Mixture Modeling and Latent Class Analysis

December 14-16, 2020
Online Webinar via Zoom
Dan Bauer
Software Demonstrations: R and Mplus



Course Description

Mixture Modeling and Latent Class Analysis is a three-day workshop focused on the application and interpretation of statistical techniques designed to identify subgroups within a heterogeneous population, including latent class analysis, latent profile analysis, and other finite mixture models. In practice, these methods are often implemented with the goal of identifying theoretically distinct subgroups (e.g., people with a liability for schizophrenia versus those without). Alternatively, they can be used as a data reduction device, to summarize prototypical patterns when working with complex multivariate data (e.g., market segmentation in consumer research). In recent years, an increasing focus has been on multivariate and longitudinal applications (e.g., growth mixture modeling). In this workshop we provide a comprehensive exploration of the foundations and uses of latent class/profile analysis and finite mixture models, with topics ranging from introductory to advanced, and applications to both single-time point and longitudinal data.

Event Details

Start date: December 14, 2020

End date: December 16, 2020

Start time: 09:00 a.m. EST

End time: 05:00 p.m. EST

Venue: Zoom Webinar

Email: info@curranbauer.org


Dan BauerDaniel J. Bauer, Ph.D.

Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches primarily graduate-level courses in statistical methods, for which he has won teaching awards from the University of North Carolina and from the American Psychological Association. Endeavoring to make advanced statistical techniques more accessible, Dan has spent the last 15 years developing and teaching workshops on a variety of topics in both the United States and abroad, including multilevel modeling, mixture modeling, longitudinal data analysis, structural equation modeling, latent curve analysis, missing data analysis, measurement, and integrative data analysis. His research interests lie at the intersection of quantitative and developmental psychology, particularly the development of problem and health-related behaviors over childhood and adolescence. He has published over 65 scientific papers, served as Associate Editor for Psychological Methods, currently serves on the editorial boards of several journals, and has reviewed grants for the National Science Foundation, National Institutes of Health, and the Institute of Educational Sciences. He received an early career award from the American Psychological Association in 2009. For more details, see his academic web page.

Course Details

Who Should Attend and Software Considerations?

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences.

Software demonstrations for mixture models are conducted using R and Mplus. Note that R can be downloaded for free. While it is helpful to have some familiarity with Mplus or R, this is not necessary. The lectures which constitute the majority of the workshop are software-independent.

The Goals of the Workshop

Our motivating goal is to provide an intense yet enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in mixture modeling and latent class analysis. We strive to strike an equal balance between core concepts of the analytic techniques along with their practical application and interpretation when implemented with real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of mixture modeling and latent class analysis and to be able to thoughtfully apply these procedures to their own data.

Reviews from Past Participants

In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our prior 2020 online offering of Mixture Modeling and Latent Class Analysis workshop:

Great examples provided throughout the course topics. It was a good balance background, theory and application.

I really like the fact the course note comes with statistical program codes and the instructors walked us through the codes during the demonstration session. It really helped me understand the concept more practically.

Clearly presenters are extremely knowledgeable and the notes they provide are amazing.

The instructors did an awesome job in balancing between the methodological details and empirical applications, which is usually hard to do but they did an excellent job. Thank you!

I have taken several short courses, but this one is the best that I have ever taken!

I was able to participate in this course because of online delivery. It is very expensive for many to cover travel, accommodation and registration costs. So we look forward to more online courses in coming years.

Thank you very much for this workshop! I hope to use mixture modeling for my thesis work, specifically LCA. Though I did some reading beforehand, this workshop was incredibly helpful.

The materials are very well-organized, easy to follow, and well-balanced in theory and practice. The Q&As were amazing and so helpful!

The handouts, especially the software ones, were amazingly detailed and much appreciated.

What is Provided

Dan Bauer will teach the workshop via Zoom. Participants will receive complete PDF copies of the course notes and the computer demonstrations as well as data and code for all examples. The PDFs are not time-limited and may be retained indefinitely but should not be distributed to others without obtaining prior permission.

For examples of our course materials see sample copies of our structural equation modeling lecture notes and associated Mplus demonstration notes, as well as our multilevel modeling lecture notes and SAS software notes.

Interactions with Instructor

Participants can ask text-based questions using a Zoom function that will be monitored by CBA staff and conveyed to the instructor. If a question cannot be answered during lecture, a text response will be provided at a later time.

Connectivity Requirements

Because participants are receiving the stream from Zoom and not broadcasting video images back, the connectivity requirements are minimal. A minimum of 150kbps (kilobytes per second) is required to participate in a video webinar, and this can be wired or wireless. Given typical home internet connections or personal WiFi access, these requirements are quite low. For example, it is recommended that a 3000kbps (or 3mbps, megabytes per second) connection be used to stream a movie on Netflix. A typical WiFi hotspot on a typical cell phone is 20-30mbps, thus any standard internet connection should allow for uninterrupted participation in the webinar. See https://www.speedtest.net/ to evaluate your own connection speed. Note that a typical source of connectivity problems in the home is linking the device to the WiFi broadcast unit, so be certain your device is has uninterrupted lines of site to the wireless modem; see, e.g., https://www.familyhandyman.com/smart-homeowner/9-simple-tips-for-faster-wi-fi/

Daily Schedule

Live-stream lectures will begin at 9:00 a.m. and finish at approximately 4:00 p.m. Eastern Time (US) each day. Computer demonstrations will be provided from approximately 4:00 p.m. to 5:00 p.m. Eastern Time (US), with separate breakout meetings for Mplus and R.

There will be 15-minute morning and afternoon breaks and a one hour lunch break, the exact times of which are determined during lecture. Local time zones within which the participant is connected must be adjusted to correspond to Eastern Standard Time.

Availability of Recordings

The livestream does not have DVR-like controls and thus cannot be paused or rewound during the session itself. However, full recordings of the live-stream will be available to all participants for 6 months following the completion of the workshop. The recordings cannot be saved by the participant and will not be available after the 6 month period.

Technical Support

CBA is not able to provide technical support for end-user issues. As such, participants are fully responsible for connectivity that supports the live-stream of audio and video. Information will be provided about the minimum required bandwidth and methods for testing connectivity. However, in the low probability that a participant is not able to connect, there will be access to the recorded sessions for 6 months following the completion of the workshop.

Tuition Reduction Opportunities

We offer reduced-price registrations for graduate students who are actively enrolled in a recognized masters or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

Support for Junior Scholars from Under-Represented Groups

We are fortunate to have the opportunity to work in collaboration with the Society of Multivariate Experimental Psychology (SMEP) to provide a limited number of financial awards to students from under-represented groups to attend methodological workshops. These awards are made to qualifying students and post doctoral fellows with available funds of up to $1000 per student. Please see Support for Students from Underrepresented Groups to Attend Methodological Workshops for full details on both of these sources of support.

Cancellation Policy

Curran-Bauer Analytics will fully refund registration fees, minus transaction fees, for cancellations made with one week or more notice prior to the event. Registration fees are non-refundable if a cancellation is made less than one week before the event.


Chapter 1. Introduction
1.1 Introduction and Organization of the Workshop
1.2 When and How Clustering can be Useful
1.3 Brief Introduction to Matrix Algebra

Chapter 2. Finite Normal Mixture Models with One Variable
2.1 The Development and Application of Finite Mixture Models
2.2 Explicating the Model
2.3 How to Specify, Estimate, and Interpret the Model
2.4 Determining the Number of Latent Classes

Chapter 3. Finite Mixture Models with Multiple Variables
3.1 Extending the Normal Mixture Model to Multiple Variables
3.2 Model Specifications for Multivariate Normal Mixtures
3.3 Latent Class Analysis with Binary or Ordinal Indicators
3.4 Self-Study: Mixtures of Non-Normal Continuous Distributions

Chapter 4. Relating Latent Classes to Predictors and Distal Outcomes
4.1 One-Step versus Three-Step Approaches
4.2 Predicting Who's in Which Class
4.3 Determining Long-Term Outcomes of Class Membership

Chapter 5. Longitudinal Mixture Models
5.1 Distinct Trajectories of Change Over Time
5.2 Latent Class Growth Analysis / Semiparametric Groups Based Trajectory Modeling
5.3 "General" Growth Mixture Models with Random Effects
5.4 Brief Survey of Latent Transition Analysis and Survival Mixture Models

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