Summer School 2022

Our popular programme of research-methods courses will take place online between 20 June and 8 July.

What we offer

The selection includes software training, qualitative and quantitative analysis, data visualisation, research design and much more! The course content is based on approaches from across the various schools within The University of Manchester.

This year's training courses will be delivered online.

Dates

The Summer School will take place over three weeks: 20–24 June, 27 June – 1 July, and 4–8 July. 

Course details

The Summer School 2022 courses have been published and can be consulted below. Each course is to be booked individually, and a limited number of bursaries are available.

For any questions, please get in touch at methods@manchester.ac.uk and a member of our team will respond as soon as possible.

Book your ticket(s) here.

Courses commencing on week 20 - 24 June

Qualitative Research for Quantitative Researchers

20 June – 24 June 2022

Overview

This course will begin by demonstrating that quantitative and qualitative research have much more in common than most people think.

We will then consider markers of quality in qualitative research.

We will review the role of methodologies and theories in qualitative research, then proceed through qualitative context setting, data gathering, data coding, data analysis, reporting, presenting and disseminating qualitative findings.

Research ethics will be attended to throughout.

Course objectives

The objective of this course is to equip quantitative researchers with a good basic understanding of:

  • qualitative research;
  • the role of methodologies and theories;
  • qualitative methods of contextualising research;
  • gathering and analysing data;
  • reporting, presenting, and disseminating findings.

Who is this course for?

This is not a course for novices. Attendees will need a good grounding in research methods – quantitative alone is fine – and an open mind. If you are sure the ‘scientific method’ is the only approach worth using, or that an RCT is the best research method regardless of the research question or context, this is probably not the right course for you.

Course timetable

Monday, 20 June 2022

Afternoon: Introductions; similarities and differences; quality markers

Tuesday, 21 June 2022

Morning: Qualitative methodologies
Afternoon: The role of theory in qualitative research

Wednesday, 22 June 2022

Morning: Qualitative context setting, mainstream data gathering methods
Afternoon: Alternative qualitative data gathering methods

Thursday, 23 June 2022

Morning: Qualitative data coding
Afternoon: Qualitative data analysis

Friday, 24 June 2022

Morning: Qualitative reporting, presenting and disseminating

Course leader

Dr Helen Kara FAcSS has been an independent researcher since 1999 and an independent scholar since 2011. She writes about research methods and research ethics, and teaches doctoral students and staff at higher education institutions worldwide. Her books include Creative Research Methods: A Practical Guide and Research Ethics in the Real World: Euro-Western and Indigenous Perspectives for Policy Press, and she has written and edited several other books for Policy Press, SAGE and Routledge. She is an Honorary Senior Research Fellow at the University of Manchester, and a Fellow of the Academy of Social Sciences.

Course text 

Participants will receive a 30% discount on the print or ebook version of this text:

Cost

£360

A limited number of bursaries covering 100% or 50% of the course fee are available to current PhD students. To apply, please fill in the following bursary application form no later than two weeks before the course start date:

Book a place

Digital Trust and Security in the Post-COVID World: Theory and Research

20 June – 24 June 2022

Overview

Digital systems have transformed  ΜΆ- and continue transforming -  societal daily routines and economic systems, and the security risks we face. The benefits of digital systems depend on public trust in their safety, reliability, fairness, and maintenance of values, which in turn depend on secure hardware and software, fair and effective technological and behavioural security measures, and a keen awareness of the unintended or collateral consequences of digitisation. Researching each of these elements is key for advancing our understanding of trust and security in the digital world – but research on digital trust and security is not free of challenges.

The digital trust and security landscape is broad and includes issues such as:

  • How to protect social and digital systems from cyber-attacks, cyber-breaches and/or sabotage, which threaten citizens, businesses, and institutions. Attacks may be technical (for instance on software, hardware, or networks) and/or social-behavioural (for instance social engineering, malicious insiders).
  • Understanding offender behaviour and supporting law enforcement and security actions in digital spaces to mitigate the impact of cyber-criminal activities.
  • The impact of increased data surveillance, concentration of data and information in powerful organisations, and potential economic or political harms to minority or at-risk communities. And the impact of current and potential political, ethical, and regulatory systems on harm reduction.
  • Understanding what security means across varied geographic and cultural contexts. Internet usage, perceptions of data and privacy, and digitally based communities reveal heterogeneous values on privacy, security, fairness, trust, and autonomy.

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. 

Course objectives

  •  Distinguish the nature and range of cyber threats and ways to counter them;
  •  Identify the skills and knowledge required by professionals working in the industry;
  •  Gain a general understanding of the opportunities and threats associated with the growth in digital technologies;
  •  Gain a general understanding of the opportunities and challenges associated with research on digital trust and security;
  •  Apply this knowledge in your own personal, societal and business contexts;
  •  Put acquired knowledge and skills into practice to write a blog post or research proposal in teams.

Course requirements

No particular requirements.

Who is this course for?

The course is open to MRes and PGR students, researcher or practitioner who wants to learn about the emergence of new digital harms, the influence of behaviours and attitudes on digital trust and cyber security, different ways in which those harms can be reduced or prevented, and methodological approaches to researching digital trust and security.

Course timetable

Monday, 20 June 2022

Afternoon: Introduction to digital trust and security

13:00 – 13:20 Welcome to the Manchester Centre for Digital Trust and Society (Prof Nicholas Lord)
13:20 – 13:50 The digital world (Dr David Buil-Gil)
14:00 – 14:50 Core issues and concepts in digital trust and security (Prof Daniel Dresner)
15:00 – 15:50 Introducing the challenges (Dr David Buil-Gil and Justyna Urbanczyk)

Tuesday, 21 June, 2022

Morning: Digital security and crime: Financial and organised crime

10:00 – 10:40 Financial (cyber) crime (Dr David Buil-Gil)
10:40 – 11:20 Digital currencies and (anti-)money laundering (Dr Katie Benson)
11:20 – 11:50 Organised (cyber) crime (Prof Nicholas Lord)

Afternoon: Digital security and crime: Darknet markets, online sexual exploitation and investigating cybercrime

13:00 – 13:50 Darknet markets (Patrick Shortis)
14:00 – 14:50 The digital component of human trafficking and sexual exploitation (Dr Rose Broad)
15:00 – 15:50 Investigating cybercrime (Prof Daniel Dresner)

Wednesday, 22 June 2022

Morning: Digital harms beyond crime: Data privacy

10:00 – 10:50 Optional sessions

1. How to write a blog post (Dr David Buil-Gil and Justyna Urbanczyk)
2. How to write a research proposal (Dr Chloe Jeffries)

11:00 – 11:50 How do we effectively anonymise data? (Prof Mark Elliot and Dr Claire Little)

Afternoon: Digital harms beyond crime: misinformation, trust in democracy and business

13:00 – 13:50 Reframing Russia for the global mediasphere: From cold war to “information war” (Prof Stephen Hutchings)
14:00 – 14:50 The democratic opportunities and harms of digital technology (Prof Rachel Gibson)
15:00 – 15:50 Impacts of cyberattacks on businesses (Dr Xiuqin Li)

Thursday, 23 June 2022

Morning: Digital systems and cybersecurity: Technology solutions and the workplace

10:00 – 10:40 Cyber security measures for systems (Prof Daniel Dresner)
10:40 – 11:20 Privacy Enhancing Technologies (PETs) to protect user privacy (Dr Mustafa Mustafa)
11:20 – 11:50 Psychological insights to tackle phishing in the workplace (Dr Siddharth Gulati)

Afternoon: Digital systems and cybersecurity: Data analytics

13:00 – 13:50 Data and knowledge-based decision analytics (Prof Yu-Wang Chen and collaborators)
14:00 – 14:50 Geoprivacy and data sharing in research (Dr Eon Kim)
15:00 – 15:50 Challenges

Friday 24 June

Morning: Optional sessions and presentation of challenges

10:00 – 10:50 Optional sessions:

1. Digital tech and crime (Dr David Buil-Gil)
2. Trusted digital systems (Dr Lucas Cordeiro)
3. Workplace and organisational security (Dr Richard Allmendinger)
4. Democracy and trust (Beatriz Buarque)
5. Privacy and trust (Prof Mark Elliot and Dr Claire Little)

11:00 – 11:50 Presentation of challenges

Course leaders

David Buil-Gil is a Lecturer in Quantitative Criminology at the Department of Criminology of the University of Manchester, UK, and Cluster Lead for Digital Technologies and Crime at the Manchester Centre for Digital Trust and Society. His primary research interests are in crime data modelling, victimization surveys, new methods for data collection, and cybercrime. Find out more about his research.

Richard Allmendinger –Business Engagement Lead of Alliance Manchester Business School and Senior Lecturer in Decision Sciences, University of Manchester

Katie Benson – Lecturer in Criminology at the Department of Criminology, University of Manchester

Rose Broad – Senior Lecturer in Criminology at the Department of Criminology, University of Manchester

Beatriz Buarque – PhD Student in Politics at the University of Manchester and Lecturer at King's College London

Yu-Wang Chen – Professor in Decision Sciences and Business Analytics at Alliance Manchester Business School, University of Manchester

Lucas Cordeiro – Reader in ProgAnlys and CyberSec at the Department of Computer Science, University of Manchester

Daniel Dresner – Academic Cyber Security Lead and Professor of Cyber Security at the Department of Computer Science, University of Manchester

Mark Elliot – Professor in Social Statistics at the Department of Social Statistics, University of Manchester

Rachel Gibson – Professor of Political Science in the Department of Politics, University of Manchester

Siddharth Gulati – Research Associate in the Workplace and Organisational Security Cluster of the Manchester Centre for Digital Trust and Society, University of Manchester

Stephen Hutchings – Professor of Russian Studies at the School of Arts, Languages and Cultures, University of Manchester

Chloe Jeffries – Head of Strategic Funding at the Faculty of Humanities, University of Manchester

Eon Kim – Lecturer in Criminology (Digital Tech) at the Department of Criminology, University of Manchester

Xiuqin Li – Research Associate at the Manchester Institute of Innovation Research, University of Manchester

Claire Little – Research Associate at the Privacy and Trust Cluster of the Manchester Centre for Digital Trust and Society, University of Manchester

Nicholas Lord – Professor of Criminology at the Department of Criminology and Director of the Manchester Centre for Digital Trust and Society, University of Manchester

Mustafa Mustafa – Dame Kathleen Ollerenshaw Research Fellow at the Department Computer Science, University of Manchester

Patrick Shortis – PhD Candidate at the Department of Criminology, University of Manchester

Justyna Urbanczyk – Project Administrator at the Centre for Digital Trust and Society, University of Manchester

Recommended reading

  • Aldridge, J., & Askew, R. (2017). Delivery dilemmas: How drug cryptomarket users identify and seek to reduce their risk of detection by law enforcement. International Journal of Drug Policy, 41, 101-109.
  • Baines, V. (2021). Rhetoric of InSecurity: The language of danger, fear and safety in national and international contexts. Routledge.
  • Barratt, M., & Aldridge, J. (2016). Everything you always wanted to know about drug cryptomarkets* (*but were afraid to ask). International Journal of Drug Policy, 35, 1–6.
  • Brotherton, R. French, C.C., & Pickering, A.D. (2013). Measuring belief in conspiracy theories: the generic conspiracist beliefs scale. Frontiers in Psychology.
  • Elliot, M., Mackey, E., & O'Hara, K. (2020). The anonymisation decision-making framework. Second Edition. Manchester: UKAN.
  • Goldsmith, A., & Wall, D. S. (2022). The seductions of cybercrime: Adolescence and the thrills of digital transgression. European Journal of Criminology, 19(1), 98-117.
  • Holt, T. J., & Bossler, A M. (eds.). 2020. The Palgrave handbook of international cybercrime and cyberdeviance. Palgrave Macmillan.
  • Leukfeldt, R, & Holt, T. J. (eds.). 2019. The human factor of cybercrime. Abingdon: Routledge.
  • Leukfeldt, E.R., Lavorgna, A. & Kleemans, E.R. (2017). Organised cybercrime or cybercrime that is organised? An assessment of the conceptualisation of financial cybercrime as organised crime. European Journal on Criminal Policy and Research, 23(3), 287-300.
  • Miró Llinares, F., & Johnson, S. D. (2017). Cybercrime and place: Applying environmental criminology to crimes in cyberspace. In G. J. N. Bruinsma & S. D. Johnson (Eds.), The Oxford handbook of environmental criminology (883-906). New York: Oxford University Press.
  • Yar, M., & Steinmetz, K. F. (2019). Cybercrime and society. SAGE

Cost

£360

A limited number of bursaries covering 100% or 50% of the course fee are available to current PhD students. To apply, please fill in the following bursary application form no later than two weeks before the course start date: 

Book your place

Courses commencing on week 27 June - 1 July

Creative Approaches to Qualitative Research

27 June – 29 June 2022

Overview

This intermediate level course offers a hands-on introduction to creative approaches to doing qualitative research. The course is taught by an team based in Sociology, 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, mobile methods 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 be taught via a combination of pre-recorded lecture content, ‘live’ online Zoom sessions and ‘methods surgery’ drop-in sessions. Participants are also encouraged to do some reading ahead of the course and to complete short tasks ahead of some of the session – details will be provided to registered participants via the course Blackboard platform.

Course objectives

At the core of the week’s course is helping to develop an inventive orientation that puts the researcher’s creativity and imagination at the heart of methodological practice. This will include:

  • Introducing participants to creative methods both as an approach, and as a means of generating social science research data;
  • Providing an introduction to and some practical experience in the use of a range of creative methods of data collection;
  • Introducing participants to creative analytical strategies;
  • Offering participants opportunities to think about how they could use creative approaches in their own research;
  • Introducing participants to strategies for writing with qualitative data.

Course requirements

  • This is an intermediate course for participants with reasonable to good knowledge of the principles of qualitative research methods and have some experience of qualitative data collection and analysis;
  • No prior knowledge of creative qualitative methods is expected;
  • Participants will be asked to discuss their research projects and to bring their own data for analysis– therefore, in order to take part in the course it is essential that participants have data they can bring to the course;
  • The number of places on this course is limited, so please do fill in the application form by 12pm noon on 6 June 2022;
  • Form: registration survey

Who is this course for?

The course is aimed at PhD students (Year 2 and above i.e. students who have collected their data) and postdoctoral researchers who are interested in extending their knowledge of qualitative methods.

Course timetable

Monday, 27 June 2022

Morning: Introduction
Afternoon: Facet methodology; Methods surgery

Tuesday, 28 June 2022

Morning: Object and sensory elicitation
Afternoon: Mobile methods and walking interviews; Methods surgery

Wednesday, 29 June 2022

Morning: Mapping methods: researching memory
Afternoon: Analysis and writing

Course leaders

Laura Fenton

James Fletcher

Jess Mancuso

Vanessa May

Petra Nordqvist

Paul Simpson

Sophie Woodward

Recommended reading

  • Holmes, Helen and Hall, Sarah Marie (eds.) (2020) Mundane Methods: Innovative Ways to Research the Everyday, Manchester: Manchester University Press
  • Mason, J. (2018) Qualitative Researching (3rd edn), Sage: London.
  • Mason, J. (2011) ‘Facet methodology: The case for an inventive research orientation’, Methodological Innovations Online 6(3): 75-92.
  • Pink, S., 2015. Doing Sensory Ethnography (2nd edn), Sage, London.
  • Woodward, Sophie. 2020. Material Methods: Researching and Thinking with Things. London: Sage.

Cost

£270

A limited number of bursaries covering 100% or 50% of the course fee are available to current PhD students. To apply, please fill in the following bursary application form no later than two weeks before the course start date:

Book your place

  • In order to book a place for this course, please fill in the registration survey and you will be contacted by the course leader in due time.

Introduction to Social Network Analysis

27 June – 1 July 2022

Overview

This is an introductory course, covering the concepts, methods and data analysis techniques of social network analysis.

The course is based on the book Analyzing Social Networks by Borgatti et al. (2018) and all participants are advised to obtain a copy. The cost has been deducted from the normal course fee.

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 is a hands-on course largely based around the use of UCINET software, and will give participants experience of analyzing real social network data using the techniques covered in the workshop.

Course objectives

The course will:

  • introduce the idea of Social Network Analysis;
  • explain how to describe and visualise networks using specialist software (UCINET);
  • explain key concepts of Social Network Analysis (e.g. Cohesion, Brokerage);
  • provide hands-on training to use software to investigate social network structure.

Course requirements

No prior knowledge of social network analysis is assumed for this course. However, it would be useful to read Scott's (2000) Social Network Analysis: A Handbook. 

Participants are required to:

Who is this course for?

Postgraduate research students and academics wishing to use SNA in their research.

Course timetable

Subject to change

Monday, 27 June 2022

Afternoon: Introduction to social network analysis, terminology and the software UCINET/Netdraw (Borgatti et al. (2018), Chapters 1 and 2)

Tuesday, 28 June 2022

Morning: Collecting social network data and research design (Chapters 3 and 4)
Afternoon: Data management and visualisation (Chapters 5 and 7)

Wednesday, 29 June 2022

Morning: Multivariate techniques and whole networks (Chapters 6 and 9)
Afternoon: Centrality and ego networks (Chapters 10 and 15)

Thursday, 30 June 2022

Morning: Equivalence and core-periphery (Chapter 12)
Afternoon: Subgroups and two-mode networks (Chapters 11 and 13)

Friday, 1 July 2022

Morning: Testing hypothesis and large networks (Chapters 8 and 14)

Course leaders

Elisa Bellotti is a Senior Lecturer at The University of Manchester. Along with being part of the Sociology department, she is a member of the Mitchell Centre for Social Network Analysis, where she organises the weekly seminar series. She teaches introductory and advanced workshops in social network analysis and egonetworks, and in mixed methods in SNA. Before arriving in Manchester in 2008, she worked as a research fellow at the University of Turin and the University of Bozen, Italy. She completed her PhD in Sociology and Methodology of Social Research in 2006 at Catholic University of Milan. Her research interests mainly focus on relational sociology and its link with other mainstream sociological theories; and on social network analysis and mixed methods. She has taken this approach in several sociological substantive areas, such as the study of intimacy and personal relationships, sociology of science, criminal networks, inter and intra organisational ties, and sociology of consumption.

Nick Crossley is Professor of Sociology at The University of Manchester. His main work using social network analysis has focused upon music worlds, social movements and covert networks. He has also written extensively about 'relational sociology', a theoretical position that advocates a focus upon networks in sociology. His most recent book is Networks of Sound, Style and Subversion: the Punk and Post-Punk Worlds of Manchester, London, Liverpool and Sheffield, 1975-1976 (Manchester University Press).

Martin Everett is a Professor in Social Network Analysis at the University of Manchester and currently co-directs the Mitchell Centre for Social Network Analysis (with Nick Crossley). He has been a past president of the International Network for Social Network Analysis (INSNA) and is co-author of the software package UCINET and the Sage book Analyzing social networks. Martin regularly gives invited talks at major conferences and is co-editor of the journal Social Networks. He is a fellow of the Academy of Social Sciences and a Simmel award holder, the highest honour given by INSNA.

Course text

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks (2nd ed). Sage.

Recommended reading

  • Scott, J (2000) Social Network Analysis: A Handbook. Sage.

Cost

£360

Book your place

Positionality and Reflexivity in Practice

27 June – 1 July 2022

Overview

This course seeks to support 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.

We will further examine the use of a research journal in relation to doing reflexivity and exploring one's positionality. We will experiment with form and formats of recording journal entries, consider how to make choices of what to record when and trial strategies for recording information we may have missed.

Finally, we will deal with the practicalities of moving from the personal, private practice of doing reflexivity to the formal, public statement of positionality that is often asked for in theses and publications.

In line with the pedagogical principles of social constructivism the course is delivered as a mixture of interactive group tasks, discussions and lectures to enable active and experiential learning.

Course objectives

The aims of this interactive course are to provide insight into theoretical foundations and practical strategies to deal with reflexivity and positionality within research.

By the end of the course delegates will:

  • have critical awareness of what reflexivity in research is and means;
  • have practical knowledge and personal experience of practical methods and strategies to acknowledge and capture emotions and experiences;
  • be equipped to capture thoughts and views and be mindful of their impact on research;
  • have practical knowledge and personal experience of effective methods for research journaling;
  • understand the aims and purposes of research journaling;
  • have a critical awareness of the relationship between the what, why and how of journaling and sharing the entries;
  • feel permitted to 'experiment' and 'try' in research.

Course requirements

Attendees need to bring their personal research journal to the sessions. Attendees will not be asked to share those, but will need them to work through some tasks.
Attendees are also strongly encouraged to have a specific print or digital workbook, exercise book, notebook or journal to use throughout the course.

Who is this course for?

The course is most suitable for PGR students, early-career researchers and academics undertaking research or supporting others undertaking research.

Course timetable

Monday, 27 June 2022

Afternoon: What is reflexivity? | The role of reflexivity in research.

Tuesday, 28 June 2022

Morning: What do I write about? | Foundations of research journaling to explore the self.

Afternoon: Who am I as a researcher? | Exploration of the self and the relationship between (auto)ethnography and reflexivity.

Wednesday, 29 June 2022

Morning: Who am I as an embodied being? | Embodiment, the role of emotions and noticing exercises.

Afternoon: What is happening here? | Experimenting with levels of interpretation from observation to analysis. 

Thursday, 30 June 2022

Morning: How do I get from here to there? | Making the leap from personal research entries to public personality statements.

Afternoon: Is that what I want? | The role of reflexivity and personality statements in research, publications and career. 

Friday, 1 July 2022

Morning: Where am I going next? | Organising and planning for reflexivity and journaling for research and career development.

Course leaders

Dr Nicole Brown is Director of Social Research & Practice and Education Ltd and Associate Professor at UCL Institute of Education. Nicole’s research interests relate to physical and material representations of experiences, the generation of knowledge and use of metaphors to express what is difficult to express, and more generally, research methods and approaches to explore identity and body work.

Her books include Lived Experiences of Ableism in Academia: Strategies for Inclusion in Higher Education, Ableism in Academia: Theorising Experiences of Disabilities and Chronic Illnesses in Higher EducationEmbodied Inquiry: Research Methods, and Making the Most of Your Research Journal.

Nicole's most recent creative nonfiction has been published in the Journal of Participatory Research MethodsSo Fi Zine and The AutoEthnographer.
She tweets as @ncjbrown and @AbleismAcademia.

Cost

£360

Book your place

Creative Academic Writing

27 June – 1 July 2022

Overview

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.

Course objectives

Our objectives on this course will include understanding the role of the following in academic writing and academic work: 

  • fiction and fiction writing techniques;
  • poetry and poetic techniques;
  • play and screenplay writing.

Other aims include: 

  • understanding the blurred boundary between writing and drawing;
  • understanding writing as a resource as well as a task to complete;
  • knowing how to find time to write creatively, and how to use that time well;
  • drafting a piece of creative academic writing: blog post, journal article, thesis chapter or section.

Course requirements

Ability to read and write.

Participants may write on an electronic device or on paper as they prefer, though for one exercise they will need either access to a printer or to write on paper. They will also need a pair of scissors.

Who is this course for?

Postgraduate researchers and early-career researchers across all disciplines. The course may also be of interest to senior scholars, practice-based researchers, independent researchers and scholars. 

Course timetable

Monday, 27 June 2022

Afternoon: Introductions; sensory language

Tuesday, 28 June 2022

Morning: Storytelling and planning
Afternoon: Fiction writing techniques

Wednesday, 29 June 2022

Morning: Autoethnography
Afternoon: Poetry and poetic techniques

Thursday, 30 June 2022

Morning: Writing and drawing
Afternoon: Play and screenplay techniques

Friday, 1 July 2022

Morning: Writing as a resource; next steps

Course leaders

Dr Helen Kara has been an independent researcher since 1999 and also teaches research methods and ethics. She is not, and never has been, an academic, though she has learned to speak the language. In 2015 Helen was the first fully independent researcher to be conferred as a Fellow of the Academy of Social Sciences. She is also an Honorary Senior Research Fellow at the Cathie Marsh Institute for Social Research, University of Manchester. She has written several books and journal articles on research methods and ethics, including Creative Research Methods: A Practical Guide (2020 (2nd edn), Policy Press) and, with Richard Phillips, Creative Writing for Social Research (2021, Policy Press).

Course text

Participants will receive a 35% discount on the print or ebook version of this text:

  • Phillips, R. and Kara, H. (2021) Creative Writing for Social Research: A Practical Guide. Bristol: Policy Press.

Recommended reading

  • Harper, G. (2019) Critical Approaches to Creative Writing. Abingdon: Routledge.
  • Pelias, R. (2019) The Creative Qualitative Researcher: Writing That Makes Readers Want to Read. Abingdon: Routledge. (Also relevant for multi-modal and quantitative researchers.) 

Cost

£360

Book your place

Introduction to Longitudinal Data Analysis

27 June – 1 July 2022

Overview

Longitudinal data is essential in a number of research fields as it enables analysts to understand individual level change in time, the occurrence of events and helps improve our causal understanding of the world.

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 organised 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.

Course objectives

  • To gain competence in the concepts, designs and terms of longitudinal research;
  • To be able to apply a range of different methods for longitudinal data analysis;
  • To have a general understanding of how each method represents different kinds of longitudinal processes
  • To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.

Course requirements

  • Good knowledge of regression modelling;
  • Basic knowledge of R or good programming experience with a different statistical software.

Who is this course for?

The course is open to any researcher who wants to learn how to analyse longitudinal data. We will be using statistical models from a number of different fields and will apply it using the popular open source software R.

Course timetable

Monday, 27 June 2022

Morning: Data Cleaning with R
Afternoon: Visualization using R

Tuesday, 28 June 2022

Morning: Intro to SEM and auto-regressive
Afternoon: Cross-lagged models

Wednesday, 29 June 2022

Morning: Intro to multilevel model for change
Afternoon: Advanced multilevel model for change

Thursday, 30 June 2022

Morning: Intro to latent growth models
Afternoon: Advanced latent growth model

Friday, 1 July 2022

Morning: Survival analysis
Afternoon: Cox regression

Course leaders

Alexandru Cernat is an associate professor in the social statistics department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute.

His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling, new forms of data and missing data. He is also editor of the Measurement Error in Longitudinal Data book. Find out more about his research

Recommended reading

  • Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press. (available online);
  • Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc;
  • Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. Routledge.

Cost

£450

A limited number of bursaries covering 100% or 50% of the course fee are available to current PhD students. To apply, please fill in the following bursary application form no later than two weeks before the course start date:

Book your place

Courses commencing on week 4 - 8 July

A Comprehensive System of Data Analysis Using R and the Rcommander

4 July – 8 July 2021

Overview

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.  

The 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.

Course objectives

The main objective of this course is to provide a general method for modelling a wide range of data using regression-based techniques. Participant will be able to select, run and interpret models for continuous, ordered and unordered data using modern graphical techniques. 

Course requirements

There are no pre-requisites for this course as instruction is provided for all techniques. 
However, it will be of most use to those who are interested in modelling social science datasets (survey and quasi-experimental) and applying graphics to interpret these.

Who is this course for?

Postgraduate research students, academics and researchers in all social science fields.

Course timetable

Monday, 4 July 2022 

09.00 – 10.30 Introduction to the course and the software
10.30 – 11.00 morning break
11.00 – 12.30 Coding scales and data management
12.30 – 13.30 Lunch break
13.30 – 15.00 Defining research questions (additive models, interactions and experimental design)
15.00 – 15.30 afternoon break
15.30 - Discussion session: an opportunity to ask questions and discuss your own data and research.

Tuesday, 5 July 2022

09.00 – 10.30   Describing relationships (Effect displays: illustrating relationships for numeric and categorical variables).
10.30 – 11.00  morning break
11.00 – 12.30  Quantifying significance (p-values, Type II and III ANOVA tests, Analysis of Deviance).
12.30 – 13.30  Lunch break
13.30 – 15.00  Modelling continuous variables (OLS regression, t-tests, ANOVA, ANCOVA, Pearson's correlation).
15.00 – 15.30  afternoon break
15.30 - Discussion session: an opportunity to ask questions and discuss your own data and research.

Wednesday, 6 July 2022

DAY OFF

Thursday, 7 July 2022

09.00 – 10.30  Modelling count data (Poisson regression, multi-dimensional chi-square, log-linear)
10.30 – 11.00  morning break
11.00 – 12.30  Regression diagnostics and data transformation (model assumptions and dealing with transformations)
12.30 – 13.30  Lunch break
13.30 – 15.00  Modelling categorical data (logistic and multinomial logit models for unordered categories)
15.00 – 15.30  afternoon break
15.30 - Discussion session: an opportunity to ask questions and discuss your own data and research.

Friday, 8 July 2022

09.00 – 10.30  Modelling ordered categorical data (proportional odds models; non-parametric tests)
10.30 – 11.00  morning break
11.00 – 12.30  Variable selection and multi-model inference
12.30 – 13.30  Lunch break
13.30 – 15.00  Examples of analysis (how to apply these models to a wide range of data and research design).
15.00 – 15.30  afternoon break
15.30 - Discussion session: an opportunity to ask questions and discuss your own data and research.

Course leader

Graeme Hutcheson is a lecturer in the Manchester Institute of Education and has published extensively in the field of regression models and the analysis of social science data.

Recommended reading

  • Agresti, A. (1996). An Introduction to Categorical Data Analysis. Wiley.
  • Fox, J. and Weisberg, S. (2011). An R companion to Applied Regression (second edition). SagePublications.
  • Harrell, F. E. (2001). Regression modelling strategies. Springer.
  • Hutcheson, G. & Sofroniou, N. (1999). The multivariate social scientist. Sage Publications.
  • Hutcheson, G. & Moutinho, L. (2008). Statistical modelling for management. Sage Publications.

Cost

£360

A limited number of bursaries covering 100% or 50% of the course fee are available to current PhD students. To apply, please fill in the following bursary application form no later than two weeks before the course start date: 

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