24902 Research Methodology and Data Analysis Techniques
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): (((24108 Marketing Foundations OR 24109 Marketing and Customer Value) AND 24202 Consumer Behaviour AND 24309 Marketing Research) OR 24908 Research Design and Data Collection Techniques )
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject addresses comprehensive and practical considerations of research methodology, data characteristics and processing, multivariate data analysis approaches (statistical considerations and applications), and communication of marketing research results. It helps students develop advanced research skills that are critical to both knowledge generation in marketing theory and problem solving in marketing practice.
Subject learning objectives (SLOs)
| 1. | select and employ suitably relevant research methodologies |
|---|---|
| 2. | employ suitable data analysis approaches |
| 3. | interpret and communicate research findings |
| 4. | present more effectively in an informal and formal manner the findings of the group work components. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following program learning objectives:
- Communicate research and its potential impacts effectively to a range of audiences (2.1)
Contribution to the development of graduate attributes
This subject is designed to introduce students to modern research design and multivariate data analysis techniques. It helps students to develop advanced data analysis skills that are critical to both knowledge generation in marketing theory and problem solving in marketing practice. The subject will be particularly valuable to students planning careers that require strong data analysis skills.
Teaching and learning strategies
The subject is based on dynamic and interactive lecture and workshop sessions. It is taught through a combination of lectures and workshops. The lectures involve critical debate; and the computer-based workshops are built around in-class exercises and presentations. These classes will be supplemented with both printed and electronic learning materials and resources. The UTS Learning Management System will be used to share information and encourage interaction between staff and students. Students will also use appropriate computer software such as spreadsheets and statistical software packages to complete assigned tasks. All students will be provided with the opportunity for initial feedback on their performance in the subject during the first six weeks of the semester, for example, online quizzes, tutorial exercises, draft assignments and other assessment methods. Further feedback will be provided in relation to submitted assessment tasks.
Content (topics)
- Research methods
- Qualitative analysis
- Review of basic statistical theories
- Multivariate data analysis
- Structural equation modeling
- Communication of research results
Assessment
Assessment task 1: In-Class Assessment (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 This addresses program learning objectives(s): 2.1 |
|---|---|
| Weight: | 30% |
| Criteria: | *Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 2: Project report (Group)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3 and 4 This addresses program learning objectives(s): 2.1 |
|---|---|
| Weight: | 30% |
Assessment task 3: Final Exam (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 This addresses program learning objectives(s): 2.1 |
|---|---|
| Weight: | 40% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Hair, Joseph F. Jr, William C. Black, Barry J. Babin, and Rolph E. Anderson (2013), Multivariate Data Analysis: Pearson New International Edition, 7th Edition, Upper Saddle River, NJ: Pearson Education, Inc.
References
Market Research Resources
The following are textbooks on Market Research. These will assist students with the group report. Many of these texts are available second hand and from the library:
D'Alessandro, S., Lowe, B., Winzar, H., Zikmun, W. and Babin, B.J. (2020), Marketing Research: Asia-Pacific 5th Edition, Cengage Learning Australia. (ISBN 9780170369824).
Hair, Joseph F. and Bryan Lukas (2014), Marketing Research (4th Edition), McGraw-Hill, North Ryde, NSW, Australia (ISBN 9781743078761).
Joseph F. Hair, David J. Ortinau, Dana E. Harrison (2020), Essentials of Marketing Research 5th Edition, McGraw-Hill, ISBN 9781260575781
Business Statistics
An introductory business statistics textbook will be useful to gain an understanding of the statistical concepts and methods used. A suggested textbook that is used in the UTS Business School is:
Ken Black, John Asafu-Adjaye, Paul Burke, Nelson Perera, Carl Sherwood, Saleh A. Wasimi (2019), Business Analytics and Statistics (1st Edition), Wiley Publishing (ISBN: 978073035543).
SPSS resources
SPSS is a statistical software package widely used in organisations and by practitioners to analyse data collected. This subject will be using SPSS during class and in all of the assessments. SPSS is available on all UTS computers. SPSS is also available to all students via a virtual lab environment hosted on a Citrix system. Full log-in details and instructions will be provided on UTS Canvas. You will be given complete instructions on how to log in and use the software through information posted on UTS Canvas, in workshops and in lectures.
Allen, Peter, Kellie Bennett, and Brody Heritage (2014), SPSS Statistics Version 22: A Practical Guide, Melbourne: Cengage Learning Australia.
Field, Andy (2009), Discovering Statistics Using SPSS, 3rd Edition, London: Sage Publications.
Other resources
Canvas is a web-based learning management system. In this subject, Canvas is used for (1) keeping up to date via Announcements; (2) accessing learning resources via Modules; (3) asking and answering questions via Discussions; and (4) checking your grades via Marks.