Universität Wien

040501 KU Data Analysis for Marketing Decisions (MA) (2022S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
MIXED

It is absolutely essential that all registered students attend the first session (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.

By registering for this course you agree that the automated plagiarism software Turnitin processes and stores your data (i.e. project work, seminar papers, exams, etc.)

Exchange students must have successfully completed at least a basic/introductory marketing course at their home university. To be able to attend the course they must hand in a relevant transcript/certificate by March 6th, 2022.

https://international-marketing.univie.ac.at/studies/master-bwibw/courses-ss-2022/
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 60 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 04.03. 11:30 - 13:00 Digital
  • Thursday 10.03. 11:30 - 13:00 Digital
  • Friday 18.03. 11:30 - 13:00 Digital
  • Friday 25.03. 11:30 - 13:00 Digital
  • Friday 01.04. 11:30 - 13:00 Digital
  • Friday 08.04. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 29.04. 11:30 - 13:00 Digital
  • Friday 06.05. 11:30 - 13:00 Digital
  • Friday 13.05. 11:30 - 13:00 Digital
  • Friday 20.05. 11:30 - 13:00 Digital
  • Friday 27.05. 11:30 - 13:00 Digital
  • Friday 03.06. 11:30 - 13:00 Digital
  • Thursday 09.06. 11:30 - 13:00 Digital
  • Friday 17.06. 11:30 - 13:00 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

Sound knowledge of statistics and data analysis is an essential requirement for marketing research and managerial decision-making – more than one would expect! The present course discusses (a) key concepts of statistics and statistical inference (e.g., NHST, Type I and II error, confidence intervals) and (b) different methods of data analysis (e.g., t-test, χ2 test, ANOVA, regression analysis) in a series of lectures that combine theory, illustrative examples, and hands-on exercises. Although IBM SPSS software is used in the context of DAMD, the course is not a tutorial on the SPSS software package. Rather it focuses on the logic, the implementation, and the interpretation of statistical data analysis in general. Students who successfully complete DAMD will be equipped with the ability to effectively carry out data analysis projects in their later academic and professional career.

The sessions involve theory discussions accompanied by practical examples and hands-on exercises. Lectures primarily provide background knowledge in understanding the theory and logic behind the statistical techniques and finally to illustrate how to perform and interpret the data analysis using the SPSS environment as an example. Note that successful completion of DAMD depends greatly on whether students systematically review the class material.
Students are expected to participate in the online sessions using their cameras and microphones.

Assessment and permitted materials

Students’ performance in the course is assessed as follows:
Midterm exam: 30%
Group assignment: 25%
Final exam: 45%

No material other than a dictonary may be used in the final exam.

Minimum requirements and assessment criteria

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.

Examination topics

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 assignment is conducted by teams of ~ 6 students and involves answering a series of research questions by conducting and reporting the appropriate analytical techniques. The assignment reflects collective effort, so all members are expected to contribute and receive the same grade based on the evaluation of the assignment. Detailed instructions will be provided in class.

The final exam covers all topics discussed in the lectures and corresponding book chapters. The exam typically includes questions of different formats (e.g., multiple-choice questions and mini cases with open-ended questions).

Reading list

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!

Association in the course directory

Last modified: Th 11.05.2023 11:27