Analysis of Longitudinal Data
The purpose of the workshop is to provide participants with an overview of issues related to the design and analysis of longitudinal data, that is, data collected on individuals over time. These data occur frequently in research in many fields, and often present statistical challenges that cannot be addressed with the knowledge gained from typical introductory statistics courses.
The workshop is intended for participants who have the equivalent of two semesters of statistics, to the level of multiple regression and analysis of variance. Ideally, participants will have some previous experience doing data analysis in practice. The workshop will be taught through a combination of lectures, computer exercises, readings, and discussions. The workshop will be appropriate for faculty, research staff, and graduate students.
| Topics: | 1. Introduction to longitudinal
design and analysis 2. Developmental curve approaches using multi-level models 3. Dynamic approaches using structural equation modeling (path analysis) 4. Handling of dropouts and missing data 5. Analyzing categorical data with logistic and other forms of regression 6. Analysis of clustered categorical data (generalized estimating equations, generalized linear models, random effects for categorical data) 7. Survival analysis 8. Event history analysis |
| Participants in the workshop will: | 1. Understand the principles
for designing longitudinal studies and the features that differentiate
longitudinal data from other types of data, 2. Understand and be able to put into practice analyses of longitudinal data with continuous, categorical, and time-to-event outcome variables, 3. Understand the processes that lead to missing data and approaches for handling missing data, and be able to put into practice some of these approaches in the context of longitudinal data, |