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Race Differences: A Very Brief Review

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The nature of race differences, and even the mere “existence” of human races, continues to be a major source of controversy and confusion. This brief review summarizes the empirical evidence about race differences and the conceptual issues related to taxonomy, as well as practical implications for medicine and the social sciences. The review shows that human races are distinctive phenotypically and genotypically, the latter with regard to the frequencies of a very large number (millions) of alleles. Distributions of these traits are clinal rather than discrete, and human races are subject to continuous change across evolutionary time.
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MANKIND QUARTERLY 2019 60.2 142-173
Race Differences: A Very Brief Review
Emil O. W. Kirkegaard
Ulster Institute for Social Research, London, UK
The nature of race differences, and even the mere existence
of human races, continues to be a major source of controversy and
confusion. This brief review summarizes the empirical evidence
about race differences and the conceptual issues related to
taxonomy, as well as practical implications for medicine and the
social sciences. The review shows that human races are distinctive
phenotypically and genotypically, the latter with regard to the
frequencies of a very large number (millions) of alleles. Distributions
of these traits are clinal rather than discrete, and human races are
subject to continuous change across evolutionary time.
Key Words: Human races, Skin color, Allele frequencies, Genome-
wide association studies, Admixture, Evolution
Differences between human racial groups are perhaps the most controversial
topic in all of the social sciences, with almost every conceivable fact being
contested by two or more opposing factions. The matter is also scientifically
challenging because a comprehensive account of such differences and their
origins involves findings from a large number of scientific (sub)fields including
evolutionary psychology, differential psychology, psychometrics, sociology,
anthropology, population genetics, genomics, behavioral genetics, history,
archaeology, and almost every interdisciplinary field between these. On top of this
comes the fact that the topic became heavily politicized in Western countries after
World War II. The following account attempts a cautious summary of the current
thrusts of the research, which will unavoidably be seen as unsatisfactory by some.
Semantic, ontological and historical status
To begin with, both the current and historical meaning of the word race is
disputed. The most popular view in the West currently among social scientists is
that race denotes a concept of discrete/typological populations in an ancient
Greek sense (Platonic/essentialist), and that genomic data shows that such
discrete populations do not exist. Hence human races do not exist in any
biological sense, but only as (at least somewhat) arbitrary social categories (e.g.
James, 2017; Kitcher, 2007; Ousley, Jantz & Freid, 2009; Pigliucci & Kaplan,
2002; Smedley & Smedley, 2005; Sussman, 2014). The contrary minority view is
that race denotes a subspecies, breeding population, genetic cluster, extended
family (or some other biologically-based idea along those lines), and that genomic
data shows that these exist, and have or might have important relationships to
socially valued phenotypic traits among humans (e.g. Andreasen, 2000; Barnes,
2018; Fuerst, 2015; Levin, 1997; Lynn, 2015; Relethford, 2009; Rushton, 2000;
Sarich & Miele, 2004; Sesardic, 2010; Spencer, 2015). In line with standard
terminology in philosophy (Miller, 2016), the first view will be denoted the social
constructivist view (or sometimes, anti-realist), and the second the realist view
(“race realism”). This is not meant to be an endorsement of the realist view as
being more realistic in the everyday sense of the word, but only as a descriptive
term meaning the reality of something is asserted. Therefore, the difference is
essentially semantic rather than substantive.
Fuerst (2015) reviewed 12 surveys of anthropologists, anatomists and
biologists, which asked about agreement with statements such as There are
biological races in the species Homo sapiens(see also Lieberman et al., 2004).
Agreement with race realism is lower among researchers in the USA and higher
in East Europe and East Asia, however, there are substantial numbers of experts
with both views in every survey. Furthermore, agreement is higher among
physical as opposed to cultural anthropologists, lower in recent years in the
US/West 1, and higher among biologists and anatomists than anthropologists.
Agreement ranged from 14% to 75% depending on the survey year, exact
question, country, and type of researcher. Partly in response to the above
compilation, another large US survey was carried out which polled about 1900
anthropologists and included many variant question formulations (Wagner et al.,
2017). The patterns of that study replicate those above in that contemporary US
anthropologists mostly are to be found in the anti-realist or social constructivist
1 Working population geneticists in the West have generally avoided the term race since
it fell out of political favor, opting instead for synonyms or closely related terms such as
genetic cluster, population, genetic ancestry and so on (Frost, 2014). In medical
genetics, the currently preferred term is the somewhat unwieldy biogeographical
ancestry (Mersha & Abebe, 2015; Shriver & Kittles, 2004; Tishkoff & Kidd, 2004),
though this is not to say that this term does not also have its detractors (Gannett, 2014).
camp, but it depends on the specific framing of the question. Similarly, Horowitz
et al. (2019) surveyed 301 US anthropologists about various topics. One question
included was The social construct of racehas no corresponding biological
reality”, with which 76% of their sample agreed, and 15% disagreed (9% dont
know/other). All in all, we can say that opinion seems to be moving against the
realist view, but that there is not yet a consensus level of agreement, even among
There is no non-question-begging way to even write about race differences
since using race as normally done would implicitly appear to assume a realist
position of some sort, while adding scare quotes (‘race’) would indicate the
opposite. This entry does not take a position on the question but uses the normal
writing style for ease of reading. For the matters at stake, the ultimate fate of the
word race is immaterial because the ancestry associations will be there no matter
what we call them, and no matter how well typical racial classification schemes
are congruent with ancestry variation.
Overview of human populations
There is consensus in the field that when human genomic data is analyzed
with methods such as principal components analysis or cluster analysis, certain
non-arbitrary patterns can be seen in the data (J. L. Baker, Rotimi & Shriner, 2017;
Cavalli-Sforza, Menozzi & Piazza, 1994; Reich, 2018a). Specifically, for persons
who dont belong to recent migrantpopulations, those who are geographically
close tend to go together or cluster (in some sense) in the results. Recent
migration usually refers to peoples that have moved since 1492, in the post-
Columbus period. This date is a somewhat arbitrary but convenient choice since
mass migration to the Americas started at that time. There is a large number of
mathematical approaches to doing such clustering with no agreement on a single
best method (Padhukasahasram, 2014; Yuan et al., 2017). Because of this,
results from multiple methods will be summarized. Figure 1 shows the results of
a principal components analysis on a large genomic dataset from populations
across the world. It is evident there is some patterning in the data related to
geographic location.
Principal components analysis works by constructing a new dimension
(variable) based on the data such that it best explainsthe existing data in the
sense of having the highest possible variance in common with the input variables
(here, variation in genetic loci). This process is usually repeated multiple times,
giving a set of principal components. Hence, the first principal component (PC1)
summarizes the largest possible amount of the data in a single dimension. Of the
remaining variation, PC2 best summarizes that, and so on. In the figure, the two
first dimensions are shown and the subjects are colored by their geographical
origin (or origin of their remote ancestors). It is evident that similarly colored
persons are usually close to each other. There are some exceptions, however.
Mexicans (MEX) are not well separated from Indians (GIH). They are however far
apart in genetic space if one considers more than the first two dimensions, but
this is difficult to convey in a two-dimensional image.
Figure 1. Principal components analysis of HapMap3 data. Each dot is a person.
Axes are the first two principal components of the SNP (single nucleotide
polymorphism) data. Populations: ASW = African American in USA; CEU =
Central European from Utah, USA; CHB = Chinese from Beijing; CHD = Chinese
from Denver, USA; GIH = Gujarati Indians in Houston, USA; JPT = Japanese
from Tokyo, Japan; LWK = Luhya Africans from Kenya; MEX = Mexicans from
Los Angeles, USA; MKK = Maasai Africans from Kenya; TSI = Italians from
central Italy (Toscani); YRI = Yuruba Africans from Nigeria. Figure reproduced
from Abraham and Inouye (2014).
An alternative approach is to construct a phylogenetic tree (dendrogram, tree
plot) based on the most likely estimated relationships between the groups in
terms of evolutionary divergence. An example is shown in Figure 2.
Figure 2. Dendrogram based on data from 51 populations in the Human Genome
Diversity Panel. Colors represent the 7 continental clusters. Reproduced from Jun
et al. (2017).
As before, one can clearly see that geographically close populations tend to
join up’. Some other relations are more surprising and reflect older migrations
that are now mostly forgotten. For instance, northern Indians are related to
Europeans, and indeed speak related languages from the Indo-European family
(Reich et al., 2009). In general, language relatedness reflects earlier migrations
and thus genetic relatedness as well (J. L. Baker et al., 2017). There are various
exceptions to this general pattern, such as the Hungarians (a central European
population). Their language is related to those of Finns and Estonians (northeast
European populations), who live about 1,500 kilometers (900 miles) away across
several national borders and bear little genetic resemblance to Hungarians.
Various language isolate populations speak languages (apparently) unrelated to
their neighbors, with the most well-known being Basque (located in north of
The degree to which genetic relatedness mirrors geographical distance can
be impressive. Figure 3 shows a scatterplot of the first two principal components
with a map of Europe shown on top.
Figure 3. Map of European populations' genetic distance with a map of Europe.
From Novembre et al. (2008).
The relationship between genetic distance and geographic distance is good
but not perfect, thus indicating some recent population movements or
inaccuracies in the data. More fine-grained differences can also be detected,
including ones inside a single country of relatively homogeneous people. Recent
studies have looked at the relationships between geographic location and genetic
distances in the British Isles (Abdellaoui et al., 2018; Byrne et al., 2018; Kandt,
Cheshire & Longley, 2016; Leslie et al., 2015), Belgium (Van den Eynden et al.,
2018), France (Karakachoff et al., 2015), and the Nordic countries (Athanasiadis
et al., 2016; Kerminen et al., 2017).
The two approaches to analyze the data used above hail from two different
ways of looking at the genetic data. In the first approach, one is concerned with
continuous distances between persons and groups, and there are no rigid
boundaries. In the other approach, one thinks of the populations more as discrete
units which can be descended from one another. Reality is somewhere in
between these two extremes, which is called the clinal vs. cluster debate of
human genetic variation (Rosenberg et al., 2005). Both sides recognize the fact
that genetic distance between populations correlates strongly with geographic
distance (again, for populations that havent migrated recently’). Depending on
theoretical assumptions and definitions, finding certain low (high) levels of clinality
might indicate the absence (reality) of human races. Figure 4 shows a world map
overlaid with relative rates of migration.
Figure 4. Large-scale patterns of population structure in the Old World. Color
coding shows estimated rate of migration with brown indicating 'troughs', i.e.
areas across which there was little human intermixing. Reproduced from Peter
et al. (2018).
While all human variation is clinal to some degree, some areas have more
migration (and thus gene flow) than others. The areas with particularly low
migration usually correspond to geographical barriers: oceans, mountains and
deserts. With regards to the clines vs. clusters debate, the authors of the study
that produced the above map concluded:
Our rugged migration landscapes suggest a synthesis of the clusters
versus clines paradigms for human structure: By revealing both sharp and
diffuse features that structure human genetic diversity, our results suggest
that more continuous definitions of ancestry in human population genetics
should complement models of discrete populations with admixture.
This might be taken as a reasonable middle position on the clines vs. clusters
Ancestry estimates and social race
When clustering methods are used to analyze genetic data, the results allow
one to score a given individual on their proportion of genetic ancestry or
biogeographic ancestry as it is often called in medical genetics (Shriver et al.,
2003) from each cluster identified in the analysis. Such ancestry (or admixture)
analysis has since become big business (dubbed consumer genomics or
recreational genomics) with multiple competing companies offering ancestry
estimation services based on microarray data obtained from customers (Khan &
Mittelman, 2018). At the beginning of 2018, about 10 million people had been
genotyped this way. Essentially, the customer purchases a small kit (a tube with
liquid), deposits spit into it, mails it to the laboratory for analysis, and then 2-3
weeks later receives a report on a website. Ancestry analysis and presentation is
somewhat of an art, not exact science (Khan, 2017a,b), but provides valuable
information to many people who are curious about their origins. The services can
also identify distant or lost family members (most commonly siblings adopted
away, or unknown half-siblings). The ability to do this has also led to the arrest of
multiple people suspected of serious crimes based on DNA evidence left at the
crime scene that for decades could not be matched to a person but which could
be found by matching to distant relatives (Regalado, 2018; Wilson, 2018).
Figure 5. Individual ancestry estimates for three US race groups with European,
Amerindian and African ancestry. Reproduced from Bryc et al. (2015) based on
23andme customer data.
Figure 5 shows an example of an ancestry distribution for the United States
from the consumer genomics company 23andme. Each mini-pie chart represents
the distribution of self-reported race/ethnicity for a given combination of
genetically measured ancestry. European (White) Americans are almost entirely
European on average (about 99%) but Latinos and African Americans show
considerable variation, almost every person having some degree of admixture
compared to reference populations (Africans in Africa and Amerindian
populations without interbreeding since the European conquest). The mixed
nature of many human groups, especially in Latin America, and somewhat
imperceptible nature of precise genetic ancestry means that typical social labels
such as White, Black/African American, Mestizo do not map up exactly with
genetic ancestry, and in some cases, not well at all (Ruiz-Linares et al., 2014).
Still, the terms are widely used as rough proxies for genetic ancestry, which can
be valuable in situations where genetic data is missing, both in medicine
(Bonham, Sellers & Woolford, 2014; Rosenberg et al., 2002) and in other
research (Fulford, Petkov & Schiantarelli, 2016; Putterman & Weil, 2010).
Figure 6. Self-identified race among a sample of American blacks and whites
from Philadelphia. Biracial are those that identified as both black and white.
Figure from Lasker et al. (2019)
Figure 6 shows a sample of Americans and their self-identification as a
function of their genetic ancestry. In this dataset, only persons who self-identified
as White, Black or both were included. One can see that individuals with nearly
100% ancestry from either group have a nearly (but not entirely) 100% chance of
identifying as White or Black. However, for persons of mixed ancestry, the
probabilities were intermediate almost but not entirely in line with their ancestry.
It would make more sense to have the doubly identifying group exactly in the
middle, but instead we see that such persons are somewhat more European
genetically than would be expected with a maximum probability around 60%. This
seems to be a remnant of the so-called one drop rule (or law) that was present in
the USA in past times (Guo et al., 2014); or it simply means that in the US, most
individuals identifying as biracial have a white parent who is nearly 100%
European and a blackparent with substantial European admixture. Other
research has shown that a persons visual characteristics (skin color, nose shape
etc.) and social status also affect how they self-identify above and beyond their
actual genetic ancestry (Ruiz-Linares et al., 2014; Telles & Paschel, 2014).
Physical differences
Physical differences between races are much less controversial than mental
ones, at least, insofar as they relate to traits unrelated to social status or other
valued traits. The most obvious physical trait related to race is skin color. Figure
7 shows a world map of estimated skin color.
Figure 7. Worldwide distribution of human skin color, as estimated by Jablonski
There is geographical clustering which is related to the amount of UV
radiation that people in different parts of the world are exposed to. Other visible
traits that strongly covary with skin color and geographic location include
tannability, freckles, hair color, hair texture (straight, curly etc.), eye color, lip and
nose thickness (J. R. Baker, 1974). A variety of less visible physical differences
also exist, and can in many cases be identified from skeletal remains to infer the
likely ancestry/race of the decedent (Albanese & Saunders, 2006; Kennedy,
1995). Detailed cranium measures can also be analyzed with the same methods
used for large genetic datasets and tend to give similar results (Reyes-Centeno,
Ghirotto & Harvati, 2017). Another human trait that shows large race differences
is height (NCD Risk Factor Collaboration, 2016). Human height has increased
considerably during the last 100 years in almost every country while the between
country differences have generally remained large (“A rising tide lifts all boats, but
tall sails remain high”). The tallest people in the world 100 years ago (e.g. Dutch
and Scandinavians) are also among the tallest people now, but gained about 10
cm. Meanwhile some populations increased in the relative ranking, such as post-
war Japan and South Korea coinciding with rapid economic growth (North
Koreans stayed short, however). This temporal relationship between
development and height has led economic historians to create large databases
of historical data for human height for use in research as a proxy for development,
or measure of health of a population (Baten, 2000, see https://clio-
What is common for the traits discussed above is that few researchers
dispute that differences between race groups are the result of genetic differences
(though the secular trend in height is attributed to environmental improvements).
However, for more socially valuable traits, the relative contributions of genetics,
environment and their potential interactions are heavily debated. Since large
health datasets began to be collected, medical researchers have noted that race
groups differ in various disease rates. Many of the rare diseases have relatively
simple genetic causes (single-gene/monogenic/Mendelian disorders), with one or
only a few genes involved (Tibayrenc, 2017). The genetic etiology of race
differences is not disputed for these, probably because most of them are quite
rare (though not sickle cell disease) and the molecular causes are often known
to some degree. Populations that have had recent migration-related bottlenecks
usually have their own collection of special disorders they acquired from the
genetic drift induced by the bottleneck. Ashkenazi Jews, for instance, suffer from
higher than average European rates of Tay-Sachs disease, Gaucher's disease,
and BRCA-related breast cancer among others (Slatkin, 2004). Other populations
with well-known elevated rates for rare single-gene disorders include French
speaking Canadians (Scriver, 2001), Finns (Martin et al., 2018), and Amish
(Mitchell et al., 2015).
Since the advent of large datasets with SNP (single nucleotide
polymorphism, a location in the genome with a variable base) data, it has become
possible to estimate the fractional admixture of people with mixed ancestry for
large samples of people with known phenotypes. This information can then be
related to having a given disease, or the value of some continuous trait (e.g.
height, body mass index). Hundreds if not thousands of such studies now exist
that find many relationships between genetically estimated ancestry and disease
traits, which usually replicate those seen for the corresponding socially defined
racial groups (e.g. African Americans, AA). Such associations are often
interpreted causally, especially when the most plausible environmental causes
were controlled in a regression. For instance:
The role of genetic predisposition in this disparity is supported by two
admixture mapping studies of AAs which demonstrated that greater
proportion of European ancestry was inversely associated with fibroids in
AA women. (Giri et al., 2017; two other examples: Bidulescu et al., 2014;
Meigs et al., 2014)
It should be mentioned that some researchers advise against such causal
interpretations (Cooper, 2004), on account that the associations with ancestry
might just reflect a relevant but omitted environmental variable. More advanced
methods based on local ancestry analysis exist, but are not yet as widely used as
global ancestry analysis (for a brief introduction, see Shriner, 2013). These
methods have been used to examine both diseases (Tibayrenc, 2017) and
physical traits that are thought to have evolved recently by natural selection
(Chacón-Duque et al., 2018). For instance, Jeong et al. (2014) examined a
population of mixed Tibetan and Han (Chinese) ancestry, and found that those
with more Tibetan ancestry did better in higher altitudes. Furthermore, local
ancestry analysis revealed particular blood-related genes which were much more
distinctive than the rest of the genome in comparisons between Han and
highland-adapted Tibetans, indicating a causal protective effect of these.
More controversial is the topic of race differences in sports (Dutton, 2015;
Epstein, 2014). A commonly noted difference is that West Africans (the ancestors
of most Africans in the New World) tend to do very well at short distances while
East Africans tend to do well at long distance running. Currently, all top 25 records
for the 100 meter dash are held by persons of West African descent (Wikipedia,
2019a), whether born in Africa or to ancestors who emigrated to somewhere. In
contrast, the current top 25 male (and female) record holders for half marathons
(21 km) all are of Kenyan or Ethiopian descent (Wikipedia, 2019b). It seems
difficult to argue that other groups lack an interest in this sport considering the
millions of people in Western countries who enjoy running, including competitively
(Deaner, 2015). It is also hard to argue that these people dont have the necessary
wealth to pursue training and the requisite nutrition. Yet they are being beaten
consistently by persons who either hail from or grew up in very poor states, and
which are geographically distant. Thus, to many researchers (Dutton, 2015;
Entine, 2016; Epstein, 2014) it seems likely that genetics plays some role in these
Psychological differences
Psychological differences between race groups are controversial, perhaps
the most controversial topic in all of social science (Horowitz, Haynor & Kickham,
2018; Hunt & Carlson, 2007; Tabery, 2015; Yee et al., 1993). The literature on the
topic is enormous and goes back to the 1860s with Victorian English polymath
Francis Galtons pioneering work (Galton, 1869). Although there are many ways
to categorize psychological traits, I will employ a binary division according to
which psychological traits can be roughly divided into cognitive and noncognitive
domains (e.g. as used in Kaestner & Callison, 2011). Cognitive refers to cognitive
ability/intelligence related traits such as working memory, long term recall, 3d
spatial ability, verbal fluency, general intelligence and many more (Carroll, 1993).
Noncognitive refers to everything that isnt cognitive, which is a very
heterogeneous remainder category that includes personality traits (both broad
and narrow), interests, dispositions, beliefs, and psychiatric diseases. These
various traits are of course often statistically related, including across the binary
classification, and sometimes strongly enough that one might question their
independence. In other cases, the traits themselves admit both cognitive,
noncognitive and mixed conceptions, such as with emotional intelligence
(OBoyle et al., 2011). However, to attempt a summary, we must allow for some
level of simplification.
Noncognitive differences
There is broad agreement that personality is multi-dimensional. Several
approaches exist that attempt to distill personality variation to a few latent
dimensions. The most popular of these is the Big Five/Five factor model/OCEAN
approach, which summarizes personality as variation in Openness,
Conscientiousness, Extraversion, Agreeableness, and Neuroticism/Emotional
Stability (McCrae & Costa, 2006). Social group differences, including racial, in
OCEAN traits are difficult to investigate due to implicit group comparisons in the
scales, sometimes called the reference group effect (Heine et al., 2002). Most of
the data about human personality comes from subjects rating themselves on
adjectives or short phrases. These ratings are implicit comparisons to other
people, but which other people exactly? When asked whether one often attends
parties, the reference frame is some kind of typical party-going rate among other
humans in comparison to which one might be above average or not. This problem
becomes especially troublesome when one does personality comparisons across
countries where most people have little or no experience with other groups
(Kajonius & Mac Giolla, 2017; Meisenberg, 2015). Such country-level
comparisons of OCEAN traits find sizable gaps, which depending on the
demographics of the countries, may or may not reflect racial group differences.
The psychometric quality of the measurements is unfortunately low and
confounded with other traits such as intelligence (Kajonius & Mac Giolla, 2017;
Meisenberg, 2015; Meisenberg & Williams, 2008; Nye & Drasgow, 2011). Heine
et al. (2008) compared national stereotypes (termed national character
perceptions) to measures of conscientiousness from self- and other reported
personality scales, as well as objective data based on e.g. precision of public
clocks and speed of postal workers. They found that the typical personality
measures had negative (self report, mean rs -.43 and -.19) and null associations
(other report, r = .06) with national stereotypes, but that objective measures had
sizable positive correlations with stereotypes (r = .61). Based on this, one might
conclude that the stereotypes were accurate and the self-report personality data
is problematic.
To reduce the reference group problem, we might instead consider racial
group differences in OCEAN traits within a country. A very large (k = 567) meta-
analysis by Tate and McDaniel (2008) found that gaps between African
Americans and Whites in the United States were small or trivial in size: openness
d = 0.02, conscientiousness d = 0.02, extraversion d = 0.18, agreeableness d =
0.09, and neuroticism d = 0.06 (where positive values mean whites are higher).
Racial group personality differences on other personality inventories have rarely
been reported in large samples or meta-analyses and are thus hard to describe.
These results were replicated by Foldes et al. (2008) who included data from over
700 studies. While Tate and McDaniel (2008) only covered the black-white
comparison, Foldes et al. (2008) covered data from Whites, Blacks, Hispanics,
and Asians (heterogeneous as these groups are). Generally, their findings agree
with the previous study in that they find overall small gaps. The gaps are not
consistent in direction within each trait (e.g. conscientiousness), so that while
whites seemed to be favored on one facet (e.g. dependability, d = 0.05) blacks
where higher on others (e.g. cautiousness, d = -0.16). Results were similar for the
other comparisons. Exceptions related mainly to small samples, as would be
expected by sampling error alone (e.g., Asian-White gap was d = 0.63 for
agreeableness, but the Asian sample for this was only n = 93).
In general, the findings should be viewed with suspicion in the light of existing
stereotypes, which tend to be especially accurate for demographic groups
(Jussim, 2018). The question then boils down to: are the stereotypes quite
incorrect for personality traits, or are we not measuring personality correctly? The
matter requires more research to clarify. It seems unlikely that the existing
approach of collecting more self-report data can clarify matters, so it is
recommended that researchers try other approaches as well as better statistical
methods to clarify measurement invariance (Church et al., 2011; Mõttus, Allik &
Realo, 2010; Schmitt, Golubovich & Leong, 2011).
Occupational interests
Occupational (job) interest scales such as the Holland Occupational Themes
are used for guidance counseling. These tests attempt to summarize variation in
occupational interests with a few dimensions. RIASEC is the acronym of a
popular 6-factor model (Lubinski, 2000) Realistic, Investigative, Artistic, Social,
Enterprising, Conventional though much recent research has used a simpler 2-
dimensional model that distills variation down to a people-things dimension and
a data-ideas dimension (Su, Rounds & Armstrong, 2009; Tay, Su & Rounds,
2011). Studies using occupational interest scales and racial group are rare, but
Schmitt et al. (2011) reported gaps for the usual black and white comparison (in
Cohens d, positive values mean whites are higher): R = 0.31, I = 0.28, A = -0.42,
S = -0.51, E = -0.45, C = -0.17. Their design was stronger than usual because
they also used multi-group confirmatory factor analysis to guard against
measurement bias. The same study, however, also examined OCEAN traits and
found only minor differences, the same as in the meta-analysis discussed
previously. These results are in need of replication to reach firm conclusions.
Psychiatric traits
Table 1. Odds ratios of mental disorders by US racial groups, compared to the
White prevalence scaled as 1.00. Table from Coleman et al. (2016), who
calculated them based on large samples. * indicates the odds ratio was not
statistically different (p>.05), all other values differed with p<.001.
Disorder Asian Black Hispanic Mixed
Native Amer.
& Alaska
Anxiety disorder
Any psychiatric
diagnosis 0.36 0.69 0.72 0.64 1.03 0.47
Bipolar disorder
Depressive disorder
spectrum disorder 0.77 1.98 0.72 0.88* 1.18* 0.67
Other psychosis 0.50 1.13 0.61 0.34 0.80 0.51
Racial differences in rates of psychiatric disorders have long been noted,
though they are hard to estimate accurately. Table 1 shows odds ratios of mental
disorders from major US racial groups. With the exception of Native Americans
who have similar prevalence rates as whites, for most of the disorders in the table,
whites have the highest rate (the odds ratios for others are below 1). This pattern
may be caused by ascertainment bias with whites being more likely to admit
psychiatric problems, seek help, afford evaluation, or some combination of
factors, or simply by them having the highest genetic liability. The main exception
to this pattern is the higher rate of schizophrenia and other psychosis seen for
blacks, which is a heated topic of debate in the literature (Curtis, 2018).
An increasingly popular view is that psychiatric disorders are mainly a
continuation of normal personality variation. In a slogan, abnormal is normal
(Plomin et al., 2016). Based on this, one might consider race differences in
psychiatric disorders to be measures of race differences in normal personality
variation, which could result from evolutionary trade-offs (Del Giudice, 2018;
Sikela & Quick, 2018). In line with this approach, the MMPI (Minnesota
Multiphasic Personality Inventory), spans both non-diseased personality
variation and psychopathology (Sellbom & Ben-Porath, 2005). Evidence going
back to the 1970s indicates that blacks outscore whites on some of the scales of
this battery (Castro et al., 2008). Unfortunately, the scales lack good descriptive
names, so it is not easy to summarize the nature of these findings. The meta-
analysis by Hall et al. (1999) indicates that these gaps are small in size, with
Cohen ds around 0.20. More controversially, Lynn (2002) reviewed evidence
from studies of psychopathy (broadly speaking) and found that East Asians have
the lowest levels, Europeans intermediate and Africans the highest. His
conclusions have however been contested by others (e.g. DeLisi, 2018).
Generally speaking, aside from a few facts such as the higher rate of depression
among whites compared to blacks and schizophrenia among blacks compared to
whites, there is not much agreement in the field about the relationships between
race and psychiatric disorders.
Cognitive differences
Soon after the start of the 1900s when the first modern cognitive tests were
invented, a large research effort began with the purpose of documenting and
understanding racial gaps in various tested abilities. This was by no means limited
to the study of blacks (vs. whites) in USA, but also covered Aborigines in
Australia, Maori in New Zealand, Indians in South Africa, and so on (Herrnstein &
Murray, 1994; Lynn, 2015; Shuey, 1966). Most of the early studies were very
simple since they were chiefly concerned with detecting whether racial cognitive
gaps existed, and whether these were due to faulty tests or real differences. The
question of measurement bias remains very much a central topic of active
research, though the methods employed have markedly improved from the
earliest studies. Much current research is interested in the question of national
differences in cognitive ability (Jones, 2016; Lynn & Becker, 2019; Rindermann,
2018), which is of course strongly related to the deeper question of racial gaps
due to the varying demographics of countries. Lynn (2015) provides a review of
typical IQ scores for each of 12 major racial groups, shown in Table 2.
Table 2. Mean IQ scores by racial group. All groups measured in their native
habitat (e.g. Africans measured in Africa, not in Western countries). IQ normed to
UK British norms (white British = 100/15). Based on Lynn (2015).
Racial group Brain size (cm3
Mean IQ Number of studies
Arctic Peoples 1443 91 18
Northeast Asians
Europeans 1369 100 162
Native Americans 1366 86 31
South Asians 1293 84 77
North Africans 1293 83 26
Bushmen 1270 55 5
Sub-Saharan Africans
Australians 1225 62 17
Southeast Asians 1332 87 51
The values given by Lynn cannot be taken as final estimates because many
are based on small, old samples and with unclear levels of test bias. For instance,
it is difficult to accept that the true level of intelligence among Australian
Aborigines is about 60 without strong evidence of measurement invariance. As
far as the author knows, there are no recent, large, advanced measurement
studies for this population, and studies from the early 20th century can hardly be
considered informative about present-day intelligence levels. A particularly
contentious topic is the best estimate of African intelligence, with other
researchers estimating either about 80 or about 75 (Rindermann, 2013; Wicherts,
Dolan & van der Maas, 2010). Still, however, the numbers are reasonably
consistent and quite stable across time and place.
Neither can the values be taken at face value to indicate what one might call
genetic level of intelligence. Both sides in the debate recognize the importance of
environmental variation, especially for the lower scoring groups. Unfortunately, it
is difficult to estimate the relative importance of genetic and environmental factors
since these are usually correlated in practice countries with good nutrition also
have high intelligence levels, but which causes what? A few modern genetic
studies have been done on psychological traits and are worth summarizing. Piffer
(2019) found that when looking at 26 quasi-national populations, their mean
polygenic score for educational attainment/intelligence was correlated .80 to .90
with estimates of intelligence. However, it is well known that simple comparisons
of polygenic scores across groups are hard to interpret due to biases in their
construction which is mainly based on genotyping of European-origin individuals
(Berg et al., 2018; Curtis, 2018; Duncan et al., 2018; Kerminen et al., 2018; Sohail
et al., 2019). One can avoid this problem by using ancestry analysis instead, and
there are two such published studies. Kirkegaard et al. (2019) studied ~1400 US
children and youth and found that genetic ancestry predicted IQ scores even
controlling for parental education. Lasker et al. (2019) analyzed data from ~7200
US children and youth, and found that genetic ancestry predicted IQ scores even
when including a genetic score for skin color in the regression. So far, however,
no study using the stronger design of local admixture analysis has been
published, and the aforementioned studies all have limitations that make a
substantial role of genetics plausible but not conclusive. They do, however,
conform to predictions made by hereditarian researchers back in the 1960s (e.g.,
Jensen, 1969).
Finally, it is worth noting that experts have not yet reached any consensus
on this topic with regards to causation. There exist at least four surveys of experts
which asked about causes of racial or national gaps (Friedrichs, 1973;
Rindermann, Becker & Coyle, 2016; Sherwood & Nataupsky, 1968; Snyderman
& Rothman, 1988). All of these found that a sizable minority believes the gaps to
result purely from environmental causes. The average opinion, however, seems
to be that there is some unclear mix of genetics and environmental causes. For
instance, the most recent survey by Rindermann et al. (Becker, 2018;
Rindermann et al., 2016; Rindermann, Becker & Coyle, 2020) was conducted
2013 to 2014 by surveying authors who had published in the journal Intelligence,
the highest impact factor journal in the field. 86 experts answered a question
about the causes of the US black-white intelligence gap. They estimated a genetic
contribution of on average 49% (SD = 31%), with 16% believing environmental
factors to be the sole cause, and 6% believing genetics to be the sole cause. The
large standard deviation of the mean estimate indicates that experts strongly
disagree with one another, and the question remains a topic of ongoing scholarly
The present review is necessarily quite limited in scope. However, it is hoped
that it has provided a useful summary of the main findings of the many scientific
fields that contribute towards the study of race. Regarding the causes of the many
racial group differences noted above, the present author expects that advances
in genomics will relatively soon (less than 10 years from now, probably sooner)
provide crucial evidence on the relative role of genetics in causing or not causing
such gaps. David Reich, a population geneticist with impeccable credentials,
explained what we might expect in a recent New York Times article (Reich,
Recent genetic studies have demonstrated differences across populations
not just in the genetic determinants of simple traits such as skin color, but
also in more complex traits like bodily dimensions and susceptibility to
diseases. For example, we now know that genetic factors help explain why
northern Europeans are taller on average than southern Europeans, why
multiple sclerosis is more common in European-Americans than in
African-Americans, and why the reverse is true for end-stage kidney
I am worried that well-meaning people who deny the possibility of
substantial biological differences among human populations are digging
themselves into an indefensible position, one that will not survive the
onslaught of science. I am also worried that whatever discoveries are
made and we truly have no idea yet what they will be will be cited as
scientific proofthat racist prejudices and agendas have been correct all
along, and that those well-meaning people will not understand the science
well enough to push back against these claims.
This is why it is important, even urgent, that we develop a candid and
scientifically up-to-date way of discussing any such differences, instead of
sticking our heads in the sand and being caught unprepared when they
are found.
For readers interested in more in-depth reviews about race differences, see (more
realist view: J. R. Baker, 1974; Fuerst, 2015; Jensen, 1998; Lynn, 2015; Rushton,
2000; Rushton & Jensen, 2005; Sarich & Miele, 2004; Wade, 2014; Winegard,
Winegard & Boutwell, 2017; less realist view: Conley & Fletcher, 2017; Evans,
2019; Nisbett, 2009; Nisbett et al., 2012; Sussman, 2014).
This review was originally written with intent to send to the Encyclopedia of
Evolutionary Psychological Science, as I was invited to submit an entry for their
encyclopedia. However, upon completion, the editor, Todd Shackelford, sent me
an email letting me know that After further discussion, we have decided to
eliminate this entry. You are now free to send to a different publication.This
series of events should probably be interpreted in the light of a recent shaming of
Shackelford by a journalist, which happened in between the invitation and the
submission of the entry, which has made him more wary of taking on
controversial material (Schulson, 2018; for context, see Carl & Woodley of
Menie, 2019 and Woodley of Menie et al., 2018).
Abdellaoui, A., Hugh-Jones, D., Kemper, K.E., Holtz, Y., Nivard, M.G., Veul, L., … &
Visscher, P.M. (2018). Genetic consequences of social stratification in Great Britain.
BioRxiv: 457515.
Abraham, G. & Inouye, M. (2014). Fast principal component analysis of large-scale
genome-wide data. PLOS ONE 9(4): e93766.
Albanese, J. & Saunders, S.R. (2006). Is it possible to escape racial typology in forensic
identification? In: A. Schmitt, E. Cunha & J. Pinheiro (eds.), Forensic Anthropology and
Medicine: Complementary Sciences from Recovery to Cause of Death, pp. 281-316.
Andreasen, R.O. (2000). Race: Biological reality or social construct? Philosophy of
Science 67: S653-S666.
Athanasiadis, G., Cheng, J.Y., Vilhjálmsson, B.J., Jørgensen, F.G., Als, T.D., Le Hellard,
S., … & Mailund, T. (2016). Nationwide genomic study in Denmark reveals remarkable
population homogeneity. Genetics 204(2): 711-722.
Baker, J.L., Rotimi, C.N. & Shriner, D. (2017). Human ancestry correlates with language
and reveals that race is not an objective genomic classifier. Scientific Reports 7(1): 1572.
Baker, J.R. (1974). Race. London: Oxford University Press.
Barnes, J.C. (2018). A constructivist view of race in modern criminology. Journal of
Criminal Justice 59: 81-86.
Baten, J. (2000). Heights and real wages in the 18th and 19th centuries: An international
overview. Jahrbuch für Wirtschaftsgeschichte / Economic History Yearbook 41(1): 61-76.
Becker, D. (2018, May). Survey of expert opinion on intelligence: Intelligence research in
the media, the public and their self-reflection. Presented at the the 5th meeting of the
London School of Intelligence conference, 2018, Skanderborg, Denmark. Retrieved from
Berg, J.J., Harpak, A., Sinnott-Armstrong, N., Joergensen, A.M., Mostafavi, H., Field,
Y., … & Coop, G. (2018). Reduced signal for polygenic adaptation of height in UK
Biobank. BioRxiv: 354951.
Bidulescu, A., Choudhry, S., Musani, S.K., Buxbaum, S.G., Liu, J., Rotimi, C.N., … &
Gibbons, G.H. (2014). Associations of adiponectin with individual European ancestry in
African Americans: The Jackson Heart Study. Frontiers in Genetics 5.
Bonham, V.L., Sellers, S.L. & Woolford, S. (2014). Physiciansknowledge, beliefs, and
use of race and human genetic variation: New measures and insights. BMC Health
Services Research 14(1): 456.
Bryc, K., Durand, E.Y., Macpherson, J.M., Reich, D. & Mountain, J.L. (2015). The genetic
ancestry of African Americans, Latinos, and European Americans across the United
States. American Journal of Human Genetics 96: 37-53.
Byrne, R.P., Martiniano, R., Cassidy, L.M., Carrigan, M., Hellenthal, G., Hardiman, O.,
& McLaughlin, R.L. (2018). Insular Celtic population structure and genomic footprints of
migration. PLOS Genetics 14(1): e1007152.
Carl, N. & Woodley of Menie, M.A. (2019). A scientometric analysis of controversies in the
field of intelligence research. Intelligence 77: 101397.
Carroll, J.B. (1993). Human Cognitive Abilities: A Survey of Factor-Analytic Studies.
Retrieved from
Castro, Y., Gordon, K.H., Brown, J.S., Anestis, J.C. & Joiner, T.E. (2008). Examination of
racial differences on the MMPI-2 clinical and restructured clinical scales in an outpatient
sample. Assessment 15: 277-286.
Cavalli-Sforza, L.L., Menozzi, P. & Piazza, A. (1994). The History and Geography of
Human Genes. Retrieved from:
Chacón-Duque, J.-C., Adhikari, K., Fuentes-Guajardo, M., Mendoza-Revilla, J., Acuña-
Alonzo, V., Barquera, R., … & Ruiz-Linares, A. (2018). Latin Americans show wide-spread
Converso ancestry and imprint of local Native ancestry on physical appearance. Nature
Communications 9(1): 5388.
Church, A.T., Alvarez, J.M., Mai, N.T.Q., French, B.F., Katigbak, M.S. & Ortiz, F.A. (2011).
Are cross-cultural comparisons of personality profiles meaningful? Differential item and
facet functioning in the Revised NEO Personality Inventory. Journal of Personality and
Social Psychology 101: 1068-1089.
Coleman, K.J., Stewart, C., Waitzfelder, B.E., Zeber, J.E., Morales, L.S., Ahmed, A.T.,
& Simon, G.E. (2016). Racial-ethnic differences in psychiatric diagnoses and treatment
across 11 health care systems in the Mental Health Research Network. Psychiatric
Services 67: 749-757.
Conley, D. & Fletcher, J. (2017). The Genome Factor: What the Social Genomics
Revolution Reveals about Ourselves, Our History, and the Future. Princeton: Princeton
University Press.
Cooper, R.S. (2004). Critical perspectives on racial and ethnic differences in health in late
life. In: N. Anderson, R. Bulatao & B. Cohen (eds.), Genetic Factors in Ethnic Disparities
in Health. Retrieved from
Curtis, D. (2018). Polygenic risk score for schizophrenia is more strongly associated with
ancestry than with schizophrenia. Psychiatric Genetics 28(5): 85-89.
Deaner, R. (2015, December 10). Why distance running is the perfect lab for studying sex
differences in competitiveness. Retrieved August 23, 2019, from Quillette website:
Del Giudice, M. (2018). Evolutionary Psychopathology: A Unified Approach. New York,
NY: Oxford University Press.
DeLisi, M. (2018). Race and (antisocial) personality. Journal of Criminal Justice 59: 32-
Duncan, L., Shen, H., Gelaye, B., Ressler, K., Feldman, M., Peterson, R. & Domingue, B.
(2018). Analysis of polygenic score usage and performance across diverse human
populations. BioRxiv: 398396.
Dutton, E. (2015). Race and Sport. London: Ulster Institute for Social Research.
Entine, J. (2016, August 15). Kenyans sweep distance races, Jamaicans sprints: How
evolution has shaped elite sports. Genetic Literacy Project. Retrieved from
Epstein, D.J. (2014). The Sports Gene: Inside the Science of Extraordinary Athletic
Evans, G. (2019). Skin Deep: Journeys in the Divisive Science of Race. Oneworld
Foldes, H.J., Duehr, E.E. & Ones, D.S. (2008). Group differences in personality: Meta-
analyses comparing five U.S. racial groups. Personnel Psychology 61: 579-616.
Friedrichs, R.W. (1973). The impact of social factors upon scientific judgment: The
Jensen Thesisas appraised by members of the American Psychological Association.
Journal of Negro Education 42: 429-438.
Frost, P. (2014). L.L. Cavalli-Sforza: A bird in a gilded cage. Open Behavioral Genetics
1(1). Retrieved from
Fuerst, J. (2015). Nature of Race: The Genealogy of the Concept and the Biological
Constructs Contemporaneous Utility. Open Behavior Genetics.
Fulford, S.L., Petkov, I. & Schiantarelli, F. (2016). Does it matter where you came from?
Ancestry composition and economic performance of U.S. counties, 1850-2010 (SSRN
Scholarly Paper No. ID 2608567). Retrieved from Social Science Research Network
Galton, F. (1869). Hereditary Genius. Retrieved from
Gannett, L. (2014). Biogeographical ancestry and race. Studies in History and Philosophy
of Science Part C: Studies in History and Philosophy of Biological and Biomedical
Sciences 47: 173-184.
Giri, A., Edwards, T.L., Hartmann, K.E., Torstenson, E.S., Wellons, M., Schreiner, P.J. &
Edwards, D.R.V. (2017). African genetic ancestry interacts with body mass index to
modify risk for uterine fibroids. PLOS Genetics 13(7): e1006871.
Guo, G., Fu, Y., Lee, H., Cai, T., Mullan Harris, K. & Li, Y. (2014). Genetic bio-ancestry
and social construction of racial classification in social surveys in the contemporary United
States. Demography 51: 141-172.
Hall, G.C.N., Bansal, A. & Lopez, I.R. (1999). Ethnicity and psychopathology: A meta-
analytic review of 31 years of comparative MMPI/MMPI-2 research. Psychological
Assessment 11: 186-197.
Heine, S.J., Buchtel, E.E. & Norenzayan, A. (2008). What do cross-national comparisons
of personality traits tell us? The case of conscientiousness. Psychological Science 19:
Heine, S.J., Lehman, D.R., Peng, K. & Greenholtz, J. (2002). Whats wrong with cross-
cultural comparisons of subjective Likert scales? The reference-group effect. Journal of
Personality and Social Psychology 82: 903-918.
Herrnstein, R.J. & Murray, C.A. (1994). The Bell Curve: Intelligence and Class Structure
in American Life, 1st pbk. ed. New York: Simon & Schuster.
Horowitz, M., Haynor, A. & Kickham, K. (2018). Sociologys sacred victims and the politics
of knowledge: Moral foundations theory and disciplinary controversies. American
Sociologist 49: 459-495.
Horowitz, M., Yaworsky, W. & Kickham, K. (2019). Anthropologys science wars. Current
Anthropology 65: 000000.
Hunt, E. & Carlson, J. (2007). Considerations relating to the study of group differences in
intelligence. Perspectives on Psychological Science 2: 194-213.
Jablonski, N.G. (2004). The evolution of human skin and skin color. Annual Review of
Anthropology 33: 585-623.
James, M. (2017). Race. In: E.N. Zalta (ed.), The Stanford Encyclopedia of Philosophy.
Retrieved from
Jensen, A.R. (1969). Reducing the heredity-environment uncertainty: A reply. Harvard
Educational Review 39: 449-483.
Jensen, A.R. (1998). The g Factor: The Science of Mental Ability. Westport CT: Praeger.
Jeong, C., Alkorta-Aranburu, G., Basnyat, B., Neupane, M., Witonsky, D.B., Pritchard,
J.K., … & Di Rienzo, A. (2014). Admixture facilitates genetic adaptations to high altitude
in Tibet. Nature Communications 5: 3281.
Jones, G. (2016). Hive Mind: How Your Nations IQ Matters so Much More than Your Own.
Stanford CA: Stanford University Press.
Jun, S.H., Lewis, J.B. & Schwekendiek, D. (2017). The biological standard of living in pre-
modern Korea: Determinants of height of militia recruits during the Chosŏn dynasty.
Economics & Human Biology 24: 104-110.
Jussim, L. (2018). The accuracy of demographic stereotypes.
Kaestner, R. & Callison, K. (2011). Adolescent cognitive and noncognitive correlates of
adult health. Journal of Human Capital 5: 29-69.
Kajonius, P. & Mac Giolla, E. (2017). Personality traits across countries: Support for
similarities rather than differences. PLoS ONE 12(6): e0179646
Kandt, J., Cheshire, J.A. & Longley, P.A. (2016). Regional surnames and genetic structure
in Great Britain. Transactions 41: 554-569.
Karakachoff, M., Duforet-Frebourg, N., Simonet, F., Le Scouarnec, S., Pellen, N.,
Lecointe, S., … & Dina, C. (2015). Fine-scale human genetic structure in Western France.
European Journal of Human Genetics 23: 831-836.
Kennedy, K.A.R. (1995). But professor, why teach race identification if races dont exist?
Journal of Forensic Science 40: 797-800.
Kerminen, S., Havulinna, A.S., Hellenthal, G., Martin, A.R., Sarin, A.-P., Perola, M., … &
Pirinen, M. (2017). Fine-scale genetic structure in Finland. G3: Genes, Genomes,
Genetics 7: 3459-3468.
Kerminen, S., Martin, A.R., Koskela, J., Ruotsalainen, S.E., Havulinna, A.S., Surakka,
I., … & Pirinen, M. (2018). Geographic variation and bias in polygenic scores of complex
diseases and traits in Finland. BioRxiv: 485441.
Khan, R. (2017a, December 4). Genomic ancestry tests are not cons, part 1: Gene
expression. Retrieved November 12, 2018, from Gene Expression website:
Khan, R. (2017b, December 8). Genomic ancestry tests are not cons, part 2: The problem
of ethnicity. Retrieved November 12, 2018, from Gene Expression website:
Khan, R. & Mittelman, D. (2018). Consumer genomics will change your life, whether you
get tested or not. Genome Biology 19(1): 120.
Kirkegaard, E.O.W., Woodley of Menie, M.A., Williams, R.L., Fuerst, J. & Meisenberg, G.
(2019). Biogeographic ancestry, cognitive ability and socioeconomic outcomes. Psych
1(1): 1-25.
Kitcher, P. (2007). Does ‘racehave a future? Philosophy & Public Affairs 35(4): 293-317.
Lasker, J., Pesta, B.J., Fuerst, J.G.R. & Kirkegaard, E.O.W. (2019). Ancestry and IQ: The
effects of ancestry on cognitive ability in African and European-Americans. Psych 1(1):
Leslie, S., Winney, B., Hellenthal, G., Davison, D., Boumertit, A., Day, T., … & Bodmer,
W. (2015). The fine scale genetic structure of the British population. Nature 519: 309-314.
Levin, M. (1997). Why Race Matters: Race Differences and What They Mean. Westport
CT: Praeger.
Lieberman, L., Kaszycka, K.A., Martinez Fuentes, A.J., Yablonsky, L., Kirk, R.C., Strkalj,
G., & Sun, L. (2004). The race concept in six regions: Variation without consensus.
Collegium Anthropologicum 28(2): 907-921.
Lubinski, D. (2000). Scientific and social significance of assessing individual differences:
sinking shafts at a few critical points.Annual Review of Psychology 51: 405-444.
Lynn, R. (2002). Racial and ethnic differences in psychopathic personality. Personality
and Individual Differences 32: 273-316.
Lynn, R. (2015). Race Differences in Intelligence, revised edition. Augusta GA:
Washington Summit Publishers.
Lynn, R. & Becker, D. (2019). The Intelligence of Nations. London: Ulster Institute for
Social Research.
Martin, A.R., Karczewski, K.J., Kerminen, S., Kurki, M.I., Sarin, A.-P., Artomov, M., … &
Daly, M.J. (2018). Haplotype sharing provides insights into fine-scale population history
and disease in Finland. American Journal of Human Genetics 102: 760-775.
McCrae, R.R. & Costa, P.T. (2006). Personality in Adulthood: A Five-Factor Theory
Perspective, 2nd ed. New York, NY: Guilford Press.
Meigs, J.B., Grant, R.W., Piccolo, R., López, L., Florez, J.C., Porneala, B., … & McKinlay,
J.B. (2014). Association of African genetic ancestry with fasting glucose and HbA1c levels
in non-diabetic individuals: The Boston Area Community Health (BACH) Prediabetes
Study. Diabetologia 57: 1850-1858.
Meisenberg, G. (2015). Do we have valid country-level measures of personality? Mankind
Quarterly 55: 360-382.
Meisenberg, G. & Williams, A. (2008). Are acquiescent and extreme response styles
related to low intelligence and education? Personality and Individual Differences 44:
Mersha, T.B. & Abebe, T. (2015). Self-reported race/ethnicity in the age of genomic
research: Its potential impact on understanding health disparities. Human Genomics 9(1):
Miller, A. (2016). Realism. In: E.N. Zalta (ed.), The Stanford Encyclopedia of Philosophy.
Retrieved from
Mitchell, B.D., Schäffer, A.A., Pollin, T.I., Streeten, E.A., Horenstein, R.B., Steinle, N.I.,
& OConnell, J.R. (2015). Mapping genes in isolated populations: Lessons from the Old
Order Amish. In: R. Duggirala, L. Almasy, S. Williams-Blangero, S.F.D. Paul & C. Kole
(eds.), Genome Mapping and Genomics in Human and Non-Human Primates, pp. 141-
Mõttus, R., Allik, J. & Realo, A. (2010). An attempt to validate national mean scores of
Conscientiousness: No necessarily paradoxical findings. Journal of Research in
Personality 44: 630-640.
NCD Risk Factor Collaboration (2016). A century of trends in adult human height. ELife
5: e13410.
Nisbett, R.E. (2009). Intelligence and How to Get It: Why Schools and Cultures Count.
New York: W.W. Norton & Co.
Nisbett, R.E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D.F. & Turkheimer,
E. (2012). Intelligence: New findings and theoretical developments. American
Psychologist 67: 130-159.
Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A.R., Auton, A., … & Bustamante,
C.D. (2008). Genes mirror geography within Europe. Nature 456: 98-101.
Nye, C.D. & Drasgow, F. (2011). Effect size indices for analyses of measurement
equivalence: Understanding the practical importance of differences between groups.
Journal of Applied Psychology 96: 966-980.
O’Boyle, E.H., Humphrey, R.H., Pollack, J.M., Hawver, T.H. & Story, P.A. (2011). The
relation between emotional intelligence and job performance: A meta-analysis. Journal of
Organizational Behavior 32: 788-818.
Ousley, S., Jantz, R. & Freid, D. (2009). Understanding race and human variation: Why
forensic anthropologists are good at identifying race. American Journal of Physical
Anthropology 139: 68-76.
Padhukasahasram, B. (2014). Inferring ancestry from population genomic data and its
applications. Frontiers in Genetics 5.
Peter, B.M., Petkova, D. & Novembre, J. (2018). Genetic landscapes reveal how human
genetic diversity aligns with geography. BioRxiv: 233486.
Piffer, D. (2019). Evidence for recent polygenic selection on educational attainment and
intelligence inferred from GWAS hits: A replication of previous findings using recent data.
Psych 1(1): 55-75.
Pigliucci, M. & Kaplan, J. (2002). On the concept of biological race and its applicability to
humans. Philosophy of Science 70: 1161-1172.
Plomin, R., DeFries, J.C., Knopik, V.S. & Neiderhiser, J.M. (2016). Top 10 replicated
findings from behavioral genetics. Perspectives on Psychological Science 11: 3-23.
Putterman, L. & Weil, D.N. (2010). Post-1500 population flows and the long-run
determinants of economic growth and inequality. Quarterly Journal of Economics 125:
Regalado, A. (2018, June 22). Meet the DNA detective who finds killers from her couch.
Retrieved November 12, 2018, from MIT Technology Review website:
Reich, D. (2018a). Who We Are and How We Got Here: Ancient DNA and the New
Science of the Human Past. Retrieved from
Reich, D. (2018b, April 1). Opinion | How genetics is changing our understanding of ‘race’.
New York Times. Retrieved from
Reich, D., Thangaraj, K., Patterson, N., Price, A.L. & Singh, L. (2009). Reconstructing
Indian population history. Nature 461: 489-494.
Relethford, J.H. (2009). Race and global patterns of phenotypic variation. American
Journal of Physical Anthropology 139: 16-22.
Reyes-Centeno, H., Ghirotto, S. & Harvati, K. (2017). Genomic validation of the differential
preservation of population history in modern human cranial anatomy. American Journal
of Physical Anthropology 162: 170-179.
Rindermann, H. (2013). African cognitive ability: Research, results, divergences and
recommendations. Personality and Individual Differences 55: 229-233.
Rindermann, H. (2018). Cognitive Capitalism: Human Capital and the Wellbeing of
Nations. Cambridge UK, New York NY: University Printing House.
Rindermann, H., Becker, D. & Coyle, T.R. (2016). Survey of expert opinion on intelligence:
Causes of international differences in cognitive ability tests. Frontiers in Psychology 7.
Rindermann, H., Becker, D. & Coyle, T.R. (2020). Survey of expert opinion on intelligence:
Intelligence research, experts' background, controversial issues, and the media.
Intelligence 78: 101406.
Rosenberg, N.A., Pritchard, J.K., Weber, J.L., Cann, H.M., Kidd, K.K., Zhivotovsky, L.A.
& Feldman, M.W. (2002). Genetic structure of human populations. Science 298: 2381-
Rosenberg, N.A., Mahajan, S., Ramachandran, S., Zhao, C., Pritchard, J.K. & Feldman,
M.W. (2005). Clines, clusters, and the effect of study design on the inference of human
population structure. PLoS Genetics 1(6): e0010070
Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias,
W. et al. (2014). Admixture in Latin America: Geographic structure, phenotypic diversity
and self-perception of ancestry based on 7,342 individuals. PLoS Genetics 10(9):
Rushton, J.P. (2000). Race, Evolution, and Behavior: A Life History Perspective. Port
Huron, MI: Charles Darwin Research Institute.
Rushton, J.P. & Jensen, A.R. (2005). Thirty years of research on race differences in
cognitive ability. Psychology, Public Policy, and Law 11: 235-294.
Sarich, V. & Miele, F. (2004). Race: The Reality of Human Differences. Boulder CO:
Westview Press.
Schmitt, N., Golubovich, J. & Leong, F.T.L. (2011). Impact of measurement invariance on
construct correlations, mean differences, and relations with external correlates. An
illustrative example using Big Five and RIASEC measures. Assessment 18: 412-427.
Schulson, M. (2018, June 27). Kevin MacDonald and the elevation of anti-semitic
pseudoscience. Undark. Retrieved from
Scriver, C.R. (2001). Human genetics: Lessons from Quebec populations. Annual Review
of Genomics and Human Genetics 2: 69-101.
Sellbom, M. & Ben-Porath, Y.S. (2005). Mapping the MMPI–2 Restructured Clinical
Scales onto normal personality traits: Evidence of construct validity. Journal of Personality
Assessment 85: 179-187.
Sesardic, N. (2010). Race: A social destruction of a biological concept. Biology &
Philosophy 25: 143-162.
Sherwood, J.J. & Nataupsky, M. (1968). Prediciting the conclusions of Negro-white
intelligence research from biographical characteristics of the investigator. Journal of
Personality and Social Psychology 8: 53-58.
Shriner, D. (2013). Overview of admixture mapping. Current Protocols in Human Genetics
76(1): 1-23
Shriver, M.D. & Kittles, R.A. (2004). Genetic ancestry and the search for personalized
genetic histories. Nature Reviews Genetics 5: 611-618.
Shriver, M.D., Parra, E.J., Dios, S., Bonilla, C., Norton, H., Jovel, C., … & Kittles, R.A.
(2003). Skin pigmentation, biogeographical ancestry and admixture mapping. Human
Genetics 112: 387-399.
Shuey, A.M. (1966). The Testing of Negro Intelligence, 2nd ed. Social Science Press.
Sikela, J.M. & Quick, V.S. (2018). Genomic trade-offs: Are autism and schizophrenia the
steep price of the human brain? Human Genetics 137: 1-13.
Slatkin, M. (2004). A population-genetic test of founder effects and implications for
Ashkenazi Jewish diseases. American Journal of Human Genetics 75: 282-293.
Smedley, A. & Smedley, B.D. (2005). Race as biology is fiction, racism as a social problem
is real: Anthropological and historical perspectives on the social construction of race.
American Psychologist 60: 16-26.
Snyderman, M. & Rothman, S. (1988). The IQ Controversy, the Media and Public Policy.
New Brunswick, NJ: Transaction Publishers.
Sohail, M., Maier, R.M., Ganna, A., Bloemendal, A., Martin, A.R., Turchin, M.C., … &
Sunyaev, S.R. (2019). Polygenic adaptation on height is overestimated due to
uncorrected stratification in genome-wide association studies. ELife 8: e39702.
Spencer, Q. (2015). Philosophy of race meets population genetics. Studies in History and
Philosophy of Science Part C: Studies in History and Philosophy of Biological and
Biomedical Sciences 52: 46-55.
Su, R., Rounds, J. & Armstrong, P.I. (2009). Men and things, women and people: A meta-
analysis of sex differences in interests. Psychological Bulletin 135: 859-884.
Sussman, R.W. (2014). The Myth of Race: The Troubling Persistence of an Unscientific
Idea. Harvard University Press.
Tabery, J. (2015). Why is studying the genetics of intelligence so controversial? Hastings
Center Report 45(S1): S9-S14.
Tate, B.W. & McDaniel, M.A. (2008). Race differences in personality: An evaluation of
moderators and publication bias. Retrieved from
Tay, L., Su, R. & Rounds, J. (2011). Peoplethings and dataideas: Bipolar dimensions?
Journal of Counseling Psychology 58: 424-440.
Telles, E. & Paschel, T. (2014). Who is black, white, or mixed race? How skin color, status,
and nation shape racial classification in Latin America. American Journal of Sociology
120: 864-907.
Tibayrenc, M. (2017). Human intergroup variation and disease genetics. In: M. Tibayrenc
& F.J. Ayala (eds.), On Human Nature, pp. 161-175. Amsterdam: Academic Press.
Tishkoff, S.A. & Kidd, K.K. (2004). Implications of biogeography of human populations for
“raceand medicine. Nature Genetics 36: S21-S27.
Van den Eynden, J., Descamps, T., Delporte, E., Roosens, N.H.C., De Keersmaecker,
S.C.J., De Wit, V., … & Van Oyen, H. (2018). The genetic structure of the Belgian
population. Human Genomics 12(1): 6.
Wade, N. (2014). A Troublesome Inheritance: Genes, Race, and Human History. New
York: Penguin.
Wagner, J.K., Yu, J .-H., Ifekwunigwe, J.O., Harrell, T.M., Bamshad, M.J. & Royal, C.D.
(2017). Anthropologistsviews on race, ancestry, and genetics. American Journal of
Physical Anthropology 162: 318-327.
Wicherts, J.M., Dolan, C.V. & van der Maas, H.L.J. (2010). A systematic literature review
of the average IQ of sub-Saharan Africans. Intelligence 38: 1-20.
Wikipedia (2019a). 100 meter dash. Retrieved from
Wikipedia (2019b). Half marathon. Retrieved from
Wilson, C. (2018, April 27). Serial killer suspect identified using DNA family tree website.
New Scientist, retrieved November 12, 2018.
Winegard, B., Winegard, B. & Boutwell, B. (2017). Human biological and psychological
diversity. Evolutionary Psychological Science 3(2): 159-180.
Woodley of Menie, M.A., Dutton, E., Figueredo, A.J., Carl, N., Debes, F., Hertler, S.,
Irwing, P., ... & Rindermann, H. (2018). Communicating intelligence research: Media
misrepresentation, the Gould Effect and unexpected forces. Intelligence 70: 80-87.
Yee, A.H., Fairchild, H.H., Weizmann, F. & Wyatt, G.E. (1993). Addressing psychologys
problem with race. American Psychologist 48: 1132-1140.
Yuan, K., Zhou, Y., Ni, X., Wang, Y., Liu, C. & Xu, S. (2017). Models, methods and tools
for ancestry inference and admixture analysis. Quantitative Biology 5: 236-250.
... After all, the best predictor of future success is past success. All this points to stable causes of social success that are related to the origin, and thus genetic ancestry of people around the world (Easterly & Levine, 2012;Fulford et al., 2016;Kirkegaard, 2019c;Kodila-Tedika & Asongu, 2015;Putterman & Weil, 2010). One team of economists put it this way: ...
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Immigrants to Western countries typically have worse social outcomes than natives, but country of origin immigrant groups differ widely. We studied school performance in Denmark for 116 immigrant groups measured by the grade point average (GPA) of the 9th grade exam at the end of compulsory schooling. General intelligence is a strong causal factor of school outcomes and life outcomes in general for individuals. We accordingly predicted that country of origin average intelligence (national IQ) will predict immigrant group outcomes. We furthermore included as covariates immigrant generation (first vs. second) as well as the Muslim percentage of country of origin. Results show that GPA in Denmark can be predicted by national IQ r = .47 (n = 81), Muslim percentage r = -.40 (n = 81), and educational selectivity of immigrants entering Denmark r = .35 (n = 71). Regression modeling indicated that each predictor is informative when combined. The final model explained 46.3% of the variance with first generation (binary) β = -0.65, βIQ = 0.29, βMuslim = -0.21, and β education selectivity index = 0.27 (all predictors p < .001, n = 97). Our results are in line with existing research on cognitive stratification and immigration.
... In the second assignment, both male and female managed to follow essay organisation rules but with minor errors. This thus suggests that it is important for teachers to consider using blended learning methods in teaching since the methods are essential for students, particularly male students, who benefit most from the blended learning methods in developing their writing skills (Kirkegaard, 2019). The students' writing assignment scores were divided into three categories, and the first category is the evaluation of improvement in Scores. ...
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Technology has grown exponentially in this 21st-century society with the alignment of the Industrial Revolution 4.0, where communication technology devices and knowledge are made readily available at an individual’s disposal. In line with I.R 4.0 and TVET, handheld devices and computers have started to infiltrate the tertiary education level across the nation. It is hoped that the iPad Pro usage in teaching and learning could assist the students with learning disabilities in learning effectively. This study is also meant to assist them in learning in the most meaningful way and nurturing them to be autonomous learners. In reality, Apple Teacher and the application of iPad Pro is not common in the teaching and learning session particularly in Malaysia; due to the educators’ limited knowledge in utilising iPad Pro as an instrument of teaching and learning. The purposes of this study are to examine the educators’ discernment towards iPad Pro as an instructional tool and to investigate the Apple Teachers’ capabilities in utilising iPad Pro in harmonising the teaching and learning session. Eighteen (18) participants who participated in the Apple Teacher course in developing teaching and learning materials using iPad Pro were selected for this study. The data collection was conducted through an online survey and online interview sessions to investigate their discernment and capabilities in utilising iPad Pro in developing the teaching and learning sessions. The preliminary findings via the survey reveal that the educators’ capabilities of utilising iPad Pro do change their pedagogical methods in teaching and learning. In addition, the findings indicate that the usage of iPad Pro proves to have potentials and positive impacts on the teaching and learning engagement. Keywords: iPad Pro; Instructional tools; Apple Teacher; Apple Teacher Discernment and Capabilities; Teaching and Learning
... In the second assignment, both male and female managed to follow essay organisation rules but with minor errors. This thus suggests that it is important for teachers to consider using blended learning methods in teaching since the methods are essential for students, particularly male students, who benefit most from the blended learning methods in developing their writing skills (Kirkegaard, 2019). The students' writing assignment scores were divided into three categories, and the first category is the evaluation of improvement in Scores. ...
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In recent years, social media have become an integral part of online news distribution and consumption. Many news organisations have established social media accounts on various social platforms. Competition for audience influence has never been higher. Few studies have researched engagement, influence and social media information diffusion. To fill this gap, this article attempts to examine previous studies that are concerned with news content and news sharing on Twitter in the period 2010-2020 through a review of scientific, peer-reviewed articles. Fiftysix articles were selected as relevant from the skimming of abstracts from two databases (Taylor & Francis and Sage) by searching the keywords "news content, sharing, Twitter." To discover the general trend and pattern, each article was content analysed along four primary dimensions: authorship profile, manuscript characteristics, research design, and research methodology. Three central research areas—news sharing users, content, and networks—were identified and systematically reviewed. The review results find that studies' focus in recent years lies in four aspects: the content, the users, the media organisations, and the network. The review results provide critical analysis of current research and give future research suggestions in related areas in the central concluding section. Keywords: News content; News sharing; Twitter; News diffusion
... In the second assignment, both male and female managed to follow essay organisation rules but with minor errors. This thus suggests that it is important for teachers to consider using blended learning methods in teaching since the methods are essential for students, particularly male students, who benefit most from the blended learning methods in developing their writing skills (Kirkegaard, 2019). The students' writing assignment scores were divided into three categories, and the first category is the evaluation of improvement in Scores. ...
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Virtual identity is visually established through Avatars and their associated profiles. Realising that there are real people with real emotions on the other side of the screen, this research intends to uncover the virtual identity that users embrace in the virtual world. More specifically, we investigate how Avatars communicate their identity in Second Life beyond the visual representation. This research applied an ethnography approach in this study in which prolonged engagement in the online community, i.e. Second Life was undertaken to understand the culture of the virtual residents. Guided by Erving Goffman’s Theory, we conducted participant observation and open interviews to unveil the virtual identity. A total of 25.5 hours in-world time was spent in Second Life including interviews with six respondents. Data was collected through field notes and one-to-one interviews. Words, phrases and sentences were coded, and thematic analysis was employed to identify the dominant themes. The findings have shown that virtual identities are constructed through the words, phrases and sentences that the Avatars used in the online interactions with other users or Avatars. Each Avatar is unique when they carry their own identification based on the interactions. To some extent, the virtual world has strongly impacted on several aspects of human life, with identity being one of them. This study hopes that the findings may assist professionals as well as practitioners desiring to gain more understanding of virtual identity. Keywords: Virtual Identity; Second Life; Virtual World; Avatar
... In the second assignment, both male and female managed to follow essay organisation rules but with minor errors. This thus suggests that it is important for teachers to consider using blended learning methods in teaching since the methods are essential for students, particularly male students, who benefit most from the blended learning methods in developing their writing skills (Kirkegaard, 2019). The students' writing assignment scores were divided into three categories, and the first category is the evaluation of improvement in Scores. ...
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The commitments given by the users of Second Life virtual world game for an extended time has sparked an interest for the researchers to investigate how Second Life virtual world has fulfilled the needs of its residents. Previous research highlighted that Second Life has served support for learners’ socialisation and motivation in their daily lives. The researchers gather information on motivations of the Second Life users with the aim to identify differences of the motivations based on two genders; males and females by employing virtual ethnographic study. The researchers collected the data on how Second Life virtual world has served the motivations of its residents using participant observation, informal interviews and one-to-one semi-structured interviews. Eight participants have taken part to provide relevant information. The data were analysed using inductive thematic analysis which resulted in the finding of four categories of motivations: experiential, social, escapism and functional. The results reveal that the two genders share similarities in their motivations but differ in their preferences of the motivations. The study has several implications as it enhances our understanding of the motivations of the two genders and their preferences which will be useful to understand the communication process among the users. This study also highlights the features offered by the Second Life virtual world serve different needs for its users. The findings of this study make several contributions to the current literature as it has revealed the differences of main motivations between males and females to be in virtual world games. This study also provides insights on experiential motivations of male avatars in Second Life which could lead to more future research on this topic. Keywords: Virtual world game; Second Life; Motivations
... For people reporting on having an emotional mental disorder or not, there was seemingly no pattern except that "extreme liberal" reported having this higher than everybody else. As a robustness test, we ran models on whites only to avoid any potential confound with race and mental illness (Kirkegaard, 2019). However, the results were very similar and not shown here. ...
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It has been claimed that left-wingers or liberals (US sense) tend to be more mentally ill than right-wingers or conservatives. This potential link was investigated using the General Social Survey. A search found 5 items measuring one's own mental illness in different ways (e.g."Do you have any emotional or mental disability?"). All of these items were associated with left-wing political ideology as measured by self-report. These results held up mostly in regressions that adjusted for age, sex, and race. For the variable with the most data, the difference in mental illness between "extremely liberal" and "extremely conservative" was 0.39 d. This finding is congruent with numerous findings based on related constructs.
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A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008-2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = -5.97, df = 24, p = 3.7 × 10-6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.
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The cause(s) of ubiquitous cognitive differences between American self-identified racial/ethnic groups (SIREs) is uncertain. Evolutionary-genetic models posit that ancestral genetic selection pressures are the ultimate source of these differences. Conversely, sociological models posit that these differences result from racial discrimination. To examine predictions based on these models, we conducted a global admixture analysis using data from the Pediatric Imaging, Neurocognition, and Genetics Study (PING; N = 1,369 American children). Specifically, we employed a standard methodology of genetic epidemiology to determine whether genetic ancestry significantly predicts cognitive ability, independent of SIRE. In regression models using four different codings for SIRE as a covariate, we found incremental relationships between genetic ancestry and both general cognitive ability and parental socioeconomic status (SES). The relationships between global ancestry and cognitive ability were partially attenuated when parental SES was added as a predictor and when cognitive ability was the outcome. Moreover, these associations generally held when subgroups were analyzed separately. Our results are congruent with evolutionary-genetic models of group differences and with certain environmental models that mimic the predictions of evolutionary-genetic ones. Implications for research on race/ethnic differences in the Americas are discussed, as are methods for further exploring the matter.
Experts (Nmax = 102 answering) on intelligence completed a survey about IQ research, controversies, and the media. The survey was conducted in 2013 and 2014 using the Internet-based Expert Questionnaire on Cognitive Ability (EQCA). In the current study, we examined the background of the experts (e.g., nationality, gender, religion, and political orientation) and their positions on intelligence research, controversial issues, and the media. Most experts were male (83%) and from Western countries (90%). Political affiliations ranged from the left (liberal, 54%) to the right (conservative, 24%), with more extreme responses within the left-liberal spectrum. Experts rated the media and public debates as far below adequate. Experts with a left (liberal, progressive) political orientation were more likely to have positive views of the media (around r = |.30|). In contrast, compared to female and left (liberal) experts, male and right (conservative) experts were more likely to endorse the validity of IQ testing (correlations with gender, politics: r = .55, .41), the g factor theory of intelligence (r = .18, .34), and the impact of genes on US Black-White differences (r = .50, .48). The paper compares the results to those of prior expert surveys and discusses the role of experts' backgrounds, with a focus on political orientation and gender. An underrepresentation of viewpoints associated with experts' background characteristics (i.e., political views, gender) may distort research findings and should be addressed in higher education policy.
The field of intelligence research has seen more controversies than perhaps any other area of social science. Here we present a scientometric analysis of controversies involving intelligence researchers working in the democratic Western world since 1950. By consulting books and articles, conducting web searches, and contacting some of the individuals involved, we assembled a large database of controversies. Each entry in our database represents a controversy involving a particular individual in a particular year. We computed a measure of controversy by combining the number and severity of incidents, separately for each individual and each year. The individual-level distribution is highly skewed, with just a few individuals accounting for a disproportionate share of the controversy. When tracking the level of controversy over time, we find four relatively distinct ‘eras’, of which the most recent era—the ‘LCI era’—may be the most significant to date.
In recent decades the field of anthropology has been characterized as sharply divided between pro-science and anti-science factions. The aim of this study is to empirically evaluate that characterization. We survey anthropologists in graduate programs in the United States regarding their views of science and advocacy, moral and epistemic relativism, and the merits of evolutionary biological explanations. We examine anthropologists’ views in concert with their varying appraisals of major controversies in the discipline (Chagnon/Tierney, Mead/Freeman, and Menchú/Stoll). We find that disciplinary specialization and especially gender and political orientation are significant predictors of anthropologists’ views. We interpret our findings through the lens of an intuitionist social psychology that helps explain the dynamics of such controversies as well as ongoing ideological divisions in the field.
Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.