040501 VO Foundations of Marketing: Data Analysis for Marketing Decisions (MA) (2023W)
Labels
This course is particularly targeted at students of the Master's in Business Administration, who wish to take "Marketing & International 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 & International 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.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
Details
Sprache: Englisch
Prüfungstermine
Montag
11.12.2023
11:30 - 14:45
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Freitag
19.01.2024
09:45 - 12:00
Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag
20.02.2024
09:45 - 13:00
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Montag
02.10.
11:30 - 13:00
Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
Donnerstag
05.10.
09:45 - 11:15
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag
09.10.
11:30 - 13:00
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Montag
16.10.
11:30 - 13:00
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Dienstag
24.10.
11:30 - 13:00
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
31.10.
13:15 - 14:45
Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
07.11.
13:15 - 14:45
Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
Freitag
10.11.
09:45 - 11:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
14.11.
13:15 - 14:45
Digital
Montag
20.11.
13:15 - 14:45
Digital
Montag
27.11.
13:15 - 14:45
Digital
Mittwoch
06.12.
09:45 - 11:15
Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Information
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. Additional registration for any exam taken 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: 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: Di 16.01.2024 11:25