The Logit Group

A study runs for two years. The hypothesis is strong. The literature review is airtight. The grant is fully funded.

Then the data comes back, and it's compromised.

Not because the methodology was wrong. Because the fieldwork didn't hold up. AI-generated responses, identity spoofing, and panel fraud aren't edge cases anymore. They're common enough that responsible fielding now requires active countermeasures rather than periodic data cleaning.

The standard academic research has been set for data quality, which isn't academic anymore. It's the baseline for any research that has to stand up to scrutiny.

A few thoughts on what that looks like in practice:

What Rigorous Academic Research Reveals About Fieldwork Decisions Read time: 5 mins Academic research is designed to withstand scrutiny from Institutional Review Boards (IRBs), journals, and funding bodies simultaneously. That pressure doesn't just shape methodology. It exposes exactly where fieldwork decisions succeed or fail. At The Logit Group, our work support...

⚽ Before the FIFA World Cup 2026 kicked off, Narrative Research and The Logit Group surveyed Canadians to understand how they planned to engage with the tournament.

Now, as we're in the midst of Round of 16 following Canada's hard-fought exit, it's interesting to look back at what Canadians told us:

• Nearly half planned to watch matches or attend in person
• Team Canada was the clear favourite among Canadian supporters
• Toronto was the top host city for those planning to attend matches
• Argentina was the most commonly predicted tournament champion

The tournament may have taken a different turn for Canadian fans, but the passion surrounding Team Canada and the World Cup has been unmistakable.

Read the full survey results: https://hubs.ly/Q04ny2nJ0

FIFA World Cup 2026 Canada Survey: Nearly Half of Canadians Plan to Watch the Tournament Read time: 3 mins A new FIFA World Cup 2026 Canada survey conducted by Narrative Research and The Logit Group reveals strong interest in next year's tournament, with nearly half of Canadians planning to watch matches or attend in person. From overwhelming support for Team Canada to Toronto

Good tech can catch bad respondents. It still can’t fix bad sample strategy.

The industry is getting better at fraud detection, validation, and quality monitoring.

That is a good thing. But strong technology still cannot fully solve for:

Sourcing bias
Weak recruitment diversity
Poor quota design
Over-reliance on sample source profiling
Hidden composition issues

---
Good Tech Can Catch More. It Still Can’t Fix Bad Sample Strategy: https://hubs.ly/Q04mc_3l0
---

A dataset can be technically clean and still be strategically weak. That is the part we do not talk about enough.

Good tech can help detect bad actors. It can flag suspicious behavior. It can score response quality. But it cannot fully undo a weak sample framework after the fact. That still takes research judgment.

Knowing when to trust profiling
Knowing when to verify it
Knowing when quotas are too shallow
Knowing when a source mix is creating bias instead of coverage

---
Whitepaper | From Fraud Prevention to Insight Protection: https://hubs.ly/Q04mcmvL0
---

That is why the strongest quality strategies are not built on tech alone. They combine better detection with better sample decisions.

That is also where tools like Calibr8 fit best, not as a replacement for good research thinking, but as a way to strengthen it: https://hubs.ly/Q04mcKQ40.

Because clean data is not always strong data.

Calibr8: A New Standard for Data Quality in Market Research Calibr8: A New Standard for Data Quality in Market Research Market research data quality is under pressure because the way respondents generate data has changed faster than the way the industry validates it. For years,

Let’s say 32% of online survey completions are flagged as fraudulent or of poor quality across 43 respondent sources. Is that a vendor problem or a workflow problem?

Now we need to also be thinking about how the AI analysis layered on top can't fix it.

Want some of our thoughts? Hear from experts about what AI-powered insights can do and what they simply can’t do! Both are important!

https://hubs.ly/Q04lmrlx0

AI is changing market research quickly, but not always in the ways people expect.

In the latest episode of Ponderings from the Perch, Logit's Steve Male joined Priscilla McKinney to discuss the realities of AI in research, the evolving fraud landscape, and what organizations should be asking their fieldwork partners to better protect data quality.

As Logit's EVP of Innovation & Strategic Partnerships, Steve brings decades of experience in research operations, technology, and data quality. The conversation explores where AI is creating new risks, where it is delivering real value, and why strong research judgment still matters.

Listen now: https://hubs.ly/Q04mdGLc0

Poor open ends are not always the problem. Sometimes they are just the evidence.

One of the biggest mistakes in data quality is treating the symptom like the source. We see:

Weak verbatims
Contradictions
Speeders
Duplicates

---
Data Quality Symptoms Are Easy to Spot. Root Causes Are Harder to Catch: https://hubs.ly/Q04lsYYb0
---

And we focus on cleaning those up. But the bigger question is what caused them:

Weak entry controls
Misrepresentation
Bad actors
Manipulated environments
Respondents who know how to game the system

The visible issue is often just where the underlying problem finally shows up.

That is why quality cannot just be about spotting what looks bad at the end. It has to be about understanding what allowed it into the study in the first place.

---
Whitepaper | From Fraud Prevention to Insight Protection: https://hubs.ly/Q04lsSRl0
---

That is also why layered approaches matter, and why tools like Calibr8 are designed to look beyond just the final response and help surface suspicious patterns earlier in the process: https://hubs.ly/Q04lsV3Z0.

Because the symptom may be obvious. The source usually is not.

Bad data did not just show up. The industry helped create the conditions for it.

When we talk about data quality in market research, we often focus on the symptoms:

Speeders
Straightliners
Poor open ends
Duplicate responses

But those are downstream issues.

A bigger part of the story is economic.

---
The Industry Didn’t Just Inherit a Data Quality Problem. It Priced Its Way Into One: https://hubs.ly/Q04ls9js0
---

As CPIs have come down, pressure has increased across the sample ecosystem. Lower costs often mean lower participant incentives. Lower incentives can reduce engagement, reduce thoughtfulness, and make survey participation less attractive to high-quality respondents.

At the same time, pressure to maintain volume efficiently has pushed recruitment through narrower and often cheaper channels. That can limit diversity in the respondent pool, increase overexposure, and create heavier reliance on the same audiences again and again.

And when the environment is built around low cost, high speed, and easy scale, fraud has more room to grow.

So the issue is not just that we are seeing more bad data. It is that the ecosystem has made bad data easier to produce.

That is why quality cannot just be about catching poor responses after the fact. It has to look at the conditions that made those responses more likely in the first place.

---
Whitepaper | From Fraud Prevention to Insight Protection: https://hubs.ly/Q04ls03G0
---

That broader, more layered view is a big part of the thinking behind Calibr8: https://hubs.ly/Q04lsY8s0.

Not just identifying what looks wrong in the dataset, but helping uncover the signals and structural risks behind it.

Because better data quality starts upstream.

Every research tech vendor claims they're your solution. But what happens when your "solutions" don't work together?

Budget waste. Integration headaches. Teams are spending more time managing technology than conducting research.

Our ResTech Stack guide shares our favorite tech and partners that support your workflow instead of fighting against it: https://hubs.ly/Q03GZd730

Navigate the ResTech Maze with Confidence Your ResTech Stack: Our Favorite Tech & Partners for a Competitive Advantage Build a smarter, more efficient research ecosystem. Discover the tools, partners, and strategies we trust to deliver faster insights, better data quality, and stronger outcomes. See the Tech We Recommend What's Inside the G...

Your project load is growing. Your headcount is not.

Knowing what to hand off and who to trust with it changes everything for a research team trying to scale. We shared our thoughts on what to actually look for in a global research partner.

See how to choose the right global research partner. https://hubs.ly/Q04g9Mq00

A respondent showed up for a two-hour in-home interview and announced they'd switched treatments.

A focus group kept going in a total blackout, phones as flashlights, backup recorders running.

A teenager filled out a diary study to "help out" — and accidentally opened up a conversation about caregiver burden no one had thought to ask about.

These are certainly unexpected situations, but they're pretty accurate representations of what qualitative research actually looks like in field. The difference maker is having the confidence that your fieldwork partner knows how to turn the unexpected into something useful.

Jay Thordarson shares a few thoughts on what it’s like inside “Mission Control” of a research study: https://hubs.ly/Q049C8LM0