040088 UE Empirical Methods I (MA) (2021S)

This course is an introductory class on empirical methods and data analysis which precedes the follow-up class “Empirical Methods II”. The goal of this introductory course is for students to learn the fundamental techniques and obtain the basic skills required in empirical research. Our theoretical sessions will cover tools and stages required for running empirical projects (e.g. research design, measurement, methods of data collection) with a special focus on the pre-evaluation stage of an empirical work. Our applied sessions will introduce the students to basic programming skills, allowing them to prepare their data for analysis using statistical programming software. Students will participate by reading and presenting scientific articles in some of the highest ranked strategy journals. Knowledge gained in this course is also applied during a project where students actively develop the necessary steps for conducting their own empirical research projects.
This course is highly interactive and built around the idea of a laboratory setup as is typical for social sciences. The setup necessitates certain software and IT equipment. To provide every student the same opportunity to successfully participate in the course, it is usually held in one of the PC-labs at the OMP 1. Since the current situation seems not to allow this setting, but we still want to maintain a high quality and interactive class, this semester, the class will be held online using MS Teams or Zoom. In this spirit, students are required to ensure stable Internet connection and be able to join using their web cameras as well! Hence, not only an audio connection, also a video connection is required!

This course is an introductory class in which you learn the fundamental knowledge and skills to conduct empirical research. In specific, we will focus on the early stages of an empirical research project, namely basic principles of empirical research in the social sciences, research design, data collection, data preparation, data visualization and some (descriptive) analysis. Applied sessions introduce you to basic programming skills with statistical software, allowing you to start working with real data(sets). Furthermore, you’ll be improving your (digital) soft skills by collaborating and discussing ideas in student teams online – an experience that is very close to how many scientific research collaborations work (especially nowadays).

TEACHING STYLE AND COURSE DESIGN: This course is taught entirely online; consisting of both live sessions (-> expect mandatory attendance) and asynchronous exercises and assignments [I will send to all registered students a syllabus with further details at the end of February through email].
I designed the course in a way to be very interactive (and hopefully fun) to maximize your learning outcomes. Such a concept depends on your active participation:
1) You’ll have to attend and actively give input during sessions; this means that you’ll be to some extend exposed when sharing your opinion, giving/receiving feedback to/from others, asking questions or sharing other details. This requires openness, curiosity, treating others respectfully and some discipline.
2) We’ll use a variety of tools to support this and communicate in our live sessions (including also external software tools, such as padlet, miro, doodle, google forms, break-out discussion rooms in BBB/ BBCollaborate, potentially also zoom; some of these tools require you to set up an account/ install software). By signing up for this course, I assume your consent with this; please do contact me up-front before the first session via email in case you have any concerns.
3) We’ll use software for statistical computing and graphics; there are some minimum hardware requirements (i.e. a laptop with some free disk space), since you need to install software after the first session. There will be exercises and elements you’ll have to work on, both during as well as between live sessions.

** Self-study is a fundamental element of this course, enabling you to learn both more effectively as well as more efficiently. I’ll provide materials and expect you to independently acquire some knowledge and skills at home. This leaves more precious time during live sessions for interactions and your questions, for deepening your understanding and giving you feedback on your learning progress. **
** This course has 4 ECTS-Credits, i.e. an overall workload of roughly 100-120 hours: Live sessions require ~25hours, each(!) live session (of 4 hours) requires the same additional amount of time for self-study, revision of class discussions/ slides and preparation for the next session (+4hours, in total about ~25 hours). You further need to work ~25 hours for the individual assessment/exam and ~25 hours on the team project. **
==> Live sessions are very intense and dense in March. You need to reserve 4-5 hours for self-study and work after each session (and before the next one!) **

We’ll immediately deep-dive into contents and tool-supported interactions in the first session. Therefore, I’ll distribute the syllabus a couple of days before the first sessions (via email) and ask you to read it and come prepared; the first session may already contain some smaller assignments, contributing to your participation score.

I’m looking forward to seeing you online in class!