The Center for Integrated Latent Variable Research (CILVR)





taught by

Roy Levy, Arizona State University


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 and data-model fit, model comparisons, and modeling in the presence of missing data. 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 at home 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

This online, two-day short course assumes experience with introductory level Bayesian statistical modeling, such as that provided in our first course (immediately preceding this course, or taken previously) or from analogous university course exposure elsewhere that covers those topics in our first course. Content covered will draw, as time allows, from such topics as: missing data modeling, factor analysis, item response theory, structural equation modeling, latent class modeling, Bayesian networks, and multilevel modeling. Familiarity with these topics would be beneficial, but not required. Each will be reviewed from a conventional perspective before pursuing a Bayesian perspective. 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. 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. We will primarily be using Stan and R, but will also use JAGS and Netica for particular item response, latent class, and Bayesian network models. Throughout the course participants will be able to practice exercises at home using these software packages. (Participants will be instructed on how to download free versions of the software prior to the course.)


Graduate students, emerging researchers, continuing researchers


Bayesian Statistical Modeling: A First Course

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.

Bayesian Statistical Modeling: A Second Course

Participants should have a foundational knowledge, such as that provided in the first course or comparable training. Prior experience with conventional approaches to latent variable models, structural equation models, multilevel models, and missing data a plus, but not required.


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. For certain models we will use or demonstrate JAGS and Netica software. 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.


Bayesian Statistical Modeling: A First Course
June 7-9, 2021: 10:00-4:30 Eastern Daylight Time (UTC-4)

Bayesian Statistical Modeling: A Second Course
June 10-11, 2021: 10:00-4:30 Eastern Daylight Time (UTC-4)

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


First Course Only:

      -     Professional: $495     

      -     Full-time student*: $295

Second Course Only:

      -     Professional: $345

      -     Full-time student*: $195

Both First and Second Course:

      -     Professional: $795     

      -     Full-time student*: $455

*Full-time students must submit student status proof at for prompt processing of the registration.

Free for registered HDQM Department faculty and HDQM degree-seeking students, although you must register through the internal link.


One-time Registration:

- For professional and full-time students participants, please register using this link:

First Course:

Second Course:

Both Courses:

- After payment, full-time students must also submit the student status proof at  for prompt processing of the registration. Note that it may take 2-3 business days for your registration to be processed.

Bundle Registration:

- Participants who wish to register for multiple CILVR short courses in 2020-2021 as a bundle and obtain ONE receipt for the bundle registrations can submit the request at


HDQM Registration:

- HDQM department registrants can register using the following registration form after logging into the UMD Gmail account:


This workshop will be delivered entirely online via the video conferencing software Zoom ( 

Within a limited time, the video recordings of the short course will be available for both synchronous and asynchronous participants on Vimeo (


Support for students from Underrepresented Groups to attend methodological workshops (from the Society of Multivariate Experimental Psychology):


ormat: 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 6 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 two weeks 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.


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.


For any questions, please contact Ms. Yishan Ding--


Dr. Roy Levy 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 JournalBritish Journal of Mathematical and Statistical PsychologyPsychological MethodsMultivariate Behavioral ResearchApplied Psychological MeasurementJournal of Educational and Behavioral StatisticsSociological Methods and ResearchEducational 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