Borghans et al. (2016) analyze 4 datasets with diverse measures of IQ and, shockingly, concluded that the impact of IQ on social outcomes is weak compared to personality measures, despite what the earlier reviews and meta-analyses showed (Gottfredson, 1997; Poropat, 2009; Schmidt & Hunter, 2004). Indeed, as reviewed prior, most studies found that personality measures generally have weak relationship with outcomes once IQ is accounted for. Yet their work has not been subjected to critical examination, just various uninteresting comments (Ganzach & Zisman, 2022; Golsteyn et al., 2022; Stankov, 2023) and replication failures (Zisman & Ganzach, 2022).
Thomas Sowell’s book, Wealth, Poverty and Politics, provides a thorough explanation as to why nations and groups of peoples developed at different rates, how and why they rise or fall as a group or empire. There are only a few sections which I do not find convincing, such as his arguments on group differences in IQ and his complete rejection of the genetic hypothesis.
To summarize the ideas of the book, Sowell shows that 1) population differences emerged because geography has never been egalitarian, 2) cultural and geographical isolations are great impediments to development, 3) equal opportunity will not create equal outcomes between groups, 4) education is not human capital and has sometimes caused negative outcomes, 5) exploitation of the poor through either slavery or imperialism does not explain prosperity status, 6) poverty and inequality are so ill-defined to the point that comparisons are meaningless, 7) the government has a duty to please the masses through dubious tactics at the expense of economic performance.
There are convenient ways researchers can collect IQ scores and correlate the observed scores with measures of self-reported health, socio-economic attainment, personality or political views. In platforms such as Prolific or MTurk, participants make money in their spare time by completing tasks. Designing a test that displays both a high loading on the general factor of intelligence, while avoiding measurement bias and bad quality data from online participants, is quite a challenging task.
CONTENT
- Introduction page content
- Item’s pass rate and g-loading
- Lazy and dishonest test takers
- Short versus long test
- Scrolling dilemma
- Item type “write-in”
- Instruction and rules
- Cultural content and cultural bias
- Computerized Ability Test
The issues related to online testing are illustrated based on the numerous IQ tests Jurij Fedorov devised, with my assistance, using Alchemer’s professional software.
Probably the most rehearsed explanation of the gender pay gap is discrimination. After accounting for traditional labor market factors, a large residual gap remains. This residual gap is also called the unexplained gap. Researchers often commit the fallacy of equating unexplained effect to discrimination effect instead of omitted variable bias. In fact, most wage decomposition models are probably contaminated by bias. This article will explain that much of the residual gap is likely due to other causes. In particular, time flexibility. The evidence for the discrimination effect is often ambiguous.
Despite the US Supreme Court recently overturning affirmative action (AA), many scholars believe that AA bans in higher education hurt minorities’ opportunities because affirmative action actually delivered on its promises. Although the findings lack consistency, the impact of AA on the outcomes of under-represented minorities (URM) is generally either ambiguous or slightly negative. The bans exert a less negative effect on more competitive fields such as STEM. The picture is even worse when one considers that AA comes at the cost of lowering the chance of other, more capable minorities, such as Asians, and does not greatly impact the intended targets, i.e., the impoverished families among URMs.
This post is a quick update on my in-depth review of group differences in the SAT, occasioned by the publication of the College Board’s reports on the SAT scores of the 2023 high school graduate cohort. Using national-level test data as well as data from selected states, I will examine how the most recent results relate to the trends that I previously identified.
Continue reading
This article reports racial gaps in the LSAT scores for Canada (2019-2023). By using the threshold method designed by La Griffe du Lion (2007), the standardized effect sizes are computed from the proportions of members of each groups who attain specific score ranges. Results are compared with U.S. gaps in the Law School Admission Test (LSAT) and Medical College Admission Test (MCAT) scores. Details of the analysis is available here.
We recently published the IQ scores for major ethnic groups, based on the broadly representative Adolescent Brain Cognitive Development sample. These ethnic averages correlated very strongly (r = .90 to .94) with scholastic aptitude scores (SAT or ACT scores) based on nationally representative samples of American-born college students between the years 2012 and 2020. The aptitude scores came from the NPSAS surveys, which, unfortunately, have a limited number of ethnic classifications.
As Dalliard noted, understanding racial/ethnic differences in aptitude tests is important since it is a guide to the composition of the USA’s future cognitive elite. Since different ethnic groups have different political interests, which, in turn, shape policy, understanding the cognitive capital of ethnic groups is essential to predicting the trajectory of the USA in the coming century.
While no open-source national surveys provide data on SAT/ACT scores decomposed by detailed ethnic groups, Common Application provides some data for USA citizens and residents. Common App is an undergraduate college admission application service that allows one to apply to over one thousand member colleges in the USA. While the data sample is large, with over 1 million applicants each year, several issues have been reported by Freeman et al. (2021):
- The percentage of applicants reporting a test score fell from 70 and 73% in 2018-19 and 2019-20 to 40% in 2020-21. This is likely due to 89% of Common App’s members (900+ colleges) no longer requiring SAT/ACT for admission in 2020-21.
- Nearly 60% of applicants applied from ZIP Codes in the top 20% of the median household income distribution. The decline in reporting rates between 2019-20 and 2020-21 was greater in lower-income communities.
- Underrepresented minorities (not including Asians) report test scores at lower rates than non-underrepresented minorities (71% vs 77%). The drop in reporting rates between 2019-20 and 2020-21 was larger for underrepresented minorities (31% vs 47%).
- In 2019-20, 78% of non-first-generation students reported test scores versus 69% of first-generation students while in 2020-2021 the rates were 48% and 30%.
- Applications to private, more selective institutions were the most likely to include test scores (83% in 2019–20 and 44% in 2020–21), while applications to private, less selective institutions were the least likely to include test scores (67% and 28%).
The high rates of missing test scores, especially for low-achieving groups, may mean that certain group averages are biased. Moreover, many ethnic groups suffer from ethnic attrition (Emeka, 2019), in which case group identification is correlated with aptitude. To illustrate, in the case of Nigerian-Americans, Emeka (2019) noticed that Nigerians residing in poor families with parents who have not completed high school or college degrees are much more likely to drop out of the Nigerian group in favor of the African American or Black group. This is because for them, “it is not Nigerian not to go to college”.
Those caveats noted, with respect to test scores, which are reported by Kim et al. (2022), the numbers are more or less as expected. Scores for the average Asian group, average White, average Black, and Native Hawaiian or Pacific Islander are 1382, 1278, 1108, and 1181, respectively. Applicants who did not report racial/ethnic information score (1378) higher than the non-underrepresented minorities (1297). Among Asian ethnicities, Asians from India, China, Korea, Japan, Malaysia, and “None provided” scored substantially higher (around 1400 vs 1300 or less) than Asians from Cambodia, Philippines, Vietnam, Pakistan, Other Southeast, or Other South Asia (Figure 2a).
The following table reports the SAT/ACT means by race/ethnicity, including mixed races, as well as Hispanic groups by both region and race. The columns display the N (unadjusted), % of reports, average SAT/ACT, GPA, N (adjusted for % of reports), SAT/ACT in IQ metrics. The IQ metric SAT/ACT scores were computed using the NPSAS20 total SAT/ACT standard deviations.
Ethnic | N | Reports | SAT¹ | GPA | N² | IQ-metric |
White | ||||||
Average | 570400 | 0.53 | 1278 | 92 | 302312 | 100.0 |
Two+ Provided | 17640 | 0.52 | 1310 | 93 | 9173 | 102.4 |
Europe | 464670 | 0.55 | 1287 | 93 | 255569 | 100.7 |
Middle East | 22720 | 0.39 | 1236 | 90 | 8861 | 96.9 |
None Provided | 39580 | 0.43 | 1206 | 91 | 17019 | 94.6 |
Other | 25800 | 0.35 | 1159 | 88 | 9030 | 91.1 |
African American | ||||||
Average | 140010 | 0.36 | 1108 | 85 | 50404 | 87.2 |
Africa | 13840 | 0.34 | 1185 | 87 | 4706 | 93.0 |
Two+ Provided | 17740 | 0.39 | 1170 | 87 | 6919 | 91.9 |
None Provided | 670 | 0.29 | 1141 | 85 | 194 | 89.7 |
Caribbean | 10610 | 0.37 | 1116 | 85 | 3926 | 87.8 |
Other | 680 | 0.27 | 1113 | 84 | 184 | 87.6 |
U.S. Af-Am | 96470 | 0.35 | 1084 | 84 | 33765 | 85.4 |
Asian | ||||||
Average | 115490 | 0.61 | 1382 | 95 | 70449 | 107.8 |
None Provided | 1600 | 0.65 | 1438 | 97 | 1040 | 112.0 |
Korea | 10480 | 0.67 | 1421 | 96 | 7022 | 110.7 |
India | 32750 | 0.72 | 1415 | 96 | 23580 | 110.3 |
China | 24620 | 0.64 | 1414 | 97 | 15757 | 110.2 |
Other East Asia | 2800 | 0.59 | 1411 | 95 | 1652 | 110.0 |
Malaysia | 240 | 0.61 | 1380 | 95 | 146 | 107.6 |
Two+ Provided | 8520 | 0.58 | 1376 | 96 | 4942 | 107.3 |
Japan | 1330 | 0.57 | 1364 | 94 | 758 | 106.4 |
Other South Asia | 5620 | 0.45 | 1309 | 92 | 2529 | 102.3 |
Pakistan | 5500 | 0.50 | 1301 | 92 | 2750 | 101.7 |
Vietnam | 9090 | 0.55 | 1284 | 94 | 5000 | 100.5 |
Philippines | 8100 | 0.47 | 1262 | 94 | 3807 | 98.8 |
Other Southeast Asia | 4130 | 0.44 | 1261 | 92 | 1817 | 98.7 |
Cambodia | 700 | 0.36 | 1216 | 92 | 252 | 95.4 |
Pacific Islander | ||||||
Group Average | 1770 | 0.32 | 1181 | 88 | 566 | 92.7 |
None Provided | 100 | 0.36 | 1246 | 91 | 36 | 97.6 |
Guam | 220 | 0.34 | 1233 | 91 | 75 | 96.6 |
Other (Excl. Philippines) | 570 | 0.30 | 1204 | 86 | 171 | 94.5 |
Two+ Provided | 150 | 0.28 | 1187 | 87 | 42 | 93.2 |
Hawaii | 420 | 0.35 | 1180 | 88 | 147 | 92.6 |
Samoa | 320 | 0.33 | 1082 | 88 | 106 | 85.3 |
American Indian | ||||||
Average | 2760 | 0.36 | 1162 | 87 | 994 | 91.3 |
OK Citizen Potawatomi | 20 | 0.29 | 1338 | 90 | 6 | 104.5 |
OK Choctaw | 90 | 0.55 | 1267 | 93 | 50 | 99.2 |
OK Chickasaw | 50 | 0.52 | 1252 | 90 | 26 | 98.1 |
OK Muscogee (Creek) Nation | 50 | 0.59 | 1241 | 91 | 30 | 97.2 |
OK Cherokee | 140 | 0.53 | 1218 | 95 | 74 | 95.5 |
MI Sault Ste. Marie | 40 | 0.57 | 1192 | 88 | 23 | 93.5 |
NY Saint Regis | 50 | 0.16 | 1170 | 86 | 8 | 91.9 |
None Provided | 80 | 0.29 | 1153 | 87 | 23 | 90.7 |
Unenrolled | 1370 | 0.34 | 1147 | 86 | 466 | 90.2 |
Other Enrolled | 640 | 0.34 | 1146 | 87 | 218 | 90.1 |
SD Oglala Sioux | 20 | 0.18 | 1123 | 90 | 4 | 88.3 |
AZ Navajo | 160 | 0.29 | 1096 | 89 | 46 | 86.3 |
NC Eastern Cherokee | 40 | 0.41 | 1079 | 87 | 16 | 85.1 |
Two/More Races | ||||||
Group Average | 56130 | 0.50 | 1289 | 92 | 28065 | 100.8 |
Asian & White | 25400 | 0.60 | 1354 | 95 | 15240 | 105.7 |
Asian & Pacific Islander | 1020 | 0.43 | 1278 | 93 | 439 | 100.0 |
Asian & American Indian | 140 | 0.45 | 1266 | 88 | 63 | 99.1 |
White & Pacific Islander | 1010 | 0.48 | 1265 | 92 | 485 | 99.0 |
White & Native American | 4620 | 0.50 | 1248 | 91 | 2310 | 97.7 |
Three or More Races | 3610 | 0.41 | 1241 | 90 | 1480 | 97.2 |
Asian & African Am. | 2680 | 0.43 | 1224 | 90 | 1152 | 96.0 |
White & African Am. | 15680 | 0.40 | 1192 | 88 | 6272 | 93.5 |
African Am. & Pacific Isl. | 40 | 0.19 | 1118 | 83 | 8 | 88.0 |
Native Am. & Pacific Isl. | 1540 | 0.31 | 1095 | 84 | 477 | 86.2 |
African Am. & Native Am. | 380 | 0.34 | 1093 | 84 | 129 | 86.1 |
Hispanic (Region) | ||||||
Group Average | 194060 | 0.37 | 1195 | 89 | 71802 | 93.8 |
Spain | 4950 | 0.48 | 1284 | 92 | 2376 | 100.4 |
South America | 24800 | 0.46 | 1247 | 91 | 11408 | 97.7 |
Cuba | 6860 | 0.61 | 1236 | 92 | 4185 | 96.9 |
Two+ Provided | 28730 | 0.41 | 1211 | 90 | 11779 | 94.9 |
Mexico | 70270 | 0.32 | 1170 | 89 | 22486 | 91.9 |
Central America | 17400 | 0.32 | 1168 | 89 | 5568 | 91.8 |
Puerto Rico | 22540 | 0.37 | 1168 | 87 | 8340 | 91.7 |
None Provided | 1850 | 0.29 | 1161 | 87 | 537 | 91.2 |
Other | 16670 | 0.30 | 1152 | 87 | 5001 | 90.5 |
Hispanic (Races) | ||||||
Group Average | 194060 | 0.37 | 1195 | 89 | 71802 | 93.8 |
Asian | 3290 | 0.43 | 1277 | 92 | 1415 | 99.9 |
Two+ Provided | 9750 | 0.43 | 1235 | 90 | 4193 | 96.7 |
White | 96690 | 0.45 | 1219 | 90 | 43511 | 95.6 |
Hispanic or Latinx Only | 60870 | 0.27 | 1146 | 88 | 16435 | 90.1 |
American Indian | 4740 | 0.29 | 1133 | 87 | 1375 | 89.1 |
African American | 17740 | 0.29 | 1116 | 85 | 5145 | 87.8 |
Pacific Islander | 980 | 0.25 | 1097 | 85 | 245 | 86.4 |
¹SAT/ACT average
²Real N estimated by multiplying N column by % Reports
Despite issues with the data that were pointed out above, one clearly notices the strong similarity between these IQ-metric SAT/ACT scores by race/ethnicity, including various mixed-race categories, and the IQ-metric SAT/ACT estimates from the NPSAS reported in a previous post.
The full dataset made available by Common App can be found at the following link. See also the following report. Additional data is included in the file such as AP scores, the number of academic honors reported, and household income.
References
- Freeman, M., Magouirk, P., & Kajikawa, T. (2020). Applying to college in a test‐optional admissions landscape: Trends from Common App data.
- Kim, B.H., Freeman, M., Kajikawa, T., Karimi, H., & Magouirk, P. (2022). Unpacking applicant race and ethnicity, part 2: disparities in key indicators of applicant readiness and resources across detailed backgrounds. Common Application.
In this analysis of the Project Talent data, the g factor model as represented by the Spearman’s Hypothesis (SH) was confirmed for the black-white cognitive difference but not for the sex difference. Results from MGCFA and MCV corroborate each other. MGCFA detected small-modest bias with respect to race but strong bias with respect to sex cognitive difference. Full result is available at OSF.
Chuck recently published the IQ estimates for almost 30 ethnic groups/subgroups in the ABCD of the 10-year old US children. The post was an astounding hit. However, a few commenters complained that the sample sizes of some subgroups were small. I responded that if one could replicate the values and the rank order, one would have more confidence in these estimates. And this is exactly what we did here (full result available).
Continue reading