General Information

The RTP provide the joint graduate methods courses across the College of Social Sciences. Below you can find an overview of the courses on offer and the general requirements.

Course title




Research Design



Mark Tranmer

Qualitative Methods



Sonja Marzi

Quantitative Methods



David MacArthur

Applied Qualitative Methods



Kristina Saunder

Introduction to Social Theory for Researchers



Christopher Bunn

Quantitative Methods II




*Requirements by degree type:

MRes Students: 

All core

One optional

MSc Students: 

One core (conditional on programme)

PhD Students: 

All course optional (no auditing).

General enquiries

Please contact the Research Training Programme Administrator

Specific course enquiries

Please contact the respective course convenor - details available via Core Course Summary

Guides & Links

Guide to Self Enrol on MyCampus - PGR students

Researcher Development Programme

Research Design PhD/MRes (SPS5041)

Semester 1

Coordinator(s): Mark Tranmer (Director of Graduate Training)

Duration: 31.5 hours: 9 x 2 hour lectures (live & pre-recorded) +  9 x 1.5 hour tutorials

Moodle Link: 

This course aims to provide students with a broad overview of different research designs in social sciences.  A research design is a blueprint that connects the different stages of the research process in a logical way such that new knowledge can be generated in an unbiased and robust way.  There is a range of different designs, such as longitudinal and cross-sectional, or experimental and observational research designs.  The choice of research design should suit the research question to be answered.  The research design determines which methods can be used to answer the question.  Research design for qualitative and for quantitative research as mixed-methods designs exist.  The course aims to provide an introductory overview across these types of research and expose students to a range of advanced methods that are mostly commonly employed across social sciences.  It improves students’ skills around developing a strong and robust research design and outlines clear guidelines for distinguishing good research from bad research.  In addition to exposure to a variety of designs and corresponding methods as well as the different stages of the research process, students will learn how to combine these different elements in order to increase the quality of their own research.  At the end of the course, students should be able to make an informed decision on how to select a good research question, how to select cases, how to measure and collect data, and what methods to choose for the analysis in their own prospective research.  Rather than selecting methods by personal taste or abilities, students will be enabled to select appropriate methods in an informed way in order to maximise the validity of the findings they generate

Qualitative Methods PhD/Mres (SPS5042)

Semester 1

Co-ordinator(s): Sonja Marzi

Duration: 25.5 hours (10 x 1.5 hour lectures, 9 x 1 hour Dissertation Training Sessions)

Moodle Link:

Qualitative methods are those research techniques concerned broadly with non-mathematical, naturally occurring and non-experimental research practices that look to uncover the meanings and significance of the wide variety of evidence that social researchers collect. Qualitative research includes a broad range of approaches and techniques. The purpose of the course is to introduce students to a number of the most commonly used of these approaches and techniques. These tools include in-depth interviews and focus groups as well as the gathering of data based on observation and textual information. The course aims to develop a practical understanding of the philosophical underpinnings, application and analysis of qualitative methodology for those working in the social sciences. 

Quantitative Data Analysis (URBAN5127/SPS5033)

Semester 1: (URBAN5127) and Semester 2 (SPS5033)

Coordinator(s): David Mcarthur

Duration: 37 hours (11 x 2 hour lectures, 10 x 1.5 hour tutorials)

The course introduces basic statistics and data analysis from univariate summary statistics up to multivariate linear regression. The main aim of the course is to enable students to summarise, analyse, and present data in valid ways and understand the basics of statistical inference and association as required in quantitative social science research. At the end of the course, students should be able to describe, summarise, and visualise data, calculate the association between variables at various scale levels, understand sampling and inference, test hypotheses with given datasets, quantify the uncertainty arising from data, and apply, interpret, and understand the assumptions of, linear regression models.

At all times, special care is taken to ensure that students can associate the statistical techniques with real-world examples from across the social sciences, and especially a themed example chosen from the set of research themes identified by the College of Social Sciences. In addition to basic statistics, students will acquire computational skills that allow them to apply their newly acquired skills using the statistical computing environment R. The overarching aim is to enable students to transfer these skills to new datasets, possibly including their own research topics. Students will learn how to evaluate theories and claims based on data by selecting the appropriate statistical tools and applying them to the data by hand and by using R. In each session of the course, the relevant concepts are taught using words, numbers, equations, examples, and R code.

Introduction to Social Theory for Researchers (SPS5036)

Semester 2

Co-ordinator(s): Christopher Bunn

Duration:  20 hours (10 x 1 hour lectures, 10 x 1 hour tutorials)

The course will begin with a historical scrutiny of the founding figures of social science.  Then, by following the development of distinctive programmes of social research throughout the nineteenth and twentieth centuries, we will explore key theoretical and methodological questions.  The emphasis of the course will be empirical in two senses.  First, there will be a strong stress on the foundational issues underlying practical empirical research in the social sciences.  Second, the teaching of the course will be based firmly upon the close study of original texts.  The course will examine the status of the natural sciences as an exemplar of high-status knowledge in our society.  It will be argued that the scientific method, thus, provides an effective model for social inquiry.  But we will also regard scientific knowledge as itself socially explicable.

Applied Qualitative Methods (SPS5035)

Semester 2

Co-ordinator(s): Kristina Saunders

Duration: 32 hours (11 x 2 hour lectures, 10 x 1 hour tutorials)

This course aims to advance thinking around qualitative methods, and to reflect pragmatically on life in the ‘field’. The course focuses much more on how to do research, exploring the link between an ontological position (particularly a politically informed one) and available epistemologies. The course requires students to focus more strategically on designing research, gathering data and analysing materials. Further students will engage with the socio-political and ethical issues which arise as part of these research processes.

Quantitative Data Analysis II (SPS5032) – currently not active – but planned for 2023

Semester 2

Coordinator(s) – TBC

Duration: 32 hours (11 x 2 hour lectures, 10 x 1 hour tutorials)

This is an advanced course on regression modelling and focuses on the Generalised Linear Model (GLM) and the maximum likelihood principle. These techniques are frequently employed in contemporary quantitative research and can be found in publications across a range of subjects. The course starts where the course “Quantitative Data Analysis” ends. The linear model is re-interpreted as a special case of the generalised linear model, and other outcome distributions of the GLM are introduced, such as models for binary, ordinal, multinomial, count, and event history data. The maximum likelihood principle is discussed as the GLM’s main estimation strategy. Advanced specifications, such as interaction terms, random effects, and robust estimation, are introduced. The main objective of the course is to give students a solid working knowledge of regression modelling for various scenarios that go beyond the standard case of the linear model. Students will learn how to apply and interpret generalised linear models and related techniques and acquire a solid understanding of how to model social phenomena with the tools of statistical inference. The statistical techniques are taught theoretically, through the use of examples, and in the statistical computing environment R.

Quantitative Data Analysis is a requisite to sit this course.


Induction Event

The Postgraduate Research Induction for new Postgraduate Researchers including PhD, LLM and MPhil will take place first week in October 2022 and repeated in January 2023.

Some of the week’s activities will be delivered online and some in person which will include live information sessions, live daily ‘chat cafes’ to enable you to ask questions and interact with the experts who will support your journey, and interactive and engaging online courses.

Agenda and further information will be forthcoming once available.

CoSS RTP Handbook and Course Information

The documents listed below will provide more information on the Research Training Programme (RTP) as well as the courses taught as part of the programme. 

COSS RT Handbook 2022-2023

Research Training Programme slides 2022

CoSS RTP Core Course Summary

Any initial queries regarding the programme can be directed to

Assignment Submission Dates

Assignment submission dates for session 2022-2023 to be confirmed.

Extension applications should be submitted to the Research Training Programme Administrators at as soon as possible in advance of the submission deadline.

Only applications submitted before the assignment deadline will be considered. If you are unable to submit your assignment before the submission date you must complete an Application for Extension to assignment submission deadlines form.