National Centre for Research Methods

This two-day online course provides advanced training in Big Data Analysis and Management using R, focusing on efficient techniques for processing, managing, and visualizing large datasets. Participants will learn to overcome common challenges in big data workflows, including memory optimization, time series analysis, and geospatial data handling. The course combines theory with hands-on practice, equipping learners with practical skills for real-world data applications.

The course covers: 

Introduction to Big Data and R Environment

1. Big data concepts
2. Challenges of handling Big Data (Memory limits, computational efficiency)
3. R/RStudio setup, Package installations

Handling Large Datasets

1. Working with large datasets using data.table
2. Memory-efficient data wrangling with dplyr
3. Working with Out-of-Memory data (disk.frame, ff, feather)
4. Best practices for efficient pipelines

Visualization in Big Data Context

1. Challenges of visualizing big data
2. Exploratory data analysis with ggplot2 and plotly
3. Handling large datasets in visualizations (sampling, aggregation, ggforce)
4. Overview of Shiny dashboard - example

Handling Time Series (Temporal) Data

1. Temporal data structures in R (Date, lubridate)
2. Time series storage and manipulation (xts, zoo)
3. Time series aggregation and decomposition
4. Temporal visualization techniques (ggfortify, gghighlight)
5. Interactive time exploration with dygraphs

Handling Geospatial Data

1. Geospatial vector data structure in R (sf, sp)
2. Handling Raster data in R (terra, raster)
3. Projections and CRS: understanding EPSG codes and proj4 strings
4. Static Maps: ggplot2 with geom_sf, tmap for thematic mapping
5. Interactive Maps: leaflet, mapview, and plotly integration

By the end of the course participants will:

• Understand key concepts and challenges of big data analysis in R.
• Efficiently handle and process large datasets using optimized techniques.
• Apply effective visualization methods for exploratory data analysis.
• Manage and analyse time series data.
• Work with geospatial data for mapping and spatial analysis.
• Build streamlined workflows for scalable big data solutions.

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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.

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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.

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An Introduction to Ethical Open Source Investigation is a two-part methodology course which teaches practical open source skills whilst foregrounding a critical and reflexive approach to open source investigations. You will learn practical skills to evaluate social media content and satellite images, whilst integrating considerations of ethics, care and power.

It’s taught by Ray Adams Row Farr, an open source investigator currently working on the Gaza team of Forensic Architecture. She has been researching and teaching open source investigation in a human rights context for the past six years, including as an investigator at Amnesty International.

This material builds on the University of Cambridge’s Open Source Investigation for Academics course (which was co-designed by Dr Ella McPherson, Ray Adams Row Farr, Nik Yasikov and Laetitia Maurat), inspired by the university’s collaboration with Amnesty International as part of their Digital Verification Corps - an international network of universities where students trained in open source investigation contribute to Amnesty’s human rights fact-finding. 

The emphasis of the course is on learning how to combine a variety of skills into a coherent workflow that can be replicated independently. It's run online and taught interactively — amongst other exercises, across the four days we will work from start to finish on an investigation of your choice.

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

PART 1 : Thursday 20th November 2025 and Friday 21st November 2025

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

Topics covered:

• What is Open Source Research

• Vicarious Trauma

• Collection

• Verification (chronolocation, reverse image search)

PART 2 : Thursday 27th November 2025 and Friday 28th November 2025

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

Topics covered:

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

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Ethnographic methods are increasingly popular with researchers across the social sciences, but the full potential and possible pitfalls of this complex practice are often overlooked in favour of catch-phrase definitions.

This course moves beyond standard understandings of ethnography that depict it as a generic qualitative method founded on ‘participant observation’ to provide learners with a sophisticated, state-of-the-art approach based on cutting-edge academic research.

The course will blend theorical and practical considerations. On the one hand, the course examines the theoretical scaffolding of ethnography, recognising that a thorough understanding of the epistemological foundations of the methods we use is essential to conducting rigorous and ethical research.

On the other, the spirit of the course is inherently practical and pragmatic, as it aims at preparing researchers to design and conduct ethnographic fieldwork, as well as writing it up for academic and non-academic audiences.

The course covers:

• Epistemology, method, and research design: ethnography beyond participant observation
• Preparing for fieldwork: a pragmatic approach to designing research projects
• Ethics and power: access, collaboration, co-production, and the possibility of decolonising research
• Writing ethnography: from the practical to the political

By the end of the course participants will:

• Grasp the practice of ethnographic research beyond participant observation
• Understand the potential of ethnography beyond the traditional ‘study of culture’
• Have a sophisticated understanding of ethnographic research, from the design stage to its execution and writing up, including an overview of sensorial considerations and visual methods
• Be able to appreciate the ethical and power dimensions of ethnographic research
• Understand the ethics and politics of writing, publishing, and representing ethnographically

This advanced course is suitable for any researchers equipped with some prior knowledge/experience using both standard qualitative methods (interviews, focus groups, life histories, etc.) as well as ethnographic methods but is interested in advancing their understanding of ethnographic research to a professional level.

Researchers working within and outside academia (private sector, government, charitable institutions, etc.) are equally welcome to apply. The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.), particularly if enrolled or intending to enrol in a research degree (e.g., PhD, Masters by Research, Masters in Research Methods).

Please note that this course is also suitable for postgraduate researchers with an UG background in anthropology, as the course if pitched to an advanced level.

Pre-requisites

Experience using ethnographic research methods and qualitative research methods.

Preparatory Reading

Demetriou, O. (2023), ‘Reconsidering the vignette as method. Art, ethnography, and refugee studies’, American Ethnologist, 50(2): 208-222.

Hage, G. (2005), ‘A not so multi-sited ethnography of a not so imagined community’, Anthropological Theory 5, no.4: 463-475.

Ingold, T. (2014), ‘That’s enough about ethnography!’, HAU Journal of Ethnographic Theory 4, no 1: 383–395

Stefanelli, A. (forthcoming 2024) ‘Reading ethnography in the classroom: complementary strategies to develop students’ ethnographic imagination.’ Learning and Teaching in the Social Sciences.

The course will run from 09:30 to 15:15 both days.

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The aim of this two-day online training course is to introduce participants to the practice and ethics of ethnographic research.

Through a mix of plenary sessions, group and independent work, participants will learn the basic principles of participant observation and research design, as well as the foundations of ethical ethnographic research.

The course will also examine the ways in which other qualitative and creative methods of data collection may be productively integrated in ethnographic research.

The course covers:

• 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.

Some prior training in qualitative research methods, broadly defined – regardless of whether that includes ethnographic methods specifically.

Programme

Day 1

Morning session:

• 09:30-09:45  Introduction to the course
• 09:45-10:45  Plenary – The Practice of Ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-13:45  Plenary - Qualitative methods in ethnographic practice
• 13:45-14:00  Break
• 14:00-15:15  Practical exercise followed by class discussion

Day 2

Morning session:

• 09:30-10:45  Plenary - Research ethics in ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-1:345  Plenary – Writing ethnography
• 13:45-14:00  Break
• 14:00-15:00  Practical exercise, followed by class discussion         
• 15:00-15:15  Conclusions and Evaluations

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This online course will explain when and why to use documents as data for research, show you how to gather documentary data, and consider some ways to analyse that data.

The course covers:

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

By the end of the course participants will:

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

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

THIS COURSE IS TAUGHT OVER TWO AFTERNOONS (14:00 - 17:00) AND EQUATES TO A ONE DAY COURSE FOR PAYMENT PURPOSES.

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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.

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This two-hour and 30-minute workshop will explore what Gen AI tools exist that are helpful for researchers and how we can use them to optimise research processes.

It is a workshop for Gen AI beginners, not Gen AI experts.

We will explore a range of mainstream Gen AI tools (e.g. ChatGPT, Claude, Co-Pilot) and research-specific ones (e.g. Sc iSpace, Research Rabbit, Elicit) and examine how they work through hands-on tasks.

By the end of the workshop, you will feel confident in what Gen AI can do (and what it can’t!), how to prompt engineer for research tasks, and the ethics of using Gen AI as a researcher.

Pre-requisites

This is an entry-level course, no previous knowledge and no prior reading is required.

The session will only use free GenAI tools.

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email 05/09/25

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.

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This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.  This three day introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model. 

The course covers:

• What is deep learning?
• Classification by a neural network using Keras
• Monitor the training progress
• Advanced layer types
• Real world application

By the end of the course participants will:

• Define deep learning
• Describe how a neural network is build up
• Explain the operations performed by a single neuron
• Describe what a loss function is
• Recall the sort of problems for which deep learning is a useful tool
• List some of the available tools for deep learning
• Recall the steps of a deep learning workflow
• Test that you have correctly installed the Keras, Seaborn and scikit-learn libraries
• Use the deep learning workflow to structure the notebook
• Explore the dataset using pandas and seaborn
• Identify the inputs and outputs of a deep neural network.
• Use one-hot encoding to prepare data for classification in Keras
• Describe a fully connected layer
• Implement a fully connected layer with Keras
• Use Keras to train a small fully connected network on prepared data
• Interpret the loss curve of the training process
• Use a confusion matrix to measure the trained networks’ performance on a test set
• Explain the importance of keeping your test set clean, by validating on the validation set instead of the test set
• Use the data splits to plot the training process
• Explain how optimization works
• Design a neural network for a regression task
• Measure the performance of your deep neural network
• Interpret the training plots to recognize overfitting
• Use normalization as preparation step for deep learning
• Implement basic strategies to prevent overfitting
• Understand why convolutional and pooling layers are useful for image data
• Implement a convolutional neural network on an image dataset
• Use a dropout layer to prevent overfitting
• Be able to tune the hyperparameters of a Keras model
• Adapt a state-of-the-art pre-trained network to your own dataset
• Understand that what we learned in this course can be applied to real-world problems
• Use best practices for organising a deep learning project
• Identify next steps to take after this course

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:

Learners are expected to have the following knowledge:

• Basic Python programming skills and familiarity with the Pandas package.
• Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).

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This two-day online introductory course offers an overview of Artificial Intelligence (AI) and Machine Learning (ML), covering key concepts, real-world applications, basic learning algorithms, and ethical considerations. Designed for beginners, it includes hands-on activities and case studies to illustrate how AI and ML are shaping industries like healthcare, finance, and communication. The course also features a real-time application demo using R or Python programming language.

The course covers: 

• Introduction to AI and ML

1. What is Artificial Intelligence (AI)
   • Definition and history
   • AI vs. Machine Learning vs. Deep Learning
2. Real-World Applications
   • Healthcare, finance, autonomous vehicles, chatbots
3. Types of AI
4. Ethical Considerations in AI

• Basics of Machine Learning

1. What is Machine Learning?
2. Types of ML
   • Supervised Learning (Classification and Regression)
   • Unsupervised Learning (Clustering)
   • Reinforcement Learning (Brief Overview)

• How Machines Learn

1. The Learning Process
   • Data collection and processing
   • Training vs. Testing data
2. Basic Algorithms Overview
   • Linear Regression (Simple Example)
   • Decision Trees
3. Evaluation Metrics: Accuracy, Precision, Recall

• Hands on Exercises using R/Python

1. Exploring and Visualizing Data
2. Hands on exercise (supervised learning)
   • Decision Trees
   • Random Forests
   • Support Vector Machine (SVM)
3. Hands on exercise (unsupervised learning)
   • K-Means Clustering
   • Hierarchical Clustering

• AI in the Real World and Future Trends

1. Case Studies: (healthcare, finance, NLP)
2. Limitations of AI
   • Bias in AI models
   • Data privacy concerns
3. Future of AI

By the end of the course participants will:

•  Understand the core concepts and types of AI and ML.
• Recognize key real-world applications of AI across industries.
• Differentiate between supervised, unsupervised, and reinforcement learning.
• Apply basic ML algorithms using Orange open-source software.
• Identify ethical issues and limitations associated with AI systems.

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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.

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This course provides an introduction to National Pupil Database (NPD), an administrative data resource covering the education system in England.

The course covers:

• The population coverage of NPD
• The component modules of NPD and how they link together
• How to create a longitudinal picture of pupils’ lives in schools
• Key data cleaning routines
• Accessing NPD

By the end of the course participants will:

•  Be familiar with the structure of NPD
•  Understand the strengths and limitations of the data available
•  Know where to go to find more detailed information
•  Know how to apply for access

This course is suitable for anyone intending to undertake quantitative research on the school system in England. No prior knowledge of the NPD or statistical code is required to access the course.

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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.

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This two day introductory short course to software development with Python is spread across four consecutive mornings and covers the following:

Automating Tasks with the Unix Shell - 20th October  (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 - 21st October  (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 - 22nd and 23rd October (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 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.

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This one-day intermediate-level course introduces the concepts and techniques of moderation and mediation analysis using R, with a focus on social science applications. Participants will learn how to test for interaction effects (moderation) using regression models, conduct mediation analysis using structural equation modelling and combine both approaches to explore mediated moderation. Emphasis is placed on interpretation, visualization, and reproducible R workflows using real-world data examples.

The course covers: 

• A brief refresher on linear regression in R
• Moderation analysis using interaction terms in regression
• Probing and visualizing interaction effects
• Mediation analysis using path models
• Estimating indirect effects  
• Mediated moderation  
• Applied examples in social sciences
• Practical coding sessions using real datasets in R

By the end of the course participants will:

• Understand the conceptual foundations of moderation and mediation
• Conduct moderation analysis using interaction terms in R
• Interpret and visualize interaction effects  
• Specify and estimate mediation models using path analysis
• Test indirect effects and interpret mediation results
• Use SEM to explore differences in mediation across groups
• Apply techniques to real-world social science datasets

Course format  

This course will be delivered online on the 10th October 2025 from 9:00 am to 4:00 pm with lunch break from 12:00 pm to 1:00 pm. The day will be divided into two parts (morning and afternoon). The first part will cover moderation, and the second part will cover mediation. Each part will be divided in a lecture, followed by a hands on practical and then going through the solution as a group.
 
The course leader 

The course is Led by Dr Alexandru Cernat, a Professor at the University of Manchester, specialising in collecting and analysing longitudinal data. Over the past decade, he has published over 50 papers and book chapters using advanced statistical models to investigate how people and societies change. His main focus in on data quality and how to estimate it using latent variable modelling. 
Dr Alexandru Cernat is also the founder of longitudinalanalysis.com, a platform developed to help researchers and analysts learn how to collect, clean, and analyse longitudinal data.
 
Pre-requisites 
The course includes hands-on computer workshops. Participants will use the R programming language. 
All software is free and open source. Participants should have R and RStudio installed prior to the course (ideally the latest versions).  

Participants should be familiar with:

• Basic R usage (e.g., using lm(), loading data, basic plots)
• Multiple regression and interpreting coefficients

Recommended reading:  

• MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation Analysis. Annual Review of Psychology, 58(1), 593–614. LINK
• Spiller, S. A., Fitzsimons, G. J., LynchJR., J. G., & Mcclelland, G. H. (2013). Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression. Journal of Marketing Research, 50(2), 277–288. LINK 

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This one-day online workshop will introduce participants to mixed methods research and help them structure qualitative and quantitative methods' design, analysis, and integration within a single study.

Organised by: NCRM, University of Manchester and Administrative Date Research UK

The course covers: 

• Common mixed methods designs will be presented and explored, followed by analysis, approaches to integrating data and discussion of the application of the sessional learning to your own developing study design.
• Content will be presented in sessions which include lecture, interactivity and peer discussion.
• Embedded within the workshop are applied learning sessions, where participants apply their learning to their own planned or in-progress mixed methods work (where applicable).
• Time to work independently and in groups on delegates’ own Mixed Methods work will be facilitated by the workshop lead.
• Participants will come away from the workshop with a clearer understanding of the purposes and potential of mixed methods research, as well as commonly used mixed methods designs and analysis procedures, focusing on how these approaches can aid the design of participants' own work.

By the end of the course participants will: 

• Participants will come away from the workshop with a clearer understanding of the purposes and potential of mixed methods research, as well as commonly used mixed methods designs and analysis procedures.
• Participants will be able to consider, plan and design a mixed methods study using the knowledge and skills learned at the workshop.  

Course format  
This full-day course will be delivered online on the 22nd of September 2025 from 9:00 am to 5:00 pm.
 
The course leader 
The course is led by Dr Rebecca Johnson, an Associate Professor of Health Services Research Education at the University of Warwick. Rebecca has been conducting research and teaching in the health sciences since 2008. She undertook her PhD from 2010 – 2013 studying the implementation and effectiveness of mental wellbeing interventions delivered in community settings. She is a Senior Fellow of the Higher Education Academy. 

Rebecca has been teaching and researching since 2009. She has led on Mixed Methods education delivery since 2015 and has delivered numerous mixed methods workshops in the UK and Ireland. Rebecca is a current member of the NCRM research methods pedagogy network and remains an active researcher as well as educator using mixed methods.
 
Pre-requisites

• The course is recommended for those who have prior knowledge of quantitative and qualitative methods. It is ideal for delegates who plan to begin or are currently working on a mixed methods project.
• A personal laptop may be useful for participants on this course and will be essential for the course. An internet connection is essential, as well as access to Teams or Zoom.

Preparatory reading: 

• A paper will be sent out in advance of the course for participants to read

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PAR aims to create a space for researcher and participants to co-produce knowledge and where relevant, action for change. PAR is considered as a research paradigm in itself, that embodies a particular set of concepts under which researchers operate (Minkler and Wallerstein 2008). These include respect for diversity, community strengths, reflection of cultural identities, power-sharing, and co-learning (Minkler 2000). In this session we will explore these principles, the cyclical approach to PAR and what this means in practice. Participants will be given the opportunity to learn terminology, understand participation in community engaged research, explore how power and positionality can change health outcomes in PAR, and learn about a variety of participatory methods and how they have been applied in different contexts, globally and within the UK. Participants will also be provided with the space to explore challenges they are facing in designing or implementing community engaged collaborative research within a discussion clinic forum. 

Course Content:

The course will take place between 9:30am and 3.30/4.00pm on both days.

Mornings: online teaching and discussion with example videos and guests

Day 1: The history of PAR and underpinning orientation.

• Planning and setting up a PAR project
• Skills required for a PAR study
• Ethical considerations specific to a PAR study
• Participatory research with children and young people
• Photovoice methodology
• Independent activity
• Group discussion

Day 2 : Doing co-analysis

• Participatory research methods (examples of other visual methods, social mapping, seasonal calendars and other non-visual methods but still participatory such as narratives and others that have been used)
• Participation and inclusion
• Dissemination and writing for PAR projects – different approaches, narratives/thematic analysis, thesis, publications, policy briefs, blogs and others
• Group discussion on pre-workshop task
• Advice clinic

Afternoons: independent learning and practical exercises

• Day 1: Photovoice activity and reflections
• Day 2: Individual PAR project outline and feedback

Preparatory reading and videos will be shared beforehand.

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This online course, taught over four mornings, aims to teach participants how to conduct qualitative field research, particularly participant observation and ordinary language interviewing. The course provides an understanding of the distinctiveness of ethnographic fieldwork compared to other data collection methods. By the end of the course, students should be able to understand how to conduct ethnography rigorously and the skills needed to produce high-quality ethnographic research. Students will be able to practice data collection methods associated with ethnography, such as participant observation, field notes, and ordinary language interviews. Finally, the course will discuss how to use fieldwork data to produce new and general theoretical insights.

The course covers: 

• Introduction to Ethnography 

• Ordinary Language Interview 

• Participant Observation

• Digital Ethnography 

• Theory building with qualitative data 

By the end of the course participants will:

• Explain the distinctive features of ethnographic fieldwork, particularly how participant observation and ordinary language interviewing differ from other qualitative research methods.
• Apply core ethnographic methods such as participant observation, field notes, digital ethnography, and interviews in their own research projects
• Critically assess the methodological and ethical considerations involved in designing and conducting ethnographic research.
• Analyse fieldwork data to generate theoretical insights

Target Audience

• Postgraduate students (Master’s and PhD) in political science, sociology, anthropology, international relations, cultural studies, linguistics, arts, geography, archaeology, anthropology, and development studies, and related fields who are interested in incorporating ethnographic methods into their research;
• Early-career researchers and practitioners studying political or social dynamics who wish to strengthen their qualitative fieldwork skills—especially in participant observation and interviewing;
• Students planning or currently conducting fieldwork, particularly those working on topics like political parties, social movements, state institutions, or the everyday practices of politics.

There are no prerequisites. The course is designed to be accessible to those new to ethnographic research, though some familiarity with qualitative methods may enhance your experience.

PLEASE NOTE THIS COURSE EQUATES TO 1.5 DAYS FOR PAYMENT PURPOSES.

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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.

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

The course covers:

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

By the end of the course participants will:

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

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

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

Programme:

Day One

09:30    Welcome and introductions

09:40    Research ethics in context: presentation

10:00    Discussion, Q&A

10.15    When do we need research ethics?

10:20    Video and discussion

10:40    Trauma-informed research: presentation

10:50    Debiasing techniques: presentation

11.00    Discussion, Q&A

11.10    Break time

11.25    Ethical research design: discussion

11.35    Ethical context-setting: discussion         

11:45    Ethical data gathering: discussion

11:55    Ethical data analysis: discussion

12:05    Video and discussion

12:20    Q&A

12:30    Close

Day Two

09:30    Welcome, questions arising from Day 1

09:40    Ethical research reporting: discussion

09:50    Ethical research presenting: discussion

10.00    Ethical research dissemination: discussion

10.10    Ethical aftercare: discussion

10.20    Researcher wellbeing

10.30    Unethical research today – presentation

10.45    Video and discussion

11.10    Break time

11.25    Real-life ethical dilemmas from research #1

11:40    Real-life ethical dilemmas from research #2

11:55    Real-life ethical dilemmas from research #3

12.10    Discussion, Q&A, evaluation

12.30    Close

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Many organisations store their data in relational databases rather than spreadsheets or CSV files, requiring SQL (Structured Query Language) to access, extract and analyse data. This hands-on course introduces researchers to essential database concepts and fundamental SQL skills for data extraction and analysis, empowering participants to confidently engage with data providers and collaborate effectively with technical teams. Through practical workshops, participants will learn how to write queries, combine data from multiple tables, and integrate SQL into their research workflows. No prior database or SQL experience is required.

This one day (10am - 4:30pm) online course covers: 

• Database concepts and why organisations use relational databases

• Writing SQL queries to extract, filter and sort data

• Connecting data from multiple tables using joins

• Summarising and aggregating data

• Integrating SQL with statistical software (e.g. R, Stata, Python, Excel)

• Resources and tools for continued learning

By the end of the course participants will:

• Understand what distinguishes relational databases from other data storage methods, and why organisations choose them over spreadsheets or flat files
• Be aware of the four main types of SQL operations (SELECT, INSERT, UPDATE, and DELETE) and their purposes
• Be able to write SELECT queries to extract, filter and sort data
• Be able to use aggregate functions and GROUP BY to summarise data
• Be able to combine data from related tables using INNER JOIN and LEFT JOIN
• Be able to extract data from a database for further analysis using statistical software
• Be aware of tools and methods for connecting to databases relevant to their own fields
• Understand how SQL skills fit into typical research and data analysis workflows, and know where to find resources for continued learning

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The measurement of health in surveys involves collecting data from individuals about their health status, health-related behaviours, and experiences, often as part of multi-purpose surveys. These surveys may include both subjective self-reports (e.g., self-assessed health measures) and objectively measured health data (e.g., physical health assessments, blood-based biomarkers, or DNA data).

Survey measurement of health plays a vital role in advancing social science research on health. It is essential for the analysis of health inequalities, for informing policy decisions, and evaluating the effect of interventions. By capturing individual health behaviours alongside the social, economic, and environmental contexts influencing health outcomes, surveys offer a nuanced understanding of the complex interplay between society and health.

However, measurement error in health data can significantly affect and compromise the quality of social science research in health. Importantly, such errors are not confined to self-reported data. While self-reports are often susceptible to certain types of inaccuracies, other sources of error can arise, including those associated with nurse-administered health assessments or blood-based biomarker data.

Survey mode – e.g. face-to-face interview, telephone interview, online questionnaire - is also well known to affect the distribution of the survey variables so is a special case of measurement error.  Mode effects are hypothesised to be driven by well-known sources of survey bias such as social desirability, positivity and satisficing and the presence (or not) of an interviewer. Mode effects can be defined in the same way as causal or treatment effects and estimated from mixed-mode surveys, and estimated using the same methods.

We provide an overview of methods for understanding measurement error and mode effects. We will also provide practical sessions and illustrative examples demonstrating the impact of measurement error and mode effects in the social and health sciences.

The course covers: 

• Collection of health data in multi-purpose social science surveys

• Measurement errors in health data, including both self-reports and objective measures

• Implications of measurement errors in health data for existing social science research in health

• The nature of mode effects and the connection with classical measurement error

• The definition and identification of different kinds of mode effect from mixed-mode surveys and a case study based on Wave 8 of Understanding Society.

• Practical sessions with illustrative examples of measurement error and mode effects

By the end of the course participants will be able:

• Understand the basis on the process of survey measurement of health, including the collection of both self-reported and nurse-collected health data.
• Explain the role of survey measurement in advancing social science research, particularly in understanding health inequalities, guiding policy, and tracking interventions.
• Recognize the impact of measurement error in health data, including errors in self-reported, nurse-administered, and biomarker data.
• To understand the potential impact of survey mode on survey data, learn how to estimate different kinds of mode effects from mixed-mode surveys, and how to do so robustly using instrumental variable estimation.
• Apply practical knowledge of how measurement error in survey health data can affect the accuracy and interpretation of social science research.

This course is aimed at Post-graduate researchers and analysts, including (but not limited to): Academics, Government Researchers, Third sector organisations, (Health) Consultancy analysts and Survey methodologists. Participants will need a basic knowledge of STATA.

Programme TBC

Day 1

9:00-11:00 Measurement error in self-reported health measures regularly available in large-scale multipurpose datasets

11:00-11:15 (Virtual) coffee break (Q&A session)

11:15-12:45 Beyond self-reported health measures – characterizing and quantifying measurement errors in administrative health data and nurse-collected bio-measures.

12:45-13:45 Lunch

13:45-14:45 Assessing the potential implications for the existing research in economics and social science that rely on health data

14:45-15:45 Practical s

This online course will introduce participants to visual and embodied methodologies (VEM) and how they can be used in social science research. VEM can increase understanding about the social world, enhance participation and collaboration through the arts-based or creative research process, and add meaning and value to the analysis and communication of research data. By learning about VEM participants will learn how these methods can be used to develop original research approaches, work with vulnerable and minoritised groups, and address key social science research challenges.

This modular course consists of one core ‘Introduction to VEM’ module followed by a selection of optional modules on specific VEM methods. All participants undertake the core introductory module and can then select optional modules to customise the training according to the VEM methods of interest.

The Introduction to VEM module (13th October 2025 09:00 - 11:00) covers:

• What are visual and embodied methodologies?
• How can VEM be used to understand and address social science research challenges?
• Differentiating between different forms of creative research collaborations and interventions.
• Key ethical issues when using VEM.
• How to manage research collaborations with artists and participants.
• Creative data analysis.

The ‘pick and mix’ two-hour sessions on particular VEM methods include:

13th October 2025

• Photovoice - 12:00 - 14:00
• Body Mapping - 15:00 - 17:00

14th October 2025

• Digital Storytelling - 09:00 - 11:00
• Poetry - 12:00 - 14:00
• Policy - 15:00 - 17:00

15th October 2025

• Performance and participatory theatre - 09:00 - 11:00
• Zine-making and collage - 12:00 - 14:00
• Textiles, Banners and Sewing - 15:00 - 17:00

The course is charged as one day for the core module plus up to two modules from the ‘pick and mix’ options above, two days for the core module plus up to five modules and three days for the core module plus up to eight modules. Please book the required number of days and you will be contacted to choose which modules you wish to attend.  Numbers will be limited to 12 participants on each module.

Learning outcomes

• By the end of the course participants will:
• Understand what VEM methods are and how they can be applied to social science research
• Assess the risks and opportunities of using VEM
• Be able to design an ethical VEM research methodology
• Have the tools to manage researcher-participant relations
• Understand and think critically about specific VEM methods, their applications, processes, advantages and limitations

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