[ICPSR] 2-week workshop on Bayesian methods - register soon!

[Scott Campbell]

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Learn Bayesian measurement techniques
Just a few more weeks to register for this workshop!

*Bayesian Multilevel and Measurement Models*

*Dates:* June 7-18, 2021
*Instructor: *Ryan Bakker, University of Essex
*Registration Deadline*: June 3

This workshop introduces the Bayesian multilevel and measurement model
frameworks. Bayesian methods allow for an extremely flexible approach for
estimating hierarchical models with a variety of different types of
dependent variables. The Bayesian approach simplifies several of the
assumptions of the classical techniques for MLMS and directly estimates a
variety of quantities of interest that require post-estimation methods in
the non-Bayesian framework. The Bayesian approach is also ideal for
estimating a variety of measurement models and these approaches offer
several advantages to classic techniques. Topics covered will be the
hierarchical linear model, as well as models with limited dependent
variables, summarizing results, in and out of sample predictions, and
measures of model fit. Additionally, we will cover Bayesian versions of
factor analyses, IRT models, latent class analysis, and several variants of
these. No prior knowledge of Bayesian modeling is required, but will be
beneficial.

*Bio:*  Ryan Bakker is a reader in Comparative Politics at the University
of Essex. He received his Ph.D. in political science from the University of
North Carolina at Chapel Hill. His research and teaching interests include
applied Bayesian modeling and measurement, IRT models, missing data,
maximum likelihood estimation, generalized linear models, Western European
politics, EU elections and politics, terrorism, and international conflict.
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Scott Campbell
ICPSR Summer Program Communications & Video Services Coordinator
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