Universität Wien FIND

Return to Vienna for the summer semester of 2022. We are planning to hold courses mainly on site to enable the personal exchange between you, your teachers and fellow students. We have labelled digital and mixed courses in u:find accordingly.

Due to COVID-19, there might be changes at short notice (e.g. individual classes in a digital format). Obtain information about the current status on u:find and check your e-mails regularly.

Please read the information on https://studieren.univie.ac.at/en/info.

040501 KU Data Analysis for Marketing Decisions (MA) (2017W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Summary

1 Halkias , Moodle
2 Halkias , Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Registration information is available for each group.

Groups

Group 1

It is absolutely essential that all registered students attend the first session on October 4th, 2017 (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.

Exchange students must have successfully completed at least a basic/introductory marketing course at their home university. To be able to attend the course they must hand in a relevant transcript/certificate by October 10th, 2017.

http://international-marketing.univie.ac.at/teaching/master-bwibw/courses-ws-1718/#c637029

max. 30 participants
Language: English
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 04.10. 09:45 - 11:15 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 11.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 18.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 25.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday 03.11. 11:30 - 13:00 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 08.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday 10.11. 15:00 - 16:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 15.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 22.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 29.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 06.12. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 13.12. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 10.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 17.01. 09:45 - 11:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock

Minimum requirements and assessment criteria

In total, a minimum of 50 percent needs to be attained 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 who fail must repeat the entire course (and must register in the usual way next time the course is offered). No opportunities for make-ups will be offered.

Group 2

It is absolutely essential that all registered students attend the first session on October 4th, 2017 (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.

Exchange students must have successfully completed at least a basic/introductory marketing course at their home university. To be able to attend the course they must hand in a relevant transcript/certificate by October 10th, 2017.

http://international-marketing.univie.ac.at/teaching/master-bwibw/courses-ws-1718/#c640675

max. 30 participants
Language: English
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 04.10. 09:45 - 11:15 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 11.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 18.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 25.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday 03.11. 11:30 - 13:00 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 08.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday 10.11. 15:00 - 16:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 15.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 22.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 29.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 06.12. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 13.12. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 10.01. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 17.01. 09:45 - 11:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock

Minimum requirements and assessment criteria

In total, a minimum of 50 percent needs to be attained 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 who fail must repeat the entire course (and must register in the usual way next time the course is offered). No opportunities for make-ups will be offered.


Information

Aims, contents and method of the course

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 as well as (b) different methods of data analysis in a series of lectures that combine theory with applied examples and hands-on exercises. The application of the analytical techniques is carried out using the IBM SPSS software package. However, this course is not a tutorial on SPSS but it rather focuses on the logic, the implementation, and the interpretation of statistical data analysis. Students who successfully complete DAMD will be equipped with the ability to effectively conduct research projects in their later academic (e.g., other university courses, dissertations) and/or professional career.

The major topics covered in the course are:
Theoretical introduction to basic research terms: data, variables, models, research process, sample, population, measurement scales, etc.
Introduction and familiarization with the statistical software SPSS
Clearing and preparing data for further analysis
Descriptive statistics: central tendency, variability, skewness, kurtosis
Testing statistical assumptions: normality, homogeneity of variance, homoscedasticity
Inferential statistics and hypothesis testing: parameter estimates, sampling error, confidence intervals, Type I and Type II errors, p-values, t-values
Performing comparisons: chi-square test, independent samples t-test, paired-sample t-tests, analysis of variance
Investigating relationships: bivariate correlation, partial correlation
Regression models: simple linear regression, multiple linear regression, logistic regression
Finding structures using Factor Analysis
Presenting, reporting and interpreting results
Identifying practical and theoretical implications drawn from statistical analyses

The classes involve a combination of formal theory lectures and practical lab sessions. Formal lectures primarily provide background knowledge on statistical inference and the selection of appropriate statistical techniques to analyse data. On the other hand, lab sessions and hands-on exercises introduce the SPSS environment and illustrate how to conduct and interpret different types of data analyses.

Assessment and permitted materials

Performance in the course will be assessed as follows:
Midterm exam: 20%
Team assignment: 35%
Final exam: 45%

No material other than a dictonary may be used in the final exam.

Examination topics

The midterm exam is based on the topics covered in sessions 1 to 5 and the corresponding book chapters. The exam will include a combination of multiple-choice/single-choice questions.

The team assignment is a more complex homework conducted by teams of 3 to 5 students; the same grade will be awarded to students belonging to the same team. Detailed instructions will be provided in the course.

The final exam is in written form and will be in English. Examinable material includes all topics covered in theory and practice sessions as well as the corresponding book chapters. The exam will include questions of multiple formats (single choice questions, open-ended questions, mini cases, etc.).

Reading list

The required textbook is: Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 978-1-4462-4918-5 (pbk)]. An accompanying website provides additional useful material (http://www.uk.sagepub.com/field4e/).

A recommended additional textbook is: 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].

Reading the course material (slides, book chapters) is an essential part of the course (especially as preparation for the sessions!) and as important as attending lectures.

Association in the course directory

Last modified: Mo 07.09.2020 15:29