Validity
Response consistency, speed, straight-lining and social-desirability indicators. Computed before anything else, and able to invalidate a session outright.
No proprietary mystery. The model, the scoring approach and the current state of the evidence, written so a head of people and an I/O psychologist can both check it.
The model
Response consistency, speed, straight-lining and social-desirability indicators. Computed before anything else, and able to invalidate a session outright.
The stable bright-side facets: drive, discipline, curiosity, composure, sociability, openness to adaptation.
Nine behaviours that help until pressure arrives. In development: the items are collected, but this layer is not scored or reported yet. When it is, it will be reported as risk zones with base rates, never as a diagnosis.
What the person is motivated by. In development: collected but not yet scored or reported. When live, it will be compared against what the work demands to produce fit, not ranking.
Learning agility, tolerance for ambiguity, and measured AI fluency — the layer that makes this an instrument for this decade.
Five group-level indices report today — cohesion, safety spread, role coverage, cognitive diversity and risk density — gated at five respondents. Energy concentration depends on forced-choice driver items that need a calibration we have not collected, so it renders as not yet available.
Layers feed forward: L0 can invalidate everything after it, LA–LD describe the person, and LE is composed from LA–LD plus consensus items. Nothing in LE is ever traced back to an individual's item responses in a manager view.
Scoring philosophy
Polytomous items are scored with a graded response model, so an item that discriminates well counts for more than one that doesn't. Item parameters live in versioned calibration data — never in code — and every score records which calibration produced it.
The long-form instrument uses ranked blocks to blunt impression management. Because ranked data is ipsative, it is scored into a normative space before anything is reported, and the platform structurally refuses to compare people on raw ipsative output.
Every norm table carries a population definition, a collection window, an n, and a version. A rebuild requires n ≥ 300 and produces a new version rather than overwriting the old one, so a score from last year can still be reproduced exactly.
All arithmetic happens in z-space. Percentiles, stens and 0–100 values are derived at the display edge only, and percentiles are never averaged — a rule the pipeline enforces rather than documents.
Reliability is estimated per construct (α and ω, tracked nightly), converted to a standard error of measurement, and drawn as a likely range around every reported score. Where bands overlap, the platform says the difference is not measurable.
Items are screened for differential item functioning across available groups, and the bias-audit workbench computes impact ratios with both the four-fifths and 2-SD tests, exportable as a dated artefact.
Evidence status
Under-claiming is the brand. This table is maintained as evidence accumulates, and we would rather lose a deal than round a status up.
Internal consistency of the disposition facets (LA)
α and ω computed per construct on every calibration sample and re-estimated nightly against live data. Facets below threshold are held out of reporting.
Graded-response calibration and SEM derivation
Item parameters estimated and exportable for independent replication in R (mirt). Every reported score carries the calibration and norm version that produced it.
Criterion validity against performance outcomes
Longitudinal collection under way with design partners. Until effect sizes are published here, we make no predictive-validity claim of any kind.
Thurstonian scoring of the forced-choice long form
Implemented and running; normative recovery is being benchmarked against the Likert form on a shared sample.
Adaptability and AI-fluency construct validity (LD)
A new construct in a fast-moving domain. Convergent evidence against observed tool-use behaviour is being collected; treat LD as developmental.
Cross-cultural measurement invariance beyond en-AU / en-GB / en-US
Norms exist for English-language populations only. We will not ship a locale before the invariance testing that justifies it.