Sat, 22 Oct 1994 14:40:06 -0700 for From: lichter@nicco.sscnet.ucla.edu (Michael Lichter) Date: Sat, 22 Oct 1994 14:40:01 +0000 To: socgrad@UCSD.EDU Subject: History & Murray (Long) So as not to bombard you, I've put together four messages from PEN-L and PSN that address the IQ controversy and Murray's new book. The first is primarily about politics; the second both comments on the book (Ted actually read it!) and includes a lenghty history of the erroneous application of "the bell curve" to IQ and social phenomena including test scores; the third adds an anecdote; and the final message, by Joe Feagin, documents some of the historical uses of IQ testing, including labeling of Southern and Eastern European immigrants as "of inferior races". |Date: Sat, 22 Oct 1994 02:30:43 -0700 |From: djones@uclink.berkeley.edu (jones-bhandari) |Subject: fascism |X-Comment: Progressive Economics List | |I am hoping that we can use this line to think through our responses to |Murray and Herrenstein's latest "research". | |I want to raise here two points: their concern with differential birth |rates and the critique of positivism. For those of you whose appreciation |for the delete key grows as my posts grow, I do recommend skipping to the |passage which I quote on positivism. | |1. So-called race suicide | |What strikes me is their complete revival of the fears of race suicide. |They are concerned with not only the intellectual potential and |reproduction rates of the oppressed but with the alleged differential |reproduction rates of classes bifurcated by their IQ's. In the Atlantic |Monthly in 1989, Herrenstein lamented that high IQ women are not having |enough children, while Murray is now openly arguing that the welfare system |is a defacto fertility policy--"The technically precise description of |America's fertility policy is that it subsidizes births among poor women, |who are also disproportionately at the low end of the intelligence |distribution." | |Of course IQ has been collapsed here with intelligence without argument, |and Murray has intimated once again that AFDC increases birth rates, |instead of merely ameliorating poverty especially among children. But if |further cutbacks in AFDC do not further his programme to control the |already declining birth rate among the most oppressed, what other means |will be available to him--see Troy Duster's disturbing book Backdoor to |Eugenics. Already they are proposing to make birth control devices and |information more widely available to the poor. | |And one wonders what means will be proposed to increase the birth rates of |high IQ women. Remember people like George Gilder have argued that the |total re-subordination of women in patriarchal, nuclear families is the key |prop to rekindling the spirit of enterprise: only when bourgeois men have |children and dependent wives (family ties) do they work hard enough to and |have the motive to save--which then cures the economy of the savings |shortfall engendered by a rising depreciation rate. Savings and investment |are treated as subjective variables. The influence of Gilder's |neo-Schumpeterian theories on contemporary family policy has been noted by |Pamela Abbot in the Family and the New Right, as well as indirectly by |Stephanie Coontz in The Way We Never Were. | |What seemed at first as a right-wing attempt to resubordinate all women in |patriarchal families is turning out to be a policy directed towards |curtailing the reproduction of some women and at the very least lamenting |the lack of births among others. It seems to me that the Moynihan thesis |is now being used to justify a policy approaching eugenics: since Black men |cannot be the heads of the Brady Bunch (on genetic grounds) and since |children should not be raised in women-headed households, Black women |should be discouraged from having children at all. So whereas before |Black men were to be singled out for job training, while Black women were |induced to become dependent upon them, they are now both to be ignored--on |account of a good deal of poverty getting down to an intractable core. But |then again, as Adolph Reed has pointed out, the job program the goal of |which was putatively to make good patriarchs of Black men was service in |the man-short army during the Vietnam War. Quasi-genocidal policies from |that recommendation to the very process of ghettoization have been part of |America...since its inception. | |In other words, the concern here is not simply excessive reproduction of |the oppressed but with differential reproduction between "classes", which |has often been the object of "corrective" state policy in the twentieth |century. It seems to me that what we need a rigorous marxist theory of such |policy, theory which clarfies what is at stake: socialism or barbarism. | |2. the critique of M and H's positivism | |It may be most effective to strike at Murray's very methods. From John |Hoffman's 1975, Marxism and the Theory of Praxis: | |Positivism tries to restrict science to the world of "appearances" and thus |leaves it vulnerable to fetishism of every kind. The truth of a phenomenon |is only intellible when we really *understand* it, when we can begin to |explain it, relate it, dig out its causes, in short, reasn about it....if |people of different classes or "races" look different, behave differently, |think differently, then this is somehow "empirical" proof that class and |"race" can only be explained in physiological terms. The historical forces |which make people what they are, which shape them and mould them, giving |them a specific appearance at a specific moment in time--these are simply |ignored--and the momentary form is ossified into a timeless reality. No |real change is possible: all that remains is for charlatans and mystics to |carry out their fascist-type experiments in order to coerce the "defective" |and the "aberrant" to "genetically adust" to a capitalist status quo. |Postivism with its dogmas of socially irresponsible (allegedly |"value-free") science, of theory without practice, brings to an ugly head |the age-old philosophical activity of trying to freeze historical |development into timeless "verities", mental abstractions, which leave the |world the world as it is. Sacrificing objective reality for its empirical |fragments, postivism strikes viciously at the roots of reason, our ability |to control the world around us, and defends instead a religion of passivity |and helplessless in the name of "science": we are all victims of |circumstance, genetic inheritance, accident, instinctual impulses which |nobody can control, and the only bit of philosophy we have to guide us |through life is to follow the will of those who know better (p.15-16) | | |jb | | |Date: Sat, 22 Oct 1994 06:35:56 -0600 |From: Ted Goertzel |Subject: Myth of the Bell Curve |X-Comment: PROGRESSIVE SOCIOLOGISTS NETWORK | |I've followed the commentary on The Bell Curve with interest. I |believe I am the first commentator so far who has actually read |the book. It is very well written and thought out, and really |exemplary in communicating the results of quantitative research |to a statistically unsophisticated audience. By most objective, |academic, scientific standards, it is an excellent book. This is |not to say that it doesn't have its weaknesses, but they are not |in overlooking the kinds of rhetorical points that most PSN |commentators have made. Most of the arguments made on PSN are |anticipated in the book and responded to with extensive citations |and/or quantitative findings. If PSN members believe this book |is important, they will have to do a lot of hard work to refute |it. Just going into battle with the kinds of arguments expressed |on the network will make the left look rhetorical and shallow |compared to the rigorous science of Herrnstein and Murray. | |There has been little discussion of the actual recommendations in |the book. They do not, for example, propose abandoning |affirmative action, but limiting the margin given to blacks or |other minorities on tests such as the GRE and LSAT to half a |standard deviation. They give statistics showing that in elite |schools the minority mean is often more than a standard deviation |less than the white mean. This seems unfair, especially to white |students from modest economic circumstances who feel treated |unfairly. Their recommendation is a plausible policy, different |from the policy of having a quota for minorities and admitting |the strongest minority candidates regardless of how they compare |to the majority. What policy do PSN members advocate? Quotas? |Class as opposed to racial criteria? Herrnstein and Murray also |address the question of socioeconomic vs. ethnic or racial |criteria. | |Some of the weak points in the book are mentioned in the current |issue of Newsweek - e.g., the "Flynn effect" which they use to |explain away the fact that IQ scores seem to be increasing over |time rather than falling as their theory predicts. Probably the |most fundamental weakness is overlooking the importance of |cultural factors. | |Another flaw which happens to interest me in the image of "The |Bell Curve" itself. I published a paper (with Joe Fashing) on |this back in 1981 in Humanity and Society. Since this is a very |difficult journal to find, and I believe the paper will interest |you, I am sending an adaptation of it along. Feel free to use |this in your courses, cite it in your writings, etc. It has been |buried long enough. | |Ted Goertzel | |PS |I am thinking of revising and updating this. If any of you have come |across misuses of the normal curve, please send them on to me. |Ted Goertzel, Rutgers University, Camden NJ 08102 | | | | The Myth of the Bell Curve | by Ted Goertzel | |Adapted and condensed from: Ted Goertzel and Joseph Fashing, "The |Myth of the Normal Curve: A Theoretical Critique and Examination |of its Role in Teaching and Research," Humanity and Society 5:14-31 |(1981), reprinted in Readings in Humanist Sociology (General Hall, |1986). | | | Surely the hallowed bell-shaped curve has cracked from top to | bottom. Perhaps, like the Liberty Bell, it should be enshrined | somewhere as a memorial to more heroic days. | | -Earnest Ernest, Philadelphia Inquirer. 10 November 1974. | | | | The myth of the bell curve has occupied a central place in the |theory of inequality (Walker, 1929; Bradley, 1968). Apologists |for inequality in all spheres of social life have used the theory |of the bell curve, explicitly and implicitly, in developing moral |rationalizations to justify the status quo. While the misuse of the |bell curve has perhaps been most frequent in the field of |education, it is also common in other areas of social science and |social welfare. When Abraham de Moivre made the first recorded |discovery of the normal curve of error (to give the bell curve its |proper name) in 1733, his immediate concern was with games of |chance. The normal distribution, which is nothing more than the |limiting case of the binomial distribution resulting from random |operations such as flipping coins or rolling dice, was a natural |discovery for anyone interested in the mathematics of gambling. De |Moivre was unhappy, however, with the lowly origins of his |discovery, He proceeded to raise its status by attributing to it an |-importance beyond its literal meaning. In his age, this could best |be done by claiming hat it was a proof of the existence of God. He |announced: | | And thus in all cases it will be found, that although Chance | produces irregularities, still the Odds will be infinitely | great, that in process of Time, those irregularities will bear | no proportion to the recurrency of that Order which naturally | results from Original Design .... (Walker, 1929:17). | | De Moivre's discovery of the bell curve did not attract much |attention. Gamblers are perhaps better served with discrete |distributions. Theologians, for their part, no doubt preferred to |base their case for God's insistence on less probabilistic grounds. |Serious interest in the distribution of errors on the part of |mathematicians such as Laplace and Gauss awaited the early |nineteenth century when astronomers found the bell curve to be a |useful tool to take into consideration the errors they made in |their observations of the orbits of the planets. | Further developments in the myth of the bell curve were left |not to the astronomers or theologians but to the early quantitative |social scientists. Systematic collection of population statistics |began in the late eighteenth and early nineteenth centuries as a |response to the social upheavals of the time and the consequent |concern with understanding the dynamics of mass behavior. These |early sociologists were not concerned with theology, but they were |seeking proof of the orderliness of society. Relying on the |justifiably great prestige of Laplace and Gauss as mathematicians, |they took the bell curve as proof of the existence of order in the |seemingly chaotic social world. Unfortunately, the early |social scientists often had a poor understanding of the fact that |the mathematical formulas of Gauss and Laplace were based on |assumptions not often met in the empirical world. As Fisher (1923, |Vol. 1: 18 1) points out: | | the Gaussian error law came to act as a veritable | Procrustean bed to which all possible measurements | should be made to fit. The belief in authority so typical of | modern German learning and which has also spread to America | was too great to question the supposed generality of the law | discovered by the great Gauss. | | The mathematicians, on the other hand, did not feel that it |was their domain to check whether or not the empirical world |happened to fit their postulates. The bell curve came to be |generally accepted, as M. Lippmnan remarked to Poincare (Bradley, |1969:8), because "...the experimenters fancy that it is a theorem |in mathematics and the mathematicians that it is an experimental |fact." | Adolph Quetelet, the father of quantitative social science, |was the first to claim that the bell curve could be applied only |to random errors but also to the distributions of social phenomena |(Landau and Lazarsfeld, 1968; Wechsler, 1935:30-31). The myth of |the bell curve was part of Quetelet's theory of the Average Man |(Quetelet, 1969). He assumed that nature aimed at a fixed point in |forming human beings, but made a certain frequency of errors. The |mean in any distribution of human phenomena was to him not merely |a descriptive tool but a statement of the ideal. Extremes in all |things were undesirable deviations. His doctrine was a |quantification of Aristotle's doctrine of the Golden Mean, and it |is susceptible to the same criticisms. While there may be traits |where the average can reasonably be considered to be the ideal, the |argument's application is severely limited. One might argue, for |example, that average vision is ideal, whereas nearsightedness and |farsightedness are undesirable deviations. But is this true of |physical strength or of mental abilities, or even of physical |stature (one variable for which there is actually substantial |evidence of an approximately normal distribution)? Quetelet, like |Aristotle, exempted mental abilities, arguing that those who were |superior to the average in intelligence were mere forerunners of a |new average that was to come. | Quetelet's doctrine of the Average Man was ill suited to a |society that was more in need of a rationalization for inequality |than a glorification of the common man. His use of the bell curve, |however, was useful as part of the social Darwinist ideology that |was emerging as a justification for the inequities of laissez-faire |capitalism. | The myth of the bell curve found its most enthusiastic and |effective champion in Francis Galton and the eugenics movement of |which he was a major founder. The importance that he attributed to |the bell curve can be illustrated by the following quotation |(Galton, 1889:66): | | I know of scarcely anything so apt to impress the imagination | as the wonderful form of cosmic order expressed by the "Law of | Frequency of Error." The law would have been personified by | the Greeks and deified, if they had known of it. It reigns | with serenity and in complete self-effacement amidst the | wildest confusion. The huger the mob, the greater the apparent | anarchy, the more perfect is its sway. It is the supreme law | of Unreason. Whenever a large sample of chaotic elements are | taken in hand and marshalled in the order of their magnitude, | an unsuspected and most beautiful form of regularity proves to | have been latent all along. The tops of the marshalled row | form a flowing curve of invariable proportions; and each | element, as it is sorted into place, finds, as it were, a | preordained niche, accurately adapted to fit it. | | Galton went beyond Quetelet not only in his enthusiasm |for the bell curve but also in his attempt to gather data to |demonstrate its general applicability. He obtained data on a |number of physical traits that he was interested in improving, such |as height, weight, strength of the arms and of the grip, swiftness |of the blow, and keenness of eyesight. The variables tended to be |approximately normally distributed, but the fit was not perfect. |He consequently converted his data into a type of standard score |and averaged the standard scores together (Galton, 1889:201). |These average scores fit the fit the normal curve very well as |might be expected since he had averaged together a number of |largely unrelated variables and created a mean score that reflected |little more than random error. | Karl Pearson (best known today for the invention of the |product-moment correlation coefficient) was Galton Professor of |Eugenics at the University of London and Galton's biographer. He |accepted the ideology of the eugenics movement and was preoccupied |with curing social problem by creating a race of superior blue-eyed |and golden-haired people (Pearson, 1912). He was, however, too |good a statistician to repeat Galton's methodological errors or to |accept the Gaussian model on the basis of authority. He used his |newly developed Chi Square test to check how closely a number of |empirical distributions of supposedly random errors fitted the bell |curve. He found that many of the distributions that had been cited |in the literature as fitting the normal curve were actually |significantly different from it, and concluded that "the normal |curve of error possesses no special fitness for describing errors |or deviations such as arise either in observing practice or in |nature" (Pearson, 1900: 174). | |The Myth in Testing Theory | | Pearson's conclusions were not sufficient to stop the |application of the normal curve of error as a norm in assigning |classroom grades or in psychological testing. Most objective tests |that are in practical use today rely on summated scaling |techniques. This means that the person taking the tests answers a |large number of items and receives a total score corresponding to |the number of items that he or she answers correctly. This type of |measurement, which is also used in Likert-scaling in sociological |research, has an inherent bias toward the normal distribution in |that it is essentially an averaging process, and the central limit |theorem shows that distributions of means tend to be normally |distributed even if the underlying distribution is not (if the |means are based on large random samples). This inherent |bias is most likely to be realized if the responses to the test |items are poorly intercorrelated (i.e., if the test or scale is |poorly constructed to measure a central factor). | If a large number of people fill out a typical multiple choice |test such as the Scholastic Aptitude Test (or a typical |sociological questionnaire with precoded responses such as |"strongly agree, agree") at random using a perfect die, the scores |are very likely to be normally distributed. This is true because |many more combinations of responses give a sum that is close to the |theoretical mean than give a score that is close to either extreme. |This characteristic of the averaging process is useful in |calculating probable errors in random sampling and is consequently |discussed in elementary statistics books (e.g., Blalock, |1960:138-141). When averaging is used in testing or measurement, |however, it means that the greater the amount of error present, the |greater the likelihood of a normal distribution of scores, even if |the variable being measured is not normally distributed. | All objective tests contain a certain amount of error in that |the chance of a respondent's getting a given item right depends not |only on the central factor being measured but also on other general |factors and on characteristics idiosyncratic to that item (not to |mention the element of luck). Thus it is not surprising that |summated scaling devices tend to give normal distributions. The |problem comes when this tendency is interpreted not as a result of |unavoidable error, but as a confirmation of a preconceived idea |that the variable being measured is in fact normally distributed. | The early developers of standardized intelligence tests were |pleased to find that their distributions of scores were |approximately normal, although they were disturbed by the fact that |perfect normal distributions were rarely, if ever, achieved. |Tborndike (1926:521-555) went so far as to average together scores |achieved by the same respondents on eleven different intelligence |tests in order to achieve a more normal distribution. He thus |repeated Galton's mistake by averaging together somewhat diverse |measures and then assuming that the resultant distribution was due |to the normality of the underlying variable rather than to the |increased measurement error. (The importance of this, of course, |depends on how different the various tests were.) He also |discounted the fact that the intelligence tests themselves |were standardized in such a way as to give normal distribution. | Despite the efforts of prominent psychometricians such as |David Wechsler (1935:34) to counter it, the myth of the bell curve |was widely disseminated in psychological texts (Goodenough, |1949:148-149; V , 1940-16-17; Anastasi, 1968:27) and is widely used |as a criterion for test construction. More modern texts usually |recognize that there is no theoretical justification for the use of |the normal curve, but justify using it as a convenience (Cronbach, |1970:99-100). | The clear assertion by prominent psychologists such as |Wechsler and Cronbach that psychological phenomena are not somehow |inherently normally distributed is a clear advance over the type of |indoctrination that students of educational psychology typically |received in the 1930s and 1940s. This methodological advance |coincided with a general trend in the social sciences away from |sociobiological arguments. The close tie between methodological |presuppositions and ideological concerns is illustrated by the fact |that the myth of the bell curve has recently been reactivated |precisely as part of an attempt to reassert racist arguments about |the biological determinants of human abilities. In his highly |controversial article on genetics and I.Q., Arthur Jensen (1969) |went to considerable length in an attempt to demonstrate that I.Q. |scores are approximately normally distributed. | In 1994, Richard Herrnstein and Charles Murray used the phrase |"The Bell Curve" as the title of their widely reviewed book on |Intelligence and Class Structure in American Life. While their |book presents elaborate statistical justifications for most of its |assertions, however, the claim that intelligence is normally |distributed is defended on common sense grounds. Herrnstein and |Murray (1994: 557) simply assert that "it makes sense that most |things will be arranged in bell-shaped curves. Extremes tend to be |rarer than averages." They note that the bell curve "has a close |mathematical affinity to the meaning of the standard deviation," a |concept which they use extensively in the book, and remark that: | | It is worth pausing a moment over this link between a | relatively simple measure of spread in a distribution and the | way things in everyday life vary, for it is one of nature's | more remarkable uniformities. | | In reality, there is nothing remarkable about the fact that |measures which contain a good deal of random variation will fit a |measure designed to measure random variation. | The question whether intelligence is or is not normally |distributed is actually irrelevant to the thesis that observed |differences in I.Q. scores between racial groups reflect innate |biologic differences. Jenson, Herrnstein and Murray apparently |introduce the topic of the normality of I.Q. score distributions |because readers who have been led to accept the myth of the normal |curve in other contexts may assume that a normal distribution |proves that the measurement was valid. If the normal distribution |were properly understood as nothing more than a distribution of |random errors, it would not lend any weight to their arguments. |tests. | |The Myth of the Bell Curve in Grading | | The myth of the normal bell curve also lives on in educational |institutions, where students and faculty often casually refer to |"grading on the curve" or "curving the grades." Many |administrators resemble the superintendent of schools in "Elmtown" |(Hollingshead, 1961) in assuming that a normal distribution of |scores indicates that a good job of grading was done. Often, |instructors are expected to turn in an approximately normal distri- |bution of grades and any substantial deviations must be justified. |In a 1970-1972 dispute at a large state university, conflict over |grading and other issues led to a situation in which all but one of |the full-time junior faculty members were fired, denied tenure, or |resigned under pressure (Goertzel and Fashing, 1969). | The initial controversy arose when some administrators became |concerned about the tendency toward "grade inflation" on campus, an |issue that has been of some national concern as well (Jencks and |Riesman, 1968). The dean of the college distributed statistics |showing that the mean grade point average had been increasing over |time and in comparison to other institutions. There was also |considerable difference in the average grades given out by |departments on campus. The Sociology Department was particularly |singled out for its high average grades, and pressure was put on |the department chair to bring his faculty members into line. | One junior faculty member was told that he must use "common |sense" standards in grading that would result in a "more or less |normal distribution" of grades. The teaching assistants in the |chairman's introductory sociology class were given more explicit |instructions: The combined average grades for each of their four |classes was not to exceed 2.6 (or a low B -). Five teaching |assistants were summarily dismissed after they refused to sign a |document declaring their willingness to carry out the intent of the |chairman's directive. | The issue became a major focus of conflict on campus, leading |the dean and other senior faculty and administrators to enunciate |assumptions which are not often states so clearly. They made it |clear that their concern went beyond the question of the "average" |or mean grade. They were also concerned that the number of As be |relatively small. Indeed, they insisted that the usual distribution |of grades should approximate a normal distribution in that most |grades should be clustered around the mean (or C) with relatively |few at the extremes. Most of the spokesmen who supported a normal |distribution said they thought that such a distribution was the |"usual," "natural" or "common sense" result to be obtained from |correct grading procedures. | In a more traditional view of grading as representing |objective academic standards, instructors should grade papers |according to their intrinsic merit and give out whatever grades |result even if the distribution results in a lot of A's or F's. On |tests, an instructor should know, before looking at the results, |what score will be required for each grade. This practice, |however, may be administratively inconvenient for several reasons. |Enrollments may drop if too many students fail. Admissions to |elite programs may be too large if too many students receive high |grades. The myth of the bell curve serves administrative |convenience by assuring that a predictable proportion of students |can be channeled into each strata of the educational and |occupational system. | | |The Bell curve in Theory and Research | | The use of the myth of the bell curve in research serves to |reinforce some persistent biases, as well as to disguise sloppy |research practices. These biased research findings may then be used |to justify the assumption that abilities and talents are normally |distributed and that grades and other social rewards should be |distributed according to the bell curve. | The assumption that social phenomena should be normally |distributed is consistent with pluralist or other multicausal |theoretical models, since a large number of unrelated and |equipotent causes lead to a normal distribution. Indeed, the early |pluralists in political science expected political attitudes to be |normally distributed, since they believed them to be caused by |numerous, equipotent independent factors (Rice, 1928:72). |Similarly, if social status is determined by a number of |independent factors, we would expect it to be normally distributed. |If, as Marxists and others argue, it is largely determined by a |single variable, such as the relationship to the means of |production, there would be no reason for this to be the case. | In point of fact, income is not normally distributed in the |United States or any other known society. Income can be measured |easily in monetary units, this is well accepted. A graph of the |income distribution in the United States can even be found in |Herrnstein and Murray's book (1984: 100), and it is not a bell |curve. Other measurements used by social scientists, however, |provide only a rough index of the underlying trait. If sufficient |error is present in these measuring instruments, a normal |distribution may well result. | Lundberg and Friedman (1943), for example, compared three |measures of socioeconomic status in a rural community. These tests |measured social status by arbitrarily assigning points to the |furniture and other objects observed in the respondents' living |rooms. After applying several tests to the same families and |plotting the resulting distributions, the authors noted: | | assuming that in a random sample, socioeconomic status is | normally distributed, the distortion of the normality of the | distribution by the Guttman version of the Chapin scale | suggests the presence of spurious factors .... | | In other words, the bell curve was used as a standard for |deciding which test was valid. | The commentators on the article (Knupfer and Merton, |1943) were quick to point out that this was an unjustified |assumption. Income, property, education, and occupational status |are not normally distributed; why should socioeconomic status as |measured by a summated scale of the paraphernalia in the |respondents' living rooms be? Yet the assumption that distribution |should be normal is widely used, perhaps in the absence of any |other criterion to demonstrate that a good job of measurement has |been done. A U.S. Forest Service Report (1973:24a), for example, |reports with satisfaction that scores on an index of the wilderness |quality of roadless areas were quite normally distributed. There |is no reason why this should be the case except that the Forest |Service has averaged together a number of possibly unrelated |variables (scenic character, isolation, variety). (In |fact, distribution found by the Forest Service deviates |significantly from normality; but, as if often the case, they did |not check the goodness of fit.) The use of normality as a |criterion reinforces sloppiness in scale construction, since a |sloppy scale has more error and is thus more likely to approximate |a normal distribution. | The myth of the bell curve is also consistent with theories |that assume that social behavior is a reflection of individual |differences (provided, also, that it is assumed that individual |differences are normally distributed). Stuart Dodd (1942:251-262), |for example, used the bell curve in developing his theory of social |problems. A social problem, to Dodd, consisted in a deficit of |some characteristic that is socially desirable. The 2% of the |population that falls below two standard deviations from the mean |on a desirable characteristic are the "minimals," and they |constitute the social problems. These "minimals" include |divorcees, prostitutes, illegitimates; the sick, blind, crippled, |or insane; the poor and unemployed; criminals and political |refugees; inferior races such as Bushmen and Pygmies; the |illiterate or ignorant; the overworked and underprivileged; the |offensively vulgar; atheists; foreign language minorities; hermits |and social isolates. | Dodd was certainly aware that not all phenomena are normally |distributed, and he realized that the two percent figure may not |always be appropriate. Yet, only the assumption of normality led |him to even suggest this figure; otherwise, what possible reason |could there be for suggesting that the divorce rate, poverty rate, |unemployment rate, to say nothing of the proportion of foreign |language minorities, should fall at 2%? | Dodd also used the bell curve to estimate the possible range |of human characteristics, determining that it was unlikely for the |range to exceed 12.5 standard deviations (Dodd, 1942:261-262). He |noted, however, that the range of incomes in our "capitalistic |culture" exceeded 2000 standard deviations. His suggestion that the |variance in incomes should be limited to correspond to the variance |in abilities is perhaps a good one, but more rigorous data show |that the assumption of normality cannot be used m determining the |range of these abilities. Weschler (1935) shows on the basis of |much better data, that the range of human traits rarely exceeds a |ratio of 3:1 (the range ratio of Binet Mental Age scores is |2.30:1). | Nothing in this paper should be taken as questioning the use |of the normal distribution where it is appropriate (e.g., in |estimating confidence intervals from random samples). To make |this correct usage clear, it might be wise to revert to the |earlier phrase, "normal curve of error." This would make it clear |that the normal bell curve is "normal" only if we are dealing |with random errors. Social life, however, is not a lottery, and |there is no reason to expect sociological variables to be nor- |mally distributed. Nor is there any reason to expect psycho- |logical variables to be if they are influenced by social factors. |Certain physiological traits, such as length of the extremities, |are often approximately normally distributed within homogeneous |populations. Other traits, such as weight, which are affected by |social behaviors, are not. Indeed, if a phenomenon is found to be |normally distributed, this is very likely an indication that it |is caused by random individual variations rather than by social |forces. | The myth that social variables are normally distributed has |been shown to be invalid by those methodologists who have taken |the trouble to check it out. Its persistence in the folklore and |procedures of social institutions is a reflection of |institutionalized bias, not scientific rigor. | | | References |Anastasi, A. | 1968 Psychological Testing. New York: Macmillan. |Blalock, H. | 1960 Social Statistics. New York: McGraw-Hill. |Bohrnstedt, E. and C. Bohrnstedt | 1972 'How One Normally Constructs Good Measures, | Sociological Methods and Research, I, 3-12. |Bradley, J.V. | 1968 Distribution-free Statistical Tests. Englewood Cliffs, | N.J.: Prentice-Hall. | |Cronbach, L. | 1970 Essentials of Psychological Testing. New York: | Harper & Row. |Dodd, S. | 1942 Dimensions of Society. New York: Macmillan. |Fisher, A. | 1922 The Mathematical Theory of Probability. New | York: Macmillan. |Forest Service, U.S.D.A. | Roadless and Undeveloped Areas Within National Forests. | Springfield Va.: National Technical Information Service. |Galton, F. | 1889 Natural Inheritance. London: Macmillan. |Goertzel, T. and J. Fashing | 1981 "The Myth of the Normal Curve: A Theoretical Critique | and Examination of its Role in Teaching and Research" | Humanity and Society 5: 14-31. |Goodenough, F. | 1949 Mental Testing. New York: Rinehart. |Herrnstein, R. and C. Murray | 1994 The Bell Curve: Intelligence and Class Structure in | American Life. New York: Free Press. |Hollingshead, A. | 1961 Eltmtown's Youth. New York: Wiley. |Hoyt, D.P. | 1965 "The Relationship Between College Grades and Adult | Achievement." Iowa City: American College Testing Program, | Research Report No. 7. |Jencks, C. and D. Riesman | 1968 The Academic Revolution. New York: Doubleday. |Jencks, C., et al. | 1972 Inequality. New York: Basic Books. |Jensen, A. | 1969 "How Much Can We Boost I.Q. and Scholastic Achievement?" | Harvard Educational Review 39, 1-123. |Knupfer, G. and R. Merton | 1943 "Discussion." Rural Sociology 8, 236-239. |Landau, D. and P.F. Lazarsfeld | 1968 "Adolphe Quetelet." In Vol. 13 of International | Encyclopedia of the Social Sciences. New York: | Macmillan and Free Press. |Lundberg, G. and P. Friedman | 1943 "A Comparison of Three Measures of SocioEconomic Status." | Rural Sociology 8, 227-236. |Pearson, K. | 1912 Social Problems: Their Treatment, Past, Present and | Future. London: Dulau. | 1900 "On the Criterion That a Given System of Deviations From | the Probable in the Case of a Correlated System of | Variables Is Such That It Can Be Reasonably Supposed to | Have Arisen from Random Sampling." The London, Edinburgh | and Dublin Philosophical Magazine and Journal of Science | 50, 157-175. |Quetelet, L.A.J. | 1969 A Treatise on Man. Gainesville, Fla.: Scholar's | Facsimiles and Reprints. Rice, S. | 1928 Quantitative Methods in Politics. New York: Knopf. |Thorndike, E.L., el al. | 1927 The Measurement of Intelligence. New York: Columbia | University Press. |Thurstone, L.L. | 1959 The Vectors of the Mind. Chicago: University of | Chicago Press. |Vernon, P. | 1940 The Measurement of Abilities. London: University of | London Press. |Walker, H. | 1929 Studies in the History of Statistical Method. | Baltimore: Williams and Wilkins. |Wechsler, D. | 1935 The Range of Human Abilities. Baltimore: Williams and | Wilkins. | | |Date: Sat, 22 Oct 1994 09:27:30 -0600 |From: "Thomas D. [Tom] Hall, THALL@DEPAUW.EDU" |Subject: IQ & Goertzel |X-Comment: PROGRESSIVE SOCIOLOGISTS NETWORK | |I want to read Ted's post and mull all this over. | |However, I thought I'd share a few things passing the hallways here at |DePauw (Dan Quayle's alma mater--but also Barbara Kingsolver & Vernon |Jordan). By the criteria Murray & Herrs. suggest, old Danny Boy should not |even have gotten into college--If I am to trust the comments from the few |senior faculty members who actually had him as a student. | |The merits of DQ |are beside the point, but I cannot help but wonder how ardent those who |want to use IQ to deny opportunities to some would be if it were more |publicly pointed out that some of their heroes would also be denied! Of |course DQ had access to beau coup $, and as ole bobby zimmerman says, |"money doesn't talk, it swears!" | |If I recall some of Lewis Feuer's work on Einstein, he too would have been |denied. | |random thoughts on a sunny saturday.... | |Tom Hall |thall@depauw.edu |Department of Sociology |DePauw University |Greencastle, IN 46135 |317-658-4519 | | |Date: Sat, 22 Oct 1994 11:30:31 -0600 |From: FEAGIN2@NERVM.NERDC.UFL.EDU |X-Comment: PROGRESSIVE SOCIOLOGISTS NETWORK | | This is a section from a textbook of mine, wrtten more than |a decade ago. Some may find it useful for contextualizing the |current so-called "IQ" debate: | | In the early 1900s popular writers, scholars, and members of |Congress warned of the peril of allowing inferior stocks from |Europe into the United States. Kenneth L. Roberts, a prominent |journalist, wrote of the dangers of the newer immigrants making |Americans a mongrel race: "Races can not be cross-bred without |mongrelization, any more than breeds of dogs can be cross-bred |without mongrelization. The American nation was founded and |developed by the Nordic race, but if a few more million members |of the Alpine, Mediterranean and Semitic races are poured among |us, the result must inevitably be a hybrid race of people as |worthless and futile as the good-for-nothing mongrels of Central |America and southeastern Europe." The "Alpine, Mediterranean, and |Semitic races" generally covered countries of heavy emigration |other than those of northern Europe; the Italians and European |Jews were thought by such writers to be examples of " |inferiority." | Half-truths about disease and illiteracy were circulated |about the southern and eastern European immigrants. It was true |in some years between 1880 and 1920 that half the adult Italian |immigrants could not read and write, but in other years the |overwhelming majority were literate. In no year were the charges |of total illiteracy leveled at Italian Americans by the press and |politicians accurate. Particularly hostile was the leap from the |proportions illiterate to assumptions of low intelligence. |In the first three decades of the twentieth century stereotypes |of intellectual inferiority were based in part on misreadings of |the results of new psychological tests inaccurately labeled |intelligence (IQ) tests. The term "intelligence test" is |inaccurate because the tests measure only selected, learned |verbal and quantitative skills, not a broad or basic |intelligence. In 1912 Henry Goddard gave Binet's diagnostic test |and related tests to a large number of immigrants from southern |and eastern Europe. His data supposedly showed that 83 percent of |Jewish and 79 percent of Italian immigrants were "feeble-minded," |a category naively defined in terms of low scores on the new |tests. | With the coming of World War I some prominent psychologists |developed verbal and performance tests for large-scale testing of |draftees. Although the results were not used for military |purposes, detailed analyses were published in the 1920s and |gained public and congressional attention because of the |racial-inferiority interpretation some psychologists placed on |the test results of the southern and eastern Europeans among the |draftees. | In 1923 Carl Brigham, a prominent young Princeton |psychologist who would later play a role in developing today's |college entrance tests, wrote a detailed analysis of the alleged |intellectual inferiority of immigrant groups, including Italian |Americans, drawing on data from army tests. The average scores |for foreign-born draftees ranged from highs of 14.87 for English |and 14.34 for Scotch draftees, to an average of 13.77 for all |white draftees, to lows of 10.74 for Polish and 11.01 for Italian |draftees. The low test scores for such groups as the Italian |Americans were boldly explained in racial terms; those European |groups were then considered inferior "races" or inferior "racial |stocks." These results were used by psychologists such as Brigham |to support the prevalent ideology of "Nordic" intellectual |superiority being espoused by racist theorists such as Madison |Grant. Brigham went on to argue that the sharp increases in |southern and eastern European immigration had lowered the general |level of American intelligence. | The political implications of Brigham's analysis were |proclaimed: immigration limits were necessary. Political means |should be developed within the United States to prevent the |continued "propagation" of "defective strains" in the population. |Here was pseudoscientific support for such government action as |passage of the 1924 Immigration Act, which would severely |restrict Italian and other southern European immigration on |racial grounds. | An important aspect of this stereotyping of Italian and |other European immigrants is the role of the government. The |definition of these immigrants as undesirable racial groups was |stimulated by social psychologists working with and for state |agencies, in this case the U.S. armed forces, and their research |was used by another branch of government, the Congress, to |restrict immigration. | The "intelligence" differences measured by psychological |tests were assumed to reflect the inferior or superior genetic |background of European "racial" stocks. In those decades few |seriously considered the possibility that the linguistic |(English), cultural (northern European American), and educational |bias in the tests and in interpretive procedures could account |for the differences. These debates over the inferiority of |European "racial" groups are now a historical curiosity. No |social scientists today would advance arguments of white ethnic |inferiority on the basis of paper-and-pencil test data. | | Some immigrant leaders developed humorous strategies for |dealing with concern over their intelligence and their "blood" |lineage. One prominent Italian American leader, Fiorello La |Guardia, suffered personal attacks that incorporated stereotypes. |For his criticism of officials such as President Herbert Hoover |he received letters such as the following: "You should go back |where you belong and advise Mussolini how to make good honest |citizens in Italy. The Italians are preponderantly our murderers |and boot-leggers." La Guardia's countertactic was biting humor. |When asked to provide material on his family background for the |New York World, he saw the ghost of "blood" inferiority behind |the request and commented: "I have no family tree. The only |member of my family who has one is my dog Yank. He is the son of |Doughboy, who was the son of Siegfried, who was the son of |Tannhuser, who was the son of Wotan. A distinguished family |tree, to be sure-but after all he's only a son of a bitch."