From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.
Semester 1 (Early-Start) - Dual-Delivery
|Fees||Look up fees|
This subject provides an introduction to multivariate data analysis in the behavioural and social sciences, including the nature, rationale and application of a number of widely used multivariate data analysis models. For each model, issues covered include the nature of the model and its assumptions; situations in which the model might be applied; diagnostics for model adequacy; estimation and inference; interpretation; the use of statistical software for model-fitting. Models will be selected from multiple regression; logistic regression; an introduction to path analysis and structural equation modelling; multivariate analysis of variance and discriminant analysis; multilevel models; principal components analysis and factor analysis; models for multivariate categorical data; cluster analysis and multidimensional scaling.
The first two lectures/tutorials of the subject will be taught on one day (six hours) in Orientation Week, thereby allowing students time to work on assessment tasks at the beginning of the semester.
Intended learning outcomes
Upon completion of the subject, students should demonstrate the following competencies in psychology:
Students should demonstrate knowledge of:
- The forms of major multivariate techniques including multivariate analysis of variance and variants, multilevel models, methods for categorical data analysis and structural equation modelling;
- The role of, and methods for, exploratory analysis of multivariate observations such as factor analysis, multidimensional scaling, and clustering;
- The appropriate analysis tool required for a particular dataset.
On completion of the subject students should be able to:
- Execute complex multivariate methods for data analysis;
- Explore and visualize data using clustering and multidimensional scaling;
- Reduce complex data to a set of interpretable factors using principal components analysis and factor analysis.
Application of knowledge and skills
On completion of the subject, students should be able to apply their knowledge and skills to:
- Design research studies requiring complex quantitative observations;
- Apply major multivariate techniques to large datasets with psychological variables
- Critically evaluate and interpret complex quantitative information.
On completion of this subject, students should have a greater ability to:
- present and analyse complex quantitative information;
- synthesize, interpret, and communicate information in ways that others can understand
- think critically about which tools are required for different types of problems
- critically evaluate assumptions, advantages, and limitations of different analytic techniques
Last updated: 29 July 2022