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National Centre for Research Methods

National Centre for Research Methods

Advances in Diary Method for Qualitative Researchers

Advances in Diary Method for Qualitative Researchers

This advanced training event will help you to use solicited diary method to gather qualitative information. You will learn about gathering, handling, analysing and reporting data sourced through solicited diary method, through real-life diary data, small group work, and mini lectures.

The event will cover the practicalities, as well as the methodological and ethical decisions involved in using solicited diary method, such as getting diaries back, providing financial incentives, protecting the well-being and privacy of diarists and their diaries during the data collection phase, and techniques for analyzing diary data, including visual diaries.  In particular, we will consider how diary method is used and modified with participants who may lack the capacity to write, remember events, or think clearly and logically, including for example young children, adults with learning disabilities, people with dementia, and people living in low income countries. 

StartEndPlaces LeftCourse Fee 
01/12/201701/12/2017[Read More]
Applied GIS modelling for social science

Applied GIS for social science applications

This two day course is aimed at researchers interested in upgrading their spatial data analysis skillset. Participants with some basic experience of using GIS and spatial data will become confident in applying more advanced tools to address a range of applied research questions in the social sciences. We will work with ESRIs ArcGIS software and combine hands-on practical activities with supporting lectures. 

StartEndCourse Fee 
27/06/201728/06/2017[Read More]
Applied Multilevel Modelling

Applied Multilevel Modelling 2017

This course will focus on the application of multilevel models to hierarchically clustered data structures. Topics will include types of multilevel data structures, linear multilevel and logistic multilevel models, random intercept and random slope models, and the use of graphical methods to display results. The course will focus primarily on situations where the dependent variable is continuous, but methods will also be introduced for dealing with binary response data. The course will include a mixture of lectures and practical workshops using the multilevel modelling software MLwiN.

  • Multilevel data structures
  • Random intercept and random slope models
  • Contextual effects and cross-level interactions
  • Diagnostic checking and model specification
  • Binary response models
  • Repeated measures analysis and non-nested data

The course will have a practical emphasis with computer workshops allowing participants to work through examples using the MLwiN software.

By the end of the course participants should:

  • Have a practical understanding of the ideas and methods of modelling data with a multilevel data structure, and know when their use is appropriate
  • Have a detailed understanding of how to critically interpret results from multilevel models
  • Gain a working knowledge of the multilevel statistical package MLwiN
  • Be able to apply these methods to continuous and binary response data

Participants are expected to have a good working knowledge of simple statistical methods, including a good understanding of linear regression. No familiarity with the software MLwiN is required.

StartEndPlaces LeftCourse Fee 
13/09/201715/09/2017[Read More]

Big Data Analysis for Social Scientists

This course introduces participants to the collection and analysis of socially-generated ‘big data’ using the R statistical software. The main emphasis is on applying social network analysis and quantitative text analysis to data from social media and the WWW. The course will also provide an opportunity for participants to learn how these data and techniques are being used in social science research.

The course covers:

R and RStudio refresher, installing R Packages

Collecting YouTube video comment data, Facebook fan page data and twitter data with socialMediaLab R package (please note with one day course only one of these data sources will be covered in detail)

Basis social network analysis in R/igraph (graph visualisation, core node-and network-level metrics, network clustering, constructing two-mode networks)

Basic text analysis in R (building a corpus, descriptive analysis, wordclouds)

StartEndPlaces LeftCourse Fee 
06/07/201706/07/2017[Read More]
Designing and Implementing Mobile Web Surveys

Designing and Implementing Mobile Web Surveys

Mobile devices (smartphones and tablets) are increasingly being used by respondents to complete Web surveys. This presents a number of design challenges for survey researchers. Smartphones also offer a number of added possibilities for survey designers, such as the use of GPS to track movement, apps to trigger measurement at set times (ecological momentary assessment), the possibility of capturing images, and other features. This course will focus on the design implications of the rise of mobile device use for survey research. The research evidence will be reviewed, and the various options for accommodating mobile Web users will be discussed. The challenges of using the enhanced features of mobile phones for general population surveys will also be reviewed. The course is focused on situations where respondents are using their own devices, i.e., the designer has little control over the device used. Participants are encouraged to bring their own example surveys to the course to discuss.

The course covers:

  • The rise in mobile Web use
  • Coverage and nonresponse implications of mobile Web
  • How to identify mobile Web users (user agent strings and paradata)
  • How Web surveys on a mobile device are different from those on a PC
  • How to design Web surveys to accommodate mobile users
  • Challenges and opportunities of exploiting the features of mobile surveys


By the end of the course participants will:

  • Understand the varieties and uses of paradata in Web surveys
  • Have the knowledge or tools to collect, process, and analyse paradata
  • Understand how to extract meaning from paradata
StartEndCourse Fee 
08/12/201708/12/2017[Read More]

Gathering and analysing social media data from Twitter and YouTube

This course introduces free Windows-based software for collecting tweets and for downloading the sets of comments on YouTube videos. It also describes a range of keyword, network and graphical methods to analyse the data for social science research purposes, as supported by the software. This is a small scale style of big data research. The course includes a brief discussion of ethical and practical issues associated with social media data analysis. The practical sessions give an introduction to the free program, with supporting documentation for those wishing to become advanced users.


Introduce methods to gather and analyse social media data, with a focus on Twitter and YouTube comments.

Learn how to use the free Windows-based social media data analysis and gathering software Webometric Analyst.


Participants should have at least basic knowledge of social media sites, including both Twitter and YouTube, and basic familiarity with Windows.

The course is designed for social science and humanities researchers that would like to learn new methods to gather and analyse social media data, or that need to create corpora of social media data to research.

Recommended Reading

Wilkinson, D. & Thelwall, M. (2012). Trending Twitter topics in English: An international comparison. Journal of the American Society for Information Science and Technology, 63(8), 1631-1646.

Thelwall, M., Sud, P., & Vis, F. (2012). Commenting on YouTube videos: From Guatemalan rock to El Big Bang. Journal of the American Society for Information Science and Technology, 63(3), 616–629.


9.45 – 10:00 Registration

10:00– 11:00 Lecture 1

11:00 – 11:30 Coffee/tea

11:30 – 13:15 Practical 1

13:15 – 14:15 Lunch

14:15 – 15:15 Lecture 2

15:15 – 15:45 Coffee/tea

15:45 – 17:30 Practical 2

StartEndCourse Fee 
14/06/201714/06/2017[Read More]

Introduction to Latent Class Analysis

Latent Class Analysis (LCA) is a branch of the more General Latent Variable Modelling approach. It is typically used to classify subjects (such as individuals or countries) in groups that represent underlying patterns from the data. In addition to this application LCA provides a flexible framework that can be used in a wide range of contexts: in longitudinal studies (e.g., mixture latent growth models, hidden Markov chains), in evaluation of data quality (e.g., extreme response style, cross-cultural equivalence), non-parametric multilevel models, joint modelling for dealing with missing data.

In this course you will receive an introduction to the essential topics of LCA such as: what is LCA, how to run models, how to choose between alternative models, how to classify observations, how to evaluate and predict classifications. You will also apply this knowledge to a number of more advanced models that look at the relationship between latent class variables and at longitudinal data.

The course covers:

  • Refresher of basic concepts in categorical analysis: (marginal) probability, odds ratios, logistic regression;
  • Basic concepts and assumptions of latent class analysis;
  • Introduction to Latent GOLD software;
  • Model fit evaluation: global, local and substantive evaluation;
  • Classification of cases;
  • Apply these concepts to a number of models looking at: predicting class membership, relationships between latent classes, hidden Markov chains.

 By the end of the course participants will:

  • Know what is Latent Class Analysis;
  • Be able to estimate and interpret results from Latent Class Analysis;
  • Be able to choose between alternative Latent Class Models;
  • Understand latent class classification and how to predict it;
  • Be able to investigate the relationship between latent class variables.
StartEndPlaces LeftCourse Fee 
16/11/201717/11/2017[Read More]
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Introduction to Spatial Analysis for Researchers

This two day course is designed to introduce researchers to the industry leading GIS software – ArcGIS. Participants will learn about data management, spatial analysis (including proximity analysis) and data presentation. No previous experience of using GIS is required.


The course covers:


  • Introduction to GIS theory
  • ArcGIS – ArcMap and ArcCatalog
  • Data Management
  • Spatial analysis
  • Data editing
  • Data presentation – creating maps


By the end of the course participants will:


  • Understand the fundamental concepts of GIS
  • Be able to carry out basic spatial analysis
  • Be able to create and edit spatial data
  • Be able to create a map layout – including North Arrow, Scale bar etc
  • Be able to use some basic geoprocessing tools


The course is designed for those who have little or no GIS knowledge and have never used ArcGIS but who wish to develop skills in using ArcGIS for spatial analysis and mapping. It is for users from all disciplines who are interesting in learning how to get the most out of their data by mapping and spatial analysis.

StartEndPlaces LeftCourse Fee 
04/10/201705/10/2017[Read More]

Longitudinal Data Analysis

This course starts by reviewing the advantages of collecting and analysing longitudinal data. After discussing the various types of longitudinal data, we focus on panel data containing repeated measures. Topics will include: methods for exploring longitudinal data; alternative approaches for modelling repeated measures data for continuous and categorical responses with particular attention to population average and subject-specific models; and methods for handling complex survey designs, weights and non-response.


This course will include the following topics:

  • Issues when analysing longitudinal survey data
  • Overview of approaches to analysing longitudinal survey data
  • Population average (marginal) models
  • Random effects models
  • Fixed effects models
  • Methods for categorical responses
  • Handling complex survey designs, weights and non-response

The methods will be illustrated and compared using analyses of a variety of socio-economic, attitudinal and health outcomes collected in the British Household Panel Survey and in Understanding Society. The course will have a strong practical emphasis, with regular computer sessions using STATA enabling participants to work through examples.

Learning outcomes:

  • To provide an introduction to various approaches for analysing longitudinal survey data, including methods for handling complex surveys, weights and non-response.
  • To enable participants to identify the important issues when analysing longitudinal survey data.
StartEndCourse Fee 
03/07/201705/07/2017[Read More]

Making and measuring impact

Maximising the impact from research is now a requirement of most research funders. But how do we plan, engage and monitor our knowledge exchange and collaboration activities to ensure we create impact, and can demonstrate this?

This one day training session will draw on the growing body of literature about research utilisation to explore and problematize the processes of research impact and measurement. The first part will focus on developing understandings of how research is taken up, used and re-used by non-academics, and how this process might lead to the kind of changes in awareness, policies and practices.  The second part will address issues in assessing impact, and allow participants to start to develop an impact framework for their own research.


The course covers:

  • How research is used in non-academic context
  • How to define and operationalise concepts of research impact
  • issues in assessing impact
  • how to develop an impact framework

By the end of the course participants will:

  • Have a clearer evidence-based understanding of the processes of research utilisation and impact
  • Be able to draw on theoretical concepts of research utilisation to inform their own impact plans
  • Have started to develop an impact framework for their own research

Feel more confident and able to engage with the impact requirements of research funders

Participants should be able to think in detail about a research programme, project or body of work they wish to assess the impact of. This will be their worked example during the session

StartEndPlaces LeftCourse Fee 
30/05/201730/05/2017[Read More]

Managing Danger in Oral Historical Fieldwork

This one-day workshop will introduce participants to the literature on anticipating and managing danger in qualitative fieldwork as it pertains to the practice of oral history both in relatively benign and in overtly hostile settings. It offers an alternative perspective to the widespread assumption that oral history is an inherently positive endeavour that results in good relationships and positive outcomes, and explores some of the circumstances through which danger can emerge in the course of oral historical fieldwork. It also offers preliminary recommendations for anticipating and managing these forms of harm as it relates to different stages in the fieldwork process, including

(a) pre-fieldwork research design and ethics approval

(b) the recruitment and interview phase

(c) analysis and dissemination aimed at informing academic and public audiences.

StartEndPlaces LeftCourse Fee 
06/09/201706/09/2017[Read More]

Methodological considerations in Biosocial Research using Understanding Society data

The course is an introduction to biosocial research for social science researchers. The 1 day course will cover a conceptual framework for biosocial research, key methodological considerations when carrying out biosocial research, and some hands on practical work using biosocial data from Understanding Society.

The course covers:

  • A conceptual framework for biosocial research
  • A description of how biological data are collected in large social surveys
  • Methodological considerations when using biosocial data from Understanding Society

By the end of the course participants will:

  • Understand some of the principles underlying biosocial research
  • Develop an awareness of the methodological considerations when using biological data from large surveys
  • Create their own biosocial model based on data from Understanding Society

Target Audience

Quantitative social science researchers who have not analysed biological data before. These could include some health researchers who wish to become familiar with the biosocial data available in large surveys.

09:30-10:00 Registration and refreshments

10:00-11:00 What is Biosocial Research? What are Biosocial research questions? Biosocial conceptual frameworks.

11:00-11:15 Tea/Coffee break

11:15-12:00 How are biological data collected in large surveys?

12:00-12:30 Practical- design your own biosocial data collection

12:30-13:30 Lunch

13:30-14:30 Methodological considerations in biosocial research

14:30-15:00 Tea/Coffee break

15:00-16:00 STATA Practical: using Understanding Society biosocial data

16:00-16:30 Reflections

StartEndCourse Fee 
16/06/201716/06/2017[Read More]

Spatial Interaction Modelling

This two day course is designed to equip participants with the knowledge and skills to build, calibrate and apply powerful spatial interaction models (SIMs). SIMs are used to estimate flows between origins and destinations and have a broad range of applications within geography, planning, transportation, social science and the commercial sector. We assume no prior experience of working with SIMs (or gravity models as they are also known) and focus on both the theoretical and technical components of model building using examples which are intuitively straightforward and familiar to participants (shopping behaviours and migration).

StartEndPlaces LeftCourse Fee 
19/10/201720/10/2017[Read More]

Thinking with Ethics in and beyond the field

This workshop focuses on ethics in the space in which research is practised. Ethics as research practice involves going beyond the moments of ethics regulation and review to consider how ethical dilemmas feature at all stages of the research process.


Ethical dilemmas are often played out in grey zone areas where regulations and codes of conduct for research integrity are not sufficient to guide the individual researcher. Ethical dilemmas are thus often difficult to talk about or write about – fear of being judged as unethical can make researchers feel vulnerable – and so be reluctant to expose their experiences, this in turn can make it harder to learn. This workshop provides a  safe space in which participants – including the workshop convenors – will share reflections on ethics dilemmas across the research process (from inception, through fieldwork and analysis to dissemination).


We offer an opportunity to unpack hesitancy as a way of dealing with real-life research concerns and potential vulnerabilities that have no obvious or easily anticipated solution. Hesitancy offers a productive moment:  in refraining from action, we resist the idea of prescriptive formula for ethical research practice, and draw on underpinning knowledge of ethics codes and regulatory systems as a framework for thinking productively about grey zones in research ethics.  In this way, the workshop will offer a containing space, where the vulnerabilities of ethical dilemmas and decisions can be shared, and strategies developed through shared reflection and discussion. We will consider how hesitancy can bring about a productive pause, creating a space in which to question immediate impulses and attend to aspects of the situation that are more complicated and difficult to resolve, before moving forwards in considered action.


When out in the field, or engaged in writing and dissemination, the researcher is often left on her own in deciding and manoeuvring potential ethical pitfalls, looking to do the right ethical thing at critical points in the research process.  This workshop is designed to provide participants with skills and strategies for recognising and managing uncertainty and not-knowing in the face of ethical risks and dilemmas in the field and in writing about research, drawing in particular on the concept of ‘ethical hesitancy’ as a strategy for thinking with ethics.


Bringing together perspectives from Denmark, the UK and beyond, the workshop will examine the ways in which national contexts and cultural perspectives frame our understandings of what is ethical, what is risky, and who is vulnerable, and hence how we should deal with ethics in research.

StartEndPlaces LeftCourse Fee 
07/09/201707/09/2017[Read More]
Time Series Analysis for Political and Social Data

Time Series Analysis for Political and Social Data

This course provides an introduction to time series methods and their application to social science research. Many of our theoretical questions in the social sciences are implicitly temporal in nature – such as whether a change in public policy leads to a change in social behaviour, whether that relates to recycling, voting or offending, and the widespread availability of social and political longitudinal data make it possible to address them. There are specific issues associated with time series data – due to their temporal structure and dependence – that requires careful attention. This course will introduce participants to the time serial structure of social and political data, fundamental concepts in time series analysis, diagnostic tests for different time series processes (i.e. stationarity and unit root), and static and dynamic regression models (including “ARIMA”, autoregressive distributed lag and error-correction models) for social and political variables.


The course covers:

  • The structure of political and social time series.
  • Fundamental concepts in time series analysis.
  • Diagnostic tests for autocorrelation, moving average and stationary/integrated processes.
  • Univariate and static/dynamic regression models.


By the end of the course participants will:

  • Have developed an understanding of the theoretical structure of time series data, and be able to organise their own data in this format.
  • Be able to apply diagnostic tests for time series processes to their own data.
  • Be able to select the appropriate model for univariate and multivariate specifications, and estimate and interpret the short- and long-run effects of variables, lag distributions and rates of ‘error-correction’.


The target audience for this event is academics or government researchers from the social sciences (not including economics) with some background in quantitative methods in general, but no experience of time series analysis specifically. This may range from PhD students to more advanced researchers looking for an introduction to a new method.

If possible, please bring one or more social/political time series that are relevant to your research (e.g. crime rates, survey data on vote intentions or support for government cuts, social trust indicators).




Some knowledge of linear regression models is assumed but prior training in time series analysis is not required or expected. The course will make use of basic algebra. The computer workshop will use Stata. Some familiarity with Stata would be helpful but for those without preparatory materials will be provided ahead of the course and the teaching materials will provide a crash course during the session.


Preparatory Reading


Paul Kellstedt and Guy Whitten. (2013). The Fundamentals of Political Science Research. Cambridge University Press. Chapter 11.


Mark Pickup. (2014). Introduction to Time Series Analysis. Sage.


Janet M. Box-Steffensmeier, John R. Freeman, Jon C. W. Pevehouse and Matthew Perry Hitt. (2014). Time Series Analysis for the Social Sciences. Cambridge University Press.


StartEndCourse Fee 
20/06/201721/06/2017[Read More]

Using Creative Research Methods

This 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, research using technology, mixed methods, 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. In the afternoon you will have the opportunity to try applying these methods in practice. Attention will be paid to ethical issues throughout. The day will include plenty of practical advice and tips on using creative methods in research.


The course covers:

  • Arts-based methods
  • Research using technology
  • Mixed methods
  • 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 traditional 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 traditional research methods.

StartEndPlaces LeftCourse Fee 
14/07/201714/07/2017[Read More]
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Web Survey Paradata

Paradata are the data automatically generated when respondents answer Web surveys. There are many different kinds of Web survey paradata, including email tracking tools, user agent strings (to identify devices used), and server-side and client-side paradata providing information on things like response times, mouse-clicks, scrolling behaviour, and so on. This course will provide participants with an overview of the different types of Web paradata, and how they can be collected, managed, and analysed to provide useful information on data quality and nonresponse in Web surveys, leading to design improvements.


The course covers:

  • Different types of Web paradata and their uses
  • Capturing paradata in Web surveys
  • Processing Web survey paradata
  • Analysing paradata


By the end of the course participants will:

  • Understand the varieties and uses of paradata in Web surveys
  • Have the knowledge or tools to collect, process, and analyse paradata
  • Understand how to extract meaning from paradata
    • Introduction and overview
    • What do we mean by paradata: client-side versus server-side paradata
    • Classification of different types of paradata
    • Capturing paradata in Web surveys: conceptual and practical issues
    • How paradata can be used: examples of use
    • Processing Web paradata: concept and practical issues
    • Analysing Web paradata: conceptual and practical issues
    • Combining paradata with other sources of information
    • Using paradata to improve online instruments


    Detailed programme to follow
StartEndCourse Fee 
07/12/201707/12/2017[Read More]

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