The Adaptive IQ Test

Development and Norming

The Adaptive IQ Test was developed using established psychometric principles found in leading cognitive assessments, creating original pattern-based questions that measure the same cognitive constructs. The test underwent extensive norming with both online participants from a diverse global population and a smaller proctored baseline study, with both norming methods yielding remarkably consistent results in score distributions and psychometric properties.

Key features of the norming process included:

• Acceptance of only first attempts to prevent practice effects
• Automatic exclusion of entries where participants minimized the browser window or switched tabs
• Computer-generated questions that change with each page load, effectively preventing answer lookups

The norming data demonstrated high reliability, with analysis showing that participants typically required 7 or more attempts to achieve meaningful score improvements. Investigation revealed that most early score variations were attributable to guessed answers rather than genuine performance changes. The convergence of results between online and proctored administration provides strong evidence for the test's validity, reliability, and g-loading.

Views on g-loading

The developers of this test maintain a critical perspective on traditional g-loading claims in psychometrics. They argue that g-loading values are systematically inflated by test developers for commercial purposes, achieved through selective participant sampling and data manipulation. The developers contend that testing a truly diverse population would likely yield g-loadings below 0.6 for most instruments.

This skepticism is reinforced by concerns about data pruning practices among psychologists and the field's troubling reproducibility rate of only 36%, as documented by the Open Science Collaboration ^[1].

Several prominent researchers have challenged the validity and utility of g:

• Stephen Jay Gould argued that g is merely a statistical artifact rather than a real phenomenon, presenting extensive critiques in The Mismeasure of Man ^[2].
• Howard Gardner's theory of multiple intelligences directly challenges the notion of a single general intelligence factor ^[3].
• Robert Sternberg demonstrated that practical intelligence operates independently of g, suggesting multiple cognitive domains ^[4].
• van der Maas et al. proposed that correlations between cognitive abilities stem from mutualistic development processes rather than an underlying g factor ^[5].
• James Flynn's documentation of the Flynn Effect—substantial IQ gains over generations—raises fundamental questions about what g actually measures ^[6].
• Cosma Shalizi has argued that g is a statistical myth, demonstrating how factor analysis can produce a general factor from any positively correlated data ^[7].
• Nassim Taleb criticized IQ testing and g as lacking mathematical rigor and practical validity, particularly at the extremes ^[8].

These critiques suggest that the psychometric community's emphasis on g-loading may reflect professional and commercial interests rather than scientific validity. The persistence of high g-loading claims despite methodological concerns and low reproducibility rates indicates potential systemic issues within intelligence research.

References

1. Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251). doi:10.1126/science.aac4716
2. Gould, S. J. (1996). The Mismeasure of Man (Revised ed.). W. W. Norton & Company. ISBN: 978-0393314250
3. Gardner, H. (1998). A multiplicity of intelligences. Scientific American Presents, 9(4), 19-23. doi:10.1080/0969595980050102
4. Sternberg, R. J. (2000). Practical Intelligence in Everyday Life. Cambridge University Press. doi:10.1017/CBO9780511977244
5. van der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842-861. doi:10.1037/0033-295X.113.4.842
6. Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171-191. doi:10.1037/0033-2909.101.2.171
7. Shalizi, C. (2007). g, a Statistical Myth. Three-Toed Sloth
8. Taleb, N. N. (2018). Skin in the Game: Hidden Asymmetries in Daily Life. Random House. ISBN: 978-1400067824