Nonparametric estimation of mean and covariance structures for longitudinal data

2013 ◽  
Vol 41 (4) ◽  
pp. 557-574 ◽  
Author(s):  
Huazhen Lin ◽  
Jianxin Pan
Keyword(s):  
Longitudinal Data ◽  
Nonparametric Estimation ◽  
Covariance Structures ◽  
Mean And Covariance Structures

scholarly journals Multilevel modeling for longitudinal data: concepts and applications

2019 ◽  
Vol 54 (4) ◽  
pp. 459-489 ◽  
Author(s):  
Joseph F. Hair Jr. ◽  
Luiz Paulo Fávero
Keyword(s):  
Longitudinal Data ◽  
Multilevel Modeling ◽  
Random Effects ◽  
Repeated Measures ◽  
Design Methodology ◽  
Multilevel Models ◽  
Covariance Structures ◽  
Content Type ◽  
Intraclass Correlations ◽  
Fixed And Random Effects

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.

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