Research Report
No. 2009-1

Socioeconomic
Status and theRelationship
Between the SAT
and Freshman
GPA: An Analysis
of Data from41 Colleges andUniversities®

Paul R. Sackett, Nathan R. Kuncel,
Justin J. Arneson, Sara R. Cooper,
and Shonna D. Waters

www.collegeboard.com

College Board Research Report No. 2009-1

Socioeconomic Status andthe Relationship Between the®
SAT and Freshman GPA:
An Analysis of Data from41 Colleges and Universities

Paul R. Sackett, Nathan R. Kuncel, Justin J. Arneson,
Sara R. Cooper, and Shonna D. Waters

The College Board, New York, 2009

Paul Sackett is the Beverly and Richard Fink Distinguished
Professor of Psychology and Liberal Arts at the Universityof Minnesota.
Nathan Kuncel holds the Marvin D. Dunnette
Distinguished Chair in Industrial and Organizational
Psychology at the University of Minnesota.
Justin Arneson, Sara Cooper, and Shonna Waters were
graduate students in the Department of Psychology
at the University of Minnesota when the research was
conducted. Justin Arneson is now at Target Corporation.
Shonna Waters is now at the Human Resources Research
Organization.
Researchers are encouraged to freely express theirprofessional judgment. Therefore, points of view or
opinions stated in College Board Reports do not necessarily
represent official College Board position or policy.
The College Board
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Each year, the College Board serves seven million
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Acknowledgments
This research was supported by a grant from the College
Board to Paul Sackett and Nathan Kuncel. The order of
the three junior authors is alphabetical; all contributed
equally to the project.

Contents
Abstract  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 1Introduction .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 1Method .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 2Sample .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 2Measures .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 3Analyses .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 3Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Comparison of Restricted and Unrestricted
Means and Standard Deviations  .  .  .  .  .  .  .  .  .  . 4SES–Test Relationships . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 4SES–Grade Relationships . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5Test–Grade Relationships . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5Test–Grade Correlations Controllingfor SES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5SES–Grade Correlations Controlling for Test
Score . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5Parallel Analyses of the Role of SES in HighSchool GPS–College GPA Correlations . .  .  .  .  . 5Discussion  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 6Limitations  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 6Conclusion .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 7References . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 7Appendix: Correlations and Partial Correlations
Among SAT, College GPA, HSGPA, and SES by
School  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 14

Tables1.	Overview of Characteristics of the
41 Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.	Means, SDs, and Correlations Among SAT,HSGPA, and SES for National SAT
Population  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.	Unrestricted and Restricted Means andStandard Deviations for SES by School . . . . . . . . 9
4.	Unrestricted and Restricted Means andStandard Deviations for SAT Verbal andMathematics Scores by School . . . . . . . . . . . . . . 10
5.	Unrestricted and Restricted Means andStandard Deviations for High School GPA by
School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.	Means and Standard Deviations for Freshman
GPA by School . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.	Summary of Mean SES–Test, SES–Grade, andTest–Grade Correlations Across Schools . . . . . 13
8.	Summary of Mean SES–High School GPA, SES–
College Grade, and High School GPA–College
Grade Correlations Across Schools . . . . . . . . . . 13
Figures
1a. Model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1b. Model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Abstract
Critics of educational admissions tests assert that tests
measure nothing other than socioeconomic status(SES), and that their apparent validity in predicting
academic performance is an artifact of SES. We examine
relationships among SAT®, SES, and freshman grades in
41 colleges and universities and show that (a) SES is related
to SAT scores (r = 0.42 among the population of SAT
takers), (b) SAT scores are predictive of freshman grades(r = 0.47 corrected for school-specific range restriction),and (c) statistically controlling for SES reduces theestimated SAT-grade correlation from r = 0.47 to r = 0.44.
Thus, the vast majority of the SAT–grade relationship is
independent of SES: The SAT–grade relationship is not an
artifact of common influences of SES on both test scoresand grades.

Introduction
Millions of Americans take postsecondary admissions
tests (e.g., SAT, ACT, GRE, LSAT, MCAT, and GMAT)
each year. Given their prominent role as a standardized
national metric, these tests are of great interest to the
public, and they undergo considerable scrutiny. A
common assertion among test critics is that test scores
used for high-stakes decisions (e.g., college admission)
measure nothing more than socioeconomic status (SES).
Examples of this assertion include the claim that “in theinterest of truth in advertising, the SAT should simply
be called a ‘wealth test’” (Guiner, cited in Zwick, 2002),
that “the SAT merely measures the size of students’houses” (Kohn, 2001), and that the “only thing the SAT
predicts well now is socioeconomic status” (Colvin,1997). Implicit in these criticisms is that SES has an
artificial and irrelevant effect on test scores: high SES
leads to higher test scores (e.g., via knowledge of testtaking techniques), but not to higher true standing on
the characteristic the test is intended to measure (i.e.,developed abilities relevant to academic performance).
This assertion can be paired with another one, namely,that SES has a similar artificial effect on academic
performance measures (e.g., grading is biased in favor ofhigh SES students), and thus that the appearance of test
validity (i.e., test–grade correlations) is also an artifact.
If SES inflates both test scores and grades of high-SES
students, and deflates both test scores and grades of lowSES students, then a test that is, in fact, completely invalid
as a predictor of academic performance will appear
valid due to the common effects of SES on both test andgrade. Assertions that the appearance of test validity is

an artifact of SES are also prominently placed within
the psychological literature. One claim is that “…[I]t has
now been documented with massive data sets from the
University of California that SAT I scores lose any ability
to predict freshman year grades if the regression analyses
control for socioeconomic status (Crosby, Iyer, Clayton,
and Downing, 2003, p. 1001).” Similarly, “…SAT scores
used for college admission do not predict freshman year
grades when socioeconomic status is controlled (Biernat,
2003, p. 1023).” The most visible critic of the SAT, former
president of the University of California (UC) system
Richard Atkinson (2005), states that “after controllingfor (SES)…the relationship between SAT I scores andUC grades virtually disappears.” Moving beyond thespecific issue of SES and test validity, it is noteworthy
that the report of a task force commissioned by theAmerican Psychological Association to examine SES andmake recommendations for directions for psychologicalresearch and practice has recently been issued (Saegert,
Adler, Bullock, Cauce, Liu, and Wyche, 2007). This task
force affirms the criticality of understanding the role ofSES.
We concluded that a systematic explorationof the degree to which SES accounts for test–grade
relationships was in order. Our goal was to examine
three relationships: (1) the correlation between scoreson the SAT and SES, (2) the correlation between the
SAT and indices of subsequent academic performance(e.g., grades), and (3) the correlation between SES andthese indices of academic performance. With estimatesof these three relationships, we can statistically control
either SAT or SES to shed light on the nature of the
SES–SAT–academic performance relationships.
We contrast two conceptual models of therelationships between these two variables. Model 1,
implicit in the position of the critics noted above, isreflected visually in Figure 1a. SES influences testscores, and SES influences grades, but there is no causal
relationship between the characteristics measured by thetest and grades. Any correlation between test scores andgrades is an artifact of the common influences of SES
on both test scores and grades. If this model is correct,
then the correlation between test scores and grades will

Test
SES
GradesFigure 1a. Model 1.

1.	The term “SAT I” for the purposes of this report means “SAT.” The term “SAT II” may be understood to mean “SAT Subject
Tests™.”

1

SES

Test

GradesFigure 1b. Model 2.

drop to zero when statistically controlling for SES. Thismodel is statistically and conceptually consistent withthe criticisms discussed earlier. A weaker version of thismodel would concede the possibility of a weak test–
grade relationship after controlling for SES, but would
nonetheless posit that much or most of the apparent test
validity is an artifact of SES. Thus a comparison of thetest–grade correlation with the test–grade–correlation
controlling for SES is the crucial test of this model.
A finding of a test–grade relationship that changes
minimally, if at all, when controlling for SES would bestrong evidence against the assertion that the test–grade
correlation is an artifact of the joint association of bothvariables with SES.
Figure 1b offers an alternate conceptual model ofthe relationship between these variables. Here SES
affects the characteristics measured by tests, which
subsequently affect grades. A key feature of this model,
though, is that SES is not posited to have a direct
relationship with grades; its link to grades is a resultof the mediating role of test scores. While SES has
an influence on test scores, the test scores truly are
predictive of academic performance. The test–grade
relationship is not an artifact of the joint influence of
SES on both test and grade. The crucial test of this model
is a comparison of the SES–grade correlation with the
SES–grade correlation after controlling for test score.
A finding that the SES–grade correlation is reduced tozero or near zero after controlling for test score, pairedwith the finding of a substantial test–grade correlation
after controlling for SES, would be evidence strongly insupport of this model. If ability accounts for nearly allof the SES–grade relationship but accounting for SES
has little effect on a substantial test–grade correlation
we can have confidence that the ability test is capturing
information about the individual that is related tosubsequent success but does not appear to be related to
SES. This ability test effectively treats the relationshipbetween SES and the test as entirely the result of nonmerit or unfair factors. In fact, it is certainly possible
for SES to have a legitimate influence on subsequent testscores due to better education, nutrition, health care,and safe family environments. Although differencesin SES among people may not be socially desired, it
does not follow that an overlap between SES and ability
test scores means that tests are measuring irrelevantcharacteristics. In our study we set aside this caveat andevaluate ability tests using a harsh test in a single large
data set.

2

Both of the models articulated above posit SES–
test relationships. Model 1 views this relationshipas artifactual: Controlling for SES, the test–grade
performance drops to zero or near zero. Model 2 views
the relationship as reflecting a real advantage conferredby high SES: Higher SES leads to higher developed ability,
which leads to higher academic performance. Were
Model 1 true, continued test use would be inappropriate.
Were Model 2 true, then test scores contain meaningful
information predictive of academic performance, and thefocus shifts to the question of the societal consequencesof the fact that being higher in SES confers a meaningfuladvantage. This may lead some to call for interventions
to alleviate the advantage conveyed by high SES. It may
also lead some to question test use, but it is important
to differentiate between criticizing tests on the grounds
that they are not valid measures of academically relevant
skills and criticizing tests on the grounds that one is
not comfortable with the social consequences of using
a test, despite its being a valid predictor of academic
performance.
As one part of a broader investigation of the role of
SES in test validity, we analyzed a large data set collectedby the College Board with the help of 41 colleges anduniversities, which contains SAT scores (verbal andmathematics), SES measures, and freshman grades forover 150,000 students from multiple entering classes
at these institutions. Because we also have SAT scoresand SES measures for a nationwide population of morethan 2.5 million SAT takers over a three-year period, we
can examine the degree of range restriction that takes
place on SAT and SES within each of the 41 schools,
and perform psychometric corrections to estimate thecorrelation of interest in applicant samples, rather thanin samples selected in part on the basis of test scores.

MethodSampleThe College Board collected SAT, SES, and freshman grade
information from three entering cohorts (1995, 1996, and1997) in collaboration with a group of 41 colleges anduniversities. These were selected to be geographicallydiverse, to include large and small schools, to include
public and private institutions, and to cover a broad
range in terms of school selectivity on SAT scores. The
41 schools included 15 private and 26 public institutions.
Mean freshman class size was 1,359 (Standard Deviation[SD] = 1,234), with a range from 98 to 6,172. The meanof the mean high school GPAs among entering students
across the 41 schools was 3.51 (SD = 0.27), with a range
from 2.96 to 4.05 (grades above 4.0 reflect extra credit

for Advanced Placement® and honors courses). The meanof the mean SAT total scores across schools was 1127
(SD = 112), with a range from 945 to 1395. (The above
values are based on computing values for each school foreach of the 1995, 1996, and 1997 cohorts, and averaging
them; for example, a value of 6172 for freshman class sizemeans that the school’s mean entering class across thesethree cohorts was 6,172). Table 1 (see page 8) provides
full information on class size, public versus private,mean high school GPA, and mean SAT for each school.
A smaller subset of schools provided cumulative grades
for at least four years; we focused on freshman grades tomaximize the number of participating institutions (seeBridgeman, Pollack, and Burton, 2004, for prior researchusing this data set).

Measures
SAT mathematics and SAT verbal scores were obtained
from College Board records; we combined these intoa unit-weighted composite. Three SES variables wereobtained from questionnaires completed by students atthe time they took the SAT: father’s education, mother’s
education, and family income; the natural log of family
income was used. The mean school-specific correlation
between the two education variables was 0.57; father’sand mother’s education had mean correlations of 0.43
and 0.35, respectively, with family income. In the entire
population of SAT takers, the correlation between thetwo education variables was 0.60; father’s and mother’s
education correlated 0.46 and 0.41, respectively, withfamily income. We created an equally weighted compositeof these three variables. Freshman GPA was provided by
the college or university. We also obtained data on highschool performance from the student questionnaires andused this information in a multivariate correction for
restriction of range.

Analyses
We initially performed separate analyses of SES–SAT,
SES–grade, and SAT–grade correlations for eachentering cohort for each school (1995–1997); finding no
meaningful cohort differences, we pooled the data acrossthe three cohorts for each school. All analyses were thenconducted separately by school, and these school-specificcorrelations were combined by meta-analysis.
We sought to obtain applicant population data inorder to correct correlations among test, grades, and SES
for range restriction. Range restriction refers to the factthat variance in test scores is reduced when the sample
available for study has been selected in part on the basis ofscores on the test in question (e.g., computing SAT–grade
correlations in samples where SAT scores were part of theselection process). Restricted variance on the test results

in a lower test–grade correlation than would be the caseif the relationship were examined in applicant samples(Sackett and Yang, 2000).
The data needed are the unrestricted means, SDs,and correlations among the variables. We obtained two
separate sources of information for this purpose. The
first was means, SDs, and correlations between SAT and
SES among the entire population of individuals taking
the SAT and completing a questionnaire reporting SES
in 1995, 1996, and 1997 (more than 2.5 million students).
Thus these data estimate the population for whom thetest is relevant. Table 2 contains these broad populationmeans, standard deviations, and correlations.
Our second goal was to obtain estimates of themeans, SDs, and correlations in the applicant pool foreach specific college or university, in order to obtain
unrestricted estimates of the correlation of interest
among each school’s applicant population. While suchdata are not available, we were able to obtain data that we
believe provide a reasonable proxy to the school-specificapplicant pool. When students take the SAT, they indicate
the schools to which they wish their scores to be sent;
we used the set of students who asked that their scores
be sent to a given school as our estimate of the applicant
pool for that school, a strategy also used by Boldt (1986).
Thus, we made multivariate range restriction corrections
using both the school-specific estimates of the applicant
pool and the entire SAT-taking population as the referent
population (Sackett and Yang, 2000). It is important tocarefully choose the population from which one draws
unrestricted SD and correlation estimates. Students
choose which colleges to apply to based in part on how
their SAT scores and HSGPAs match colleges’ standards,
and therefore, SAT and HSGPA variability will be smaller
within any given college’s applicant pool than in the total
population of college applicants. Correcting for range
restriction using each college’s applicant pool, SDs andcorrelations as unrestricted values estimate how well SAT
and HSGPA could be expected to predict grades within
the average college’s applicant pool. Correcting using
SDs and correlations drawn from the entire populationof college applicants estimates how well SAT and HSGPA
could be expected to predict grades if SAT and HSGPA
variance was not reduced by students’ self-selecting into
Table 2Means, SDs, and Correlations Among SAT, HSGPA,
and SES for National SAT Population
HSGPA

SAT

SES

SAT

0.54

SES

0.20

0.42

Mean

3.21

1012.77

0

SD

0.66

206.4713certain colleges’ applicant pools. Neither correction isnecessarily “correct”; both answer important questions
about how well SAT and HSGPA predict college grades.
Thus, we separately present both types of corrections forcomparison purposes.

Results
Comparison of Restricted
and Unrestricted Means andStandard Deviations
While Table 2 presented the SES, SAT, and HSGPA means
and standard deviations for the full national test-taking
population, Tables 3, 4, and 5 (see pages 9, 10, and 11)
present school-specific unrestricted means and standard
deviations for SES, SAT, and HSGPA, respectively. Eachtable also presents the ratio of restricted to unrestricted
standard deviation, which serves as an indicator of thedegree of range restriction on each variable for eachschool.
The results for SES in Table 3 indicate considerable
variability from school to school in the mean SES of
those in the applicant pool. SES is expressed in a metricin which the mean and standard deviation among thenational population of SAT takers are zero and one,respectively. Thus a school-specific mean greater thanzero indicates a school-specific value above the nationalmean, and a standard deviation less than one indicates anarrow range of SES for the school than for the national
population. Clearly, SES is linked to the self-selection
process by which students choose the schools to which
they will apply. However, SES does not appear linked
to the selection process used by schools, as the ratioof restricted to unrestricted SES standard deviations
is generally very close to 1.0, averaging 1.09 across the
41 schools. Thus, within an applicant pool, SES is notstrongly linked to the process by which a class is selected
and subsequently enrolled. That the ratio of restricted tounrestricted SDs averages over 1.0 is consistent with thenotion that colleges seek an entering class that is diversein terms of SES and/or variables related to SES.
The results for SAT in Table 4 also show considerable
variability from school to school in the mean SAT ofthose in the applicant pool, with means ranging from
940 to 1294. Thus, like SES, SAT is linked to the process
by which students choose the schools to which they
will apply. Unlike SES, though, SAT is also linked tothe selection process used by schools, as the restricted
SAT standard deviations are generally smaller thanthe unrestricted standard deviations, with the ratio of

4

restricted to unrestricted SD averaging .86 across schools.
This ratio does vary across schools, from .65 to 1.01; asa result, some schools are more selective on the SAT,
while others (i.e., the three schools with ratios of .97, .99,
and 1.01) are either nonselective or select on variables
uncorrelated with the SAT.
The results for high school GPA are shown in Table
5, and indicate a pattern very similar to that of the SAT.
Mean GPA in applicant pools varies from 2.96 to 4.05,
signaling self-selection, and the ratio of restricted tounrestricted SD averages 0.85. The ratio does vary acrossschools, from 0.64 to 0.98. Interestingly, the mean andthe range of the ratios are very similar for SAT and highschool GPA, signaling that the two are used to a roughly
comparable degree in selecting students. Across the 41
schools, the correlation between the SD ratio for SAT and
the SD ratio for high school GPA is 0.65, indicating that
it is not the case that schools choose to emphasize either
the SAT or high school GPA (in which case a negative
correlation would be expected), but rather that there is an
overarching issue of school selectivity: Schools that are
more selective on the SAT tend to also be the schools thatare more selective on high school GPA.
Table 6 (see page 12) presents freshman GPA means
and standard deviations by school. These GPAs average
2.78, with an SD of 0.68, and range from 2.15 to 3.30.
Consequently, there are substantial differences in the
distribution of grades by school.

SES–Test Relationships
Answers to the question “How strongly are test scores and
SES related?” vary as a result of the type of data examined.
As shown in Table 7 (see page 13), the mean SES–SAT
correlation among students enrolled at a given college
or university is 0.22. (Correlations for each individual
school are listed in the appendix.) This increases to0.31 among applicants to a specific school. It increases
further to 0.42 when one examines the relationshipin the entire SAT-taking population. The difference
between the correlation of 0.42 in the entire test-taking
population and 0.31 in the population of applicants toa given school reflects self-selection on either or bothvariables: Both SES and knowledge of typical SAT scoresof admitted students may affect student application
decisions. The difference between the correlation of 0.31
in the school-specific applicant pool and 0.21 among
enrolled students reflects a combination of these selfselection factors and the school’s use of the SAT scoresas a factor in admissions decisions. Thus, samples ofenrolled students underestimate SES–test relationships inthe college-bound population, leading to the conclusionthat the population of interest must be specified when one
estimates the correlation between SES and test scores.

SES–Grade Relationships
As Table 7 shows, the mean within-school SES–grade
correlation was 0.12. (Correlations for each individual
school are listed in the appendix.) After correcting forrange restriction, a mean correlation of 0.19 for applicants
to a specific school, and a mean correlation of 0.22 forthe full population of SAT takers was found. As a result,institutional or self-selection on SES or on correlates of
SES (e.g., test scores) reduces the SES–grade correlation inenrolled student samples. In short, SES is correlated withgrades, though the correlation is relatively low, and lower
than its correlation with test performance.

Test–Grade Relationships
As Table 7 shows, the mean within-school SAT–grade
correlation was 0.35. (Correlations for each individual
school are listed in the appendix). After correcting forrange restriction, mean correlations of 0.47 for applicants
to a specific school, and 0.53 for the full population of SAT
takers, were found. Institutional or self-selection on the
SAT or on correlates of the SAT (e.g., SES) reduces the test–
grade correlation in enrolled student samples. Thus, theSAT–grade relationship varies as a result of decisions about
whether or how to correct for range restriction. We positthat correcting for school-specific applicant pools gives
the best estimate of the relationship of operational interest,
namely, how well the SAT predicts grades given the set of
applicants who present themselves for consideration at agiven school. We note that school-specific applicant pool
information is often not available, and it is not uncommon
to use the SAT-taking population as the reference groupin making range restriction corrections. This answers
a hypothetical question (i.e., what would the validity of
the SAT be if the applicant pool for a given school werea random sample of the SAT-taking population?), rather
than the operational question of the validity of the SAT
for existing applicant pools. An argument for using
broader applicant pools as the basis for correction isthat some students decide not to apply to a given school
based on knowledge of their own test scores and of thetypical scores of students enrolling at a given school.
Thus, test score variance at a given school is restricted
due to both the school’s selection processes and individual
students’ self-selection processes. Consequently, while both
estimates are of interest, we focus on current operational
validity (i.e., how well the test predicts among thosecurrently applying), while acknowledging that test scores
can also play a role in student self-selection. The findings
demonstrate that using the SAT-taking population as thereference group results in a larger estimate of operational
validity (0.53) than is obtained using a school-specificapplicant pool (0.47).

Test–Grade Correlations
Controlling for SES
To test the proposition that the test–grade relationship wasan artifact of the relationship between SES and both testscore and grades, the test–grade correlation partiallingout SES was computed to determine the degree to which
controlling for SES reduced the test–grade relationship.
Contrary to the assertion of test critics, observed test–
grade correlations are at most nominally affected whencontrolling for SES: the mean SAT–grade correlation of
0.35 drops to 0.33 when controlling for SES. We argued
earlier that the test–grade correlation corrected for schoolspecific range restriction (r = 0.47) was the best estimateof operational test validity for predicting grades. Thisvalue drops to 0.44 when controlling for SES. Therefore,
contrary to the claim that the relationship drops to nearzero when controlling SES, our conclusion is that tests
retain virtually all of their predictive power when SES iscontrolled, and that test validity is not an artifact of SES.
The appendix presents these partial correlations foreach individual school. We examined these school-specificcorrelations to determine whether there are some schools
for which partialling SES does substantially reduce theSAT–grade correlation. For 29 of the 41 schools, the SAT–
grade correlation changes by 0.01 or less; for 6 schools thecorrelation changes by 0.02; for the remaining 6 schools,
the correlation changes by 0.03–0.06. Thus, there are noschools for which the posited reduction of the SAT–grade
correlation to near zero is observed.

SES–Grade Correlations
Controlling for Test Score
To test the Model 2 proposition that the observed
correlation between SES and grades was mediated bytest performance (i.e., that SES did not influence grades
other than via its relationship with test performance),
test performance was partialled from the SES–grade
relationship. Consistent with this proposition, SES–grade
correlations did drop substantially when controlling for
grade. The SES–grade correlation of 0.22 in the entire
SAT-taking population drops to –0.01 when controllingfor SAT score. As a result, the relationship between SES
and grades is mediated by test score.

Parallel Analyses of the Role of
SES in High School GPA–College
GPA Correlations
The primary role of high school grade data in this reportis to act as a variable in the multivariate range restriction
correction process because the report focuses on the5role of SES in SAT–grade relationships. However, we
report a parallel set of analyses investigating the role of
SES in high school GPA–college GPA relationships. The
above analyses show that SAT–college grade relationships
are not an artifact of SES. Table 8 (see page 13) showsthat the same is true for high school GPA–college GPA
relationships. The mean HSGPA–college GPA correlation
across the 41 schools is 0.40; it remains 0.40 when SES iscontrolled. The mean high school GPA (HSGPA)–college
GPA correlation correcting for school-specific range
restriction is 0.51, which drops to 0.50 after controllingfor SES. The mean HSGPA–college GPA correlation
correcting for range restriction at the national populationlevel is 0.58, which drops to 0.56 after controlling for
SES.
Table 8 also shows that, correcting for school-specificrange restriction, the SES–HSGPA relationship (0.12)
is weaker than the SES–SAT relationship (0.31). The
table also shows that the SES–college GPA correlation
decreases from 0.19 to 0.16 when controlling for HSGPA.
In contrast, the SES–college GPA correlation drops
substantially when controlling for SAT. Thus, the SES–
college GPA relationship is largely mediated by SAT, andonly mediated to a modest degree by HSGPA.

Discussion
Our analyses produce several findings. First, SES is indeed
related to test scores. In broad unrestricted populations,this correlation is quite substantial (i.e., r = 0.42 among
the population of SAT takers). Second, test scores areindeed predictive of academic performance, as indexed
by grades. Observed correlations in samples of admitted
students average r = 0.35; applying range restriction
corrections to estimate the validity for school-specificapplicant pools results in an estimate of 0.47 as theoperational validity. Third, the test–grade relationshipis not an artifact of common influences of SES on bothtest scores and grades. Partialling SES from the above
estimate of the operational validity of tests (r = 0.47)
reduces the estimated validity to 0.44. Fourth, the SES–
grade relationship is largely explainable via a mediating
mechanism in which SES influences test scores, which
are subsequently predictive of grades. SES has a near-zero
relationship with grades other than via this SES–test–
grade chain of relationships.
The results presented here are at odds with the critics’claims presented earlier that large-scale University ofCalifornia data show that the predictive power of the
SAT drops to zero when SES is controlled. This contrast
warrants some specific discussion. While such a claim is
made by Geiser and Studley (2002) in their analysis of the
University of California data, their work does not, in fact,
actually provide data supporting those conclusions, as has
6been pointed out by other scholars who have reanalyzed
their data (Johnson, 2004; Zwick, Brown, and Sklar, 2003).
Such a conclusion would require regression analyses
showing that the SAT’s predictive power disappears when
SES is added to the model. However, Geiser and Studley’s
main focus was a comparison of SAT I and the newer SAT
II tests. They estimated a regression model with both SAT
I and SAT II, high school grades, and SES as predictorsof GPA. Thus, they are asking questions about theincremental contribution of one test over another, which
is very different from asking whether the SAT I remains
predictive of grades when SES is controlled. Because
SAT I and SAT II are highly correlated, the incremental
contribution of either one over the other will be quite
small, even if both are predictive of grades. In fact,
the reanalyses reveal that the SAT II–GPA correlation
changes from 0.38 to 0.35 when partialling out SES, afinding fully consistent with the data we present here
(Johnson, 2004; Zwick et al., 2003).

Limitations
In terms of limitations, we note that we rely on selfreports of SES, as these measures are obtained fromquestionnaires completed by students. It is possible thatthese self-reports may be in error for some students. We
believe students are generally in a good position to reportparents’ occupation and educational attainment. Looker(1989) reviews studies of agreement between student andparent reports; focusing on twelfth-grade samples, wefind a mean student–parent correlation of 0.82 for father’soccupation, 0.86 for father’s education, and 0.85 formother’s education. Error in reports of parental income
may be more likely. We note, though, that SES–test and
SES–grade correlations are highly similar regardlessof the SES indicator used, and also note that we used acomposite across multiple SES indicators throughout the
study.
We also note that we have focused on the influence of
SES on SAT–freshman grade relationships. Although it isclear that these types of tests predict a range of important
outcomes (Kuncel and Hezlett, 2007; Kuncel, Hezlett,and Ones, 2004) and grades are related to important life
outcomes (Roth, BeVier, Switzer, and Schippmann, 1996;
Roth and Clark, 1998), it would be valuable to further
extend these analyses to other outcome and performancemeasures.

Conclusion

References

Although our work does not address the mechanisms
underlying the SES–SAT relationship, which may include
issues such as access to higher-quality education, accessto out-of-school educational experiences, parentalrole models and support for academic achievement,
and genetic factors, it is simply not the case that theSAT is merely a proxy for family income and parental
education. The original purpose of the SAT was to obtain
information about basic academic skills and talent that
was as free of class-based opportunity as possible. As
such, most standardized tests are not tightly linked to agiven curricula. Instead, these measures evaluate basic
language skills and mathematical reasoning based on
fundamental instead of advanced mathematics. It appears
that this goal has not been entirely missed.
In conclusion, our work focuses on the predictive
power of admissions tests and shows that this power is
not an artifact of SES. There is a substantial SES–test
relationship, though it is important to note that testscores reflect far more than SES. About a quarter of thevariance in test scores is shared with SES, and thus thereis large variability in test scores at any given level of SES.
Thus, claims that tests are merely proxies for SES are
unfounded.

Atkinson, R. C. (2005). College admissions and the SAT: A
personal perspective. APS Observer, 18, 15–22.
Biernat, M. (2003). Toward a broader view of social stereotyping.
American Psychologist, 58, 1019–27.
Boldt, R. F. (1986). Generalization of SAT validity across colleges.
(College Board Research Report No. 86-3; ETS Research
Report 86-24). New York: The College Board.
Bridgeman, B., Pollack, J., & Burton, N. (2004). Understanding
what the SAT reasoning test scores add to high school grades:
A straightforward approach. (College Board Research Report
No. 2004-4). New York: The College Board.
Colvin, R. L. (1997, October 1). Q & A: Should UC do away with
the SAT? Los Angeles Times, p. B2.
Crosby, F. J., Iyer, A., Clayton, S., & Downing, R. A. (2003).
Affirmative action: Psychological data and the policy
debates. American Psychologist, 58(2), 93–115.
Geiser, S., & Studley, R. (2002). UC and the SAT: Predictive
validity and differential impact of the SAT I and SAT II at
the University of California. Educational Assessment, 8(1),1–26.
Johnson, J. W. (2004). Not all affirmative action rewards merit:
Comment. American Psychologist, 59, 123–24.
Kohn, A. (2001). Two cheers for an end to the SAT. Chronicle ofHigher Education, 9, B-12.
Kuncel, N. R., & Hezlett, S. A. (2007). Standardized tests
predict graduate students’ success. Science, 315, 1080–81.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic
performance, career potential, creativity, and job
performance: Can one construct predict them all? Journal of
Personality and Social Psychology [Special Section, Cognitive
Abilities: 100 Years after Spearman (1904)], 86, 148–61.
Looker, E. D. (1989). Accuracy of proxy reports of parental
status characteristics. Sociology of Education, 62, 257–76.
Roth, P. L., BeVier, C. A., Switzer, F. S., & Schippmann, J. S.
(1996). Meta-analyzing the relationship between grades
and job performance. Journal of Applied Psychology, 81 (5),548–56.
Roth, P. L., & Clark, R. L. (1998). Meta-analyzing the relation
between grades and salary. Journal of Vocational Behavior,
53, 386–400.
Sackett, P. R., & Yang, H. (2000). Correction for range restriction:
An expanded typology. Journal of Applied Psychology, 85,
112–18.
Saegert, S. C., Adler, N. E., Bullock, H. E., Cauce, A. M., Liu,
W. M., & Wyche, K. F. (2007). Report of the APA task
force on socioeconomic status. Washington, DC: American
Psychological Association.
Zwick, R. (2002). Is the SAT a “wealth test?” Phi Delta Kappan,
84(4), 307–11.
Zwick, R., Brown, T., & Sklar, J. C. (2003). California and theSAT: A reanalysis of University of California admissions data.
Berkeley, CA: Center for Studies in Higher Education.

7

Table 1
Overview of Characteristics of the 41 Schools
SchoolEntering Class N

1

1,911

Public (0)/Private(1)

HSGPA Mean

SAT Mean

SES
Mean

0

3.32

1082.97

0.06

2

1,271

0

3.21

951.11-0.16

33,821**

1

3.52

1134.71

0.33

4

2,333

0

3.41

1090.71

-0.06

5

1,160

1

3.75

1250.850.61

61,007**

1

3.98

1294.22

0.81

7

5,54

1

3.64

1101.17

0.51

8

829

1

3.25

1084.56

-0.02

9

763*

0

3.03

939.54

-0.26

10

961

0

3.17

960.61

-0.18

11

372**

1

3.38

967.98

-0.07

12

297

1

3.64

1058.05

0.07

13

2,007

0

3.81

1124.35

0.47

14

371

1

3.67

1204.14

0.75

15

1,296

0

2.96

948.77

-0.23

16

395

0

3.58

1154.45

0.58

17

334

1

3.78

1263.91

0.73

18

1,029

0

3.29

1024.77

-0.14

19

3,444

0

3.75

1190.93

0.74

20

1,011

0

3.32

974.27-0.18

211,624**

03.51

1031.4

0.09

22

453

0

3.63

1044.3

0.26

23

652

1

4.05

1220.57

0.77

24

1,467

0

3.36

971.05

0.00

25

1,146

0

3.21

976.36

-0.17

26

1,210

0

3.07

941.07

-0.34

27

421**

1

3.33

1044.39

0.17

28

365*

1

3.57

1070.87

0.44

29

195

1

3.55

994.79-0.10

30

1,604**

03.73

1151.050.19

315,544**

03.82

1060.95

0.32

32

2,836*

03.49

1004.110.18

33

1,152*

1

3.71

1190.98

0.57

34

2,031

0

3.35

984.170.01

35

3,498*

03.81

1084.44

0.06

36

3,215

0

3.94

1107.6

0.46

37

1,898

0

3.42

1074.22

0.33

38

6,172

0

3.74

1084.36

0.41

39

1,507

1

3.57

1114.71

0.51

40

3,529

0

3.73

1097.510.28

411,136**

0

3.16

944.2

-0.10

Note: Single asterisk indicates the school provided data for the 1995 cohort only; double asterisk
indicates the school provided data for the 1995 and 1996 cohorts; no asterisk indicates the school
provided data for the 1995, 1996, and 1997 cohorts.

8

Table 3
Unrestricted and Restricted Means and Standard Deviations for SES by School
SchoolUnrestricted Mean

Unrestricted SD

Restricted Mean

Restricted SD

Ratio of Restricted toUnrestricted SD

1

0.28

0.90

0.06

1.01

1.12

2

-0.10

0.78

-0.16

0.82

1.05

3

0.46

0.87

0.33

0.95

1.09

4

0.24

0.90

-0.06

1.03

1.14

5

0.61

0.83

0.61

0.86

1.04

6

0.86

0.78

0.81

0.87

1.12

7

0.45

0.87

0.51

0.89

1.02

8

0.10

0.80

-0.02

0.87

1.09

9

-0.07

0.77

-0.26

0.82

1.06

10

0.03

0.81

-0.18

0.92

1.14

11

0.00

0.80

-0.07

0.90

1.13

12

0.18

0.73

0.07

0.75

1.03

13

0.34

0.84

0.47

0.81

.96

14

0.79

0.77

0.75

0.81

1.05

15

-0.09

0.76

-0.23

0.81

1.07

16

0.64

0.81

0.58

0.84

1.04

17

0.88

0.75

0.73

0.82

1.09

18

0.10

0.81

-0.14

0.83

1.02

19

0.69

0.84

0.74

0.84

1.00

20

-0.06

0.77

-0.18

0.85

1.10

21

0.22

0.80

0.09

0.89

1.11

22

0.27

0.81

0.26

0.84

1.04

23

0.54

0.86

0.77

0.85

.99

24

0.13

0.79

0.00

0.87

1.10

25

-0.06

0.78

-0.17

0.83

1.06

26

-0.21

0.73

-0.34

0.80

1.10

27

0.44

0.80

0.17

1.01

1.26

28

0.47

0.83

0.44

0.78

.94

29

0.02

0.85

-0.10

1.05

1.24

30

0.40

0.90

0.19

1.11

1.23

31

0.28

0.82

0.32

0.87

1.06

32

0.22

0.82

0.18

0.88

1.07

33

0.70

0.81

0.57

0.90

1.11

34

0.07

0.81

0.01

0.86

1.06

35

0.31

0.88

0.06

1.23

1.40

36

0.43

0.86

0.46

0.88

1.02

37

0.41

0.83

0.33

0.90

1.08

38

0.34

0.86

0.41

0.93

1.08

39

0.50

0.84

0.51

0.82

.98

40

0.41

0.82

0.28

0.93

1.13

41

-0.03

0.80

-0.10

0.85

1.06

.887

1.09

Mean

.297

.817

.212

9

Table 4
Unrestricted and Restricted Means and Standard Deviations for SAT Verbal andMathematics Scores by School

10

Unrestricted SD

Ratio of Restricted
to Unrestricted SD

SchoolUnrestricted Mean

Restricted Mean

Restricted SD

11082.972

951.11

162.701112.45128.480.79

122.591002.27

121.740.99

31134.71169.871180.80140.010.82

41090.71169.781101.45

142.380.84

51250.85

155.851309.76135.780.87

61294.22141.351395.23113.900.81

71101.17164.931157.91

144.670.88

81084.56

164.71

1072.07152.730.93

9939.54143.24955.86126.000.88

10960.61

162.16999.95145.760.90

11967.98160.151036.83

146.110.91

121058.05163.751097.62

166.171.01

131124.35

188.46

1264.14122.410.65

141204.14161.181223.75

141.680.88

15948.77143.58957.86131.850.92

161154.45150.801193.22139.450.92

171263.91147.16

1286.82

127.240.86

181024.77

163.121081.69

140.540.86

191190.93179.651228.74

139.270.78

20974.27148.551040.52

135.840.91

211031.40

163.361070.45

159.060.97

221044.30160.791120.24

143.490.89

231220.57

187.731393.44139.930.75

24971.05144.13983.75142.430.99

25976.36143.191022.29

127.440.89

26941.07144.39944.86141.980.98

271044.39163.811053.60

138.990.85

281070.87

151.721095.76129.290.85

29994.79172.491094.32

155.760.90

301151.05163.031182.56141.540.87

311060.95

164.791161.98

144.030.87

321004.11160.341036.53

147.130.92

331190.98

154.441251.14129.760.84

34984.17163.111081.00

129.270.79

351084.44200.021201.63138.660.69

361107.60

184.191196.20147.000.80

371074.22161.461103.09149.430.93

381084.36

182.151196.08

146.330.80

391114.71160.901166.32

122.610.76

401097.51

170.491144.63154.010.90

41944.20160.661006.26129.020.80

Mean1073.05

161.481126.95

139.000.86

Table 5
Unrestricted and Restricted Means and Standard Deviations for High School GPA by School
SchoolUnrestricted Mean

Unrestricted SD

1

3.28

2

3.10

3
4
5

3.69

6

3.81

Ratio of Restricted toUnrestricted SD

Restricted Mean

Restricted SD

0.54

3.32

0.44

0.81

0.57

3.21

0.53

0.93

3.45

0.55

3.52

0.50

0.91

3.37

0.55

3.41

0.46

0.84

0.50

3.75

0.45

0.90

0.42

3.98

0.31

0.74

7

3.47

0.59

3.64

0.47

0.80

8

3.35

0.62

3.25

0.59

0.95

9

3.00

0.58

3.03

0.52

0.90

10

3.07

0.62

3.17

0.56

0.90

11

3.17

0.62

3.38

0.61

0.98

12

3.54

0.57

3.64

0.49

0.86

13

3.52

0.59

3.81

0.40

0.68

14

3.62

0.54

3.67

0.46

0.85

15

2.97

0.57

2.96

0.53

0.93

16

3.51

0.54

3.58

0.50

0.92

17

3.64

0.48

3.78

0.40

0.84

18

3.11

0.57

3.29

0.47

0.83

19

3.60

0.54

3.75

0.40

0.74

20

3.12

0.58

3.32

0.48

0.83

21

3.34

0.60

3.51

0.51

0.86

22

3.41

0.57

3.63

0.48

0.84

23

3.81

0.47

4.05

0.32

0.69

24

3.30

0.56

3.36

0.52

0.92

25

3.09

0.58

3.21

0.53

0.91

26

3.07

0.62

3.07

0.59

0.95

27

3.37

0.58

3.33

0.53

0.92

28

3.47

0.57

3.57

0.47

0.82

29

3.41

0.58

3.55

0.55

0.95

30

3.54

0.50

3.73

0.37

0.73

31

3.55

0.54

3.82

0.41

0.76

32

3.43

0.56

3.49

0.52

0.92

33

3.59

0.54

3.71

0.49

0.90

34

3.19

0.62

3.35

0.55

0.88

35

3.50

0.57

3.81

0.41

0.71

36

3.57

0.57

3.94

0.36

0.64

37

3.35

0.57

3.42

0.51

0.90

38

3.54

0.54

3.74

0.45

0.83

39

3.48

0.53

3.57

0.44

0.82

40

3.50

0.55

3.73

0.41

0.75

41

3.03

0.57

3.16

0.50

0.88

Mean

3.39

0.56

3.52

0.48

0.85

11

Table 6
Means and Standard Deviations for Freshman
GPA by School

12

SchoolMean Freshman
GPA

SD of Freshman
GPA

1

2.67

0.77

2

2.45

0.75

3

2.87

0.62

4

2.69

0.73

5

3.01

0.71

6

3.22

0.42

7

2.88

0.57

8

2.60

0.88

9

2.35

0.83

10

2.35

0.91

11

2.62

0.78

12

3.08

0.62

13

2.69

0.77

14

3.12

0.59

15

2.15

0.89

16

3.00

0.57

17

3.27

0.44

18

2.45

0.79

19

3.08

0.57

20

2.67

0.68

21

2.77

0.69

22

3.08

0.59

23

3.30

0.51

24

2.28

0.88

25

2.52

0.73

26

2.48

0.82

27

2.81

0.57

28

3.05

0.50

29

2.83

0.72

30

2.99

0.57

31

2.74

0.71

32

2.58

0.89

33

2.92

0.62

34

2.53

0.91

35

3.04

0.51

36

2.90

0.63

37

2.84

0.64

38

2.89

0.79

39

2.97

0.56

40

3.07

0.52

41

2.50

0.74

Mean

2.79

0.68

Table 7
Summary of Mean SES–Test, SES–Grade, and Test–Grade Correlations Across Schoolsr SES–Testr SES–Gradesr Test–Gradesr Test–Grades
Controlling forSES

r SES–Grades
Controlling for
Test

Uncorrected

0.22

0.12

0.35

0.33

0.04

Corrected for SchoolSpecific Range
Restriction

0.31

0.19

0.47

0.44

0.05

Corrected for National
Population Range
Restriction

0.42

0.22

0.53

0.50

-0.01

Table 8
Summary of Mean SES–High School GPA, SES–College Grade, and High School GPA–College
Grade Correlations Across Schoolsr SES-HSGPAr SES-College Grades r HSGPA- College
GradesrHSGPA-College
Grade Controllingfor SESrSES-College Grade
Controlling for
HSGPA

Uncorrected

-0.01

0.12

0.40

0.40

0.14

Corrected for SchoolSpecific Range
Restriction

0.11

0.19

0.51

0.50

0.16

Corrected for National
Population Range
Restriction

0.19

0.22

0.58

0.56

0.13

13

Appendix
Correlations and Partial Correlations Among SAT, College GPA, HSGPA, and SES by School
SES–Grade

SES–SATHSGPA–SATHSGPA–
Grade

0.20

0.05

0.26

0.28

0.35

0.28

0.04

0.14

0.30

0.42

School

SAT–Grade

1
2

SAT–
Grade.
SES

SES–
Grade.
SATHSGPA–
Grade.
SES

0.03

0.19

0.00

0.35

-0.09

0.28

0.00

0.43

SES–
HSGPA

3

0.44

0.05

0.14

0.40

0.44

-0.06

0.44

-0.01

0.45

4

0.24

0.08

0.27

0.32

0.35

0.06

0.23

0.02

0.35

5

0.23

0.10

0.20

0.32

0.30

-0.07

0.22

0.06

0.31

6

0.35

0.21

0.34

0.18

0.30

0.01

0.30

0.10

0.31

7

0.49

0.16

0.21

0.43

0.54

0.03

0.47

0.07

0.54

8

0.26

0.09

0.09

0.34

0.41

-0.03

0.25

0.07

0.42

9

0.27

0.06

0.17

0.30

0.42

-0.02

0.26

0.02

0.42

10

0.26

0.07

0.22

0.24

0.41

-0.04

0.25

0.01

0.41

11

0.48

0.05

0.05

0.42

0.58

-0.04

0.48

0.03

0.58

12

0.47

0.18

0.18

0.47

0.61

0.08

0.45

0.11

0.61

13

0.32

0.13

0.16

0.23

0.40

-0.02

0.31

0.08

0.40

14

0.41

0.07

0.20

0.36

0.49

-0.03

0.41

-0.01

0.50

15

0.30

0.09

0.16

0.32

0.44

-0.02

0.29

0.05

0.45

16

0.40

0.04

0.13

0.37

0.41

-0.14

0.40

-0.01

0.42

17

0.30

0.09

0.14

0.28

0.36

-0.08

0.29

0.05

0.37

18

0.31

0.08

0.11

0.34

0.42

-0.01

0.30

0.05

0.42

19

0.34

0.20

0.27

0.30

0.31

0.05

0.30

0.12

0.31

20

0.38

0.13

0.20

0.30

0.45

0.01

0.36

0.06

0.45

21

0.42

0.12

0.22

0.39

0.46

0.04

0.41

0.03

0.46

22

0.37

0.08

0.11

0.42

0.48

-0.04

0.37

0.04

0.49

23

0.46

0.26

0.34

0.41

0.35

0.01

0.41

0.12

0.36

24

0.30

0.10

0.17

0.23

0.41

-0.07

0.29

0.05

0.42

25

0.30

0.08

0.18

0.32

0.47

-0.05

0.29

0.03

0.48

26

0.31

0.10

0.14

0.42

0.46

0.03

0.30

0.06

0.46

27

0.46

0.06

0.18

0.37

0.50

-0.02

0.46

-0.03

0.50

28

0.47

0.02

0.05

0.38

0.51

0.01

0.47

-0.00

0.51

29

0.41

0.07

0.20

0.38

0.45

-0.03

0.41

-0.01

0.45

30

0.34

0.13

0.42

0.36

0.33

0.16

0.32

-0.01

0.32

31

0.41

0.17

0.21

0.29

0.41

0.00

0.39

0.09

0.41

32

0.38

0.12

0.18

0.36

0.46

-0.03

0.37

0.06

0.47

33

0.37

0.06

0.08

0.32

0.43

-0.09

0.37

0.03

0.44

34

0.20

0.07

0.10

0.26

0.39

-0.02

0.20

0.05

0.39

35

0.41

0.25

0.42

0.33

0.38

0.10

0.35

0.09

0.37

36

0.46

0.26

0.35

0.24

0.35

-0.03

0.41

0.12

0.37

37

0.36

0.09

0.19

0.30

0.44

-0.03

0.35

0.02

0.45

38

0.40

0.14

0.27

0.26

0.40

-0.01

0.38

0.04

0.41

39

0.42

0.03

0.12

0.33

0.46

-0.06

0.42

-0.02

0.46

40

0.39

0.16

0.29

0.31

0.39

0.04

0.37

0.05

0.39

41

0.31

0.08

0.19

0.32

0.40

-0.03

0.30

0.02

0.41

Note: Tabled values are correlations. Values with labels of the form “SAT–Grade.SES” are partial correlations, i.e., the correlation
between SAT and grades controlling for SES.

14

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