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Epidemiology, Biostatistics and Prevention Institute (Prof. Dr. rer. nat. Ulrike Held)
Description
The course offers an introduction to data analysis in the transdisciplinary field of empirical (medical) research in the programming language R. The R system for statistical computing is openly available at https://www.r-project.org and provides a simple and flexible software environment for statistical analyses and graphics. Tailored to the application in empirical research, the course covers the basics of programming and data formats in R, as well as the essential steps of a data analysis including data manipulation, descriptive statistics, statistical tests and graphical representations. Reflections on research methodology and transdisciplinarity are addressed and critical thinking is encouraged.
The Get R_eady module enhances digital skills in the context of data analysis and graphics. It stimulates critical thinking and provides tools for reproducible research. In applications corresponding to the course participants' backgrounds, the concepts are explained. Hands-on sessions facilitate the understanding of the participants and enhance the discussion.
The course is building on problem based learning and held in an interactive and diverse format, with short lectures, demonstrations and hands on practical exercises to be solved in small groups.
Target group
Students at Master's or PhD level, involved in empirical or medical research. Participants should have basic knowledge in statistics and should be beginners with the software R.
Course dates
Friday 14.00 - 17.00
21.10.2022, 28.10.2022, 04.11.2022
The course will be held in English.
Offered in
Every semester
Assessment / ECTS Credits
The assessment is based on preparatory work before the course starts, active course participation, short presentations in the second and third part of the course and homework.
1 ECTS
Description
Larger collections of data are becoming increasingly available. To exploit their potential, statistical analysis skills are needed. The direct link between data and visualization/reporting of results is highly relevant in all empirical research disciplines, as several scientific fields have recently been criticized for lack of reproducibility.
Dynamic reporting tools can be used to directly link data, visualization and analysis outputs, allowing for rapid adaption after possible changes in the dataset, e.g. after data preparation, validation or in the context of manuscript revision.
Tailored to applications in empirical research, the course covers the basics of dynamic programming in R, including examples of dynamic reports for presentations, manuscripts, and html websites. Research methodology is reflected upon, especially in relation to reproducibility, Open Science and transdisciplinarity. Exemplary reports from different disciplines will be compiled and presented by the students.
Target group
Students at Master's or PhD level who are involved in empirical research. Participants should have basic knowledge in statistical methods and in the programming language R, equivalent to completion of the course "Get R_eady: Introduction to Data Analysis for Empirical Research". Students should be beginners with software R Markdown, R Notebook and R Sweave.
Course dates
Friday 14.00 - 17.00
18.11.2022, 25.11.2022, 02.12.2022
The course will be held in English.
Offered in
Every semester
Assessment / ECTS Credits
The assessment is based on preparatory work before the course starts, active course participation, short presentations in the second and third part of the course and homework.
1 ECTS
If you want to combine the Get R_eady Modules with other Modules from Open and Reproducible Science, offered by the School for Transdisciplinary Studies, we suggest a combination totalling 3 ECTS.
