APPLIES TO ACADEMIC YEAR 2015/2016

GRA 6437 Marketing Research and Multivariate Analysis

Responsible for the course
Auke Hunneman

Department
Department of Marketing

Term
According to study plan

ECTS Credits
6

Language of instruction
English

Introduction
In this course you will learn to analyze and interpret multivariate analysis techniques with the purpose to reduce the uncertainty and increase the profitability of marketing decisions. This course builds on knowledge the students are assumed to have acquired in previous marketing research and data analyses courses.

The focus in the course is to teach the students to use data analyses tools in selected cutting-edge multivariate analysis techniques along with the ability to understand where each technique can best be used.
This is not a statistics course - even though all the techniques are based on the theory of statistics, the approach taken here is managerial-based, rather than formula-based. Therefore, you will not be a statistics expert at the end of this course. The course focuses in training students to know and apply the techniques in a practical manner.

Students are expected to have working knowledge of SPSS or an equivalent software (e.g. JMP/R) before the course starts.

Learning outcome
The overriding learning outcome is to be able to see the particular benefits of analytic decision-making in marketing. Closely related to this ability is the ability to discern and choose between various possible analysis techniques as well as the ability to apply the chosen technique appropriately, given the need to solve a particular marketing problem. A relevant marketing problem would typically be the research questions the students address in their master thesis.

To achieve this general learning outcome, the students must be able to the following:

Explain the differences and similarities between key techniques.
Understand each technique in terms of:
- The underlying design providing the data
- Its statistical principles
- Its assumptions
- Its output
Interpret the outputs (results)
Communicate/write up the results

Prerequisites

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Compulsory reading
Books:
Janssens, Wim ... [et al.]. 2008. Marketing research with SPSS. Prentice Hall/Financial Times
Malhotra, Naresh K. 2010. Marketing research : an applied orientation. 6th ed. Pearson

Collection of articles:
A collection of scientific articles

Other:
During the course there may be hand-outs and other material on additional topics relevant for the course and the examination.


Recommended reading

Course outline
- Problem definition, design (experimental and survey-based) and data sources
- Data analysis
- Data interpretation
- Presentation of results

Computer-based tools
JMP
Lisrel
R
SPSS

Learning process and workload
A course of 6 ECTS credits corresponds to a workload of 160-180 hours.

The learning process of this course involves learning the principles underlying various important statistically based techniques in practical marketing research and theoretical research in marketing and hands-on practice with these methods. In particular,

Part 1 - Identify and design the project, prepare data for analysis and consider the quality of the data

Part 2 - Analyse data using appropriate statistical techniques:
1. A refresher on univariate statisitcs
2. ANOVA (Factorial design) for testing experimental data
3. ANOVA with repeated measures for testing multiple responses in an experiment
4. ANCOVA and mediation analysis
5. OLS regression analysis for prediction purposes and hypothesis testing
6. Logistic regression analysis for prediction purposes
7. Factor analysis for customer segmentation
8. Conjoint analysis for designing optimal product and service offerings

This list may be subject to change.
Part 2 will be thought partly in ordinary class sessions and partly in data laboratory classes

Part 3 - Interpret the results

Part 4 - Write-up of results

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on the course homepage/It's learning or text book.


Examination
The course grade will be based on the following activities and weights:
A 3-hour written examination (60%) and two assignments (account for 20% each).

Students work on the assignments individually and hand them in before specified deadlines. Solutions to the assignments are subsequently discussed in class/lab.