TY  - JOUR
AB  - 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.
VL  - 54
IS  - 4
SN  - 2531-0488
DO  - 10.1108/RAUSP-04-2019-0059
UR  - https://doi.org/10.1108/RAUSP-04-2019-0059
AU  - Hair Jr. Joseph F.
AU  - Fávero Luiz Paulo
PY  - 2019
Y1  - 2019/01/01
TI  - Multilevel modeling for longitudinal data: concepts and applications
T2  - RAUSP Management Journal
PB  - Emerald Publishing Limited
SP  - 459
EP  - 489
Y2  - 2024/04/25
ER  -