- Population-referenced norming instead of norms that quietly shift under a convenience sample.
- Versioned, explicitly activated scoring models. No silent updates or unreviewed logic.
- Measurement and prediction are evaluated separately, making it easier to locate weakness in either layer.
- Local norm development and technical manuals are delivered, not withheld.
Researchers and Partners
FSI Calibrate
Rigor Behind the Results
Pilot and researchThe measurement backbone of the platform: instruments built and validated against your own data, not a borrowed norm table.
Who It's For
For people who need the numbers to hold up.
Built for the people who have to answer for a score.
How It Works
Five pillars of the calibration process.
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01
Build and validate against local data
Instruments are built and checked against the population they'll actually be used on.
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02
Anchor to a stable reference population
Scores are anchored to a stable, known reference population.
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03
Version and explicitly activate every model
Every model is versioned and explicitly activated before going live.
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04
Separate measurement from prediction
Measurement models and prediction models are validated separately, on their own terms.
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05
Documentation is a deliverable
Local norms and technical documentation ship as part of the product, not an afterthought.
What Makes It Different
Rigor you can point to, not just trust.
Early conversations
Talk through calibration for your data.
Evaluating an instrument or validating against a local population? Reach out.