0053 Marketing Research
Daniel Winkler, MSc (WU)
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
17.09.2019 bis 22.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Montag 14.10.2019 08:30 - 13:00 LC.2.064 Raiffeisen Kurslabor
Montag 21.10.2019 08:30 - 13:00 LC.-1.038
Montag 28.10.2019 08:30 - 13:00 LC.-1.038
Montag 04.11.2019 08:30 - 13:00 LC.-1.038
Montag 11.11.2019 08:30 - 13:00 LC.-1.038
Montag 02.12.2019 10:30 - 15:00 TC.0.03 WIENER STÄDTISCHE

Inhalte der LV

This course covers the statistical methods most commonly used in the field of marketing:

Foundations: Dependent vs. independent variables, descriptive statistics

Measurement & scaling: Types of scales and descriptive & inferential statistics, reliability & validity, measurement error

Data collection & sampling: Taking samples from a population, sampling distribution of the mean, central limit theorem, confidence intervals, sample size

Exploring data with graphs: Histogram, scatter plot etc.

Exploring assumptions: Testing whether a distribution is normal, testing for homogeneity of variance

Basics of hypothesis testing: Different types of tests and assumptions, e.g., parametric vs. non-parametric, normal vs. t-distribution, chi-2 distribution, degrees of freedom

Comparing means: t-test, analysis of variance (ANOVA)

Correlation and regression: Looking at the relationships, simple and multiple regression

This course also gives an introduction to the statistical software R. R is a language and environment for statistical computing and graphics, which provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, …) and graphical techniques, and is highly extensible. With the knowledge gained in this course, you will be ready to undertake your very first own data analysis including the statistical methods most commonly used in the field of marketing.

Lernergebnisse (Learning Outcomes)

At the end of this course, you will be able to:

  • Interpret statistical analyses used in the field of marketing.
  • Learn how to perform exploratory data analysis on a data set by using the statistical software R.

All methods are deepened by practical examples in the context of typical marketing applications.


Regelung zur Anwesenheit

The students are allowed to miss only one (1) session. 


The course is taught using a combination of material presented by the lecturer and supported by practical examples. During the sessions, students will have the chance to apply methods covered utilizing R. 



Leistung(en) für eine Beurteilung

The performance of students is assessed based on various exercises (delivery via Learn@WU) and a final examination:

  • Active participation in computer exercises  (10% = 4 x 2.5%)
  • 3 assignments (30% = 3 x 10%)
  • A final exam (60% with a requirement of minimum 30%)

The students are allowed to miss only one (1) session. 

Students have to be prepared to present their assignment solutions in class, based on their uploaded document.

For a positive grade, students have to fulfill 60% of the requirements.

For this course we have the following scale:

< 60%                                fail (5)

60% bis 69,99%               sufficient (4)

70% bis 79,99%               satisfactory (3)

80% bis 89,99%               good (2)

>= 90%                              excellent (1)


1 Autor/in: Institute for Interactive Marketing and Social Media

Verlag: Institute for Interactive Marketing and Social Media
Auflage: 1st
Anmerkungen: Online script
Jahr: 2019
Prüfungsstoff: Ja
Diplomprüfungsstoff: Nein
Empfehlung: Unbedingt notwendige Studienliteratur für alle Studierenden
Art: Skriptum
2 Autor/in: Field, A., Miles, J., & Field, Z.

Discovering Statistics using R

Verlag: Sage Publications Ltd.
Auflage: 1st Edition
Jahr: 2012
Prüfungsstoff: Ja
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch

Erreichbarkeit des/der Vortragenden

I am happy to answer your questions so feel free to send me a short email if you would like to talk to me in person. I will also try to be available in the class-room after each class or during the breaks of each class.


Zuletzt bearbeitet: 03.12.2019