UV9257U – Multilevel and Longitudinal Modeling
Schedule, syllabus and examination date
Welcome to this 4 day PhD-course lead by Professor Sophia Rabe-Hesketh, University of California, Berkeley, and Professor Anders Skrondal, CEMO & Norwegian Institute of Public Health & University of California, Berkeley.
The short course introduces models for multilevel or clustered data, such as cross-sectional data with students nested in schools, or longitudinal data with repeated measures/panel waves nested in subjects. Models and concepts are introduced via examples from a variety of disciplines, equations and illustrative graphs, keeping the mathematics as simple as possible (avoiding matrix algebra and calculus). Software is not discussed, but the handouts include Stata commands for all the results that are presented and the data are available online. The short course is based on successful semester-long graduate-level courses by the presenters at Berkeley and London School of Economics.
- Rabe-Hesketh, S. and Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata (3rd Edition). College Station, TX: Stata Press →Vol. 1 on “Continuous Responses” is sufficient together with Chapter 10 on “Dichotomous or Binary Responses” from Vol. 2: http://www.stata-press.com/books/mlmus3_ch10.pdf
- Snijders, T.A.B., and Bosker, R.J. (2011). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling (2nd Edition). London, Sage.
Part 1 of the course covers linear multilevel models for continuous responses, including random-intercept, random-coefficient, and three-level models. (Restricted) maximum likelihood estimation of model parameters and empirical Bayes prediction of random effects are introduced at a non-technical level. Part 2 focuses on longitudinal data analysis, starting with application of random-coefficient models for growth, contrasting this approach with marginal modeling and giving a brief overview of methods from panel data econometrics. Part 3 introduces multilevel logistic regression for binary responses.
By the end of the course, they should have an understanding of the model assumptions, be able to choose an appropriate model for a given situation and interpret the parameter estimates.
Registration deadline: May 7
Limited number of Places
Recommended previous knowledge
Prior to the course, participants must be familiar with linear and logistic regression.
Outline of Topics
Part 1: Linear mixed models
Predicting random effects
Part 2: Longitudinal data analysis
Growth curve models
Panel data econometrics
Part 3: Multilevel models for binary data
Review of multiple logistic and probit regression
Multilevel logistic models
Estimation and prediction