You will find bellow some general standards of data quality, from the UK Data Archive:
Quality control of data is an integral part of all research and takes place at various stages: during data collection, data entry or digitization, and data checking. It is important to assign clear roles and responsibilities for data quality assurance at all stages of research and to develop suitable procedures before data gathering starts
1. Quality control measures during data collection:
• calibration of instruments to check the precision, bias and/or scale of measurement
• taking multiple measurements, observations or samples
• checking the truth of the record with an expert
• using standardized methods and protocols for capturing observations, alongside recording forms with clear instructions
• computer-assisted interview software to: standardize interviews, verify response consistency, route and customize questions so that only appropriate questions are asked, confirm responses against previous answers where appropriate and detect inadmissible responses
2. Quality control during digitalization, entry or coding of data
The quality of data collection methods used strongly influences data quality and documenting in detail how data are collected provides evidence of such quality. When data are digitized, entered in a database or spreadsheet, or coded, quality is ensured and error avoided by using standardized and consistent procedures with clear instructions. These may include:
• setting up validation rules or input masks in data entry software
• using data entry screens
• using controlled vocabularies, code lists and choice lists to minimize manual data entry
• detailed labelling of variable and record names to avoid confusion
• designing a purpose-built database structure to organize data and data files
More information on data entry checks
3. Qualitative data transcription quality
Transcription is a translation between forms of data, most commonly to convert audio recordings to text in qualitative research. Whilst transcription is often part of the analysis process, it also enhances the sharing and reuse potential of qualitative research data. Full transcription is recommended for data sharing.
If transcription is outsourced to an external transcriber, attention should be paid to:
• data security when transmitting recordings and transcripts between researcher and transcriber
• data security procedures for the transcriber to follow
• a non-disclosure agreement for the transcriber
• transcriber instructions or guidelines, indicating required transcription style, layout and editing
Best practice is to:
• consider the compatibility of transcription formats with import features of qualitative data analysis software, e.g. loss of headers and formatting, before developing a template or guidelines
• develop a transcription template to use, especially if multiple transcribers carry out work
• ensure consistency between transcripts
• anonymize data during transcription, or mark sensitive information for later anonymization. Want some help? Check this out
Transcripts should:
• have a unique identifier that labels an interview either through a name or number
• have a uniform layout throughout a research project or data collection
• use speaker tags to indicate turn-taking or question/answer sequence in conversations
• carry line breaks between turn-takes
• be page numbered
• have a document cover sheet or header with brief interview or event details such as date, place, interviewer name, interviewee detail
4. Data checking
During data checking, data are edited, cleaned, verified, cross-checked and validated.
Checking typically involves both automated and manual procedures. These may include:
• double-checking coding of observations or responses and out-of-range values (more information on outlier)
• checking data completeness
• verifying random samples of the digital data against the original data
• double entry of data
• statistical analyses such as frequencies, means, ranges or clustering to detect errors and anomalous values
• peer review
Best practices for data cleaning can be found here.
Finally, note that data quality best practices highly depend on the type of data collected and the research field. Therefore, contact your research institute to see whether some best practices are already available.
Go further:
Want some help to describe data quality process? Check this out
Want to create a data quality plan? Check this out
Want more information on how to ensure data quality in your research?