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.
Multilevel modeling; Longitudinal data; Fixed effects; Random effects; Stata
