Most Accurate National IQs Possible

2 weeks ago 1

Methodology: a total of 5,645 means were compiled into the meta-analysis, most drawing from Becker’s dataset, PISA, PIRLS, TIMSS, the working memory meta-analysis, and the harmonised learning outcomes.

These means were classified into three categories: international scholastic tests, IQ samples (from Becker’s dataset + some other sources), and the samples from the working memory meta.

The IQ samples were adjusted for the effect of perceived selectivity (e.g. college students or white collar workers would be highly selected samples, janitors would be negatively selected) and location (urban, rural, national, etc). All samples were age-normed prior to the conduction of statistical analysis.

The memory samples were adjusted for the age of testing, year, perceived selectivity, and the type of test (e.g. forward span vs backward span). The standard deviations for these variables also had to be adjusted for these factors. IQs were then calculated using these means and standard deviations. The quality of these averages was nothing short of awful, despite my best efforts; according to the model, some of the samples had average IQs as high as 150 and as low as 40.

Then, all of the samples were combined into one file and subject to an anchoring process which controls all of the samples for bias at the category level. For example, if the PISA math test from 2008 gives scores 20 points higher in comparison to what would be expected from the set of tested countries, the scores were adjusted downwards by 20 points. The reference group (IQ = 100) is white Britons.

I then grouped the tests into six different categories: PIRLS, TIMSS, memory, PISA, IQ, and all other tests (other). I gave weights of 3 to the PIRLS, TIMSS, PISA, and ‘other’ averages, a weight of 2 to the IQ averages, and a weight of 1 to the memory averages. No manual revisions were made and I refused to impute the IQs of Turkmenistan, Guyana, Suriname, and French Guinea with proxies like performance in the IMO.

I also calculated the standard errors of these estimates by taking the standard deviation of the sample means and dividing it by the square root of the number of them. Countries with only one sample had their standard errors estimated based on the observed relationship between number of samples and standard error:

On average, each country had a standard error of 1.53. This is a 41% improvement from the prior dataset (V2) which had an estimated average standard error of 2.58.

Do IQ tests have a regional bias?

I reviewed the literature in this preprint and couldn’t come to a definitive conclusion. There is one study (Wicherts’, I believe) that found that GPA and IQ were less correlated in Africa than they were in Europe, but that could also be an artefact of worse grading. Most of the tools traditionally used to assess bias (differential item testing, comparing g-loadings across groups) find little evidence of bias, but I personally don’t think these methods work well.

When it comes to bias testing, the golden standard is taking variables that are assumed to be racially unbiased (e.g. education, income), regressing those onto IQ between groups, and observing if the slopes/intercepts differ to a practical/statistical extent. To my knowledge, there has been no attempt to do this across countries.

Speaking clearly, I think the largest issue with comparing cognitive tests across countries are education and effort. Education causes people’s IQ scores to increase because they become more familiar with standardised testing and improve their skills in maths/reading, but don’t actually get more intelligent. As such, more educated countries should have higher IQ scores that are not reflective of superior general intelligence. Some people have theories that IQ tests are biased by effort across countries, but I don’t see much evidence for this; empirical attempts to assess the question have found the opposite (check the appendix of said paper).

Alternatively, one could argue that there are factors that shrink the observed averages, like using between group standard deviations and imperfectly g-loaded tests. Overall, I would guess that the differences between countries are slightly inflated.

Is the average IQ in Sub-Saharan Africa really 70?

More or less. The score is reflective of their ability to take cognitive tests. An IQ of 70 is commonly used as a cutoff for intellectual disability in the United States, but I should note that this is not a hard cutoff and there is general agreement that IQ alone cannot be used to diagnose intellectual disability. I suspect that, controlling for education and measurement invariance, the true IQ of Sub-Saharan Africa is closer to 75.

Is the average IQ in China really 101?

The average is inflated by several points because Eastern provinces and urban areas are oversampled. A nationally representative sample of China would probably score somewhere between 95 and 100.

How much does biased collection play a role in the differences across countries?

Almost none. Lynn made some mistakes in the collection of the data but he was making an honest effort from what can be inferred.

About half of the data come from international scholastic assessments that take (roughly) representative samples of students and compare them in terms of their ability. Despite the samples being massive and the data collection being conducted by an independent body, the scores on these tests correlate reasonably well with the ones found in IQ tests:

Are any of these values based on geographical imputations?

No.

Do you have any concerns with the estimates of specific countries?

I think that China probably has an IQ in the upper 90s, Mynamar in the mid 80s, Kazakhstan in the upper 80s, and North Korea in the low 90s.

I think the North Korean IQ is deflated by sampling refugees who live in South Korea and I think the Chinese IQ is inflated because Eastern provinces higher in intelligence are disproportionately sampled. I don’t have any specific comments on the others but they seem to be at odds with their levels of development and cultural prominence.

Average IQ by region:

region mean_IQ <chr> <dbl> 1 Eastern Asia 99.823 2 Western Europe 99.794 3 Northern Europe 98.801 4 Australia and New Zealand 98.721 5 Northern America 96.111 6 Eastern Europe 96.004 7 Southern Europe 91.588 8 South-eastern Asia 88.311 9 Polynesia 86.841 10 Central Asia 85.608 11 Western Asia 83.863 12 Micronesia 82.047 13 Latin America and the Caribbean 82.007 14 Southern Asia 78.556 15 Melanesia 78.513 16 Northern Africa 78.503 17 Sub-Saharan Africa 69.308

Values:

alpha3 NIQ se <chr> <dbl> <dbl> 1 HKG 105.689 0.846 2 SGP 105.317 0.923 3 JPN 104.964 0.556 4 TWN 104.87 1.073 5 KOR 103.27 0.915 6 LIE 102.05 0.734 7 EST 101.553 0.315 8 FIN 101.335 0.838 9 CHN 101.286 1.503 10 MAC 101.206 0.61 11 CHE 101.055 0.669 12 AUT 100.735 0.905 13 NLD 100.724 0.654 14 CAN 100.106 0.817 15 IRL 99.923 0.637 16 HUN 99.791 0.422 17 SWE 99.778 0.348 18 AUS 99.619 0.703 19 LUX 99.479 0.594 20 RUS 99.432 0.431 21 GBR 99.406 0.584 22 CZE 99.396 0.283 23 DEU 99.152 0.724 24 DNK 99.025 0.414 25 VNM 98.904 1.706 26 SVN 98.841 0.513 27 USA 98.77 0.465 28 POL 98.398 0.611 29 BEL 98.38 0.641 30 BLR 98.035 1.918 31 SVK 97.884 0.381 32 NZL 97.823 0.546 33 NOR 97.804 0.472 34 LVA 97.423 0.407 35 SCO 97.308 0.384 36 LTU 97.123 0.358 37 FRA 96.774 0.609 38 HRV 96.464 0.696 39 ISL 96.14 0.531 40 ITA 95.478 0.471 41 PRT 95.223 0.691 42 MMR 95.079 3.362 43 ISR 94.981 0.776 44 ESP 94.733 0.902 45 CYP 93.698 0.559 46 BGR 93.345 0.864 47 GRC 92.931 0.551 48 KAZ 92.868 1.177 49 BMU 92.844 1.624 50 GRL 92.725 2.491 51 SRB 92.354 1.147 52 MLT 92.233 0.595 53 MYS 91.854 0.788 54 MNG 91.801 3.429 55 UKR 91.572 0.613 56 MDA 91.524 0.76 57 BRB 91.365 2.785 58 ARM 91.097 1.132 59 TUR 90.913 0.544 60 ROU 90.664 0.534 61 ALB 90.51 1.015 62 BRN 90.06 0.92 63 WSM 90 2.493 64 CHL 89.46 0.625 65 TCA 89.4 2.494 66 THA 89.181 0.949 67 URY 89.088 0.464 68 COK 89 2.494 69 BIH 88.339 1.122 70 CRI 88.19 1.048 71 MEX 88.046 0.839 72 MNE 88 0.351 73 PRI 87.93 1.849 74 TJK 87.71 2.495 75 TTO 87.523 0.547 76 LKA 87.375 2.541 77 GEO 87.199 0.526 78 ARE 86.866 0.526 79 BHR 86.7 0.727 80 AZE 86.512 1.111 81 ARG 86.172 0.897 82 VIR 86.1 3.695 83 VEN 85.802 3.771 84 PRK 85.5 3.5 85 NCL 85 2.496 86 MUS 84.978 1.716 87 BOL 84.97 4.784 88 BHS 84.733 2.557 89 MKD 84.587 0.705 90 BRA 84.389 0.864 91 COL 84.365 1.157 92 PER 84.265 0.694 93 FJI 84 2.497 94 MHL 83.96 2.497 95 IRN 83.751 0.752 96 TUN 83.486 1.322 97 UZB 83.321 2.307 98 JAM 82.915 2.188 99 QAT 82.755 1.064 100 CUB 82.507 1.489 101 ECU 82.209 1.688 102 IDN 82.162 0.816 103 OMN 82.026 0.845 104 LBN 81.966 0.94 105 TON 81.522 2.52 106 SAU 81.421 0.787 107 LAO 81.35 3.336 108 DOM 81.23 1.02 109 SYC 81.198 1.061 110 KIR 81.18 2.499 111 ANT 81.035 2.499 112 MNP 81 2.499 113 KSV 80.954 0.573 114 JOR 80.884 0.815 115 KHM 80.729 2.817 116 PSE 80.63 0.912 117 GTM 80.366 0.663 118 PAN 80.087 0.66 119 IRQ 79.664 3.252 120 PHL 79.475 1.841 121 SLV 79.326 1.051 122 HND 79.29 1.433 123 KWT 79.162 1.305 124 DZA 79.134 0.811 125 SDN 78.713 1.604 126 KGZ 78.535 2.334 127 IND 78.533 1.428 128 KEN 78.456 1.253 129 SYR 78.222 2.132 130 NIC 77.966 2.501 131 LBY 77.812 2.58 132 PRY 77.764 1.595 133 GAB 77.537 1.97 134 SWZ 77.407 0.635 135 NPL 77.332 0.303 136 EGY 77.331 1.627 137 TLS 77.304 0.702 138 BWA 76.902 0.657 139 AFG 76.4 2.502 140 MRT 76.4 2.502 141 BGD 76.188 3.66 142 CYM 76 2.502 143 SLB 75.486 2.502 144 BDI 75.312 2.094 145 SOM 75.202 2.503 146 MAR 74.541 0.781 147 PNG 74.381 4.383 148 TZA 74.15 2.578 149 VUT 73.695 2.504 150 ZNZ 73.537 3.345 151 ETH 73.141 3.002 152 ZWE 73.024 1.761 153 BFA 72.936 2.31 154 ERI 72.619 5.112 155 MOZ 72.547 2.391 156 RWA 72.367 1.998 157 GMB 72.063 1.734 158 ZAF 71.156 1.485 159 VCT 70.88 2.505 160 PAK 70.311 3.617 161 UGA 70.099 2.101 162 SEN 69.972 1.108 163 AGO 69.817 5.257 164 GNQ 69.667 2.506 165 MWI 69.269 1.7 166 SSD 68.818 2.062 167 LBR 68.757 1.57 168 NGA 68.535 1.617 169 BLZ 67.914 2.507 170 BEN 67.181 1.894 171 HTI 66.902 2.008 172 NAM 66.873 1.252 173 LSO 66.423 0.662 174 CMR 66.199 1.147 175 DMA 66.04 0.25 176 COG 65.653 1.459 177 COD 64.951 1.042 178 YEM 64.839 3.115 179 GHA 64.516 1.224 180 CAF 64 2.51 181 MDG 63.983 2.912 182 ZMB 63.654 1.541 183 SLE 62.818 2.718 184 CIV 62.755 0.815 185 TGO 62.21 1.179 186 GIN 62.094 1.757 187 COM 60.355 2.512 188 MLI 60.348 1.919 189 TCD 60.046 1.076 190 DJI 60 2.512 191 NER 58.249 1.981

Correlation matrix: