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Bayesian Statistics in Social Sciences

General data

Course ID:	2500-PL-PS-SP15-16
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 Statistics in Social Sciences
Name in Polish:	Bayesian Statistics in Social Sciences
Organizational unit:	Faculty of Psychology
Course groups:
ECTS credit allocation (and other scores):	2.00 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.
Language:	English
Prerequisites (description):	(in Polish) Kryteria wejścia: Y2 Social Research Specialization
Short description:	The main goal of this course is to familiarize students with basics of Bayesian data analysis and its applications in social sciences.
Learning outcomes:	Upon completion of this course:  students know basics of Bayesian data analysis and its theoretical underpinnings  students know how to perform basic Bayesian analyses with R and brms  students know potential applications of Bayesian models in social and behavioral sciences and are able use some of them in their own research

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO TU W TH CW FR
Type of class:	Classes, 15 hours more information
Coordinators:	(unknown)
Group instructors:	Wiktor Soral
Students list:	(inaccessible to you)
Examination:	Course - Grading Classes - Grading
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, flexibility in data modeling and easiness of interpretation of the results makes Bayesian data analysis a natural candidate to approach problems in modeling 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, and regression analyses, and how to perform them using open-source software (R and brms).
Bibliography:	Handbook for this course is: McElreath, R. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton: CRC Press.

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