Faculty of Business: Marketing
Credit points: 6 cp

Subject level: Undergraduate

Result Type: Grade and marks

Requisite(s): 24309 Introductory Marketing Research
These requisites may not apply to students in certain courses.
There are also course requisites for this subject. See access conditions.

Handbook description

This subject introduces students to quantitative methods used in marketing, concentrating on the analysis of survey data and use of multivariate statistical techniques. It combines a theoretical but non-mathematical understanding of the statistical techniques with their practical application in a marketing context. A computer statistical package, SPSS, is used to illustrate the lectures and allow students to develop practical data-analysis skills. The subject emphasises the 'when' and 'how' of multivariate analysis and the interpretation and implications of results.

Subject objectives/outcomes

On successful completion of this subject students should be able to:

1. Understand the role that modern computer-based analytical techniques can play in enhancing marketing decision-making
2. Possess new skills in using statistical software tools to solve real world marketing problems
3. Demonstrate an appreciation of a range of multivariate statistical techniques in terms of their purpose, limitations, and practical implications
4. Develop quantitative data analysis skills that are required for the Honours program.

Contribution to graduate profile

This subject is designed to introduce students to a variety of quantitative data analysis techniques used in marketing research. It helps students to develop analytical skills to solve real world marketing problems such as market segmentation, targeting and positioning, new product design and development, and sales forecasting. The subject is recommended for potential Honours students and will be particularly valuable to students planning careers that require strong data analysis skills.

Teaching and learning strategies

The subject will be taught using a combination of lectures and tutorials in a computer lab. Therefore an experiential approach is adopted based on the philosophy of learning by doing. Lectures will normally be of one-hour duration. The structure will vary slightly depending upon the material to be covered. However, they will usually comprise of presentation of theoretical underpinnings of various analysis methods and interpretation of data analysis results. Tutorial sessions will normally consist of individual exercises designed to teach students how computer based statistical packages, such as SPSS for Windows, and spreadsheet software, such as Microsoft Excel, may be used to illustrate the lecture topics and allow students to develop practical data analysis skills to tackle various marketing problems. Relevant data files and other course material are posted on UTS Online website: http://online.uts.edu.au

Content

• Introduction to the subject including its aims, structure, and rationale
• Overview of statistical theories, including univariate, bivariate, and multivariate statistics
• Hypothesis testing procedure and two types of errors
• Introduction to the concepts of causality and experimental designs
• Analysis of variance, covariance, and hierarchical regression models
• Market demand and sales forecasting techniques
• New product design using conjoint analysis and binary choice modelling method
• Market segmentation and targeting using cluster analysis
• Market positioning using perceptual mapping techniques.

Assessment

In-class tests (Individual)	30%
This addresses objectives 1–4.
Project report (Group)	30%
This addresses objectives 2 and 3.
Take-home tests (Individual)	40%
This addresses objectives 1–4.

The lecturer will conduct in-class tests during class time. Students are required to submit their take-home assignments within one week and each student must submit a report that is completely his or her own work. Both the individual take-home test and group report assignments will be secured through a combination of updating of assessment tasks across semesters and/or plagiarism detection software.

Recommended text(s)

Malhotra, Naresh K., John Hall, Mike Shaw, Peter Oppenheim (2002), Marketing Research: An Applied Orientation, 2nd ed., Sydney: Pearson Education Australia Pty Ltd.

Indicative references

Coakes, Sheridan J. and Lyndall G. Steed (2001), SPSS: Analysis without Anguish (Version 10), Brisbane: John Wiley & Sons Australia, Ltd.

Hair, Joseph F. Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black (1998), Multivariate Data Analysis, 5th ed., Upper Saddle River, NJ: Prentice-Hall, Inc.

Lilien, Gary L. and Arvind Rangaswamy (2002), Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, 2nd ed., Sydney: Pearson Education International.