School of Social & Political Sciences

Semester 2

Co-ordinator(s): 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 2

Co-ordinator(s): Rebecca Tapscott Rebecca.Tapscott@glasgow.ac.uk

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

This course helps students develop skills in conducting fieldwork in the social sciences, with a focus on working internationally and in conflict-affected or non-western country contexts. The course will provide students with theoretical and conceptual tools to understand fieldwork and its unique contribution to knowledge production, as well as practical tools to prepare them for the often challenging and unpredictable nature of fieldwork. Students will also have the opportunity to develop skills related to the practice of academic research, including topics such as developing equitable partnerships, conducting ethically-sound research, pursuing impact, and contributing to an inclusive research culture.

Course catalogue: link to detail course description

Semester 2

Co-ordinator(s): Thees Spreckelsen Thees.Spreckelsen@glasgow.ac.uk

Duration:  30 hours (10 x 2 hour seminar, 10 x 1 hour computer labs)

The course offers students a presentation of advanced quantitative methods to aid in examining the world around them and to serve as a stepping stone for future quantitative training. Through the course students will develop a critical view of data and statistics that can be applied to published information they are exposed to in their day-to-day lives and academia. Finally, the course especially its labs will equip students with quantitative and analytical skills to evaluate the data that they are exposed to, and to critically reflect on the impact of this data on social issues and policy formation.

This course is ideal for students having done some introductory methods.

Course catalogue: link to detail course description

Semester 2 - 

Co-ordinator(s): Christopher Claassen  Christopher.Claassen@glasgow.ac.uk

Duration: 20hrs (10x1h lecture, 5x1h seminar, 5x1h computer lab)

This course offers a hands-on introduction to designing public opinion surveys and analysing secondary survey data. Students will learn to design effective surveys, analyse survey data, and interpret results - valuable skills for dissertation research and a wide range of careers.

Course catalogue: link to detail course description

Semester 2

Co-ordinator(s): Michael Heaney Michael.Heaney@glasgow.ac.uk

Duration: 20hrs (10x1h lecture, 10x1h seminar)

Social populations are connected through people living in the same area, working or studying in the same organisation, or being in the same network. Substantive questions around these phenomena relate to area effects, organisational variations, and peer effects on individual outcomes. Here, multilevel modelling can often be used as a quantitative method. Students will build on the techniques presented in introductory quantitative methods or the Semester 1 courses of the MSc Social Science Research Methods. The necessary theory will be illustrated with real life examples, including key concepts in social network analysis. This course also has a practical element; students will use "R" software to carry out multilevel and network analyses.

Course catalogue: link to detail course description

Semester 2

Co-ordinator(s): Alasdair Stewart Alasdair.Stewart@glasgow.ac.uk

Duration: 30hrs (10x1h lecture, 10x2h seminar)

In this practice-led advanced qualitative analysis course, students will complete a qualitative analysis project from start to finish. Using a large archive of text data and working with qualitative data analysis software and open source tools, students will learn how to prepare and manage qualitative data, apply different qualitative analysis approaches, and move from coding and analysis to writing up.

Course catalogue: link to detail course description

Semester 2 (SPS5033)

Coordinator(s): 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