Methodology

Science behind the score

Standardized, image-based, language-independent. Below is exactly how every score is produced.

Methodology

Each test draws a balanced sample of image-based questions from five cognitive domains. Items are language-independent, eliminating verbal-fluency bias common in classic tests.^[1]

Item difficulty is calibrated against historical attempts. Each delivered test mixes difficulty bands so that ceiling and floor effects are minimized regardless of the taker's ability level.

Validity

Construct validity

Five sub-skills load onto a general factor (g) consistent with Cattell-Horn-Carroll theory.^[2] Sub-skill correlations are monitored each quarter.

Reliability

Internal consistency targets Cronbach's α ≥ 0.85 per sub-skill. Test-retest reliability over 30 days targets r ≥ 0.80 with randomized item bank.

Fairness

Items are reviewed for differential item functioning (DIF) across regions and languages. Items showing significant DIF are retired from the active bank.

Normalization

Raw scores are normalized against a reference distribution (mean 100, SD 15) and adjusted with a bounded time bonus that rewards efficient solving without penalizing careful work.

rawAccuracy = correct / total
timeBonus   = clamp(1 + (expected - actual) / expected, 0.7, 1.3)
iqRaw       = 100 + ((rawAccuracy - 0.5) / 0.15) * 15 * timeBonus
iq          = round(clamp(iqRaw, 40, 200))

As the population sample grows, the reference distribution is re-fit periodically to account for drift.^[3]

Limitations

IQ is one signal, not destiny. Test fatigue, stress, and connection issues can affect a single attempt. We recommend taking the test under good conditions and re-taking after a break before drawing conclusions.

References

[1]Raven, J.C. (1936). Mental tests used in genetic studies. Standardised image-based progressive matrices methodology.
[2]Cattell, R.B. (1971). Abilities: Their structure, growth, and action. Foundation for fluid vs. crystallized intelligence distinction.
[3]Flynn, J.R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Norm re-fitting basis.

Science behind the score

Standardized, image-based, language-independent. Below is exactly how every score is produced.

Methodology

Each test draws a balanced sample of image-based questions from five cognitive domains. Items are language-independent, eliminating verbal-fluency bias common in classic tests.^[1]

Item difficulty is calibrated against historical attempts. Each delivered test mixes difficulty bands so that ceiling and floor effects are minimized regardless of the taker's ability level.

Validity

Construct validity

Five sub-skills load onto a general factor (g) consistent with Cattell-Horn-Carroll theory.^[2] Sub-skill correlations are monitored each quarter.

Reliability

Internal consistency targets Cronbach's α ≥ 0.85 per sub-skill. Test-retest reliability over 30 days targets r ≥ 0.80 with randomized item bank.

Fairness

Items are reviewed for differential item functioning (DIF) across regions and languages. Items showing significant DIF are retired from the active bank.

Normalization

Raw scores are normalized against a reference distribution (mean 100, SD 15) and adjusted with a bounded time bonus that rewards efficient solving without penalizing careful work.

rawAccuracy = correct / total
timeBonus   = clamp(1 + (expected - actual) / expected, 0.7, 1.3)
iqRaw       = 100 + ((rawAccuracy - 0.5) / 0.15) * 15 * timeBonus
iq          = round(clamp(iqRaw, 40, 200))

As the population sample grows, the reference distribution is re-fit periodically to account for drift.^[3]

Limitations

References

[1]Raven, J.C. (1936). Mental tests used in genetic studies. Standardised image-based progressive matrices methodology.
[2]Cattell, R.B. (1971). Abilities: Their structure, growth, and action. Foundation for fluid vs. crystallized intelligence distinction.
[3]Flynn, J.R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Norm re-fitting basis.