Universität Wien
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040126 UK Repetitorium: Data Analysis for Marketing Decisions in practice (2025W)

Please note that from WS2023 repetition courses (Repetitorium) from the Faculty of Business, Economics and Statistics cannot be used for modules within a curriculum.

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

This course is particularly targeted at students of the Master's in Business Administration/International Business Administration, who wish to advance their quantitative/analytical skills and write their Master thesis in "Marketing & International Marketing".
The course is strongly recommended for students who have already taken "Foundations of Marketing: Data Analysis for Marketing Decisions (VO)".
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

  • Dienstag 02.12. 09:45 - 14:40 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Freitag 05.12. 09:50 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 09.12. 09:50 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Freitag 12.12. 09:45 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Quantitative methods, data analytics, and statistical analysis is a key component of decision-making
for business practitioners and policy makers. This course aims to equip students with the hands-on
knowledge and skills necessary to implement, interpret, and communicate quantitative data analysis
using computer software and other tools.

The course assumes that students already have a theoretical understanding of statistical inference and
basic knowledge of key concepts in research methods. Hence, the emphasis is not placed on analytical
theory, but on training students in analyzing data to predict behavioral tendencies (e.g., relative product
preferences, purchase choices, and willingness to pay), make forecasts about future outcomes (e.g.,
likelihood of customer switching, probability of being hired/fired, and expected product sales), make
comparisons (e.g., across gender, nationality, or market segments), and assess the efficacy of alternative
interventions.

The course primarily relies on IBM SPSS and JAMOVI software packages, but utilizes additional statistical packages
and tools such as PROCESS and G*Power. Overall, the course provides students with a toolbox
of practical skills that are essential in carrying out empirical projects.

The sessions involve a brief introduction to the underlying logic behind the different analytical methods
and then focus on hands-on demonstrations and exercises. Sessions are highly interactive with
students working individually and/or in groups to solve practical problems in class using specific tools
and software under the guidance of the professor, who will provide feedback on how to effectively
perform, report, and interpret the various analytical techniques.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Assessment is based on class attendance and interaction, relying on the following dimensions:
▪ Class participation
▪ Class exercises
▪ Assignment

Mindestanforderungen und Beurteilungsmaßstab

The Repetitorium DAMDiP does not result in a numerical grade. Students receive either a “+” (pass) or
a “─“ (fail), depending on whether they have performed adequately in the assessment dimensions
mentioned above.

Prüfungsstoff

Students work independently but also in groups to address practical business research questions that require performing quantitative data analysis and presenting the results. The exercises focus on the application of individual statistical techniques and typically take place during the sessions. A final assignment may consist of a more comprehensive case study, where students are requested to identify, perform, and report the appropriate analyses to address several different questions. Class interaction and students’ participation is highly recommended to ensure effective learning and successful completion of the course.

Literatur

Required textbook: Field, A. (2018), Discovering Statistics Using IBM SPSS Statistics (5th edition), Sage Publications: London [ISBN: 9781526445780].

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

Complementary material (open-access) material will be available on Moodle.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 06.10.2025 10:25