Data skills modules

Key data skills

There is a wealth of data available for reuse in research and reports – but how do you get started with finding good quality data, understanding it and starting your analyses?

These free, interactive modules are designed for anyone who wants to start using secondary data.

You can follow the modules in your own time and dip in and out when needed, and you can get a certificate of completion at the end of each module.

These modules have been created in the Rise 360 platform. If you’re new to Rise you can find some tips on how to navigate the platform in our document on Using Rise 360 and see our Accessibility tips for Rise 360 document.

Please let us know if you have any feedback or suggestions for enhancements or other topics.

We hope you enjoy the modules!

These modules cover the following topics:

Introductory modules

Themed and dataset modules

Census data modules

Research data management skills

If you would like to learn about research data management, try these interactive modules.

Introduction to survey data

Learn about how to find survey data from the UK and around the world, what you need to know about it before you can start analysing it, and how to produce simple tables and graphs for your research or reports.

Level: Introductory (no previous knowledge assumed).
Time: Approx. 2-2.5 hours (you can dip in and out of the materials and return as you like).
Software: We use SPSS in the examples but you can do the examples in other statistical software. You can follow the module without having statistical software.
Dataset used: Quarterly Labour Force Survey, January-March 2015: Unrestricted Access Teaching Dataset.
Certificate of completion available: Yes.

Introduction to longitudinal data

Longitudinal studies collect data about individuals, households, businesses or any other unit of observation over time so they can be used for following changes over the life course. Learn what longitudinal studies are available, key features and issues with using longitudinal data and how to start some basic analyses.

Level: Introductory – but if you are new to surveys, we suggest you complete the surveys module first.
Time: Approx. 2 hours (you can dip in and out of the materials and return as you like).
Software: We use SPSS in the examples but you can do the examples in other statistical software. You can follow the module without having statistical software.
Dataset: UK Household Longitudinal Study (Understanding Society) (End user licence version).
Certificate of completion available: Yes.

Introduction to aggregate data

Aggregate data are data combined or ‘aggregated’ from several measurements and are often produced at geographical levels such as countries or regions. Learn where to find these data, how to produce summary statistics using them and how to create a choropleth map.

Level: Introductory (no previous knowledge assumed).
Time: Approx.1.5 hours (you can dip in and out of the materials and return as you like).
Software: Excel or Google sheets (Unit 3) and QGIS (Unit 5). You can follow the module without having these software.
Dataset: World Bank (2016) Subnational Population Database, and Census aggregate and boundary data.
Certificate of completion available: Yes.

Sourcing data ethically

What ethical issues should researchers consider when sourcing data for secondary analysis? This module explores how core principles of responsible research guide ethical access to, and use of, existing datasets. You’ll learn how to navigate common ethical considerations when selecting and working with secondary data. In this module, you will:

Review key ethical research principles and how they apply when sourcing data.
Learn how responsible data repositories support ethical access to research data.
Explore the implications of data licensing and intellectual property rights (IPR) for ethical use.
Consider the ethical challenges involved in working with social media data for secondary analysis.

Level: Introductory (no previous knowledge assumed).
Time: Approx. 30 minutes (you can dip in and out of the materials and return as you like).

Exploring crime surveys with R (Beta version)

Learn where to find survey data to explore crime and how to access it. This module also covers getting started with survey data using R Studio including basic functions, exploratory analysis, visualisations and conducting weighted analyses.

Level: Introductory for crime survey data, R and R Studio. Basic knowledge of exploratory analysis of surveys is assumed. If you are new to using surveys, you may find it useful to complete the Introduction to survey data module first.
Time: Approx. 3 hours (you can dip in and out of the materials and return as you like).
Software: R and R Studio (open-source software).
Dataset: Crime Survey for England and Wales, 2013-2014: Unrestricted Access Teaching Dataset.
Certificate of completion available: Not yet (will be available soon).

Introduction to the British Social Attitudes Survey

Learn about the British Social Attitudes (BSA) survey design, questionnaires and weights, how to access the data from the UK Data Service, how to explore variables using documentation and how to create population estimates using weights and survey design variables.

Level: Basic knowledge of exploratory analysis of survey data using R or SPSS is assumed. If you are new to using surveys, you may find it useful to complete the 'Introduction to survey data' module first.
Time: Approx. 1.5 hours. Dip in and out of the materials and return as you like.
Software: R or SPSS (using syntax).
Datasets: BSA 2017 and BSA 2020 (End User Licence versions).
Certificate of completion available: No.

Getting started with the Family Resources Survey (FRS)

The Family Resources Survey (FRS) is the primary source of income data in the UK. Learn about its survey design, questionnaires, weights, related datasets, how to access data via the UK Data Service, navigate documentation, and merge datasets.

Level: Basic knowledge of exploratory analysis of survey data using R or SPSS is assumed. If you are new to using surveys, you may find it useful to complete the 'Introduction to survey data' module first.
Time: Approx. 1.5 hours. Dip in and out of the materials and return as you like.
Software: We have R and SPSS code examples, but you can do the examples in other statistical software.
Datasets: FRS 2022-23 (End User Licence versions).
Certificate of completion available: No.

Understanding Society (UKHLS): Data and research potential

Understanding Society – the UK Household Longitudinal Study (UKHLS) – offers a rich, ongoing source of data on the lives of people and households across the UK. This module introduces how this unique study works and how large-scale longitudinal data can be used to answer a wide range of social research questions. In this module, you’ll explore:

The research value of Understanding Society and other household panel studies.
How the data are collected and the topics they cover.
How these data can be applied to real social research.

Level: Introductory (no previous knowledge assumed).
Time: Approx. 30 minutes (you can dip in and out of the materials and return as you like).

Getting started with census microdata

Census microdata offers a robust resource to explore detailed information about individuals and households across the UK. This module introduces you to the essentials of working with microdata, from understanding its structure to carrying out your own analysis. You’ll build practical skills that help you move from raw data to meaningful results. In this module, you will:

Learn how census microdata is organised and accessed.
Explore how to interpret variables using documentation.
Practice preparing and analysing data through a guided activity.

Level: Introductory (some basic knowledge of data analysis is helpful).
Time: Approx.45 minutes (you can dip in and out of the materials and return as you like).

Concepts of ecological analysis using census data

Ecological analysis provides a powerful way to explore relationships between social and economic characteristics across different places. They can reveal important insights, but they can also be misleading if spatial context is not carefully considered. This module explains how spatial context, data structure, and analytical choices influence the patterns we observe and how we interpret them. In this module, you will:

Learn how spatial data links information to places and how it is represented.
Explore how to prepare and integrate data for spatial analysis.
Learn how to recognise issues such as the Modifiable Areal Unit Problem (MAUP).
Develop an understanding of key spatial analysis principles and their interpretation.

Level: Introductory (no previous knowledge assumed).
Time: Approx. 25 minutes (you can dip in and out of the materials and return as you like).

Introduction to UK census geographies

Census data provides valuable insights into populations and communities across the UK. These data are organised into a range of geographical units, each designed for different purposes. They also vary between UK nations. This module will guide you through these structures and help you build a clear understanding of how geography shapes data analysis. In this module, you will:

Learn how census geographies are structured across the UK.
Understand the difference between administrative, electoral, and statistical geographies.
Interpret census outputs with greater geographical awareness.

Level: Introductory (no previous knowledge assumed).
Time: Approx. 25 minutes (you can dip in and out of the materials and return as you like).

De-identification and anonymisation of transcript data

Learn how to anonymise interview transcripts effectively. The module covers the principles of anonymisation, offers practical steps for identifying and redacting personal information, and discusses the ethical considerations involved. The module is designed to help researchers ensure that their data is securely anonymised while retaining its usefulness for analysis.

De-identification and anonymisation of quantitative data

Learn how to anonymise quantitative data effectively. This module explains the principles of anonymising datasets, provides practical techniques for safeguarding information, and explores the ethical implications of data anonymisation. It's designed to assist researchers in ensuring that their quantitative data is anonymised while maintaining its integrity for analysis.

Ethical consent and data sharing

Learn how to navigate the ethical and consent considerations in research. This module covers the principles of obtaining informed consent, managing participant data responsibly, and ensuring ethical compliance throughout the research process. It’s designed to guide researchers in upholding ethical standards while conducting studies, ensuring that participants' rights and privacy are respected.

Best practices for documenting data collections

Learn how to effectively document research data collections. This module guides you through the principles of data documentation, offering practical advice on how to create thorough and clear documentation that enhances the usability and discoverability of your data collections. It’s designed to help researchers ensure that their data collections are well-documented, facilitating future reuse and analysis.