Controversy over the predictive validity of IQ on job performance

Sackett et al. (2022) recently questioned prior meta-analytic conclusions about the high IQ validity since the studies by Schmidt & Hunter decades ago. The crux of the issue is complex, but while the debate regarding range restriction correction is not (at least not completely) resolved yet, one thing is certain. The validity depends on the measure of job performance that is used in the meta-analysis. It is unlikely that the importance of IQ has declined in recent years.

CONTENT
1. The groundbreaking study
2. The controversy
3. The choice of criterion (dependent) variable matters

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How IQ became less important than personality: A critical examination of Borghans et al. (2016)

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).

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Wealth, Poverty and Politics: A must read for understanding group differences

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.

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The Structure of Well Designed Online IQ Tests

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

  1. Introduction page content
  2. Item’s pass rate and g-loading
  3. Lazy and dishonest test takers
  4. Short versus long test
  5. Scrolling dilemma
  6. Item type “write-in”
  7. Instruction and rules
  8. Cultural content and cultural bias
  9. 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.

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Gender wage gap: Why the discrimination theory (likely) fails

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.

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Affirmative action failed: An extensive and complicated literature review

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.

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Canadian Race/Ethnic Differences on the LSAT (2019-2023)

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.

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SAT/ACT Scores by Detailed Race/Ethnicity From Applicants on Common App (2021)

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

  1. Freeman, M., Magouirk, P., & Kajikawa, T. (2020). Applying to college in a test‐optional admissions landscape: Trends from Common App data.
  2. 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.

Spearman’s g Explains Black-White but not Sex Differences in Cognitive Abilities in the Project Talent

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.

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