0842 Marketing Research Methods
Tatiana Karpukhina, Ph.D.
Contact details
Weekly hours
Language of instruction
09/24/20 to 10/01/20
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 10/14/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 10/28/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 11/04/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 11/11/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 11/18/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 12/02/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 12/09/20 10:00 AM - 01:00 PM Online-Einheit
Wednesday 12/16/20 11:00 AM - 01:00 PM Online-Einheit

Procedure for the course when limited activity on campus

Most part of the course is planned to take place online in a dynamic lecture mode no matter the current regulations on campus activity. Last lecture of the course will take place off-line in a hybrid mode: students will be able to attend the lecture provided the auditorium allows for safe distancing. This lecture will be conducted in the online setting as well. Students will be informed about the format of the last lecture at least a week before (unless urgent measures are installed in a shorter time period).



Every day decisions as well as grand changes in most companies today are directed by data. In order to make smart marketing decisions one should know the market situation, watch out for competition, be familiar with the preferences of the target group and objectively evaluate the capabilities of a product.

This course covers the base set of topics and focuses on initial development of the skills relevant for data collection, analysis and critical interpretation necessary for marketing decision making.

Learning outcomes

After the completion of the course students should be able to plan and conduct a marketing research project: collecting, analyzing and interpreting the data and presenting the results convincingly.  

In particular the students will be able:

  • Define research questions and hypotheses
  • Develop a questionnaire
  • Collect the data
  • Prepare and evaluate the data in available statistical software (MSExcel)
  • Run various statistical tests (T-tests, Chi-square test, Analyses of variance, Correlation and Regression analyses)
  • Make critical conclusions and present research outcomes in a comprehensive way

Attendance requirements

For the successful completion of the course students are required to complete minimum 80% of the Online-Lectures (including the completion of in-lecture individual exercises).

Teaching/learning method(s)

Most part of the course will take place online by means of dynamic lecture flow facilitating interactive hands-on learning and timely personal feedback.

The core topics of the course will be introduced by the lecturer with further in-class examples, video explanations and exercises. The use of statistical software will also be introduced in class with further possibility of training both in the course of the sessions in the form of in-class exercises and as part of homework.

Students are required to study theoretical reading material, practice statistical analysis as preparation for the sessions, and for successful completion of the final group project.

The course provides:

  • Opportunity of individual lecturer2student interaction based on dynamic lecture format,
  • Hands-on learning of data analysis, evaluation and interpretation
  • 3 group projects (a short recent research summary presentation, creation of own survey and final group project)
  • Accessible theoretical literature
  • Coaching session before quizzes and the final project presentation


Individual Work:

  • Home assignment - 5%
  • Class participation (esp. completion of in-class exercises) - 5%
  • Quizzes (including theoretical knowledge and practical use of statistical software) – 50%


  • Development of the questionnaire – 10%
  • Recent Research Summary - 10%
  • Final Project – 20%


from 90 %: Excellent
from 80 %: Good
from 70 %: Satisfactory
from 60%: Sufficient
< 60%: Not Sufficient


Prerequisites for participation and waiting lists

Aufnahme in die SBWL Marketing

Basic MSExcel Knowledge (if abset, materials for self-study will be provided at the beginning of the course)

Availability of lecturer(s)

Individual appointments by e-mail

Last edited: 2020-09-29