Research Data Management: Reusing Existing Data
Bringing together University resources and services to facilitate researchers in the production of high quality data.
At a Glance
Reusing existing data can be cost effective & time saving but may require significant effort to get to know the data.
Help@UCD: ISSDA Reusing Research Data
- ISSDA: Reusing Research DataIrish Social Science Data Archive (ISSDA) tips for reusing existing reseach data.
Help@UCD: ISSDA Data Sources
Reusing Data Pros & Cons
- Datasets may impossible to create within the scope of your research project
- I can be cost effective & time saving to use data which has already been collected
- Ethical issues about data collection have already been dealt with
- You can spend bulk of time analysing data
- There is a huge breadth of data available, even in an Irish context
The main disadvantage to reusing existing data is that the data were not collected to answer your specific research questions.
- Particular information may not have been collected
- The data may refer to a different geographic region than you are interested in studying
- The data may refer to a different time period than you are interested in studying
- Variables may have been defined or categorised differently than you would have liked
- You were not directly involved in the data collection process
- There may have bee a low response rate
- Anonymisation may be quite extensive, so variables you are interested in may not be available
Reusing Existing Research Data
It is essential to:
- Check out the documentation for collection procedures, data cleaning procedures and other technical information.
- Spend time getting to know and understanding the data.
- Be practical about whether data are suitable (good enough) for your research.
Documentation helps you to understand the meaning of the data & to evaluate suitability for your research question. It can help you understand exactly what information was collected, from whom, where & when, as well as what was done to the resulting data before it was archived.
Documentation can include:
- Study description (metadata)
- User guide
- Codebook or data dictionary
- Survey questions
- Official reports