This course introduces key methods for analysing datasets with multiple variables per observation. It covers techniques such as factor analysis, multiple regression, multivariate analysis of variance (MANOVA), and cluster analysis. Statistical software is used to implement and interpret these analytical approaches.

5 x 2 hour lectures

5 x 1 hour workshops

None

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1. Written assignment, including essay: Individual; 1700 words; 100%; ILOs 1-3.

Applied Multivariate Analysis introduces key techniques for analysing multivariate data in management contexts. The course develops applied knowledge and research skills across a range of methods, including factor analysis, multiple regression, multivariate analysis of variance (MANOVA), and cluster analysis. It aims to explain these techniques, guide the selection of appropriate methods for specific research problems (including business applications), and examine the conceptual and statistical issues associated with multivariate analysis in management research.

By the end of this course, students will be able to:

1. Critically assess the suitability, assumptions, and limitations of multivariate techniques, including factor analysis, multiple regression, MANOVA, and cluster analysis, in business and management research contexts.

2. Prepare and analyse observational datasets using appropriate multivariate techniques and statistical software to address research questions.

3. Interpret and evaluate multivariate analysis results, including parameter estimates and model quality indicators, to support evidence-based conclusions.