Question 1

How can I be sure of AI object-/scene-detection in ethnography footage?

Accepted Answer

To trust AI object and scene detection in ethnography videos, you need validation steps that confirm the system's accuracy in context. Researchers typically start by testing the AI on sample footage where the true contents are known, comparing its tags to human-coded results. High agreement (or precision/recall metrics) gives confidence, and any mismatches highlight where the model needs tuning. Additionally, using AI tools that provide confidence scores or flag uncertain detections lets you or a human reviewer double-check critical moments, ensuring the final analysis of ethnographic footage is reliable rather than blindly accepted from the AI. Also, ethnographic contexts can be unusual for off-the-shelf models, so fine-tuning the AI on footage similar to yours or using a human-in-the-loop for tricky cases helps ensure nothing important is missed.

Question 2

How is connected health technology changing medical device research priorities?

Accepted Answer

Connected health technology (like smart medical devices, wearables, and health apps) is shifting medical device research toward user experience and data integration priorities. Traditionally, research focused on a device's clinical effectiveness and safety in isolation, but now teams must also prioritize how the device connects to other systems and fits into patients' daily lives. For example, a modern glucose monitor study doesn't just test accuracy; it also evaluates the device's mobile app interface, real-time data sharing with healthcare providers, and patient adherence when using continuous monitoring. Data privacy and security have become key research angles as well, since these devices transmit sensitive health information. Additionally, connected devices generate continuous real-world data, so researchers increasingly analyze usage patterns and outcomes over time to ensure the technology delivers sustained value. In essence, connected health is broadening the scope: successful medical device development now demands excellence in digital features, interoperability, and patient engagement, not just the core medical function.

Question 3

When should qualitative follow surveys to decode financial advice misalignment?

Accepted Answer

Follow-up qualitative research should be done after a survey when there's a clear misalignment in financial advice perceptions that numbers alone can't explain. If a survey reveals, for example, that clients aren't following their advisor's recommendations despite claiming to be satisfied, interviews or focus groups can probe why. Qualitative methods are ideal whenever data shows a disconnect — advisors might think they're communicating clearly while clients feel confused or mistrustful. In those cases, talking directly to clients and advisors helps decode emotions, misunderstandings, or unmet needs behind the survey responses. In short, whenever quantitative findings on financial advice don't add up or reveal a gap in expectations, a qualitative deep dive should follow to uncover the "why."

Question 4

How can AI improve engagement tracking and retention in research panels?

Accepted Answer

Artificial Intelligence (AI) can boost both engagement tracking and retention in research panels by personalizing and predicting participant interactions. Machine learning algorithms analyze panelists' participation patterns (response rates, completion time, etc.) to spot early signs of disengagement. With these insights, the system might automatically send tailored reminders or adjust survey frequency to re-engage those members. For instance, if AI predicts a certain panelist is likely to drop out, it could trigger a special retention campaign-like a personalized message or bonus incentive-to win them back. AI can also personalize content and incentives; for example, recommending surveys that match a panelist's interests or adjusting rewards based on individual engagement levels. By continuously learning what keeps different people active, an AI-driven platform helps participants feel valued and reduces the likelihood of attrition over time.

Question 5

How should we measure equitable reach in public opinion research?

Accepted Answer

You measure equitable reach in public opinion research by examining whether your sample truly reflects all key segments of the population, without underrepresenting any group. In practice, this means comparing the demographics of your respondents to the target population benchmarks (for example, census data or known community profiles) to see if groups like different races, age brackets, income levels, or regions are included in proportion to their share of the population. If certain groups have a much lower response rate or presence in the sample than they should, the reach wasn't equitable. One way to quantify this is to calculate a representation ratio or inclusion index for each demographic (the percentage of responses from that group divided by its percentage in the population) and see how close it is to 1. Ideally, all major groups score evenly — indicating no one's voice is disproportionately missing — thus demonstrating that your public opinion research achieved equitable reach.

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Market Research FAQs with Expert Insights

How can I be sure of AI object-/scene-detection in ethnography footage?

How is connected health technology changing medical device research priorities?

When should qualitative follow surveys to decode financial advice misalignment?

How can AI improve engagement tracking and retention in research panels?

How should we measure equitable reach in public opinion research?