Norming of IQ tests on a ratio scale
This article is a preprint and has not been certified by peer review [What does this mean?].
Author(s) / Creator(s)
Melão junior, Hindemburg
Abstract / Description
In this article, we present a solution to one of the central problems of psychometrics over
the past 100 years: a method for norming cognitive tests by producing scores on a ratio
scale. This new approach is accompanied by many other complementary solutions,
including the ability to accurately determine consistent values for the test ceilings, even
when the theoretical rarity level at the top is much larger than the sample size. For example,
in a sample of 3,000 subjects from a non-selective population, we can accurately determine
the ceiling at the rarity level of 1 in 3 million by grouping the lower scores to compose a
metric that allows us to estimate the higher scores while preserving the scale’s internal
consistency. The higher the ceiling and the more difficult it is to norm using traditional
methods, the greater the benefits of this method. In a non-selective sample of 50,000
people, we can determine the ceiling down to the level of 1 in 1 billion. Alternatively, with a
sample of only a few hundred people with significantly above-average IQs, heavily biased
by self-selection, we were able to determine the ceiling down to the level of 1 in a few billion
even without knowing the parameters of this sample’s distribution. In samples severely
contaminated by self-selection, we were able to neutralize this effect naturally, without the
need for epicycle. We corrected for distortions in the dense tails and obtained a series of
other advantages compared to traditional methods based on IRT and TCT [10], in addition
to combining all the typical conceptual advantages inherent in ratio scales. We also opened
the possibility of measuring animal and artificial intelligence on the same scale as human
intelligence, among other possibilities yet to be discovered. We call this new procedure the
“HM Method”.
Keyword(s)
HM Method ratio scale Elo system Thurstone Rasch model Birnbaum model Lord model Item Response Theory IRT Sigma Test Extended Sigma Test Titan Test Mega Test IQ Test WAIS Stanford—Binet 1PL 2PL 3PL 5PL pIQ rIQ Cognitive Ability Game Quantitative Methods Cognitive Skills Genius Diagnostic High Ability Artificial Intelligence Intelligence Measurement Artificial General Intelligence (AGI) LLM Benchmarking Neural Scaling Emergent Abilities Foundational Models Cognitive Architectures Animal IntelligencePersistent Identifier
Date of first publication
2025-12-15
Publisher
PsychArchives
Citation
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Psy_Norm_IQ_ratio_scale.pdfAdobe PDF - 4.85MBMD5 : a2bf59ff12c371cf877ed4c1e8204e8b
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There are no other versions of this object.
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Author(s) / Creator(s)Melão junior, Hindemburg
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PsychArchives acquisition timestamp2025-12-15T15:29:24Z
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Made available on2025-12-15T15:29:24Z
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Date of first publication2025-12-15
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Abstract / DescriptionIn this article, we present a solution to one of the central problems of psychometrics over the past 100 years: a method for norming cognitive tests by producing scores on a ratio scale. This new approach is accompanied by many other complementary solutions, including the ability to accurately determine consistent values for the test ceilings, even when the theoretical rarity level at the top is much larger than the sample size. For example, in a sample of 3,000 subjects from a non-selective population, we can accurately determine the ceiling at the rarity level of 1 in 3 million by grouping the lower scores to compose a metric that allows us to estimate the higher scores while preserving the scale’s internal consistency. The higher the ceiling and the more difficult it is to norm using traditional methods, the greater the benefits of this method. In a non-selective sample of 50,000 people, we can determine the ceiling down to the level of 1 in 1 billion. Alternatively, with a sample of only a few hundred people with significantly above-average IQs, heavily biased by self-selection, we were able to determine the ceiling down to the level of 1 in a few billion even without knowing the parameters of this sample’s distribution. In samples severely contaminated by self-selection, we were able to neutralize this effect naturally, without the need for epicycle. We corrected for distortions in the dense tails and obtained a series of other advantages compared to traditional methods based on IRT and TCT [10], in addition to combining all the typical conceptual advantages inherent in ratio scales. We also opened the possibility of measuring animal and artificial intelligence on the same scale as human intelligence, among other possibilities yet to be discovered. We call this new procedure the “HM Method”.en
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Publication statusother
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Review statusnotReviewed
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/16900
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.21511
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Language of contenteng
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PublisherPsychArchives
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Keyword(s)HM Method
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Keyword(s)ratio scale
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Keyword(s)Elo system
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Keyword(s)Thurstone
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Keyword(s)Rasch model
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Keyword(s)Birnbaum model
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Keyword(s)Lord model
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Keyword(s)Item Response Theory
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Keyword(s)IRT
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Keyword(s)Sigma Test Extended
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Keyword(s)Sigma Test
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Keyword(s)Titan Test
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Keyword(s)Mega Test
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Keyword(s)IQ Test
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Keyword(s)WAIS
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Keyword(s)Stanford—Binet
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Keyword(s)1PL
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Keyword(s)2PL
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Keyword(s)3PL
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Keyword(s)5PL
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Keyword(s)pIQ
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Keyword(s)rIQ
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Keyword(s)Cognitive Ability
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Keyword(s)Game Quantitative Methods
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Keyword(s)Cognitive Skills
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Keyword(s)Genius Diagnostic
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Keyword(s)High Ability
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Keyword(s)Artificial Intelligence
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Keyword(s)Intelligence Measurement
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Keyword(s)Artificial General Intelligence (AGI)
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Keyword(s)LLM Benchmarking
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Keyword(s)Neural Scaling
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Keyword(s)Emergent Abilities
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Keyword(s)Foundational Models
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Keyword(s)Cognitive Architectures
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Keyword(s)Animal Intelligence
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Dewey Decimal Classification number(s)150
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TitleNorming of IQ tests on a ratio scaleen
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DRO typepreprint