Achievement Versus Aptitude in College Admissions a Cautionary Note Based on Evidence From Chile

International Journal of Educational Development 33 (2013) 106–115

Achievement versus aptitude in college admissions: A cautionary note based onevidence from Chile

Mladen Koljatic *, Monica Silva, Rodrigo Cofre

Escuela de Administracion, Pontificia Universidad Catolica de Chile, Vicuna Mackenna 4860, Santiago, Chile

A R T I C L E I N F O

Keywords:

College admission

Achievement tests

Aptitude tests

Fairness in testing

Chile

A B S T R A C T

In recent years there has been a debate over the alleged superiority of achievement tests over aptitude

tests on the grounds that the first would be fairer for college admissions and less influenced by family

background. The switch from aptitude tests to achievement tests in Chile presented a unique opportunity

to examine this claim. Regression analysis was used to assess the impact of the change in test

performance using data from seven cohorts of test-takers. The evidence does not support the superiority

of achievement tests, particularly when these assess extensive contents.

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1. Introduction

Most countries in the world face an expanding number ofstudents who demand college education, particularly in develop-ing countries (World Bank, 2000). Sorting out those who have theabilities to pursue college education is a difficult task. Not only is itnecessary to devise a systematic decision-making process tochoose the best qualified among applicants, but such a processneeds to be equitable with respect to subgroups defined byethnicity, gender and socioeconomic status.

From Brazil to China, policy makers debate which is the best andfairest method to select students for higher education. In recentyears Brazil has moved away from custom-designed institutionaladmission tests for a national entrance exam used by publicuniversities (Wildavsky, 2010). In China, some officials at theministry of education are questioning the fairness of theirtraditional ‘‘gaokao’’ exam because of the ‘‘gulf in quality betweenrural and urban schools’’ (The Chronicle, 2010).

In the U.S. the quest for fair college admission has a long historyand was the starting point of aptitude testing in the early twentiethcentury, when James B. Connant, president of Harvard Universityaimed to open a venue for the more capable students to gainadmittance to the institution. He was aware that the tests in use atthe time were much too influenced by the prior academicopportunities of the students and sought to find a way to detecttalented individuals independently of the quality of theireducational experiences. Unlike achievement tests which assessedmastery of specific subjects taught in school, aptitude or reasoning

* Corresponding author. Tel.: +56 2 354 4371; fax: +56 2 553 1672.

E-mail addresses: [email protected] (M. Koljatic), [email protected] (M. Silva),

[email protected] (R. Cofre).

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doi:10.1016/j.ijedudev.2012.03.001

tests focused on measuring verbal and mathematical abilities notdirectly tied to the curriculum, with an emphasis on reasoningskills, critical thinking and problem solving that were deemedrelevant for college-level studies. The idea behind the quest foraptitude tests was to help ‘‘find extraordinarily talented studentswhom you’d otherwise miss because they haven’t had the chanceto go to good schools’’ (Lemann, 2004, p. 11).

Almost one century later, the controversy over the nature ofcontent and fairness of tests is still unsettled. While some haveargued for a return to achievement tests (e.g., see Atkinson, 2001),others believe that aptitude tests deserve consideration if the aimis to identify talent (Lohman, 2004). Still, others affirm that thepursuit of academic excellence and the enhancement of diversityare possible through the use of appropriate measurementinstruments, albeit not the traditional ones (Sternberg, 2006).

In the U.S., the recent changes introduced to the SAT in 2005were fueled, among others, by concerns of equity and fair access. Asexpressed by the President of the University of California:‘‘Achievement tests are fairer to students because they measureaccomplishment rather than ill-defined notions of aptitude. . . theyare less vulnerable to charges of cultural or socioeconomic bias’’(Atkinson, 2001, p. 35).

Supporters of achievement tests have argued that admissiontests based on material taught in the classroom would yieldsmaller score differences among economically deprived andaffluent groups than traditional aptitude tests, a notion that wasquestioned by Zwick (2004) who found that the stronger linkage ofthe ACT test to high school curricula did not translate into smallerscore gaps when compared with the SAT.

Even though the controversy regarding the benefits ofachievement tests appears to be far from settled, a recent reportfrom the National Association for College Admission Counseling(NACAC) has also claimed alleged benefits of achievement tests

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M. Koljatic et al. / International Journal of Educational Development 33 (2013) 106–115 107

over aptitude tests, echoing Atkinson’s (2001) view that anemphasis on achievement tests would encourage improvementof high school teaching and also ‘‘reduce the inequities inherent inthe current system’’ (NACAC, 2008, p. 11).

The controversy in the U.S. over the benefits of achievement orcontent-based tests for students of lower socioeconomic level andthe convenience of eliminating the use of the SAT I in collegeadmissions had its reverberations in Chile, where local authoritiesin charge of the national university admission process decided in2001 to eliminate the aptitude tests in use and replace them byachievement tests. The arguments proffered for the need to changethe existing tests were strikingly similar to those forwarded byAtkinson (2001), as pointed out by a high official from theUniversity of California while on a visit to Chile (Henrıquez, 2003).

As in the U.S., the new achievement tests were presented as avenue to increase the opportunities of access for low SES studentsand to simultaneously improve the quality of secondary education(Bravo et al., 2000; Ministerio de Educacion, 2000). Aptitude testswere declared to be unfair since scores reflected the cultural capitalof the home (Brunner, 2002). Conversely, achievement tests, byfocusing on school learning and personal effort, were assumed tobe more equitable. According to one of the leaders behind the moveto switch tests, increasing the amount of contents assessed in theadmission tests should help advance fairness. He argued that themore contents added to the tests the greater chance to make themmore equitable, since the impact of factors such as familybackground and cultural capital—present in aptitude tests—wouldbe reduced (Diario Austral, 2002).

The following sections offer an account of the context of thechange of tests in Chile, address the purpose of the study and themethods employed for the analysis, report the results, and discussthe implications of the findings.

2. The context of the change of tests

In the year 2000, an educational reform was underway in Chile.The national secondary education curriculum was being revisedand ministry officials were committed to change the SAT-type tests(APT) that had been in use for three decades for a new set ofachievement tests (ACH) aligned with the revised secondary schoolcurricula. In addition to sorting students for admission purposes,the new achievement tests were aimed to promote the sustain-ability of the secondary education reform and to assess itseducational outcomes (Ministerio de Educacion, 2000; WorldBank, 2001).

Since 1967 and until the change of tests in 2003, the APT Verbaland Mathematics tests in use were similar to the American SAT,tapping on what were considered basic reasoning abilities requiredfor success in college. The tests were offered once a year andrequired for admission to any university receiving public funds.

The APT tests were complemented by a set of achievement testsin the vein of the SAT-II Subject Tests that assessed the knowledgeof applicants in advanced math, physics, biology, chemistry andsocial sciences. Still, the pillar of the admission system was the APTsince the subject tests were only required by a few of the moreprestigious institutions for admittance to some of their programs.Unlike the case of the U.S. where admission decisions were madeon the basis of a comprehensive review of the background of thestudent—including test scores, grades, recommendation forms,essays, and diversity considerations—admission decisions in Chilewere made solely on the basis of the scores obtained in theadmission tests and high-school grade point average (HSGPA).Additionally, scores in the admission tests carried weightyconsequences for students, since state-funded scholarship pro-grams and financial aid were tied to outcomes on the nationaladmission tests. Student test score performance was also relevant

for institutions of higher education, with universities vying toattract the top scorers in the admission tests because theenrollment of the best translated into additional public fundingfor the institutions.

3. The development of the new tests

In 2001, the Ministry of Education informed the public that aproject to develop new admission tests was under way to eliminatethe APT battery and substitute it for a set of four mandatoryachievement tests in Mathematics, Verbal (Language Skills), SocialSciences, and Science. The new admission battery would examine100% of the newly reformed national curriculum from grades 9 to12 that could be assessed via multiple choice tests. All applicants topublic-funded universities would be required to take the four testsin order to be eligible for admission. The project was the focus of apublic debate on the convenience of the change. The reservationsregarding the new tests stemmed from the nature of the new testsand the highly segmented secondary education system character-ized by a markedly heterogeneous quality of schools in the nation(Beyer, 2002; Eyzaguirre and Le Foulon, 2002).

In Chile, schools fell into three broad categories: municipal,which were state funded public schools; private-subsidized, whichreceived some funding from the state and charged a small tuitionfee; and private-paying schools. The type of school attended wasassociated with socioeconomic status (SES) and the quality ofeducation varied significantly among municipal, private-subsi-dized and private-paying schools according to national andinternational tests. The private-paying schools educated the mostsocioeconomically advantaged student group; the private-subsi-dized schools attracted lower to middle-income families while themunicipal schools catered to the poorest sections of society. Boththe private-subsidized and the municipal schools offered a dualtrack curricular system: the general track (GT) and the vocationaltrack (VT). The new ACH tests were to be aligned with the nationalcurriculum. Although the required curricular or ‘‘minimum’’contents in subjects such as Mathematics and Language wereallegedly the same, the VT and GT curricula differed in the numberof class hours devoted to academic subjects. For example, while theGT curriculum included Philosophy, Biology, Chemistry andPhysics in the last 2 years of high-school, the VT curriculum didnot.

Although all schools were expected to cover the ‘‘minimum’’contents prescribed in the national curriculum not all schoolsachieved this goal. Thus, the quality of the education received bythe students and the coverage of school content depended largelyon the kind of school attended and track choice, with private-paying schools providing the best quality of education for thosethat could afford it (OECD, 2009).

The students attending the municipal and private-subsidizedschools offering the vocational tracks lagged behind in the qualityof education they received, particularly those attending themunicipal schools that catered to the poorest segment of thepopulation. Still, admittance to prestigious public funded univer-sities was an aspiration for all, including the students from the VTswhere over 60% of its graduates took the admission tests and only9% expressed a desire to enter the labor force straight out of high-school (Ministerio de Educacion, 2008). Many prestigious univer-sities in the nation offered a variety of technical programs andprofessional degrees via the centralized admission process thatselected students through a combination of standardized testscores and grades.

In lieu of the public controversy that ensued over theelimination of the APT tests and the concerns expressed byeducational experts about the hasty process of change and the risksentailed in it for the educationally disadvantaged test-takers

Fig. 1. Selected milestones of the process of change of tests.

1 The 2006 version of the ACH tests was run separately because of conflicting

information as to whether it included full contents. The coefficients for the type of

school and test interactions were consistent with those for the high-content ACH

tests.

M. Koljatic et al. / International Journal of Educational Development 33 (2013) 106–115108

(Fontaine, 2002; Eyzaguirre and Le Foulon, 2002), the authoritiesmade some concessions in order to assuage the transition processbetween the two batteries. Applicants would only be required totake three of the four ACH tests. The Verbal and Math tests wouldbe compulsory and the choice of the third test would depend on thespecific requirements of the academic programs where theapplicant sought admission. Additionally, contents included inthe first versions of the ACH tests were reduced. However, thereduction in the contents was only temporary since additionaltopics would be added yearly until the admission process of 2007,when the ACH battery would include 100% of the contents of thenational GT high-school curriculum.

The ACH tests were first applied for the 2004 universityadmission process with a 15% fall in the number of test-takersrelative to the previous year (Koljatic and Silva, 2006). The loss oftest-takers mostly affected graduates from municipal schools. Thenumber of test-takers recovered and surpassed the 2003 level onlyafter the Ministry of Education started a fee-waiver policy in 2007aimed at low income test-takers (Fig. 1 shows some milestones ofthe change process).

In the years that followed the introduction of the ACH tests, theperformance gap between municipal and private-paying schoolsgrew. Newspaper headlines underscored the trend, but testdevelopers responded that the growing gap between types ofschool was merely a consequence of the change in the SES profile oftest-takers across the years, particularly after the onset of the feewaiver policy of 2007, ruling out that the increase could be in partattributable to the type of test or the amount of contents assessedin them (PSU: Consejo de Rectores, 2008; Dalgalarrando, 2007;Molina, 2008).

4. Purpose of the study

The type of admission test and the amount of contents assessedare matters of consequence in developing nations with centralizedadmission processes that heavily rely on standardized tests foradmission purposes. Whereas SES and access to high qualityeducation cannot be readily changed to enhance fair access tohigher education, the type of admission tests used and theamounts of contents assessed in these can be modified in the short-run. Under conditions of equal predictive capacity, the type of testthat proves to be less detrimental to the opportunities of the mostvulnerable groups should be preferred.

The purpose of this study is to assess whether ACH tests areindeed beneficial for the assessment of students from lowsocioeconomic groups as has been claimed in the literature. Thesuperiority of ACH tests when assessing socioeconomicallydisadvantaged groups has not been examined under conditionsof markedly unequal quality of education and varying amounts ofcontents assessed in the tests. For this reason, the Chilean dataoffered a good, albeit not ideal, opportunity to test this claim. Theideal way for assessing the relative merits of both types of tests

would have been to require all applicants to take both the APT andthe ACH tests and directly compare the outcomes in terms of scoreperformance gaps and prediction in the same sample before optingfor one type of test. However, test developers in Chile downplayedthe need to conduct this sort of comparative study beforeeliminating the APT tests in use (Vision Universitaria, 2002). Theeducational authorities opted to abide by the test-developers’judgment, forgoing further studies. Still, the data available can beexamined to assess whether ACH tests appear to be better suitedthan APT tests for fair assessment of disadvantaged students. Tworesearch questions are addressed:

(1) Are low-content ACH tests better suited than APT tests toassess test-takers from low SES? After accounting fordifferences in SES, does the use of achievement tests favorthose exposed to high quality of education?

(2) Are high-content ACH tests better suited than APT tests toassess test-takers from low SES? After accounting fordifferences in SES, does the use of achievement tests favorthose exposed to high quality of education?

5. Data and methods

5.1. Data

The study used a set of data files that contained the officialtest score records of all test-takers between 2002 and 2008 andwere provided by officials at Pontificia Universidad Catolica deChile, who received the original files directly from the testingagency. In the 2002 and 2003 data files the test scorescorresponded to APT tests. From 2004 onwards the scoresrepresented performance in ACH tests. The version of the ACHtest that examined the lower amount of contents was offered in2004. Additional contents were added to the ACH tests yearlyuntil 2007 when full-curricular content was included in all ACHtests.1 The data files also included socio-demographic variablesand high-school grades, but no data was available on motivationto pursue studies or attendance to coaching programs since itwas not required by the testing agency.

5.2. Measures

Information on high-school achievement in the database wasthe grade point average (HSGPA) obtained by the student duringthe 4 years of high-school expressed in a scale that ranged between200 and 800 points, with a mean of 540 points and a standarddeviation of 100 points.

2 As a validity check, the regression analyses were also run for the group

comprising the deferred cohort of test-takers and repeat test-takers. In general, the

results were consistent with those obtained for the first-timers, albeit with less

variance explained by the model for the deferred cohort.


Three variables associated with SES—self-reported familyincome, mother’s education and father’s education—were codedas ordinal scales in the database. The number of scale categories foreach variable changed across the years rendering non-equivalentcategory descriptions, so they had to be redefined in order to makethem comparable.

A 6-point scale measured self-reported family income between2002 and 2007; an 8-point scale was employed in 2008. To have acommon and comparable measure of income for the period understudy, the data was collapsed into three broad categories: low,middle and high income.

Similarly, the number of scale categories for parental educationchanged from 10-categories to 13-categories in 2006. In this case,the categories were made homogeneous by collapsing them intofour levels of educational attainment: elementary education, highschool, technical training, and university education.

A proxy index for SES was calculated following Donaldson et al.(2008) that linearly combined three demographic variables: self-reported income, father education and mother education. Theindex ranged from 0 to 1, with higher values indicating higher SES.

Schools were categorized in six types according to the type ofschool (TS) attended (i.e. municipal, private-subsidized or private-paying) and the choice of program (vocational or general track).Yet, only five categories were used in the analysis—private-paying/general track (PP_GT), private-subsidized/general track (PS_GT),private-subsidized/vocational track (PS_VT), municipal/generaltrack (MU_GT) and municipal/vocational track (MU_VT). The lastcategory ‘‘private-paying/vocational track’’ was not included sinceover 99% of the students from private-paying schools followed thegeneral track curriculum. The combination of type of schoolattended and track (vocational vs. general track) were consideredto be a proxy of the quality of education. The best quality ofeducation was offered by the PP_GT schools and the lower end wasrepresented by the vocational track at the municipal schools.

Finally, in order to control for the changes in the scaling proceduresapplied to the APT and ACH tests and render scores in a common scale,test scores prior to 2006 were rescaled to approximate the scores tothe same percentiles across the years. The re-scaled score distribu-tions had a mean of 500 and a standard deviation of 110 (seeAppendix A). The re-scaling procedure was based on the total scoressince item-level data was not provided in the databases.

5.3. Methods

The decision of test makers and educational authorities toswitch tests without allowing for a period of simultaneousapplication of both the APT and the ACH tests required the useof statistical control of confounding variables in order to addressthe relative merits of APT tests, low-content ACH tests, and high-content ACH tests as college admission tools. A hierarchicalmultiple regression analysis was employed to examine the impactof the selected predictors of interest and their interactions on thedependent variable (test scores).

The regression analysis was conducted in two steps. The firststep estimated the impact of type of test (TT), SES, and theinteraction variable SES*TT on test scores. Type of test (TT) wasincluded as a dummy variable (APT = 0, ACH = 1). The SES*TTinteraction component served to examine the claim that achieve-ment tests are better suited for assessing students from low SES. Ithas a value equal to zero for APT, while it takes the value of SES forACH. If the argument espoused by proponents of achievement testsholds—i.e. that achievement tests are better suited for assessingstudents of lower SES—the regression coefficient associated withSES*TT should be significant and negative.

The second step introduced of the four school dummy variablesthat captured the combinations of type of school and track (PS_VT;

MU_GT; PS_VT; PP_GT), where the reference category comprisedstudents attending the vocational track at municipal schools(MU_VT), along with four two-way interaction terms representingthe types of schools by type of test interactions (PS_VT*TT,PS_GT*TT, MU_GT*TT and PP_GT*TT). This model assessed whethertype of school (TS) and type of school by type of test interactions(TS*TT) contributed to the explanation of score variance over andbeyond that explained by the variables introduced in the first step.Positive and significant regression coefficients for these interac-tions represented the gains associated with the use of ACH tests bythose attending the different types of schools, after controlling forSES. This strategy provided a stringent test of the additionalvariance contributed by second step variables to the predictionbecause variables that are entered in the first step capture varianceshared with variables entered later in the model.

Given that the focus of the present analysis lies in theinteraction components, both standardized and unstandardizedregression coefficients will be reported, since the latter are non-arbitrary metrics that allow for meaningful comparisons betweengroups (as recommended by Jaccard and Turrisi, 2003).

As is often the case in educational research, the predictorvariables under study were correlated with each other, thereforethe weights and the interpretations arising from them werecontext specific and subject to change radically with the additionor the deletion of a single predictor. Because of the correlationamong SES and type of school predictors, the unique contributionof each depends on the order in which these are entered in theanalysis, and thus the customary test of added subsets could bemisleading and the magnitude of the regression coefficients per semay not be a good indicator of the usefulness of the predictor.Consequently, in order to correctly interpret the relative impor-tance of the predictors in the model we opted to also includeinformation on structure coefficients and a commonality analysis,as recommended by Courville and Thompson (2001), Zienteck andThompson (2006), and Nimon (2010).

The benefit of employing a commonality analysis in conjunc-tion with the analysis of structure coefficients is that it is possibleto determine how much variance each variable (or set of variables)uniquely contributes and how much each shares with every othervariable in the regression. Commonality analysis partitions aregression effect into constituent, non-overlapping components.The partitioning process produces unique and common effectsbetween variables (Nimon, 2010; Zienteck and Thompson, 2006).

The results presented below are based on data for the first-timetest-takers who took the tests immediately following high-schoolgraduation. First-timers differ from the deferred-cohort of test-takers in terms of demographic variables; the latter are an oldergroup and obtain higher test scores. There is no evidence to gaugewhether this pattern is due to differences in motivation, coachingor a combined effect of these and other variables.2

Separate regressions were estimated for the entire sample offirst-time test-takers and the subset of higher-performers in high-school (i.e. those in the upper tercile of HSGPA). The group of high-performers in high school was deemed to be an important segmentof the test-taking population since needy students in the upperrange of high-school grades have a greater opportunity of securingmerit grants and financial aid if they perform well in the admissiontests.

Statistical analyses were conducted for the scores for the twomandatory tests, Verbal and Mathematics. Since both yielded


similar results with slightly higher variance explained for theMathematics test, only the latter are reported here.

6. Results

6.1. Descriptive statistics

Table 1 shows the profound socioeconomic stratificationprevalent in the Chilean secondary school educational system.Students enrolled in the vocational tracks at the municipal schoolsranked lowest in all SES-related variables, followed by those in thevocational tracks at the private-subsidized schools.

Table 2 shows the evolution of test score averages across theyears.

As expected, high correlations were observed between testscores and SES and test scores and type of school (see Table 3). Thecorrelation between type of school and SES (rTS/SES) remainedstable throughout the years indicating the pervasive segmentationof SES by type of school.

The following sections deal with the magnitude of the changesand the extent to which the gap growth can be attributed to boththe changes in the type of test and the amount of contents assessedin them.

6.2. APT test vs. low-content ACH test

The results of the regression analysis that examined whetherlower-content ACH tests (ACH2004 and ACH2005) are better

Table 1Selected socio-economic statistics of test-takers according to type of school, track and

2002 2003 2004

Father% Attaining elementary education only

Private-paying/GT 2.9 2.4 1.8

Private subsidized/GT 14.0 13.4 12.3

Private subsidized/VT 31.8 32.7 31.2

Municipal/GT 28.7 27.9 26.7

Municipal/VT 38.8 38.4 37.2

% Of fathers employed in blue collar/menial jobs






Mother% Attaining elementary education only






% Of mothers employed in blue collar/menial jobsa

Private-paying/GT 1.0 .9 .8





Family% Reporting family income of US $ 500 dollars/month or less






GT—general track; VT—vocational track.a The relatively lower rates of maternal employment in menial jobs is consistent with

female employment rates (OECD, 2011). Paternal unemployment of timely test-takers wa

group attending private-paying schools reported higher rates of maternal employment

suited than APT tests (APT2002 and APT2003) to assess disadvan-taged groups are presented in the first four columns of Table 4, forall first-time test-takers and the subset of high-performers in high-school.

For all test-takers and for the subset of high-performers in highschool, the set of variables at the first step explained 22%(F = 36,190; p < .00) and 31.5% (F = 17,313; p < .00) of the variance,respectively. For the full sample, albeit small, the regressioncoefficient for the SES*TT interaction appeared to support the claimby proponents of achievement tests that its use favored test-takersfrom low SES (b = �13.4, t = �11.63, p < .00). However, for high-performers the SES*TT coefficient was non-significant (b = �1.7,t = �.98, p < .33).

The inclusion of the type of school (TS) variables and theirinteractions at Step 2 increased variance explained by the model byapproximately 2.7% (Fchange = 1732; p < .00) for the full sample and5.6% (Fchange = 1260; p < .00) for the high-performing group,respectively, indicating an additional contribution of TS and TS*TTinteractions beyond variance accounted for by Step 1 variables. Forboth the entire sample and the high-performers, the values of theregression coefficients for SES*TT showed a small positive effect fordisadvantaged groups (b = �18.8, t = �12.60, p < .00; b = �11.9,t = �4.93, p < .00). However, the TS*TT interactions indicated smallgains in scores for those attending general tracks when assessed byACH tests, i.e. favoring the more affluent groups of the population.The highest gains when assessed by ACH tests were associatedwith attendance to private-paying schools (b = 17.2, t = 11.90,p < .00) and the municipal schools (b = 15.8, t = 14.90, p < .00). For

year (percent).

2005 2006 2007 2008

1.7 1.6 1.6 1.3

11.2 11.0 12.3 12.3

30.5 28.6 31.9 32.4

26.2 26.2 28.4 28.6

35.7 36.7 38.9 39.0

1.2 1.2 1.4 1.2

11.6 12.4 14.5 15.6

29.5 29.6 34.0 35.4

26.2 27.3 31.1 32.9

36.0 38.4 42.0 43.3

1.5 1.5 1.3 1.3

11.7 10.7 11.5 11.9

30.0 28.1 31.7 31.5

26.0 25.8 27.8 28.1

35.4 36.9 38.2 38.6

.7 .8 0.8 .8

6.3 6.5 7.3 8.0

14.8 15.1 15.7 17.5

10.8 11.6 12.1 13.1

14.7 16.1 16.2 17.5

8.1 6.4 6.6 15.8

46.9 45.2 48.2 46.1

75.8 73.0 82.4 77.4

71.9 71.1 75.1 72.1

83.2 83.4 88.7 85.4

data from international studies where Chile ranks among the countries with lower

s less than 4% across the years while maternal unemployment climbed over 50%. The

.

Table 2Mathematics test score means, standard deviations and sample size of first-time test-takers by year.

APT2002 APT2003 ACH2004 ACH2005 ACH2006 ACH2007 ACH2008

Private-paying_GT

Mean 588 603 577 586 591 603 609

SD 145 145 112 110 111 106 103

N 20.917 19.116 18.011 18.433 18.333 18.708 18.871

Private-subsidized_GT

Mean 492 495 500 503 502 511 510

SD 131 134 100 101 101 101 100

N 30.005 32.887 33.566 40.203 46.634 49.460 54.191

Private-subsidized_VT

Mean 411 406 430 430 432 434 433

SD 93 96 85 88 87 85 85

N 12.669 11.930 10.560 11.589 12.364 18.563 19.981

Municipal_GT

Mean 445 449 466 476 475 475 475

SD 123 130 109 109 108 107 108

N 37.969 36.252 32.251 33.913 35.616 39.051 39.780

Municipal_VT

Mean 418 411 434 435 431 431 427

SD 93 96 83 86 85 82 84

N 16.176 16.729 12.956 14.072 14.581 22.040 22.554

Total

Mean 475 477 488 493 492 491 491

SD 137 141 112 113 112 112 112

N 117.736 116.914 107.344 118.210 124.528 148.622 155.377


high-performers in high-school, the same trend was observed,with even larger gains for those attending the general tracks atprivate-paying schools or municipal schools when assessed byACH tests (b = 22.5, t = 9.54, p < .00; b = 22.1, t = 12.47, p < .00).

The squared structure coefficients showed a consistent patternwith the regression coefficients for both the total sample and thesubset of high-performers, indicating that the highest predictor ofscores was SES (rs(SES)

2 = 0.887; rs(SES)2 = 0.848) and attendance to

private-paying schools (rs(PP_GT)2 = 0.528; rs(PP_GT)

2 = 0.518).Among the interactions, the highest squared structure coefficientscorresponded to the PP_GT*TT interactions (rs(PP_GT*TT)

2 = 0.249;rs(PP_GT*TT)

2 = 0.239) and the SES*TT interactions (rs(SES*TT)2 = 0.236;

rs(SES*TT)2 = 0.218) indicative of their relevance for score prediction

for the total sample and the high-performers.The partitioning of variance through commonality analysis for

the full sample revealed that Step 1 variables, contributed 6.9% ofunique variance while school-related variables accounted for 2.7%of unique variance. The common contribution of SES and TSvariables (i.e. shared variance) amounted to 15.1%. For the highperformers the unique variance accounted by Step 1 variables was6.8%; the unique variance associated with TS and interactionvariables was 5.6% while shared variance was 24.7%. Commonvariance between SES and TS exceeded their unique contributionsfor both groups. The unique variance of TS variables, albeit smallerthat that for SES was still substantive, particularly for the group ofhigh-performers in high-school.

Table 3Correlations of Mathematics test scores, SES and type of school (TS).

rMath/SES RSES/TS rMath/TS

2002 .48 .58 .37

2003 .49 .58 .39

2004 .46 .58 .39

2005 .45 .56 .39

2006 .45 .56 .40

2007 .50 .58 .43

2008 .52 .55 .44

6.3. APT test vs. high-content ACH test

The last four columns of Table 4 present the results of theregression analyses that examines whether APT tests (APT2002/APT2003) are better suited than high-content ACH tests (ACH2007/ACH2008) for disadvantaged groups.

The set of variables at Step 1 explained 24.6% (F = 47,177;p < .00) and 33.4% of the variance (F = 20,836, p <. 00) for theentire sample and the high-performers, respectively.

The inclusion of the type of school variables and theirinteractions at Step 2 increased variance explained by the modelby 2.9% (Fchange = 2190; p < .00) for the entire sample and 6.3%(Fchange = 1619; p < .00) for the subset of high performers,respectively. The SES*TT interaction was not significant for thefull sample (b = 2.3, t = 1.63; p > .01) whereas for high performersthe regression coefficient albeit small, was in the predicteddirection favoring the lower SES students (b = �9.20, t = �4.10;p < .00). However, just as in the case of the lower content ACH test,the inclusion of type of school dummies along with theirinteraction variables revealed overriding gains associated withthe use of ACH tests, for those attending the general tracks,particularly private-paying schools (b = 25.9, t = 18.9; p < .00) witheven larger gains for high-performers (b = 31.7, t = 14.3; p < .00).

The squared structure coefficients again showed a relativelyconsistent pattern with the regression coefficients, revealing thatthe highest predictor of scores was SES both for the total sample andthe high-performing group (rs(ses)

2 = 0.885, rs(ses)2 = 0.837) followed

by attendance to private-paying schools (rs(PP_GT)2 = 0.488,

rs(PP_GT)2 = 0.496). Among the interactions, the coefficients for

(PP_GT)*TT and SES*TT were consistently high for both groups.The commonality analysis indicated that the unique variance

associated with SES and test-related variables amounted to 7.4%for the full sample. School and test-related variables contributed2.9% of unique variance. Shared variance between the two set ofpredictors was 17.2%. For high-performers the differences betweenthe unique contributions of SES and TS variables were considerablyless pronounced (6.8% and 6.3%, respectively) with shared variancebetween the two amounting to 26.6%.

Table 4Regression of math scores for low- and high-content tests (standard errors in parentheses).

Low-content High-content

All High-performers All High-performers

b b b b b b b b

N 385,579 112,971 434,340 124,632

Step 1

Intercept 404.9 (.40)* 470.1 (.69)* 404.9 (.39)* 470.1 (.67)*

SES 194.8 (.80)* .484 198.9 (1.22)* .563 194.8 (.79)* .480 198.9 (1.20)* .568

TT 9.9 (.58)* .043 4.6 (.98)* .021 5.54 (.52)* .024 6.6 (.91)* .030

SES*TT �13.4 (1.15)* �.034 �1.7 (1.74) �.005 11.7 (1.07)* .029 5.7 (1.63)* .017

R2 adj. .220 .315 .246 .334

Step 2

Intercept 403.2 (.64)* 451.1 (1.05)* 403.23(.62)* 451.1 (1.02)*

SES 146.3 (1.05)* .363 136.7 (1.69)* .387 146.3 (1.03)* .360 136.7 (1.64)* .390

TT 1.8 (.94) .008 �6.2 (1.51)* �.028 �4.6 (.82)* �.020 �5.5 (1.38)* �.025

SES*TT �18.8 (1.49)* �.048 �11.9 (.2.41)* �.035 2.3 (1.40)* .006 �9.2 (2.23)* �.027

PP_GT 59.0 (.99)* .193 84.4 (1.63)* .323 59.0 (.97)* .181 84.4 (1.58)* .315

PS_GT 32.7 (.77)* .132 61.0 (1.29)* .252 32.7 (.75)* .134 61.0 (1.54)* .259

MU_GT 12.3 (.72)* .050 44.0 (1.24)* .177 12.3 (.70)* .049 44.1(1.20)* .177

PS_VT �11.4 (.91)* �.03 �3.0 (1.59) �.008 �11.4 (.88)* �.033 �3.0 (1.54) �.008

PP_GT*TT 17.2 (1.49)* .041 22.5 (2.36)* .064 25.9 (1.37)* .056 31.7 (2.21)* .087

PS_GT*TT 8.3 (1.10)* .027 16.0 (1.84)* .052 13.9 (.98)* .048 20.6 (1.68)* .073

MU_GT*TT 15.8 (1.06)* .049 22.1 (1.77)* .067 21.7 (.94)* .068 26.6 (1.63)* .083

PS_VT*TT 2.4 (1.32) .005 3.9 (2.27) .007 10.7 (1.15)* .024 10.8 (2.04)* .022

R2 adj. .247* .371* .275* .397*

R2 change .027* .056* .029* .063*

Squared structure coefficients at Step 2

SES .887 .848 .885 .837

TT .005 .001 .002 .002

SES*TT .236 .218 .302 .272

PP_GT .528 .518 .488 .496

PS_GT .026 .019 .045 .028

MU_GT .006 .028 .040 .023

PS_VT .123 .155 .116 .155

PP_GT*TT .249 .239 .272 .269

PS_GT*TT .016 .012 .004 .024

MU_GT*TT .011 .005 .006 .003

PS_VT*TT .054 .077 .061 .084

Unique variance (%)

Step 1 variables 6.9 6.8 7.4 6.8

Step 2 variables 2.7 5.6 2.9 6.3

Shared variance (%) 15.1 24.7 17.2 26.6

* p < .001.


In summary, there is an increase of variance explained in testscores associated with SES and type of school variables whenachievement tests assess higher amounts of contents. Thus, underconditions of unequal quality of education, the amount of contentsassessed in ACH tests does not appear to be an irrelevant variable interms of test scores. The findings hold for all test-takers, butparticularly for the group of high-performers in high-school.

The examination of the regression models does not support theclaim of ACH test proponents of their inherent superiority for theassessment of underprivileged groups when quality of schooling istaken into account. In particular, high-content tests appear toprovide an additional edge to test-takers from affluent groups whocan afford quality education.

7. Discussion

The change of admission tests that took place in Chile sheds lightfrom a practical and also a conceptual perspective on the benefits ofachievement tests over aptitude tests to promote equitable access.The findings from the present study—albeit correlational in nature—provide credible evidence that the benefits of achievement tests forthe assessment of disadvantaged students do not hold indepen-dently of amount of contents assessed in them and under conditionsof unequal access to educational opportunities. Any marginally

positive effects of using achievement tests for disadvantaged groupsof society may only hold for light content tests; when achievementtests evaluate larger amounts of contents, the benefits are washedaway.

The gains associated with the interaction of type of school andtype of test were consistent throughout the analyses, with largerincreases in scores accrued for those attending private-payingschools when ACH tests assessed larger amounts of contents. Theless favored groups by the switch in the tests were those attendingthe vocational tracks at municipal and private-subsidized schoolsthat cater to the poorer sectors of society.

As expected, SES explained the largest proportion of uniquevariance in test performance. However, quality of educationvariables should be taken into account when deciding on theimplementation of admission tests. Particularly in nations wherethe poor receive a lower quality of education it cannot be assumedthat the use of achievement tests per se will be beneficial fordisadvantaged students, as was done in Chile. It has been arguedthat the difference between aptitude and achievement tests mayhave been overstated, since aptitude tests require knowledge ofvocabulary, reading skills, and mathematical operations that aretaught in schools and quality achievement tests reflect curriculumstandards that emphasize reasoning over mere memorization offacts (Bridgeman et al., 2004, p. 286). However, the similarity


between aptitude and achievement tests may hold up better whenthe comparison involves low-content achievement tests.

The present findings offer empirical evidence that the switchfrom aptitude to high-content achievement tests may bedetrimental to underprivileged test-takers in nations where accessto quality education is the privilege of an elite. The detrimentaleffects may seem marginal, but in a nation like Chile where equityof access to higher education has been so hard to achieve, a smallstep backwards can be of consequence, as reflected by theprogressive drop in the enrollment of students from municipalschools at top Chilean universities (Simonsen, 2008). This trend isconsistent with findings by Koretz (2000) in the U.S., who reportedthat when scores count heavily in admissions differences in scoresbetween extreme groups will have a major impact on theprobability that students from the disadvantaged group will beadmitted, affecting the composition of the student body.

Although there is a need to guarantee that the tests cover anadequate range of requisite knowledge and skills that are requiredin order to succeed in college, aligning the tests with thecurriculum does not require an unrestrictive inclusion of contents,even if these are prescribed in the high-school curriculum. Indeveloping nations that suffer from unequal access to qualityeducation, admission tests may contribute to enhance equity iffocused on selected contents that are demonstrably predictive ofsuccess in the first year of college.

In Chile, the inclusion of extensive contents in the admissiontests did not favor equitable access nor improved prediction.Recent data from a system-wide study of predictive validity for thenew ACH tests revealed a substantial drop in the predictivecapacity of the Math ACH Test, from an average correlation of .29for the admission cohort of 2004 (i.e. the test with the lightestcurricular content) to .25 in 2007 when the tests included fullcontents (Bravo et al., 2010). Thus, the increase of contentsassessed in the tests may have been detrimental not only for fairaccess but also in terms of the tests predictive capacity.

Although Atkinson (2001, p. 36) expressed that ‘‘the movementaway from aptitude tests towards achievement tests [was] anappropriate step for U.S. students, schools and universities,’’ therisks in generalizing outside the realm of the U.S. need to beunderscored. In nations where educational systems are character-ized by widely variable educational inputs and where the poorreceive the lowest quality of schooling, the claim that ‘‘admissioncriteria that emphasize demonstrated achievement over potentialability are better aligned with the needs of disadvantaged studentsand schools’’ (Geisser, 2009, p. 18) remains unsupported. On thecontrary, the evidence from Chile is more consistent with theassertion that in social contexts where school quality varieswidely, the use of achievement tests for selection purposes shouldbe avoided, since these are likely to measure the individual’sopportunity to learn rather than ability to learn (Heyneman, 1987).

7.1. Limitations of the study

The limitations of the analyses presented here stem from thenature of the data available to study the question of interest. Test

developers did not make provisions to properly examine whetherACH tests would be fairer for the assessment of low SES students. Ifboth APT and ACH tests had been applied simultaneously to thesame pool of applicants, the questions regarding the relative meritsof APT and ACH tests and the impact of increasing the amounts ofcontents tested when assessing disadvantaged groups could havebeen responded in a straightforward manner. This oversightrequired the analysis of data arising from different samples takingdifferent tests with varying amounts of contents.

The analysis was also constrained by lack of access to item-leveldata which would have allowed for a more precise rescaling of thetests through the years. The re-scaling procedure employed wasthe best approximation possible given the limited data access.

Additionally, information on key variables such as coaching andmotivation to pursue university education was unavailable. Type oftest training, number of hours devoted to it and amount of resourcesinvested in coaching for the test, if available could have allowedimproving prediction and gaining a fuller understanding of therelevance of coaching for the different types of admission tests.International evidence indicates that the implementation of highlycompetitive tests may play against the disadvantaged groups whenthese have received a low quality of secondary education unless theycan compensate their handicap through coaching or tutoringcourses (Lewis and Dundar, 2002). Thus, the role of coachingpractices in test performance remains to be examined.

7.2. Lessons learned

Policy makers in developing nations that have a nationalcurriculum should be wary about assuming that achievement testsare superior to aptitude tests to promote fair access. Althoughproponents of achievement tests for selection purposes haveargued that the benefits of national achievement tests may beimpossible to attain in the absence of a national curriculum(Geisser, 2009), this statement should be further qualified. Even innations that have a national curriculum, such as Chile, the allegedbenefits of achievement tests for the socially and educationallydeprived groups have not been attained.

The conception of fairness as opportunity to learn should becritically analyzed when considering the use of achievement tests,particularly high-content tests for admission purposes in nationswhere access to quality education is the privilege of an elite. Asstated by the Standards for Educational and Psychological Testing(1999, p. 76) ‘‘when test-takers have not had the opportunity tolearn the material tested, the policy of using their test scores as abasis for [a high-stakes decision]. . . is viewed as unfair.’’

Finally, the Chilean experience with the change of tests shouldserve to underscore the need to avoid simplistic approaches in thepursuit of equity of access to higher education and highlights thecosts of ignoring evaluative research in the process of change. Anyrevisions and modifications in national admission systems shouldbe backed by a solid validity framework in order to guarantee thatthe expected benefits of the change are attained and to prevent theoccurrence of negative unanticipated consequences, particularly,for disadvantaged groups.

Appendix A

Scores corresponding to percentiles of the Math tests for APT and ACH tests (prior to re-scaling).Math test.

Percentile APT2002 APT2003 ACH2004 ACH2005 ACH2006 ACH2007 ACH2008 ACH2009

5 319 308 301 304 310 300 310 312

10 327 325 342 345 358 353 355 356

15 344 334 377 378 380 376 375 376

20 353 351 403 392 398 395 393 395

25 361 359 414 416 415 413 408 410

30 378 368 425 435 429 428 433 425

35 387 385 442 450 441 441 444 449

40 404 402 457 464 462 463 463 460

45 412 419 470 476 471 472 471 469

50 438 436 487 493 486 487 486 485

55 455 462 497 507 505 501 506 498

60 480 488 511 520 516 517 517 515

65 506 514 527 532 534 531 532 529

70 540 548 541 551 547 549 550 546

75 574 582 559 569 565 568 567 564

80 608 617 577 587 586 585 585 584

85 650 651 602 609 607 609 608 607

90 693 702 631 636 637 636 636 636

95 744 754 667 680 680 681 682 677

96 752 771 680 694 690 692 688 694

97 769 780 720 713 707 705 708 708

98 778 788 759 725 740 728 727 724

99 795 814 780 795 777 763 761 763

Note: As may be observed from the table, scores in the APT tests and ACH were not comparable before re-scaling. For example, a student who scored in the 95th percentile in

the APT 2003 had 754 points. The following year, the score corresponding to the same 95th percentile was 87 points lower: 667. Conversely, the score assigned to the 40th

percentile in the ACH 2004 was 55 points higher than the one assigned in APT 2003. In essence, the new ACH scaling boosted the scores at the lower end of the distribution and

deflated the upper end of the distribution. Consequently, scores prior to 2006 were rescaled before conducting the statistical analyses.


References

Atkinson, R., 2001. Achievement versus aptitude in college admissions. Issues inScience and Technology 18 (2), 31–36.

Beyer, H., April 25, 2002. Sobre las pruebas de ingreso a las universidades. ElMercurio, p. A2.

Bravo, D., Bosch, A., del Pino, G., Donoso, G., Manzi, J., Martinez, M., Pizarro, R.,2010. Validez diferencial y sesgo de predictividad de las pruebas deadmision a las universidades chilenas. Mimeo. Documento del Comite Tec-nico Asesor Consejo de Rectores de las Universidades Chilenas, Santiago,Chile.

Bravo, D., Manzi, J., Larranaga, O., Contreras, D., Himmel, E., Rosas, R., Sevilla, M.,2000. Reformulacion de las pruebas de seleccion a la educacion superior.Unpublished manuscript. Octavo Concurso Nacional de Proyectos de Investiga-cion y Desarrollo, FONDEF-CONICYT, Santiago, Chile.

Bridgeman, B., Burton, N., Cline, F., 2004. Replacing reasoning tests with achieve-ment tests in university admission: does it make any difference? In: Zwick, R.(Ed.), Rethinking the SAT: The Future of Standardized Testing in UniversityAdmissions. Routledge Falmer, New York, pp. 277–288.

Brunner, J., June 19, 2002. SIES: Tres preguntas y la responsabilidad de las uni-versidades. La Segunda, p. 14.

Courville, T., Thompson, B., 2001. Use of structure coefficients in published multipleregression articles: b is not enough. Educational and Psychological Measure-ment 61 (2), 229–248.

Dalgalarrando, G., January 9, 2007. Crece brecha de puntajes entre educacionpublica y privada. El Mercurio, p. C1.

Diario Austral, July 21, 2002. El SIES comenzara a ser aplicado a contar del 2003. ,p. A12.

Donaldson, K., Lichtenstein, G., Sheppard, S., 2008. Socioeconomic status and theundergraduate engineering experience: preliminary findings from four Ameri-can universities. Paper Presented at the Meeting of the American Society forEngineering Education, 22–25 June, Pittsburgh, PA.

Eyzaguirre, B., Le Foulon, C., 2002. SIES: Un proyecto prematuro, vol. 87. EstudiosPublicos, pp. 39–53.

Fontaine, A., 2002. Peligro en el SIES, vol. 86. Estudios Publicos, pp. 25–55.Geisser, S., 2009. Back to the basics: in defense of achievement (and achievement

tests) in college admissions. Change (January/February), 16–23.Henrıquez, J., August 4, 2003. Me impresiona que estemos haciendo el mismo

cambio en EE.UU y Chile. La Segunda, p. 9.Heyneman, S., 1987. Uses of examinations in developing countries: selection,

research, and education sector management. International Journal of Educa-tional Development 7 (4), 251–263.

Jaccard., J., Turrisi, R., 2003. Interaction Effects in Multiple Regression. Sage Pub-lications, Thousand Oakes.

Koljatic, M., Silva, M., 2006. Equity issues associated with the change of collegeadmission tests in Chile. Equal Opportunities International 25 (7),544–561.

Koretz, D., 2000. The impact of scores differences on the admission of minoritystudents: an illustration. NBETPP Statements 1 (5), 1–16.

Lemann, N., 2004. A history of admission testing. In: Zwick, R. (Ed.), Rethinking theSAT: The Future of Standardized Testing in University Admissions. RoutledgeFalmer, New York, pp. 5–14.

Lewis, D., Dundar, H., 2002. Equity effects of higher education in developingcountries: access, choice, and persistence. In: Chapman, D., Austin, A.E.(Eds.), Higher Education in the Developing World: changing Contexts andInstitutional Responses. Greenwood, Westport, CT, pp. 169–194.

Lohman, D., 2004. Aptitude for college: the importance of reasoning tests forminority admissions. In: Zwick, R. (Ed.), Rethinking the SAT: The Future ofStandardized Testing in University Admissions. Routledge Falmer, New York,pp. 41–56.

Ministerio de Educacion, November 22, 2000. Comision nuevo curriculum de laensenanza media y pruebas del sistema de admision a la educacion superior:Informe sometido en consulta previa a la Ministra de Educacion. Mimeo,Ministerio de Educacion, Chile.

Ministerio de Educacion, 2008. Bases para una Polıtica de Formacion Tecnico-Profesional en Chile. Informe Ejecutivo. Mimeo, Ministerio de Educacion, Chile.

Molina, P., December 18, 2008. La PSU aumenta la brecha. El Mercurio, p. D3.NACAC, 2008. Report of the Commission on the Use of Standardized Tests in

Undergraduate Admission. National Association for College Admission Counsel-ing, Arlington, VA.

Nimon, K., 2010. Regression commonality analysis: demonstration of an SPSSsolution. Multiple Linear Regression Viewpoints 36 (1), 10–17.

OECD, 2009. Reviews of National Policies for Education: Tertiary Education in Chile.OECD, Paris.

OECD, 2011. Families are changing. OECD, Paris.PSU: Consejo de Rectores analiza mecanismo de seleccion universitaria, 2008.

http://uchile.cl/un49226 (accessed 20.07.11).Simonsen, E., November 30, 2008. Ingreso de alumnos municipales a universidades

top baja 10% en 6 anos. La Tercera, p. 40.Standards for Educational and Psychological Testing, 1999. American Educational

Research Association, American Psychological Association and National Councilon Measurement in Education. American Educational Research Association,Washington, DC.

Sternberg, R., 2006. How can we simultaneously enhance both academic excellenceand diversity? College & University 82, 3–9.

The Chronicle of Higher Education, June 7, 2010. China Begins to Reform itsControversial College-entrance Exam. http://chronicle.com/article/China-Begins-to-Reform-Its/65804/ (accessed 20.07.11).

http://uchile.cl/un49226

http://chronicle.com/article/China-Begins-to-Reform-Its/65804/

http://chronicle.com/article/China-Begins-to-Reform-Its/65804/


Vision Universitaria, Julio 2002. Seminario prueba de admision a las universidadeschilenas: habilidades generales o conocimientos. Pontificia Universidad Cato-lica de Chile, Santiago, Chile.

Wildavsky, B., November 11, 2010. Why Brazil’s Standardized Entrance TestDeserves to be Salvaged. The Chronicle of Higher Education. http://chronicle.com/blogs/worldwise/why-brazils-standardized-entrance-test-deserves-to-be-salvaged/27577 (accessed 20.07.11).

World Bank, 2000. Higher Education in Developing Countries: Peril and Promise.World Bank, Washington, DC.

World Bank, 2001. Implementation Completion Report, CL-Secondary Education:World Bank Report No. 22979.

Zienteck, L., Thompson, B., 2006. Commonality analysis: partitioning variance tofacilitate better understanding of data. Journal of Early Intervention 28 (4),299–307.

Zwick, R., 2004. Is the SAT a wealth test? The link between educational achievementand socioeconomic status. In: Zwick, R. (Ed.), Rethinking the SAT: The Future ofStandardized Testing in University Admissions. Routledge Falmer, New York,pp. 203–216.

http://chronicle.com/blogs/worldwise/why-brazils-standardized-entrance-test-deserves-to-be-salvaged/27577



Documents

Achievement Versus Aptitude in College Admissions a Cautionary Note Based on Evidence From Chile