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 web page

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 emphasises student knowledge acquisition via efforts to understand the parameters of, and solutions to, complex real-world situations. Such case studies will involve engaging and contemporary challenges around the identification of patterns and relationships across themes such as education, deprivation and social exclusion, social justice, climate and environment, and health and wellbeing.
In practical accounts of PBL approaches to quantitative methods, complex case studies are presented, accompanied by data, which encourage students to engage with deeper conceptual problems around the use and method of quantitative enquiry when studying relationships. For each study students will need to engage with the data, potentially identifying and learning techniques for statistical or quantitative which will allow them to understand and address the case study and the deeper underlying problem. As such it is customary for students to be expected to consult a well-chosen and accessible text relating to statistical methods as a source for technical background material.
This approach, when applied to the teaching of quantitative research methods is very innovative, though earlier studies (e.g. Karpiak (2011)) suggest that learning outcomes are improved for students who study the subject in this fashion. Moreover, the PBL approach more closely aligns the mode of learning with the research process such that students will first encounter the scenario, generate the problem, before working to acquire the skills and knowledge required to address said problem.
The use of complex and involved case studies allows multiple learning outcomes to be associated with a single problem; as students develop their knowledge they are able to iteratively explore the problem in greater depth over several weeks. The course will be based around case studies which address 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
  The methods of teaching, and the emphasis on student understanding through self-directed inquiry, differentiates this module from other thematically similar modules at this level taught at the university (and more widely).
  References:
  Karpiak, C.P. (2011) Assessment of Problem-Based Learning in the Undergraduate Statistics Course. Teaching of Psychology, 38(4): 251-254

Learning outcomes

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

• Demonstrate an understanding and usefulness of the key concepts in describing relationships in data, the meaning and of descriptive statistics used to describe such relationships, and the generation and application of such statistics using real-world data.
• Use regression analysis to evaluate linear bivariate relationships in real-world data, understand issues in data and its collection

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