National Centre for Research Methods

In this online one day course (taught over two mornings) you will learn about advanced spatial analysis techniques using QGIS. You will gain proficiency in complex GIS operations, such as spatial overlays and point-based access analysis, equipping them with the skills to leverage spatial data for insightful research outcomes. You will also learn how to work with a variety of different data sources and types and using spatial overlays, point in polygon analysis and spatial joins.

The course covers:

• How to work with different data sources
• 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:

• 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

This course is ideal for anyone who wishes to use spatial data in their role. This includes students, academic, 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 GIS and spatial data can be used for, and how you can better represent your data with maps.

This is an Advanced course. Participants can either complete the introductory course (Introduction to QGIS), which requires no prior knowledge, or attend this advanced course if they already have experience with QGIS and spatial data. Contact Dr. Nick Bearman if you need clarification about whether your existing knowledge is sufficient for this course.

THIS COURSE IS TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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.

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.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This exciting interactive workshop will develop your knowledge and skills in using creative and participatory research methods. Creative and participatory methods are increasingly being utilised by social researchers to tackle complex research questions, enhance participant inclusivity and to generate wide ranging research impact for a broad range of stakeholders. 

This session begins with an overview of developments in creative and participatory research, highlighting the opportunities and challenges in the context of social policy, research impact and advancing academic knowledge. Across the two days, the course covers how and why we use a variety of creative and participatory methods and how to bring them together in analysis, forming a constellation. The workshop will address ethics, opportunities, benefits and challenges during the research process and how to generate multi-level impact from grassroots to social policy. Participants will be given the opportunity to explore how to incorporate creative and participatory approaches (such as zines and photovoice) in their own research, and how to analyse and disseminate effectively.

Over the course you will:

• Be introduced to key debates in creative and participatory research
• Understand the potential for, and the challenges of, using creative and participatory research methods
• Explore how to ethically engage in creative and participatory research
• Learn from active peer-researchers involved in co-creating research

By the end of the course participants will:

• Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)
• Develop skills in designing, conducting, analysing and disseminating creative and participatory research
• Learn how such methods can be incorporated into the generation of meaningful research impact

Indicative Schedule:

The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes.

Day 1

• What do we mean by creative and/or participatory methods?
• The value of creative/participatory research methods
• Planning and setting up creative/participatory research tools.
• FOCUS ON (1): zines as creative/participatory methods
• Ethical considerations specific to creative/participatory research (part 1)

Day 2

• Ethical considerations specific to creative/participatory research (part 2)
• Creative/participatory research with children and young people
• Creative/participatory research with marginalised communities    
• FOCUS ON (2): co-creation – creative and participatory research in action*
• Doing co-analysis and co-dissemination
• Creative/participatory methods for generating meaningful research impact
• Wrapping up the workshop/advice clinic

*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects.

Presenters:

This course will be delivered by Dr Linzi Ladlow, Senior Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They are editors of the book, Insights into Creative and Participatory Research: Key Issues and Innovative Developments (2026) Policy Press. 

Target audience:

This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

In the era of big data and rapidly advancing AI technologies, the real challenge is no longer whether data exist, but how to extract credible, interpretable, and policy-relevant evidence from massive and highly complex datasets. This four-course series uses International Large-Scale Assessment (ILSA) data as its central example to guide participants—from beginners to those with prior analytical experience—through the process of analysing complex large-scale data in the social sciences. Confronting the challenges inherent in complex secondary datasets—such as multilevel structures, cross-national sampling, complex weighting schemes and latent constructs—the series moves from foundational concepts to advanced applications, covering traditional statistical methods (e.g. multilevel models, structural equation modelling, causal inference models), machine learning techniques, and emerging uses of generative AI in data analysis. Participants will develop an understanding of the differences, strengths, limitations, and complementarities of these approaches within the context of secondary data analysis.

This is a series of four courses and more information on each course/session can be found using the links below.

• Introduction to Machine Learning and AI Methods to analyse large-scale data: An example using International Large-Scale Assessment Data
• Can Machines Really Understand Data? — Using Machine Learning and AI methods to Analyse Survey and International Large-Scale Assessment Data
• How Can Generative AI (LLMs) Help with Analysing Survey and International Large-Scale Assessment Data
• Which analytical choices are better? – Comparing Traditional Statistical Methods with Machine Learning and AI-supported methods

You may register for any number of sessions individually. If you choose to register for all four, a discount will be applied.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a payment.

Improve the efficiency and reliability of your research with this introductory course. Learn foundational computational skills including automating tasks using the command line on your computer, tracking changes to your work using version control and building simple programs using the programming language python. These skills are the foundation of many powerful data analysis techniques including using Artificial Intelligence or High Performance Computing.

This two day introductory short course is spread across four consecutive mornings and covers the following:

Automating Tasks with the Unix Shell - 3rd March  (half day - morning)

Learn the basic concepts of using a text-based interface with a computer. Use the shell to run basic productivity commands and then chain these commands to perform more complicated actions. Use loops to automate time-consuming tasks such as processing or moving large numbers of individual data files. Use the powerful search features to find text in files.

Version Control with Git - 4th March  (half day - morning)

Track changes to files made by yourself to be able to more effectively recover from mistakes. Track changes made by a team to be able to work more effectively in collaboration.

Building Programs with Python - 5th and 6th March (2 x half days - mornings)

Learn the basics of the Python programming language. Learn how to document and add comments to Python code to make it sustainable and user-friendly. Use Python libraries to access the massive amount of tools available from the Python community. Learn the basics of making graphs using MatPlotLib.

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).

This online training workshop will take place over the course of one day between 10:00 and 16:00, with 1 hour for lunch between 12:30 and 13:30.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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.

A one day introduction to machine learning using Scikit Learn in Python.  Learners will be introduced to several machine learning techniques including regression, clustering, dimensionality reduction, and neural networks.  The course also includes a brief overview of the ethics and implications of machine learning.

The course covers:

• Introduction to machine learning
• Regression
• Introducing Scikit Learn
• Clustering with Scikit Learn
• Dimensionality reduction
• Neural networks
• Ethics and implications of machine learning 

By the end of the course participants will:

• Gain an overview of what machine learning is and the techniques available.
• Understand how machine learning and artificial intelligence differ.
• Be aware of some caveats when using Machine Learning.
• Apply linear regression with Scikit-Learn to create a model.
• Measure the error between a regression model and input data.
• Analyse and assess the accuracy of a linear model using Scikit-Learn’s metrics library.
• Understand how more complex models can be built with non-linear equations.
• Apply polynomial modelling to non-linear data using Scikit-Learn.
• Use two different supervised methods to classify data.
• Learn about the concept of hyper-parameters.
• Learn to validate and cross-validate models
• Understand the difference between supervised and unsupervised learning
• Identify clusters in data using k-means clustering.
• Understand the limitations of k-means when clusters overlap.
• Use spectral clustering to overcome the limitations of k-means.
• Recall that most data is inherently multidimensional.
• Understand that reducing the number of dimensions can simplify modelling and allow classifications to be performed.
• Apply Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimensions of data.
• Evaluate the relative peformance of PCA and t-SNE in reducing data dimensionality.
• Understand the basic architecture of a perceptron.
• Be able to create a perceptron to encode a simple function.
• Understand that layers of perceptrons allow non-linear separable problems to be solved.
• Train a multi-layer perceptron using Scikit-Learn.
• Evaluate the accuracy of a multi-layer perceptron using real input data.
• Understand that cross validation allows the entire data set to be used in the training process.
• Consider the ethical implications of machine learning, in general, and in research.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

Pre-requisites:

A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic. The ‘Introduction to Software Development’ (20th-23rd October 2025  ) covers sufficient background.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

In this online one day course (taught over two mornings) you will learn what GIS is, how it works and how you can use it to create maps. 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 gain practical experience in data importation, map creation, and analysis techniques, empowering them to enhance their research insights with compelling spatial visualisations.

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

By the end of the course participants will:

• Be able to set up QGIS and add data
• Understand how to add data with latitude & longitude coordinates
• 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

This course is ideal for anyone who wishes to use spatial data in their role. This includes students, academic, 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 GIS and spatial data can be used for, and how you can better represent your data with maps. No previous experience of spatial data is required.

THIS COURSE IS RUN OVER TWO MORNINGS (10:00-13:00) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This online course on questionnaire design, explores question wording issues and the questionnaire as a whole with a focus on web surveys and mobile-friendly web surveys. The course is full of practical advice. It also provides tips for anyone moving from interviewer-administered surveys to web surveys. Mirroring in-person training, there course will be interactive. There will also be 6 small group workshops to facilitate putting the course concepts into practice.

Questionnaire Design

Getting started with a new questionnaire
Trade-offs – short and simple versus clear
Four cognitive stages a respondent goes through in answering a survey question
Solutions to ambiguous term, understanding recall error and reducing question sensitivity
Question wording guidelines - This about the do's and don'ts of writing survey questions for any context.
Workshop 1 - Critiquing a survey question
Some additional issues with factual questions
Highlights from mini appendix: Demographic questions are always the most difficult to write
Workshop 2 - Writing a survey question
Mini appendix on actual versus usual behaviour
Highlights from mini appendix on some additional issues with subjective questions
Know the deeper issues with open and closed questions
Problematic question formats to be aware of or avoid (agree / disagree)
Mini appendix on other problematic formats (satisfaction, tick all that apply, ranking and hypothetical questions)
Web surveys

Don't rely on web survey software templates
Workshop 3 - Critiquing web survey software templates
Modes of quantitative data collection

Modes of quatitative data collection: Mixing modes
Modes of quantitative data collection: Overall mode differences (the obvious ones)
Mini Appendix on mode effects due to satisficing
Workshop 4 - Interpreting data from a mixed mode experiment
Back to web surveys

Determining the web survey itself
Day 2 appendix - 8 question testing methods for web surveys
The special things that web surveys can do, but should we?
Visual versus not visual
Mini appendix on tips for paper self-completion
Workshop 5 - Visual problems
Web surveys for mobile phones - earlier evidence, current thinking
Highlights from mini appendix on data collection differences: What should you do?
Mini appendix on "push to web"
Back to questionnaire design

Examples of question revisions based on testing results
Workshop 6 - Revising survey questions
Highlights from mini appendix on extra tasks on mobile phones 
By the end of the course participants will:

Have greater questionnaire design skills in general and the ability to critique existing web survey software templates
Have the ability to create effective web survey questionnaires as well as mobile-friendly ones
Have better knowledge about questionnaire-related mode differences and effects
This course is for anyone interested in questionnaire design for web and mobile web surveys. Ideally participants need some familiarity with surveys and questionnaire design.

Preparatory Reading (desirable):

Link 1

Link 2

PLEASE NOTE THIS COURSE IS TAUGHT OVER THREE DAYS (10:00-15:00), AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

Complex Large-Scale Data Analysis -A Course Series on Traditional Statistical Modelling

In the era of big data and rapidly advancing AI technologies, the real challenge is no longer whether data exist, but how to extract credible, interpretable, and policy-relevant evidence from massive and highly complex datasets. This four-course series uses International Large-Scale Assessment (ILSA) data as its central example to guide participants—from beginners to those with prior analytical experience—through the process of analysing complex large-scale data in the social sciences. Confronting the challenges inherent in complex secondary datasets—such as multilevel structures, cross-national sampling, complex weighting schemes and latent constructs—the series moves from foundational concepts to advanced applications, covering traditional statistical methods (e.g. multilevel models, structural equation modelling, causal inference models), machine learning techniques, and emerging uses of generative AI in data analysis. Participants will develop an understanding of the differences, strengths, limitations, and complementarities of these approaches within the context of secondary data analysis.

This is a series of four courses and more information on each course/session can be found using the links below.

• Introduction to Machine Learning and AI Methods to analyse large-scale data: An example using International Large-Scale Assessment Data
• Can Machines Really Understand Data? — Using Machine Learning and AI methods to Analyse Survey and International Large-Scale Assessment Data
• How Can Generative AI (LLMs) Help with Analysing Survey and International Large-Scale Assessment Data
• Which analytical choices are better? – Comparing Traditional Statistical Methods with Machine Learning and AI-supported methods

PhD students from Southampton are invited to attend all four sessions at the special rate of £25 in total.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a payment.