IP110 Quantitative Methods for Undergraduate Research

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

• Understand the strengths of quantitative research methods and their complementarity with other research methods.
• Demonstrate an understanding of key descriptive statistics, data distributions, probabilities, and implications for analysis.
• Demonstrate an understanding of different data types, sources, methods of collection, and issues to be considered in data collection.
• Use computer software to analyse data, produce descriptive statistics, and present data in an appropriate and intuitive way.
• Demonstrate understanding of the relationship between sample and population data, challenges associated with bias and non-representative data, and how we use sample data to make inferences about the entire population.
• Draw on appropriate statistical techniques to support quantitative research and dissemination.

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.

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.

Across the module, we explore quantitative methods through four key themes

• Why we use quantitative methods:
  We examine the philosophical and practical foundations of quantification, including:
  -- The role of numbers in society and decision‑making
  -- The epistemological assumptions behind measurement and generalisation
  -- The strengths and limitations of quantitative approaches
  -- Core principles of quantitative research design
• How complex information can be communicated clearly:
  We focus on how data becomes meaningful through summarisation and representation:
  -- Key descriptive statistics and what they reveal
  -- Types of data and their implications
  -- Effective and ethical data presentation
• How we judge certainty and uncertainty in results:
  We explore the logic of variation, probability, and inference:
  -- Data and sampling distributions
  -- Probability and its calculation
  -- How probability underpins statistical inference
• How data should be collected — and what goes wrong when it isn’t:
  We consider the generation of data and the consequences of poor practice:
  -- Data sources, collection methods, and common pitfalls
  -- The relationship between samples and populations
  -- Using samples to infer population characteristics
  -- Confidence intervals and hypothesis testing

Learning outcomes

The case study structure of the course means that each problem will be associated with in-depth news articles, data, academic journal articles, and other source materials relating to a contemporary issue (possible themes include the study of issues in education, deprivation and social exclusion, social justice, climate and environment, and health and wellbeing).

It is essential to the course that students have access to a well-chosen technical reference in order to assist in the technical side of their learning. Such technical sources might include (or be similar to):

Lomax, R.G., Hans-Vaugn, D.L. (2012) An introduction to statistical concepts (3e). Routledge, New York

Coursework:

• Group Presentation (40%)
• Mini-Quizzes (15%)
• Final test (45%)

Through this module, you will develop different skills that are sought by employers which will support your professional development. We have highlighted this to enable you to identify and reflect on the skills you have acquired and apply them throughout your professional journey including during the recruitment process whether this is in a CV/application form or at an interview.

• Data analysis: Qualitative and quantitative analysis techniques and evaluation methods using tools such as Excel, STATA.
• Teamwork: Collaborating with peers and multiple partners on project briefs involving sharing ideas, knowledge and best practice.
• Time and self-management: Developed through planning and managing weekly tasks, working towards agreed group schedules, as well as on your own initiative without supervision.
• Research: Developed through carrying out research using sample data involving formulating research questions, conducting literature reviews, identifying appropriate measurement variables, and analysing and interpreting results. Additionally, writing and communicating the research in an appropriate manner.