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 course provides an introduction to coding when working with administrative data. The course will include an overview of best practices in coding, with hands-on sessions using synthetic data and different programming languages: R, Python and SQL. It will also discuss the use of AI in coding.

The course will be delivered in three interactive sessions, with breakout rooms in each session, as well as opportunities to engage with the instructors.

All Participants need to attend the first session on Tuesday 29 July (09:30-11:00), with options on the additional sessions. The price is the same, irrelevant of how many sessions you attend.

Tuesday 29 July 2025

• 09:30 – 10:30 Writing efficient code
• 10:30 – 11:00 Using data ethically and legally

Options:

• Option 1: Tuesday 29th July / 11:15 – 14:45 (with 45 minute lunch break) Programming using SQL & Datacise
• Option 2: Wednesday 30th July / 09:30 – 12:30 Programming using Python & ASHE-Census synthetic data
• Option 3: Wednesday 30th July / 13:30 – 15:00 Use of AI in coding using ASHE-Census synthetic data
• Option 4: Thursday 31st July / 09:30 – 12:30 Programming using R & ASHE-Census synthetic data

Registrants will be contacted following booking to confirm which sessions they wish to attend.

By the end of the course participants will:

• Be familiar with best practices in writing efficient, reproducible code
• Understand best practices in using data to ensure transparency, integrity and compliance
• Have practised programming using SQL & the Datacise platform
• Be familiar with coding standards using Python and practised programming using Python & ASHE-Census synthetic data
• Be familiar with how AI can be used throughout the code development lifecycle
• Be familiar with coding standards using R and practiced programming using R and ASHE-Census synthetic data

This course is open to anyone who uses, or will use administrative data. Some knowledge of data manipulation is required.

Participants will be sent instructions as to how they can access Datacise and synthetic ASHE-Census data. Please note that, in order to download the synthetic ASHE-Census data, you will need to register with the UK Data Service. 

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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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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 two-hour and 30-minute workshop will explore what Gen AI tools exist that are helpful for researchers and how we can use them to optimise research processes.

It is a workshop for Gen AI beginners, not Gen AI experts.

We will explore a range of mainstream Gen AI tools (e.g. ChatGPT, Claude, Co-Pilot) and research-specific ones (e.g. Sc iSpace, Research Rabbit, Elicit) and examine how they work through hands-on tasks.

By the end of the workshop, you will feel confident in what Gen AI can do (and what it can’t!), how to prompt engineer for research tasks, and the ethics of using Gen AI as a researcher.

The course will take place on Teams or Zoom on the 1^st of July from 10:00 to 12:30.

Pre-requisites

This is an entry-level course, no previous knowledge and no prior reading is required.

The session will only use free GenAI tools.

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email 30/04/25

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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This one-day online, interactive course will provide a practical introduction to Qualitative Secondary Analysis. The morning session will explore key debates and histories characterising the beginnings of this methodology, including how and why we might use existing qualitative data, how to sample from and across datasets and repositories, the unique ethical considerations of using existing qualitative data, and the potential of Qualitative Secondary Analysis to produce new and relevant research findings. The afternoon session will focus on the practicalities of analysing existing data, exploring the possibilities for these methods for delegates’ own research and the diversity of research designs made possible through data reuse. The course will comprise two lectures and two interactive workshops (see below for further details). The course will be delivered by Kahryn Hughes and Anna Tarrant, both specialists in QSA, and the authors of numerous books and papers based on research innovating in this methodology. 

The course will run from 10am to 4pm. 

In the first presentation of the day, delegates will be introduced to the key debates and challenges characterising the histories of Qualitative Secondary Analysis (QSA). They will  be introduced to early debates interrogating the particularities of reusing qualitative data and the evolution of questions from whether we should reuse qualitative data to how we might do it. 

In the second presentation of the day, delegates will be introduced to methods and ethics of QSA, sources of qualitative data and the practicalities of sampling within and across datasets and repositories

In afternoon workshops, delegates will get more ‘hands on’ with existing data with an opportunity to analyse short excerpts from real world research, and engage in guided reflection on the possibilities and limitations of this methodological approach.  A second practical session will offer delegates the opportunity to consider possible research designs incorporating QSA in their own research.

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 Timescapes Archive.

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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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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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This course provides an introduction to National Pupil Database (NPD), an administrative data resource covering the education system in England.

The course covers:

• The population coverage of NPD
• The component modules of NPD and how they link together
• How to create a longitudinal picture of pupils’ lives in schools
• Key data cleaning routines
• Accessing NPD

By the end of the course participants will:

•  Be familiar with the structure of NPD
•  Understand the strengths and limitations of the data available
•  Know where to go to find more detailed information
•  Know how to apply for access

This course is suitable for anyone intending to undertake quantitative research on the school system in England. No prior knowledge of the NPD or statistical code is required to access the course.

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Over the two days of this in-person course, you will gain valuable skills that you can market to employers, gain confidence in your ability to work with data, and create a knowledge base that you can build on for years to come.

Topics to be covered:

• How to navigate the R-Studio environment
• Reading data into R
• Tidyverse programming and Base R, including packages such as ggplot
• Data wrangling, including cleaning data and creating variables
• Data visualisation, including how to create the right graphs for your data
• Data analysis, both descriptive and inferential, including t-tests and an introduction to regression modelling

By the end of the course participants will:

• Be able to use R-Studio to analyse data
• Have improved their understanding of programming in R-Studio

Course Outline:

Day 1:

• RStudio principles and key terms
• Reading data into RStudio
• Creating variables
• Visualising data with ggplot

Day 2:

• Subsetting and grouping data
• Analysing survey data
• Analysing experiment data
• Performing a regression analysis

Software:

Students are able to use the computers in the lab during the course. If they are using their own laptops, they will need to install R and R-Studio, available free from https://cran.rstudio.com/ 

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To prevent obesity or smoking initiation among teenagers, who should be targeted in an intervention? How can we contain the spread of an infectious disease under limited resources? Who should be vaccinated first in order to be most effective during vaccination shortages? How can we dismantle a terrorist organization, a drug distribution network or disrupt the communication flow of a criminal gang?

Social network analysis offers the theoretical framework and the appropriate methodology to answer questions like these by focusing on the relationships between and among social entities. Unlike transitional research methods, we shift the object of study from the individual as the unit of analysis, to the social relations that connect these individuals. A network is therefore a structure composed of units and the relationships that connect them. Network analysis is about the position of these units, the overall structure and how these affect the flow of information.

The focus of this two day online course is not so much on how to express these concepts formally through mathematics, but rather on how to use appropriate software to acquire measurements for these concepts in the data and use them rigorously in empirical hypothesis testing. The majority of the course will focus on descriptive methods of network analysis, but we will also discuss network-specific models and inferential methods for network analysis.

The course covers:

• Foundations social networks data: relational structures and data collection;
• Manipulation of network data (matrix algebra and graph theory);
• Node-level measurements;
• Graph-level measurements;
• Network visualization.

Learning outcomes:

• Navigate the key areas of research in social networks;
• Acquire knowledge of data collection and suitable data structures for analysing social networks;
• Develop an understanding of social phenomena through the lenses of the social networks theory;
• Learn how to operate software package for the analysis of social network data;
• Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;
• Acquire the ability and comprehension to independently read scientific literature using social network analysis methodology;
• Learn the fundamentals of social network analysis to acquire the necessary proficiency to explore more advanced topics autonomously.

Basic knowledge of Excel and data matrices will be required.

This course will run from 9.00am - 5.30pm each day.

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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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The problem: Your team collected thousands of words of data. You try a traditional thematic analysis of the text. Soon, colour coding, close reading, writing ad hoc reflections about the text become too onerous a task. You doubt the validity of your observations. You wish there was another way to streamline the process, that would extract key themes in data in a faster and empirically-valid way.

Solution: Join us for a session in which we showcase empirical methods for the extraction and analysis of meaning, concepts, and themes in texts. The session will provide training in corpus linguistics and mixed-method tools that enable the analysis of texts in an empirical, bottom-up fashion. Through a range of case-studies, you will be guided to extract meaning and other thematic patterns from texts to gain insight into thoughts and behaviours of authors of those texts. We will share best practises on the thematic analysis of various data types, such as diaries, interview transcripts, data scraped from the web, and outputs of both new and traditional media. We also demonstrate ways of building the results of such analyses into answering research questions, developing business strategy, or a public policy.

This session will be run by researchers from the University of Sussex’s Concept Analytics Lab (https://conceptanalytics.org.uk/). We will demonstrate solutions developed for a variety of problems and text types coming from our work with medical sciences, psychology, economics, and the energy industry. We will also show how linguistic patterns within or between texts (e.g. those that differ demographically or diachronically) can be explored, particularly through the use of new visualisation techniques. The workshop will conclude with a showcase of next-generation textual analysis tools that have been developed at Concept Analytics Lab.

This will be a practical session, enabling attendees to develop hands-on experience with using corpus analysis tools. The course will consist of six hours of training over the course of one day [9.30am - 5pm] and will be delivered online. 

The course covers: 

• How to extract meaning from large textual data
• How to build a corpus using textual data 
• How to engage with existing corpora, such as multi-billion word corpora scraped from the web
• How to use corpus methods for bottom-up and top-down research
• Techniques for the visualisation of unstructured language data
• An introduction to discourse analysis and its application to corpora (corpus-assisted discourse analysis)

By the end of the course participants will:

• Know how to engage a suite of mixed-method corpus linguistic tools to extract meaning from a corpus
• Be able to use corpora to answer a variety of research questions
• Be able to build their own corpora
• Conduct comparative corpus analysis (e.g. between texts that differ demographically or diachronically)

Programme:

9:30: Welcome and introduction to corpus linguistics

10:00: Interrogating existing corpora - quantitative analysis

12:00: Lunch

13:00: Interrogating existing corpora - qualitative analysis

15:00: Break

15:15: Building your own corpus

16:15: The Concept Cruncher: The next generation of text analysis

16:45: Final remarks

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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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Queering data introduces participants to issues surrounding the ways that sex, gender and sexuality are represented in UK data. The course focuses on the production of data via surveys, helping participants design survey questions in an inclusive, transparent and reflexive manner. It takes place over four half days (10th & 11th and 17th & 18th June), with each session on a specific theme. The first two sessions engage with critical theories surrounding how populations are categorised and counted and dives into the UK data context. The final two sessions provide participants with insights and tools for designing their own survey questions and engaging with data in an informed and reflexive manner. The recommendations provided in this course are based on a mixed method research project that directly engaged with people with relationships to sex, gender and sexuality overlooked in UK survey data. 

The course covers: 

• Introduction to critical queer and feminist theories on data collection 
• Introduction to the UK census and the controversies around them 
• The pros and cons of data visibility for LGBTI+ people 
• Being a reflexive data collector 
• Being transparent data collector 
• Ways to design sex, gender and sexuality survey questions based on research with overlooked populations 

By the end of the course participants will:

• Be able to summarise the pros and cons of LGBTI+ data collection as seen by queer theorists 
• Be able to summarise feminist critiques of quantification 
• Know what the UK census are, what they are for, how they are designed and the latest debates surrounding how they represent sex, gender and sexuality 

Course format:

• Four half days over two weeks - 10th & 11th and 17th & 18th June 2025
• All sessions running from 10:00-13:00 with two 15 minute comfort breaks
• A mix of lectures and group discussion based activities.
• Some short post session activities, should take no more than 15 minutes.
• Additional reading which the participants can choose to do if they wish.

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Human-computer interaction (HCI) is an ever-more pervasive phenomenon. Many societies are at the point where avoiding interaction with digital technologies is hugely challenging. In this way HCI – both as a phenomenon and as a field of research – has the potential for widespread relevance well beyond its initial disciplinary origins (which stem largely from university computer science and psychology departments).

Simultaneously, approaches from the human sciences (and arts and humanities) have pushed well into HCI’s mainstream. One strand of this having significant formative impact in HCI is, broadly, what we might gloss as ‘sociological interactionism’ or pragmatics (although ‘pragmatics’ is a less used term in HCI); that is, research approaches that foreground ‘interaction’ with / around digital technologies, infrastructures and services, and simultaneously formulate this as constitutively interactional in nature.

This course will explore one key version of this trend: video-based studies of social interaction in which digital technologies play a role, whether in so-called ‘naturalistic’ settings or as part of more experimental rollouts of technology. The course will focus on approaches grounded strongly in traditions of ethnomethodology and conversation analysis. As part of this, the course will contextualise the use of video to study social life with technology, both in terms of human-computer interaction as everyday, routine phenomena, and with respect to HCI as a field (and its connections with both technical and sociotechnical fields of research).

A corresponding practical element will complement these discussions. By looking at various existing examples coupled with participants having a go at their own analyses, the course will provide pointers for what is involved in doing video based EMCA studies of technology in use, including what kind of outcomes they might produce.

The course covers:

• Introduction to human-computer interactions (HCI) and collaborative computing as phenomena
• Understanding and situating HCI as a ‘discipline’: Methods, approaches, disciplinarity
• Social turns and the ‘missing what’: introduction to and critical review of traditions of ethnomethodology and conversation analysis in / of HCI
• Video as aid: Why study human-computer interactional phenomena with video?

Half of the course will be dedicated to practical hands-on work:

• Discussion of existing cases studies (e.g., prior work)
• Analysing some examples of data via individual work (experimenting with forms of transcription) and a joint data session (presenting and discussing findings amongst the group)

Schedule: The course is run across two consecutive mornings with individual and group assignments inbetween and equates to one teaching day for payment purposes.

MORNING 1

9:30    Welcome, 20 second intros around the room (10 mins)

9:40    Teaching session 1 (1hr – short break included) – laying the ground work

Presentations on:

1) Brief intro to human-computer interactions (scoping HCI as phenomena and                   discipline);

2) Primer on EMCA and HCI research and its position within HCI broadly

10:40   Break (20 mins)

11:00   Teaching session 2 (1hr – short break included) – thinking about analysis

Presentations on continued intro to EMCA, and intro to using video to study human-computer interactions – note video segment will include interactive components as preparatory for exercise

12:00   Break (10 mins)

12:20   Briefing (20 mins) on the practical activity / task (video analysis)

12:40    End     

Afternoon session (own time):

Practical activity taking place in own time in groups and / or individually (TBD)

MORNING 2

9:30     Welcome back / recap (5 mins)

9:35     Teaching session 3 (1.5hrs inc. 15 min break) – analysing data

Interactive data session / feedback on video analysis task from groups / individuals

11:00   Break (10 mins)

11:10   Teaching session 4 (max 1hr – short break included if necessary) – wrap up

Open: depending upon outcomes of session 3, could be further group analysis of data, opportunity for discussion and deeper questions from the course, or more structured discussion around EMCA and technology / further studies, future of HCI discourses, etc.

12:10   End

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The measurement of health in surveys involves collecting data from individuals about their health status, health-related behaviours, and experiences, often as part of multi-purpose surveys. These surveys may include both subjective self-reports (e.g., self-assessed health measures) and objectively measured health data (e.g., physical health assessments, blood-based biomarkers, or DNA data).

Survey measurement of health plays a vital role in advancing social science research on health. It is essential for the analysis of health inequalities, for informing policy decisions, and evaluating the effect of interventions. By capturing individual health behaviours alongside the social, economic, and environmental contexts influencing health outcomes, surveys offer a nuanced understanding of the complex interplay between society and health.

However, measurement error in health data can significantly affect and compromise the quality of social science research in health. Importantly, such errors are not confined to self-reported data. While self-reports are often susceptible to certain types of inaccuracies, other sources of error can arise, including those associated with nurse-administered health assessments or blood-based biomarker data.

Survey mode – e.g. face-to-face interview, telephone interview, online questionnaire - is also well known to affect the distribution of the survey variables so is a special case of measurement error.  Mode effects are hypothesised to be driven by well-known sources of survey bias such as social desirability, positivity and satisficing and the presence (or not) of an interviewer. Mode effects can be defined in the same way as causal or treatment effects and estimated from mixed-mode surveys, and estimated using the same methods.

We provide an overview of methods for understanding measurement error and mode effects. We will also provide practical sessions and illustrative examples demonstrating the impact of measurement error and mode effects in the social and health sciences.

The course covers: 

• Collection of health data in multi-purpose social science surveys

• Measurement errors in health data, including both self-reports and objective measures

• Implications of measurement errors in health data for existing social science research in health

• The nature of mode effects and the connection with classical measurement error

• The definition and identification of different kinds of mode effect from mixed-mode surveys and a case study based on Wave 8 of Understanding Society.

• Practical sessions with illustrative examples of measurement error and mode effects

By the end of the course participants will be able:

• Understand the basis on the process of survey measurement of health, including the collection of both self-reported and nurse-collected health data.
• Explain the role of survey measurement in advancing social science research, particularly in understanding health inequalities, guiding policy, and tracking interventions.
• Recognize the impact of measurement error in health data, including errors in self-reported, nurse-administered, and biomarker data.
• To understand the potential impact of survey mode on survey data, learn how to estimate different kinds of mode effects from mixed-mode surveys, and how to do so robustly using instrumental variable estimation.
• Apply practical knowledge of how measurement error in survey health data can affect the accuracy and interpretation of social science research.

This course is aimed at Post-graduate researchers and analysts, including (but not limited to): Academics, Government Researchers, Third sector organisations, (Health) Consultancy analysts and Survey methodologists. Participants will need a basic knowledge of STATA.

Programme TBC

Day 1

9:00-11:00 Measurement error in self-reported health measures regularly available in large-scale multipurpose datasets

11:00-11:15 (Virtual) coffee break (Q&A session)

11:15-12:45 Beyond self-reported health measures – characterizing and quantifying measurement errors in administrative health data and nurse-collected bio-measures.

12:45-13:45 Lunch

13:45-14:45 Assessing the potential implications for the existing research in economics and social science that rely on health data

14:45-15:45 Practical sessions using Stata and illustrative examples

Day 2

9:30-11:00 Connection between mode effects and measurement error, definition of different mode effects, identification of mode effects from mixed-mode surveys where people non-randomly select survey mode

11:00-11:15 (Virtual) coffee break (Q&A session)

11:15-12:45 Case study: Using regression and instrumental variable-based methods to estimate mode effects from Wave 8 of Understanding Society

12:45-13:45 Lunch

13:45-14:45 Practical session using Stata and illustrative examples

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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), Christian Spielmann (Bristol) 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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This online course will introduce participants to visual and embodied methodologies (VEM) and how they can be used in social science research. VEM can increase understanding about the social world, enhance participation and collaboration through the arts-based or creative research process, and add meaning and value to the analysis and communication of research data. By learning about VEM participants will learn how these methods can be used to develop original research approaches, work with vulnerable and minoritised groups, and address key social science research challenges.

This modular course consists of one core ‘Introduction to VEM’ module followed by a selection of optional modules on specific VEM methods. All participants undertake the core introductory module and can then select optional modules to customise the training according to the VEM methods of interest.

The Introduction to VEM module (13th October 2025 09:00 - 11:00) covers:

• What are visual and embodied methodologies?
• How can VEM be used to understand and address social science research challenges?
• Differentiating between different forms of creative research collaborations and interventions.
• Key ethical issues when using VEM.
• How to manage research collaborations with artists and participants.
• Creative data analysis.

The ‘pick and mix’ two-hour sessions on particular VEM methods include:

13th October 2025

• Photovoice - 12:00 - 14:00
• Body Mapping - 15:00 - 17:00

14th October 2025

• Digital Storytelling - 09:00 - 11:00
• Poetry - 12:00 - 14:00
• Policy - 15:00 - 17:00

15th October 2025

• Performance and participatory theatre - 09:00 - 11:00
• Zine-making and collage - 12:00 - 14:00
• Textiles, Banners and Sewing - 15:00 - 17:00

The course is charged as one day for the core module plus up to two modules from the ‘pick and mix’ options above, two days for the core module plus up to five modules and three days for the core module plus up to eight modules. Please book the required number of days and you will be contacted to choose which modules you wish to attend.  Numbers will be limited to 12 participants on each module.

Learning outcomes

• By the end of the course participants will:
• Understand what VEM methods are and how they can be applied to social science research
• Assess the risks and opportunities of using VEM
• Be able to design an ethical VEM research methodology
• Have the tools to manage researcher-participant relations
• Understand and think critically about specific VEM methods, their applications, processes, advantages and limitations

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.