Universität Wien

040501 VO Principles of Data Analysis for Marketing and Management Decisions (MA) (2025S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
GEMISCHT

An/Abmeldung

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

Details

Sprache: Englisch

Prüfungstermine

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Montag 03.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 07.03. 09:45 - 11:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Montag 10.03. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 17.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 24.03. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 31.03. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 07.04. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 11.04. 09:45 - 11:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Montag 05.05. 11:30 - 13:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 12.05. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 19.05. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is particularly targeted at students of the Master's in Business Administration, who wish to take "Marketing" as a major or minor, as well as at students of the Master's in International Business Administration, who have chosen "Marketing & International Marketing" in the in-depth phase. Students who wish to write their Master thesis in "Marketing" are strongly encouraged to attend.
This course is also open to students from other programs as well as guest students who meet the study requirements. Note that this course can also be complemented by the UK Repetitorium: Data Analysis for Marketing Decisions in practice (DAMDiP – Course 040213) which trains students in analyzing data with hands-on software applications in the Computer Lab.

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 pow-er, 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 imple-mentation, 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 quan-titative 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 con-cepts 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.

It is recommended that Erasmus students have successfully completed a basic/introductory marketing course at their home university. DAMD is a prerequisite for the Seminar Marketing.

Course policies:A
- The course and any material related to it (lectures, readings, exams, etc) is in English.
- Attendance is not mandatory (yet, highly recommended).
- Students must register to the course to get access to the corresponding Moodle page.
- To take the Final Exam, students must register separately for the chosen exam date.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students’ performance in the course is assessed through a comprehensive, final exam. 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).

To take the Final Exam, students must register separately for the chosen exam date!
Students can take the exam for maximum 4 times. Additional registration for any exam taken is mandatory

Mindestanforderungen und Beurteilungsmaßstab

In total, a minimum of 50 percent is needed to pass the course. The grading system is as follows: 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.

Prüfungsstoff

The final exam covers all topics discussed in the lectures and corresponding book chapters.

Literatur

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)

Systematically reviewing the course material (book chapters and lecture slides) is as essential as being physically and mentally present in the lectures!

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 06.03.2025 10:25