What is Data Quality Rate?
Data Quality Rate (DQR) is the percentage of survey responses that meet predefined quality criteria (complete, valid and usable) out of all responses collected. It helps teams judge how much of their data is reliable for analysis and decision-making.
Data Quality Rate measures how many of the responses you collected are actually useful. Instead of counting every submission equally, DQR applies simple checks — for example: required questions answered, answers not clearly automated or gibberish, minimum length for free-text fields, attention-checks passed, and basic validity (timestamps, non-duplicate submissions, language detection). The rate is usually expressed as a percentage: (number of responses that pass quality checks ÷ total responses) × 100. Teams can use a single set of rules or weight different checks depending on the survey and context.
Usage example
A local council ran a neighbourhood consultation and received 1,200 submissions. Using their quality rules (all required fields completed, no duplicate entries, and minimum three-word answers for open text), 960 responses passed — giving a DQR of 80%. The engagement team used only those 960 responses for their analysis and reported the DQR alongside headline results.
Practical application
Why it matters: raw response counts can be misleading if many submissions are incomplete, spammy or unintelligible. DQR lets you: prioritize reliable responses for analysis, compare outreach approaches (for example, different languages or channels) on a like-for-like basis, spot problems early (low DQR can indicate confusing wording, poor translations, or a bot attack), and track improvements over time as you refine wording or translation quality. For Hearo users, monitoring DQR is especially useful for ensuring multilingual responses are both readable and correctly translated before you act on them.
FAQ
How exactly is DQR calculated?
At its simplest, DQR = (number of responses that meet your quality rules ÷ total submissions) × 100. Quality rules can include required-question completion, minimum answer length, passing attention checks, non-duplicate submissions, valid timestamps, and language-detection checks. You can apply all rules equally or weight them depending on your needs.
What is a ‘good’ Data Quality Rate?
There’s no universal threshold — a good DQR depends on your survey type and audience. Many public-facing consultations aim for 80%–95% for usable responses, while short polls may tolerate lower rates. Look at DQR trends over time and compare across segments (languages, channels) to decide what’s acceptable for your project.
Can translation issues affect DQR?
Yes. Poor wording or mistranslation can cause participants to skip questions, give very short or irrelevant answers, or flag items as confusing — all of which lower DQR. With Hearo, you can monitor DQR by language and use participant feedback on translations to improve wording and raise quality.
Will low-quality responses be removed automatically?
That depends on your workflow. DQR is primarily a measurement and screening tool: you can choose to exclude low-quality responses from analysis, flag them for manual review, or adjust quality rules. Automated removal is possible but should be used carefully to avoid discarding legitimate but brief responses.