Hierarchical Linear Models For The Analysis Of Longitudinal Data With Applications From Hiv/Aids Program Evaluation


Book: 
Proceedings of the sixth international conference on teaching statistics, Developing a statistically literate society
Authors: 
O'Connell, A.
Editors: 
Phillips, B.
Category: 
Pages: 
Online
Year: 
2002
Publisher: 
International Statistical Institute
URL: 
http://www.stat.auckland.ac.nz/~iase/publications/1/3l2_ocon.pdf
Abstract: 

In this paper, two examples of multilevel modeling as part of the analysis of data from HIV evaluation studies are presented. Strategies for teaching multilevel models for each type of data are discussed. The first, a panel study, uses multiple linear regression models to show how a hierarchical linear model can be developed. The second, a repeated cross-sectional design, uses simple analysis of variance models to show how a random coefficients model can be fit to the data. Complex multilevel models may be easier to understand and apply when broken down into these more familiar strategies. Analyses are presented using the HLM program and SAS.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education