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040213 UK Repetitorium: Data Analysis for Marketing Decisions in practice (MA) (2026S)

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

  • Mittwoch 27.05. 09:45 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Freitag 29.05. 09:45 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 02.06. 09:45 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Mittwoch 03.06. 09:45 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Quantitative methods, data analytics, and statistical analysis are key components 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 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 the IBM SPSS and the JAMOVI statistical packages but also utilizes additional 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.

Course structure:
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 also provide feedback on how to effectively perform, report, and interpret the various analytical techniques.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students’ performance in the course is assessed on the following dimensions:
- Class participation
- Class exercises
- Home assignment

Mindestanforderungen und Beurteilungsmaßstab

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

Prüfungsstoff

Students work individually or in groups to address business research questions that require performing quantitative data analysis and presenting the results. The exercises focus on individual statistical techniques and take place during the sessions, with the instructor guiding the students throughout the process. The assignment consists of a more comprehensive case study, where students address several different questions by identifying, performing, and reporting the appropriate quantitative techniques.
* Class participation and interaction are key components of effective learning and ensure the 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: Di 03.03.2026 11:05