• Rohrbach Tobias

• Kukles Yulia

This course is designed for Master's students to enhance their foundational knowledge of statistics and data analysis through hands-on experience. Students will acquire practical skills in representing various data structures and performing essential statistical analyses commonly used communication research, including descriptive analyses, regression analysis, and dimensionality reduction techniques, with an emphasis on visualizing and reporting key outputs effectively. In addition to quantitative methods, the course introduces advanced techniques (meta-analysis, automated text analysis, basic machine learning) and encourages critical reflection on contemporary methodological topics, such as open science. Students will also explore qualitative approaches to data analysis, enabling a comprehensive understanding of data visualization. By the end of the course, participants will be equipped to analyze and present data confidently and critically. Note: The course will heavily rely on statistical software R. Previous knowledge of R is encouraged but not necessary.

• Develop the ability to represent, analyze, and report quantitative and qualitative relevant to communication research
• Conduct essential statistical analyses, including descriptive statistics, regression analysis, and dimensionality reduction techniques
• Effectively visualize and communicate key outputs from data analyses
• Understand and be able to interpret advanced techniques such as meta-analysis and automated text analysis in research contexts
• Reflect critically on contemporary methodological topics, including open science practices.
• Explore and integrate qualitative approaches to enhance data analysis and visualization skills.

Students will be required to bring their personal computer to the course.

A 60-minute written exam

The course will be evaluated in form of an open-book exam, consisting of an applied data analysis and visualisation task. In addition, students will complete two pass or fail mini-exercices during the semester in preparation for the final exam. Students will be required to bring their own laptops to the exam!

A 60-minute written exam

The course will be evaluated in form of an open-book exam, consisting of an applied data analysis and visualisation task. In addition, students will complete two pass or fail mini-exercices during the semester in preparation for the final exam. Students will be required to bring their own laptops to the exam!