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Center for Integrated Latent Variable Research

Bayesian Statistical Modeling: First Course and Second Course
Program

Bayesian Statistical Modeling: First Course and Second Course 2024

Starts Jun 3, 2024

$825 Enroll

Full program description

REGISTRATION OPTIONS

On this page, you could enroll in both the first course and the second course at the discounted bundle rate. Based on their specific interests and needs, however, participants could also choose to only register for one of the courses at the following links separately:

 

SHORT COURSE DESCRIPTION

BAYESIAN STATISTICAL MODELING: A FIRST COURSE

This online, three-day short course assumes no prior experience with Bayesian statistical modeling, and is intended as both a theoretical and practical introduction. An understanding of Bayesian statistical modeling will be developed by relating it to participants’ existing knowledge of traditional frequentist approaches. The philosophical underpinnings and departures from conventional frequentist interpretations of probability will be explained. This in turn will motivate the development of Bayesian statistical modeling. It is assumed that participants have expertise with frequentist approaches to statistics (e.g., hypothesis testing, confidence intervals, least-squares and likelihood estimation) in contexts up through multiple regression. Although not required, a participant’s experience in this course will be enhanced by additional prior coursework or experience with advanced statistical modeling techniques (e.g., general linear modeling, multivariate models for multiple outcomes) and/or by familiarity with the basics of probability theory (e.g., joint, marginal, and conditional distributions, independence).

To introduce Bayesian principles in familiar contexts we will begin with simple binomial and univariate normal models, then move to simple regression and multiple regression. Along the way, we will cover aspects of modeling including model construction, graphical representations of models, practical aspects of Markov chain Monte Carlo (MCMC) estimation, evaluating hypotheses, model comparisons, and model-data fit. Although Bayesian statistical modeling has proven advantageous in many disciplines, the examples used in presentations draw primarily from social science and educational research. Examples will be accompanied by input and output from Stan and R. Throughout the course participants will be able to practice exercises using these software packages. (Participants will be instructed on how to download free versions of the software prior to the course.)

 

BAYESIAN STATISTICAL MODELING: A SECOND COURSE

Day 1 - Building off an understanding of Bayesian regression modeling (covered in the first course), we will cover Bayesian factor analysis in depth, time permitting including model evaluation and comparison. Though the focus is on factor analysis, the components of these models serve as the grounding for more advanced models participants may encounter. In addition, the presentation is intended to illuminate broader ideas of Bayesian statistical modeling, such that key principles can be abstracted even for those researchers who do not work with factor analysis models.

Day 2 - In the second day, we will build off of the material covered on the first day to treat structural equation modeling, multilevel modeling, and missing data modeling. The presentation of each of these topics is intended to illuminate broader ideas of Bayesian statistical modeling, such that key principles can be abstracted even for those researchers not working with the particular type of model at hand.

Familiarity with these topics would be beneficial, but not required. Each will be reviewed from a conventional perspective before pursuing a Bayesian perspective. Although this material is necessarily more complex, it will be presented in a manner targeting the applied researcher, with examples primarily from social science and educational research, accompanied by input and output from software. Examples will be accompanied by input and output from Stan and R. Throughout the course participants will be able to practice exercises using these software packages. (Participants will be instructed on how to download free versions of the software prior to the course.) 

 

DATES AND TIMES

BAYESIAN STATISTICAL MODELING: A FIRST COURSE

June 3-5, 2024 (Monday-Wednesday)

BAYESIAN STATISTICAL MODELING: A SECOND COURSE

June 6-7, 2024 (Thursday-Friday)

 

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

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

 

COURSE FEES

Professional: $825

Full-time student*: $455

 

*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 ($20). 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.

 

INSTRUCTIONS AFTER ENROLLING

How to begin each course in a program: For program courses, you must begin each course through your Open Learning Student Dashboard to gain access to the Canvas course content. From your dashboard, click the "Begin Course" button for each course in the Bayesian Statistical Modeling Program to begin. 

 

 

 

TARGET AUDIENCE

Graduate students, emerging researchers, continuing researchers

 

REQUISITE KNOWLEDGE

Participants should have a foundational knowledge up through multiple regression. Prior experience with more advanced models and/or probability theory methods is a plus, but not required.

SOFTWARE

Models and exercises for these short courses will be conducted using the R and Stan software, through multiple R packages for interfacing with Stan and processing output. Participants will be instructed on how to download (free) versions of the software prior to the course, and will be given access to datasets and code for the examples. Code for other software platforms (including Mplus, JAGS, and BUGS) will also be included for several of the examples, but will not be the focus of instruction.

 

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

Dr. Roy Levy is is a Professor of Measurement and Statistical Analysis in the T. Denny Sanford School of Social and Family Dynamics at Arizona State University, where he teaches coursework in Bayesian statistical modeling. He is the co-author of the book, Bayesian Psychometric Modeling, and his research has appeared in such journals as Structural Equation Modeling: A Multidisciplinary Journal, British Journal of Mathematical and Statistical Psychology, Psychological Methods, Multivariate Behavioral Research, Applied Psychological Measurement, Journal of Educational and Behavioral Statistics, Sociological Methods and Research, Educational and Psychological Measurement, and Journal of Probability and Statistics. He is a past chair of the structural equation modeling special interest group of the American Educational Research Association, has served on the editorial boards of several journals, and was a 2010 recipient of the Presidential Early Career Award for Scientists and Engineers by the President of the United States. Dr. Levy holds a B.A. in Philosophy, an M.A. in Measurement, Statistics and Evaluation, and a Ph.D. in Measurement, Statistics and Evaluation from the University of Maryland. He may be reached at roy.levy@asu.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 bayes.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.