Quality control of data is an integral part of all research and takes place at various stages, during data collection, data entry or digitisation, and data checking. It is vital to develop suitable procedures before data gathering starts.
During data collection researchers must ensure that the data recorded reflect the actual facts, responses, observations and events. The quality of data collection methods used strongly influences data quality, and documenting in detail how data are collected provides evidence of such quality.
Quality control measures during data collection may include:
Digitisation and data entry
When data are digitised, transcribed, entered in a database or spreadsheet, or coded, quality is ensured by standardised and consistent procedures for data entry with clear instructions. This may include:
Data checking is when data are edited, cleaned, verified, cross-checked and validated. Checking typically involves both automated and manual procedures. This may include:
Because digital information can be copied or altered so easily, it is important to be able to demonstrate the authenticity of data and to be able to prevent unauthorised access to data that may potentially lead to unauthorised changes.
Best practice to ensure authenticity is to:
Researchers can add significant value to their datasets by including additional variables or parameters that widen the possible applications.
Including standard parameters or generic derived variables in data files may substantially increase the potential reuse value of a dataset and provide new avenues for research. For example, geo-referencing data may allow other researchers to more easily add value to data and apply the data in geographical information systems. Equally, sharing field notes from an interviewing project can help enrich the research context.
Quality assurance of recorded interviews
The quality of interview data gathered by means of recorded interviews depends on both the skill of the interviewer and the quality of the audio-visual equipment. Initially a researcher should think about:
Taking steps to create audio recordings of good quality increases their usefulness. Good quality sound recordings should prevent mis-transcription and reduces the chance of sections of an interview remaining untranscribed due to poor sound quality. Although some recording equipment can be expensive, it is a good investment if it is to be used time and again during a project, or even again on future projects.
The UK Data Service has recently upgraded its data discovery and access systems. Read more about the changes to our new Data Catalogue and ‘My Account’ area.