Introductory description

This module aims to provide doctoral students with a grounding in the set of advanced quantitative analysis methods that are increasingly necessary for conducting and publishing world-leading business and management science.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Quantitative Analysis, Data, and Reality
2. Hypothesizing about The World: Causation, Mediation and Moderation
3. Measure Validity, Factor models
4. Introduction to Multiple Regression and its Extensions: Dummy, Logistic, Interactions
5. Structural Equation Models
6. Panel Data, and Fixed Effects Models
7. Introducing Random Effects and Multi-level models
8. The Principles of Experimental Research in Business and Management: The GLM and Extensions (ANOVA, Factorial, ANCOVA, MANOVA etc)
9. Introduction to Bayesian Thinking
10. Algorithms, Analytics, and Prediction
11. Quantitative Computing
12. Introduction to Numerical Methods, Optimization and Other Models
13. Statistical Inference, Power, and Effect Sizes: Good Scientific Practice and Open Science

Learning outcomes

By the end of the module, students should be able to:

• Understand the link between research questions, scientific hypotheses, and data analysis.
• Design an analytic strategy to robustly test their hypotheses.
• Implement their analysis using appropriate software and evaluate the results.

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Understand the link between research questions, scientific hypotheses, and data analysis.
Design an analytic strategy to robustly test their hypotheses.
Implement their analysis using appropriate software and evaluate the results.

Transferable skills

Problem solving abilities.
Communication skills.
Analytical skills.
Confidence as user of statistical software.