Multilevel model analysis using R

Nicolae-Marius JULA (
Nicolae Titulescu University of Bucharest


The complex datasets cannot be analyzed using only simple regressions. Multilevel models (also known as hierarchical linear models, nested models, mixed models, random coefficient, random-effects models, random parameter models or split-plot designs) are statistical models of parameters that vary at more than one level.
Multilevel models can be used on data with many levels, although 2-level models are the most common.
Multilevel models, or mixed effects models, can be estimated in R. There are several packages available in CRAN. In this paper we are presenting some common methods to analyze these models.
JEL Classification: B23, C23, C33, C87

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Romanian Statistical Review 2/2014