Universität Wien

040501 VO Foundations of Marketing: Data Analysis for Marketing Decisions (MA) (2022W)

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 03.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Freitag 07.10. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Freitag 14.10. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Freitag 21.10. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Freitag 28.10. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag 07.11. 09:45 - 11:15 Digital
Freitag 11.11. 11:30 - 13:00 Digital
Freitag 18.11. 11:30 - 13:00 Digital
Freitag 25.11. 11:30 - 13:00 Digital
Montag 05.12. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Freitag 09.12. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag 12.12. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock

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. The course is not tied to a any specific statistical software (IBM SPSS software is only used for demonstration). Rather it focuses on the logic, the implementation, and the interpretation of statistical data analysis in general – regardless of the program used. 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 then illustrate how to interpret quantitative data analytic methods. Note that successful completion of DAMD depends greatly on whether students systematically review the class 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.

Prüfungsstoff

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).

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

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 11.05.2023 11:27