National Centre for Research Methods

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 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.

Session 1: creative methods and ethics in a pandemic
Session 2: enhanced and mobile interviews
Session 3: using comics and animation in research
Session 4: using video in research
Session 5: poetic inquiry
Session 6: metaphor collection and analysis
The course will focus on gathering and analysing data. Exercises will offer hands-on experience, and an online space will be available for discussions and feedback in between the Wednesday sessions.

Social science data is increasingly complex and routinely requires substantial preparation prior to analysis. This aspect of the data analysis workflow is often overlooked in many courses covering statistical modelling.

This one-day course will demonstrate a range of useful functions using R, through the extensive capabilities of the dplyr and tidyverse suit of commands.

The workshop is specifically designed for social scientists, and social science data and examples will be showcased throughout the workshop.

The event is intended to be engaging and informative, it will be delivered online during the COVID-19 crisis.

This is a hands-on workshop using R.

The aim of this training session is to explore the role of the researcher and more specifically, the researcher's emotions within the process of qualitative research. Emotions are a fundamental part of how we live in and experience the world around us, and both, researchers and participants have emotions and feel emotional responses throughout the research process, even if they are sometimes difficult to acknowledge, express, capture and process (that is recognise, name, accept and then potentially act on).

This training event draws on embodied and creative techniques to focus on the presence of emotions in research and to present practical strategies for practising reflexivity – how we can attend to and become aware of them in ourselves as researchers and in our participants, how we can hold emotional space for ourselves and our participants to express emotions safely, how we can capture the emotional resonance created in research, again in ourselves and in our participants, and how we can process the emotions we feel directly, and as a result of our participants’ experiences.

The session will be delivered as a combination of group discussions, creative tasks using materials participants will have available at home and short presentations from the course leader.

Course Contents:

• Emotions and reflexivity in research, particularly in relation to data collection and analysis
• Theoretical underpinning to reflexivity
• Practical strategies and methods for developing and maintaining reflexivity
• Consideration of what to do with "reflexivity", "reflexive data" and "emotions" in the research process
• The role of the researcher's emotions in data collection and analysis

Learning Outcomes:

• Have knowledge and critical awareness of what reflexivity in qualitative research is and means
• Have practical knowledge and personal experience of practical methods and strategies to acknowledge and capture emotions and experiences
• Be equipped to capture emotions and be mindful of their impact on research
• Have a working understanding of dealing with emotions in self and others
• Feel permitted to "experiment" and "try" in qualitative research

The course will be delivered online and the course will run from 10:00 - 17:00 with an hour break for lunch

Target Audience: This course is suitable for anyone who would like to experience and learn more about reflexivity and the role of the researcher's positionality within qualitative research. It is expected that participants will have prior experience of and with qualitative research. Participants at the early, middle and final stages of a qualitative research project will benefit, but if possible, as it is best to consider emotions and reflexivity at all stages of the research.

The online course gives a non-technical introduction to understanding and estimating quantile treatment effects. Quantile treatment effects have become more popular as the importance of heterogeneity in treatment effects is recognized and interest in distributional estimators increases.

We will start with a very brief review of the potential outcome framework for estimating treatment effects and discuss how this framework can be extended beyond the mean, as well as the challenges posed by this extension. We will then discuss three classes of estimators: conditional quantile regressions, unconditional quantile regressions and distribution regressions, focusing on how to (and how not to) interpret them. Finally, we look at the links between these estimators and how conditional quantile regressions and particularly distribution regressions can be used to calculate quantile treatment effects.

The course covers:

• The potential outcomes framework to evaluate treatment effects
• Going beyond the mean: quantile treatment effects: what they are and why they are of interest
• Conditional quantile regressions and their interpretation
• Unconditional quantile regressions and their interpretation
• Distribution regressions and their interpretation
• Conditional, unconditional quantile and distribution regressions: how to (and how not to) use them to estimate quantile treatment effects
• Using Stata to estimate conditional, unconditional and distribution regressions

By the end of the course participants will:

•  Understand what quantile treatment effect are and their relevance from a treatment effects perspective
•  Understand the uses (and abuses) of conditional, unconditional quantile and distribution regressions
•  Understand how quantile treatment effects can (and how they cannot) be estimated
• Use Stata to estimate their own conditional, unconditional, and distribution regressions models

This course is aimed at postgraduate students and early career academics in social sciences, as well as research staff at government institutions and charities. The course is meant to be relatively non-technical and focuses on the intuition behind the methods, but some mathematical notation and equations will be used.

The course assumes very good familiarity with distributional statistics (e.g., mean, median, quantiles) and the regression framework. It also assumes some knowledge of the potential outcome framework used to estimate treatment effects (this will be only briefly reviewed in the course). Good working knowledge of Stata is necessary to be able to fully take part in the practical sessions.

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 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.

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
• Campbell, M. L., & Gregor, F. (2002). Mapping social relations: A primer in doing institutional ethnography. Garamond Press.

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.

Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences.
In this course you will learn both how to clean longitudinal data as well as the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis.
The course is organized as a mixture of lectures and hands on practicals using real world data. During the course there will also be opportunities to discuss also how to apply these models in your own research.

Objectives:
- To gain competence in the concepts, designs and terms of longitudinal research;
- To be able to apply a range of different methods for longitudinal data analysis;
- To have a general understanding of how each method represents different kinds of longitudinal processes;
To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.

This course is aimed at all quantitative researchers, academic and non-academic, with experience/knowledge of performing causal analysis with data from observational studies and of some of the challenges (e.g. adjusting for confounding bias/selection on observables, non-random selection, endogenous regressors).  It should be suitable for junior researchers or senior researchers who wish to get a hands-on introduction to this topic.

Some prior knowledge of programming would be desirable but not essential.  Experience with some statistical package should be sufficient to understand and run the exercises. Some familiarity with high-level programming concepts. Ideally, if you want to participate in the practical element of the course, have a python interpreter installed in your computer.

This workshop will

1. Introduce the basic principles of causal modelling (potential outcomes, graphs, causal effects) and emphasise the key role of design and assumptions in obtaining robust estimates.
2. Introduce the basic principles of machine learning and the use of machine learning methods to do causal inference (e.g. methods stemming from domain adaptation and propensity scores).
3. Show how to implement these techniques for causal analysis and interpret the results in illustrative examples.

The course covers:

• Fundamentals of causal analysis
• Basic machine learning techniques
• Running simple causal analysis using machine learning on real data sets

By the end of the course participants will understand

•  The distinction between associations and causal effects and the key role played by study design and untestable assumptions in causal analysis
•  How the training and testing steps in machine learning work and play a similar role to significance testing in traditional statistics
•  The basics of Python and how to set up, run and interpret the output from causal learning algorithms

This course is suitable for all researchers and analysts interested in the measurement of socio-economic inequality in health and health care, including (but not limited to): Academics, Government and Third-Sector Researchers. It will be assumed that all participants have some experience of analysing observational data (e.g. from surveys) using statistical regression models.

The course will be conducted using Python in the user-friendly Google Colab environment (participants will be given details of how to register and use the platform)

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

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

This short online course aims to introduce participants to time diary analysis, a multidisciplinary field which has made a sustained contribution to social science over the last 50 years. It is targeted at academics, doctoral students, post-doctoral as well as public or private sector researchers interested in studying the way people spend their time throughout the day.  It requires basic to intermediate prior knowledge of statistics and basic experience with statistical programming.

Across the social sciences there is widespread agreement that longitudinal data (e.g. studies that repeatedly contact individuals) provides powerful research resources to examine both social change and social stability. There is now a broad portfolio of longitudinal data available to social science researchers. Many social science research questions can be adequately answered without longitudinal data; however, most research projects will benefit from the addition of longitudinal data analysis, and some research questions can only feasibly be answered using longitudinal data.

This is a two day workshop on longitudinal data and research using statistical methods.

The workshop is specifically designed for social scientists, and social science data and examples will be showcased throughout the workshop. The workshop will focus on the research value of longitudinal data and explore sources of longitudinal data. Participants will be introduced to the analysis of repeated cross-sectional data, duration models and models for panel data. The emphasis will be on interpreting outputs (e.g. from data analysis software packages) and understanding results (e.g. in published papers).

The event is intended to be engaging and informative and there will be audience participation and practical demonstrations.

This is not a practical workshop and it does not provide training in the use of data analysis software. It will however provide a strong theoretical foundation for future engagement at practical workshops that are designed to provide hands-on training in data analysis.

A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters degree) or have attended the one day workshop on Statistical Modelling (held the day before).

Researchers who are at any career-stage are welcome.

A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters degree) or have attended the one day workshop on Statistical Modelling (held the day before).

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

The course covers:

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

By the end of the course participants will:

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

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

This course will provide participants with an understanding of how Hospital Episode Statistics (HES) data are collected and coded, their structure, and how to clean and analyse HES data. A key focus will be on developing an understanding of the strengths and weaknesses of HES, how inconsistencies arise, and approaches to deal with these. Participants will also learn how to ensure individuals’ anonymity and confidentiality when carrying out analyses and publishing results based on HES. The course consists of a mixture of lectures and practicals for which participants will use Stata software to clean and analyse HES data.

The course covers:

• HES data collection and coding

• HES data structure

• How to clean and manage HES data

• How to ensure anonymity and confidentiality

• How to carry out basic analyses using HES data

• Sources of variation in HES data

• How to apply for HES data

By the end of the course participants will:

• understand how and why HES data are collected
• become aware of the strength and weaknesses of using HES data for research
• know how to carry out basic cleaning, management and analysis tasks using HES data
• know how to ensure anonymity and confidentiality when using HES

The course is for researchers and data analysts in academia, government and private sector at all levels who are using or planning to use HES for their work.

There are no pre-requisites for the lectures. Computer practicals will involve analysis of simulated data therefore previous experience of programming in Stata, R or SAS will be helpful. Instructions for how to set up data in Stata and Stata code with solutions to all practicals will be provided to all participants.

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

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

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

This course will introduce the age-period-cohort identification problem, and what it means for longitudinal social science research. We will consider some supposed solutions to the identification problem, some old (such as the combining age categories) and some new (such as the hierarchical age-period-cohort model) and consider the extent to which these can and cannot produce accurate age-period-cohort estimates. Finally, we will consider what we can do as researchers interested in age, period and cohort effects – both in terms of statistical models, and data visualisation

The course will include practical elements, taught using R.
R and RStudio – students should install these on their computer in advance of the course.

Course Timings: 09:30 – 17:00
Students should have some familiarity with R, since the course will not have time to go over the basics of the software (although code will be provided for students to follow). Students should also have a decent understanding of multiple regression models, and ideally some experience of multilevel models (although we will give a brief introduction to this in the class).

Course Contents:
• The age period cohort identification problem
• Statistical models with various combinations of age, period and cohort, and the assumptions they make
• The problem with statistical “solutions” to the identification problem
• Best practice with APC analysis: statistical models
• Best practice with APC analysis: data visualisation – lexis plots

By the end of this workshop you will:
•    Understand the age period cohort identification problem, and the challenges it raises for longitudinal research
•    Be able to critique supposed solutions to the identification problem, carefully considering the assumptions that those solutions make
•    Develop statistical models that make APC assumptions explicit, based on theory
•    Produce data visualisations (in particular, lexis plots) to show APC patterns in 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.

Ludwig Wittgenstein is widely considered one of the greatest and most influential philosophers of the 20th century who in his later work produced a radical and distinctive approach to philosophical analysis, which had far-reaching ramifications for research in the social and human sciences. What does Wittgenstein and Witgensteinian philosophy have to say to Social Scientists and Psychologists in the 21st century?

Wittgenstein’s impact on the social sciences can be divided into a number of phases, some of which (2 & 3) spawned distinctive research programmes:

1. Via reception of the work of the Wittgensteinian philosopher Peter Winch and his influential book The Idea of a Social Science and its Relation to Philosophy, published in the mid-20th Century.

2. The combining of Wittgensteinian insights with Ethnomethodology and Conversation Analysis (EMCA) beginning in the early 1970s and found in the work of the ‘Manchester School’ of Ethnomethodology and Conversation Analysis in the work of authors such as Wes Sharrock, Rod Watson and Bob Anderson in addition to associated figures such as Mike Lynch (Cornell) and Jeff Coulter (Boston).

3. The combining of Wittgensteinian insights with Michael Billig’s Rhetorical Analysis, Ethnomethodology and Conversation Analysis to form Discursive Psychology, initiated by Johnathan Potter, Derek Edwards and Margaret Wetherell, beginning in the 1980s at Loughborough University and being developed by others in the decades that have followed.

Run across two afternoons, this course will revisit the philosophical sources that inform a Wittgensteinian approach to questions in the social and human sciences and explore these alongside the approach to interaction found in Ethnomethodology.

Humans are connected in various ways, forming social networks. The quality of social ties and the structure of social networks strongly influences individual-, group- and society-level outcomes. Building on social theory, this course introduces theoretical foundations and methodological tools for the analysis of small social networks (e.g., school cohorts, organisations) relevant for social scientists. We will learn to visualize social networks, to describe the positions of individuals within their networks, and to identify subgroups. After a general introduction to the statistical modelling of social networks and to the most important model families, we will gain deeper insight into a few important models. Practical information about how to collect social network data, as well as ethics of such data collection will be briefly discussed. Participants not familiar with R are advised to learn the basics in order to be able to follow the course.

The course covers:

• Centralities in social networks
• Dyads, triads, cliques, and communities
• Data collection and research ethnics
• Visualisation of social networks
• Social network mechanisms
• Statistical models
• Exponential Random Graph Models
• Stochastic Actor-Oriented Models

By the end of the course participants will:

•  be able to describe and visualise small social networks
•  be familiar with the basic principles of social network modelling
•  be able to apply some social network models to appropriate data

Pre-requisites

Basic knowledge of R (an online introductory course would be sufficient)

Basic knowledge of quantitative social science methods and statistics (e.g., linear and logistic regression)

Programme

Day 1: Introduction, basic concepts, descriptive Social Network Analysis, visualisation

Day 2: Data collection, data ethics and statistical models of social networks

Day 3: Exponential Random Graph Models or Stochastic Actor-Oriented Models

THIS COURSE IS BEING RUN OVER 3 MORNINGS (10AM-1PM) ON 2-4 NOVEMBER AND EQUATES TO 1.5 DAYS OF TRAINING FOR PAYMENT PURPOSES.

Economists (and more broadly social scientists) 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 two-day course aims to postgraduate researchers and analysts interested in the quantitative analysis of inequity and (socio-economic and regional) inequality in health. 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 surveys 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 may significantly affect and contaminate the measurement of socio-economic inequality in health when relying on self-reported health measures. We will draw conclusions on the potential implications of measurement error in self-reported health measures for research in inequalities in health.

We will also provide some 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 health data and its potential implications for the socio-economic inequality in health research that relies on self-reported health.

• Practical sessions and illustrative examples on the measurement of 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 conduct basic research into health inequalities.

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.

The social world is complex and messy. Statistical models provide a formal approach to evaluate data, test ideas and investigate research questions.

This is a one-day Stata workshop on statistical models for social science data analysis.

The workshop will concentrate on models within the generalized linear modelling framework. It will cover linear regression, and models for binary, categorical, ordered categorical and count data. The focus of the workshop will be on social science applications, and social science data and research questions will be showcased throughout. The emphasis will be on interpreting outputs (e.g. from data analysis software packages) and understanding published results.

•    Be able to interpret statistical modelling results
•    Understand linear regression models
•    Understand models for binary, categorical, ordered categorical and count data
•    Understand the model building process (e.g. selecting variables, goodness of fit and model criticism)
•    Be aware of some statistical data analysis software solutions

The social world is complex and messy. Statistical models provide a formal approach to evaluate data, test ideas and investigate research questions.

This is a one day workshop on statistical models for social science data analysis. It will introduce the underlying concepts associated with multivariate analysis using statistical models. The workshop will concentrate on models within the generalized linear modelling framework. It will cover linear regression, and models for binary, categorical, ordered categorical and count data. The focus of the workshop will on social science applications, and social science data and research questions will be showcased throughout. The emphasis will be on interpreting outputs (e.g. from data analysis software packages) and understanding published results.

The event is intended to be engaging and informative and there will be audience participation and practical demonstrations.

This is not a practical workshop and it does not provide training in the use of data analysis software. It will however provide a strong theoretical foundation for future engagement at practical workshops that are designed to provide hands-on training in data analysis.

A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters degree).

Researchers who are at any career-stage are welcome.

A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters degree).

Increasingly, film and video are used in research as tools of data collection, as outputs in which dialogue between researchers and participants are shared, and as a creative means of disseminating research results. This innovative and transdisciplinary series of training programmes will develop your knowledge and skills about the use of film making in research, particularly in research with groups often identified as marginalized, vulnerable and/ or excluded.

The aim is to consider how the use of film in research opens up alternative ways of knowing and doing, storytelling, with opportunities for using mixed methods in data collection, capturing reflections, democratising the research process and for research findings shared in a format that is accessible to a range of audiences.  Technological advances mean films and/or videos are effective tools for raising awareness and advocating for change.

This one day-course is designed for researchers that are interested in time-use research. Participants will be introduced to the state of art in time-use research and the analytical opportunities provided by existing time-use resources in the United Kingdom. The course will also cover topics surrounding time-use data collection, including diary design as well as advantages and disadvantages of different modes of time-diary data collection, including web and app technologies. The course will also cover the main statistical modelling techniques that can be applied to time-use data. The course is suitable for researchers interested in collecting time-use data as well as those interested in secondary analyses of large-scale time-use surveys.

Course Timings: 10:00 – 16:00
A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters-level degree)

Since the turn of millennium research funders, governments, and publishers, both nationally and internationally, have increasingly demanded transparency in all social research, and more latterly required transparency in qualitative research. What impact will this have on qualitative researchers?

The vocabulary of the transparency agenda can be confusing, with terms such as, inter alia ‘open science’, ‘open access’, ‘open data’, ‘data sharing’, and ‘data access’ replete throughout funders guidelines and publishers’ statements. The ethical and practical considerations associated with the production, storing and sharing of the many and varied forms of qualitative data can be daunting. Moreover, the very idea of sharing ‘our own’, what can feel personal and even intimate, qualitative data is hotly debated and controversial.

The aim of the workshop is to introduce and debate these issues. Following an introductory presentation, the session will encourage participants to debate these issues. Ideally, where possible, researchers will bring examples from their own experiences of generating qualitative data and share reflections on factors that hinder and enhance its transparency. Others may be thinking about carrying out qualitative research and want to think about how they should ensure they can meet the requirements of transparency. Still others may resist the idea of transparency in qualitative research altogether. All contributions and considerations are welcomed.  

Course Timings: 10:00 – 16:00
Where possible participants are asked to bring along their own examples of their previous, current or anticipated research and note any questions and issues they want to discuss relating to how their work might meet the criteria of transparency (in what every shape or form they think transparency might take).

This online course will outline creative research methods and show you how to use them appropriately at every stage of 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 more conventional research methods and are often particularly useful when addressing more complex research questions. You will have the opportunity to try applying some of these methods in practice, and 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 more conventional methods
•  Understand when to use creative methods in research
• 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.

Participants on this one-day training course will learn how to conduct research aimed at improving accountability for climate change.  The course will show participants how to generate data that is focussed on key actors in a particular geographic/industrial/economic context, and build a case study that they can use in their research project, or in a stand alone project.

The course will teach participants to navigate specialist secondary sources and apply investigative methods to produce unique analysis of responsible actors, the role they play in climate change and the benefits they gain from environmentally harmful practices.