Introductory description

This module has two main aims: to provide an introduction to secondary data acquisition, management and analysis in the social sciences; prepare to acquire further statistical training and make use of statistics in future research projects. It consists of nine weeks of teaching. Seminars are designed as discussions on theory and concepts, as well as ‘hands-on’ computer workshops, giving direct experience of exploring and analysing data in the free R environment.

The module introduces to different kinds of data, such as individual surveys, country-level aggregate data, corporate statistics, administrative records, and data collected online. In class, we primarily work with survey data and country-level aggregate data.

This module does not require any prior knowledge of mathematics or statistics. You only need to understand the importance of statistics for empirical social sciences, and show willingness to learn about them. Great importance is placed on understanding how each method works and how we can apply it to real-word research problems in practice. You will be invited to use in this module skills that you have been developing in other domains, that is, logical and causal reasoning, research design, reading and writing. Statistical reasoning and computer languages require a good command of natural language (in this case, English), as in any other module.

If you already have some knowledge or practice of statistics, please be aware that this module is designed for a range of levels, including beginners, as you may skim or skip the parts that you are confident with, and spend more time on more advanced readings and weekly “Going further” exercises. On the contrary, if you are a beginner, you may rather focus on understanding the lecture, reading on the aspect of the module where you lack background (either statistics, coding, or social scientific reasoning), and doing the “Core” exercises.

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. The ABC of quantitative research: design and concepts
2. Understanding single variables
3. Working with variation: probability and sampling
4. From Sample to population: confidence intervals and significance tests
5. Why statistics can mislead: errors and power explained
6. (reading week)
7. Causality in statistical reasoning
8. Beyond patterns with bivariate regression
9. The real world: multiple regression
10. Transformation of variables: unlocking patterns

Learning outcomes

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

• Explain the importance of quantitative methods in social science analysis
• Apply research design principles by clearly conceptualising theoretical propositions, selecting appropriate measures, and justifying methodological choices to produce valid, reliable, and transparent empirical analysis
• Understand the principles and core issue of statistical inference
• Distinguish between correlation and causation
• Apply bivariate and multivariate regression analysis
• Critically engage with quantitative social and political science literature
• Conduct introductory level analysis in the software R

Indicative reading list

Specific reading list for the module

Research element

PO91Q teaches how to learn and practice quantitative research.

Interdisciplinary

PO91Q applies quantitative methods to all social sciences, with some degree of transferability beyond this domain.

International

The worked examples used on PO91Q are drawing on data from around the world.

Subject specific skills

• Quantitative Research Design
• Process of Conceptualisation and Measurement and its relevance in social and political science research
• Calculation of descriptive statistics
• Analysis of bivariate relationships,
• Application of bivariate and multiple regression analysis to empirical problems,
• Conduct quantitative research autonomously

Transferable skills

• Written communication skills
• Oral communication skills
• Problem-solving skills
• Skills in the use of information technology
• Skills of interpretation and the critical analysis of primary and secondary sources
• The ability to digest, retain and apply complex information and ideas
• Ability to conduct research and reference your work appropriately
• Time management skills and the ability to meet deadlines
• The ability to reflect critically on the extent and limitations of how and what you have learned, discovered and understood