Bayesian Models in Psychology

General data

Course ID:	2500-EN-F-204
Erasmus code / ISCED:	14.4 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Bayesian Models in Psychology
Name in Polish:	Bayesian Models in Psychology
Organizational unit:	Faculty of Psychology
Course groups:	(in Polish) Academic basket (in Polish) Elective courses (in Polish) electives for 4 and 5 year Methodology, Statistics and Psychometrics basket
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective courses
Short description:	The main goal of this course is to familiarize students with basics of Bayesian data analysis and its applications in psychology.
Full description:	Bayesian data analysis is an alternative to the classical (frequentist) approach to statistics, that deals directly with problems of uncertainty and probability in research problems. As pointed by many authors, power, flexibility, and easiness of interpretation of Bayesian data analysis makes it a natural candidate to approach problems in modeling of psychological processes. During this lab we will learn the basics of Bayesian approach to statistics. We will learn strengths of Bayesian alternatives to t-test, ANOVA, correlation and regression analyses, and how to perform them using open-source software (R and JAGS).
Bibliography:	1. Bayes Rule and it’s applications - Krushke, chapter 2 and 5 2. Binomial and Normal Models - Krushke, chapter 4 and 6 3. More on priors - additional chapter from Gill (2014), scan provided by lecturer 4. Intro to Bayesian computation with R - Krushke, chapter 3 5. Computing Bayesian models with simulations (in R) 6. Basics of Markov Chain Monte Carlo - Krushke, chapter 7 7. Using JAGS and rjags - Krushke, chapter 8 8. Midterm exam 9. Bayesian t-test - Krushke, chapter 18 10. Comparing more than two groups - Krushke, chapter 19 11. Factorial design analysis in Bayesian approach - Krushke, chapter 20 12. Bayesian regression - Krushke, chapter 17 13. Bayesian multiple regression - Krushke, chapter 18 14. Bayesian logistic regression - Krushke, chapter 21 15. Final exam Handbook for this course is: Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
Learning outcomes:	Upon completion of this course:  students know basics of Bayesian data analysis and it’s theoretical underpinnings  students know how to perform basic statistical computations with R  students know how to perform basic Bayesian analyses with R and JAGS  students know potential applications of Bayesian models in social and behavioral sciences and are able use some of them in their own research
Assessment methods and assessment criteria:	Students are allowed to miss 2 classes without excuse, 2 more classes in case of excuse, but will not pass the course in case of more than 4 absences. Additional work is assigned in case more than 2 classes are missed (even in cases of valid excuse). The Final grade will be determined by three components: midterm and final exam scores and amount of points gathered from the home assignments throughout the semester. The final grade will be the weighted average computed according to the following formula: 30% * (midterm score) + 30% * (home assignments) + 40% * (final exam score) = total score Total score and both exam scores should be at least at the 50% level to pass the course. Grading scale: 95%+ = 5! 90-94% = 5 80-89% = 4.5 70-79% = 4 60-69% = 3.5 50-59% 59% = 3 below 50% = 2 (fail)

This course is not currently offered.

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