Syllabus

Title
1219 Marketing Research
Instructors
Alexander Kulumbeg, MSc (WU)
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/13/21 to 09/24/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Monday 10/11/21 09:00 AM - 01:30 PM LC.-1.038
Monday 10/18/21 09:00 AM - 01:30 PM LC.-1.038
Monday 10/25/21 09:00 AM - 01:30 PM LC.-1.038
Monday 11/08/21 09:00 AM - 01:30 PM LC.-1.038
Monday 11/15/21 09:00 AM - 01:30 PM Online-Einheit
Monday 12/06/21 10:30 AM - 03:00 PM Online-Einheit

Contents

This course will be held in presence mode.

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

Foundations: Dependent vs. independent variables, descriptive statistics & introduction to probability theory/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.

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.

 

Attendance requirements

The students are allowed to miss 20% of the course which translates to one (1) session. 

Teaching/learning method(s)

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. 

 

 

Assessment

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

  • Group Project (30%)
  • Programming exercises (20% = 4(+1) x 5%, best out of 5)
  • Participation (10%)
  • Final exam (40%)

The results for all exercises should be clearly stated in the documents handed in.

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

For this SBWL 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)

 

Please note that copying/cheating on individual assignments (computer exercises, exam) will result in immediate exclusion from the course and a failing grade (5).

 

 

Readings

1 Author: Institute for Interactive Marketing and Social Media
Title:

Publisher: Institute for Interactive Marketing and Social Media
Edition: 1st
Remarks: Online script
Year: 2020
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Script
2 Author: Field, A., Miles, J., & Field, Z.
Title:

Discovering Statistics using R


Publisher: Sage Publications Ltd.
Edition: 1st Edition
Year: 2012
Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
3 Author: DataCamp
Title:

Additional R Help can be found here:

 

https://www.datacamp.com/


Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Script

Recommended previous knowledge and skills

All the material covered in the following courses is considered a prerequisite:

- Einstieg in die SBWL: Service und Digital Marketing

- Digital Marketing

- Mathematik (STEOP)

- Statistik (CBK)

 

Availability of lecturer(s)

There will be office hours announced for you to ask your all questions.

 

Last edited: 2021-11-14



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