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In his September 14, 2007 op-ed piece in the New York Times, David Brooks tells his impression of the latest research in cognitive ability. Unfortunately, he not only misses the forest, but he bungles a few trees as well. Article and comments below.

A nice phenomenon of the past few years is the diminishing influence of I.Q.

Right out of the block he is off. In what domain was there once a non-zero IQ-outcome relation, but now, X number of years later, the relation has shown a systematic decrease? From the generality of the statement, one would expect this to hold across most, if not all, pertinent domains (e.g., occupation, academic success, etc.). However, that is not the case. Not only do the IQ-achievement, and IQ-occupation relationships still hold, but now there is a burgeoning new field in the area: cognitive epidemiology, that looks to see how health outcomes are related to cognitive ability. Deary et al give a terse summary here, and Gottfredson gives a conceptual overview here. But, perhaps more interesting, researchers who have no interest in intelligence per se are finding similar results: a case-in-point is Yakov Stern’s cognitive reserve research that shows people with higher IQ scores tend to have have less severe symptoms of Alzheimer’s symptoms. As this is a new area of inquiry, the exact nature of the relationship has not been identified, but one thing we can say for sure is that there is no diminishing influence of cognitive ability.

For a time, I.Q. was the most reliable method we had to capture mental aptitude. People had the impression that we are born with these information-processing engines in our heads and that smart people have more horsepower than dumb people.

These two statements have little to do with each other. IQ (at least as derived from a Full Scale score) has been, and still is, very reliable for most age groups and subpopulations, no matter how you measure reliability. For example, the Woodcock-Johnson, one of the more theoretically sound measures of cognitive ability, reports in their new normative update that the coefficient alpha values (which are a lower bound of reliability) above .90 for all ages ranging from 3 to over 80. Given that the maximum value alpha can take is 1 (under almost all circumstances), this is pretty good evidence. If you look at the technical manual for the Wechsler, Stanford-Binet, or Reynolds Intellectual Assessment Scales, you’ll find very similar values (I refer to these only because their norms span a very large age group, and the full scale score is derived from multiple subtests). I challenge Mr. Brooks to find a more reliably-measured psychological construct in psychology, nay, in the social sciences.

The second statement, while perhaps overstated, is true. People are born with brains, these brains process information, and smarter people (as measured by IQ scores) tend to process information faster (see, for example, here and here). What impression should people have instead? People are born with a blank slate and all of life is little more that the acquisition of stimulus-response patterns? Skinner died in the 1990s, and strict adherence to this view died long before that (a great book about this).

And in fact, there’s something to that. There is such a thing as general intelligence; people who are good at one mental skill tend to be good at others. This intelligence is partly hereditary. A meta-analysis by Bernie Devlin of the University of Pittsburgh found that genes account for about 48 percent of the differences in I.Q. scores. There’s even evidence that people with bigger brains tend to
have higher intelligence.

No disagreement here.

But there has always been something opaque about I.Q. In the first place, there’s no consensus about what intelligence is. Some people think intelligence is the ability to adapt to an environment, others that capacity to think abstractly, and so on.

Ah, the slippery slope begins. These arguments are so old, and well-answered in the literature that it is almost painful to repeat them. I refer the interested (and Mr. Brooks) to Seligman’s phenomenal, non-technical introduction, as well as Deary’s brilliant literary corpuscle. First, IQ and intelligence are two different things. One is a measuring instrument’s scale and the other is a psychological construct that is measured, to one degree or another, by an IQ test. We don’t confuse inches and paper, so why do we confuse IQ and intelligence? Second, few scholars actually study intelligence. While the word might be used in common parlance, there is no common definition. Instead, most serious scholars study general intelligence (g) or one of its sub-constructs (e..g, fluid abilities, crystallized abilities; see here or here or here). Once you make the jump to g, the definition becomes much more consensual. There are technical debates (as there are in any branch of science), but it’s measurement (by factor analysis of one flavor or another) is virtually undebated. For most purposes in daily life, it is OK to quasi-equate intelligence and g, as well as IQ scores and
intelligence, but they really are quite different concepts.

Then there are weird patterns. For example, over the past century, average I.Q. scores have risen at a rate of about 3 to 6 points per decade. This phenomenon, known as the Flynn effect, has been measured in many countries and across all age groups. Nobody seems to understand why this happens or why it seems to be petering out in some places, like Scandinavia.

IQ scores, across generations, need re-calibrated for valid comparisons. We have ways that do this very well (latent trait models), that have very sound theory behind them. You have to periodically re-calibrate your bathroom scale, and you have no question about what it is measuring; why should IQ be any different? As a side note, this phenomenon is not at all confined to IQ tests, and it has been known about in the psychometric literature for decades, although it is called item parameter drift there. Moreover, just because there is no consensus as to why cross-generational scores tended to rise in the mid-twentieth century, this does nothing to invalidate the validity of interpreting IQ scores within a generation.

I.Q. can also be powerfully affected by environment. As Eric Turkheimer of the University of Virginia and others have shown, growing up in poverty can affect your intelligence for the worse. Growing up in an emotionally strangled household also affects I.Q. One of the classic findings of this was made by H.M. Skeels back in the 1930s. He studied mentally retarded orphans who were put in foster homes. After four years, their I.Q.’s diverged an amazing 50 points from orphans who were not moved. And the remarkable thing is the mothers who adopted the orphans were themselves mentally retarded and living in a different institution. It wasn’t tutoring that produced the I.Q. spike; it was love.

Brooks is telling all parents of children who have Mental Retardation or Borderline Intelligence that their children’s low cognitive ability is a direct result of parental inadequacy. If these parents would love their children more, the Mental Retardation would go away. If I were king, I would mandate that any person with the gumption to make asinine statements like this do two things (a) read Spitz’s chef d’oeuvre, and (b) spend a week with a family who have a child diagnosed with Mental Retardation. Not just a daily visit, but an in vivo experience. Then get back to me about how easy it is raise the cognitive ability of people with mental retardation.

By the way, Turkheimer’s studies look at the ability of the environmental variance to modify heritabilty estimates. Specifically, people who grow up in more impoverished environment have a more variable environments, which, almost by definition, decreases heritability estimates. This is a very long cry from showing “growing up in poverty can affect your intelligence for the worse”.

Then, finally, there are the various theories of multiple intelligences. We don’t just have one thing called intelligence. We have a lot of distinct mental capacities. These theories thrive, despite resistance from the statisticians, because they explain everyday experience. I’m decent at processing words, but when it comes to calculating the caroms on a pool table, I have the aptitude of a sea slug.

What? A few paragraphs ago general intelligence existed, now it doesn’t? Anyway, it is an awful shame when everyday experience does not map onto what data tell us: Beth Visser recently (gasp!) gathered data to test Gardner’s theory. What did she find? Basically what John Carrol said she would find a decade ago: these multiple intelligence all positively correlate (sans kinesthetic intelligence) and a strong g factor can be extracted when the measures are factor analyzed.

I.Q., in other words, is a black box. It measures something, but it’s not clear what it is or whether it’s good at predicting how people will do in life. Over the past few years, scientists have opened the black box to investigate the brain itself, not a statistical artifact.

I wish I had the luxury of being able to write blatantly false statements in a national paper. There is over 100 years of empirical literature investigating the construct validity of IQ. There is also 100 years of literature examining what, and how well, IQ scores predict life outcomes. A simple perusing of Jensen’s g factor or Brand’s g factor (this one is even available for free!) would have sufficed here; but who wants data to interfere with a good opinion?

Now you can read books about mental capacities in which the subject of I.Q. and intelligence barely comes up. The authors are concerned instead with, say, the parallel processes that compete for attention in the brain, and how they integrate. They’re discovering that far from being a cold engine for processing information, neural connections are shaped by emotion.

…and you can read books about journalism in which the subject of sophism barely comes up. Namely because the books are concerned about journalism, not logical arguments. Why would a cognitive scientist who is writing a book about attention necessarily include a chapter about intelligence? As a rule, cognitive scientists tend to be concerned with general processes, not individual differences. The field can learn much from each other, but they are concerned about very different areas of investigation.

Antonio Damasio of the University of Southern California had a patient rendered emotionless by damage to his frontal lobes. When asked what day he could come back for an appointment, he stood there for nearly half an hour describing the pros and cons of different dates, but was incapable of making a decision. This is not the Spock-like brain engine suggested by the I.Q.

By all means, lets infer from one person with severe brain damage to the entire population. But if we want to play this game, I had a patient once who had just started Kindergarten, but could do addition, subtraction, multiplication and long division (the latter of which he deduced how to do pretty much on his own). He did not need a school to teach him any of this, so lets get rid of elementary schools for everyone. After all, if my patient could figure out long division, so should every other 5 year old.

Today, the research that dominates public conversation is not about raw brain power but about the strengths and consequences of specific processes. Daniel Schacter of Harvard writes about the vices that flow from the way memory works. Daniel Gilbert, also of Harvard, describes the mistakes people make in perceiving the future. If people at Harvard are moving beyond general intelligence, you know something big is happening.

Harvard never was a bastion for the study of general intelligence. It was the University of London. In fact, except for Yerkes, Herrnstein, and, to some extent, Pinker, I can’t think of too many profs. there who contributed much to the study of general intelligence. And since when did Harvard’s Psychology department become the measuring stick by which the importance of a research agenda was measured? I’m sure much of the work they do there furthers the general field of psychology, but what makes their research more special than, say, Berkeley, Stanford, UT-Austin, etc.?

The cultural consequence is that judging intelligence is less like measuring horsepower in an engine and more like watching ballet. Speed and strength are part of intelligence, and these things can be measured numerically, but the essence of the activity is found in the rhythm and grace and personality — traits that are the products of an idiosyncratic blend of emotions, experiences, motivations and inheritances.

This paragraph is quite confusing, perhaps due to the mixing of automotive and ballet metaphors. I think Brooks is trying to tell his readers he thinks personality is important for modern culture. I agree. And that has absolutely no bearing on the importance (or lack thereof) of cognitive ability in the same culture.
>

Recent brain research, rather than reducing everything to electrical impulses and quantifiable pulses, actually enhances our appreciation of human complexity and richness. While psychometrics offered the false allure of objective fact, the new science brings us back into contact with literature, history and the humanities, and, ultimately, to the uniqueness of the individual.

What? First, psychometrics (and specifically, the study of cognitive ability) has always held as paramount the uniqueness of the individual. Second, how has the study of cognitive ability NOT shown the complexity of humanity? Sir Cyril Burt, one of the pioneers in the field, was enamored with the complexity of students he encountered while a school psychologist in London. In fact, he was such an ardent supporter of psychological measurement so that he could begin to quantify, and, ultimately, understand and predict, this variability(see a bibliography here). More modern techniques, such as fMRIs, extend the work of psychometrics, in that they add to our ability to quantify individual variability at a much more precise level. However the two are quite complementary. From here:

Despite the sometimes contentious controversy about whether intelligence can or should be measured, the array of neuroimaging studies reviewed here demonstrates that scores on many psychometrically-based measures of intellectual ability have robust correlates in brain structure and function. Moreover, the consistencies demonstrated among studies further undermine claims that intelligence testing has no empirical basis.

In the world of academia, to have your ideas printed in a reputable journal, you have to go through the peer-review process. While there are arguments for the pros and cons of this process, at least it frequently squashes ill-informed, blatantly false propaganda from reaching the masses. After reading op-ed like this, one wishes the NYT had a similar mechanism in place.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: David Brooks, General Intelligence, IQ 
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History

The study of intelligence goes back many millennia, but, as such, it was usually defined as a nebulous construct and it fell more under the domain of philosophy than, say, science. Enter Francis Galton. With his Darwinian ancestry and precocious nature, Galton became fascinated by human variability and spent most of his life pursuing various distributaries from this river (e.g., dactylography, anthropology). Most important to the field of individual differences, was his study of the nature of human cognitive abilities. That is, he was one the first (if not the first) to make a systematic study of human variation in cognitive abilities. In doing so, he developed a cadre of “brass instruments” to measure various aspects of basic human abilities, which, to him, were all related to this underlying, general cognitive ability.


People lay too much stress on apparent specialities, thinking over-rashly that, because a man is devoted to some particular pursuit, he could not possibly have succeeded in anything else. They might as well say that, because a youth had fallen desperately in love with a brunette, he could not possibly have fallen in love with a blonde. He may or may not have more natural liking for the former type of beauty than the latter, but it is more probable a not the affair was mainly or wholly due to a general amorousness of disposition (Galton, 1869, p. 6)

While in his time, his elementary task/sensory discrimination data did not support his hypothesis that they were related to other “common sense” criteria such as education and occupation, later, when Fisherian analysis were applied, Galton was proved to be correct–that is, there were group differences in average scores (Johnson, McClearn, Yuen, Nagoshi, Ahern, & Cole, 1985).

In addition to his interest in elementary tasks, Galton was also interested in more traditional psychometrics. In fact, he convinced the British Association for the Advancement of Science to conduct a survey of mental capacities throughout British schools. William McDougal was appointed to head this up and his student, Sir Cyril Burt, got his initial taste of the field of applied psychometrics from this project (Burt, 1972).

Measurement

As important as Galton was in developing the underpinnings to modern intelligence research, he was not able to conceive of a way to measure general cognitive ability. Instead, this task was accomplished by engineer-turned-psychologist Charles Spearman (1904). Spearman was able to accomplish this based on two of his mathematical “inventions:” Classical Test Theory (CTT) and Factor Analysis (FA). Neither one of these is particularly easy to explicate via BLOG form, but the bottom line is: (a) CTT allowed for one to find the correlation between two variables, disattenuated by (random) measurement error; and (b) FA allowed for one to extract commonalities in groups of correlations. That is: If variable A, variable B, and variable C are all highly correlated with each other, then they likely have something in common. FA allows one to “get at” the thing (loosely speaking) that they have in common.

For example, if we have the following correlations for A, B, and C, then the last row has the correlations between the variable and the common factor (i.e., factor loadings)

A B C
A
B 0.7
C 0.8 0.75
g loadings
0.864 0.810 0.926

Spearman called that underlying factor general intelligence, and that is still what is meant today when the moniker Spearman’s g is used, even though the factor analytic techniques have greatly advanced since Spearman’s day.

After Spearman’s developments, there was a period of controversy as to (a) whether g existed, (b) if it existed, was it the only factor that could be extracted, and (c) if other factors could be extracted, could g be extracted at the same time? The details of this (needless?) argumentation need not concern this post (for a succinct summary, see Carroll, 1993), with the eventual conclusion being that, given a sufficient diversity of tests, g could be extracted, but other, more primary factors (e.g., Working memory, Long-term memory, Quantitative knowledge) could also be extracted. A picture is given below:

Spearman’s g is at the apex, the more primary ability are the circles below, and the tests from which the factors were extracted are represented by the boxes [the circles are used as that is the common way of representing latent variables; likewise, boxes are common way of representing manifest variables]

IQ

Around the same time Spearman and his London School contemporaries were doing their work in g theory, the field of intelligence testing was arising–due in large part to Binet and Simon’s work in France, Goddard and Terman’s work in the US, and Burt’s work in the UK. Today, intelligence is often used synonymously with IQ scores, which, outside of differential psychology and psychometrics, is also used synonymously with Spearman’s g. They are similar concepts, undoubtedly, but they need distinction.

Intelligence. A nebulous construct at best, it had eluded a century of definition, and, in Arthur Jensen’s (1998) own words, “psychologists are incapable of reaching a consensus on its definition” (p. 48) As it cannot be defined, we will not use it any further.

Spearman’s g. It is the primary factor extracted from the correlation matrix of a group of variables that all measure some aspect of cognitive ability. That is, it is the part of the covariance that all the variables have in common with each other.

Intelligence Quotient (IQ). An imperfect measure of Spearman’s g. That is, in modern IQ tests, IQ scores are the weighted average of performance all the subtests involved. This is sometimes referred to as “intelligence in general” as opposed to “general intelligence” (i.e., Spearman’s g), but for general purposes an IQ score can be thought of as rough measure of Spearman’s’ g, plus some (random) measurement error. Usually these scores are scaled such that most people will have a score between 90 and 110; mental retardation is a serious consideration for people with IQs below 70, as giftedness is a serious consideration for people with IQs greater that 120.

Why All the Fuss?

As presented, one may easily come to the conclusion of, so what? IQ/g sounds like it is another entry in the massive world of psychobabble, along with mental bonds, closure, and life coaching. The fuss is this:

No other variable in the history of psychology has (strongly) predicted such a wide variety of life outcomes.


  • Educational Outcomes (Deary, Strand, Smith, & Fernandes, 2007; Kuncel, Hezlett, & Ones, 2004)
  • Physical Health/Accidents (Gottfredson, 2004; Gottfredson & Deary, 2004).
  • Reaction Time to Cognitive Tasks (Jensen, 2006)
  • Occupation Status (Gottfredson, 1986; Herrnstein & Murray, 1994)
  • Job Success (Schmidt & Hunter, 1998, 2004)
  • Crime (Ellis & Walsh, 2003).
  • Race Differences (Lynn, 2005; Rushton & Jensen, 2005)
  • Sex Differences (Lynn & Irwing, 2004)
  • GDP (Lynn & Vanhanen, 2006)

And this is to just name a few.

If I were to stop here, one might be under the impression that g/IQ are important, but (a) there are other forms of “intelligence”; and (b) that IQ is just a product of the environment and can be raised (almost) at will.

Multiple Intelligences

The theory of Multiple Intelligences (MI) stems from Howard Gardner who (now) posits that g exists, but so do other forms of independent “intelligences” that (equally) predict life success. His other forms are things like interpersonal skills, intrapersonal knowledge, and kinesthetic ability. Since in the 25 years since MI has been around, Gardner has refused to test his hypotheses, it really is not even worth mentioning anymore. Thus, I won’t (for some empirical work showing why Gardner is, well, wrong, see Visser, Ashton, & Verson, 2006, under review).

Triarchic Theory of Intelligence

This works stem from the work of Robert Sternberg, and his theory of cognitive ability that, similar to Gardner, posits that g exists, but that there are independent cognitive entities that are useful in life, such as practical intelligence; he even goes so far as to say that these independent entities are better predictors of life outcomes than g. Unfortunately, like Gardner, he doesn’t readily submit his theories to much empiricism, and his claims, to date, are unsubstantiated (for an excellent critique, see Gottfredson, 2003).

Stability and Raising g/IQ

If one is under the impression that the environment can have massive influence on g, the logical product of that belief is that massive government programs should be able to raise cognitive abilities. In short, they do not. They produce short-term gains, but the gains do not last long (see, for example, Spitz, 1986, 1992). This is not to say that other things, such as nutritional supplementation, might not be able to increase cognitive performance, but massive environmental programs, at least as implemented in the past 50 years, have not. Moreover, IQ scores measured when one is 10ish are consistent, very consistent, with IQ scores measured almost 70 years later on the same individuals (Deary, Whalley, Lemmon, Crawford & Starr, 2000). That is, despite a life’s worth of diversity of experience, your IQ when you are in Middle School is very predictive of your IQ when you retire.

Take Home Message

g is ubiquitous in cognitive tasks, it is stable across time in individuals, and no other variable in the history of psychology has been able to predict so many life outcomes, so well.

References

Burt, C. L. (1972). Inheritance of general intelligence. American Psychologist, 27, 175–190

Carroll, J.B. (1993) Human cognitive abilities. Cambridge University Press.

Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R., & Starr, J. M. (2000). The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence, 28, 49-55.

Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13-21.

Ellis, L., & Walsh, A. (2003). Crime, delinquency, and intelligence: A review of the worldwide literature. In H. Nyborg (Ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 343-365). New York: Pergamon.

Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. London: MacMillan

Gottfredson, L. S. (Ed.) (1986). The g factor in employment. Journal of Vocational Behavior, 29 (3). (Special Issue)

Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343-397.

Gottfredson, L. S. (2004). Intelligence: Is it the epidemiologists’ elusive “fundamental cause” of social class inequalities in health? Journal of Personality and Social Psychology, 86, 174-199.

Gottfredson, L., & Deary, I. J. (2004). Intelligence predicts health and longevity: but why? Current Directions in Psychological Science, 13, 1-4.

Herrnstein, R. & Murray (1994) The Bell Curve: Intelligence and class structure in american life. New York: Free Pres

Jensen, A. R. (1998). The g factor. Westport, CT: Praeger.

Jensen, A.R. (2006). Clocking the mind: Mental chronometry and individual differences. Oxford: Elsevier.

Johnson, R. C., McClearn, G. E., Yuen, S., Nagoshi, C. T., Ahern, F. M., & Cole, R. E. (1985). Galton’s data a century later. American Psychologist, 40, 875–892

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86, 148-161.

Lynn R. (2005). Race differences in intelligence: An evolutionary analysis. Augusta, GA: Washington Summit.

Lynn, R. and Irwing, P. (2004) Sex differences on the Progressive Matrices: a meta-analysis. Intelligence, 32, 481-498.

Lynn, R. & Vanhanen, T. (2006). IQ and global inequality.
Atlanta, GA: Washington Summit.

Rushton, J. P., & Jensen, A. R. (2005). Thirty years of research on race differences in cognitive ability. Psychology, Public Policy, and Law, 11, 235-294.

Schmidt, F. L., & Hunter, J. (1998). The validity and utility of selection methods in personnel psychology practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262-274.

Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86, 162-173.

Spearman, C. E. (1904). “General intelligence”: Objectively defined and measured. American Journal of Psychology, 15, 201–292.

Spitz, H. H. (1986). The raising of intelligence: A selected history of attempts to raise retarded intelligence. Lawrence Erlbaum Associates.

Spitz, H. H. (1992). Does the Carolina Abecedarian Early Intervention Project prevent sociocultural mental retardation? Intelligence, 16, 225-237

Visser, B.A., Ashton, M.C., & Verson, P.A. (under review). Self-estimated general and “multiple” intelligence(s): Accuracy, sex differences, and personality.

Visser, B.A., Ashton, M.C., & Verson, P.A. (2006). Beyond g: putting Multiple Intelligences theory to the test. Intelligence, 34, 487-502.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science 
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Wherever the abilities involved are sufficiently distinct–and that is in the great majority of cases–our tetrad equation is satisfied with surprising exactitude, so that here each ability must be divisible into g and s. The letter g becomes, in this manner, a name for the factor–whatever it may be–that is common to mental tests of such a description. This is the very definition of g. (Spearman, 2005, p. 161)

General intelligence (g) has been one of the most, if not the most, aggressively studied constructs in psychology. Type the search string “general intelligence” in PsycInfo and you will return over 2000 entries, and a similar search in Pubmed pulls up over 400. If you broaden the term to just “intelligence”, the respective number of entries are 65405 and 37166. While not all of the results focus on g , (e.g., AI, “social intelligence”), a large portion of them do, and the prospect of meandering your way through can be intimidating. Fortunately, the overall literature is consistent and, at least for me, highly engaging.

The study of g can be bifurcated into two distinct areas: vertical and horizontal g . Vertical g is the domain that studies g’s biological relationships. It is the area that is going to eventually assimilate enough data and literature to elucidate, unquestionably, the causal mechanisms of g . From this field of study, we know that g is correlated with a variety of neural mechanisms, such at glucose metabolism (Haier, 2003), cortical development (Shaw et al., 2006), and biochemical activity (Jung et al., 2005). We know that g is highly heritable, both when measured psychometrically (Plomin & Spinath, 2002) or chronometrically (Beaujean, 2005). We know that g decreases with inbreeding (Jensen, 1983) and increases with hybrid vigor (Nagoshi & Johnson, 1986). As genome scanning becomingmore popular, we are now even beginning to see some specific genes that are implicated g.

As interesting as vertical g is, however, this entry is going to instead focus in the horizontal aspects of g . That is, how does g play out into “everyday life.” Specifically, we will look three different, although related, areas: education, occupation, and general life outcomes. The reasons for doing so are twofold: (a) the more the science of horizontal g is positively promulgated, then, perhaps, the more likely people are to support the needed research into vertical g and (b) even though this area of research has been around for over a century (e.g., Galton, 1869), there are still new, important findings.

Before delving into horizontal g, however, it would behoove us to delineate a mechanism by which g could influence education, occupation, and general life outcomes.For our purposes, that mechanism is information processing. Generally defined, information processing is the pathway and mechanisms by which stimuli are perceived, attended to, retrieved, and/or used to solve problems and/or cope with exigencies in the environment (Jensen, 1998a). The cognitive psychology literature is chalked full of the nuances of the various information-processing theorists, the specifics are which cannot be delineated here (an easy-to-read intro: Ormrod, 2004). Yet, within all these theories lies the idea that people respond to stimuli in a way that involves many mechanisms (e.g., sensory register, primary memory)and a variety of neurological regions (e.g., hippocampus, amygdala, mammillary bodies). The consequence? There is ample room for individual differences in the speed and efficiency in which information is processed.

From another perspective (e.g., Kline, 1998), information is processed in irreducibly small pieces (often called bits) and the time it takes to process those bits is the BIP, the Basic period of Information Processing. Now, the time it takes Johnny to process the fact that the only integer between 2 and 4 is 3 is going to be different than the time it takes Jane. Multiply those differences by the number of people processing the fact, and voila! individual differences.

Educational Outcomes

This is probably the area most replete with data and, unsurprisingly, the g-educational achievement relationship is strong. In fact, although it differs by grade level (with it decreasing as grade level increases), most of the non-random variance in scholastic performance is accounted for by g (Thorndike, 1984). Jensen (1989, 1998b) writes that this is so due to the fact that “school learning” is, itself, quite g -loaded. Of course, there are those who write that g is just a product of education (e.g., Ceci, 1991; for a review of others, see Gottfredson, 1986), or, perhaps more egregious, that g and educational achievement are just products of the tests designed to measure them (for review and rebuttal, see Jensen, 1984). But these arguments quickly dissipate when looking at the evidence.

For example, in the latest issue of Intelligence, there were two longitudinal studies (Deary, Strand, Smith, & Fernandes, 2007; Watkins, Lei, & Canivez, 2007) that showed a strong IQ –> Educational Achievement relationship (approx. 70 from Deary), but reverse (i.e., EA –> IQ) was not there (from the Watkins study). Further evidence comes from the two major “We can improve you Education by improving your IQ” projects: Head Start and the Abecedarian Study. With regard to the former, Head Start just does not produce long-term IQ gains and, hence, does not produce long-term academic gains (Caruso, Taylor, & Detterman, 1982; Holden, 1990; Kreisman, 2003). With regard to the latter, while there has been acrimonious debate, the overall conclusion is that, like Head Start, the initial IQ gains do not last, giving even more evidence that educational achievement cannot be raised independently of g (Spitz, 1986, 1992, 1993b, 1993a).

Yet another line for arguing against the prominence of g in education is the idea that there are other traits that are just as necessary for academic success, such as motivation, personality, etc. To risk sounding like to broken record, the data shows that these traits are not nearly as potent predictors as g in predicting academic outcomes. For example, Gagne and St. Pere (2002) gives us reason to believe that motivation might just be an impotent variable in predicting academic achievement. Likewise, Laidra, Pullmann, and Allik (2007) have shown that while personality factors contribute some to the variance in educational achievement, they are dwarfed in comparison to the contribution of g.

Occupational Outcomes

There are many theories as to how g and occupational outcomes relate (see Gottfredson, 1986), but the one that is most supported by data is best explicated by Frank Schmidt and John Hunter

[g] predicts both the occupational level attained by individual and their performance within their chosen occupation. [g] correlates above .50 with later occupational level, performance in job training programs, and performance on the job. Relationships this large are rare in psychological literature and are considered “large” . . . weighted combinations of specific aptitudes (e.g., verbal, spatial, or quantitative aptitude) tailored to individual jobs do not predict job performance better than [g] measures alone, thus disconfirming the specific aptitude theory. It has been proposed that job experience is a better predictor of job performance than [g], but the research findings . . . support the opposite conclusion. . . . Nearly 100 years ago Spearman (1904) proposed that the construct of [g] is central to human affairs. The research . . . supports his proposal in the world of work, an area of life critical to individuals, organizations, and the economy as a whole.(Schmidt & Hunter, 2004, p.171; cf.Schmidt & Hunter, 1998)

One could argue
that, given the high g -education relationship, that the g-occupation relationship is just a natural outgrowth.That is, once education is controlled, the g-occupation relationship significantly shrinks. But to make that argument, one would have to have a Sternberg-like approach to intelligence (Sternberg & Wagner, 1993). That is, that the cognitive skills needed for a successful education are somehow vastly different than those needed for everyday life. The data, however, indicate that the same generative process that tends to makes one successful in the educational arena is also the mechanism that tends to make one successful in the occupational arena: g (Kuncel, Hezlett, & Ones, 2004). This is not to say that other things are not important in occupational or educational outcomes; but, as with education, they are not nearly as potent predictors (Gottfredson, 2002).

Life Outcomes

Over the last decade or so, an area that has become of more interest to the intelligence community is the influence of g on general life outcomes. That is, beyond educational and occupational outcomes, does g contribute to life success? The answer here, too, seems to be a resounding yes.

IQ scores [a proxy for g] predict a wider range of important social outcomes and they correlate with more personal attributes than perhaps any other psychological trait. The ubiquity and often-considerable size of g’s correlations across life’s various domains suggest g truly is important in negotiating the corridors of daily life. (Gottfredson, 2003, p. 326)

But how does g relate to general life outcomes? Believe it or not, it appears that the same information-processing mechanisms that are so potent for educational and occupational outcomes also play a role in day-to-day life (Gottfredson & Deary, 2004). Gottfredson (2003, 2004b) elaborates this mechanism as follows: Life is is made up of many tasks with a wide array of complexity (Gordon, 1997). In the US and most Western nations, society is “free enough” for competence (read: g ) to make a substantial difference in who succeeds in life. As those who have “higher g” are more able to tackle the day-to-day activities of life successfully with less exerted effort, they are able to progress in life with fewer impediments (e.g., untreated illness, accidents; Gottfredson, 2004a), thus allowing them to (a) have more resources to successfully compete and (b) be able to use their resources more efficiently. This then not only allows for a higher probability of achieving satisfying life outcomes (e.g., adequate income, occupational autonomy), but also allows for a lower probability of being involved with unsatisfying life outcomes (e.g., having children without means to support them, crime/delinquency) (cf. Ellis & Walsh, 2003; Herrnstein & Murray, 1996)

Conclusion

Given the ubiquity of g in fostering success in many life outcomes from education achievement to occupational success, from health outcomes to criminal recidivism, social science in general and psychological science in particular would be remiss to “pretend it doesn’t matter” (Gottfredson, 2000). Rather, if these fields want to strengthen their scientific integrity and acumen, they should do exact opposite. That is, bring the large, cumulative database on g and its influence on life outcomes to the forefront of a wide array of research agendas so that this corpus of data can serve as the strong underlying foundation of a generation of new investigations on g’s life implications. While this line of investigation may never get to the underlying (vertical) mechanisms by which g operates, it can help foster the acceptance of doing such research and pave the way for its societal implications, whatever they may be.

References

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Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27, 703-722.

Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13-21.

Ellis, L., & Walsh, A. (2003). Crime, deliquency, and intelligence: A review of the worldwide literature. In H. Nyborg (Ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 343-365). New York: Pergamon.

Gagne, F., & St. Pere, F. (2002). When IQ is controlled, does motivation still predict achievement? Intelligence, 30, 71-100.

Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. London: MacMillan.

Gordon, R. A. (1997). Everyday Life as an Intelligence Test: Effects of Intelligence
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Gottfredson, L. S. (1986). Societal consequences ofthe g factor in employment. Journal of vocational behavior, 29, 379-410.

Gottfredson, L. S. (2000). Pretending that intelligence doesn’t matter. Cerebrum, 2, 75-96.

Gottfredson, L. S. (2002). g: Highly general and highly practical. In R. J. Sternberg & E. L. Grigorenko (Eds.), The general factor of intelligence: How general is it? (pp. 331-380). Mahwah, NJ: Erlbaum.

Gottfredson, L. S. (2003). g, jobs, and life. In H. Nyborg (Ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 293-342). New York: Pergamon.

Gottfredson, L. S. (2004a). Intelligence: Is it the epidemiologists’ elusive “fundamental cause” of social class inequalities in health? Journal of Personality and Social Psychology, 86, 174-199.

Gottfredson, L. S. (2004b). Life, death, and intelligence. Journal of Cognitive Education and Psychology, 4, 23-46.

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Haier, R. J. (2003). Brain imaging studies of intelligence: Individual differences
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Herrnstein, R. J., & Murray, C. (1996). Bell curve: Intelligence and class structure in American life. New York: Free Press.

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Jensen, A. R. (1989). The relationship between learning and intelligence. Learning
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Jensen, A. R. (1998a). The g factor and the design of education. In R. J. Sternberg & W. M. Williams (Eds.), Intelligence, instruction, and assessment: Theory into practice (pp. 111-131). Mahwah, NJ: Lawrence Erlbaum.

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potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86, 148-161.

Laidra, K., Pullmann, H., & Allik, J. (2007). Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and Individual Differences, 42, 441-451.

Nagoshi, C. T., & Johnson, R. C. (1986). The ubiquity of g. Personality and Individual Differences, 7, 201-207.

Ormrod, J. E. (2004). Human learning (4th ed.). Upper Saddle River, NJ: Pearson.

Plomin, R., & Spinath, F. M. (2002). Genetics and general cognitive ability (g). Trends in Cognitive Science, 6, 169-176.

Schmidt, F. L., & Hunter, J. (1998). The validity and utility of selection methods in personnel psychology practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262-274.

Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86, 162-173.

Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., et al. (2006, Mar 30). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676-679.

Spearman, C. E.(1904). “General intelligence”: Objectively defined and measured.
American Journal of Psychology, 15, 201-292.

Spearman, C. E.(2005). The abilities of man: Their nature and measurement. New York: Blackburn Press (Original work published 1927).

Spitz, H. H. (1986). The raising of intelligence: A selected history of attempts to raise retarded intelligence. Hillsdale, NJ: Lawrence Erlbaum Associates.

Spitz, H. H.(1992). Does the Carolina Abecedarian Early Intervention Project prevent sociocultural mental retardation? Intelligence, 16, 225-237.

Spitz, H. H. (1993a). Spitzs reply to Ramey’s response to Spitz’s first reply to Ramey’s first response to Spitz’s critique of the Abecedarian Project. Intelligence, 17, 31-35.

Spitz, H. H. (1993b). When prophecy fails: On Ramey’s response to Spitz’s critique of the Abecedarian Project. Intelligence, 17, 17-23.

Sternberg, R. J., & Wagner, R. K. (1993). The g-ocentric view of intelligence and job performance is wrong. Current Directions in Psychological Science, 2, 1-5.

Thorndike, R. L.(1984). Intelligence as information processing: The mind and the computer. Bloomington, IL: Center on Evaluation, Development, and Research.

Watkins, M., Lei, P. W., & Canivez, G. L. (2007). Psychometric intelligence and achievement: A cross-lagged panel analysis. Intelligence, 35, 59-68.

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• Category: Science • Tags: Education, General Intelligence, IQ 
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I received my latest APS Observer in the mail, and one of the main articles reports that 3 psychologists have been placed on the National Science Board, one of whom is Camilla Benbow.

This is significant for multiple reasons. While Dr. Benbow’s academic record is exemplary (here are some pdfs), her record of research on sex differences and mathematics (along with Stanley and Lubinski) ranks her among the few in this area who do not fall into the “there are no differences” camp (a la Hyde; pdf). Moreover, her writings on the cogency of cognitive ability and its influence in educational and life outcomes (no doubt stemming from her many years working with precocious youth) is very much in line with the general London School mindset. (must read: Benbow, C. P., & Stanley, J. C. (1996). Inequity in equity: How current educational equity policies place able students at risk. Psychology, Public Policy, and Law, 2, 249-293.). Consequently, I think this is the first time in the NSB’s History an Individual Differences researcher is on board. I am not sure what, if any, changes are in store at the NSF, but this a big step for the field of differential psychology and (hopefully) a sign of things to come.

Three cheers for Dr. Benbow!

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Count ‘em: one, two, three new studies on the relationship between IQ and academic achievement in the latest issue of Intelligence (volume 35, issue 1)

Before the studies’ precis, a little background on why such studies are necessary. More than anything, such studies are needed because folks such as S. Ceci and R. Sternberg (very prominent and oft-cited) advocate that (traditional) IQ tests are just measuring little more than school related achievement. So, IQ and academic achievement are only related because, for reasons X, Y, and Z (pick your own environmental variables), some folks get more out of school, and it just so happens that the same folks do well on IQ tests due largely (if not entirely) because school achievement and IQ tests are measuring the same thing. Consequently, g is an irrelevant artifact of those damned psychometricians.

An alternative hypothesis (explicated nicely in Jensen1,2), however, is that due largely to genetic factors (which influence both individual differences and environmental influences), people enter school with wide variability in cognitive ability and “readiness to learn.” This initial variability then heavily influences (although not completely determines) the amount a given student will pick up as he/she matriculates. As a student gains more information, his/her initial ability and the new information acquired then interact so he/she is able to expand his/her knowledge further, and so on and so forth. Therefore, while one needs access to “information,” the child’s general cognitive ability is the engine driving his/her educational achievement.

Don’t miss the point here. These are two separate, testable, hypotheses. (A) IQ and academic achievement are synonymous. That is, people are smart (or not so smart) almost solely because they had (or did not have) a good education. (B) IQ is independent of academic achievement, although the former significantly influences the latter. That is, you can come from a good school, but not be so bright, and do poorly on achievement tests; likewise, you can come from a school that is not so good, (but meets some very minimum standard), but be bright, and do very well on academic achievement tests.

Now, the studies…….

1) Treena Eileen Rohde and Lee Anne Thompson: Predicting academic achievement with cognitive ability

This study is likely the weakest only because they used a group of college students from an elite university. Not that there is anything wrong with this, but when you see the samples in the studies below, it is a noticeable concern.

Their major contribution was that in predicting (standardized) academic achievement, speed of information processing and spatial ability can explain small, but significant, amounts of variance unexplained by general vocabulary (Mill Hill) and perceptual organization (Raven’s Matrices), although the latter two tests, hands down, did the best in predicting academic achievement across various indicators.

In their own words:

General cognitive ability measures (Raven’s, Vocabulary) and specific cognitive abilities (working memory, processing speed, spatial ability) collectively accounted for between 16% [GPA] and 54% [SAT] of the variance in academic achievement.

2) Marley W. Watkins, Pui-Wa Lei and Gary L. Canivez: Psychometric intelligence and achievement: A cross-lagged panel analysis

This study had 3 advantages over the former: (1) it is longitudinal, (2) the data is from a much wider scope of IQs, and (3) the data comes from all over the US. The drawback, and major caveat, is that the data is all from special education (broadly defined) testing, so the applicability to the entire population is in question. Still, the mean Full Scale IQ score from the WISC-III (the IQ instrument used) is 90 with a SD of 15 (in the general population it is 100 and 15), and the subtest scores hover around 8 with SDs that hover around 3 (in the general population it is 10 and 3).

Because they have longitudinal data on both standardized IQ and standardized achievement tests, they can specifically test the IQ—>Achievement hypothesis (see preamble). What do they find?

This notion of intelligence estimating a student’s ability to succeed in school assumes the temporal precedence of intelligence to achievement. . . Regardless, the present study supports the view that intelligence, as measured by the VC [Verbal Comprehension] and PO [Perceptual Organization] dimensions of the WISC-III, influences or is related to future achievement whereas reading and math achievement do not appear to influence or are not related to future psychometric intelligence.

Stated more bluntly:

. . . the present study provides evidence that psychometric intelligence is predictive of future achievement whereas achievement is not predictive of future psychometric intelligence. This temporal precedence is consistent with the theoretical position of Jensen (2000)[1] that intelligence bears a causal relationship to achievement and not the other way around.

3) Ian J. Deary, Steve Strand, Pauline Smith and Cres Fernandes: Intelligence and educational achievement

Before getting on to the study, a brief word about Dr. Deary. He is the current badass of differential psychology. Because of his background (degrees in medicine and psychology), he is able to investigate psychometric, chronometric, genetic, and neurological aspects (often concurrently) of both intelligence and personality (look at the range on his vita). As if that were not enough, he has challenged the whole field of differential psychology by obtaining multiple population level, longitudinal data sets. So instead of trying to infer from a sample a few hundred to the target population, he is gathering population level samples of thousands of individuals. Case in point:

Deary’s study looked at how cognitive ability measured at age 11 predicted academic achievement at age 16. Unsurprisingly, the IQ-Achievement correlations for the Sciences are around .6 (math highest, chemistry lowest), with similar coefficients form Arts/Humanities and Social Studies. Surprisingly, for practical fields (e.g., P.E., Art) the coefficients are a little lower, but not that much, averaging around .5. Here is a pic of the correlation table: (the n is in parentheses; it obviously changes as not every student took every class)

Deary took the analysis a step further however and did a little latent variable modeling. As the IQ test had three components/subtests (verbal, nonverbal, quantitative), he correlated a latent g factor with a latent academic factor using the following subtests: English, English Literature, Math, Science, Geography, French (n=12519). The correlation between the latent factors was .81. That is: 66% of the variance in latent (general) academic achievement can be explained by latent cognitive ability—measured 5 years previously. While he hypothesizes that such things as “school ethos” and “parental support” are good areas to search for the other 34%, based on Rohode’s work, it is likely going to be found in residual, first order factors (see Carroll or McGrew).

Take home message: While general cognitive ability and academic achievement are not isomorphic, the former is necessary for the latter, while the converse is not necessarily true. Spearman suggested this more than a century ago, and, to quote the last sentence in Deary’s work,

These data establish the validity of g for this important life outcome.

1. Jensen, A. R. (2000, August). The g factor and the design of education. Paper presented at the annual meeting of the American Psychological Association, Washington, DC.

2. Jensen, A. R. (1989). The relationship between learning and intelligence. Learning and Individual Differences, 1, 37-62.

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….to ruin a perfectly good theory. So says Beth Visser and colleagues in the latest edition of Intelligence. She tested Gardner’s theory of Multiple Intelligences (MI), gasp!, empirically. What did she find?

Our analyses of tests measuring the “intelligences” of Gardner’s MI theory revealed that many of those tests were substantially intercorrelated, despite representing different domains of Gardner’s framework, and also showed strong loadings on a g factor and strong correlations with an external test of general intelligence. These results are difficult to reconcile with the core aspects of MI theory.

More specifically, the researchers combed over quite a few tests to find two or three that match Gardner’s own explication of his intelligences. When they gave them to a group of college students, (surprise surprise) they all had a strong correlation with each other, except the tests that measured Bodily-Kinesthetic, Intrapersonal, and Musical “intelligence.” I don’t think anyone outside of Gardner and his sycophants think that tests that measure Bodily-Kinesthetic skills are actually a subset under intelligence. Likewise, Intrapersonal intelligence is little more than self-esteem/self-concept, which we know doesn’t really predict anything; the low musical correlations, however, were a bit of surprise (esp. given Carroll’s opus), but in all likelihood, this a solely a function of the tests horrible reliability.

What can we conclude? (a) sans physical ability and self-esteem, Gardner’s intelligence domains can best be thought of as second-stratum factors of Carroll’s hierarchy, (b) that being said, before any more time, effort, and energy is exerted in MI, Gardner or his colleagues need to provide “falsifiable, testable, MI-based hypotheses that would predict results different from those predicted by existing models of the structure of mental abilities,” and (c) any educational curriculum based on MI theory is, currently, a waste of teacher time and student energy.

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J. Philippe Rushton has two new things of interest:

1) A new homepage with his full vita–with many of the newer articles being downloadable.

2) A new article(co-authored with the late Douglas Jackson), showing pre-collegiate males are smarter (“have higher g”), at least as measured by the SAT, than females across ethnic groups and SES. This seems to hold for both SAT-Math and SAT-Verbal. Of note: a) the sample size is huge: 100,000 people; 2) males were “overrepresented” at the higher tails of the SAT/IQ distribution, females were “overrepresented” at the lower tails—but, of course, folks with Mental Retardation and Borderline Cognitive Functioning do not, in general, take the SAT, and since the sample used was the 1991 SAT “validity” folks, the lower tails of the SAT distribution should be around 90ish in a regular IQ distribution. Thus, what we have here, is some more evidence that males are more likely to be in the upper tails of the IQ distribution, while females are more likely to be in the center. This, then, averages to about a 3 IQ points advantage for males—a trend that others have also found (e.g.,)

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The NYT has an article up about how supposedly malleable IQs are by a professor trying to get more money for early childhood education programs. Aside from the arguments about funding a program that is not that effective , the merits of the NYT article are questionable.

Kirp’s argument is based on two studies by the same researcher: Michel Duyme. Before anything is stated about the studies, I’ll point out that while the participants in these studies may be new, the theory and designs are not. Herman Spitz’s masterful work goes over many of these types of studies, and the overall result is not too impressive for those thinking IQ is easily changed. If your educational psychology professor didn’t make that book a required reading, join Questia and read it, or snag it from the local library.

Now on to the NYT article…….

About the first study, Kirp writes:

Regardless of whether the adopting families were rich or poor, Capron and Duyme learned, children whose biological parents were well-off had I.Q. scores averaging 16 points higher than those from working-class parents. Yet what is really remarkable is how big a difference the adopting families’ backgrounds made all the same. The average I.Q. of children from well-to-do parents who were placed with families from the same social stratum was 119.6. But when such infants were adopted by poor families, their average I.Q. was 107.5 – 12 points lower. The same holds true for children born into impoverished families: youngsters adopted by parents of similarly modest means had average I.Q.’s of 92.4, while the I.Q.’s of those placed with well-off parents averaged 103.6. These studies confirm that environment matters – the only, and crucial, difference between these children is the lives they have led.

Darth Quixote has already deeply delved into this study, and there is no need to repeat him here. I will add:

  • the design was non-experimental, post-test only. While these designs have merit, to infer causality is tenuous as the adoptees’ IQ could easily have fit the same pattern before adoption. Moreover, since there was no randomness in the adoption process, we have no idea as to why the children were placed where they were.
  • the researcher’s did not test to see if the instrument they were using was functioning the same across comparison groups. A (good) way to check this Multigroup confirmatory factor analysis (e.g.,), but with the n so small, it is difficult to do.1 As a proxy, I checked for equality of covariances: B+ vs B-: not significantly different; A+ vs A-: not significantly different; B+A+ vs B-A-: significantly different! (chi-square: 154.79, df= 55, p < .0001). This could mean multiple things. Two that come to mind are: a) the B-A- group's pattern of cognitive functioning has not matured at the same rate as the B+A+ group, so give them some time and it may converge. b) the B- kids were placed into the A- home for a specific reason (i.e., they had some type of deficit that made A+ parents pass on them).
  • So where does leave us? Capron and Duyme sum it up best:

    Although these findings clearly indicate that the biological parents’ background contributes to observed differences in IQ between extreme groups, as does that of adoptive parents, more detailed interpretation is difficult (p. 553)

    which is a far cry from Kirp’s interpretation:

    the only, and crucial, difference between these children is the lives they have led.

    About the second study, Kirp writes:

    A later study of French youngsters adopted between the ages of 4 and 6 shows the continuing interplay of nature and nurture. Those children had little going for them. Their I.Q.’s averaged 77, putting them near retardation. Most were abused or neglected as infants, then shunted from one foster home or institution to the next.

    Nine years later, they retook the I.Q. tests, and contrary to the conventional belief that I.Q. is essentially stable, all of them did better. The amount they improved was directly related to the adopting family’s status. Children adopted by farmers and laborers had average I.Q. scores of 85.5; those placed with middle-class families had average scores of 92. The average I.Q. scores of youngsters placed in well-to-do homes climbed more than 20 points, to 98 – a jump from borderline retardation to a whisker below average. That is a huge difference – a person with an I.Q. of 77 couldn’t explain the rules of baseball, while an individual with a 98 I.Q. could actually manage a baseball team – and it can only be explained by pointing to variations in family circumstances.

    One has to do some digging elsewhere, but the study he is referring to is from PNAS. Since the study is free to read, I’ll only go over the major points:

  • The research team picked children/teenagers who met five criteria: They had all been (1) neglected and/or abused during infancy, having been definitively removed from their biological family by court order after judicial procedures; (2) placed in many foster families and/or institutions before adoptive placement; (3) had an IQ 60 in the year preceding adoptive placement; (iv) aged 4-6 at the time of the adoptive placement and (v) aged 11-18 and being raised by the same two adoptive parents at the time of the second IQ test (i.e., 5-14 years with same adoptive parents)
  • The original IQ test was either a) the first or second edition of the Stanford Binet (their wording and reference date makes it hard to discern), b) the original Bayley Scale of Infant Development, or c) “other French tests of Intelligence”. The post-adoption IQ was either the WAIS or the WISC-R.
  • The Children/teenagers were classified into one of three groups based on adoptive father’s occupation: High, Middle, or Low
  • There was a universal gain in IQ scores for all participants, averaging 13.88 points.
  • There was a (range restriction corrected) correlation of 0.67 between IQs before and after adoption, which “indicates a degree of stability close to the stability found in longitudinal studies of biological children who have not undergone an environmental change. . . Thus, on the basis of IQ at the end of the preschool period, the results show that there is a moderate stability for rank. This is a near-universal finding.
  • As to a critique, first the selection criteria is loaded. No one with any sense about them would think that being abused/neglected from birth so bad that the legal system has to intervene would not decrease cognitive functioning. This is not at all what is meant when discussing “Average Expectable Environment.” So right off the bat we know that these kids are not at all the same as a regular Joe (or Jane) off the street who has Borderline IQ. Thus, right off the bat we would expect that after any semblance of stability, the kids’ cognitive functioning would improve (i.e., return t
    o its normal state).

    Second, while there is some difference between the IQs across groups, it isn’t major (max: Low SES PIQ vs High SES PIQ, d = .9 [r2=.17]; min: Low SES VIQ vs. High SES VIQ, d=.5 [r2=.06]), indicating SES explains somewhere between 6 and 17 percent of the variance in post-adoption IQ scores. While there is definitely an effect, given the extreme nature of the study’s categories, it does not appear to be much of one.

    Third, it is very difficult to say with much certainty that IQ scores from scale, developed with one set of theoretical guidelines and one set of 1950s norms are directly comparable other IQ scores from a different scale, developed with a totally different theoretical orientation that uses a set of 1970s/1980s norms. This is even more so when the Bayley scale is used. For those of you that have given the Bayley (all 2 of you!), you know it is an extreme pain to administer and that it is about as unlike the Wechsler instruments as you can find a test that still measures cognitive ability.

    Fourth, while there was an overall mean increase in IQ, in addition, there was a universal gain in variance (stnd. dev. increased approx 9 IQ points). Thus, assuming that the before and after IQs were directly comparable, the majority of the scores fell into these ranges:

    95% Range of IQ (i.e., -/+ 2 SD)

    Pre-Adoption
    Post-Adoption

    Low SES High SES
    64.23 91.43 50.54 118.54
    64.97 90.17 68.8 1272

    A few things to notice. 1. There were at least some kids in both categories who, pre-adoption, were in the average range. Given the stark conditions of their pre-adoption upbringing, that is amazing – and likely has little to due with a nurturing environment. 2. There were at least some kids in both categories who, post-adoption, were in the Mild to Moderately Deficient range. Meaning – they still were classified as MR. If the environment is so powerful, why didn’t their IQ rise also? 3. It is a fair assumption that even the Low SES households were much better off for the kids than their abusive/neglectful original home. If the environment is so powerful, then why are some children doing worse, IQ-wise, post-adoption?

    Fifth, buried in the next to last paragraph of the study, we find this statement:

    This study shows that stability for rank can be found following a marked environmental change after 4 years of age regardless of the SES of adoptive families. The factors explaining this stability are undoubtedly different from those explaining the gains in mean IQs.

    Interpretation: the worst performers before the adoption tended to be the worst performers after the adoption. The only thing that happened was a linear transformation of scores (i.e., post-score ≈ a*pre-score + b) across all SES groups, which could be due to many things, including: changing IQ scales and/or many years of living in an “average expectable environment.”

    So, what we end up with is a picture much more complex and intricate than Kirp allows. What we can definitely conclude is that abusing or neglecting your children so bad that the government has to take them away tends to produce lower IQs (although some kids will still be in the average range), but was this really in question? What we can also conclude is that for some children in the MR range, adoption into high SES environments will not significnalty improve thier IQ. If the environment is so powerful to change cognitive abilities, then how did this happen? Last, I think we can conclude that Kirp’s attempt to glorify the ability of the environment to change IQ is not much more than the wheel, reinvented.

    [1.] I did it using the bootstrap feature in AMOS. I can go into more detail by email or in the comment box.

    (Republished from GNXP.com by permission of author or representative)
     
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    There have been many posting here on Colleges of Education, and the Education profession in general. I think this may take the cake as one of the most asinine and psychologically bankrupt1 educational policies I have ever come across.

    A sneak preview

    one school in the town of Wellingborough is allowing pupils to swear at teachers, providing they only do so no more than five times in a class. A tally of how many times the f-word is used will be kept and if the class exceeds the limit, they will be “spoken” to…

    Eventus stultorum magister

    1. I would think anyone with a class in rudimentary learning theory would give a thumbs down to this. Ironically, I don’t think that is a requirement in all COEs.

    (Republished from GNXP.com by permission of author or representative)
     
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    Article in the San Bernardino News suggesting Ebonics as a way to raise academic achievement for Black students.

    There are so many things wrong in that article, I’ll stick the two most basic that it is hard to believe I have to state them:

    1) I would like to see one, data-backed and independently tested (i.e., subject to peer-review and multiple investigations) theory that posits speaking Ebonics either a) keeps Black students interested in core academics (e.g., math, reading, chemistry) above and beyond the norm or b) has a positive effect on college admissions to mildly selective universities. Note, I didn’t say a thing about raising IQ scores, which is really what this whole thing is all about.

    2) The article says:

    A pilot of the policy, known as the Students Accumulating New Knowledge Optimizing Future Accomplishment Initiative, has been implemented at two city schools.

    Have any results been published? Can I have access to the data? Or is this going to be another fraud in the name of social justice.

    I searched PsychInfo and the Internet in general (i.e., Google and Google Scholar) and found not one article, much less a peer-reviewed one, with the following key words: “Students Accumulating New Knowledge Optimizing Future Accomplishment Initiative” or “Mary Texeira”.

    I challenge anyone involved in this project to give citations of where the public can go to review a) their research design, b) their data collection instrumentation, and (if available) c) their analysis.

    Until such a time, this effort, to paraphrase KA, will have to be classified as ass.

    *Thanks to Scott for the heads up on this article.

    (Republished from GNXP.com by permission of author or representative)
     
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