Instructor: Stavroula Chrysanthopoulou
Textbook: Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel Hierarchical Models. Cambridge University Press. ISBN 978-0-511-79094-2.
Description: This course provided a survey of statistical methods for modeling and analyzing data that are collected at multiple levels of sampling. The topic of multilevel data was approached primarily from the angle of regression modeling with a focus on generalized linear mixed effects specified in a hierarchical fashion. In addition to using R programming to carry out the majority of analyses, real data examples were used to motivate the material.
Multilevel data structures are found in longitudinal studies, clustered designs, and many other settings where data are structured at multiple levels of aggregation. Statistical methods for analyzing this type of data take into account their hierarchical structure to draw proper inferences and appropriately partition multiple sources of variation.
Assignments:
Spring ‘22