The importance of data quality in the market research industry is growing, and it is no surprise that it is considered
the most crucial factor when choosing a market research partner or supplier (GRIT Report 2020). To address
concerns regarding survey fraud and poor quality data, here are five key principles that should be applied to any
quantitative market research study:
Fraud is a continuously evolving issue –
The quality assurance (QA) checks that worked in the past may no longer be sufficient to prevent fraudulent
survey responses. As panels improve their tools to block fraud, fraudsters become more sophisticated in
their ability to get into surveys. Therefore, it is crucial to develop the right tools and strategies to prevent
fraud, which is a continuous effort.
There is no one solution to prevent fraud –
Unfortunately, there is no single solution to detect and prevent fraud. Instead, the problem should be
approached by implementing layers of protection throughout the research process, including panel sourcing,
fraud detection software, programming logic built into the survey, and backend checks.
Automation alone cannot solve data quality issues –
While technology can spot outliers, manual labor is still essential in identifying suspicious patterns in data.
Human intelligence is necessary in designing robust questionnaires, measuring fraud, setting appropriate
thresholds for suspicious respondents, and identifying systematic issues.
Human error is not equivalent to fraud –
Mistakes happen, and survey respondents may misread a question or get frustrated, but these do not
necessarily invalidate an entire survey. Systematic fraud, which is organized and malicious, is harder to
identify and has a more significant impact on data.
Everyone has a role to play in ensuring data quality –
Everyone involved in the research process, including panel providers and researchers, has a role to play in
ensuring data quality. Researchers are best positioned to assess data quality, as they typically know what to
expect from the results.
By adhering to these principles, researchers can uphold the data quality standards they hold themselves to. Being
aware of data quality challenges and keeping up-to-date with new tools and strategies in the industry is critical to
improve data quality and prevent fraud.
