IB9MH-15 Quantitative Methods I
Introductory description
This module aims to provide doctoral students with skills and knowledge in quantitative analysis. The module introduces, at pace, core statistical methods alongside hands-on experience coding with business and management data.
Module aims
Understand how data can be used to conduct world leading research in business and management
Develop skills to interpret the results and analyses in world leading research
Develop skills to design and apply appropriate analysis strategies
Develop skills in coding for data analysis
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.
Coding in R
Wrangling data with dplyr
Visualising data with ggplot2
t-tests
Estimation and confidence intervals
ANOVA
Regression and multiple regression
Power
Repeated measures and panel data
Logistic regression and linear probability models
Poisson regression
Pre-registration
Experimental design and Qualtrix
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Learning outcomes
By the end of the module, students should be able to:
- Understand the strengths and limitations of different types of data
- Design appropriate analysis strategies 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 ed). Belmont, CA: Duxbury Press.
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25, 7–29.
Cumming, G. and Calin-Jageman, R. (2016). Introduction to the new statistics: Estimation, open science, and beyond. New York: Routledge.
Spiegelhalter, D. (2019). The art of statistics: Learning from Data. London: Pelican Books.
Subject specific skills
Understand the strengths and limitations of different types of data
Design appropriate analysis strategies robustly to test hypotheses
Implement analyses using appropriate statistical packages and software, and interpret the results
Transferable skills
Problem solving abilities
Communication skills.
Analytical skills.
Confidence as user of statistical software.
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 3 hours (20%) |
| Private study | 48 hours (32%) |
| Assessment | 72 hours (48%) |
| Total | 150 hours |
Private study description
Reflective learning and reading.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Individual assignment | 100% | 72 hours | Yes (extension) |
Reassessment component is the same |
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Feedback on assessment
Module leader feedback.
Post-requisite modules
If you pass this module, you can take:
- IB9MD-15 Introduction to Advanced Quantitative Analysis
Courses
This module is Core optional for:
- Year 1 of TIBS-N1QY Postgraduate Taught Business and Management (Master of Research)