24911 Multivariate Data Analysis
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Credit points: 6 cp
PostgraduateResult type: Grade and marks
There are course requisites for this subject. See access conditions.
This subject provides students with a systemic and critical understanding of multivariate data analysis and marketing models, with special attention to the underlying theory and assumptions of the models used. Data analysis techniques such as multiple regression models, endogeneity and instrumental variable estimation, multivariate analysis of variance, linear mixed models, factor analysis, structural equation modelling, meta-analysis, multi-level modelling, and choice modelling are discussed. Through hands-on practical sessions, participants develop specialised research skills that enable them to carry out their own academic research in the field of marketing.
Subject learning objectives (SLOs)
|Critically reflect on the applicability of various data analysis techniques and marketing models in specific contexts.
|Evaluate and adapt different statistical techniques to specific applications.
|Develop and implement different research methods using current statistical computational software.
Contribution to the development of graduate attributes
This subject is designed not only to expose students to foundational literature on consumer research but also to support the development of academic research skills. It is based on a hands-on approach whereby students critically analyse existing research, participate in in-class research discussions with instructor and other students, and create research ideas with potential to significantly contribute to the literature. Hence, this subject contributes to the development of the following graduate attributes:
- Business knowledge and concepts
- Critical thinking, creativity and analytical skills
- Communication and interpersonal skills
Teaching and learning strategies
The subject is based on collaborative learning activities that include lectures, in-class discussions, and applied computer exercises. The lectures will explain the different statistical tools, discussing how, when and why they may be used and explore the underlying theories of the statistical tools. Between lectures, students are expected to examine how the various data analysis techniques and marketing models they have learned about are applied and reported in academic articles to understand these requirements for their own research. The computer exercises take place in a workshop environment and allow the students an opportunity to apply the techniques to relevant data sets. This helps to uncover various misunderstandings from the other components and develops the practical skills necessary to undertake analysis in their own research.
This subject is constructed so as to challenge the students and encourage them to develop independent thinking. Students will be given plenty of opportunities to collaborate on various activities during the sessions, particularly when undertaking computer exercises and during in-class discussions. These collaborative learning activities provide students with repeated opportunities for them to practice their data analysis skills to solidify their understanding and boost their confidence to apply these skills to their own research.
Students are expected to prepare for class by reading the assigned literature or by attempting assigned computer exercises prior to class so that the instructor can facilitate in-class discussions and other collaborative learning activities. Formative feedback is given continually both in class and outside class time.
The learning management system will be used to share information (including additional readings and assignments) and encourage interaction between staff and students. Students will also use appropriate computer software to complete assigned tasks.
- Overview of data analysis and marketing models
- Multiple regression models
- Endogeneity and instrumental variable estimation
- Multivariate analysis of variance and tests for mediation / moderation
- Linear mixed models and multi-level modelling
- Factor analysis and structural equation modelling
- Choice modelling
Assessment task 1: Four computer exercises (20% each)
This addresses subject learning objective(s):
1, 2 and 3
Assessment task 2: Journal article critique
The assignment of journal article critique allows students to critically evaluate existing academic literature and identify research gaps that warrant further inquiry.
This addresses subject learning objective(s):
1 and 2
In order to pass the subject students must achieve a pass mark (>=50%) on all components.