The Science of Prediction
A modern, five-axis quantitative methodology for predicting leadership potential; execution velocity; and role fit.
1The Problem with Legacy Assessments
Most personality assessments available today (such as industry-standard preference tests) measure preferences; how someone feels or what they value. While useful for therapy; preferences are unreliable predictors of job performance.
When an executive is under immense pressure, they don't act on their preferences; they act on their conative instincts. The Trait Institute ABIF™ methodology was built specifically to measure these instinctive, action-oriented drivers.
The 5-Axis Framework
Our proprietary algorithm measures human action through five continuous dimensions. Rather than boxing a candidate into a static personality type, we plot their exact coordinates on these axes:
Dominance (D)
Measures the drive for control, problem solving, and confronting challenges. High-D individuals are pioneers who thrive in friction. Low-D individuals seek harmony and build consensus.
Processing (P)
Measures the speed of decision making vs. analytical depth. High-P individuals need vast amounts of data before moving. Low-P individuals trust their gut and execute rapidly.
Social (S)
Measures the drive for interaction, persuasion, and sociability. High-S individuals energize others and excel in sales environments. Low-S individuals prefer deep, undisturbed focus.
Structure (C)
Measures the need for systems, rules, and predictability. High-C individuals create procedures and protect quality. Low-C individuals thrive in chaos and constantly disrupt the status quo.
Intensity (I)
The final axis is a multiplier. It measures raw energy, stamina, and drive for achievement across the other four axes. High Intensity acts as an amplifier for a candidate's core traits.
Statistical Validity & Scoring
Unlike simple Likert scale surveys (1 to 5 ratings) that are highly susceptible to candidate faking, the ABIF™ utilizes a forced-choice ipsative mechanism.
Candidates are presented with sets of behavioral descriptors and must choose the one that is Most like them and Least like them. This methodology forces candidates to prioritize their true instincts, eliminating "middle-of-the-road" or socially desirable answering patterns.
Our scoring engine uses Euclidean distance vectors to map these selections against tens of thousands of previous profiles, instantly generating a normalized score (0-100) on all five axes.