Multilevel Models II: Advanced Topics

Instructor(s):

  • John Poe, University of Kentucky

This course is designed to extend the basic multilevel skills that participants receive from an introductory applied class to more sophisticated and complex models like nonlinear and non-hierarchical mixed effects models.

In the first week, we will review the basics of multilevel models, discuss models with non-hierarchical structures, and compare likelihood and Bayesian multilevel models. In the second week, we discuss generalized linear mixed models for non-linear outcomes. In the third week, we discuss models focused on group-level endogeneity or omitted variable bias. In the final week, we focus on mixed effects models for causal inference with observational data.

Prerequisites: The course is at about the same technical level of other Track III courses, such as Maximum Likelihood Estimation II: Advanced Topics. Prior exposure to maximum likelihood or basic categorical models as well as some previous background in either multilevel or longitudinal modeling is a prerequisite. Ideally, students will have had courses like the Maximum Likelihood Estimation I and Multilevel Models I classes at ICPSR prior to attending. The primary goal of the course will be to allow students to use advanced models and to understand how and when more sophisticated techniques will provide practical benefits.

Software: While we do not require that you use a particular software package, we will be able to provide the most practical help with R and Stata. Code and examples will be provided using R and Stata and their implementations of STAN. Other software programs (e.g. SAS, Mplus, HLM) can do many of the types of models that we cover but you will be responsible for learning how to implement them in your preferred software.

Fees: Consult the fee structure.

Tags: multilevel models, hierarchical linear models

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): July 23 - August 17

Time: 1:00 PM - 3:00 PM

Instructor(s):

  • John Poe, University of Kentucky

Syllabus: