A review of Multilevel Modeling: some methodological issues and advances

Multilevel modeling is a recently new class of statistical methods to handle nested data. Mainly thanks to the wide range of applicability and the great increase of statistical softwares, in the last decades multilevel modeling has enjoyed an explosion of published papers and books in both methodological and application field. Currently, there is a need to not only develop the research on multilevel approach for the analysis of complex data, but also to have instructions to properly address the usage. This work aims at summarizing methodological aspects related to multilevel models, illustrating good-practices, advantages and limits by reviewing applications in various fields, such as socio-economic, educational, health and medical sciences.We further focus our attention on the latest advances of multilevel modeling towards, e.g., the inclusion of latent variables and the Bayesian approach.