Universität Wien FIND

040501 VO Foundations of Marketing: Data Analysis for Marketing Decisions (MA) (2023S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften


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


max. 60 Teilnehmer*innen
Sprache: Englisch



Termine (iCal) - nächster Termin ist mit N markiert

Freitag 03.03. 09:45 - 11:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Montag 06.03. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Freitag 10.03. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag 14.03. 16:45 - 18:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag 20.03. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Freitag 31.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag 17.04. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag 20.04. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Dienstag 02.05. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Montag 08.05. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch 10.05. 13:15 - 14:45 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß


Ziele, Inhalte und Methode der Lehrveranstaltung

Sound knowledge of statistics and data analysis is an important requirement for business practice and marketing decisions – 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, statistical power, effect sizes) and (b) different methods of data analysis (e.g., t-test, χ2 test, AN(C)OVA, regression analysis) in a series of lectures that combine theory with illustrative, practical examples.
The course does not focus on programming/coding nor it aims to demonstrate how to use a specific statistical software (e.g, R, Excel, SPSS, JMP, Minitab, etc). Instead, it focuses on the logic, the implementation, and the interpretation of statistical data analysis in general – regardless of the program employed! Students who successfully complete DAMD will be equipped with a solid foundation of quantitative data analysis and be able to effectively understand, interpret, and communicate a wide range of analytical approaches; an essential asset for their professional development and career prospects.

The sessions involve theory discussions accompanied by practical cases and hands-on examples. Data simulations and visualization tools are also employed to illustrate the theoretical/computational concepts discussed. Lectures typically provide background knowledge in understanding the theory and logic behind the statistical techniques and then illustrate how to interpret quantitative data analytic methods. Note that successful completion of DAMD depends greatly on whether students systematically review the relevant reading material.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students’ performance in the course is assessed through a comprehensive, final exam.
No material other than a dictonary may be used in the final exam.

Mindestanforderungen und Beurteilungsmaßstab

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 can take the exam for maximum 4 times. The additional registration for the exam is mandatory.


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


Required textbook: Diamantopoulos, D., Schlegelmilch, B., & Halkias, G. (2023), Taking the Fear out of Data Analysis: Completely Revised, Significantly Extended and Still Fun, Edward Elgar: London [ISBN: 978 1 80392 985 9].

Recommended textbook: Field, A. (2018), Discovering Statistics Using IBM SPSS Statistics (5th edition), Sage Publications: London [ISBN: 9781526445780]. (previous editions are also fine)

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 20.03.2023 10:28