Faculty of Business: Marketing
Credit points: 6 cp

Subject level: Postgraduate

Result Type: Grade and marks

Handbook description

This subject introduces students to various advanced research methods in the marketing discipline, with an emphasis on quantitative and multivariate analytical techniques. It adopts an applications-oriented approach to applying advanced statistics and computer software to theory development and theory testing in marketing. It aims to develop advanced skills in conducting sound and rigorous research projects in both theoretical and applied research settings.

Subject objectives/outcomes

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

1. Understand the critical role that modern research design and multivariate analytical techniques can play in advancing knowledge.
2. Achieve fluency in the language of research methods in general and multivariate data analysis in particular.
3. Identify and understand a wide range of pitfalls in research design and data analysis that can pose serious threat to the reliability and validity of research findings.
4. Demonstrate skills in the use of advanced data analysis software packages.

Contribution to graduate profile

This subject introduces students to modern quantitative research design and multivariate data analysis techniques. It helps students to develop advanced data analysis skills that are critical to both knowledge generation in theory and problem solving in practice. The subject will be particularly valuable to research students planning careers that require strong quantitative 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 various statistical software packages, such as SPSS, SAS, and AMOS, may be used to illustrate the lecture topics and allow students to develop advanced data analysis skills to solve various marketing problems. Relevant data files and other course material are posted on UTS Online.

Content

• Review of statistical theories, including univariate, bivariate, and multivariate statistics
• Product positioning using simple correspondence analysis
• Advanced experimental and quasi-experimental designs
• General linear models, including two-way ANOVA, MANOVA, and repeated measures ANOVA
• Principal components analysis and exploratory factor analysis
• Reliability analysis for multi-item scales
• Confirmatory factor analysis, including construct reliability, convergent validity, and discriminant validity
• Confirmatory tetrad analysis for formative measures
• Full-scale structural equation modelling techniques
• Unconditional logit and conditional logit models

Assessment

In-class tests (Individual)	40%
Addresses objectives 1-4.
Project report (Group or Individual)	15%
Addresses objectives 2 and 3.
Take-home tests (Individual)	45%
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 tests and group report assignments will be secured through a combination of updating of assessment tasks across semesters and/or plagiarism detection software.

To pass the subject, students must achieve at least 50% of the final overall grade.

Recommended text(s)

Hair, Joseph F. Jr., William C. Black, Barry J. Babin, Rolph E. Anderson, and Ronald L. Tatham (2006), Multivariate Data Analysis, 6th ed., Upper Saddle River, NJ: Prentice-Hall, Inc. ISBN: 0-13-228139-2

Indicative references

Sharma, Subhash (1996), Applied Multivariate Techniques, New York: John Wiley & Sons, Inc.

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

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