# MULTILEVEL/HIERARCHICAL MODELING

JULY 2-6, 2018

Welcome to the page for my workshop on Multilevel/Hierarchical Modeling at the IPSA-NUS Methods School!

This course introduces participants to the analysis of multilevel, hierarchical, or structured data. These data are ubiquitous in all of the social sciences and include observations that are "nested" in higher-level units, such as groups of survey respondents in different countries, students in different schools, or country-level observations at repeated time points. Analyzing multilevel data can be challenging, but provides many opportunities for statistical inference. Participants will learn how to appropriately estimate such quantities of interests as effects that vary across units and/or time or how much of a change in an outcome of interest is associated with individual- or group-specific features.

Covering both theory and practical applications, the course will explore the following topics:

- Characteristics of multilevel data structures
- Data management for multilevel data
- Fixed and random effects
- Multilevel regression for continuous and categorical outcomes
- Multilevel regression for time-series cross-sectional data
- Multilevel regression and post-stratification
- Post-estimation (incl. marginal effects and predicted probabilities)
- Model assessment and comparison

- Distinguish the concepts of fixed and random effects in the context of multilevel data
- Estimate regression models with varying slopes and varying intercepts
- Generate such post-estimation quantities as marginal effects, predicted probabilities, etc. from multilevel regression models
- Use graphical tools to present results from multilevel regression models