040213 UK Repetitorium: Data Analysis for Marketing Decisions in practice (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.02.2023 09:00 bis Mi 22.02.2023 12:00
- Abmeldung bis Mi 03.05.2023 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Freitag 21.04. 09:45 - 13:00 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Freitag 05.05. 09:45 - 13:00 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Freitag 12.05. 13:15 - 16:30 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Montag 22.05. 09:45 - 13:00 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Freitag 26.05. 13:15 - 16:30 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Freitag 02.06. 09:45 - 13:00 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Students’ performance in the course is assessed on the following dimensions:
▪ Participation
▪ Group exercises
▪ Final group assignment
▪ Participation
▪ Group exercises
▪ Final group 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.
a “─“ (fail), depending on whether they have performed adequately in the assessment dimensions
mentioned above.
Prüfungsstoff
Students mainly work 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 typically take place during the sessions. The final group assignment consists of a more comprehensive
case study, where groups 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.
data analysis and presenting the results. The exercises focus on individual statistical techniques
and typically take place during the sessions. The final group assignment consists of a more comprehensive
case study, where groups 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.
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), Taking
the Fear out of Data Analysis: Completely Revised, Significantly Extended and Still Fun, Edward Elgar:
London [ISBN: 978 1 80392 985 9].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]. → this and other open-access material will be available on Moodle
Publications: London [ISBN: 9781526445780].Recommended additional 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].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]. → this and other open-access material will be available on Moodle
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 20.03.2023 10:28
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 the IBM SPSS software, but also utilizes additional statistical packages
and tools such as PROCESS, JAMOVI 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 also provide feedback on how to effectively
perform, report, and interpret the various analytical techniques.