This course in data analysis is designed to provide a relatively complete practical introduction to  data analysis for post-graduate students. Analyses for many different types of data are included (numeric, count, ordered and unordered categorical) using generalized regression techniques such as OLS, logistic, Poisson, proportional-odds and multinomial logit models.

This course deals with the process of defining models, selecting an analytical technique and interpreting the results using graphical and tabular output. Graphical displays are extensively used, making the task of interpreting and communicating results much simpler. Participants will also learn about the use of contrast coding for categorical variables, interpreting and visualising interactions, regression diagnostics and data transformation and issues related to multicollinearity and variable selection.

This course is practically based with participants encouraged to analyse a variety of data and produce their own analyses. The software package R is used in conjunction with the R-commander and the R-studio. These packages provide a simple yet powerful system for data analysis. No previous experience of using R is required for this course, nor is any previous experience of coding or using other statistical packages. Participants are provided with instructions and examples for installing the software onto their own computers before the course starts.

Every scholar has to write, whatever their discipline. Academic writing is always creative: putting words together to make sentences, sentences to make paragraphs, and so on, as nobody has ever done before.

However, in any discipline, some academics write more creatively than others.

This course will raise your awareness of the scope for creativity in academic writing, and provide you with the skills you need to use creative techniques in ways that will make your academic writing more engaging and accessible for your readers.

This course offers a hands-on introduction to creative approaches to doing qualitative research. The course is taught by an interdisciplinary team based in Sociology and Human Geography, all members of the Morgan Centre for Research into Everyday Lives. The various stages of research will be covered, from data collection and analysis through to writing with qualitative data.

We begin by introducing what we mean by doing qualitative research creatively and discussing facet methodology. Course participants will also provide short introductions of their research projects. Participants will be given a practical and hands-on introduction to object interviews, creative ways of researching memory, sketching as a research method and sensory elicitation.

The course will also cover creative ways of analysing qualitative data and practical and intellectual strategies for writing with qualitative data. The course includes workshop exercises involving creating qualitative data and data analysis. Participants will also have the opportunity to discuss methodological issues related to their ongoing research projects.

This course will enable you to understand the nature of digital harms, from online crimes to the malicious manipulation of information, the influence of behaviours and attitudes, and transcending the digital boundaries to effect physical and psychological attacks. We will explore the different ways in which those harms can be reduced or prevented so that you can make well-informed choices about countering security threats in personal, societal and business contexts. Moreover, we will learn about cross-cutting research on digital trust and society and discuss the methodological challenges and opportunities associated with research in this domain.

This course provides a comprehensive introduction to harnessing NVivo to accomplish qualitative and mixed methods analysis. It follows the principles of ensuring analytic strategies drive the use of software tools as described in the CAQDAS pedagogy, the Five-Level QDA method, providing participants with both theoretical understandings of the role of software like NVivo in the analysis process, and practical experience with operating the software to accomplish a variety of analytic tasks.

In this course you will learn both how to clean longitudinal data as well as the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis.

The course is organized as a mixture of lectures and hands on practicals. Throughout the course you will be using real world data to prepare you for your own research and the challenges that appear when doing independent work. The course will also cover how to clean and visualize longitudinal data in an efficient way using R.

The course begins with a general introduction to the distinct goals and perspectives of social network analysis, followed by a practical discussion of network data, covering issues of collection, validity, visualization, and mathematical/computer representation. We then take up the methods of detection and description of structural properties, such as centrality, cohesion, subgroups and positional analysis techniques.

This course seeks to support qualitative researchers in dealing with the key concepts of positionality and reflexivity. During the course attendees will explore the role of the researcher at the insider/outsider divide as well as experience and practise practical strategies and exercises to learn how to do reflexivity. Drawing on embodied and creative techniques we practise conscious noticing, we discuss our being, assumptions and beliefs and how they impact research.

This 1.5-day course provides a relaxed space for researchers to share experiences, questions and concerns they have about their own physical and emotional safety and/or that of those they are supervising, collaborating with, or assisting. It combines an awareness of existing guidelines and protections researchers may use to safeguard themselves, combined with a deeper understanding.