The course is conducted in English。

While this course is primarily aimed at graduate-level students, undergraduates may be considered on a case-by-case basis under exceptional circumstances. IMPORTANT: IF YOU COULD NOT BOOK THE CLASS - USE THIS FORM - WILL THEN GET IN TOUCH WITH YOU: https://forms.gle/JNC5EpsY1Ko3tG7U7 You can also directly contact me: adrian.rauchfleisch@gmail.com This course offers a comprehensive overview of research methods in communication science. You will be introduced to various research designs, including experiments, surveys, content analysis, and computational approaches to data analysis. You will also learn how to critically evaluate social science research, including open science's importance and how qualitative and quantitative methods can be used in conjunction. We will utilize the programming language R to analyze and visualize data throughout the course as well as some Python for automatic content analysis.

- learn the basic methods in social research - learn how to use R - be ready to conduct your own research

1. Assignments (20%) - Short weekly assignments in the first third of the semester 2. Midterm quiz (25%) 3. Research proposal (5%) - Write a 1-2 page research proposal. 4. Final presentation (5%) - Present preliminary results of your research project 5. Research report (45%)

Research methods in social sciences (available in the course folder) Keyton, J. (2019). Communication research: Asking questions, finding answers (Fifth edition). McGraw-Hill Education. Babbie, E. R. (2013). The practice of social research (Thirteenth edition). Belmont, Calif: Wadsworth Cengage Learning. Croucher, S. M., & Cronn-Mills, D. (2021). Understanding Communication Research Methods. Routledge. Singleton, R., & Straits, B. C. (2010). Approaches to social research (5th ed). New York: Oxford University Press. Salganik, M. J. (2018). Bit by bit: social research in the digital age. Princeton: Princeton University Press. (https://www.bitbybitbook.com/en/1st-ed/preface/) Data visualizations and statistics (available in the course folder) Healy, K. (2018). Data visualization: a practical introduction. Princeton, NJ: Princeton University Press. (http://socviz.co/) Wheelan, C. J. (2014). Naked statistics: stripping the dread from the data. New York: Norton. Kabacoff, R. (2015). R in action: data analysis and graphics with R (Second edition). Shelter Island: Manning.