MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
Robert Price, Christina Nicole Lewis
Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.
Thesis - Open Access
Khatiwada, Aastha, "Multilevel Models for Longitudinal Data" (2016). Electronic Theses and Dissertations. Paper 3090. http://dc.etsu.edu/etd/3090
Copyright by the authors.