The Center for Integrated Latent Variable Research (CILVR)

presents

BAYESIAN STATISTICAL MODELING: A FIRST COURSE, JULY 8-10, 2020

taught by

Roy Levy, Arizona State University

 
ONLINE SHORT COURSE DESCRIPTION

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.)

TARGET AUDIENCE

Graduate students, emerging researchers, continuing researchers

REQUISITE KNOWLEDGE

No prior experience with Bayesian statistical modeling is required. Participants should have a foundational knowledge of conventional (frequentist) approaches 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 this short course will be done using the R and Stan software. Participants will be instructed on how to download free versions of the software prior to the course. Also, the short course itself will use Zoom, so participants should have the Zoom conferencing program installed.
DATES AND TIMES

July 8-10, 2020: 9:00-4:30 (log-in 8:30-9:00) Eastern Daylight Time (UTC-4)

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

LOCATION

Wherever you have reliable WiFi!  

COURSE FEES

$395 for all three days; $295 for full-time students (free for registered HDQM Department faculty and degree-seeking students, although you must register). 

UNDERREPRESENTED GROUPS

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

https://www.smep.org/resources/underrepresented-fellowships

DETAILS

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 two weeks 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 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: Real-time content support for on-line participants is limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. On-line participants may e-mail the instructor for content support, but that cannot be addressed in real-time.

HOW TO REGISTER

Please register using the preferred on-line registration form (non-HDQM department registrants): 

HDQM department registrants use the following registration form:

(Note: For those students who are not in the HDQM department please fill out the paper registration form below and upload it when prompted within the online registration system).

For those who prefer not to use the on-line registration system, please complete and submit the following paper registration form: BayesRegForm2020.pdf

Note that it may take 2-3 business days for your registration to be processed.

 

 

QUESTIONS?

Contact Ms. Yishan Ding -- bayes2020.cilvr@gmail.com

THE INSTRUCTOR

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 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 has served on the editorial boards of several journals, and has received awards from the President of the United States, the American Educational Research Association, and the National Council on Measurement in Education. 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.