National Centre for Research Methods

In this online course, we outline a comprehensive framework for understanding the trade-offs involved in designing and remodelling social surveys. Our framework is rooted in the Total Survey Error and Total Survey Quality approaches, balancing the need to reduce sources of error against the constraints of a project, time and costs. Through real-life examples and case studies, we discuss the advantages and disadvantages of different research designs, with a focus on mixed-mode surveys, and the key steps involved in making informed decisions and remodelling surveys.

This course is for anyone involved in the design of survey research and will be particularly relevant for those who are running an existing survey and exploring alternative modes of data collection.

Age, period and cohort (APC) are three ways in which things change over time; however they are exactly collinear, in that if we know an individual’s age and year of measurement, we can work out their birth year (age=period-cohort). This presents problems for any longitudinal analysis, because we cannot include all three APC terms in a statistical model without some kind of constraint. Yet if we fail to include all three terms in a model, we can radically mis-apportion affects: what can, for example, appear to be an age effect, could in fact be a combination of period and cohort effects.

Netnography involves studying online socialites and cultures to gain insights into emergent social phenomena. The method was developed by Professor Robert Kozinets in the 1990s as a way of understanding the techno-social dynamics of online spaces. Typically, netnography involves a combination of qualitative research methods, including immersion journaling, participant observation and interviews amongst others. Various interpretive approaches to data analysis can be used, including hermeneutic and thematic analysis.

Owing to the increasingly digital nature of social life, netnography is well established as a rigorous way to gain creative, deep, and cultural insights into a range of phenomena. Given that the data collected in a netnography is often spontaneous, indigenous, interactive, and naturally occurring, it offers fresh and often uncovered perspectives straight from a public of people, dwelling online.

This one-day workshop will:

• Discuss the data management techniques needed to use child and youth data (merging data, creating variables, linking parental data) and how these may differ based on the type of data (panel, cohort). 
• Refresh the basic analytical methods for longitudinal analysis of child and youth data (OLS regression, fixed and random effects models and path models).
• Show how to implement these data management and analytical techniques in child and youth data using practical examples.

Ethical Open Source Investigation: A Deep Dive into Key Skills is a two-part methodology course which teaches open source skills whilst foregrounding a critical and reflexive approach to open source investigations. You will learn practical skills to evaluate social media content, online databases and satellite images, whilst integrating considerations of ethics, care and power.

This material is developed from the University of Cambridge’s Open Source Investigation for Academics course (which was co-designed by Dr Ella McPherson, Ray Adams Row Farr, Nik Yasikov and Laetitia Maurat) and inspired by the university’s collaboration with Amnesty International as part of their Digital Verification Corps - an international network of universities where students trained in open source investigation contribute to Amnesty’s human rights fact-finding. It brings together long-standing academic considerations of positionality, reflexivity and ethics with a practical introduction to the methodologies of open source investigation. 

This exciting opportunity to engage with Open Source Investigation is open to anyone interested in using OSI in any part of their work, whether a PhD student, Early Career Researcher, researcher in the field or academic. This is an intensive course and there is preparation and follow-up work expected from you. If you are unable to commit to the course in its entirely (2 days over 2 weeks) then you are unable to apply.

This course offers a deep dive into the analytical techniques used to dissect election and survey data, providing insights into the mechanics of political behaviour and electoral dynamics. 

Participants will explore a range of statistical methods and models, from measuring ideological leanings and voter segmentation to modelling electoral behaviour.

Through a blend of theory and practical application using real-world data and case studies, the course aims to equip attendees with a nuanced understanding of the complex

This two-day in person course aims to provide participants with an opportunity to learn about and practice creative participatory methods that have been applied with people and communities to understand needs, set priorities and co-develop actions for social change.

The methods presented will include transect walks, photovoice, body mapping, stepping stones and others. Learners will hear how researchers and community partners globally have used these methods to co-create new knowledge together and the impact these have had. They will then be provided with tools to practice these methods within a group or individually, and to reflect on being a participant, facilitator, or observer/documenter. Participants will also be involved in a discussion about power and inclusion in participatory research.

This will be followed by hands-on experience of facilitating a co-analysis exercise, triangulating data across methods and developing themes. 

This exciting interactive online 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 half-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.

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

The target audience of the course are professionals and researchers, especially those approaching experimental methods for the first time. The course does not require any previous knowledge of experimental design or statistics and is open to participants with basic secondary school knowledge of mathematics. Prospective participants with some prior knowledge of experimental methods that would like to deepen their knowledge and skills on one specific approach (lab, field, or survey), can selectively sign up for sessions across the three days covering those approaches. 

The workshop objective is to teach participants to become critical readers of the experimental literature, and to equip them with the ability to design, implement, analyse, and report their first experiment. 

The workshop will also briefly overview service providers for marketing experiments (Facebook, Google), access panels and online marketplaces (Lucid, MTurk, Prolific, etc.), and survey providers that support survey experiments (Yougov, Ipsos etc.).  

The workshop will be taught by an interdisciplinary team of faculty members of the Centre of Behavioural Experimental and Action Research (C-BEAR), leveraging examples from Politics, Economics, Business and Psychology.

The workshop will address the responsible conduct of research during experimental studies, covering research ethics, pre-registration, and debriefing practices for deceptive research designs.

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 laboratory, survey and field experiments.

By the end of the course participants will:

• Be able to read critically the literature on experimental methods in a variety of different disciplines.
• Know how to interact more effectively and efficiently with service providers that offer A/B testing experiments, surveys experiments, or participants’ panels.
• Know how to design, pre-register, implement, analyse and present simple experiments.
• Be familiar with classic lab, survey, and field experiments.
• Be familiar with the ethics and legal requirements for conducting experiments with human subjects.
• Be familiar with the statistical methods required for analysing simple experiments.

The target audience of the course are professionals, members of public institutions and researchers that are approaching experimental methods for the first time and are interested to implement an experiment for the first time or to commission an experiment to a survey company or other service provider.

The course does not require any previous knowledge of experimental design or statistics and is open to anybody with basic high school knowledge of mathematics.  The level (junior, senior, etc.) of the course is open.

The workshop is designed to have no requisite beyond a basic understanding of secondary school mathematics. The first two days will provide the students the mathematical and statistical tools to engage effectively with the rest of the course.

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. 

Ethnographic methods are increasingly popular with researchers across the social sciences, but the full potential and possible pitfalls of this complex practice are often overlooked in favour of catch-phrase definitions. This course moves beyond standard understandings of ethnography that depict it as a generic qualitative method founded on ‘participant observation’ to provide learners with a sophisticated, state-of-the-art approach based on cutting-edge academic research. The course will blend theorical and practical considerations. On the one hand, the course examines the theoretical scaffolding of ethnography, recognising that a thorough understanding of the epistemological foundations of the methods we use is essential to conducting rigorous and ethical research. On the other, the spirit of the course is inherently practical and pragmatic, as it aims at preparing researchers to design and conduct ethnographic fieldwork, as well as writing it up for academic and non-academic audiences.

The course covers:

· Epistemology, method, and research design: ethnography beyond participant observation

· Preparing for fieldwork: a pragmatic approach to designing research projects

· Ethics and power: access, collaboration, co-production, and the possibility of decolonising research

· Writing ethnography: from the practical to the political

By the end of the course participants will:

· Grasp the practice of ethnographic research beyond participant observation

· Understand the potential of ethnography beyond the traditional ‘study of culture’

· Have a sophisticated understanding of ethnographic research, from the design stage to its execution and

writing up, including an overview of sensorial considerations and visual methods

· Be able to appreciate the ethical and power dimensions of ethnographic research

· Understand the ethics and politics of writing, publishing, and representing ethnographically

This advanced course is suitable for any researchers equipped with some prior knowledge/experience using both standard qualitative methods (interviews, focus groups, life histories, etc.) as well as ethnographic methods but is interested in advancing their understanding of ethnographic research to a professional level. Researchers working within and outside academia (private sector, government, charitable institutions, etc.) are equally welcome to apply. The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.), particularly if enrolled or intending to enrol in a research degree (e.g., PhD, Masters by Research, Masters in Research Methods). Please note that this course is also suitable for postgraduate researchers with an UG background in anthropology, as the course if pitched to an advanced level.

Pre-requisites

Experience using ethnographic research methods and qualitative research methods.

Preparatory Reading

Demetriou, O. (2023), ‘Reconsidering the vignette as method. Art, ethnography, and refugee studies’, American Ethnologist, 50(2): 208-222.

Hage, G. (2005), ‘A not so multi-sited ethnography of a not so imagined community’, Anthropological Theory 5, no.4: 463-475.

Ingold, T. (2014), ‘That’s enough about ethnography!’, HAU Journal of Ethnographic Theory 4, no 1: 383–395

Stefanelli, A. (forthcoming 2024) ‘Reading ethnography in the classroom: complementary strategies to develop students’ ethnographic imagination.’ Learning and Teaching in the Social Sciences.

The course will run from 09:30 to 15:15 both days.

• 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.

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 above or contact X. 

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.

This in-person 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

This online 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

While ethical issues are faced by all social researchers the ethical issues posed by some digital platforms, digital spaces and mediated interactions can raise distinct ethical challenges (and opportunities).

This one-day course will consider what it means to ethically collect data and to work with potentially sensitive data when using digital research tools and when researching digital spaces and objects.  It will introduce researchers to principles underpinning ethical practice in digital contexts and will explore the complexities of managing issues of informed consent, anonymity and confidentiality in and through digitally mediated environments.

Course Timings: 11:00 – 16:00

This course is suitable for researchers at any career stage who wish to gain an understanding of ethics in digital research.

Short case study material will be provided in advance of the course and students are asked to read the case studies prior to attendance.

The training is designed to introduce researchers and PGRs to using drawing methodologies in Social Science Research and give them practical experience of doing so. It will provide an overview of the use of drawing in social sciences, focusing particularly on the turn towards 'live sociology' and creative ethnographic methods in recent years.

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 1 day on Wednesday 11th December between 10:00 and 16:00, with 1 hour for lunch between 12:30 and 13:30. 

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.

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 Bren Neale, a specialist in QL research and the author of two books on this methodology.

This in-person course is for anyone interested in agent-based modelling for public health research. During the course, participants will be introduced to principles, concepts, and steps of building and analysing agent-based models, including conceptual model development, agent-based model development and verification, model documentation, and running and analysing of experiments. The course is primarily based on hands-on activities using the free, open-source software NetLogo, complemented by lectures and readings.

This course comprises four full days (9am - 4pm) of activities. No previous knowledge or skills in agent-based modelling or NetLogo is required. We welcome everyone interested in the course, with no minimum academic degree required. Participants will benefit from the assistance of 2 experienced modellers throughout the course and solutions to the exercises will be provided.

This practical, one-day hands-on course provides a guided introduction to the workflow in ArcGIS Online (AGOL) for uploading and sharing your spatial data. You will learn how to publish existing vector data to AGOL, create views, set sharing and group privileges, create a web map that drives data collection and forms the foundation to an Experience Builder web application. 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.
• Edit a layer and conduct data capture.
• Create an Experience Builder application from the web map.
• 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 must be familiar with the basics of using ArcGIS Pro and spatial data, familiarity with AGOL is an advantage but not essential. Training will be run at our dedicated training suite located on the Highfield Campus, University of Southampton. Room 1065 in building 44 (Shackleton).  GeoData will provide access to ArcPro 3.x and ArcGIS organisational logins for the course duration.

This course will run from 09:30-17:00.

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 organized 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

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.

This introductory online course will provide students with an understanding of how social interactions may relate to health behaviour and outcomes, the approaches that can be used to study social network influences on health, and network approaches to improving health.

The course is aimed at those with no knowledge of network analysis but with an interest in population health, delivering community services or implementing health interventions. The course will focus on non-communicable disease e.g., mental health, health risk behaviours but not infectious diseases. The course focuses on patterns of relationships between individuals, and not on social media networks.

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.

In this in-person introductory course, you will acquire the foundations to understand, execute and communicate data analysis in a widely recognised software platform that was built for statistics. R is used in a number of professions, from academia to government departments and from data journalists to pollsters. 

Over two days, 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.  

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.

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.

This two-day intensive workshop will provide delegates with an introduction to mixed methods research and help them to structure the design, sampling, analysis and integration of qualitative and quantitative methods within a single study.

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. 

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. 

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. Ran over four half days, 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.

Randomised controlled trials (RCTs) are heralded as the gold standard of research design in the social sciences. RCT principles are used in research at all levels of complexity from evaluating national social policies to experimenting with the impact of website designs (there often known as A/B testing). This course is for social researchers who have a firm grasp of the foundations of quantitative research methods (e.g., linear regression and confidence intervals) and would like to learn how to design and analyse randomised controlled trials. The course incorporates a blend of presentations and participatory sessions, using examples from the social sciences.

Dr Andi Fugard (they/them) is a Research Director in NatCen Evaluation. They have experience designing, project managing, and analysing data from randomised controlled trials in mental health and education. Before joining NatCen, Andi was a Senior Lecturer in Social Science Research Methods at Birkbeck, University of London, where they directed postgraduate programmes in Social Research, and Lecturer in Educational Psychology Research Methods at University College London.

The course considers key social debates that flow from demographic change such as overpopulation, fertility decline, rural population sustainability and population ageing.

You will learn about the demographic data and methods and theory that underpin such debates with hands-on opportunities to undertake analysis using real demographic data. 

The course includes a set of practicals which involve downloading real demographic data and undertaking analysis using Excel. An understanding of the use of formulas in Excel is expected.

Economists (and social scientists more broadly) are increasingly focusing on the measurement and causes of inequality in health. This reflects the concern that health inequality reflects social injustices, and it is also in response to the trend away from a narrow focus on income inequality to broader inequality in wellbeing analysis.

This three-day online course aims to postgraduate researchers and analysts interested in quantitative analysis of inequity and (socio-economic and regional) inequality in health and health care. This consists of lectures and practical sessions on measurement and interpretation of inequity and inequality in health and health care. Specifically, this course provides a gentle introduction to the concept of inequity, socio-economic inequality, and inequality of opportunity in health, i.e., the “egalitarian” framework that does not necessarily indicate equality of the distribution of outcomes per se but emphasises the role of individual responsibility in defining a “fair” distribution of health in the society.

Recent advances in the survey measurement of health, in the context of large-scale social science datasets, allow us to access and collect physical measurements and markers derived from biological samples, in addition to self-reported health assessments. Measurement error in self-reported health data (as well as potential measurement errors in “more objectively” measured nurse-collected indicators in social science surveys) may significantly affect and contaminate the measurement of socio-economic inequality in health research when relying on these health measures. We will draw conclusions on the potential implications of measurement error in self-reported and measured health indicators for research in inequalities in health. Additional sessions will also take place on specific topics in health inequalities such as: a) the social and economic factors which may drive the observed regional inequalities in health within and between countries with the presentation of international evidence and practical sessions, and b) the role of reforms in shaping socio-economic inequality in health and healthcare.

We will also provide a good set of practical sessions and illustrative examples on the measurement of inequality in health using subjective and more objectively measured health indicators.

The course covers:

•  A gentle introduction to inequity and socio-economic (and regional) inequality in health and health care
• A number of approaches (employed by economists, social scientists and bio-social researchers) on the measurement of socio-economic inequality in health and healthcare
• The concept of inequality of opportunity in health
• Measurement of inequality and inequality of opportunity in health
• Measurement error in self-reported (and measured) health data in social science surveys and its potential implications for the socio-economic inequality in health research.
• The social and economic factors which may drive the observed regional inequalities in health within and between countries.
• The role of reforms on shaping socio-economic inequality in health and healthcare.
• Practical sessions and illustrative examples on the measurement of health inequality, measurement error in health outcomes and the potential implications for existing research in health inequality. 

By the end of the course participants will:

• be able to understand several approaches (employed by economists, social scientists and bio-social researchers) on the measurement of inequity and socio-economic inequality in health and healthcare
• be comfortable in computing health inequality measures using Stata
• understand the concept of inequality of opportunity in health and its measurement via practical sessions in Stata
• have the theoretical and practical knowledge to undertake basic research into health inequalities.
• be familiar with measurement error in health data available at social science surveys and its potential implications for existing research in socio-economic health inequality. 

This course is aimed at Postgraduate researchers and analysts interested in the measurement of socio-economic inequality in health and health care, including (but not limited to): Academics, Government Researchers, Third sector organisations and (Health) Consultancy analysts. Participants will need intermediate knowledge of Stata.

Data and statistics form the basis of much political discussion about crime, provide the foundation for evidence-based research on justice interventions, and shape our understanding of possible biases in the justice system.
However, working with any social science data can be complex, and there are particular features of working with criminological data that can pose challenges for researchers.

Data and statistics form the basis of much political discussion about crime, provide the foundation for evidence-based research on justice interventions, and shape our understanding of possible biases in the justice system.
However, working with any social science data can be complex, and there are particular features of working with criminological data that can pose challenges for researchers.

This is a two-day course on statistical methods for meta-analysis using the package STATA. The first day gives an overview on traditional techniques used in meta-analysis. The second day present more recent state-of-the-art modelling including mixed Poisson and binomial regression. The teaching style of the course is a mix of lectures and practical work. 

The course covers: 

• Basic elements: Effect measures of interest; Mantel-Haenszel, case studies, continuous outcomes; graphical techniques, precalculated effect measures
• Heterogeneity: why is there heterogeneity, study as random effect, sub-group analysis, meta-regression
• Meta-analysis of rare events: what are the issues? Mantel-Haenszel estimation 
• Modelling approaches for rare meta-analysis: Poisson and binomial with fixed and random effects
• Meta-analysis for rare events: single- and double-zero studies and their effect
• Conditional logistic regression

By the end of the course participants will:

•  Be able to do a simple statistical meta-analysis using traditional techniques
•  Be able to do a more advanced model-based meta-analysis 
•  Know in which meta-analysis setting to apply the most appropriate statistical approach 

Preparatory Reading

Introduction to Meta-Analysis, second edition (authors: Michael Borenstein, Larry V Hedges, Julian P T Higgins and Hannah R Rothstein.

Knowledge in STATA is helpful but not an essential pre-requisite.

This course will take place at the University of Southampton from 9am to 5pm both days (please note refreshments will be provided but lunch will not).

Deliberative research is emerging as a critical method for exploring public attitudes particularly on social and policy problems that are contested, complex or uncertain.  More broadly deliberation - through methods such as Citizens assemblies and juries - is used in society as a way to engage citizens in policy decisions.

This live online course explores the principles, benefits and limitations of deliberative approaches to social research and engagement, with a particular focus on the challenges and opportunities of delivering these online. We cover a combination of theory and practical examples to consider both doing deliberative research and being a deliberative researcher.

It is suitable for those with existing experience of the theory and practice of qualitative research and aimed at those who have responsibility for designing, commissioning and overseeing the delivery of research projects. 

This course will be useful to you if:

• You want to expand the range of participative qualitative methods you use for research
• You want to learn more about the use of online approaches to social research
• You have used deliberative research approaches in face to face studies and want to consider how to bring it online
• You want to know more about deliberation in general and how to deliver deliberative processes

This course will offer an in-depth exploration of decolonising academia 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 explore some of them 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.

Spread across three days, this online short course introduces participants to sources of quantitative and qualitative data about fathers, or ‘dad data’, in the UK for research about children, young people, interparental relationships and families. Featuring discussion of both quantitative and qualitative datasets, the training will provide hands on experience of using ‘dad data’ and explore the potential and the value of re-using different data sources for advancing new knowledge. The course will showcase existing datasets in the UK; six national longitudinal studies about children and families that contain ‘dad data’ for the purpose of quantitative analysis, as well as the qualitative longitudinal datasets that feature the voices and experiences of fathers, that are stored in the Timescapes Archive at the University of Leeds. 

Via a mix of synchronous presentations and an asynchronous ‘data dive’, participants will be supported to develop their knowledge and hone their skills in identifying, accessing, reusing, and analysing existing quantitative and/or qualitative data about fathers. There will also be opportunities to explore the relevance of ‘dad data’ for your own work, as well as identifying new research questions and future directions. A group Q&A session on the last day will support exploration of challenges, new questions, and future opportunities for research with ‘dad data’.

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
• Transformative research frameworks

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.  Refreshments will be provided over the two days, however, lunch will not.  There are various places nearby to purchase something.

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.

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 2024 Introduction to Python and Python for Data Analysis (ncrm.ac.uk)