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Center for Integrated Latent Variable Research

Course

Introduction to Finite Mixture Modeling

Started Apr 24, 2024

$525 Enroll

Full course description

SHORT COURSE DESCRIPTION

Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. Finite mixture models represent a type of latent variable model that expresses the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of variables is due to the existence of two or more distinct homogeneous subgroups, or latent classes, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor analytic models. This three-day course is intended as both a theoretical and practical introduction to finite mixture modeling as it pertains to statistical methods regularly used in educational, behavioral, and social science research.

We will begin by introducing mixture modeling principles in familiar contexts such as univariate and multivariate distributions and quickly move to more complex modeling environments such as regression, factor analysis, and latent growth modeling. Along the way, we will cover aspects of mixture modeling including model construction and specification, graphical representations of models, estimation, class enumeration, evaluating hypotheses, assessing data-model fit, and model comparisons. In addition, content covered will draw, as time allows, from such topics as: latent class analysis, hidden Markov models, latent transition analysis, multilevel mixture modeling. Although this material is necessarily more complex, it will be presented in an approachable hands-on manner. 

Examples used in presentations draw primarily from social science research, including the fields of education and psychology, and will be accompanied by annotated input and output using the Mplus software package. Throughout the course, participants will be able to do practice exercises using Mplus.

 

DATES AND TIMES

April 24-26, 2024 (Wed-Fri)

10am-5pm Eastern Daylight Time (UTC-4)

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 and methods under the general linear model (GLM) umbrella. Prior experience with latent growth models and with more advanced topics in structural equation modeling (SEM) or multilevel modeling (MLM) is a plus, but not required.

 

SOFTWARE

Models and hands-on exercises for this workshop will be done using Mplus statistical software. Participants are welcome to have the software loaded on their own computer, although this is not required.

 

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

Jeffrey R. Harring is Professor of Quantitative Methodology: Measurement and Statistics in the Department of Human Development and Quantitative Methodology at the University of Maryland, where he teaches coursework in general and generalized linear models, longitudinal modeling, finite mixture modeling, and simulation techniques. He has numerous methodological workshops on topics including longitudinal data analysis, finite mixture modeling, and using R. Dr. Harring’s research interests include statistical models of repeated measures data and mixture modeling and his work has appeared in such journals as Structural Equation Modeling: A Multidisciplinary Journal, Psychological Methods, Multivariate Behavioral Research, Journal of Educational Measurement, Psychometrika, and Journal of Educational and Behavioral Statistics. Additionally, Dr. Harring co-authored a book entitled, Comparing Groups: Randomization and Bootstrap Methods Using R, which was published in 2011, and published co-edited volumes, Advances in Longitudinal Methods in the Social and Behavioral Sciences with Gregory R. Hancock in 2012, Advances in Multilevel Modeling for Educational Research with Laura M. Stapleton and S. Natasha Beretvas in 2016, and Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton with Gregory R. Hancock in 2019. Dr. Harring is the past editor of Multivariate Behavioral Research (MBR) and currently sits on the editorial board of Psychological Methods and Structural Equation Modeling. Dr. Harring holds M.S. and Ph.D. degrees from the University of Minnesota. He may be reached at harring@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 the CIVLR team at mixture.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

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.