Upon completion of this module, students 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

• 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, issues to be considered in data collection, and to undertake collection and analysis of primary data

• 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

• Read and understand basic data descriptions and analysis in published academic literature such as journals or textbooks

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 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 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. 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 in quantitative research:

I. 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

II. 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

III. 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

IV. 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).

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:

• 1 x 1,250-word technical report (30%)
• 1 x Group technical report (15%)

Practical:

• 1 x 15-minute in-class group presentation (15%)

Examination:

• 90-minute computer-based exam (40%)

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.