Introductory description

This module aims to provide doctoral students with skills and knowledge in quantitative analysis, particularly as it relates to behavioural data.

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. Data quality and preparation/cleaning of behavioural data
2. Experiment design
3. Estimating and measuring statistical power for behavioural research design
4. Quasi experimental data
5. Generalized Linear Model as applied to behavioural data analysis
6. Logistic regression and choice data
7. Random effects and multi-level models for individual differences
8. Behavioural process data (incl. reaction time analysis)
9. Model comparison/competition and hypothesis testing in behavioural theories
10. Parameter estimation in models of behaviour/decision making

Learning outcomes

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

• Understand the strengths and limitations of different types of behavioural data
• Design appropriate analysis strategies for behavioural data to robustly test hypotheses
• Implement analyses using appropriate statistical packages and software, and interpret the results

Indicative reading list

Howell, D. C. (2017) Fundamental statistics for the behavioral sciences. 9th edn. Belmont, CA: Duxbury Press.
Cumming, G. (2014) ‘The new statistics: Why and how’, Psychological Science, 25, pp. 7–29
Cumming, G. and Calin-Jageman, R. (2016) Introduction to the new statistics: Estimation, open science, and beyond. New York:
Routledge.
Russo, R. (2003) Statistics for the behavioural sciences: an introduction. 1st edn. New York, N.Y. : Psychology Press

Subject specific skills

Understand the strengths and limitations of different types of behavioural data.
Design appropriate analysis strategies for behavioural data to robustly test hypotheses.
Implement analyses using appropriate statistical packages and software, and interpret the results.
An in-depth knowledge of how behavioural data can be used in management research.
An in-depth understanding of assumptions underlying quantitative analyses with behavioural data.
An in-depth knowledge of analytics tools commonly used in modern behavioural data analysis.

Transferable skills

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