CASE4Quality

Are You Falling for These 5 Data Quality Myths and Biases? ?? Bad data is creeping into research—and outdated assumptions might be to blame. Discover the 5 biggest myths about data quality, how AI is changing the game, and what industry leaders say must happen next.

Our Open Letter to the Insights Industry Has Been Published! The consumer insights industry is at a critical turning point. Our open letter, featured in the January/February edition of Quirk's Media Magazine, exposes the survey fraud crisis threatening our entire field. Want to understand why 38% of research data is being discarded? Read the full letter here: https://lnkd.in/d8XqhuXf Would you like to join the other industry leaders who are taking a stand against survey fraud? Email us at [email protected] if you would like to sign our letter.

?? ???????? 4! ?????? ???????????? ?????????? ???????????????????? ?????????????? ?????????????? ?????? ?????????? ?????????????????? ??????????. ?? Fraudsters don’t get caught for speeding, straight-lining, or bad open-ends. They know all the tricks. Meanwhile, real people make all kinds of mistakes - because they’re not robots. It’s 2025. If you’re not catching fraudsters in ???????? ????????, they’ll slip right through. Every survey platform should have built-in, in-survey behavioral fraud detection by now but they don’t. ?? It’s on us to take ownership of the data quality process.?Here’s a good place to start (in no particular order)! Rep Data (Research Defender) CloudResearch (Sentry) OpinionRoute (Clean ID) Redem GmbH dtect Realeyes - Vision AI b3Intelligence Roundtable Ayda DeviceForensIQ If I forgot anyone - sorry! ??I am thrilled to partner with CASE4Quality for this ????????-?????????????? ???????????? on online sampling.?Myth 5 and 6 are coming up - hit the bell if you don’t want to miss out. The full article will be published in the February issue of Quirk's Media. Stay tuned! #mrx #dataquality #survey

Enough is enough. According to Kantar's The State of Online Research Panels, "Researchers are discarding on average 38% of survey data due to quality concerns.” That’s more than one-third of your data—bought, paid for, and then thrown away—because it’s riddled with fraud and bad responses. We are fighting back. 1Q's Keith Rinzler shares how to tackle data fraud and the bot crisis through proactive, structural changes in his open letter to the industry—supported by the Insights Association, Quirk's Media, Greenbook, and others. Read the Open Letter to the Consumer Insights Industry here: https://lnkd.in/e6m-2iR6 #InsightsAssociation #Greenbook #QuirksMRX #DataFraud #DataQuality #MarketResearch #MarketResearchIndustryTrends #MRX #BeatTheBots

This was one of the most eye-opening findings of the CASE fraud study — respondents are entering an average of 21 surveys per day — some over 100 or 1000!! Client-side quality standards used to include a lockout of respondents who completed a survey within 3, 6 or 9 months. I wonder how many client side researchers understand this stat and the implications. ?? Thanks Karine Pepin for keeping the conversation going. And to Efrain Ribeiro, Tia Maurer and Carrie Campbell for your continuing expertise and collaboration on this important topic.

??Thrilled to partner with CASE4Quality for a ????????-?????????????? ???????????? on online sampling! If you care about data quality, you won’t want to miss this. ?? Our ????????-?????????????? ?????????????explores how evolving sampling practices have led to issues like fraud, low-quality respondents, and a lack of transparency. More importantly, we’ll share practical strategies to help client-side researchers protect research integrity, demand transparency, and raise the bar for quality standards. ?? Sneak Peek Time! ?? We’re rolling out insights over the next three weeks, leading up to a full article in Quirk's Media at the end of the month. ??Ready for some hard truths? ?? Let’s start with ???????? #1:? ?????????????????? ???????? ?????????????????? ?????? ???????????????? ?????????? ?????????????? ???? ???????????? ????????, ????????????, ?????? ?????????????????? ???????????????????????? ?????? ???? ????????????. ??Hit the bell to stay in the loop! If you’re a client-side researcher, don’t hesitate to reach out to CASE with all your burning questions (see info in the comments ??) #dataquality #surveys #mrx

It's important for research buyers -- especially client-side -- to understand the current issues in data quality and technologies being created to help. For client-side researchers, if your suppliers are purporting the use of these technologies, ask for the results to be appended to your study findings so that you can easily access and review them in case something is amiss. It also ensures that these techniques are actually being employed on ALL of your studies if that's your expectation. Mayank Agrawal -- thanks for sharing. FYI -- Efrain Ribeiro Karine Pepin Tia Maurer Carrie Campbell Mary Beth Weber

???? ???????? ???? ???????????????? ???????????? ?????????? ???????? ?????????????? ?????? ???????????? ???????? ???????? ?????? ?????? ????????????????. ????’?? ???????????????????? ?????? ???????? ?????? ??????????. My resolution for 2024 was to dedicate 100 hours (??) to reviewing the academic literature on data quality and determining which QC checks are most effective. Why did I take on this challenge? Because we’re making too many mistakes- flagging good respondents while letting fraudsters slip through the cracks. The literature on poor data quality covers a range of satisficing behaviors, such as careless responding, random answering, insufficient effort responding, inattentiveness, straightlining, and so on. ???????????? #??: I had no idea what I was getting myself into. Despite spending over 100 hours, I feel like I’ve only scratched the surface. ?? ???????????? #??: While there are a handful of QC checks I feel more confident in, no silver bullet emerged from my research.???? ?????? ?????? ????????????????: The future of data quality lies in passive in-survey behavior monitoring (like cut/paste, mouse movement, etc.). Without this information, we’re relying too heavily on assumptions about participant intent. ??You heard it here first! That’s my prediction for 2025 -we’ll see more behavioral tools (AI or not, as long as it solves the problem I don’t care!!) that flag suspicious in-survey behavior. ?? BUT: Who knows how long that will remain effective ??♀? . The only real solution to the data quality crisis is to start validating participant identity. The other way will be to crack down on payments. There shouldn't be 60 profiles linked to the same bank account/PayPal, and we have the technology to monitor that. What are your predictions for data quality in 2025? #mrx #dataquality #surveys

Perhaps client-side researchers should consider asking if their sample providers are doing these 5 things ... Thanks for sharing, Karine Pepin!

Do online surveys really have a data quality problem? A few weeks ago, Dynata, a major player in the market research industry, claimed a 99.8% sample acceptance rate in its ‘Seven Vendor Quality Study’. This quickly became ‘the talk of town’ among market researchers since the high sample acceptance rate could suggest an absence of data quality issues in online surveys, a topic of extensive debate for over a decade. In this article, I will share the findings from my investigation of Dynata’s ‘Seven Vendor Quality Study’ to fuel the debate on data quality in online surveys. Let me know what you think!