Bad data is one of the most widespread issues in global market research. As fraud grows, bots evolve, and respondent attention declines, teams worldwide are facing serious risks around survey data quality, research data accuracy, and respondent fraud detection.
In our last webinar “Bad data quality is killing your research (let’s fix it!)”, we talk about how to fix your Data Quality. Here are the 10 most common data quality problems affecting research today and how to fix each one using modern tools, best practices, and AI-driven safeguards.
1. Junk respondents passing your screeners
One of the biggest contributors to unreliable insights is the presence of poor quality respondents who slip through traditional screeners. This includes bots, click farms, speeders, and even real humans who rush through surveys without reading.
Many outdated survey platforms rely only on basic checks that can no longer detect advanced survey fraud, making it easy for bad actors to mimic real respondent behavior.
How to fix it:
Modern respondent fraud detection requires multi-layer protection. Use rotating screeners, hidden logic checks, digital fingerprinting, IP validation, and behavior monitoring. Combine these with machine learning signals that detect unusual response patterns. When done well, you dramatically reduce the volume of unreliable survey data entering your sample.
2. Overcomplicated surveys leading to low-quality answers
Long surveys with confusing structures often trigger survey fatigue, which directly affects survey completion rates and produces inaccurate data. Respondents who feel overwhelmed tend to skim, guess, or abandon the survey entirely.
Poor survey UX also leads to misclicks, inconsistent answers, and low engagement with open-ended questions.
How to fix it:
Apply survey design best practices by streamlining your question flow, removing unnecessary items, and ensuring your language is clear and accessible. Use mobile friendly layouts, clear progress indicators, and logical branching. Optimizing for readability and speed not only reduces fatigue but increases the likelihood of thoughtful, high-quality responses.
3. Poor timing and respondent fatigue
Even well designed surveys can produce bad data if they hit respondents at the wrong moment. High fatigue hours, seasonal rush periods, or late night delivery often correlate with sloppy, rushed, or incomplete answers.
When respondents feel mentally overloaded, respondent engagement drops sharply, regardless of how good your survey questions are.
How to fix it:
Follow survey timing best practices and schedule research during periods of higher alertness. Shorten the expected completion time and communicate it upfront. Provide incentives that match the effort required. These steps help combat survey fatigue, leading to cleaner and more reliable responses.
4. Bots generating fake responses that look real
Bots have evolved far beyond simple scripts. Many now generate text that resembles human writing and can bypass traditional checks, resulting in a dangerous mix of fake but convincing responses. This automated fraud can heavily skew insights.
Today’s AI generated survey responses are sophisticated enough to imitate reading patterns and fill in logic based questions.
How to fix it:
Use platforms equipped with bot detection for surveys that track behavioral signals like cursor movement, decision speed, typing irregularities, and metadata. Combine these with strong fraud detection tools such as device fingerprinting, repeat respondent detection, and open-end verification models.
5. Biased or leading questions that distort insights
Even the cleanest sample cannot save a biased survey. Poorly phrased questions introduce survey question bias, influencing respondents toward a specific option and dramatically reducing research data accuracy.
Leading wording, imbalanced scales, and confusing phrasing are some of the most common causes of skewed results.
How to fix it:
Eliminate bias in surveys by rewriting questions with neutral language, keeping scales symmetrical, and testing questions across multiple teams. Short cognitive interviews or soft launches can reveal issues before they harm your dataset. Improving question clarity improves the accuracy of the insights you deliver.
6. Targeting the wrong audience
Even if your data is “clean,” it does not matter if it comes from the wrong audience. A mismatch between your study requirements and sample source leads to major sampling errors.
Poor panel quality or inadequate screening can result in respondents who do not fit the demographic, behavioral, or attitudinal criteria needed.
How to fix it:
Boost sampling accuracy by using verified consumer panels, applying layered screening, integrating zero-party data, and performing ongoing respondent validation. Work with partners who offer transparent audience sourcing and monitoring to ensure your sample reflects the group you actually want to study.
7. Low-quality open ends from disengaged participants
Open-ended questions reveal the truth of respondent quality. Spam phrases, repeated answers, vague comments, or copy-pasted content are often signs of disengagement or fraud.
Without proper open end analysis, these responses contaminate your dataset.
How to fix it:
Use AI text scoring and natural language processing to evaluate the quality of each open end. Modern text data cleaning tools can detect irrelevant, repetitive, or AI generated submissions, helping you flag respondents who are not providing meaningful input.
8. Inconsistent answers within the survey
Contradictions are a clear indicator of low engagement or deceitful intent. Respondents who speed through questions often select random options, resulting in data that cannot be trusted.
Response consistency checks are essential for ensuring data reliability across key variables.
How to fix it:
Incorporate hidden logic checks, compare answers across related items, and identify contradictory answers. Combine this with behavior analysis tools to identify patterns such as rhythmic clicking or repeating the same response across scales.
9. Ignoring behavioral signals like timing and click patterns
Many teams rely solely on content based checks while ignoring behavioral data. Yet respondent behavior analytics often reveal more truth than the answers themselves.
Speeders, laggers, and patterned clickers can slip through unnoticed without proper tracking.
How to fix it:
Use speeding detection, track time-to-complete analysis, and monitor click distributions to identify abnormal behavior. Authentic responses contain natural pauses, varied timing, and a mix of reading and answering rhythms. Fraudulent behavior rarely does.
10. Falling behind on AI driven fraud tactics
AI is rapidly reshaping the research landscape. Fraudsters now use AI models to generate entire surveys. Research teams who do not adopt modern AI fraud detection tools risk falling behind.
Automated quality checks are now essential for staying ahead of fast evolving survey fraud trends.
How to fix it:
Adopt platforms that specialize in AI in market research, combining machine learning models, metadata tracking, behavioral indicators, and fraud scoring. Keep up with advancements in AI tools that help filter out synthetic or manipulated responses.
Strengthen your research quality with expert insight
Want to master these techniques and improve the accuracy of your research data?
Check out our last session “Bad data quality is killing your research (let’s fix it!)” with:
- Crystal Wiese, Director of Marketing at QuestionPro
- Bob Fawson, Co-Founder at Data Quality Co-op
- Tim Cornelius, Director of Audience at QuestionPro
You will learn how to detect fraud, improve sample integrity, increase data reliability, and protect your studies from noisy or misleading responses.
Start improving your survey data quality today!
