January 2024 Online Short Course:
Introduction to Multilevel with R
Introduction to Multilevel with R
Dr. Paul Bliese
Wednesday, January 3 – Friday, January 5
Taught daily from 10:00 AM ET – 4:00 PM ET
Instructor Biography
Paul D. Bliese is the Jeff B. Bates Professor of Management at the Darla Moore School of Business. He received a Ph.D. from Texas Tech University and a B.A. from Texas Lutheran University. After graduating in 1991, he worked for a year for the Bureau of Labor Statistics. In 1992, he joined the US Army, where he spent 22 years as a research psychologist at the Walter Reed Army Institute of Research. In his last military assignment, he served as the director of the Center for Military Psychiatry and Neuroscience and retired at the rank of Colonel in 2014. Over his military career, Bliese directed a large portfolio of research initiatives examining stress, leadership, well-being and performance. From 2007 to 2014, he oversaw the US Army’s Mental Health Advisory Team program assessing the morale and well-being of soldiers deployed to Iraq and Afghanistan. Throughout his professional career, Bliese has led efforts to advance statistical methods and apply analytics to complex organizational data. He developed and maintains the multilevel package for the open-source statistical programming language R, and his research has been influential in advancing organizational multilevel theory. He has published in numerous outlets and served on many editorial boards. He was an Associate Editor for the Journal of Applied Psychology from 2010 to 2017 and is the incoming Editor-in-Chief for Organizational Research
Course Description
The CARMA Introduction to Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct basic multilevel analyses. Emphasis will be placed on techniques for traditional, hierarchically nested data (e.g., children in classrooms; employees in teams). The first part of the course introduces issues related to multilevel theory (e.g., multilevel constructs; principles of multilevel theory building; cross-level inferences and cross-level biases). The second part of the course discusses issues related to multilevel measurement (e.g., aggregation; aggregation bias; composition and compilation models of emergence; estimating within-group agreement). The last part of the course focuses on the specification of basic 2-level models (e.g., children nested in classrooms; soldiers nested in platoons; employees nested within work teams) analyzed via multilevel regression (i.e., random coefficient regression; hierarchical linear model; mixed effects model). The R software package will be introduced, explained, and emphasized during this short course in preparation for the advanced short course offered in Session II. Participants who prefer HLM, SAS, SPSS, or MPlus (and have expertise with these programs) have the option of completing some assignments with these programs. Participants are encouraged to also bring datasets to the course and apply the principles to their specific areas of research. The course is best suited for faculty and graduate students who are familiar with traditional (i.e., single-level) multiple regression analysis, but have little (if any) expertise related to conducting multilevel analyses.
Short Course Introduction
