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Multilevel Models: Conceptual Framework and Applicability

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  • Roxana-Otilia-Sonia Hritcu

    (Alexandru Ioan Cuza University)

Abstract

Individuals and the social or organizational groups they belong to can be viewed as a hierarchical system situated on different levels. Individuals are situated on the first level of the hierarchy and they are nested together on the higher levels. Individuals interact with the social groups they belong to and are influenced by these groups. Traditional methods that study the relationships between data, like simple regression, do not take into account the hierarchical structure of the data and the effects of a group membership and, hence, results may be invalidated. Unlike standard regression modelling, the multilevel approach takes into account the individuals as well as the groups to which they belong. To take advantage of the multilevel analysis it is important that we recognize the multilevel characteristics of the data. In this article we introduce the outlines of multilevel data and we describe the models that work with such data. We introduce the basic multilevel model, the two-level model: students can be nested into classes, individuals into countries and the general two-level model can be extended very easily to several levels. Multilevel analysis has begun to be extensively used in many research areas. We present the most frequent study areas where multilevel models are used, such as sociological studies, education, psychological research, health studies, demography, epidemiology, biology, environmental studies and entrepreneurship. We support the idea that since hierarchies exist everywhere, multilevel data should be recognized and analyzed properly by using multilevel modelling.

Suggested Citation

  • Roxana-Otilia-Sonia Hritcu, 2015. "Multilevel Models: Conceptual Framework and Applicability," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 11(5), pages 72-83, October.
    • Handle: RePEc:dug:actaec:y:2015:i:5:p:72-83
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    File URL: http://journals.univ-danubius.ro/index.php/oeconomica/article/view/2959/2849
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    1. Robin Lamarche-Perrin & Sven Banisch & Eckehard Olbrich, 2016. "The Information Bottleneck Method For Optimal Prediction Of Multilevel Agent-Based Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(01n02), pages 1-45, February.

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