Syllabus

Title
4624 Marketing Research Methods
Instructors
Dr. Lukas Maier, Marie Louise Brand, MSc.
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/17/23 to 02/27/23
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Tuesday 03/14/23 10:00 AM - 01:00 PM TC.0.03 WIENER STÄDTISCHE
Tuesday 03/28/23 10:00 AM - 01:00 PM TC.-1.61
Tuesday 04/11/23 10:00 AM - 01:00 PM Online-Einheit
Tuesday 04/18/23 10:00 AM - 01:00 PM Online-Einheit
Friday 04/21/23 10:00 AM - 01:30 PM LC.2.064 Raiffeisen PC Raum
Tuesday 04/25/23 10:00 AM - 01:00 PM Online-Einheit
Tuesday 05/09/23 10:00 AM - 01:00 PM Online-Einheit
Tuesday 05/16/23 12:00 PM - 03:30 PM TC.3.02
Tuesday 05/23/23 10:00 AM - 01:00 PM D3.0.222
Tuesday 05/30/23 10:00 AM - 01:00 PM TC.5.04

Contents

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 lectures. This includes the completion of in-lecture individual exercises: failure to complete dynamic lecture flow exercises is considered as failure to attend the lecture.

Flexible scheduling: Students will have the flexibility to complete online lecture flows (lectures 3 through 6) in their preferred time and pace within the completion time.  Specifically, each online lecture flow should be completed within 3 days from the scheduled date of the class (from Tuesday to Thursday of the respective lecture weeks).

Fixed Scheduling:

Students have to be available for the completion of the online course quizzes for one hour starting at the class scheduled time. Specifically:

  • Quiz I -scheduled time of Lecture 2 (28.03.2023)
  • Quiz II - scheduled time of Lecture 5 (25.04.2023)
  • Quiz III - scheduled time of Lecture 6 (09.05.2023)

Students have to be available during the entire time of the scheduled in-class sessions

  • Lecture 1 - 14.03.2023
  • Lecture 2 - 28.03.2023
  • Mandatory Coaching Session - 21.04.2023 - time slots available from 10:00 - 16:00*
  • Final Project Presentation - 23.05.2023

* please note that you only have to be available for your booked time slot

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 online class flow 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
  • 2 group projects (creation of own survey and final group project)
  • Accessible theoretical literature
  • Coaching session before quizzes and the final project presentation

Assessment

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%

Teamwork:

  • Development of the questionnaire – 10%
  • Final Project – 30%

ATTENTION: Team projects will receive a grade for the team, however an individual grade for team task could vary. Upon completion of the team work, students will be asked to provide peer evaluation. In case multiple group members report over/under-proportionate effort of a group member, this member's grade will be increased/decreased respectively (the degree of increase/decrease will be calculated based on the averaged peer rating).

 

Grades:
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 is necessary for this course: if absent, materials for self-study will be provided at the beginning of the course

Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Availability of lecturer(s)

Individual appointments by e-mail

Last edited: 2022-12-01



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