Research
May 18, 2026 · 8 min read
Classical Test Theory treats every item as equally informative. Item Response Theory (IRT) models each item's difficulty and discrimination separately, then estimates a candidate's underlying trait (theta) given their pattern of responses.
The practical effect: with a 2PL IRT model and Computer Adaptive Testing, we converge on a stable trait estimate in 10–25 items instead of 50+. Candidates spend less time, drop-off goes down, and the score you get is psychometrically tighter than a sum of right/wrong answers.
For hiring teams, that means fewer noisy decisions and a defensible audit trail when a candidate or regulator asks how the score was produced.