**Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously. **

Ryan Bakker, from 2006-2008 was a post-doctoral fellow at the University of Oxford. From 2008-2014, he was an assistant professor at the University of Georgia. Between 2015-2019, he was associate professor and director of the Center for the Study of Global Issues at UGA. In 2019 – , he became a Reader at University of Essex. “Measuring Party Positions in Europe: The Chapel Hill Expert Survey Trend File, 199-2010”, Party Politics 21(1). “Using Bayesian Aldrich-McKelvey Scaling to Study Citizens’ Ideological Preferences and Perceptions” AJPS 59(3). “The European Common Space: Using anchoring vignettes to scale party positions across Europe” JOP 76(4). His research interests include parties and elections in the EU and Bayesian latent variable models.

**Course Content:**

This course introduces the basic theoretical and applied principles of Bayesian statistical analysis. The Bayesian paradigm is particularly well-suited for the types of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed. The course begins with a discussion of the strengths and weaknesses of the Bayesian approach and the philosophical differences between the Bayesian and frequentist approaches. Most of the course content will focus on estimating and interpreting a variety of models (linear, dichotomous and polytomous choice, poisson, missing data, latent variable, and multilevel) from an applied Bayesian perspective.

**Course Objectives: **

Participants will learn the theoretical and empirical foundations of the Bayesian approach to statistical modelling and will leave with an improved understanding of conditional probability and mathematical statistics in general. They will also be introduced to state of the art computing tools. At the end of the course, participants will possess a suite of code that will allow them to estimate and present results for a wide-range of statistical models. The Bayesian approach is particularly useful for researchers with interests in latent variable models, multilevel models, and models with missing data.

**Course Prerequisites:**

Participants are expected to be well-versed in the linear model and proficient in maximum likelihood models and probability theory. Additionally, participants should have some basic understanding of derivative calculus and matrix algebra and some familiarity with R.

**Representative Background Reading: **

Gary King. 1986. “How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science.” American Journal of Political Science. 30, pp. 666-687.

**Required Texts:**

Gelman, A. and Hill, J. Data Analysis Using Regression and Multilevel/hierarchical Models. 2007. Cambridge University Press. **This book will be provided by ESS as part of the course material for this course.**

Gill, Jeff, Bayesian Methods: A Social and Behavioural Sciences Approach, 3rd Edition, 2014. Chapman and Hall/CRC Statistics.

**Background knowledge required**

*Mathematical: *

Calculus: Elementary

Linear Regression: Elementary

*Statistics*

OLS = strong

Maximum Likelihood = moderate

*Computer Background*

R = elementary