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The Importance of Data Validation in Market Research
In the Market Research industry, data is everything. This data is primarily collected through surveys, and its value lies in its accuracy, relevance, and integrity. However, the insights derived from such data are only as good as the methods used to collect and validate it. Without a scientific and structured approach to data validation, the results can be misleading or outright incorrect.
Common Data Quality Issues in Surveys
Based on our experience, several common issues often compromise the integrity of survey data:
1. Straight Liners
Respondents who select the same answer across all items in a rating scale (e.g., a 5-point Likert scale) are often termed straight liners. This behaviour suggests a lack of engagement and indicates that the respondent is more interested in completing the survey quickly than providing thoughtful answers.
2. Red Herring Checks
Red herrings are strategically placed questions designed to identify inattentive or disengaged respondents. These questions help differentiate between genuine and random responses.
3. Bad Open-Ends (Bad OEs)
Some respondents use offensive or nonsensical language in open-ended responses. These are classified as Bad OEs. While it's difficult to control this behaviour during data collection, such responses need to be flagged and removed during post-survey data cleaning.
4. Speeders
Respondents who complete survey in significantly less time than the expected Length of Interview (LOI) are known as speeders. Such behaviour often leads to lower quality data, and these responses usually need to be discarded after the survey is completed.
While it's difficult to control this behaviour during data collection, such responses need to be flagged and removed during post-survey data cleaning.
5. Survey Logic Issues
Even with thorough testing, logic issues such as skip logic or piping or auto punch errors can slip through. While these are ideally identified during survey testing, they may only become evident during the soft launch phase.
The Cost of Poor Data Validation
If such issues go undetected, they can lead to:
  • Misleading insights
  • Costly recontacts or re-surveys
  • Delays in project timelines
  • Additional expenses for fieldwork and data processing
How to Address These Issues?
The key lies in robust data validation at multiple levels:
✅ RDG (Random Data Generator) Checks
Most survey tools offer RDG capabilities to simulate responses and test logic flows. These checks help catch a majority of programming and logic errors before the survey goes live.
✅ Soft Launch Validation
Conducting a soft launch (typically with 10% of the total sample) allows you to validate data with real respondents. This phase is crucial for catching issues that RDG might miss.
✅ Interim and Final Data Checks
Interim and final data checks play a critical role in ensuring data quality. Interim validation during data collection helps identify issues such as straight-lining, speeding, or red herring failures, allowing for timely intervention. Post-collection cleaning then ensures that only valid responses are retained for analysis.

Conclusion

To ensure high-quality insights and reliable decision-making, data validation must be a fundamental part of the market research process. It minimizes the risk of bad data, improves data integrity, and ultimately leads to more accurate, actionable, and trustworthy results.
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