Universität Wien FIND

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Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter https://studieren.univie.ac.at/info.

040501 KU Data Analysis for Marketing Decisions (MA) (2018S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

It is absolutely essential that all registered students attend the first session on March 6th, 2018 (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 March 11th, 2018.




max. 30 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Dienstag 06.03. 09:45 - 11:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag 13.03. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 20.03. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 10.04. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 17.04. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 23.04. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag 24.04. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch 02.05. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 08.05. 09:30 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 15.05. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch 23.05. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 05.06. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag 12.06. 09:45 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 18.06. 11:30 - 13:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock


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) as well as (b) different methods of data analysis (e.g., t-test, χ2 test, ANOVA, regression 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, master thesis) 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 theoretical discussions that are accompanied by several practical examples and hands-on exercises. Primarily, the lectures provide background knowledge on the statistical theory, the selection, and the understanding of various data analysis techniques. In addition, hands-on exercises introduce the SPSS environment and illustrate how to perform and interpret statistical data analyses. Note that successful completion of DAMD depends greatly on students’ effort to systematically review the material and suggested homework throughout the semester.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Performance in the course will be assessed as follows:
Midterm exam: 25%
Group project: 30%
Final exam: 45%

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

Mindestanforderungen und Beurteilungsmaßstab

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.


The midterm exam is based on the topics covered in sessions 1 to 5 and the corresponding book chapters. The exam typically (but not necessarily) involves a combination of multiple-choice/single-choice questions.

The group project is an assignment conducted by teams of 3 to 5 students and involves the analysis of a dataset as well as the interpretation and the presentation of the relevant results. The grade of the group project takes into account both group and individual performance and is determined by the overall quality of the assignment weighted by the individual contribution of each member to the group project (as determined by peer-evaluation). Thus, a different grade might be awarded to students belonging to the same team. Detailed instructions will be provided in class.

The examinable material of the final exam includes all topics covered in the lectures and the corresponding book chapters. The exam will include questions of different formats (e.g., multiple choice questions and mini cases with open-ended questions).


Required textbook is: Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 978-1-4462-4918-5].
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].

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:29