Personality and Individual Differences 60 (2014) 3–7

Contents lists available at ScienceDirect

Personality and Individual Differences

journal homepage: www.elsevier .com/locate /paid

Higher-order g versus blended variable models of mental ability:Comment on Hampshire, Highfield, Parkin, and Owen (2012) q

0191-8869/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.paid.2013.09.024

q This research was supported by Social Sciences and Humanities ResearchCouncil of Canada Grant 410-2011-0089.⇑ Corresponding authors. Address: Department of Psychology, Brock University,

St. Catharines, ON L2S 3A1, Canada. Fax: +1 905 688 6922 (M.C. Ashton). Address:Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada(K. Lee).

E-mail addresses: mashton@brocku.ca (M.C. Ashton), kibeom@ucalgary.ca(K. Lee), bethvisser@trentu.ca (B.A. Visser).

Michael C. Ashton a,⇑, Kibeom Lee b,⇑, Beth A. Visser c

a Department of Psychology, Brock University, Canadab Department of Psychology, University of Calgary, Canadac Department of Psychology, Trent University, Oshawa, Canada

a r t i c l e i n f o

Article history:Available online 26 October 2013

Keywords:Structure of mental abilityGeneral intelligenceHigher-order factor

a b s t r a c t

Hampshire et al. (2012) suggested that the positive correlations among all mental ability tasks do notnecessarily imply the existence of a general intelligence (g) factor (e.g., Spearman, 1904), but couldinstead be explained by ‘‘task mixing’’, that is, the influence of multiple independent ability factors onthe various tasks. We note here that the task mixing model is conceptually very similar to the blendedvariables model proposed in the personality domain (Ashton et al., 2009) as an alternative to higher-orderpersonality factors. Here we use CFA to compare a higher-order g model with a task mixing or blendedvariable model in relation to the data of Hampshire et al., and we find that the higher-order g modelprovides a much closer fit to the data. Following Thurstone (1938), we suggest that it is conceptuallyimplausible that every task is influenced by every factor of mental ability. We also suggest that the non-existence of g would be demonstrated by finding mutually orthogonal markers of those factors; however,the data of Hampshire et al. and other mental ability datasets suggest that this cannot be achieved.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Hampshire, Highfield, Parkin, and Owen (2012, p. 1229)recently called into question the existence of a higher-order g factorof general intelligence:

. . .in classical intelligence testing, first-order components gen-erated by factor analyzing the correlations between task scoresare invariably correlated positively if allowed to rotate intotheir optimal oblique orientations. A common approach is toundertake a second-order factor analysis of the correlationsbetween the obliquely oriented first-order components. Theresultant second-order component is often denoted as ‘‘g’’. Thisapproach is particularly useful when tasks load heavily on mul-tiple components, as it can simplify the task to first-order com-ponent weightings, making the factor solution more readilyinterpretable. A complication for this approach, however, is thatthe underlying source of this second-order component is

ambiguous. More specifically, while correlations betweenfirst-order components from the PCA may arise because theunderlying factors are themselves correlated (for example, ifthe capacities of the MDwm and MDr networks were influencedby some diffuse factor like conductance speed or plasticity),they will also be correlated if there is ‘‘task mixing,’’ that is, iftasks tend to weigh on multiple independent factors. In behav-ioral factor analysis, these accounts are effectively indistin-guishable as the components or latent variables cannot bemeasured directly.

Hampshire et al. administered a set of mental ability tasks toa large internet sample of respondents and obtained three prin-cipal components from those tasks. They suggested that thesecomponents were analogous to distinct functional networks ofthe brain, and they used simulations based on neuroimagingdata to show that (p. 1233) ‘‘when the tendency for cognitivetasks to corecruit a combination of these functional networksis accounted for, there is little evidence for a higher-orderintelligence factor’’.

In this article, we note that the task mixing model of Hampshireet al. (2012) is conceptually equivalent to the blended variablesmodel that has previously been proposed in the personality litera-ture as an alternative to higher-order factors of personality (Ash-ton, Lee, Goldberg, & de Vries, 2009). We then construct CFAmodels of the relations among the mental ability task scores of

Fig. 1. Blended variable (task mixing) model of Hampshire et al. (2012) subtests.

4 M.C. Ashton et al. / Personality and Individual Differences 60 (2014) 3–7

Hampshire et al., comparing the fit of a higher-order g model witha blended variable model. Finally, we discuss the conceptual plau-sibility of a task mixing or blended variable model in the domain ofmental abilities, and we address some additional aspects of theHampshire et al. report.1

1 As noted above, Hampshire et al. (2012) also reported neuroimaging data.Specifically, they reported analyses of within-person data in which the activity ofmany brain voxels was assessed during performance of the mental ability tasks. Fromthe correlations among the 12 tasks’ activation levels across voxels, Hampshire et al.extracted three components, which broadly resembled those obtained from theanalyses of actual task performance. They used the neuroimaging-derived componentloadings to generate simulated scores for ‘‘participants’’ on the 12 tasks (afteraddition of some ‘‘noise’’ or error variance). The resulting simulated data producedcomponents that were somewhat similar to the components based on real partic-ipants’ task score data; also, when the simulated components were rotated obliquely,they showed intercorrelations close to those observed for the obliquely-rotatedcomponents from the real data. According to Hampshire et al., this result demon-strated that the correlations among mental ability task scores can be attributedentirely to ‘‘task mixing’’ and that ‘‘diffuse factors’’ (those that would underlie a gfactor) need not be invoked.

In this report, we do not discuss the neuroimaging data of Hampshire et al. (2012)or their simulated individual difference data generated from those data. As describedabove, the neuroimaging components of Hampshire et al. were derived fromcorrelations among tasks’ activation levels across brain voxels. Those componentsdo not themselves represent individual difference dimensions, and it is not knownwhether or not they should relate isomorphically, if at all, to such dimensions.Therefore, it is not at all clear that the generation of simulated individual differencedata from the within-person neuroimaging data is a meaningful exercise.

2. Ashton et al.’s blended variables model and Hampshire et al.’stask mixing model

Ashton et al. (2009) addressed the suggestion that the majordimensions of personality—whether conceptualized in terms ofthe Big Five (e.g., Goldberg, 1990) or the HEXACO (e.g., Ashton& Lee, 2007) models—are themselves correlated and define twohigher-order factors (DeYoung, 2006) or even a single ‘‘generalfactor of personality’’ (Musek, 2007). They pointed out that thefinding of correlations among scales assessing personality factorsmight not be due to some underlying higher-order factors butmight instead be due to the prevalence within those scales oftraits that represent same-signed blends of two or more factors(see Ashton et al., 2009, Figs. 1 and 2). Thus, the task mixing mod-el of Hampshire et al. (2012), in which ‘‘tasks weigh on multipleindependent factors’’ (p. 1226) is equivalent to the previouslyproposed blended variables model of Ashton et al. (In a latersection of this article, we discuss the task mixing model inrelation to early work in the structure of intelligence byThurstone and by Thomson.)

Ashton et al. demonstrated using CFA that blended variablesmodels did in fact outperform higher-order factor models inexplaining the correlations among personality variables thatdefined the Big Five or HEXACO factors. In addition, however,Ashton et al. also examined some mental ability data—specifi-cally, the WISC-IV normative sample subtest intercorrelations—and found that a higher-order factor model involving a g factorand four lower-order factors easily outperformed a blendedvariables model involving four orthogonal factors and manysecondary loadings. In this report, we use a similar approach inthe context of the data reported by Hampshire et al., comparingthe fit of a higher-order g model against that of a blended variable(or ‘‘task mixing’’) model.2

3. Factor structure of the Hampshire et al. subtests

Before comparing the competing models of the structure of theHampshire et al. (2012) mental ability tasks, we first report anexploratory factor analysis of those variables. (We thank AdamHampshire for providing the correlation matrix.) Table 1 showsthe loadings of the 12 tasks administered by Hampshire et al.(2012) on three varimax-rotated common (principal axis) factors.Our use of an orthogonal rotation is intended to show the patternof secondary loadings that is required given the assumption ofindependent factors.

The content of the three factors corresponds very closely to thatof the three principal components interpreted by Hampshire et al.as representing Reasoning, Short-Term Memory, and Verbal abili-ties.3 We have doubts about some of these interpretations: forexample, the loadings of the Verbal Reasoning and Deductive Rea-soning tasks on the Reasoning factor are rather modest, as is theloading of Digit Span on the Short-Term Memory factor; moreover,the loadings of Digit Span and Paired Associates on the Verbal factorseem to weaken the interpretation of that factor. Regardless of theinterpretation of the factors, we note that the loadings are very

2 Although we use a higher-order factor model in the present report, we shouldnote that one can also model the loadings of tasks on g and additional factors withoutusing higher-order factors. A nested factor or bi-factor model (e.g., Holzinger &Swineford, 1937), in which tasks’ g loadings are estimated directly rather thanthrough their loadings on lower-order factors, tends to produce somewhat closer fit tothe data but with somewhat reduced parsimony.

3 We report common factors rather than principal components. Because principalcomponents analysis is based on all variance in the variable set rather than on onlythe covariance among variables, the first few principal components tend not toreproduce the original correlation matrix closely. This distortion is particularlyprominent in the dataset of Hampshire et al., where scale intercorrelations wererather low.

Fig. 2. Higher-order factor model of Hampshire et al. (2012) subtests.

Table 1Loadings of Hampshire et al. mental ability tasks on three Varimax-rotated commonfactors.

Subset Factor

1 2 3

Spatial Rotation .53 .15 .07Feature Matching .44 .18 .15Spatial Planning .38 .30 .10Interlocking Polygons .38 .11 .16Deductive Reasoning .31 .15 .03Spatial Span .25 .57 .12Visual Spatial Working Memory .24 .55 .15Self-Ordered Search .22 .44 .22Color-Word Remapping .11 .36 .25Verbal Reasoning .33 .08 .47Digit Span .00 .20 .42Paired Associates .35 .20 .37

Note: N = 44600. Primary loadings given in bold. Maximum absolute residual cor-relation = .03. Factor interpretations following Hampshire et al. (2012): 1, Reason-ing; 2, Short-Term Memory; 3, Verbal.

M.C. Ashton et al. / Personality and Individual Differences 60 (2014) 3–7 5

low for a mental abilities dataset, with none reaching .60 and onlythree exceeding .50. These low loadings reflect the low correlationsamong the variables: The highest correlation in the matrix was .41,and five of the correlations fell between .05 and .10 (all of these in-volved Digit Span, whose highest correlation was only .22).4

As seen in Table 1, the tasks did not load univocally on the fac-tors. Although there are only three factors, nine of the 12 taskshad secondary loadings at least one-fourth as large as their primaryloadings on both of the other two factors, and all 12 tasks had atleast one such secondary loading. (We emphasize that althoughthese secondary loadings are quite small, they are not especiallysmall in relation to the modest size of the primary loadings.) Someof these secondary loadings seem consistent with the content andoperations involved in the tasks (see the Supplemental Online Mate-rial of Hampshire et al. (2012) for task descriptions), but other sec-ondary loadings are much less plausibly interpreted in this way. Forexample, it is not at all clear as to why the Verbal factor should influ-ence performance on such tasks as Interlocking Polygons, Visual-Spatial Working Memory, or Feature Match, all of which involveentirely nonverbal stimuli. We realize that one might construct

4 These low intercorrelations may reflect the brevity of the tasks and theprocedures of administration or of scoring. Consider for example the deductivereasoning subtest, whose correlations with the other subtests ranged from .05 to .20.As noted in the supplementary online material of Hampshire et al., this subtest was90 s in duration (and thus extremely brief for a test of reasoning), with an unusualmethod of item administration (a computer adaptive format whereby correctresponses were followed by more difficult items, but without this difficultycontributing to total scores) and an unusual scoring algorithm (whereby points weresubtracted for incorrect responses).

some plausible post hoc ‘‘task mixing’’ explanation of any or all ofthese secondary loadings, and we will return to this point in a latersection of this article.

Note that if we compute an oblique rotation of the three factorsusing the promax criterion (with kappa = 4), the correlationsamong the factors range from .54 to .62. These results suggest thata higher-order factor model containing a g factor would outper-form a blended variable (or ‘‘task mixing’’) model. To test thispossibility formally, we constructed a blended variable modeland a higher-order g model using CFA. For the blended variable

5 We note that the blended variables model is similar to the model of severalindependent factors which Thurstone (1938) entertained, but that it differs from the‘‘sampling theory’’ model proposed by Thomson (1916, 1951). Thomson argued that gmay actually represent a pool of mental ‘‘bonds’’ (rather than, as per Spearman, asingle continuous quantity of ‘‘mental energy’’), but he did not argue for severalindependent factors each representing a distinct kind of mental ability. AlthoughThomson in his earliest work used the term ‘‘group factors’’ for what he later called‘‘bonds’’, he was not referring to factors such as those reported by Thurstone, whichwere derived psychometrically and were interpretable in terms of the content or theprocesses of their defining tasks. Thomson’s model allowed correlated lower-orderfactors of the kind reported by Thurstone and Thurstone (1941), and interpreted themas representing overlapping subpools of the total pool of bonds.

6 M.C. Ashton et al. / Personality and Individual Differences 60 (2014) 3–7

model (see Fig. 1), we allowed one secondary loading for eachvariable, choosing the highest secondary loadings of Table 1. Forthe higher-order factor model (see Fig. 2), we simply allowed eachsubtest to load on its designated lower-order factor, and allowedeach of those factors to load on a higher-order g factor. (Note thatalthough we did not incorporate any secondary loadings into thehigher-order g model, such loadings could be added.) The fit ofthe higher-order g model (v2

(51) = 2724.18, SRMR = .024,CFI = .961, RMSEA = .032) was better than that of the blendedvariables model (v2

(44) = 5286.26, SRMR = .057, CFI = .924,RMSEA = .052), even though the latter model had many secondaryloadings (and hence fewer degrees of freedom than did the higher-order g model).

4. On the plausibility of highly complex models of the structureof mental abilities

The comparison of CFA models reported above might be criti-cized on the grounds that although the task mixing or blended var-iable model contained many secondary loadings, many othernontrivial loadings have been omitted from that model. Accordingto this view, a close approximation to the correlation matrix wouldbe achieved only by allowing every variable to load on every factor,hence producing a model that cannot be identified in CFA but canbe expressed through the analysis reported in Table 1.

The difficulty with this argument, in our view, is that if a givenfactor solution is extremely complex—in the sense that every var-iable loads substantially on several factors—then it is likely to bepsychologically implausible. That is, if we postulate the existenceof several orthogonal factors of mental ability, then we cannot ex-pect that every variable will be influenced to an important degreeby all or most of those factors. Here we follow the thinking of Thur-stone (1938, pp. 71–72), an early proponent of the idea of severalmental ability factors:

Let us suppose that there exist distinct factors, mental facultiesor powers, such as facility with numbers, facility with words,inductive resourcefulness, ability to think in visual terms,quickness of perception of detail, and retentiveness. Let therebe a list of eight or ten such faculties or possibly fifty or sixtyof them.Now consider a matrix of order n � r which represents the nor-mal or average factorial composition of n tasks and r factors.Each row of such a factorial matrix then represents one of thetasks. List at random such tasks. It is almost certain that noone of the tasks will involve all the abilities or faculties. In fact,it would be difficult, if not impossible, to invent a unified task orproblem or test question which required the exercise of all thefactors just enumerated. The best way to convince one’s selfabout the psychological plausibility of this assumption is toattempt the assembly of a list of tests so designed that eachone of them demands the exercise of each one of the enumer-ated mental powers. It is almost certain that any collection oftests that we can assemble will have a factorial description witha very large number of vanishing entries. For example, a test inwhich the subject writes the opposites of given words asquickly as he can think of them is almost certain to make littleor no demand on number facility or the perception of detail.A numerical task is likely to make a negligible demand on wordfluency or memory. It is by these psychological considerationsthat we have confidence in being able to find, by rotation ofthe axes, such a reference frame that a unique set of vanishingentries appears in the factorial matrix.

It is of some interest in this context to consider the evolution ofThurstone’s thinking on the structure of mental ability. In his 1938study cited above, in which he examined mental test scores in a

sample of University of Chicago students, Thurstone reportedorthogonal factor solutions, and his factor loading and correlationmatrices did suggest that there were roughly mutually orthogonalmarkers of some but not all of the factors. However, the correla-tions among the tests in this study were attenuated due to the re-stricted range of ability within this highly selected group ofsubjects, whose average level of overall mental ability was farabove average. In later work based on less-selected samples, Thur-stone found that an orthogonal simple structure could not be ob-tained, and he therefore reported oblique factors that couldproduce a higher-order factor (e.g., Thurstone & Thurstone,1941), which he interpreted as being equivalent to the g factor asdiscovered by Spearman (1904, 1927). Thus, although Thurstonehad endeavored to identify independent factors of mental ability,he later considered it necessary to use correlated factors and to in-voke a general factor to account for those factor intercorrelations(see Thurstone, 1947, for a reconciliation of his ideas with thoseof Spearman).5

As noted above, one might attempt to generate task mixingexplanations for the many secondary loadings of Table 1, includingthose that we consider to be conceptually implausible. We suggestthat the best way to support the task mixing model would be to de-velop mutually orthogonal marker tasks for the proposed factors(or, better still, for the four or five broad factors that are commonlyobtained in mental abilities research; see, e.g., Canivez & Watkins,2010; Gustafsson, 2002). But it seems that this task is impossible:Hampshire et al. (2012, p. 1225) have themselves noted the‘‘intractability of developing tests that measure individual cogni-tive processes’’, and we remind readers of the extraordinarily cre-ative efforts of the Thurstones (e.g., Thurstone, 1938; Thurstone &Thurstone, 1941) to create maximally independent sets of tasks. Ifthe task mixing model is to be considered as a model of the struc-ture of mental ability, however, then one should be able nearly toisolate the content or processes involved in each factor of mentalability and thereby to develop tasks that draw almost exclusivelyon that factor. Recall that in the domain of personalitystructure—a domain characterized by a distinct lack of simple struc-ture—blended variable models have been supported through theidentification of traits representing roughly mutually orthogonalmarkers of the major personality dimensions (Ashton et al., 2009).

5. On the interpretation and correlates of g, and the utilityof global IQ scores

Hampshire et al. (2012) referred in several places to the ideathat intelligence is ‘‘unitary’’, asking whether ‘‘the entire distribu-tion of human intelligence can be accounted for by just one generalfactor’’ (p. 1225). These statements lead the reader to infer thatintelligence researchers entertain a model in which individual dif-ferences in mental ability are attributed almost entirely to a singlefactor. We should clarify that this is not the case and that it has notbeen the case since at least the 1920s, when even the discoverer ofthe g factor acknowledged the existence of additional common fac-tors while emphasizing the importance of g (Spearman, 1927). The

M.C. Ashton et al. / Personality and Individual Differences 60 (2014) 3–7 7

long-held consensus view among intelligence researchers is thatthe variance in mental ability test scores is attributable in part toa higher-order g factor, in part to several residualized lower-orderfactors, and in part to the specific variance of each individual task(note that in some variations on this model, there are two levels oflower-order factors). For example, Canivez and Watkins (2010) re-ported that in normative sample data for the WAIS-IV core sub-tests, a higher-order g factor accounted for about 43% of thevariance in the test battery, four residualized first-order factors to-gether accounted for 21% of the variance, and subtest specificityaccounted for the remaining 36% of the variance. Althoughresearchers have long agreed that the domain of intelligence hasa large and important general factor, they have also long agreedthat this factor does not capture all of the variance shared betweenmental ability tests.

Related to the above issue is the question of whether global IQscores can provide useful indices of individuals’ levels ofintelligence. Here we should note that even if there were no g fac-tor—even if there did exist several independent factors of mentalability—an index of overall mental ability, computed as an averagescore across those factors, might still have considerable practicalutility insofar as some important outcome variables might dependon that overall ability. But given that the various lower-order intel-ligence factors are substantially intercorrelated, with the resultthat the higher-order g factor typically accounts for the majorityof the common variance shared by mental ability tasks, it is clearthat an overall IQ score approximates rather closely a majordimension of general intelligence.6

Finally, Hampshire et al. (2012) reported that different aspectsof intelligence, as represented by scores on three principal compo-nent scores in their test battery, showed differing relations withvarious demographic variables such as participant sex, education,region of birth, and others. This result was taken as evidence forthe relative independence of these three abilities. However, thispattern of results does not demonstrate the existence of indepen-dent factors of mental ability. Even if each lower-order factor isloaded strongly on a higher-order g factor, it is entirely possiblethat demographic and other individual difference variables willalso be related to the non-g variance of those lower-order factors,thereby producing substantial differences between similarlyg-loaded tasks in their correlations with those variables.7

6. Conclusion

We summarize the major points of this article as follows.The task mixing model proposed by Hampshire et al. (2012) is

conceptually equivalent to the blended variable model that Ashtonet al. (2009) used in disconfirming the existence of higher-orderpersonality factors.

6 Hampshire et al. suggested that g ‘‘is defined as the measure taken by classicalpen and paper IQ tests such as Raven’s matrices . . . or the Cattell Culture Fair [test]’’.Although some particular mental ability tasks, such as the nonverbal matrixreasoning task developed by Raven, have sometimes been found to be highly g-loaded, the best approximation to g is obtained not from any single task but insteadfrom many diverse mental ability tasks (Gustafsson, 2002).

7 See the textbook by Ashton (2013) for various examples of cases in whichcomparably g-loaded tasks representing different lower-order factors differ in theirassociations with other individual difference or demographic variables.

In the domain of mental abilities, a blended variable or taskmixing model cannot explain the correlations among tasks aseffectively as can a higher-order g model, even when manysecondary loadings are specified. Only by assuming that every var-iable is influenced by most or all postulated factors of mental abil-ity can a blended variable or task mixing model account for thecorrelations almost as effectively as can a higher-order g model.For most tasks, however, such extreme multifactorial influence ishighly implausible, as noted decades ago even by Thurstone, theoriginal proponent of multiple-factor models.

The non-existence of higher-order personality factors has beendemonstrated through the construction of mutually orthogonalmarkers of several independent personality factors. Likewise, thenon-existence of g would be demonstrated by constructing mutu-ally orthogonal markers of factors representing the major aspectsof mental ability. However, the data of Hampshire et al. do not pro-vide such markers, and the classic findings of the Thurstones sug-gest that this task is impossible.

References

Ashton, M. C. (2013). Individual Differences and Personality (2nd ed.). San Diego, CA:Academic Press.

Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages ofthe HEXACO model of personality structure. Personality and Social PsychologyReview, 11, 150–166.

Ashton, M. C., Lee, K., Goldberg, L. R., & de Vries, R. E. (2009). Higher-order factors ofpersonality: Do they exist? Personality and Social Psychology Review, 13, 79–91.

Canivez, G. L., & Watkins, M. W. (2010). Investigation of the factor structure of theWechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV): Exploratory andhigher order factor analyses. Psychological Assessment, 22, 827–836.

DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informantsample. Journal of Personality and Social Psychology, 91, 1138–1151.

Goldberg, L. R. (1990). An alternative ‘‘Description of personality’’: The Big-Fivefactor structure. Journal of Personality and Social Psychology, 59, 1216–1229.

Gustafsson, J.-E. (2002). Measurement from a hierarchical point of view. In H. I.Braun, D. N. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychologicaland educational measurement (pp. 73–95). Mahwah, NJ: Erlbaum.

Hampshire, A., Highfield, R. R., Parkin, B. L., & Owen, A. M. (2012). Fractionatinghuman intelligence. Neuron, 76, 1225–1237.

Holzinger, K. J., & Swineford, F. (1937). The bi-factor method. Psychometrika, 2,41–54.

Musek, J. (2007). A general factor of personality: Evidence for the Big One in thefive-factor model. Journal of Research in Personality, 41, 1213–1233.

Spearman, C. (1904). ‘‘General intelligence’’, objectively defined and measured.American Journal of Psychology, 15, 201–292.

Spearman, C. (1927). The abilities of man: Their nature and measurement. London:Macmillan.

Thomson, G. H. (1916). A hierarchy without a general factor. British Journal ofPsychology, 8, 271–281.

Thomson, G. H. (1951). The factorial analysis of human ability (5th ed.). London:University of London Press.

Thurstone, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.Thurstone, L. L. (1947). Multiple-factor analysis: A development and extension of The

vectors of mind. Chicago: University of Chicago Press.Thurstone, L. L., & Thurstone, T. G. (1941). Factorial studies of intelligence. Chicago:

University of Chicago Press.