SHORT COURSE DESCRIPTION

This workshop will allow students to obtain a firm grounding in the statistical theory of multilevel modeling as it is used in the social and behavioral sciences. More specifically, students will:

• gain an understanding of the central statistical concepts underlying the methods (e.g., intraclass correlations, random coefficients, variance components);
• learn about a variety of multilevel models, with multiple types of outcome variables;
• and gain experience in estimating multilevel models with R, interpreting results, and drawing meaningful substantive conclusions.

In general, the course will proceed through topics in multilevel modeling basics and estimation and will then address more advanced applications and issues.

LEARNING OBJECTIVES

This short course is meant to introduce participants to the issues in modeling data that are from nested structures and, most importantly, to understand the research questions that can be answered with such models. At the end of the short course, participants should be able to:

• interpret information found in articles that share results from multilevel models
• express research questions that could be answered with a multilevel analysis
• identify the data structures that would facilitate a multilevel analysis
• run basic multilevel models and examine the assumptions underlying those analyses

 

DATES AND TIMES

Dec. 14-15, 2023 (Thurs-Fri)

10am-5pm Eastern Standard Time (UTC-5)

The instructor will determine timing of breaks.

 

COURSE FEES

Professional: $375

Full-time student*: $195

 

*Full-time students need to submit student status proof at https://go.umd.edu/CILVR-STUDENT-23 to request a discount code prior to registration.

 

*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM-23.

 

 

HOW TO REGISTER

 

Prior to registration, participants not affiliated with UMD need to get a valid UMD associate account in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details about the UMD associate account, please click here.

 

For UMD affiliated participants, you may register using your existing UMD directory ID. 

 

To request the promotional code prior to registration: 

- Full-time students can submit the student status proof at https://go.umd.edu/CILVR-STUDENT-23 to request a student discount code prior to registration. Note that it may take 2-3 business days for your request to be processed.

- HDQM department registrants can request the HDQM discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM-23. Note that it may take 2-3 business days for your request to be processed.

 

TARGET AUDIENCE

The target audience for this course is any individual with an intermediate knowledge of statistical analyses who seeks to conduct or understand applications of multilevel modeling. Graduate students and researchers working within academic institutions or research agencies are the ideal audience.  Content is not discipline-specific – participants from a wide range of fields are welcome (e.g., education, public health, psychology, medicine, engineering, sociology).

 

REQUISITE KNOWLEDGE

This workshop assumes knowledge of estimation in a multiple regression context (intercept and slope coefficients and their interpretations, standard errors of these parameter estimates, proportion of variance reduction).

 

SOFTWARE

 

Examples, hand-on exercises, and support will be provided for R.

LOCATION AND PLATFORM

 

• The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/)
• This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).
• Within a limited time, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.

 

 

IMPORTANT COURSE DETAILS

 

Platform: Participants need to have a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.

 

Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.

 

Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.

 

Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

 

Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.

 

Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.

 

THE INSTRUCTOR

Dr. Tracy Sweet is an Associate Professor of Quantitative Methodology: Measurement and Statistics (QMMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland. Dr. Sweet has taught several workshops on social network models and machine learning for the social sciences and has recently taught graduate courses on multilevel models. She has also taught a number of graduate-level quantitative methods courses on general linear models,  Bayesian inference, social network models, and statistical consulting. Her research interests include developing statistical models for social networks as well as data mining methods for multilevel social network data. Dr. Sweet has published methodological papers in prominent journals such as Journal of Educational and Behavioral Statistics and Social Networks, and application papers in American Educational Research Journal, Sociology of Education, and Educational Psychologist. Her research has been funded by the Institute of Educational Sciences (IES). She currently serves as an Associate Editor for Journal of Educational and Behavioral Statistics.  Before joining the QMMS faculty in the fall of 2013, Dr. Sweet worked as a postdoctoral researcher at Carnegie Mellon University where she received her Ph.D. in 2012. She may be reached at tsweet@umd.edu.

 

REFUND POLICY

Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

 

CONTACT

For any further questions, please contact mlm.cilvr@gmail.com.

To request a copy of the payment receipt, please contact the OES office at oes-finance@umd.edu.

 

CILVR Short Course Series

• Analysis of Complex Survey Data (from NCES)
• Introduction to Multilevel Modeling
• Structural Equation Modeling: From Beginner to Intermediate
• Introduction to Longitudinal Structural Equation and Latent Growth Modeling
• Item Writing and Questionnaire Design
• Introduction to Meta-Analysis
• Introduction to Finite Mixture Modeling
• Introduction to Machine Learning in Social Sciences
• Bayesian Statistical Modeling: A First Course
• Bayesian Statistical Modeling: A Second Course
• Bayesian Statistical Modeling: First and Second Courses

 

Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)

CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Quantitative Methodology: Measurement and Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. QMMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.