Students’ performance in the course is assessed as follows:
Midterm exam: 25%
Group project: 30%
Final exam: 45%
UPDATE 18.03.2020: Students’ performance in the course is assessed as follows:
Mini group assignment: 25%
Group project: 30%
Final exam: 45%
No material other than a dictonary may be used in the final exam.
The course has “prüfungsimmanenten Charakter”, therefore attendance is mandatory throughout the semester – more than three absences automatically results in a grade of 5 (“fail”).
In total, a minimum of 50 percent is needed to pass the course. The grading system is the following: 0 to 49% - grade 5, 50 to 59% - grade 4, 60 to 69% - grade 3, 70 to 79% - grade 2, 80 to 100% - grade 1.
Students who fail must repeat the entire course (and must register in the usual way next time the course is offered). No opportunities for make-ups will be offered.
The midterm exam is based on the topics covered in sessions 1 to 5 and the corresponding book chapters. The exam typically (but not necessarily) involves a combination of single-choice/true-false questions.
The group project is an assignment conducted by teams of 3 to 5 students and involves the analysis of a dataset as well as the interpretation and presentation of the relevant results. The grade of the group project takes into account both group and individual performance and is determined by the overall quality of the assignment weighted by the individual contribution of each member to the group project (as determined by peer-evaluation). Thus, a different grade might be awarded to students belonging to the same team. Detailed instructions will be provided in class.
The final exam covers all topics discussed in the lectures and corresponding book chapters as well as provided materials on moodle. The exam typically includes questions of different formats (e.g., multiple-choice questions and mini cases with open-ended questions).
UPDATE 18.03.2020: Instead of the Midterm exam students will have to complete mini assignments with their group. These will ask you to answer/solve a research problem or mini case within a certain time frame. For instance, groups will either log in and start an online assignment on Moodle which will have to be completed within, let’s say, 20mins or they will be given an assignment with a submission deadline of a few days (e.g., submit by next week).
UPDATE 20.04.2020: The final exam will take place online via Moodle. The final exam covers all topics discussed in the lectures and corresponding book chapters as well as provided materials on moodle. The exam typically includes questions of different formats (e.g., multiple-choice questions and mini cases with open-ended questions). Date and time remains as announced.
Required textbook: Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 9781446249185] OR (new edition): Field, A. (2018), Discovering Statistics Using IBM SPSS Statistics (5th edition), Sage Publications: London [ISBN: 9781526445780].
Recommended additional textbook: Diamantopoulos, D. and Schlegelmilch, B. (2000), Taking the Fear out of Data Analysis (2nd edition), South-Western CENGAGE Learning: London [ISBN: 978-1-86152-430-0].
Complementary material: Marshall, E. (2016), The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests, University of Shefield - Statstutor Community Project, [Retrieved from www.statstutor.ac.uk]. → will be available on Moodle
Systematically reviewing the course material (slides, book chapters, and exercises) is as essential as being (physically and mentally) present in the lectures!