Day 1: General Overview of Longitudinal Analysis (by Rajulton Fernando)
Advantages and challenges of using longitudinal data, data layout and notations, covariance/correlation structures, GEE models for categorical data analysis.
Day 2: Markov Transition Models and Binary Sequence Models (by Rajulton Fernando)
Analysis of turnover tables and binary sequence models. Emphasis is on using models for nominal and ordinal variables, with link functions logit, probit, and complementary log-log.
Day 3: Survival Analysis (by Alain Gagnon)
Discrete-time survival models with various link and risk functions. After introducing basic model building strategies with person-period data, the instructor shows how to account for unobserved heterogeneity and sample selection bias with discrete-time survival data.
Day 4: Growth Curve Models (by Piotr Wilk)
Introduction to structural equation modelling, comparison between hierarchical and structural equation model specification and estimation, unconditional and conditional latent curve models for binary, ordered categorical and count variables.
Day 5: Multilevel Growth Trajectories (by Piotr Wilk)
Introduction to the analysis of longitudinal data using multilevel models for binary and categorical variables. Examples of data preparation are given in STATA and model analysis are demonstrated using HLM.