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IP309 Quantitative Research Methods: Understanding Relationships in Data

Dr Lauren Bird



Term 1

10 weeks

Moodle Platform »

Principal Aims

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 model 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.

This module utilises an innovative Problem-Based Learning approach to teaching intermediate quantitative concepts which promotes self-directed and reflective learning. Through tackling multifaceted and complex social issues, students will begin to generate, appreciate and understand broader, underlying, conceptual problems around why quantitative approaches are relevant, and to uncover the appropriate methodologies. In addition to improved learning outcomes, this approach also aligns learning activities with the processes of independent research – effectively preparing students for independent project work or modules which encourage individual enquiry.

Through group discussion and research around the provided cases, students will begin building their knowledge and confidence in plotting and estimating bivariate relationships, uncover the core technical approaches we use for this, and the conditions under which these approaches are appropriate. They will build on existing knowledge of distributions to study the principles of hypothesis testing to understand how we can use results based on a sample to make inferences about the wider population.

As the course develops, problems will move toward the requirement to understand more complex multivariate relationships, the importance of control variables in reducing ‘noise’ in our models, and finally extensions which allow us to use our frameworks to plot non-linear relationships.

The combination of PBL discussion classes and practical workshops will build students’ confidence at using statistical computer packages to put into practice the concepts they uncover through their research, and to take their first steps in statistically modelling the relationship between two or more variables.

This module builds on the introductory understanding students acquired in IP110 Quantitative Methods for Undergraduate Research and serves as a pre-requisite for the specialised techniques studied in IP306. Students will also find the concepts in this module complementary to the approaches taken in IP201 (Sustainability).

Principal Learning Outcomes

On completion of this course, students will 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 bi-variate relationships in real-world data, understand issues in data and its collection which impact on the analysis of relationships, and demonstrate an understanding of the conditions under which using this approach is appropriate and how we verify that these conditions are met.
  • Demonstrate an understanding of how estimated relationships based upon sample data can be used to make inferences about relationships in the wider population, and the associated principles of distributions and hypothesis testing.
  • Employ multivariate regression techniques to investigate real-world data to evaluate linear and non-linear relationships, and demonstrate understanding of the conditions under which such approaches are appropriate.
  • Use statistical computer software packages to manage data and perform data analysis tasks.


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.

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 and critically examine 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:

 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

 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

 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

 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.

Indicative Bibliography

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 and relationships 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 one or more well-chosen technical references in order to assist in the technical side of their learning. Such technical sources might include (or be similar to):

Angrist, J.D, and Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, 2009

Gujarati, D. Econometrics by Example. 2nd Edition. MacMillan Higher Education, 2014

Contextual sources will be confirmed based upon relevant and current topics used as examples, and may include both book chapters and academic journal articles


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

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

1 x Group technical report (15%)

1 x 90-minute computer-based exam (40%) (questions seen 7 days before the test)