Commentary

August 1998

IQ Since “the Bell Curve”

Christopher F. Chabris

PAST January, Governor Zell Miller of Georgia asked his legislature for enough money to give a cassette or CD of classical music to every newborn child in the state. The governor cited scientific evidence to support this unusual budget request. "There's even a study," he declared in his State of the State address, "that showed that after college students listened to a Mozart piano sonata for ten minutes, their IQ scores increased by nine points." And he added: "Some argue that it didn't last, but no one doubts that listening to music, especially at a very early age, affects the spatial-temporal reasoning that underlies math, engineering, and chess."

The so-called "Mozart effect" is one of the most publicized recent examples of our ongoing preoccupation with intelligence, a subject that not only refuses to go away but continues to raise whirlwinds of controversy. The largest such controversy, of course, surrounds The Bell Curve (1994), by the late Richard J. Herrnstein and Charles Murray. A mountain of essays and books purporting to refute that work and its conclusions grows and grows to this day. But now we also have the magnum opus of Arthur Jensen,1 a leading figure in IQ research and, like Herrnstein and Murray, a favorite target of academic liberals, as well as a posthumous volume by another leading IQ researcher, Hans Eysenck.2 So it is a good moment to look again at what we know, what we do not know, and what we think we know about this vexed subject.

IN The Bell Curve, Herrnstein and Murray set out to prove that American society was becoming increasingly meritocratic, in the sense that wealth and other positive social outcomes were being distributed more and more according to people's intelligence and less and less according to their social backgrounds. Furthermore, to the extent that intelligence was not subject to easy environmental control, but was instead difficult to modify and even in part inherited, genetic differences among individuals, Herrnstein and Murray posited, would contribute significantly to their futures.

The evidence for this thesis came largely from an analysis of data compiled in the National Longitudinal Study of Youth (NLSY), an ongoing federal project that tested over 10,000 Americans in 1980, with follow-up interviews regularly thereafter. Each participant completed the Armed Forces Qualifying Test (AFQT)--which, like any diverse test of mental ability, can be used as a measure of intelligence--and was then evaluated for subsequent social outcomes (including high-school graduation, level of income, likelihood of being in jail, likelihood of getting divorced, and so forth). As a rule, a person's intelligence turned out to predict such outcomes more strongly than did the socio economic status of his parents. This relationship held for all ethnic groups; indeed, when intelligence was statistically controlled, many "outcome" differences among ethnic groups vanished.

Herrnstein, a professor of psychology at Harvard with an impeccable reputation for scientific integrity, died of cancer just a week before The Bell Curve arrived in bookstores. This in itself may have had something to do with the frenzy of the public response. Had Herrnstein lived to participate in the debate, critics might have found the book harder to malign than it became when Murray, whose training was not in psychology but in sociology, was left to promote and defend it by himself.

Not that Murray, the author of Losing Ground (1984) and a vocal critic of the liberal welfare state, failed to do so energetically. But his lack of credentials as a hard scientist, and his overabundant credentials as a scourge of liberalism, made him a tempting target for an attack that was itself motivated as much by political as by scientific differences, and that was almost entirely focused on a side-issue in the book. That side-issue was differences in intelligence not among individuals but among groups--and specifically between whites and blacks--the degree to which those differences might or might not be explained genetically. So heated, and so partisan, was the furor at its peak that even President Clinton was asked about the book at a press conference. (He had not read it, but disagreed with it nonetheless.)

But the overreaction to what was in essence a moderate and closely reasoned book would also not have surprised Herrnstein in the least. If anything, it was a replay--actually, a more civilized replay--of what had happened to him after he published his first article on intelligence in the Atlantic in 1971. That article, entitled "IQ," besides bringing to public attention several points raised by Arthur Jensen in a 1969 paper in the Harvard Educational Review, offered a more speculative version of the argument that would be fleshed out and documented with NLSY data in The Bell Curve 23 years later.

Just as with The Bell Curve, only a small portion of Herrnstein's 1971 article dealt with differences among groups, and only a portion of that portion dealt with possible genetic influences on those differences; and, just as with The Bell Curve, these were the passages that received the greatest attention. In his article, Herrnstein concluded that "although there are scraps of evidence for a genetic component in the black-white difference, the overwhelming case is for believing that American blacks have been at an environmental disadvantage" (emphasis added). This did not stop one Nathan Hare from writing in response that "one would think that the pseudo-scientific generalizations surrounding race and IQ had long been put to rest. But the ghoulish die hard." Nor did it keep students at Harvard and elsewhere from putting up posters accusing Herrnstein of racism and calling him "pigeon-man" (in reference to his animal-learning research). His lectures were filled with protesters, and his speeches at other universities were canceled, held under police guard, or aborted with last-second, back-door escapes into unmarked vehicles. Death threats were made.

PEOPLE OFTEN react most defensively when challenged not on their firmly held beliefs but on beliefs they wish were true but suspect at some level to be false. This is the psychology behind the controversy that ensued after "IQ" in 1971 and The Bell Curve in 1994.3 On each occasion intemperate articles were written (some by the same people, barely updated), and the most strident positions were taken by those least qualified to comment on the science.4

By now, five major books have been published in direct response to The Bell Curve. Two of them, though critical, are within the bounds of reasonable discourse. Thus, Intelligence, Genes, and Success (1997), edited by four professors from the University of Pittsburgh who seem opposed to the book's public-policy conclusions, offers a fairly balanced range of scholarly views. On the sensitive question of heritability, what is especially notable is that the argument takes place mainly at the margins; although some of the book's contributors contend that the heritability of intelligence falls within a range lower than the 40-80 percent given by Herrnstein and Murray, that range is in every case much greater than zero.

A tougher line is taken in Inequality by Design: Cracking the Bell Curve Myth (1996), written by six Berkeley sociologists. This book addresses Herrnstein and Murray's main argument--that intelligence is an important determiner of social outcomes in America. To their credit, the authors do some old-fashioned hard work, reanalyzing the NLSY data and even making one correction that strengthens The Bell Curve's conclusions. But their main effort is to show, by adding variables other than parental socioeconomic status to the mix of factors predicting outcomes, that intelligence is not as important as The Bell Curve claims. Murray has since responded to this argument in a pamphlet entitled Income Inequality and IQ (published by the American Enterprise Institute); there, by considering only the NLSY data from sibling groups, within which parental background is by definition equal, he is able to show that intelligence still has very strong effects.

The conclusion one may reasonably draw from these two books, and from Murray's response, is that while intelligence may matter more or less than family background, it certainly matters, and that if it is not entirely heritable, it is heritable in some degree. It is useful to bear this in mind when considering the other three books, for one would scarcely know from reading them that such a view has any reputable backing at all. Though a few chapters in Measured Lies (1996), the most vituperative and scientifically irrelevant of the five volumes under consideration, attempt data-based argumentation, most settle for sarcasm, self-righteousness, and name-calling. And then there are The Bell Curve Debate and The Bell Curve Wars (both published in 1995); the former is an anthology of historical documents and reviews, mostly negative, which the editors rightly claim represent the general trend among responses to Herrnstein and Murray's book; the latter is a set of essays, also mostly negative, that originally appeared in a single issue of the New Republic when The Bell Curve was first published, with a few similar pieces added for effect.

According to its back cover, The Bell Curve Wars "dismantles the alleged scientific foundations . . . of this incendiary book." Since, however, the vast majority of those commenting on The Bell Curve in the anthology's pages have little or no scientific authority, whoever wrote those last words probably had in mind the single entry by the Harvard zoology professor Stephen Jay Gould. That essay, entitled "Curveball," was originally published in the New Yorker and appears both in The Bell Curve Wars and The Bell Curve Debate, occupying the first position in each. In it, Gould repeats many of the same accusations of racism and attributions of political motive that he made in his 1981 book, The Mismeasure of Man, written in response to the earlier controversy sparked by Jensen and Herrnstein.

WITHIN THE social-science community and the academic world in general, Gould's critique has been widely accepted as the canonical demonstration that the concepts of intelligence and its heritability are at best nonscientific and at worst racist and evil. (For instance, all of the contributors to Measured Lies who cite Gould's essay do so approvingly, if we count the one who asserts that it does not go far enough.) Indeed, so well has The Mismeasure of Man endured that in 1996 its publisher reissued it with a new introduction and appendices, including the ubiquitous "Curveball," but left the main text essentially unrevised.

Gould charges that the craniometrists of the 19th century, and later intelligence researchers as well, operated from racist assumptions, and implies that on those grounds their work should be ignored or even suppressed. Insofar as the charge is meant to include figures like Herrnstein and Murray, it is absurd as well as malicious. But even in those cases in the past in which racist assumptions can indeed be demonstrated, the proof of the pudding remains in the eating, not in the beliefs of the chef. Useful science can proceed from all sorts of predispositions; nor--it seems necessary to add--do the predispositions of scientists always point in the same direction, especially where discussions of human nature are concerned.

Before World War II, for example, the anthropologist Margaret Mead, presumably basing herself on her observations of non-Western cultures, wrote: "We are forced to conclude that human nature is almost unbelievably malleable, responding accurately and contrastingly to contrasting cultural conditions." Later, Mead admitted that what forced this conclusion was not the data she had collected but the political goals she espoused: "We knew how politically loaded discussions of inborn differences could become. . . . [I]t seemed clear to us that [their] further study . . . would have to wait upon less troubled times." As the shoot-the-messenger responses of Gould and others show, the times may still be too troubled for the truth.

But what about Gould's main scientific contention--that, as he puts it in his 1996 introduction to The Mismeasure of Man, "the theory of unitary, innate, linearly rankable intelligence" is full of "fallacies"?

The theory that Gould is attacking usually goes under the name of general intelligence. Its advocates, practitioners of the hybrid psychological-statistical discipline known as psychometrics, argue simply that while individuals differ in their abilities in a wide range of intellectual realms, a relationship exists among these variations that can be attributed to a common factor. This common factor is what the psychometricians label general intelligence, or g.

A brief example will illustrate the evidence they adduce for this proposition. Suppose a group of students takes a set of ten, timed mental-ability tests, five based on verbal materials (such as choosing antonyms) and five based on spatial materials (such as drawing paths through mazes). Each student will receive ten scores, and each student will have a unique profile of scores, higher on some tests than others.

Now suppose we correlate mathematically the students' scores on the five verbal tests. We will probably find them positively, though not perfectly, correlated--that is, the score on one will predict reasonably well the scores on the others. With the aid of a statistical procedure known as factor analysis, we can examine the pattern of these positive correlations and infer that they can be explained by the existence of a common factor, the most logical candidate being the "verbal ability" of the students who took the tests. Analogous results would likely occur if we factor-analyzed the set of five spatial tests.

What if we combined all ten tests in a single analysis, looking at all the possible correlations? Most likely we would find separate verbal and spatial factors at work. But those factors themselves will almost always be correlated. A superordinate, or "general," factor--g--can then be extracted to account for the commonalities across all the tests, though this factor will be revealed more by some tests than by others; such tests, known as "highly g-loaded," are taken as especially good measures of general intelligence.

TO THE extent that it is not simply political, the debate that followed The Bell Curve and "IQ," and that lies at the heart of Gould's critique in The Mismeasure of Man, is over the very existence and coherence of general intelligence. Each side has made the same points over and over, and each side believes it has refuted the other side's arguments. The reason this is so is that the two sides proceed according to different definitions of intelligence.

The psychometric camp, which includes Herrnstein and Murray, Jensen, Eysenck, John Carroll (whose 1993 treatise, Human Cognitive Abilities, offers the most extensive factor-analysis of mental tests), and most psychologists who have traditionally studied the topic, hold to a conception of intelligence that closely matches what common sense and the dictionary tell us the term means. The opposing side, which sports a more eclectic set of disciplinary backgrounds and prides itself on a more sophisticated and inclusive perspective, divides human abilities into broad classes--logical, spatial, interpersonal, verbal, etc.--and labels each class an "intelligence." The two sides then proceed to talk past each other.

Scientists make bad dictionary writers and worse philosophers. Their main skills are in constructing experiments and generating explanations for what they observe. Neither of these endeavors requires agreement on what the words involved "mean" in any deep or absolute sense, only on ways of converting the elements of the theory at issue into operations that can be carried out in an experiment and repeated later if necessary. Measurement is the most important such operation; as Kelvin pointed out long ago, without a way to measure something it cannot be studied scientifically.

This is why the oft-repeated phrase, "intelligence is nothing more than what intelligence tests measure," is, as an objection, merely a tautology. The truth is that as long as intelligence can be reliably measured--it can be, with a variety of tests--and validly applied--it can be, to predict a variety of outcomes--it is intelligence. If we suddenly started calling it "cognitive ability," "cognitive efficiency," or even "the tendency to perform well on mental tests," it would still have the same scientific properties. Nothing about the natural world would change.

One way to test the schemes of the proponents of "multiple intelligences" would be to apply research techniques that might (or might not) suggest a role in them for general intelligence. But this is an exercise the advocates of multiple intelligences tend to rule out of consideration a priori. Thus, as Howard Gardner correctly notes in Frames of Mind (1983), there is good evidence that different parts of the brain are responsible for different abilities. However, when at a recent seminar a member of Gardner's research group was asked how abilities in the various intelligences are measured, the swift response was, "We don't measure them."

The reason is obvious: any reasonable system of measurement would produce a set of scores whose correlations could be calculated, and the pattern of those correlations would likely reveal a common factor--in other words, g--accounting for some fraction of the total variation. Gardner's theory is very popular in educational circles these days, as is the idea, espoused by Daniel Goleman in Emotional Intelligence (1995), that skill at managing one's own emotions and interpreting those of others is very valuable to social interaction and success in life. Surely both of these ideas are correct, as far as they go. But neither one of them addresses intelligence in a complete way.

ANOTHER CRITICISM of the notion of general intelligence is that it is based on factor analysis, an indirect procedure that deals with the structure of tests rather than the nature of the mind and brain. This is a point raised with special vehemence by Gould. In The g Factor: The Science of Mental Ability, Arthur Jensen shows that the objection is without foundation.5

The g Factor is a deep, scholarly work laden with hundreds of tables, graphs, and endnotes, some of them with tables and graphs of their own. It is balanced and comprehensive, summarizing virtually all the relevant studies on the nature of intelligence and demolishing most of the challenges and alternative explanations of the major findings. (It is not, however, an easy book for nonspecialists to read, which is why we are also fortunate to have Hans Eysenck's much more accessible and even entertaining Intelligence: The New Look.)

In refuting Gould's point, Jensen demonstrates that mental-test scores correlate not just with one another but with many measures of information-processing efficiency, including reaction time (how quickly you can press a button after a light flashes), inspection time (how long two separate line-segments must be displayed for you to judge accurately which is longer), and working-memory capacity (how many random items of information you can remember while doing something else). Jensen also reviews the many direct biological correlates of IQ, such as myopia (a very heritable condition), brain electrical activity, estimates of nerve-conduction velocity (the speed at which brain cells communicate with one another), and the brain's metabolism of glucose. Even brain size, the study of which is richly derided by Gould, has been found with modern imaging technology to correlate with IQ.

These chapters, among the most impressive in Jensen's book, put general intelligence as a psychological trait on a more solid foundation than is enjoyed by any other aspect of personality or behavior. They also speak persuasively to the issue of its heritability, the argument for which becomes more plausible to the extent that intelligence can be associated with biological correlates.

One can go farther. To Stephen Jay Gould and other critics, belief in the heritability of intelligence is inextricably--and fatally--linked to belief in g; destroy the arguments for one and you have destroyed the arguments for the other. But as Kevin Korb pointed out in a reply to Gould in 1994, and as Jensen affirms here, the g factor and the heritability of intelligence are independent concepts: either hypothesis could be true with the other being false. In some alternate reality, intelligence could be determined by wholly uncorrelated factors, or for that matter by wholly environmental (i.e., nonheritable) factors. It is simply less of a stretch to imagine that a general factor both exists and is somewhat heritable, since, as Jensen shows, this combination describes our own reality.

STILL ANOTHER line of attack used by the detractors of g is to point to studies allegedly showing that intelligence is easy to change (and, therefore, a meaningless concept).

Arthur Jensen raised a firestorm three decades ago when he asked, "How much can we raise IQ and scholastic achievement?" and answered: not much. This brings us back to the Mozart effect, which purports to do in ten minutes what years of intensive educational interventions often fail to accomplish.

The Mozart effect was first shown in a study by Frances Rauscher, Gordon Shaw, and Katherine Ky that was reported in the British journal Nature in 1993. It is difficult to determine their experimental procedure with precision--their article was less than a page in length--but the essentials appear to be as follows. Thirty-six college students performed three spatial-ability subtests from the most recent version of the Stanford-Binet intelligence test. Before one of the tests, the students spent ten minutes in silence; before another, they listened to ten minutes of "progressive-relaxation" instructions; and before still another, they listened to ten minutes of Mozart's Sonata for Two Pianos in D Major (K. 448). The subjects performed the tests in different orders, and each test was paired with equal frequency against each listening option. The results, when converted to the scale of IQ scores: 110 for silence, 111 for relaxation, and 119 for Mozart.

"Mozart makes you smarter!" said the press releases as new classical CD's were rushed to market. A self-help entrepreneur named Don Campbell trademarked the phrase "The Mozart Effect," published a book by the same name, and began selling cassettes and CD's of his own, including versions designed specially for children. Frances Rauscher testified before a congressional committee and gave many press interviews.

What was wrong with this picture? The article in Nature did not give separate scores for each of the three Stanford-Binet tasks (necessary for comparative purposes), and it used dubious statistical procedures in suggesting that listening to Mozart enhanced overall "spatial IQ" or "abstract reasoning." Nor did the researchers analyze separately the first task done by each subject, to rule out the possibility that prior conditions may have influenced the Mozart score. Finally, they claimed that the effect lasted for only ten to fifteen minutes, but gave no direct evidence; since the subjects were apparently tested only immediately after each listening episode, there was no way to see how this interval was calculated.

IN AN attempt to reproduce the finding that classical music enhances "abstract reasoning," Joan Newman and her colleagues performed a simple experiment: each of three separate groups comprising at least 36 subjects completed two separate subsets of Raven's Matrices Test (a good measure of g) before and after listening to either silence, relaxation instructions, or the Mozart-effect sonata. All three groups improved from the first test to the second, but by the same amount; in other words, Mozart was of no particular help. In another experiment along the same lines, a group led by Kenneth Steele asked subjects to listen to ever-longer strings of digits and repeat them backward; it, too, found no benefit from prior exposure to Mozart. Other independent tests reported similar failures or equivocal results.

In response to these experiments, Rauscher and Shaw have considerably narrowed the scope of their original findings. They now concede that the post-Mozart increase in spatial performance occurred on just one of the three Stanford-Binet tasks, while on the others, varying the listening condition made no difference. According to their revised estimate, only "spatiotemporal" tasks, which require the transformation of visualized images over time, are affected by complex music, not spatial ability or reasoning in general.

Unfortunately, however, neither Nature nor any journal of similar stature has given space to the follow-up experiments, most of which have been reported in Perceptual and Motor Skills or other low-prestige journals that many psychologists never read. And the media have of course moved on, leaving the babies of Georgia with state-sponsored gifts and the public with the vague idea that if ten minutes of music can "make you smarter," then IQ cannot signify very much.

Similarly feeding this mistaken impression are other recent examples of brief treatments affecting performance either negatively or positively on IQ-type tests. Thus, the researchers Claude Steele and Joshua Aronson told one group of black undergraduates at Stanford that a difficult verbal test would diagnose their abilities and limitations, and another group that their answers would be used only for research on verbal processing. In four separate experiments, students did worse under the former conditions than under the latter.

Analogous results have been obtained with Asian female students in math tests: stressing to them that the test measures the abilities of their sex reduces their scores (women typically do worse than men in math), but stressing that it measures the abilities of their ethnic group increases their scores (Asians typically do better than other groups). But as Jensen points out, in such cases we are dealing with a stereotype about group differences that serves to increase or decrease test anxiety. That performance goes down when anxiety gets too high is a common enough finding in testing research, and says nothing about g.

What all these experiments do illustrate is that the human brain is a dynamic system whose functioning can change quite quickly. But this is not the same thing as changing intelligence itself. A few weeks of Prozac or another modern antidepressant can radically alter a person's behavior, but we still accept that his basic identity has not changed--he is still the man we knew before. Intelligence, too, is a stable characteristic of a person's behavior across a wide range of situations. He will be able to perform a task much better in one context than in another, with special training than without; but he is still the same person, and his intelligence is also still the same. Although the Mozart effect was promoted as though it were bad news for The Bell Curve and IQ, it is not.

AND NEITHER, finally, is the much-talked-about "Flynn effect." Over the past 50 years, the average score on intelligence tests has risen about three points per decade. This means that we are all getting smarter--indeed, the average adult of 1998 is, psychometrically at least, smarter than 84 percent of the population was in 1948.

The Flynn effect is named after James Flynn, who has been studying it for over fifteen years (although the phenomenon had been noted as early as the 1930's). In a chapter of a new book, The Rising Curve: Long-term Gains in IQ and Related Measures,6 Flynn notes that gains are occurring steadily in every country sampled, mainly in the West and the industrialized parts of Asia, though their size and specific nature varies in different cases. He believes the start of the increases coincided with industrialization in the 19th century, though the data are of course less reliable the farther back one goes. What he does not know is why the gains have been occurring, and the other contributors to The Rising Curve can offer only tentative theories at best.

Psychologists, like all scientists, prefer to test their theories with controlled experiments, but such experiments cannot be performed when the phenomenon to be explained is occurring throughout the world continuously over time. The difficulty in comparing times is that many things have changed with the times: the items on IQ tests are different; education is different; nutrition is better; airplanes, cars, radio, television, movies, computers, and the Internet have been invented; society has become more permissive and also more rewarding of risk-taking; testing is more widespread and people are more accustomed to being tested; birth rates are lower; and so on. Encompassing all of these time-correlated variables, the change in what might be called our cognitive environment has been simply tremendous over the past 150 years. The most relevant factors here are probably better nutrition--a topic Eysenck studied at the end of his career--plus greater, more diverse, and more complex stimulation of the brain by our everyday experiences.

Evidence of such a dramatic environmental effect on IQ scores should reinforce skepticism concerning a genetic basis for group differences. But, in any case, psychometric theory makes no claims about average or absolute levels of intelligence within or between populations, and behavioral genetics allows for complex environmental influences on traits that are still significantly heritable. And so, again contrary to popular belief, the concept of general intelligence remains as sound and as meaningful as ever, Flynn effect or no.

HAVING WITHSTOOD so many attacks, will the psychometric study of intelligence survive? Alas, not necessarily. In a pattern reminiscent of an earlier episode in the annals of modern psychology, the impact of Stephen Jay Gould's critique has been reinforced by the lack of a forceful response to it by psychometricians themselves, leaving the impression even within psychology at large that general intelligence has been routed.

Just as Gould, a paleontologist, has chided psychologists for misunderstanding genetics, so, in a review of B.F. Skinner's Verbal Behavior in 1959, the linguist Noam Chomsky chided behavioral psychologists for misunderstanding language. Like Gould, who has caricatured and ridiculed the notion of general intelligence and the factor analysis used to document it, Chomsky caricatured the tenets and methods of behaviorism, which argued that the task of psychology is to measure only behavior and to explain it only in terms of environmental and genetic causes, without referring to what goes on inside the head.

It took eleven years before a leading behaviorist, Kenneth MacCorquodale, answered Chomsky; the reason none of his colleagues had bothered to reply earlier, he explained, was that they found Chomsky's arguments simply uninformed and irrelevant to the work they did. In the meantime, however, Chomsky's review was widely read and subscribed to by the new wave of cognitive psychologists who were building a framework for psychology that remains dominant today.

Gould's book seems to have had a similar effect on young intelligence researchers. Although Jensen and several others did review The Mismeasure of Man very negatively at the time it appeared, like MacCorquodale they replied in obscure journals read mainly by their own supporters. Thanks in part to Gould's influence (and, of course, to the outrage directed against Jensen and Herrnstein in the 70's), the most popular new theories in the 1980's came to minimize the role of general intellectual ability in favor of other factors, to posit multiple "intelligences," and to give little attention to heritability. Now Eysenck, one of the heroes of psychometrics, and Herrnstein, one of its leading supporters, have died, Jensen and Carroll are approaching the end of their careers, and the psychometricians risk going into the same sort of extended bankruptcy proceedings as the behaviorists before them.

The great irony is that this is occurring just as the field of behavioral genetics has begun to thrive as never before. One of its most striking successes has been to document, through the convergence of numerous family and twin studies, the heritability of intelligence. Now researchers have been able to identify a specific gene whose variations are associated with differences in intelligence. This is a crucial step in building a complete theory of intelligence that can explain individual differences in biological as well as psychological terms. But the new generation of cognitive scientists, who focus on characteristics of the mind and brain that are common to everyone, are not too interested in differences among people, while the psychometricians, who stand to be vindicated, have been sidelined on their own playing field.

THE MOST basic claim put forth by Herrnstein and Murray was that smart people do better than dumb people. What is so troubling about that? We rarely encounter an argument over the fact that beautiful people do better than ugly people, or tall people better than short ones, though each of these propositions is also true. Is an intellectual meritocracy less just or moral than a physical one?

The answer, unfortunately, is that whenever intelligence is said, "race" is heard; whenever race is said, "genetics" is heard; and whenever genetics is said, "inferiority" is heard--even though these issues are not necessarily connected in any way. When I mentioned to friends that I was writing an article on intelligence, many were surprised, and some wanted to know why. I can only imagine how Herrnstein was treated by his colleagues during the last 25 years of his life. The public protests may have bothered him less than the fact that people in his own community never thought of him in the same way again: he had disturbed a pleasant conversation by bringing up unpleasant facts.

Since The Bell Curve, intelligence is stronger than ever as a scientific concept, but as unwelcome as ever as an issue in polite society. It would be reassuring to think that the next twenty years, which promise to be the heyday of behavioral genetics, will change this state of affairs. But if the past is any guide, many more phony controversies lie ahead.

CHRISTOPHER F. CHABRIS, here making his first appearance in COMMENTARY, is a Ph.D. candidate at Harvard specializing in cognitive neuroscience. He is at work on a book about the chess-playing machine Deep Blue and artificial intelligence.

¹ The g Factor: The Science of Mental Ability. Praeger, 672 pp., $39.95.

² Intelligence: The New Look. Transaction, 232 pp., $29.95.

³ For Richard J. Herrnstein's account of what happened after he published his Atlantic article, see his "On Challenging an Orthodoxy," COMMENTARY, April 1973. For Charles Murray's account of the later controversy, see his "The Bell Curve and Its Critics," COMMENTARY, May 1995 and subsequent letters, August 1995.--Ed.

4 In the case of The Bell Curve, a special committee set up by the American Psychological Association to report on the basic science eventually backed all of the book's main claims, as did an open letter signed by several dozen of the nation's most qualified intelligence researchers.

⁵ Jensen's work should not be confused with another of almost the same title, The g Factor: General Intelligence and its Implications, by Christopher Brand. This provocative but worthy book was published in the United Kingdom early in 1996 and was withdrawn within two months, after negative media coverage and a frenzy reminiscent of the early 1970's. The publisher, Wiley, also canceled the book's distribution in the United States before any copies went on sale. Brand has since been fired from his teaching position at Edinburgh University, and has yet to find a new publisher.

⁶ Edited by Ulric Neisser. American Psychological Association, 400 pp., $39.95.

Commentary on some of the empirical and theoretical support for The Bell Curve.

Kranzler, John
Vol. 24, School Psychology Review, 01-01-1995, pp 36.

John H. Kranzler, PhD, received doctorate from the University of California ay Berkeley in 1990. He is currently an Assistant Professor of School Psychology at the University of Florida. His research focuses on the structure, development, and assessment of human cognitive abilities Address all correspondence concerning this article to John H. Kranzler, College of Education, University of Florida, 1403 Norman Hall, Gainesville, FL 32611-2053.

Abstract: This commentary discusses two important components of The Bell Curve: Intelligence and Class Structure in American Life. The first is the original empirical evidence presented by Herrnstein and Murray to demonstrate the central role of intelligence in American life. The second is Spearman's g, the general factor underlying individual differences in all tests and performances involving cognitive ability. This article concludes that: (a) although the results of Herrnstein and Murray's multiple regression analyses of the National Longitudinal Survey of Youth (NLSY) cannot be easily dismissed, these data do not provide unequivocal support for theft policy recommendations; and (b) despite the fact that a considerable amount of contemporary research substantiates the importance of g as a psychological construct, educational and public policy should be based on more than psychometrics and statistics.

The Bell Curve: Intelligence and Class Structure in American Life, by the late Richard J. Herrnstein and Charles Murray (1994), is about the implications of individual and group differences in intelligence for educational and public policy. According to Herrnstein and Murray:

To try to come to grips with the nation's problems without understanding the role of intelligence is to see through a glass darkly indeed, to grope with symptoms instead of causes, to stumble into supposed remedies that have no chance of working. (pp. xxii-xxiii)

Briefly, they argued that, because intelligence is largely inherited and substantially immutable, social programs based on environmentalist ideals (e.g., Affirmative Action) are unlikely to succeed and should be drastically altered or eliminated. Herrnstein and Murray's recommendations for educational policy include: increasing the funding of programs for the intellectually gifted (at the expense of programs for those with learning difficulties); allowing all parents to choose the schools their children attend; and emphasizing the classical notion of the "educated person" as the overarching goal of public schools.

The Bell Curve received an incredible amount of almost entirely negative attention in the media, mainly because it addressed, in part, the controversial issues of the heritability of intelligence and racial- ethnic group differences in cognitive ability. According to Herrnstein and Murray, the possibility that group differences in intelligence are partly responsible for many of the social and economic inequalities that exist across racial-ethnic groups has long been considered too sensitive to discuss in public, yet badly needs airing. Many have disagreed, asserting that the book will only fan the flames of racism and exacerbate ethnic balkanization. Criticism of The Bell Curve has been harsh. In The New Republic (October 31, 1994), for example, respondents referred to it as "pseudo-scientific racism" and "errant nonsense. " One commentary was even entitled, "Neo-Nazis!" Stephen Jay Gould (1994), the Harvard paleontologist who wrote The Mismeasure of Man, characterized Herrnstein and Murray's policy recommendations as " anachronistic social Darwinism" (p. 138) and called the book a "manifesto of conservative ideology" (p. 141).

For better or worse, the arguments presented in The Bell Curve are being widely contemplated, if book sales and media coverage are any indication. My intention in this commentary is not to review The Bell Curve in its entirety. Space limitations preclude a thorough discussion of all the complex issues addressed by Herrnstein and Murray. Instead, two important components of The Bell Curve will be examined. The first is the original empirical evidence presented by Herrnstein and Murray to demonstrate the central role of intelligence in American life. The second is Spearman's g. Although not discussed in depth in The Bell Curve, the g factor is, as Gould (1994) noted, "the sine qua non of their entire argument" (p. 143).

EMPIRICAL SUPPORT

Herrnstein and Murray provided considerable empirical support for their detailed arguments in The Bell Curve. A significant portion of their 845-page book presents the results of original analyses of data from the National Longitudinal Survey of Youth (NLSY). The NLSY began in 1979 as a nationally representative sample of 12,686 individuals between 14 and 22 years of age, each of whom has been resurveyed annually for the past 15 years. The demographic information gathered for the NLSY includes the youths' childhood environment and parental socioeconomic status (SES), as well as their subsequent educational and occupational attainment, work history, and family formation. Participants in the NLSY also have been administered a number of intelligence and cognitive ability measures. As stated in The Bell Curve, the NLSY is "the mother lode for scholars who wish to understand the relationship of cognitive ability to social and economic outcomes" (p. 119).

Herrnstein and Murray analyzed the NLSY data with multiple regression. In most of their analyses, they used intelligence and SES to predict important social and economic outcomes. Herrnstein and Murray's main objective was to determine the relationship between intelligence and these outcomes after controlling for socioeconomic background, and vice versa. They asserted that, "if the role of IQ remains largely independent of SES, then it is worth thinking about, for it may cast social behavior and public policy in a new light" (p. 123). To measure cognitive ability, they used the Armed Forces Qualification Test (AFQT), a highly g-loaded instrument with excellent psychometric properties. To assess socio-economic background, Herrnstein and Murray created a new index based on family income, education, and occupation. They re-potted a Cronbach's alpha reliability coefficient of .76 for this measure, but no evidence of validity.

According to Herrnstein and Murray, results of these analyses underscore the predominant influence of intelligence in determining wealth, poverty, and social status. They asserted that their findings also demonstrate the integral relationship between intelligence and a host of important social behaviors, such as parenting, welfare dependency, crime, child neglect, and illegitimacy, among others. Herrnstein and Murray concluded that intelligence is a much better predictor of these behaviors than socioeconomic background. In fact, after controlling for intelligence, they found that the relationship between socio -economic background and many social, educational, occupational, and economic outcomes was negligible. Despite the fact that the substantive conclusions reached by Herrnstein and Murray in The Bell Curve rest largely on the analysis of one set of data with one statistical technique, their findings cannot be easily dismissed. Not only did they analyze an excellent set of data with appropriate statistical techniques, but their findings are generally consistent with the results of past studies (see Brody, 1992; Jensen, 1980, 1993a, for reviews).

Nevertheless, Herrnstein and Murray's interpretation of these statistically significant results has been subject to criticism. Judis (1994), for example, argued that "Murray and Herrnstein acknowledge the difference between demonstrating correlation and causation, but consistently use the language of causation when they merely have demonstrated correlation" (p. 18). As a result, he maintained, "a distorted picture of social change emerges" (p. 18) in their recommendations for public policy. Gould (1994) raised the issue of practical versus statistical significance of empirical results. He asserted that:

In violation of all statistical norms that rye ever learned, they plot only [emphasis in the original] the regression curve and do not show the scatter of variation around the curve, so their graphs do not show anything about the size of the relationship -that is, the amount of variation in social factors explained by IQ and socio-economic status. (pp. 145-146)

After examining the analysis of variance tables in Appendix 4 of The Bell Curve, Gould concluded that "their own data indicate that IQ is not a major factor in determining variation in nearly all the social behaviors they study" (p. 146). He stated that "although low figures are not atypical for large social-science surveys involving many variables, most of Herrnstein and Murray's correlations are very weak -- often in the 0.2 to 0.4 range" (p. 147). Gould also accused Herrnstein and Murray of "pervasive disingenuousness" (p. 140), partly because information on the variance explained in their regression analyses is "tucked away" in an appendix, instead of discussed in the text along with graphic presentation of their results.

Although Herrnstein and Murray did consider the fact that substantial dispersion is bound to exist around regression lines for moderately correlated variables, they argued that "the exceptions [to the general relationship] do not invalidate the importance of a statistically significant correlation" (p. 68). They also provided a rationale for deemphasizing the variance accounted for by the independent variables in their analyses. As Herrnstein and Murray stated:

A crucial point to keep in mind abut correlation coefficients... is that correlations in the social sciences are seldom much higher than .5 (or lower than-.5) and often much weaker -- because social events are imprecisely measured and are usually affected by variables besides the ones that happened to be included in any particular body of data. A correlation of .2 can nevertheless be "big" for many social science topics. In terms of social phenomena, modest correlations can produce large aggregate effects. (p. 67)

Given Herrnstein and Murray's focus on the implications of their results for educational and public policy, emphasizing the interpretation of statistically significant regression slopes rather than the variance explained seems reasonable. In addition, many researchers in the social and behavioral sciences would not agree with Gould's (1994) description of correlations in the 0.2 to 0.4 range, which explain between .4 and 16% of the variance, as "very weak." According to Cohen (1977), a "large" effect explains more than 15% of the variance, a "medium" effect about 6%, and a "small" effect about 1%. On this "scale," Herrnstein and Murray's results are best described as medium to large. Regardless of the label one attaches to the amount of variance explained in Herrnstein's and Murray's analyses, it is important to note that these are still correlational results. Causality is not established by the existence of a correlation between variables.

One potential problem with the multiple regression analyses reported in The Bell Curve that has not been mentioned in prior reviews concerns measurement error. Reliability of measurement affects correlations. The maximum correlation that can be obtained between any two variables is the square root of the product of their reliabilities. When the reliability of either or both is less than perfect, the correlation will be attenuated. Because some degree of measurement error is the rule rather than the exception, correlations usually underestimate the true degree of association between variables. Fortunately, they can be corrected for measurement error to estimate the true population value. Correlations corrected for attenuation are larger than uncorrected correlations, sometimes substantially so.

In contrast to the predictable effect of measurement error on zero- order correlations, in multiple regression the effect of measurement error is unpredictable. True regression parameters will be either overestimated or underestimated as a function of measurement error, depending upon the pattern of correlations among the variables and their reliabilities (Pedhazur, 1982). The reliability of a variable partialed out in multiple regression is especially important. Regarding the independent variables used by Herrnstein and Murray, the reliability of the AFQT is quite high, but the reliability of their index of socioeconomic background is only moderate (i.e., less than 0.80). Nevertheless, Herrnstein and Murray apparently did not correct for attenuation while conducting their multiple regression analyses. Results of their analyses of the NLSY data, therefore, must be viewed with caution until the unpredictable effect of measurement error has been examined, particularly when controlling for socioeconomic background.

THEORETICAL SUPPORT

The Bell Curve is based on six conclusions about tests of cognitive ability that, according to Herrnstein and Murray (1994), are beyond significant dispute in the scientific literature:

1. There is such a thing as a general factor of cognitive ability on which human beings differ.

2. All standardized tests of academic aptitude or achievement measure this general factor to some degree, but IQ tests expressly designed for that purpose measure it most accurately.

3. IQ scores match, to a first degree, whatever it is that people mean when they use the word intelligent or smart [emphases in the original] in ordinary language.

4. IQ scores are stable, although not perfectly so, over much of a person's life.

5. Properly administered IQ tests are not demonstrably biased against social, economic, ethnic, or racial groups.

6. Cognitive ability is substantially heritable, apparently no less than 40 percent and no more than 80 percent. (pp. 22-23)

Herrnstein and Murray derived these six conclusions from what they referred to as the classical paradigm of intelligence research and theory. Classicist researchers maintain that the structure of mental ability is best described as hierarchical, with Spearman's g at the apex (e.g., see Carroll, 1993). Although g is recognized as but one of many factors of cognitive ability, they contend that it is the most important one. As Jensen (1992a) stated:

In recent years, the study of general mental ability [emphasis in the original], or g, has begun to look as a science should. Along with the increasing realization of the tremendous importance of this subject, there has been an unusually rapid growth of theoretical and empirical research, both psychometric and experimental. (p. 271)

Herrnstein and Murray agreed with researchers in the classical paradigm on the importance of g, yet did not discuss this central concept in sufficient detail. My aim here is to sketch some of the most fundamental things known about g, so that readers of The Bell Curve may better evaluate its theoretical foundation.

What is g?

Spearman's g is a scientific construct, not a real thing that resides in the brain. It is used to explain an observable phenomenon known as the "positive manifold," which is the empirical fact that correlations between tests of cognitive ability are almost always positive. The positive manifold indicates that a common source of variance underlies individual differences in all tests and performances involving cognitive ability. Spearman (1994) invented factor analysis to measure g empirically. For any particular battery of mental tests, the g factor is estimated equally well by the first (unrotated) principal factor, the first principal component, the single highest-order factor in a Schmid-Leiman hierarchical factor analysis, and LISREL methods of factor analysis (Jensen & Weng, 1994; Ree & Earles, 1991). Estimates of g also are quite stable. According to Thorndike (1987), the g factor extracted from any large and varied battery of mental tests will be essentially the same g.

No test or performance involving cognitive ability measures only g, however. The nonerror variance of most tests also reflects specificity and group factors. Nevertheless, g accounts for more variance than any other independent component of individual differences in tests of cognitive ability (see Jensen, 1980). In fact, the g factor often accounts for more variance than all other group factors combined, even when tests are designed without the expressed intent of measuring g. For example, Kranzler and Weng (in press) recently found that a battery of tests developed by Naglieri, Das, Stevens, and Ledbetter (1991) to measure the constructs of the planning, attention, and simultaneous- successive (PASS) processes theory of human cognition primarily measured g. The second-order g accounted for 59.3% of the common factor variance among the PASS tests, which is considerably more than that accounted for by the g of conventional IQ tests, such as the Weschler Intelligence Scales for Children-3rd Edition (see Sattler, 1992).

Another well-established fact about g is that it is related to processing complexity. Cognitive tasks' g-loadings are an increasing monotonic function of the complexity of information processing required for successful task completion. Loadings on g are not related to the task' s surface characteristics. As Jensen (1992a) asserted, "The knowledge and skill content of performance on mental ability tests is merely a vehicle for g, which reflects the overall capacity and efficiency of information processes by which the knowledge and skill are acquired and used" p. 275, emphases in the original).

Why is g Important?

The importance of g lies in the fact that it correlates substantially with phenomena outside the domain of psychometric tests and factor analysis. Psychometric g is therefore not simply a mathematical artifact. For example, g is more predictive of outcomes in educational achievement, job training, and job success than any other factor derived from tests of cognitive ability (Jensen, 1992b, 1993a; Ree & Earles, 1992). In addition, Jensen (1993b) argued that the size of the black-white group difference on cognitive tasks is a direct function of the tasks' loading on g. Psychometric g also is related to the heritability (i.e., the proportion of genetic variance) of cognitive tests, which indicates that individual differences in g are determined in part by genetics and therefore influenced by biological functioning (e. g., Bouchard, Lykken, McGue, Segal, & Tellegen, 1990).

A great deal of contemporary research is now aimed at determining the neurophysiological and psychological mechanisms that underlie g (for review, see Jensen, 1992a; Vernon, 1993). The significant correlates of g identified by researchers thus far include: averaged evoked potentials (e.g., Barrett & Eysenck, 1992), nerve conduction velocity (e.g., Vernon & Mori, 1992), speed of neural and synaptic transmission in the visual brain as measured by the positron emission tomography scanning technique (e.g., Haler, Siegel, Crinella, & Buchsbaum, 1993), and the speed and efficiency of elementary cognitive processes (see Vernon, 1990a, for a review). The consistency and coherence of these data reflect real scientific progress that must be explained by any viable theory of intelligence. Many now believe that the results of this research substantiate a neural efficiency model of g (see Vernon, 1993). According to Vernon (1900b), this model postulates that "persons who perform well on intelligence tests (who have high "IQs") have brains that can operate faster and more efficiently than those of persons who perform less well" (p. 295).

In The Bell Curve, Herrnstein and Murray have gone further than most in discussing the implications of individual and group differences in g for educational and public policy. Some would say they have gone too far. Because human abilities obviously comprise more than g, it is misleading, or possibly even hazardous, to hold g as the sine qua non of American life. Policy decisions must be based on more than psychometrics or statistics. Science should inform policy as best it can, but policy also should be based on political, social, and moral considerations.

CONCLUSION

The Bell Curve should be discussed seriously and rationally by school psychologists, not suppressed. Whether we like it or not, Herrnstein and Murray's data, and the conclusions they draw from them, will not fade away. Although they may not be correct in all their assertions, there are lessons to be learned from The Bell Curve and from how the media reacted to it. The worst possible outcome would be for this discussion not to take place.

REFERENCES

Barrett, P T., & Eysenck, H. J. (1992). Brain evoked potentials and intelligence: The Hendrickson paradigm. Intelligence, 16, 361-382.

Bouchard, T. J., Jr., Lykken, D. T. McGue, M., Segal, N., L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart. Science, 250, 223-258.

Brody, N. (1992). Intelligence (2nd ed.). New York: Academic Press.

Carroll, J. B. (1993). Human cognitive abilities: A survey of factor- analytic studies. New York: Cambridge University Press.

Cohen, J. (1977). Statistical power for the social sciences. New York: Academic Press.

Gould, S. J. (1994, November 28). Curveball. The New Yorker, pp. 139- 149.

Haier, R. J., Siegel, B. V., Crinella, E M., & Buchsbaum, M. S. (1993). Biological and psychometric intelligence: Testing an animal model in humans with positron emission tomography. In D. K. Detterman (Ed. ), Current topics in human intelligence: Individual differences and cognition (vol. 3; pp. 157-170). Norwood, NJ: Ablex Publishing Corporation.

Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York: Free Press.

Jensen, A. R. (1980). Bias in mental testing. New York: The Free Press.

Jensen, A. R. (1992a). Understanding g in terms of information processing. Educational Psychology Review, 4, 271-308.

Jensen, A. R. (1992b). Commentary: Vehicles of g. Psychological Science, 3, 275-278.

Jensen, A. R. (1993a). Psychometric g and achievement. In B. R. Gifford (Ed.), Policy perspectives on educational testing. Norwell, MA: Kluwer Academic Publishers.

Jensen, A. R. (1993b). Spearman's hypothesis tested with chronometric information-processing tasks. Intelligence, 17, 47-78.

Jensen, A. R., & Weng, L. (1994). What is a good g? Intelligence, 18, 231-258.

Judis, J. J. (1994, October 31). Taboo you. The New Republic, p. 18.

Kranzler, J. H., & Weng, L. (in press). The factor structure of the PASS cognitive tasks: A reexamination of Naglieri et al. (1991). Journal of School Psychology.

Naglieri, J. A., Das, J. P., Stevens, J. J., & Ledbetter, M. F. (1991). Confirmatory factor analysis of planning, attention, simultaneous, and successive cognitive processing tasks. Journal of School Psychology, 29, 1-17.

Naglieri, J. A., Das, J. P, Stevens, J. J., & Ledbetter, M. F. (1991). Continuatory factor analysis of planning, attention, simultaneous, and successive cognitive processing tasks. Journal of School Psychology, 29, 1-17.

Pedhazur, E. J. (1982). Multiple regression in behavioral research 2nd ed.). New York: Holt, Rhinehart, and Winston.

Ree, M., & Earles, J. A. (1991). The stability of convergent estimates of g. Intelligence, 15, 86-89.

Ree, M., & Earles, J. A. (1992). Intelligence is the best predictor of job performance. Psychological Science, 3, 86-89.

Reed, T. E., & Jensen, A. R. (1992). Conduction velocity in a brain neural pathway of normal adults correlates with intelligence level. Intelligence, 16, 259-272.

Sattler, J. M. (1992). Assessment of Children (rev. 3rd ed.). San Diego: J. M. Sattler, Publisher.

Spearman, C. E. (1904). "General intelligence" objectively determined and measured. American Journal of Psychology, 15, 201-293.

Thorndike, R. L. (1987). The stability of factor loadings. Personality and Individual Differences, 8, 585-586.

Vernon, P A. (1990a). An overview of chronometric measures of intelligence. School Psychology Review, 19, 399-410.

Vernon, P. A. (1990b). The use of biological measures to estimate behavioral intelligence. Educational Psychologist, 25, 293-304.

Vernon, P. A. (1993). Intelligence and neural efficiency. In D. K. Detterman (Ed.), Current topics in human intelligence: Individual differences and cognition (Vol. 3; pp. 171-187). Norwood, NJ: Ablex Publishing Corporation.

Vernon, P A., & Mori, M. (1992). Intelligence, reaction times, and peripheral nerve conduction velocity. Intelligence, 16, 273-288.

The g Factor: The Science of Mental Ability

Reviewed by J. Philippe Rushton, Professor of Psychology at the University of Western Ontario
London, Ontario N6A 5C2, Canada.
Appeared in Politics and the Life Sciences, 1998, 17, 230-232.

Few scientists have effects or laws named after them. Arthur Jensen’s name is listed in a number of dictionaries as an "ism!" The Random House and Webster’s Unabridged Dictionaries contain the following entry:

Jen-sen-ism (jen´se niz´em), n. the theory that an individual’s IQ is largely due to heredity, including racial heritage. [1965-1970]; after Arthur R. Jensen (born 1923), U.S. educational psychologist, who proposed such a theory; see -ism] --Jen´sen-ist, Jen´sen-ite´, n., adj.

The "theory" attributed to Jensen has, in fact, been around since the time of Francis Galton (1822-1911), whose Hereditary Genius (1869) predated by exactly one century Jensen’s famous Harvard Educational Review article that led him to be labeled a "hereditarian." The dictionary definition can’t be overly derided, however, as Jensen’s (1969) review of the evidence that IQ is heritable and that genetic factors are involved in the Black-White IQ gap had enormous impact.

Jensenism, one of the great heresies of 20^th century science, is partly responsible for getting the Darwinian-Galtonian paradigm back on track in differential psychology after it had been derailed in the behavioral sciences for at least a generation following World War II. In a brilliant 40-year career that has earned him a place among the most frequently cited figures in contemporary psychology, Arthur Jensen has systematically researched and extended Charles Spearman’s (1927) seminal concept of g, the general factor of intelligence. The g Factor is an awesome and monumental exposition of the case for the reality of g. It does not draw back from its most controversial conclusions—that the average differences in IQ found between Blacks and Whites has a substantial hereditary component, and that this difference has important societal consequences.

However, The g Factor is not about race, as such. The first five chapters deal with the intellectual history of the discovery of g and various models of how to conceptualize intelligence. Other chapters deal with the biological correlates of g (excluding race), its heritability, and its practical predictive power. The fact that psychometric g has many physical correlates proves that it is not just a methodological artifact. Among biological variables, gloads on heritability coefficients determined from twin studies and inbreeding depression scores calculated in children born from cousin-marriages. g is also related to brain size measured by Magnetic Resonance Imaging (MRI), brain evoked potentials, and intracellular brain pH levels. It (g) is a product of human evolution and is also found in non-human animals.

Despite these caveats, The Bell Curve affair allows one to safely predict that The g Factor’s coverage of race will strike many as of central importance. All the issues Jensen raised in 1969 are still with us today. Indeed, much of the opposition to IQ testing and heritability would probably disappear if it were not for the stubborn and unwelcome fact that, despite extensive well funded programs of intervention, the Black-White difference refuses to go quietly into the night. Chapter 11 of The g Factor fully documents that, on average, the American Black population scores below the White population by about 1.2 standard deviations, equivalent to 18 IQ points. (This magnitude of difference gives a median overlap of less than 15 percent, meaning that less than 15 percent of the Black population exceeds the White average of 50 percent).

The difference between Blacks and Whites in average IQ scores has scarcely changed over the past 80 years (despite some claims that the gap is narrowing) and can be observed as early as three years of age. Controlling for overall socioeconomic level only reduces the mean difference by 4 IQ points. Culture-fair tests tend to give Blacks slightly lower scores, on the average, than more conventional tests, as do non-verbal tests compared with verbal tests, and abstract reasoning tests compared with tests of acquired knowledge. On average, Blacks also score 1 standard deviation below Whites in academic achievement throughout the period from grades 1 through 12 (and also considerably below all other disadvantaged minorities tested — Puerto Rican, Mexican-American, and American Indian).

International IQ Distribution

Inspired by "Jensenism," researchers like Richard Lynn and Philip E. Vernon not only pushed the envelope, but extended the ‘outside of the envelope’ and made the race-IQ debate international in scope with their findings that East Asians average higher on tests of mental ability than do Whites, whereas Caribbeans (and especially Africans) average lower. East Asians, measured in North America and in Pacific Rim countries, typically average IQs in the range of 101 to 111. Caucasoid populations in North America, Europe, and Australasia typically have average IQs from 85 to 115 with an overall mean of 100. African populations living south of the Sahara, in North America, in the Caribbean, and in Britain typically have mean IQs from 70 to 90. (Blacks in sub-Saharan Africa score about 2 standard deviations [approximately 30 IQ points] below the mean of Whites on nonverbal tests.)

Spearman’s Hypothesis

But the 18 point IQ difference between American Blacks and Whites is only an average. On some sub-tests the Black-White difference is smaller and on other sub-tests the Black-White difference is larger. Black-White differences are markedly smaller on tests of rote learning and short term memory than on tests of reasoning and those requiring transformation of the input. For example, on the Forward Digit Span Test, in which people are asked to recall a series of digits in the same order as that in which they were presented, Black-White differences are quite small, but on the Backward Digit Span Test in which people recall a series of digits in the reverse order to that in which they were presented, they are quite large. One day, while re-reading Spearman’s (1927) The Abilities of Man, Jensen tells us that he noted the suggestion (which appears on page 379), that Black-White differences on various tests are a function of each tests’ g loading. Here, Jensen thought, was the essential phenomenon that would explain, in much broader, more fundamental terms, the specific psychometric phenomenon that gave rise to the variation in the Black-White average differences.

The g Factor summarizes the results of numerous investigations of Spearman’s hypothesis on a wide variety of psychometric tests administered to large representative samples of Whites and Blacks. Chapter 11, for example, describes the results from 17 independent data sets on a total of nearly 45,000 Blacks and 245,000 Whites derived from 171 psychometric tests. G loadings consistently predict the magnitude of the Black-White difference (r = +.63). Spearman’s hypothesis is borne out even among three-year-olds administered eight subtests of the Stanford-Binet. The rank correlation between the g loadings and the Black-White differences is +.71 (p <.05).

These g related race differences are not due to factors such as the reliability of the test, social class differences, or tautologies based on some inevitability of factor analysis. Indeed, it is not even universally true that all groups that differ, on average, in their overall score on a test battery will conform to Spearman’s hypothesis. In South Africa, although the nearly 1 standard deviation difference between Whites and East Indians showed no correlation between g loadings and standardized mean differences, the 2 standard deviation difference between Whites and Blacks showed a correlation of +.62.

Spearman’s hypothesis even applies to the g factor extracted from performance on elementary cognitive tasks. In some of these studies, 9-to-12-year-olds are asked to decide which of several lights is illuminated and move their hand to press a button that turns that light off. All children can perform the tasks in less than one second, but children with higher IQ scores perform faster than do those with lower scores, and White children, on average, perform faster than Black children. The correlations between the g loadings of these types of reaction time tasks and the Black-White differences range from +.70 to +.81.

Jensen also applied Spearman’s hypothesis to East Asian-White comparisons using these same reaction time measures. The direction of the correlation is opposite to that in the Black-White studies, indicating that, on average, East Asians score higher in g than do Whites. No one so far seems to have looked at East Asian-White differences on conventional psychometric tests as a function of their g loadings. From the study just mentioned, however, Jensen’s prediction is clear: One should find the reverse of Spearman’s hypothesis for Black-White differences.

Are Race Differences Heritable?

Chapter 12 presents Jensen’s technical arguments for why he believes that race differences are about 50 percent heritable. He emphasizes the fact that it is precisely those components of intelligence tests that are most heritable and that most relate to brain size which most profoundly differentiate Blacks from Whites. Thus, Black-White differences on 11 sub-tests of the Wechsler Intelligence Scale for Children are predicted by the amount of inbreeding depression on the same 11 sub-test scores from Japan (r = +.48). The inbreeding prediction was a sufficiently robust predictor to overcome generalization from the Japanese in Japan to Blacks and Whites in the U.S. There really is no non-genetic explanation for the inbreeding effect and its ability to predict Black-White differences in scores on IQ tests.

The g Factor also cites the evidence of transracial adoption studies. Three studies have been carried out on Korean and Vietnamese children adopted into White American and White Belgian homes. Though many had been hospitalized for malnutrition, prior to adoption, they went on to develop IQs ten or more points higher than their adoptive national norms. By contrast, Black and Mixed-Race (Black-White) children adopted into White middle-class families typically perform at a lower level than similarly adopted White children. In the well known Minnesota Transracial Adoption Study, by age 17, adopted children with two White biological parents had an average IQ of 106, adopted children with one Black and one White biological parent averaged an IQ of 99, and adopted children with two Black biological parents had an average IQ of 89.

The g Factor also devotes a fair amount of space to racial differences in brain size. Chapter 6 reviewed the literature that found that the brain-size/IQ relation was most clearly shown using Magnetic Resonance Imaging (r = .44 across eight separate studies). Chapter 12 documents the three-way racial gradient in brain size established by aggregating data from studies using four kinds of measurements: (a) wet brain weight at autopsy, (b) volume of empty skulls using filler, (c) volume estimated from external head sizes, and (d) volume estimated from external head measurements and corrected for body size. East Asians and their descendants average about 17 cm3 (1 in3) larger brain volumes than do Europeans and their descendants, whose brains average about 80 cm3 (5 in3) larger than do those of Africans and their descendants. Jensen calculated an "ecological" correlation (widely used in epidemiological studies) of +0.99 between median IQ and mean cranial capacity across the three populations of "Mongoloids," "Caucasoids," and "Negroids."

The g Factor also considers the race differences from an evolutionary perspective. Jensen endorses the "Out-of-Africa" theory, that Homo sapiens arose in Africa about 100,000 years ago, expanded beyond Africa after that, and then migrated east after a European/East Asian split about 40,000 years ago. Since evolutionary selection pressures were different in the hot savanna where Africans evolved than in the cold Arctic where Mongoloids evolved, these ecological differences had not only morphological, but also behavioral effects. The farther north the populations migrated ‘Out of Africa,’ the more they encountered the cognitively demanding problems of gathering and storing food, gaining shelter, making clothes, and raising children during prolonged winters. As these populations evolved into present-day Europeans and East Asians, they underwent selective pressure for larger brains.

In recent years, the equalitarian dogma has run headlong into some bad karma. In the wake of the success of The Bell Curve (Herrnstein & Murray, 1994), and other recent books about race (including my own) to provide race-realist answers to the question of differential group achievement, there has been an intense effort to get the ‘race genie’ back in the bottle, to get the previously tabooed toothpaste back in the tube. By firmly establishing the psychometric, neurophysiological, behavior genetic, and comparative evidence for the existence and importance of Spearman’s g, Jensen’s The g Factor makes it near certain that such efforts will end up shredded by Occam’s razor.

References

Galton, F. (1869). Hereditary Genius. London: Macmillan.

Herrnstein, R.J., & Murray, C. (1994). The Bell Curve: Intelligence and class structure in American life. New York: Free Press.

Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement? Harvard Educational Review, 39, 1-123.

Spearman, C. (1927). The Abilities of Man: Their nature and measurement. New York: Macmillan.

The Genetic Basis of Intelligence

Farnoosh Tayyari
Graphics: Jen

Can Intelligence be Quantified?

Intelligence is a very difficult concept to define. Intellect is described as “the power of the mind to think in a logical manner and acquire knowledge”¹. Even psychology experts have not agreed upon what this actually means. ². Intelligence can be divided into various subcategories such as reasoning, problem solving, and memory, and so creating a consistent scale by which one can measure intelligence is quite difficult.

		Figure 1. IQ testing is a widely used measure of g.

Many researchers working on intelligence use a psychometric definition for intelligence, termed “general mental ability” or “the g factor”. This concept originated from the work of Sir Francis Galton and Charles Spearman³ of the London School of psychology. Today many psychometricians worldwide accept the theory of general mental ability. According to this theory, on average, those who do well on one mental test also are likely to do well on other mental tests.

In most genetic studies, researchers lump the different subcategories of intelligence into Intelligence Quotient (IQ), a single scale used to quantify intelligence. The concept of IQ was first developed by Alfred Binet, a French psychologist and lawyer in 1905. In 1914 Stern, a German psychologist, created the measurement scale of IQ by dividing the subject’s mental age by his or her chronological age. Later Terman, a professor at Stanford University, removed the decimal from Stern’s formula by multiplying it by 100. Wechsler developed a similar test for adults called the Wechsler Adult Intelligence Scale⁴.

To determine whether scores such as IQs are of genetic or environmental origin, scientific validation is required. Arthur Jensen points to the statistical and biological realities of the g factor. The correlation of g with the overall size of brain, its glucose metabolic rate during problem solving, the complexity and speed of brain waves, as well as estimates of heritability point towards genetic influence².

Genetics of Intelligence

The study of intelligence genetics examines how much and by what manner mental abilities are affected by genes. Since many genetic and environmental factors influence intelligence, it is considered a complex trait. However, we do not know much about the quantity and character of genes responsible for mental abilities. We know even less about the factors responsible for expression of these genes.

Intelligence is of primary interest to human genetic research. The first methodical set of experimental observations can be traced back to Galton’s work in 1865, a year before Mendel’s influential article on the laws of heredity⁵. Galton evaluated the transmission of several traits in families using statistical tools. He concluded that many traits including mental ability are genetically transmitted and normally distributed in the general population. He did not analyze the role of the common environment within the families, biasing his conclusions towards the genetic. He did, however, recognize significant general principles such as regression to the mean in children of parents with extreme phenotypic traits.

The first adoptee and twin studies on intelligence were carried out in 1920s. Later animal studies on maze-bright and maze-dull rats investigated individual differences in intelligence. Studies on inbred mice also demonstrated the critical role of genetics in individual differences for aspects of learning. By the 1960s genetic studies on intelligence resulted in declining interest in the environmental origins of intelligence in psychology, enhancing acceptance of a genetic influence on intellect [reviewed in 6]. Then in 1969, in the Harvard Educational Review Jensen suggested that while cultural factors contributed to the 15-point difference in average IQ between black and white Americans, genes could not be ruled out. This declaration made Jensen a target of student protests, acts of vandalism, death threats, and introduced the word “Jensenism” ². However, Jensen pointed to an issue that brought strong criticism to genetic research on intelligence, leading to a generation of bigger behavioral genetic studies.

Large sample size studies in monozygotic (MZ) and dizygotic (DZ) twins raised together show an average correlation of .86 for MZ twins, while the correlation for DZ twins is only .60. Twenty-five years later, in The Bell Curve, Herrnstein and Murray⁷ claimed that intelligence is strongly inherited with a heritability estimation of .60 + .2 within whites. Based on data from the National Longitudinal Survey of Youth (NLSY), a federal project testing over 10,000 youths in the 1980’s, they declared that social intervention had very little effect on IQ. A large group of scientists and researchers see this claim as racist.

The g factor has a normal distribution in the general population, suggesting g is probably a product of several genes that interact with the environment. Moreover, although g correlates with the parental value, it has a tendency to be closer to the population mean, suggesting a regression to the mean. These observations suggest that some genetic variants that influence g will vary between populations rather than within populations. For instance, certain Asian populations have a frequency of 0.60 in COMT Met158 allele, which predicts lower COMT-enzyme activity and thereby better cognitive performance, while Caucasians have a frequency of 0.42 for the same allele⁸.

Studies show a moderate increase in g over time in developed countries, correlating with improvements in nutrition, health and education. Environmental conditions such as socio-economic status have been shown to play a significant role in intelligence. A study by Wahlsten⁹ showed that children transferred from a home with low socio-economic status to a home with high socio-economic status improved their test scores as much as 16 points. Moreover, in a study by Plomin et al. there was a .19 correlation for adoptive parents and their adopted children and a .32 correlation for the adopted siblings who had no genetic relatedness, suggesting the shared environment could be responsible for a third of the total variance of g between individuals ⁶. It has been reported that heritability for intelligence increases during development, resulting in heritability as high as 80% in adulthood while there is also some evidence that heritability might be lower in adulthood than in childhood ^10,11.

Molecular Biology of Intelligence

The correlation between DNA sequence and behavioral differences such as intelligence is considered causal because DNA variations can lead to behavioral differences but behavioral differences do not change DNA sequences ⁶ . Several methods have been used for DNA research including linkage and association studies.

In contrast to Mendel’s second law of independent assortment, if two genes or a gene and a DNA marker located on the same chromosome are close they can be inherited together within families. This is called linkage. Linkage studies have discovered the location of many single-gene disorders. However, linkage studies are only useful in situations that a single gene has a very large effect: Huntington’s disease, for example.

Cognitive problems including learning disabilities or dementia appear to be complex disorders affected by both multiple genes and environment. Multiple-gene systems result in dimensions (quantitative traits) as opposed to disorders (qualitative dichotomies). Hence, genes in multiple-gene systems are called quantitative trait loci (QTLs). The objective of QTL study is to discover multiple genes with different effect sizes, contributing to the variation of the trait. Improved QTL linkage studies, which use many small families instead of few large ones, can be used to study the extremes of a dimension and are capable of finding genes with more than 10% effect size ⁶ . Cardon et al.¹²identified the first QTL linkage, a linkage for reading disability.

QTLs can also be identified with a simpler method that can detect QTLs with even much smaller effect sizes on the variance of the trait. This method is called allelic association and detects the correlation between a specific allele and trait in the population.

Benjamin et al.¹³ reported one of the first associations for personality: the association between dopamine D4 receptor (DRD4), and novelty seeking. DRD4 gene has two types of alleles, short and long. Novelty seeking scores were higher in subjects with long DRD4 alleles. However, long alleles are also associated with hyperactivity ¹⁴. Association methods have also been used to study diseases such as Alzheimer’s disease ¹⁵. The allele associated with this disease is apolipoprotein E (APOE)-4.

QTLs associated with intelligence are being investigated under the IQ QTL Project. The project focuses on ability rather than disability. The hypothesis of this project is that being very high functioning calls for most of the positive alleles and hardly any of the negative ones. The aim is to use very high functioning individuals to identify QTLs that work all through the total distribution⁶. Studies of complex traits such as intelligence have 80% power to detect QTLs when they are responsible for 1% heritability. This means that when a single marker is studied, the size of an unselected sample should be about 800. Some of the studies that have used such a large-scale sample size of unselected individuals have reported positive association between normal variation in g and candidate genes including Cathepsin D (CTSD) and cholinergic muscarinic 2 receptor (CHRM2)^16,17 , and the association of catechol-o-methyltransferase gene (COMT) with working memory^18,19. The studies need to be replicated: Wickelgren et al.²⁰ reported a positive association between insulin-like growth factor receptor gene with learning and memory but using a larger sample Hill et al.²¹ did not find such an association. Moreover, there are many QTLs that are responsible for less than 1% heritability, which means detecting them is difficult.

Functional Genomics

While genomics provides information about what proteins could potentially be in a cell, only functional genomics determines which of those proteins actually exist. Proteins also undergo modifications and interactions that cannot be predicted from genomics alone. Functional genomics operates under three major categories: gene manipulation, gene expression profiling, and proteomics.

In gene manipulation studies the sequence of a gene of interest in an animal model can be deleted, “knocking out” the gene. This method has been used in intelligence related studies. Silva et al.²², for example, found that knocking out a particular kinase gene interferes with mouse spatial tasks. Another approach involves “knocking down” the genes using antisense DNA, which binds a complimentary RNA and prevents its translation. Using this approach, Guzowski and McGaugh²³ reported the importance of CRE-binding protein (CREB) gene in memory formation. While knocking out or knocking down a gene can affect a behavior this does not mean that the gene causes that behavior: it is possible the gene along with hundreds of others is involved. Conversely, redundancy in living systems can result in little effect when genes are knocked down and out. Moreover, since different knock out strains can show different results, the importance of other genes is evident. Lastly, knocking out or knocking down genes of interest does not exactly parallel natural control behavior.

Gene expression can be determined by the presence of mRNA which could be transcribed into proteins. Microarrays can detect the expression of many genes at the same time. Gene expression depends on the tissue that is sampled, making the approach difficult for human studies because brain tissue is required. Nevertheless, gene expression profiling has been broadly used in mice.

Proteomics is the study the entire protein complement (proteome) of a cell, tissue or organism. The level of mRNA expression is not always a good reflection of protein level, thus proteomics yields a much more accurate snapshot of a cell’s proteome.

Conclusion

There remains a lack of scientific precision for the definition of intelligence, although many scientists use the psychometric definition, g (general cognitive ability). Whether nature or nurture influences intelligence remains a matter of debate between geneticists and environmentalists, and sits at about 50/50. Intelligence appears to be controlled in part by quantitative trait loci (QTLs). Multiple-gene systems like intelligence result in dimensions (quantitative traits) as opposed to disorders (qualitative dichotomies). The objective of QTL study is to discover multiple genes with different sized effects contributing to the variation of the trait. Several methods including linkage and association have been used for discovering the QTLs. Postgenomics holds promise for understanding the genetics of intelligence.

Glossary

psychometricians-mental testing experts

normal distribution- a distribution that can be represented as a symmetrical bell-shaped curve, with the greatest frequency in the center.

adoptee and twin studies- adopted individuals can acquire traits from their adopted family only environmentally, and from their biological parents only genetically, making them ideal for study of environmental/genetic effects. Twins raised apart share genetic material, but have different environments.

effect size-strength of the relationship of two variables, calculated by dividing the difference between two population means by their standard deviation

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Two Views of The Bell Curve

The following book reviews will appear in the May 1995 (Volume 40, Number 5) issue of Contemporary Psychology, APA's journal of book reviews.

Richard J. Herrnstein and Charles Murray
The Bell Curve: Intelligence and Class Structure in American Life
New York: Free Press, 1994. 845 pp. ISBN 0-02-914673-9. $30.00

Reviews by Thomas J. Bouchard, Jr., and Donald D. Dorfman

Breaking the Last Taboo

Review by Thomas J. Bouchard, Jr.

"We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain inalienable Rights, that among these are Life, Liberty, and the pursuit of Happiness." With these words Jefferson introduced one of America's most treasured documents, the Declaration of Independence. Successive generations of Americans have not only embraced Jefferson's noble sentiments, they have embellished them. Equality of political rights and legal standing has been expanded into a belief in literal equality; today, differences in outcome are taken as prima facie evidence of unequal opportunity. In an egalitarian society such as ours, the existence of significant and enduring individual or group differences in intelligence is seen as a challenge to our highest ideals. This challenge is taken up by Richard J. Herrnstein and Charles Murray in The Bell Curve.

The Bell Curve has a simple but powerful thesis: There are substantial individual and group differences in intelligence; these differences profoundly influence the social structure and organization of work in modern industrial societies, and they defy easy remediation. In the current political milieu, this book's message is not merely controversial, it is incendiary. As scholars such as Daniel Moynihan, Arthur Jensen, and E. O. Wilson have learned, the mainstream media and much of the scientific community have little tolerance for those who would question our most cherished beliefs. Herrnstein and Murray have received similar treatment. They have been cast as racists and elitists, and The Bell Curve has been dismissed as pseudoscience, ironically by some commentators who broadly proclaim that their critique has not benefited from a reading of the book. The book's message cannot be dismissed so easily. Herrnstein and Murray have written one of the most provocative social science books published in many years. The issues raised are likely to be debated by academics and policymakers for years to come.

The emergence of a cognitive elite

Commentators from across the political spectrum have documented the profound social changes that all industrialized societies are undergoing at the end of the 20th century--erosion of the middle class, loss of well-paying manufacturing jobs, and an emerging information age in which individual success will depend on brains not brawn. The Bell Curve tells a similar story regarding the United States. It differs from other works by focusing on intelligence, rather than education or social class as a causal variable. The authors tell us that true educational opportunity as a function of ability (measured by IQ tests) did not arrive in the United States until about 1950. Until that date only about 55 percent of high school graduates in the top IQ quartile went directly to college. From 1950 to 1960, this number jumped to 72 percent, and in 1980 over 80 percent of graduates in the highest ability quartile went to college. In addition, sorting by cognitive ability continues as students move through college. It also occurs across colleges, with the elite schools selecting the more intellectually talented students. Finally, it continues across careers in the world of work. The authors argue that intellectual stratification through occupations is driven by powerful economic pressures. This argument is based on a number of different and compelling lines of evidence. If Herrnstein and Murray are correct, current social inequalities reflect, in large part, the achievement of a meritocracy based on cognitive ability.

The notion of a meritocracy is not, in itself, an affront to American sensibilities. Social scientists have carefully documented that social mobility does occur from one generation to the next and that cognitive ability is a major factor in determining whether an individual will achieve greater or lesser social status than did his or her parents (Waller, 1971). When each generation resorts in this way, the elements of fairness and opportunity are preserved. If, however, as The Bell Curve asserts, the heritability of IQ is quite high and there is a strong tendency for those similar in ability to marry, there will be less regression toward the mean in the cognitive ability of children of the intellectually talented and, therefore, less intergenerational reassortment. Under these circumstances a meritocracy begins to look like an aristocracy, a perception that is strongly reinforced when the intellectual elite segregate themselves from the rest of society by living in separate neighborhoods, sending their children to private schools, and supporting social institutions that cater to their own unique interests.

The authors do argue that general cognitive ability (i.e., "g") is a major determiner of social status and that variance in general mental ability is largely attributable to genetic factors--propositions that are certainly endorsed by many experts in the field. The book explicitly disclaims, however, that general mental ability is the only determinant of social status, or that g is the sum total of an individual's social worth.

The role of social class of origin

The Bell Curve carefully documents in table after table, graph after graph that cognitive ability has become a more important determinant of social status than social class of origin. Although this may come as a surprise to many, it is consistent with a large body of evidence. Research methodology in the domain of individual differences has changed dramatically in the past 20 years. Many investigators in this domain now accept two major methodological principles: that single studies based on small samples are inherently uninformative and that correlations calculated from data gathered within biological families are seriously confounded. Understanding both of these principles is important when evaluating evidence often brought to bear against The Bell Curve.

Results from a single modest study carry little more weight than does a single anecdote, no matter how compelling the finding. Most social scientists, but certainly not all, have adopted the methodology of meta-analysis, a statistical tool that systematically combines the results from many studies to provide a single reliable conclusion. In a similar fashion, behavioral geneticists combine the results from numerous kinships weighted by their sample sizes to provide the best estimate of the degree of environmental and genetic influence on any particular trait. Any single study is viewed as providing only weak evidence on its own.

The confound generated by data drawn from within biological families provides numerous pitfalls when assessing this book's claims and reviewers' counterclaims. Within a biological family, correlations (e.g., parental socioeconomic status x child's IQ) are ambiguous because the cause of the correlation could be the family environment or the parent's genes. Within biological families, the correlation between parental socioeconomic status (SES) and child's IQ, based on a meta-analysis of the literature, is .333 (White, 1982). However, in studies where genetic effects are held constant, through twin or adoption designs, the correlation drops dramatically (Bouchard, Lykken, McGue, Segal, & Tellegen, 1990; Scarr & Weinberg, 1978). Another striking exemplar of this phenomenon is the IQ correlation between unrelated individuals reared together who share a common family environment but lack a common genetic background. When the cognitive ability of these "unrelated siblings" is measured in adulthood the correlation is zero (McGue, Bouchard, Iacono, & Lykken, 1993). Thus the correlation between parental SES and offspring IQ in biological families is due, in some measure, to genetic endowment. Consequently, when examining the relationship between IQ and a dependent variable, to "hold constant" the SES of biological parents (on the grounds that SES is a competing "environmental explanation") results in an underestimate of the true influence of IQ. As early as 1970, Paul Meehl warned that "the commonest error in handling nuisance variables of the `status' sort (e.g., income, education, locale, marriage) is the error of suppressing statistically components of variance that, being genetic, ought not be thus arbitrarily relegated to the `spurious influence' category" (pp. 393-394). In this book, intended for lay readers as well as academicians, the authors have purposefully provided simple and straightforward analyses of SES and cognitive ability. They have, in many instances, understated the role of cognitive ability by holding SES constant. We can expect to see numerous reanalyses and the presentation of many more complex models derived to support both sides of the debate. The careful reader will remember Meehl's caution when examining the data and drawing conclusions.

Cognitive classes and social behavior

Part II of The Bell Curve reviews the role of cognitive ability in areas of social dysfunction. In this section, the data are more complicated, conclusions more equivocal. In spite of claims to the contrary by some reviewers, the book makes it clear that with regard to the issues discussed in this section of the book (e.g., poverty, schooling, unemployment, idleness and injury, family matters, welfare dependency, parenting, crime, civility, and citizenship), IQ "almost always explains less than 20 percent of the variance, . . . usually less than 10 percent and often less than 5 percent" (p. 117). These analyses deal only with non-Latino Whites and make use of the National Longitudinal Survey of Labor Market Experience of Youth (NLSY). This large nationally representative survey, begun in 1979, incorporated the Armed Forces Qualification Test (AFQT). The AFQT provides an excellent measure of g, and the survey contains sufficiently detailed information that questions regarding the influence of g on the outcomes listed above can now be addressed systematically.

I discuss the results regarding poverty as an exemplar. First, it must be noted that the decline in poverty from 1940 to 1970 is dramatic and linear, dropping from over 50 percent to less than 15 percent. It has remained nearly constant since 1970. This means that the rise in crime, drug abuse, and many other discontents over the past 25 years cannot be ascribed to poverty per se. It also means the analyses in The Bell Curve are being carried out on a very different population than would have been used had the analysis been carried out before 1970. Consequently, comparisons with earlier research are problematical. The evidence strongly supports the conclusion that high IQ is an important protective factor, and low IQ is an important risk factor. Parental SES is not nearly as protective or nearly as debilitating. IQ has an effect even when education is held constant. When one looks at poverty among women with children, the situation is quite different. For separated, divorced, or never married White mothers with very low IQs, the probability of being in poverty is almost 70 percent. For the same group of mothers with very high IQs, the risk of poverty is about 10 percent. For married mothers, however, the range is from under 20 percent to near zero. IQ is influential, but marriage is clearly more important. Thus poverty among children is strongly associated with the marital status of their mothers. Holding IQ constant washes out any influence of parental SES for both types of mothers but leaves a large marital effect. Similar empirical demonstrations, with numerous twists and turns, are made regarding the other dependent variables enumerated above.

The national context

Part III of The Bell Curve contains the most controversial chapter in the book, "Ethnic Differences in Cognitive Ability." The data reviewed here are neither new nor surprising and find strong support in the current psychological literature (Humphreys, 1988). East Asians, living in Asia or America, score above White Americans in tests of cognitive ability; the best estimate of that difference is about three points with findings ranging from no difference to a 10-point spread in test scores. The difference in measured IQ between African Americans and Whites has remained at about 15 IQ points for decades, although there is some indication of very modest convergence due to fewer low scores in the African American population. Controlling for SES reduces but does not eliminate this difference, and of course, controlling for SES in ethnic group contrasts may eliminate a valid source of IQ variance. Moreover, ethnic differences on cognitive tests cannot be attributed to test bias.

As described earlier, The Bell Curve asserts that differences in cognitive ability between individuals are due in part to differences in their genetic endowment. A great deal of research supports this conclusion (Bouchard, 1993; Pedersen, Plomin, Nesselroade, & McClearn, 1992). The question is, What can we infer from these findings about the origins of ethnic group differences? As any graduate student knows, the source of individual differences in a trait cannot be taken as evidence for the source of group differences in the same trait. A great deal of indirect evidence points to both genetic and environmental contributions to ethnic group differences in IQ. None of this evidence, however, is as firm as the evidence for genetic influence on individual differences in IQ. Many experts in the field (Snyderman & Rothman, 1988) agree with Herrnstein and Murray when they state that "it seems highly likely to us that both genes and the environment have something to do with racial differences. What might the mix be? We are resolutely agnostic on the issue; as far as we can determine, the evidence does not yet justify an estimate" (p. 311).

Science, ethics, and social policy

The Bell Curve closes with a review of the policy implications of their findings. What is the role of the social scientist in the formulation of social policy? I agree with Kendler (1993) that it is clearly within the scientific realm to comment on the likely consequences of competing social policies. Judging the value, as opposed to the costs, of such policies is, however, a matter of political rather than scientific discourse. As Kendler documents, many social scientists confuse these two functions. Herrnstein and Murray have been vigorously chastised for discussing policy implications on the basis of the work reviewed and the data analyzed in their book. Similar assertions are, however, regularly made by many investigators in the social sciences. For example, the implications of specific research projects are regularly found in grant applications where they are used to justify the request for funds. Seldom are the value judgments underlying these implications explicitly stated, but they are easily inferred. Herrnstein and Murray have, in my opinion, been much more "up front" about these matters than many social scientists, and their discussions fall clearly within the boundaries discussed by Kendler. They argue, for example, with regard to affirmative action, "Our contribution (we hope) is to calibrate the policy choices associated with affirmative action, to make costs and benefits clearer than they usually are" (pp. 387-388).

In writing the Declaration of Independence, Jefferson was attempting to give birth to a shared political goal--freedom, as expressed in the right to life, liberty, and the pursuit of happiness. Herrnstein and Murray also address this important theme. They make it clear that a meritocracy need not be a Darwinian jungle and that a responsible society should make a place for everyone. Their description of the ideal meritocracy will not be to everyone's taste, but it is neither more foolish nor more naive than many proposals that have been suggested in the past. Nevertheless, predicting the future is an extremely hazardous enterprise. We have recently seen the virtual collapse of a number of societies that were based on a totally different conception of human nature than that underlying The Bell Curve. Virtually no one predicted this dramatic outcome for one of history's largest social experiments. Undoubtedly, Herrnstein and Murray's arguments are wrong in some of the details, and they may be wrong about the larger picture. Nevertheless, one of the goals of the intellectual enterprise is to question received wisdom, to ask difficult questions, and to seek novel and "better" solutions to both new and old problems. They have succeeded admirably at this task.

This is a superbly written and exceedingly well-documented book. It raises many troubling questions regarding the organization of our society. It deserves the attention of every well-informed and thoughtful citizen.

References

Bouchard, T. J., Jr. (1993). The genetic architecture of human intelligence. In P. A. Vernon (Ed.), Biological approaches to the study of human intelligence (pp. 33-93). Norwood, NJ: Ablex.

Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart. Science, 250, 223-228.

Humphreys, L. G. (1988). Trends and levels of academic achievement of Blacks and other minorities. Intelligence, 12, 231-260.

Kendler, H. H. (1993). Psychology and the ethics of social policy. American Psychologist, 48, 1046-1053.

McGue, M., Bouchard, T. J., Jr., Iacono, W. G., & Lykken, D. T. (1993). Behavior genetics of cognitive ability: A life-span perspective. In R. Plomin & G. E. McClearn (Eds.), Nature, nurture and psychology (pp. 59-76). Washington, DC: American Psychological Association.

Meehl, P. E. (1970). Nuisance variables and the ex post facto design. In M. Radner & S. Winokur (Eds.), Minnesota studies in the philosophy of science IV (pp. 373-402). Minneapolis: University of Minnesota Press. Pedersen, N. L., Plomin, R., Nesselroade, J. R., & McClearn, G. E. (1992). A quantitative genetic analysis of cognitive abilities during the second half of the life span. Psychological Science, 3, 346-353.

Scarr, S., & Weinberg, R. A. (1978). The influence of family background on intellectual attainment. American Sociological Review, 43, 674-692.

Snyderman, M., & Rothman, S. (1988). The IQ controversy: The media and public policy. New Brunswick, NJ: Transaction Books.

Waller, J. H. (1971). Achievement and social mobility: Relationship among IQ score, education and occupation in two generations. Social Biology, 18, 252-259.

White, R. K. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91, 461-481.

Soft Science With a Neoconservative Agenda

Review by Donald D. Dorfman

"Is there a danger that current welfare policies, unaided by eugenic foresight, could lead to the genetic enslavement of a substantial segment of our population? The possible consequences of our failure seriously to study these questions may well be viewed by future generations as our society's greatest injustice to Negro Americans" (Jensen, 1969, p. 95).

So said Arthur Jensen in 1969 in a Harvard Educational Review article on race and general intelligence. General intelligence is often called IQ for short. In the most controversial parts of The Bell Curve, a book written for the general reader, Richard J. Herrnstein and Charles Murray present much the same theories and general concerns as did Jensen with regard to Black cognitive intelligence, while extending the analysis to include Latinos. They greatly expand on the evidence, present possible causal links between IQ and socially undesirable behaviors, and at the end of the book suggest implications for public policy. They are especially worried about a supposed downward pressure on the distribution of IQ in the United States, which they call dysgenic pressure. Dysgenic is a term borrowed from population biology. As does Jensen, the authors believe that Blacks "are experiencing even more severe dysgenic pressures than Whites" (p. 341). Part of the problem may be differences in reproductive strategies among the races, according to J. Philippe Rushton's theory discussed in the book (pp. 642-643). Herrnstein and Murray mention Rushton's theory that Blacks have the largest genitals and the highest frequency of sexual intercourse among the three major races (p. 642). Consistent with customary academic standards of scholarly objectivity and neutrality, Herrnstein and Murray reserve judgment on whether Rushton is right or wrong: "We expect that time will tell whether it [Rushton's theory] is right or wrong in fact" (p. 643).

In addition to supposed downward pressures on the distribution of intelligence in this country produced by high fertility rates in Blacks, Herrnstein and Murray believe that Latinos are also experiencing more severe dysgenic pressures than Whites (p. 341) and that Latino immigration is putting downward pressure on the distribution of American national intelligence. So should we be worrying about dysgenic pressure on our national cognitive intelligence? They conclude, "Putting the pieces together--higher fertility and a faster generational cycle among the less intelligent and an immigrant population that is probably somewhat below the native-born average--the case is strong that something worth worrying about is happening to the cognitive capital of the country" (p. 364).

The authors present a large number of research analyses that they performed themselves, in which they pit parental socioeconomic status (SES) against IQ on a variety of economic and social behaviors. They conclude that the major cause of economic and social behaviors is IQ, not SES. The authors' research analyses are based on data collected in the National Longitudinal Survey of Youth (NLSY). None of their research analyses on the relation between IQ, SES, and social behaviors has ever been published in peer-reviewed scientific journals. The Bell Curve is written for the general reader and does not assume that the reader has had a course in statistics. The authors have even included an appendix for those readers who are sure they can not learn statistics, titled "Statistics for People Who Are Sure They Can't Learn Statistics" (Appendix 1, pp. 553-567). Scientists first publish their research in peer-reviewed scientific journals, not in books written for the general reader who may not have the technical background needed to detect flaws in data and misinterpretations of data analyses. It is inappropriate for a scientist to do otherwise.

Herrnstein and Murray's research analyses--never published in peer-reviewed scientific journals--investigate the relation of IQ and SES to marriage, to divorce, to illegitimacy, to welfare dependency, and to parenting. They conclude that IQ is the primary problem, not SES: "People with low cognitive ability tend to be worse parents" (p. 232). The authors believe that low birth weight and high infant mortality are indications of poor parenting and are probably caused by "prenatal negligence" (p. 233) on the part of mothers with low cognitive ability rather than inadequate prenatal medical care on the part of society. They also present unpublished research analyses on the relation between crime and low cognitive intelligence, and between civility and high cognitive intelligence. "A smarter population is more likely to be, and more capable of being made into, a civil citizenry" (p. 266), according to the authors.

In the final part of The Bell Curve, titled "Living Together," Herrnstein and Murray propose a solution to the supposed dysgenic downward pressures on our national intelligence caused by the large number of children born to "low-IQ women," and to the recent large-scale Latino immigrations to the United States. They argue that America's current fertility policy "subsidizes births among poor women, who are disproportionately at the low end of the intelligence distribution" (p. 548). They seem to urge eugenic foresight to counteract dysgenic pressure: "We urge generally that these policies, represented by the extensive network of cash and services for low-income women who have babies, be ended" (p. 548). With regard to the supposed dysgenic effects of Latino immigration on national intelligence, their central thought about immigration "is that present policy assumes an indifference to the individual characteristics of immigrants that no society can indefinitely maintain without danger" (p. 549). "But," they conclude, "we believe that the main purpose of immigration law should be to serve America's interests" (p. 549). For those members of groups who will not be excluded from the American dream by eugenic foresight or new immigration laws, Herrnstein and Murray propose "that group differences in cognitive ability, so desperately denied for so long, can best be handled--can only be handled--by a return to individualism" (p. 550).

Who are the authors of The Bell Curve? Are they right? The first author, Richard Herrnstein, was a professor of psychology at Harvard University for 36 years. He died a very short time ago. One would presume that The Bell Curve represents Herrnstein's final summing up of a lifetime of objective scholarly research published in peer-reviewed scientific journals on the genetic basis of IQ. Regrettably, the media seem to be totally unaware of the fact that the deceased Harvard professor never published any scientific research on the genetic basis of IQ and its relation to race, poverty, or social class in peer-reviewed scientific journals in his entire 36-year academic career. Richard Herrnstein's actual area of expertise is the experimental analysis of decision making in pigeons and rats, and he never studied the genetic basis of any behavior in those laboratory animals. The first presentation of his theory on the genetic basis of IQ, social class, and poverty appeared in a magazine article titled "I.Q." published in the September 1971 issue of the Atlantic Monthly magazine. As we all know, scientists publish their data and theories in peer-reviewed scientific journals or in peer-reviewed technical books, not in popular magazines or in nontechnical books written for the general reader.

In 1973, Herrnstein published a nontechnical Atlantic Monthly Press book titled I.Q. in the Meritocracy that expanded on his theory of the genetic basis of IQ and poverty. Herrnstein had never collected data on IQ, so the book drew on the work of others, especially the "data" of Sir Cyril Burt. According to Leslie Hearnshaw (1979), Burt's biographer and distinguished historian of British psychology, Burt had probably invented much of his highly cited data on the genetic basis of IQ. While doing research on Burt's data for an article that I later published in Science (1978), I discovered that Herrnstein had in fact laundered Burt's own descriptions of Burt's widely publicized and highly cited study "Intelligence and Social Mobility" (Burt, 1961). Burt had described his own study "merely as a pilot inquiry" (p. 9) and his data as "crude and limited" (p. 9). Burt had not even reported the number of subjects he had tested in his crude and limited study. In describing Burt's study, however, Herrnstein (1973) failed to tell the reader about the deficiencies that Burt, himself, had mentioned. In addition, Herrnstein (1973) said Burt's sample size was "1,000" (p. 203), later revising that figure to "40,000" in response to criticism. In reply to a critical letter by Jerry Hirsch (1975), Herrnstein (1975) revised his 1973 figure: "It is true that Burt's sample was 40,000, not 1,000 as I said" (p. 436), while failing to acknowledge that Burt had never reported the number of subjects he had tested. Leon Kamin (1974) appears to have been the only psychologist to notice and publicly report that Burt had failed to give the sample size of his celebrated 1961 study of IQ and social mobility. Presumably, Herrnstein and other psychologists who had publicized the results of that study had never noticed that Burt had not reported the sample size of his famous study.

The second author of The Bell Curve, Charles Murray, has a doctorate in political science from the Massachusetts Institute of Technology and is currently a Bradley Fellow with the American Enterprise Institute, a conservative research group in Washington, DC. Murray often publishes his research and theories in The Public Interest (e.g., Murray, 1994), a neoconservative magazine edited by Irving Kristol, also a fellow of the American Enterprise Institute and sometimes considered the founding father of neoconservatism (Atlas, 1995). In an article recently published in The Public Interest, Murray listed the first priority of his political agenda: "And so I want to end welfare" (1994, p. 18). Inasmuch as the media sometimes refer to The Bell Curve as Murray's book, perhaps the book represents Murray's summing up of a body of objective scholarly research that he had published in scientific journals on the genetic basis of IQ and poverty. But like his coauthor Richard Herrnstein, Murray has never conducted or published any research in scientific journals on the genetic basis of IQ and poverty in his entire career.

The Bell Curve is not a scientific work. It was not written by experts, and it has a specific political agenda. Still, it is possible that the major scientific premises of the book may be correct. If two monkeys were put before a typewriter, it is theoretically possible for those two monkeys to produce a Shakespearean sonnet. Perhaps Herrnstein and Murray produced a valid scientific work. I will now evaluate the major premises of The Bell Curve.

The rewriting of history: The Burt affair

In 1972, Leon Kamin exposed the empirical unsoundness of the most important evidence in support of the IQ hereditarian position, Sir Cyril Burt's data (Hearnshaw, 1979). He later published his results in a book attacking Burt's data as well as the secondary sources who publicized those data (Kamin, 1974). In 1979, Leslie Hearnshaw (1979) published a biography of Burt in which he concluded on the basis of personal diaries and other material that it was highly likely that Burt had fabricated some of his most celebrated data. Hearnshaw, distinguished historian of British psychology, delivered the memorial address at Burt's Memorial Service and was later asked by Marion Burt, Burt's sister, to write a full-length biography of Burt. The result was the well-known Cyril Burt: Psychologist (1979). In their discussion of the Burt affair, Herrnstein and Murray suggest that some of Burt's "leading critics were aware that their accusations were inaccurate" (p. 12), suggesting a possible conspiracy against Burt. There is, however, no mention whatsoever of Hearnshaw's book in their half-page synopsis of the Burt affair, and Hearnshaw's book does not appear anywhere in their 57-page bibliography of references. This misrepresentation of the Burt affair by omission of important historical facts is not uniquely associated with The Bell Curve. In 1982, Richard Herrnstein published an article in The Atlantic Monthly in which he attacked the media for misrepresenting the evidence in the IQ controversy (Herrnstein, 1982). In that magazine article, the Harvard professor wrote "that most psychometricians had stopped trusting Burt's data years before, partly because of inconsistencies first noted in a 1974 article by Arthur Jensen" (p. 70), while omitting any mention of Leo Kamin, the psychologist who in reality first noted inconsistencies in Burt's data.

Does the distribution of IQs follow a bell curve?

The distribution of IQ test scores cannot be expected to follow a bell curve unless it is constructed by the tester to do so (Dorfman, 1978). The shape of the distribution of IQ test scores will depend on the average difficulty of the test items as well as their intercorrelations. The high item intercorrelations in IQ tests imply that the IQ distribution can take a variety of shapes. The central limit theorem does not apply to random variables with positive intercorrelations (Lamperti, 1966). Frederic Lord (1952), one of the fathers of modern test theory and former president of the Psychometric Society, gave results on this question: "The results given are sufficient to show that the distribution of test scores cannot in general be expected to be normal, or even approximately normal. The question naturally arises as to what possible shapes the frequency distribution fs, as given in (76) [Lord's Equation (76)], may assume. The answer is that this function may assume any shape whatsoever, provided the item intercorrelations are sufficiently high" (Lord, 1952, pp. 32-33). The symbol fs refers to the distribution of test scores.

Does cognitive ability consist of a single general factor?

The book uses factor analysis to infer the existence of a single hypothetical general factor of cognitive intelligence that is presumed to account for most of cognitive performance. One of the problems with factor analysis as a tool for determining the underlying structure of a system is that neither the factors nor the loadings are uniquely defined if you have more than one factor (Lawley & Maxwell, 1963), and it is difficult to determine if you have only one factor. In experimental cognitive psychology, factor analysis is virtually never used as a tool to determine the underlying cognitive structure. It is a tool for correlational cognitive psychology, not experimental cognitive psychology. I inspected the subject index of some well-known texts in experimental cognitive psychology and found that the term factor analysis never appears in the subject index (e.g., see Anderson, 1985; Matlin, 1994; Reed, 1982). Why not? Kendall and Stuart (1966) may provide the answer: "Application of the same technique [factor analysis] to physical systems very often results in weighted sums of variables to which no clear interpretation can be given" (p. 310). In short, "The main difficulty, as a rule, is to know what the results mean" (p. 310), Kendall and Stuart point out.

Can you measure the heritability of IQ?

The most direct way of estimating heritability is from data on monozygotic twins reared apart (MZA) and separated in early infancy (Bouchard, Lykken, McGue, Segal, & Tellegen, 1990). This MZA design allows for the estimation of heritability if the following major assumptions are met: (a) environments are a random sample from the population of environments, (b) genotypes are a random sample from the population of genotypes, (c) there is no genotype-environment correlation, and (d) there is no genotype-environment interaction. If the pairs of MZAs differ in age, then these assumptions will not be met. If these assumptions are met, then the intraclass correlation between IQ scores of MZA twin pairs directly measures heritability. Sir Cyril Burt's (1966) study of 53 MZAs appears to have met the first three assumptions. Unfortunately, Burt's data appear to have been invented (Hearnshaw, 1979). Bouchard et al.'s (Minnesota) survey of MZAs provides the next best data set. Unfortunately, to the best of my knowledge, the detailed case-study records of the Minnesota MZAs have never been released and have therefore not been subjected to public scrutiny to determine the degree to which assumptions have been met and the degree to which the MZAs told the truth to the Minnesota group. Finally, if there is genotype-environment interaction--then the fourth assumption is not met--and heritability is undefined. But this is the most controversial assumption underlying the MZA design. Herrnstein and Murray present no convincing evidence to justify the fourth assumption.

Does high within-group heritability of IQ imply between-group heritability of IQ?

The authors have made a fundamental error well-known by professional geneticists. It is sometimes called "Jensen's error." Jensen made that error in his famous 1969 Harvard Educational Review article. The critical importance of that error was first clearly illuminated by Roger Milkman, a professor of biology at the University of Iowa and a world authority on population genetics and evolutionary biology. The article, "A Simple Exposition of Jensen's Error," was published in the Journal of Educational Statistics in 1978 (Milkman, 1978). Melvin Novick was editor of that journal when Milkman's article was published. Novick, professor of statistics and education at the University of Iowa at the time, later became president of the Psychometric Society. What is Jensen's error? It is that within-race heritability has no implications for between-race heritability. The Bell Curve is therefore flawed with regard to inferring between-race heritability in IQ from within-race heritability in IQ.

Does IQ or SES cause socially undesirable behaviors?

Herrnstein and Murray use logistic regression to determine which is more important--IQ or SES--in determining socially undesirable behaviors. Logistic regression is a form of regression in which the dependent variable is binary. In all of their analyses, they assume a simple additive model in which the logit (a transform of the sample proportion) is assumed to equal B0 + B1IQ + B2SES + B3 age + random residual [numbers after Bs should read as subscripts]. They assume no IQ-SES interaction. They use the standardized beta weights to determine the relative importance of IQ and SES in determining the probability of various undesirable or desirable behaviors. Unfortunately, IQ and SES are highly intercorrelated (collinearity).

There are two major problems with Herrnstein and Murray's attempts to determine whether IQ or SES is more important. First, there is the collinearity problem. Weisberg (1985) describes the collinearity problem in linear regression: "When the predictors are related to each other, regression modeling can be very confusing. Estimated effects can change magnitude or even sign depending on the other predictors in the model" (p. 196). Next, there is the problem of deciding that the predictor with the largest standardized beta weight is the most important. Weisberg describes why this approach is faulty: "Unfortunately, this logic is faulty because the scaling depends on the range of values for the variables in the data" (p. 186). Perhaps these are the reasons why Herrnstein and Murray never published their logistic analyses in peer-reviewed journals.

Were Herrnstein and Murray as lucky as the proverbial monkeys at a typewriter? That depends on your point of view.

References

Anderson, J. R. (1985). Cognitive psychology and its implications (2nd ed.). New York: W. H. Freeman.

Atlas, J. (1995, February 12). The counter counterculture. The New York Times, pp. 32-39, 54, 61-62, 65.

Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart. Science, 250, 223-228.

Burt, C. (1961). Intelligence and social mobility. The British Journal of Statistical Psychology, 14, 3-24.

Burt, C. (1966). The genetic determination of differences in intelligence: A study of monozygotic twins reared together and apart. British Journal of Psychology, 57, 137-153.

Dorfman, D. D. (1978). The Cyril Burt question: New findings. Science, 201, 1177-1186.

Hearnshaw, L. S. (1979). Cyril Burt: Psychologist. London: Hodder & Staughton.

Herrnstein, R. J. (1971, September). I.Q. The Atlantic Monthly, 43-64.

Herrnstein, R. J. (1973). I.Q. in the meritocracy. Boston: Atlantic-Little, Brown.

Herrnstein, R. J. (1975). Herrnstein replies. Contemporary Psychology, 20, 436.

Herrnstein, R. J. (1982, August). IQ testing and the media. The Atlantic Monthly, 68-74.

Hirsch, J. (1975). Hirsch on Herrnstein and Jensen. Contemporary Psychology, 20, 436.

Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement? Harvard Educational Review, 39, 1-123.

Kamin, L. J. (1974). The science and politics of I.Q. New York: Wiley.

Kendall, M. G., & Stuart, A. (1966). The advanced theory of statistics (Vol. 3). New York: Hafner.

Lamperti, J. (1966). Probability: A survey of the mathematical theory. New York: W. A. Benjamin.

Lawley, D. N., & Maxwell, A. E. (1963). Factor analysis as a statistical method. London: Butterworth.

Lord, F. M. (1952). A theory of test scores. Psychometric Monographs (Whole No. 7).

Matlin, M. W. (1994). Cognition (3rd ed.). Fort Worth, TX: Harcourt Brace.

Milkman, R. (1978). A simple exposition of Jensen's error. Journal of Educational Statistics, 3, 203-208.

Murray, C. (1994, Spring). Does welfare bring more babies? The Public Interest, 17-30.

Reed, S. K. (1982). Cognition: Theory and applications. Monterey, CA: Brooks/Cole.

Weisberg, S. (1985). Applied linear regression (2nd ed.). New York: Wiley.

Biographies

Richard J. Herrnstein (deceased) was Edgar Pierce Professor of Psychology at Harvard University (Cambridge, Massachusetts) and is author of IQ in the Meritocracy and coauthor, with J. Q. Wilson, of Crime and Human Nature and, with E. G. Boring, of A Sourcebook in the History of Psychology. Herrnstein is coeditor, with R. C. Atkinson, G. Lindzey, and R. D. Luce, of Stevens' Handbook of Experimental Psychology, Vol. 1: Perception and Motivation, and Vol. 2: Learning and Cognition (2nd ed.); and, with M. L. Commons, S. M. Kosslyn, and D. B. Mumford, of Quantitative Analyses of Behavior, Vol. 8: Behavioral Approaches to Pattern Recognition and Concept Formation, and Vol. 9: Computational and Clinical Approaches to Pattern Recognition and Concept Formation.

Charles Murray, currently a Bradley Fellow at the American Enterprise Institute, is author of Losing Ground: American Social Policy 1950-1980.

Thomas J. Bouchard, Jr., professor of psychology at the University of Minnesota (Minneapolis) and director of the Minnesota Center for Twin and Adoption Research, is immediate past president of the Behavior Genetics Association and American Psychological Association Distinguished Scientist Lecturer for 1995. Bouchard is author of the chapter "The Genetic Architecture of Human Intelligence" in P. E. Vernon (Ed.) Biological Approaches in the Study of Human Intelligence and of the chapter "IQ Similarity in Twins Reared Apart: Findings and Responses to Critics" in the forthcoming R. J. Sternberg and E. L. Grigorenko (Eds.) Intelligence: Heredity and Environment. Bouchard is coeditor, with P. Propping, of Twins as a Tool of Behavior Genetics.

Donald D. Dorfman, professor of psychology and radiology and faculty member in the graduate program in applied mathematical and computational sciences at the University of Iowa (Iowa City), is author of the chapter "Group Testing" in S. Kotz and N. L. Johnson (Eds.) Encyclopedia of Statistical Sciences, Vol. 3 and coauthor, with J. T. Cacioppo, of the chapter "Waveform Moment Analysis: Topographical Analysis of Nonrhythmic Waveforms" in J. T. Cacioppo and L. G. Tassinary (Eds.) Principles of Psychophysiology: Physical, Social, and Inferential Elements.

Published Tuesday, January 18, 2005
Harvard Chief Defends His Talk on Women

By SAM DILLON
New York Times

The president of Harvard University, Lawrence H. Summers, who offended some women at an academic conference last week by suggesting that innate differences in sex may explain why fewer women succeed in science and math careers, stood by his comments yesterday but said he regretted if they were misunderstood.

"I'm sorry for any misunderstanding but believe that raising questions, discussing multiple factors that may explain a difficult problem, and seeking to understand how they interrelate is vitally important," Dr. Summers said in an interview.

Several women who participated in the conference said yesterday that they had been surprised or outraged by Dr. Summers's comments, and Denice D. Denton, the chancellor designate of the University of California, Santa Cruz, questioned Dr. Summers sharply during the conference, saying she needed to "speak truth to power."

Nancy Hopkins, a professor of biology at the Massachusetts Institute of Technology who once led an investigation of sex discrimination there that led to changes in hiring and promotion, walked out midway through Dr. Summers's remarks.

"When he started talking about innate differences in aptitude between men and women, I just couldn't breathe because this kind of bias makes me physically ill," Dr. Hopkins said. "Let's not forget that people used to say that women couldn't drive an automobile."
The Boston Globe first reported yesterday about Dr. Summers's remarks and the stir they created.

Not all reactions were negative. Some female academics and the organizer of the two-day conference that Dr. Summers addressed on Friday at the National Bureau of Economic Research, a nonprofit economic research organization in Cambridge, defended the remarks as a well-intentioned effort to speak candidly about the persistent underrepresentation of women in university departments of mathematics, engineering and physical sciences.

"A lot of people who absolutely disagreed with him were not irritated, and he said again and again, 'I'm here to provoke you,' " said Richard Freeman, an economics professor at Harvard who directs the bureau's labor studies program and invited Dr. Summers to speak. "He's very good at stimulating debate, but he cares deeply about increasing diversity in the science and engineering workforces, especially since we have many more women getting Ph.D.'s in science and engineering than ever before."

About 50 academics from across the nation, many of them economists, participated in the conference, "Diversifying the Science and Engineering Workforce: Women,

Underrepresented Minorities, and their S. & E. Careers." Dr. Summers arrived after a morning session and addressed a working lunch, speaking without notes. No transcript was made because the conference was designed to be off-the-record so that participants could speak candidly without fear of public misunderstanding or disclosure later.

In his presentation, Dr. Summers addressed the question of why so few women were on math and engineering faculties at top research universities.

"I began by saying that the whole issue of gender equality was profoundly important and that we are taking major steps at Harvard to combat passive discrimination," he recalled in yesterday's interview. "Then I wanted to add some provocation to what I understand to be basically a social science discussion."

He discussed several factors that could help explain the underrepresentation of women. The first factor, he said, according to several participants, was that top positions on university math and engineering faculties require extraordinary commitments of time and energy, with many professors working 80-hour weeks in the same punishing schedules pursued by top lawyers, bankers and business executives. Few married women with children are willing to accept such sacrifices, he said.

Dr. Hopkins said, "I didn't disagree, but didn't like the way he presented that point because I like to work 80 hours a week, and I know a lot of women who work that hard."
In citing a second factor, Dr. Summers cited research showing that more high school boys than girls tend to score at very high and very low levels on standardized math tests, and that it was important to consider the possibility that such differences may stem from biological differences between the sexes.

Dr. Freeman said, "Men are taller than women, that comes from the biology, and Larry's view was that perhaps the dispersion in test scores could also come from the biology."
Dr. Summers said, "I was trying to provoke discussion, and I certainly believe that there's been some move in the research away from believing that all these things are shaped only by socialization."

It was at this point in his presentation that Dr. Hopkins walked out, and shortly thereafter, Dr. Denton told the Harvard president that she believed his assertions had been contradicted by research materials presented at the conference. Dr. Summers said he responded that "I didn't think for a moment that I had proven anything, but only that these are things that need to be studied."

A late phone call yesterday to Dr. Denton at the University of Washington, where she is the dean of engineering, was not returned.

Paula E. Stephan, a professor of economics at Georgia State University, said Dr. Summers's remarks offended some participants, but not her. "I think if you come to participate in a research conference," Dr. Stephan said, "you should expect speakers to present hypotheses that you may not agree with and then discuss them on the basis of research findings."

Catherine Didion, a director of the International Network of Women Engineers and Scientists, said she was "surprised by the provocation in tone and manner" of Dr. Summers's remarks.

"Initially all of the questions were from women, and I think there was definitely a gender component to how people interpreted his remarks," Dr. Didion said. "Male colleagues didn't say much afterwards and later said they felt his comments were being blown out of context. Female colleagues were on the whole surprised by his comments."

Amr.Ren. April 1995

Three Words and You're Out

Poor Francis Lawrence. Last November, the president of Rutgers University said the following at a faculty meeting:

"The average S.A.T. for African-Americans is 750 [out of 1600]. Do we set standards in the future so that we don't admit anybody with the national test? Or do we deal with a disadvantaged population that doesn't have the genetic hereditary background to have a higher average?"

Someone was recording his remarks – which went unchallenged at the time – and leaked them to the press in January. Dr. Lawrence has been apologizing and backpedaling ever since. "What I intended to say," he now explains, "was that standardized tests should not be used to exclude disadvantaged students on the trumped-up grounds that such tests measure inherent ability." [John Nordheimer, Rutgers Leader disavows linking race and ability, NYT, 2/1/95, p. B5.]

There is much to ponder here. First, non-whites at Rutgers have been baying for his head, despite the fact that Dr. Lawrence has been one of the most ardent advocates of racial preferences, speech codes, and multiculturalism on any American campus. His record and his abject apologies meant nothing; he had to be fired. Thus do non-whites reward their benefactors. In February, when the university's Board of Governors announced that Dr. Lawrence would be kept on, some black activists shed tears of grief.

Clearly, many blacks believe that Dr. Lawrence meant what he said at the faculty meeting. It would be significant if, indeed, he believes that blacks do not have the same genetic endowments as whites, but still deserve affirmative action. This might become the fallback position for the defenders of racial preferences, once the facts about IQ have become too well known to be ignored. Race-based handouts are all the more necessary, it might be argued, if some races face built-in limitations.

Genes determine the funniest things (Just don't tell Larry Summers' enemies)
Jonah Goldberg

June 24, 2005

From:

TownHall.com

http://www.townhall.com/columnists/jonahgoldberg/jg20050624.shtml

Here are some recent headlines from the world of science:

"Researchers Say Intelligence and Diseases May Be Linked in Ashkenazic [Jewish] Genes" - New York Times

"Some Politics May Be Etched in the Genes" - The New York Times

"Feminists Feed on Lawrence Summers' Flesh, Vital Organs; Pancreas Swallowed Whole, 'like a Cocktail Peanut.' " - New York Times

OK, I made the last one up. Feminists didn't actually feed on the president of Harvard University, but it's certainly been all-you-can-eat-at-Sizzler night, metaphorically speaking. In January, you might recall, Larry Summers raised the possibility - nay, the hypothesis! - that as a statistical matter biological differences may partially account for the disproportionately low number of women at the top ranks of science. In response, an activist feminist professor from MIT contracted a case of the vapors, and when she arose from her fainting couch she was on the "Today Show" complaining to a supportive Katie Couric about what a bigot Summers is. Fast forward from her Café Vienna moment with Katie, through more groveling than Jake Blues offered to Carrie Fisher at the end of "The Blues Brothers," and we have the recent announcement that Summers will spend an additional 50 million of someone's tuition dollars over the next 10 years to atone for his - and Harvard's - alleged bigotry toward (just-as-smart-as-you-Mister-Man) female scientists.

The flames of the Summers' auto-da-fe cast a useful light on the cognitive dissonance, schizophrenia and bad faith dotting the intellectual and political landscape today when it comes to genetics.

Consider the other headlines I mentioned above. One paper by a respected independent researcher suggests that Jews from Northern Europe (aka Ashkenazi Jews) are more likely to get certain diseases, like Tay Sachs, in part because Jews have been selectively breeding for intelligence for centuries. Central to the theory is the fact that Jews have been middleman traders, financiers and bankers since the Middle Ages - occupations that require high levels of intelligence.

Or consider the new study that claims, as reported just this week in The New York Times, that political attitudes are in some part genetically determined. The study itself, which appeared in the American Political Science Review, is far more cautious than the Times' coverage of it. But the basic gist is that studies of twins have revealed that genetics plays a significant, but far from ironclad, role in political attitudes. Identical twins are more likely to see politics through a similar prism than other siblings. Or so the authors claim.

"It is not that opinions on specific issues are written into a person's DNA," the Times's Benedict Carey reassures readers. "Rather, genes prime people to respond cautiously or openly to the mores of a social group."

Sounds plausible, but there's good reason to be skeptical. Since the 1930s, the Left has offered a string of theories suggesting that conservatives are simply wired "wrong" and that our views can be ascribed to mental defects rather than conviction. It was this thinking that prompted 1,189 psychiatrists in 1964 to take out newspaper ads declaring Barry Goldwater to be "psychologically unfit" to be president. Just two summers ago, a Berkeley study claimed to prove that conservatism was more akin to a personality disorder than an actual political philosophy.

Indeed, one gets a whiff of this sort of thinking in Carey's coverage . The most ominous concern he raises at the end is that the study calls into doubt "the future of bipartisan cooperation or national unity." Why? "Because men and women tend to seek mates with a similar ideology . the two gene pools are becoming, if anything, more concentrated, not less."

This last bit strikes me as piffle. Did mates select for bipartisanship more during the Bronze Age?

Here's how I read Carey: The bipartisan divide exists because those chromosomally damaged right-wingers aren't going away until we can find a cure.

In other words, the Times is showing it's biases.

Which brings us back to the mortification of Larry Summers. Is it so unreasonable to assume there are greater genetic cognitive and behavioral differences between men and women than between, say, Jews and gentiles - never mind conservatives and liberals? If genes make us more open to some group mores, why can't they make one gender more open to one field of study? The animal kingdom is replete with enormous male-female disparities. Even among the branch of humans we call feminists, it's a widely held view that men and women think and behave differently.

Such views only become controversial when some aggrieved group's self-esteem is on the line - and the possibility of extortion is in play. Then, suddenly, Dr. Summers' pancreas becomes a cocktail peanut.

The problem is these sorts of stories are going to be pouring forth daily for the next 20 years, and there's just not enough of Summers for everybody with low self-esteem to feed on.

Jonah Goldberg is editor-at-large of, a Townhall.com member group.

©2005 Tribune Media Service