National Centre for Research Methods

Critical Praxeological Analysis (CPA) is a new approach which provides a way of conducting critical qualitative research. 

Critical Praxeological Analysis (CPA) synthesises ideas from three key areas: Wittgensteinian philosophy, particularly the method of grammatical investigation; ethnomethodology and conversation analysis, with a focus on praxeological gestalts; and critical research, especially critical phenomenology. This synthesis provides a robust method for critical qualitative research. 

In this two day online advanced course, the authors of the approach, Khadijah Diskin and Phil Hutchinson, will help participants identify and plan a project of study, handle data, and make a start on developing a research article. 

This course will assume an understanding of the basics of CPA and spend minimal time on a brief recap of these (see NCRM's online course Introduction to CPA - 10-11 September 2025  if you feel you need an introductory course first). 

The idea is for participants to make significant progress on work towards a CPA article for publication, either individually or working with other participants on the course as co-authors. The convenors will provide advice on every stage of the process and offer follow-up 1:1 sessions to participants.

The course (22 October) will focus on supporting participants in designing and carrying out a project and writing a paper. Two weeks after the course (29 October) there will be a data session, where those who attended the course can bring their data for the group to analyse. 

By completion of the course:

• Participants will have made a start on a CPA project and in some cases a paper for publication.
• Participants will learn and understand the stages of CPA project design.
• Participants will have hands-on experience of handling and analysing data as a CPA researcher and participating in a data session.
• Participants will have tips on overcoming the hurdles which are commonly faced in a CPA project.
• Participants will have the opportunity to make use of subsequent support sessions, which will support them to the completion and submission of their paper over 8 months following the course.

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In this online course, run over two mornings, we will show you how to prepare and conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques. We will also cover how to use GeoDa to perform exploratory spatial data analysis, including making use of linked displays and measures of spatial autocorrelation and clustering.

The course covers: 

• Understanding and being able to interpret Spatial Autocorrelation measure Moran's I
• Understanding Local Indicators of Spatial Association statistic
• Perform Spatial Decision Making in R
• Perform Point in Polygon analysis using different approaches
• Be aware of the advantages and disadvantages of using point based or polygon based data
• Using buffers as a part of spatial decision making

By the end of the course participants will:

• Be aware of some spatial statistics concepts and be able to apply them to their own data using GeoDa
• Be able to perform spatial decision making 
• Understand the limitations and benefits of working with data in this way

This course is aimed as PhD students, post-docs and lecturers who have some existing knowledge of using R as a GIS and want to develop their knowledge of spatial stats and spatial decision making in R. Some prior knowledge of both R and GIS is required. It is also appropriate for those in public sector and industry who wish to gain similar skills. 

Students will be using R, RStudio and GeoDa. 

Students need to have completed my Introduction to Spatial Data and Using R as a GIS (https://www.ncrm.ac.uk/training/show.php?article=13142) course, or have equivalent experience.

This includes:

• Using R to import, manage and process spatial data
• Design and creation of choropleth maps
• Use of scripts in R
• Working with loops in R to create multiple maps

For more information, please look at the link.

Students will need R (v > 4.0), and the sf, tmap, dplyr libraries. They will also need RStudio (v > 2023.01 or greater)

No prior knowledge of GeoDa is needed. It can be downloaded following the instructions at https://nickbearman.github.io/installing-software/geoda. Version 1.20 or greater is required. 

THIS COURSE WILL RUN OVER TWO MORNINGS (10AM TO 1PM) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

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This exciting interactive workshop will develop your knowledge and skills in using creative and participatory research methods. Creative and participatory methods are increasingly being utilised by social researchers to tackle complex research questions, enhance participant inclusivity and to generate wide ranging research impact for a broad range of stakeholders. 

This session begins with an overview of developments in creative and participatory research, highlighting the opportunities and challenges in the context of social policy, research impact and advancing academic knowledge. Across the two days, the course covers how and why we use a variety of creative and participatory methods and how to bring them together in analysis, forming a constellation. The workshop will address ethics, opportunities, benefits and challenges during the research process and how to generate multi-level impact from grassroots to social policy. Participants will be given the opportunity to explore how to incorporate creative and participatory approaches (such as zines and photovoice) in their own research, and how to analyse and disseminate effectively.

Over the course you will:

• Be introduced to key debates in creative and participatory research
• Understand the potential for, and the challenges of, using creative and participatory research methods
• Explore how to ethically engage in creative and participatory research
• Learn from active peer-researchers involved in co-creating research

By the end of the course participants will:

• Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)
• Develop skills in designing, conducting, analysing and disseminating creative and participatory research
• Learn how such methods can be incorporated into the generation of meaningful research impact

Indicative Schedule:

The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes.

Day 1

• What do we mean by creative and/or participatory methods?
• The value of creative/participatory research methods
• Planning and setting up creative/participatory research tools.
• FOCUS ON (1): zines as creative/participatory methods
• Ethical considerations specific to creative/participatory research (part 1)

Day 2

• Ethical considerations specific to creative/participatory research (part 2)
• Creative/participatory research with children and young people
• Creative/participatory research with marginalised communities    
• FOCUS ON (2): co-creation – creative and participatory research in action*
• Doing co-analysis and co-dissemination
• Creative/participatory methods for generating meaningful research impact
• Wrapping up the workshop/advice clinic

*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects.

Presenters:

This course will be delivered by Dr Linzi Ladlow, Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They have hosted a BSA funded symposium and are editing a forthcoming collection on creative and participatory methods.

Target audience:

This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed.

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This five-day workshop introduces participants to the theory and practice of experimental methods in the Social Sciences. It provides an overview of prevalent approaches—specifically lab, field, and survey experiments—offering a solid introduction to experimental methodology and the practical skills needed to design, implement, analyse, and present experiments.

The course is designed for researchers, PhD students, professionals, and members of public institutions, particularly those new to experimental methods or those with experience in one method (survey, field, or lab) who wish to deepen their knowledge of the others. No prior knowledge of experimental design or statistics is required. The course is also suitable for those looking to commission an experiment through a survey company or other service provider.

The workshop aims to equip participants with the skills to design, implement, analyse, and report experiments, as well as to critically evaluate experimental literature.

Additionally, the workshop will provide a brief overview of service providers for marketing experiments (Facebook, Google), access panels and online marketplaces (Lucid, MTurk, Prolific), and survey providers that support experiments (YouGov, Ipsos). Participants will also be introduced to Qualtrics, Stata, R, and Excel as tools for experimental research with practical exercises.

An interdisciplinary team of faculty members from the Centre for Behavioural, Experimental, and Action Research (C-BEAR) will lead the workshop, drawing on examples from Politics, Economics, Business, and Psychology. Each day, two faculty members from different disciplines will co-teach, fostering a dynamic and dialogic learning environment. The workshop will be interactive and hands-on, incorporating group work and practical exercises.

The workshop will also cover responsible research conduct in experimental studies, including research ethics, pre-registration, and debriefing practices for deceptive research designs.

Course Content

Days 1 and 2  will provide the basic knowledge to design, analyse and present experiments such as randomised controlled trials (RCTs), while Days 3, 4, and 5 will focus on survey, field and laboratory experiments.

Participants need to bring their own device that can run basic office suites, and free versions of R and Stata. A tablet with a keyboard might also work.

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The aim of this two-day online training course is to introduce participants to the practice and ethics of ethnographic research.

Through a mix of plenary sessions, group and independent work, participants will learn the basic principles of participant observation and research design, as well as the foundations of ethical ethnographic research.

The course will also examine the ways in which other qualitative and creative methods of data collection may be productively integrated in ethnographic research.

The course covers:

• Research design
• Qualitative methods in ethnographic research
• Access and power
• Research ethics in participant observation

By the end of the course participants will:

• Understand the epistemological foundations of ethnographic research
• Have a solid understanding of ethnographic research in action
• Be able to design and conduct research integrating qualitative and ethnographic research methods
• Be able to conduct ethical ethnographic research

The course is suitable for any professional researchers interested in learning more about using ethnographic methods – whether within or outside academia (private sector, government researchers, etc.).

The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.) with prior knowledge of any qualitative research methods, but not necessarily of ethnography.

Some prior training in qualitative research methods, broadly defined – regardless of whether that includes ethnographic methods specifically.

Programme

Day 1

Morning session:

• 09:30-09:45  Introduction to the course
• 09:45-10:45  Plenary – The Practice of Ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-13:45  Plenary - Qualitative methods in ethnographic practice
• 13:45-14:00  Break
• 14:00-15:15  Practical exercise followed by class discussion

Day 2

Morning session:

• 09:30-10:45  Plenary - Research ethics in ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-1:345  Plenary – Writing ethnography
• 13:45-14:00  Break
• 14:00-15:00  Practical exercise, followed by class discussion         
• 15:00-15:15  Conclusions and Evaluations

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This in-person three day course will provide participants with an understanding of the complex intervention research process. Presentations and activities will relate to the main concepts of developing/identifying and evaluating complex interventions and support participants to apply the principles to their own research. It will focus on the overarching considerations required to develop complex intervention research projects, rather than the details of study design, and enable researchers to develop and conduct research that will provide the most useful evidence for decision making. The course will be structured around the MRC/NIHR Framework for Developing and Evaluating Complex Interventions. There will be a mix of lectures and small group activities to put learning into practice. 

It is for anyone interested in developing, evaluating and implementing interventions with the intention of positive health and/or social change. This could be academic or other researchers, practitioners, or others interested in implementing the best process for their intervention development or evaluation. Participants should have some familiarity with the framework for developing and evaluating interventions, and some experience of working with complex interventions. 

The course covers: 

• Introduction to using the Framework
• Developing and identifying interventions
• Feasibility research
• Evaluation research
• Implementation & round up

Course timetable:

The course runs across three days on 6th, 7th & 8th May 2025 between 10am - 4pm each day.

By the end of day 1 participants will:

• Have an overview of the journey through complex intervention research.
• Have an awareness of the challenges and limitations of complex intervention research. 
• Understand how to approach complex intervention research & the key overarching considerations.
• Begin to see how you can apply the framework to your own complex intervention research. 
• Understand what resources/steps to use in developing a complex intervention
• Know how to take forward an ‘identified’ intervention
• Know how to develop a programme theory and make use of it throughout the research process
• Be aware of key issues to consider for the implementation of complex interventions

By the end of day 2, participants will: 

• Understand what feasibility studies are and why they are important ​
• Have learned how to approach designing a feasibility study​
• Have learned how the core elements can be applied at this stage ​
• Understand how feasibility study findings can inform decision about next steps​
• Be able to consider what complexity means for the evaluation of interventions
• Understand that a focus on usefulness of evidence requires methodological pluralism
• Have some understanding of how to choose between evaluation approaches
• Have an understanding of the six key considerations (core elements of the framework) at the evaluation stage
• Have an understanding of what economic evaluation is and why we do it
• Have an understanding of key methods/different types of economic evaluation

By the end of day 3, participants will: 

• Be aware of framework checklist and how it can be applied
• Understand the importance of considering Implementation throughout the phases of the framework and specifically at the Implementation Phase 
• Review and reinforce the key motivations and innovations of the new framework
• Be aware of research questions and methods particularly relevant to a systems perspective
• Have the opportunity to raise questions to reinforce learning and fill gaps.

Pre-requisites:

Participants should be familiar with the process of intervention development and evaluation, with some experience in intervention research.

No software or sign up required.

Essential reading: 

Skivington, Matthews, Simpson et al (2021). A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance BMJ 2021; 374 doi: https://doi.org/10.1136/bmj.n2061 (Published 30 September 2021).

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This short online course provides an introduction to the Education Outcomes Linkage (EOL) 2018/19-2021/22, which is a longitudinal relational database comprised of a range of tables containing post primary schools’ data for pupils aged 14 and over in Northern Ireland. The course will include a mixture of lectures and interactive discussions with experts.

The course will be delivered in three interactive sessions, with breakout rooms in each session, as well as opportunities to engage with conference facilitators, expert users and data owners.

Session 1:

• Introduction/housekeeping 

• The demography and education system in NI

• The datasets: 

• the datasets included

• how they are linked 

• coverage and variables in the EOL 

• inclusion and exclusion criteria

Session 2: 

• Case studies using the linked dataset

• Accessing the data – Application

• An overview of the EOL user guide and metadata

• Summary of morning sessions and Q & A

Session 3:

• Output Clearance – statistical disclosure controls

• The future of EOL

• Q & A with expert users and data owners

• Summary of the course

By the end of the course participants will:

• Have an awareness of the demography and education system in Northern Ireland
• Be familiar with the content of the data including some strengths and limitations
• Understand the application process to access the data share
• Know how to successfully create outputs for clearance
• Have knowledge of previous and current research using the EOL dataset, and future plans for the EOL and the LEO NI

This course will suit anyone interested in conducting quantitative data analyses using Northern Ireland post primary school data for pupils aged 14 years and over. This may include, but is not limited to, quantitative researchers in academic, government, or third sector settings. People at any stage in their research career would be welcome, but the course most likely be of interest to master’s and PhD students, early career researchers, and mid-career researchers.

No specialist prior knowledge of the EOL, or statistical software is needed to attend, but a basic knowledge of research design and quantitative data analysis would be beneficial.

Background reading on the EOL is recommended:

Education Outcomes Linkage (EOL) 2018/19 - 2021/2022 | Northern Ireland Statistics and Research Agency

THIS COURSE EQUATES TO HALF A DAYS TRAINING FOR PAYMENT PURPOSES.

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How does ideological positioning, such as identifying as right-wing in the UK, compare across countries like Germany or Sweden? How can a single question in a cross-national survey account for contextual differences (where they exist)? Who should we ask to ensure nationally representative results, and how should we analyse and interpret the data across different countries?

This two-day online workshop equips participants with the tools to design, analyse, and interpret cross-national surveys, tackling key challenges such as measurement equivalence, sampling techniques, data harmonization, and weighting adjustments. Participants will explore how the Total Survey Error (TSE) Framework can help identify and address measurement error, nonresponse error, and sampling error—key factors that influence data quality and comparability.

The course will focus on practical issues like the impact of question wording on survey results, the influence of social desirability bias across cultures, and the role of cultural and demographic variability in shaping responses. Participants will be introduced to Multi-group Confirmatory Factor Analysis (MG-CFA) as a method for assessing measurement equivalence in diverse contexts. We will also cover essential sampling methods in cross-cultural survey research, as well as weighting techniques designed to make survey results nationally representative, while addressing the challenges of weighting in cross-national surveys.

The course covers: 

• Introduction to Measurement in Comparative Surveys

• Testing for Equivalence in Comparative Data

• Data harmonisation in Comparative Surveys

• Comparative Sampling Fundamentals

• Weighting Adjustments in Comparative Surveys

• Advanced sampling techniques and Ethical Considerations

By the end of the course participants will:

• Learn how to identify and apply the right strategies to ensure survey data comparability.
• Have an introductory understanding of key statistical tools such as Multi Group Confirmatory Factor Analysis to test for measurement equivalence in cross-national survey data.
• Understand the principles and implications of using different sampling methods.
• Learn about types of weights and the importance of weighting in comparative research.
• Learn to autonomously address common data quality issues (eg. nonresponse bias, social desirability bias).
• Enhance the ability to analyse and interpret cross-national survey data. 

This course is aimed at all researchers interested in using cross-national survey data, mainly from social sciences and related disciplines would benefit from the course. 

Participants should have a basic understanding of linear regression analysis.

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In this one-day online training workshop you will be introduced to four qualitative research methods to better understand diverse lives - Photo Go-Alongs, Collage, Life History Interviews and Participant Packs.

When researching social groups, researchers may focus on categories such as age, gender, sexuality and so on. These categories can turn catch-all terms into catch-all agendas. Treating groups of people with one shared characteristic as homogenous risks a cookie-cutter approach which overlooks diverse lives and needs. Given the complexity of what it means to be a person, a one-size fits all approach to engagement cannot suffice.

The methods introduced in this training workshop are beneficial in exploring diverse lives and can be used when researching with any group. 

The session is aimed at PhD students and academics of all career stages across the UK who want to better understand: 

• The specific place-based needs of people 
• The everyday practices of people
• The world from participants’ perspectives
• How to work with people in an inclusive and accessible way

This online training workshop will be structured as follows:  

• Introductions
• Origins and Approach 
• Methods deep dive: 
• Photo Go-Alongs
• Participant packs
• Collage 
• Life Histories 
• Workshops 
• Learnings and close 

By the end of the course participants will:

• Be able to think critically about how creative, participatory methods might be incorporated into their research and/ or teaching. 
• Have broadened their understanding of research methods from tools of data collection to techniques for capacity building.
• Have workshopped four qualitative methods for creatively engaging with people (Photo Go-Alongs, Collage, Life Histories and Participant packs).

This online training workshop will take place over the course of one day between 10:00 and 16:00, with 1 hour for lunch between 12:30 and 13:30.

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This workshop is run as a collaboration between the National Centre for Research Methods and Administrative Data Research UK and is part of a series on short courses on administrative data.

The aim of the workshop is to provide an introduction to the Growing Up in England (GUiE) dataset. GUiE is a flagship ADR dataset and provides a link between 2011 Census data and longitudinal administrative data from the education and children’s social care systems.

The workshop provides an introduction and overview of GUiE including:

• The different component datasets that are brought together in GUiE
• Waves and linkage methods
• Coverage, years and gaps
• Strengths and limitations
• Exemplar analysis
• The application process, documentation and support

By the end of the workshop participants will:

• Be familiar with the structure of GUiE
• Understand the content, coverage and years of the GUiE Waves 1-3 data
• Be aware of the strengths and limitations of the data available
• Be aware of existing studies and analysis using GUiE
• Have knowledge of GUiE documentation 
• Understand the application process, conditions of use and where to go for further information and support

Knowledge of administrative data research datasets and SDC processes would be helpful but are not essential. This is an introductory course and prior knowledge of or experience using GUiE is not required. 

PLEASE NOTE THIS WORKSHOP IS TAKING PLACE OVER TWO MORNINGS (10:00-13:00) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

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This online workshop aims to give participants a range of practical approaches they can adopt when writing about methodology in the social sciences.

Using a range of exercises throughout, the course focuses on 20 or so writing strategies and thought experiments designed to provide more clarity and power to the often-difficult challenge of writing about methods.

The course also looks at common mistakes and how to avoid them when writing about methods. The focus throughout is on building confidence and increasing our repertoire of writing strategies and skills.

The course covers:

• A range of practical writing strategies for handling methodology
• The challenges of writing a PhD methodology chapter or a methods section in a research paper
• Writing for qualitative and quantitative research approaches
• Understanding different audiences and the needs of different academic markets

By the end of the course participants will:

• Better understand who and what ‘methodology writing’ is for
• Know the differences and similarities between PhD methods chapters, research paper methods sections and methods books
• Understand and reflect on 21 principles (or starting points) of best practice in methodology writing
• Focus writing on audience needs and expectations
• Be aware of common mistakes and misunderstandings and so avoid them
• Reflect on the relationship between methodology writing and other parts of your manuscript
• To develop learning and best practice through exercises and examples

Target Audience:

PhD students, post-docs and junior researchers in the social sciences working on their doctoral theses or supervising doctoral students.

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This online workshop will introduce Institutional Ethnography (IE), an interdisciplinary feminist approach to social research that focuses on how texts and language organise our everyday lives. IE is not just a methodology, but an entire approach to research with a specific ontology of how the social world works and the organising role of texts and language.

In IE, the researcher ‘takes sides’ using a specific version of standpoint to explore how institutions work in practice rooted in peoples’ experiences. This often involves researching as, with, or alongside marginalised groups and making visible how institutions exclude or make invisible certain groups of people and experiences. 

The overall aim of the workshop is to provide attendees with a comprehensive overview of institutional ethnography as an approach and the opportunity to translate their own research ideas and projects into an IE research proposal and do a small piece of text-focused analysis. This hands-on workshop is suitable for students, academics, and anyone else interested in feminist methodologies, text and discourse analysis, and institutional or organisational ethnographies. No prior training in, or knowledge of, IE is required. 

The course covers: 

• An overview of Institutional Ethnography and the work of feminist sociologist, Dorothy Smith, who developed Institutional Ethnography 
• Case studies of Institutional Ethnography research projects to show how it works in practice in different disciplines
• How to translate your research into an Institutional Ethnography project using a research proposal framework
• Practical explanation of how to do text and discourse analysis within Institutional Ethnography through a short text analysis activity 

By the end of the course participants will:

• understand of the origin and development of Institutional Ethnography
• know how to use Institutional Ethnography to analyse texts, processes, and discourses
• have an outline of how their research ideas could become an Institutional Ethnography project 

This course is aimed at academics, students, any other qualitative researchers, including policymakers, organisers, and activists interested in analysing organisational processes.  Participants must have at least some experience in qualitative research methods, but no experience of Institutional Ethnography is required.

Preparatory Reading

Required:

• 1 hour lecture by Dorothy Smith summarising Institutional Ethnography - https://www.youtube.com/watch?v=1RI2KEy9NDw
• Murray, Ó.M., 2020. Text, Process, Discourse: Doing feminist text analysis in institutional ethnography, Available at: https://doi.org/10.1080/13645579.2020.1839162

Desirable:

• Earles, J., & Crawley, S. L. 2020. Institutional ethnography. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), Foundation: SAGE research methods. Retrieved July 17, 2020, from: http://dx.doi.org/10.4135/9781526421036759274
• Smith, D.E. & Griffith, A.I., 2022. Simply Institutional Ethnography: Creating a Sociology for People. Toronto: University of Toronto Press.

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This one-day online, interactive course will provide a practical introduction to qualitative longitudinal enquiry. The morning session will explore key design features of this methodology, including how to build time into a study, how to sample through time, how to generate temporal data, the ethics of longitudinal enquiry, and the potential to create real-time impact in policy processes. The afternoon session will focus on the intricate nature of QL analysis. The course will comprise two lectures and two interactive workshops (see below and attached programme for further details). The course will be delivered by Anna Tarrant and Kahryn Hughes, specialists in QL research and authors of multiple books and papers on this methodology.

The course will run from 10am to 5pm and covers the following: 

• In the first presentation of the day, delegates will be introduced to the principles and design features of qualitative longitudinal (QL) research. The unique power of this methodology – the combination of its real-time operation and its interactive, real-world engagement - will be explored, and its application in policy/practice settings and processes will be introduced.   

• In the morning workshop, delegates will work individually and in pairs to generate temporal data. They will be introduced to life mapping techniques and will have the opportunity to construct a life map and use this as the basis for life journey interviewing. 

• In the second presentation of the day, delegates will be introduced to the principles and practice of QL analysis (QLA): including the tripartite logic of working across cases, themes and processes, and the abductive reasoning that underpins this strategy.

• In the afternoon workshop, delegates will brainstorm the analysis of data from the Following Young Fathers Study. They will have an opportunity to build a longitudinal case history for a young father, drawn from several waves of interviews; and to engage in cross-case comparative analysis of contrasting cases. 

 By the end of the course participants will:

• Have an appreciation of the key design features of Qualitative Longitudinal Research,
• Have gained insights into working with and through time to generate temporal data about unfolding lives,
• Have a greater understanding of the principles of QL analysis, and some experience in working analytically with data and collectively brainstorming new insights. 

The course is suitable for doctoral and established researchers who are either new to this methodology or wish to refresh or enhance their research practice. Delegates will receive a course pack, comprising powerpoint slides and data files from the Following Young Fathers Study. 

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ArcGIS Online (AGOL) is Esri’s cloud-based GIS platform that allows users to create maps and other web applications for data sharing, collaboration and analysis.

This practical, one-day hands-on course provides a guided introduction to AGOL. You will learn how to publish existing vector data to AGOL, set sharing and group privileges and create a web map that drives field-based data collection. You will use Experience Builder to create a web application that is used for sharing and visualising spatial data. The course comprises hands-on exercises each introduced with a short presentation and a live demonstration.

The course covers: 

• An introduction to ArcGIS Online (AGOL).
• Sharing your vector data from ArcGIS Pro to AGOL.
• Controlling access (settings, groups and views).
• Create a web map from your shared data.
• Build a web map application in Experience Builder.
• Configure layers for data capture.
• Accessing data in AGOL directly within ArcGIS Pro.

By the end of the course participants will be able to:

• Prepare and upload vector data from desktop ArcGIS Pro to ArcGIS Online.
• Create views, groups and set access privileges for hosted feature layers.
• Create a web map, set layer symbology and configure pop-ups.
• Configure data for data capture.
• Create an Experience Builder application from the web map to share and visualise data.
• Access layers in AGOL directly within ArcGIS Pro for data analysis.

This course is intended for users of ESRI’s ArcGIS Pro software who wish to improve their technical knowledge and understanding in ArcGIS Online (AGOL), the cloud-based counterpart, to traditional desktop GIS. Delegates should be familiar with the basics of using ArcGIS Pro and spatial data (e.g. using spatial layers, working with an attribute table, setting symbology, vector data).

Delegates should be familiar with the basics of using ArcGIS Pro and spatial data (e.g. using spatial layers, working with an attribute table, setting symbology, vector data).

This is a 1-day online course starting from 9:30 and completing at 17:00, with a mid-morning break (10 mins), a 30 minute lunch and an optional mid-afternoon break (10 mins).

Delegates are provided with the data used to take part in the course and electronic copies of the course materials (presentations and exercises). The course comprises of 7 modules. Each module has a short introduction presentation, followed by a live demonstration to reinforce what was discussed in the presentation.

Delegates will then work through an exercise on that topic for around 15 to 45 minutes. The trainer will be present to answer any queries. The course is delivered online using Zoom desktop. The trainer will be present throughout the course and additional trainers will support if required.

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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 course covers: 

• The Basics of Probability
• Bayes’ Theorem and Bayesian inference
• Probability Functions
• Bayesian Conjugates
• Markov Chain Monte Carlo
• Applications

By the end of the course participants will:

• Be able to understand the basics of Bayesian analysis
• Perform simple Bayesian analyses
• Apply simple models to their own work

The course will be delivered over two days, with a two-hour live session on each day  (26/03/2025 and 02/04/2025 at 2pm).  Participants will be given access to a two-hour pre-recorded lecture one week prior to each live session, which they are expected to work through in preparation.  The live sessions will provide an opportunity to apply concepts from the lectures, and participants are encouraged to come with questions.

This course will be taught using STATA - Some basic knowledge of STATA will be required. 

The course leader will prepare the basics of probability refresher which can be used as part of the preparatory work for this course. It will be available 2 weeks before the course start date.  Students who are not as confident with using probability or haven’t worked with it recently are encouraged to complete this. 

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Critical Praxeological Analysis (CPA) is a new approach which provides a way of conducting critical qualitative research.

Critical Praxeological Analysis (CPA) synthesises ideas from three key areas: Wittgensteinian philosophy, particularly the method of grammatical investigation; ethnomethodology and conversation analysis, with a focus on praxeological gestalts; and critical research, especially critical phenomenology. This synthesis provides a robust method for critical qualitative research. 

In this online introductory course, the authors of the approach, Khadijah Diskin and Phil Hutchinson, will provide newcomers with a foundation in CPA, by laying out the philosophical background, outlining the stages of project development and analysis, and then ending the day by facilitating a CPA data session on some recent data that might serve as a topic for CPA studies.

This course will assume no prior knowledge of either CPA, Critical Phenomenology or Ethnomethodology and Conversation Analysis and so works as perfect introduction for those who are looking for a method of critical qualitative research.

The course will cover the following:

CPA – Respecifying Critical Research. 

We explain how the purpose of CPA is to provide a procedure for conducting critical qualitative research. Where much critical research is based in theoretical or formal analyses, CPA provides a process for conducting qualitative praxeological analyses which are designed to recover the experiences of members of society.

CPA – The philosophical resources of CPA. 

We introduce the resources that feed into CPA. These are the grammatical investigations of Wittgensteinian philosophy, the praxeological Gestalts of ethnomethodology and CPA’s concept of discordant Gestalts and the extending of the unique adequacy requirement. Each of these will be explained in easy-to-grasp ways with the use of examples.

CPA – Selecting a topic and designing a project. 

We take you through a step-by-step process of selecting a topic, designing, and executing a CPA project.

CPA – Data Session

We end the course by conducting a group data session on some recently acquired data. This will help participants become familiar with how a CPA researcher handles data and what it is to be attuned to aspects of the material as a CPA researcher. The data will be audio or video data gathered in the ‘wild’ and will be from recent events (i.e. 2024).

Learning Outcomes:

• Participants will have a good understanding of the purpose of conducting a CPA, understand the philosophical building blocks of CPA, and have a working understanding of what it is to be a CPA researcher.
• Participants will learn and understand the stages of CPA project design.
• Participants will have hands-on experience of handling and analysing data as a CPA researcher.

Schedule:

10th September: 9am - 4:30pm

11th September: 9am - 2pm

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Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences. 

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 using real-world data. During the course, there will also be opportunities to discuss also how to apply these models in your own research.

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.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

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Technological advancements have not only driven the digitisation of society and the emergence of novel socio-political issues, but have also resulted in significant developments in algorithms, computational power, and increasingly large datasets. 

This practical-based face to face session will be delivered over two days and will provide you with both the technical programming skills and understanding of data science techniques that you will need to research pre-existing and novel social-political and economic issues and the kind of transferable skills that are currently in demand in the job market. Specifically, it will introduce you to the Python programming language, assuming zero prior-experience, and give you the skills necessary to use it for data analysis.

This training can be standalone or taken in conjunction with Web-scraping with Python and Introduction to text data with Python on 24th and 24th April 2025 

This course covers:

• Introduction to coding principles and architecture

• Introduction to Python syntax

• Important Python modules for data analysis

• Introduction to data analysis

• Using Python for basic data analysis

By the end of this course participants will:

• Understand the basic principles of computer coding

• Use Python for a range of different tasks

• Understand how to use Python specifically for basic data analysis

Computer workshops:

All sessions requiring the use of a computer will take place in PC suites where you will have access to University of Exeter PCs. However, if you wish to bring your own laptop please ensure that the following is installed:

Anaconda Python distribution: https://www.anaconda.com/download

Target Audience:

Beginners with no prior experience with coding or data analysis, or those who are familiar with one but want to learn the other.

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In this one day course (online over two mornings) we will explore how to use R to import, manage and process spatial data. We will also cover the process of making choropleth maps, as well as some basic spatial analysis.

Finally, we will cover the use of loops to make multiple maps quickly and easily, one of the major benefits of using a scripting language to make maps, rather than traditional graphic point-and-click interface.

The course covers:

• Using R to import, manage and process spatial data
• Design and creation of choropleth maps
• Basic spatial analysis
• Working with loops in R to create multiple maps

 By the end of the course participants will:

• Use R to read in CSV data & spatial data
• Know how to plot spatial data using R
• Join spatial data to attribute data
• Customize colour and classification methods
• Understand how to use loops to make multiple maps
• Know how to reproject spatial data
• Be able to perform point in polygon operations
• Know how to write shapefiles

This course is ideal for anyone who wishes to use spatial data in their role. This includes government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map.

This course is also suitable for those who wish to have an overview of what spatial data can be used for. Although no previous experience of spatial data is required it would be beneficial (eg Google Maps).

This course will be taught over two mornings (10:00 – 13:00, including a mid morning break) and equates to one teaching day for payment purposes.

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This course is designed to familiarize students and researchers with various facets of qualitative research, particularly focusing on challenging fieldwork environments involving complex and intimate inquiries, expansive research scopes and diverse participant types. We will draw on our personal experience of undertaking ethnographic work and collecting semi-structured interviews with adults and children, presenting examples from the field to illustrate key challenges. The course will particularly benefit researchers engaging in qualitative research with vulnerable communities for short-term periods and in international contexts.

This course will discuss:

• Making sense of the field: As researchers working on sensitive issues and with families living in precarious conditions, how can one effectively understand and document the field (space, participants, communities and surroundings)? How can we make decisions about community engagement in research while recognizing that our participants are part of existing networks and communities? How can our research ensure that voices are heard without causing harm or disruption to people’s daily lives and social structures? How does our definition of the boundaries of a ‘community’ influence how we define and include community/peer researchers? Further, do we concentrate on noticeable elements that define the field for us, or should we pay attention to aspects that may not be prominent to us but hold significance for the participants? What leads us to make these decisions? Likewise, in informal discussions when new subthemes of our primary research objective emerge, what strategies can we employ to capture the evolving field effectively. 

• Working with ‘vulnerable’ participants: How can we define vulnerability in a way that respects participants’ right to participate and be heard but also attends to situated realities? How do you interact with participants who are traditionally seen as vulnerable, considering both the environment they live in and the potential vulnerability their involvement in the research might entail?

  • Research with children

  • Researching daily wage labourers in factory settings

  • Navigating the complexities of posing tough questions in qualitative research

• Locating researcher and participant vulnerabilities in qualitative research: While participants may be structurally vulnerable and situated in precarious circumstances, it is likely that both researchers and participants will encounter additional vulnerabilities during the research process. How should these challenges be managed and navigated as the research progresses?

  • Managing unanticipated challenges during fieldwork

  • Understanding and iteratively addressing multi-layered power dynamics in working with community/peer researchers

• Ethics as an ongoing process in qualitative research: How does one navigate ethical dilemmas in the field while collecting data and later representing participants’ and their experiences in academic writing? How does one continue to maintain ethical rigour throughout the research and beyond the application process?

  • Navigating positionality constraints in short-term field research

  • Adopting inclusive research practices

  • Praxis-oriented reflexive research

  • Adopting more collaborative methods, including working with community/peer researchers

By the end of the course participants will:

• Understand key challenges and ethical considerations in qualitative research
• Be able to articulate their own positionality and why it might matter during fieldwork
• Have a nuanced view of how to define a ‘vulnerable’ group and understand the methodological and ethical challenges while working with such groups
• Understand what a praxis-oriented, reflexive approach entails
• Understand how local contexts might shape participants’ understandings
• Be able to identify non-disruptive community engagement strategies
• Identify benefits and challenges of working with community/peer researchers
• Be able to identify some of the unique considerations involved in international research
• Acquire essential insights into the challenges and experiences of working with children

This course is aimed at students, researchers and academics in the social sciences with little or no training in qualitative methods.

The course will run from 11:00-16:00 and equates to one teaching day for payment purposes.

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This online course will provide participants with an understanding of how Hospital Episode Statistics (HES) data are collected and coded, their structure, and how to clean and analyse HES data.

A key focus will be on developing an understanding of the strengths and weaknesses of HES, how inconsistencies arise, and approaches to deal with these. Participants will also learn how to ensure individuals’ anonymity and confidentiality when carrying out analyses and publishing results based on HES.

The course consists of a mixture of lectures and practicals for which participants will use Stata software to clean and analyse HES data.

The course covers:

• HES data collection and coding
• HES data structure
• How to clean and manage HES data
• How to ensure anonymity and confidentiality
• How to carry out basic analyses using HES data
• Sources of variation in HES data
• How to apply for HES data

By the end of the course participants will:

• understand how and why HES data are collected
• become aware of the strength and weaknesses of using HES data for research
• know how to carry out basic cleaning, management and analysis tasks using HES data
• know how to ensure anonymity and confidentiality when using HES

The course is for researchers and data analysts in academia, government and private sector at all levels who are using or planning to use HES for their work.

There are no pre-requisites for the lectures. Computer practicals will involve analysis of simulated data therefore previous experience of programming in Stata, R or SAS will be helpful. 

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In this two day course (which will be taught online over 4 mornings), you will learn what GIS is, how it works and how you can use it to create maps and perform spatial analysis.

We assume no prior knowledge of GIS and you will learn how to get data into the GIS, how to produce maps using your own data and what you can and cannot do with spatial data. You will also learn how to work with a variety of different data sources and types (including XY coordinate data and address or postcode data) and using spatial overlays, point in polygon analysis and spatial joins.

The course covers:

• What is GIS and spatial data?
• How to classify data for a choropleth map
• How to create a publication ready map
• How to work with different data sources including XY coordinate and postcode data
• Using attribute and spatial joins
• Using spatial overlays and spatial analysis
• How to apply these skills to your own data

By the end of the course participants will:

• Be able to set up QGIS and add data
• Know how to classify data for a choropleth map
• Be able to join tabular data to spatial data
• Designing and producing a publication ready map in QGIS
• Understand how to import a range of data types into QGIS
• Be able to locate and open a range of GIS data sets
• Know how to apply GIS analysis tools including spatial overlays and point in polygon.
• Be confident at applying the skills to their own data

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PAR aims to create a space for researcher and participants to co-produce knowledge and where relevant, action for change. PAR is considered as a research paradigm in itself, that embodies a particular set of concepts under which researchers operate (Minkler and Wallerstein 2008). These include respect for diversity, community strengths, reflection of cultural identities, power-sharing, and co-learning (Minkler 2000). In this session we will explore these principles, the cyclical approach to PAR and what this means in practice. Participants will be given the opportunity to learn terminology, understand participation in community engaged research, explore how power and positionality can change health outcomes in PAR, and learn about a variety of participatory methods and how they have been applied in different contexts, globally and within the UK. Participants will also be provided with the space to explore challenges they are facing in designing or implementing community engaged collaborative research within a discussion clinic forum. 

Course Content:

The course will take place between 9:30am and 3.30/4.00pm on both days.

Mornings: online teaching and discussion with example videos and guests

Day 1: The history of PAR and underpinning orientation.

• Planning and setting up a PAR project
• Skills required for a PAR study
• Ethical considerations specific to a PAR study
• Participatory research with children and young people
• Photovoice methodology
• Independent activity
• Group discussion

Day 2 : Doing co-analysis

• Participatory research methods (examples of other visual methods, social mapping, seasonal calendars and other non-visual methods but still participatory such as narratives and others that have been used)
• Participation and inclusion
• Dissemination and writing for PAR projects – different approaches, narratives/thematic analysis, thesis, publications, policy briefs, blogs and others
• Group discussion on pre-workshop task
• Advice clinic

Afternoons: independent learning and practical exercises

• Day 1: Photovoice activity and reflections
• Day 2: Individual PAR project outline and feedback

Preparatory reading and videos will be shared beforehand.

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Qualitative Diary Methods (QDMs) are increasingly recognised as a valuable and important method in social science research, due to concern across disciplines with an overreliance on cross-sectional research, a lack of focus on temporality, and the need to capture evolving processes and the daily dynamics of phenomena.

QDMs offer a range of innovative approaches and tools for social science researchers that enable us to capture and subsequently begin to understand, how phenomena are experienced in the moment, as well as how they evolve over time. However, they remain a methodological blindspot in much postgraduate research training.

This workshop will provide researchers with a new range of methods to add to their methodological toolkit, as well as support and guidance in managing some of the challenges associated with these methods, including insights into qualitative diary (longitudinal) analysis approaches.

The course covers:

• Intro to Qualitative Diary Methods (QDMs)
• When and why to use different qualitative diary methods
• Addressing challenges associated with QDMs
• Ethical considerations when using QDMs
• Designing pen and paper diaries
• Designing audio and video diaries
• Designing photo-elicitation diaries
• Designing qualitative ‘App’ diaries
• Analysing data from QDMs: Moving beyond thematic analysis

By the end of the course participants will:

• Have a comprehensive understanding of the benefits and challenges of QDMs, and how to manage these challenges
• Be able to evaluate when different QDMs should be used and why
• Be able to design appropriate QDM research projects to address particular research questions
• Have a critical awareness of particular ethical issues associated with the use of QDMs
• Have a comprehensive understanding of analytical approaches suited to data gathered using QDMs

Programme Outline:

Welcome & Introductions 10:30 am

Session 1: 10:45 – 12:45

• An introduction to diary methods
• When and why to use different qualitative diary methods
• Collecting qualitative diary data: challenges and practical considerations
• Ethical considerations and participant reflexivity
• Sampling approaches for qualitative diary studies
• Pre- and post-diary interviews
• Audience Q&A and Task: Design a diary for your own research project/ interest – Justify design choices in line with sample, data needs and research questions.

Lunch Break 12:45 – 1:45

• Opportunity for informal discussions and networking

Session 2: 2:00 – 4:30

• Designing pen and paper diaries: Tips & Examples
• Designing audio and video diaries: Tips & Examples
• Designing photo-elicitation diaries: Tips & Examples
• Designing qualitative ‘App’ diaries: Some Examples
• Analysing data from qualitative (longitudinal) diary projects: Moving beyond thematic analysis - Thematic Trajectory Analysis (TTA)
• Audience Q&A & Group Task; Group discussions around proposed diary projects, including analytical approaches. Feedback from participants and presenters.

4:30 pm Conclusion & Finish

The event runs from 10:30am-4:30pm

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In this live online course, learn about questionnaire design in the context of different modes of data collection. Explore question wording issues, the questionnaire as a whole and visual concerns when moving from interviewer-administered to web survey, when creating a web survey in general and when facing the questionnaire design challenges in creating mobile-friendly web surveys. Mirroring in-person training this will be an interactive course and will also have workshops throughout.

The course covers:

• The push towards mixed mode, web and mobile web surveys
• Questionnaire design revision - Getting started, trade-offs, general guidelines, beware of certain question formats
• Question design solutions for comprehension issues - Appendix for memory and sensitivity issues
• Don't rely on survey templates
• Mixing modes of data collection, some overall mode differences, mode effects by question content and format
• From interview survey to web survey
• Web survey questionnaire requirements and options, web surveys can include . . . but should we?, importance of visual layout, unexpected issues with HTML formats
• Push to web
• Questionnaires for mobile web surveys - earlier evidence, later findings, current thinking on making a questionnaire mobile-friendly

By the end of the course participants will:

• Have better knowledge about questionnaire-related mode differences and effects
• Have the skill to change an existing interviewer-administered questionnaire to a web survey
• Have the ability to create effective web survey questionnaires as well as mobile-friendly ones
• Have greater questionnaire design skills in general and the ability to critique existing survey templates

This course is for anyone involved in mixed-mode, web and/or mobile web surveys.  Participants need familiarity with surveys and questionnaire design.

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This three-day workshop introduces participants to the theory and practice of experimental methods in application to scholarship research in higher education (HE). It delivers an overview of prevalent approaches, specifically using survey experiments, mixed methods and randomised control trials (RCT) providing a solid introduction to key experimental methodology. The course will focus on the practical skills needed to design, implement, and analyse scholarship in HE. Instructors include Caroline Elliott (Warwick) Fabio Arico (UEA), Paolo Spada (Southampton), Douglas McKee (Cornell), Jadrian Wooten (Virginia Tech), Annika Johnson (Bristol), Thomas Gall (Southampton) and Jana Sadeh (Southampton).

The course covers: 

Day 1: Introduction to Scholarship Research in HE, choosing appropriate methods, and ethics of research in HE.

Day 2: Survey and mixed methods: design, implementation and analysis

Day 3: RCT: design, application and analysis.

By the end of the course participants will:

• Understand the breadth of methods available for scholarship research in HE.
• Be able to design a research project using survey, mixed or RCT methods
• Be able to implement a research project using survey, mixed or RCT methods
• Be able to analyse data generated from a research project using survey, mixed or RCT methods 
• Understand the ethical frameworks and challenges surrounding scholarship research

This course is aimed at Teaching Fellows and Senior Teaching Fellows embarking on scholarship research in the higher education (HE) sector for the first time or who are interested in trialling new methodologies in their research. Non-teaching faculty members interested in doing scholarship research in HE.

This workshop is designed by trainers from the field of Economics but will be relevant to other Social Sciences staff with some quantitative background. Participants should have relatively sound knowledge of basic statistics.

This will course run from 09:00-18:00 each day.

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In recent years, there has been an upsurge in calls to decolonise academia. This has involved discussion about the need to decolonise curricula, pedagogy, campus spaces, and relationships within universities.

It has also been suggested that research methods and methodologies need to be decolonised too. This course will offer an in-depth exploration of this area by asking: What are the possibilities when it comes to decolonial research? This one-day course will be split into four sections, as follows. 

Firstly, the course will examine the decolonial critique of academia. This will involve exploring some of the main themes found within decolonial scholarship and activism with a specific focus on the decolonial critique of social science research. 

Secondly, the course will explore some of the key predecessors of decolonial research which have had a significant impact on current thinking about decolonial research. More specifically, consideration will be given to the relationship between feminist research, Indigenous research, and decolonial research. 

Thirdly, the course will consider various strategies which have been suggested to decolonise research. This will involve discussing some of the practical strategies that may be used when trying to produce decolonial research.

Fourthly, the course will offer a critique of decolonial research. This will involve recognising the limitations of decolonial research which make it an imperfect approach to conducting research. The course will be delivered in an interactive workshop format which will involve a mixture of lecture-style teaching, interactive large-group discussions, and small-group conversations. Researchers from all disciplines, expertise, and backgrounds are welcome to attend.

The course covers: 

• Theoretical and practical insights into what decolonial research is and how it can be conducted.
• Robust exposition of decolonial theory and its critique of contemporary academia.
• Examination of the significant influence that feminist research and Indigenous research has had on decolonial research.
• Consideration of various strategies which have been adopted in the pursuit of decolonial research.
• Exploration of the limitations of decolonial research

By the end of the course participants will:

• Be well-versed in the decolonial critique of academia, particularly in relation to social science research.
• Be able to identify the core principles and strategies which constitute decolonial research.
• Be prepared to offer a lucid critique of decolonial research.

This is an in-person course, taking place in Cardiff on the 9th of May from 10am to 5pm. 

The course is suitable for all levels. It is structured in such a way so as to ensure that it progresses from entry-level, to intermediate-level, to advanced-level. 

This course is designed for doctoral researchers within social science disciplines and anyone else who would like to further explore decolonial research methods and methodologies.

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This two day course will outline creative research methods and show you how to use them appropriately throughout the research process.

The course assumes that you have a good working knowledge of conventional research methods, and builds on that knowledge by introducing arts-based methods, embodied methods, research using technology, multi-modal research, and transformative research frameworks such as participatory and activist research.

Any or all of these techniques can be used alongside conventional research methods and are often particularly useful when addressing more complex research questions. You will have several opportunities to try applying these methods in practice. Attention will be paid to ethical issues throughout.

The course will include plenty of practical advice and tips on using creative methods in research.

The course covers:

• Arts-based methods
• Embodied methods
• Research using technology
• Multi-modal research

By the end of the course participants will:

• Have a good level of knowledge of creative research methods
• Understand how to use creative methods alongside conventional methods
• Understand when to use creative methods in research
• Have more resources to counter any opposition to the use of creative methods
• Know how creative methods can add value to funding bids

This course will be relevant for researchers from the third sector, public services (e.g. health, criminal justice, social care, education, local or national government), and those who work in independent research organisations or academia. It is an intermediate level course and attendees will need a good working knowledge of conventional research methods.

The course will run from 10.30-17.30 on Day One and  9:00-16:00 on Day Two at the University of Liverpool. Please note refreshments will be provided, but lunch will not (there are various outlets nearby to purchase something, or bring your own).

Preparatory Reading

Although not required participants may wish to purchase the book on which the course is based: Creative Research Methods: A Practical Guide (2nd edn), by the trainer, published by Policy Press. NB: if participants sign up for the monthly e-newsletter produced by Policy Press, they will receive a substantial discount on the book.

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Technological advancements have not only driven the digitisation of society and the emergence of novel socio-political issues, but have also resulted in significant developments in algorithms, computational power, and increasingly large datasets. 

This practical-based face to face session will be delivered over two days and will provide you with both the technical programming skills and understanding of data science techniques that you will need to research pre-existing and novel social-political and economic issues and the kind of transferable skills that are currently in demand in the job market.

Text data surrounds us in our lives and comes in different shapes and sizes, e.g. newspaper articles, tweets, product reviews, song lyrics, etc. While it might seem at first glance that this information can hardly be summarized and compared, certain computational techniques allow extracting meaningful information from text data. This course provides the foundations for you to understand, execute and communicate text data analysis in a widely recognised software platform that was built for data analysis

Specifically, it will introduce additional skills using the Python programming language, and requires prior introductory experience with Python. 

This training can be standalone with prior Python experience or as a follow on from the Introduction to Python sessions, on 22nd and 23rd April 2025 Introduction to Python for Data Analysis.

Web scraping with Python

• Introduction to Google Colab (students need a functioning gmail/google account they can log into)
• Pandas dataframes and uploading external data to Colab
• How to scrape a web page and extract text with Beautiful Soup 
• How to analyse and visualise text content using the Seaborn library

Introduction to Text Data with Python

• Text preprocessing
• Bag of words modelling and count vectorizer
• Lexicon based sentiment analysis using spacy
• Comparative visualisation

By the end of the course:

• Participants will be able to use Google Colab for collaborative data science projects, and

• have improved their Python skills and be able to import and evaluate text data

Computer workshops:

Students need a functioning gmail/google account they can log into. Students can use the computers in the lab or bring their own laptops.

Pre-requisites:

Basic Python or completion of Introduction to Python for Data Analysis.

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