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Secondary analysis of qualitative and quantitative data

Secondary analysis is the reanalysis of either qualitative or quantitative data that has already been collected for a previous study, by a different researcher typically looking to address a new research question.

Research data are collected across a range of social science disciplines using a variety of research methods. Social surveys and interviewing projects represent the most common methods, but primary data can also be gathered from fieldwork observation, diaries, self-completion questionnaires and other activities. Administrative and routine business data collected in the course of government activities also represent a rich source of statistical information. These resources represent an abundant stock of material that can be reanalysed, reworked, used for new analyses, and compared or combined with contemporary data.

In time, archived data become historically important research materials. Using existing data also enables research where the required data may be expensive, difficult or impossible to collect, for example in the case of global administrative data, large-scale surveys or historic data.

The Economic and Social Research Council (ESRC) promotes secondary analysis through its Secondary Data Analysis Initiative (SDAI). The aim is to deliver high-impact policy and practitioner-relevant research through the deeper exploitation of major data resources.

Find out more about secondary analysis from our Reusing qualitative data guide (PDF).

We provide on-demand interactive modules, designed for anyone who wants to start using secondary data.

Share your data and help support long-term research access to important data resources that support wider society. The Economic and Social Research Council (ESRC) fully supports data sharing and was the first UK funding body to adopt a formal data sharing policy. ESRC award holders are contractually required to offer all created research data to the UK Data Service (or another responsible repository) for archiving and dissemination.

Share research data to enable:

• The scrutiny of research findings.
• Replicability of research.
• Greater transparency and accountability of your research.
• Greater impact and visibility of research.
• Credit for research outputs.
• Potential for increased citation rates.
• New collaborations between data users and data creators.
• The improvement and validation of research methods.
• Cost savings by not duplicating data collection.
• Promotion of innovation and potential new data uses.
• Greater return on investment for research funders and researchers.
• Great resources for education, training and Knowledge Exchange.

Journals and publishers increasingly require data that underpin publications to be shared or deposited within an accessible database or repository for analysis by readers.

UK Research and Innovation (UKRI) have prepared a set of common principles on data policy, which provide an overarching framework for individual research councils.

Many other funders such as the Natural Environment Research Council (NERC), the Medical Research Council (MRC), the Biotechnology and Biological Sciences Research Council (BBSRC), the Engineering and Physical Sciences Research Council (EPSRC), Cancer Research UK and the Wellcome Trust have similar data policies that mandate or encourage researchers to share data.

Any research publication, whether printed, electronic or broadcast, based wholly or in part on the data collections accessed through the UK Data Service must be accompanied by the correct citation and acknowledgement information. Citing data correctly is not only best practice but also ensures compliance with the terms and conditions of use.

• Watch our video Why cite data? Experts from the University of Essex discuss why and how the research community should cite data correctly.
• For a quick overview, see our Top ten tips to citing data (PDF).
• For more information about the importance of data citation and the role of DOIs, see the ESRC data citation web page and brochure Data Citation: what you need to know (PDF).
• Read our blog Spotlight on #CiteTheData: Make the data count which discusses citation and the benefits it brings to researchers and research.
• Publishing and citing research data presentation (PDF).
• ICPSR video: Why should I cite data?
• Cite The Data – Communications Campaign Toolkit (PDF) for organisations wanting to promote data citation with their stakeholders.
• Reasons to cite data correctly.

Citing multiple studies

Researchers using multiple studies from the same data series can use the series citation, which covers the whole series and includes a DOI. The series citation is available on the main series abstract page (see this example for British Social Attitudes Survey). The series citation can be used if it is not possible to cite all the studies used individually. Still, researchers are encouraged to clearly state the individual study numbers used, and their edition numbers, alongside it to ensure reproducibility.

Researchers using multiple studies that are not part of a series should cite them individually.

Use our data citation tool

Our citation tool is designed to make citing data easy and straightforward. You will find it on every catalogue record page.

The tool provides you with a full and accurate citation for that dataset in a format of your choice e.g. APA, DataCite, Harvard.

By clicking on the select citation format box you will get a drop down menu of formats. Simply choose the format you require and the citation will change accordingly.

You can then copy and paste that citation and use it in the acknowledgements section of your research document.