This is the home page of Pop510: Multilevel Models, offered in the Spring of 2012 (Session II). For Pop509: Survival Analysis, also offered in the Spring of 2012 (Session I) click here. For resources related to my own research on multilevel models click here.
Pop 510 - Multilevel Models
This half-course, offered in the second session of the 2012 spring term, provides an introduction to statistical methods for the analysis of multilevel data, such as data on children, families, and neighborhoods.
- We review fixed- and random-effects models for the analysis of clustered and longitudinal data before moving on to multilevel random-intercept and random-slopes models.
- We discuss model fitting and interpretation, including centering and estimation of cross-level interactions.
- We cover models for continuous as well as binary and count data, reviewing the different approaches to estimation in common use, including Bayesian inference.
The course emphasizes practical applications. Prerequisite: WWS509 or equivalent.
A list of my papers on multilevel models including abstracts and links to JSTOR where available, as well as the Rodríguez-Goldman data, may be found at http://data.princeton.edu/multilevel.
We start with simple variance-component models using data on language scores from Snijders and Boskers. Part 2 has random intercept and random slope models, and Part 3 has a model with a level-2 predictor, where the random intercept and slopes depend on school SES.
The next analysis considers 3-level linear models using growth curve data on 1721 students in 60 schools. We start with simple variance-component models and hen move to growth curves with random intercepts and then random slopes. Our analysis concludes with a comparison of growth curves in schools that differ in observed and unobserved characteristics.
We next move to multilevel logit models, starting with an application to longitudinal data on union membership from the NLS, focusing on a comparison of marginal and subject-specific models and the calculation of intraclass correlation for latent and manifest outcomes.
We continue with an application to contraceptive use in Bangladesh, where we consider random-intercept and random-slope models. We also illustrate the estimation of random effects using maximum likelihood and posterior Bayes estimates.
For a three-level logit model consider the analysis of immunization in Guatemala. The data are available on the multilevel section of the website and the book by Rabe-Hesketh and Skrondal has a substantial analysis.
The notes on how to run multilevel logit models using winBUGS are here, with a link to a compound document that can be run from WinBugs.
Our final application follows the analysis of infant and child mortality in Kenya which you will find in my chapter in the Handbook of Multilevel Analysis.