National Centre for Research Methods

Survey and administrative data are frequently affected by data quality issues as a result of problems at the sampling and data collection stages (e.g. non-response, coverage error, social desirability bias or recording errors). Just one of those problems in its most basic form, like random measurement errors, can exert strong biases in multivariate analysis relying on this data. Yet, attempts to adjust for the impact of these types of data quality problems remain rare. 

We believe this is because of most approaches suggested in the literature, are either: i) designed for ad hoc solutions; ii) require additional forms of data often unavailable to researchers; or iii) are too complex, involving the adoption of different estimation methods and specialised software. 

In this course we introduce a combination of – relatively - simple adjustment methods available in R, that can be applied across a wide range of missing data, measurement error and misclassification problems, even in instances when all that we have is an educated guess of the extent of the problem. 

The course covers an introduction to common data quality problems and adjustment methods:

• Missing data, completely at random, at random, and not at random
• Coverage error
• Measurement error, additive, multiplicative, random, systematic, and differential
• Misclassification
• Multiple imputation
• SIMEX
• Sensitivity analysis

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

• Anticipate contexts in Social Science research where measurement error and missing data can be expected to be particularly prevalent.
• Distinguish between different types of measurement error and missing data problems employing the precise terminology.
• Adjust – or at least illustrate - the potential biasing effect associated to different types of data quality problems. 

This course is aimed at Social Science researchers of all backgrounds and disciplines, who undertake multivariate analysis with data prone to either measurement error, missingness, or both. It is essential that participants possess at least a beginner level of familiarity with R. Some basic understanding of regression modelling is also required.

This two day course will run from 10:00-17:00 on 10th January and 09:00-14:00 on 11th January 2024.

Preparatory Reading

The following references provide a useful reading list covering the methods that we will see in this course. They are listed in order of relevance:

Van Buuren, S. (2018). Flexible imputation of missing data. CRC press. https://stefvanbuuren.name/fimd/

Blackwell, M., Honaker, J., & King, G. (2017). A unified approach to measurement error and missing data: overview and applications. Sociological Methods & Research, 46(3), 303-341. 

Lederer, W., & Küchenhoff, H. (2006). A short introduction to the SIMEX and MCSIMEX. The Newsletter of the R Project, 6, 26. 

Gallop, M., & Weschle, S. (2019). Assessing the impact of non-random measurement error on inference: a sensitivity analysis approach. Political Science Research and Methods, 7(2), 367-384.

Pina-Sánchez, J., Buil-Gil, D., Brunton-Smith, I., & Cernat, A. (2022). The Impact of Measurement Error in Regression Models Using Police Recorded Crime Rates. Journal of Quantitative Criminology, 1-28. 

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.

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:

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 is a six-week course covering creative research methods and ethics in theory and in practice.

The course runs for 1.5 hours online each week, from 2-3.30 pm on Wednesdays, with associated readings, videos, exercises to do and online discussions in between the online sessions. 

A one day in-person hands-on workshop introducing the fundamentals of data visualisation and infographic design.

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

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.

This short course provides students with a state-of-the-art introduction to the intricacies and principles of experimental research in the social sciences. Participants will be guided through best practices for online experimental design and analysis, as well as learn how to carry out a range of Web-based experiments. 

The course will be structured with a blend of lectures and hands-on practical sessions, allowing students to actively engage and apply their learning.

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).

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 take place over the course of 1 day on Wednesday 14TH February between 10:00 and 16:00, with 1 hour for lunch between 12:30 and 13:30. 

In this one-day online training workshop you will be introduced to four qualitative methods to better understand diverse lives - Photo Go-Alongs, Collage, Life History Interviews and Participant Packs. When researching social groups, it can be tempting to  focus on categories such as age, gender, sexuality and so on. However, 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 working or researching with any group. 

The session is aimed at professionals working in the community sector (and those organisations who support such activity) 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 projects and programmes. 
• 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 7th February 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 course will overview popular causal inference methods and their applications in the study of inequality in education.

By discussing papers that use specific methods to investigate inequality in education, we will cover the econometric theory and how to implement the estimation methods in Stata statistical software.

This module is an introduction into quantitative methods for comparisons of educational achievement across countries. The module aims to familiarise students with the key OECD databases (e.g. PISA; PIAAC) and describe the statistical methods used to draw robust comparisons across countries.

Each session will be mirrored by a practical workshop seminar where students will put the analytical techniques introduced in the lectures to use. Students will learn how to analyse a large international databases using Stata.

By the end of the course, students should be able to use the OECD databases to conduct robust international comparisons.

Many students in the social sciences, especially in political science, public policy and public administration who decide to undertake qualitative or interpretive research feel they are unqualified to do so. They express deep-seated confusion about the reliability and generalizability of data, results, and conclusions. They feel that interpretive approaches lack the type of specialised training that has become commonplace in quantitative political science. The aim of this course is to redress this gap. We will equip students with a toolkit that will enable them to both conceptualise and execute an interpretive project. 

The course covers:

• Situating the interpretive approach in relation to other ways of doing political science research by reference to the philosophical, epistemological, and methodological assumptions on which these approaches are based;
• The theoretical and analytical tools students need to design and conduct their research project;
• The toolkit of methods used by interpretive scholars to collect data, including ethnographic and interview-based methods;
• The standards that will both ensure results are reliable and maximise the impact of findings; and
• Guidance on the norms and principles used to analyse data in an interpretive project.
• An introduction to comparative interpretive research

By the end of the course participants will:

• Be able to describe the strengths and features of the interpretive approach   
• Be able to develop and justify a sophisticated design for interpretive research
• Have experience interpreting rich qualitative data

This workshop will introduce Institutional Ethnography (IE), an interdisciplinary feminist approach to social research that focuses on how texts and language organise our everyday lives. IE is not just a methodology, but a comprehensive feminist ontology of how the social world works which advocates using a form of standpoint to explore from specific perspectives. IE research ‘takes sides’, often researching as, with, and/or for, marginalised groups who are often made invisible by, or excluded from, organisations and institutions. The focus on texts – conceptualised as replicable materials objects that carry messages – allows IE researchers to ethnographically explore the organising power of language and institutions, made material in institutional texts which act as bridges between different people and places.

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

The course covers:

• An overview of the work of feminist sociologist, Dorothy Smith, who developed Institutional Ethnography
• Three Institutional Ethnography case studies from Sociology and Human Geography
• Three text and discourse analysis methods within the Institutional Ethnography approach
• How to translate your research ideas or projects into an Institutional Ethnography proposal/plan

By the end of the course participants will:

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

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

Preparatory Reading

Required:

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

Desirable:

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

This short course is designed to give participants a practical introduction to ECHILD (Educational and Child Health Insights from Linked Data). ECHILD is a collection of linked, longitudinal administrative datasets covering health, education and children’s social care. More information about the data can be found in the ECHILD User Guide. The course is aimed at both analysts intending to use ECHILD and researchers who want to understand more about how the data can be used for policy relevant research. This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.  

Day 1 will provide information on the strengths and limitations of the different component datasets of ECHILD, through case studies of the National Pupil Database, Hospital Episode Statistics, Maternity Services Data, Mental Health Services Data, and the Community Services Dataset. Interactive lectures / tutorials will teach participants how to design a research study to answer a specific research question in ECHILD, focusing on the power and complexity of working with linked datasets. We will also discuss how to extract ECHILD data from SQL tables on the ONS Secure Research Service platform. 

Day 2 will include a series of practical sessions allowing participants to progress through an exemplar research study using ECHILD, covering phenotyping, developing cohorts, and analysing ECHILD cohort data.  

The course covers: 

• Overview of the component datasets of ECHILD 
• Accessing the data on the ONS SRS 
• Strengths and limitations of ECHILD 
• Development of electronic cohorts in ECHILD
• Phenotyping in ECHILD 
• Analysing ECHILD data

By the end of the course participants will:

• Understand what data are available in ECHILD 
• Know how to access ECHILD data
• Understand the strengths and limitations of ECHILD
• Know how to developing electronic cohorts in ECHILD  

The course is aimed at analysts and researchers who would like to know more about ECHILD and how ECHILD could be used in their own research, or who would like to know how it is currently being used to generate policy-relevant research. 

Participants should have a basic understanding of epidemiological research methods and statistical analysis. They should be comfortable in using R or Stata. Experience using administrative data is not required but would be an advantage. 

Participants should bring their own laptop and should have access to Stata or R for the practical sessions. 

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.

This online course (taught over two mornings) 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)

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

Session 1: Thursday 7 March 2024

- 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

Session 2: Thursday 21 March 2024

- 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).

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.

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.



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. 

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

Open science involves making scientific methods, data, and outcomes transparent to everyone. It includes making as transparent and available as possible (1) steps taken in data collection, processing and analysis that lead to the production of results, (2) study plans, data, materials and associated processing methods and (3) the results generated by the research. Reproducible science involves the potential for others to recreate reported results by repeating the original data processing and analyses with the original data.

Transparent and reproducible science results from open science workflows that allow you to easily share work and collaborate with others as well as openly publish your data and workflows to contribute to greater scientific knowledge. Facilitating openness, reproducibility and transparency in social science is important as it advances collaboration, scientific progress, trust in science, and the reusability of research. It also touches on ethical questions, for example in navigating between the creation of science as a public good and the protection of research subjects. 

This course introduces the principles and practical steps of doing cutting-edge open, reproducible and transparent social science research. Participants will learn how to conduct research that is easy to check and understand by providing easy-to-use access to methods and data. They will also learn how to conduct reproducible research the results of which can be easily recreated using the original data and steps in data processing and analysis.

The course covers an introduction to principles and practices of transparent and reproducible social science research:

• Motivations behind and principles of open, reproducible and transparent research
• Pre-analysis plan / pre-registration / Registered Report
• Multiverse analysis, sensitivity analysis, specification curve analysis
• Meta-analysis / systematic evidence synthesis
• Transparent reporting standards and disclosure
• Replication
• Data management and data sharing
• Script/code sharing and version control (e.g., using Github)
• Reproducible workflows (e.g., using R Markdown)
• Open access (preprints and publisher models)

By the end of the course participants will:

• understand the philosophical and meta-research-based rationale for doing open, reproducible and transparent social science research;
• have conceptual and practical knowledge of the main building blocks of open, reproducible and transparent social science research:
  • transparent study planning,
  • assessment of the robustness of research results,
  • sharing of research outputs to enable easy reproduction and replication.

This course is aimed at Social science researchers of all backgrounds, disciplines and levels (junior and senior) who undertake data analysis (quantitative and qualitative).  It is essential that participants possess at least a beginner level of familiarity with R. Some basic understanding of regression modelling is also recommended.

R and RStudio will be installed on all the desktop computers available in the teaching room. However, if you bring your own laptop, we recommend installing the R and RStudio in advance. You may also want to get an account on GitHub and download a desktop version of GitHub.

Preparatory Reading

The following references provide a useful reading list covering the methods that we will see in this course. They are listed in order of relevance:

Christensen, G.S., Freese, J. and Miguel, E. 2019. Transparent and reproducible social science research: How to do open science. Oakland, CA: University of California Press. https://doi.org/10.2307/j.ctvpb3xkg  

Moody, J.W., Keister, L.A. and Ramos, M.C. 2022. Reproducibility in the social sciences. Annual Review of Sociology. 48(1), pp.65–85.

Miguel, E., Camerer, C., Casey, K., Cohen, J., Esterling, K.M., Gerber, A., Glennerster, R., Green, D.P., Humphreys, M., Imbens, G., Laitin, D., Madon, T., Nelson, L., Nosek, B.A., Petersen, M., Sedlmayr, R., Simmons, J.P., Simonsohn, U. and Laan, M.V. der 2014. Promoting transparency in social science research. Science. 343(6166), pp.30–31.

Freese, J., Rauf, T. and Voelkel, J.G. 2022. Advances in transparency and reproducibility in the social sciences. Social Science Research. 107, Article 102770.

'Our Interlocked Universe': Sociohistorical Network Analysis; Methods, Applications and New Directions

‘Our Interlocked Universe’ is an interdisciplinary conference exploring the methods and applications of social network analysis (SNA) within historical contexts, offering scope to engage with demonstrations – both quantitative and qualitative – of historical SNA in practice alongside methodological discussions around the uses and limitations associated with using SNA in historical research. The event will be structured around themed panels and a keynote speaker.

Further information can also be found at https://ourinterlockeduniverse.wordpress.com/

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.

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.

Ethical research is better quality research. This online course (one day, taught over two mornings) is designed to raise your awareness of why and how you need to think and act ethically in practice throughout your research work. The current system of ethical review by committee can lead to the misleading sense of having ‘done ethics’. This course shows you how to conduct research which is truly ethical. It also provides the opportunity for discussion of your own ethical dilemmas, if you wish.

The course covers:

• Research ethics in context – ethical breaches past and present, ethics dumping, ethics activism
• Potential ethical pitfalls at each stage of the research process, from question setting to aftercare
• How to think and act ethically throughout research

By the end of the course participants will:

• Recognise the importance of context for ethical decision-making
• Understand why they need to think and act ethically throughout research work
• Be clearer about potential ethical pitfalls at different stages of the research process
• Know how to approach ethical thought and action at any point in their research

This course is aimed at Doctoral students, early career researchers (any discipline). Also practice-based/applied researchers. Possibly government researchers too, and independent researchers.

Reanimating data: A method for secondary analysis, historical enquiry and participatory data collection

This course will give you an overview of creative and participatory approaches to working with archived research materials.

The aim of this interactive workshop is to explore data reanimation as a qualitative research method. The focus will be on creative, participatory and innovative ways of working with archived qualitative research materials for the purpose of secondary analysis, historical enquiry and/or data collection.

The course is structured around analysing the demographic components of change (fertility, mortality and migration) and finishes with a discussion of cohort component projection methodologies. Recent advances and debates in demography will be considered.

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. 

The course covers:

• The Demographic Equation
• Demographic Data
• Fertility: Period and Cohort Approaches
• Mortality: Measuring Mortality and the Life Table
• Migration: measurement and associated issues
• Population projections
• Recent advances and debates in demography

By the end of the course participants will have:

• understanding of the way demographic pattern and process influence our understanding of changing populations and of related social issues
• knowledge and understanding of the key theory and principles underlying demographic analysis
• ability to calculate and interpret a range of measures for demographic analysis
• the ability to access and use appropriate data sources for demographic analysis
• an ability to use demographic data sources and demographic method intelligently in a range of real world applications

The only pre-requisite for the workshop is an understanding of the use of formulas in Excel, however an interest in demographic processes and some knowledge of rudimentary statistics will also be helpful.

Course Timings: 09:30 - 17:00

This one day-course is designed for researchers that are interested in researching disability inequalities. It aims to provide researchers with a firm understanding of the strengths and challenges of quantitative and qualitative approaches in the field of disability research. The course will cover topics surrounding different models of disability, and their implications for measurement. Participants will also be introduced to existing cross-sectional and longitudinal UK resources that can be used to document and/or explain disability inequalities. Limitations of existing data will be reviewed, alongside with priorities for future data collection. The course will also focus on qualitative data collection covering a wide range of topics such as inclusive methods and lived experiences, longitudinal approaches, intersectional designs as well as sampling of hard-to-access disabled populations. The course will therefore provide a well-rounded introduction on key challenges for those interested in primary data collection and/or secondary data analysis of existing data to systematically research disability inequalities. This course is ideal for disability researchers, social scientists interested in social inequality, and professionals from the third sector looking to broaden their knowledge on disability research. 

Course Timings: 10:00 - 16:00

This workshop is devoted to the use of ‘sequence analysis’ to study life trajectories, such as changes in peoples employment, housing, and family formation over-time. Sequence analysis covers a range of techniques; this workshop will focus on those techniques that help group people with similar life trajectories, and the generation of data visualisations to interpret life trajectories. The workshop is aimed at a social science audience, and will work through practical examples to introduce the theoretical concepts of sequence analysis. Basic techniques of sequence analysis using R will be covered; however, by providing some theoretical background, this will help users applying sequence analysis techniques using other software (i.e., STATA).

The course covers:

• Different methods of managing longitudinal data to enable sequence analysis 
• Production of descriptive statistics and visualisaitons of life trajectories
• Methods of identifying and grouping people with ‘similar’ life trajectories

By the end of the course participants will:

• Know what data can be used in sequence analysis and how it needs to be formatted
• Describe and visualise life trajectories
• Create and describe ‘typologies’ of people based on similar patterns of life trajectories

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).

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

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

This 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.

This workshop focuses on visual and graphical possibilities in R. The workshop will introduce the ‘grammar of graphics’ and enable users to create visualizations that summarise data and statistical results. The workshop will emphasise producing informative and elegant graphics for presentations and for research publications.