National Centre for Research Methods

The central concern of Membership Categorisation Analysis (MCA) is the description of the array of categorisation practices observable in members’ “naturally-organised activities”. This workshop is focused on exploring and understanding what Harvey Sacks called the “member’s machinery” and how that foundation was later developed into a concern with the analysis of “culture-in-action”. Sacks’ early analyses considered how relevant categories are ‘used’ not only to categorise individuals as ‘representative’ members of a given category but, in a broader sense, to both produce and recognise the orderly character that scenes and activities observably have. In this sense, MCA is not a formal method of inquiry as such but forms a live ‘resource’ for members in the accomplishment of reasoning, sense-making, and social organisation. For members, such practices are employed in a range of everyday practices both in forms of talk and conversation (e.g. in telling a story about some event), but also in mobility practices (such as forming a queue or ‘flow file’ in public space) or accomplishing visual order (for example, of producing and viewing memes). For analysts, an attentiveness to categorisation practices provides a powerful means of accessing people’s “improvised cultural practices” (Hester and Francis, 2017) which provide the very grounds upon which the sense of the world is built.

This training course provides doctoral students with the necessary methodological tools for policy evaluation. Macro-level policy interventions, such as foreign aid in developing countries, are not randomly assigned, which complicates analysis of their causal effects on development outcomes. The course introduces students to the commonly used remedies to the problem of causal identification, including matching on observables, regression discontinuity designs, instrumental variable regression, and difference-in-difference estimation. The course will further a conceptual understanding of these techniques, discuss their pros and cons, and provide practical guidance using examples from the aid effectiveness literature.

The aim of this two-day 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.

This 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.

The training is designed to introduce researchers and PGRs to using drawing methodologies in qualitative research (which has a fieldwork element) and give them practical experience of doing so. It will provide an overview of the use of drawing in social sciences in the core aspects of carrying out a research project: project design, fieldwork, analysis and representation. In addition to presentations from the convenors, who have both used drawing-based methods, the two-day training session will include many practical group drawing exercises, with opportunities for discussion and reflection. Participants will conduct mini research projects based on a research question. The workshop will end with a group exhibition of drawings representing their findings. Participants will have the opportunity to discuss the potential uses of drawing in their current and potential research projects, including how to navigate ethics across the life-course of any project.

This 2-day course, delivered in-person, introduces participants to a range of measures of geographical accessibility, utilising examples of everyday services such as healthcare facilities and retail stores.

This hands-on course considers a range of spatial analysis tools and techniques which can be used to capture measures of geographical accessibility at a range of spatial scales. It uses freely available datasets and tools to equip participants with the skills and understanding to incorporate geographical accessibility within their research and analysis. These include measures of proximity to healthcare, network-based measures of accessibility to everyday services and composite measures of neighborhood accessibility to food stores.

We also consider modelling tools that can be used to optimise the location of facilities in order to maximise coverage and accessibility and consider sources of powerful routing and travel time data which can be used to enhance measures of geographical accessibility. The course concludes with a more self-directed and open-ended activity in which participants can put into practice skills and understanding from the course, with support and advice from fellow participants and the course tutor.

This course is suitable for researchers, analysts and policy makers who have some familiarity with spatial data - for example those who have worked with data related to small areas or neighbourhoods. It would be helpful if all participants have some experience in using a GIS for basic spatial data visualization or analysis, but this is not essential, all concepts and techniques will be taught form first principles. The tools we use are all menu-driven and comprehensive guidance notes are provided to all participants alongside hands-on support.

This course will be delivered in-person - participants should bring their own laptop and will require access to Microsoft Excel and QGIS. QGIS is an open source desktop GIS system which works on Windows and Mac operating systems. It is available via a free download from https://www.qgis.org/en/site/forusers/download.html.

Prior to the course, further information will be provided to registered participants to ensure that all participants have access to the required software

This 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 short course is designed to give participants a practical introduction to data linkage and is aimed at both analysts intending to link data themselves and researchers who want to understand more about the linkage process and its implications for analysis of linked data—particularly the implications of linkage error. Day 1 will focus on the methods and practicalities of data linkage (including deterministic and probabilistic approaches) using worked examples. Day 2 will focus more on analysis of linked data, including concepts of linkage error, how to assess linkage quality and how to account for the resulting bias and uncertainty in analysis of linked data. Examples will be drawn predominantly from health data, but the concepts will apply to many other areas. This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.

The course covers:

· Overview of data linkage (data linkage systems, benefits of data linkage, types of projects)

· Overview of linkage methods (deterministic and probabilistic, privacy-preserving)

· The linkage process (data preparation, blocking, classification)

· Classifying linkage designs

· Evaluating linkage quality and bias (types of error, analysis of linked data)

· Reporting analysis of linked data

· Practical sessions (no coding required; see below)

By the end of the course participants will:

· Understand the background and theory of data linkage methods

· Perform deterministic and probabilistic linkage

· Evaluate the success of data linkage

· Appropriately report analysis based on linked data

The course is aimed at analysts and researchers who need to gain an understanding of data linkage techniques and of how to analyse linked data. The course provides an introduction to data linkage theory and methods for those who might be implementing data linkage or using linked data in their own work. Participants may be academic researchers in the social and health sciences or may work in government, survey agencies, official statistics, for charities or the private sector. The course does not assume any prior knowledge of data linkage. Some experience of using Excel or other software will be useful for the practical sessions.

Preparatory Reading

Recommended (not required):

· Doidge JC, Christen P and Harron K (2020). Quality assessment in data linkage. In: Joined up data in government: the future of data linking methods. https://www.gov.uk/government/publications/joined-up-data-in-government-the-future-of-data-linking-methods/quality-assessment-in-data-linkage

· Harron K, Doidge JC & Goldstein H (2020) Assessing data linkage quality in cohort studies, Annals of Human Biology, 47:2, 218-226, DOI: 10.1080/03014460.2020.1742379

· Harron KL, Doidge JC, Knight HE, et al. A guide to evaluating linkage quality for the analysis of linked data. Int J Epidemiol. 2017;46(5):1699–1710. doi:10.1093/ije/dyx177

· Doidge JC, Harron K (2019). Reflections of modern methods: Linkage error bias. International Journal of Epidemiology. 48(6):2050-60. https://doi.org/10.1093/ije/dyz203

· Sayers A, Ben-Shlomo Y, Blom AW, Steele F. Probabilistic record linkage. Int J Epidemiol. 2016;45(3):954–964. doi:10.1093/ije/dyv322 · Doidge JC, Harron K. Demystifying probabilistic linkage: Common myths and misconceptions. Int J Popul Data Sci. 2018;3(1):410. doi:10.23889/ijpds.v3i1.410

Programme

Day 1

· Overview

· Deterministic linkage algorithms

· Linkage error

· Probabilistic linkage theory and practical demonstration

· Practical considerations (including variable selection, handling missing data and managing processing

requirements)

· Overview of advanced topics including privacy preservation, string comparators and linkage of multiple files

Day 2

· Recap: Common myths and misconceptions about probabilistic linkage

· Linkage error bias

· Linkage quality assessment

· Handling linkage error in analysis

· Reporting studies of linked data

· Software demonstration: Splink – open-source toolkit for probabilistic record linkage and deduplication at scale

This Hybrid course is available in-person at the University of Cambridge and also online. It will run over two mornings on Tuesday 21st February and Monday 6th March 2023, 10:00 - 12:30, and equates to one teaching day for paypment purposes.

This training is designed for researchers and students who are interested in podcasting as a research practice, sonic method, and as effective means of communication and public engagement with research. No prior experience is required.

This training explores the potential of podcasting as a creative, qualitative research method. While academics have already started to discover podcasting as an effective digital medium for research communication, podcasting can also be used as part of a creative research practice and methodology. In this training, which consists of two sessions of 2,5 hours each, we explore some of the ways in which podcasting can be used to this end. This firstly includes podcasting as a participatory action research method, with podcasts serving as public outward-facing platforms for collective action, reflection, and public engagement. Secondly, podcasting can be used as a sonic elicitation technique during interviews and focus groups to elicit rich, detailed, embodied, and affective responses from participants.

Participants will have the opportunity to get hands-on experience with podcasting by creating their own ‘mini-podcast’ (1 short episode). The topic of this exercise can be related to the participant’s own current research practice, where desired and appropriate. For this introductory-level training, you do not require any prior experience with podcasting or other forms of audio production.

The course covers:

In this training, we will cover a range of topics related to podcasting in research contexts, from ethical considerations and methodological applications to the practice of podcasting itself. This includes the design and set-up of a podcast (such as genre, format, ‘branding’, and determining your target audience), practical approaches to audio recording, basic editing and (post-)production, and finally the distribution of episodes to public platforms such as Spotify and Apple Podcasts.

By the end of the course participants will:

•  Have a basic understanding of how podcasting can be used as a research method
•  Be able to produce a podcast for research purposes
•  Have an understanding of how to publish and promote a podcast (post-production)

This course is aimed at students and researchers interested in podcasting as a research practice.

Participants will need to sign up to the Anchor app for podcast production. Alternatively, participants may choose to work with software such as GarageBand (for more advanced editing options), but no previous experience with either of these two programmes is required.

Programme

Tuesday 21 February, 10:00 – 12:30

Session 1:

- Introduction to podcasting as research method (PAR method, and podcast elicitation)

- Ethics of podcasting, strengths and weaknesses of podcast methods

- Demonstration of podcast production and instruction for hands-on assignment

Monday 6 March, 10:00 – 12:30

Session 2:

- Listening to excerpts of the mini-podcasts produced by the participants

- Group discussion and feedback 

- Time for Q & A

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).

Britain’s energy regulator Ofgem is set to increase its cap on energy prices by 54% this April 2022. This is in response to the skyrocketing price of gas, aggravated by demand picking up as countries relaxed lockdown measures, low-wind speeds, and bottlenecks in supply chains. Over the same period, a recent ONS survey found that of the adults who reported a rise in the cost of living, 79% reported energy bills among the relevant causes.

This two-day online course aims to postgraduate researchers and analysts interested in quantitative analysis of energy poverty and its effect on people’s wellbeing. This consists of lectures and practical sessions on measurement of energy poverty and on (causal) analysis on its effect of people’s health and wellbeing outcomes.

The measurement of fuel poverty can be explored from two key perspectives. The objective approach relies primarily on household income and expenditure on energy bills to measure the prevalence of fuel poverty. In contrast, the subjective (sometimes referred to the ‘consensual’) approach uses households stated ability to afford energy at a reasonable price as well as characteristics of the home (e.g., damp). We will explore the advantages and disadvantages of these approaches. In addition, we will explore key associations between fuel poverty and outcomes that affect the health, wellbeing and wealth of individuals.

The course covers:

• The set of different measures to define energy poverty;
• (Causal) analytical methods to explore the association of energy poverty with health and wellbeing outcomes;
• Empirical evidence on the association between various definitions of energy poverty and wellbeing outcomes;
• Practical example and real word evidence using Stata. 

By the end of the course participants will:

• be able to understand the different definitions of energy poverty;
• be aware of the set of analytical methods to be used in the analysis of the association between energy poverty and wellbeing outcomes;
• implement analysis using Stata on the measurement of energy poverty based on its various definitions;
• implement (causal) econometric analysis to explore the association between energy poverty and a set of wellbeing outcomes.

This course is suitable for postgraduate researchers and analysts interested in energy poverty research including (but not limited to): Academics, Government Researchers, Third sector organisations and Consultancy analysts.

This course will focus on the conceptual understanding of multilevel modelling and its relevance for robust analysis of evidence from randomised controlled trials, with case studies from educational and clinical trials. It will focus on ‘meaning’ and application of multilevel models instead of computations. The course will run for three days with the first day focusing on the transition from linear regression models to multilevel models. Practical examples with simple exercises will be used to motivate the need for a more robust approach than t-tests or linear regressions in randomised controlled trials. The different sources of variability will be discussed as well as their implications on effect size. The course will primarily be taught in R, but we would also be able to support individual exercises in STATA. This is an intermediate course that requires good understanding of the linear regression model as a prerequisite.

The course covers:

• Simple and multiple linear regression
• Overview of multisite and cluster randomised controlled trials
• Hierarchical and correlated data structures
• Random intercepts models
• Random site by intervention models
• Multilevel models for longitudinal data

By the end of the course participants will:

• Gain practical skills in converting data to long form
• Make a link between study design and analytical choice
• Gain practical skills in applying multilevel models and interpreting results
• Acquire necessary skills to check robustness of results from educational or clinical trials

This course is suitable for postgraduate students, researchers, trial statisticians and methodologists and participants should have a basic understanding of statistical methods including the linear regression model and analysis of variance. Participants require access to computer with R enabled software. It is recommended to use RStudio for coding and running R.

This 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. Instructions for how to set up data in Stata and Stata code with solutions to all practicals will be provided to all participants.

In this two day course (which will be taught online over 4 mornings), you will learn what GIS is, how it works and how you can use it to create maps and perform spatial analysis. We assume no prior knowledge of GIS and you will learn how to get data into the GIS, how to produce maps using your own data and what you can and cannot do with spatial data. You will also learn how to work with a variety of different data sources and types (including XY coordinate data and address or postcode data) and using spatial overlays, point in polygon analysis and spatial joins.
The course covers:
• What is GIS and spatial data?
• How to classify data for a choropleth map
• How to create a publication ready map
• How to work with different data sources including XY coordinate and postcode data
• Using attribute and spatial joins
• Using spatial overlays and spatial analysis
• How to apply these skills to your own data

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

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. 

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

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

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

This course is aimed at researchers and teachers who have previously mostly worked in the qualitative tradition of research but wish to understand or begin to research in the quantitative tradition.  It assumes no more than a lay knowledge of quantitative methods (such as surveys or polling) and will take participants on a journey from the methodological and epistemological foundations of quantitative methods, through design, sampling and principles of analysis. No prior statistical knowledge is required. The course will mostly follow a problem based learning approach.

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

The course covers:

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

By the end of the course participants will:

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

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

This one-day course alerts participants to the potential of performance as a method for research in the humanities and social sciences, and the utility of performance in widely disseminating research findings. It will use the work of the course leader’s own research-led production company Sidelong Glance as a focal point for discussion. After introducing the company and its past, current, and planned future productions, Sidelong Glance founder/director Dr Eleanor Lybeck will perform the original one-woman show Wild Laughter; this performance will be followed by an audience Q&A. The second half of the course will be dedicated to the practicalities of production, including funding applications to academic and non-academic sources, and group and individual sessions during which participants will have the opportunity to discuss their ideas for performance projects emerging out of their own research.

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.

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.