Universität Wien
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

040501 KU Data Analysis for Marketing Decisions (MA) (2021S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
DIGITAL

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 7th, 2021.

https://international-marketing.univie.ac.at/studies/master-bwibw/courses-ss-2021/
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Freitag 05.03. 11:30 - 13:00 Digital
  • Donnerstag 11.03. 11:30 - 13:00 Digital
  • Freitag 19.03. 11:30 - 13:00 Digital
  • Mittwoch 24.03. 13:15 - 14:45 Digital
  • Freitag 26.03. 11:30 - 13:00 Digital
  • Donnerstag 15.04. 11:30 - 13:00 Digital
  • Freitag 16.04. 11:30 - 13:00 Digital
  • Freitag 23.04. 11:30 - 13:00 Digital
  • Freitag 30.04. 11:30 - 13:00 Digital
  • Freitag 07.05. 11:30 - 13:00 Digital
  • Freitag 14.05. 11:30 - 13:00 Digital
  • Freitag 21.05. 11:30 - 13:00 Digital
  • Freitag 28.05. 11:30 - 13:00 Digital
  • Freitag 04.06. 11:30 - 13:00 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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

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

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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 approx. 5 students and involves the analysis of a dataset as well as the interpretation and presentation of the relevant results. The group project is a collective effort and all members are expected to contribute as the same grade is 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. The exam typically includes questions of different formats (e.g., multiple-choice questions and mini cases with open-ended questions).

Literatur

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!

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:12