All Social Sciences Research Methods Programme courses

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

• Session 1: Exploratory Factor Analysis Introduction
• Session 2: Factor Analysis Applications
• Session 3: CFA and Path Analysis with STATA
• Session 4: Introduction to SEM and programming

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software Stata.

Topics covered include:

• Interaction effects in regression models: how to estimate these and how to interpret them
• Marginal effects from interacted models
• Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models.

The aim of this course is to introduce students to comparative historical research methods and encourage them to engage with practical exercises, to distinguish between different approaches in comparative historical research methods in social sciences.

Through the reading and seminars students will learn how to distinguish between different texts, theorists and approaches and learn how to apply these approaches to their own research and writing.

Comparative historical sociology studies major social transformations over periods of time and across different states, societies, and regions.

Content analysis has been widely used to study different sources of data, such as interviews, conversations, speeches, and other texts. This module adopts an interactive approach, where students are introduced to the key elements of content analysis, how to conduct content analysis, and a range of examples of the use of content analysis. This module offers two practical workshops, where students have a hands-on opportunity to practice performing content analysis, followed by guided reflection.

This module is for anyone considering studying on an SSRMP module but not sure which one/s to choose. It provides an overview of the research process and issues in research design. Through reflection on a broad overview of empirical research, the module aims to encourage students to consider where they may wish to develop their research skills and knowledge. The module will signpost the different modules, both quantitative and qualitative, offered by SSRMP and encourage students to consider what modules might be appropriate for their research and career development.

Please note: This module has pre-recorded lectures which need to be watched before the live workshop session.

This module introduces focus group research as a qualitative research method. Attention is given to the key elements and methodological consideration of conducting focus group research. It also explores the process of conducting focus group research, where students are given the opportunity to design focus group questions, and to experience the role of researcher in the practical workshops.

This module introduces focus group research as a qualitative research method. Attention is given to the key elements and methodological consideration of conducting focus group research. It also explores the process of conducting focus group research, where students are given the opportunity to design focus group questions, and to experience the role of researcher in the practical workshops.

This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:

• Ways of reading data into Python
• How to manipulate data in major data types
• How to draw basic graphs and figures with Python
• How to summarise data using descriptive statistics
• How to perform basic inferential statistics

This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques.