Race

[Anonymouse]

Originally posted by Fadix Answer to "Taboo: Why Black Athletes Dominate Sports and Why We're Afraid to Talk About It," by JONATHAN MARKS(Jonathan Marks teaches biological anthropology at the University of California, Berkeley, and is author of "Human Biodiversity.")

This doesn't disprove that black athletes do not dominate, and in fact it admit to differences but clouds them in the term "complex", as Taboo has already taken account of Marks' claims, if you had bothered to read the article I posted. Moreover, we already know different muscle builds account for different athletic skills, such as why African sprinters are the top sprinters of the world - fast-twitch versus slow-twitch muscles, vascular density around muscles, different chemical reactions and energy conversion cell densities, which have their root in physiological differences, ergo genetic, not genes that have been mapped yet, no different than before the human genome when certain genes were not mapped. Marks makes a good point, individuals are individuals, and there are individual exceptions to the rule, however, as far as his article attempts to address the points raised by Taboo, it fails on many grounds.
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Next Saturday afternoon, in less time than it has taken me to type this sentence, the fastest man at the Olympics will take the 100m gold medal. That man may be the pre-Olympic favourite, the American Maurice Greene. It may be Trinidad's Ato Boldon. It may even be Britain's Dwaine Chambers, who has run into impressive form in the last few weeks. But whoever it is, of one thing we can be certain: he will be black. Indeed, you've probably got more chance of winning the lottery next Saturday than a white man has of even making it to the final. The last time that a white athlete participated in an Olympic 100m final, Jimmy Carter was still in the White House. And the last time a white athlete held the 100m world record, Khrushchev was ensconced in the Kremlin. Over the past decade, the 10 second mark in the 100m has been broken 200 times - but not once by a white athlete. Nor is it just at the 100m that whites are so noticeably absent. Every men's world record at every commonly-run track distance from 100m to the marathon now belongs to a runner of African descent.

Nor is there any respite for white sportsmen away from the Olympics. In 1950, the American Basketball Association was almost entirely white. Today it is 80 per cent black; among the stars the figure rises to 95 per cent. Sixty per cent of American footballers are black. France won the football World Cup and Euro 2000 with a team in which more than a third of the players were black. In boxing, the two world heavyweight champions - Lennox Lewis and Evander Holyfield - are black; there is not a single serious white contender for their crowns.

What lies behind such black domination of sport? The traditional liberal answer points the finger at social factors. Blacks, so the argument runs, have been driven into sport because racism has excluded them from most areas of employment. Racism also makes blacks hungrier than whites for success, and so they more often end up on the winners' rostrum. In the postwar world, largely as a consequence of the experience of the Holocaust, there has been a great reluctance to see human differences, indeed to view any aspect of human behaviour, in biological terms. Humans, we have come to believe, can be explained purely in terms of culture.

Increasingly, this antipathy to biology is wearing away. More and more, biologists, anthropologists and athletes themselves are looking to nature not nurture for an explanation of black domination. 'Blacks are made better', argues Carl Lewis, the African American athlete who won four golds at the 1984 Olympics. The American journalist Jon Entine dismisses the environmentalist theory of black athletic prowess as 'political correctness'. Entine's book, Taboo: Why Black Athletes Dominate Sports and Why We're Afraid to Talk About It was published in America earlier this year to great controversy. The liberal consensus, Entine argues, has served only to disguise the truth about the black domination of sport - which is that blacks are built to run and jump. It's an argument that's winning a hearing on this side of the Atlantic too. Last week, the BBC transmitted The Faster Race, produced by its Black Britain team, which argued the case for a natural black athleticism. Channel 4 begins shortly a three-part series, The Difference, which explores genetic differences between races, including in sport. It's time we put away our fears of talking about racial differences, the series argues, and faced up to the facts of genetic diversity.

The view that black sportsmen and women have a natural superiority rests on the evidence of physiological research, largely into two groups of athletes: East African long distance runners and West African sprinters. East Africa, and in particular Kenya, is the powerhouse of middle and long distance running. The top 60 times in the 3000m steeple chase are all held by Kenyan athletes, who also hold more than half the top times at 5000 and 10,000 metres. Kenyan men have won the world cross-country championship every year since 1986. At the Boston marathon, often considered the world's premier event, Kenyan men have not lost since 1990. Most remarkably, the vast majority of top Kenyan runners come from one area in the country - the Kalenjin region along the western rim of the Great Rift Valley, adjacent to Lake Victoria. Kalenjin runners have won more than seventy per cent of Kenya's Olympic medals in world running and all but one Kenyan-held world records.

A number of lines of research suggest that the secret of such spectacular success lies in superior biology. All muscle contains two kinds of fibres - fast-twitch and slow-twitch. The former is good at producing explosive bursts of energy, the latter at sustaining muscle effort over long periods. Physiologists have shown that the muscles of Kenyan athletes have a higher proportion of slow-twitch fibres than those of white or West African athletes. Kenyans also enjoy a slighter body profile, have relatively longer legs and larger lung capacities, and possess more energy-producing enzymes in their muscles which are better able to utilise oxygen.

Athletes of West African descent - which include most African American, Caribbean and black British athletes - have, on the other hand, a physique which is suited to explosive events, requiring sprinting and jumping. Such athletes possess what biologists call a mesomorphic physique with bigger, more visible muscles including a larger chest. Their muscles contain a higher proportion of fast-twitch fibres than do whites or East Africans. Athletes of West African descent also possess less body fat, a higher centre of gravity, narrower hips, and higher levels of testosterone in their blood.

For Entine such physiological and biomechanical differences demonstrate the natural superiority of black athletes. For Entine's critics, on the other hand, the very search for such differences demonstrates a racist outlook. 'I don't think it matters what the biological conclusions are', argues former footballer Garth Crooks. 'It forges a distinction between black and white athletes which is unhealthy, unhelpful and untrue.' According to the prestigious science journal Nature, 'The danger that interracial comparisons will be inhibited by considerations of political correctness is less serious than that interracial studies will be wrongly used.' 'There are some things better left unsaid', concluded the New York Times.

Such critics are responding to a long history of racism in which black athletic superiority has often been seen as evidence of intellectual backwardness. 'The Negro excels in the events he does because he is closer to the primitive than the white man', claimed Dean Cromwell, the head coach to the US team at the 1936 Berlin Olympics. 'It was not long ago that his ability to sprint and jump was a life-and-death matter to him in the jungle.' Today, too, scientific racists, such as the controversial Canadian psychologist Philippe J. Rushton, argue that there is a trade-off between brain and brawn, and that black athletic superiority has been purchased at the price of lower intelligence. In The Faster Race Rushton explained (with a perfectly straight face) that Asian and white infants are born with bigger heads than black infants. Hence Asian and white women have a bigger pelvic girdle than do black women. A smaller pelvis, Rushton claimed, is better suited to running. Asians and whites are brainier, blacks more athletic.

Such claims may seem to us deeply offensive. But this is no reason to close our eyes to scientific arguments about racial differences in sporting ability. The cause of antiracism is not strengthened by ignoring science or censoring data. Racial science is a pseudo-science, which ignores the truth about human differences; antiracists should not try to ape it. Moreover, the debate about differences in sporting abilities is part of a wider debate about the meaning of new knowledge about genetic diversity. Channel 4's The Difference links racial variation in physical attributes to racial variation in intelligence. The final programme in the series is largely given over to Charles Murray, co-author of The Bell Curve, to argue that black populations are naturally less intelligent that whites and Asians. Liberals who refuse to engage in the debate about natural difference are simply leaving the terrain open to the likes of Rushton and Murray.

The real problem with the 'blacks are born to run' thesis is not that it is politically incorrect and hence should be ignored but that it is factually incorrect and should be challenged. The most basic difficulty is the confusion of racial and population differences. Different population groups are clearly physically distinct. The Masai in Kenya tend to be taller and longer limbed than the stocky, short-limbed Inuit in the Arctic, because the body-forms of both have been shaped by natural selection to suit their particular environments. But the fact that there are physical differences between human groups does not mean that such differences can be reduced to racial distinctions, nor that such differences need have a meaningful consequence in human endeavour, whether that be sport or IQ tests.

It is certainly possible to divide humanity into a number of races, as we conventionally do, according to skin colour and body form. But it is also possible to do it many other ways - using, for instance, blood group, lactose-tolerance, sickle cell, or any other genetic trait, as the basis for our new 'races'. Genetically, each would be as valid a criterion as skin colour. The distribution of one physical or genetic characteristic - say skin colour - is not necessarily the same as that of another - such as blood group. The current division of the world into black, white, Asian and Oriental races is, in other words, as rooted in social convention as in genetics.

Entine rejects such criticisms as mere 'semantics'. But his own argument shows why it is not so. According to Entine, East Africans are naturally superior at endurance sports, West Africans at sprinting and jumping, and 'whites fall somewhere in the middle'. But if East and West Africans are at either end of a genetic spectrum of athletic abilities why consider them to be part of a single race, and one that is distinct from whites? Only because conventionally we use skin colour as the criterion of racial difference.

To understand why genetic notions of population difference are at odds with social ideas of race, consider the Australian athlete Cathy Freeman. Freeman, an Aborigine, is the hottest Australian athlete, and a good tip for the 400m Olympic gold. Because of their skin colour, Aborigines are often bracketed with sub-Saharan Africans as a 'black' race. Racial scientists have often argued that Australian Aborigines and black African are the two most primitive races in the world. Since Freeman's rise to prominence, there has been much speculation that Aborigines, like black Africans, are natural athletes. Genetically, however, there is no population in the world more distinct from those of sub-Saharan Africa than Australian Aborigines. Freeman is genetically closer to white athletes such as Britain's Katherine Merry than to black athletes such as America's Marion Jones. Here, as in much else, appearances can be deceptive.

Not only are genetic notions of population differences distinct from political concepts of race, but the physiology of human differences is not easy to interpret in sporting terms. Entine suggests that West Africans have relatively slender calves compared to whites, and that this helps their sprinting ability. It is difficult to see how, because muscle-power increases with cross-sectional area; smaller calves should make it harder, not easier, to excel in explosive sprinting events. Indeed 'slender calves' is the main biological reason given for the lack of African-Americans in ice hockey. Yet the same attribute is seen as enhancing their performance on the track.

It is true that athletes of West African descent living in North America, Western Europe and the Caribbean dominate many sports. But contemporary West Africans don't. This is the opposite of what one should expect if athletic ability was predominantly genetic. In America, considerable intermixing between black and white populations has meant that the African American population embodies, on average, some 30 per cent of genes from populations of European descent. Hence African Americans should be poorer athletes than West Africans. The reverse is true.

What all this suggests is that the relationship between sports, culture and genetics is much more complex than either liberal antiracists or 'race realists' like Entine and Murray will allow. Athletic talent is at least in part inherited, and there are undoubted genetic differences between populations. Nor should we dismiss the possibility that West Africans and Kenyans have a genetic advantage when it comes to sprinting or long distance running. It has not been proved beyond reasonable doubt, and there is clearly much more to sport than natural ability, but in principle there is no reason to assume that certain populations have physical characteristics more suited to particular athletic activities. But are blacks naturally better athletes than whites? Not necessarily. We should be highly suspicious of any and all attempts to confuse the genetics of populations and the politics of race.

[Anonymouse]

The Story Behind the Amazing Success of Black Athletes, by Jon Entine

PART II:
Shattering Racist Myths: The Science Behind Why Kenyans Dominate Distance Running

Even a casual mention that meaningful genetic differences exist between populations can ignite a firestorm and threaten a career. Ask Jimmy "the Greek" Snyder. Or Roger Bannister, the first man to break the four-minute barrier in the mile, in 1954. In a speech before the British Association for the Advancement of Science in 1995, Sir Roger Bannister, the distinguished neurologist and retired Oxford dean was showered with ridicule for venturing his opinion "as a scientist rather than a sociologist" that all athletes are not created equal. "I am prepared to risk political incorrectness," he said, "by drawing attention to the seemingly obvious but under stressed fact that black sprinters and black athletes in general all seem to have certain natural anatomical advantages."

That's the explosive "N" word - natural. Because of the pseudo-science that has historically plagued research into human differences, assertions that biology predetermines or even significantly influences human behavior runs into a wall of political incorrectness. That's the politics.

While everyone readily accepts that evolution has turned out blacks with a genetic proclivity to contract sickle cell and Jews of European heritage who are 100 times more likely than other populations to be afflicted with the degenerative mental disease Tay-Sachs, it is widely perceived as racist to suggest that blacks of West African ancestry have evolved into the world's best sprinters, Asians among the best divers, East Africans the premier distance runners, and whites the top weightlifters.

Yet the science is quite clear and the empirical evidence consistent and overwhelming. A look at the ancestry (or home country) of runners holding the top 100 times in eight distances, from the 100 meters to the marathon, makes it clear that African domination is deep as well as broad:

-Blacks who trace their ancestry to West Africa, including African Americans, hold more than 95 percent of the top times in sprinting;
-Whites are virtually absent from the top ranks of sprinting; though whites have traditionally done well in the longer endurance races, particularly the marathon, their ranks have thinned in recent years;
-Athletes from one country, Kenya, make up more than one-third of top times in middle and long distance races; including top performances by other East Africans (most from Ethiopia), that domination swells to almost 50 percent.
-North Africans do well at middle distances;
-Mexicans (Native Americans), are strongest at the longest races, 10,000 meters and the marathon;
-East Asians are competitive only at the event requiring the most endurance, the marathon, and at ultra-marathons.

Why do athletes of African ancestry dominate running? Whereas the West African population evolved in the lowlands, East Africans (who are relatively slow sprinters but the world's best distance runners) trace their ancestry to mountainous terrain. Kenya, with 28 million people, is the powerhouse. It is a genetic stew, with studies indicating a mixture of genes from invading Arabs and Middle Easterners. One tiny district, the Nandi, with only 500,000 people, sweeps an unfathomable 20 percent of major international distance events, marking it as the greatest concentration of raw athletic talent in the history of sports.

At the Seoul Olympics in 1988, Kenya shocked the running world when it's top male runners won the 800m, 1500m and 5,000 meters, plus the 3,000-meter steeplechase. Based on population percentages alone, the likelihood of such a performance is one in 1.6 billion. The Kalenjins of the Great Rift Valley adjacent to Lake Victoria, a tribe of half a million people, win 40 percent of top international distance running honors - and three times as many distance medals as athletes from any other nation in the world.

This East African domination (and by some Moroccans and Algerians who are much closer, genetically, to East than West Africans) has been slow to emerge. Ethiopia's Abebe Bikila shocked the world at the 1960 Rome Olympics when, running barefoot, he won the marathon. But most East Africans did not have the money or means to compete. By the1980s Africans began trickling into long distance running, although soccer, at which Kenyans (and East Africans generally) fair poorly, was and is the national sport.


THE MYSTERY OF MUSCLES

Over the years there have been more than two hundred studies comparing the body composition of athletes, with the work of British physician James M. Tanner the most famous. His The Physique of the Olympic Athlete, published in 1960 after the Rome Olympics, found an ideal body for each sport, although the study noted considerable overlap in types - a classic bell curve. Sprinters were the most muscular. Beginning at 400 meters on up to the marathon, athletes competing in these events were progressively less muscular in the upper body. Long-distance runners were generally small, short-legged, narrow-shouldered, and ectomorphic, or lacking in muscle.

"Amongst competitors in both track and field events there are large significant racial differences," Tanner wrote. As nature would have it, different populations are better suited to excel at anaerobic activities such as sprinting, jumping, and lifting, than at aerobic sports such as distance running, cycling, and swimming.

We see these differences on the playing field, but they are apparent at the micro level as well. In the mitochondria of cells, the body's powerhouse, oxygen combines with the glucose released by carbohydrates and, eventually, fats to produce sustained energy. When the body demands quick bursts, it breaks down carbohydrates quickly, if incompletely. At roughly 400 meters, about 40-50 seconds of running for a top athlete, or 100 meters in swimming, the body has depleted much of its anaerobic capacity. That is the point at which anaerobic athletes experience an accumulation of lactic acid, the waste product of the muscles. If physical activity continues past this bio-physiological divide, the body begins to process energy more deliberately. Scientists are definitive in their findings that athletes of West African ancestry are the most anaerobically efficient athletes, East African are the fittest aerobically, and whites fall in the middle.

For years it was axiomatic that muscles have two types of fibers - white, or fast-twitch, which were thought to be adapted for power movements, such as leaping or sprinting; and red, or slow-twitch, which were adapted for endurance. Now we know the model is more complicated. There are in fact two different types of fast-twitch fibers, one more metabolically efficient. Whites on average have a higher percentage of slow-twitch fibers than West African blacks who generally have more of both types of fast-twitch fibers.

Geneticist and exercise physiologist Claude Bouchard at Laval University in Quebec City, has run numerous experiments comparing two populations, French-Canadian and West African students. Using long needles inserted into the thighs of test subjects, Bouchard's team extracted tiny sections of fibers, which look to the naked eye like pieces of raw meat. They were chemically treated to reveal metabolic differences, put on a glass slide, and slipped under a high-power microscope, where they appeared as a collage of tiny red and white crocodile scales. The West Africans, by a ratio of approximately two to one, had more of the larger fast-twitch fibers. The researchers concluded that the force generating capacity of type-II muscle fibers at high velocity, the speed and tempo of movements, and the capacity of an individual to adapt to exercise training are all genetically influenced.

Although physical activity can improve fitness, it generally cannot alter a person's biological endowment by converting fast-twitch fibers to slow-twitch ones, or vice versa (although people do gradually and permanently lose fast-twitch muscles as a result of aging). It's estimated that 40 percent is due to environmental influences such as exercise, whereas 45 percent is associated with genetic factors (the remaining 15 percent is due to sampling error). At the far end of the performance bell curve in sprinting, where small differences can be crucial, genetics clearly circumscribes possibility.


THE SCIENCE OF ENDURANCE

Although fiber composition can significantly affect performance, it is not sufficient by itself to ensure high performance. Since endurance is only about 25 percent inherited, training plays an integral role-but more so in blacks than whites. Experiments show that with only a modest amount of training, blacks can experience an explosive rise in exercise capacity, while even with far more effort whites don't improve nearly as much. In contrast, it appears that no amount of training can break through genetically-imposed, inherited limits on anaerobic capacity-the ability to sprint and jump. Thus, although fiber type alone does not itself guarantee a champion, if an athlete does not have a certain proportion of fast-twitch muscles, he or she can't hope to be a champion sprinter or jumper. In practical terms, this detail suggests that sprinters are born, not made.

If genetics and race really do matter in athletic performance, then we might expect to find noticeable differences in the ways different population groups sustain anaerobic and aerobic functioning. Sure enough, by applying population genetics to athletic performance and examining the aerobic/anaerobic energy cycle, scientists are beginning to understand the racial pattern in sports.

Timothy Noakes, long-time director of the Sport Science Center at the University of Cape Town Medical School, and author of many scholarly books, including Lore of Running, has observed that black South Africans, who share much of their genetic ancestry with East Africans, sweep more than 90 percent of the top places in endurance races held in his country, despite the fact that blacks represent no more than one-quarter of the active running population. Noakes has attempted to figure out why in his laboratory. In a treadmill study, black marathoners consistently bested whites. Although white runners matched or exceeded the black runners at distances up to 5,000 meters, blacks were "clearly superior at distances greater than 5km." The fine print in the data was particularly revealing. There was a dramatic difference in the ability of the blacks to run at a higher maximum oxygen capacity. In the case of the marathoners, blacks performed at 89 percent of the maximum oxygen capacity, while whites lagged by nearly 10 percent. The muscles of the black athletes also showed far fewer signs of fatigue as measured by lactic acid.

Noakes noted a link between his findings and the training habits of well-known Kenyan runners who report favoring low-mileage, high-intensity workouts. This presented a nurture/nature conundrum: Does hard training lead to a change in oxidative capacity and fatigue resistance, or does it merely reflect a genetically well-endowed athletic machine?

The answer can be found in the wild card in performance: muscle efficiency. David Costill, former head of the Human Performance Laboratory at Ball State in Muncie, Indiana, has shown that the adaptability of the muscle fiber for aerobic metabolism - its oxidative potential - is more important than the basic composition of the muscle. More aerobically efficient fibers produce fewer fatigue-producing lactate toxins, resulting in better performance. And although fiber composition is genetically fixed, which effectively limits the pool of possible successful athletes in each event, exercise can help muscles better utilize oxygen.

A team from South Africa and Australia, including Noakes, has found an apparent link between oxidative capacity, resistance to fatigue, and race. The researchers measured "running economy"-the amount of metabolic work (and therefore oxygen consumption) that is required to run at a given speed, much like the fuel economy of a car. Running economy can be affected by a variety of factors both environmental, such as running technique, and physiological, such as body-mass distribution and muscle elasticity. "We've shown that the oxidative enzyme capacity of the [black] athletes we looked at was one and a half times higher on average than the white runners," reported Kathy Myburgh, a co-author of the report and senior lecturer at the University of Stellenbosch in South Africa. Comparing black and white athletes with nearly identical race times, the researchers found that blacks were both more efficient runners and able to utilize a considerably higher percentage of their maximum oxygen potential - a decided advantage if two athletes otherwise have the same capacity.

"Whilst the current study does not elucidate the origins of these differences," the report concluded, "the findings may partially explain the success of African runners at the elite level." A subsequent study determined that the superior fatigue resistance during high-intensity endurance exercise is partially related to the higher skeletal-muscle oxidative capacity and lower plasma lactate accumulation found more commonly in blacks.

[Anonymouse]

COMPARING KENYANS AND SCANDINAVIANS

Noakes's colleague, Bengt Saltin, head of the Muscle Research Institute in Denmark, has come to the conclusion that certain population groups, including Northern Europeans, who are notable endurance runners and cross-country skiers, may have superior fatigue resistance encoded in their genes. He has found that Scandinavian distance runners, Kenyans, and South African blacks all have consistently lower blood-lactate levels and perform more efficiently than athletes from other regions, the likely result of their having evolved in mountainous regions. Population genetics - ancestry - is the key determinant.

Saltin brought a half-dozen established Swedish national class runners to St. Patrick's High in Iten, Kenya, in 1990 to see how they might match up against up-and-coming East African schoolboys. It was a demoralizing experience for the Swedes. National champion after national champion was soundly trounced in races from 800 meters to 10 kilometers. Stunned, Saltin estimated that in this one tiny area of the Rift Valley there were at least five hundred school boys who could best his national champions at 2,000 meters.

In a subsequent study Saltin brought several groups of Kenyans to the Karolinska labs in Sweden, where he was then working. Muscle-fiber distribution was similar for the Kenyans and Swedes. But biopsies of the quadricep muscles in the thighs indicated that the Kenyans had more blood-carrying capillaries surrounding the muscle fibers and more mitochondria within the fibers. That's important because mitochondria act a little like power stations, processing the glucose with oxygen brought in by breathing into energy. The Kenyans also were found to have relatively smaller muscle fibers than the Swedes, which Saltin speculated might serve to bring the mitochondria closer to the surrounding capillaries. This process aids in oxidation, bringing more "fuel" to the mitochondria, the engine of the muscles.

The Kenyans also showed little ammonia accumulation in their muscles from protein combustion, and less lactic-acid buildup. They have more of the muscle enzymes that burn fat, and their glycogen reserves are not burned as quickly, which improves endurance. Most impressively, they are able to take months off from regular training and then regain their old form quickly. When they do train, more than half of their total mileage occurs at heart rates of 90 percent of maximum, far higher than the rate for Europeans or Americans. In general, Saltin reported a 5 to 15 percent greater running economy at far less mileage, but at a higher intensity. Saltin has privately suggested that Kenyans appear to be innately efficient, durable, and fast - with the most perfect aerobic potential measured so far on earth.

Although there is no one genetically consistent African population, the environment appears to have resulted in some characteristics shared across populations. Bouchard is persuaded that a large fraction of both West and East African blacks enjoy biological advantages for power or sprint events and endurance activities, respectively. "West Africans have more of the key anaerobic enzymes in their skeletal muscle, those enzymes being responsible for regenerating the energy in the muscle cell through anaerobic pathways," wrote Bouchard. We are talking here about a two-fold difference between a significant fraction of blacks from West Africa and whites from North America. East Africans, on the other hand, seem to have more ability to mobilize the energy stored in adipose tissue in other body depots and to use that energy for prolonged periods of exercise. The key point is that these biological characteristics are not unique to either West or East African blacks. These characteristics are seen in all populations, including whites. However, based on the limited number of studies available, there seems to be more African Blacks with such characteristics than there are in other populations.

Considering the hotter-than-hot nature of the debate, it is not surprising that the most balanced views originate with those outside the United States. "There is an environment in the US which prevents individuals from even talking about this topic," said Bouchard. "I do not feel this pressure at all here in Canada. We talk openly about this issue and treat it as a scientific matter. I believe that we need to look at the causes of differences in performance between races as legitimately as we do when we study or discuss differences in diseases between the various races. I have always worked with the hypothesis that ignorance fosters prejudice. [Critical inquiry] is the greatest safeguard against prejudice."

Measured by fractions of a second, or wins and losses, sport comes as close as we can get to an objective, racially neutral scoring system. "I've been asked many times how an academic can waste time studying the differences between black and white people," said exercise physiologist Kathy Myburgh. "I said, 'Well, if you're a scientist and you're studying obesity, who do you compare obese people with? You compare them with thin people. But if you are a physiologist and you want to compare your best runners with those not quite as good, you compare the black ones with the white ones, because the blacks clearly are performing better.'"

Arthur Ashe, Jr., the first black man to win the US Open, Australian Open, and Wimbledon singles tennis titles, wrestled with this controversy while writing his groundbreaking A Hard Road to Glory, which catalogues the history of black accomplishment in sports. He accumulated thousands of anecdotes of how cultural and environmental forces had shaped black success in sports. Still, Ashe could not put the genetic issue to rest. When asked about what he had come to believe after years of research, whether blacks had a physical advantage, Ashe responded deliberately: "The results are outstanding, nothing short of stellar.

"Damn it," he sighed, frustrated at the political incorrectness of his own beliefs. "My heart says 'no,' but my head says 'yes.' Sociology can't explain it. I want to hear from the scientists. Until I see some numbers [to the contrary], I have to believe that we blacks have something that gives us an edge."

Ashe of course is only recognizing what we all intuitively know unequivocally: Population genetics and ancestry matter, a lot. Yet it should never be forgotten that genes are not the ultimate factors in elite performance. While genetics will determine if you have a chance to be an elite athlete, intelligence, dedication, and serendipity are the final arbiters of who wins and loses.

"It's the brain, not the heart or lungs, that is the critical organ," Roger Bannister told me. "But one would have to be blind not so see a pattern here. I hope we are not at a time and place where we are afraid to talk about remarkable events. I hope not."

[Fadix]

Dan, there is no such thing as Negroid genes. Some markers will be present for peoples in a location whom will be black, the only thing this study reflect is that 40% of the genetic makeup was from people from that location, whom are mostly all black. You can not say what were the rest of the 60%, what you are doing is not science, it is just a claim based on your belief that Blacks alone can not do anything... Just to clarify things, this same marker is found among Taiwanese, Indonesians etc... whom are considered as "Mongoloid"(see: International Journal of Molecular Medecine, Vol. 12, N. 4, 2003)

Dan, my biased articles? Dan, the materials your side posted, the researchs that support your claims, 95% of them have been writen by Rushton, and 100% of them were financed from the same organization which has been founded by white supremacists. It is not my articles that are biased, it is yours.

As well, now you guys divert the whole point by bringing sport, the correlation between black success and sport is environment based, it is rather the location from where those blacks come from than them being black, black is only a skin color, there are blacks with more whites characteristics(excluding skin color) than other blacks etc...


So you read them right? so you read my articles? Sure Dab, I do believe you, if you read them, how come you have not moved an inch from your preconcieved thought, when I have demonstrated that nearly all biologists, anthropologists, geneticists etc... reject your claims? One wonder, are they not those best placed to judge those things?

Now regarding literacy rate. Yes! my mistake, but if in fact you really have read what I posted you would understand what I meant. I posted in the past about this and Lynn remember? I was reffering to the fact that when they passed the test most were illetrate(many of those tests were conducted in the 60s, so what I said stend true).

http://www.suz.unizh.ch/volken/pdfs/IQWealthNation.pdf

Here a critic of those numbers, but I dought again that you will even read it.

Another point, 59 as IQ with 85,7% is very unlikely, 59 as IQ is the IQ of someone suffering from trisomy, if we take the Bell function, on the left side(left percentils) you will find more than 7% of the population in the range under the range 55-65 ... in a scholarised and Western country someone suffering from trisomy with all the ressources that are offered can learn basic reading and writting... but in a country that does not have those ressources, it is impossible. With an IQ of national average of the one that someone suffer from trisomy has, there is no way that the literacy rate could be above 30%

So I am making assumptions Dan? OK! then, feel free to show me my "contradictions."

The rest of your post has no relevancy.

[Fadix]

Equal opportunity and racial differences in IQ

Joseph F. xxxan, , a and Cynthia R. Holland, b

a Department of Psychology, Case Western Reserve University, Cleveland, OH 44106-7123, USA
b Liberal Arts, Cuyahoga Community College, 11000 West Pleasant Valley Road, Parma, OH 44130, USA
Received 2 October 2000; revised 2 October 2001; accepted 10 January 2002. Available online 6 February 2002.


Abstract

The intelligence quotient (IQ) differs for various racial-ethnic groups. Blacks and Whites, for example, differ, on the average, by about 15 points in IQ. The present study finds that differences in knowledge between Blacks and Whites for items tested on an intelligence test, the meanings of words, can be eliminated. They are eliminated when equal opportunity for exposure to the information to be tested has been experimentally assured. The data support the view that cultural differences in the provision of information may account for racial differences in IQ.


1. Introduction

In the present study we present an empirical test of the source of intelligence quotient (IQ) differences between racial groups. A note on nomenclature is necessary before we proceed. In the present text, we employ the racial designations used by the Public Health Service (see page KK of the PHS 398 Grant Form, Rev. 4/98). Specifically, participants in the present studies were asked to identify themselves as a member of one of four racial groupings: Asian (or Pacific Islander), Black (not of Hispanic origin), Hispanic, or White (not of Hispanic origin). The focus of the present study was on Blacks and Whites. Differences in IQ between Blacks and Whites on the order of about 15 IQ points are well documented (Jensen, 1985) and are present as early as 3 years of age (Montie and Peoples).

The search for the causes of Black–White differences in IQ is guided by one's theoretical interpretation of the IQ score or, more broadly, of intelligence. Jensen (1998), for example, considers racial differences in IQ to be due to differences in basic intellectual ability. Jensen bases his explanation of racial-ethnic differences in IQ on the fact that scores on the various tasks making up an IQ score are intercorrelated. Tasks that share common variance are said to load on a factor. Tasks used to derive an IQ score tend to load more or less heavily on what Jensen calls g or the general factor of intelligence. Theoretically, Jensen considers g to be the essence of intellectual ability and points out that the difference between races in IQ becomes more evident as the g loading of a task increases (Jensen and Naglieri). In making this argument, Jensen assumes that the opportunity for exposure to the information being tested has been the same for Blacks and Whites no matter how g-loaded the task.

Why do Blacks and Whites differ in IQ or g? How much of the difference in IQ between Blacks and Whites can be considered to be due to genetics and how much can be said to be due to the environment? Jensen (1998) has advanced what he calls the default hypothesis to explain what he believes to be the source of IQ or g differences between Blacks and Whites. The default hypothesis assumes that the differences in IQ between Blacks and Whites result from the same environmental and genetic factors, in the same ratio, that underlie individual differences in intellectual ability among the members of each racial group. Jensen's critics point out that the manner and the degree to which heredity causes differences in IQ among individuals may have nothing to do with what causes differences in IQ between groups of people. Logically, the critics are correct. But Jensen's reply is that the facts, as known, are more consistent with his view than with the view that the extent to which the genetic plan causes differences in IQ within groups of individuals may be irrelevant for explaining group differences in IQ (Jensen, 1998).

In contrast to Jensen, we assume (xxxan; xxxan; xxxan; xxxan and xxxan) that the IQ is a measure of how much a person knows in comparison to people their own age. How much a person knows (their IQ) depends on both the person's intellectual ability and on the information that has been provided to that person. Intellectual ability (how well one processes information) depends on one's genetic plan and on those aspects of the physical environment (e.g., nutrition, illness, trauma, etc.) that alter the functioning of the brain. The information one is given to process depends on one's culture. It is obvious that some circumstances of birth are associated with what information people are exposed to and, hence, with differences in how much they know as reflected by their IQ score. For example, the fact that you might have been born just before or just after an arbitrary cutoff date for school entry has nothing to do with your information processing (intellectual) ability. But your birth date has a great deal to do with whether you find yourself in one grade in school or another and, consequently, on how much you know. Nine-year-olds in the fourth grade achieve higher IQ scores than 9-year-olds in the third grade (Cahan & Cohen, 1989). Why? Because fourth graders have been exposed to more information than have third graders. Interestingly, as Cahan and Cohen (1989) note, schooling age also influences scores on the Raven's Progressive Matrices (Raven, Court, & Raven, 1975). Thus, performance on a test (the Raven's) that is highly g-loaded and shows large IQ differences between Blacks and Whites Jensen (1993) is subject to an obvious cultural influence.

Group differences in IQ due to an additional year of schooling are easily attributed to what has been taught to children. Other group differences in IQ are not so easily explained. Arguments for group differences in IQ as being more likely due to genetics, the physical environment, or to the culture can be made and are strongly held. We do not seek to praise or condemn any particular position on the causes of group differences in IQ. Rather, we seek to use theory to guide the search for the causes of group differences in IQ. Thus, to understand why people differ in IQ we feel that we must study the contributions that both intellectual ability and access to information make to knowledge. In our view, if group differences in IQ are accompanied by group differences in intellectual ability, then the search for the causes of the IQ differences should be directed toward genetic effects on information processing or to the effects of the physical environment on information processing. If group differences in IQ are not accompanied by group differences in information processing, then the search for the causes of the IQ differences should be directed toward cultural influences.

We assume that different experiences lead to different amounts of knowledge. Different amounts of knowledge produce differences in IQ scores. Given these assumptions, if people vary, because of their racial identity, in the opportunity they have had to be exposed to information, than Jensen's default hypothesis may be incorrect. Differences between racial groups in IQ may not be apportioned, in the same ratio, to the genetic and environmental causes that underlie differences in IQ among individuals within a racial-ethnic group. It may be that IQ differences between racial groups are due principally to environmental causes (exposure to information) while, within a racial group, the differences among individuals in how much has been learned are largely due to genetically influenced differences in intellectual ability. As Scarr (1996) points out, people are not apt to move easily between racial groups by virtue of their intelligence. Hence, genetic variability for a characteristic such as intelligence tends to be more pronounced within racial groups than it is between racial groups. Thus, according to our view, it is quite possible for differences in IQ between racial groups to be influenced more by one cause while individual differences in IQ within a racial group to be influenced more by a different cause.

In the present study, guided by our theoretical view and by that of Jensen, we sought to provide an empirical test of the question as to the source of racial differences in IQ. In doing so, we sought, first, to study a relatively complex task, because Jensen feels that when we make tasks more demanding or complex we are employing a task that is highly g-loaded and most apt to reveal Black–White differences in IQ. In effect a complex task would be a task commonly used on standard IQ tests, a task that loads heavily on the g factor, and, most importantly, a task that typically results in racial group differences in IQ. Second, we experimentally ensured that both Blacks and Whites would have equal opportunity to be exposed to the information to be processed and tested.

Specifically, in the present study, we compared people from different racial-ethnic backgrounds for their knowledge of the meanings of words. Knowledge of the meanings of words is a standard task on which IQ scores are based, a task that loads heavily on g, and a task that shows IQ differences between Blacks and Whites (Jensen, 1998). In addition, it is likely that Blacks have had less exposure to different words than have Whites. Hart and Risley (1995), for example, conducted a landmark longitudinal study of the frequency of verbal stimulation and the resulting language development of children from 1 to 3 years of age. In the Hart and Risley study, frequent observations of parent–child interaction in the home ensured that language experience and language knowledge was well documented. They found that amount of exposure to language predicted the vocabulary development and the IQ scores of the children at 3 years. They also found that the children of professionals (typically, Whites) were exposed to five times the amount of words than were children of parents on welfare (typically, Blacks). We are not claiming that such differences in exposure to information are the only or even the chief cause of the differences in IQ between Blacks and Whites in the Hart and Risley sample. The Hart and Risley results do tell us, however, that such differences early in life in exposure to information on the part of Blacks and Whites are an empirical fact that must be dealt with.

In the present study, we ensured that Blacks and Whites were given equal opportunity to learn the meanings of relatively novel words and we conducted tests to determine how much knowledge had been acquired. If, as Jensen suggests, the differences in IQ between Blacks and Whites are due to differences in intellectual ability per se, then knowledge for word meanings learned under exactly the same conditions should differ between Blacks and Whites. In contrast to Jensen, we assume that an IQ score depends on information provided to the learner as well as on intellectual ability. Thus, if differences in IQ between Blacks and Whites are due to unequal opportunity for exposure to information, rather than to differences in intellectual ability, no differences in knowledge should obtain between Blacks and Whites given equal opportunity to learn new information. Moreover, if equal training produces equal knowledge across racial groups, than the search for racial differences in IQ should not be aimed at the genetic bases of IQ but at differences in the information to which people from different racial groups have been exposed.

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2. General method

To discover if Blacks and Whites would differ in vocabulary knowledge following equal opportunity to learn, we taught young adults the meanings of words. Prior to training, the words were essentially meaningless to them. We obtained meaningless words by picking 82 words out of a dictionary. We picked words that we assumed few people have ever seen e.g., words such as "venter," which means belly. A dozen students attending a private university with strict entrance requirements and representing a variety of academic major interests (median age 19 years; 6 males and 6 females; 11 Whites, 1 Black) were tested for their knowledge of the meanings of these rare words. They were given a multiple-choice test with five alternatives. Based on the performance of the dozen students, we chose, for succeeding experiments, 40 words for which correct choices averaged no better than a chance level of 20%.

A group of 254 students (57 males, 197 females) most of whom (236) attended a local community college (18 students, 10 Whites and 8 Blacks, attended a private university) participated in two initial studies in which the meanings of these unknown words were taught. Of the 254 students, 171 were members of groups that usually do well on standard IQ tests (10 Asians and 161 Whites) and 83 were members of groups that, typically, do less well on standard IQ tests (2 Hispanics and 81 Blacks). The average age of the students was 26.5 years (S.D. 8.7).

We trained the students on the meanings of the unusual words by putting each word in a sentence. For example: "Tubby had a big, fat venter." We then asked a simple question about the unknown word in the sentence to see if the person being trained understood the meaning of the sentence. They had to indicate, for example, whether a venter was a body part or a mental state. After the training materials were collected, the students were given an irrelevant task to perform for 15 minutes (rating faces they had never seen before for attractiveness). Following this delay, we then determined their knowledge of the newly learned words by giving a multiple-choice test. The choices for venter, for example, were height, candle, badge, belly, and opening. A good learner would choose belly.

We also wished to control for the possibility that the particular people we chose to represent each racial-ethnic group might, by chance, simply have been equal in general vocabulary knowledge to begin with. Thus, the tests of a person's knowledge of the newly learned words were intermixed with similar multiple-choice tests for knowledge of the meanings of different words, words that were more apt to be known, words for which no special training in the experimental situation had been provided. The choices for a word more apt to be known such as "situate" (to place), for example, were: "wear, add, take, study, and place."

We conducted a third and a fourth study of 115 community college students in which Blacks and Whites were tested for their knowledge of vocabulary in which we used items from a standard intelligence test. Each racial group was given an opportunity, prior to testing, to learn the meanings of half of the words on the test. Knowledge for the meanings of the other half of the words on the test was also measured, but no opportunity was provided in the experimental situation for education as to the meanings of these words prior to the test. In a fifth experiment, 93 community college students were given a standard administration of the intelligence test employed in the third and fourth studies. The conventional testing allowed us to obtain IQ scores that would tell us how representative in intellectual functioning our study samples were of the general population.

For each of the initial four experiments, raw scores for knowledge of vocabulary for students in all racial groups combined were converted to standard (z) scores for each of the two conditions of training, i.e., in one condition students were given, in the experimental procedure, equal opportunity to learn the meanings of words prior to test and, in the other condition, they were not given such opportunity. Conversion to standard scores was done to remove irrelevant variance from statistical analyses. The irrelevant variance was due to the fact that vocabulary tests for trained and untrained materials, in Experiments 1 and 2, differed in numbers of items. The conversion to z scores, while equating for absolute level of performance due to differences in number of items, left any variance in vocabulary knowledge due to racial group membership free to vary. More importantly, conversion to standard scores allowed a meta-analysis of the hypothesis under test across all the experiments in the series, a meta-analysis that will be presented following considerations of each experiment.

In accord with the general literature on racial-ethnic differences in IQ, we assumed that the racial-ethnic groups tested in the present experiments would differ in general vocabulary knowledge. The question was whether they would differ in their knowledge for newly learned words.

3. Experiment 1

The students in Experiment 1 differed from those in Experiment 2 in that they were pretested to ensure that the clues subsequently given to train them as to the meanings of novel words would be understood. Specifically, they began the session by being asked to choose which one of five words went with a particular phrase. The phrase, for example, might be "Big, fat Tubby." The choices would be: "height, candle, badge, belly," and "opening." The student would choose the word "belly." This pretest was given to ensure that when the students were later trained as to the meaning of the novel word "venter" (as in the sentence "Tubby had a big, fat venter") we could be confident that the terms "Tubby," "big" and "fat" would elicit "belly" as an associate.

3.1. Method

3.1.1. Subjects

The first study included 97 students (78 Whites, 19 Blacks, 21 males, 76 females) with an average age of 25.4 years, S.D. 8.7.

3.1.2. Procedure

The 97 students were tested for their knowledge of 40 novel words whose meanings they had just learned and were also tested for their knowledge of the meanings of 60 other, presumably somewhat familiar, words. The students were tested in a classroom. They were given a set of pages that contained first, a consent form and, second, a sheet that asked for information as to their age, sex, and their racial identity. The consent form introduced the experiment as one on "... the relative contributions to knowledge of information processing and previous knowledge." They were told that they would be "... asked to look at a series of pictures, study 40 sentences, and complete a 100-word vocabulary study." In asking for their racial identity, the form stated "According to the United States Public Health Service, `women and members of minority groups and their subpopulations must be included in all National Institute of Health supported biomedical and behavioral research' and researchers are asked to `describe the composition of the proposed study population in terms of gender and racial/ethnic group.' To aid us in following the U.S. Public Health Service guidelines for research, please check the appropriate category." The student then checked one of five categories labeled "American Indian or Alaskan Native," "Asian or Pacific Islander," "Black, not of Hispanic origin," "Hispanic," or "White, not of Hispanic origin." The students were then, as noted above, pretested to ensure that the clues given later in the session to train them as to the meanings of 40 novel words would be understood. Their instructions were: "Here is a brief vocabulary survey that should be fairly easy. Circle a, b, c, d, or e for the word that best fits the phrase in question. It is very important that you answer each question. Guess, if necessary. But be sure to answer each question." They were then given 40 brief phrases, each with five possible answers. As noted earlier the phrase might be "Big, fat Tubby" and the choices would be "height, candle, badge, belly, opening." The students then handed in the sheets they had filled out. New sheets were distributed and the students were asked to rate faces (the faces were unknown to them) of college age males and females for attractiveness. (The ratings of faces were not relevant for the present experiments. They were given simply to provide some time between the various training and test sessions concerned with vocabulary knowledge.) The students were then presented with sheets containing the 40 novel words and were instructed. "Now you are going to see how some unusual words are used in sentences. Read each sentence carefully but quickly. Circle a or b to answer the question that follows each sentence. Again, read each sentence and answer each question." To continue the example given earlier, a sentence might read "Tubby had a big, fat venter." and their choices as to the meaning of "venter" would be "a. a body part, b. a mental state." Following completion and collection of the training materials, the students were given new forms. Again they rated novel faces for attractiveness and then were given a 100-item vocabulary test with the instructions: "On the following pages you will be asked to complete a word knowledge survey. Circle the letter (a, b, c, d, or e) next to the word that you think is the correct definition of the term. Work as quickly but as accurately as possible. It is important that all the questions be answered. So, if you don't know a word, guess. Many of these terms are very unusual, so don't feel bad if you do not know many of them. Do the best you can and give an answer for every question." To continue with our example, the word "venter" would be presented as one of the 100 words with "a. height, b. candle, c. badge, d. belly, and e. opening" as choices.

The entire session lasted about 45 minutes.

3.2. Results

The students had no difficulty in matching (98%) a set of terms such as "Big, fat Tubby" to a defining word such as "belly" in the pretest prior to training. Similarly, they were invariably correct (98%) during the training phase in picking a term such as "a body part" to link to a defining phrase such as "Tubby had a big, fat venter."

But, what of vocabulary knowledge? In accord with what is typically found in standard IQ testing, the racial-ethnic groups differed in their general vocabulary knowledge, i.e., in knowledge of word meanings where equal opportunity for exposure was not experimentally assured. The Whites' mean number of words known (out of a possible 60) was 27.5 (S.D. 9.7), or 46% correct, a number significantly greater (t=2.0, df 95, P<.05) than that of 23.0 words known (S.D. 7.6) by the Blacks (38% correct). But what about knowledge for the meanings of the unusual words for which the groups had received equal training? The results were straightforward. Knowledge of the meanings of the 40 novel words for which the groups had been equally trained did not differ for the Whites (a mean of 19.9 correct out of a possible 40, or 50%, S.D. 9.3) when compared to the Blacks (mean 21.5, or 54%, S.D. 8.6) at t=0.7, df 95.

We checked these raw scores to ensure that there were no ceiling or floor effects in the data. The lowest percent correct (38% for the Blacks' performance on untrained words) was significantly greater than a chance value of 20% (t=6.3, df 18, P<.001), thus avoiding a floor effect. With the highest performance, in general, at moderate difficulty (54% for the Blacks when allowed equal training), ceiling effects were of no concern.

The z scores derived from the means and standard deviations of the raw scores for vocabulary knowledge were entered into a 2 (racial groups, Black and White)×2 (training uncontrolled or controlled) ANOVA with repeated measures on the second factor. The only significant effect yielded by the ANOVA was a Racial Groups×Training Condition interaction (F=6.5, df 1/95, P<.01). The interaction was due to the fact that the Whites were superior to the Blacks in general (untrained) vocabulary knowledge with a mean z score of .11, S.D. 1.0 for the Whites and a mean z score of -.40, S.D. 0.8 for the Blacks with t=2.0, df 95, and P<.05. However, when equal opportunity for exposure to the meanings of words was experimentally assured, the Whites and the Blacks were equal in vocabulary knowledge with a mean z score of .00, S.D. 1.0 for the Whites and a mean z score of .13, S.D. 1.0 for the Blacks. This slight superiority on the part of the Blacks was not significant (t=0.5, df 95).

We interpret such results to mean that equal intellectual ability between racial-ethnic groups, plus equal opportunity to learn, results in equal knowledge.

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4. Experiment 2

We took pains to ensure, in Experiment 1, that, prior to the final vocabulary test, the correct definitions on that vocabulary test had never been presented in the same context as the novel words they served to define. Consider the novel word "venter" and its definition "belly." The term "belly" had been presented, in the pretest, in association with "Big, fat Tubby." During training as to the meanings of novel words, "Tubby had a big, fat venter" had been associated with "A body part." It was not until the final vocabulary test that belly appeared (as one of the definitional choices) in the same context as the word venter. Thus, we can have some confidence that the students in Experiment 1 were not simply recognizing that venter and belly had appeared together. However, in giving a pretest to ensure that a phrase such as "Big, fat Tubby" would be linked to belly there was an advantage and a disadvantage. The advantage of the pretest in Experiment 1 was that it proved that students had no difficulty in matching a set of terms to a defining word. But the pretest also had a disadvantage. It familiarized the students with all of the correct terms on the later vocabulary test. Perhaps we simply predisposed the students in Experiment 1 to respond to belly when they later saw it as a choice on the vocabulary test, regardless of the fact that it was the correct definition of venter. Perhaps they were just responding to a familiar item. Maybe the equality in vocabulary knowledge between Blacks and Whites in Experiment 1 was not due to equal opportunity for exposure to information but was due to equally adept recognition memory between racial-ethnic groups. To ensure that such was not the case, the essential conditions of Experiment 1 were carried out again in Experiment 2. However, in Experiment 2, we omitted the pretesting condition in which the vocabulary choices that appeared on a pretest also later appeared on a vocabulary test. Thus, in Experiment 2, recognition memory played no role.

4.1. Method

4.1.1. Subjects

The sample included 157 students (57 males, 197 females; 83 Whites, 10 Asians, 2 Hispanics, 62 Blacks) with a mean age of 26.5 years (S.D. 8.6).

4.1.2. Procedure

The procedure in Experiment 2 was the same as in Experiment 1 with two exceptions. First, the pretest, as noted, was omitted. Second, the session in Experiment 1 being a bit lengthy, the students in the second study learned the meanings of and were tested for their knowledge of 35 novel words and were also tested for their knowledge of 45 common words.

4.2. Results

Again, as in Experiment 1, the students in Experiment 2 were typically correct (98%) during the training phase in picking a term such as "a body part" to link to its defining phrase such as "Tubby had a big, fat venter." Also in accord with the results of Experiment 1, in Experiment 2 the Asians' and the Whites' knowledge of the meanings of the more common words at a mean correct of 21.5 (S.D. 6.7) out of a possible 45 (47% correct) was greater (t=4.1, df 155, P<.001) than the mean of 16.9 (S.D. 7.2) or 38% achieved by the Blacks and the Hispanics. Most importantly, however, in Experiment 2 (as in Experiment 1) knowledge of the meanings of newly learned words did not differ for the Asians and Whites (a mean of 12.0, S.D. 4.9 out of 35 words or 34%) when they were compared to the Hispanics and Blacks (a mean of 11.0, S.D. 4.9, or 31%). The t value of 1.3, df 155, proved not to be significant (P>.05). As the percentages indicate, the vocabulary tests were of high to moderate difficulty. Again, as in Experiment 1, t tests revealed no floor effects in that all of the four mean raw scores just reported were significantly greater (at P<.001) than a chance level of 20%.

The z scores derived from the means and standard deviations of the raw scores for vocabulary knowledge were entered into a 2 (racial groups, Black and White)×2 (training uncontrolled or controlled) ANOVA with repeated measures on the second factor. The ANOVA yielded a significant effect due to racial groups at F=9.0, df 1/155, P<. 003. This main effect due to racial groups, however, must be interpreted within the context of the other significant effect to emerge from the analysis, a Racial Groups×Training Condition interaction (F=6.3, df 1/155, P<.01). The interaction was due to the fact that the Whites (and Asians) were superior to the Blacks (and Hispanics) only in general (untrained) vocabulary knowledge with a mean z score of .26, S.D. 0.9 for the Whites (and Asians) and a mean z score of -.35, S.D. 1.0 for the Blacks (and Hispanics) with t=3.9, df 155, and P<.00001. Most importantly for our present purposes, however, when equal opportunity for exposure to the meanings of words was experimentally assured, the Whites (and Asians) when compared to the Blacks (and Hispanics) were equal in vocabulary knowledge with a mean z score of .09, S.D. 1.0 for the Whites (and Asians) and a mean z score of -.10, S.D. 1.0 for the Blacks (and Hispanics). This slight superiority on the part of the Whites (and Asians) was not significant (t=1.2, df 155).

Again, we interpret the results to mean that equal intellectual ability between racial-ethnic groups, plus equal opportunity to learn, results in equal knowledge.

5. Experiment 3

There may be those who would be more convinced that IQ differences between Blacks and Whites are due to cultural differences in the provision of information if the results we have just seen in Experiments 1 and 2 were based on a standard IQ test. We did a third and a fourth experiment in which community college students were tested for their knowledge of vocabulary by being given a standard IQ assessment, the Peabody Picture Vocabulary Test––Revised (PPVT-R; Dunn & Dunn, 1981). The PPVT-R includes 175 words to be tested. It is used to measure intelligence from childhood to adulthood. We selected the 72 items on the PPVT-R numbered from 104 to 175. The lowest numbered items can be solved easily by adolescents and the highest numbered items can be solved only by adults with superior knowledge. In Experiment 3, equal opportunity for Blacks and Whites for exposure to information was provided by allowing the participants to read sentences defining half of the words on which they would later be tested for their knowledge of the meanings of those words. In the fourth experiment, standard dictionary definitions provided such information.

5.1. Method

5.1.1. Subjects

The sample in Experiment 3 included 79 community college students (41 Whites and 38 Blacks; 13 males and 66 females, with an average age of 26.6 years, S.D. 9.6).

5.1.2. Procedure

Students were seen, in groups, in their classrooms. Prior to vocabulary testing, all participants received training on the meanings of 36 of the words on the PPVT-R (all even-numbered words from item 104 to item 174). The printed instructions were: "On the following pages you will see how 36 words are defined. Carefully read the sentences for each word, then answer the question about each word by circling a or b. Do not skip any words. Please answer each question. Thank you." Training consisted of a sentence explaining the meaning of the word followed by an easily answered question about the word. For example:

Thus, each racial group had an equal opportunity to learn half the words on the IQ test. For the odd-numbered words on the test, no training was given. Exposure to the 36 untrained (odd-numbered) words was left free to vary as a result of each racial group's previous cultural experiences. Following training, the materials were collected, and the students were given sheets of paper containing six PPVT-R items per page. The items, which were xeroxed, had been reduced from their standard size to fit on the page. The students were asked to choose, for each item, the correct one of four pictures that defined a particular word. The instructions read: "On the following pages you will see sets of pictures for 72 words. Please circle the picture that goes with each word. Please circle a picture for each word. Don't skip any words. If you are not sure, make your best guess." The word TALON, for example, was printed under four line drawings; the head of a unicorn, a hawk's head, a hawk's wing, and a talon common to birds of prey. The session lasted about 30 minutes.

5.2. Results

Knowledge for the meanings of the lowest numbered items on the PPVT-R, words that are easily understood by adolescents, whether training was given or not, proved to be much too easy for our participants. On the initial, untrained items (the 9 odd-numbered items from 105 to 121), the students averaged 92% correct (91% for the Whites and 94% for the Blacks, a difference that was not statistically reliable). On the initial, trained items (the 9 even-numbered items from 104 to 120), the students averaged 93% correct (95% for the Whites and 92% for the Blacks, again, a statistically insignificant difference). Conversely, the final, untrained items (the 12 odd-numbered items from 153 to 175, which even people of superior intelligence find difficult to understand) proved to be too difficult for the Whites (at 30% correct) and for the Blacks (at 33% correct), two values that were not statistically different from one another and each close to a chance performance of 25%. Because of these ceiling (too easy) and floor (too difficult) effects, our search for differences in general vocabulary knowledge between Blacks and the Whites centered on the 15, odd-numbered items from 123 to 151, the untrained items that proved to be intermediate in difficulty.

The results were consistent with those of the first two experiments. The performance of the Whites at 77% correct (a mean of 11.5 items out of a possible 15, S.D. 2.7) was significantly superior (t=2.5, df 77, P<.02) to the performance of the Blacks at 67% (a mean of 10.0, S.D. 2.2). Thus, for words of intermediate difficulty taken from a standard intelligence test, words for which no special training was given, Whites knew more than Blacks, a standard finding.

What about the performance of the Blacks as compared to the Whites on the words whose meanings they were given equal opportunity to learn? No statistically significant differences between Blacks and Whites in knowledge for the trained items were obtained at any point. For items of intermediate difficulty (the 15 even-numbered words listed from 122 to 150 on the PPVT-R) the Whites at 89% correct (mean 13.3 words out of 15, S.D. 2.1) did not differ (t=0.6, df 77) from the Blacks at 87% correct (mean 13.0, S.D. 1.6).

We also compared the Whites and the Blacks for their knowledge of trained items somewhat later in the test series. Specifically, we compared the Whites and Blacks for their knowledge of the even-numbered items from 134 to 162. Absolute performance on these later, trained items was more similar in difficulty to the earlier, untrained items (odd-numbered items 123 to 151) on which the Whites and Blacks differed when no equal opportunity for exposure was provided. The Whites' performance of 76% correct (mean of 11.4 items correct, S.D. 2.7) on these 15 trained items was the same (t=0.4, df 77) as the Blacks' performance of 75% correct (mean of 11.2 items correct, S.D. 2.7). Neither did the Whites (61% correct, mean 7.4 items, S.D. 2.8) and Blacks (57% correct, mean 6.8 items, S.D. 2.3) differ in performance (t=1.0, df 77) on the trained items (even-numbered words 152–174) that were the most difficult to learn.

As a final analysis, consistent with the analyses performed in the first two experiments, we converted the scores for all the students on the untrained items of intermediate difficulty (odd-numbered items 123–151) to z scores. We followed the same procedure for the trained items of intermediate difficulty (even-numbered items 134–162). As noted above, absolute performance on these later, trained items (134–162) was more similar in difficulty to the earlier, untrained items (odd-numbered items 123–151). The z scores were entered into a 2 (racial groups, Black and White)×2 (training uncontrolled or controlled) ANOVA with repeated measures on the second factor. The only significant effect yielded by the ANOVA was a Racial Groups×Training Condition interaction (F=4.6, df 1/77, P<.03). The interaction was due to the fact that the Whites were superior to the Blacks in general (untrained) vocabulary knowledge with a mean z score of .26, S.D. 1.1 for the Whites and a mean z score of -.28, S.D. 0.9 for the Blacks with t=2.5, df 77, P<.02. However, when equal opportunity for exposure to the meanings of words was experimentally assured, the Whites and the Blacks were equal in vocabulary knowledge with a mean z score of .04, S.D. 1.1 for the Whites and a mean z score of -.04, S.D. 1.0 for the Blacks. This slight superiority on the part of the Whites was not significant (t=0.4, df 77).

Once again, as in the first two experiments, when given equal opportunity for exposure, Blacks and Whites were equal in knowledge.

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6. Experiment 4

In the first three experiments we provided Blacks and Whites with equal opportunity for exposure to information by embedding an unusual word in a simply worded sentence. Our aim was to clarify the meaning of the word. In ordinary discourse, however, teachers and authors do not always make the effort to ensure that the meaning of an unusual term is clear to their audience (mea culpa). If you were to encounter a sentence like "The wound was to the venter," for example, you would not know the meaning of the word venter. As our parents and teachers taught us, when in doubt, go to the dictionary. In a dictionary we find, not sentences, but a more common term for the word in question. A dictionary would tell us that venter means belly. In Experiment 4, we asked what the effect on word knowledge would be if standard dictionary definitions taken from The American Heritage Dictionary of the English Language (Morris, 1978) were employed to provide Blacks and Whites with exposure to information.

6.1. Method

6.1.1. Subjects

The sample included 36 community college students (19 Whites and 17 Blacks; 25 who listed themselves as female and 8 who listed themselves as male, and 3 who were noncommittal as to gender). The average age of the group was 27.2 years, S.D. 12.4.

6.1.2. Procedure

Again, students were seen, in groups, in their classrooms. Prior to vocabulary testing, all received training on the meanings of 27 of the words on the PPVT-R (all even-numbered words from items 122 to 174). The written instructions were: "On the following pages you will see how 27 words are defined. Carefully read the definitions for each word. Then circle the number from 1 to 5 that that indicates how well you, yourself, understand the word given the definition of the word. A 1 means you understand the word from the definition very well. A 3 means you probably understand the word from the definition. A 5 means you don't understand the word from the definition. Do not skip any words. Please rate your own understanding of each word from the definition given."

For example:

For 27 odd-numbered words on the test (items 123–175), no training was given. Exposure to the 27 untrained (odd-numbered) words was, as in Experiment 3, left free to vary as a result of each racial group's previous cultural experiences. Following training, the training materials were collected, and the students were tested for their knowledge of the meanings of the words on the PPVT-R, as a group, following the same testing procedure employed in Experiment 3.

6.2. Results

Taking advantage of the data gathered and analyzed in Experiment 3 as to floor and ceiling effects on the PPVT-R, our search for differences in general vocabulary knowledge between the Blacks and Whites who participated in Experiment 4 was focused on the 15 odd-numbered items from 123 to 151. The results were consistent with those of the preceding three experiments. The general vocabulary knowledge of the Whites, at 83% correct (a mean of 12.4 items, S.D. 1.7) was superior (t=2.8, df 34, P<.01) to the knowledge of the Blacks at 71% (a mean of 10.6 items, S.D. 2.2). Thus, when opportunity for exposure to information is allowed to vary, Whites know more about the meanings of words than do Blacks.

But what of knowledge of the meanings of words when Blacks and Whites are given equal opportunity for exposure to information? To find out, we compared the Whites and Blacks for their knowledge of the meanings of the 15 words numbered, evenly, from 134 to 162. As in Experiment 3, absolute performance on these later, trained items was more similar in difficulty to the earlier, untrained items (odd-numbered items from 123 to 151). As we found in each of the three prior experiments, Whites and Blacks did not differ in knowledge of word meanings when equal opportunity for exposure to information was experimentally assured. Specifically, The performance of the Whites in Experiment 4 at 71% correct (mean 10.7 items, S.D. 2.2) on these 15 items was virtually identical (t=0.2, df 34) to that of the Blacks' performance of 70% correct (mean 10.5 items, S.D. 2.8).

As you may recall, we asked the students to rate the understandability of the dictionary definitions we provided for these 15 words. The Blacks' mean ratings over the 15 words of 1.9, S.D. 0.6 and the Whites' mean ratings of 1.7, S.D. 0.5 did not differ at t=1.5, df 34. A rating of 1 meant that the definition explained the word very well. Moreover, dictionary definitions appear to be as effective for conveying the meaning of words as are the use of sentences. Specifically, the mean number, 11.3 (S.D. 2.5), of even-numbered items from 134 to 162 correctly identified on test for all 79 participants in Experiment 3 who were given sentences did not differ from a mean of 10.6 items (S.D. 2.5) achieved by the students in Experiment 4 (t=1.4, df 113) who were provided with dictionary definitions.

Finally, as we did in the three prior experiments, we converted the scores for the participants in Experiment 4 (odd-numbered items 123–151 and even-numbered items 134–162) to z scores. The z scores were entered into a 2 (racial groups, Black and White)×2 (training, uncontrolled or controlled) ANOVA with repeated measures on the second factor. The sole statistically significant effect yielded by the ANOVA was a Racial Group×Training Condition interaction (F=5.9, df 1/34, P<.02). The basis of the interaction was obvious. The Whites were superior to the Blacks when training was not controlled (mean z for the Whites of .39, S.D. 0.8, and a mean z for the Blacks of -.45, S.D. 1.0 with t=2.8, df 34, P<.01). But, when equal opportunity for exposure to information was assured, the Whites and Blacks were equal in knowledge of word meanings (mean z of .03, S.D. 1.1 for the Whites and a mean z of -.03, S.D. 0.9 for the Blacks with t=0.2, df 34).

Thus, consistent with the results obtained from the prior three experiments, when given equal opportunity for exposure to information, Blacks and Whites were equal in knowledge.

7. Experiment 5

When one achieves consistent results across a series of experiments one must grow wary. There may be a problem somewhere. In Experiment 5, and in the meta-analysis of Experiments 1–4 to follow, we attempted to address the obvious questions that we, or any erstwhile critic, might raise. Specifically, in Experiment 5 we sought to answer the question of the generality of our results. Perhaps, in Experiments 1–4, we had been selecting groups of Blacks and Whites who were not representative of the general population with regard to IQ. The experimental question is straightforward. What are the average IQ levels of the population from which our Blacks and Whites groups came? How large are the actual IQ differences between Blacks and Whites from such a population?

We felt confident that the Blacks and Whites tested in Experiments 1–4 were representative of the general population of the United States, per se. Of the 369 students who participated in Experiments 1–4, 357 were kind enough to reveal their age and 361 to note their educational level. Some 98% of the students (350 out of 357) fell between the ages of 18 and 49 years, with a mean age of 26 years. According to the data published by the U.S. Census Bureau (1999), people in the age range of 18–49 years constitute 48% of the U.S. population (the range of ages listed by the Census Bureau is birth to over 100 years). The mean age of the people in the age range of 18 to 49 years is 30.5 years. Whites and Blacks appear to be equally represented, proportionately, in such an age range, e.g., some 44.4% of Whites and some 44% of Blacks in the United States are between 20 and 49 years of age.

The educational level of the participants in Experiments 1–4 averaged 14.0 years of schooling with a range from 10 to 18 years. The vast majority of our participants (351 of 369 or 95%) were commuters attending an urban community college on a part-time basis. In 1998, according to the Census Bureau, 58% of Whites and 61% of Blacks in the United States that were 25 years of age or older listed educational levels falling between a high school degree (12 years) and an associates degree (14 years). Some 75.6% of Whites and 71.6% of Blacks over 25 years of age listed educational levels falling between a high school degree and a bachelor's degree (16 years). Moreover, in 1998, 37% of Whites and 42% of Blacks enrolled in college were attending 2-year community colleges.

In brief, both our Black and our White participants in Experiments 1–4 appeared to be representative of the U.S. population with regard to age and educational level. The actual IQ levels for students comparable in age and in education to the students tested in Experiments 1–4 were ascertained directly in Experiment 5.

7.1. Method

7.1.1. Subjects

The sample included 93 students (67 Whites and 26 Blacks), 14 males and 79 females with an average age of 26.5 years (S.D. 9.5, ranging from 18 to 52 years) attending the same community college as the participants in Experiments 1–4. The mean reported educational level of the group was 14.2 years, S.D. 1.5, range 10 to 20 years. Statistical comparisons of the Whites and the Blacks revealed no significant differences in age (Whites at a mean of 25.3 years, S.D. 9.0 and Blacks at 29.7, S.D. 10.3) or in educational level (Whites at 14.3 years, S.D. 1.5 and Blacks at 14.0, S.D. 1.2).

7.1.2. Procedure

Students were seen, in groups, in their classrooms. All students in Experiment 5 were asked (under the same instructions employed in Experiments 3 and 4) to complete the six items per page, group-administered, 72-item (items 104–175) version of the PPVT-R that had been used in Experiments 3 and 4. In Experiment 5, however, the students received no exposure in the experimental situation to any of the 72 items prior to test.

7.2. Results

The PPVT-R tests were scored in the standard manner, i.e., basal (last 8 consecutively correct responses) and ceiling (6 incorrect responses out of a series of 8) were obtained, number of correct items between basal and ceiling were established, and conversion of number of correct responses to standard IQ scores was based on the number correct by age tables listed in the Dunn and Dunn (1981) manual.

Jensen (1998, p. 353) points out that the average IQs of groups of Whites and Blacks from large representative samples in the United States are, respectively, 100 and 85. Are the samples of Whites and Blacks in Experiment 5 representative of the U.S. population in IQ? Using Jensen's figures, if we consider a group of 93 individuals, 67 of whom are White and 26 of whom are Black (as in Experiment 5) we would expect a mean IQ for that group of 93 people to be about 95.8, i.e., (67×100+26×85)/(93). In the present study, the mean IQ for all 93 participants was quite representative of the U.S. population at 93.8 (S.D. 14.5), with a range from 62 to 131.

What about the standard IQ difference between Whites and Blacks of approximately 15 points (100 minus 85)? In the present study, the IQs of the 67 Whites averaged 98.1 (S.D. 12.4, range 67 to 131) and the IQs of the 26 Blacks averaged 82.6 (S.D. 13.7, range 62 to 112), a difference of 15.5 IQ points (t=5.3, df 91, P<.0001). Again, the mean IQs of the Whites and the Blacks and the scope of the difference in mean IQ between the racial groups for the participants in Experiment 5 (who were drawn from the same population as the participants in Experiments 1–4) are comfortably close to those of representative samples of Whites and Blacks in the U.S. population. In brief, throughout Experiments 1–4 we found that Whites and Blacks, when given equal opportunity for exposure to information presented on IQ tests, were equal in their knowledge of that information. The results of Experiment 5 tell us that such consistent findings were, indeed, obtained for samples representative of the general U.S. population in intellectual functioning.

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8. Meta-analysis

As noted earlier, for each of the first four experiments, raw scores for all participants were converted to standard (z) scores for the condition in which they were given, in the experimental procedure, equal opportunity to learn the meanings of words prior to test and, also, in the condition where previous opportunity for exposure to information was allowed to vary. This conversion to standard scores allowed a meta-analysis, across all the experiments, of the hypothesis that, given equal opportunity for exposure to information, Blacks and Whites will be equal in knowledge.

The meta-analysis based on z scores also allowed us to test a procedural question. Were our results noted thus far, Blacks and Whites differing in general vocabulary knowledge but equally knowledgeable of newly learned word meanings, simply a consequence of our method? That is, by some artifact of experimental design, procedure, or mathematical happenstance, would any two groups of people, people of the same race, be equal in their knowledge of newly learned word meanings even though they differ in their general knowledge of word meanings? To test such a possibility, we formed, for Whites and for Blacks, two samples (within each racial group). The samples differed (as indexed by their z scores), in their general vocabulary knowledge. The difference between the high-scoring Whites and the low-scoring Whites on general vocabulary knowledge and the difference between the high-scoring Blacks and the low-scoring Blacks on general vocabulary knowledge was approximately as large as the difference in general vocabulary between the Blacks and the Whites had been in each study. Specifically, we randomly selected, within each racial-ethnic group (within each of the four experiments) two thirds of the people in that racial group who had scored above the group median for general vocabulary knowledge and combined them with a randomly selected one third of the people in that racial group who scored below the median. These high-scoring individuals (on the average) on general vocabulary knowledge were compared, as a group, to their remaining racial-ethnic fellows who formed (on the average) a low-scoring group in general vocabulary knowledge, i.e., the remaining two-thirds in that racial group who had scored below the median combined with the remaining one-third in that racial group who had scored above the median. Again, by general vocabulary knowledge, we mean knowledge of the meanings of the words for which no special training in the experimental situation had been provided.

Please note that grouping people on the basis of their general word knowledge was made without any regard to the individuals' knowledge for newly learned word meanings. Thus, we were able to discover if two groups of people of the same race who differed in general vocabulary knowledge would or would not be alike in their knowledge of newly learned words.

All students' z scores were entered into a 2 (racial groups, Black and White)×2 (test for general vocabulary knowledge and test for knowledge of newly learned words)×2 (high-scoring and low-scoring on general vocabulary knowledge within each racial group)×4 (Experiments 1, 2, 3, and 4) ANOVA with repeated measures on the second factor. The ANOVA yielded a significant effect due to racial groups at F=12.0, df 1/353, P<.001. This main effect due to racial groups must be interpreted within the context of the only significant interaction effect to emerge from the analysis, a Racial Groups×Training Condition interaction (F=20.7, df 1/353, P<.0001).

The interaction was due to the fact that the Whites were superior to the Blacks only in general (untrained) vocabulary knowledge with a mean z score of .22, S.D. 1.0, for the 231 Whites and a mean z score of -.35, S.D. 0.9, for the 138 Blacks, with t=5.5, df 367, and P<.001. Conversely, when equal opportunity for exposure to the meanings of words was experimentally assured, the Whites when compared to the Blacks were equal in vocabulary knowledge with a mean z score of .05, S.D. 1.0, for the Whites and a mean z score of -.05, S.D. 1.0, for the Blacks, a not statistically significant difference (t=0.9, df 367). Thus, as we have seen repeatedly in the present series of experiments, when Blacks and Whites are given equal opportunity to acquire the meanings of words, the Blacks' and the Whites' knowledge of the meanings of these words does not differ.

What of the high and low scorers on general vocabulary knowledge within each racial group? Did they also yield a Group×Training Condition interaction? Not at all. The high- and low-scoring Group×Training Condition interaction was virtually nonexistent with F=0.2, df 1/353. The reason for such a lack of interaction was quite clear. Performance under the condition where opportunity for exposure to information was not controlled was, as one might well expect, much better for those students (Blacks and Whites) selected because they were, indeed, more knowledgeable under that condition. The high-scoring group of 186 students (Blacks and Whites) had a mean z score of .38, S.D. 1.0, in general vocabulary knowledge and were superior (t=7.8, df 367, P<.0001) to the 183 students (Blacks and Whites) selected as low in general vocabulary knowledge (mean z of -.38, S.D. 1.0). Of most importance, however, was the fact that the two groups also differed in their knowledge of newly learned word meanings at a mean z score of .35, S.D. 1.0, for those selected for high general vocabulary scores as compared with a mean z score of -.33, S.D. 0.9 for those in the low-scoring group (t=7.1, df 367, P<.0001).

In the present analysis, the lack of a significant triple interaction involving race, groups within race varying in general vocabulary knowledge, and training condition is also meaningful. It meant, for both Whites and Blacks, that the groups we selected to differ in general vocabulary knowledge also differed in knowledge for newly learned words. Specifically, The 116 Whites selected to be high in general vocabulary knowledge had a mean z score of .60, S.D. 0.9, in general vocabulary knowledge and were superior (t=6.4, df 229, P<.0001) to the 115 Whites selected as low in general vocabulary knowledge (mean z of -.16, S.D. 0.9). Notably, the two groups of Whites also differed in their knowledge of newly learned word meanings at a mean z score for newly learned words of .38, S.D. 1.0, for those selected for high general vocabulary scores as compared with a mean z score for newly learned words of -.29, S.D. 0.9, for those in the low-scoring group on general vocabulary knowledge (t=5.4, df 229, P<.0001). The same effect occurred for the 138 racial minority students. The 70 selected to be high in general vocabulary knowledge had a mean z score of .02 in general vocabulary knowledge, S.D. 0.9, which was greater (t=5.1, df 136, P<.0001) than the mean z score of -.73, S.D. 0.8, of the 68 minority students selected to low in general vocabulary knowledge. But a similar difference in knowledge for newly learned word meanings was also apparent with a mean z score of .30, S.D. 1.0, for those Blacks selected for high general vocabulary scores as compared with a mean z score for newly learned words of -.41, S.D. 0.8, for those Blacks in the low-scoring group on general vocabulary (t=4.6, df 136, P<.0001).

In brief, the result of creating groups within each race that differed in general vocabulary knowledge was quite clear. Differences in general vocabulary knowledge within racial-ethnic groups were always accompanied by a similar gap in knowledge for newly learned word meanings. We will consider, in our discussion, the theoretical implications of the fact that differences in IQ between races apparently do not have the same bases as differences in IQ among individuals of a particular race. For our present purposes, these consistent differences in the ability to learn new word meanings within racial-ethnic groups who differ in general vocabulary knowledge allow us be confident that our finding that differences in general vocabulary knowledge between racial groups were never accompanied by differences in knowledge for newly learned word meanings was not an artifact of our method.

Finally, in asking people to learn the meanings of novel words did we simply pick a task that, in and of itself, bore little or no relationship to general vocabulary knowledge? It is highly unlikely, of course, that the ability to learn the meanings of new words would be unrelated to the person's acquisition of word meanings over a lifetime. In fact, our results indicate that how well a person learns the meanings of new words is highly predictive of what that person has learned about the meanings of words over a lifetime. Specifically, in the initial sample of 97 students in Experiment 1, the correlation between knowledge for the meanings of newly learned words and general knowledge for the meanings of words was r=.57 for the total sample (P<.0001) with r=.54 (N=78, P<.0001) for the Whites and r=.54 (N=19, P<.009) for the Blacks. In the sample of 157 students in Experiment 2, the correlations were r=.48 for the total sample (P<.0001) with r=.56 for the 93 Whites and Asians (P<.0001) and r=.43 for the 64 Hispanics and Blacks (P<.0001). For the sample of 79 students in Experiment 3, the correlation between knowledge for trained (even-numbered) words of intermediate difficulty (items 134 to 162) and knowledge for untrained (odd-numbered) words of intermediate difficulty (items 123 to 151) was r=.53 (P<.0001) with r=.59 (P<.0001) for the 41 Whites and r=.46 (P<.0001) for the 38 Blacks. Similarly, for the 36 students tested in Experiment 4, the correlation between untrained (odd-numbered) words (items 133–165) and trained (even-numbered) words (items 132–164) was r=.56 (P<.0001) with r =.57 (P<.01) for the 19 Whites and r=.62 (P<.01) for the 17 Blacks. We performed the analyses in the third and fourth studies on items of intermediate, and, roughly equal, difficulty so that the correlations would not be artificially altered by floor or ceiling effects. In addition, a meta-analysis, across all four experiments, was undertaken with z scores. In the meta-analysis the correlation between knowledge for the meanings of newly learned words and general knowledge for the meanings of words was r=.50 (N=369, P<.0001) for all the participants, r=.54 (N=231, P<.0001) for the Whites, and r=.45 (N=138, P<.0001) for the Blacks.

Of course, all of these reported correlations are underestimates of the relation between how well people acquire knowledge of the meanings of unfamiliar words and how many words they have learned the meanings of over the years. The underestimation is due to the less than perfect reliabilities of the tests employed. Thus, we recomputed the correlations between the two kinds of knowledge after estimates of each had been corrected for unreliability. Specifically, we applied the Kuder–Richardson Formula 21 to the means and standard deviations and numbers of items tested to obtain the reliabilities of the performances of the students in Experiments 1 and 2. For the participants in Experiments 3 and 4, we employed reliabilities of .82 based on the reports by Dunn and Dunn (1981) for adults in the standardization sample of Form L of the PPVT-R. These reliabilities, along with the obtained correlations, were then employed in the standard formula used to obtain coefficients corrected for unreliability. The corrected correlations indicting the extent of the relationship between knowledge of newly learned words and general word knowledge were .62, .64, .65, and .68 (with a mean of .65) for the participants in Experiments 1, 2, 3,and 4, respectively. The mean for the Whites across samples was .68 (.62, .69, .72, and .70 for the four studies) and, for the Blacks, was .64 (.65, .57, .56, and .76, respectively). For the meta-analysis based on z scores we employed the mean reliabilities gained by our study-by-study analysis (above) to correct the coefficients between newly gained and previous knowledge of word meanings computed for z scores. The coefficients, corrected for unreliability, were .63 for all 369 participants, .69 for the 231 Whites, and .55 for the 138 Blacks. Thus, new knowledge of the meanings of words gained under conditions of equal opportunity for exposure to the information to be processed is strongly related to one's general vocabulary knowledge.

9. Discussion

The IQ differs for various racial-ethnic groups. Blacks and Whites, for example, differ, on the average, by about 15 points in IQ. In the present study, we sought an empirical answer to the source of IQ differences between Blacks and Whites. Specifically, we compared people from different racial-ethnic backgrounds for their knowledge of the meanings of words, a task on which IQ scores are based, a task that loads heavily on g, and a task that shows IQ differences between Blacks and Whites. We employed a procedure in which Blacks and Whites were given equal opportunity to learn the meanings of novel words. If, as Jensen suggests, the differences in IQ between Blacks and Whites are due to differences in intellectual ability, then knowledge for word meanings learned under exactly the same conditions should differ between Blacks and Whites. If differences in IQ between Blacks and Whites are due to unequal opportunity for exposure to information, then no differences in knowledge should obtain between Blacks and Whites given equal opportunity to learn new information. We found, in accord with the general literature on racial-ethnic differences in IQ, that the racial-ethnic groups tested in the present experiments did indeed differ in general vocabulary knowledge. The question was whether they would differ in their knowledge for newly learned words. They did not. Thus, the present study found that differences in knowledge between Blacks and Whites for items tested on an intelligence test, the meanings of words, could be eliminated. They were eliminated when equal opportunity for exposure to the information to be tested had been experimentally assured.

The data of the present study support the view that cultural differences in the provision of information may account for racial differences in IQ, at least for IQ tests based on knowledge of word meanings. Specifically, our results indicate that IQ differences between Blacks and Whites have to do with experience. In what follows we will focus on the implications that the results of the present study have for conceptual, empirical, and practical issues that are associated with or spring from the question of the origins of racial differences in IQ.

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9.1. Test bias

According to Herrnstein and Murray (1994, pp. 625–627), external evidence of test bias based on predictive validity can be inferred from data that show that randomly selected Blacks and Whites who have the same test scores on a test where Blacks are typically inferior to Whites, nonetheless have different outcomes on some measure of achievement, a measure that the original test predicts. They give, as a hypothetical example, Blacks doing better in school than Whites when the Blacks and Whites have been chosen to have equal scores on a test predictive of achievement in school, a test on which Whites typically score higher than Blacks. Thus, such a test would be considered biased against Blacks. They further point out that such data is the "ultimate criterion" for test bias.

Evidence of test bias based on predictive validity may be inferred from the results of the present series of studies. In the present studies, Blacks and Whites were given tests measuring their general knowledge of vocabulary. They were also given "schooling" as to the meanings of novel words. The results of the schooling were measured. Let us assume that the test we suspect to be biased is the test measuring the person's general knowledge of vocabulary. It is a test that shows group differences between Blacks and Whites. It is also a test that is predictive (at about r=.63 as we noted in the meta-analysis of the results of the present studies) of how well a person will learn the meanings of novel words. If we select Blacks and Whites who have the same scores on general knowledge of vocabulary, will the Blacks do better than the Whites on the learning of new word meanings? To answer such a question we selected all the Whites and all the Blacks whose z scores in our meta-analysis ranged from .15 to -.95 on the test of general (untrained) vocabulary knowledge. That particular range of scores ensured the largest number of Blacks and Whites that would, as groups, be equal in their general vocabulary knowledge. Specifically, the 87 Whites so chosen with a mean z score of -.39 (S.D. 0.32) did not differ (at t=1.1, df 142) from the 57 Blacks so chosen whose mean score was -.45 (S.D. 0.33). The groups did differ, however, in their acquisition of word meanings when equally trained (t=2.1, df 142, P<.05) with Whites achieving an average z score of -.32 (S.D. 0.78) and Blacks achieving a higher mean of -.03 (S.D. 0.85). Most importantly for our present purposes, the Whites mean scores of -.39 and -.32 did not vary over the two conditions (t=0.9, df 86) while the scores for the Blacks increased significantly (t=3.7, df 56, P<.0001) from -.45 to -.03. A 2 (racial groups)×2 (untrained and trained vocabulary) ANOVA confirmed this Race×Training Condition interaction (F=6.6, df 1/142, P<.01).

In brief, the present study finds statistically significant external evidence of test bias based on predictive validity for a test on intelligence based on general vocabulary knowledge. Jensen (1980) in an early summary of the results of studies on test bias concluded that there was little, if any, evidence for test bias based on race. Our results are contrary to those generally found. As Jensen (1980, p. 466) points out, since our results are unusual, we should consider the ways in which our study may differ from others. The difference lies in assumptions about the information available to people who differ in previous knowledge. Investigators of test bias assume that people with a poor knowledge base and more knowledgeable people who are given the same, new curriculum have the same amount of information available to them from that curriculum. We do not believe that to be the case. Why? Because we assume that associations to or the interpretations of incoming information are necessarily based on what a person already knows. If the new information a person is given to process must be understood in the context of what that person already knows, a poor knowledge base limits further learning. What happens when the new information that Blacks and Whites are given to learn is presented in such a way that the effect of a poor knowledge base is avoided? In the present studies, we provided easily understood, well-known terms to teach students the meanings of novel words. In doing so, we avoided the limitations on new learning that might have obtained if we had used instructional materials falling beyond the bounds of the existing knowledge base. In doing so, we found evidence for test bias. Whether further studies in which limitations on new learning due to an existing knowledge base are avoided also find evidence of test bias remains an interesting empirical question.

9.2. The default hypothesis

To what degree do factors that produce IQ differences among members of a racial group also produce IQ differences between racial groups? Jensen has advanced what he calls the default hypothesis to explain the source of IQ differences between Blacks and Whites. The default hypothesis assumes that the average difference in IQ of about 15 points between Whites and Blacks is the result of the same environmental and genetic factors, and in the same ratio, which underlie individual differences in IQ among the members of each racial group (e.g., Jensen, 1998). The present results are not consistent with the default hypothesis. Our racial groups differed in knowledge of word meanings (a standard IQ test) when no attempt was made to ensure equal opportunity for exposure to the information to be tested. However, any racial group difference in knowledge of word meanings was eliminated when equal opportunity for exposure to the information to be tested was assured. Contrary to what the default hypothesis would predict, however, the within racial group analyses in our study stand in sharp contrast to our between racial group findings. Specifically, individuals within a racial group who differed in general knowledge of word meanings also differed in performance when equal exposure to the information to be tested was provided. Thus, our results suggest that the average difference of 15 IQ points between Blacks and Whites is not due to the same genetic and environmental factors, in the same ratio, that account for differences among individuals within a racial group in IQ.

While our results indicate that the sources of IQ differences between Blacks and Whites are to be sought in the environment, it is important to realize that the present results in no way contradict the idea that differences among individuals in intellectual ability are due, to some extent, to genetic causes. In fact, in the present studies, we also found consistent differences in the ability to learn new word meanings among individuals within racial-ethnic groups selected to differ in general vocabulary knowledge. Consistent individual differences in the learning of new information within a racial group support the assumption that differences in IQ among individuals within a racial group are determined, to some substantial degree, by differences in information processing ability rather than by differences in exposure to information.

Findings similar to the present results with regard to group differences in IQ due to differential experience accompanied by consistent individual differences in IQ within a group have been reported by Ramey and his associates (Ramey and Ramey). The Ramey studies were aimed at discovering if providing poor children with preschooling early in life would raise the children's IQs. The IQs of the children increased with schooling. While IQs increased with training, individual differences in IQ before and after schooling remained stable. We would account for such effects by assuming the IQ score to be a measure of knowledge, knowledge determined by how well one processes information and by what one has been taught. Children given more information will, as a group, know more (have higher IQs) following training than they did prior to enrichment. At the same time, children in such an enriched group who process information well will learn more of that information than children given the same enrichment who process information less well.

In brief, differences in information processing ability, undoubtedly, have some genetic basis. In general, however, the present results do raise the question of the accuracy of estimates of the influence of genetics on intelligence that are based on IQ scores obtained from populations composed of people who represent various races or who, for other reasons, vary in their opportunity for exposure to the information tested on standard IQ tests. Our results suggest that obtaining the most accurate estimates of the genetic and environmental contributions to intelligence will involve the application of behavior genetic models to measures of IQ where equal opportunity for exposure to information has been objectively assured for the individuals tested.