National Centre for Research Methods

This course equips participants with the knowledge and skills to build, calibrate and apply powerful spatial interaction models (SIMs). SIMs are statistical models used to predict origin-destination flows. They are widely applied within geography, planning, transportation and the social sciences to predict interactions/flows related to commuting, migration, access to services etc. They are also widely applied across the commercial sector – for example to model flows of consumers between home and retail centres – with broad applications in commercial decision making and policy evaluation.

This hands on course equips participants with the skills to build, calibrate and apply spatial interaction models suitable for addressing a range of research questions. We don’t assume any prior knowledge of spatial interaction modelli

Variational Bayes is a tool used to allow scalable Bayesian inference and is applicable in a huge variety of applications. This course will give an introduction to Variational Bayes and describe its use in some common regression settings. The course will consist of a mixture of taught lectures and R practicals, giving participants an understanding of both some of the theory of variational methods, and the practical implementation in some common models.

We often learn statistical data analysis skills using data which is assumed to arse from a simple random sample of the target population. However, most large scale multi-purpose social survey data resources use complex sampling strategies. These include many of the UK’s large scale infrastructural data resources such as Understanding Society or the Millennium Cohort Study. Complex sample designs need to be taken into account when analysing these data if we intend to make inferences to a wider population.

This workshop will provide students with the skills to appropriately analyse complex samples data using Stata.

Students need access to Stata, and some familiarity with Stata is assumed.

Course Timings: 11:00 – 15:00 each day

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. The session will end with a space to design your own research protocol using creative methods; participants will be able to bring their own projects to work on or a scenario will be set as a practical example. Feedback will be provided by experienced participatory researchers and peers, including a discussion of adapting methods to suit different contexts and communities.

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.

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 explain when and why to use documents as data for research and show you how to gather and analyse documentary data.

The course covers:

• When and why to use documents as data
• How to find personal and official documents, historical documents, mainstream print media documents, virtual documents, and self-published documents
• How to assess the quality and usefulness of documents for research purposes
• How to analyse documentary data

By the end of the course participants will:

• Know how to find the documents they need for their research
• Know how to assess the quality and usefulness of documents
• Understand how to approach the analysis of documentary data
• Have an action plan for using documents in their own research

This is an intermediate level course assuming a good basic knowledge of research methods. It would suit postgraduate students, early career researchers in academia, practice-based and independent researchers. Participants will need a good basic knowledge of research methods.

THIS COURSE IS TAUGHT OVER TWO MORNINGS (09:30-12:30) AND EQUATES TO A ONE DAY COURSE FOR PAYMENT PURPOSES.

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. It’s taught by Ray Adams Row Farr, an open source investigator for Amnesty International’s Evidence Lab. She has been researching, training and teaching open source investigation in a human rights context for the past five years.

PART 1

Over the two days of this first part of the course, you’ll develop your understanding of what open source is and learn the stages involved in the research. On day one, drawing from relevant case studies, you’ll be introduced to open source research, its potential applications and how to engage with your own wellbeing as a researcher. On day two, we’ll build on this ground work by diving into the stages of an open source investigation, split into discovering and verifying content.

Topics covered:

• What is Open Source Research
• Vicarious Trauma
• Discovery
• Verification (chronolocation, reverse image search)

PART 2

Over two days in this second part of the course, you’ll build on and deepen the practical and theoretical skills learnt in Part 1 by learning geolocation, archiving and data curation, digital footprints and ethics more broadly. On Day 1 you’ll build on your practical skills verifying content by diving into chronolocation and geolocation, before moving on to consider how to structure and archive content in an open source investigation. On Day 2, we’ll broaden the discussion and bring together all methodological components discussed so far, to think about ethics in the context of open source investigations.

Topics covered:

• Geolocation
• Satellite imagery
• Data Curation
• Archiving
• Digital Footprint
• Ethics

This exciting opportunity to engage with Open Source Investigation is open to anyone, whether a PhD student, Early Career Researcher, researcher in the field, academic or interested in using OSI in any part of your work. This is an intensive four day course and there is preparation and follow-up work expected from you. Please note that you are required to attend both parts of this course, the dates are as follows:

Part I: Thursday 20th July 2023 and Friday 21st July 2023

Part II: Thursday 27th July 2023 and Friday 28th July 2023

This short course will be facilitated online

This course introduces the analysis and modelling of event history data. Event history analysis (EHA - also known as survival analysis or failure time analysis) is widely used in the social sciences where interest is on analysing time to events such as job changes, marriage, birth of children or time to divorce. The course draws on different data examples to illustrate event history approaches. This includes describing event history data, the semi-parametric Cox proportional hazards model, alternative parametric approaches and the discrete time modelling approach.

Course Timings: 10:00 – 16:00
The course will use the Stata software in the most recent version available.

Some familiarity with (and access to) Stata. Familiarity with alternative statistical analysis packages that transfer to Stata is also sufficient. Understanding of standard statistical analysis techniques such as OLS regression and/or other GLM models would be beneficial.

Target Audience: Postgraduate students and earlier career researchers who have some background in statistically orientated social science data analysis. Anyone curious about incorporating event history analysis into their methodological repertoire. People who have experience of regression and would like to expand on this would benefit from this course. The course is also a good stepping-stone between regression and other longitudinal modelling approaches.

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.

PLEASE NOTE THIS COURSE IS TAKING PLACE AT

University of London, Senate House, Malet Street, London, WC1E 7HU

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

The online one day course (which will be taught over two mornings) will introduce you to various empirical, quantitative methods that can be used to estimate the impact of a specific policy intervention. These methods can be referred to as “programme evaluation”, “impact assessment”, “causal estimation” or “impact evaluation”. The course assumes basic statistical concepts (mean, median, correlation, expected value, statistical significance and confidence intervals), and algebra is optional. It does not teach participants how to implement any of these methods using statistical software.

 The course covers:

• The evaluation problem, and how randomized experiments solve the problem
• An intuitive explanation of the advantages and disadvantages of matching, including propensity score matching; quasi-experimental methods such as instrumental variables; and difference-in-differences
• It does not teach participants how to implement any of these methods using statistical software

 By the end of the course participants will:

• Be able to think about evaluation in terms of “counterfactuals” and “informative contrasts” (or comparisons)
• Be able to explain intuitively the conditions under which propensity score matching, instrumental variables and difference-in-differences are likely to produce unbiased estimates of the impact of an intervention
• Be able to assess whether an actual or proposed design for an impact evaluation is likely to give reliable results, given the nature of the policy under consideration

This course is aimed at Government researchers and analysts interested in quantitative methods for impact evaluation, Third sector researchers and analysts interested in quantitative methods for impact evaluation and PhD students and junior researchers.

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 live online course covers why we need to deal with nonresponse bias in surveys and how weighting can be a good solution.  The course explores the 3 levels of weighting: (1) correcting for unequal probabilities of selection in the sampling design, (2) adjusting for nonresponse and (3) calibrating to population totals. But the focus is on nonresponse adjustment. Learn about all the weighting methods that can be used and their pros and cons.

Participants should have knowledge about surveys. It is helpful for participants to be familiar with the statistical concepts of sampling variance, confidence intervals and regression analysis.

Latent variable models relate a set of observed (or manifest) variables to a set of latent (or unmeasured) variables. In practice, this entails the use of models to summarise the relationship between a series of highly associated variables. It will be demonstrated that these models are specific examples of a wider family of measurement models.

This one-day course will showcase latent variable models through the application of these models to social science data. Particular emphasis will be given to estimation and interpretation of latent class models in Stata.

It is most suited to empirical social science researchers with knowledge of statistical data analysis methods.

Course will be delivered on-line from 10:00 – 16:00

Participants will need to use their own computers and have Stata installed. Familiarity with Stata is helpful but not essential.

This course will use Stata v17. To estimate latent class models in Stata using gsem commands, you will require Stata version 15 or newer.

The demonstration and practice exercises will use Stata. Familiarity with Stata is helpful but not essential.

The course is best suited to intermediate users of Stata, i.e. those with some prior knowledge of using Stata to analyse data. Those who have not used Stata before may find unfamiliarity with commands makes the material a little more challenging.

The course is available for all who conduct research using quantitative data. This may include those working in academia, the public or private sectors.



Policymakers need to know whether social programmes and policies make a difference, and often when randomised experiments are unethical or unfeasible. Causal matching and weighting, types of quasi-experiments, can satisfy this need. Using a range of examples, we will begin by exploring intuitive understandings of how matching and weighting methods work and progress towards a gentle introduction to key concepts that underpin them. A central message will be that a robust theory from the substantive topic area is an essential ingredient for quasi-experiments to deliver the conclusions that policymakers demand. By the end of the session, participants will be empowered to tackle the technical literature and learn more.

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.

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.

Reflexivity within participatory research goes beyond reflection of applying methods to recognising and attending to shifting power relations within and alongside community engaged research projects (Kindon et al. 2007).

Participatory and action-based research with communities requires an examination of collective and self-identity, positionality and power. It involves an immediate, dynamic and continuing self-awareness that reminds the researcher to deconstruct their positionality with the aim of producing a more trustworthy, transparent and honest account of the research (Finlay 2002; McGee 2002; Finlay and Gough 2003).

This one-day in person course aims to provide learners with an understanding of the key concepts and principles behind reflexivity in participatory research, including an understanding of the impact that positionality and power have on equitable research partnerships and outcomes. The course will provide practical tools and techniques for building reflexive practice into projects from research design to dissemination.

This in person course is taking place at The University of Liverpool Management School and will run from 9:30am to 4pm.

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.

The concept of “social networks” is increasingly a part of social discussion, organizational strategy, and academic research. The rising interest in social networks has been coupled with a proliferation of widely available network data, but there has not been a concomitant increase in understanding how to analyse social network data.

This course presents concepts and methods applicable for the analysis of a wide range of social networks, such as those based on family ties, business collaboration, political alliances, and social media.

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.

Deliberative research is emerging as a critical method for exploring public attitudes particularly on social and policy problems that are contested, complex or uncertain.  This live online course explores the principles, benefits and limitations of deliberative approaches to social research and in particular the challenges and opportunities of delivering these online. 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 as well as overseeing the delivery of research projects.  It covers a combination of theory and practical examples to consider both doing deliberative research and being a deliberative researcher. It does not focus on the analysis of deliberative data.

This one day online course will explore temporal research methods, in particular looking at qualitative longitudinal interviewing techniques and creative activities used in support of interviews to encourage participants to think over extended time periods. The course assumes that you have some knowledge of qualitative research methods – particularly interviewing, and builds on that knowledge by introducing qualitative longitudinal interviews and the use of creative activity packs.

The course will include practical advice and tips on using creative methods in research, as well as an opportunity to try out some of the activity packs. These include mapping, ranking exercises, photo elicitation, and the use of pre-existing images and video, amongst others.