Intelligence 37 (2009) 62–67

Contents lists available at ScienceDirect

Intelligence

Culture-fair prediction of academic achievement

Joseph F. Fagan a,⁎, Cynthia R. Holland b,1

a Department of Psychology, Case Western Reserve University, Cleveland, OH 44106-7123, USAb Liberal Arts, Cuyahoga Community College, 11000 West Pleasant Valley Road, Parma, OH 4130, USA

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +1 216 368 6476; fax:E-mail addresses: jff@case.edu (J.F. Fagan), cindy.h

(C.R. Holland).1 Tel.: +1 216 987 5144; fax: +1 216 987 5066.

0160-2896/$ – see front matter © 2008 Elsevier Inc. Adoi:10.1016/j.intell.2008.07.004

a b s t r a c t

Article history:Received 14 December 2007Received in revised form 2 July 2008Accepted 23 July 2008Available online 30 August 2008

A theoretically based, culture-fair test of new learning ability is predictive of academicachievement. A sample of 633 adults, 121 of minority status, drawn from urban privateuniversities, colleges, and community colleges were given information as to the meanings ofpreviously unknownwords, sayings, similarities, and analogies. They were also tested for theirexisting knowledge of vocabulary, opposites, and analogies with a brief version of the ScholasticAssessment Test (SAT). New learning ability proved to be culture-fair, reliable, and predictive ofgrades and of the brief version of the SAT.

© 2008 Elsevier Inc. All rights reserved.

Keywords:Culture and abilityEqual opportunity

1. Introduction

The present research tests the validity of a culture-fair testof the ability to process new information for the prediction ofacademic achievement (exam scores in college courses). Theculture-fair test of new learning is based on a theory whichdefines intelligence as information processing ability and theintelligence quotient (IQ score) as a measure of knowledgeresulting from processing ability and from the informationprovided by the culture for processing (Fagan, 2000).

The search for a culture-fair test predictive of academicachievement begins with a question. Are majority–minoritydifferences in IQ due to differences in innate intellectualability or to cultural variations in exposure to information(Jensen, 1985)? There is no agreed upon answer and there isevidence for both sides of the argument (Gottfredson, 2005;Nisbett, 2005; Rushton & Jensen, 2005a,b; Sternberg, 2005;Suzuki & Aronson, 2005). Therefore, many (Cooper, 2005;Helms, 2007; Hunt & Carlson, 2007; Newman, Hanges, &Outtz, 2007; Sternberg, Grigorenko, & Kidd, 2005) argue forthe need for new theoretical approaches to the question of the

+1 216 368 4891.olland@tri-c.edu

ll rights reserved.

sources of racial inequality in IQ. The theoretical approachtaken in the present study assumes that group differences inIQ not accompanied by group differences in informationprocessing ability are due to group differences in access toinformation (Fagan, 2000). Based on these assumptions,studies by Fagan & Holland (2002, 2007) offer a theoreticallyguided, empirical approach to the question of the basis ofracial differences in IQ.

Fagan and Holland (2002) investigated the contributionsthat information processing ability and access to informationmake to racial differences in IQ. Majority and minority groupmembers were compared for their knowledge of the mean-ings of words, a task that typically results in racial groupdifferences in IQ. Fagan and Holland (2002) insured that thepeople in each group were given equal opportunity to learnthe meanings of novel words and tested to determine howmuch knowledge had been acquired. General knowledge ofword meanings was also tested to control for the possibilitythat the particular people chosen to represent each racialgroupmight, by chance, simply have been equal in vocabularyknowledge. The majority group members were, as expected,superior to those in the minority in general vocabularyknowledge. However, when equal opportunity for exposureto the meanings of words was experimentally assured, bothracial groups were equal in knowledge. Fagan and Holland(2007) explored the generality of their original findingsby testing majority and minority group members for their

63J.F. Fagan, C.R. Holland / Intelligence 37 (2009) 62–67

knowledge of sayings, analogies, or similarities. Material waspresented in such a way that knowledge of the concepts andterms employed in each test were commonly available forindividuals of either race. Participants were also tested fortheir understanding of sayings, similarities, and analogiestypically given in assessments of IQ, assessments which varywith race (Jensen,1980; Jensen,1981). As in their earlier study(Fagan & Holland 2002), knowledge such as that tested onconventional IQ tests varied with racewhile knowledge basedon information made generally available did not vary withrace.

1.1. Summary

In brief, the data of Fagan and Holland (2002, 2007), basedon some 1000 participants, support the view that differencesamong races in knowledge typically tapped on standard IQtests have to do with experience. A chief implication of suchfindings is that it may be possible to develop culture-fair testsof intelligence. As Williams (2000, p. 17) notes “Fagan's ideas”are “relevant to the debate on intelligence testing andaffirmative action because .… a true measure of processingefficiency (if it could be devised) would be fair to members ofall racial and ethnic groups”. The goal of the presentinvestigation was to discover if a culture-fair test of informa-tion processing based on new learning ability is predictive ofacademic achievement. Specifically, adults drawn fromprivate universities, colleges, and community colleges in amajor urban setting were tested for their ability to acquirenew information concerning the meanings of previouslyunknown words, sayings, similarities, and analogies. Theywere also tested for their knowledge of vocabulary, opposites,and analogies via a brief version of the verbal section of theScholastic Assessment Test (SAT-V) constructed for thepurposes of the present study. Associations among perfor-mance on the culture-fair tests of new learning, a moreconventional estimate of academic aptitude (the brief SAT),and academic achievement (objective test scores in collegecourses) were analyzed. For a small number of the partici-pants standard SAT-V scores were available which allowed anestimate of whether the brief SAT constructed for the presentstudy was comparable to the standard SAT-V in predictingclass exam scores.

2. Methods

2.1. Participants

The sample included 633 students (392 females, 241males). Racial identity was voluntarily provided by thestudent who checked, on a form, one of five categorieslabeled “American Indian or Alaskan Native”, “Asian or PacificIslander”, “Black or African-American, not of Hispanic origin”,“Hispanic”, or “White, not of Hispanic origin”. The categoriesused were based on the designations employed by the UnitedStates Public Health Service and were consistent with the useof the same categories employed in the Fagan and Holland(2002, 2007) studies. Of the 630 who provided information asto their race, those of majority status (461 Whites, 48 Asians)constituted 80% of the sample and those of minority status(100 African-Americans, 18 Hispanics, 3 American Indians)

constituted 20%. The mean age was 21.3 years (SD 6.0 years),the mean education 13.7 years (SD 1.3 years). Some 49% of thestudents were enrolled at two private universities, 3%attended a small, private, liberal arts college, and 48%attended a two-year, community college. Admission to thecommunity college is not based on standard tests such as theSAT or the ACT, only proof of completion of high school isrequired. Including community college students as well asstudents in private universities and colleges allowed arepresentative estimate of performance across a wide rangeof ability. All were registered for psychology classes at theundergraduate level.

2.2. Apparatus and materials

A culture-fair test of the students’ ability to acquire newinformation concerning the meanings of previously unknownwords, sayings, similarities, and analogies was given. Exam-ples will be noted below. In addition to the culture-fair tests ofnew learning all participants were given a brief SAT type testbased on questions available from books containing practicequestions for the SAT (examples will be given below). Thebrief SAT was given to insure that all participants would begiven the same test, since community college students are notrequired to take the SAT for admission. In addition, studentsfrom other countries and transfer students did not alwayshave SAT scores on record. Measures of exam performance onall of the objective tests taken during a semester (expressed as% correct out of 100) were obtained from instructors ofpsychology courses, with the students' consent.

2.3. Procedure

2.3.1. Culture-fair tests of new knowledgeTests of new knowledge were based on a training phase

and a testing phase. All training and testing was done in agroup setting. Training for the learning of the meanings ofnew words, consisted of a form that said: “Now you are goingto see how some unusual words are used in sentences. Readeach sentence carefully.” An example of such a sentence was“It cost 1500 BEZANTS to buy the rug in Byzantium.” Trainingwould then continue for the remaining 11 words of the 12item set. Instructions for learning the meaning of new sayingswere: “On the following pages you will see how 16 sayingsfrom various cultures are explained in English. Carefully readthe explanation for each saying.” An example of such a sayingwas “IN THE SOUP: Stuck. Not able to escape. Can't get away.”Training for learning the meanings of new similarities andanalogies was accomplished using pairs of nonsense words.Each of 20, two-word sets was explained. Later, 10 pairings ofwords were used to test for knowledge of the similaritybetween the words and 10 pairings were used to estimatenewly gained knowledge of how thewords fit into an analogy.The training instructions for two examples follow: “On thefollowing pages you will see how simple words from rarelanguages are explained in English. Carefully read theexplanation for each set of words. BRILLIG and CIDY: ABRILLIG is easily picked from a low branch and a CIDY from offa vine. Both a BRILLIG and a CIDYare juicy and Delicious. KODTand VALD: Big drills went into the earth hoping to find solid,shiny KODT or energy-rich, flowing VALD.”

Table 1Estimates of reliability for new knowledge, brief SAT scores, and class examscores (N=539)

Test Mean SD % correct Reliability

New knowledge 25.8 6.8 53.8 .77Brief SAT 11.8 5.4 49.2 .82Class exam scores 80.9 12.3 80.9 .91

64 J.F. Fagan, C.R. Holland / Intelligence 37 (2009) 62–67

The students then handed in their training materials andreceived a set of multiple choice tests on the newly learnedmaterial. To test new knowledge of 12 word meanings, thestudent was asked to choose among four possible answers.For example: “BEZANT a. Hotel b. Coin c. Mill d. Harbor”. Totest knowledge for the 16 newly learned sayings the studentswere given multiple choice tests such as: “IN THE SOUPmeans a. Broke b. Rich c. Trapped d. Knowing”. To test forknowledge of how a pair of newly learned words was mostsimilar students saw 10 questions such as: “BRILLIG and CIDYa. Round b. Colored c. Fruit d. Vitamins”. To test for knowledgeof newly learned analogies students saw 10 questions such as:“KODT is to silver as VALD is to_____ a. Gold b. Mecca c. Oil d.Eggs”

2.3.2. The brief SATQuestions of the sort traditionally tested on the SAT-V

were taken from practice texts for the SAT-V (Robinson &Katzman, 2002) and the Graduate Record Exam (Martinson,2000). They comprise what will be referred to here as thebrief SAT. The test included 24 items, eight of which testedknowledge of the meanings of words. For example: “Henryviewed Melissa as_________; she seemed to be against anyposition regardless of its merits. a. Heretical b. Disobedient c.Contrary d. Inattentive e. Harried”. A second set of eightquestions tested knowledge of opposites. For example:“EXONERATE a. Testify b. Engender c. Accuse d. Inundate e.Abrogate.” A set of eight questions tested knowledge ofanalogies. For example: “WATERFALL: CASCADE:: a. Snow :Freeze b. Missile : Launch c. Tree : Exfoliate d.Wave : Undulatee. Monarch : Reign”. An entire session (training and testing)lasted about 40 min.

3. Results

3.1. Measures

Total scores across tests of newly acquired meanings ofwords, sayings, and similarities; total scores based on thebrief SAT type tests of knowledge of meanings, opposites andanalogies; and class exam scores as the index of achievementwere the measures of interest.

3.2. New learning is culture-fair

The relationships among race, knowledge of newlylearned material, past knowledge as estimated by the briefSAT scores and class exam scores were explored in a series ofmultiple regression analyses. The first two of these analyseswere based on 628 participants for whom scores on newlearning and the brief SAT were available and who hadindicated their racial identity. Both race and new learningability were expected to play a role in past knowledge (briefSAT scores). Indeed, such was the case. A regression analysisyielded a multiple R of .65, F (2/627)=232.7, Pb.001, with Betavalues of .06 (t=2.0, Pb.05) and .64 (t=21.0, Pb.001) for raceand new learning, respectively, for the prediction of brief SATscores.

Past knowledge (i.e. brief SAT scores), but not race, wasexpected to be related to the ability to process new informa-tion. The results were as predicted. A regression analysis

yielded a multiple R of .65, F (2/625)=230.9 Pb.0001, withBeta values of .04 (t=1.3, PN.18) and .65 (t=21.0, Pb.0001) formajority–minority status and brief SATscores, respectively, forthe prediction of new learning.

A similar analysis was undertaken for the contributions ofrace and new learning ability to class exam scores. Theseanalyses were based on 539 participants for whom scores onnew learning and class exam scores were available and whohad indicated their race. Age was entered into the regressionanalysis as well, since a preliminary analysis indicated somerelationship between age and grades. Both race and newlearning ability were expected to play a role in class examscores. They did, with a multiple R of .44, F (3/533)=43.2,Pb.0001, with Beta values of .10 (t=2.4, Pb.02) for majority–minority status, .42 (t=10.6, Pb.0001) for new learning ability,and .15 (t=3.7, Pb.0001) for age, respectively, for theprediction of class exam scores.

If both information processing ability and cultural expo-sure to information determine knowledge, class exam scores,but not race, should be related to the ability to process newinformation. The results confirmed such a prediction. Theregression analysis yielded a multiple R of .43, F (3/533)=39.8, Pb.0001, with a non-significant Beta value of .007(t=0.2) for majority–minority status, and significant Betavalues of .42 (t=10.6, Pb.0001), and − .11 (t=−2.7, Pb.006)for class exam scores and age, respectively, for the predictionof new learning.

The present findings are in accordwith the results of Faganand Holland (2002, 2007) where minority andmajority groupmembers did not differ in the ability to process novelinformation but did differ when previous exposure toinformation was not controlled (brief SAT and class examscores). For our present purposes, the results confirm theculture-fair nature of the items chosen in the present study tomeasure the ability to acquire new knowledge.

3.3. Reliability

Correlations either uncorrected or corrected for unrelia-bility were computed to determine the relationships amongthe indices of new knowledge, the brief SAT scores and classexam scores. The means and standard deviations for the testof new learning, the brief SAT test and class exam scores alongwith estimates of reliability based on Kuder–Richardsonformula 21, (Cronbach, 1960) are listed in Table 1 for the539 participants in the sample who indicated their racialgrouping, who completed each test and who consented tohave their class grades made available. As one can see fromthe data in Table 1, both the new learning and the brief SATscores were of the same level of difficulty and of the samelevel of reliability.

Table 3Correlations uncorrected (r) or corrected (R) for unreliability between newknowledge and class exam scores and brief SAT and class exam scores formajority and minority participants

New knowledge Brief SAT

Majority r .40⁎⁎ .52⁎⁎R .48⁎⁎ .60⁎⁎

Minority r .46⁎⁎ .50⁎⁎R .56⁎⁎ .58⁎⁎

** Pb.0001.

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3.4. Predictive validity

The next focus of analysis was on the predictive validity ofthe culture-fair test of new learning. Obtained coefficients,either uncorrected or corrected for attenuation due tounreliability, for the total sample of 539 participants arelisted in Table 2.

The data given in Table 2 indicate that class exam scoreswere predicted at a moderate and statistically significant levelby the culture-fair test of new knowledge (r= .41, R= .50) andthere was a substantial and significant relationship betweenthe culture-fair test of new knowledge and the brief SAT index(r= .66, R= .83).

Table 3 lists the correlations (uncorrected or corrected forunreliability) among new knowledge, the brief SAT, and classexam scores presented separately for majority and minorityparticipants. As can be seen, the finding that tests of newlearning are valid in predicting achievement for the sample asa whole are as true for minority participants as they are formajority participants with no significant differences obtainedin any comparisons of corresponding coefficients for majorityvs. minority members.

In considering the magnitude of the predictive validitycoefficients listed in Tables 2 and 3 it should be borne in mindthat the coefficients obtained between class exam scores andeither new knowledge or the brief SAT are attenuated by thefact that exam scores from one class to another are not exactlycomparable. Teachers vary in the difficulty of the tests theyconstruct, readings required, clarity of lectures, etc., and, thus,in the class exam scores obtained by their students. Thus,differences among teachers remains as an (undetermined)attenuating factor in assessing the accuracy of predictivevalidity coefficients.

It is important to note, for the total sample (Table 2), thatthe relationships between the brief SAT and class exam scores(r= .51, R= .59) are somewhat higher than the relationshipsbetween new learning and class exam scores (r= .41, R=.50)with the difference (.10) between the r values of .51 and .41for the 541 participants being reliable at t=3.3, df 536,Pb.001. According to the theory guiding the present studysuch a disparity is to be expected. Information processingability (measured here by the new learning task) plays a rolein how much knowledge is gained over time, hence newlearning predicts both brief SAT scores as well as class examscores. The brief SAT scores, however, are based not only onprocessing ability but on specific information provided byone's culture. The exposure to information provided by one'sculture necessary to solve items on the brief SAT (e.g.knowledge of word meanings) also plays a role in under-

Table 2Correlations among new knowledge, brief SAT, and class exam scores,uncorrected (r) or corrected (R) for unreliability for N=539

Brief SAT Class exam scores

New knowledge r .66⁎⁎ .41⁎⁎R .83⁎⁎ .50⁎⁎

Brief SAT r .51⁎⁎R .59⁎⁎

**Pb.0001.

standing course material. Thus, one would expect the briefSAT scores to be somewhat better predictors of class examscores than the new learning scores.

A further multiple regression analysis based on the totalsample asked if the test of new knowledge, being highlycorrelated with the brief SAT, would add independentvariance to the prediction of class exam scores. Such wasthe case, as indicated by a multiple R of .52, F (2/536)=101.5,Pb.00001, with Beta values of .14 (t=2.8, Pb.005) and .42(t=8.7, Pb.0001) for new learning and the brief SAT,respectively, for the prediction of class exam scores on thepart of the 539 participants.

3.5. Comparisons of the SAT-V and the brief SAT

How representative of the standard SAT-V is the brief SATdesigned for the present study? The sample happened toinclude 192 students with an average age of 19.3 years (SD1.6), attending two private universities who had taken theSAT's in 2005–2006. The 192 students included 92 males, 100females, 167 majority group members and 25 minority groupmembers. An initial analysis found that the brief SAT (M=15.5,SD=3.8) and the standard SAT-V scores (M=624, SD=79) werehighly correlated at r= .66 (Pb.001) with a correlationcorrected for unreliability (R) of .82. Did the brief SAT testpredict class exam scores as well as the SAT-V? Yes. Thepredictions from each test to class exam scores were virtuallyidentical. No significant differences were found between theSAT-V predictions of class exam scores (r= .38, Pb.01) or thebrief SAT test's prediction of class exam scores (r= .40, Pb.01).A comparison of coefficients corrected for unreliability, in fact,indicated, if anything, a superiority in prediction for the briefSAT (R= .54) over that for the standard SAT verbal score(R= .42) at t=3.3, df 189, Pb.001).

4. Discussion

4.1. Empirical conclusions

The present study asked whether a racially unbiased testbased on the ability to process new informationwould predictsuccess in college classes. The test of new learning employedin the present study proved to be culture-fair, reliable, andpredictive of both numerical scores on class exams and of abrief version of a standard test of scholastic aptitude (theScholastic Assessment Test-Verbal). Further, the resultsdemonstrate that tests of new knowledge and tests of existingknowledge (such as the brief SAT) each contribute indepen-dent variance to the prediction of performance on exams in

66 J.F. Fagan, C.R. Holland / Intelligence 37 (2009) 62–67

class. A final finding is that the brief, 24 item version of theSAT-V created for the present study is as predictive ofperformance in class as the standard SAT-V.

4.2. Theoretical significance

The present experiment serves as an example of how along lived and still much debated issue such as culture-fairtesting can be addressed by a theory which defines intelli-gence as information processing and by experimental studiesguided by such a theory. The present findings and those ofFagan and Holland (2002, 2007) are consistent with findingsfrom other studies which have attempted to experimentallyinsure equal opportunity for exposure to information topeople of different races. For example, training verbalstrategies can erase differences between African-Americanschool children andWhite school children on tests of analogysolution (Bridgeman & Buttram, 1975). Teaching cognitiveskills and strategies to African children in Tanzania increasestheir scores (relative to children not so trained) on tests ofsyllogisms, sorting, and twenty questions (Sternberg et al.,2002). Black college students in South Africa given amediatedlearning experience reap significantly more benefit from suchtraining on tests of matrix solution than do similarly trainedWhite peers (Skuy et al., 2002), although the strength of thedifferential training effect reported by Skuy, et al. has beencalled into question by te Nijenhuis, van Vianen, and van derFlier (2007) in a reanalysis of a subset of the Skuy et al. data.

Why, in the present study and in related previous studies(Fagan & Holland, 2002, 2007) do majority group membersand minority group members perform equally well on newlylearned information but differ on similar tasks involvingmaterial supposedly learned over a lifetime?Why, indeed, arepeople with less previous knowledge able to process newinformation as well as people with more previous knowl-edge? No racial differences emerged for newly acquiredinformation because the experimental procedures employedassured that all participants had equal opportunity to acquirethe new information in a setting where past knowledge wascommon to all participants. The tasks in the present studyinvolved knowledge of the meanings of word, similarities,and analogies. The procedures used to estimate informationprocessing or new learning ability put previously unknownwords into sentences along with words known to allparticipants, sentences such as “The man walked down thestreet with his GLIP and his GLOP on a leash”. Later, tests weregiven to see how well the participants had learned themeanings of GLIP and GLOB or in what way GLIP and GLOBwere most similar or the use of GLIP and GLOB in an analogy.African-Americans and Whites did not differ on such tasks.Why not? They did not differ because the dictum of “equalopportunity for exposure to information” was assured by theexperimental procedures allowing the equality of races as toinformation processing ability (i.e. intelligence) to be empiri-cally demonstrated. Why did the same groups differ on theirknowledge of material supposedly learned over a lifetime?The theoretical presumption would be that they differed inknowledge acquired over a lifetime because equal opportu-nity for exposure to that information over a lifetime had notbeen assured by the culture in which the different racial-ethnic groups were raised.

A question may also be raised as to why some informationprocessing tasks show racial-ethnic differences in perfor-mance while the tasks employed in the present study do not.The theory of intelligence as processing guiding the presentstudy contains a two page discussion of the fact that somecognitive or information processing tasks that show racialdifferences are also quite subject to obvious cultural influ-ences such as cutoff dates for school entry (Fagan, 2000, pp.173–174). The pointmade in Fagan (2000) is that performanceon tasks called information processing tasks may be culturallyinfluenced. Thus one must be cautious in attributing racialdifferences in IQ to processing differences on the basis of anyor all such tasks. Fagan (2000, p.174) then goes on to point outthat findings by Jensen (1993) as to racial differences onwhatare called complex information processing tasks (mentalarithmetic) may also have a cultural basis, since age of schoolentry (a cultural factor) also alters performance on the speedof solution of mental arithmetic problems. The tasks used inthe present study, tasks involving the acquisition of newverbal knowledge and the use of such knowledge in thesolution of vocabulary, similarities, and analogies, are tasksmore complex than any of the information processing tasksjust noted (e.g. more complex than speed of mentalarithmetic). Yet, African-Americans and White Americansdid not differ on these complex verbal tasks when equalopportunity for exposure to the information underlying thetasks was experimentally assured. The fact that the racial-ethnic groups in the present study did not differ inperformance had nothing to do with the simplicity or thecomplexity of the tasks. It had to do with the fact, notedabove, that the dictum of “equal opportunity for exposure toinformation” was assured by the experimental proceduresallowing the hypothesis of the equality of racial-ethnic groupsas to information processing ability (i.e. intelligence) to betested.

A final theoretical question that may be raised is whetherthe new learning tasks used in the present study are devoid ofthe general factor (g) and, thus, show no racial-ethnicdifferences in performance. Such is not the case. In accor-dance with the manner in which Jensen (1998) derives g,estimates of g in the present study were based on a principalfactor analysis (un-rotated). In order to compare the g valuesbased on the three subtests making up the brief SAT to thefour subtests making up the new knowledge test, correctionswere made for the fact that the two tests differed in numberof subtests. To compare the g value obtained from the threesubtests of the brief SAT, to a comparable g value for newlearning, the four subtests making up the new learning testwere grouped into four unique sets of three subtests and aprincipal component, un-rotated factor was derived for eachset. The mean of those four g factors was used to estimate gfor a three subtest version of the new learning test. Thereliabilities of the g factors for the brief SAT and for the newlearning test were then derived by a formula provided byJensen (1998, pp. 99–100) employing the number of subtestsand the eigenvalues of the first principal components. Theseestimates of reliability were used to obtain final, corrected (G)factors for the brief SAT, and the new learning test for theentire sample of 539 employed in the earlier analyses, for theracial-ethnic majority (N=447) and for the racial-ethnicminority (N=92). These computations revealed that both

67J.F. Fagan, C.R. Holland / Intelligence 37 (2009) 62–67

the brief SAT and the new learning tests were each heavilyloaded on G with values of 79.7% and 73.2%, respectively.Moreover, majority andminority participants showed equal Gloadings on the brief SAT (79.6% and 77.3%, respectively) andon the culture-fair test of new learning (72.9% and 72.3%,respectively).

4.3. Practical significance

For economic as well as for educational purposes valid,unbiased estimates of intellectual ability are needed to meetthe challenges of recruiting, selecting, assigning and promot-ing people to positions where they can function mosteffectively. Other recent attempts at the development ofsuch a culture-fair test have been promising in finding thatracial group differences in test scores can be substantiallyreduced (Naglieri, Rojahn, Matto, & Aquilino, 2005; Naglieri,Rojahn, & Matto, 2007; Sternberg, 2006). These reductions inperformance disparities between races hold out the hope offairer access to higher education on the part of racialminorities (Sternberg, 2006). In an earlier article, the hopewas expressed that “culture-fair … tests that are based onprocessing may provide an objective means of selectingcandidates for employment or for advanced education”(Fagan, 2000, p. 177). In fact, the present study increasesthat hope by providing evidence that a reliable, culture-fairtest of information processing based on the ability to acquirenew information is a valid predictor of academic achieve-ment. Such a brief, cost-effective estimate of cognitive abilitycan be used in the selection of candidates for advancededucation or training in complex situations and may providean incentive to achievement and bolster the hopes ofadvancement on the part of minorities.

Acknowledgements

The present study was supported, in part, by a UnitedStates Army Research Institute for the Behavioral and SocialSciences contract W74V8H05K006 (Joseph F. Fagan, PI), in part bya Leffingwell Professorship (Joseph F. Fagan), and by an NIHgrant under the Initiatives for Minority Students: Bridges tothe Baccalaureate Program 2R25 GM49010 (Cynthia R. Hol-land, PI). The rights of study participants were protected andapplicable human research guidelines were followed. Theview, opinions, and/or findings contained in this paper arethose of the authors and should not be construed as an officialDepartment of the Army position, policy, or decision.

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