All Social Sciences Research Methods Programme courses

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging.

This short course introduces Embodied Inquiry as a research method interested in knowledge generated through the body, not just knowledge of the body. Embodied Inquiry has gained traction as a creative research method capable of challenging the mind-body split and exploring the possible role of the body in research, both for the researcher and for participants. The course will provide a broad overview of the theoretical grounding for embodied inquiry, what embodied inquiry can look like within the social sciences as well as the benefits and pitfalls of embodied inquiry as a method. In addition, the course will provide opportunities to consider how embodied inquiry might relate to individual’s research projects and identifying where to find out more about embodied inquiry.

With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research.

NB. This module has video content that needs watching prior to the advertised start date, which can be found on the Moodle page.

This module is designed to help students who will need to use archives in their research, and consists of four sessions. The first session will deal with the large variety of material which can be found in archives, how it is organised, and how to use their various different catalogues and use of finding devices. The second session will look at how to plan an archive visit when it is necessary to consult stored documents. Increasingly more archives are making their material available online, and this session will examine how to find out what is available to view and can be download. The final session on overseas archives is given as part of the History Faculty general training.

This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between mini-lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions, in which you will learn how to analyse qualitative data using the software.

The sessions will introduce participants to the following:

• consideration of the advantages and limitations of using qualitative analysis software
• setting-up a research project in Atlas.ti
• use of Atlas.ti's menus and tool bars
• importing and organising data
• starting data analysis using Atlas.ti’s coding tools
• exploring data using query and visualization tools

Please note: Atlas.ti for Mac will not be covered.

This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data.

Techniques to be covered include:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA
• Ordinary Least Squares (OLS)

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

The module introduces causal inference methods that are commonly used in quantitative research, in particularly social policy evaluations. It covers the contexts and principles as well as applications of several specific methods - instrumental variable approach, regression discontinuity design, and difference-in-differences analysis. Key aspects of the module include investigations of the theoretical basis, statistical process, and illustrative examples drawn from research papers published on leading academic journals. The module incorporates both formal lecturing and lab practice to facilitate understanding and applications of the specific methods covered. The module is suitable for those who are interested in quantitative research and analysis of causality across a range of topics in social sciences.

The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA.