Introductory description

As individuals and scholars, we are frequently confronted with the claim that A causes B, or the requirement to verify whether a relationship between A and B exists. While anecdotal accounts can help inform our opinion, it is dangerous to rely on one-off observations to verify more general relationships. Equally, even where we believe there may be a relationship, we can be misled by confounding influences which obscure or mislead us.
This is where quantitative approaches can help us untangle the relationships we observe around us, and help us answer question of whether these relationships hold in the wider population. The skills acquired on this module will be invaluable for any student wishing to pursue research which involves large numbers of participants, or which involves the analysis of datasets from official sources.

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.

The following outline represents the core knowledge and competency gain associated with the course activities. In order to facilitate the acquisition of knowledge and competency, the course is delivered via Problem-Based Learning which emphasizes student knowledge acquisition via efforts to understand the parameters of, and solutions to, complex real-world situations. Study will encourage engagement in contemporary challenges across themes such as education, deprivation, social statistics, climate and environment, and health and wellbeing.. The course will be based around four key conceptual problems around the use of quantitative research to elicit relationships:

1. Why employ quantitative approaches to relationships, and why not? Which will allow students to develop knowledge and understanding around:

• Core statistical concepts used to describe relationships
• Differences between quantitative and qualitative approaches and their respective strengths
• Why correlation isn't causality

1. How can we model the strength of a relationship? Which will allow students to develop knowledge and understanding around:

• Core principles of linear regression modelling
• Estimating straight line relationships using ordinary least squared (OLS) approaches
• The required assumptions of ordinary least squared modelling and what happens when we ignore them
• Using regression analysis of data samples to make inferences about the population

1. How can we deal with complex multifaceted relationships? Which will allow students to develop knowledge and understanding around:

• Control variables, confounding influences, and the usefulness (and shortcomings) of multivariate modelling
• Estimating linear relationships using OLS when we have many variables
• The required assumptions of multivariate ordinary least squared modelling and what happens when we ignore them
• Using multivariate regression analysis of data samples to make inferences about the population

1. What happens when we don't have a linear relationship? Which will allow students to develop knowledge and understanding around:

• The role of interactions in data
• Inclusion of data transformations in regression models and what they mean
• Interpreting binary variables in the context of statistical models

Learning outcomes

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

• Demonstrate an understanding of the key concepts in describing relationships in data, including descriptive and inferential statistics.
• Evaluate linear bivariate relationships in real-world data using regression modelling.
• Apply sound statistical techniques using real-world data.
• Understand issues in data collection, sampling, analysis and interpretation.

Research element

This is an optional module on the Liberal Arts course which aims to facilitate the acquisition by students of a range of methods of enquiry from various disciplines and equip them to deploy those skills in research. Research skills are embedded into the teaching strategy of all of the course's modules which, collaboratively, seek to develop and enhance students’ capacity to conduct independently original research into a current problem. Specifically, the approach used in this module serves to align learning activities with the processes of independent research – effectively preparing students for independent project work or modules which encourage individual enquiry.

Interdisciplinary

This is an optional module on the Liberal Arts course which adopts an interdisciplinary approach spanning the arts, humanities, social and natural sciences fields in order to engage with debates on topical, local national and international issues.

International

This is an optional module on the BA in Liberal Arts course which offers a unique transdisciplinary learning experience allowing students to achieve breadth and depth of knowledge.

Subject specific skills

Skills in data analysis using a range of methods

Transferable skills

Skills in using statistical computer software packages to manage data and perform data analysis tasks
Problem solving
Information technology
Numeracy
Oral and written communication
Digital literacy