Statistics of Intelligence Quantifier by assessment

Introduction

This report contains statistics of Intelligence Quantifiers (I.Q.'s) established via a combination of observation, interview, experiment, and testing. While strictly speaking not psychometrics, this approach is extremely robust against score inflation, fraud, answer leakage and the like, as well as population-independent. The latter means that these I.Q.'s are able to reveal possible changes over time of a population distribution, such as average or spread, as the I.Q.'s are not relative to any population. For clarity, I.Q.'s that are normed on a population can not reflect any such changes, as they go along with those changes and thus hide them.

Norms

The relation of I.Q. with protonorms and other norms is shown in the protonorm table. The assessed I.Q.'s may be used in future adjustments of the relation between protonorms and norms, to help keep that relation stable over time and population-independent.

Scores on Intelligence Quantifier by assessment as of 26 July 2021

Contents type: Observation.   Period: 2010-present

• n: 144
• Median: 139.0
• Quartile deviation: 9.0
• Range: 106
• Maximum possible: ∞
• Minimum possible: -87
• Male median: 139.0 (n = 121)
• Female median: 131.0 (n = 23)
• Resolution: 0.61

Scores by males

n = 121

Scores by females

n = 23

Correlation of Intelligence Quantifier by assessment with other tests by I.Q. Tests for the High Range

Weighted average of correlations: 0.723 (N = 1210, weighted sum = 875.00)

Conservatively estimated minimum g loading: 0.85

Ranking in above table is based on the unrounded correlations. All available data is present in this table, no tests are left out except for those with less than 4 score pairs. All known pairs are used, including possible floor/ceiling scores or outliers.

Correlation of Intelligence Quantifier by assessment with tests by others

Weighted average of correlations: 0.291 (N = 327, weighted sum = 95.27)

Ranking in above table is based on the unrounded correlations. All available data is present in this table, no tests are left out except for those with less than 4 score pairs. All known pairs are used, including possible floor/ceiling scores or outliers.

Please be aware that correlations with these external tests are in most cases affected (depressed, typically) by one or more of the following: (1) Little overlap with the object test because of the much lower ceilings and inherent ceiling effects of the tests used in regular psychology; (2) Candidates reporting scores selectively, for instance only the higher ones while withholding lower ones; (3) Candidates reporting, or having been reported by psychometricians, incorrect scores.

Correlation of Intelligence Quantifier by assessment with other tests by I.Q. Tests for the High Range - for females

Weighted average of correlations: 0.858 (N = 50, weighted sum = 42.88)

Conservatively estimated minimum g loading among females: 0.93

Ranking in above table is based on the unrounded correlations. All available data is present in this table, no tests are left out except for those with less than 2 score pairs. All known pairs are used, including possible floor/ceiling scores or outliers.

Note: The threshold was set to 2 pairs per test because of the smaller amount of data for females. That the g loading within females is higher than among all candidates combined need not be significant yet, although the same has been observed in several other tests for which it has been possible to compute the within-females g loading so far.

Estimated loadings of Intelligence Quantifier by assessment on particular item types

These are estimated g factor loadings, but against homogeneous tests (containing only particular item types) as opposed to non-compound heterogeneous tests. Although tending to surprise the lay person, it is not uncommon for tests to have high loadings on item types they do not actually contain themselves. Such loadings reflect the empirical fact that most tests for mental abilities measure primarily g, regardless of their contents; that the major part of test score variance is caused by g, and only a minor part by factors germane to particular item types. It is of key importance to understand that this is a fact of nature, a natural phenomenon, and not something that was built into the tests by the test constructors.

N = 975

Compound tests have been left out of this table to avoid overlap.

Balanced g loading = 0.83

National medians for Intelligence Quantifier by assessment

For reasons of privacy, only countries with 2 or more candidates are included in this table. Ranking is based on the medians, and then alphabetic.

Correlation with national I.Q.'s of Intelligence Quantifier by assessment

Correlation of this test with national average I.Q.'s published by Lynn and Vanhanen:

• r = 0.01 (n = 144)

Correlation of Intelligence Quantifier by assessment with personal details

Correlation with personal details of Intelligence Quantifier by assessment - within females

Note: The moderate positive correlation with year of birth in females is significant and remarkable, especially compared to that for males which is slightly negative (and significant). It suggests, considering that these assessments were made within a limited period and therefore year of birth is more or less equivalent to reversed age, that in adult females there is a decline of intelligence with age which is not apparent in males. This will be studied in closer detail when possible and reported on in High-range I.Q. scores by age group. In the meantime, do not panic, it may just be a peculiarity of these 21 females after all.

Correlation with personal details of Intelligence Quantifier by assessment - within males

Estimated g factor loadings upward and downward of particular scores

In parentheses the number of score pairs on which that estimated g factor loading is based. The goal of this is to verify the hypothesis that g becomes less important, accounts for a smaller proportion of the variance, at higher I.Q. levels. The mere fact of restricting the range like this also depresses the g loading compared to computing it over the test's full range, so it would be normal for both values to be lower than the test's full-range g loading.

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