UV9916V1 – Bayesian Statistics for Education Research
Schedule, syllabus and examination date
Bayesian statistics has long been overlooked in the quantitative methods training for social and behavioral scientists. Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class. This is not surprising. First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software. Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful (and free) statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.
On Day 1 of the workshop we will explore the major differences between the Bayesian and frequentist paradigms of statistics, with particular focus on how uncertainty is characterized. The implications of the Bayesian perspective for hypothesis testing will be highlighted. Next, we will explore the basics of model building and model evaluation. On Day 2, we will discuss Bayesian computation focusing exclusively on packages available through the R statistical programming environment. A brief introduction to R will be given. Also, on Day 2, student will have time to run simple regression models using data from the IEA Program on International Reading Literacy Study (PIRLS). On Day 3 we will consider somewhat more advance models -- particularly Bayesian multilevel models and Bayesian factor analysis. The workshop will close with a discussion of the relative advantages of the Bayesian perspective, again leaving time for practice.
- Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press.
- Kaplan, D. & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (ed.), Oxford Handbook of Quantitative Methods. (pp 407-437) Oxford: Oxford University Press.
- Kaplan, D. & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling. (pp 650-673), New York: Guilford Publications, Inc.
- van de Schoot, R., Kaplan, D., Denissen, J., Asndorpf, J. B., Neyer, F. J. & van Aken, M. A. G. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. DOI: 10.1111/cdev.12169
- Major differences between the Bayesian and frequentist paradigms of statistics.
- Bayes’ theorem; The likelihood, The nature of priors; The posterior distribution.
- Bayesian hypothesis testing; Contrasts with frequentist hypothesis testing.
Afternoon: Brief introduction to R
- Bayesian model building.
- Bayesian model evaluation.
- Bayesian model averaging.
- Bayesian linear regression.
- Bayesian computation; MCMC.
- Student analyses – Bayesian regression analysis
- Advanced topics; HLM; factor analysis (time permitting)
- Final philosophical issues
- Final student analysis
The orientation of this course is to introduce education researchers to the basic elements of Bayesian statistics and to show through discussion and practice, why the Bayesian perspective provides a powerful alternative to the frequentist perspective.
Registration deadline: 6 Jan 2016
Recommended previous knowledge
It is assumed that students of the workshop will have a background in basic statistical methods up to, and including, regression analysis. Some exposure to multilevel modeling and factor analysis is desirable.
Dates: 19, 20, 21 Jan 2016
Location: Room 2, Georg Sverdrups hus
Time: 09.00-16.00 all days
Lecturer: Professor David Kaplan, University of Wisconsin
To obtain 1 study point 80% attendance in the lectures is required.
To obtain 3 study points a short paper needs to be submitted after the
Grades are awarded on a pass/fail scale.