Core Methodology
Raven’s Progressive Matrices
Your assessment is based on the Raven's Progressive Matrices (RPM) scale, the gold standard in non-verbal cognitive testing. Unlike traditional tests that rely on language or general knowledge, RPM measures Fluid Intelligence ($g_f$) — the ability to reason and solve new problems independently of previously acquired knowledge.
This ensures your score reflects pure cognitive potential, free from cultural, linguistic, or educational bias.
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Pattern Recognition
Evaluates your ability to perceive relationships between complex geometric shapes.
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Abstract Reasoning
Measures the capacity to think logically and solve problems in novel situations.
Scoring Model
The Standard Deviation & Bell Curve
IQ scores are relative. We map your performance against a Normal Distribution curve (Gaussian Distribution). The global average is set at 100, with a Standard Deviation (SD) of 15.
AVG (100)
85
115
Top 2% > 130
This psychometric approach ensures your score is not just a random number, but a precise statistical percentile ranking compared to the general population.
Psychometric Reliability
Fluid vs. Crystallized Intelligence
Our methodology focuses strictly on Fluid Intelligence ($g_f$). While Crystallized Intelligence represents knowledge gained through education and experience (vocabulary, math skills), Fluid Intelligence represents your raw processing power. Research indicates that $g_f$ is highly correlated with professional success in complex environments requiring adaptation and quick learning.
Internal Consistency
To ensure validity, our test items are calibrated to measure the same underlying construct (General Intelligence factor, or g). Internal pilot testing has demonstrated a high consistency coefficient (Cronbach's $\alpha > 0.85$), minimizing the margin of error and ensuring that your results are a reliable indicator of your cognitive capabilities.
Age Calibration
Cognitive processing speed peaks in early adulthood and changes over time. To provide an accurate measurement, we apply an age-correction factor derived from global census data. This prevents younger or older candidates from being unfairly penalized based on biological processing speed differences.