Introductory description

In our capacity as learners, researchers, and members of society, we are continually exposed to statistics and data which, while it can shed light on the nature of the world around us, also has the capacity to confound and mislead. As such, it is vital that we understand both how to interpret the data which surrounds us, and also understand how to use this data to our advantage.

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 section outlines the approach of the module and 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. Such case studies will involve engaging and contemporary challenges around themes such as education, deprivation and social exclusion, social justice, climate and environment, and health and wellbeing. Students will need to regularly engage with data, potentially identifying and learning techniques for statistical analysis which will allow them to understand and address the identified problems. 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 in quantitative research:

1. Why do we use quantitative methods?
   Which will allow students to develop knowledge and understanding around:

• Numbers in society
• Advantages and disadvantages of quantitative approaches
• Key principles of quantitative research

1. How can we convey complex information?
   Which will allow students to develop knowledge and understanding around:

• Key descriptive statistics, their meaning and significance, and how they are calculated
• Different data types, how we present data

1. How certain of our results can we be when we use data?
   Which will allow students to develop knowledge and understanding around:

• Introduction to data distributions, sampling distributions, and their implications for understanding data
• The principle of probability and its calculation
• The relationship between probability and distribution

1. How should data be collected, and what impacts can poor practice have? Which will allow students to develop knowledge and understanding around:

• Data sources, collecting data, challenges, and pitfalls
• The relationship between sample and population
• Using samples to make inferences about the population
• Confidence intervals and hypothesis testing
  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:

• Articulate the usefulness of quantitative analysis as a mode of research within the broader portfolio of research techniques —demonstrating an understanding of its strengths and advantages, but also understanding weaknesses, and where alternative complementary approaches may be more appropriate
• Demonstrate an understanding of key descriptive statistics used to describe a sample, their meaning, and how they are calculated
• Demonstrate an understanding of different data types, sources, methods of collection, and issues to be considered in data collection
• Read and understand basic data descriptions and analysis in published academic literature such as journals or textbooks
• 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
• Demonstrate an awareness of data distributions, the way key variables are distributed, and their implications for the analysis of data
• Demonstrate an understanding and calculation of simple, compound, and conditional probabilities, and understand the relationship between probability and data distribution

Indicative reading list

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
22. Learning outcomes
Successful completion of the module leads to the learning outcomes. The learning outcomes identify the
knowledge, skills and attributes developed by the module.
Learning Outcomes should be presented in the format "By the end of the module students should be able to..." using the table at the end of the module approval form:

Research element

This is a core 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.

Interdisciplinary

This is a core 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.

Subject specific skills

Students will learn to understand different data types, sources, methods of collection, issues to be considered in data collection, and be able to undertake collection and analysis of primary data.
They will acquire an 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.
They will have an awareness of data distributions, the way key variables are distributed, and their implications for the analysis of data.
They will be able to demonstrate an understanding and calculation of simple, compound, and conditional probabilities, and understand the relationship between probability and data distribution
They will learn to read and understand basic data descriptions and analysis in published academic literature such as journals or textbooks

Transferable skills

As a module delivered with a problem-based learning approach, this module will develop enhanced skills of problem identification, articulation and analysis as well as highly developed skills of data analysis. They will be able to articulate the usefulness of quantitative analysis as a mode of research within the broader portfolio of research techniques — demonstrating an understanding of its strengths and advantages, but also understanding weaknesses, and where alternative complementary approaches may be more appropriate
Other skills supported by this module are:
Oral and written communication
Digital literacy
Professional communication
Working with others
Information technology
Numeracy
Research across various disciplines and using a variety of methods