distance mode, this course will be held as an online session - structure/lessons of the course will be still the same.
Language of instruction
|Monday||10/12/20||02:00 PM - 06:00 PM||Online-Einheit|
|Tuesday||10/27/20||02:00 PM - 06:00 PM||Online-Einheit|
|Monday||11/09/20||02:00 PM - 06:00 PM||Online-Einheit|
|Monday||11/23/20||02:00 PM - 06:00 PM||Online-Einheit|
|Wednesday||12/09/20||02:00 PM - 06:00 PM||Online-Einheit|
|Monday||12/21/20||02:00 PM - 06:00 PM||Online-Einheit|
At the beginning of the course, students will be assigned to groups to develop a research project that they will develop throughout the course. The first step of the project is to identify a real problem – driven by environmental challenges that organizations face – challenges that need urgent action based on good information. Students are free to choose a topic about which they care henceforth to develop the necessary skills to reason about it. After identifying a problem, groups search the academic literature to structure the path towards a solution. Then, groups are asked to develop a theoretical framework; design an experiment or build a questionnaire, and then collect data. For each step, students prepare a presentation to report the state of their advancement in front of the class. Each group is assigned to mentor another group throughout the course, provide them with feedback and help them develop stronger and better ideas, and strengthen the analysis.
At the end of the course, students are asked to present their work in front of the plenary of students and write a formal report in which they address the problem, present results, and offer recommendations to the company they chose to study.
As students start from broad grand challenges to develop a research question and an appropriate research design, the topics that will be covered during the lectures will include: basic differences between inductive vs deductive research, the steps of a research project, descriptive statistics, measures of association and difference amongst groups, sampling and sample size estimation, and linear regression.
As the competitive environment changes rapidly, organizations need to adapt to survive. Adaptation requires informed decisions. Informed decisions are good choices that do not stem from managers’ gut feelings, but rather from an accurate analysis of real-world data. In this course, students will hone skills that will allow them to monitor external information and therefore better decision-makers.
Accordingly, the goal of the course is to let students understand and use the basic concepts of research design, data analysis, and statistical analysis that are used in social sciences and increasingly in organizations. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students pragmatic tools for assessing statistical claims and conducting their own basic statistical analyses.
Attendance is mandatory. Maximum 1 lesson can be skipped
Lectures, group discussions, students’ presentations, group works around theoretical questions and practical problems. Seminars on research methods and data analysis. Autonomous reading of academic literature.
Students are invited to bring their laptop to the lecture room
The evaluation will be partly based on group work, partly on individual contributions
Group assignment 1 - 10%
Group assignment 2 - 15%
Group assignment 3 - 15%
Group assignment 4 - 25%
Final written report - 25%
Feedback to peers - 10%
** Group work will count 75% of the grade & Individual contributions, assessed via peer evaluation, will count 25%. Thereby the final grade will to the following formula:
Individual grade = group vote * group quota (75%) + group vote * individual quota (25%) * individual contribution
For example, Martin’s contribution to the group-work has been valued 80% by his peers, and his group has achieved .825 overall.
Martin’s grade = .825*.75 + .825*.25*.80 = .784 or 78%, which then will be translated in the final grade according to the following conversion rate.
Not sufficient 0-60%; Sufficient 60,1-70%; Satisfactory 70,1-80%; Good 80,1-90%; Very good 90,1-100%
Students are expected to have some familiarity with Excel, basic statistical jargon and concepts, and management theories.
Your instructor will be available after each class
This course will be as a rotation mode. The class will be contacted by the teacher after the registration ends how the groups/sessions will be structured.