School of Social & Political Sciences

PGR students passing the "core" courses and two options will be awarded the CSSRM by the Associate Dean for Methods & Skills Prof. Jo Ferrie.

Please contact the Research Training Programme Administrator socpol-pgt-rm-courses@glasgow.ac.uk

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 Training Handbook 2023-24

Research Training Programme slides 2022

CoSS RTP Core Course Summary

Any initial queries regarding the programme can be directed to socpol-pgt-rm-courses@glasgow.ac.uk.

Semester 1

Coordinator(s): Diego Maria Malara DiegoMaria.Malara@glasgow.ac.uk

Duration: 5 weeks - 30 hours: 10 x 2 hour lectures +  5 x 2 hour tutorials

Moodle Link: https://moodle.gla.ac.uk/course/view.php?id=39087

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.

Course catalogue: https://www.gla.ac.uk/coursecatalogue/course/?code=SPS5041

Semester 1

Co-ordinator(s): Miriam Snellgrove Miriam.Snellgrove@glasgow.ac.uk and Phillippa Wiseman Phillippa.Wiseman@glasgow.ac.uk

Duration: 5 weeks - 30 hours (10 x 2 hour lectures, 5 x 2 hour tutorials)

Moodle Link: https://moodle.gla.ac.uk/course/view.php?id=39088

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. 

Course catalogue: link to detailed course description

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

Coordinator(s): Serena Pattaro Serena.Pattaro@glasgow.ac.uk / Colin Mack Colin.Mack@glasgow.ac.uk 

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.

Course catalogue: link to detail course description

Semester 2

Co-ordinator(s): Christopher Bunn Christopher.bunn@glasgow.ac.uk / Ashli Mullen Ashli.Mullen@glasgow.ac.uk 

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.

Course catalogue: link to detail course description

Semester - This course has been withdrawn

Co-ordinator(s): --

Duration: --

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.

Course catalogue: link to detail course description

Semester 2

Coordinator(s) – TB

Semester 2

Coordinator(s) – Michael Heaney Michael.Heaney@glasgow.ac.uk

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

This course examines tools for analysing quantitative data beyond the basic linear model. Students will gain expertise in managing common problems in data analysis, such as violated assumptions and missing data, and complex data formats, such as panel data.  Topics will include binomial, multinomial, proportional, and count data; generalised linear models; fixed and random effects in panel data; and multiple equation models (such as instrumental variables estimators).

Quantitative Data Analysis is a requisite to sit this course.

Course catalogue: link to detailed course description

C

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.