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Course module: 328049-M-6
328049-M-6
Introduction to Research in Marketing
Course info
Course module328049-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Marketing; TiSEM: Marketing;
Is part of
M Marketing Analytics
M Marketing Management
Contact persondr. G. Knox
Lecturer(s)
Coordinator course
dr. G. Knox
Other course modules lecturer
Lecturer
dr. G. Knox
Other course modules lecturer
Lecturer
I.W.A. Weeterings, MSc
Other course modules lecturer
Starting block
BLOK 1
Course mode
Full-time
RemarksThis information is not up to date. Check the Course Catalog 2019 or select the course via “Register”.
Registration openfrom 14/08/2018 09:00 up to and including 31/07/2019
Aims
Objectives
We survey several statistical and marketing research techniques including ANOVA, cluster analysis, conjoint analysis, factor analysis, logistic regression, and multidimensional scaling. After this course you should be able to judiciously use these methods to support strategic and tactical marketing decisions.

The objectives of the course are twofold:
  • Generate Knowledge:
    • Theoretical: students should know the objectives, principles and assumptions of selected multivariate methods
    • Marketing: students should be able to identify proper methods to solve marketing problems on hand
  • Develop Skills: students should be able to apply multivariate methods/solve marketing problems using SPSS
 
Specifics
This course is only available for MSc Marketing Management and MSc Marketing Analytics students
 
This course is offered in block 1 and in block 3. This course prepares students for the more advanced research courses in the MSc Marketing Management and MSc Marketing Analytics program. As a consequence, students entering the MSc program in block 1, should take the block 1 (fall) course. Students entering the MSc program in block 3, should take the block 3 (spring) course. Note that students are only allowed to follow this course 1 time per cohort, with the exam and resit associated with the course from the respective block. Students get 2 exam chances a year (and not 4).
 
Repeater Rules

If you fail the course you must do the whole course again in the same semester you were before. So if you failed FALL, you must do the course again next FALL. If you failed SPRING, you must do the course again next SPRING.
 
 
Content
The course comprises two parts (1) a theoretical part and (2) a practical part.
 
The theoretical part consists of offline mandatory lectures, accompanied by course materials to be studied by the students and deals with a set of multivariate statistical method(s).
 
In the practical part, in depth explanations of each method are followed by a demonstration (including SPSS instructions) on a marketing case.  The practical part consists of video tutorials provided on the Blackboard site, which you can of course watch whenever, wherever.  We have reserved 4 computer lab rooms at the same time each week; this is only for answering questions about the video tutorials or assignments.  No material is presented.  Attendance is optional. If you do not have questions or need help with the assignment, you do not need to go. 

Type of instructions
Lectures + Interactive sessions/Video lectures

Type of exams
Computer Closed Book Exam + Assignments

Compulsory Reading
  1. Sheets and reader.

Recommended Reading
  1. Hair, Black, Babin, Anderson, Tatham, Multivariate Data Analysis: Pearson New International Edition,, Pearson, 2013, ISBN 9781292021904. Or earlier editions are fine also.
Timetable information
328049-M-6|Intro. to Research in Marketing Fall
Written test opportunities
Omschrijving/DescriptionToets/TestBlok/BlockGelegenheid/OpportunityDatum/Date
Written test opportunities (HIST)
Omschrijving/DescriptionToets/TestBlok/BlockGelegenheid/OpportunityDatum/Date
Digital Exam / Digital ExamEXAM_01BLOK 1117-10-2018
Digital Exam / Digital ExamEXAM_01BLOK 1209-01-2019
Required materials
-
Recommended materials
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Tests
Digital Exam

Paper

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