An Introduction to Multilevel Models

Half-day short course (2:00pm – 5:30pm)

Short course #5

Nested data is common in the social sciences where individuals are not randomly assigned to clusters, resulting in outcomes that are more similar within a cluster than between clusters. Some common examples include students nested in classrooms or schools, employees nested in organizations, and individuals nested in neighborhoods, and special models called multilevel models can be used for these data. Participants in this course will learn about common multilevel models, the types of research questions that can be addressed with these models, and how to fit these models in R. 

Intended Audience

Anyone interested in learning about multilevel modeling; experience with regression and R is helpful but not necessary.

About the Instructor

Tracy Morrison Sweet (University of Maryland)

  Tracy Sweet is an Associate Professor in the Quantitative Methodology: Measurement & Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology.  She completed her Ph.D. in Statistics at Carnegie Mellon University and a M.A. in Mathematics at Morgan State University. Her research focuses on methods for social network analysis with particular focus on multilevel social network models. Recent projects include network interference, measurement error, and missing data. She serves as the Associate Director of Research for UMCP for the Maryland Longitudinal Data System Center and is currently overseeing projects applying data science and statistical methods to large-scale educational data. Finally, Dr. Sweet is committed to improving diversity in the fields of statistics and quantitative methodology. She serves on the DEI committee for her department and the College’s Council on Racial Equity and Justice, and she is interested in exploring how race and ethnicity is analyzed in quantitative methods.