SES and IQ: Longitudinal twin study

Yet another study demonstrating the significant relation between SES and intelligence, this time a large longitudinal study of twins. Click on images to enlarge. Prior SES posts can be found here.







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CHC g+specific abilities and college math achievement

Click on images to enlarge. Copy of paper available here.






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Does Head Start improve g (general intelligence) or just unique subtest variance?

Interesting meta-analysis of Head Start IQ studies that suggests that improvements in IQ scores in Head Start evaluation programs are not due to changes in general intelligence (g), but are more due to short-term and transient changes to unique, specific abilities of subtests in IQ tests.

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More on Greenspan's model of personal competence: Relationship between IQ and social, practical, and conceptual abilities

I am pleased to see that, after a relatively long draught in published research, someone is again investigating the relations between general intelligence, and the primary domains of adaptive behavior, in models (that when examined closely) that are investigating aspects of Greenspan's' model of personal competence. The title, abstract, and key figure from this new research follow. The article can be read here. Kudos to these researchers

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My primary criticism of this study is that it completely ignores the primary foundation research in this area that occurred between 1990 and 2000, some of which are the primary research studies cited in the AAIDD manuals to support the domains of practical, conceptual and social competence (Greenspan's model). I have provided a list of that research, and results from the most prominent article from that group of researchers, below.












Yes, my name is all over these MIA studies (in the current featured article) so some could see my comments as academic sour grapes for being overlooked. But I see their omission as a lack of scholarly rigor by the researchers and the journal who published the current article. All of the MIA studies can be found at the MindHub--scroll down until you see the list of studies shown above. Then click away and download and read. It would have been nice if the new study results would have been integrated with the extant personal competence research literature.

In the final analysis I am pleased that someone is conducting much needed research on these constructs given the pivotal role they play in the definition and assessment of MR/ID.


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Psychometric taxonomic models of intelligence: A visual tour from g to CHC

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Does g (general intelligence) exist? Schneider & McGrew's (2012) position

Click on images to enlarge for reading. This is from "The Cattell-Horn-Carroll Theory of Intelligence" chapter (warning...thus is large 20MB file)in Flanagan and Harrison's 3rd edition of Contemporary Intellectual Assessment.


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SLODOR and national IQ

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The Flynn effect, g, and ability differentiation

Another FE article to be added to the Flynn Effect Archive Project On next update

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Another article implicating dlPFC and P-FIT model of intelligence--Importance to general intelligence

Another study implicating dorsolateral prefrontal cortex (dlPFC) and PFIT model of intelligence with regard to general intelligence (g), working memory and white matter tract-moderated functional brain network connectivity. Supports significant components of the three-level explanatory model articulated in MindHub Pub #2.

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IAP101 Brief #12: Use of IQ component part scores as indicators of general intelligence in SLD and MR/ID diagnosis

   
            Historically the concept of general intelligence (g), as operationalized by intelligence test battery global “full scale” IQ scores, has been central to the definition and classification of individuals with a specific learning disability (SLD) as well as individuals with an intellectual disability (ID).  More recently, contemporary definitions and operational criteria have elevated intelligence test battery composite or part scores to a more prominent role in diagnosis and classification of SLD and more recently in ID.

            In the case of SLD, third-method “consistency” definitions prominently feature component or part scores in (a) the identification of consistency between low achievement and relevant cognitive abilities or processing disorders and (b) the requirement that an individual demonstrate relative cognitive and achievement strengths (see Flanagan, Fiorello & Ortiz, 2010).  The global IQ score is de-emphasized in the third-method SLD methods.

            In contrast, the 11^th edition of the AAIDD Intellectual Disability: Definition, Classification, and Systems of Supports manual (AAIDD, 2010) placed general intelligence, and thus global composite IQ scores, as central to the definition of intellectual functioning.  This has not been without challenge.  For example, the AAIDD ID definition has been criticized for an over-reliance on the construct of general intelligence and for ignoring contemporary psychometric theoretical and empirical research that has converged on a multidimensional hierarchical model of intelligence (viz., Cattell-Horn-Carroll or CHC theory).

The potential constraints of the “ID-as-a-general-intelligence-disability” definition was anticipated by the Committee on Disability Determination for Mental Retardation, in its National Research Council report “Mental Retardation:  Determining Eligibility for Social Security Benefits” (Reschly, Meyers & Hartel, 2001).  This national committee of experts concluded that “during the next decade, even greater alignment of intelligence tests and the IQ scores derived from them and the Horn-Cattell and Carroll models is likely.  As a result, the future will almost certainly see greater reliance on part scores, such as IQ scores for Gc and Gf, in addition to the traditional composite IQ.  That is, the traditional composite IQ may not be dropped, but greater emphasis will be placed on part scores than has been the case in the past” (Reschly et al., 2002, p. 94).  The committee stated that “whenever the validity of one or more part scores (subtests, scales) is questioned, examiners must also question whether the test’s total score is appropriate for guiding diagnostic decision making.  The total test score is usually considered the best estimate of a client’s overall intellectual functioning.  However, there are instances in which, and individuals for whom, the total test score may not be the best representation of overall cognitive functioning.” (p. 106-107).

            The increased emphasis on intelligence test battery composite part scores in SLD and ID diagnosis and classification raises a number of measurement and conceptual issues (Reschly et al., 2002).  For example, what are statistically significant differences?  What is a meaningful difference?  What appropriate cognitive abilities should serve as proxies of general intelligence when the global IQ is questioned?  What should be the magnitude of the total test score? 

Appropriate cognitive abilities will only be the only issue discussed here.  This issue addresses  which component or part scores are more correlated with general intelligence (g)—that is, what component part scores are high g-loaders?  The traditional consensus has been that measures of Gc (crystallized intelligence; comprehension-knowledge) and Gf (fluid intelligence or reasoning) are the highest g-loading measures and constructs and are the most likely candidates for elevated status when diagnosing ID (Reschly et al., 2002).  Although not always stated explicitly, the third method consistency SLD definitions specify that an individual must demonstrate “at least an average level of general cognitive ability or intelligence” (Flanagan et al., 2010, p.745), a statement that implicitly suggests cognitive abilities and component scores with high g-ness.

Table 1 is intended to provide guidance when using component part scores in the diagnosis and classification of SLD and ID (click on images to enlarge and use the browser zoom feature  to view; it is recommended you click here to access a PDF copy of the table..and also zoom in on it).  Table 1 presents a summary of the comprehensive, nationally normed, individually administered intelligence batteries that possess satisfactory psychometric characteristics (i.e., national norm samples, adequate reliability and validity for the composite g-score) for use in the diagnosis of ID and SLD.

The “Composite g-score” column lists the global general intelligence score provided by each intelligence battery.  This score is the best estimate of a person’s general intellectual ability, which currently is most relevant to the diagnosis of ID as per AAIDD.  All composite g-scores listed in Table 1 meet Jensen’s (1998) psychometric sampling error criteria as valid estimates of general intelligence.  As per Jensen’s number of tests criterion, all intelligence batteries g-composites are based on a minimum of nine tests that sample at least three primary cognitive ability domains.  As per Jensen’s variety of tests criterion (i.e., information content, skills and demands for a variety of mental operations), the batteries, when viewed from the perspective of CHC theory, vary in ability domain coverage—four (CAS, SB5), five (KABC-II, WISC-IV, WAIS-IV), six (DAS-II) and seven (WJ III) (Flanagan, Ortiz & Alfonso, 2007; Keith & Reynolds, 2010).   As recommended by Jensen (1998), “the particular collection of tests used to estimate g should come as close as possible, with some limited number of tests, to being a representative sample of all types of mental tests, and the various kinds of test should be represented as equally as possible” (p. 85).  Users should consult sources such as Flanagan et al. (2007) and Keith and Reynolds, 2010) to determine how each intelligence battery approximates Jensen’s optimal design criterion, the specific CHC domains measured, and the proportional representation of the CHC domains in each batteries composite g-score.

Also included in Table 1 are the component part scales provided by each battery (e.g., WAIS-IV Verbal Comprehension Index, Perceptual Reasoning Index, Working Memory Index, and Processing Speed Index), followed by their respective within-battery g-loadings.[1]  Examination of the g-ness of composite scores from existing batteries (see last three columns in Table 1) suggests the traditional assumption that measures of Gf and Gc are the best proxies of general intelligence may not hold across all intelligence batteries.[2] 

In the case of the SB5, all five composite part scores are very similar in g-loadings (h² = .72 to .79).  No single SB5 composite part score appears better than the other SB5 scores for suggesting average general intelligence (when the global IQ score is not used for this purpose).  At the other extreme is the WJ III where the Fluid Reasoning, Comprehension-Knowledge, Long-term Storage and Retrieval cluster scores are the best g-proxies for part-score based interpretation within the WJ III.  The WJ III Visual Processing and Processing Speed clusters are not composite part scores that should be emphasized as indicators of general intelligence.  Across all batteries that include a processing speed component part score (DAS-II, WAIS-IV, WISC-IV, WJ III) the respective processing speed scale is always the weakest proxy for general intelligence and thus, would not be viewed as a good estimate of general intelligence. 

            It is also clear that one cannot assume that composites with similar sounding names of measured abilities should have similar relative g-ness status within different batteries.  For example, the Gv (visual-spatial or visual processing) clusters in the DAS-II (Spatial Ability), SB5 (Visual-Spatial Processing) are relatively strong g-measures within their respective battery, but the same cannot be said for the WJ III Visual Processing cluster.  Even more interesting are the differences in the WAIS-IV and WISC-IV relative g-loadings for similarly sounding index scores. 

For example, the Working Memory Index is the highest g-loading component part score (tied with Perceptual Reasoning Index) in the WAIS-IV but is only third (out of four) in the WISC-IV.   The Working Memory Index is comprised of the Digit Span and Arithmetic subtests in the WAIS-IV and the Digit Span and the Letter-Number Sequencing subtests in the WISC-IV.  The Arithmetic subtest has been reported to be a factorially complex test which may tap fluid intelligence (Gf-RQ—quantitative reasoning), quantitative knowledge (Gq), working memory (Gsm), and possible processing speed (Gs; Keith & Reynolds, 2010; Phelps, McGrew, Knopik & Ford, 2005).   The factorially complex characteristics of the Arithmetic subtest (which, in essence, makes it function like a mini-g proxy) would explain why the WAIS-IV Working Memory Index is a good proxy for g in the WAIS-IV but not in the WISC-IV. The WAIS-IV and WISC-IV Working Memory Index scales, although named the same, are not measuring identical constructs.

A critical caveat is that the g-loadings cannot be compared across different batteries.  g-loadings may change when the mixture of measures included in the analyses change.  Different "flavors" of g can result (Carroll, 1993; Jensen, 1998). The only way to compare the g-ness across batteries is with appropriately designed cross- or joint-battery analysis (e.g., WAIS-IV, SB5 and WJ III analyzed in a common sample).

The above within and across intelligence battery examples illustrates that those who use component part scores as an estimate of a person’s general intelligence must be aware of the composition and psychometric g-ness of the component scores within each intelligence battery.  Not all component part scores in different intelligence batteries are created equal (with regard to g-ness).  Also, not all similarly named factor-based composite scores may measure the same identical construct and may vary in degree of within battery g-ness.  This is not a new problem in the context of naming factors in factor analysis, and by extension, factor-based intelligence test composite scores, Cliff (1983) described this nominalistic fallacy in simple language—“if we name something, this does not mean we understand it” (p. 120). 

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[1] As noted in the footnotes in Table 1, all composite score g-loadings were computed by Kevin McGrew by entering the smallest number (and largest age ranges covered) of the published correlation matrices within each intelligence batteries technical manual (note the exception for the WJ III) in order to obtain an average g-loading estimate.  It would have been possible to calculate and report these values for each age-differentiated correlation matrix for each intelligence battery.  However, the purpose of this table is to provide the best possible average value across the entire age-range of each intelligence battery.  Floyd and colleagues have published age-differentiated g-loadings for the DAS-II and WJ III.  Those values were not used as they are based on the use of the principal common factor analysis method, a method that  analyzes the reliable shared variance among tests.  Although principal factor and principal component loadings typically will order measures in the same relative position, the principal factor loadings typically will be lower.  Given that the imperfect manifest composite scale scores are those that are utilized in practice, and to also allow uniformity in the calculation of the g-loadings reported in Table 1, principal component analysis was used in this work. The same rationale was used for not using the latent factor loadings on a higher-order g-factor in SEM/CFA analysis of each test battery.  Loadings from CFA analyses represent the relations between the underlying theoretical ability constructs and g purged of measurement error.  Also, frequently the final CFA solutions reported in a batteries technical manual (or independent journal articles) allow tests to be factorially complex (load on more than one latent factor), a measurement model that does not resemble the real world reality of the manifest/observed composite scores used in practice.  Latent factor loadings on a higher-order g-factor will often differ significantly from principal component loadings based on the manifest measures, both in absolute magnitude and relative size (e.g., see high Ga loading on g in WJ III technical manual which is at variance with the manifest variable based Ga loading reported in Table 1) 

[2] The h² values are the values that should be used to compare the relative amount of g-variance present in the component part scores within each intelligence battery.

IAP Applied Psychometrics 101 Brief Report # 11: What is the typical IQ and adaptive behavior correlation?

What is the typical relation (correlation) between standardized measures of adaptive behavior (AB)  and measures of intelligence (IQ)?  This is an important question given the role both play in the definition diagnosis of mental retardation (MR) / intellectual disability (ID). 

During the late 1970's and 1980's this was an active area of research.  Numerous studies were published that reported correlations between a wide variety of adaptive behavior scales and intelligence tests.  Probably the best synthesis of this research was provided by Harrison (1987).  Harrison's review included a table of over 40+ correlations.  This is Table 2 in the above referenced and linked article.  Harrison concluded, as have most others who have reviewed the literature, that "the majority of correlations fall in the moderate range" (p.39).  When the correlations with maladaptive measures are excluded from Harrison's table, the correlations range from .03 to .91.  This is a wide range.  Harrison could not identify a specific explanation for the variability or range of correlations.  Harrison speculated that variables might impact the magnitude of the correlations were the specific adaptive behavior or measure of intelligence used and differences in sample variability.

Subsequently the Committee on Disability Determination for Mental Retardation published a National Research Council report (Mental Retardation:  Determining Eligibility for Social Security Benefits; Reschly, Meyers & Hartel, 2001) that also addressed the AB/IQ relation. The report concluded that AB/IQ studies report correlations "ranging from 0 (indicating no relationship) to almost +1 (indicating a perfect relationship).  Data also suggest that the relationship between IQ and adaptive behavior varies significantly by age and levels of retardation, being strongest in the severe and moderate ranges and weakest in the mild range.  There is a dearth of data on the relationship of IQ and adaptive behavior functioning at the mild level of retardation" (p. 8).  Factors identified as moderating the AB/IQ correlation were scale content, measurement of competences versus perceptions, sample variability, ceiling and floor problems of the scales, and level of mental retardation.

Given the above, it is hard to render an objective statement on the approximate typical AB/IQ correlation.  With this in mind, an informal research synthesis was completed and is reported here.

First, only the AB/IQ correlations (IQ/maladaptive correlations were excluded) from Harrison's 1987 table were extracted (n = 43 correlations).  Then, the technical manuals for the current editions of the three most frequently used contemporary adaptive behavior scales were reviewed for additional correlations.  This included the Vineland Adaptive Behavior Scale (Sparrow, Cicchetti & Balla, 2005; n = 2 correlations of .12, .20) and the Adaptive Behavior Scales--II (Harrison & Oakland, 2008; n = 10 correlations ranging from .39 to .67; median = .51).

Although six different correlations were reported in the Scales of Independent Behavior-Revised manual (SIB-R; Bruininks, Woodcock, Weatherman & Hill, 1996), the values were not used as they are inflated estimates when compared to the type of correlations typically reported.  For example, very high correlations of .79, .82 and .91 are reported for certain groups.  A close reading of the tables reveals that the SIB-R correlations with either the WJ or WJ-R intelligence test were calculated on the basis of the W-score growth metric.   By definition, a growth metric includes age variance.  If correlations are reported across wide age groups the correlations convey variance related to the correlation between the AB  and IQ constructs but also contains shared variance due to the influence of general age-base development (age).  Thus, the SIB and SIB-R correlations with IQ, although not wrong and providing different information, are not comparable to all other reported correlations where age variance has been removed (typically by correlating age-based standard scores).  Clear evidence for this point comes from McGrew and Bruininks (1990) who used the same SIB/WJ subject data reported in the SIB and SIB-R manuals, but who removed the W-score confounded age variance prior to the calculation of latent factor correlations (via confirmatory factor analysis) between latent practical intelligence (SIB adaptive behavior) and conceptual intelligence (WJ IQ) factors.  The resulting AB/IQ correlations for three different age groups were .38, .56 and .58--far below the values in the .70 to .92 range.  Thus, the values from McGrew and Bruininks (1990) were included for estimates of the SIB/SIB-R IQ correlations in the current synthesis. 

Finally, latent AB/IQ correlations (as estimated from confirmatory factor analysis models)  of .27 and .39 were included from Ittenbach, Spiegel, McGrew and Bruininks (1992) and Keith, Fehrmann,Harrison and Pottebaum (1987), respectively.  This process resulted in the addition of 17 AB/IQ correlations to the 43 from Harrison, for a total of 60 correlations.

Descriptive statistics for this collection of 60 AB/IQ correlations are as follows: range of correlations from .12 to .90,  a mean of .51 and a median of .48, and a standard deviation of .20.  Below is a figure that includes a frequency polygon (and smoothed normal curve overlay) and a box-whisker plot of the data set.  A review of the box and whisker plot (at the bottom) shows the median correlation (.48) as a vertical line within the rectangle.  The rectangle includes the 50% middle of the distributions of correlations and shows an approximate range of just below .40 to just above .65.  Of particular note is the shape of the frequency polygon and smoothed normal curve.  The shape of the frequency polygon is consistent with a normal curve.  In quantitative research synthesis this type of normal distribution suggests that total data set included in the review is not biased--both studies that are likely under- or overestimates of the "true" population correlation (due to method or sampling factors) are included.  More importantly, the "bunching" up of the majority of the correlations in the middle provide confidence that the median of this distribution is a reasonable unbiased estimate of the populaiton correaltion.  This type of relatively normal distribution suggests that the current collection of 60 AB/IQ correlations is likely a reasonable approximation of the complete set of population AB/IQ correlations.

Based on this informal (and admittedly incomplete review of all possible AB/IQ correlation research) one can conclude that a reasonable estimate of the typical AB/IQ correlation is approximately .50 (mean = .51; median = .48), with most ranging from approximately .40 to .65.  This finding is consistent with Harrison's 1987 conclusion of a "moderate" correlation.  The current analysis continues to reinforce Harrison's (and others) conclusions that adaptive behavior and intelligence are statistically related constructs, but  they are still independent.   An average correlation of .50 indicates that AB and IQ share approximately 25 % common variance (approximately 15% to 40 % common variance if one looks at the range of the 50% middle of the distribution of values).  In practical terms this means that for any individual, standard scores from AB and IQ tests will frequently diverge and not always be consistent.  

Harrison (1987) provides a nice explanation for the primary reasons for the moderate correlation between AB and IQ.  Her quote is reproduced below

Numerous caveats need to be applied to this analysis and report.  The most important are:

• A comprehensive review of all possible published and unpublished AB/IQ research studies was not completed.  Clearly there are more studies "out there" that could be added to the synthesis. 
• The analysis makes no attempt to determine if there are moderator effects.  That is, is the typical correlation likely to systematically vary as a function of AB measures, IQ measures, variability in the sample's level of functioning, manifest/measured versus latent variable correlations, level of ability, etc.? 
•  This has not been peer reviewed.

 It is hoped that this ad hoc update of Harrison's (1987) review, augmented by quantitive organizational methods, will serve to stimulate a formal meta-analysis by others (hint---a nice study or thesis for someone?)

Research byte: Dependability of g (general intelligence) test loadings--replication of Floyd et al. (2009)

Major et al (2011) replication of Floyd et al. (2009) study (I was part of this author team---at the bottom of the batting order). Double click on image to enlarge.


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General intelligence: To g or not to g? Dr. Joel Schneider comments

Last week there was a spirited exchange on CHC listserv regarding the status of the theoretical construct of general intelligence (g). Dr. Joel Schneider provided a very thought provoking response that included some of his recent writings on the subject. I asked Joel if I could share on IQs Corner, and he agreed. Below are his comments "as is." As the reader will learn from some of his comments, he was responding to other individuals who made some statements about g on the list.


Yes, opinion polling is not the way to do science but ultimately science IS about consensus-building. A single researcher can produce evidence so compelling that the entire field is forced to change its mind. When it comes to g, however, there is no compelling evidence about what it is or is not. Here are three excepts from a chapter I wrote that is in preparation:

"Spearman’s (1904) little g caused a big stir when it was first proposed and has, for over a century now, been disrupting the natural state of harmony that would otherwise prevail amongst academics. Many a collegial tie has been severed, many a friendship has soured, perhaps even engagements broken off and marriages turned into dismal, loveless unions because of the rancor this topic provokes. I have seen otherwise mild-mannered professors in tweed jackets come to blows in bars over disagreements about g — okay…not really…but I have seen some very sarcastic emails exchanged on professional listservs!"

"It turns out that these two groups [the mono-g-ists and the poly-G-ists] are not merely on opposite sides of an intellectual debate — they are members of different tribes. They speak different dialects, vote for different candidates, and pray to different gods. Their heroic tales emphasize different virtues and their foundation myths offer radically different but still internally consistent explanations of how the world works. If you think that the matter will be settled by accumulating more data, you have not been paying attention for the last hundred years."

"The theoretical status of g will not cease to be controversial until something extraordinary happens to the field. I do not pretend to know what this might be. Maybe a breakthrough from biology will resolve the matter. Maybe divine intervention. Until then, I feel no need to join either tribe. I will remain agnostic and I will not get too excited the next time really smart people eagerly announce that finally, once and for all, they have proof that the other side is wrong. This has happened too many times before."

Shifting topics:

You are right, I have estimated a person's intelligence and said something about it out loud. In like manner, I have said about different people, "She's nice." "He's a jerk!" "He's funny!" "She's impressive." "He's a good person." I agree with Spearman that "intelligence" is a pre-scientific folk concept, just as nice, jerk, funny, and good are folk concepts. There is nothing wrong with these terms. They communicate pretty clearly what I want to say. However, I do not believe that there is an underlying personality variable called "goodness" or "impressiveness." Such terms probably do have an indirect relationship to more fundamental cognitive structures, though.

Here is an excerpt from an early draft of the forthcoming chapter I wrote with Kevin McGrew. Almost of all of this section was removed because the chapter was starting to look like it was going to be over 200 pages. Editing the chapter down to 100 pages was painful and many parts we liked were removed:

Is g an ability?

The controversy about the theoretical status of g may have less fire and venom if some misunderstandings are cleared up. First, Spearman did not believe that performance on tests was affected by g and only g. In a review of a book by his rival Godfrey Thomson, Spearman (1940, p. 306) clarified his position.

“For I myself, no less than Thomson, accept the hypothesis that the observed test-scores, and therefore their correlations, derive originally from a great number of small causes; as genes, neurones, etc. Indeed this much seems to be accepted universally. We only disagree as to the way in which this derivation is to be explained.”

Second, Spearman (1927, p. 92) always maintained, even in his first paper about g (Spearman, 1904, p. 284), that g might consist of more than one general factor. Cattell (1943) noted that this was an anticipation of Gf-Gc Theory. Third, Spearman did not consider g to be an ability, or even a thing. Yes, you read that sentence correctly. Surprisingly, neither does Arthur Jensen, perhaps the most (in)famous living proponent of Spearman’s theory. Wait! The paper describing the discovery of g was called “‘General Intelligence’: Objectively Determined and Measured.” Surely this means that Spearman believed that g was general intelligence. Yes, but not really. Spearman thought it unproductive to equate g with intelligence, the latter being a complex amalgamation of many abilities (Jensen, 2000). Spearman believed that “intelligence” is a folk concept and thus no one can say anything scientific about it because everyone can define it whichever way they wish. Contemplating the contradictory definitions of intelligence moved Spearman (1927, p. 14) to erupt,

“Chaos itself can go no farther! The disagreement between different testers—indeed, even between the doctrine and the practice of the selfsame tester—has reached its apogee. […] In truth, ‘intelligence’ has become a mere vocal sound, a word with so many meanings that finally it has none.”

Spearman had a much more subtle conceptualization of g than many critics give him credit for. In discussing the difficulty of equating g with intelligence, or variations of that word with more precise meanings such as abstraction or adaptation, Spearman (1927, p.88) explained,

“Even the best of these renderings of intelligence, however, always presents one serious general difficulty. This is that such terms as adaptation, abstraction, and so forth denote entire mental operations; whereas our g, as we have seen, measures only a factor in any operation, not the whole of it.”

At a conference in which the proceedings were published in an edited volume (Bock, Goode, & Webb, 2000), Maynard Smith argued that there isn't a thing called athletic ability but rather it is a performance category. That is, athletic ability would have various components such as heart volume, muscle size, etc. Smith went on to argue that g, like athletic ability, is simply a correlate that is statistically good at predicting performance. Jensen, in reply, said, "No one who has worked in this field has ever thought of g as an entity or thing. Spearman, who discovered g, actually said the very same thing that you're saying now, and Cyril Burt and Hans Eysenck said that also: just about everyone who has worked in this field has not been confused on that point." (Bock, Goode, & Webb, 2000, p. 29). In a later discussion at the same conference, Jensen clarified his point by saying that g is not a thing but is instead the total action of many things. He then listed a number of candidates that might explain why disparate regions and functions of the brain tend to function at a similar level within the same person such as the amount of myelination of axons, the efficiency of neural signaling, and the total number of neurons in the brain (Bock, Goode, & Webb, 2000, p. 52). Note that none of these hypotheses suggest that g is an ability. Rather, g is what makes abilities similar to each other within a particular person’s brain.
In Jensen’s remarks, all of the influences on g were parameters of brain functioning. We can extend Jensen’s reasoning to environmental influences with a thought experiment. Suspend disbelief for a moment and suppose that there is only one general influence on brain functioning: lead exposure. Because of individual differences in degree of lead exposure, all brain functions are positively correlated and thus a factor analysis would find a psychometric g-factor. Undoubtedly, it would be a smaller g-factor than is actually observed but it would exist.

In this thought experiment, g is not an ability. It is not lead exposure itself, but the effect of lead exposure. There is no g to be found in any person’s brain. Instead, g is a property of the group of people tested. Analogously, a statistical mean is not a property of individuals but a group property (Bartholomew, 2004). This hypothetical g emerges because lead exposure influences all of the brain at the same time and because some people are exposed to more lead than are others.

In the thought experiment above, the assumptions were unrealistically simple and restrictive. It is certain that individual differences in brain functioning is influenced in part by genetic differences among individuals and that some genetic differences affect almost all cognitive abilities (Exhibit A: Down Syndrome). Some genetic differences affect some abilities more than others (e.g., William’s Syndrome, caused by a deletion of about 26 genes on chromosome 7, is associated with impaired spatial processing but relatively intact verbal ability). Thus, there are general genetic influences on brain functioning and there are genetic differences that effect only a subset of brain functions.

The fact that there are some genetic differences with general effects on cognitive ability (and there are probably many, Plomin, 20??) is enough to produce at least a small g-factor, and possibly a large one. However, there are many environmental effects that effect most aspects of cognitive functioning. Lead exposure is just one of many toxins that likely operate this way (e.g., mercury & arsenic). There are viruses and other pathogens that infect the brain more or less indiscriminately and thus have an effect on all cognitive abilities. Many head injuries are relatively focal (e.g., microstrokes and bullet wounds) but others are more global (e.g., large strokes and blunt force trauma) and thus increase the size of psychometric g. Poor nutrition probably hampers the functioning of individual neurons indiscriminately but the systems that govern the most vital brain functions have more robust mechanisms and greater redundancy so that temporary periods of extreme malnourishment affect some brain functions more than others. Even when you are a little hungry, the first abilities to suffer are highly g-loaded and evolutionarily new abilities such as working memory and controlled attention.

Societal forces probably also increase the size of psychometric g. Economic inequality ensures that some people will have more of everything that enhances cognitive abilities and more protection from everything that diminishes them. This means that influences on cognitive abilities that are not intrinsically connected (e.g., living in highly polluted environments, being exposed to water-borne parasites, poor medical care, poor schools, cultural practices that fail to encourage excellence in cognitively demanding domains, reduced access to knowledgeable mentors among many many others) are correlated. Correlated influences on abilities cause otherwise independent cognitive abilities to be correlated, increasing the size of psychometric g. How much any of these factors increase the size of psychometric g (if at all) is not yet known. The point is that just because abilities are influenced by a common cause, does not mean that the common cause is an ability.

There are two false dichotomies we should be careful to avoid. The first is the distinction between nature and nurture. There are many reasons that genetic and environmental effects on cognitive abilities might be correlated, including the possibility that genes affect the environment and the possibility that the environment alters the effect of genes. The second false choice is the notion that either psychometric g is an ability or it is not. Note that if we allow that some of psychometric g is determined by things that are not abilities, it does not mean that there are no truly general abilities (e.g., working memory, processing speed, fluid intelligence, and so forth). Both types of general influences on abilities can be present.

In this section, we have argued that not even the inventor of g considered it to be an ability. Why do so many scholars write as if Spearman believed otherwise? In truth, he (and Jensen as well) often wrote in a sort of mental shorthand as if g were an ability or a thing that a person could have more of or less of. Cattell (1943, p. 19) gives this elegantly persuasive justification:

Obviously "g" is no more resident in the individual than the horsepower of a car is resident in the engine. It is a concept derived from the relations between the individual and his environment. But what trait that we normally project into and assign to the individual is not? The important further condition is that the factor is not determinable by the individual and his environment but only in relation to a group and its environment. A test factor loading or an individual's factor endowment has meaning only in relation to a population and an environment. But it is difficult to see why there should be any objection to the concept of intelligence being given so abstract a habitation when economists, for example, are quite prepared to assign to such a simple, concrete notion as "price" an equally relational existence.


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Joint CFA (Floyd et al., 2010) of WJ III and DKEFS: Guest comments by John Garruto

John Garruto took advantage of my offer and thus, now provides his comments regarding the following recently published research study.  John has been a regular guest blogger at IQ's Corner....how about the rest of you!!!!!!! 

I am open to any topic, but am particularly interested in guest posts regarding articles that have been FYI-mentioned at this blog (typically under Research Bytes tag)---and I especially would like to encourage graduate students to send me possible guest posts...as a way to get experience with analyzing research and providing brief summaries.  Maybe some of my professorial colleagues could make the submission of one guest blog post a requirement in one of their classes :)

• Floyd, R. G., Bergeron, R., Hamilton, G. & Parra, G. R. (2010).  How do executive functions fit with the Cattell-Horn-Carroll model? Some evidence from a joint factor analysis of the Delis-Kaplan Executive Function System and the Woodcock-Johnson III tests of cognitive abilities.  Psychology in the Schools, 47(7), 721-738. (click here to view/read)

[Note...these are John's comments with only minor copy editing by the blogmaster/dictator]

Before I go into some thoughts regarding the article by Floyd, Bergeron, Hamilton, and Parra, please bear a bit of a sidetracking set of thoughts that are relevant to this article.  When I was in my masters training program for school psychology, we took a course called “Analysis of Individual Learning I”.  My textbook was authored by Samuel Kirk and James Chalfant (copyright was 1984).  I don’t remember being asked to read anything from that text and it wasn’t until well after I graduated that I learned Samuel Kirk was the person who coined the term “learning disability.” I pulled the book of my shelf and decided to take a look at it.  Chapter 3 is entitled “Causes and contributing factors”.  The entire chapter (which wasn’t long…13 pages) was devoted to the brain and neuropsychology. 

Since that book was published, the law and professional opinion have differed  much on what learning disabilities are.  For years, the psychometric framework has seemingly reigned supreme, using a discrepancy approach and an intuitive paradigm (if the child isn’t working to ability, something must be getting in the way).  For the past six years, RTI has permeated the field of LD with an even more distant framework that Kirk conceptualized…that a learning disability is the failure of a child to respond to research based instruction.  Unfortunately, the brain has largely been left out, with a few exceptions. 

Fortunately, there has been hope.  We’re now seeing a surge in publications that are coauthored by those schooled in both the psychometric and neuropsychological traditions.  They’re not distinct entities-they’re two sides to the same coin (or perhaps “face of the die” is more appropriate).  I remember when I first dove into CHC theory after trying the WJ-III (sadly my program did not use any Woodcock tests in our training)-a paradigm altering notion was that cognition and achievement were not distinct constructs-they were on the same continuum.  This marriage of the psychometric and neuropsychological traditions is happening in the same manner.

This brings us to the article, which has examined ways to categorize executive functions under CHC theory.  The authors performed joint-factor analyses of tests from the WJ-III and Delis-Kaplan Executive Function System (DKEFS).  I initially stopped myself from reading the article and made my guesses.  I scratched down “Retrieval Fluency-Verbal Fluency”, “Concept Formation-Sorting Test”, “Tower-Planning/Spatial Relations”.  “Verbal Comprehension-Word Context”.  I then wrote “Ga-nothing” and “Gsm-nothing”.  These were the subtests that I hypothesized would be grouped together given the nature of their tasks.  Some of the results of this study confirmed my hypotheses and some were not close.

The results presented a six first-order factor model conceptualized under the CHC hierarchy.  The factors included Crystallized Ability (Gc), Processing Speed (Gs), Long Term Storage and Retrieval (Glr), Short-Term Memory (Gsm), Executive Functioning, and Visual-Spatial Processing (Gv).  The groupings were Gc: (DKEFS-Free Sorting, Sort Recognition, Word Context; WJ-III-Verbal Comprehension, General Information); Gs: (DKEFS-Color Word Interference-Inhibition; WJ-III-Visual Matching, Pair Cancellation, Decision Speed); Glr: (DKEFS-Verbal Fluency, Trails switching; WJ-III-Retrieval Fluency, Rapid Picture Naming); Gsm: (DKEFS-Trails switching; WJ-III-Numbers Reversed, AWM, Mem for Words); EF: (DKEFS-Verbal Fluency Switching, Design Fluency Switching; WJ-III Rapid Picture Naming, Concept Formation) and Gv: (WJ-III-Planning, Spatial Relations, Picture Recognition). 

The following were huge surprises for me:

• Sorting as a measure of Gc…I always saw this test as more of a Gf task (although if one thinks about it-Similarities is a Gc task (with some Gf-ness) even if it hasn’t panned out in some analyses)

• Tower not having a Gv load

• Trails not having any Gs variance

• Concept formation as mental flexibility.  I absolutely see it-but more as hypothesis testing rather than set shifting.  I would have thought Gf and sorting would have “hung” together-perhaps perhaps the verbal sorts must have had an influence?

One cannot read the research of Dr. Floyd and not appreciate his contributions to research on the general factor.  His findings are not only interesting but also important.  He noted that although the strongest loaders on the general factors were from the WJ-III, the DKEFS had more subtests that loaded strongly on the general factor.  This is important as many of us tend to think of EF as regulation of thinking--but clearly there is a higher order ability to EF.  In fact, of the six factors, EF was number two (Gc number one) for ranking of an overall g load. 

Floyd and colleagues did address some of the same musings I had-identifying that sorting and 20 questions are probably better measures of Gf but not word context.  I see word context as also having a Gf nature to it (deductive reasoning).  One must engage in hypothesis testing and construct a mental Venn diagram-the key word must fit all clues.  Perhaps we’re looking at a hybrid of Gf-Gc (could Raymond Cattell be trying to tell us something?) 

Nevertheless, the importance of this article cannot be underscored enough.  There are many skills tapped by EF tests that can impact school performance.  How well would we expect a student who has trouble set-shifting to do on calculations with mixed operations (set-shifting constantly being tapped)-or even long division (requiring three sequential operations for each place value of the quotient).  Also, we can see that tests that review some aspects of neuropsychological performance fit very well within CHC theory. 
I’d like to share one more memory.  I once performed  an evaluation of a a student with TBI that was returning to school. The case highlighted the importance of the ongoing fusion of psychometric and neuropsychological traditions.  The hospital indicated that a WISC and WIAT would be fine (that’s rarely fine for me though!)  I decided to accompany the evaluation with VAL, Retrieval Fluency, and Rapid Picture naming from the WJ-III.  For the latter two, the student said, “I did tasks like these at the hospital.”  Indeed.

Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, Gf, Gc, Gv, Ga, Gsm, Glr, Gs, Gq, Grw, working memory, executive function, DKEFS, WJ III, Woodcock-Johnson, factor analysis

Current research in Cattell-Horn-Carroll (CHC) based intelligence testing: Special PITS isue is out

I'm excited to announce that the special issue of Psychology in the Schools, Current Research in Cattell-Horn-Carroll-Based Assessment (guest editors where Jocelyn Newton and myself), is now published.  Yippeee.  To be honest, Dr. Newton deserves the major credit....she did all the heavy lifting and I road her coat tails.  Also thanks to Dr. David McIntosh for suggesting and overseeing the special issue

A review of the TOC can be found by clicking here.  A copy of the article (Cattell-Horn-Carroll cognitive achievement relations:  What we have learned from the past 20 years of research) I co-authored with Barb Wendling can be found by clicking here and the introduction to this issue I co-authored with Dr. Newton is available here.

If you do not have access to this journal and would like to read 1 or more of the articles, I'd be willing to privately share PDF copies in exchange for a guest blog post review here at IQ's Corner.  Now how can folks resist such an offer?....to learn more and to become a guest blogger.  It doesn't get any better.

Enjoy.

Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, school achievement, general intelligence, Gf, Gc, Gv, Ga, Gsm, Glr, Gs, Gq, Grw, Psychology in the Schools, learning disabilities, RTI

New book on intelligence by Earl Hunt: Available November 2010

I just learned of an exciting new book on intelligence by a giant in the field, Earl Hunt.  The book Human Intelligence will be available this November from Cambridge University Press.

I look forward to reviewing the contents when I can secure a copy.  I have added the book to IQs Corner Book Store

Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, Cattell-Horn-Carroll, factor analysis, IQs Corner, IQ, IQ tests, IQ scores, Earl Hunt, human intelligence

Research bytes 4-24-10: Broad factor 1.0 g loadings power issue; domain general mental resource mechanism

Matzke, D. Dolan, C., & Molenaar, D. (in press). The issue of power in the identification of “g” with lower-order factors.  Intelligence.

Abstract

In higher order factor models, general intelligence (g) is often found to correlate perfectly with lower-order common factors, suggesting that g and some well-defined cognitive ability, such as working memory, may be identical. However, the results of studies that addressed the equivalence of g and lower-order factors are inconsistent. We suggest that this inconsistency may partly be attributable to the lack of statistical power to detect the distinctiveness of the two factors. The present study therefore investigated the power to reject the hypothesis that g and a lower-order factor are perfectly correlated using artificial datasets, based on realistic parameter values and on the results of selected publications. The results of the power analyses indicated that power was substantially influenced by the effect size and the number and the reliability of the indicators. The examination of published studies revealed that most case studies that reported a perfect correlation between g and a lower-order factor were underpowered, with power coefficients rarely exceeding 0.30. We conclude the paper by emphasizing the importance of considering power in the context of identifying g with lower-order factors.

Vergauwe, E., Barrouillet, P., & Camos, V. (2010). Do Mental Processes Share a Domain-General Resource? Psychological Science, 21(3), 384-390.

Abstract

What determines success and failure in dual-task situations? Many theories propose that the extent to which two activities can be performed concurrently depends on the nature of the information involved in the activities. In particular, verbal and visuospatial activities are thought to be fueled by distinct resources, so that interference occurs between two verbal activities or two visuospatial activities, but little or no interference occurs between verbal and visuospatial activities. The current study examined trade-offs in four dual-task situations in which participants maintained verbal or visuospatial information while concurrently processing either verbal or visuospatial information. We manipulated the cognitive load of concurrent processing and assessed recall performance in each condition. Results revealed that both verbal and visuospatial recall performance decreased as a direct function of increasing cognitive load, regardless of the nature of the information concurrently processed. The observed trade-offs suggest strongly that verbal and visuospatial activities compete for a common domain-general pool of resources.

Technorati Tags: Psychology, educational psychology, school psychology, cognition, cognitive psychology, neuropsychology, intelligence, general intelligence, g, domain general, domain general cognitive mechanism, working memory, shared mental resources, IQs Corner

Research Byte 4-23-10: Flynn effect and black/white IQ score differences

Another Flynn Effect related article, this time focused on the black/white race IQ gap.  This article will be included in the next update to the Flynn Effect on-line archive.....hopefully soon.

Rushton, J. P., & Jensen, A. R. (2010). The rise and fall of the Flynn Effect as a reason to expect a narrowing of the Black White IQ gap. Intelligence, 38(2), 213-219.

Abstract

In this Editorial we correct the false claim that g loadings and inbreeding depression scores correlate with the secular gains in IQ. This claim has been used to render the logic of heritable g a “red herring” and an “absurdity” as an explanation of Black–White differences because secular gains are environmental in origin. In point of fact, while g loadings and inbreeding depression scores on the 11 subtests of the Wechsler Intelligence Scale for Children correlate significantly positively with Black–White differences (0.61 and 0.48, P < 0.001), they correlate significantly negatively (or not at all) with the secular gains (mean r = -0.33, P < 0.001; and 0.13, ns, respectively). Moreover, heritabilities calculated from twins also correlate with the g loadings (r = 0.99, P < 0.001 for the estimated true correlation), providing biological evidence for a true genetic g, as opposed to a mere statistical g. While the secular gains are on g-loaded tests (such as the Wechsler), they are negatively correlated with the most g-loaded components of those tests. Also, the tests lose their g loadedness over time with training, retesting, and familiarity. In an analysis of mathematics and reading scores from tests such as the NAEP and Coleman Report over the last 54 years, we show that there has been no narrowing of the gap in either IQ scores or in educational achievement. From 1954 to 2008, Black 17-year-olds have consistently scored at about the level of White 14-year-olds, yielding IQ equivalents of 85 for 1954, 82 for 1965, 70 for 1975, and 81 for 2008. We conclude that predictions about the Black–White IQ gap narrowing as a result of the secular rise are unsupported. The (mostly heritable) cause of the one is not the (mostly environmental) cause of the other. The Flynn Effect (the secular rise in IQ) is not a Jensen Effect (because it does not occur on g).

Technorati Tags: psychology, forensic psychology, forensic psychiatry, neuropsychology, intelligence, school psychology, psychometrics, educational psychology, IQ, IQ tests, IQ scores, adaptive behavior, adaptive functioning, intellectual disability, mental retardation, MR, ID, criminal psychology, criminal defense, criminal justice, ABA, American Bar Association, Atkins cases, death penalty, capital punishment, AAIDD, IQs Corner, Atkins MR/ID listserv, ICDP blog, Flynn Effect, race IQ differences