Category: Asian-White IQ Gap

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.

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The SAT and Racial/Ethnic Differences in Cognitive Ability

The SAT is the most popular standardized test used for college admissions in the United States. In principle, SAT scores offer a good way to gauge racial and ethnic differences in cognitive ability. This is because, psychometrically, the SAT is just another IQ test–that is, it is a set of items the responses to which can be objectively marked as correct or incorrect.[Note 1] Unsurprisingly, SAT scores correlate strongly with scores from other IQ tests (Frey & Detterman, 2004). It is also advantageous that SAT-takers are generally motivated to get good scores, and that large numbers of young people from all backgrounds take the test each year, enabling precise estimation of population means.

However, the SAT has at least two major limitations when used for group comparisons. Firstly, it is a high-stakes college entrance test, which means that it is a target for intense test preparation activities in ways that conventional IQ tests are not, potentially jeopardizing its validity as a measure of cognitive ability. Secondly, taking the SAT is voluntary, which means that the participant sample is not representative but rather consists of people who tend to be smarter and more motivated than the average.

This post will attempt to address these shortcomings. I will investigate whether racial and ethnic gaps in the SAT are best understood as cognitive ability gaps, or if other factors make a significant contribution, too. An important method here is to compare the SAT to other tests that are not subject to extraneous influences such as test prepping. Another goal of the post is to come up with estimates of racial/ethnic gaps in the test that are minimally affected by selection bias. This can be done with data from states where entire high school graduate cohorts take the SAT. Other topics that will receive some attention in the post include ceiling effects, predictive validity, and measurement invariance.

Because racial/ethnic gaps in the SAT have changed over time, an essential part of the analysis is understanding these temporal trends. In particular, Asian-Americans have performed extraordinarily well in the test in recent years. Getting a better handle on that phenomenon was a major motivation for the post. Comparisons of trends in the national and state-level results turned out to be informative with respect to this question.

The post is multipronged and somewhat sprawling. This is because the (publicly available) SAT data do not yield straightforward answers to many important questions about racial and ethnic differences. The only way to get some clarity on these issues is to examine them from multiple angles, none of which alone supplies definitive answers, but which together, hopefully, paint a reasonably clear picture. I have relegated many technical details of the statistical methods used, as well as many ancillary analyses, to footnotes so as make the main text less heavy-going. The data and R code needed to reproduce all the calculations, tables, and graphs in this post are included at the end of each chapter, or, in the case of some ancillary analyses, in the footnotes.

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