Multilevel modeling for longitudinal data: concepts and applications

Joseph F. Hair and Luiz Paulo Fávero

RAUSP Management Journal, 2019, vol. 54, issue 4, 459-489

Abstract: Purpose - This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used. Design/methodology/approach - The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation. Findings - From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level. Originality/value - Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

Keywords: Multilevel modeling; Longitudinal data; Fixed effects; Random effects; Stata (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:rauspp:rausp-04-2019-0059

DOI: 10.1108/RAUSP-04-2019-0059

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