whereas the random variance component parameters are predicted via the restricted maximum likelihood (REML) estimation method. The model is employed to analyse the FEBS data. A Monte Carlo simulation study is conducted to evaluate the performance of estimators.