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Educational Measurement: Issues and PracticeSummer 2010, Vol. 29, No. 2, pp. 3–12

Can High School Achievement Tests Serveto Select College Students?

Adriana D. Cimetta, University of Arizona, Jerome V. D’Agostino, Ohio StateUniversity, and Joel R. Levin, University of Arizona

Postsecondary schools have traditionally relied on admissions tests such as the SAT and ACT toselect students. With high school achievement assessments in place in many states, it is importantto ascertain whether scores from those exams can either supplement or supplant conventionaladmissions tests. In this study we examined whether the Arizona Instrument to Measure Standards(AIMS) high school tests could serve as a useful predictor of college performance. Stepwiseregression analyses with a predetermined order of variable entry revealed that AIMS generally didnot account for additional performance variation when added to high school grade-point average(HSGPA) and SAT. However, in a cohort of students that took the test for graduation purposes, AIMSdid account for about the same proportion of variance as SAT when added to a model that includedHSGPA. The predictive value of both SAT and AIMS was generally the same for Caucasian, Hispanic,and Asian American students. The ramifications of universities using high school achievementexams as predictors of college success, in addition to or in lieu of traditional measures, arediscussed.

Keywords: achievement testing, aptitude, college admissions

An ongoing debate in college admissions testing is whetherachievement examinations can or should replace nation-

ally standardized admission tests for selection decisions. Ac-cording to USA Today (Bruno, 2008), in recent years a num-ber of universities and colleges, including several top-rankedschools, have stopped using the traditional SAT ReasoningTests (formerly known as the Scholastic Aptitude Test andthe Scholastic Assessment Test) and ACT (formerly knownas the American College Test). Other institutions, led by theUniversity of California system, have struggled with decid-ing on the role that traditional admission tests should playin admitting students. Much of the debate on which tests tochoose stems from perceptions by university decision makersthat achievement tests are more fair and valid than commonlyused admissions tests, and focusing on achievement sends themessage to students that doing well in school is more impor-tant than possessing socioeconomic privilege (e.g., Atkinson,2001; Geiser, 2008). After reviewing the evidence, a commis-sion of the National Association for College Admission urgedpostsecondary schools to focus more attention on achieve-ment indices and to deemphasize reliance on traditional se-lection tests (NACAC, 2008).

Adriana D. Cimetta is a doctoral candidate, Department of Educa-tional Psychology, College of Education, University of Arizona, Tuc-son, AZ 85721. Jerome V. D’Agostino is Associate Professor of Quanti-tative Methods, School of Educational Policy and Leadership, OhioState University, Columbus, OH 43210; [email protected]. JoelR. Levin is Emeritus Professor of Educational Psychology, Collegeof Education, University of Arizona, Tucson, AZ 85721.

Besides achievement tests designed specifically for collegeselection purposes, most states have developed assessmentsto gauge the degree to which high school students have at-tained state academic content standards. However, becausethese tests were not created to predict students’ postsec-ondary success, questions persist about the degree of verticalalignment between secondary and postsecondary expecta-tions. In this study, we investigated whether the high schoolexit examinations from one state, Arizona, could either addto (supplement) or replace (supplant) the SAT/ACT as a pre-dictor of students’ grade point averages (GPA) at one of thelarge state institutions, the University of Arizona (UA). Be-cause public school students are required to take state highschool examinations (usually beginning in the 10th grade),it is critical to ascertain the usefulness of those tests foruniversity admissions decisions.1

Predictive Validity Evidence for Achievement andTraditional Admissions Tests

Research on the comparative predictive capabilities of ad-missions and achievement tests has yielded somewhat mixedfindings, which likely has resulted from differences acrosstests, differences among study samples and criterion mea-sures, grading and selection policy variations across postsec-ondary institutions, and, perhaps most importantly, ambigui-ties regarding the constructs tested by various tests.

Although it has been demonstrated repeatedly that scoresfrom conventional admission tests such as the SAT andACT correlate with students’ future grade-point averages

Copyright C© 2010 by the National Council on Measurement in Education 3

(GPAs) in various colleges (e.g., Bridgeman, McCamley-Jenkins, & Ervin, 2000; Burton & Ramist, 2001; Camara &Echternacht, 2000; Morgan, 1989; Munday, 1965; Noble &Sawyer, 2002), at least one study revealed no difference inthe predictive power of College Board achievement exami-nations (predecessors of the SAT II: Subject Tests) and theSAT (Crouse & Trusheim, 1988); and another study foundthat SAT II: Subject Tests were better predictors of collegeGPA than the SAT (Geiser & Studley, 2001; see also Geiser,2008).2

Other studies on the SAT and SAT II: Subject Tests wereconducted to elucidate potential reasons for Geiser and Stud-ley’s (2001) findings at the University of California, Berkeley.Based on data from 39 colleges, Ramist, Lewis, and McCamley-Jenkins (2001) found that correlations between individualSAT II: Subject Tests and college GPA varied considerablyfrom .17 for some language tests to .58 for mathematics andchemistry tests. Using the UC-Berkeley data, Bridgeman, Bur-ton, and Cline (2001) discovered that when high school GPA(HSGPA) was taken into account, there was virtually equalpredictive capacity of two of three available SAT II: SubjectTests and SAT Math and Verbal exams. The authors con-cluded that any apparent increase in explained college per-formance variance for the SAT II: Subject Tests in the Geiserand Studley study likely was due to the inclusion of a thirdsubject-matter test. In addition, other studies have revealedthat SAT II: Subject Tests varied in their predictive power,depending on which two subject-matter tests were includedas predictors.

A more recent study that examined the predictive validityof the newer SAT Reasoning tests (Critical Reading, Math, andWriting) and the newer SAT Subject tests found that the SATReasoning tests were slightly better predictors of Universityof California students’ GPAs than the prior SAT I Math andVerbal subtests, but that the newer subject tests were notmore effective predictors than the older SAT II: Subject Tests(Agronow & Studley, 2007).

In addition to studies using College Board achievementmeasures, at least two studies have been conducted on statestandards-based assessments. Coelen and Berger (2006) an-alyzed the predictive capability of the Connecticut AcademicPerformance Test (CAPT) by following students who took theCAPT in 1996 during their sophomore year. After obtainingstudents’ SAT scores and college GPAs, Coelen and Bergerfound that CAPT Mathematics and SAT quantitative scoreswere interchangeable as college GPA predictors. Specifically,neither measure uniquely predicted GPA when both variableswere included in a regression model, but each measure ac-counted for the same proportion of GPA variability whenexamined individually. Students’ CAPT language arts andSAT verbal scores, however, both accounted for unique GPAvariance when included as simultaneous predictors. McGee(2003) compared the predictive capability of the Washingtonstate high school exit examination (the WASL) and SAT. Bothpredictors accounted for approximately the same proportionof variance in University of Washington students’ GPAs, lead-ing the author to conclude that WASL and SAT scores werecomparable in terms of predicting students’ college success.

What is Measured by Traditional Admissions andAchievement Tests?

Although universities are looking more closely at achieve-ment measures to select students, the content domain

differences among commonly used admissions tests and morerecently developed achievement measures are ambiguous andoften overlap. Most traditional admissions tests were origi-nally designed to measure aptitude, which denotes aptnessor readiness to learn or work in a new situation, includingcognitive as well as conative and affective attributes that in-dicate suitability to perform successfully on a future set oftasks (Snow, 1992). In contrast, achievement refers to thedegree to which a student has developed the cognitive andnoncognitive learning objectives of schools. Because manysecondary schooling objectives relate to preparing studentsfor postsecondary school, the content of the domains naturallyoverlap, and thus, early aptitude tests contained items thateasily could have been considered indicators of achievement.Also, early aptitude tests in the 1920s contained elements ofintelligence tests, mainly due to the expertise of the originalSAT designer, Carl Brigham (Lohman, 2004), which furtherblurred the direct measurement of the construct “aptitude.”

Over time, considerably more elements of achievementwere integrated into the SAT (Lawrence, Rigol, Van Essen, &Jackson, 2002). The SAT verbal emphasis shifted in 1994 fromdecontextualized antonym, analogy, and sentence completionquestions to reading passage-based questions. The focus ofthe National Council of Teaching of Mathematics (NCTM) onreal-world problem solving, probability and statistics, appli-cation in new situations, and analysis led to changes in themathematics test. Since 1994, the SAT mathematics test hascontained fewer contrived problems and more questions thatare better aligned with the NCTM standards.

Because the SAT content was modified to be more sensitiveto students’ learning experiences in schools, one might won-der how that admissions test now differs from achievementtests such as state standards-based assessments or SAT Sub-ject Tests in English and Mathematics. With its present focuson national content standards, the SAT likely covers a broaderset of learning objectives that are not specific to any state aca-demic standards. Furthermore, formal content analyses haverevealed that: (1) college admission mathematics tests (SATand ACT) contain a larger proportion of intermediate algebra,trigonometry, and logic items in comparison with a sample ofstate high school mathematics tests; but (2) the state assess-ments frame questions in more realistic contexts (Le, 2002).Another apparent difference between college admissions andhigh school achievement tests might reside in the intellec-tual skills measured by each. Le discovered that although ad-missions and state mathematics assessments both containedlarge proportions of knowledge items, the admissions testshad larger proportions of problem-solving and conceptual un-derstanding items, on average. In language arts, admissionstests were found to contain a greater proportion of inference-based items relative to state assessments.

The Role of State High School Exams

Presently all states are required by the No Child Left BehindAct (NCLB) of 2001 (2002) to test students at least oncebetween grades 10 and 12 in language arts, mathematics, andscience, based on state content standards. About half of thestates also use their high school achievement tests as exitexaminations, meaning that students are required to pass theexams to graduate from high school. Because some states withrather large populations, such as California, Texas, and NewYork, have exit examinations, more than half (52%) of the stu-dents in the country take an exit exam (Gayler, Chudowsky,

4 Educational Measurement: Issues and Practice

Hamilton, Kober, & Yeager, 2004). Some states use the ACTor SAT as high school tests, but for different purposes. InIllinois and Michigan, students take the ACT (with augmen-tation) to meet NCLB requirements. Students in Coloradoare administered the ACT for school accountability purposes,and in Maine students take the SAT for NCLB. Despite thelikelihood that college admissions tests are less aligned withstate academic standards, those states have integrated theACT or SAT into their state testing programs to encourage allstudents to consider higher education, to make higher educa-tion more accessible for all, and to facilitate greater verticalalignment between high school and college expectations.

Analyses of state academic and college-level standardshave revealed a disconnect between the messages that highschools send to students regarding their preparedness forcollege and what they will face in terms of academic chal-lenges in school beyond the 12th grade (Achieve, Inc., 2004;Brown & Conley, 2007). In response to this evidence, manystates are attempting to strengthen secondary and postsec-ondary alignment by enhancing the rigor of their state testsand standards. By 2007, 12 states had aligned their contentstandards with college expectations and another 32 stateswere working toward that goal. Colleges in nine states wereusing state tests as readiness indicators and postsecondaryschools in 21 additional states were considering the use ofexit examinations for that purpose (Achieve, Inc., 2007).

Objectives of the Present Study

Given the focus on state achievement tests for college admis-sions purposes, we sought to examine the degree to whichconventional tests such as the SAT, along with a standards-based state test, predict students’ college performance.Unlike past studies conducted in Connecticut (Coelen &Berger, 2006) and Washington (McGee, 2003), we were ableto examine the differential predictive power of Arizona Instru-ment to Measure Standards (AIMS) scores of: (a) a cohort ofstudents (1999) who took the test when it was not a gradu-ation requirement; and (b) a cohort of students (2000) whotook the test the following year when the state did requireit for graduation purposes.3 Thus, these naturally occurringcircumstances allowed us to examine whether changing theperceived stakes for students (i.e., altering the perceived con-sequences/payoff of students’ AIMS test performance) wasassociated with differences in the test’s predictive validity ofstudents’ performance at UA.

By tracking UA students for four years, we also were ableto examine the predictive validities of SAT and AIMS scoreswith respect to their first-year college GPA (Y1GPA) andcumulative four-year GPA (CGPA). Selecting these two cri-teria led to two subsamples of students, one that includeda greater diversity of early collegiate achievement patterns,and the other that represented a pool of students who hadcompleted (or were well on their way to having completed)their degrees. We were particularly interested in determiningwhether AIMS could serve as an additional predictor to theSAT and HSGPA, or as a substitute for the conventional ad-missions test scores. Finally, we sought to determine whetherAIMS or SAT scores predicted college GPA better for certainethnic subgroups than others.

More specifically, we addressed the following primary re-search questions: (1) Can AIMS add to (or supplement) thetraditional measures used to predict college GPA, including

HSGPA and SAT scores? and (2) Can AIMS replace (or sup-plant) the traditional measures used to predict college GPA?Our ancillary research question was: (3) Does either AIMSor SAT test performance differentially predict college GPA intwo designated ethnic-group samples (Asian American andHispanic) compared to the majority Caucasian sample?

MethodData Collection

Data for this study were obtained from the Arizona Depart-ment of Education (ADE) and the Department of StudentEnrollment and Management at UA. Scores for all high schoolsophomores completing the AIMS tests in 1999 and 2000 wereobtained from the ADE.4 A unique identifier was created forstudents completing AIMS in 1999 and 2000. This identifierwas then used to search the UA student database to identifystudents attending the UA. Identified students were assigneda UA student identification number. Admissions informationconsisting of HSGPA, SAT scores, and college academic per-formance (Y1GPA and CGPA), were obtained from the UAdatabase.

Assessments

The AIMS high school tests in 1999 and 2000 consisted ofthree subject tests (Reading, Mathematics, and Writing) thatwere designed to measure the Arizona Academic Standards.The reading subtest had 37 multiple-choice items (one pointeach) and four short-answer questions (three worth twopoints each and one worth one point) for a total of 44 points.The mathematics subtest comprised eight short-answer ques-tions, worth two points each, and 81 multiple-choice items,worth one point each, for a total of 97 points. The writ-ing subtest had 56 total points and consisted of six short-answer questions (one item was worth one point, three itemswere worth two points each, and two items were worth threepoints each), 31 single-point multiple-choice items, and anextended writing prompt scored with a six-trait rubric by tworaters worth 12 points. All three examinations produced scalescores ranging from 200 to 800. External subject-matter ex-perts who reviewed the 2005 AIMS reading and mathematicshigh school examinations concluded that both tests were suf-ficiently aligned to the state academic standards (D’Agostino,Welsh, & Cimetta, 2005); however, no external alignmentanalyses had been conducted on the 1999 or 2000 tests.

Sampled students most likely took the SAT I Verbal andMath examinations during their junior or senior year of highschool, which would have been in 2000 or 2001 for the 1999cohort, and 2001 or 2002 for the 2000 cohort. It is impor-tant to note that since that time period, the College Boardhas considerably revised the SAT to enhance curricular andinstructional alignment with secondary and postsecondaryschools (Kobrin & Melican, 2007). A writing component alsohas been added to the new SAT. Thus, this study comparesthe predictive capacity of AIMS to the prior version of theSAT that presumably was less sensitive to high school andcollege instructional objectives. The SAT I Math and Verbaltests yielded scale scores ranging from 200 to 800. Students’AIMS and SAT scale scores were used in all analyses.

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Students Included

Students with valid AIMS reading, mathematics, and writingscores, along with SAT I Verbal and Math scores, comprisedthe data-analysis sample. The total number of students withdata that were usable for the analyses was 1,673 for the 1999cohort and 2,222 for the 2000 cohort. Students who took theACT in lieu of the SAT (approximately an additional 18% ofthe students) were omitted from the data analyses (but seeFootnote 7). For each cohort, students were also classifiedaccording to their ethnic group, to determine whether theSAT and AIMS tests predicted college GPAs similarly in eachsubgroup. The ethnic subgroups that were considered largeenough (N > 100) for meaningful analyses to be conductedwere Caucasian (Ns = 1,156 and 1,561 for the 1999 and 2000cohorts, respectively), Hispanic (Ns = 248 and 312), andAsian American (Ns = 170 and 126).

Analyses

Primary research questions. To determine whether theAIMS test can supplement traditionally used measures forpredicting college GPA (both Y1GPA and CGPA), a step-wise regression analysis with a predetermined order of vari-able entry was conducted.5 The measures included werehigh school HSGPA and the two SAT subtests (Verbal, SAT-V; and Mathematics, SAT-M), with the three AIMS sub-tests (Reading, AIMS-R; Mathematics, AIMS-M; and Writ-ing, AIMS-W) then added to the regression model. A seconduser-determined stepwise regression analysis addressed thequestion of whether AIMS can supplant traditional college en-trance examination scores. In these reverse-ordered analyses,the three AIMS subtests followed HSGPA into the model, withstudents’ two SAT subtest scores then added to determinewhether that addition would result in improved prediction(both statistically and substantially).

This process was followed for each cohort. Because: (a)many different hypotheses were tested; and (b) our sam-ples were relatively large (more than 1,600 students for thetotal-sample analyses) for the major research questions, tosafeguard against uncovering minimal effects a Type I errorprobability (α) of .01 was allocated to the statistical testof each predictor variable entered into the regression equa-tions. Increments in R2 were examined when offering con-clusions about the importance of each statistically significantpredictor.

Ancillary research question. Additional user-determinedstepwise regression analyses were conducted to determinewhether there were similar or different patterns of supple-mental AIMS prediction of college GPAs in the three ethnicsubsamples. One set of analyses included the Caucasian andHispanic subsamples and another set included the Caucasianand Asian American subsamples. In these analyses, students’dummy-coded ethnic status was entered first, followed by HS-GPA (one predictor variable), SAT (two predictor variables),AIMS (three predictor variables), and finally the productsof ethnic status and AIMS (three predictor variables). Thefinal three products represent the interaction between eth-nic group and AIMS, which indicates the degree to which theAIMS test statistically predicts college GPA similarly or dif-ferently in the two ethnic groups under consideration. Eachset of predictor variables’ contribution to the prediction of

students’ college GPAs was statistically evaluated based onan α of .05.

ResultsBecause of the concern that high school examinations testmore basic skills targeted at 9th and 10th grade students, wefirst explored whether the present AIMS scores demonstratedmore range restriction than did SAT scores. We analyzed re-striction of range in two ways. First, we compared the coeffi-cient of variations (CVs) for the measures among the samplesof UA students. Second, we examined the decrement in CVsfrom population to sample data for the various measures.Table 1 presents the means, standard deviations, and CVs for

Table 1. 1999 and 2000 Cohort PredictorDescriptive Statistics by Subsample

CoefficientMean (SD) of Variation

1999/2000 Cohort1st-Year GPA (N = 1,673/2,222)

HSGPA 3.56 (.37) .103.50 (.40) .11

SATVerbal 554.94 (91.40) .16

545.19 (91.21) .17Mathematics 563.21 (92.64) .16

557.47 (96.86) .17AIMS

Reading 568.07 (51.06) .09574.87 (55.51) .10

Mathematics 493.09 (42.20) .09503.73 (36.26) .07

Writing 527.09 (38.18) .07526.52 (34.68) .07

Cumulative GPA (N = 1,326/1,788)HSGPA 3.60 (.35) .10

3.55 (.38) .11SAT

Verbal 562.49 (88.99) .16552.19 (90.88) .16

Mathematics 570.69 (90.01) .16565.30 (96.64) .17

AIMSReading 570.73 (51.46) .09

577.34 (55.99) .10Mathematics 496.58 (41.19) .08

506.54 (36.28) .07Writing 529.91 (38.28) .07

528.97 (34.48) .07Population Parameters

SATa

Verbal 531.00 (103.60) .20Mathematics 540.40 (106.80) .20

AIMS (1999/2000)bReading 512.47 (57.06) .11

523.32 (58.89) .11Mathematics 441.05 (48.49) .11

449.98 (50.85) .11Writing 469.26 (56.49) .12

473.84 (53.47) .11aFrom Dorans (1999).bDerived from analysis based on the Arizona AIMS census dataconducted by the authors.


HSGPA, SAT (Verbal and Mathematics), and AIMS (Reading,Mathematics, and Writing) scores for the four subsamples(1999 and 2000 cohorts for college Y1GPA and CGPA). As canbe seen from the table, the CVs are almost twice as large forSAT than AIMS in both college cohorts, indicating greatervariation of test scores among students on the traditionaladmissions test as compared to the state achievement mea-sures. Population values for SAT and AIMS are provided atthe bottom of the table. By dividing the sample CVs by thepopulation CVs, it can be seen that the sample SAT valuesare 80%–85% of the population parameters. Except for AIMSReading, the AIMS sample CVs are 58%–73% of the populationCVs. Thus, more variability is lost for AIMS Mathematics and

Writing than for either SAT test when students were selectedto the UA.

Primary research questions. Correlations among the vari-ables examined here are presented in the upper and lowertriangles of Table 2 for the 1999 and 2000 cohorts, respec-tively. There it can be seen that there are at least moderaterelationships among the predictor variables included, withthe correlations between SAT-M and AIMS-M being in the.70s and .80s.

The ability of the AIMS test to supplement HSGPA andSAT in predicting both Y1GPA and CGPA is shown in Step3(i) of Tables 3 and 4. Although AIMS afforded a statistically

Table 2. Correlation Matrix of Variables Included in the User-Determined Stepwise RegressionAnalyses of Students’ 1st-Year and Cumulative 4-Year Grade-Point Averages

Variable HSGPA SAT-V SAT-M AIMS-R AIMS-M AIMS-W Y1GPA CGPA

HSGPA — .292 .362 .292 .421 .368 .554 .541.281 .354 .279 .407 .377

SAT-V .322 — .675 .546 .574 .567 .400 .399.307 .660 .528 .558 .547

SAT-M .358 .666 — .446 .756 .478 .406 .408.345 .658 .410 .747 .449

AIMS-R .280 .536 .394 — .447 .482 .293 .282.262 .519 .369 .416 .457

AIMS-M .436 .614 .811 .433 — .517 .409 .390.420 .608 .807 .416 .503

AIMS-W .407 .615 .520 .454 .574 — .364 .350.389 .608 .509 .436 .562

Y1GPA .579 .377 .340 .285 .370 .402 — .904a

CGPA .561 .382 .344 .278 .383 .398 .899b —

Note: HSGPA = High school grade-point average; Y1GPA = College first-year grade-point average; CGPA = College cumulative 4-yeargrade-point average; SAT-V = SAT Verbal score; SAT-M = SAT Mathematics score; AIMS-R = 10th-grade AIMS Reading scale score; AIMS-M =10th-grade AIMS Mathematics scale score; AIMS-W = 10th-grade Writing scale score. The first six variables are predictor variables and the last twovariables are outcome variables. The 1999 cohort correlations are in the table’s upper triangle for Y1GPA (N = 1,673) and CGPA (N = 1,326) andthe 2000 cohort correlations are in the table’s lower triangle (Y1GPA N = 2,222; CGPA N = 1,788). Each pair of within-cell correlations involvingHSGPA, SAT, and AIMS represents the values associated with Y1GPA (first row) and CGPA (second row).aN = 1,318; bN = 1,777.

Table 3. Summary of User-Determined Stepwise Regression Analysis for (i) AIMS or (ii) SATTests Supplementing HSGPA and (i) SAT or (ii) AIMS Tests in Predicting Y1GPA and CGPA(1999 Cohort)

1st Year College GPAa 4-Year Cumulative College GPAb

Variables Entered R2 �R2 �R2 p R2 �R2 �R2 p

Step 1HSGPA .307 .307 <.001 .293 .293 <.001Step 2i. HSGPA .375 .068 <.001 .367 .075 <.001SATii. HSGPA .371 .049 <.001 .338 .045 <.001AIMSStep 3i. HSGPA .378 .003 .071 .368 .001 .621SATAIMSii. HSGPA .378 .022 <.001 .368 .030 <.001AIMSSAT

Note: The variables entered at each step are in bold. SAT = Verbal and Mathematics subtests; AIMS = Reading, Mathematics, and Writing scalescores.aFinal equation (standardized coefficients): Y1GPA=.442(HSGPA) + .168(SAT-V) + .078(SAT-M) − .007(AIMS-R) + .046(AIMS-M) + .048(AIMS-W).bFinal equation (standardized coefficients): CGPA = .433(HSGPA) + .179(SAT-V) + .110(SAT-M) + .001(AIMS-R) + .016(AIMS-M) + .031(AIMS-W).

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Table 4. Summary of User-Determined Stepwise Regression Analysis for the (i) AIMS or (ii) SATTests Supplementing HSGPA and the (i) SAT or (ii) AIMS Tests in Predicting Y1GPA and CGPA(2000 Cohort)

1st Year College GPAa 4-Year Cumulative College GPAb

Variables Entered R2 �R2 �R2 p R2 �R2 �R2 p

Step 1HSGPA .335 .335 <.001 .315 .315 <.001Step 2i. HSGPA .376 .041 <.001 .365 .050 <.001SATii. HSGPA .373 .039 <.001 .362 .047 <.001AIMSStep 3i. HSGPA .383 .007 <.001 .372 .008 <.001SATAIMSii. HSGPA .383 .010 <.001 .372 .010 <.001AIMSSAT

Note: The variables entered at each step are in bold. SAT = Verbal and Mathematics subtests; AIMS = Reading, Mathematics, and Writing scalescores.aFinal equation (standardized coefficients): Y1GPA=.482(HSGPA) + .135(SAT-V) + .023(SAT-M) + .027(AIMS-R) − .014(AIMS-M) + .107(AIMS-W).bFinal equation (standardized coefficients): CGPA = .456(HSGPA) + .146(SAT-V) + .005(SAT-M) + .024(AIMS-R) + .033(AIMS-M) + .101(AIMS-W).

significant increment in predicting college GPA for the two2000 cohort measures, those increments were both of mini-mal practical importance (.7% and .8% for Y1GPA and CGPA,respectively). With HSGPA and AIMS included in the regres-sion equations, SAT also was a statistically significant sup-plemental predictor for both cohorts and criteria, but unlikeAIMS, SAT tended to account for sizable additional variation[see Step 3(ii) of Tables 3 and 4], with the increment beingas high as 3% for CGPA in the 1999 cohort. Final equationstandardized regression coefficients are presented in the bot-tom of Tables 3 and 4.6 Although certain SAT and AIMS co-efficients in these and following tables are negative, they aregenerally small in magnitude and statistically nonsignificant(i.e., essentially zero), and they may also be functioning assuppressor variables to some extent.

In the analyses to determine whether, when added to HS-GPA, AIMS prediction of college GPA can supplant the SAT, itwas found that only in the 2000 cohort were the Step 2 contri-butions of AIMS comparable to those of SAT from a practicalimportance standpoint. Although AIMS afforded statisticalprediction for both college GPA measures in both cohorts,with reasonably impressive additions to HSGPA in the per-centages of variance that it accounted for (4% to 5%), inthe 1999 cohort the corresponding Step 2 additions for theSAT test were somewhat larger (7% and 7.5%). Potential rea-sons for the differences in results between the 1999 and 2000cohorts are considered in the Discussion.7

Ancillary research questions. In neither the 1999 nor 2000cohorts did the ethnic subgroup user-determined stepwiseregression analyses reveal any differential contributions ofthe three AIMS subtests when added to HSGPA and thetwo SAT subtests in either the Caucasian and Hispanic orthe Caucasian and Asian American subsamples, in that allethnic groups by AIMS interactions had associated ps >.08.8 Results of these analyses indicate that after controllingfor other precollege measures, the additional proportions ofcollege-outcome variance accounted for by the three AIMSsubtests are statistically comparable in the different ethnic

subgroup samples. Separate subsample results, including thefinal equation standardized regression coefficients, are pre-sented in Tables 5 and 6.

Despite the lack of any AIMS by ethnic group interactions,a few salient entries in Tables 5 and 6 are noteworthy. First,in Table 5 (the 1999 cohort) it may be seen that the threeAIMS subtests added both statistically significant and educa-tionally important prediction of first-year college grades (anadditional 4% of additional variance accounted for) in theAsian American subsample, though not in the Caucasian orHispanic subsamples. Second, in Table 6 (the 2000 cohort),although the three AIMS subtests made a statistically signifi-cant contribution in the Caucasian subsample, the proportionof additional variance accounted for there was minimal (.7%)and so that statistical effect must be interpreted with respectto the much larger Caucasian sample sizes (1,561 and 1,249for Y1GPA and CGPA, respectively). In addition, a journal re-viewer correctly pointed out that the absence of any AIMS byethnic subgroup interactions does not imply that the adjustedmean college-outcome performances are comparable in thevarious ethnic subgroups. In fact, in two of the eight relevantanalyses, statistical differences (p < .05) were detected. Inparticular: (1) the 1999 cohort comparison of Caucasian andHispanic students’ mean Y1GPAs revealed a difference favor-ing the former, adjusted Ms = 2.95 and 2.85, respectively,t(1,396) = 1.99, p = .047; and (2) the 1999 cohort compar-ison of Caucasian and Asian American students’ mean CG-PAs indicated a difference also favoring the former, adjustedMs = 3.21 and 3.10, respectively, t(1,051) = 2.34, p = .019.

Although not a purpose of the present study, the sameethnic-group analyses were conducted with the two SAT sub-tests as the final variables added to (or replacing) the threeAIMS subtests for predicting Y1GPA and CGPA. In those analy-ses, for the 2000 cohort there was some evidence of differentialcontributions of SAT when comparing Caucasian and AsianAmerican students. For example, in the Caucasian studentsubsample, when the SAT subtests were added to HSGPA theyaccounted for an additional 2.9% of the Y1GPA variance (withfinal-equation standardized partial regression coefficients of


Table 5. Summary of Ethnic Group User-Determined Stepwise Regression Analyses for the AIMSTest as a Supplemental Predictor of Students’ Y1GPA and CGPA (1999 Cohort: Caucasian Ns =1,156 and 910, Hispanic Ns = 248 and 191, Asian American Ns = 170 and 149, for Y1GPA andCGPA, Respectively)

Ethnic Group

Caucasian Hispanic Asian American

Predictors/Outcomes R2 �R2 �R2 p R2 �R2 �R2 p R2 �R2 �R2 p

Step 1HSGPA Y1GPA .317 .317 <.001 .208 .208 <.001 .342 .342 <.001

CGPA .302 .302 <.001 .226 .226 <.001 .325 .325 <.001Step 2HSGPA Y1GPA .379 .062 <.001 .264 .056 <.001 .431 .088 <.001SAT

CGPA .375 .073 <.001 .261 .035 .013 .405 .079 <.001Step 3HSGPA Y1GPA .380 .001 .612 .275 .011 .296 .470 .040 .008SATAIMS CGPA .376 .001 .809 .263 .002 .938 .436 .031 .053

Note: The variables entered at each step are in bold. SAT = Verbal and Mathematics subtests; AIMS = Reading, Mathematics, and Writing scalescores.Final equations (standardized coefficients):

Caucasian : Y1GPA = .462(HSGPA) + .176(SAT − V) + .096(SAT-M) + .007(AIMS-R) − .007(AIMS-M) + .037(AIMS-W)Hispanic : Y1GPA = .381(HSGPA) + .180(SAT-V) − .054(SAT-M) − .001(AIMS-R) + .150(AIMS-M) + .030(AIMS-W)

Asian American : Y1GPA = .389(HSGPA) + .021(SAT-V) + .126(SAT-M) + .038(AIMS-R) + .097(AIMS-M) + .210(AIMS-W)Caucasian : CGPA = .460(HSGPA) + .179(SAT-V) + .138(SAT-M) + .025(AIMS-R) − .005(AIMS-M) − .022(AIMS-W)

Hispanic : CGPA = .404(HSGPA) + .099(SAT-V) + .082(SAT-M) + .029(AIMS-R) − .009(AIMS-M) + .037(AIMS-W)Asian American : CGPA = .436(HSGPA) + .097(SAT-V) + .106(SAT-M) − .052(AIMS-R) + .046(AIMS-M) + .219(AIMS-W).

Table 6. Summary of Ethnic Group User-Determined Stepwise Regression Analyses for the AIMSTest as a Supplemental Predictor of Students’ Y1GPA and CGPA (2000 Cohort: Caucasian Ns =1,561 and 1,249, Hispanic Ns = 312 and 258, Asian American Ns = 126 and 102, for Y1GPAand CGPA, Respectively)

Ethnic Group

Caucasian Hispanic Asian American

Predictors/Outcomes R2 �R2 �R2 p R2 �R2 �R2 p R2 �R2 �R2 p

Step 1HSGPA Y1GPA .367 .367 <.001 .218 .218 <.001 .342 .342 <.001

CGPA .333 .333 <.001 .256 .256 <.001 .364 .364 <.001Step 2HSGPA Y1GPA .395 .029 <.001 .261 .043 <.001 .496 .155 <.001SAT

CGPA .356 .023 <.001 .360 .105 <.001 .494 .130 <.001Step 3HSGPA Y1GPA .403 .007 <.001 .270 .010 .263 .498 .001 .963SATAIMS CGPA .363 .007 .003 .377 .017 .080 .495 .002 .959

Note: The variables entered at each step are in bold. SAT = Verbal and Mathematics subtests; AIMS = Reading, Mathematics, and Writing scalescores.Final equations (standardized coefficients):

Caucasian : Y1GPA = .521(HSGPA) + .112(SAT-V) + .034(SAT-M) + .028(AIMS-R) − .042(AIMS-M) + .106(AIMS-W)Hispanic : Y1GPA = .406(HSGPA) + .169(SAT-V) − .004(SAT-M) − .076(AIMS-R) + .046(AIMS-M) + .102(AIMS-W)

Asian American : Y1GPA = .411(HSGPA) + .302(SAT-V) + .115(SAT-M) + .038(AIMS-R) + .003(AIMS-M) + .020(AIMS-W)Caucasian : CGPA = .497(HSGPA) + .099(SAT-V) − .012(SAT-M) + .046(AIMS-R) + .009(AIMS-M) + .088(AIMS-W)

Hispanic : CGPA = .415(HSGPA) + .271(SAT-V) − .049(SAT-M) − .071(AIMS-R) + .081(AIMS-M) + .142(AIMS-W)Asian American : CGPA = .473(HSGPA) + .296(SAT-V) + .100(SAT-M) + .006(AIMS-R) + .060(AIMS-M) − .041(AIMS-W).

.16 and .02 for SAT-V and SAT-M, respectively), comparedto 15.5% (with standardized partial regression coefficients of.33 and .13) for Asian American students, p = .055. A similarpattern was found (p = .076) for predicting Y1GPA when thetwo SAT subtests were added to both HSGPA and the threeAIMS subtests, where SAT accounted for an additional .8%

of the variance in the Caucasian sample (with standardizedpartial regression coefficients of .11 and .03) and 4.8% of thevariance in the Asian American sample (with standardizedpartial regression coefficients of .30 and .12). No differentialSAT contributions were found in the 1999 cohort compari-son of Caucasian and Asian American students, all ps > .64,

Summer 2010 9

or in either cohort comparison of Caucasian and Hispanicstudents, all ps > .10.

DiscussionRecently, a number of higher education institutions have ei-ther moved away from, or limited the role of, conventionaladmissions tests such as the SAT and ACT. Schools are con-sidering the value of achievement tests for selection purposesbecause they are viewed as less dependent on factors that areoutside of a candidate’s control, and thus, signal to a studentthe importance of doing well in school. Considering that moststates have standards-based high school examinations, manyof which are used for graduation decisions, the worth of thetests in selecting students for college should be looked atmore closely.

By analyzing the degree to which AIMS high school testscores predicted both Y1GPA and CGPA in two successivecohorts in the context of various prediction models that in-cluded HSGPA and SAT, we were able to elucidate the po-tential “value added” of such achievement scores in one stateand at one large university. Some research has suggestedthat high school standards-based examinations reflect ratherbasic skills, and consequently, would not yield scores thatadequately indicate college readiness (Achieve, Inc., 2004;Brown & Conley, 2007; Le, 2002). Indeed, we discoveredthat for UA students in each cohort, AIMS-M and AIMS-Wscores had less variability than SAT scores, and compared topopulation parameters, AIMS-M and AIMS-W demonstratedgreater variability decrements than SAT when UA selected itsstudent classes. These findings potentially revealed the na-ture of criterion-referenced tests designed to measure highschool achievement compared to a norm-referenced test de-signed to select students who are prepared for college. The re-duced variation for AIMS, however, was not dramatic enoughto override its ability to be an effective discriminator of stu-dents’ collegiate attainment levels.

To answer our primary research questions, we employeduser-determined stepwise regression analyses with HSGPA,SAT, and AIMS as predictors. In both cohorts, AIMS did notimportantly supplement HSGPA and SAT in predicting Y1GPAor CGPA. Specifically, after including HSGPA and SAT aspredictors of college GPA, AIMS did not account for muchvariation in students’ college performance (i.e., .8% or less).In the 1999 cohort, however, we found that with HSGPA andAIMS included as predictors, SAT accounted for a reasonablepercentage of additional Y1GPA and CGPA variation (2.2%and 3.0%, respectively).

In terms of supplanting the SAT, we discovered that AIMSdid not perform as well as SAT in adding explained varia-tion above HSGPA for the 1999 cohort (about 2 to 3 % lessexplained variability of AIMS relative to SAT across the twoGPA criteria), but for the 2000 cohort, AIMS functioned nearlyas well as an additional predictor compared to SAT, account-ing for about the same proportion of variance explained whenHSGPA was included in the analyses.9 Similar to the findingsof research conducted on the Connecticut (Coelen & Berger,2006) and Washington (McGee, 2003) achievement tests, wediscovered that AIMS scores could have served as a good SATsubstitute, at least for one of two cohorts. But unlike the Coe-len and Berger findings in Connecticut, we did not identifyany unique effects for any of the three AIMS subtests withHSGPA and SAT included as predictors.

Given the differences in how AIMS performed in generalacross the two cohorts, it is worth noting key changes to theArizona testing program from 1999 to 2000. AIMS was a brandnew test in 1999, and not only did high school students takethe tests for the first time that year, they did not have topass the tests to graduate from high school. Within this con-text, one can assume there was little motivation to performwell on the new examinations. By 2000, the state enacted alaw that high school students had to pass AIMS to graduate.After 10th-graders took the test in 2000, the state delayedthe AIMS graduation requirement until 2006 to allow highschools more time to align their curriculums with the stateacademic standards. The added perceived high-stakes natureof the testing context might have increased the accuracy andpredictive capability of AIMS for the 2000 cohort, but with-out actual student motivation data, this explanation remainsspeculative. Furthermore, because students were not ran-domly assigned to either cohort and we did not statisticallyequate them on relevant preexisting variables, AIMS and SATprediction differences across the two cohorts could have beenat least partially due to group composition effects.10

In our examination of the differential prediction accuracyof AIMS and SAT by ethnic subgroup, we found very few effectsother than for Asian American students.11 In particular, forstudents from this ethnic group: Adding AIMS to HSGPA andSAT improved prediction accuracy for the 1999 cohort; and forthe 2000 cohort, SAT supplemented HSGPA and AIMS. Theseresults apparently revealed the fruitfulness of including asmuch test score information as possible to better understandthe subsequent collegiate performance for 1999 and 2000cohort Asian American students at UA. It is possible thatbesides measures of high school achievement (HSGPA andAIMS), a conventional measure of college readiness (SAT)detects unique skills that ultimately play a role in determininghow students from that ethnic group achieve at the collegelevel. Again, it is vital to note that our study was based on anow-defunct version of the SAT. With changes to the new SATthat include greater high school curricular sensitivity, it isnot clear if these results would be detected in future studies.

Taken together, what implications can be drawn from ourfindings for university administrators who are considering us-ing state standards-based achievement measures in lieu ofor in addition to conventional admissions tests? First andforemost, we found no evidence that state exams were su-perior to the circa 2000 version of the SAT; and for logisticreasons that we will discuss below, it likely would be disad-vantageous for postsecondary institutions to ignore the use-fulness of conventional measures—especially if such institu-tions fundamentally strive to be fair to all students regardlessof ethnicity. Nonetheless, we also generated considerable ev-idence to support the notion that state tests (at least in onestate) are aligned enough with college expectations to ren-der them useful as indicators of college readiness. For the2000 cohort, the state tests performed nearly as well as theconventional (nationally standardized) measures in terms ofselection accuracy for all students combined.

In light of the greater AIMS range restriction compared toSAT, hypothetically we would have detected superior predic-tive validity for AIMS scores if we had corrected for restrictionof range, particularly for the 2000 regression models. If stateswere to enhance their state tests to increase the verticalalignment between their exams and college expectations, assuggested by researchers and policy analysts (Achieve, Inc.,


2004; Brown & Conley, 2007; Kirst & Reeves Bracco, 2004),the tests might actually outperform traditional measures. Fur-thermore, it must be considered that AIMS was administeredat least one year earlier in high school than SAT for most sam-pled students, and thus was a more distal measure of highschool readiness. In many states, especially those that re-quire students to pass the exams to graduate, students wouldcontinue to take portions of the exams not passed at leastthrough the 12th grade. In Arizona, students can continue totake already passed subtests to earn a college scholarship byreaching the “Exceeds” performance level on all three tests.It is possible that more proximal retest scores for students in11th or 12th grade would yield greater prediction accuracy ofcollege performance.

From a student perspective, not having to take an addi-tional admissions test for a fee that one would have to incurwould likely be welcome, and focusing on doing as best aspossible on a single set of exams that reflect state standardswould be an advantage. Nevertheless, there are logistic is-sues that many state education agencies and postsecondaryinstitutions would face if state scores were used for admis-sion purposes. Like in Arizona, many state education agenciesand postsecondary institutions have not developed systemsto share student data. Thus, a state would need to develop aprocess by which applicants’ state test scores are provided tocolleges and universities. Furthermore, not all students ap-plying to a university would have scores from the same test.Out-of-state students and in-state students who claimed resi-dency after the high school tests were administered wouldeither have no scores or scores from another state test.Without conversion tables to compare scores across statetests, there would be no way for a university to equate a stu-dent’s score on another state test with the home state exam.Thus, although dropping conventional admissions tests wouldreduce student testing and save students the time and finan-cial costs of taking them, the tests do provide universitieswith standardized scores that apply across the nation.

One possible solution for universities would be to acceptstate achievement scores for students who took those exams,and conventional admissions test scores for those studentswho did not. If a university had sufficient data from studentswho had taken both measures, conversion tables or compara-ble performance thresholds could be established for the two.Again, however, logistic issues pertaining to data access andmanagement would need to be solved.

We are now aware of three studies including ours that haveexamined the usefulness of state exams for collegiate ad-mission decisions (see Coelen & Berger, 2006; McGee, 2003).However, the rigor and content emphasis of state exams, alongwith the expectations of postsecondary schools vary enoughacross the country to question the degree to which the extantbody of evidence generalizes across the various study set-tings. As more states make strides toward better alignmentbetween high school and college demands, state achievementtests likely will become better indicators of college readiness.Ultimately, if the goal is to use scores from state-standardstests to select students for college, empirical investigations ofthe predictive capacity of the state test in question will needto be conducted.

AcknowledgmentsThe research reported here is based on data supplied bythe Arizona Department of Education and the Admissions

Research Office, University of Arizona. We are indebted toboth agencies for their support. We are also grateful to RonaldMarx and Lawrence Aleamoni for their input to the study.

Statutes

No Child Left Behind Act of 2001, Pub. L. No. 107-110, 115 Stat. 1425(2002).

Notes1It should be kept in mind, however, that the ostensible pri-mary purpose of state high school achievement tests (or “grad-uation examinations”) is to assess whether the prescribedcurricular content was actually taught to and acquired bygraduating students.

2Geiser and Studley (2001) examined the predictive powerof an SAT II composite comprised of three equally weightedsubject examinations.

3A few months after the latter cohort had taken AIMS, thestate delayed the graduation requirement until 2006. Thisresulted in the 2000 students’ scores having no actual impacton their high school completion or on their acceptance into astate university, even though the students expected AIMS tohave an impact when the tests were taken.

4The fact that the state-mandated AIMS test was taken bystudents in their sophomore year (i.e., two years before theirgraduation) means that: (a) students in the present studypotentially did not possess the complete set of curricularreadiness that would be expected of entering college students;and (b) relative to the SAT and ACT tests taken by studentsin their senior years, the AIMS test represented a more distalpredictor of college GPA here. As will be revisited later, theseissues need to be taken into account when interpreting ourfindings.

5This has traditionally been referred to as a “hierarchi-cal” regression analysis in the statistical literature. A journalreviewer suggested, however, that because of the advent ofhierarchical linear modeling, our use of “hierarchical” mightcreate some confusion or ambiguity among readers. Hence,throughout this article we will refer to the procedure as a“user-determined stepwise regression analysis” (e.g., Maras-cuilo & Levin, 1983, p. 112).

6It should also be noted that whereas in the previously citedGeiser and Studley (2001) study of UC-Berkeley students itwas found that the HSGPA and SAT II collectively accountedfor about 21% of the variance in college GPAs, from ourTables 3 and 4 it may be seen that HSGPA and SAT togetheraccounted for a substantially higher percentage of variance,at least 36% (i.e., Step 2 R2 ≥ .36 in all cases), with HSGPAby itself accounting for at least 29%.

7As noted, approximately 18% of the initial student samplehad to be dropped from the foregoing analyses because somestudents had not taken the SAT test. Nonetheless, analysesbased on students who had either just ACT test scores or bothACT and SAT test scores (1999 cohort Ns = 973 and 753 forY1GPA and CGPA, respectively; 2000 cohort Ns = 1156 and906 for Y1GPA and CGPA, respectively) yielded substantially(and statistically) the same predictive-validity results basedon the ACT as those just reported based on the SAT.

8Additional analyses confirmed these results when AIMSwas added to HSGPA without the presence of SAT in theequation, in that all ethnic group by AIMS interactions hadassociated ps > .06.

Summer 2010 11

9As an interesting aside, Atkinson (2001, p. 3) reported thatin the UC-Berkeley study the “single best predictor of studentperformance turned out to be the SAT II writing test.” We per-formed the just-reported regression analyses with students’AIMS-W scores replacing their AIMS-R, AIMS-M, and AIMS-W values. In those analyses, we found that the conclusionsrelated to our “supplement” hypothesis were identical, in thatthe AIMS writing subtest added virtually the same amountto HSGPA and SAT prediction of college GPA as did all threeAIMS subtests combined. However, with respect to our “sup-plant” hypothesis we found that, compared to AIMS’ threesubtests, the AIMS writing subtest accounted for somewhatless variance in college GPA (including 5% less in the 1999cohort’s CGPA) when added to HSGPA; and, thus, SAT ac-counted for proportionally more college GPA variance whenadded to HSGPA and AIMS-W scores.

10A similar caution should be directed at the interpreta-tion of Salins’s (2008) recent nonrandomized assessment ofthe SAT’s predictive validity at State University of New Yorkcampuses that did and did not raise SAT score standards foradmission.

11Yet it should be pointed out that in both cohorts, the abil-ity of the complete set of precollege variables used to predictstudents’ college outcomes (as reflected by the final-equationR2s) was generally best in the Asian American sample, worstin the Hispanic sample, and intermediate in the Caucasiansample.

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