SHORT COURSE DESCRIPTION

This three-day course assumes no prior experience with SEM, and is intended as both a theoretical and practical introduction. An understanding of SEM will be developed by relating it to participants’ previous knowledge of multiple linear regression, and then by expanding it to allow for correlated and causally related latent constructs. We will start with path analysis among measured variables (including mediation/moderation models, actor partner interdependence models), move into confirmatory factor models (including models for reliability), then structural models involving latent causality and multi-group models, as well as dealing with real data challenges and a preview of more advanced topics. Examples from a variety of disciplines will be accompanied by example code and output from the Mplus software package. (Parallel R/lavaan code will also be made available.)

 

DATES AND TIMES

Jan 3-5, 2024 (Wed. - Fri.)

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

The instructor will determine timing of lunch break, as well as morning and afternoon breaks.

 

COURSE FEES

Professional: $525

Full-time student*: $295

 

*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

Graduate students, emerging researchers, continuing researchers

 

REQUISITE KNOWLEDGE

Participants should have a foundational knowledge up through multiple regression. Prior experience with exploratory factor analysis and multivariate methods is a plus, but not required. Prior experience with the Mplus software is also not required.

 

SOFTWARE

Models and hands-on exercises for this workshop will be done using the Mplus software. Participants are welcome to have the package loaded on their own computer, although this is not required. (Parallel R/lavaan code will also be made available.)

 

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/). 

·      Typically within 24 hours, 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 who are not affiliated with UMD need to get 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

Gregory R. Hancock is Professor and Distinguished Scholar-Teacher, long-time Director of the Quantitative Methodology: Measurement and Statistics program in the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park, and Director of the Center for Integrated Latent Variable Research (CILVR). He is also co-host of the popular quantitative methods podcast Quantitude. His research interests include structural equation modeling and latent growth models, power, reliability, and the use of latent variables in (quasi)experimental design. His research has appeared in such journals as Psychometrika, Multivariate Behavioral Research, Structural Equation Modeling: A Multidisciplinary Journal, Psychological Methods, British Journal of Mathematical and Statistical Psychology, Journal of Educational and Behavioral Statistics, Educational and Psychological Measurement, Review of Educational Research, and Communications in Statistics: Simulation and Computation. He also co-edited the volumes Structural Equation Modeling: A Second Course (2006; 2013), The Reviewer's Guide to Quantitative Methods in the Social Sciences (2010; 2019), Advances in Latent Variable Mixture Models (2008), Advances in Longitudinal Methods in the Social and Behavioral Sciences (2012), and Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton (2019). He is past chair of the SEM special interest group of the American Educational Research Association (three terms), serves on the editorial board of a number of journals including Psychological Methods, Multivariate Behavioral Research, and Structural Equation Modeling: A Multidisciplinary Journal, and has taught over 200 methodological workshops in the United States, Canada, and abroad. He is a Fellow of the American Psychological Association, American Educational Research Association, Association for Psychological Science, Society of Multivariate Experimental Psychology, and also received the 2011 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring by the American Psychological Association. Dr. Hancock holds a Ph.D. from the University of Washington. He may be reached at ghancock@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 Ashani Jayasekera at sem.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.