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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.

Suggested Citation

  • Nicolae-Marius Jula, 2014. "Multilevel model analysis using R," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 55-66, June.
  • Handle: RePEc:rsr:journl:v:62:y:2014:i:2:p:55-66

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    CRAN; Multilevel analysis; package; R;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software


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