UNIVERSITY OF CALIFORNIA, ACADEMIC SENATEBERKELEY • DAVIS • IRVINE • LOS ANGELES • MERCED • RIVERSIDE • SAN DIEGO • SAN FRANCISCO

Kum-Kum Bhavnani
Telephone: (510) 987-9303
Email:kum-kum.bhavnani@ucop.edu

SANTA BARBARA • SANTA CRUZ

Chair of the Assembly of the Academic Senate
Faculty Representative to the Regents
University of California
1111 Franklin Street, 12th Floor
Oakland, California 94607-5200

February 3, 2020

CHAIRS OF SENATE DIVISIONS AND COMMITTEES:
Re: Systemwide Review of the Report of the Academic Council’s Standardized Testing Task
Force (STTF)
Dear Colleagues,
The UC Academic Senate, at President Napolitano’s request, has been conducting an
examination of undergraduate admissions to the University of California, in accordance with itsdelegated authority over admissions.
The Academic Council’s Standardized Testing Task Force (STTF) has been meeting over the
past year to carry out the work of offering policy recommendations on this complex issue. I am
pleased to forward for systemwide Academic Senate review the attached STTF report andrecommendations.
Please submit comments to the Academic Senate office at SenateReview@ucop.edu by March 23,
2020 to allow us to compile and summarize comments for the Academic Council’s April 1
meeting. As always, any committee that considers these matters outside its jurisdiction or charge
may decline to comment.
I think you will agree that the report offers a thorough, critical, and empirically based picture of
the role of standardized testing in UC admissions. We expect the systemwide review to bethoughtful and robust. Based on your feedback, we intend to issue a final set of policy
recommendations to the Assembly of the Academic Senate in April 2020, and then to President
Napolitano for transmittal to the Regents.
Please do not hesitate to contact me if you have additional questions.
Sincerely,

Kum-Kum Bhavnani, Chair
Academic Councilcc:

Academic Council
Senate Directors

UNIVERSITY OF CALIFORNIA

BERKELEY • DAVIS • IRVINE • LOS ANGELES • MERCED • RIVERSIDE • SAN DIEGO • SAN FRANCISCO

STANDARDIZED TESTING TASK FORCE (STTF)
Eddie Comeaux and Henry Sánchez, Co-Chairs

SANTA BARBARA • SANTA CRUZ

Assembly of the Academic Senate
1111 Franklin Street, 12th Floor
Oakland, CA 94607-5200

January 27, 2020
KUM-KUM BHAVNANI, CHAIR, ACADEMIC COUNCIL
Dear Kum-Kum:
Please find attached the final report of the Academic Council’s Standardized Testing Task Force(STTF).
In January 2019, at the request of President Napolitano, Senate Chair Robert May empaneled the
STTF to examine the University’s current use of standardized testing for admission and consider
whether the University and its students are best served by UC’s current testing practices, amodification of current practices, another testing approach, or the elimination of testing. Chair
May asked the STTF to develop a set of actionable recommendations to BOARS and theAcademic Council.
The STTF and its Writing Subcommittee consulted dozens of studies concerning standardized
tests, their predictive value, and their impact on access and diversity. It met with the national
testing agencies, critics of standardized testing, State education leaders, UC campus admissions
officers, UCOP institutional researchers, BOARS, and other UC-based and non-UC content
experts. It examined the effectiveness of UC’s current comprehensive review and “9 x 9”
admissions policies, and considered the potential benefits and drawbacks of eliminating thetesting requirement or moving to a “test optional” policy. It also considered potential alternative
assessment tools, as well as alternative tests that might be more effective in meeting UC’s goals
around academic success and diversity.
The STTF engaged in complex and principled conversations about how to define and measureacademic preparedness without contributing to preexisting disparities in educational opportunity
along lines of race and class. It assessed how to weigh the goal of accurate prediction of UC
performance against other, potentially competing goals, such as educating future leaders,
representing the diversity of California, promoting socioeconomic mobility, and countering
inequality. It investigated concerns about the potential correlation between race and SES status,on the one hand, and standardized test scores, on the other, as well as the impact of school
segregation and resource inequality in California schools on UC applicants. These discussions
revealed the complexity of the task of selecting students who should be admitted to UC,
consistent with the goals of ensuring preparedness, pursuing diversity, and supporting
opportunity, access, and fairness. The STTF and its subcommittee requested many additional

analyses, including regressions and simulations of the effects of various possible reforms, toassist in the development of recommendations.
The resulting report and recommendations, grounded in evidence-based research and UC values,
reflects the high quality of work one expects of UC faculty. The STTF co-chairs would like torecognize the hard work and dedication exhibited by the entire Task Force over the past year,
and in particular the efforts of the STTF Writing Subcommittee (Chair Jonathan Glater, Julian
Betts, Li Cai, Patricia Gandara, Andrea Hasenstaub, and George Johnson), whose extraordinaryand sustained dedication made this report possible. The Subcommittee conducted original
analyses of new data, endured long and intense conversations about the analyses andrecommendations—including over the winter holiday season, on weekends, while on family
vacation, and after hours—and relentlessly kept at the task of writing to meet accelerated
deadlines of completing the report.
We also would like to thank Robert May for empaneling an outstanding group of UC faculty,
graduate student representative, and Academic Senate staff. Finally, we thank you, Kum-Kum,for your leadership in guiding the Task Force through a set of challenging deadlines. We believe
the attached report and appendix represents a living document to help frame actionable next steps
to advance the University of California missions of access, opportunity, diversity, inclusion, andfairness.
Sincerely,

Henry Sánchez,
STTF Co-Chair

Eddie Comeaux,
STTF Co-Chair

Report of the UC Academic Council
Standardized Testing Task Force(STTF)

January 2020
Systemwide Academic Senate
University of California

Table of Contents
Executive Summary ...................................................................................................................................... 3
I.

Introduction........................................................................................................................................... 8II.

How UC Uses Standardized Tests in Admissions .............................................................................. 14

III. Rationale for Current Use of Standardized Tests ............................................................................... 17
IV. Concerns Over Use of Standardized Tests in UC Admissions ........................................................... 63
V.

Questions & Answers ......................................................................................................................... 78

VI. Reforms the Task Force Considered but Does Not Recommend ....................................................... 89
The Task Force does not recommend adopting Smarter Balanced Assessment in lieu of currently
used standardized tests. ....................................................................................................................... 89
The Task Force does not at this time recommend that UC make standardized tests optional for
applicants ............................................................................................................................................ 95
VII. Recommendations of the Task Force.................................................................................................. 99
Recommendation 1. Review and update components of the statewide eligibility index. ................. 100
Recommendation 2. Expand Eligibility in the Local Context. ......................................................... 100
Recommendation 3. Further analyze factors contributing to disproportionate representation. ........ 105
Recommendation 4. Study and expand student academic support services. .................................... 105
Recommendation 5. Obtain data to perform item-level analysis of current standardized tests. ....... 107
Recommendation 6. Develop a new assessment that will be continuously accessible to students andthat will assess a broader array of student learning and capabilities than any of the currently available
tests. .................................................................................................................................................. 109
VIII. List of Appendices ........................................................................................................................... 118

2

Executive Summary
In July 2018, President Napolitano wrote to 2017–18 Academic Senate Chair Shane White asking theAcademic Senate to examine the current use of standardized testing for admission to the University ofCalifornia (UC or the University); review the testing principles developed in 2002 1 by the Board of
Admissions and Relations with Schools (BOARS) and revised by BOARS in 2010 2; and determine
whether any changes in policies on use of test scores are needed.
In early 2019, 2018–19 Senate Chair Robert May empaneled an eighteen-member Standardized Testing
Task Force (STTF) to consider whether the University and its students are best served by UC’s current
testing practices, a modification of current practices, another testing approach, or the elimination of
testing. Chair May asked the STTF to develop a set of actionable recommendations to the Academic
Council; to approach its work analytically, without prejudice or presupposition; and to consult withexperts from a broad range of perspectives. The STTF met 12 times between February 2019 and January
2020, and empaneled a six-member subcommittee to draft specific recommendations.
Chair May’s charge to the STTF identified the following questions:
•
•
•
•
•

How well do UC’s current standardized testing practices assess entering high school students for
UC readiness?
How well do UC current standardized testing practices predict student success in the context ofits comprehensive review process?
Do standardized testing assessments fairly promote diversity and opportunity forstudents applying to UC?
Does UC’s use of standardized tests increase or contract the eligibility pool compared to two
other possibilities: 1) de-weighting standardized tests; or 2) eliminating the testing requirement?
Should UC testing practices be improved, changed, or eliminated?

By way of background, the University admits students through a two-stage process: first, a determinationof eligibility for the University overall, then selection by a specific campus using comprehensive review.
Test scores are used to establish eligibility for some (but not all) students and are also used in admissions
decisions. Some campuses also use test scores for purposes other than admissions, such as awarding ofscholarships, placement in classes, identification of students who might benefit from extra support, andadmissions to honors programs. Standardized test scores tend to exhibit differences along lines of raceand class, with students who belong to many of the demographic groups historically excluded fromeducational (and other) opportunities on average receiving lower scores.
How well do UC’s current standardized testing practices assess entering high school students for UC
readiness? How well do UC current standardized testing practices predict student success in the context
of its comprehensive review process?
The STTF found that standardized test scores aid in predicting important aspects of student success,including undergraduate grade point average (UGPA), retention, and completion. At UC, test scores are
currently better predictors of first-year GPA than high school grade point average (HSGPA), and about as
good at predicting first-year retention, UGPA, and graduation. 3 For students within any given (HSGPA)
band, higher standardized test scores correlate with a higher freshman UGPA, a higher graduation UGPA,
1

https://senate.universityofcalifornia.edu/_files/committees/boars/admissionstests.pdf
https://senate.universityofcalifornia.edu/_files/reports/hp2mgy_boars-testing_010609.pdf
3UCOP, Relationship of the SAT/ACT to College Performance at the University of California (2019).
2

3

and higher likelihood of graduating within either four years (for transfers) or seven years (for freshmen).
Further, the amount of variance in student outcomes explained by test scores has increased since 2007,
while variance explained by high school grades has decreased, although altogether does not exceed 26%.
Test scores are predictive for all demographic groups and disciplines, even after controlling for HSGPA.
In fact, test scores are better predictors of success for students who are Underrepresented Minority
students (URMs), who are first-generation, or whose families are low-income: that is, test scores explain
more of the variance in UGPA and completion rates for students in these groups. One consequence ofdropping test scores would be increased reliance on HSGPA in admissions. The STTF found that
California high schools vary greatly in grading standards, and that grade inflation is part of why thepredictive power of HSGPA has decreased since the last UC study.
Do standardized testing assessments fairly promote diversity and opportunity for students applying to
UC? Does UC’s use of standardized tests increase or contract the eligibility pool compared to two otherpossibilities: 1) de-weighting standardized tests; or 2) eliminating the testing requirement?
The racial and ethnic makeup of the population admitted to the University differs significantly from that
graduating from California high schools. In 2019, only 37% of the California resident students in theadmitted freshman class, and 26% of all admitted students, were URMs, defined as Latino, AfricanAmerican, and Native American students. Approximately 59% of California high school graduates wereURMs. Further, the 37% (or 26%) are not distributed evenly across campuses; at Berkeley, for example,
only 26% of admitted California freshmen (and only 18% of the admitted students overall) were URMs.
The difference in fraction of URMs between the high school student population and the undergraduate
population is a matter of concern. The Task Force was concerned about the extent to which standardized
tests may contribute to this underrepresentation of URMs at UC.
Analysis of admissions results by the Task Force concluded that UC takes into account students’ contexts
when evaluating test scores. Applicants from less advantaged demographic groups are admitted at higher
rates for any given test score as a result of comprehensive review, which is a process that evaluatesapplicants’ academic achievements in light of the opportunities available to them and takes into
consideration the capacity each student demonstrates to contribute to the intellectual life of the campus.
The point was not what the STTF expected before we commenced our data analysis. Task Force members
took note of the average differences in test scores among groups and expected to find that test score
differences explain differences in admission rates. That is not what we found. Instead, the STTF foundthat UC admissions practices compensated well for the observed differences in average test scores among
demographic groups. This likely reflects UC’s use of comprehensive review, as well as UC’s practice ofreferencing each student’s performance to the context of their school.
The Task Force found that test scores appear to receive less weight in admission than high school grades:
for example, for every campus, the admit rate is much higher for applicants with high HSGPA and low
SATs than for those with high SATs and low HSGPAs. On average, disadvantaged and URM students
applied to UC with HSGPAs significantly lower than the HSGPAs of more advantaged groups, and theUC admissions process did not appear to compensate for HSGPA as it does for test scores.
The Task Force found that of the 22,613 students guaranteed admission through the statewide index (theadmissions pathway in which test scores can compensate for lower HSGPA) but not through Eligibility inthe Local Context (ELC), which only considers high school grades, about 25% were members ofunderrepresented minority groups, and 47% were low-income or first generation students. These students
would not have been guaranteed admission on the basis of their grades alone.
The Task Force examined the pipeline for high school seniors for the class of 2018 to the admitted pool offreshmen that year. There was a 22-percentage point gap between the share of URMs in the grade 12 class4and the pool of California resident students admitted by UC. The Task Force concluded that multiple
factors contribute to this underrepresentation at UC. Roughly 75% of the opportunity gap arises fromfactors rooted in systemic racial and class inequalities that precede admission: lower high school
graduation rates for URMs, lower rates of completion of the A-G courses required by UC and CSU, andlower application rates. The most significant contributor was lack of eligibility as a result of failure tocomplete all required A-G courses with a C or better. Roughly 25% of underrepresentation was due to UC
admissions decisions overall. Test scores play a role in those decisions, and thus account for some of that
25%, even if they are not the primary barrier to admission. It is also unknown to what extent low scoreson the standardized tests deter some students from even submitting applications.
In sum, mean differences in standardized test scores between different demographic groups are often verylarge, and many of the ways these tests could be used in admissions would certainly produce strong
disparate impacts between groups. However, UC weights test scores less strongly than GPA, andcomprehensive review appears to help compensate for group differences in test scores. The distributionsof test scores among applicants are very different by group, but the distributions of test scores among
admitted students are also very different by group, and in almost exactly the identical way. The Task
Force did not find evidence that UC’s use of test scores played a major role in worsening the effects ofdisparities already present among applicants and did find evidence that UC’s admissions process helped
to make up for the potential adverse effect of score differences between groups.
Yet this is not to conclude that consideration of test scores does not adversely affect URM applicants. If
standardized test scores must be compensated in order to achieve the entering class sought by UC, that isreason to question whether it is necessary to use the tests at all, and/or whether it is possible to design analternative instrument that does not require such compensation. UC admissions practices do not fully
make up for disparities that persist along lines of race and class. Whether these disparities arise from testscores, GPA, or others among the 14 factors that comprise comprehensive review at UC, the outcome of
UC admissions processes is that many of the populations historically excluded from opportunity are still
underrepresented by wide margins. Some members of the Task Force emphasized UC’s responsibility toassist disadvantaged and URM students who attend schools with lesser resources than those attended by
students from affluent families, and they worried that continuing to use tests would help preserve thestatus quo. These members contended that the University has an obligation to interrupt perpetuation of
inequality, especially when the state played a historic role in creating it, and must do more to serve thestate’s aspiring college students more equitably.
Should UC testing practices be improved, changed or eliminated?
In reaching its recommendations, the Task Force weighed both the importance of admitting students most
likely to succeed at UC and the importance of enrolling classes representative of California’s diversity.
STTF members had varying perspectives on the relative importance of these goals. Members of the Task
Force were also aware of the public scrutiny of admissions practices generally and consideration ofstandardized test scores in particular. The Task Force carefully considered the relative risks and benefitsof possible steps that UC could take to ensure that the population of admitted students reflects thediversity of the state.
The STTF produced the following consensus recommendations, offered below with brief explanations:
1) Review and update components of the statewide index used to identify UC-eligible students. While
the University admits students to campuses based on 14 factors, we base systemwide eligibility on
only two — HSGPA and standardized test scores. The Task Force recommends that BOARS consider
whether, among the 14 factors, there are additional, quantitative features that could be included in thestatewide eligibility index.

52) Expand the ELC pathway, which admits students in the top 9% of each school based on HSGPA
alone, to admit more than the top 9%, so as to increase access and representation, while leaving thestatewide index formula unchanged. This would result in growth in the student body, but would avoid
likely opposition that would result if students who would have been admitted under the current
statewide eligibility rules are excluded as a result of shrinking the statewide pool to accommodate
growth in the ELC pool. In addition, allowing growth in the undergraduate population that would
come from expanding ELC would send a signal to Californians about the high value that UC places
on becoming more representative of the state over time.
3) Undertake further analysis of the admissions process to identify those points in the process and thefactors at work at each point that contribute to disproportionate declines in representation of students
who belong to populations that have historically been excluded from higher education opportunity.
This research matters because the Task Force’s preliminary analysis indicates that disparities alonglines of race, ethnicity, and socioeconomic status (SES) evident in the undergraduate population of
the University are a function of multiple factors, and that the SAT and ACT are smaller contributors.
For example, disparities in access to and completion of A-G courses account for a disproportionate
lack of UC eligibility for students who are members of underrepresented groups. Differences in A-G
completion rates across schools also reduce the yield of students through ELC. This study may informnew reforms to the admissions process to achieve greater equity in access to UC.
4) Study and expand student academic support services. A multi-pronged effort to study, fine-tune andexpand the system of academic and socio-emotional supports that UC provides to its undergraduate
students would begin by identifying and reporting on existing programs, then proceed to evaluate
them. UC would then be in position to design and evaluate new support programs implementing bestpractices, as needs are identified. The Task Force believes this effort will promote University efforts
to improve important measures of student success, including undergraduate GPA and likelihood of
graduation, for students who are members of groups historically excluded from and underrepresented
on college campuses, and who might benefit from support to excel in UC classes.
5) Obtain the necessary item-level data to perform an updated analysis of potential bias in current
standardized tests. With the most recent operational student-level data from College Board and ACTin the form of raw item responses, as well as necessary applicant and school background information
as they are available, UC can attempt to provide a definitive answer to the question of whether thetests and the items making up the tests are biased against members of particular subgroups of testtakers.
6) Develop a new assessment system that will be continuously accessible to students and that will assessa broader array of student learning and capabilities than any of the currently available tests. This newassessment system should provide more detailed feedback to schools and to students, allow students
to learn the item types through multiple opportunities to practice, and align programs in
Recommendation 4 to be targeted to students’ learning needs. The new assessment may enable UC toadmit classes of students more representative of the diversity of the state. A new system should allow
the measurement of student progress over time and development of a broader range of skills andattributes relevant to an applicant’s success at UC, reflecting the principle that the University "should
select those students who demonstrate a strong likelihood that they will persist to graduation." Inparticular, based on the research reviewed, the Task Force believes that UC will be able to articulate a
set of skills and attributes that a) can be measured in a uniform and quantitative way, b) are likely tobe predictive of success at UC, and c) could potentially show smaller disparities than current
measures along the lines of race, ethnicity, and SES. The tests should continue to evaluate math andreading achievement due to their foundational importance in college and career success. The formats

6

of the tasks on the new assessments can and should also take full advantage of technological advances
to let the aspiring applicants exercise a wider range of knowledge, skills, and reasoning than multiplechoice questions do.
The timeline for development of a new suite of assessments would put widespread implementationnine years out. Members of the Task Force differed on the question of whether to recommend that UC
cease consideration of standardized test scores sooner — in all likelihood before availability of thereplacement suite of assessments. The differences reflected uncertainty within the Task Force, given
the data available to us, over the relative risks and benefits of possible, additional steps that UC
should take to ensure that the population of admitted students reflects the diversity of the state.
The Task Force considered, but does not recommend, the following possibilities.
7) The Task Force does not recommend that UC make standardized tests optional for applicants at thistime. UC should conduct additional research on the impact of going “test optional” before deciding
whether and how to implement such a policy. STTF had pragmatic concerns about how campuses
would evaluate and compare applicants who submit standardized test scores relative to applicants
who do not; whether and how campuses would impute, explicitly or implicitly, test scores toapplicants; and ethical concerns about how to treat students in the two groups fairly. The current UC
admissions practice of putting each applicant’s test scores into context by comparing them to allapplicants from the same school, thus allowing readers to identify students who performed
exceptionally well given available opportunities, could no longer be used if students could choose
whether or not to submit their test scores. Finally, given the state of research and UC’s size andunique system of “in the context” review, it was not clear that going “test-optional” would increase
diversity of incoming classes, and the likely impact on preparation of incoming classes was not clear.
8) The Task Force does not recommend adopting the Smarter Balanced (SBAC) Assessment in lieu ofcurrently used standardized tests. The Task Force was deeply concerned with a wide range of risks
related to the adoption of the SBAC to meet UC admissions requirements, including (but not limitedto) test security, item exposure control, and item bank size, as well as inconsistent implementation of
the SBAC across states, ambiguity regarding instructional validity, and the likelihood that this change
would reduce the utility of SBAC for its main purpose.

7

I.

Introduction

The University of California (“UC” or “the University”) faces a crisis of legitimacy around its
undergraduate admissions processes. On one hand, some believe that the prior academic achievements ofentering undergraduates affect UC’s institutional excellence and that students’ scores on standardized
admissions tests – the SAT and ACT – reflect academic achievement. Consequently, for this group,
abandoning consideration of test scores in admissions risks compromising the legitimacy of the
University’s claim to excellence. On the other hand, some believe that continued consideration ofstandardized test scores undermines institutional legitimacy from a different perspective: that by using
these tests, on which students who belong to historically excluded groups receive lower scores, theUniversity is complicit in perpetuating existing, longstanding inequities in educational opportunity.
The debate over the admissions process consequently implicates additional debates over what it means tobe an excellent institution – i.e., are admitted students’ standardized test scores critical to excellence – andwhat it means to be a public institution. We believe that the path to reconciliation rests on development ofa definition of excellence that enables the University both to assert academic preeminence and to use its
powerful role to interrupt inequality of educational opportunity along lines of race, class, and ethnicity.
We believe that the role of a public university is not simply to identify smart students but also to develop
informed citizens who will contribute to the common good.
The Organic Act of 1868 establishing the University of California included a set of principles to guide theinstitution. Among these was a definition of whom it would serve:“[It] shall be the duty of the regents, according to population, to so apportion the representation of
students, when necessary, that all portions of the State shall enjoy equal privilege therein.”(Organic Act, 1868, Sec. 14)
To some extent UC has been grappling with this principle ever since. Its founders could not haveimagined how the state and the University would change over time or how “all portions of the state”
might come to be re-defined. In the nearly 60 years since the Master Plan for Higher Education4 wasestablished, limiting eligibility for UC to the top 12.5% of California high school graduates, the state’s
population has changed dramatically. In 1960, California’s population was overwhelmingly white andEnglish speaking and immigration was at an historic low point. Income inequality was also at a low point
and California enjoyed a reputation for having some of the nation’s best schools. California had invested
heavily in both its K-12 and higher education systems. The College Board’s SAT, and later the ACT,
were incorporated into UC’s admissions requirements in part to determine the eligible pool of students
and in part to standardize and establish a common metric for all applicants. However, as the California
population has become increasingly diverse, the University has come under greater pressure to ensure thatthe students it admits reflect the state’s high school graduates with respect to race, ethnicity, and socioeconomic status (SES). These student characteristics were not considered in the language of the 1960
Master Plan.
In 2017, the most recent year for which we have data, 61% of California high school graduates were
underrepresented minorities (URM), a category that UC defines as students who are African-American,
Native American, and Latino) while just 31% of freshman enrollees to UC were URMs. 5 At Berkeley,fewer than 18% of freshman enrollees were URMs, and at UCLA, fewer than 28% were. URM students
45

https://www.ucop.edu/acadinit/mastplan/MasterPlan1960.pdf
https://www.universityofcalifornia.edu/infocenter/admissions-residency-and-ethnicity

8are enrolled disproportionately at the University’s less competitive campuses. These disparities across UC
campuses have raised questions about fairness among some critics.
There are reasons unrelated to standardized tests that some URM students are not eligible to attend UC,
and other hurdles to admission contribute to their disproportionate exclusion. But the tests play a role ineligibility and admissions decisions. It is ironic that the SAT was developed during the early part of thelast century for the purpose of identifying intellectually talented male students without ties to the nation’s
elite families, or who resided outside the closed circle of prep schools in the Northeast. 6 This allowed
elite colleges to enroll a somewhat more diverse student body. Today, some critics assert that use of thetest scores has the opposite effect.
A recent nationwide scandal that involved wealthy families paying for fraudulent test scores on
standardized admission tests brought further scrutiny of the use of these tests in admissions. It alsobrought an outcry against the tests from many quarters; some University Regents, state legislators,
community groups and public interest lawyers have demanded an end to their use.7 Several UC leaders
have weighed in on the issue. Amidst these concerns, as well as concern over excessive testing in K-12
schools and recognition that admissions tests measure only a fraction of the skills and knowledge students
are likely to require in college and later life, a growing number of colleges and universities has abandoned
the SAT and ACT test requirements or gone test-optional. The University of Chicago’s decision in 2018
to make the SAT and ACT tests optional has been particularly noteworthy as it, like UC, is very selective.
It is also noteworthy, however, that the debates over the use of standardized tests in undergraduate
admissions is longstanding and dates back to the mid-1960s. 8
In July 2018, UC President Janet Napolitano asked the Academic Senate to undertake a study to “examine
the current use of standardized testing for UC undergraduate admission; review the testing principles
developed in 2002 and revised in 2010; and determine whether any changes in admission testing policies
or practices are necessary to ensure that the University continues to use standardized tests in theappropriate way.” At the September 2018 meeting of the University of California Regents, Robert May,
then chair of the Academic Senate, accepted the request and in January 2019 laid out the charge to theStandardized Testing Task Force (STTF). The charge included “examin[ing] how well our current
standardized testing is working, how well it predicts student success in the context of our comprehensive
review process for admissions; could it be improved, changed or eliminated; and is it fair and unbiased forthe diversity of students applying to UC?” 9
The work of the STTF is guided in part by existing University policies. In May 1988, the UC Regentsaffirmed its position on undergraduate admissions in Policy 2102, which has remained in force to thepresent. It includes the following text:
The entrance requirements established by the University follow the guidelines set forth in the
Master Plan, which requires that the top one-eighth of the state's high school graduates, as well as
those transfer students who have successfully completed specified college work, be eligible foradmission to the University of California. These requirements are designed to ensure that all
eligible students are adequately prepared for University-level work.
6

Lemann, N. (1999). The Big Test. The Secret History of the American Meritocracy. New York: Farrar, Straus andGiroux7
Watanabe, T. (2019). Put to the test, GPA and SATs may not tell all, Los Angeles Times (December 22, 2019), at
A1.
8
Douglass, J.A. (2004). The Conditions of Admission. Access, Equity and the Social Contract of Public
Universities. Palo Alto: Stanford University Press.
9
https://senate.universityofcalifornia.edu/_files/committees/sttf/standardized-testing-tf-charge.pdf

9Mindful of its mission as a public institution, the University of California has an historiccommitment to provide places within the University for all eligible applicants who are residents
of California. The University seeks to enroll, on each of its campuses, a student body that, beyond
meeting the University's eligibility requirements, demonstrates high academic achievement or
exceptional personal talent, and that encompasses the broad diversity of cultural, racial,
geographic, and socioeconomic backgrounds characteristic of California.
Because applicant pools differ among the campuses of the University, each campus shall establish
procedures for the selection of applicants to be admitted from its pool of eligible candidates. Suchprocedures shall be consistent with the principles stated above and with other applicable
University policies. 10
The loss of affirmative action in UC admissions policy, first in 1995 by a vote of the Regents on Special
Policy 1 (SP-1), and then in 1996 by a state-wide referendum, 11 dealt a blow to the University’s ability toadmit a diverse class from which UC has never fully recovered. 12 An important tool for diversity wasremoved, which put greater pressure on the system to find new ways to support diversity in admissions.
This is the third time in almost two decades that the University of California has undertaken a review of
its admissions procedures to ensure fairness to all applicants. The most recent studies were undertaken by
the Board of Admissions and Relations with Schools (BOARS) in 2002 and in 2010. In 2002, BOARS
concluded unanimously that “admissions tests serve a useful purpose in helping both to determine UC
eligibility and to select applicants for admission to campuses that cannot accommodate all UC-eligible
applicants. While the grade point average (GPA) in UC-approved college preparatory courses taken inhigh school is clearly the best indicator of likely success, standardized tests can provide additional
information that is helpful in illuminating the high school record …” (BOARS, 2002, executive summary,p. 2). 13 In this report, BOARS also articulated a set of principles to be applied to all admissions tests.
BOARS wrote:
1. Admissions tests will be used at the University of California• To assess academic preparation and achievement of UC applicants;• To predict success at UC beyond that predicted by high school GPA;
• To aid in establishing UC eligibility; and• To aid in selecting students for admission at individual UC campuses.
2. The desired properties of admissions tests to be used for these purposes include the following:• An admissions test should be a reliable measurement that provides uniform assessment andshould be fair across demographic groups.
• An admissions test should measure levels of mastery of content in UC-approved high school
preparatory coursework and should provide information to students, parents, and educators
enabling them to identify academic strengths and weaknesses.
• An admissions test should be demonstrably useful in predicting student success at UC andprovide information beyond that which is contained in other parts of the application. (It is
recognized that predictors of success are currently limited, and generally only include first-

10

https://regents.universityofcalifornia.edu/governance/policies/2102.html
Proposition 209.
12
Kidder, W. & Gándara, P. (2015). Two Decades After the Affirmative Action Ban: Evaluating the University ofCalifornia’s Race-Neutral Efforts. Princeton, NJ: Educational Testing Service.
13
https://senate.universityofcalifornia.edu/_files/committees/boars/testsummary.pdf
11

10

•

year college GPA and graduation rate. As this field advances, better predictors should be
identified and used in validating admissions tests.)
An admissions test should be useful in a way that justifies its social and monetary costs.

BOARS also recommended that, as a matter of principle, the faculty regularly review UC’s admissions
testing policy and practices to ensure that tests are being used in a way that is consistent with theseprinciples and desired properties of admissions tests. In 2003, the UC Regents accepted the BOARS
recommendations, and College Board and ACT revamped their tests to better align with high school
subject matter.
In 2010, BOARS reaffirmed the testing principles and recommended that the Regents remove theprovisional status of the SAT-R (the revised SAT designed to better align with high school subject
matter). BOARS also noted that it planned “to continue to study and monitor the use of tests in UC
admissions, revise the testing principles, and determine whether alternative paths are possible to diminish
reliance on tests in the future” [italics added]. 14 Thus in 2010, the door was left open to consider otheradmissions practices and procedures.
Beginning in 1998, not long after the passage of SP-1, the Berkeley campus instituted a form ofcomprehensive review that evolved over time, and that the other campuses came to adopt in varying
forms.
In 2001, BOARS developed a set of principles for Comprehensive Review, which all campuses were
expected to adhere to, each in its own autonomous way. These procedures are very relevant to theexamination of fairness of the UC admissions processes as they are conducted at each campus. The UC
Regents define comprehensive review as: “the process by which students applying to UC campuses areevaluated for admission using multiple measures of achievement and promise while considering thecontext in which each student has demonstrated academic accomplishment.” 15 In designing campus
procedures, campus admissions committees should adhere to the following guiding principles:
1. The admissions process honors academic achievement and accords priority to students of highacademic accomplishment. At the same time, merit should be assessed in terms of the full rangeof an applicant’s academic and personal achievements and likely contribution to the campus
community, viewed in the context of the opportunities and challenges that the applicant has faced.
2. Campus admissions procedures should involve a comprehensive review of applications using abroad variety of factors to select an entering class. 16
3. No fixed proportion of applicants should be admitted based solely on a narrow set of criteria.
4. Campus policies should reflect continued commitment to the goal of enrolling classes that exhibit
academic excellence as well as diversity of talents and abilities, personal experience, andbackgrounds.
5. Faculty on individual campuses should be given flexibility to create admission policies andpractices that, while consistent with University-wide criteria and policies, are also sensitive to
local campus values and academic priorities.
6. The admission process should select students of whom the campus will be proud, and who give
14

Admissions Tests and UC Principles for Admissions Testing, available athttps://senate.universityofcalifornia.edu/_files/reports/hp2mgy_boars-testing_010609.pdf
15
https://regents.universityofcalifornia.edu/governance/policies/2104.html
16Comprehensive review includes 14 factors that are to be considered in all applications, including GPA andstandardized tests scores, but also special talents and achievements, proficiency in other languages and academic
achievement in the context of disadvantage, among others. The full list can be found athttps://admission.universityofcalifornia.edu/how-to-apply/applying-as-a-freshman/how-applications-arereviewed.html.

11evidence that they will use their education to make contributions to the intellectual, cultural,social, and political life of the State and the Nation.
7. The admissions process should select those students who demonstrate a strong likelihood thatthey will persist to graduation.
8. Campus selection policies should ensure that no applicant will be denied admission without acomprehensive review of his or her file.17
Because differences in educational opportunity vary substantially by race and class among California’s
students, the 2010 Recommendations on Comprehensive Review state that a comprehensive review
involving both quantitative evaluation as well as evaluation of non-quantified criteria, should beundertaken for each applicant who meets the minimum criteria for UC eligibility. 18 Since 2010,
incremental changes have increased the fraction of students admitted through this “Entitled to Review”(ETR) pathway as well as the fraction of students admitted through “Eligibility in the Local Context”(ELC), under which students whose grades put them in the top 9% in each California public high school
are deemed eligible 19 based on GPA in a specific set of courses regardless of admissions test scores.
Although each review of admissions procedures has resulted in some changes, standardized tests haveremained a part of the freshman admissions evaluation since they were instituted. The comprehensive
review is the latest major reform to the admissions process, but it still includes consideration ofstandardized test scores, which some campuses weigh heavily and to varying degrees. The charge given
to the STTF to review current admissions procedures necessarily begs the question of the goal of a
University of California education: Is the goal to maintain UC’s reputation for excellence, which somecontend depends on admitting students who have shown academic achievement in the form of high testscores that correlate with greater likelihood of receiving higher grades and graduating? Or is it to admit aclass representative of the public that UC was created to serve, thereby acknowledging some of the vastinequities in California society? Currently, the University is under increasing pressure to ensure that thestudent body more closely represents the state population and includes a broader range of students,
especially at the flagship campuses. Standardized tests may identify some number of students overlookedby other metrics, but they also operate to exclude some number of students.
The choices before the STTF were to recommend changes to admissions policy that either eliminated oraltered the way the tests are used in the process of admitting students or to maintain the status quo, while
possibly tweaking the system once again at the margins. On the one hand, BOARS and the Academic
Senate have reviewed the use of standardized tests several times before and decided repeatedly to retain
them in some form. These decisions are defensible in that the standardized tests do have predictive power
above and beyond high school GPA (HSGPA), they provide a common metric across students from very
different schools and communities, and as admissions officers told the STTF, “We know how to do this.”Any change would require doing something that UC does not yet know how to do and could conceivably
result in greater inequities and worse student outcomes, particularly if changes were mandated
immediately and without adequate consultation with admissions officers on the various campuses. On theother hand, if UC chooses to resist (or appears to resist) the pressures to change the current admissions
procedures, outside political forces might compel change, and mandated reforms likely would be lessinformed, less effective, and more prone to produce unexpected and undesired outcomes than reforms

17
University of California, Guidelines for Implementation of University Policy on Undergraduate Admissions, p. 2,
at https://www.ucop.edu/student-affairs/_files/guidelines-for-implementation-of-undergraduate-admissions--rev-72019.pdf.
18
For example, students must have met the A-G requirement. This and other requirements are specified in Regents
Policy 2103, https://regents.universityofcalifornia.edu/governance/policies/2103.html
19
That is to say, not admitted to a particular campus, but eligible for admission to one of the UC campuses.

12

produced within the University. Such reforms might also miss the opportunity to place greater emphasis
on holistic review than is now practiced, especially on some campuses.
The Task Force also considered that if UC chooses not to revise how it uses standardized test scores inadmissions decisions, the University could be passing up an opportunity to address the extent to which
public higher education in the state should weigh more heavily in admissions the non-quantitative, nonacademic achievements reflected principally in comprehensive review factor 11. 20
University admissions pursues multiple goals, including developing academic excellence, advancing
equity in access to higher education opportunity, and producing graduates who will serve the campus andlater the larger society well in whatever capacity, with an eye to the greater good. These goals will not be
met by strictly prioritizing students who have received the highest grades and scores on standardized tests
at the time they apply to college. As expressed in Regents Policy 2106:
The University of California is committed to excellence and equity in education for all ofCalifornia's students to secure the social well-being and economic prosperity of the individual andthe State. The University affirms that a fundamental part of its mission is to engage in efforts topromote the academic achievement and success of all students, including students who, because
they are educationally disadvantaged and underrepresented, therefore need additional
assistance. 21
It was with this understanding that the STTF reviewed the options before it.

20

Factor 11 is defined as follows: “Special talents, achievements and awards in a particular field, such as visual andperforming arts, communication or athletic endeavors; special skills, such as demonstrated written and oral
proficiency in other languages; special interests, such as intensive study and exploration of other cultures;experiences that demonstrate unusual promise for leadership, such as significant community service or significant
participation in student government; or other significant experiences or achievements that demonstrate the student's
promise for contributing to the intellectual vitality of a campus.”
https://senate.universityofcalifornia.edu/_files/committees/boars/documents/guidelines-implementation-of-ugadmission-rev-7-2019.pdf
21https://regents.universityofcalifornia.edu/governance/policies/2106.html

13

II.

How UC Uses Standardized Tests in Admissions

The University of California (“UC” or “the University”) admits students through a two-stage admissions
process: first, a determination of eligibility for the UC system overall, and then selection by a specificcampus using comprehensive review. Test scores are used to establish eligibility for some (but not all)students. Test scores are used in admissions decisions. Test scores are also used at some campuses forpurposes beyond campus admissions, such as part of scholarship decisions, for placement, to identify
students who would benefit from extra support, and in honors program admissions. These uses of tests aredescribed below, beginning with a review of the two-stage UC admissions process and the uses of tests inadmissions.
Eligibility versus selection. UC campuses consider 14 factors in shaping their freshman classes byselecting among qualified applicants. These “comprehensive review” factors include quantifiable featuresof academic records such as high school GPA (HSGPA) and standardized test scores, the number of A-G
and honors classes the student completed before the end of the junior year, and how many A-G andhonors courses the student is taking in the senior year. The University also considers factors related tobackground, opportunities, and socioeconomic status (such as first-generation, family income, andacademic opportunities in their local school), as well as more difficult to quantify factors such as a
student’s trajectory of improvement and the strength of a student’s extracurricular activities. However,UC eligibility is based on only two of these factors: 1) GPA in a specific set of sophomore and junior year
courses, and 2) in some cases, test scores. Eligible students are guaranteed admission to UC (i.e.,
guaranteed to be admitted to at least one campus). The concept of eligibility applies only to in-state public
and private high school students, not to out-of-state, international, or homeschooled students.
Eligibility. Applicants may be eligible for UC admission in either “the local context” or the “statewide
context.” Any student who is UC eligible is guaranteed admission to at least one UC campus, although
not necessarily their top choice. The top 9% of students in each participating high school in California are
deemed “eligible in the local context” (ELC). This determination is made based on HSGPA in a specific
set of courses, without considering admissions test scores. In 2019, roughly 32% of California freshman
applicants to UC had earned eligibility for admission based on eligibility in the local context. The
rationale for ELC was that “Using superior scholarship identified in every high school in the state as abasis for eligibility will foster equal opportunity to attend UC for students from all parts of California,
regardless of their socio-economic background or the richness of educational opportunities available tothem.” 22 In addition, the top 9% of students statewide are deemed “statewide eligible” based on an index
that combines HSGPA with a core test score. The index allows a student’s strength in one measure tocompensate for weakness in the other. Index eligibility allows UC to admit academically strong students
who are not in the top 9% of their high school class.
Individual UC campuses may also enroll students who are neither index eligible nor ELC (i.e. Entitled to
Review [ETR]), provided they have completed a specific complement of A-G courses with a 3.0 GPA andsubmit a SAT or ACT score but there is no guarantee of admission as there is with students meeting thestatewide index or ELC. There is no minimum test score requirement for ETR students. In addition, onethird of the seats in each junior class are reserved for transfer students from California community
colleges, who are admitted based on the community college courses taken and grades received and are notrequired to take a core admissions test.

22

Regents item: Establishment of UC Freshman Eligibility in the Local Context (ELC), Proposed Changes in
Academic Course Requirements for UC Freshman Eligibility (VPA Requirement), Proposed Changes in GPA
Calculation for UC Freshman Eligibility. Available at:https://regents.universityofcalifornia.edu/minutes/1999/edpol399.pdf

14

Selection through comprehensive review. UC considers itself to be “one system with many campuses.”
Each undergraduate campus selects its own incoming class: that is, each campus is independently
responsible for issuing a set of offers that will produce an enrolled freshman pool that 1) matches overall
enrollment to physical and instructional capacity 2) balances enrollment across majors 3) is academicallyqualified, such that most students will re-enroll after the freshman year and ultimately complete degrees
4) admits out-of-state/international students who are more qualified, on average, than in-state students 5)“demonstrates high academic achievement or exceptional personal talent, and encompasses the broad
diversity of backgrounds characteristic of California”. 23
While each campus has a different strategy for achieving these (sometimes conflicting) goals, all UC
campuses select students for admission by conducting a comprehensive review of all application
information, including standardized test scores. It is here useful to review the principles forComprehensive Review developed by BOARS in 2001. 24 BOARS defines comprehensive review as: theprocess by which students applying to UC campuses are evaluated for admission using multiple measuresof achievement and promise while considering the context in which each student has demonstrated
academic accomplishment. The comprehensive review criteria established by BOARS include bothquantitative and non-quantitative criteria. As noted in the Introduction, BOARS has identified thefollowing principles of comprehensive review, noting that all campuses are expected to adhere to theseprinciples, with each campus given leeway to adapt as best suits local priorities and circumstances:
1) The admissions process honors academic achievement and accords priority to students of highacademic accomplishment. At the same time, merit should be assessed in terms of the full range ofan applicant’s academic and personal achievements and likely contribution to the campus
community, viewed in the context of the opportunities and challenges that the applicant has faced.
2) Campus admissions procedures should involve a comprehensive review of applications using abroad variety of factors to select an entering class.
3) No fixed proportion of applicants should be admitted based solely on a narrow set of criteria.
4) Campus policies should reflect continued commitment to the goal of enrolling classes thatexhibit academic excellence as well as diversity of talents and abilities, personal experience, andbackgrounds.
5) Faculty on individual campuses should be given flexibility to create admission policies andpractices that, while consistent with University-wide criteria and policies, are also sensitive to
local campus values and academic priorities.
6) The admission process should select students of whom the campus will be proud, and who give
evidence that they will use their education to make contributions to the intellectual, cultural, social,
and political life of the state and the nation.
7) The admissions process should select those students who demonstrate a strong likelihood thatthey will persist to graduation.

23

https://regents.universityofcalifornia.edu/regmeet/may01/re28new.pdf
Guidelines for Implementation of University Policy on Undergraduate Admissions, Guiding Principles For
Comprehensive Review available athttps://senate.universityofcalifornia.edu/_files/committees/boars/CRGUIDELINES.pdf
24158) Campus selection policies should ensure that no applicant will be denied admission without acomprehensive review of his or her file.
Each campus implements comprehensive review in different ways. Most use holistic review, in which no
applications are filtered out based only on standardized test scores or only on HSGPA, no fixed weights
are assigned to any of the 14 factors, and applications are read by trained readers who see – in addition to
raw GPAs, test scores, number of honors courses taken, and so on – a rich array of information that places
these raw numbers “in the context” in which the achievements were obtained. For instance, GPAs areshown both as raw GPAs and as percentiles with respect to the local high school pool of applicants to UC
as a whole and to the specific campus, in addition to the percentile of all applicants to UC as a whole andto the specific campus. Number of honors courses taken is shown next to the number of courses available
at the school. Test scores are shown next to the percentile the test score represents with respect to testtakers in the local high school class and with respect to the applicant pool overall.
Two campuses incorporate a fixed-weight system in which applications are scored prior to comprehensive
review: Riverside uses a fixed weight system in which test scores are ~41% of the total weight (GPA
represents ~50% total weight), and Santa Barbara uses a data-driven complex system in which tests
ultimately comprise 20-25% of the total admissions score (depending on major). Even at score-using
campuses, applications are read as well as scored. Admissions decisions are ultimately made based on areview of all the information available in the applicant’s file.
Students who are eligible for UC admission, but are not selected for admission by one of the campuses towhich they applied, are offered admission to another campus/major combination (generally Merced)
through the “referral pool.”
Scholarships. Some, but not all, campuses consider test scores in assigning scholarships. Davis uses an“Academic Index Score,” which includes test scores, to assign the Regents’ Scholarship, Wakeman
Scholarship, and other scholarship funds. Irvine includes test scores in the holistic review that determines
assignment of Regents’ Scholarships and Chancellors’ Excellence Scholarships. San Diego uses testscores for assigning scholarships in a similar way. Overall, most campuses that use test scores forscholarship assignment use them holistically, in the context of the entire application, rather than by
assigning a fixed weight to the test score.
Placement. All campuses allow the University’s Entry Level Writing Requirement to be met bystandardized test scores. Other uses of test scores for placement vary on a campus by campus basis. For
instance, at Irvine, introductory hard-science courses allow students to enroll with high enough testscores, as an alternative to completing prerequisite coursework or locally-developed placement
examinations. Los Angeles’ minimum graduation requirement for quantitative reasoning can be satisfied
using test scores. San Diego allows using test scores for math placement, as an alternative to taking amath placement exam.
Other. UC has an “admission by examination” mechanism, to allow students from nontraditional
backgrounds (such as homeschooled students) who are unable to meet A-G requirements to be admitted
solely based on standardized test scores. This mechanism is rarely used and amounts to less than 1% ofadmissions. Some campuses, including Merced and Riverside, require minimum test scores for students
who are admitted “by exception” (i.e., without completing the required A-G classes with a 3.0 GPA).
Some campuses use test scores to identify students who may struggle without additional academic
support and provide support to those students – for instance, San Diego uses test scores to identify
candidates for the Triton Summer Success Program. Some campuses use test scores to identify candidates
for honors programs (e.g. San Diego’s Revelle program).

16

III.

Rationale for Current Use of Standardized Tests

1) Recent history of standardized testing at the University of California (“UC” or “theUniversity”).
Before discussing why UC is considering changing its use of tests, it is important to review the reasons
why UC currently uses admissions tests at all. When BOARS studied this issue in 2001–2002, they noted
three “different, but related assumptions regarding the value of tests in making admissions decisions:
1. Admissions tests are a valid measure of student preparation and/or promise that havebeen proven statistically to add to an institution’s ability to predict student successbeyond the predictive information that high school grades alone provide.
2. Admissions tests provide a standardized measure of preparation that is independent of
the variability among grading patterns inevitably present when reviewing the records ofstudents from thousands of high schools across the country.
3. Admissions tests can identify as-yet undeveloped talent in students who for a variety ofreasons may not have worked to their full potential in high school, but who will
nonetheless excel in college.” 25This 2002 BOARS report noted each of these assumptions was in principle testable against UC
admissions and college success data, that such tests had not been adequately done in the past, and thatexamination of this data was essential for rational decision making in the future.26 BOARS concluded that
only the first two factors were well-justified by available data and should form the rationale for continued
use of standardized tests at UC. The report identified four policy issues that should be considered inregular reviews of the use of tests:
1. “The relative merits of tests that measure achievement versus those that purport tomeasure aptitude.
2. The messages that our test requirement sends to students and their families.
3. The degree to which admissions tests should be related to the curriculum UC applicants
are expected to study in high school.
4. The burden that our test requirements place on students and schools.”
These policy issues, combined with the previously identified value of admissions tests as a standardized
measure that increases predictability of student success, led to the following principles for use ofstandardized tests at UC:
1) Admissions tests will be used at the University of California:• to assess academic preparation and achievement of UC applicants;• to predict success at UC beyond that predicted by high school GPA;
• to aid in establishing UC eligibility; and25

The Use of Admissions Tests by the University of California, available at:https://senate.universityofcalifornia.edu/_files/committees/boars/admissionstests.pdf
26
The University of California has been publicly debating this issue for at least one hundred years. “Assuggested above, most arguments concerning the efficiency of college entrance examinations as a test offitness to succeed in college are based merely upon mere opinions; there have been few careful studies todetermine the facts.” “A suggested simplification in the present method of admitting freshmen to theuniversity,” W. Scott Thomas, The University of California Chronicle, 1914.

17• to aid in selecting students for admission at individual UC campuses.
2) The desired properties of admissions tests to be used for these purposes include
the following.
• An admissions test should be a reliable measurement that provides uniform
assessment and should be fair across demographic groups.
• An admissions test should measure levels of mastery of content in UC-approved highschool preparatory coursework and should provide information to students, parents,and educators enabling them to identify academic strengths and weaknesses.
• An admissions test should be demonstrably useful in predicting student success at
UC and provide information beyond that which is contained in other parts of theapplication. (It is recognized that predictors of success are currently limited, andgenerally only include first-year college GPA and graduation rate. As this fieldadvances, better predictors should be identified and used in validating admissions
tests.)• An admissions test should be useful in a way that justifies its social and monetary costs.
BOARS also recommended that “as a matter of principle, the faculty regularly review UC’s admissions
testing policy and practices to ensure that tests are being used in a way that is consistent with theseprinciples and desired properties of admissions tests.”
When BOARS addressed the issue again over three years of meetings in 2007–2009, it conducted
additional statistical analyses focused on outcomes and predictability. BOARS affirmed that, at thattime, the UC use of tests was generally compatible with the UC principles on use of tests, but revised
the testing principles to include “to increase the options available for applicants to demonstrate their
preparedness for college.”
2)

Does UC’s current use of tests meet UC/BOARS criteria?

This section of the report focuses on whether the University’s current use of tests is compatible with theprinciples for use of tests at UC as they are currently formulated (i.e., as they have been inherited fromprevious generations of BOARS reports). In subsequent sections of the report, we will examine whether
UC’s principles on testing should be modified, and more broadly, whether continued adherence to the
California Master Plan for Higher Education and the ongoing prioritization of college preparation andacademic merit should be revised in favor of an approach to college admissions more focused on
demographic equity and the righting of historic wrongs.
This section is divided into five subsections that answer the following questions:
A. Are admissions test scores currently useful to predict student success beyond the predictive
information in high school grades alone?
B. Does UC appropriately weight tests in admissions?
C. Does UC’s use of standardized test scores inappropriately exclude members of certain groups?
D. Do standardized test scores counterbalance the variability among grading patterns at different highschools?
E. Additional rationales implicit in UC’s current use of tests.
Much of the data presented here comes from the UCOP Department of Institutional Research andAcademic Planning (IRAP) study entitled “Relationship of the SAT/ACT to College Performance at the
University of California, January 2020,” attached as Appendix I to this report.

18

A. Admissions tests add substantially to UC’s ability to predict student success beyond thepredictive information in high school grades alone.
A.1. What does it mean to succeed in college, and why is predictive validity important? The
definition of college success can be broader and more nuanced than is commonly appreciated. Some
aspects of success are nearly universally agreed on: for instance, there is broad consensus that success incollege requires graduating with a credential rather than leaving college partway through with debt and nodegree. The relative importance of other metrics of success is debated. While in the past BOARS hasgenerally agreed that college grades are a valuable measure of student success, the specific importance offreshman GPA as an outcome metric is arguable 27. It can be argued that as long as students complete theirdegrees, their grades are largely irrelevant to their success in life after they have graduated. It can also beargued that good grades are important for students who will attend graduate or professional school, andsince UC is the primary research arm of the state’s tripartite college and university system, it is reasonable
to ensure that our undergraduate students are well prepared for post-baccalaureate activity. Many otheraspects of success are difficult to measure or the University does not keep systematic records on them(e.g., participation in campus research opportunities or leadership).
In order to ensure public accountability, UC is obligated to make data-driven admissions decisions andreport these decisions to the public, as it does in the annual Accountability Report. 28 Thus, despite debate
on what exactly qualifies as success in college and consensus that the definition of success could include
more metrics than are currently used, admissions accountability must focus on measurable (andmeasured) aspects of college success. The measurable outcomes that UC and BOARS typically focus on
include 1) retention to second year, 2) first-year GPA, 3) graduation (whether on-time or not), and 4)
GPA at graduation. The use of these metrics to determine whether admissions criteria are valid andappropriate goes back at least to the Master Plan in 1960 29; their use has been regularly re-affirmed sincethen, including in the 2002 and 2010 BOARS studies 30 of the use of standardized tests at UC.
The primary conclusions of the IRAP report and the STTF’s studies on the usefulness of admissions testscores in identifying successful students by these four metrics include the following.
1) Overall, both grades and admissions test scores are moderate predictors of college GPA at UC.
The predictive power of test scores has gone up, and the predictive power of high school grades
has gone down, since the 2010 BOARS study of this issue. At present, test scores are a slightly
better predictor of freshman grades than high school grades are. Both grades and scores are27

https://senate.universityofcalifornia.edu/files/committees/boars/admissionstests.pdf
https://accountability.universityofcalifornia.edu
29
From the Master Plan: “The Technical Committee suggested as a guiding principle that admission
requirements are valid for any one college if, first, they serve to qualify for admission those applicants whose
educational purposes are properly met by the college and whose abilities and training indicate probablescholastic success in the college and, secondly, they serve to eliminate applicants not meeting theserequirements. The Technical Committee suggested the following measures of validity:
28

1.
2.
3.
4.

Scholastic success in the first semester or year
Continuance in college
Rate of dismissal for poor scholastic performance
Comparative standing on objective tests

The Technical Committee regards scholastic success as the best single measure of validity. The Survey Teamagrees, but prefers the use of several criteria in combination.” (Emphasis added.)30

https://senate.universityofcalifornia.edu/committees/boars/reports.html

19

stronger predictors of early outcomes (freshman retention and GPA) than of longer-term
outcomes (eventual graduation and graduation GPA).
2) Test scores contribute a statistically significant increment of prediction when added toregression analyses that already include high school grades as predictors. This improved
prediction can translate to fairly large differences in predicted student outcomes (e.g. fourfold
changes in non-retention rates, even for students with similar high school GPAs [HSGPAs],
depending on test scores).
3) Test scores contribute significant predictive power across all income levels, ethnic groups,
across both first-generation and non-first-generation students, and across all campuses andmajors. There are meaningful differences between groups in the predictive improvement
conferred by test scores.
4) Although there are large test score gaps between applicants from different demographic
groups, UC does not use test scores in a way that prevents low-scoring students fromdisadvantaged groups from being admitted to UC as long as their applications show academic
achievement or promise in other ways. Some students admitted with weak test scores throughcomprehensive review do perform well at UC. However, the lowest-scoring students typicallydo not perform well at UC.
A.2. What does a 25% [15%, 10%] correlation mean for student outcomes? Later chapters of thisreport will discuss whether a given increment in predictive power is worth considering in admissions
decisions. Here, we provide three guides for interpreting correlation coefficients. 31 A detailed explanationof how correlations are computed and interpreted is attached as Appendix IV.
1) In statistical studies of human behavior,a. a correlation above 0.5, corresponding to R2 or share of variance explained ofabove 0.25, is considered high (the R2 is the square of the correlation);b. R2 / share variance explained of roughly 0.1 to 0.25 is considered moderate;c. R2/ share variance explained of below 0.1 is considered low. 322) Given the very wide range of scores and grades presented by applicants for admission,even a low correlation coefficient can reveal large differences between the expectedsuccess levels for students in different grade or score bands. “The SAT ‘explaining’ lessthan 10% variance given HSGPA may seem trivial, but the difference between a 16%
success rate and a 73% success rate for students with similar high school records, butdifferent SAT scores, appears less trivial.” 33 (Underlines are added by the STTF foremphasis.)3) The practical usefulness of a given strength of correlation depends on the fraction of
applicants who can be selected. 34 At UC overall, and particularly at certain campuses,
space constraints, financial constraints, and the requirements of the Master Plan allow
admitting only a small fraction of applicants. “For nearly a century it has been
understood…that the smaller the selection ratio, the greater the payoff a particular
correlation will yield.” 35 When the fraction of applicants who can be admitted is small,
31

Correlation coefficients are used to measure/summarize the strength of the association between two variables
Zwick; Cohen 1998.
33
Bridgeman, Burton, and Pollack 2008. Predicting Grades in College Courses: A Comparison of Multiple
Regression and Percent Succeeding Approaches. Bridgeman, Brent; Pollack, Judith; Burton, Nancy. Journal ofCollege Admission, n199 p19-25 Spr 2008. Available at https://files.eric.ed.gov/fulltext/EJ829428.pdf
34
Taylor and Russell 1939. Taylor, H. C., & Russell, J. T. (1939). The relationship of validity coefficients to thepractical effectiveness of tests in selection: discussion and tables. Journal of Applied Psychology, 23(5), 565–578.
35
Zwick, “Who Gets In Zwick, R. (2017). Who gets in? Strategies for fair and effective college admissions.
Cambridge, MA: Harvard University Press
32

20

even a weakly correlated predictor can produce a substantial increase in the proportion of
students who will perform at a given level (e.g., maintaining a minimum GPA or
graduating).
A.3. Predictive validity: College success measures correlate with admissions test scores. At UC,
each of the primary success measures is clearly correlated with, and predicted by, scores on standard
college admissions tests. Extensive tables and analyses are shown in the IRAP study included as
Appendix I. A table of regression slopes and squared correlation coefficients for admissions years 20102012 is shown as Table 3A-1. Additional correlations are shown in Appendix II (Tables 1-4). In datafrom 2010-2012, 21% of variance in first-year GPA was predicted by SAT score. This number may soundsmall, but it translates into large differences in student outcomes. For instance, among students admittedwith SAT scores below 700, 35% left UC after only one year and only 50% graduated within seven years;
among students admitted with SAT scores above 1400, only 3% were not retained past freshman year and
92% graduated within seven years. Outcomes data are shown in Figure 3A-1.

HSGPA compared to SAT as a predictor of UC freshman
grades across demographic groups, 2010-2012
CategoryN

GPA-only models

SAT-only models

All

90504

Slope
0.39

R^2
0.16

Slope
0.46

R^2
0.21

<$30K
$30-$60K$60-$120K$120K+

20178
19232
19307
21339

0.34
0.36
0.36
0.41

0.12
0.13
0.13
0.17

0.41
0.42
0.39
0.35

0.17
0.18
0.15
0.12

Asian
Black
Hispanic
White

37899
3652
22522
23046

0.38
0.29
0.33
0.38

0.15
0.09
0.11
0.14

0.42
0.37
0.39
0.34

0.18
0.14
0.15
0.11

Not 1stGen
First Gen

47626
40405

0.39
0.34

0.15
0.12

0.37
0.4

0.14
0.16

Table 3A-1. High school GPA (HSGPA) compared to SAT as predictors offreshman grades at UC, for all students and for different demographic groups,
2010-2012. SAT is calculated as SATRW + SATRM. Both SAT and HSGPA are
normalized (z-scored) prior to ordinary least-squares regression.

21

Figure 3A-1 Percent of freshmen who fail to return the following fall, average first year UC GPA, percent ofstudents who fail to earn a degree within seven years, and average UC GPA at graduation by SAT bands. SAT =SATRM+SATRR. All panels but Graduation GPA contain data from 2010-2012 admits. Graduation GPA was
only available for 2010 admits. Retention/graduation: Lines represent mean rate of non-retention or nongraduation. GPA: Lines represent medians and ranges shown are interquartile intervals.

A.4. Predictive validity: Standardized test scores provide information beyond what is available in
HSGPA. Contrary to the narrative that is commonly expressed, the information available in test scores is
not redundant with the information available in grades. Even among students with similar high schoolacademic records, we still see a substantial relationship between test scores and college retention, grades,and graduation. For any given high school GPA, a student admitted with a low SAT score is between two
and five times more likely to drop out after one year, and up to three times less likely to complete theirdegree compared to a student with a high score (Figure 3A-2).

Figure 3A-2 Percent of freshmen who fail to return the following fall, average first year UC GPA, percent ofstudents who fail to earn a degree within seven years, and average UC GPA at graduation by SAT and HSGPA
bands. Dot area is proportional to number of students in each SAT and HSGPA band. SAT scores are summed
SATRM + SATRW from the 2010-2012 application pools, except for the plots of graduation GPA which are for2010 only. HSGPAs are weighted. Color indicates HSGPA bands

22

HSGPA plus SAT to predict UC freshman
grades across demographic groups, 2010-2012
Category

N

Combined models

All

90504

GPA
slope
0.25

SAT
slope
0.36

R^2
0.26

<$30K
$30-$60K$60-$120K$120K+

20178
19232
19307
21339

0.23
0.23
0.25
0.32

0.33
0.34
0.29
0.21

0.22
0.23
0.20
0.20

Asian
Black
Hispanic
White

37899
3652
22522
23046

0.26
0.19
0.22
0.29

0.32
0.30
0.31
0.22

0.24
0.17
0.20
0.18

Not 1stGen
First Gen

47626
40405

0.29
0.23

0.26
0.32

0.21
0.21

Table 3A-2. High school GPA (HSGPA) combined with SAT
to predict freshman grades at UC for different demographic
groups, 2010-2012. SAT is calculated as SATRR + SATRM.
Both SAT and HSGPA are normalized (z-scored) prior to
ordinary least-squares regression.

These results are derived from UC admissions data in the 2010–2012 cohort. We chose this cohort
because it is the most recent cohort for which seven-year graduation rates are available. Some STTF
members felt that overreliance on four-year graduation rates might be unnecessarily discriminatory
against low-income students, who are more likely to have to interrupt their studies to take care of family
members or earn money to support themselves 36. The graduation GPA data comes only from the 2010
cohort because 2011-2012 graduation GPAs were not yet available from UCOP at the time of this study.
These results are broadly consistent with studies from other public university systems and nationwide
studies as well as with UC historical data. 37 The major difference is that in current UC data, test scores
explain a greater fraction of outcome variance than at universities nationwide. Indeed, the variance inoutcomes explained by test scores has increased since 2007, while the variance explained by grades has
decreased. Analysis in the IRAP report (attached as Appendix I) concludes that this is likely due to theway UC uses grades and test scores in admissions, which has changed over this time: the Regentsincreased the minimum HSGPA required for UC freshman eligibility from 2.8 to 3.0, and rapid growth of
the qualified applicant pool has increased selectivity (and thus reduced the fraction of students admittedwith low HSGPAs) at all campuses. The fraction of students admitted through the Eligibility in the Local
36

Soria, Krista M.; Weiner, Brad; and Lu, Elissa C. (2014) "Financial Decisions among Undergraduate
Students from Low-Income and Working-Class Social Class Backgrounds," Journal of Student Financial Aid:
Vol. 44: Iss. 1
37
Reviewed in “Eight Myths about Standardized Admissions Testing,” Paul Sackett and Nathan Kuncel, bookchapter in “Measuring Success: Testing, Grades, and the Future of College Admissions”, ed. Buckley et al 2018.

23

Context (ELC) program (which admits students with high grades without regard to their test scores) has
increased. At the same time, “the distribution of HSGPAs was increasingly compressed as a result of highschools’ grade inflation,” 38 “students have tended towards taking larger courses with more formal testbased examinations,” 39 and “high-HSGPA students are increasingly enrolled in low-grade courses likeEngineering and Economics.” 40
We also note that because the data supplied to STTF by the UC Office of the President (UCOP) only
included years for which seven-year graduation data were available, the STTF was unable to directly
examine outcomes of students admitted after the ELC expansion. This highlights the relationship between
two closely related issues: what do we believe are the most important goals of the admissions process, andhow often UC should consider non-incremental changes to its admissions strategy? Following the last
BOARS report on admissions tests in 2010, UC made significant changes to its admissions procedures in2012 (expanding the ELC program and adding a new “Entitled to Review” [ETR] category guaranteeingthat a student who submits a complete application and has a 3.0 GPA in A-G courses will not be denied
admission without a comprehensive review). We do not yet know how the students admitted throughthese expanded access programs have fared in the long term. In addition, the new iteration of the SAT 41 is
only a few years old; only one-year outcomes are available for students admitted using the new SAT and
we do not know how those students will fare in long-term metrics. There is a tradeoff between prioritizing
long-term outcome measures and being able to rapidly analyze the consequences of changes in policy.
One reason why BOARS analyses have often focused on freshman-year grades is that they are assessable
rapidly, and allow BOARS to justify more rapid changes in policy than could be supported based on
longer-term outcome metrics. 42 Caution is warranted in making another large change to UC admissions
procedures before we understand the consequences of the last large changes to UC admissions
procedures.
A.5. Predictive validity: Standardized test scores are predictive of outcomes for all demographic
groups. Many on STTF were concerned that the predictive power of test scores might differ for membersof different demographic groups, and that therefore members of certain groups typically underrepresented
at UC would be disadvantaged by mandatory consideration of test scores in their application. This issue
was noted in the BOARS 2002 report on testing, which stated that “It is well known that admissions tests
38

IRAP report, page 44.
IRAP report, page 45.
40
IRAP report, page 45.
41
STTF reviewed the College Board’s preliminary report, showing generally similar relationships between
scores, demographics, and outcomes for the new version of the SAT as for prior SAT versions. STTF was
unable to College Board representatives back after our initial meeting with them to go over their statisticalreport in more detail, due to the shortened turnaround for this report.
42
The 2002 BOARS report noted the same issue but chose to prioritize rapid turnaround over evaluation of
longer-term outcomes: “It should be noted here that historically the measure of “student success” mostcommonly used in validity studies of admissions tests—and the one on which the College Board and
Educational Testing Service base statistical studies of SAT I and SAT II scores—has been the freshman-year
GPA. This is because intervening variables (including differing academic programs pursued by individual
students and the intellectual and personal development students experience during their college years) serve to
weaken substantially the predictive relationship between pre-college factors like high school GPA and testscores and eventual college GPA. Predictive validity studies justify use of freshman-year GPA by citing its
correlation with GPA at graduation. Nonetheless, faculty and admissions officers involved in determining
eligibility and selection criteria and processes point out that maximizing freshman year GPA is at best a
relatively minor goal of the admissions evaluation process. While highly cognizant of the weaknesses involved
in using freshman GPA, BOARS could not identify an alternative and therefore followed the generally accepted
practice of using freshman-year GPA as the outcome variable indicating success in college.”https://senate.universityofcalifornia.edu/files/committees/boars/admissionstests.pdf
3924

of all types — along with high school grades and other indicators of academic achievement — arestrongly correlated with family income. This does not reflect bias in the tests, but rather the inescapable
fact that schools in California — like those throughout the country — vary widely in available resources
and students from poor families are more likely to attend schools with fewer resources. The members of
BOARS are well aware that they cannot eliminate this level of “disparate impact” admissions tests haveon students from socio-economically disadvantaged circumstances.” 43
IRAP and STTF also performed predictive validity analyses that disaggregated UC students according todemographic factors including parental education, family income, and racial/ethnic identity. Figures 3A3, 3A-4, and 3A-5; Tables 3A-1 and 3A-2 above, and the IRAP report show the findings. The IRAP
study showed that models combining test scores with GPA explained 14-22% of the variance in UC
grades for all ethnic groups, and 18-20% for all incomes (see Appendix C of IRAP report). As describedabove, this may or may not sound like a large amount of variance, but it corresponds to fairly significant
changes in outcomes between students with high and low test scores, even for students with equivalenthigh school GPA and similar demographic profiles. The STTF study determined the increment inpredictive power attributable to adding test scores to HSGPA and the relative statistical power of the two
measures for the set of students enrolling in the years 2010-2012.
Perhaps counterintuitively, we found that test scores were better predictors of outcomes forunderrepresented groups than for majority groups.
For instance, for the wealthiest incoming freshmen (those from families earning above $120,000 per year
– the top quartile of UC’s income distribution), HSGPA-only models explained about 17% of variance infreshman GPA (Figure 3A-3; Table 3A-1 above; Appendix II Table 2). Adding SAT to a HSGPA-onlymodel produced about a 20% improvement in predictive power (Table 3A-2). Further, the predictive
power of SAT systematically increases as student income decreases: for students in the lowest income
quartile (<$30K household income), HSGPA-only models explain about 12% of variance in freshman
GPA, while adding SAT to HSGPA improves predictability to 20%, an 80% increase. Similarly, forstudents in the highest income quartile, HSGPA-only models explained about 16% of the variance ingraduation GPA, while SAT-only models explained only 10% of the variance for outcomes among thesehigh-income students. Models in which SAT and HSGPA were combined accounted for 19% of thevariance, only a 17% improvement over models containing HSGPA alone. Yet for students in the lowest
income quartile, adding SAT to a HSGPA-only model produces a 60% improvement in predictability of
final GPA.
Similarly, for non-first-generation students, a HSGPA-only model explains about 15% of variance infreshman GPA (Table 3A-1) and adding SAT to HSGPA increased explained variance to 21%, about a
40% increase (Figure 3A-4; Table 3A-2). But for first-generation students, a HSGPA-only model only
explained 12% of variance in freshman GPA (Table 3A-1), and combining SAT and HSGPA increased
explained variance by 75% over HSGPA alone (Table 3A-2). For non-first-generation students, a
HSGPA-only model explained 16% of graduation GPA variance, and adding SAT to HSGPA increased
explained variance to 19%, about a 20% increase. Yet for first-generation students, a HSGPA-only model
explained 11% of variance, and combining SAT and HSGPA increased explained variance by 55% above
GPA alone (Figure 3A-4; Appendix II Tables 3 and 4). In other words, the increase in predictive power
was greatest for the student population less well represented at UC.

43

https://senate.universityofcalifornia.edu/_files/committees/boars/admissionstests.pdf

25

Figure 3A-3 Mean percent of freshmen who fail to return the following fall, average first-year UC GPA,
percent of students who fail to earn a degree within seven years, and average UC GPA at graduation by
SAT and HSGPA band for students of varying family incomes. Dot area is proportional to number ofstudents in each SAT and HSGPA band (normalized by number of students in each demographic group).
SAT scores are summed SATRM + SATRW from the 2010–2012 application pools, except for the plots ofgraduation GPA which are for 2010 only. HSGPAs are weighted. Color indicates HSGPA bands.

26

Figure 3A-4. Mean percent of freshmen who fail to return the following fall, average first-year UC GPA,
percent of students who fail to earn a degree within seven years, and average UC GPA at graduation by SAT and
HSGPA band for students who self-reported as having (top) or lacking (bottom) at least one parent who attended
college. Dot area is proportional to number of students in each SAT and HSGPA band (normalized by number ofstudents in each demographic group). SAT scores are summed SATRM + SATRW from the 2010–2012
application pools, except for the plots of graduation GPA, which are for 2010 only. HSGPAs are weighted.
Color indicates HSGPA bands.

We observe the same phenomenon among different racial and ethnic groups. When added to a statisticalmodel relating first-year GPA to HSGPA, test scores increase predictability of freshman GPA by 29% for
Caucasian students, by 60% for Asian students, by 77% for Hispanic students, and by 96% for AfricanAmerican students. Standardized test scores increase predictability of graduation GPA by 22% for
Caucasian students, by 39% for Asian students, by 61% for Hispanic students, and by 56% for AfricanAmerican students (Figure 3A-5; Appendix II Table 4).

27

Figure 3A-5. Mean percent of freshmen who fail to return the following fall, average first-year UC GPA, percent
of students who fail to earn a degree within seven years, and average UC GPA at graduation by SAT and
HSGPA band for students self-reporting as belonging to different racial/ethnic groups. Dot area is proportional tonumber of students in each SAT and HSGPA band (normalized by number of students in each demographic
group). SAT scores are summed SATRM + SATRW from the 2010–2012 application pools, except for the plotsof graduation GPA, which are for 2010 only. HSGPAs are weighted. Color indicates HSGPA bands.

28

Similarly, the utility of test scores for identifying students who were likely to persist past the first year, or
students who were likely to graduate – or conversely, for identifying students who are at great risk for
non-persistence or non-graduation without additional support – was greater for members ofunderrepresented groups (Figures 3A-3 to 3A-5, columns 1 and 3).
Again, the results in the universitywide predictive validity study are based on UC admissions data from2010–2018, while the figures are based on UC admissions and outcomes data from 2010–2012. Both setsof results are consistent with the broad literature on the topic, including other public university systems,
other selective institutions, and nationwide 44.
A-6. Predictive validity: Performance prediction is strongest in quantitative disciplines. IRAP alsostudied the predictive power of grades and test scores on college grades of students in different majors.
This analysis disaggregated freshman GPA at the course level, which allowed for a more precise
examination of the effects of test scores and HSGPA in explaining student performance in different
academic disciplines. IRAP identified first-year courses taught in four broad areas of study – humanities,
social sciences, natural sciences, and engineering – then modeled the student outcomes in those fourcourses separately in terms of tests and HSGPA. This analysis considered the effects of each of the threecomponents of the SAT that were taken at the time, mathematics, reading and writing, separately.
The results are summarized in Table 6 of the IRAP report (see Appendix I). The first four models
consider each of the four academic indicators (HSGPA, SAT Reading, SAT Math and SAT Writing)independently. In every case, the percent of within-course variation explained by these indicators in
isolation was least for HSGPA, sometimes dramatically so. For the natural sciences, for example, the
GPA only model had an R2 of 4.7%, while the SAT Math only model had an R2 of 13.5%. As might be
expected, the SAT reading and writing components are most explanatory variables for the humanities andsocial sciences, while the SAT math is most explanatory for the natural sciences and engineering.
Combining the indicators increased the amount of variance explained substantially above what isavailable using only HSGPA. For the humanities, the four-indicator model (Model 6 in Table 6 of the
IRAP report), has an R2 that is 2.7 times that of the HSGPA-only model. For engineering, R2 for the fullmodel is 5.2 times that of the HSGPA-only model. Clearly, the tests are providing a great deal of
information on prospective student performance that is not available in high school grades alone.
We note that the results of this subsection do not speak to the question of institutional values. That is, thestatistical power of tests in predicting outcomes does not address whether UC ought to consider expectedcollege success when deciding which students to admit. These results address the empirical question ofwhether admissions test scores can assist in identifying students likely to succeed at UC. They do not
answer the moral and practical questions of whether we ought to use that information to select foradmission students whose records indicate that they will likely persist to graduation and do well in theirclasses, whether we ought to use the information to target aid towards students who will struggle without
additional assistance, or whether we ought to ignore the information entirely. Policy implications of thesefindings are discussed and debated in the following sections.

44

Reviewed in “Eight Myths about Standardized Admissions Testing”, Paul Sackett and Nathan Kuncel, bookchapter in “Measuring Success: Testing, Grades, and the Future of College Admissions”, ed. Buckley et al 2018.

29

B. UC does not appear to violate BOARS principles of comprehensive review by prioritizing
standardized test scores in admissions. UC admissions place more value on the high school record.
B.1. It is very rare that otherwise qualified students could be blocked from UC based only on testscores. Prior BOARS studies of this issue investigated whether students with strong grades could beblocked from UC admission based on a low test score, and conversely, whether students with high testscores were being admitted to UC despite weak grades. The current STTF repeated this analysis on the2018 dataset to look for evidence that UC inappropriately uses test scores to block otherwise strong
students from admission. Test scores and grades are generally moderately correlated (R2 of 28%). This isa strong enough correlation that it is unusual for students to have “discrepant” grades and scores – that is,
to have grades more than one standard deviation above the mean but scores more than one standard
deviation below the mean, or vice versa. Of the ~18,000 students with SATs more than one standard
deviation below average, only ~300 of these had grades more than one standard deviation above theaverage, and 84% of those students were admitted. In other words, of the 120,000+ California resident
applicants to UC for 2018, fewer than 50 students with strong grades and low scores were denied
admission. (We do not know whether these students were denied admission based on test scores or basedon other weaknesses in their applications; this analysis simply tells us that no more than 50 students fall
into this category.) Conversely, of the 18,000 students with HSGPAs more than one standard deviation
below the mean, about 400 had test scores more than one standard deviation above the mean, and only
41% of these were admitted. This indicates that, consistent with BOARS policy, UC admissions is at mostvery rarely blocking otherwise qualified students based on scores, and generally appears to reward highgrades more than high test scores.
B.2. UC appears to consider grades more than test scores in admissions decisions. To examine thispoint more fully, we looked at the admit rates by campus as well as by the system as a whole. In thisanalysis, each of the academic indicators is normalized by computing the number of standard deviations
that a particular indicator is away from the campus- or system-level mean and dividing that difference by
the relevant standard deviation. If the original data follows a normal or Gaussian distribution, thenormalized data follows standard normal distribution with a mean of zero and a standard deviation of 1.
That way one can compare the influence of HSGPA and SAT with each other using the same scale. The
tables below show the percent of applicants admitted along each of four divisions of the academic
indicators: High (one standard deviation or more above the mean), Mid-High (between the mean and one
standard deviation above), Mid-Low (between the mean and one standard deviation below), and Low (onestandard deviation or more below the mean). Given the importance of HSGPA and SAT in admissions, it
is natural to suppose that an applicant who is in the “high” category in both indicators is much more likely
to be admitted than a student who is “low” in both. Indeed, this is the case.
Table 3B-1 shows the admit rates for the system as a whole. For clarity, we discuss this table in somedetail. Tables for a few campuses are also included in the body of the report. Tables for all campuses may
be found in Appendix III.

30

Percent of Applicants Admitted to at Least One Campus
High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPANet

High SAT
95%
85%
65%
41%

Mid-High SAT
95%
75%
51%
25%

Mid-Low SAT
94%
73%
43%
17%

Low SAT
84%
64%
28%
9%

Net
95%
75%
43%
16%

86%

68%

52%

27%

58%

Table 3B-1. Admission rate for the UC system for California resident applicants who submitted an SAT score for fall2018. High (one standard deviation or more above the mean), Mid-High (between the mean and one standard
deviation above), Mid-Low (between the mean and one standard deviation below), and Low (one standard deviation
or more below the mean). Colors correspond to the fraction of applicants admitted in each group. The large
differences between rows indicate that admission probability changes very much depending on HSGPA. The smallerdifferences between columns indicate that admission probability is less dependent on SAT.

The top row shows the admit rate for applicants who all reported high HSGPAs, divided into cells
according to their SAT scores. As discussed above, among students with high HSGPAs, even students inthe lowest SAT range had an 84% admit rate. The leftmost column has results for applicants with highSAT scores divided across the HSGPA ranges. Again, as discussed above, for this group of applicants,
the admit rate was only 41% for students in the lowest HSGPA range, less than half that for applicantswith high grades and low test scores. Nearly 27% of applicants with low SAT were admitted comparedwith about 16% for those with low HSGPA. The clear implication of this is that as a system, UC values
high school grades more than test scores in making admission decisions.
Campus-level results can be even more extreme, depending upon the selectivity of the campus. Table 3B2 shows results for Berkeley, one of the most selective campuses (16% overall admit rate), Table 3B-3
shows results for Santa Barbara (29% admit rate), and Table 3B-4 shows results for Merced (68% admitrate).

Percent of Applicants Admitted to Berkeley
High SATHigh HSGPA
Mid-High HSGPA

Mid-High SAT

Mid-Low SAT

54%
36%

42%
20%
2%

43%
18%
2%

30%
14%
2%

47%
23%
2%

36%

17%

11%

4%

16%

Mid-Low HSGPA

Low SATNet

Low HSGPA
Net

Table 3B-2. Admission rate for California resident applicants to Berkeley who submitted an SAT score for fall 2018.
High (one standard deviation or more above the mean), Mid-High (between the mean and one standard deviation
above), Mid-Low (between the mean and one standard deviation below), and Low (one standard deviation or more
below the mean). Colors correspond to the fraction of applicants admitted in each group. The large differences
between rows indicate that admission probability changes very much depending on HSGPA. The smaller differences
between columns indicate that admission probability is less dependent on SAT. To preserve student privacy, cellswith fewer than 30 students are blanked.

31Percent of Applicants Admitted to Santa Barbara
High SATHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA

Mid-High SAT

Mid-Low SAT

Low SAT

90%
55%
11%

74%
32%
7%

69%
31%
8%

42%
17%
2%

63%

31%

19%

5%

Low HSGPA
Net

Net

80%
35%
6%
1%
29%

Table 3B-3. Admission rate for California resident applicants to Santa Barbara who submitted an SAT score for fall2018. High (one standard deviation or more above the mean), Mid-High (between the mean and one standard
deviation above), Mid-Low (between the mean and one standard deviation below), and Low (one standard deviation
or more below the mean). Colors correspond to the fraction of applicants admitted in each group. The large
differences between rows indicate that admission probability changes very much depending on HSGPA. The smallerdifferences across columns indicate that admission probability is less dependent on SAT. To preserve student
privacy, cells with fewer than 30 students are blanked.

Percent of Applicants Admitted to Merced
High SATHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

99%
98%
83%
16%
91%

Mid-High SAT

Mid-Low SAT

99%
97%
74%
15%
82%

98%
90%
62%
10%
66%

Low SAT

91%
57%
19%
25%

Net

99%
90%
59%
9%
68%

Table 3B-4. Admission rate for California resident applicants to Merced who submitted an SAT score for fall 2018.
High (one standard deviation or more above the mean), Mid-High (between the mean and one standard deviation
above), Mid-Low (between the mean and one standard deviation below), and Low (one standard deviation or more
below the mean). Colors correspond to the fraction of applicants admitted in each group. The large differences
between rows indicate that admission probability changes very much depending on HSGPA. The smaller differences
across columns indicate that admission probability is less dependent on SAT. To preserve student privacy, cells withfewer than 30 students are blanked.

For every campus, the admit rate was much higher for applicants with high HSGPA and low SAT
than for those with high SAT and low HSGPA, offering campus-level validation for the suggestionthat grades matter more than tests in admission decisions at UC. At the highly selective ormoderately selective campuses (admit rates less than 35%), the admit rate for applicants withhigh GPA and low SAT was at least ten times that of applicants with high SAT and low GPA.
These results show that UC campuses use test scores to help select from the large number of applicantswith high HSGPAs, but that test scores by themselves do not block students from UC admission, and thattest scores are generally less important in the admissions process than grades.

32

B-3. Under the current eligibility system, tests do identify otherwise ineligible applicants who comefrom relatively disadvantaged backgrounds. Admission tests find talented students who do notstand out in terms of high school grades alone. The SAT was originally designed to identify students
who might not have had high HSGPA or attended elite or well-resourced high schools but who showed
academic promise. The SAT and ACT appear to play that role for many students at the UC today. Many
students who do not qualify for ELC, (i.e., being in the top 9% of their classes based on HSGPA), become
eligible to attend UC through statewide eligibility. Unlike ELC, statewide eligibility, which is also
referred to as Index Eligibility, uses the SAT as well as GPA.
Moreover, this population of students does not consist solely of affluent students in high- performing
competitive high schools. On the contrary, in the 2018 applicant pool, 22,613 students were guaranteed
admission to UC through the statewide eligibility index but not ELC, meaning that their SAT score madethem eligible. Of these students for whom the SAT score was decisive in guaranteeing them admission toUC, 4,931 were low-income and 5,704 were first generation college. Turning to race/ethnicity, 5,609
were under-represented Minorities (URMs), comprised of 4,442 Latino students, 999 African-American
students, and 168 Native American students.
To put these numbers into context, Figure 3B-1 shows, for the students in each of these disadvantaged
groups who were guaranteed UC admission, the percentages who earned their spots because of the SAT.
That is, they were statewide index eligible and not eligible through ELC, the latter of which uses only
GPA ranking within each high school.
The figure shows that about one quarter of all low-income, first-generation college and URM students
who were guaranteed admission earned this guarantee through the statewide eligibility path, but not
through ELC. This means that these students had relatively high SAT scores but their GPA was not highenough to guarantee them admission through ELC. This is a substantial proportion of these disadvantaged
groups of students who would not have been guaranteed admission had they not had high SAT scores.
The three rightmost bars in the figure divide the URM category into the three ethnic/racial groups thattogether constitute under-represented minorities. Just under a quarter of Latino students earned theirguarantee of admission through their SAT score. The percentage of African-American and Native
American students who gained admission through Statewide Eligibility but not ELC is markedly higher at
40% and 47% respectively.

33

Figure 3B-1. The percentage of students in disadvantaged groups who were guaranteed admission in 2018 solely
due to the statewide eligibility index. The denominator for these calculations is the total number of students in thegiven group who were eligible through: i) Statewide index only, ii) Eligibility in the Local Context (ELC) Only,and iii) Both Statewide Index and ELC. These three pools do not represent all admits, because students are also
admitted through ETR and by exception. To give some perspective, in 2018, about 17,000 out of 71,000 admits
were admitted through means other than the three guaranteed admission categories.

To sum up, the SAT allows many disadvantaged students to gain guarantees of admission to UC. As ashare of all students in disadvantaged groups who are guaranteed admission to UC, the percentages whoearn this guarantee due to their SAT scores range from a low of 24% for Latino students to highs of 40%
and 47% for African-Americans and Native Americans. The original intent of the SAT was to identify
students who came from outside relatively privileged circles who might have the potential to succeed inuniversity. This original intent is clearly being realized at UC.
B-4. Some students admitted with weaker standardized test scores through comprehensive review
do perform well at UC. However, the lowest-scoring students typically do not perform well at UC.
The STTF has considered whether UC should increase admissions through the ELC pathway (in which
the highest scoring students at each high school are admitted irrespective of scores) or through the ETR
pathway (in which all students with a 3.0 in A-G courses have their application comprehensively
reviewed at the campuses to which they apply) in order to increase enrollment of underrepresented
groups. The question arises of whether admissions test scores should still be required for those applicants.
Figures 3B-2 and 3B-3 show side-by-side the relationship between SAT score and outcomes forexamples of groups underrepresented based on family income (left), ethnicity (center), and family
educational history (right). Figure 3B-2 shows rate of retention to the sophomore year, and Figure 3B-3
shows the graduation rate. These data suggest that in some ways, admissions test scores are a helpful andinformative predictor of whether the students of diverse backgrounds targeted by ELC/ETR will succeed

34

at UC. These data indicate that whether a students’ test score is high, fairly high, or moderately high has
little effect on their odds of graduating UC. However, students admitted with the lowest test scores show
a strikingly lower probability of graduating (top row). This pattern remains evident when stratifying
applicants based on high school GPA (bottom row).

Figure 3B-2. Students with poor test scores are at especially high risk of leaving UC within one
year. Top row: SAT scores are summed SATRM + SATRW from the 2010-2012 admissions
pool. Lines represent mean rate of non-retention. Bottom row: Dot area is proportional to numberof students in each SAT and HSGPA band (normalized by number of students in eachdemographic group). HSGPAs are weighted. Color indicates HSGPA bands. Bottom row panels
are the same data as in Figures 3A-3, 3A-4, and 3A-5.

35

Figure 3B-3. Students with poor test scores are at especially high risk of leaving UC with nodegree. Top row: SAT scores are summed SATRM + SATRW from the 2010-2012 admissions
pool. Lines represent mean rate of non-retention. Bottom row: Dot area is proportional to numberof students in each SAT and HSGPA band (normalized by number of students in eachdemographic group). HSGPAs are weighted. Color indicates HSGPA bands. Bottom row panels
are the same data as in Figures 3A-3, 3A-4, and 3A-5.

This does not necessarily mean UC should not admit those students. One of the goals of UC admissions is
to expand college access by providing opportunities to promising students who have not had equal
opportunities to manifest high academic achievement. This goal has been previously endorsed by BOARS
in its statements on Comprehensive Review 45. However, these results show that45

https://senate.universityofcalifornia.edu/_files/committees/boars/BOARSREPORTCOMPREHENSIVEREVIEW2012.pdf “One measure of excellence relates to the traditional academic indicators, GPA, standardized test scores,
numbers of A-G and honors courses, discussed in the prior section, but an important test of comprehensive review iswhether selection processes fulfill the mission of a great public university: to provide access to diverse quarters of
the state. It is critical to this mission for admission policies and practices to balance increasing selectivity while
acknowledging persistent inequalities in California schools and other opportunity deficiencies that impede the abilityof talented students to meet their potential. The extent to which UC is fulfilling this mission can be assessed byexamining systemwide and campus-specific outcomes using a range of demographic indicators, and by placing theacademic indicators discussed in the previous section in context.”

36

1) Discussions about expanding ELC, and particularly about increasing admission throughthe ETR pool, must be made with full understanding that test scores do provide predictive
power about UC outcomes.
2) In the absence of other changes to support for vulnerable students, modifications to UC
admissions processes to admit higher numbers of students with low test scores (as the
STTF is recommending) are likely to decrease retention, decrease GPA, increase time-todegree, and decrease graduation rates. This is the basis for the Task Forcerecommendation that campuses examine whether their supports for vulnerable students
are adequate.

C. UC does not appear to use standardized test scores in a way that amplifies racial disparities orthat prevents low-scoring students from disadvantaged groups from being admitted to UC.
Mean differences in test scores between members of various racial, ethnic, and socioeconomic groups canbe very large (hundreds of points on the SAT, one standard deviation or even more). Prior BOARS
committees, UC governing bodies, and outside advocacy and lobbying groups have at times expressed
concern that this may needlessly block talented members of certain groups from UC access: in otherwords, that there are applicants who are otherwise prepared, would do well at UC, and are prevented from
attending UC based on a single test score. It is clear that many of the possible ways of using test scores inadmissions (for instance, hard score cutoffs or fixed-weight systems) could produce this result. UC’s
policy on comprehensive review is in part designed to address this concern, identify such applicants, andensure that they are offered UC admission irrespective of their test scores. “Guiding Principles forComprehensive Review” in the BOARS document entitled “Guidelines for Implementation of University
Policy on Undergraduate Admissions” 46 defines comprehensive review as “The process by which
students applying to UC campuses are evaluated for admission using multiple measures of achievement
and promise while considering the context in which each student has demonstrated academic
accomplishment” and includes the provision that: “No fixed proportion of applicants should be admitted
based solely on a narrow set of criteria.”
The current Task Force shares the concern that large differences in test scores across demographic groups
could lead to large disparities in admissions rates across these groups for students who were otherwisequite similar. STTF members undertook a series of analyses aimed at determining how often this happens
and how well our comprehensive review system functions to promote access.
C-1. How UC campuses use standardized test scores in selection. Here, it is useful to review how most
UC campuses use test scores in selection. UCOP is responsible for producing contextual data – aimed atensuring that an applicant’s academic accomplishments are evaluated relative to peers in their highschool. To do this, UCOP annually generates percentile tables for all high schools that have at least 20
UC applicants across the past three years. Percentiles are provided within the applicant pool and within aparticular school, by individual UC campus and systemwide for standardized test scores as well as forhigh school GPA, number of A-G courses, and honors/AP courses. Normalizing academic achievements
and test scores to each high school allows strong students to stand out even if the school is not aparticularly high performing or well- resourced school. Centralizing this data production at UCOP
provides systematization, fairness, and economies of scale, ensuring all campuses have access to theinformation that would support comprehensive review.

46

https://senate.universityofcalifornia.edu/_files/committees/boars/documents/guidelinesimplementation-of-ug-admission-rev-7-2019.pdf

37

C-2. A note on test optional. It is important to note that this system works as well as it does because
UCOP receives both test scores and grades for all the applicants to any UC campus from a given highschool. Because UCOP receives scores from so many of the students at each school, they can supply thecampus admissions officers with scores normalized by high school, thus letting the readers judge whether
a student performed exceptionally well in the local context. A switch away from mandatory submission oftest scores to a “test-optional” regime in which students choose whether or not to take a test/submit ascore would remove UCOP’s ability to normalize scores by school and thus to compensate for school toschool variability in educational quality.
C-3. Family income and first-generation status play a strong role in admissions. UC does not use
hard score cutoffs. UC admits members of different groups with widely varying test scores. It is wellknown that students in disadvantaged groups tend, on average, to have lower HSGPAs and test scoresthan students without such disadvantage. The UC application asks students to report, among many otherthings, their annual family income and whether they would be the first member of their immediate family
to graduate from a four-year institution (first-generation status). Table 3C-1 presents the differences inaverage HSGPA and SAT for three groups: low-income vs. not low-income; first-generation vs. not firstgeneration; and applicants who are both low-income and first-generation vs. those who are neither. These
group average differences are substantial, especially for those applicants who are both low-income andfirst-generation 47.
IncomeFirst Generation

Both

Average Difference in HSGPA

0.18

0.20

0.25

Average Difference in SAT

194

216

226

Table 3C-1. Differences in average HSGPA and SAT between various demographic groups, for applicants to
any UC campus for fall 2018. Breakdown of this result for individual campuses is in Appendix V, Tables 1-3,
which show generally similar trends at the highly selective campuses, and indeed at almost all campuses, withthe exception of Riverside and Santa Cruz

Examination of the admission of applicants for fall 2018 show clearly that family income and firstgeneration status play a strong role in UC admissions. Figure 3C-1 and Figure 3C-2 show the percentage
of applicants who were admitted to any campus by bands of raw (not normalized) SAT scores and
HSGPAs. Focusing on the vertical (% admitted) difference between the disadvantaged and notdisadvantaged groups, the results show that applicants who were either low-income, first-generation, or
especially both were much more likely to have been admitted within nearly all bands of SAT and
HSGPA.

47

The difference of average SAT scores of 226 points between those who are both low-income and firstgeneration is about 1.3 standard deviations below the average of the non-disadvantaged group.

38

Figure 3C-1. Admit rate for fall 2018 comparing applicants in various demographic groups by SAT.
Breakdown of this result for individual campuses is in Appendix V, Figures 11-30 D, which show generally
similar trends at the highly selective campuses, and indeed at almost all campuses, with the exception ofRiverside and Santa Cruz.

39

Figure 3C-2. Admit rate for fall 2018 comparing applicants in various demographic groups by HSGPA.
Breakdown of this result for individual campuses is in Appendix V, Figures 11-30 B, which show generally
similar trends at the highly selective campuses, and indeed at almost all campuses, with the exception ofRiverside and Santa Cruz.

If campuses did nothing to account for the fact that students are members of these groups whose
academic indicators are likely to be lower than the entire applicant pool, their admission rates would
be very low. However, consistent with university policy on comprehensive review, 48 UC does appear
to use an applicant’s non-academic information in making admission decisions.
Race, ethnicity and gender are not, of course, considered in admissions, but other demographic factors
such as family income and first-generation status are. BOARS has identified 14 groups of factors thatshould be used in some combination during comprehensive review of applications. One of these states:
13. Academic accomplishments in light of the applicant's life experiences and special
circumstances. These experiences and circumstances may include, but are not limited to,disabilities, low family income, first generation to attend college, need to work,
disadvantaged social or educational environment, difficult personal and family situations or
circumstances, refugee status, or veteran status. 49

48

https://senate.universityofcalifornia.edu/_files/reports/hp2mgy_boars-testing_010609.pdf
https://senate.universityofcalifornia.edu/_files/committees/boars/documents/guidelines-implementation-of-ugadmission-rev-7-2019.pdf
4940

% of group who are low
income% of group who are
first-generation% of low income total% of first-generation total

Racial/Ethnic Group
AfricanAmerican
Asian
AmericanIndianLatinoWhite

Unknown

61%

17%

32%

51%

28%

32%

78%

19%

33%

50%

34%

30%

57%

9%

24%

8%

~0%

2%

61%

9%

21%

6%

~0%

2%

Table 3C-2. Fraction of applicants receiving low-income and first-generation preferences.

This preference for low-income and first-generation students benefits all applicants in these categories, so
it is relevant to consider what percentage of applicants in various racial/ethnic groups are affected. This isshown in Table 3C-2. The groups with the highest in-group percentages for both low-income and firstgeneration are Latino and African-American applicants. Within these groups, 61% of Latino applicantsand 51% of African-American applicants are identified as being low-income, while 78% of Latino
applicants and 50% of African-American applicants are identified as being first-generation. Thus, thesegroups should benefit substantially from the preference given to low-income and first-generation status,
even though race and ethnicity are not explicitly considered.
C-4. UC’s admissions process does not appear to increase racial/ethnic disparities by giving undue
weight to admissions test scores. STTF members were keenly aware of the large differences between
mean test scores among different racial and ethnic groups. The STTF was concerned that the uniquechallenges faced by members of certain minority groups may produce disparities in admissions outcomes,
mediated at least in part by differences in test scores, which may not be fully compensated for byconsidering parental income, educational history, or the other comprehensive review factors UC is legallyallowed to consider in admissions. This section expands upon the work presented in the previous
subsection to assess the extent to which racial and ethnic disparities are ameliorated by use of the 14
factors in comprehensive review.
Table 3C-3 shows the mean SAT scores of applicants by group. The rightmost column displays gaps
between the given group and a comparison group, as stated in the column headers. Figure 3C-3 shows
the distribution of SAT scores of applicants by racial/ethnic group. On average, applicants from different
racial/ethnic groups do have significantly different test scores, with gaps in average test scores as large as
234 points between Asian and Latino applicants.
Because of these gaps, STTF was concerned that UC might be using the SAT in ways that disparately
affects particular racial/ethnic groups. STTF regarded this as a plausible and sensible claim that an
intelligent layperson concerned about diversity would be likely to make. In the absence of persuasive
evidence that this did not happen, the STTF was prepared to recommend significant restrictions to UC’s
use of tests. Indeed, elsewhere in this document the Task Force has marshalled the main arguments for
dropping admissions tests, and that section writes stirringly about differences by group in SAT scoressuch as those shown in Table 3C-3, which many consider to be evidence that the SAT has a disparate
impact on admissions by racial and socioeconomic groups.

41

Figure 3C-3. SAT Scores of 2018 Applicants by Racial/Ethnic Group. Breakdown of this result forindividual campuses is in Appendix V, Figures 1-10 Cab, which show generally similar trends at thehighly selective campuses, and indeed at almost all campuses, with the exception of Riverside and Santa
Cruz.
Table 3C-3. Mean SAT Scores of 2019 Applicants by Racial/Ethnic Group. Source: Mean SAT scoresprovided by UCOP. (Note: The 2019 SAT scores are from the new version of the SAT, on which
students tend to receive higher numerical scores than on the old SAT.) Breakdown of this result forindividual campuses is in Appendix V, Tables 4-6, which show generally similar trends at the highly
selective campuses, and indeed at almost all campuses, with the exception of Riverside and Santa Cruz.

We did not find evidence that UC’s use of test scores worsened disparities that are already present among
applicants. Rather, we found that, even without explicitly considering race or ethnicity in admissions,UC’s use of test scores along with the other factors incorporated into the comprehensive review of eachapplication compensated for the majority of the score differences between groups. If the SAT matteredsignificantly for admissions, we would expect to see that the admission rate is very low up to some SAT
cutoff, and then rise steeply. This would greatly reduce the admission rate for the groups in Table 3C-3
with lower mean test scores. We did not observe this to be the case.
Table 3C-4 repeats the columns in Table 3C-3, but also adds a column showing the mean SAT score ofthose admitted to UC for each group. Table 3C-4 also provides information for other student groupings,
including grouping based on family income and whether either parent has graduated from college. The
column on the left shows large differences in mean SAT scores between low-income and non-low-income
applicants, and between first-generation and non-first-generation families. For instance, the gap in mean
SAT scores between applicants who are first generation and those who are not is 216 points, a gap almost
as large as that between Latino and Asian applicants.

42

2019 Admissions
Race/Ethnicity
African-American
Native American
Asian
LatinoPacific IslanderWhite
Unknown

First-Generation College
First-Generation
Not First-Generation
Unknown

Low-Income Status
Low-Income
Not Low-Income
Unknown

Applicants

AdmitsMean SAT

Gap
Vs. Asian

Mean SAT

Gap
Vs. Asian

1166
1259
1356
1122
1216
1338
1356

190
97
0
234
140
18
0

1230
1308
1386
1172
1269
1368
1406

156
78
0
214
117
18
-20

1145
1361
1286

Vs. Not
First
Generation
216
0
75

1201
1393
1341

Vs. Not
First
Generation
192
0
52

1143
1337
1348

Vs. Not
Low
Income
194
0
-11

1200
1371
1392

Vs. Not
Low
Income1710-21Table 3C-4. Mean SAT Scores of 2019 Applicants and Admits by Racial/Ethnic Group.
Source: Mean SAT scores provided by UCOP. (Note: The 2019 SAT scores are from the newversion of the SAT, on which students tend to receive higher numerical scores than on the oldSAT.) Lines above distributions show means and standard deviations. Means and standard
deviations are also printed on each panel. Breakdown of this result for individual campuses isin Appendix V, Tables 4-6, which show generally similar trends at the highly selective
campuses, and indeed at almost all campuses, with the exception of Riverside and Santa Cruz.

The rightmost column in Table 3C-4 shows the gap in test scores between those admitted in each groupand in a selected group (that with the highest mean test score). If the SAT was the predominantadmissions factor, and UC ignored an applicant’s context in its comprehensive review, these gaps in mean
test scores among admits should be much smaller than among applicants. The gaps remain large –approximately the same size as the gaps in the test scores of applicants. For instance, the Latino/Asian testscore gap among applicants, 234 points, is almost exactly preserved among the admitted students, with agap of 214 points. The implication is that comprehensive review effectively renorms SAT scores throughits consideration of the broader set of 14 factors.

43

This important finding, that admissions procedures appear to compare students’ SAT scores withinsocioeconomic groupings, but not across groupings, is reinforced by using graphs to look at the entire
distributions of tests scores of applicants and accepted students. Figure 3C-4 shows SAT scores ofstudents of different racial/ethnic groups accepted to UC overlaid on scores of applicants to UC. Counterto the belief implicit in the claim that the SAT plays too big of a role, and that UC pays less attention toother factors, the mean SAT scores of those admitted varies dramatically by group. This level of intergroup difference would be implausible if the SAT played such a predominant role in admissions as isoften thought. Just as Table 3C-4 suggests, the SAT scores seem to be renormed to take into accountstudents’ personal context.

Figure 3C-4. Distributions of SAT scores among applicants and among admits, by
racial/ethnic group. The figure shows the distribution of SAT scores for applicants (left, andgrayed-out on right for ease of comparison) and admits (right) by racial/ethnic group. Data
represent admission decisions for students applying for admission in fall 2018. Lines above
distributions show means and standard deviations. Means and standard deviations are also
printed on each panel. Breakdown of this result for individual campuses is in Appendix V,
Figures 1-10 C, which show generally similar trends at the highly selective campuses, andindeed at almost all campuses, with the exception of Riverside and Santa Cruz.

The STTF considered whether this implied that the use of tests in admissions was small enough that testscores could be dropped without serious consequences. Yet STTF considered one goal should be toidentify students who are the most likely to be prepared to take advantage of UC’s rigorous educational
opportunities. As shown in Section II-A of this chapter, the SAT is highly predictive of a broad range ofstudent outcomes at UC, within, not merely across, specific demographic groups. Further, the admissions

44

procedures do take into account differences in test scores within group. The rationale for use of test scoresis that it increases the likelihood that students in the admitted pool will graduate from UC.
Figure 3C-5 (left column, top row) shows even more clearly that UC comprehensive review policy
effectively compensates for most group differences in the SAT scores, by plotting the admission rate ofeach racial/ethnic group by SAT score. It shows that less advantaged racial/ethnic groups are admitted at
higher rates for any given SAT score. This finding is inconsistent with claims that UC considered SAT ina way which fails to take into account disadvantage. Indeed, to some readers, the differences in admission
rates by group, holding constant SAT scores may seem surprisingly large. To give one striking example:
for students with SAT scores of 1000, admission rates are about 30% for whites but roughly 50% forLatino students. How high do SAT scores need to be for whites to have the same 50% admission rate as
Latino students with scores of 1000 do? The answer is that whites do not reach admission rates of 50%
until they reach an SAT score of 1200. As Table 3C-4 shows, this gap almost exactly matches the gap inmean test scores among different groups of applicants. The fact that it is socioeconomic factors that are atwork here is clear from the fact that on average Asian students benefit more than white students due to thefact that the percentage of Asians who are low-income and/or first-generation students is roughly twicethat of whites. (See Table 3C-2.)

Figure 3C-5. Admission rates by racial/ethnic group, parental education level, andfamily income level for given SAT scores or HSGPA. Data represent admission
decisions for students applying for admission in fall 2018. Breakdown of this resultfor individual campuses is in Appendix V, Figures 1-30 B and D, which show
generally similar trends at the highly selective campuses, and indeed at almost all
campuses, with the exception of Riverside and Santa Cruz.

Figure 3C-5 (center column, top row) shows corresponding admission rates based on whether applicants
are the first in their family to graduate from college (similar to Figure 3C-1). It shows large advantages inadmission rates for first-generation students compared to not-first-generation students. Students whoseparents have not graduated from college are admitted at rates 15 to 20 percentage points higher for all45

levels of SAT scores except the very lowest range, roughly below 900 (where both groups are equally
likely to be admitted), and those approaching a perfect 1600 score (who are likely to be admitted nomatter their family educational history).
Figure 3C-5 (right column, top row) shows admission rates for each SAT score based on whether thefamily is low-income or not (similar to Figure 3C-1). Again, the relatively disadvantaged group has
substantially larger admission rates for students with SAT scores between about 900 and 1500.
Further confirming that the comprehensive review process has the practical effect of compensating for thevariations in SAT scores across groups, Figure 3C-4 above shows for each racial/ethnic group, andFigure 3C-6 below shows for each income and educational group, the distribution of applicants’ testscores (left) and the distribution of admits’ test scores (right). Although there is considerable overlap in
the admits’ test scores across groups, the figure clearly shows that campuses are taking into accountdisparities in educational opportunities and socioeconomic status of the various groups.

Figure 3C-6. Distributions of SAT scores among applicants and among admits by parental education (left) andfamily income level (right). Data represent admission decisions for students applying for admission in fall 2018.
Distributions of test scores for applicants are shown in paler colors, alone (left columns) and underlaid beneaththe distributions of test scores for admitted students (darker colors, right columns). Lines above distributions
show means and standard deviations. Means and standard deviations are also printed on each panel. Breakdownof this result for individual campuses is in Appendix V, Figures 11-30 A and C, which show generally similar
trends at the highly selective campuses, and indeed at almost all campuses, with the exception of Riverside andSanta Cruz.

46

C-5. The overall admissions procedures at UC make a smaller than expected contribution to racialand ethnic differences in admissions rates. Failure to complete minimum admissions requirements
is a substantially larger contributor than factors differentiating among eligible students. UC has aunique two-stage selection process: determination of systemwide eligibility, followed by campus- andmajor-specific selection leading to admissions, in which campuses choose among students meeting theminimum admissions requirements. Strategies for addressing underrepresentation at UC will be very
different depending on whether the disparities arise from failure to meet minimum UC requirements, orwhether they arise from differences in test scores, HSGPAs, curricular rigor, or other factors causing
campuses to choose among eligible students at different rates. The STTF was particularly concerned thatthe requirement that students even take a standardized test might be a major contributor to the
racial/ethnic differences in admissions rate we observed.
Surprisingly, the STTF’s preliminary study of the factors limiting minority enrollment found that much of
the difference between the racial makeup of UC’s resident undergraduate population and the state’s highschool population depends on other factors that limit eligibility, particularly UC/CSU’s requirement thatapplicants complete a broad college preparatory curriculum (A-G) with grades of C or higher. These datasuggest that a move to drop testing and instead to rely on HSGPA alone for eligibility, and the remaining
12 factors for comprehensive review, would not do as much to increase diversity as might be expected,
because these other factors play such large roles in reducing the enrollment of Latino, African-American,
and Native American students at UC.
First, consider overall patterns when these three groups are combined. In the 2017-18 school year, theURM groups constituted 59.1% of high school seniors in California, but only 37.0% of UC admits. The
gap of 22.1% is large by any standard.
What factors lead to this opportunity gap? Students need to complete a series of three actions before they
can be considered for admission to a UC campus. At a minimum, 1) they must have graduated from highschool. In order to be eligible for UC admission, 2) they must also have satisfied the A-G requirements.
Having graduated with the requisite courses, 3) they must then apply, and finally 4) an admission decision
is made.
Figure 3C-7 shows the contributions of these four factors. The three pre-admission factors explain muchof the disparity. The single biggest contributing factor is the lower shares of URM students who graduate
having completed the A-G requirements. URM students are also less likely to graduate from high school
than other students, accounting for just over 2 percentage points of the 22 percentage point gap.
Differences between URM students and others in their rates of application and rates of admission accountfor about 5 percentage points each.
Figure 3C-8 depicts this information differently by showing the percentage contributions to theadmissions gap of each of these factors. The figure shows that the single largest contributing factor isthe lower than average completion rate by URM of A-G college preparatory coursework in highschool. This factor alone accounts for 40% of the gap between the shares of URM students in highschool and in the admitted freshmen at UC. Lower rates of application to UC and acceptance to UC
conditional upon having applied each explain about 25% of the gap. Finally, lower high school
graduation rates explain about 10% of the gap.

47

Figure 3C-7 The contributions of various factors to the gap
between underrepresented minority shares of high school
seniors and UC in-state admits, 2017-2018. Notes: Seenotes to Table 3-C-5.

Figure 3C-8 The percentage contributions of various
factors to the gap between underrepresented minority
shares of high School seniors and UC in-state admits, 20172018 (numbers add to 100%). See notes to Table 3C-5.

48

Table 3C-5 below shows the data underlying the above figures, while also providing details by
ethnic/racial group. The first three columns show for California the racial makeup in 2017–18 of highschool seniors, high school graduates, and graduates meeting the UC/CSU’s A-G requirements withgrades of C or higher, all obtained from the California Department of Education. 50 This last piece of
information is crucial, because students must complete the A-G requirements to be eligible to attend
UC. The rightmost columns add information on the racial mix of applicants to UC and admits to UC
for fall 2018. 51 52 The rows for the three groups that UC considers as underrepresented minorities arehighlighted in yellow. The final row combines the three URM groups. (It is these data that appear in
Figures 3C-7 and 3C-8.)
Table 3C-6 calculates the differences between various columns in Table 3C-5 to show how the
racial mix changes at each step as we compare high school seniors to graduates, graduates tograduates who complete the A-G requirements and so on. The first column shows the difference
between each group’s percentage in the high school senior population and the percentage in UC’s
admit population. At the bottom of the first column, we see that overall there is a 22.1 percentage
point gap between the share of under-represented minorities in the high school class of 2018 and thestudents admitted to UC in 2018. This crucial first column also shows that Latino students are underrepresented among UC admits by 18.2%, while the corresponding percentages are 1.85% for
African-Americans and 0.28% for Native Americans. Because these three groups vary in their sharesof the high school population, it makes sense to compare these gaps to their overall shares in thehigh school class of 2018, shown in the first column. Relative to their share of the high school
population, Latino students are under-represented by about a third, compared to just over half for
African-Americans and two-thirds for Native Americans.

50

For source see notes to the table.
For source see notes to the table.
52
It is important to note that the way UC presents most data by race and ethnicity does not match the federal
standard. The data in the above table and the preceding graphs in this subsection all uses the federal
ethnicity/race standard. The difference is that the UCOP uses the following hierarchical order to determine
ethnic/racial classification for an applicant when more than one ethnicity is selected: African-American,
Chicano, Latino, American Indian, Asian, Pacific Islander, White, Missing/Unknown. This means that when
an applicant selects African-American and Latino, UC puts this applicant in the African-American category.
The federal standard takes a different approach in which choice of Hispanic/Latino ethnicity by a person
trumps any choice of race that person might make. Federal guidelines can be found at
http://data.ucop.edu/support-training/support-resources-files/Business%20Rules%20%20UC%20and%20IPEDS%20Ethnicities.pdf. For UC applicants and admits, the biggest difference this makesis that UC counts students with race of black and ethnicity of Hispanic/Latino as black students. Under thefederal definition, these students would be listed as Hispanic/Latino. Elsewhere in this report, calculations wemade were based on a database of individual students that use the UC definition which under the federal
definition will overcount blacks and undercount Hispanic/Latino students. The differences in the racial mix that
result from the UCOP system are small in terms of percentage points, but can be large in terms of percentages(proportions) for the smaller groups such as African-Americans. To give one example, in 3C-5 below, we
calculate using federal definitions that African-American, Native American and Latino students account for
2.8%, 0.1% and 37.0% of UC admits. If we had used the UCOP racial/ethnic rules, the respective numbers
would have been 4.8%, 0.5% and 33.0% respectively.
51

49

Table 3C-5 The Racial/Ethnic Mix of Students in High School and in Applications Data for the UC, 2017-2018
Notes: * Asian group includes Filipinos. Data from the first three columns from Dataquest downloaded 11/2019
at https://dq.cde.ca.gov/dataquest/dqcensus/CohRate.aspx?cds=00&agglevel=state&year=2017-18. Data fromthe final two columns downloaded 11/19 (CA Residents only) fromhttps://www.universityofcalifornia.edu/infocenter/admissions-residency-and-ethnicity. The first three columns
show a different racial/ethnic breakdown than the last two columns because the federal government and UC
divide students by racial/ethnic identity along different lines.

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Table 3C-6 Decomposition of the Gap between the Racial/Ethnic Mix of Students in High School and UC
Admitted Students, 2017-2018. See notes to Table 3C-5. The rightmost column calculates the third column as apercentage of the first column.

The columns further right in Table 3C-6 break down the percentage gaps into gaps due to various
factors. The second column compares the racial mix of high school seniors to the racial mix of highschool graduates who complete the A-G requirements. The calculations show that 11.1 percentage
points of the 22.1 percentage point gap in the share of under-represented minorities between highschool seniors and UC admits can be accounted for by the lower than average rates at which underrepresented minorities graduate having completed A-G requirements. The column furthest to the rightshows the extent to which the gap between applications and admissions can explain the overall gaps
between each group’s shares of the high school class of 2018 and the students admitted to UC. For
Latino and African-American students, about one-quarter of the gap can be explained by admission
decisions. For Native Americans, about 5% can be explained by admission decisions.
To obtain a better sense of the relative contributions of all of the factors, Figure 3C-9 shows thebreakdown on the relative contributions of the four factors, ranging from differences in high school
graduation rates to differences in admission rates to UC. The first bar repeats the overall results for
all three URM groups combined, while the bars to the right show the results by specific racial/ethnic
group.

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Figure 3C-9 The Percentage Contributions of Various Factors to the Gap between Each Underrepresented
Minority’s Share of High School Seniors and UC In-State Admits, 2017-2018 (Numbers add to 100%).
Notes: See notes to Table 3C-5.

For all groups, about one half of the gap between the given group’s share in the high school class of 2018
and the admit pool comes from lower than average high school graduation rates and lower A-G
completion rates. These pre-UC-application factors were especially important for Native American and
African-American students. Lower than average UC application rates for these groups also contributed,
and were particularly important for Native Americans, for whom this factor explained 38.1% of thedisparity. This factor was least important for African-American students.
Turning to the item of key interest, the contribution to the disparity made by lower than average UC
acceptance rates ranged from a low of 4.6% of the total gap for Native Americans, to 25.3% for AfricanAmericans and 26.9% for Hispanic/Latino students.
The conclusion from this analysis is that the comprehensive review admissions procedures at UC, ofwhich standardized test scores and HSGPA are two components, is not the main determinant of the gap
between the racial mix of high school seniors and the racial mix of UC admits.
C-6. HSGPA also shows strong racial disparities. Much of the gap between the proportions of URM
high school seniors and the representation of URMs among UC admit pools reflect disparities in
graduating from high school or completing the A-G coursework. Nonetheless, a smaller fraction ofunderrepresented minority students are admitted to UC than graduate with A-G courses completed. We
examined whether factors other than differences in test scores account for any of this further drop-off.

52

One example of the possibility that other elements among the 14 admissions criteria may contribute toracial/ethnic gaps is the distribution of HSGPA by racial/ethnic group. Each row of Figure 3C-10 below
shows, for one of the four racial/ethnic groups most represented at UC, the distribution of HSGPA among
applicants on the left, and admits on the right (with the applicant distribution shown in the paler colors on
the right as well). The figure shows large differences in the distribution of HSGPA among applicants,
with African-American and Hispanic students having much lower HSGPAs on average. The bottom rowof the figure overlays the various racial/ethnic groups, which makes the disparities in HSGPA amonggroups even clearer. The right hand side of the figure shows the distribution of HSGPA among admits.
UC appears to admit very few of the students with low GPAs. Differences in HSGPA distributions aremuch lower among admitted students than among applicants.

Figure 3C-10. Distribution of High School GPA (HSGPA) among Applicants and Admits to
UC in 2018, by Racial/Ethnic Group. Means and standard deviations for each group are
represented above each distribution and displayed numerically on each panel. Breakdown of thisresult for individual campuses is in Appendix V, Figures 1-10 A, which show generally similar
trends at the highly selective campuses, and indeed at almost all campuses, with the exceptionof Riverside and Santa Cruz.

This implies that UC takes low grades quite seriously, and as a result of lower grades, smaller shares ofapplicants from these two groups are admitted than for the other groups. Importantly, this pattern is quite

53different from the pattern shown in Figure 3C-4, which showed that differences in SAT score
distributions among admitted students were similar to the differences among applicants (implying that
UC’s comprehensive review process effectively renormed test scores between groups), and is consistent
with the results in Section II-B showing that UC’s admissions process more heavily weights high school
grades than test scores.
The same pattern holds when students are grouped based on family income or family educational history.
Figure 3C-11 shows the distribution of HSGPAs among applicants and among accepted students, forstudents who are the first in their families to go to college (cyan) or who had a college-going parent(orange), and for students who are from low-income families (blue) or who are not low-income (red). Just
as we saw in the ethnicity data, the income and education data also reveal that the differences in HSGPAs
between groups of applicants are substantial, but that the differences in HSGPAs among groups ofaccepted students are smaller. This is quite different from the pattern of SAT score differences between
groups, which are equally large among applicants as among accepted students (previously shown in
Figure 3C-6). This indicates that UC heavily considers HSGPA in admissions, and does not appear torenormalize HSGPA between groups in the same way as SAT.

Figure 3C-11. Distributions of SAT scores among applicants and among admits by parental education (left) andfamily income level (right). Data represent admission decisions for students applying for admission in fall 2018.
Distributions of test scores for applicants are shown in paler colors, alone (left columns) and underlaid beneaththe distributions of test scores for admitted students (darker colors, right columns). Lines above distributions
show means and standard deviations. Means and standard deviations are also printed on each panel. Breakdownof this result for individual campuses is in Appendix V, Figures 11-30 A, which show generally similar trends at
the highly selective campuses, and indeed at almost all campuses, with the exception of Riverside and Santa Cruz.

The upshot: lower grades of African-American and Hispanic students appear to play a role in the disparity
between the racial mix of high school seniors and UC students. The SAT does not appear to be the only
factor, or even the disproportionate factor, in the existing differences in the racial mix of high school54

graduates completing A-G and enrollees at UC. Differences across racial groups in high school GPA
clearly are a substantial factor.
C-7. Summary of results on test scores and representation of minorities at UC. Overall, this section
has shown that for URM students overall and the constituent racial/ethnic groups, there exists a disparity
between the given group’s share in the graduating class in high school and UC admits. The gaps,
averaging 22% overall for URM groups combined, are big enough to cause concern, especially in light of
the Regental goal that UC reflect the state’s diversity.
Do these large gaps result from the way UC admits students? The answer is that UC admissions do play arole in these gaps, but a relatively small role. For URM students overall and also for Latino and AfricanAmerican students, lower than average UC admission rates contribute about one quarter of the gap. For
Native Americans, UC admission rates explain only 4.6% of the under-representation at the University.
So what are the more important factors than UC admissions decisions? For URM students overall as well
as for the three URM subgroups, relatively low rates of completion of A-G coursework is the single
biggest factor explaining the under-representation. The next most important factor varies by group. For
Latino students, relatively low application rates and admission rates are tied as the next most importantfactors. For African-American students, the second most important factor is UC admission decisions,followed closely by lower than average gaps that originate in UC admission decisions to inter-groupdifferences in the SAT. A comparison of Figures 3C-4 and 3C-14 above provides clear evidence thatSAT scores do not play a predominant role in the different UC admissions rates, but that differences in
GPA do play a substantial role.
UC considers 14 factors in admissions. Although we could not examine all 14 factors, our study foundthat URM applicants to UC on average have lower GPA, have completed fewer A-G courses, and havecompleted fewer honors courses as well. This means we cannot assume that eliminating the test
requirement would raise the URM share of the admissions pool much closer to their share in the pool ofhigh school graduates completing the A-G requirement.
C-8. Summary of results on test scores and demographic indicators overall. These analyses indicatethat mean differences in standardized test scores between different demographic groups are often verylarge. Many of the ways these tests could be used in admissions would certainly produce strong disparate
impacts between groups. Yet UC uses the test scores in ways that minimize the differential impacts on
admission rates by demographic group. UC weights test scores less strongly than GPA, and through theuse of comprehensive review in the admissions process appears to largely renormalize test scores bygroup. Although it is natural to infer that differences in test scores among applicants must cause
differences by group in admission rates, this does not seem to be the case for the way that UC uses thisinformation.
D. Do standardized test scores counterbalance the variability among grading patterns at different
high schools?
A direct consequence of dropping admissions tests would be that HSGPA would become the only
quantitative measure of academic preparation. This is problematic because HSGPA is not an objective
measure of achievement. Its relation to objective measures of achievement can vary from teacher toteacher, and from school to school. We can refer to this as variations in grading standards. HSGPA is
also a less than perfect measure of changes in academic preparation over time, due to evidence of grade
inflation.

55

These two problems are related, with variations in standards making it difficult to compare thepreparation of two students from different schools in a given year, while grade inflation raises the
problem that HSGPA’s relation to objective measures of achievement may change over time, and indifferent ways for different high schools.
Research provides ample evidence of both phenomena.
D-1. Grading standards differ across schools, and wealthy schools typically award higher grades.
Betts and Grogger (2003) used a nationally representative sample of 798 U.S. public high schools to
examine the relation between individual students’ math grades and performance on a standardized test ofmath achievement. 53 Their model allowed for the possibility that some schools might have more rigorous
grading standards, in the sense that the math test scores would on average be higher for a given letter
grade. The authors found that the probability that all high schools grade students used the same standard
was virtually zero.
Here is one way to get a sense of how big the variations in grading standards were in the data. The
standard deviation provides a measure of how much variation there is in a variable. The standard
deviation in students’ grade 10 math scores was 9.7 points. The standard deviation across high schools in
grading standards, measured in grade 10 math scores, was 3.5, which represents about one third of theoverall variation in student test scores. This means that high schools with very different levels of mathachievement gave similar grades.
More recent data comes from the state of North Carolina, where Gershenson (2018) takes advantage ofmandatory end-of-course (EOC) exams and ACT test-taking in that state.54 He finds that EOC exams
predict ACT scores later in high school quite well, but that grades in high school classes are much lesspredictive. More affluent high schools tended to have higher grades than less affluent schools.
Why might public schools vary so much in what a grade of A or B or C means? The most obvious
explanation is that, implicitly or not, teachers curve their grade distributions in part to reflect the academic
preparation of their students. At a school with below-average student achievement, teachers are not goingto restrict all grades to D and F, just as in a school with above-average achievement teachers are not goingto give a grade of A to all students. By ensuring that a certain percentage of students must receive A’s
regardless of their objective performance, one school with low achievement can institute lower grading
standards than another school with higher achievement. It is also likely that schools serving similargroups of students also differ in their grading standards for idiosyncratic reasons.
The consequence is that HSGPA is not a uniform measure of academic performance. This problem is amajor concern because if a university used HSGPA alone to measure academic performance many
students could be admitted or excluded based on variations in the meaning of grades across high schools.
D-2. Grade inflation is increasing and is statistically biased towards wealthy schools. Gradeinflation, in the sense we are using here, refers to changes over time that lead to higher HSGPA relative toobjective measures of achievement. National studies have shown increases in HSGPA have been
occurring for several decades. For example, Hurwitz and Lee (2017) estimate mean HSGPA holding
constant SAT scores for a national sample of students. They find that between 1998 and 2016 HSGPA
steadily increased, holding constant SAT scores, by 0.15 grade point. Looking at their raw data, they find53

Betts, Julian R. and Jeff Grogger. 2003. “The Impact of Grading Standards on Student Achievement, Educational
Attainment, and Entry-Level Earnings,” Economics of Education Review, (22:4), August, pp. 343-352.
54
Gershenson, Seth. 2018. “Grade Inflation in High Schools (2005-2016), The Thomas V. Fordham Institute.

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that over this period mean HSGPA rose from 3.27 to 3.38. This alone does not mean grade inflation has
taken place, but SAT scores for the same period and the same students actually dropped, from 1026 to
1002. 55
Another concern is that changes in grading standards nationwide seem to be working in favor of moreadvantaged populations. In the preface to this book chapter, the book editors Buckley, Letukas, andWildavsky succinctly state the major policy concerns raised in the Hurwitz and Lee (2017) analysis:
As Hurwitz and Lee explain, as average grades inflate toward a natural “ceiling”(traditionally 4.0, although some high schools have raised this) there is, by mathematicalnecessity, less variability in grades and therefore less predictive power in the admissions
context. But even more important, the authors show conclusively that grade inflation is notoccurring evenly in society. On average, they observe greater grade inflation among white,
Asian, wealthy, and private school students than among less advantaged students and thosein the public schools. In other words, the use of high school grades in admissions is fraught
with equity issues and is not a panacea for concerns with standardized tests. (Emphasis
added by the STTF.)
This is a major finding and should give pause to those who believe that admissions tests should beabandoned in favor of using HSGPA as the main measure of academic preparation. Other studies point in
the same direction. For example, the aforementioned study of North Carolina finds that grades have risen
in North Carolina over time, but more so in affluent schools. We lack a similar study in California. We doknow that the HSGPA of applicants to UC has risen steadily over time, which could indicate grade
inflation. But on the other hand, SAT scores among UC applicants have also risen over time. Figure 3D-1
below shows that by 2018 almost 80% of UC enrollees had a weighted HSGPA of 3.8 or higher. Test
scores have risen somewhat but as shown in the figure, the trends are not nearly as clear. This raises
concerns about grade inflation. Regardless of whether higher grades in California over time mean higher
actual achievement, the data in Figure 3D-1 show that “grade compression” is severely limiting theinformation conveyed to admissions officers by the HSGPA measure.
There is ample evidence of grade compression occurring in California high schools. Figure 3D-1 shows
this in one way. Figure 3D-2 shows the severe grade compression that is making HSGPA lessinformative in another way. It depicts UC applicants’ SAT scores and GPA from two unnamed California
high schools. Average achievement as measured by SAT is markedly different at the two schools.
Both show very high HSGPAs with a similar distribution having most students clustered together near the
top of the allowable GPA range. This example shows evidence both of variations in grading standards andthe reduction in the information provided by grades due to the clustering of students near the top possible
GPA values.

55

Hurwitz, Michael and Jason Lee. 2017. “Grade Inflation and the Role of Standardized Testing,” inBuckley, J., Letukas, L., & Wildavsky, B. (Eds.). Measuring success : Testing, Grades, and the Future ofCollege Admissions. Johns Hopkins University Press.

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Figure 3D-1 Trends in High School Grades, A-G Courses Completed and Test Scores of Entering UC
Freshmen. Source: Figure 1.3.1, UC Office of the President, 2019, “University of California Annual
Accountability Report 2019.” Note: Test scores show the average of the SAT math and verbal, not the sum.

Figure 3D-2 HSGPA and SAT scores for applicants to
UC from two California high schools (2018). Students
from the two schools are indicated in different colors.

58

One counter-argument that varying grading standards across schools limits the value of GPA in
admissions decisions is that the job of admission officers is in part to get to know all the high schools andtheir standards, and to make adjustments. This makes sense, but only to a degree. It assumes, for example,
that grading standards do not change over time.
We already have national evidence that grading standards have fallen. Even more important, the relative
amount of grade inflation at one school relative to another can change. Hurwitz and Lee (2017), in theirnationwide study, document that high schools differed dramatically in how much grading standards
relative to SAT scores changed between 1998 and 2016. They found something quite different from a
gradual predictable increase in GPA inflation across all high schools. The bottom 10% of high schools,
ranked by increases in grade inflation, showed mean GPA dropped considerably, from 3.4 to 3.1. The
highest decile of schools, meanwhile, showed an increase in mean GPA rising from 3.1 to 3.6 in 18 years.
It is not clear that admissions officers, given all of their other duties would be able to detect andremember swings in grading standards until quite a few years had passed.
A related concern is that the predictive power of HSGPA for first-year GPA at UC has declined markedly
over the last few years. Recall from section A.4 earlier in this section that between 2007 and 2015 theability of the SAT to explain first-year UC GPA remained roughly constant, while the predictive of abilityof HSGPA fell roughly by a third. This too is consistent with rising GPA among high school applicants
not leading to comparable increases in GPA at UC, that is, rising grade inflation and grade compression
over time. Ironically, in spite of the current debate about admission tests at UC, this data suggests that weneed additional measures of achievement beyond GPA more than at any other time in the last few
decades.
E. Additional rationales implicit in UC’s current use of tests.
E-1. Test scores are essential to increasing access through enrollment management. UC campuses
issue admissions offers each spring, but it is the students and their parents who decide whether to accept
them. In order to decide how many freshman admission offers to issue, admissions officers must estimate
what fraction of students will accept the offer of admission (“yield rate”). Test scores strongly improve
quantitative modeling of yield predictions 56 57 58). That is, the additional information from test scores
improves predictions of how many students will enroll. This lets the campuses offer admission to as many
students as possible without overcommitting. Without SAT or ACT test scores, UC would lose much ofits capability to model yields. As a consequence, UC campuses would have to choose: keep admissions
levels the same, likely resulting in more frequent and more severe over-enrollment incidents in which theuniversity is unable to meet its housing and instructional commitments 59; or choose to reduce the number
of offers made, thus reducing access to UC.
E-2. Admissions tests measure core academic skills essential for UC success. As previously discussed,
the sine qua non for UC admission is completion of 15 A-G courses with a mean grade of B or higher.
This allows students to demonstrate proficiency in a broad range of academic disciplines and skills, but as
56

Weiler, W “Factors influencing the matriculation choices of high ability students.” Economics of Education
Review, Vol. 15, Issue 1, Feb 1996, Pgs 23-36.
57
Nurnberg, P., Schapiro, M., & Zimmerman, D. “Students choosing colleges: Understanding the matriculation
decision at a highly selective private institution.” Economics of Education Review, Vol. 31, Issue 1, Feby 2012, Pgs
1-8.
58
Hill, C. and Winston, G. “Low-income students and highly selective private colleges: Geography, searching, andrecruiting.” Economics of Education Review. Vol 29, Issue 4, Aug 2010, Pgs 495-503.
59https://www.nytimes.com/2017/07/29/us/uc-irvine-acceptance-rejected.html
https://ucsa.org/wp-content/uploads/2018/02/UCSA-Over-Enrollment-Resolution-1.pdf

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we have seen, school grading standards vary so widely that a “B” in a mathematics class, for example,
does not always indicate the level of preparation needed to succeed at college level academic work, andnot all students have access to the same breadth and quality of A-G coursework. The core SAT and ACT
tests complement A-G by measuring mastery of English language arts and mathematics, the mostfundamental skills needed for University-level academic work. Students in all UC majors must be able toread and answer questions about what they read, and to approach problems quantitatively. These are coreskills students must have in order to learn in college level courses, and particularly to benefit from therigorous academic opportunities a research university such as UC provides.
E-3. Admissions tests are key to administering the “Compare Favorably” requirement for nonresident students. Regental policy, dating back the original Master Plan, requires UC to ensure that
undergraduates who are admitted from other states and other countries must be more qualified, on
average, than resident undergraduates. This policy is known as “Compare Favorably.” UC is audited
regularly to ensure compliance, as is appropriate for an institution that is fundamentally a public resource
held in trust for the people of California.
Although UC admits students based on 14 comprehensive review factors, public auditors generally
evaluate Compare Favorably only using the two factors that are most easily quantified: HSGPA andstandardized test scores. Yet as we have seen in Section III-D, course titles, course content, and grading
standards vary notably across states and even more so across countries. Thus, UC relies on standardized
tests to allow for direct, objective comparisons of non-resident students’ qualifications to those of
California applicants, using comprehensive review. While nonresidents’ GPAs are only 0.15 points
higher, on average, than the GPAs of California residents who are admitted (about 0.45 of a “standard
deviation,” the mean student-to-student difference in HSGPA among the applicant population),
nonresidents’ SAT scores are about 200 points higher (roughly 1.5 times the standard deviation inSAT). 60
E-4. Most aspects of opportunity and preparation are not equally distributed between groups;
eliminating the use of standardized tests could worsen inequality. UC rightly incorporates aspects of
merit that are not purely academic in the admissions process. Leadership, meaningful volunteer work, andsignificant extracurricular interests indicate that the student is likely to contribute to the campus
community in ways beyond simply showing up for class. Challenges the applicant has faced andovercome are evidence that the applicant shows determination or grit, qualities intrinsically worth
rewarding and that are also predictive of long-term success. 61 An application that compellingly presents
the student’s non-academic, difficult to quantify “softer” factors places the student at a significant
advantage, compared to an application that minimizes or ignores those factors.
Yet constructing such an application is facilitated greatly by financial resources and a sophisticatedunderstanding of how the admissions system works. This introduces statistical biases that are hard to
overcome for low-income students or those who may not be part of a college-going family (i.e. first
generation students). Many of the ways an applicant can demonstrate extracurricular interests are either
60

https://senate.universityofcalifornia.edu/_files/committees/boars/Compare-Favorably-Report-to-President-July2017.pdf
61
E.G. Gifford, Denise D.; Briceno-Perriott, Juanita; Mianzo, Frank “Locus of Control: Academic
Achievement and Retention in a Sample of University First-Year Students.” Journal of College Admission,v191 p18-25 Spr 2006. Muenks, K., Wigfield, A., Yang, J. S., & O'Neal, C. R. (2017). How true is grit?
Assessing its relations to high school and college students’ personality characteristics, self-regulation,
engagement, and achievement. Journal of Educational Psychology, 109(5), 599–620 Reviewed in Credé, M.,
Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature.
Journal of Personality and Social Psychology, 113(3), 492–511.

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costly outright (e.g. sports teams, mission trips) 62 or carry the “opportunity cost” of competing with timespent working for wages, as many low-income students must do. 63 64 Some require large timecommitments on the part of the parents as well, which can be prohibitively difficult for parents who work
multiple jobs or jobs with unpredictable schedules. Many advantaged applicants have adults in their lives– well-connected parents, admissions counselors in their schools, or even hired admissions advocates –who can advise them to become involved in leadership or extracurricular activities early, advise them on
the balance between breadth and depth of commitment to a single interest, and coach them on how toanswer personal insight questions in ways that demonstrate depth of commitment or grit. Even if a
disadvantaged student has compelling soft factors, they are unlikely to have the same access to adultassistance in crafting an application that highlights these factors. 65 To quote UC Professor emerita
Rebecca Zwick, “…the less specific the demands of university gatekeepers, the more important the roleof the candidates’ cultural capital – in this case, knowledge about the university culture. Thus, the fuzzier
the admissions criteria, the greater the disadvantage suffered by those not already steeped in the academic
ethos.” 66This concern extends to academic features of the application as well. Should a student take an additional
honors class (and risk a low grade or lowering their other grades for the term), or take a non-honors classwith weaker grading standards? Extra math or an extra year of language, or a course on a new topic todemonstrate academic breadth? Disadvantaged children are less likely to attend schools with ready accessto college counselors who can advise them on how to make these curricular choices. Indeed, URM, firstgeneration, and low-income children are less likely to take these extra courses, 67 less likely to attend
schools that even offer these extra courses, and more likely to attend schools in which honors enrollment
is capped. Even if the courses are available, in order that the classwork and extracurricular work will beready to display in the college application, these decisions must be made early in the high school career –earlier than many parents and children, particularly those from low-SES backgrounds or from groups that
do not have high levels of college attainment, start planning for college admissions. 68 As discussed in
Section II-D, not only are GPAs systematically and significantly higher at wealthy schools, but grade
inflation is increasing more rapidly at wealthy schools. All these factors further advantage children of62

David B. Grusky, Timothy M. Smeeding, C. Matthew Snipp Kaisa Snellman, Jennifer M. Silva, Carl B.
Frederick, Robert D. Putnam “The Engagement Gap: Social Mobility and Extracurricular Participation among
American Youth” The Annals of the American Academy of Political and Social Science Volume: 657 issue: 1,page(s): 194- 207 201563https://repository.library.georgetown.edu/handle/10822/1052638
64
https://pdfs.semanticscholar.org/eac9/ac094f886900ea2aa69d4c0b400c69837b54.pdf
65
LW Perna, MA Titus “The relationship between parental involvement as social capital and college enrollment: An
examination of racial/ethnic group differences” The Journal of Higher Education, 2005
66

Zwick, R. (2017). “Who Gets In?” Cambridge, MA: Harvard University Press.”
This is evident in the patterns of course-taking across different demographic groups. UC “weights” honors
and AP courses more heavily than non-honors courses in admissions; honors courses are worth one extra point.
In 2018 only 8.5% of Black students and 10.6% of Hispanic/Latino applicants to UC had weighted GPAs above
4.0, compared to 22.7% of Asian-American applicants and 24.6% of Caucasian applicants. The median Blackapplicant to UC had taken 10 honors courses, the median Hispanic applicant had taken 12, while the median
Asian and White applicants had taken 15 and 14, respectively.
68
Hearn, J. C. (1984). The relative roles of academic, ascribed, and socioeconomic characteristics incollege destinations. Sociology of Education, 57(1), 22–30. Cabrera, A.F. & La Nasa, S.M. On the Path toCollege: Three Critical Tasks Facing America's Disadvantaged. Research in Higher Education (2001) 42:
119
67

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high-SES and college-going families. Indeed, in the past, UC has been sued by the ACLU on behalf of
low-income and minority students for considering these factors in admissions. 69 70
Considered in this light, the potential for admissions tests to increase opportunity – but only when usedappropriately – seems clear. 71 72 They test only a limited set of skills that are most important for college
success, which all high schools teach and which students have other opportunities to learn. Scoring
rubrics and university use of the scores are both relatively transparent. A student does not need to begin
polishing their CV in eighth grade to achieve a strong admissions test score. Preparation material is freely
available through libraries and online services such as Khan Academy. This is not ideal: there is noquestion that more affluent students have greater access to material that teaches the core academic skills
tested by the SAT and ACT, as well as to test-specific preparation resources, just as they have greater
access to every other resource that prepares a student to construct a strong college application and tosucceed in college once admitted. But the inequities in access to materials needed to score well on a testof basic writing and language skills are more easily remedied than are inequities in access to A-G
coursework, school grading standards, 73 and extracurricular opportunity.

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Daniel v. State of California (1999) https://www.aclu.org/press-releases/class-action-lawsuit-aclu-says-castudents-are-denied-equal-access-advanced-placement "There is no reason why such students should be denied
the ability to compete equally for admission to California's elite universities," she added, "or to succeed incollege degree programs, simply because their schools did not provide an adequate AP program."
70
Castaneda v. Regents of the University of California (1999)
71
Wooldridge A, “A True Test” In Defense of the SAT.” The New Republic, 1998. “What makes this whole
exercise so ironic, of course, is the fact that, until very recently, standardized testing was an instrument of
upward mobility—and not a barrier to opportunity. Napoleon used tests to introduce the aristocrat-destroying
principle of a "career open to the talents." British Whigs such as Macaulay and Trevelyan used tests to open
Oxbridge and the civil service, hitherto playgrounds for the idle rich, to lower-class meritocrats. In the UnitedStates, testing played a vital role in forcing the WASP-dominated Ivy Leagues to admit Jews and whiteethnics—and then blacks, Asians, and Hispanics. When, in 1933, Harvard's President James Bryant Conant
launched a revolutionary scholarship program that awarded money solely on the basis of academic promise, theperson he employed to detect that promise was Henry Chauncey, who later went on to found the Educational
Testing Service. During a 1955 Supreme Court hearing to debate ways of implementing Brown v. Board of
Education, Thurgood Marshall, then a lawyer for the NAACP, relied on testing to explode the segregationist
argument that the average black child was so far behind the average white that it made no sense to educate themin the same schools. Why not test all children, black as well as white, he argued, and educate them according totheir test results rather than by the color of their skin?”72“Thurgood Marshall: His Speeches, Writings, Arguments, Opinions, and Reminiscences”, ed. Mark Tushnet;
Lawrence Hill Books, Chicago, IL, 2001.
“A Meeting in Atlanta”, The New Yorker, Bernard Taper, March 9 1956.
73
This was part of the original BOARS rationale for adopting standardized tests: “In addition, their use would
tend to reduce the inequities resulting from differences among school marking systems, and thus would be in
the interest of the applicant.” (UC Academic Senate, 1967
https://oac.cdlib.org/view?docId=hb6p3006xs&brand=oac4&doc.view=entire_text)

62

IV.

Concerns Over Use of Standardized Tests in UC Admissions

As noted in the prior section, there are large disparities in admissions along lines of race, ethnicity, andclass. The STTF acknowledges interlocking systems of inequalities, racism and other forms of
oppression, and the role they play in limiting educational opportunity, achievement and university
admissions. In emphasizing the lack of A-G completion as a significant factor in preparedness andadmission to UC, the STTF does not attempt to discount the very real environmental factors that create
and contribute to race- and class-based opportunity gaps. This section offers a perspective on the data andanalysis presented in Section III above, then situates admission to the University in a broader, historicalcontext to show the importance to all stakeholders of striving for fair access.
The presentation of data and analyses in the prior section cannot settle questions of policy, nor decide thebest balance of practices to meet the obligations and responsibilities of UC as an institution. A sober
evaluation and interpretation of the data presented in this report, however, should inform policy decisions
by UC regarding admissions and could help all stakeholders, including students and parents, understandbetter what the implications of various policy choices regarding the continued use of standardized tests
for admissions may be. Task Force members debated long and hard how to balance its empirical findings– including that the use of standardized tests for admissions are not the sole, nor even the main, culprit
causing disparities in admissions to UC – with the duty of UC, along with other institutional partners, toaddress the deep structural and institutional causes of disparity in K-12 educational quality and access tohigher education across the state. The charge to the Task Force was specific: to consider continued use ofstandardized tests in UC admissions and make recommendations to the Academic Council of the
University. The implications of our empirical findings for what policies might improve fairness of accessand our recommendations for how to reach this goal require consideration of the larger historical context
when drawing conclusions from our analysis.
Standardized Test Scores and Underrepresentation
While African-American, Latino, and Native American students – who make up the Underrepresented
Minority (URM) category as defined by the University of California – account for nearly 60% of highschool graduates in the state, they account for about one-third of UC admitted resident freshmen. As
shown in the chart below, taken from the annual Accountability Report 74 produced by the UC Office of
the President’s Institutional Research and Academic Planning (IRAP) team, disparities grow at each stage
in the pipeline into the University:74

https://accountability.universityofcalifornia.edu/2019/chapters/chapter-7.html

63Figure IV-1.

As large as these disparities are, they are based on an overcount of the actual percentage of URM students
in the total admitted freshman class. The gap is only between California URM and California nonURM. In fact, the admit pool is about 30% larger than this, including out-of-state and international
students. Taken altogether URM admit represented only 26% of the freshman students admitted in 2019.
This compares to the nearly 60% of students who were URM in California schools in the 12th grade in thesame year. While the data are not available to run analyses for the total pool of admits, it is critical tounderstand that only about 1 in 4 seats is being offered to California URM students.
Concern has grown in recent months and years that the consideration of standardized test scores in UC’s
admissions process might be a contributing cause of underrepresentation in particular of AfricanAmerican and Latino students. The correlation between the test scores and the pattern depicted in Fig. IV1 above is acknowledged. 75 The lower scores received by African-American and Latino students might beconcerning in one way if scores were a major cause of disparate underrepresentation of these students inthe pool of admitted students.
However, as made clear in Section III, this correlation should not be confused with causation. The
analysis of Section III concludes that in fact, because test scores are considered in the context ofcomprehensive review, which in effect re-scales the scores to help mitigate between-group differences.
No analyses have been conducted for admission of students whose first language is not English, but thedata show large discrepancies in SAT/ACT scores for these students as well. (See Table IV-1).

75

College Board, 2019 SAT Suite of Assessments Annual Report, https://reports.collegeboard.org/pdf/2019-totalgroup-sat-suite-assessments-annual-report.pdf. The same pattern is evident in ACT scores as well. See, e.g.,https://www.act.org/content/dam/act/unsecured/documents/P_99_999999_N_S_N00_ACT-GCPR_National.pdf.

64

Table IV-1 Average SAT Scores by Race/Ethnicity and first language
California high school graduates, 2019
Race/Ethnicity
Average Score
Asian
1214
White
1168
Latino
976
Black
948
Native American
950
English not first language
1036
Source: College Board, SAT participation and performance, 2019

In California, about 80% of these students come from Spanish-speaking homes and so are a subset of theLatino population. Research has shown that standardized tests likely under-estimate these students’capabilities because of lack of sensitivity of the tests to issues of language difference. 76 More than 40% of
students begin school in California as English learners. 77 This represents a very large percentage of
URMs who are vulnerable to mis-assessment but not accounted for in the existing analyses. To address
this issue, one prominent recommendation is to use performance-based rather than traditional
standardized tests with students whose first language is not English. One of the STTF’s recommendations
is to develop a new assessment instrument that incorporates performance-type assessments. If
implemented, a new UC-designed test incorporating performance-assessments would not be ready for an
estimated nine years. This notional timeline changes the context in which to consider how to respond tothe other Task Force recommendations in the meantime as well as establishing the appropriate basis forsuch policy decisions. The options we see are to maintain the status quo of comprehensive review(including the current role of standardized tests), modify UC’s review criteria (as described in several of
the Task Force’s recommendations), or to drop standardized tests and substantially revise theimplementation of the remaining 13 criteria of comprehensive review at each of the nine undergraduate
campuses.
The analyses in Section III and the above chart show that standardized test scores are probably not thehighest barrier to admission for URMs. See Figure IV-2 below:

76

Solano-Flores, G. (2008). Who is given tests in what language by whom, when, and where? The need forprobabilistic views of language in the testing of English language learners. Educational Researcher, 37(4), 189–199;
Solano-Flores, G., & Trumbull, E. (2003). Examining language in context: The need for new research and practiceparadigms in the testing of English-language learners. Educational Researcher, 32(2), 3–13; Jamal Abedi & Patricia
Gándara (2006).Performance of English Language Learners as a subgroup in large scale assessment: Interaction ofresearch and policy, Educational Measurement Issues and Practice, 25, 36-46
77
California Department of Education, Dataquest, 201965

Figure IV-2 (same as Figure 3C-9) Factors in UC Admissions Process by Race and Ethnicity

The discussion in Section III asserts a few critical points. First, that factors other than standardized testscores disproportionately affect African-American and Latino students in the state; second, that thestandardized tests are valuable because they enable more accurate prediction within disadvantaged
groups, of likely student outcomes; and third, that UC considers standardized test scores in admissions
decisions, in the sense that comprehensive review helps to mitigate mean test score differences amonggroups. In the paragraphs that follow, we identify sources of concern regarding the interpretation forpolicy change of each of these assertions. In the main, these concerns result from consideration of the
purposes, goals, and mission of the University and the State. These concerns do not rest on the validity ofthe empirical findings and original analysis completed at great effort (thousands of hours of work
analyzing data and interpreting findings by several Task Force members and IRAP staff, including overthe holiday seasons, on weekends, while on family vacations, and after hours), but on the meaning
attached to these findings and analysis and, in some cases, on the normative assumptions underlying the
research questions posed.
Scores on standardized tests do play a role in the University’s admissions decisions. The analysis
shows that 25 – 27% of the final admission decision is due to whatever happens in the admission process.
The Task Force could not ascertain the extent to which SAT/ACT scores play a role in the decision to
grant or deny admission, although they do seem to be considered less than HSGPA. The discussion ofSection III suggests that the consideration assigned to test scores in admissions decision-making can help
to mitigate the effects of between-group differences, but the discussion cannot and does not explain
whether that sense of appropriateness extends to questions of the broader context of admissions

66

management for inclusiveness or the challenge of removing systemic inequality in public education. If
students from historically excluded groups tend to receive lower scores and those scores are a factor inallocation of precious slots in the University’s entering class, how should that factor be used? The data
show that UC help to mitigate mean score differences between disadvantaged and advantaged groups ofapplicants. The data do not show that UC allocates enough slots to students from disadvantaged groups tomeets its obligation to reflect the diversity of the state, nor that scores are used appropriately to implement
selectivity among students within groups. This is not only an empirical question and the answer will not
emerge solely from analysis of data about current and past practices. It must be the result of the weighingof values and sense of fairness.
Further, we also know that implicit bias of admissions officers and potential applicants based on having
seen these scores can be a factor. 78 Once the scores are seen they cannot be unseen and, at least in somecases, they are likely to influence the application reader as well students deciding whether to apply. More
important, however, is the fact that 25% represents a large portion of the total process when considering
the very significant underrepresentation of this group. Add to this, the category of “did not apply” almost
certainly includes some portion of students who declined to apply after considering their chances with a
low test score. The test score may count less in admissions decisions than Task Force members initiallybelieved, but students likely do not know this and it appears that the test (and also likely grades) loom
large in their decision whether and where to apply and in their own self-assessment. In assessing thesehypotheses about perceptions of what figures in UC eligibility and admissions, more data about whatleads students to apply (or not) is needed. Indeed, it seems plausible to hypothesize that since admissions
practices are opaque to the public and even to UC faculty, potential applicants conflate eligibility withadmission criteria and assume that both are highly correlated with their individual admission probabilities.
In sum, combining the 25% in the category of denial of admission to some percent of the category of “didnot apply” constitutes an unknown but potentially significant portion of the final outcome that may be
associated with the test, even if the weight accorded in UC admissions practices is much smaller thanmany people think. Policy-makers should consider various possible reforms, from providing better
explanation of the actual use of test scores together with incremental adjustment of admissions criteria
would be the more effective remedy, all things considered, to problems of implicit bias or self-selection
not to apply.
The analysis in Section III shows that URM students are more likely to be admitted with high GPAs andlower test scores than the reverse. In fact, the conclusion is that the test scores do not matter significantly
in admission outcomes. One is then compelled to ask, if the potential adverse impact of the scores iscompensated for in comprehensive review, why does UC need them? The answer appears to be that they
are useful for prediction purposes, to select those students who are most likely to succeed at UC. This is
in tension with the statement that the tests do not play a large role in the admission decision – it isdifficult to have it both ways. If test scores are weighed in decisions, they adversely affect those withlower scores, who are disproportionately African American and Latino; if they do not, then they are notessential to admissions decisions at all.
The discussion in Section III suggests that through comprehensive review, consideration of test scores
does not adversely affect URM applicants and does facilitate more accurate prediction of future academic
performance among students within each group disadvantaged prior to application to UC. This reportdetails the issues of the use of test scores for eligibility in various contexts (Statewide, ELC, and ETR) in
relation to their use for campus admissions decisions. Because test scores have predictive power among78

Hodson, G., Dovidio, J. F., & Gaertner, S. L. (2002). Processes in racial discrimination: Differential weighting ofconflicting information. Personality and Social Psychology Bulletin, 28, 460–471; Nosek, B. A., Smyth, F. L.,
Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., & Banaji, M. R. (2007). Pervasiveness and correlates of
implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36-88.

67

students within particular disadvantaged groups (e.g. URM, first-generation, low family income, lowparental education level), they are used effectively to select among students within each group who areless likely to succeed and to admit students who are more likely to succeed. Thus, the actual use of thetest scores is a balancing act at the point of admissions decisions to implement the BOARS principle
requiring assessment of preparedness for UC-level coursework, persistence beyond the first year, andeventual successful graduation on the one hand, and on the mandate that UC reflect the diversity of thestate on the other hand. The actual use of test scores at subsequent points of evaluating matriculated
students who may need additional assistance toward these goals or who may qualify for scholarships orother aid varies at the different campuses. This balancing act does involve comparing students within
subgroups of applicants and selecting those with higher probability of success over others from similar
educational contexts. Whether the selectivity of UC campuses should be lowered by broadening the rangeof test scores in the eligibility criteria in order to potentially increase access and equity would be arebalancing that BOARS, Academic Council and UCOP should consider only in light of the obligation
UC would then incur to invest appropriate resources to insure at-risk students not only have a fair chanceof admission, but also a fair chance of success once admitted.
The predictive power of the standardized test scores is higher for those student groups who are underrepresented at UC, as shown in Figures 3-3, 3-4, and 3-5 in Section III. Thus, consideration of test scores
allows campuses to select those students from underrepresented groups who are more likely to earnhigher grades and to graduate on time. Conversely, consideration of standardized test scores yields
weaker predictions for students from higher income families, who are white or of Asian descent, or who
are not first-generation. One implication is that consideration of test scores allows greater precision whenselecting from URM populations. Whether this is desirable is a normative question.
If, but for consideration of the standardized test score, more students from a URM group would have been
admitted in the first place, then the ability to make more precise predictions within that group may notadvance the overall cause of inclusion or diversity as an admissions goal. Again, the normative meaning
to be attached to the interpretation of the findings matters: is the goal of consideration of test scores to
exclude certain applicants relative to others who also belong to historically excluded or otherwise
disadvantaged groups and who otherwise might access UC? The data in Section III show that UC
comprehensive review compensates for disparity in test scores between disadvantaged and advantaged
groups of applicants. However, there remains a 5.5% gap between the pool of URM high school seniors
and UC admits that relates to the admissions decisions themselves. As shown in section III-C5,
underrepresentation of URM students by about 22% still existed in 2018 between the pool of URM
students in grade 12 and the pool of California resident students admitted by UC. 79 About 75% of that gap
is attributable to factors that precede admissions. So, about 25% of the remaining gap (or 0.25 x 0.22 =0.055 or 5.5%) of admissions still reflects a URM underrepresentation due to the admissions decisionitself. Section III showed, unexpectedly, that test scores likely make a surprisingly modest contribution tothis gap because of the way UC takes into account students' educational context when considering testscores. Differences in GPA, and the key role that GPA plays in UC admissions decisions, appear likely toplay a larger role.
Comprehensive review cannot compensate for disparities between applicants and those who do not apply,
e.g. because of understandable but false beliefs about how UC campuses actually use test scores foradmissions decisions, or because of lack of opportunity or advice or advantage during their K-12
experience, leading students to fail to meet eligibility criteria (e.g. in meeting A-G course requirements,
minimum GPA) or to be unaware of opportunities to apply or to estimate their likelihood of admission. A
decision to make an admissions policy change at UC should not be driven solely by data and empiricalfindings. It should be driven by data together with a thoughtful evaluation of what purposes, goals, and79

Fall 2018 UC Admit Rates, source (2018_freshman_profile_allcampuses.pdf)

68mission are served well by the admissions process using the data it can gather. What the data of Section
III do not tell is whether mitigating the quantitative gap in test scores by means of comprehensive review
can help us answer normative questions regarding these purposes and goals.
5.5% of 217,036 students admitted to UC campuses for fall 2018 is 11,937 students. A system that may
be inequitable for 12,000 students a year may simply be an unjust system. The question, though, is notwhether the system is inequitable. We know that it is. Assuming inequity is intolerable; the question iswhich way of living in an intolerable system while we work to change it should we choose? Dropping thetests altogether would require adjustments to current comprehensive review practices for admissions at allnine undergraduate campuses, with unknown consequences for the extent of the impacts they are likely tohave as remedy to the remaining inequity. The simulations discussed in Section III and in therecommendations in Section VI suggest that dropping the tests might make it harder in the short (1-3
years) to medium (3-10 years) term rather than easier for UC to make up disparities due to the otherfactors (such as race, HSGPA, family income, parental education level). These other factors produce 75%of the disparity. Without substantial additional resources to expand the eligibility pool and UC
enrollments overall, and disregarding the substantial costs and risks of admissions errors in the short term
without adequate preparation of admissions officers and processes, even a well-meaning policy change
could fail.
Even in the medium term, if UC were to develop its own test as we recommend below, it would remain
unknown until the new test was evaluated on whether UC is doing better or worse by the 12,000 students
in the still underrepresented gap between the California high school URM pool and UC admissions pool.
Evaluation of a new test would take several admissions cycles beyond test implementation at the very
least. So, the question is, should UC risk continuing or worsening the 5.5% underrepresentation gap in themeantime by a qualitative change in admissions policy by dropping the tests or try to address it byincremental fine-tuning as recommended by this report? In blunt terms: yes, the current “system” isunequal and thus intolerable, but a qualitative change without more data, more simulations, and morereflection could make things worse rather than better. No change to UC policy can “fix” the interlocking
systems of inequity and oppression that operate in society today. On the other hand, an inequitable system
is unjust and the experience of what can be tolerated on any time scale depends significantly on who has
to do the tolerating and how their own past experiences of oppression and injustice inform theirimpatience with incremental change. For some, the principle of cancelling any practice that may
contribute to injustice, even if its contribution is modest, could be an overriding consideration leading to
the conclusion that standardized testing should be dropped from UC admissions considerations, despite
the likelihood of considerable disruption and expense in the short term (for example due to inaccurate
estimates of yield rates causing significant over- or under-enrollment, reduced graduation rates,etc.). Such a step should not be undertaken, however, without weighing the potential costs to theinstitution (which the Task Force judges may be considerable) and even the potential harms to UC
applicants, including those from disadvantaged groups.
Beyond the significance of denial of admission to the thousands of students affected, there is theparticular meaning of racial disparities within that group of applicants. Disproportionately affected are
students who are members of groups that have been excluded from all manner of opportunity - including
educational opportunity - in and by this country for centuries and in and by this state for more than acentury. That historical context and the reality of contemporary, ongoing societal discrimination make thenormative argument for reforms, up to and including cessation of considering test scores in UC
admissions, considerably stronger.
UC already pursues goals other than prediction in admissions. According to the most recent data
available, roughly one in two students admitted to UC received an SAT score below 1199, and one in six

69

received a SAT score 80 below 1000. 81 It is unlikely that admission of these students, who are more likely
to earn lower grades and to fail to complete, reflects flaws in UC’s process. Rather, their admission isevidence that UC pursues goals in addition to admitting the students most apparently likely to earn higher
grades and to complete on time. The decision to admit such students reflects a normative judgment bornout in the data analysis in Section III; showing that UC admits students with a very wide range of testscores in ways that helps to address diversity and compensate for inequality, in so far as inequalities are
manifested in score differentials. As the data also show, disparities between disadvantaged andadvantaged groups are not entirely mitigated. UC admissions data do not speak to the context in whichhigh school students decide (or miss the opportunity to decide) whether to apply to UC in the first place.
Prediction of student success raises questions about what constitutes success as well as what the
responsibility of a public university is to the people of the state who support it. Success here has beenmeasured in terms of first-year grades, retention and graduation within a certain number of years. Successcan be defined differently, such as the longer-term contributions to the society as described by William
Bowen and Derek Bok. 82 Such outcomes are certainly less easily measured, but are arguably far more
important.
In defending its test recently, ACT’s chief executive, Marten Roorda, argued that the test should not be
blamed for disparities in scores along lines of race and class. In a statement to the Los Angeles Times, Mr.
Roorda said: “We don’t fire the doctor or throw away the thermometer when an illness has been
diagnosed. Differences in test scores expose issues that need to be fixed in our educational system.” 83
UC has addressed these differences in test scores by helping to mitigate score disparities through its
comprehensive review policies and two-stage process of determining UC eligibility and campus
admission. That is not to say that UC has fixed problems in the K-12 educational system producing thestudents who apply to UC. The Task Force grappled with these larger issues providing context for ourrecommendations of changes to UC admissions policy. Members of the Task Force are very concernedthat debate about what the tests measure might deflect attention from the much more fundamental
discussion we should be having about what is wrong with our educational system. It would be a tragedy
to merely fix the instrumental problem of exactly what weight to accord standardized tests in UC
admissions and foreclose thoughtful debate about higher education opportunity and disproportionate
access along lines of race and class. The evidence-based description of current UC practices should not be
mistaken for a normative judgment that because UC is doing well with comprehensive review to address adifficult, bad situation in California, that anyone is off the hook regarding the fundamental problem ofunequal access to education in California K-12 schools and institutions of higher learning.
Prediction of likelihood of success at UC in the absence of robust support for students who are eligible
but somewhat less prepared, raises other questions. Given the enormous inequalities in education in thestate of California (discussed further below), what is the responsibility of UC to help ameliorate these80

SATRM + SATRR, or ACT equivalent
Table 1.3.1, https://accountability.universityofcalifornia.edu/2019/chapters/chapter-1.html#1.3.1. The SAT wasredesigned for 2017 and scores reflect the average of the math scores and the evidence-based reading and writingscores. In prior years the percentage was higher but due to change in the tests, 2017-18 data are not directlycomparable to prior years.
82
Bowen, William & Bok. Derek (1998). The Shape of the River. Princeton, NJ: Princeton University Press. Former
presidents of Princeton and Harvard respectively collected longitudinal data on
African-American students, many admitted under affirmative action, from highly selective institutions and foundthat these students tended to be much more civically involved than their non-black classmates.
83
Teresa Watanabe, UC Violates Rights of Disadvantaged Students by Requiring SAT for Admission, Lawsuits
Say, Los Angeles Times, Dec. 10, 2019, at https://www.latimes.com/california/story/2019-12-10/uc-violates-civilrights-of-disadvantaged-students-by-requiring-sat-for-admission-lawsuit-alleges.
8170

unjust conditions? What is the responsibility of UC to help interrupt the vast inequalities that exist – thatis, to prevent the predicted poor outcome? To continue with such major discrepancies in access is to becomplicit in the perpetuation of inequality. Some argue that the California Master Plan for Higher
Education accounts for this problem by offering admission to the California State University system or
the community colleges. Who has access to UC matters greatly in the lives of students and theircommunities. A college degree provides significant social mobility and a host of other benefits, including
better health and marital stability. 84 The average baccalaureate degree holder in 2015 earned 67% morethan the individual with only a high school diploma. 85 UC graduates also earn more than their peers who
attended less selective colleges once they are in the labor market. 86 But the likelihood of actuallycompleting a degree differs greatly by the selectivity of the college/university one attends. Controlling forprior grades and test scores, students who go to more selective colleges and universities complete
degrees at much higher rates than those who attend less selective institutions. 87 For example, in 2018, theCSU system posted an all-time high 61% six-year graduation rate across all campuses, but there was large
variation among campuses, with several campuses in the 42-48% range. 88 The average graduation rate at
the typical CSU is 25-30% lower than at UC (Bleemer, 2018). Moreover, Bleemer studied the effect of
ELC enrollment at four UC campuses on graduation rates for URM students in the lowest quartile of testscores versus their peers who attended less selective schools and foundURM students and students from the bottom SAT quartile of high schools obtain even larger 30
percentage point increases in graduation rates, providing evidence against ability ”mismatch” that
could diminish educational attainment (in terms of degree receipt) as a result of attending a morecompetitive university. 89
In other words, these students, on average, defied the prediction that they would not do well in thecompetitive UC environment. There is abundant evidence that attending a more selective institution
boosts graduation rates significantly above those at less selective institutions for similarly preparedstudents and this serves to interrupt historic inequities in access to degree attainment for URMs. And
there are other advantages to attending a UC campus. UC graduates are more likely to attend graduate
school and gain further credentials than students from less selective institutions, and as a result they are
more likely to achieve leadership positions, which is of critical value to their communities.
The University’s Role in Interrupting Persistent Inequality. The underlying reasons for disparities intest scores should play into UC’s decision whether to consider the scores in admissions. If group score
differences reflect historic injustices, then continuing to use them contributes to perpetuating the effectsof those prior injustices. For example, URMs in particular whose families are more likely to have fewer
resources, to K-12 schools that also have fewer resources and that consequently are less able to prepare
students for the University, and then uses that lack of preparedness to justify disproportionate exclusion,that is unfair.
84

Ma, Jennifer, Pender, Matea & Welch, Meredith (2016). Education Pays. New York: College Board.
Ibid.
86
Bleemer, Zachary (2018). The Effect of Selective Public Research University Enrollment: Evidence from
California. Center for Studies of Higher Education, UC Berkeley. Research & Occasional Paper Series:
CSHE.11.18.
87
Alon, Signal & Tienda, Marta. (2005) Assessing the “mismatch” hypothesis: Differences in college graduation
rates by institutional selectivity. Sociology of Education, 78(4), 294–315; William Bowen and Derek Bok (1998)
The Shape of the River. Princeton: Princeton University Press.
88
Gordon, Larry (2019). California State University graduation rates show uneven progress, some backsliding.
Edsource. https://edsource.org/2019/california-state-university-graduation-rates-show-uneven-progress-somebacksliding/608158
89
Bleemer, 201885https://cshe.berkeley.edu/sites/default/files/publications/rops.cshe.11.18.bleemer.effectofselectiveenrollment.9.27.18.pdf

71That is the pattern shown in data from 2013, the last year that California ranked its public schools by the
now defunct Academic Performance Index (“API”). The table below shows what kind of school, fromlow-performing (starting on the left with category 1) to high-performing (on the right with category 10),students attend. The rankings were based on test scores and other factors; the table shows the distributionof students by race/ethnicity across API deciles. African-American, Latino and English learner students
are clustered at the low end of school performance while white and Asian students are clustered at theupper end, in the schools that are higher performers.

Table IV-4 Percent Race/ethnicity/language by API Decile, California, 2013
1

2

3

4

5

6

7

8

9

10

White

2

4

4

6

18

10

12

15

20

20

Asian

3

4

5

5

5

8

9

12

16

33

AfricanAmer

14

13

12

11

10

10

9

9

8

4

Latino

12

15

15

13

11

10

9

7

6

3

EL*

14

15

14

13

11

10

8

6

5

4

API

Source: Gándara 90, 2016, Author’s Calculations; * refers to English Learner

The share of white and Asian students in each decile increases as the Academic Performance Index
increases, with between 40% and 49% of these students clustered in the top two highest performing
deciles. For Black, Latino and English learner students, between 9% and 12% of these students attend thehighest performing schools; they are overwhelmingly clustered in the lowest performing schools. In short,opportunities to learn are devastatingly disparate.
The data analyzed by the Task Force in Section III show that UC’s comprehensive review practices help
to mitigate group score differences in its use of test scores for admissions. We would not endorse a
decision to maintain status quo admissions practices on the grounds that inequity in the state’s K-12
educational system is the culprit, not that UC has no responsibility to try to remedy inequities created by
unfair distribution of primary and secondary educational opportunities by the state of which UC is a part,and no responsibility to the students who disproportionately attend under-resourced schools. It would becold comfort to tell students in groups that attend lower-performing schools and who disproportionately
receive lower scores on standardized tests that once K-12 educational equity is achieved, they can expect
their representation on UC campuses to increase. That is an argument not to increase the role ofstandardized tests in UC admissions. It is not an argument that, since current UC admissions practicehelps to mitigate test score disparities among disadvantaged groups, we have a reason to be complacent,have no duty to reform admissions practices to better reflect statewide diversity and reverse historicalinjustices, nor have a duty to improve the quality of a UC education and the success of all our students.
Rather, we recognize that the University has an immediate obligation to admit and support promising
90

Gándara, Patricia (2016). The students we share, Mexican Studies/Estudios Mexicanos, Oakland: University ofCalifornia Press, Pp.357-378, p. 370.

72

students who are eligible, but are affected by historic and adverse discrimination, rather than to allow its
effects to justify continued exclusion. This is one of the reasons that the University already takes into
account the “local context” in admissions, illustrative of UC’s commitment to try to enroll a diverse class.
However, as this report documents, consistent and persistent disparities (even if the exact magnitudes
remain unclear) are evidence that UC should do more to achieve this goal. The goal of admitting students
more likely to perform well at UC, as predicted91 by test scores, should not be singled out as the standard
and should not prevent the University from fulfilling its mandate to serve Californians fairly should be
judged.
Disparities in test scores should be evaluated in social and historical context.
•

Racial inequality runs deep in California. Enormous differences in income and wealth along lines ofrace mean that parents have very different resources to invest in their children to prepare them forcollege. Among these material resources are private schools, tutoring, extracurricular classes, sports
teams, educational materials (including test prep), and educational experiences like travel. And thedifferences in these expenditures have been growing over time, widening the expenditure andopportunity gaps between URMs and others. 92 For example, California has the seventh mostinequitable economy of the 50 states. 93 The typical white household has $9.72 in wealth for every $1
that a typical African-American family has and $8.26 in wealth for every $1 that a typical Latino
family has. 94 African-American and Hispanic households of any given income level typically live inneighborhoods that are substantially poorer than do whites and Asians of the same income level. In
fact, lower income white families are more likely than middle-income families of color to live in a
middle-class community. 95 This is the result of vast differences in wealth as well as income and thelingering effects of state-sanctioned housing discrimination. 96 This means that African-American and
Latino families are locked into housing in neighborhoods that often offer only under-resourced, low
performing schools, creating an unending cycle of lack of access to a strong college preparatory
curriculum.

•

Educational opportunity in the state of California is not equally or equitably distributed across thehigh school student population. Students who are Latino, who are African-American, whose parents
have lesser social and financial capital, disproportionately attend schools that are under-resourced
and, in addition and more specifically, are less likely to offer prerequisite coursework (i.e., A-G
courses) necessary to gain admission to the University. 97 These students also have extremely limitedaccess to a college counselor who can inform them of their options and enroll them in the available
A-G courses. California is 49th in the nation with respect to student-counselor ratios and assigns three91

BOARS, Report to the Regents on Admissions Tests and UC Principles for Testing, p. 18. Available athttps://senate.universityofcalifornia.edu/_files/reports/hp2mgy_boars-testing_010609.pdf
92
Sean Reardon (2011) The Widening Academic Achievement Gap Between the Rich and the Poor: New Evidence
and Possible Explanations in Gregory Duncan & Richard Murnane (eds) Whither Opportunity? Rising Inequality,
Schools, and Children’s Life Chances. New York: Russell Sage. Pp. 91-116.
93
Estelle Sommeiller and Mark Price (2018), The New Gilded Age: Income Inequality in the U.S. by State,
Metropolitan Area, and County. Washington DC: Economic Policy Institute.
94
Esi Hutchful (2018). The Racial Wealth Gap: What California Can Do About a Long-Standing Obstacle to Shared
Prosperity. Sacramento: California Budget and Policy Center.
95
Reardon, S.F., Townsend, J., & Fox, L. (2017). A continuous measure of the joint distribution of race and income
among neighborhoods. The Russell Sage Foundation Journal of the Social Sciences, 3, 34-62.
96
Richard Rothstein (2017). The Color of Law. New York: Liveright Publishing.
97
BOARS, Impact of the New Freshman Eligibility Policy at the University of California: A Report to the Regentsof the University of California (Nov. 2013), p. 8 (tbl. 2), available athttps://senate.universityofcalifornia.edu/_files/committees/boars/Nov52013BOARSReporttoRegents-Final.pdf.

73

times the recommended number of students to each counselor. 98 Moreover, most counselors haveneither the time nor the training to provide college counseling specifically. Affluent families canoffset this lack of counseling resources by paying for private counseling. The very unequal
assignment to low performing schools is also associated with high teacher and administrator turnover,disproportionate numbers of inexperienced and unqualified teachers, higher absentee rates foreducators, and a weaker curriculum. 99 Peers who are educationally ambitious and headed for college
can be a significant support for each other,100 but when students are segregated with only other lowincome and marginalized peers, this constitutes a weak resource.
•

While the current versions of the SAT and ACT tests are purported to assess “academic preparation”
rather than “aptitude” (as they once claimed to do), scoring well on these tests is related both toexposure to academic material and to cognitive strategies employed to answer the questions
appropriately (which is why “test prep” can increase scores without necessarily increasing academic
knowledge). If students have limited exposure in primary and secondary classes to the material tested,
as a result of a weak curriculum and/or receiving instruction in low-performing schools, and/or if
students have limited experience with the cognitive strategies that the tests are designed to elicit, they
are likely to perform less well on the tests. The tests evaluate a narrow slice of human achievement
and knowledge that to some extent is class- and culture-bound. 101 Eminent psychologists havedemonstrated that tests that are designed to elicit quite different skills from those on the market today
can produce novel and accurate ways to solve problems. 102 Students with strong academic preparation
and a proclivity for abstract reasoning have an advantage on these tests, but given the same primary
and/or secondary school inputs and opportunities or targeted postsecondary support, others may
perform equally well in UC classes. Fundamentally, the standardized tests are measuring a student’s
opportunities to learn, over which the student has little control.

•

Stereotype threat is a well-established phenomenon among URMs and women. When black (andother URM) students believe their intellectual skills are being tested, this can activate a concern about
validating a negative stereotype about their lesser ability and cause a level of stress that depresses
their scores. Claude Steele and Joshua Aronson 103 tested this theory initially with highly talented
African-American and white students through a series of studies in which students were told that thetest was designed to accurately assess their verbal aptitude. Black students in the verbal aptitudecondition responded as predicted, their scores were depressed compared with white students. In the
non-threatening condition in which students were told the test was not testing aptitude, black students
performed at expected levels. Steele and Aronson, and others, have replicated the studies many times
with reasonably consistent results for women in mathematics and for Latinos, among others. 104 Thesefindings strongly suggest that the high stakes of the SAT/ACT by themselves can have the effect of98
American School Counselor Association (n.d.) State-by-State Student Counselor Ratios. Ten year Trends: 2004-5
to 2014-15. https://www.schoolcounselor.org/asca/media/asca/Publications/ratioreport.pdf
99Donald Boyd, Hamilton Lankford, Susanna Loeb and James Wyckoff (2005). Explaining the short careers ofhigh-achieving teachers in schools with low-performing students, American Economic Review, 95 (2), 166-171;
Charles Clotfelter, Helen Ladd & Jacob Vigdor (2009). Are Teacher Absences Worth Worrying about in the US?
Washington DC: Urban Institute; Desiree Carver-Thomas and Linda Darling-Hammond (2017). Teacher turnover:Why it matters and what we can do about it. Palo Alto, CA: Learning Policy Institute
100
Margaret Gibson, Patricia Gándara and Jill Koyama (2004). School Connections. US Mexican Youth, Peers, andSchool Achievement. New York: Teachers College Press.
101
See, for example, Sternberg, Robert (1998). Ability testing, instruction, and assessment of achievement: Breaking
the vicious circle, NASSP Bulletin, 82, 4-10.
102
Ibid.
103
Claude Steele & Joshua Aronson (1995), Stereotype threat and the intellectual test performance of AfricanAmericans, Journal of Personality and Social Psychology, 69(5):797-811.
104
Aronson, Joshua (2004). The threat of stereotype, Educational Leadership, 62 (3), 14-19.

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depressing scores even for highly talented URM students who otherwise perform at high levels andare prepared to succeed in college.
•

Students from more affluent homes can take advantage of an entire industry dedicated to increasing
their test scores. In addition to test preparation materials and classes that can cost on average about$1,000 each, families can purchase coaches or counselors who for several thousand more dollars will
work with students with the preparation materials and provide tailor-made assessments and advice.
These coaches promise significant gains on the tests and they produce them. 105 For example,
Princeton Review guarantees a minimum 200-point gain in scores or your money back. 106 The factthat the evidence is mixed regarding whether test prep can boost performance this much does notmean that test prep is not effective in changing the psychology of the test-taker and may lead some tochoose not to take the test in the first place. In extreme cases, some affluent parents have paid“coaches” to falsify test scores in order to gain admission to a selective college. 107 Such incidents not
only are corrupt, they undermine students’ belief in the fairness of the admissions process.

Students with lower test scores can often rise to the challenge. But many have not had the opportunity todemonstrate what they are capable of and could achieve with the benefit of academic support that UC
campuses already offer. 108 The most direct way to interrupt this inequality is through increased and more
equitably distributed educational opportunity, including at the university level.
Under the Constitution of the State of California, the University “constitute[s] a public trust, to beadministered by the existing corporation known as ‘The Regents of the University of California,’ with fullpowers of organization and government.” 109 The Regents, in turn, have made the following statement intheir Policy 4400 on the mission of the University, worth quoting extensively:
The diversity of the people of California has been the source of innovative ideas and creative
accomplishments throughout the state’s history into the present. Diversity – a defining feature of
California’s past, present, and future – refers to the variety of personal experiences, values, andworldviews that arise from differences of culture and circumstance. Such differences include
race, ethnicity, gender, age, religion, language, abilities/disabilities, sexual orientation, gender
identity, socioeconomic status, and geographic region, and more.
Because the core mission of the University of California is to serve the interests of the State ofCalifornia, it must seek to achieve diversity among its student bodies and among its employees.
The State of California has a compelling interest in making sure that people from all backgrounds
perceive that access to the University is possible for talented students, staff, and faculty from allgroups. The knowledge that the University of California is open to qualified students from allgroups, and thus serves all parts of the community equitably, helps sustain the social fabric of theState.
Diversity should also be integral to the University’s achievement of excellence. Diversity canenhance the ability of the University to accomplish its academic mission. Diversity aims to
105

Park,J.J. & Becks, A.H. (2015). Who benefits from SAT prep? An examination of high school context andrace/ethnicity. The Review of Higher Education, 39(1), 1-23.
106
Astin, Alexander (2016). Are you Smart Enough? Sterling, VA: Stylus Publishing.
107
See for example, Hussain, Suhauna (2019). College admissions scandal hits UC Berkeley: fake SAT scoreallegedly submitted. Los Angeles Times, March 19. https://www.latimes.com/local/lanow/la-me-uc-berkeley-crewcollege-admissions-scandal-20190319-story.html
108
For example, campuses offer academic support targeted to first generation students. See, e.g., University ofCalifornia, Irvine, Student Success Initiatives, http://ssi.uci.edu/.
109
Cal. Const. Art. IX, §9.

75

broaden and deepen both the educational experience and the scholarly environment, as students
and faculty learn to interact effectively with each other, preparing them to participate in an
increasingly complex and pluralistic society. Ideas, and practices based on those ideas, can bemade richer by the process of being born and nurtured in a diverse community. The pluralistic
university can model a process of proposing and testing ideas through respectful, civilcommunication. Educational excellence that truly incorporates diversity thus can promote mutual
respect and make possible the full, effective use of the talents and abilities of all to foster
innovation and train future leadership.
Therefore, the University of California renews its commitment to the full realization of its historicpromise to recognize and nurture merit, talent, and achievement by supporting diversity and equal
opportunity in its education, services, and administration, as well as research and creative
activity. The University particularly acknowledges the acute need to remove barriers to therecruitment, retention, and advancement of talented students, faculty, and staff from historically
excluded populations who are currently underrepresented. 110
The Regents’ statement of mission is consistent with arguments by scholars who have written on barriers
to access and who have emphasized the affirmative role of higher education not merely to identify but tofoster students’ abilities.
The Demographics of the State of California Have Changed. The California Master Plan for Higher
Education was developed in a state very different from today’s California. When the Master Plan was
adopted in 1960, California’s schools ranked with the best funded in the nation. Economic inequality was
at a low point and the state was 92% white, 111 with a small and uncounted Hispanic population bundled inwith “whites,” estimated to be about 10%. 112 Socioeconomic, racial, and ethnic diversity did not figure in
the plan. The Hispanic or Latino population was so small and concentrated in specific areas that the US
Census did not consider it important to count for the states. People of Asian descent were similarly
uncounted in 1960 but estimated to be about 2% (based on the “non-white” population listed by the US
Census Bureau). African-Americans were 6% of the state’s population. At the time, the notion of a threetiered system with access for everyone coming from strong K-12 schools was hailed as an enviable modelfor the nation, with the top 12.5% of a largely homogenous population admitted to UC where students
could prepare for graduate and professional schools. The top 33% were eligible for admission to the CSU,
where students could pursue teaching or another applied degree. The community colleges were open
access with the right to transfer to the four-year colleges and universities if all prerequisites weresatisfactorily completed. The model was a cost effective use of state resources and in its early years thetransfer rates from the community colleges reached nearly 50%. 113
Virtually everything has changed since 1960 except the basic features of the Master Plan. In 2018, Latinos
comprised nearly 40% of the general population, Asians had grown to 15.3% and African-Americans
remained at about 6.5% of the population. The white population stood at 36.8% of the population. 114 Our
K-12 public schools are even more diverse, with Latinos at 55%, Asian/Pacific Islanders at 10%, African110

Regents Policy 4400, Regents of the University of California,
https://regents.universityofcalifornia.edu/governance/policies/4400.html.
111
Callan, Patrick (2012). The Perils of Success: Clark Kerr and the Master Plan for Higher Education. In S.
Rothblatt (ed) Clark Kerr's World of Higher Education Reaches the 21st Century. NY Springer Table 3.6 p.74
112
1970 is the first year in which the US Census reports “Hispanics” in California. In 1970, the Census reported the
Hispanic population to be 12%. Allowing for some growth in this population, and the fact that 1960 marked anhistoric low point in immigration, 10% is a reasonable upper bound estimate.
113
Douglass, John A. (2004). The Conditions for Admission: Access, Equity and the Social Contract of Public
Universities. Palo Alto: Stanford University Press.
114
US Census Quick Facts, California. https://www.census.gov/quickfacts/CA

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Americans at 5.4%, and white students at 23%. 115 The K-12 schools have gone “from first to worst” 116among the states, economic inequality has skyrocketed, our schools are among the most segregated byrace and income in the nation, 117 and the one strategy UC had used at least partially successfully to try toequalize access across racial lines—affirmative action—was outlawed in 1996. In 2017, 59% of
California high school graduates were URMs, but across the UC system just 31% of enrollees wereURMs. Projections are that without a change in current policy the discrepancies will grow.118 Latino
students disproportionately attend the community colleges where relatively few are successful intransferring to four-year institutions. In fact, UC’s entering freshman class is more diverse than its
entering transfer class. Getting into UC has become an intense race for limited and coveted slots.
Some would argue that part of the fairness solution lies in expanding opportunity. Rather than strictly
rationing the benefits of a UC education, the goal should be expanding access. 119 The difference in thedebate this time is that the majority of the state’s student population is URM and many of their advocatesboth from within the University as well as outside have voiced strong objections to the use of the tests. 120
They are right to voice strong concern. Some members of the Task Force began our investigation with anunderstanding shared with many members of the public that the large disparities in test scores among
various groups were directly reflected in distributions of students admitted because the correlations
between test scores and demographic groupings are fairly strong. We learned through close, careful,painstaking study of the actual results of UC’s comprehensive review, even as it is implemented indifferent ways across the nine campuses, helps to mitigate disparities in test scores.
The above examination of the mission of the University in the context of a changing California, and theargument that it is incumbent upon us to do more to interrupt historical inequality, is not meant to takeanything away from efforts already undertaken. The University already takes into account additional
student characteristics in order to evaluate test scores in context, thereby helping to mitigate some of thepotential adverse impact of scores on applicants, and UC does more than many institutions to promote
access and socioeconomic mobility. 121 But we can and should do more.

115

Fingertip Facts on Education in California – CalEdFacts, California Department of Education.
https://www.cde.ca.gov/ds/sd/cb/ceffingertipfacts.asp
116
The Merrow Report: First to Worst, California’s schools: America’s Future. PBS, Learning Matters, Inc, 2004.
This widely distributed video features California educators and policymakers decrying the fact that the state has
fallen to the bottom decile of states with respect to funding and points out the totally inadequate facilities that many
low-income students attend with broken toilets and windows, no heat or air conditioning, lack of playgrounds. It
also contrasts these ‘worst” schools with those in affluent neighborhoods where parents contribute to the school’s
resources, creating huge inequities among “public” schools.
117Frankenberg, Erica, Ee, Jongyeon, Ayscue, Jennifer & Orfield, Gary (2019). Brown at 65: Damaging Our
Common Future. Los Angeles: Civil Rights Project/Proyecto Derechos Civiles, UCLA.
118
Carnavale, Anthony, Van Der Werf, Martin, Quinn, Michael, Strohl, Jeff & Rapnikov, Dmitri (2018). Our
Separate and Unequal Public Colleges. Washington DC: Georgetown Center on Education and the Workforce.
119
Ibid.
120
See, e.g., Watanabe, Teresa, Drop the SAT and ACT as a requirement for admission, top UC officials say, Los
Angeles Times, Nov. 23, 2019, at https://www.latimes.com/california/story/2019-11-23/uc-officials-recommenddropping-sat-admission-requirement.
121
See, e.g., David Leonhardt, The Assault on Colleges and the American Dream, N.Y. Times, May 28, 2017, at SR
6 (and accompanying graphic, at https://www.nytimes.com/interactive/2017/05/25/sunday-review/opinion-pelltable.html, finding that “based on a combination of the number of lower-and middle-income students that a college
enrolls and the price it charges these students,” the top five institutions nationwide are UC campuses).

77

V.

Questions & Answers

To this point our report has explained how SAT and ACT scores are used at the University of California(“UC” or “the University”), why test scores play an important role in admissions, and how UC’s use of
tests differs substantially from what many in the public, and indeed at UC, might have believed. The priorsection summarized the main concerns about the use of tests.
This section has two goals. First, it will bring the preceding sections together by recapitulating the mainconcerns about testing expressed in the prior section along with other concerns that Task Force members
have heard, and then interrogating the earlier sections of the report to answer these questions. The second
goal is to set the stage for the final section of the report, which provides details on the Task Force’s main
recommendations. Many of these recommendations originated in part from the members’ deliberation of
the questions set out in this section. By posing these questions here in a direct and sometimes even bluntway, we hope to shed light on how UC admissions uses student information, to illustrate themisconceptions that may have existed in our own and others’ beliefs about how UC uses test scores, andto explain the genesis of the Task Force’s recommendations.
Q: I have read that the group of students admitted to UC are very different from the students in
California high schools, based on race/ethnicity and socioeconomic measures. Is this true? Isn’t thisevidence that UC admissions procedures somehow unjustifiably reduce admissions rates for somegroups?
It is true that the racial mix of students admitted into the freshman class at UC is quite different from theracial mix of high school seniors from the same year. Consider the groups that UC collectively refers to as
Underrepresented Minorities (URM): Latino, African-American students and Native American students.
In 2017-18, these groups constituted 59.1% of high school seniors in California, but only 37.0% of UC
admits among California residents.
The second question is whether this 22.1 percentage point gap arises due to UC admissions decisions. The
Task Force considered this in detail. Figure 3C-7 shows that about one-quarter of the gap is due to theadmissions decisions of the UC campuses, but the remaining three-quarters of the gap relates to outcomesthat occur before UC admissions officers read files. The single biggest factor is that relatively few of thestudents in the three underrepresented racial/ethnic groups complete the A-G coursework that both UC
and CSU require for students to become eligible to apply. The other key factors include differences in therates of graduation from high school and in applying to UC.
The fact that admission decisions explain only about one-quarter of the disparity in racial mix of highschool seniors and admitted UC freshmen is important, but a reasonable person could wonder whether
this contribution, although relatively small, might indeed indicate bias of some sort against applicantsfrom some groups relative to others in admissions itself. We will look into this in responses to some ofthe questions below.
Q: Don’t SAT score averages look a lot lower for some groups than others?
Yes, on average, SAT scores are lower for some groups than others, with lower scores for less affluent
groups and for underrepresented minorities. As shown in Table 3C-4, in 2019, test score gaps were as
wide as 234 points between Latino and Asian UC applicants, and 216 points between students who would
be the first in their family to graduate from college and those who are not “first-generation”.

78

Q: But don’t these large differences in SAT scores across demographic groups cause large
variations in UC admissions rates? Isn’t that unfair given inequalities in school quality and otherinequalities in California society?
This is a reasonable pair of questions. It turns out, though, that differences in average test scores between
groups of applicants do not cause highly different admit rates. Campus admissions officers appear to renorm, i.e., compensate for, SAT scores by comparing a student’s test scores not to the average SAT
scores across all applicants, but rather to the SAT scores of other students who come from similarly
disadvantaged groups. UC does not have a specific cutoff for SAT scores below which admission is notpossible. If it did, then all admits would have very high SAT scores, and average score differences
between disadvantaged groups and advantaged groups, although large in the applicant pool, would begreatly reduced in the pool of students who were admitted. But this is not what happens. The rightmost
column in Table 3C-4 shows that the test score gaps between racial/ethnic groups, between income
groups, and between first- and non-first-generation students among applicants remain virtually as large
among admitted students, which means that the test score gaps that are correlated with race, firstgeneration status, family income, and parental education level are almost fully compensated for inadmissions decisions; otherwise the test score gaps in the applicant pool would be different than the gaps
in the admitted pool.
Figures 3C-4 through 3C-6 show that for any SAT score, students from disadvantaged groups have ahigher probability of being admitted than students from advantaged groups. In other words, how UC uses
the test scores appears to be to help identify students within each socioeconomic group who are most
likely to succeed.
To re-state this more simply, large inter-group differences in SAT scores do not translate into majordifferences across student groups in admission rates at UC. This is probably the most important finding
the Task Force has made in its data analyses.
The second part of the above question is whether UC is ignoring differences in outcomes that arise in part
because of differences in the quality of education available to affluent students and less affluent students.
Key here is our finding that UC seems to effectively re-norm the SAT to take into account the context inwhich each group of students was educated. What this means is that UC takes careful note of inequality inschool resources and adjusts its admission decisions to take the local context into account. It also reflects
UC’s commitment to reflecting the diversity of the state in its admissions decisions by compensating fortest score gaps between groups of applicants but using test score differences among individuals withingroups of admits to help identify UC students potentially at risk of not succeeding. UC must find abalance between its mandate and value placed on diversity and its obligation to help its at-risk students.
The predictive role of the test is used to evaluate underprepared enrolled students and also to selectamong students within each group (URM, family income, and so forth) those students who alreadydemonstrate academic achievement on a variety of measures, including high school GPA and test scores.
It is possible that a small fraction of this narrow role of test scores in UC admissions and evaluation ofpotential at-risk students is still biased. For that reason, the Task Force makes recommendations to collect
more “granular” data about individual questions on the tests to further study potential racial (and otherforms of) bias in the tests.
Q: Earlier you said that about one-quarter of the racial gap between UC freshman admits andCalifornia high school seniors results from the admissions decisions themselves. You also said that
admissions offices effectively re-norm SAT scores, comparing less affluent students to each otherand students from high schools that have similar demographic profiles to each other. What explainsthese gaps, then? Are SAT scores the major factor causing the differences in admission rates
between racial/ethnic groups? If not, what other factors matter in the admissions process?

79Given the Task Force’s findings that the SAT scores are evaluated in a way that effectively renormalizes
scores to take disadvantage into account, SAT scores do not appear to play a big role in differences inadmission rates between disadvantaged and advantaged groups. Other admission factors play a role in
reducing the share of disadvantaged groups that are selected in the admissions process (as well as inreducing the share of disadvantaged groups in the admissions pool). A prime example is high school
GPA. The report shows that variations in GPA matter more than variations in SAT in explaining
admission rates. For example, Tables 3B1-4 show that, both for UC overall and for individual campuses,changes in high school grade point average (HSGPA) have large effects on admission probability while
equivalently-sized changes in SAT have smaller effects on admission probability. Figure 3C-5 shows that
UC admission probabilities rise steeply with HSGPA, and that the relation between admission and GPA isquite similar across racial/ethnic, educational, and socioeconomic status (SES) groups. Figure 3C-10
shows that disadvantaged groups tend to have significantly lower GPA in high school and that UC
admissions does not “renormalize” high school grades nearly as much as it renormalizes SAT scores.
Taken together, these imply that GPA likely plays an important role in differences in admission rates
across socioeconomic groups.
The comprehensive review process can take into account 14 factors, of which test scores and GPA are
only two. The Task Force could not evaluate the role, if any, played by the remaining 12 factors, but it
seems quite possible that more advantaged groups of students might have had opportunities that would
allow them to be ranked highly on some of the 12 other factors as well. For example, some of thesefactors relate to how many advanced courses a student takes, and the difficulty of the grade 12 course
schedule. Consider also item 11 on the list of 14 factors, which presents a long list of examples of special
talents, achievements, and awards, including athletic activities, and signs of leadership that UC considers.
Here again it seems plausible that more affluent students have more opportunity to shine in this regard.
Q: Wait a minute, earlier you said that three-quarters of the gap in the racial mix of UC freshman
admits and high schools reflects things that happen prior to the admissions decision. Isn’t this justan attempt to shift responsibility for any disparity among groups admitted to California’s K-12
system?
Section III of our report is simply reporting the data. No blame or shifting of responsibility is being
assigned or should be inferred. The report is not saying: “somebody else should fix K-12 education andthen UC’s current admissions practices will work as we hope they will.” That said, we know that despite
decades of reform, school resources and school quality vary across California’s high schools, as pointed
out eloquently in the prior section of this report. It seems likely that these variations have at least someinfluence on variations across students in their academic progress. What children experience outside ofschool matters at least as much as what happens within school. The pre-school years are especially
important. More affluent parents have access to private pre-schools, and California does not yet have
universal pre-school for all needy children. Not all children leave high school with the same academic
preparation. We have to know what problem we are trying to solve.
UC does not believe that it can, by its own admission policies, singlehandedly rectify the large differencesin academic preparation among high schoolers that relate to family income and race/ethnicity. But giventhat UC has always faced two mandates – to strive to admit prepared students who at the same timereflect the state’s diversity – UC does strive to do its part to amend these inequalities. The way in which
UC assesses SAT scores within the context of the student and his or her high school, the Blue and Gold
Scholarship program to eliminate tuition for families with limited means, and the many support programs
for undergraduates all speak to the diversity aspect of the UC mission. Most of the recommendations the
STTF has made derive from the desire to see UC improve its existing efforts.

80Q: But let’s push further on this. You say that some students arrive at UC ill prepared and often do
not graduate. What does UC do to support these students? Couldn’t UC solve its own problem of
having to rely on test scores and grades to select students who will likely perform well at UC? For
instance, can’t UC better support admitted students who are predicted to struggle?
This is an excellent point. The statistics we have presented do show ample evidence that UC students withlower test scores and grades have a lower probability of having high grades, persisting past freshman
year, and graduating. But these statistics are backward looking, and do not consider the possibility that
UC could improve its supports for students, especially in their freshman year.
UC has a wide variety of programs to support students in their freshman and later years. For example, UC
campuses provide writing programs for freshmen who have yet to demonstrate adequate mastery ofreading and writing. The campuses also invest a lot in orientation programs including some “live-in”
programs to help students, especially those who might need additional supports, with a chance to makefriends and develop a feel for living on campus well before classes start in the fall.
UC has not, to the best of our knowledge, conducted a rigorous evaluation of these many support
programs. The Task Force is thus recommending that UC first catalog these programs, and then perform arigorous multi-campus evaluation of the impact of these programs, with the goal of expanding programs
that work and either fine-tuning or if warranted, shrinking programs that do not prove to be as helpful as
hoped. We also propose a robust series of innovative new support programs that in each case should be
carefully evaluated. Some of the Task Force’s recommendations would likely result in enrollment
increases across the system, which would put even more pressure on UC to evaluate the impact of these
programs and invest resources to expand the ones that work.
Q: I have heard that SAT scores do not really measure differences in achievement. They are reallyjust measures of family income, right? How could UC ethically use SAT and ACT scores if thesetests simply measure income?
There is a lot to unpack here! It is true that SAT and ACT scores are positively correlated with family
income, as are many other measures of student achievement. The appendix to this report includes a reportfrom UCOP that documents that the correlation between family income and SAT scores has grown overthe last decade, and is now about 0.4, which is a meaningful amount of correlation.
The correlation is problematic because it could be a symptom of a causal relationship that UC might beable to do something about. The positive correlation between scores and family income, however, does
not show that family income level causes the scores to be what they are, nor that the test scores cause
family income level to be what it is, for any individual student. Also, the pattern of correlation amonggroups does not show causation for a whole group of students, because both family income and scores
could be the result of something else causing both, like race-based oppression that leads some families tohave low income and students in those families to have low test scores. For example, students suffering
racial oppression might be forced to attend lower quality schools due to economic segregation and thatwould tend to cause both lower test scores and also reflect lower family income at the same time, but thefamily income would not be a direct cause of low test scores. What the data show, using other statisticalanalyses than correlation studies, is that family income may be one contributing cause among several.
Then the question becomes: how important a factor is family income compared to the other contributing
causes in explaining any disparities in admission rates?
Your second question was, whether it was ethical for UC to use tests that are positively correlated withfamily income. This is a good question, but what really matters is how UC uses these test scores. If UC
campuses simply set a single SAT cutoff below which no student could possibly be admitted, without

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taking into account the role that family income can play, this indeed would be questionable. But what the
Task Force’s analysis shows differs markedly from this scenario. What we found is that UC admissions
practices essentially take into account the entire gap in average SAT scores between the most advantaged
and least advantaged groups. This statement is true whether we group students by race/ethnicity, family
income, or first-generation status.
So what is the use of SAT scores? Our analysis shows that even within specific socioeconomic groups
SAT scores are quite predictive of student performance once at UC. See for example Figure 3A-3, which
divides students into four equally sized groups based on family income. Even after controlling for family
income in this way, and additionally taking into account students’ high school GPA, the SAT score ishighly predictive of a variety of UC outcomes ranging from first year GPA to the probability ofgraduating within seven years. Admissions decisions take into account differences in SAT scores while
carefully taking into account the student’s socioeconomic context. This means that any of the other 12
factors in comprehensive review besides test score and high school GPA could override a low test scorein individual cases. This seems both ethical and sensible.
Q: But don’t wealthier families tend to spend a lot on test prep for their families? I could
understand why these families do this, but is this fair to students from less affluent families? Aren’t
there some things UC could do to level the playing field?
We have three responses to this question. First, it seems likely that more affluent families are more likely
to pay for test prep for their high school students. Evidence about the impact of test prep on SAT scorestends to show that these programs boost test scores, but the studies disagree on the degree to which testscores in fact rise. If it were the case that affluent families are wasting money on test prep programs
because test scores are not boosted enough to make a difference in UC admissions, access to test prep
would not be a reason for UC to drop standardized testing from admissions criteria or even to adjust its
current comprehensive review practices using test scores as one factor among 14. On the other hand,
perhaps extra confidence is created simply by having had test prep, so students having test prep but notrepeating the test do better than they would have done had they not had the test prep. That would be an
unfair advantage to the affluent. We cannot answer whether that is the case with data we have evaluated.
On the question about how much test prep boosts scores, we refer the interested reader to Sackett andHuncel (2017). 122 These authors review the literature and find virtually no support for the claims
reportedly made by some test prep providers that test prep will boost SAT scores by 100 to 200 points.
The exception was one study that found an increase in SAT verbal scores of 121 points, but the study
lacked credibility as it used an extremely small sample of only 35 students. Sackett and Huncel report thatsome studies overstate the impact of test prep by comparing students whose families buy test prep withother students who do not do so, but without accounting for the many other academic activities that theformer group of students were also involved in. Sackett and Huncel conclude that the most careful study
in the literature suggests that test prep may boost test scores on average by about 14 points on the SAT
math and 5 points on the SAT verbal. Such gains are not large enough to make much of a difference to
UC admissions decisions. (See for example Figure 3C-1, which shows that the probability of admission
at UC rises quite slowly as SAT scores rise.)Second, it is important to remember that admissions readers consider each student’s application in thecontext of his or her own background and the GPA and SAT scores of all other applicants from the sameschool. This has the important effect of taking into account variations across neighborhoods in practices
122

Sackett, P.R. and N.R. Kuncel. 2017. “Eight Myths about Standardized Admissions Testing,” in Buckley, J.,
Letukas, L., and B. Wildavsky (Eds.), Measuring Success : Testing, Grades, and the Future of College Admissions,”
Baltimore: Johns Hopkins University Press, pp. 13-39.

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like using test prep services. The report shows that SAT scores are effectively re-normed bysocioeconomic group. This means that on average, the advantage that is conferred upon more affluent
students by test prep is being taken into consideration indirectly by UC. Nonetheless, withinsocioeconomic groups, test prep is unobserved and could favor one student over another to some degree.
Third, we do think that private test prep services would be far less valuable if the UC admissions test
differed from current admissions tests in two important ways. First, suppose that students could have avast number of practice admissions tests available online, to level the playing field between more affluent
families which can buy access to many sample tests and coaching, and less affluent families? Second,
suppose that the UC admissions test, when practiced online, generated useful information for students,
their parents and their teachers about specific content areas where the student needed to improve? More“diagnostic” tests could benefit students by showing them what specific topics they need to learn more
about. Such a test would change the nature of admissions testing in a way that would help all students,
regardless of income, to prepare and to learn from practice exams what they need to study. With this inmind, the Task Force recommends the development of a new admissions test to replace the SAT and ACTthat would have both of these beneficial features. The final section of this report contains the detailed
recommendation.
Q: I have heard that the SAT and tests in general are racially biased. Is this true, and if so what is
UC going to do about it?
The Task Force takes this possibility seriously. The definition of bias in a test question thatpsychometricians use is, roughly speaking, that two groups perform systematically differently on thegiven question even though the members of the two groups have the same underlying achievement level.
For example, suppose that a question on a math test assumed that students had knowledge of how to playa sport, say football. If a math question could be understood only if the student understood the sport thatwas used for the premise of the question, and the two groups had different knowledge of how that sport
worked, this would lead to one group systematically underperforming the other even if on average the twogroups had the same level of ability with regards to the given mathematical skill.
Our review of the existing literature suggests that racial bias in the SAT, at least the version of the SAT inplace in 1999, is, at most, a minimal problem. A key paper, by Santelices and Wilson (2010) reviews theearlier work in the field and concludes that there is no evidence of SAT racial bias in the math test butthat there is mixed evidence for black/white differences on the verbal test. 123 The authors then conduct
updated analyses that take into account some criticisms that had emerged of the earlier work. They
confirm that the math test is not biased for either of two racial comparisons. Similarly, they conduct
analyses of bias on the SAT verbal test and find no evidence of bias in Latino-white comparisons. Theyfind evidence of bias in African-American-white comparisons on the SAT verbal test, but depending on
the difficulty of the question, the direction of the bias reverses, with the test biased in favor of whites foreasier questions and in favor of African-Americans for more difficult questions.
So, in three of four cases (math for both racial/ethnic comparisons and verbal for Latino/white
comparisons) no evidence of racial bias emerges. In the fourth case, black/white comparison on the SAT
verbal test, some evidence of bias exists, but the bias is against white students on some questions against
black students in other cases. Furthermore, our analysis of the results suggest that for this one, the effects
are far too small to explain much of the SAT gap in test scores between black and white students.

123

Santelices, M.V. and Wilson, M.. 2010. “On the Relationship between Differential Item Functioning and Item
Difficulty: An Issue of Methods? Item Response Theory Approach to Differential Item Functioning.” Harvard
Educational Review Vol. 80 No. 1 Spring, pp. 5-36.

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An important qualification of this result is that it uses data from California high school students gatheredin 1994 and 1999, a period when the version of the SAT in use was two versions earlier than the current
version of the SAT test. The existing evidence is persuasive, yet outdated.
With this concern in mind, the Task Force recommends later in this report that UC request item level
student results on both the SAT and the ACT so that UC researchers can update these tests for racial bias.
This is an important task that would benefit both students nationwide and the testing services, regardlessof which way the findings would go.
This answer considers racial bias at the level of individual test items. It could be that high-stakes
standardized tests are racially biased in other ways. One way discussed in Section IV is “stereotype
threat.” It could be that if members of one group felt threatened by a test that members of another group
did not, it might be the case that this group would answer ALL questions on a test correctly at lower rates.
The Task Force did not have time to gather and analyze data bearing on this important question, e.g. tomeasure the size of the effect of stereotype threat in order to compare it as a possible source of racial bias
to other possible sources.
Q: The SAT tests verbal and math abilities. My child is a lot more than the sum of her test scores.
Why does UC look so narrowly at the results of a single test? What changes, if any, is UC thinking
of to look at my student from a much broader perspective?
This question was on the minds of many in the Task Force. To those who believe that the SAT plays adecisive role both in admission decisions for individual students and in creating disparities in admission
rates across socioeconomic groups, we refer them to the above dialogue and the content in Section III of
the report. It establishes that admission decisions are much more sensitive to high school grades than to
SAT scores. Even more important, it shows compelling evidence that admissions decisions take into
account differences across groups in mean SAT scores. Any claim that UC looks narrowly at SAT scores
does not turn out to be correct. That does not mean that UC does as well as it could in explaining how UC
admissions decisions are made, nor that high school students understand how it works. The report notesthat high school guidance counseling services are overburdened and at least anecdotal evidence from
UC’s office of admissions suggests that our outreach efforts to high schools, though already extensive,
could be improved. One of the STTF’s goals in including the analyses and visualizations in this report
was to improve transparency about the UC admissions process.
Furthermore, UC uses 14 criteria in evaluating applications of which the SAT is only one. In fact, UC
looks at the “whole person”, and the whole transcript. The 14 items include the number of advanced
courses taken, whether the student plans to take a challenging set of courses in grade 12, whether students
have shown marked improvement across grades, and, as discussed earlier, UC admissions officers lookcarefully for evidence of special skills, talents, and leadership activities. Evidence of having overcome asignificant life challenge also is among the 14 factors.
Could UC do a better job of looking at the whole person? It seems very possible. For instance, with this inmind, the Task Force recommends later in the report that UC consider adding new factors to the statewide
eligibility criteria for guaranteed admission. (The statewide “index” guarantees admission to UC forstudents who meet or exceed any of a number of possible combinations of GPA and SAT scores.) For
example, we recommend that UC consider making these criteria more flexible, perhaps by giving
additional consideration to students whose GPA has improved over time.
Q: Why does UC need to use any admissions test at all? Why not just look at high school GPA?

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This might be the most important question yet. Given the evidence gathered by the Task Force, we know
how the current admissions tests are used and not used in the admissions process. With some confidence,
we can predict that the following would happen if UC stopped using admission tests and relied solely on
GPA and other aspects of the students’ transcript for the academic appraisal of applicants. The following
statements are very likely true because our analysis shows that SAT scores predict outcomes even after
taking into account GPA and a student’s socioeconomic status.
Likely Impacts of Dropping Admission Tests:•

The average student admitted would have a lower first-year GPA, a lower probability of persisting toyear 2, a lower probability of graduating within seven years, and a lower GPA upon graduation. The
reason for this is that UC would no longer be able to use admissions tests to identify, withinsocioeconomic groups, the students most likely to succeed.

•

UC would have a lessened ability to deliver academic and socio-emotional supports to freshmen
because it would no longer be able to identify as accurately the students at risk of faring poorly at UC.

•

The impacts on diversity depend largely upon how the remaining 13 admission factors were used inadmissions. Our analysis in the recommendations section later in this report suggests that expanding
Eligibility in the Local Context, a program that admits the top 9% of graduates in each high school as
ranked by GPA, would modestly improve diversity. But countering this is the fact that many
underrepresented students who are currently guaranteed admission to UC through the statewide
eligibility criteria would probably no longer be guaranteed admission. These students, who are
guaranteed admission through the statewide index but not by the Eligibility in the Local Context(ELC) program, by definition have been admitted based on their SAT scores. The numbers of
disadvantaged students who would lose guaranteed admission if UC dropped SAT tests issurprisingly large. In 2018, about one quarter of low-income, first-generation and underrepresented
minorities who were guaranteed admission to UC earned this guarantee solely by virtue of their SAT
scores. African-American and Native American students would be especially hurt by dropping the
SAT: among the students guaranteed admission to UC, 40% of African-Americans and 47% of Native
Americans won their guarantee because of their SAT scores. Figure 3B-1 and surrounding text
explains these surprising facts.

•

The average subsidy that the state of California would provide to California students throughsubsidized tuition (and complete tuition waivers for lower income students) would need to rise due toa longer time to graduation for the average student. Furthermore, the total subsidy expenditure per
Bachelor’s graduate would rise, not only due to longer time to graduation, but due to a larger
percentage of undergraduates not graduating. These increased subsidies may be socially desirable; wemerely note here that there is a financial consequence from dropping admissions tests.

•

The UC Regents have long had a policy that out-of-state undergraduates must “compare favorably” toin-state undergraduates. UC takes this requirement seriously, and has used the SAT and ACT test
results as objective measures of academic preparation. Comparing resident and non-resident students
based solely on GPA would be very difficult and unconvincing, given that grading standards differ inother states, and especially so, in other countries.

The above consequences of using only high school GPA and not admissions tests are quite likely, due tothe lessened ability of UC to pick the students within each socioeconomic group most likely to succeed
and to identify the students most at-risk, if it did not use the SAT. However, this statement is predicatedon UC not making other changes that could somehow compensate for this change in the mix of students
admitted. A case in point is that UC could react to a decision not to use admissions tests by evaluating its

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support programs for undergraduates, with a view to improving the programs and expanding the mostsuccessful programs. This reform in theory could prevent graduation rates from falling and UC GPA’sfrom falling were admission tests to be abandoned. The STTF has recommended that UC implement arigorous evaluation of the impact of its undergraduate support programs on student outcomes, tosupplement the other recommendations including the development of a broader admissions test. Clearly,
this recommendation could prove at least as valuable in the event that UC dropped admissions testing.
(PARENT A) Q: Doesn’t UC, as a publicly supported institution, have a moral obligation to reflect
the diversity of the state of California as a whole?
Yes, UC absolutely has a moral obligation to do the best it can to enroll an undergraduate population thatreflects the diversity of the state. As mentioned in Part I of this report, UC has been under a stipulation tohave its students represent the entire state since as early as 1868!
(PARENT A) Q: But really, how is UC doing? Is it simply admitting affluent students, who tend tobe from certain racial/ethnic groups? If so, isn’t UC simply perpetuating the income gaps between
rich and poor, and between racial and socioeconomic groups, and transmitting these inequalities tothe next generation? How is this fair to my child, who has not benefited from being raised in an
affluent family?
UC’s admissions procedures are designed to ensure fairness to all applicants. As the report has shown,
differences in SAT scores that relate to family context are carefully taken into account, so much so that in
the admissions pool underrepresented groups have lower average SAT scores than more advantaged
groups, and these differences mirror the gaps in test scores in the applicant pool.
In addition, one of the 14 factors that UC considers in making admission decisions is item 13: “Academic
accomplishments in light of your life experiences and special circumstances, including but not limited to:disabilities, low family income, first generation to attend college, need to work, disadvantaged social or
educational environment, difficult personal and family situations or circumstances, refugee status orveteran status.” 124 This item gives students who have faced adversity the opportunity to describe thisexperience, so that campuses can look at both grades and test scores in the proper context.
There is a third UC practice that illustrates UC’s efforts to take disadvantage into account and to be fair to
all students. Most students who enroll at UC were guaranteed admission through the statewide eligibility
criteria, the ELC criteria, or both. But what happens to students who do not receive a UC admissions
guarantee through either path? UC requires that campuses give a full review to all applicants who meetthe minimum eligibility criteria, namely having completed the A-G course requirements with grades of C
or higher, and who have an overall GPA of at least 3.0. These students are “Entitled to Review” (ETR),
which means each campus to which such students have applied, who do not meet either of the guaranteed
admissions sets of criteria, must be given a full review. (See Section I of the report for more details.) ETR
is not a small program. In 2019, about 40,000 students were ETR, of whom about 17,000 were admitted
to at least one UC campus. This amounts to just under 24%of all admitted students.
These three examples show the efforts UC makes to be as fair as possible to students who come from lessadvantaged situations. But that does not really answer your question of how well UC is doing by way of
disadvantaged applicants. It is not the case that UC merely accepts only affluent students. The reportprovides ample evidence about this.
124

Guidelines for Implementation of University Policy on Undergraduate AdmisssionAdmission,
https://senate.universityofcalifornia.edu/_files/committees/boars/documents/guidelines-implementation-of-ugadmission-rev-7-2019.pdf

86Independent reviews of UC’s approach could help to answer the question of how UC is doing in acomparative way. One example of this is the US News and World Report rankings of national universities
on social mobility. 125 The rankings examine students with lower family income who qualify for federal
Pell Grants to help them enroll in college. The US News Social Mobility rankings compare universities
based on the six-year graduation rates of Pell grantees, while giving additional credit to universities wherea larger proportion of students hold Pell grants. Another factor is the relative six-year graduation rates ofPell Grant holders and other students. 126 In these rankings of all national universities, UC campuses
account for the top three universities, 6 of the top 10 universities, and 8 of the top 30 national universities
in the United States. The implication is that relative to other national universities, UC is doing more toenroll lower-income students and to support them successfully through to graduation.
This is great news, but does this mean that UC is doing all it can? Not necessarily. The Task Force has
looked at a number of actions that could be taken to increase both the admission rates of disadvantaged
students, and their rates of success once enrolled. Among the recommendations that the Task Force ismaking, with a view to improved diversity and improved success rates, are:
• Expansion of the ELC program, which uses grades only;• Enhancing the statewide eligibility index to take into account more than SAT scores and GPA, forinstance by explicitly giving weight to applicants whose GPA has improved over time;• Rigorously evaluating UC’s many student support programs, and then expanding the mostsuccessful programs;• Performing an updated check for racial bias in existing tests, and also analyzing the current
admissions tests both to find out on a more granular level which types of math and verbal
questions best predict UC success, and which types of questions account for gaps in test scores
across socioeconomic groups;• Supporting the creation of an entirely new admissions test that would test a broader set of skills
than math and verbal achievement, and that would give all students ample opportunities to takepractice tests and to learn in a detailed way about specific skills they need to work on. It seems
quite possible that by testing a broader set of skills, the new test will improve diversity inadmissions as well.
This was a long answer, but we hope it addresses your concern that UC does not care enough about
diversity and about disadvantaged students.
(PARENT B) Q: I am sorry, but I do not like this line of questioning about diversity. My student isa white male, from an affluent family, and happens to have excellent grades and test scores. I hear
what this other parent has been saying, but it isn’t my son’s fault that he was born into an affluent
white family. Doesn’t UC have an obligation to admit the best prepared students? Why all this fuss
about the need for diversity?
This is a natural question to ask. We are glad that both of you, Parent A and Parent B, can hear eachother’s questions and our replies. You are both asking, from different perspectives, about questions offairness. We hope that you can each put yourselves in each other’s shoes, and can see that fairness for one
student must be balanced by fairness towards the other student.
The Task Force has worked hard to think through the tension that exists here. On the one hand, the UC
Regents have long required UC to admit students who are prepared for the rigorous undergraduate
curriculum. But the Regents also want UC to reflect the diversity of the state. The UC response has been125
126

See https://www.usnews.com/best-colleges/rankings/national-universities/social-mobility?_mode=table .
See https://www.usnews.com/education/best-colleges/articles/how-us-news-calculated-the-rankings .

87balanced. On the one hand, about two-thirds of admits in 2019 were guaranteed admission under stateeligibility guidelines that emphasize GPA and SAT scores. Of these just over half were also ELC,
meaning that their within-school GPA ranking also guaranteed them admission. Another twelfth ofstudents were guaranteed admission through ELC only. This group was admitted regardless of their testscores, because their GPA ranking signaled good things about their work ethic and grit. Another quarterof admitted students were not guaranteed admission, but impressed one or more campuses sufficiently togain admission under the Entitled to Review program. The goal here is to balance measures of academic
achievement and the Regental goal of diversity.
Q: I think that the testing culture in our society has gone way too far. Why are we so focused on
admissions tests, where just a few points difference could make a big difference in admissions?
We hope that the earlier part of this conversation will have convinced you that at UC, admissions officers
do not focus narrowly or solely on SAT scores. Test scores represent one of 14 factors in admission, andthe test is used both to predict who will do well, and, among admits, who might most need additional
supports starting in their freshman year. We have also pointed out that differences of a few points on the
SAT do not make a big difference in admissions decisions in most cases.
Another reason to continue using admissions tests, which we have not talked about much yet, is that in
California, HSGPA is becoming less useful as predictor of UC performance (in first-year UC GPA) overtime. In fact, at UC it is now the case that SAT predicts performance better than HSGPA. The problem
with HSGPA is that grading standards differ across schools and teachers, and are not objective. The otherproblem is that nationwide HSGPA has been drifting upward for several decades, without concomitant
improvements on tests like the SAT and other tests. There is simply less and less information thatHSGPA by itself can provide about student prowess. See Part D in Section III for an extended discussionof the issues with relying solely on HSGPA without having a standardized admissions test as a second
tool for evaluating student achievement.
Q: Okay, but all this emphasis on tests that produces a couple of test score numbers, without
making students as a whole better students. How does this testing effort help students learn, or help
their teachers figure out how to provide more support in areas in which students are struggling?
Surely there must be a better way.
We agree that undue emphasis on activities such as memorizing techniques to help on admissions tests
does not necessarily boost student achievement or enhance learning. This is why the Task Force isrecommending the development of an ambitious new form for testing that will test skills more broadly,
and that actually could have the effect of improving the quality of schooling. The new test will achieve
the latter by giving students and their teachers detailed feedback on specific areas within a subject area
where the student needs to improve. Further, the feedback can be frequent, as students can take practicetests online at will, with instant feedback being provided.
Q: Well, suppose you are right that test scores do not matter as much for admissions as the public
thinks. Isn’t it UC’s obligation to explain this better?
This is a valid point, and UC could probably help the public understand more fully the role that
admissions tests play. One service that UC already provides in this regard is online information on therange of SAT scores among the middle 50% of admits at each campus. Perhaps UC could make clearer
that each campus is willing to accept students with a wide range of SAT scores by also showing the rangeof SAT scores of the middle 80% of admitted students. The hope of the Task Force is that what we, as
individual members, have had the privilege to learn while serving on the Task Force will be widely sharedpublicly. That is certainly among our goals as we wrote this report.

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VI.

Reforms the Task Force Considered but Does Not Recommend

The Standardized Testing Task Force (STTF) evaluated two possible reforms to the University’s
admissions process that have received public attention: 1) adoption of the Smarter Balanced (SBAC)
Assessment Consortium’s high school assessments in place of the SAT and ACT, and 2) giving
applicants to the University of California the option of whether to take the SAT or ACT rather thanrequiring the tests. We do not at this time recommend either. Below we summarize our concerns about
each of these possibilities.
The Task Force does not recommend adopting Smarter Balanced Assessment in lieu of currently used
standardized tests.
One alternative to the continued use of the SAT and ACT in University admissions is adoption of thecurrent Smarter Balanced Assessment Consortium’s high school assessments as an alternative. While
there are a number of positives associated with this alternative, the Task Force does not recommend thisoption after the analysis described below.
I.

Background

Current SBAC assessments were originally developed with grant funding under the Obamaadministration. At the time, many states, including California, were adopting new college and career
readiness content standards in math and English Language Arts (ELA): the Common Core State
Standards. Because of the adoption of new standards, new tests aligned to these standards were needed tosatisfy federal K-12 accountability requirements. A group of states (California among them), led by
Washington as the fiscal agent, jointly developed the SBAC tests. After the federal grant funding ran out
in 2014, SBAC was established as a UC-operated program financially supported by the SBAC states’
membership fee payments. SBAC was initially housed at UCLA, and since 2017, located at UC Santa
Cruz Extension. In California, SBAC tests are managed by the California Department of Education(CDE) with the Educational Testing Service (ETS) currently serving as the main test administration
contractor. As required by the federal Every Student Succeeds Act (ESSA) and state education legislation,
students in grades 3-8, as well as in the 11th grade, are assessed annually with SBAC tests in California inmath and in ELA.
SBAC’s management and governance is codified in a set of intergovernmental agreements that the UC
signed with all the SBAC states. Contrary to popular belief, it was not an assessment specificallydesigned for California, though California plays a major role in the continuing success of SBAC.
Governed by the consensus of the states on all major decisions since its inception, it is a system thathas coalesced over time to serve the needs of many partners.
One of the most prominent components of SBAC assessments, which is the subject of the present
discussion, is its annual summative test in the 11th grade. In California, SBAC summative tests areadministered toward the end of the school year during a weeks-long testing window to capture thecumulative learning achieved by the students over the academic year through instruction. The summative
component stands in contrast with SBAC’s interim component, which are given several times during thecourse of instruction to provide more timely feedback to educators. The summative test consists of twoparts: an item-by-item Computerized Adaptive Test (CAT), and teacher-led performance tasks. Test
scores are generally made available to the parents and students late in the summer or early in the fall of
the following academic year.

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Purposes of Smarter Balanced Tests
Current SBAC summative tests were originally designed as assessments to measure student achievement
and growth with respect to the Common Core to inform program evaluation and school, district, and stateaccountability. As an accountability test, SBAC’s design and test score use are different from typicalhigh-stakes admissions tests.
First, current SBAC test scores carry low stakes for the individual students taking the test. While theCalifornia State University (CSU) system accepts SBAC test scores (along with the SAT and ACTscores) to meet conditions of enrollment, they are not used as part of the admissions process. CSU
admissions are also not as selective as UC. The current use of SBAC scores at CSU are intended to
inform the placement decisions of students in appropriate college-level coursework. The current design
and administration procedures of SBAC are not intended for it to withstand high-stakes usage in highly
selective UC admissions. More technical discussions on this point will be provided in the sections that
follow.
Second, an important distinction between accountability testing vs. admissions testing is that a K-12
accountability test aims to capture differences over larger aggregated groupings (e.g., schools or districts)
as opposed to refined differentiation among individuals of varying levels of proficiency or preparedness.
It is the aggregated performance on SBAC tests that contributes information into the state’s K-12
educational accountability system. A test designed and optimized for one purpose is not automaticallyappropriate for another. The 2014 edition of the Standards for Educational and Psychological Testingstated that:
Clear articulation of each intended test score interpretation for a specified use should be set
forth, and appropriate validity evidence in support of each intended interpretation should be
provided. (p. 23)127
It is incumbent upon the test providers to document validity evidence that supports the purposes for which
the scores are being used. Currently available evidence reviewed by the STTF does not support theimmediate alteration of the purpose of the SBAC tests from a designated K-12 accountability assessment
in California to a UC admissions test.
II.

Analysis

The Committee evaluated the possibility of using SBAC test scores in a similar way to how UC currently
uses the SAT and ACT. There are many reasons to consider this change. However, the STTF is concerned
about a wide range of risks related to the adoption of SBAC to meet UC admissions requirements.
Proponents of SBAC use recent data to suggest that adopting SBAC may, a) potentially improve therepresentativeness of UC matriculants of the broader population (equity); they also make four additional
arguments in support of SBAC’s adoption: b) the tests assess content accessible from school-based
instruction (instructional validity); c) the tests are considered aligned to college and career readinessstandards (content alignment); d) the tests are free and administered by teachers in schools (testing
experience); and e) it uses a novel CAT format (technical sophistication). Here we address each of thesein turn.

127

American Educational Research Association, American Psychological Association, & National Council on
Measurement in Education (2014). Standards for educational and psychological testing. Washington, DC: American
Educational Research Association.

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a.

EquityIn an early administration, when there were no stakes for student performance in relation to UC eligibilityor admissions, SBAC high scorers were more racially diverse than SAT/ACT high scorers. An
implication is that using SBAC instead of SAT/ACT might allow UC to increase the racial diversity ofmatriculants. However, the available data showing benefits to diversity is from a low-stakes
administration of the test. It is most likely that test-taker and parental behavior would change if the test
were to become high-stakes and used in admissions into selective UC campuses. This could not only
mean that some of the supposed diversity-related benefits might not be realized, but also that the current,
federally mandated purpose for which the SBAC tests were developed would be gravely undermined. The
discussions on instructional validity also help explain why adoption of the current SBAC test may in fact
be detrimental to fairness and equity due to unequal access to opportunities to learn across K-12 schools.
b.

Instructional validity

A main perceived benefit is that the SBAC tests assess content that should already be covered by schoolbased instruction aligned to the Common Core, which is freely available to all students, unlike paid SAT
coaching. As such, teachers’ instruction becomes the ultimate test preparation and therefore disparities
should become smaller in this criterion-referenced measure. These are good observations, but all tests,
independent of their claimed design attributes, almost invariably reflect disparities related to backgroundand prior experience unless there have been effective instructional interventions to mitigate theirinfluence.
Criterion-referenced tests were created with the idea that a specified domain of content and cognitive
skills could be codified into consistent standards and that teachers could teach to them. The purpose ofsuch aligned instruction would be to interrupt and to reduce, at least for the lower end of the achievement
distribution, the relationship between background and test performance. 128
For a criterion-referenced test to achieve its stated instructional validity, it should first display sensitivity
to instruction, that is, scores on the test should change when learning occurred. This is a property of themeasurement instrument that can and should be checked empirically with experimental or well-conductedquasi-experimental studies. A second element of instructional validity relates to the instruction that occurs
in the classrooms. From the angle of K-12 assessments, teaching and instruction have been termed“opportunity to learn” in recent times, meaning that opportunities to learn the content to be assessed aremade available to students through classroom instruction. Ideally if instruction is aligned to standards inthe entire K-12 system, the opportunity to learn should be more evenly available, and a test sensitive to
instruction should be able to detect it as a positive change.
Unfortunately, there has been little evidence of instructional validity for most achievement tests. There isa general lack of empirical data regarding the tests’ sensitivity to instructional differences. There areinconsistent results on instructional sensitivity from different data collection approaches, 129 including
instructional artifacts. 130 Significant portions of standards or content may also be underused in classroom

128
Baker, E. L., Chung, G. K. W. K., Cai, L. (2016). Assessment gaze, refraction, and blur: The courseof achievement testing in the past 100 years. Review of Research in Education, 40, 94-142.
129
Ing, M. (2018). What about the ‘‘instruction’’ in instructional sensitivity? Raising a validity issue in research on
instructional sensitivity. Educational and Psychological Measurement, 78, 635-652.
130
Burstein, L., McDonnell, L. M., Van Winkle, J., Ormseth, T., Mirocha, J., & Guiton, G. (1995). Validating
national curriculum indicators. Santa Monica, CA: RAND Corporation.

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teaching. 131 With present levels of inequalities in K-12 education, the uneven availability of opportunity
to learn means that students from less well-resourced schools will be systematically disadvantaged, again
raising a more fundamental fairness and equity concern.
An empirical alternative to obtaining detailed evidence about instructional validity could be inferred fromincreasing levels of performance, a result of a system convergence around the standards and tests.
However, K-12 test performance in math and reading has been relatively flat for more than a decade in
the US. 132 SBAC has largely replicated that national trend in California. The STTF observed that over
five years of administration and implementation of Common Core aligned instruction and assessment,
there continued to exist persistent achievement gaps across key student subpopulations along
race/ethnicity lines. There have even been year-to-year drops in student performance in the later grades (7and above). 133
Finally, the STTF noted that the utility of the test as a predictive measure was much less than previously
anticipated. Given that SBAC was designed specifically to be in close alignment to college and career
readiness standards and it is directly linked to several years of preparation in the form of high school
instruction, it should in theory be a much stronger predictor of typical outcomes than SAT/ACT. From thelimited data and analyses that STTF has access to, 134 the STTF’s conclusion is that in terms of predicting
first-year GPA at UC, SBAC in fact does not do better than the SAT, even when SBAC scores are notcurrently used in UC eligibility or admissions determination. The last observation is important because in
the current analysis, the SAT scores and high school GPA are already range-restricted due to their being
used in admissions, leading to attenuated multiple correlation coefficients, 135 but SBAC test scores do notcurrently suffer from such range restriction. If SBAC were to become an admissions test for the UC,
the multiple correlations reported today would decrease.
c.

Content alignment

Thus far, SBAC tests have been built and used for accountability purposes as a broad achievement
measure to capture group differences in achievement levels with respect to college and career readiness
content standards (the Common Core). A key claim in favor of SBAC’s adoption as new admissions tests
for UC is that the SBAC tests are considered aligned to the Common Core. The STTF notes that purposesand conditions of test use are inherently tied to validity. One cannot retrofit an existing test to a new
purpose solely because of content alignment, without collecting and evaluating pertinent validity evidence
for the new use.
This precept is symmetrical, that is, it applies to using group achievement tests in an individual highstakes setting (current situation) and to using admissions tests to evaluate school effectiveness. The STTF
is not alone in taking this measured position regarding test validation and use of evidence. As a matter offact, in June 2019, the National Council on Measurement in Education specifically issued a Position
Statement on the Use of College Admissions Test Scores as Academic Indicators in State Accountability

131

Opfer, V. D., Kaufman, J. H., Pane, J. D., & Thompson, L. E. (2018). Aligned curricula and implementation ofcommon core state mathematics standards: Findings from the American Teacher Panel. Santa Monica, CA: RAND
Corporation.
132
https://nces.ed.gov/nationsreportcard/data/
133
https://www.cde.ca.gov/nr/ne/yr19/yr19rel68.asp
134
Kurlaender, M., & Cohen, K. (2019). Predicting college success: How do different high school assessments
measure up? Policy Analysis for California Education.
135
Meade, A. W. (2010). Restriction of range. In Salkind, N. J. (Ed.) Encyclopedia of research design (pp. 12791280). Thousand Oaks, CA: Sage.

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Systems 136 precisely because under ESSA, states have been given considerable flexibility in using the
SAT and ACT as tests for school accountability. These admission tests may or may not be consideredvalid for the new purpose unless validity evidence is collected and interpreted. If one accepts this as a fairrequirement, then the principle of symmetry would imply that SBAC should be subject to the same levelof evidence gathering and validation for the intended new use. Content alignment alone is one piece in theoverall validation of test score use for a particular purpose and not the only piece.
Finally, it is important to note that content alignment is a matter of degree and not an absolute statement.
The STTF notes that claims of alignment to the college readiness content standards have also been madeabout the ACT, the newly revised SAT, as well as other K-12 measures (e.g., the Partnership for
Assessment of Readiness for College and Careers, or PARCC), to varying degrees. Alignment studies thathave historically been employed to verify adherence to content standards are based on expert judgment.
These studies are not free from possible bias in human judgment and decision-making. Even thoughjudges may be independent and the judging panels may follow clearly defined procedures, there istypically substantial variation/discordance both within and between judges and panels on the degrees towhich an item or a test is aligned to a set of standards.
d.

Testing Experience

There are many positives associated with the current testing experience of SBAC. It is administered in a
familiar environment, the student’s own school, and the student does not need to make special
arrangements to reach the testing site. It is free to the student and mandatory, unless parents opt out of
testing. While there is a clear perception that parental wealth may confer an unfair advantage on test
takers’ SAT/ACT scores due to intensive paid test preparation, the ability to take the tests multiple times,
and having better access to various resources that can make the testing experience more comfortable andmanageable, SBAC does not yet currently cast such a negative image. Stereotype threat may also be lessof an issue.
There is, however, a scarcity of practice materials relative to what is available freely for the SAT/ACT,even when the SBAC interim assessments, which are primarily intended as tools for teachers to check on
student progress, 137 are also counted as practice material. If SBAC were adopted by UC, it is most likelythat test preparatory services would develop to serve students whose families have the means to pay forthem. Even if one makes the argument that the best test preparation for SBAC is school-based instruction,
the lack of access to preparation material and highly skilled instructors still poses fairness questions forstudents who attend schools that are less well resourced.
Per California state law, the SBAC is administered once in the 11th grade, with a district-determined
testing window of at least 25 days after 66% of the instructional days of the year have been completed. Asan accountability test, these are entirely sensible choices. But as a high-stakes test that could help
determine a student’s future career at UC, the testing window leaves students and their families withlimited choices when the unexpected happens. Issues such as illness that last for more than a few days,
having to move from one part of the state to another, a family emergency that requires out-of-town travel,
etc., could cause chaos for the students involved with no clear means of redress that is fair to theindividuals involved and to the others at the same school. This issue is especially problematic if the event
falls on the last few days of the testing window, or if the time is close enough to June 15, when state-wideavailability of SBAC ends. An expanded testing window may address this specific problem, but as
discussed below, it will also introduce more pointed concerns related to exposure and validity.
136

https://higherlogicdownload.s3.amazonaws.com/NCME/c53581e4-9882-4137-987b4475f6cb502a/UploadedImages/Documents/Admission_Statement_06-16-19.pdf
137
http://www.smarterbalanced.org/assessments/interim-assessments/

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In terms of test security, the fact that SBAC is a school-based teacher-proctored test is more advantageousin helping address the problem of cheating by substitution (where someone is paid to take the test for a
student). On the other hand, in high-stakes testing, more testing sites also introduces more opportunities
for test security protocols to be breached. They are also more difficult to directly monitor. This may
become more problematic when the current SBAC tests are delivered over weeks-long testing windows.
Furthermore, because current SBAC is curriculum-based and given at different times of the year fordifferent schools, students may perform better if the test is administered later in the year, systematicallyadvantaging students from later administering sites. But state-wide uniformity in the opening and closingof the testing window would also necessarily take away the control that the local education agencies
needed to have for appropriate planning. Students at later administering sites may also gain preknowledge of assessment content because of item exposure in Computer Adaptive Testing (CAT). In
other words, some students may know some of the items in the CAT before taking the test simply because
others have already taken the test and remembered what was on the test. This last point will be further
elaborated in the next section.
e.

Technical Sophistication

Converting a low-stakes item-by-item computerized adaptive accountability test into a high-stakes
admissions test will have unintended consequences that may compromise the validity of the use of the testscores for both its original purpose, and the new purpose. To fully appreciate this concern, it is necessary
to understand how adaptive testing works, both theoretically, and in the real world.
In theory, an item-by-item adaptive test chooses the next item from a pool of items (the item bank) using aprovisional estimate of the test taker’s achievement or proficiency based on answers to items alreadyadministered. It also needs the definition of an optimal item to choose next. For example, an optimal item
could be defined as an item in the item bank whose level of difficulty matches the provisional proficiency
most closely. In this regard, the test is said to be “tailored,” with each individual essentially taking a uniqueform of the test most suitable for the test taker, all put together on-the-fly by an adaptive algorithm.
On paper, adaptive testing is an ideal way to put together a test. Indeed, when the stakes are low, such asin accountability testing, CAT may be helpful. For example, even though each individual CAT is oflimited test length, when many CATs are aggregated over students, the result is a set of much more
broadly sampled assessment content at the level of schools or districts than traditional fixed form tests canaccommodate. The richness of information is helpful for making inferences about school-level or districtlevel performance.
On the other hand, high-stakes testing at the individual level poses a completely different set of threats tovalidity. While an item bank may contain hundreds of items, and there is, in principle, a combinatoricallylarge number of possible alternate pathways through the test for test takers of all stripes, a real test mustcover a set of required content domains and use a variety of approaches to assess the test taker’s
knowledge due to design and validity considerations, thereby narrowing the number of possible alternate
paths significantly. This leads to a critical problem for item-by-item CAT: item exposure control. An item
is called “exposed” whenever a CAT algorithm selects the item for administration for a test taker. The
notion is that an exposed item is more amenable to be disclosed to others who may take the test at a latertime.
To understand the role exposure plays, it is helpful to revisit the storied history of the computerized
adaptive GRE, a high-stakes admissions test for graduate and professional schools. Shortly after ETS
started the GRE CAT in the 1990s, the test preparation industry (in both North America and Asia)

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discovered that by memorizing test items and sharing them after the test, individuals outside of ETS could
accumulate enough items from the exposed part of the item bank to compromise the validity of the test.
Knowing a portion of the items ahead of time can give the appearance of higher proficiency to theadaptive testing algorithm, thereby pushing the algorithm to give the test taker items that the test taker’s
true proficiency may never indicate. The size of the item bank only marginally helps limit the impact ofitem exposure, because one must also simultaneously satisfy content balancing constraints. The
ascendance of the internet also enabled the sharing of items at a larger scale than anyone ever anticipated.
Ultimately, ETS stopped the GRE CAT in some parts of the world and reverted back to paper-and-pencil
testing.
At present, adaptive algorithms used in SBAC rely on more rudimentary methods to control exposure. In
general, adaptive tests with rudimentary exposure control methods tend to have highly uneven exposure
across items, meaning that a small subset of the available items are seen by a disproportionately larger
share of students, increasing the chances that key items may be compromised. If SBAC item exposure is
not appropriately controlled and the item bank size remains modest in comparison to earlier high-stakes
CAT for admissions purposes, exposure will undoubtedly compromise the validity of the use of the
SBAC test scores for its original or new purposes, and possibly leading to more lawsuits. Importantly, thisrequired no technology in the testing sessions, only enough brains capable of remembering some of theitems, and the capability to share them. It is substantially easier to share content today than in the 1990s.
f.

Lack of Out-of-State Availability or Comparability

Finally, SBAC is not available uniformly in all states, let alone being available at all internationally. The
CCSS as adopted by the states nearly always have (small but still noticeable) changes to fit the standards
into state-specific contexts. The implementation of SBAC testing itself also varies across states. As aresult, it would be difficult to directly compare SBAC scores for out of state students to SBAC scoresfrom in-state students, – thus SBAC scores could not be used to establish that out of state students“compare favorably” to in state students, one of the other major uses of test scores for the accountabilityof UC to the public.
For these reasons, the STTF does not recommend adopting the currently available SBAC test in UC’s
admissions process.

The Task Force does not at this time recommend that UC make standardized tests optional for
applicants
At this time, the Task Force recommends that UC conduct additional research on the impact of going “testoptional” before deciding whether to implement such a policy. We have pragmatic concerns about how
campuses would evaluate and compare applicants who submit standardized test scores relative toapplicants who do not. We also have concerns about how campuses could best impute, explicitly or
implicitly, test scores to applicants. We have ethical concerns about how to treat students in the twogroups fairly. Finally, given the state of research, we are uncertain what the effects of going “testoptional” would be on either the preparation or composition of incoming classes.
I.

BackgroundAs has been widely reported in the press, several hundred colleges and universities have moved toeliminate the requirement that students include standardized test scores in their applications. Below, weturn to the scholarly assessments of what the effects of these moves have been. At the outset we note,95however, that none of these institutions is as large as UC, and we believe that the results of taking such a
step here might have effects unlike those described in extant research.
We also note that UC admissions may already use test scores in a manner unlike that of other institutions.
For example, currently applicants may be admitted to UC through Eligibility in the Local Context (ELC)
without consideration of test scores – although individual campuses consider test scores building theirentering classes. UC campus directors of undergraduate admissions informed us that standardized testscores are a useful tool to advance diversity of incoming classes and shared concerns that without use of
the scores as a tool, student diversity could actually decline. After all, as more than one admissions
director observed, California’s high schools produce tens of thousands of graduates with HSGPAs
exceeding 4.0, and if HSGPA were to become the dispositive assessment for UC admissions purposes,
then the demographics of those students with the highest grades would become the demographics of the
UC entering class. That might reduce diversity.
Because of the degree of uncertainty and because we found persuasive the argument made by directors ofundergraduate admissions across the campuses that as used at the University, standardized test scores help
to identify and admit students who are members of historically excluded groups and/or first generation,
we do not recommend making the tests optional.
II.

Research

The Task Force reviewed a sampling of the research, guided by a literature review prepared to update a2007 investigation by BOARS. That literature review is included as one of the appendices to this report.
In the following paragraphs we summarize our conclusions based on our review of studies of the effects
of making the standardized tests optional for applicants.
Scholarship on the effects of going “test optional” is mixed. The most recent study we reviewed (Saboe
and Terrizi, 2019 138) examined the effects of test-optional admissions on retention and graduation rates at
all four-year, public and private, not-for-profit institutions granting BA degrees in the period from 2009 to
2014. This sample included 1,649 colleges that required SAT scores and 127 that had a test-optional
policy. The authors report that they “cannot conclude that SAT optional policies have a significant effecton any of our measured outcome variables.” They do not find an increase in diversity and find only“limited evidence” that test-optional policies boost applicant volume and improve retention. Similar
results are reported by Sweitzer, Blalock, and Sharma, who used a different analytical method attempting
to compare liberal arts colleges that switched to test-optional to such institutions that did not. (Sweitzer,
Blalock, and Sharma 2018, in Buckley et al., eds. 139)
Syverson, Franks, and Hiss analyzed a sample of 28 colleges and universities including large and small,
public and private, nonprofit institutions of varying degrees of selectivity. (Syverson et al. 2018 140). This
study updated prior work on the effects of going test-optional, published in 2014 (Franks and Hiss,

138

Saboe, M. & Terrizzi, S. (2019). SAT optional policies: Do they influence graduate quality, selectivity or
diversity? Economics Letters, 174, 13-17.
139
Sweitzer, K, Blalock, A, and Charma, D. (2018). [The Effect of Going Test-Optional on Diversity andAdmissions.] In Buckley, Jack, Letukas, Lynn, and Wildavsky, Ben, Eds,. Measuring Success: Testing, Grades andthe Future of College Admissions (p. 164). Baltimore: Johns Hopkins University Press.
140
Syverson, S, Franks, V, & Hiss, W. (2018). Defining Access: How Test-Optional Works. Retrieved from
NACAC website: https://www.nacacnet.org/globalassets/documents/publications/research/defining-access-report2018.pdf.

962014 141). Like Saboe and Terrizi, they find that test-optional admissions increases the number ofapplications received, but unlike Saboe and Terrizi, Syverson et al. report an increase in the representationof URM students in both the applicant pool and in the entering class. However, three of the four public
universities in the sample experienced less growth in URM enrollment than a control group consisting ofpeer institutions that were not test optional.
Zwick adds variables to the discussion in offering a critique of the Franks and Hiss study, arguing thatoutcomes differed for students who did and did not submit SAT scores in at least one way not captured by
the study: students who did not submit were more likely to pursue majors in non-STEM fields. (Zwick
2018, in Buckley et al., eds. 142). Zwick criticizes the Franks and Hiss study for failing to offer findings to
support the claim that test-optional admissions leads to increased enrollment of URM students. In
describing the uncertain effects of going test-optional, Zwick also notes that the family resources devoted
to improving performance on standardized tests would presumably be redeployed to improve any otheraspect of an applicant’s performance necessary to bolster the odds of admission.
The student-level review by Syverson et al. also permitted additional observations about the students who
did submit SAT scores (“submitters”) and those who did not (“nonsubmitters”). Nonsubmitters weremore likely to be first-generation, women, members of historically excluded groups, and/or recipients offederal Pell grants. This study also found that although nonsubmitters were admitted at lower rates, they
were more likely to enroll if admitted. Graduation rates at a small, nonprofit institution were higher forunderrepresented minority students who were nonsubmitters relative to underrepresented minority
students who did submit scores. The study also found that at a large, public institution, undergraduate
GPAs of submitters were slightly higher than those of nonsubmitters but that graduation rates weresimilar.
Belasco, Rosinger, and Hearn analyzed the effects of going test-optional but their study included only
relatively small, liberal arts colleges. (Belasco, Rosinger, & Hearn 2014 143). Their panel covered classes
at 180 selective institutions between 1992 and 2010, and the authors found that the colleges that went testoptional did not increase the share of URM or Pell grant-eligible students enrolled, relative to collegesthat continued to require the SAT. However, test-optional colleges did report higher SAT scores over time
(representing the scores of those applicants who did submit scores). These findings are largely consistent
with the conclusions of a series of simulations developed by Espenshade and Chung, who analyzed thepossible effects of going test-optional on racial and socioeconomic diversity. (Espenshade and Chung,2012 144). They found that a test-optional admissions regime could increase the number of URM and poor
and working-class students enrolled. The simulations illustrated that this could be achieved by expanding
the share of URM students in the applicant pool and/or by increasing the share of students with lower testscores in the applicant pool. However, the effects are smaller at public universities than at private,nonprofit institutions. This study, while helpful in disaggregating the admissions process used in thesimulations, uses the most cautious language in offering predictions: “[G]iven the great variety ofpostsecondary institutions in the United States – variety in terms of size, endowment, mission, geography,
degree of selectivity, and whether private or public, among others – it is difficult to generalize to all
141

Hiss, W, Franks, V. (2014). Defining Promise: Optional Standardized Testing Policies in American College andUniversity Admissions. Retrieved from NACAC website: http://www.nacacnet.org/research/research-data/nacacresearch/Documents/DefiningPromise.pdf.
142
Buckley, Jack, Letukas, Lynn, and Wildavsky, Ben, Eds,. Measuring Success: Testing, Grades and the Future ofCollege Admissions. Baltimore: Johns Hopkins University Press, 2018.
143
Belasco, A. S., Rosinger, K. O., & Hearn. J. C. (2014). The Test-Optional Movement at America’s Selective
Liberal Arts Colleges: A Boon for Equity or Something Else? Educational Evaluation and Policy Analysis, pp. 1-18.
144
Espenshade, T. J., and Chung, C. Y. (2012). [Diversity outcomes of test-optional policies.] In J. A. Soares (Ed.),SAT wars: The case for test-optional admissions (p. 177). New York: Teachers College Press. Retrieved fromhttps://www.princeton.edu/~tje/files/Espenshade_Diversity_outcomes_chapter_SAT%20Wars_Soares.pdf

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campuses about the likely consequences of a move to a test-optional admissions policy.” We share thisconclusion.
III.

Summary

Multiple studies found that one effect of going test-optional was to increase the number of applicants.
This is not a priority for the University at this time. After all, while there is much concern about UC
admissions, it does not arise because critics believe the pool of applicants is too small. We anticipate that
although some advocates of reform to admissions will call for making SAT and ACT scores optional, we
believe that such a move could have significant, unanticipated, and undesirable effects on the profile ofmatriculating classes.
The research reviewed did not go into great detail about the methods used by admissions officers. For
example, the studies did not explore how admissions offices evaluated applicants who submitted SAT
scores relative to applicants who did not, nor about whether or how admissions offices imputed scores toapplicants who chose not to provide them. These are significant questions if the admissions process is tobe transparent and fair. We believe that our concerns could be possibly addressed by an appropriate
process but thoughtful design would take time. At this point, we are unsure how best to design a fair, testoptional admissions process.
For these reasons, we do not recommend making the SAT and ACT optional for applicants for admission
to UC at this time. The potential effects of making these tests optional deserve considerably more
research than has yet been conducted, certainly at a university the size of UC. We suggest that UC
undertake such study.

98VII. Recommendations of the Task ForceThe Task Force recommends the following reforms.
Potential Reforms that the University of California (“UC” or “the University”) Could Implement in
the Near Term
[The Task Force] recommends that in the near term BOARS consider reforming the UC admissions
process by updating the UC eligibility criteria, with the goal of reducing reliance on test scores,
maintaining or improving diversity, while ensuring admitted students are prepared. In this section werecommend that BOARS consider two changes to the UC Eligibility Criteria.
Background
There is a long history at the University of California of reconsidering the respective role of grades andtest scores. Most recently, in 2012 UC implemented a number of reforms to its admissions procedures.
Overall, these reforms increased the influence of GPA relative to test scores in determining the eligibility
pool for UC admissions. The reforms increased emphasis on the Eligibility in the Local Context (ELC)
approach in which the top-ranked students in each high school, ranked by GPA, become eligible to attend
UC, subject to meeting the UC A-G course requirements and an overall GPA cutoff of 3.0. The reforms
decreased emphasis on testing, first by dropping the SAT subject tests, and second, by reducing the
proportion of the eligibility pool that would come from the “statewide context,” which is based on
students meeting various combinations of GPA and SAT or ACT score. As reported in a BOARS (2013)
study of these reforms:
The changes implemented for 2012 involved 1) elimination of the SAT subject tests as arequirement for eligibility; 2) a decrease from 12.5% to 9% of California high school graduates
who were identified as eligible in the statewide context; 3) an increase from 4% to 9% of
graduates who were identified as eligible in the local (within-school) context (ELC); and 4) the
introduction of a new category of “entitled to review” (ETR) applicants who are assured acomprehensive review of their application at all campuses to which they apply, but who are noteligible for referral to another campus for admission. The 9% statewide and 9% local eligibility
was expected to result in approximately 10.5% of California high school graduates being
identified as eligible for referral to a campus with available space if they are not admitted to a
campus to which they apply. 145
The BOARS analysis indicated that in the first two years of the program, the eligibility pool was slightly
larger than predicted. Significantly, academic preparation of eligible students remained strong while
diversity improved somewhat.
Given the Task Force’s mandate to study the ways in which tests are used in the admissions process at
UC, it seems natural to explore the possibility of further reforming the relative number of students who
become eligible through the “statewide” versus “local” contexts.

145

Impact of the New Freshman Eligibility Policy at the University of California. A Report to the Regents of the
University of California,
https://senate.universityofcalifornia.edu/_files/committees/boars/Nov52013BOARSReporttoRegents-Final.pdf

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Recommendation 1. Review and update components of the statewide eligibility index.
The University admits students to campuses based on fourteen factors but bases systemwide eligibility on
only two, HSGPA and test scores. We recommend that BOARS consider whether, among the 14 factors,
we might identify additional quantitative features that could be included in the statewide eligibility index.
For instance, improvement in GPA over the course of high school is already a factor that campuses use inadmissions. Looking for students who improve their achievement over time could help UC find students
on an upward trajectory.
Recommendation 2. Expand Eligibility in the Local Context.
The Task Force recommends that BOARS consider reforming the UC admissions process by adjusting
the relative importance of the “statewide” versus “local” eligibility criteria. Specifically, BOARS shouldcarefully evaluate expanding the Eligibility in the Local Context (ELC) pool by admitting more than the
top 9% of students in each high school, when ranked by GPA. The Task Force recommends leaving thestatewide index formula unchanged but expanding ELC for two reasons. First, it would be divisive to
exclude some prepared students who would have been admitted under the current statewide eligibility
rules, but who would have been excluded as cutbacks were made to the size of the statewide pool toaccommodate replacements from the ELC pool. Second, defining and defending growth in theundergraduate population that would come from expanding ELC over a period of several years would
send a signal to Californians about the high value that UC places on becoming more representative of thestate over time.
In expanding the ELC cutoff beyond the top 9% of students, the Task Force recommends that BOARS
take the following factors into consideration:
1. Any change to ELC or statewide eligibility should be implemented judiciously, with the goal of
preventing the overall eligibility pool from contracting or expanding in dramatic ways. This isimportant because sudden large increases in enrollment would lead to shortages of spaces inclassrooms and dormitory beds, while on the other hand sudden large decreases in enrollment would
needlessly reduce access to postsecondary education at UC.
2. If the size of the eligibility pool is kept constant, then expanding ELC will necessarily displace somestudents who would have been “statewide” eligible under the current system. This would be
unfortunate and politically divisive. A superior approach would be for UC to expand enrollment overtime, dedicating new slots in the eligibility pool to the expanded ELC program, while leaving thecurrent statewide eligibility criteria unchanged. We note that such growth would be consistent with anadditional goal, beyond our purview but subject of popular discussion, of increasing the population ofcollege-educated Californians.
3. In performing simulations of possible reforms, a factor to consider is the likely acceptance rates fromstudents who would become eligible or ineligible for admissions to UC. This analysis of the yield rate
will be crucial to control the overall enrollment levels at UC.
4. Any move to expand ELC while keeping the size of the “statewide” eligibility pool constant shouldbe accompanied by a vigorous request for full funding by the legislature of the new slots to support
the overall growth in enrollment that would result.
5. BOARS should monitor the academic progress of the pool of students admitted specifically throughany expansion of ELC.

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6. One practical issue that UC already faces with its 9% ELC program is that over time the share of highschool seniors who satisfy this requirement can change. Several school districts including the twolargest districts in California have recently made completion of A-G courses a graduation requirement(although these districts have allowed those with D grades to graduate, while UC requires grades ofC.) We believe that BOARS will need to monitor A-G completion rates on an annual basis. If ELC is
expanded, this could incentivize more students to complete the A-G courses with the required grades.
This would be a desirable outcome, given that failure to complete the A-G coursework currently
excludes many students. But such an increase in numbers makes annual monitoring of yields from theprogram even more crucial.
Likely Impacts on the Student Eligibility Pool: Some Examples
This Task Force had neither the time nor the resources to conduct detailed simulations. But with theassistance of key staff at UCOP, we have learned of the results of some early simulations, which are quite
informative. Below we present results from three variants of one simulation that in turn reduce
enrollment, keep enrollment constant or expand enrollment in a way designed to maintain the number ofstudents who were admitted to UC by “Index Only,” that is, through the statewide eligibility criterion.
This is just one of many examples that BOARS could consider.
We are grateful to staff at UCOP for performing the simulations outlined below.
1) A Simulation that Would (Inadvertently) Lower Enrollment
At one extreme, in determining eligibility one could rely solely on ELC and cease to use the statewide
eligibility that focuses on test score and GPA combinations. One such simulation examined the impact on
the eligibility pool of expanding ELC to admit students in the top 12.5% of their classes and suspending
the use of “Index” eligibility based on SAT and GPA data. The simulation, using applications for fall2017 to fall 2019, shows the following:1) The diversity of the newly eligible students is higher along several dimensions than that ofstudents who were “Index Only” eligible.
2) However, many disadvantaged students and Underrepresented Minority Students (URMs) who
were eligible based on the statewide SAT/GPA criteria would no longer have been eligible foradmission under this system.
3) The high school GPA and SAT scores of those newly eligible would be both somewhat lower
than for the students who were eligible under the Index.
4) This reform would have reduced the eligibility pool over the three years from 167,534 to 120,368,
a drop of 28%, or about 47,000 students over three years. So, on average, about 16,000 students ayear who were “Index Only” eligible would not be eligible under the simulated expanded ELC
program.
2) A Simulation that Would Keep Enrollment Constant
An immediate conclusion from this simulation is that to keep enrollments at historical levels, the UC
might have to extend ELC considerably beyond the top 12.5% of each class when ranked by GPA to meet
its enrollment targets.
Alternatively, and more in line with what we propose, it would make sense for UC, if it chose to expand
ELC from the top 9% to the top 12.5%, to continue to use the statewide GPA/SAT index to admit
students, but to make the GPA/SAT criteria more stringent.

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Of the 59,327 “Index Only” students who would not have been eligible under a top 12.5% ELC program
over 2017-2019, UC would want to make up the 47,000 student shortfall over three years by making the
top 47,000 of the 59,327 “Index Only” students eligible in the state context. This would mean taking
roughly the top 80% of the “Index Only” students.
In other words, one feasible approach would be to expand ELC from the top 9% to the top 12.5% of eachhigh school class while reducing the number eligible under statewide eligibility rules by about 20%.
In Table 7-1 the left column shows the demographic makeup during 2017-19 under the existing 9x9
program, and the right column shows the projected makeup had UC expanded ELC to the top 12.5% andreduced the statewide eligibility index pool by the same number with the goal of keeping enrollment
constant. In total, the ELC pool would have grown by 12,161 students and the statewide pool would havefallen by about the same number.
Table 7-1 The Demographic Makeup of the UC Eligibility Pool in 2017-19 under the Current Policy andunder an Alternative Policy with the ELC Pool Increased to the Top 12.5% with Corresponding Drops in
the Size of the Statewide Index Pool
Current Policy(9x9)

ELC 12.5%+ Adjusted Statewide Index

African-American
Native American
Asian
LatinoPacific IslanderWhite
Unknown (U.S.)

3.8%
0.5%
36.3%
28.3%
0.3%
27.5%
3.3%

4.0%
0.5%
34.6%
31.3%
0.3%
26.1%
3.2%

First Generation
CollegeFirst Generation
Not First Generation
Unknown

35.5%
62.6%
1.8%

38.8%
59.4%
1.8%

Low-Income Status
Low-Income
Not Low-Income
Unknown

29.2%
64.2%
6.6%

31.9%
61.8%
6.3%

GenderFemaleMale
Other/unknown

56.3%
41.4%
2.2%

57.2%
40.6%
2.2%

Ethnicity

The table shows moderate changes in the demographic mix of the eligibility pool, with growth of about
2% in share of the pool that is low-income, and growth of about 3% in the shares of students who are102Latino or first-generation. All of these changes represent a move towards better reflecting the diversity ofCalifornia. In this particular simulation, some very good applicants who in 2017-19 would have become
statewide eligible but were not eligible through the ELC would not have automatically become eligible
for admission.
What about the academic preparation of those deemed eligible in 2017-2019 under the current 9X9
system, and that of those who would be eligible under a top 12.5% ELC system with an adjusted
statewide eligibility index designed to keep overall eligibility constant? Table 7-2 shows basic statistics
on the GPA and SAT of those in the actual eligibility pools and under the simulation.
Table 7-2 Academic Preparation of the Actual 9 x 9 Eligibility Pool for 2017-2019 and the Simulated
Eligibility Pool Using An Adjusted Statewide Index Eligibility and/or ELC 12.5% Eligible
High School GPA

SAT

Group

Mean

25th
Percentile

75thPercentile

Mean

25th
75th
Percentile Percentile

9 X 9 Eligible
Applicants

4.05

3.90

4.23

1325

1220

1440

Simulated Index
Eligible and/or
ELC 12.5%
Eligible

4.04

3.90

4.23

1310

1190

1440

Note: Simulation raises the rigor of the statewide eligibility index to keep the overall eligibility pool athistorical levels.
In part because the 12,000 newly eligible students in the simulation represent about 7.3% of the historicaleligibility pool, changes in academic preparation are modest. The table shows that mean high school GPA
and SAT scores, and also their 25th and 75th percentile values, are quite similar under the actual andsimulated scenarios.
Most students would continue to be eligible under both the adjusted statewide eligibility and the adjusted
12.5% ELC criteria. That said, it is important to recognize that the expanded ELC and the statewide
eligibility index are selecting students in different ways. One way to see this is to compare those eligible
either through statewide eligibility or by ELC, but not by both. Table 7-3 below shows the academic
preparation of the subsamples of the simulated eligibility pool who would have been admitted only
through the adjusted statewide eligibility criterion or admitted only by the expanded 12.5% ELC criterion.

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Table 7-3 Academic Preparation in the Simulation of Those Who Would Have Been Eligible Only
Through the Adjusted Statewide Index or Only Through the 12.5% ELC CriterionHigh School GPA

SAT

Mean

25th
Percentile

75thPercentile

Mean

25th
75th
Percentile Percentile

Adjusted
Statewide Index
Eligible Only

3.97

3.85

4.11

1397

1340

1460

ELC 12.5%
Eligible Only

3.82

3.68

4.00

1083

1010

1160

The table shows that those who would have been eligible only through the adjusted statewide eligibility
index on average had high school GPAs of 3.97 compared to 3.82 for those who would have been eligible
only under the 12.5% ELC option. The corresponding numbers for mean SAT score were 1397 and 1083.
Similar gaps emerge when we look at the 25th and 75th percentiles.
3) A Simulation that Would Increase Enrollment, By Expanding the ELC Pool While Maintaining theSize of the Statewide Index Pool
Some may express concerns that in the above simulation, the only way to keep the eligibility poolconstant was to exclude some students who were eligible in 2017-2019 under the existing statewide
eligibility index.
Another approach would be to maintain the same rules for statewide eligibility that currently exist, while
increasing the ELC pool and thereby overall UC enrollment. This would have left the number who werestatewide eligible constant over 2017-19, while the ELC eligibility pool would have increased. Overall,
the total eligibility pool over the period would have grown by the projected growth in the ELC pool, from167,534 to 179,695, or an increase of 7.3%. Thus, this approach implies UC growing its enrollment by
about 7.3% to accommodate the ELC growth.
This growth in total enrollment would of course occur over four years, because new cohorts of freshmen
would gradually take the place of smaller earlier cohorts. The increase is certainly meaningful, but not
unprecedented. Between fall 2009 and fall 2019, for example, UC undergraduate enrollment rose from177,453 to 226,125, a rise of 27.4% across a decade. 146
We emphasize that we believe BOARS will need to take the lead in deciding whether expanding the ELC
percentage is advisable and if so by how much. The simulation referred to above provides one of many
possible ways of expanding ELC, and does not constitute a formal recommendation. Rather, we intend it
as a useful illustration of likely consequences for one specific example of such an expansion. That said,
the Task Force is recommending a meaningful increase.
Analysis of UC admissions steps that may contribute to disparities in access.

146

Calculations based on total fall undergraduate enrollment obtained fromhttps://www.universityofcalifornia.edu/infocenter/fall-enrollment-glance.

104

Our preliminary analysis indicates that the disparities along lines of race, ethnicity, and socioeconomic
status evident in the undergraduate population of the University are a function of multiple factors,
including but not limited to the SAT and ACT. For example, we note that disparities in access to andcompletion of A-G courses account for a disproportionate lack of UC eligibility for students who aremembers of underrepresented groups. Differences in A-G completion rates across schools alsosignificantly reduce the yield of students through ELC. This research matters because long-term solutions
to promote better representation of historically underrepresented groups will only be achieved byunderstanding the multiple factors that impede better representation in UC.
Recommendation 3. Further analyze factors contributing to disproportionate representation.
The Task Force strongly recommends further analysis of the admissions process to identify those points
in the admissions process, and the factors at work at each such point, that contribute to disproportionate
declines in representation of students who belong to populations that have historically been excluded fromhigher education opportunity at UC.
Recommendation 4. Study and expand student academic support services.
Background
This report has established that both the existing admissions tests and high school GPA have the ability to
predict undergraduate success along a number of important dimensions. The data analyses underlying thatwork necessarily used historical data, and are backward-looking.
Of course, though, history need not be repeated if institutions evolve. What if UC could find ways toimprove undergraduate outcomes, especially for those who enter UC with lower SAT scores and
HSGPAs? This could lessen the predictive power of admissions tests and grades by virtue of improving
outcomes for at-risk students. Some Task Force members wondered whether UC as a whole could do a
better job than it already does supporting undergraduates. Supporting students in their freshman andsophomore years may be especially crucial, as this is when many students are still adjusting to the rigors
of postsecondary education, and the emotional stresses related to moving away from home, dealing withthe financial burdens of postsecondary education, and making major decisions about career paths. Ideally,
then, undergraduate support programs should provide both academic and socio-emotional support.
This line of thinking quickly led us to the outlines of a proposal i) to catalog existing support programs
and what we know about these programs’ effectiveness, ii) to evaluate these support programs rigorously,
and iii) to design and evaluate new support programs as needed. The ultimate goal of this work would be
to improve and expand the system of support programs at UC, in the process materially improving theundergraduate success of admits whose high school academic profiles suggest they could struggle at UC.
The proposal appears below, followed by a discussion of some potential pitfalls and opportunities.
The Task Force recommends a multi-pronged effort to study, fine-tune and expand the system ofacademic and socio-emotional supports that UC provides to its undergraduate students. The efforts should
begin by identifying and learning about existing programs, and then proceed quickly to a second phasethat would evaluate existing programs and fine-tune these programs as needed. In the third phase, UC
would design and evaluate new support programs as needs are identified. As a whole, this venture would
involve the steps listed below.

105

Phase 11) Systematically catalog the support programs, both academic and socio-emotional, that UC campuses
currently provide to undergraduates, especially to those who are predicted to be less likely to succeed,
based on GPA and SAT scores.
2) Learn what UC staff already know about the impact of these programs on student outcomes. In
addition to looking at formal and informal evidence of overall impacts, look for evidence about
whether there exist specific groups of students who gain more from a given program.
Phase 23) Based on what is learned in Phase 1, conduct formal evaluations of the programs that campuses viewas the most important. Goals would include evaluation of i) overall student impacts and ii)
identification of student subgroups that benefit relatively more or less from a given program.
4) Using the evidence from step 3), expand the most successful programs, and if warranted by evidence
target some programs quite selectively towards student subgroups that benefit the most from specificprograms.
Phase 3Phase 2 is likely to find gaps in what support programs currently provide to students. Where unmet needs
are identified, UC should develop new programs. This phase should include the following elements.
Notably, the first suggestion below could be carried out as early as Phase 1.
5) Survey the literature on academic and socio-emotional support programs to identify new types of
support programs that hold promise.
6) Based both on this literature review and what Phases 1 and 2 reveal about unmet needs at UC,
develop new support programs, and evaluate them.
7) Using the evidence from step 6), maintain and possibly expand the new programs that provesuccessful, while modifying or curtailing the new programs for which evidence fails to find abenefit for student outcomes.
For all the evaluation efforts, UC should use rigorous methods such as randomized controlled trials, orstrong quasi-experimental methods if necessary. These quantitative evaluation tools should be combinedwith qualitative methods that use interviews, focus groups and related methods to learn directly fromstudent participants and program staff which aspects of a program work and which need re-thinking.
Discussion
Funding — Funding for the support programs would need to come from UC. There is a strong possibility
that some of the evaluation work, especially if it involved randomized controlled trials, could be fundedthrough grants from the U.S. Department of Education, given that its Institute of Education Sciences(IES) has announced last year and again this year that a major new area for research it is willing to fundinvolves the development and rigorous evaluation of reforms designed to improve postsecondary
education.
Buy-In from Campuses — It would take support from throughout the administration of each campus forthis to work, as well as careful and diplomatic engagement with the administrators of existing support
programs.

106Research Expertise — There exist numerous ladder rank faculty with expertise in education and either
quantitative or qualitative social science research. In addition, it seems possible that some Lecturers with
Security of Employment, depending on their area of expertise, might find some aspect of the program
development and evaluation work to be exciting and relevant to the creative activity parts of their duties.
UC may also need to make a number of strategic faculty hires to help design evidence-based support
programs evidence wherever possible. For example, there has been a great deal of recent work bypsychologists on socio-emotional supports such as mindfulness based interventions and their impact onmental health and sense of well-being, including in school- and college-age populations.
Evaluation of New versus Existing Programs — Evaluating new or existing but limited programs is easierthan evaluating full-scale programs because in the former cases, it is easier to develop application lists
and randomize who enters the programs from the application lists, thereby creating a valid comparisongroup as part of a Randomized Controlled Trial (RCT). Large support programs with almost universal
reach may not be amenable to evaluation this way. One possibility in this latter case is that if large
programs use HSGPA or SAT/ACT scores to determine eligibility cutoffs, a strong quasi-experimental
design known as Regression Discontinuity could be used to choose a valid control group, by comparing
those just barely eligible to participate with other students who are on the other side of the eligibility
cutoff. Both of these approaches, RCT’s and Regression Discontinuity, have typically been reviewed
positively in past IES grant competitions.
Relevance — It may strike some readers as odd that a Task Force on admissions testing would beconcerned about improving supports for undergraduate students. But the link is clear – our review of
evidence shows variations in undergraduate performance that can be predicted by numerous measures,
including grades and test scores, and these variations can reflect inequalities in school systems and society
more broadly. Improved support systems to help students UC admits who are predicted to have a lower
chance of succeeding in college overcome challenges would lead to a larger and probably more diverse
population of college graduates in California. At the same time, it could lessen somewhat the need for UC
to use admissions tests and grades heavily in the admissions decision.
Updated Analysis of Current Standardized Tests
Recommendation 5. Obtain data to perform item-level analysis of current standardized tests.
The Task Force strongly recommends that UC obtains the necessary data and test items to perform an
updated analysis of current standardized tests. This recommendation contains two distinct but
interconnected pieces. The first piece is an updated differential item functioning (DIF) analysis,
conducted with more modern methods, e.g., with explanatory models (De Boeck & Wilson, 2004), using
more recent data. The second piece involves a two-phased feature analysis of the test items. The
qualitative review of items is combined with a quantitative analysis of student item response data using
the extracted features that may be related to cognition, instruction, prior preparedness, and language tobetter understand the disparities in student performance on current standardized tests.
DIF Analysis
First, to refresh prior studies that were based on data from two decades ago, UC should request the mostrecent operational student-level data from College Board and ACT in the form of raw item responses, aswell as necessary applicant and school background information as they are available. With data, the UC
can provide an unbiased and definitive answer to the question of whether the tests and the items making
up the tests are indeed biased against subgroups in ways that the assessment community do not fully
appreciate or understand. This is a legitimate concern that can be immediately addressed with currently

107available statistical and psychometric techniques, given the extensive revisions made recently to the SAT,
as well as the age and relevance of currently available research
Feature Analysis
Feature analysis is an integrated process involving the qualitative rating of assessment items/tasks against
a set of attributes (along the lines of content, language, and cognition) in a particular context (UC-level
college readiness), followed by a subsequent quantitative analysis to determine how these attributes
contribute to or subtract from successful task performance.
As described by Baker et al. (2015) 147, one of the earliest references to the idea of feature analysis can be
attributed to Gordon (1970) in the Report of the Commission on Tests: II. 148 Gordon mentioned
qualitative analysis of assessments to emphasize “description and prescription,” that is, the qualitativedescription of cognitive functions leading to the prescription of the learning experiences required to more
adequately ensure academic success. Gordon suggested that existing instruments can be examined with aview towards categorization and interpretation to determine whether data can be reported in qualitative
ways, in addition to traditional quantitative ways. For instance, Gordon mentioned that response patterns
can be reported differently for information recall or vocabulary. He also referred to features such asproblem solving, expression, and information management, among many others.
To accomplish this, UC should also request the items/tasks themselves from College Board or ACT. The
items should be rendered in a human-reviewable form containing complete information as they were
presented to the students in operational administration, i.e., prompts, texts, graphs, response options, etc.
The results of this study will provide immediate answers to where the subgroup performance differenceson the standardized tests may come from. The results of the analysis may also inform UC in thedevelopment efforts related to Recommendation 6 immediately below.
Longer Term Reform: Development of a New Assessment System
The STTF believes that it is possible to create fundamentally different kinds of assessments that satisfy all
BOARS principles on testing, while retaining predictive power, providing improved feedback to students
and teachers, and broadening the demographic range of students UC is able to admit. Over the longer
term, we believe that the University should study the development of a completely new suite of highschool student assessments by “designing in” the principles developed by BOARS that govern UC
admissions, instead of attempting to “retrofit” any existing assessment programs under those principles.
UC should commit to evaluating the fairness and predictive power of any newly developed assessmentsthat satisfy UC testing principles even if they do not resemble current admissions tests in content, format,
administration, and the number or types of scores available for use by UC to gauge an applicant’s
achievement or potential.

147

Baker, E. L., Madni, A., Michiuye, J. K., Choi, K., & Cai, L. (2015). Mathematical Reasoning Project
quantitative analyses resultsQuantitative Analyses Results: Grades 4, 8, and 11. Los Angeles: University ofCalifornia, Los
Angeles, National Center for Research on Evaluation, Standards, and Student Testing. Available at
http://www.smarterapp.org/documents/FeatureAnalysis_2015-09-25.pdf
148
Report of the Commission on Tests: II. Briefs. College Entrance Examination Board, New York, NY. (1970).
Available at https://files.eric.ed.gov/fulltext/ED045704.pdf

108Recommendation 6. Develop a new assessment that will be continuously accessible to students and thatwill assess a broader array of student learning and capabilities than any of the currently available tests.
This new suite of assessments should provide more feedback to schools and to students, and may enable
UC to admit classes of students more representative of the diversity of the state.
1.

Background

Current standardized assessments measure a relatively narrow slice of academic achievement or potential,
late in an aspiring applicant’s high school career, with minimal feedback and time for corrective action
even if desired. A new system should allow the longitudinal measurement and formative development ofa broader range of skills and attributes, related to an applicant’s success at UC, reflecting the principlethat the University “…should select those students who demonstrate a strong likelihood that they will
persist to graduation.” 149
In particular, based on the research reviewed by the Task Force, we believe that UC will be able toarticulate a set of skills and attributes that 1) can be measured in a uniform and quantitative way, 2) arelikely to be predictive of success at UC, and 3) could potentially show smaller disparities than current
measures along the lines of race, ethnicity, and socioeconomic status. Of course, the tests should
continue to evaluate math and reading achievement due to their foundational importance in college
and career success. The formats of the tasks on the new assessments can and should also take fulladvantage of technological advances to let the aspiring applicants exercise a wider range of knowledge,
skills, and reasoning than multiple-choice questions do.
2.

Can We Build These Measures?

So far a number of large-scale assessments have explored technology-based performance assessments inlow-stakes settings. The most prominent examples include NAEP Problem Solving in Technology-Rich
Environments 150 and NAEP Science Interactive Computer Tasks 151 under the National Assessment of
Educational Progress (NAEP) program, and PISA 2012 Creative Problem Solving Tasks 152 and PISA
2015 Collaborative Problem Solving Tasks 153 under OECD’s Program for International Student

149

Guidelines for Implementation of University Policy on Undergraduate Admission,
https://senate.universityofcalifornia.edu/_files/committees/boars/documents/guidelines-implementation-of-ugadmission-rev-7-2019.pdf
150
Bennett, R. E., Persky, H., Weiss, A. R., & Jenkins, F. (2007). Problem solving in technology-rich environments:
A report from the NAEP Technology-Based Assessment Project (NCES 2007–466). U.S. Department of Education.
Washington, DC: National Center for Education Statistics.
151
National Center for Education Statistics (NCES). (2012). The nation’s report card: Science in action: Hands-on
and interactive computer tasks from the 2009 science assessment (Report No. NCES 2012–468). Washington, DC:
Institute of Education Sciences, U.S. Department of Education.
152
Organisation for Economic Co-operation and Development (OECD). (2014). PISA 2012 Results: Creative
problem solving: Students’ skills in tackling real-life problems (Volume V). Paris: OECD Publishing. Retrieved
from: http://dx.doi.org/10.1787/9789264208070-en
153
Organisation for Economic Co-operation and Development (OECD). (2017). PISA 2015 Results: Collaborative
problem solving (Volume V). Paris: OECD Publishing. Retrieved from: https://dx.doi.org/10.1787/9789264285521-en

109

Assessment (PISA). Another related project is SimScientists, 154 155 which represents the collaboration ofsix states to study simulation-based science assessments. The results of these projects suggest somepromising directions that the proposed new development can take.
NAEP Problem Solving in Technology-Rich Environments and NAEP Science Interactive Computer
Tasks feature distinct attempts at building fundamentally different kinds of assessment experience. It is
not a coincidence that NAEP chose to examine interactive problem solving in their entry into thedevelopment of a newer, fully-digitized generation of assessment products that will contribute to our
Nation’s Report Card. One out of 10 adults, averaged across 24 countries, reported encountering at least
one non-routine, complex problem per day requiring 30 minutes or more to solve. 156
Note that these examples illustrate the promise of large-scale technology-based performance assessments.
Numerous small-scale studies around technology-based performance assessments have been studied
beginning in the 1990s – often by UC researchers. For example, Baker led a research program on the useof computer-based concept mapping to measure students’ conceptual understanding. Students createdconcept maps (networks) to represent their understanding of how concepts were related, and also wrote
essays on the same topic explaining how the concepts were related. Concept mapping was tested innumerous studies with different content and with students spanning elementary to adults, 157, 158 withstatistically significant relations found between concept map scores and essay scores (r = .4 to .7).
Stevens 159 developed interactive multi-media exercises (IMMEX) and its use as a Web-based simulation
with middle and high school students. While intended for instructional use, IMMEX also served
assessment purposes. Cognitive process validation showed that students’ cognitive processes relating totheir simulation performance, with productive reasoning processes positively related to successful
simulation performance (r = .7) and unproductive processes relating to unsuccessful simulation
performance (r = .5). 160 These are two concrete examples of research conducted by UC faculty andintended to illustrate the maturity and predictive power of carefully designed technology-based
performance assessments. For a review of technology-based performance assessments and issues ingeneral, see Clauser, Margolis, and Clauser (2016) 161 and Katz, LaMar, Spain, Zapata-Rivera, Baird, andGreiff (2017). 162
154

Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012). Science assessments for all:Integrating science simulations into balanced state science assessment systems. Journal of Research in Science
Teaching, 49, 363-393.
155
Quellmalz, E. S., Davenport, J. L., Timms, M. J., DeBoer, G. E., Jordan, K. A., Huang, C.-W., & Buckley, B. C.
(2013). Next-generation environments for assessing and promoting complex science learning. Journal of
Educational Psychology, 105, 1100–1114. doi:10.1037/a0032220
156
Organisation for Economic Co-operation and Development (OECD). (2013). OECD skills outlook: 2013: First
results from the survey of adult skills. Paris: OECD Publishing. Retrieved from:http://dx.doi.org/10.1787/9789264204256-en
157
Chung, G. K. W. K., Harmon, T. C., & Baker, E. L. (2001). The impact of a simulation-based learning design
project on student learning. IEEE Transactions on Education, 44, 390–398.
158
O’Neil, H. F., & Chung, G. K. W. K. (2011, April). Use of knowledge mapping in computer-based assessment.
Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
159
Beal, C. R., & Stevens, R. H. (2010). Improving students’ problem solving in a web-based chemistry simulation
through embedded metacognitive messages. Technology, Instruction, Cognition & Learning, 8, 255–271.
160
Chung, G. K. W. K., de Vries, L. F., Cheak, A. M., Stevens, R. H., & Bewley, W. L. (2002). Cognitive processvalidation of an online problem solving assessment. Computers in Human Behavior, 18, 669–684.
161
Clauser, B. E., Margolis, M. J., & Clauser, J. C. (2016). Issues in simulation-based assessment. In F. Drasgow(Ed.), Technology and testing: Improving educational and psychological measurement (pp. 49–78). New York, NY:
Routledge.
162
Katz, I. R., LaMar, M. M., Spain, R., Zapata-Rivera, J. D., Baird, J. A., & Greiff, S. (2017). Validity issues andconcerns for technology-based performance assessment. In Design Recommendations for Intelligent Tutoring
System-Volume 5: Assessment Methods (pp. 209–224). U.S. Army Research Laboroatry.

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We propose that the UC look into the development of simulations and realistic performance tasks that canassess creative problem solving, inductive and deductive reasoning, and analytical capability because
these attributes are hallmark features of what has come to be expected out of higher education. Macrotrends such as automation and the shift to knowledge-based economy have generally favored careers thatinvolve nonroutine tasks, and the attributes listed above are among those that are required in this new
economic reality. 163
To build the new assessments, it is important to follow assessment development principles under the
Evidence-Centered Design (ECD) framework 164. ECD has become the de facto assessment design
process, with its core principles developed and refined over 20 years of research in numerous assessment
contexts, 165, 166, 167, 168, 169, 170, 171 including large-scale standard testing, English proficiency testing,performance assessments, simulations, and games. The essential design element in ECD is to firstestablish a good representation of the construct being measured. The construct in this case is the set ofacademically relevant and perhaps even disciplinary-specific knowledge, skills, and attributes (KSAs)
that the UC deems important for an applicant’s success as a UC undergraduate. For the new assessment
instruments to be useful in UC admissions, the development should begin with the creation and vetting ofthese KSAs with strong input from its faculty and students.
Then the assessment developers, led by UC faculty, must decompose the construct into increasingly morerefined and measurable elements. These elements may be interconnected and one may depend on another.
This results in a graphical representation of the domain – complete with nodes and links that illustrate thetheory of what underlies the construct. Then the task of the assessment designer becomes that of finding
ways that will elicit from students the evidence that can be used to tap into the various elements andultimate to form an overall measure. This design approach results in an explicit, external representation of
the target content and skills that can be reviewed by various UC stakeholders. In addition, this external
representation, if available to the public, can serve as roadmap of what knowledge and skills are expectedof UC applicants. We believe an assessment constructed this way would predict outcomes for UC
matriculants better than current tests because the assessment will have specific UC needs built in.
Another benefit of designing the new assessment in this manner is that rather than giving feedback tostudents in the form of a few numbers aimed at refined differentiation of individuals, detailed feedback
can be made available to students on their performance in different areas, for example the processes
163

Autor, D. (2010). The polarization of job opportunities in the U.S. labor market: Implications for employment
and earnings. Washington, DC: Center for American Progress and The Hamilton Project.
164
Mislevy, R. J. (2013). Evidence-centered design for simulation-based assessment. Military Medicine, 178(supplement 10), 107–114.
165
Mislevy, R. J. (1994). Evidence and inference in educational assessment. Psychometrika, 59, 439–483.
166
Mislevy, R. J. (1996). Test theory reconceived. Journal of Educational Measurement, 33, 379–416.
167
Mislevy, R. J. (2018). Sociocognitive Foundations of Educational Measurement. New York, NY: Routledge.
168
Mislevy, R. J., Almond, R. G., Yan, D., & Steinberg, L. S. (1999, July). Bayes nets in educational assessment:
Where the numbers come from. In Proceedings of the fifteenth conference on uncertainty in artificial
intelligence (pp. 437–446). Burlington, MA: Morgan Kaufmann.
169
Mislevy, R. J., Corrigan, S., Oranje, A., DiCerbo, K., Bauer, M. I., von Davier, A., & John, M. (2016).
Psychometrics and game-based assessment. In F. Drasgow (Ed.), Technology and testing: Improving educational
and psychological measurement (pp. 23-48). New York, NY: Routledge.
170
Mislevy, R. J., & Haertel, G. D. (2006). Implications of evidence‐centered design for educational
testing. Educational Measurement: Issues and Practice, 25, 6–20.
171
Haertel, G., Wentland, E., Yarnall, L., & Mislevy, R. J. (2012). Evidence-centered design in assessment
development. In Handbook on Measurement, Assessment, and Evaluation in Higher Education (pp. 277–296). New
York, NY: Routledge.

111within problem solving, or within reading and math. An example is PISA 2012, wherein the core problem
solving processes are defined as: (a) Exploring and understanding; (b) Representing and formulating; (c)Planning and executing; and (d) Monitoring and reflecting. Significant individual and group differenceswere found across these problem solving processes. More nuanced feedback not only would give students
diagnostic evidence on specific areas in which the student needs to improve over time, but once admitted
to the UC this information would allow campuses to provide tailor-made supports to students rather than
generic remedial classes that are inefficient and stigmatizing.
3.

How Do Scores Relate to Student’s Background Characteristics?

The PISA 2012 Problem Solving assessment offers an example of why we think alternatives to traditional
standardized test may yield fewer disparities along the lines of race, ethnicity, or socio-economic status.
In the PISA 2012 Problem Solving assessment, items were designed to focus on measuring distinct
problem solving processes. Generally speaking, the data suggest that there is substantial variation in
problem solving skills both within and between schools. On average across participating Organisation for
Economic Co-operation and Development (OECD) countries, the variance in student performance that isobserved within schools amounts to 61% of the OECD average total variance in student performance. The
remainder is attributable to differences in student performance between schools. In the US, thedecomposition of test score variance (for math or reading) into between and within school components
generally results in around 75% of the variance attributable to the within school part. The ratio of variance
unique to problem solving versus variance shared with core academic subjects assessed by PISA (reading,math, and science) is roughly 1:2 for both the within school and between school components, suggestingthat problem solving is distinct from the core academic subjects. In other words, it is unlikely that one canrecombine the academic subject tests to measure problem solving well. Perhaps unsurprisingly, the largest
correlation in student performance is between math and problem solving (0.81); the smallest is between
reading and problem solving (0.75). These correlations may appear large when standalone, but they areappreciably smaller than the correlation observed among student performance in the core academic
subjects of reading, math and science (often above .85). Finally, the relation of socio-economic status
with problem-solving performance is weaker than its relation with general academic proficiency in math,reading or science. 172
In a more recent study, PISA administered Collaborative Problem Solving Tasks in 2015. 173 Unlike theassessments of the core PISA subjects, the PISA 2015 collaborative problem solving assessment is not a
direct measure of individual differences, but the design of these performance tasks and the results provide
useful guidance for the present proposal. Again, the relationship between socio-economic status andcollaborative problem solving performance is weaker than its relation with performance on otheracademic subjects. On average across OECD countries that participated in the collaborative problem
solving assessment, a one-point increase in students’ socio‑economic status (as measured by astandardized index used by PISA in all countries) is associated with a 13-point improvement incollaborative problem solving performance, compared to between 17 and 19 points in the three core PISA
subjects. A one-point increase in schools’ socio-economic profile is associated with a 59-point
improvement in collaborative problem-solving performance compared to between 66 and 73 points in thethree core PISA subjects. A number of countries in PISA 2015 had sizeable immigrant populations. Thereappears to be no significant relationship between immigrant status and the uniquely collaborative aspectsof the problem solving assessment, after the relationship between diversity and socio-economic profile
has been accounted for.

172
173

Organisation for Economic Co-operation and Development (OECD), 2014, p 94.
Organisation for Economic Co-operation and Development (OECD), 2017, p. 93.

112

SimScientists 174 175 focuses on middle school science (Ecosystems, and Force & Motion). The projectstudied the psychometric quality, feasibility, and utility of simulation-based science assessments designed
to serve teacher’s formative assessment 176 needs during a unit and to provide summative evidence of endof-unit proficiencies. 177 The goal is to test whether interactive assessments may be more effective than
traditional, static assessments at discriminating student proficiency across 3 types of science practices: (a)identifying principles (e.g., recognizing principles), (b) using principles (e.g., applying knowledge tomake predictions and generate explanations), and (c) conducting inquiry (e.g., designing experiments).
The results suggest that static assessments are not as effective as interactive assessments fordifferentiating between factual knowledge and the ability to apply that knowledge in meaningful contexts.
The interactive assessments were more effective at measuring students’ ability to engage in inquiry
practices. 178 Critics have contended that English Learners and students with disabilities may beoverwhelmed by complex, dynamic simulations. In this study, evidence suggests that these populations ofstudents were better able to show their skills and potential on the simulation-based assessments than on
the conventional multiple-choice test items. 179
Based on evidence so far, we expect that the new system being envisioned can be designed in such amanner that inequalities in socioeconomic status and availability of information or resources in K-12
education do not correlate as strongly with the results. This is because the new assessments aim toquantify a student’s capacity to learn new material, rather than manifesting what the student has not had
the opportunity to learn. For this reason, we hope and expect that UC will look into the development of
this new set of assessments in place of currently used standardized tests.
4.

Outstanding Issues

The following issues are technical problems that have to be solved before implementing these newassessments at a full scale, but we expect that they can be solved to UC’s satisfaction over a development
period of several years, followed by pilot testing and refinement.
a. Age Range and Stakes
The oldest students targeted in NAEP, PISA, SimScientist, and now TIMSS are 15 years old. These arealso low-stakes assessments so the same potential criticism raised against the conversion of Smarter
Balanced into a high-stakes assessment is applicable here. However, the new assessment system will be
designed from the ground up for UC’s admissions purposes. It can be designed to accommodate highstakes use and more expanded age range. On the other hand, retrofitting a statewide accountability test for
the new purpose will be more difficult.
b. Response Format
Even for the state-of-the-art computer-based assessments, the tasks are “still quite structured and rigid,
with complex forms of learning either being neglected or measured in ways that may not correspond to
174

Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012).
Quellmalz, E. S., Davenport, J. L., Timms, M. J., DeBoer, G. E., Jordan, K. A., Huang, C.W., & Buckley, B.C.
(2013).
176
In simple terms, formative assessments are intended to gauge a student’s learning during ongoing instruction, sothat effective feedback and intervention may be provided. Summative assessments, as the name suggests, provide acomprehensive summary of what student has achieved after a unit, a semester, or perhaps a year.
177
Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012), p. 1
178
Quellmalz, E. S., Davenport, J. L., Timms, M. J., DeBoer, G. E., Jordan, K. A., Huang, C.-W., & Buckley, B. C.
(2013), p, 1112
179
Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012), p. 387
175

113

real-life situations.” 180 (p. 167). Many task formats, in the end, use multiple-choice items to collect data.
Students can manipulate variables in a simulation and see how the system changes as a result, but then aprompt is presented and students are asked to choose an answer. This is generally true for the NAEP
science tasks and for SimScientist. The range of formats so far appears to be based on what is available inthe standard UI/UX toolkit 181 182. Standard User Interface elements such as sliders, drop down, drag anddrop, text input boxes, and so on are used. More custom response inputs are rare except in game-based
measures or in concept mapping exercises where the drawing of a graph with nodes/edges are produced at
the end of the task. Efforts should be made to expand the possible response formats, e.g., withtouchscreens. This would require a close collaboration between item designers and computer
programmers that many large-scale assessment programs cannot easily accommodate.
c. Automated Scoring
This is related to the problem of Response Format. How to make sense of process data (e.g., clicks orinteractions with touchscreens) from interactive tasks to produce or supplement outcome scores remains amajor topic of research. Scoring of open-ended responses appears to be a major bottleneck in efforts toscale up the assessments. It must be tackled so that tasks more similar to real-life situations can be createdand used. Essay and speech scoring seem to be the only areas that received serious and sustained
attention. Automated scoring needs to be explainable – statistical prediction alone is not sufficient. Blackbox (prediction of human judged scores only) methods for high stakes purposes are problematic because
they are not transparent. In a high-stakes setting, transparency helps with fairness. UC is home tofoundational research on cognitive AI, and much of the recent development has resided within computerscience. Assessment is one area in which UC’s capacity in computer science may help bring neededchange.
d. Automated Generation of Assessment Content
Automated task/item generation 183 184 refers to the generation of tasks or items that all share underlying
design features but different surface features, e.g., generating items with different coefficients with thesame functional form in an equation solving task. Automated task/item generation is a necessity here
because the new assessment system must be able to provide open access to practice material at any timeby any student. The same automated engine will be used to generate the proctored assessment content,
different from the openly available practice content. With always-on availability of these authentic
practice content, students can and should be encouraged to try out the assessments for as many times as
they desire. By doing this, students can inoculate themselves against any negative testing experience,
obtain feedback about their preparation, or maintain skills and knowledge. This is only fiscally possible ifenough of the content generation is automated. It also takes automated generation of assessment content
to efficiently scale up the assessment system to meet the needs of the entire State’s applicant pool (as well
as allowing assessment of private and out of state students).
O’Leary, M., Scully, D., Karakolidis, A., & Pitsia, V. (2018). The state‐of‐the‐art in digital technology‐based
assessment. European Journal of Education, 53, 160-175.
181
Parshall, C., Harmes, J. C., Davey, T., & Pashley, P. (2010). Innovative Items for Computerized Testing. In W. J.
van der Linden & C. A. W. Glas (Eds.), Elements of Adaptive Testing (pp. 215-230): Springer New York.
182
Scalise, K., & Gifford, B. (2006). Computer-based assessment in e-learning: A framework for constructing"intermediate constraint" questions and tasks for technology platforms. The Journal of Technology, Learning andAssessment, 4(6).
183
Embretson, S. E., & Kingston, N. M. (2018). Automatic item generation: A more efficient process for developing
mathematics achievement items? Journal of Educational Measurement, 55, 112–131.
184
Gierl, M. J., & Lai, H. (2012). The role of item models in automatic item generation. International Journal of
Testing, 12, 273–298.
180

114

e. Security and Authenticity
Security for the versions of the assessment that are proctored and which are considered in the UC
admissions process should be maintained. Student access to proctored assessment sessions should be
increased, to allow a chance to take the test in more than one grade at the very least. Because we expectthat the assessment system will yield information useful and predictive at other institutions of higher
education, the scores will become as portable as current standardized tests.
5.

A Notional Timeline

We do not make a recommendation now about who should govern, finance, develop, evaluate, maintain,
and administer these assessments. An endeavor of this scale will not be inexpensive and will likely
require the collaboration of a number of stakeholders. Experience also suggests that the startup and longterm success of a new assessment system such as the one proposed here will likely involve the public,
private, and philanthropic sectors.
However, to ensure more focused consideration of the feasibility of the new assessment and its usefulness
for UC admissions, we provide the following notional development timeline as a current best estimate.
With the stakes involved, there will be considerable anticipation of the timely availability of this newassessment system. The timeline, though objectively and justifiably reasonable, given the scope andimportance, may appear to be impracticably long to key observers and decision-makers. Some of us areconcerned that momentum may be lost during the long development cycle without assurance from the
University to initiate and to support its development and evaluation, not to mention a clear commitment toadoption of the new assessment system, once accepted, to replace current standardized tests. Members of
the Task Force were divided on whether to recommend cessation of consideration of standardized testscores sooner than this timeline would contemplate – likely before the completion of development of thisnew suite of assessments. The disagreement within the Task Force mirrors that outside the University andreflects different views of the relative risks and benefits of possible, additional steps that UC could take toensure that the entering classes more closely reflect the diversity of the State.

115

MonthsTask

0-24
months

1. Solicit Input
and Build
Consensus

Brief Description

Before any large-scale assessment development work can begin,
there must be a period of fact-finding and consensus building among
the key stakeholders. We propose that the UC should spend a periodof 24 months to detail, in the form of a public-facing framework
document, what the core knowledge, skills, and attributes this newassessment system should reflect. Needless to say, UC faculty andstudents, UC alumni, and the broader California public should havetheir voices represented in the process. Part of this work will have tobe qualitative and inductive, but it is necessary so that we mayunderstand what are the missing components from our current
thinking. Part of this work will involve synthesis of knowledge fromresearch, broadly construed. The ultimate goal would be the concrete
definitions of a set of cognitive, content, and linguistic features thata new assessment should reflect.
0-24
2. Analyze
UC should examine the state of the art in assessment technology,
monthsCurrent
assessment methods, as well as the state of readiness in theTechnology
assessment field outside of research and academic settings. In aseries of RFIs, information about automated item generation, item
authoring, item rendering, test security, test delivery, and automateditem scoring should be gathered and analyzed by UC researcherswith experience in large-scale assessment development,
psychometrics, statistics, and computer science and computerengineering.
Check Point 1
At the end of 24 months, the UC should have enough information to make a decision on whether thecontinuing buildout of this new system of assessments will likely meet UC’s goals. If UC chooses tomove ahead, a sunset date of the current standardized tests can be decided at this time.
25-36
3. Development
Prototypes for a couple of select content areas or select kinds of newmonthsof a Prototype
response formats will be developed. Using iterative development,
small low-stakes tryouts should already have occurred by the end of
this task.
Check Point 2
At this point, UC will have enough evidence to determine whether the full system build out is viablefrom a time and financial standpoint. The option to stop remains open at this point, but if the decision
is to move ahead with full scale development, a sunset date of the current standardized tests should be
decided at this time for ethnical and practical reasons.
37-72
4. Full Scale
The system will be built and a large field test will be carried out. IfmonthsDevelopment
adequate resources are available, this task is the part of the project
where more dollars can translate into less time until completion.
73-96
5. Operational
The assessment enters into a provisional stage and is ready for use
monthsadministrations
by campus admissions offices.
Check Point 3
Pending two years of successful data gathering and positive evaluation outcomes, the new assessment
enters into an operational stage, and is maintained actively for future years.

116

STTF Roster and Acknowledgements
STTF Co-Chairs
Henry Sánchez, Pathology (UCSF)
Eddie Comeaux, Education (UCR)
Members
Eva Baker, Education (UCLA)
Julian Betts, Economics (UCSD)
Li Cai, Education (UCLA)
Darlene Francis, Public Health (UCB)
Patricia Gandara, Education (UCLA)
Jonathan Glater, Law (UCI)James Griesemer, Philosophy (UCD)
Andrea Hasenstaub, Otolaryngology – Head andNeck (UCSF)
George Johnson, Mechanical Engineering, (UCB)
Mona Lynch, Criminology, Law & Society (UCI)
Andrew Maul, Education (UCSB)
Yolanda Moses, Anthropology (UCR)

Kip Tellez, Education (UCSC)
Haim Weizman, Chemistry & Biochemistry (UCSD)
Anne Zanzucchi, Writing Studies (UCM)
Wendy Rummerfield, Graduate Student
Representative (UCI)
STTF Writing Subcommittee
Jonathan Glater, Chair
Julian Betts
Li CaiPatricia Gandara
Andrea Hasenstaub
George Johnson
Staff
Michael LaBriola, Academic Senate Assistant
Director
Ken Feer, Academic Senate Analyst

Special Thanks
The STTF thanks its Writing Subcommittee, whose extraordinary work, sustained dedication, and original
analyses of new data made this report possible. We also thank UC Director of Institutional Research andAcademic Planning (IRAP) Tongshan Chang, and Director of Undergraduate Admissions Han Mi Yoon-Wu,
who along with IRAP Analysts Matt Reed and James Hershey, provided invaluable analytic support to the
STTF. In addition, STTF member James Griesemer’s expert and nuanced editing of the report helped bridge
differences of opinion across the STTF. Lisa Garcia and Liz Terry in the Office of Student Affairs also
provided special editing assistance. Senate Assistant Director Michael LaBriola ensured the Task Force hadrobust and informed discussions; the Report benefited from his editing assistance ata crucial juncture. Finally, we thank the testing experts and education leaders who acted as consultants
to the STTF during the last year:
o
o
o
o
o
o
o
o
o
o
o
o
o

The College Board and ACT
Saul Geiser, Research Associate, UC Berkeley Center for Studies in Higher Education,
UC Campus Directors of Undergraduate Admissions
California State Board of Education President Linda Darling-HammondProfessor of Education Michal Kurlaender, UC Davis
Michelle Center and Stephanie Gregson, California Department of Education
Smarter Balanced CEO Tony Alpert
Professor Anthony Carnevale, Georgetown University Center on Education and the Workforce
Professor of Education William E. Sedlacek, University of Maryland
Professor of Psychology Sheldon Zedeck, UC Berkeley
Testing Consultant Jay RosnerProfessor of Education Emeritus Rebecca Zwick, UC Santa Barbara
The UC Board of Admissions and Relations with Schools

VIII. List of Appendices
Appendix I: Relationship of the SAT/ACT to College Performance at the University of California (IRAP
Report)Appendix II: Tables of Regression Coefficients
Appendix III: Percentage of Applicants Reporting SAT Scores Who Were Admitted by Campus, HighSchool GPA (HSGPA) Range and SAT Range. Fall 2018 California Resident Applicants.
Appendix IV: Interpreting R2 as Percent Explained VarianceAppendix V:• Figures 1-10: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
racial/ethnic subgroup.
•

Figures 11-20: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
family educational history.

•

Figures 21-30: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
family income category.

•

Tables 1-3: Applicant and admittee HSGPAs and test scores, by demographic group and campus.

•

Tables 4-6: Applicant and admittee HSGPAs and test scores with respect to highest-scoringgroups, by demographic group and campus.

118

STTF Report Appendix I

Relationship of the SAT/ACT to College Performanceat the University of California

University of California Office of the President
January 2020

STTF Report Appendix I

Executive Summary
For many years, high school grade-point average (HSGPA) and standardized test scores (i.e.
SAT/ACT) have served as the two most important and relied upon college admissions criteria.
However, there is constant debate about whether HSGPA and SAT/ACT are fair measures ofcollege preparation and valid predictors of college success, and whether SAT/ACT should
remain as a requirement for college admissions.
Supporters of standardized tests argue that SAT/ACT evaluates college-preparedness on an equal
standing despite inconsistent grading systems throughout the nation’s high schools. While
HSGPA compares a student to the rest of his/her school, SAT/ACT scores compare the student
to the rest of the country. Opponents of standardized tests argue that SAT/ACT is biased towards
white, upper middle class students from high performing schools. Focusing on HSGPA incollege admissions may help foster racial and socioeconomic diversity in the college student
population.
Previous research generally treats standardized test results as measures of college preparation.
The literature surrounding the predictive validity of standardized test scores on student success ismixed. Many studies found a statistically significant association between SAT/ACT scores andcollege outcomes including first-year college GPA, first-year retention, four-year graduation, andgraduation GPA. On the other hand, research also shows that demographic predictors weakened
the ability of SAT/ACT scores to be effective predictors of college success.
In light of previous research, this report presents the results of separate studies on therelationship among HSGPA, standardized tests, and demographics, and explanatory power of testscores for college success based on UC’s application and enrollment data. Findings from thisstudy are generally consistent with those from prior research.
Analysis of the relationship among standardized tests and high school GPA, UC’s Analytical
Writing Placement Exam (AWPE), and demographics shows:

Demographics are stronger predictors of SAT/ACT scores than of HSGPA. The
share of variance in SAT/ACT scores accounted for by demographic factors (parental
income, parental education, and ethnicity) increased from a low of 26 percent in the late1990’s to 43 percent in 2016. The share of variance in HSGPA accounted for by the same
demographic factors increased from 5 percent in the late 1990’s to 11 percent in 2016.
About one-third of the increase for SAT/ACT scores can be explained by disparities
between CA high schools, with the remainder explained by changes in the composition of
applicants to UC. Campus-specific estimates do not support increasing racial bias on thetests as an explanation for increase.
Standardized test scores are positively correlated with high school GPA but at
moderate level. When controlling for socioeconomic factors (family income and parental
education), the correlations between SAT/ACT scores and high school GPA fall between.30 to .51 across high schools and fall between .31 to .56 within high schools. The lowest
correlations happen between SAT/ACT writing scores and high school GPA in 2018.

STTF Report Appendix IFrom 2005 to 2018, there has been an increase in the correlations between SAT
Reading/Math as well as ACT Composite with high school GPA. Overall, the SAT and
ACT writing tests are less correlated with HSGPA than SAT Reading/Math or ACT
Composite.
New SAT EBRW (Evidence-Based Reading and Writing) and ACT ELA (English
Language Arts) scores have a strong association with passing UC’s Analytical
Writing Placement Exam (AWPE), but SAT Essay scores have only a moderateassociation with passing AWPE. The analysis of the relationship between the new SAT
and new ACT and UC’s Analytical Writing Placement Exam (AWPE) shows that highscores on the SAT EBRW and ACT ELA are associated with passing the AWPE.
However, on the SAT Essay, even at the top score of 24, only 69 percent passed the
AWPE. Based on the results of data analysis, UCOPE has accepted the new ACT ELA
and SAT EBRW scores but not the new SAT Essay scores as methods of meeting UC’s
Entry Level Writing Requirement (ELWR).

Analyses of the relationship between standardized tests and college success show thatstandardized tests add value to the prediction of college outcomes beyond HSGPA alone.








SAT/ACT scores and HSGPA are both moderate predictors of student college
GPAs, and weak to moderate predictors of student retention and graduation.
Between 2001 and 2015, SAT Reading/Math scores account for 13 to 21 percent of thevariance in freshman GPA, and 15 to 17 percent of the variance in graduation GPA. ACT
Composite scores generally account for 14 to 22 percent of the variance in freshman
GPA, and 17 to 19 percent of the variance in graduation GPA. In comparison, HSGPA
accounts for 13 to 21 percent of the variance in freshman GPA, and 15 to 18 percent in
graduation GPA.
Without controlling for student demographics, SAT/ACT scores are a stronger predictorof freshman GPA when compared to HSGPA, but have almost the same explanatory
power of graduation GPA, first year retention and graduation. After controlling forstudent demographics, HSGPA and test scores have the same explanatory power of the
freshman GPA for 2015, the latest year included in this study, but HSGPA is a stronger
predictor of the first year retention, graduation GPA and four-year graduation.
Supplementing HSGPA with SAT/ACT scores increased the explanatory power of
pre-admission measures on college success metrics. Models that combined both
SAT/ACT and HSGPA account for an additional 5 to 11 percent of the total variance offirst-year GPA when compared to models that only use HSGPA scores. Similarly,
combined HSGPA and SAT/ACT models account for an additional 3 to 11 percent ofvariance associated with UC graduation GPA when compared to models that only use
HSGPA.
Adding SAT/ACT writing to SAT/ACT scores does not increase the explanatory
power of pre-admission measures on college success. SAT and ACT writing scoresaccount for an additional 1 to 2 percent of the variance associated with most student
success metrics (i.e., freshman GPA and freshman retention), with the exception of

STTF Report Appendix Igraduation GPA, where SAT and ACT writing scores account for just as much, if not
more variance associated with graduation GPA, when compared to SAT Reading/Mathand ACT composite.
HSGPA and SAT scores are associated with course performance. They are
independently important explanatory factors for first-year students’ course performance
relative to their peers in similar courses, with the latter growing in relative importance as
SAT predictive validity improves and HSGPA variation declines.

In summary, this report presents what literature and UC data show about the validity ofstandardized test scores in terms of academic preparation and college success. Should UC decideto continue to use standardized test scores as part of its admissions evaluation criteria? To
answer this question, a series of simulations will have to be done to further examine what
admissions outcomes would look like based on new criteria to evaluate students’ academic
achievements. Institutional Research and Academic Planning (IRAP) at UCOP will continue towork on analyses and simulations.

STTF Report Appendix I

I. Introduction
In 1968, the University of California (UC) introduced a new policy that required all freshman
applicants to submit scores from the SAT I or the ACT and three College Board Achievement
Tests (later called SAT II: Subject Tests and now called SAT Subject Tests). The original
purposes of this requirement were to identify applicants’ strengths and weaknesses, to improve
UC’s ability to identify students that are likely to succeed at the university, and to adjust fordifferences in high school grading practices.1 Since then, standardized test scores have played an
important role in evaluating applicants’ academic preparation for college and predicting
students’ success at UC. One important use of test scores at UC is to create a sliding scaleeligibility index along with high school Grade Point Average (HSGPA). Beginning with theClass of 1979, such an index has been used to select the top 12.5 percent of the California publichigh school graduating class in order to meet the guidelines of the California Master Plan forHigher Education.2 In 2000, UC adjusted the eligibility index to include SAT II scores in theindex and to weigh them more heavily relative to the SAT I/ACT scores effective for the fall2003 entering freshman class because the SAT I was a relatively weaker predictor of academic
performance in the freshman year based on UC’s 1996 study.3 UC, however, has never stopped
examining the validity of standardized tests in evaluating applicants’ academic preparation andpredicting college success. Based on a series of studies and evaluations, UC has revised therequirement for the SAT and the ACT several times to meet its admissions goals over the years.
One of the most important revisions since 2000 was eliminating the requirement for the SAT
Subject Tests (formerly called SAT II: Subject Tests)
Versions of the SAT
effective for the 2012 entering freshman cohort.
The College Board and ACT have also changed their test
structures and content several times during the time period
from 2001 to 2018 covered in this study, which may have
had an impact on how universities decided to use these testscores in admissions. In 2005, the College Board announced
a major change to the SAT effective in 2006 to reflect theimportance of clear and succinct writing, which is not only
considered a skill to be used in college courses, but also one
necessary for success in a wide range of careers. To meetthis goal, the College Board changed the SAT I to the SAT
Reasoning, which included three tests, Critical Reading,Math, and Writing (formerly a subject test called SAT II
Writing), and changed the SAT II: Subject Tests to SAT
Subject Tests. These changes were mainly attributed to a

UC Academic Years 2001-2005
 SAT I: Verbal and Math SAT II : Writing
UC Academic Years 2006-2016
SAT Reasoning Critical Reading Math WritingUC Academic Years 2017-2018
 Evidence-Based Reading andWriting Math Essay

Dorothy A. Perry, Michael T. Brown, & Barbara A. Sawrey. (2004). Rethinking the Use of Undergraduate
Admissions Tests: The Case of the University of California. In Rebecca Zwick (Ed.), Rethinking the SAT: The futureof standardized testing in university admissions. (pp. 103-124). New York and London: RoutledgeFalmer.
2
University of California Office of the President, Institutional Research and Academic Planning. California Master
Plan for Higher Education. Retrieved on August 31, 2018 from https://www.ucop.edu/institutional-researchacademic-planning//content-analysis/academic-planning/california-master-plan.html.
3
University of California Eligibility and Admissions Study Group. Final Report to the President. (2004, April).
https://senate.universityofcalifornia.edu/_files/committees/boars/documents/studygroup_final0404.pdf.
1

STTF Report Appendix I

series of debates over aptitude versus achievement tests in college admissions sparked by UC
President Richard C. Atkinson’s advocacy to eliminate the SAT I in UC admissions.4 After about
a decade, the College Board made changes in the content, format, and scoring of the SAT
Reasoning again in 2016. The new SAT includes three parts: 1) Evidence-Based Reading andWriting (EBRW), 2) Math, and 3) Essay with three subscores: Reading, Analysis and Writing.
The new test was first administered in the spring of 2016, and currently costs $64.50 with theessay and $47.50 without it, though some school districts subsidize these fees.5 A validity study
by the College Board based on a sample of participants from 15 four-year institutions show thatthe redesigned SAT is as predictive of college success as the prior SAT, that redesigned SAT
scores improve the ability to predict college performance beyond high school GPA alone, andthat there is a strong, positive relationship between redesigned SAT scores and grades inmatching college course domains.6 However, the 2016 changes were not done without
controversy. For example, the new exam’s wordy math questions may unfairly penalize students
because of their language burden.7 When commenting on this, Jamal Abedi, a UC Davis
professor who specializes in educational assessments stated, “The problem is going to mostly
affect English-language learners.”
ACT added an option of a 30-minute direct writing test to
their examination beginning in February 2005. Based on
Versions of the ACTan early study focusing on the value of using the ACTUC Academic Years 2001-2005
Writing test in placing students in composition and ACT Composite
related courses, the writing test score added value to theaccuracy of course placement decisions over and above
UC Academic Years 2006-2015
the ACT English test.8 Performance on the writing test in ACT Composite
combination with performance on the English test was ACT English with Writing
incorporated into the Combined English/Writing (orEnglish with Writing) score. Ten years later, in
UC Academic Years 2016- 2018
September 2015, ACT introduced changes to the design
 ACT Compositeof this writing test, with modifications to the writing task,
 ACT English Language Arts
scoring rubric, and score reports. ACT states that many
elements of the writing task remain similar to those of theprevious task, both emphasizing argumentative writing skills that are essential for college andcareer success, but the new writing task is more consistent with the writing skills emphasized in
Zwick, R. (2004). College Admissions Testing in California: How Did the California SAT Debate Arise. In
Rebecca Zwick (Ed.), Rethinking the SAT: The future of standardized testing in university admissions. (pp. 101102). New York and London: RoutledgeFalmer.
5
College Board. (2018.) SAT Suite of Assessments: Test Fees. Retrieved on October 26, 2018 fromhttps://collegereadiness.collegeboard.org/sat/register/fees.
6
Shaw, E., Marini, J., Beard, J., Shmueli, D., Young, L., and Ng, H. (2016). The Redesigned SAT Pilot Predictive
Validity Study: A First Look. Retrieved on August 31, 2018 fromhttps://collegereadiness.collegeboard.org/pdf/redesigned-sat-pilot-predictive-validity-study-first-look.pdf.
7Dudley, R. (2016). Despite warnings, College Board redesigned SAT in way that may hurt neediest students.
Reuters. Retrieved on August 31, 2018 from https://www.reuters.com/investigates/special-report/college-satredesign/.
8ACT. ACT Writing Test Technical Report. (2009). Retrieved on August 31, 2018 fromhttps://www.act.org/content/dam/act/unsecured/documents/TechReport.pdf.
4

STTF Report Appendix I

the Common Core State Standards (CCSS) and other standards developed by ACT.9 The new
writing test score is combined with scores on the English and Reading tests and reported as the
English Language Arts (ELA) score. The current test costs $67.00 with the writing section and$50.50 without it, but some school districts subsidize these fees.10
Despite the efforts the College Board and ACT made over the years to improve the validity of
their standardized tests in predicting college success, the number of colleges using Test Optional
Policies (TOPs) in higher education admissions has dramatically increased in recent years.
According to a study by the National Association for College Admission Counseling (NACAC),
more than 1,000 institutions had officially deemphasized standardized tests in admissions byspring 2018 when the report was released.11 The report further emphasizes that the momentum
indicates that undergraduate admissions is moving away from heavy reliance on measuresincreasingly “deemed to provide a narrow assessment of human potential.” The study also
indicates that almost all institutions included in the study increased representation ofunderrepresented groups (URGs) among applicants and enrollees and there are no signs ofacademic slide in terms of college GPAs and graduation rates.
In June 2018, the University of Chicago announced that it would not require domestic freshman
applicants to submit standardized test scores any more. There were several reasons for thisdecision, which, according to an article published in the Chicago Tribune, “marks a dramatic
shift for the South Side university...”12 The University’s leaders have long wanted to increase
diversity and hoped this decision would prevent students from assuming that anything less thanan outstanding test score automatically takes them out of the running. The Undergraduate
Admissions Dean at the University also said that there was a big industry of test preparation,
which served higher-income students very well.
Although few universities (e.g., University of New England) have followed the University of
Chicago’s decision to stop requiring ACT and SAT scores for prospective undergraduates, many
universities eliminated the requirement for the SAT and the ACT Writing since spring 2019.
Harvard University announced it was dropping the requirement in March, followed byDartmouth in April, Yale and the University of San Diego in June, then Princeton, Stanford,
Brown, Duke, and the University of Michigan in July.13 By November 2018, only 12 universities
The ACT College and Career Readiness Standards available online at
http://www.act.org/content/dam/act/unsecured/documents/ACT_RR2015-4.pdf and the 2011 NAEP Writing
Framework available online athttps://www.nagb.gov/content/nagb/assets/documents/publications/frameworks/writing/2011-writingframework.pdf.
10ACT. (2018). The ACT Test: Current ACT Fees and Services. Retrieved on October 26, 2018 fromhttp://www.act.org/content/act/en/products-and-services/the-act/registration/fees.html.
11
Syverson, S., Franks, V., Hiss, W. (2018). Defining Access: How Test-Optional Works. Retrieved on August 31,
2018 from https://www.nacacnet.org/globalassets/documents/publications/research/defining-access-report-2018.pdf.
12
Rhodes, D. (2018.) University of Chicago to stop requiring ACT and SAT scores for prospective undergraduates.
Retrieved on August 31, 2018 from http://www.chicagotribune.com/news/local/breaking/ct-university-chicago-satact-20180614-story.html.
13
The Princeton Review. (2018). Who Requires SAT and ACT Essays (and why they shouldn’t). The Score (blog).
Retrieved on August 28, 2018 from https://princetonreview.blog/2018/03/18/it-is-time-to-eliminate-the-sat-and-actoptional-essays/.
9

STTF Report Appendix I

still required the SAT Essay score for admissions, among which nine were UC’s campuses,
according to a list published by CompassPrep.14 The other three schools requiring the SAT Essay
were the United States Military Academy, Martin Luther College, and Soka University ofAmerica.
What decision should UC make? An article published by Inside Higher Ed in July, 2018 used aneye-catching title to emphasize that “For fate of SAT Writing Test, watch California.” Is thistrue? Given the fact that all other research universities dropped the SAT/ACT writing
requirement for admission and only three non-UC institutions continue to require the SAT or
ACT Writing, there is no doubt that if UC drops this requirement, the list of institutions requiring
the writing tests will be quite short. However, as Henry Sanchez, the former chair of the Board of
Admissions and Relations with Schools (BOARS), noted in an interview with Inside Higher Ed,
UC has a different situation from some of the colleges dropping the requirement recently. Nearly
200,000 high school seniors apply to UC now with UCLA receiving more than 113,000
applications for fall 2018 freshman admission, the largest number among all colleges anduniversities throughout the country. To make a wise and evidence-based decision about whether
to sustain or drop this requirement for admissions, UC would benefit from a better understandingof what existing research tells us and what UC data show about SAT and ACT scores in terms of
measuring college preparation and predicting student success nationwide and at UC specificallyand whether or not SAT and ACT scores can help UC to admit students to meet its educational
philosophy.
In this report, Institutional Research and Academic Planning (IRAP) at the University ofCalifornia Office of the President (UCOP) reviews current research on the validity of the SAT
and ACT and the ability of the tests to predict college success, and uses application andenrollment data from academic years 2001 to 2018 to address two sets of questions as follows:
1. How do SAT and ACT scores relate to other measures of applicants’ academic
preparation such as high school grades? Do socioeconomic factors unrelated to a
student’s academic potential affect SAT and ACT scores?
2. How well do SAT and ACT scores predict college success as measured by freshman
GPA, first-year retention, four-year graduation, and graduation GPA at UC? Do the SAT
and ACT writing tests add any explanatory power to predict college success at UC,
above and beyond what is predicted by HSGPA and SAT Reading and Math or ACT
composite? Does the ability of SAT and ACT scores to predict college success vary by
student characteristics?
This report summarizes the findings of current research on relationships among standardized
tests (the SAT and ACT), high school grades, and demographics, and explanatory power of thetests on college students’ success, and also analyzes UC data to examine what they show about
applicants’ college preparation and student success in terms of the test scores. The report isorganized into five sections. Section I describes the research methodology. Section II
summarizes findings of the existing research on the validity of the SAT and ACT. Section III
CompassPrep. (2018). ACT Writing and SAT Essay Requirements. Retrieved on May 13, 2019 fromhttps://www.compassprep.com/act-writing-and-sat-essay-requirements/.
14

STTF Report Appendix I

examines relationships between the SAT/ACT scores and high school academic preparation
measured by high school weighted, capped GPA and UC’s Analytical Writing Placement Exam
(AWPE) scores. Section IV discusses the relative validity of the SAT and ACT in predicting
student success measured by the freshman GPA, one-year retention, four-year graduation, andgraduation GPA at UC. The last section summarizes the major findings of this study.

II. Methodology
This study uses UC’s application and enrollment data to examine the relationship between the
SAT/ACT and other college preparation measures of applicants to UC and college performanceof students enrolled at UC. The analysis focuses on California resident applicants and enrollees.
The SAT and ACT scores are derived from official and self-reported scores. If official scores are
missing, self-reported scores are used.
The current study also examines differential impacts of both SAT and ACT by campus, intended
discipline (both broad area and STEM/Non-STEM), family income, parental education level,
Pell status, and high school ranking based on the Academic Performance Index (API). These
analyses are conducted as previous research
has indicated that the predictive validity of
SAT Models
1. Sum of Reading (EBRW since 2017) and
SAT/ACT varies by student demographics.
Math Scores
2. Writing (Essay since 2017)
3. Sum of Reading (EBRW since 2017) andMath Scores and Writing Score (Essay since2017)

As noted earlier, UC accepts both SAT and
ACT scores. To understand how related eachof these two tests is to college preparation andperformance, this study analyzes the SAT and
ACT separately. In other words, two separate
ACT Models
sets of statistical models are built, one set
1. Composite
with the SAT predictors and the other with the 2. English with Writing (Writing subscore since
ACT predictors. Also, to examine what2017)
additional variance of student success at UC
3. Sum of Composite and English with Writing(Writing subscore since 2017)
the SAT and ACT Writing can explain
beyond the SAT Math and Reading scores, or the ACT Composite scores, separate models inboth sections are estimated with a predictor of the sum of the SAT Math and Reading or the ACT
composite, the SAT/ACT Writing, or two predictors of the sum of the SAT Math and Reading
and the SAT Writing, or ACT Composite and ACT Writing. The College Board and ACT havemade significant changes to their test structure over time. Therefore, a longitudinal analysis was
conducted to better understand how the changes of test structures, content, and scoring may haveaffected their validity.
To examine the relationships between standardized test scores and other measures of college
preparation, this study first provides a profile of the SAT and ACT takers among UC applicantsfrom 2001 to 2018 (see Appendix A for details), then examines the percent of varianceassociated with SAT/ACT scores and high school GPA that can be attributed to factors students
have no control over (i.e., family income, parental education and race/ethnicity). Finally, a seriesof partial correlations were calculated to examine the relationship between SAT/ACT scores andSTTF Report Appendix I

high school grades, controlling for selected demographic factors. Analyses throughout this reportuse high school weighted and capped GPA15 for high school grades.
The methodology used to analyze relationships between standardized test scores and college
performance at UC is similar to that used in a paper by Saul Geiser and Roger Studley publishedin 2002, which examined predictive validity and differential impact of the SAT I and SAT II at
UC.16 The findings in Geiser’s research will be described later in this report. However, since
their research included enrollment data from 1996 to 1999, this study does not replicate theirresearch; instead, it conducts similar analyses using enrollment data from 2001 to 2015 in order
to compare results with their findings. As many other studies (see Section III for a summary ofprevious research findings) claim that freshman GPA (or first-year GPA) is by far the mostfrequently used outcome variable measuring college success, this study employs the freshman
GPA as a criterion to measure college success, and also examines how well the SAT/ACT
predicts first-year retention, four-year graduation, and graduation GPA.
This report presents some results for all years from 2001 to 2018, and other results, especiallythose about relationships of the SAT and ACT to college preparation and performance forselected years including 2001, 2005, 2007, 2012, 2015, and 2018. These years were chosenbecause 2001 was the first year UC adopted the Eligibility in the Local Context (ELC) program,2005 was the last year students took the old SAT and ACT, 2007 was the second year students
submitted new SAT and ACT test scores, 2012 was the first year UC revised the admissions
eligibility structure with top nine percent of high school graduates from a school qualified for aguaranteed admission, 2015 was the last year before the current version of the ACT, and 2018 is
the third year students submitted new ACT scores and the second year students submitted new
SAT scores.

III. What Previous Research Findings Show about the SAT and ACT
Although there is no single definition or measure of college preparation or college readiness,
researchers, national associations of education, and test developers usually suggest thatstandardized test scores in conjunction with other measures available during high school can act
as proxies for performance in college courses and careers. Among the possible measures are
SAT/ACT test scores, high school degree completion, high school GPA, taking challenging highschool courses, and performance in high school courses.17

High school Grade Point Average (HSGPA) used in this analysis is an honors-weighted, capped GPA whereextra points up to eight semesters, no more than four in the 10th grade are added to the GPA. The UC’s
admission’s website provides more detailed information about how a weighted, capped GPA is calculated,
http://admission.universityofcalifornia.edu/freshman/requirements/gpa-requirement/index.html.
15

16

Geiser, S., & Studley, R. (2002). UC and the SAT: Predictive validity and differential impact of the SAT I andSAT II at the University of California. Educational Assessment, 8(1), 1-26.
17
Maruyama, Geoffrey. (2012). Assessing College Readiness: Should We Be Satisfied With ACT or Other
Threshold Scores? Educational Researcher, 41:7, 252 – 261. Retrieved on August 28, 2018, fromhttp://journals.sagepub.com/doi/abs/10.3102/0013189X12455095.

STTF Report Appendix I

Green and Winters developed a measure of public high school college readiness to reflect theminimum standards of the least selective four-year college.18 The standard includes earning a
regular high school diploma, completing a minimum set of course requirements, and being able
to read at a basic level (scoring at or above the basic level on the National Assessment of
Educational Progress [NAEP] reading assessment).
The National Center for Educational Statistics (NCES) measured college readiness based on a
student’s high school GPA, senior class rank, National Education Longitudinal Study (NELS)
1992 test scores, and SAT/ACT college entrance scores.19
The ACT is designed to measure academic skills and knowledge taught in high school andrequired for first year college courses.20 Therefore, it is not surprising that previous studies (byACT and others) have found that factors such as high school coursework and high school GPA
affect ACT scores. ACT scores are influenced by achievement in core subject areas of highschool courses.21 High school GPA accounts for 31% of the variance in ACT scores, more thanhigh school coursework, high school characteristics, non-cognitive factors, and demographic
characteristics. Non-cognitive factors such as parental involvement or perceptions of education
affect ACT scores via their impact on high school GPA.22 Completing more AP courses is
associated with higher ACT scores.23 Taking an AP math course and taking more AP courses areassociated with meeting benchmark scores on the ACT.24 Taking and passing AP exams is
associated with higher ACT scores, but simply enrolling in AP courses is not.25 The SAT is also
moderately correlated with HSGPA.26

Green, J.P., & Winters, M.A. (2005). Public high school graduation and college-readiness rates: 1991-2002.
Manhattan Institute. Retrieved on August 10, 2018, from https://www.manhattan-institute.org/html/public-highschool-graduation-and-college-readiness-rates-1991-2002-5911.html.
19
Berkner, L., & Chavez, L. (1997). Access to postsecondary education for the 1992 high school graduates. (NCES
98-105). Washington, DC: U.S. Department of Education, National Center for Education Statistics.
20
Ferguson, Richard. (2004). Achievement versus Aptitude in College Admissions. In Zwick, Rebecca. Rethinking
the SAT. New York and London: RoutledgeFalmer.
21
Allen, J. (2015). Influence of Achievement in Core High School Courses on ACT Scores. Retrieved on August 28,
2018 from https://www.act.org/content/dam/act/unsecured/documents/2015-Tech-Brief-Influence-ofAchievement.pdf.
22
McNeish, D., Radunzel, J., Sanchez, E. (2015). A Multidimensional Perspective of College Readiness: Relating
Student and School Characteristics to Performance on the ACT®. Retrieved on August 28, 2018 fromhttp://www.act.org/content/dam/act/unsecured/documents/ACT_RR2015-6.pdf.
23
Anderson, K. (2016). The Effectiveness of Advanced Placement Courses in Improving ACT® Scores forHigh School Students. Retrieved on August 28, 2018 fromhttp://www.kylestevenanderson.com/uploads/7/0/5/8/70582975/kyle_anderson_edd_dissertation_pdf.pdf.
24
Mo, L., Yang, F., Hu, X., Calaway, F., & Nickey, J. (2011). ACT test performance by Advanced Placement
students in Memphis City schools, The Journal of Educational Research, 104, 354–359. Retrieved on August 28,
2018 from https://www.tandfonline.com/doi/abs/10.1080/00220671.2010.486810.
25
Warne, R., Larsen, R., Anderson, B., Odasso, A. (2015). The Impact of Participation in the Advanced Placement
Program on Students' College Admissions Test Scores, The Journal of Educational Research, 108, 400-416.
https://www.tandfonline.com/doi/full/10.1080/00220671.2014.917253.
26
Shaw, E., Marini, J., Beard, J., Shmueli, D., Young, L., and Ng, H. (2016). The Redesigned SAT Pilot Predictive
Validity Study: A First Look. Retrieved on August 31, 2018 fromhttps://collegereadiness.collegeboard.org/pdf/redesigned-sat-pilot-predictive-validity-study-first-look.pdf.
18

STTF Report Appendix I

Recently, a paper was published by researcher Saul Geiser, where he examined the correlation
between demographic predictors (i.e., parental education, family income and race/ethnicity) and
SAT/ACT scores in UC California resident freshman admissions from 1994-2011.27 Geiser
found that demographic predictors accounted for more than a third of the variance associatedwith SAT/ACT scores among UC applicants, and that race/ethnicity had become the strongest of
those three predictors. This report includes results of the analysis replicated based on slightly
different methods than Geiser used for his analysis.
A large volume of research has also examined relationships between standardized tests andstudent success in college. However, findings are not always consistent across research. This
could be because different research might use different measures to evaluate college success,utilize different methods to analyze data, examine different samples, and control different
demographic variables in modeling. Most of the research on standardized tests and academic
success focuses either on the SAT alone or combines SAT and ACT scores. Very few studies
look at the tests separately. Combining scores on the two tests is often done as SAT and ACTscores are highly correlated. Based on UC’s application data, the Pearson correlation coefficient
between the sum of SAT Reading and Math scores and the ACT composite scores was 0.91 in
2001, 0.89 in 2006, and 0.93 in 2016.
Many studies that use first-year GPA (FYGPA) in college as a measure of college success havefound a strong correlation between test scores and FYGPA. In 1960, Fishman and Pasanella
reviewed 147 studies that included the SAT as a predictor of FYGPA, finding that the correlation
between SAT scores and high school record with FYGPA ranged from moderate to strong (0.34
to 0.82).28 In 1989, Morgan of the College Board analyzed the predictive validity of the SAT on
first-year GPA and found that the correlation between SAT scores and FYGPA declined over theyears, but there was less change for private institutions, small institutions, and more selective
institutions.29 More recently, Hezlett and colleagues performed a meta-analysis of approximately
3,000 validity studies, with more than one million students. They found that the SAT is a valid
predictor of FYGPA, with correlations ranging from moderate to strong (0.44 to 0.62).30
A report published in 2011 by the National Bureau of Economic Research found that the English
and math sections of the ACT were more strongly correlated with student success than were thereading and science sections.31 Specifically, the ACT math and English sections only were foundto be more strongly related to first-year GPA than the science and reading sections only. While
Geiser, S. (2015). The Growing Correlation Between Race and the SAT Scores. UC Berkeley Center for Studies
in Higher Education. Retrieved from https://cshe.berkeley.edu/publications/growing-correlation-between-race-andsat-scores-new-findings-california-saul-geiser on August 3, 2018.
28
Fishman, J.A., & Pasanella, A.K. (1960). College admission selection studies. Review of Educational Research,
30(4), 298–310.
29
Morgan, R. (1989). Analysis of the Predictive Validity of the SAT and High School Grades from 1976 to 1985.
(College Board Research Report No. 1989-7). New York: The College Board.
30
Hezlett, S.A., Kuncel, N., Vey, M.A., Ahart, A.M., Ones, D.S., Campbell, J.P., & Camara, W.J. (2001, April).
The effectiveness of the SAT in predicting success early and late in college: A comprehensive meta-analysis. Paper
presented at the annual meeting of the National Council on Measurement in Education, Seattle, WA.
31
Bettinger, E.P., Evans, B.P., and Pope, D.G. (2011). Improving College Performance and Retention. The Easy
Way: Unpacking the ACT Exam. National Bureau of Economic Research. Retrieved fromhttp://www.nber.org/papers/w17119.pdf on August 7, 2018.
27

STTF Report Appendix I

not directly challenging these findings, ACT does contest the interpretation of these findings.
They argue that most students score similarly on all four tests, and those that differ may provide
admissions officers with important additional information.32
As major fields have their own unique characteristics with different grading standards, there arelikely to be differences in the predictive validity of the SAT for cumulative college GPA byacademic program. Shaw and colleagues of the College Board showed that correlations between
SAT and cumulative GPA were of moderate strength for most majors (0.50-0.60). The strongest
correlations tended to be found in STEM (Science, Technology, Engineering, and Mathematics)
fields. The weakest correlations were found for undeclared students (r = 0.42).33 Bridgeman,
Pollack, and Burton of the College Board further pointed out high school GPA is a slightly
poorer predictor of cumulative college GPA for African American and Hispanic students. SAT
scores are moderately correlated with cumulative college GPA (.50) over four or more years. For
all minority group students, both male and female, SAT scores predict college success about aswell as they do for white students.34
Some research findings indicate that a strong relationship exists between the SAT score andcollege retention, over the past 30 years. By analyzing a national dataset including individuallevel data on nearly 150,000 students from 106 colleges and universities, Mattern and Pattersonof the College Board found a strong correlation between SAT performance and retention tosecond year.35 They found that 64 percent of students in the lowest SAT score band returned fortheir second year, compared to 96 percent of students in the highest SAT score band.36 The
relationship between SAT scores and retention to the third and fourth years was also examined,
and similar results were found.37, 38
Research has also shown that SAT scores predict graduation as well as HSGPA. Burton andRamist39 of the College Board found that high school record had an uncorrected correlation of.29 with college graduation, while each of the individual SAT sections had an uncorrected
correlation of .27, and the best combination of the two sections had an uncorrected correlation of
Mathews, J. (2011). Report finds 2 of 4 tests in ACT Poor Predictors of College Success. The Washington Post.
Retrieved from https://www.washingtonpost.com/blogs/class-struggle/post/report-finds-2-of-4-tests-in-act-poorpredictors-of-college-success/2011/07/19/gIQAOiPFOI_blog.html?utm_term=.f1505f48e84a on August 13, 2018.
33
Shaw, E., Kobrin, J., Patterson, B., and Mattern K. (2012). The Validity of the SAT for Predicting Cumulative
Grade Point Average by College Major (College Board Research Report No. 2012-6). New York: The College
Board.
34
Bridgeman, B., McCamley-Jenkins, L., & Ervin, N. (2000). Predictions of freshman grade-point average from therevised and recentered SAT I: Reasoning Test. (College Board Research Report No. 2000-1). New York: The
College Board.
35
Mattern, K. and Patterson, B. (2009). Is Performance on the SAT Related to College Retention? (College Board
Research Report No. 2009-7). New York: The College Board.
36
Mattern, K. and Patterson, B. (2009). Is Performance on the SAT Related to College Retention? (College Board
Research Report No. 2009-7). New York: The College Board.
37
Mattern, K. D., & Patterson, B. F. (2011a). The relationship between SAT scores and retention to the third year:
2006 cohort (College Board Statistical Report No. 2011-2). New York: The College Board.
38
Mattern, K. D., & Patterson, B. F. (2011b). The relationship between SAT scores and retention to the fourth year:
2006 cohort (College Board Statistical Report No. 2011-6). New York: The College Board.
39
Burton, N., & Ramist, L. (2001). Predicting success in college: SAT studies of classes graduating since 1980
(College Board Research Report No. 2001-2). New York: The College Board.
32

STTF Report Appendix I.33. Mattern, Patterson and Wyatt40 of the College Board found that students with higher SAT
scores are more likely to graduate, and graduate in a timely manner (i.e., four years), even after
controlling for HSGPA, institutional characteristics, and institutional selectivity.
Geiser and Studley41 examined the relationship between SAT scores and freshman GPA basedon the records of 77,893 students who entered UC between Fall 1996 and Fall 1999. The study
found that SAT II subject tests were better predictors of student success at UC when compared to
the SAT I aptitude tests. They suggested that using the achievement tests (SAT II subject tests)
for college admissions could be valuable to the prediction of college success with clarity inadmissions standards and close linkage to the high-school curriculum.
However, other research has questioned the predictive validity of SAT scores as it relates tostudent success within the first year, and beyond. Using a dataset containing over 400,000
individual records from 176 colleges with various levels of selectivity, researchers sought tobetter understand if the SAT was predictive of first-year GPA.42 Evidence suggests that the SAT,
while overall predictive of first-year GPA, was less predictive across subgroups. For example,
when comparing African American and White students, the SAT Critical Reading either over- or
under-predicted first-year GPA at 20 percent of colleges and universities.43
Furthermore, using UC data, Geiser44 found that demographic predictors weakened the ability of
SAT/ACT scores to be effective predictors of five-year graduation rates, and in contrast, theexplanatory power of HSGPA was not found to be affected by demographic controls. Geiser andSantelices found that HSGPA was consistently a stronger predictor of student success whencompared to SAT scores, and that its power increased as students progressed through their UC
career.45
Similar to what was found by Geiser and Santelices, recently, an article posted in Forbes
concluded that HSGPA matters more than SAT/ACT scores when it comes to student success.46
For example, even students with high SAT/ACT scores (above 1,100, with ACT concorded to
the SAT scale) have a low expected six-year graduation rate (51 percent), if their HSGPA is
Mattern, K., Patterson, B. and Wyatt, J. (2013). How Useful Are Traditional Admission Measures in Predicting
Graduation Within Four Years? (College Board Research Report No. 2013-1). New York: The College Board.
41
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.
42
Aguinis, H., Culpepper, S.A. & Pierce, C.A. (2016). Differential Prediction Generalization in College Admissions
Testing. Journal of Educational Psychology (7). 1045-1059.
43
Jaschik, S. (2016). Faulty Predictions? Inside Higher Education. Retrieved fromhttps://www.insidehighered.com/news/2016/01/26/new-research-suggests-sat-under-or-overpredicts-first-yeargrades-hundreds-thousands on October 12, 2018.
44
Geiser, S. (2016). A Proposal To Eliminate the SAT in Berkeley Admissions. UC Berkeley Center for Studies inHigher Education. Retrieved from https://cshe.berkeley.edu/publications/proposal-eliminate-sat-berkeleyadmissions on August 31, 2018.
45
Geiser, S. & Santelices, M.V. (2007). Validity of High School Grades in Predicting Student Success Beyond theFreshman Year: High-School Record v. Standardized Tests as Indicators of Four-Year College Outcomes. UC
Berkeley Center for Studies in Higher Education. Retrieved fromhttps://cshe.berkeley.edu/sites/default/files/publications/rops.geiser._sat_6.13.07.pdf on August 31, 2018.
46
Cooper, P. (2018). What Predicts College Completion? High School GPA Beats SAT Score. Forbes. Retrieved
from https://www.forbes.com/sites/prestoncooper2/2018/06/11/what-predicts-college-completion-high-school-gpabeats-sat-score/#3c6910c04b09 on August 17, 2018.
40

STTF Report Appendix I

relatively low (3.00-3.32). In contrast, students with average SAT/ACT scores (900-990) and ahigh HSGPA (3.67-4.00) have a much higher expected graduation rate (62 percent). However,the study used a sample of students who attended a group of less selective four-year public
colleges and universities. This report uses the same method to analyze timely graduation rates
based on UC’s longitudinal data.
In summary, studies that examined relationships between standardized tests and other college
preparation measures such as HSGPA suggested positive relationships between them. The
literature surrounding the predictive power of standardized test scores on student success ismixed. The College Board and supporters of standardized tests argue that they offer a checkagainst grade inflation, assess skills needed not just for higher education, but the workplace as
well, and that in combination with HSGPA provide a more “complete” assessment of students’
academic potential. To support their argument, some research does suggest that both the SAT
and the ACT are related to student success within the first year, and that the first year isimportant for student success, overall.

IV. What UC’s Data Show about the SAT and ACT
Test Takers and Average Test Scores of UC California Resident Applicants
Appendix A provides a profile of UC California resident applicants who submitted SAT and/or
ACT scores in their applications. From 2001 to 2013, more than 90 percent of UC CA applicants
submitted SAT scores. However, there has been a declining trend in freshman applicants
submitting SAT scores sine earlier 2010s. In 2018 one year after the College Board launchedtheir new SAT, only about 86 percent of UC CA freshman applicants submitted SAT scores,
including SAT Math, EBRW, and Essay scores. On the other hand, the share of CA applicants
submitting ACT Composite scores increased steadily from 28 percent in 2001 to 47 percent in2011 then fluctuated between 44 percent and 53 percent in 2012 through 2018. It is also
important to note that the share submitting ACT ELA scores was substantially lower than theshare submitting ACT Composite scores in 2017 and 2018 (at 46 percent and 38 percent
respectively). It is hard to understand why about 15 percent of those who submitted ACT
Composite scores did not submit ACT ELA scores. Were there any reasons for or obstacles tothis? More analyses are needed to answer this question. However, to determine applicant’s
eligibility for guaranteed admissions, UC has to use SAT scores for these applicants. If they did
not provide a full score of SAT tests either, they would be ineligible for guaranteed admissions
under the current ETR (Entitled to Review) policy, which requires a full score of either SAT or
ACT including the writing test.
The average SAT I Verbal/Math scores ranged from 1163 to 1177 and average SAT II Writingscores ranged from 573 to 581 in 2001 to 2005 (Appendix B). The average SAT Critical
Reading/Math scores dropped from 1161 in 2006 to 1117 in 2016 and the SAT Writing scores
dropped as well from 572 in 2006 to 550 in 2016. Students seem to have higher new SAT
Math/EBRW scores in 2017 and 2018 relative to recent SAT Critical Reading/Math averages(1203 and 1167 respectively).

STTF Report Appendix I

Despite the changes over time to the ACT, the average ACT composite scores have stayedlargely the same, at 24 or 25 in all years between 2001 and 2018 except 2017, when the average
was 26. Excluding 2017 and 2018, when very few students submitted scores, English with
Writing was 23 or 24 in all years (2006 through 2016). The average for the new ELA scores was
25 in 2017 and 2018 and the average writing subscore was 8 (on a 2-12 scale).
The Relationship between Demographic Characteristics and SAT/ACT Scores
Figure 1 below shows an apparently disturbing trend in the proportion of variation in UC
applicants’ SAT scores that are accounted for by fixed student characteristics. The left panel ofthe figure shows that annual linear regressions of UC applicants’ SAT scores on three suchcharacteristics—parental income, parental education, and ethnicity—show that the amount ofdemographically-explained variation in applicants’ SAT scores (as measured by R2) has
increased from 26 percent in the late 1990s to 43 percent in 2016, suggesting that nearly half ofthe variation captured by the SAT score across UC applicants reflects student backgroundcharacteristics, not all of which are directly observed by UC’s admissions offices. Meanwhile,
the proportion of explained variation of applicants’ high school GPAs (HSGPAs) has remained
Figure 1: Demographics' Explanatory Power for SAT and HSGPA

Note: R2 from annual OLS regressions of applicants’ SAT score or high school GPA on comprehensive parental
education indicators, ethnicity indicators, and family income (and an indicator whether family income is reported),
combined (left) and one at a time (right). Sample restricted to California-resident freshmen.

at a far lower level, rising from 5 percent in the 1990s to 11 percent in 2016. Figure 1’s rightpanel breaks out each permanent characteristic separately, showing that all three demographics
each explain an additional 10 percentage points of SAT variation since the 1990s.47
A number of modeling assumptions are necessary in the production of this chart, and our choices differ from thosemade by the previous version of this brief (September, 2018) as well as from Geiser (2015). First, consider the threedemographic characteristics analyzed in this study:
1. Parental Income: Both our earlier version and Geiser include only log CPI-adjusted parental income astheir measure of income. This technique implicitly drops two important groups of applicants from thesample: (a) applicants who report 0 parental income, since the log of 0 is non-finite (about 4% of thesample), and (b) applicants who do not report parental income on their applications, usually because they
do not intend to receive financial aid, indicating high-income households (about 12% of the sample).
47

STTF Report Appendix I

There are at least two well-known interpretations of this important finding. The first is that theSAT is increasingly racist, classist, or otherwise biased against students from disadvantaged
backgrounds over time, such that despite their unchanged average aptitude over the past 25
years, disadvantaged applicants’ SAT performance has deteriorated (unlike their HSGPA
performance, which may not face the same systematic issues of bias). The second is that
increased residential segregation by ethnicity and class, combined with increased educational
disparities across California secondary schools, have led to actual average declines in college
preparation among disadvantaged groups, as accurately reflected by their declining SAT scores(but perhaps not by HSGPAs, which are normed within increasingly-disparate high schools).
A third possible explanation is that the trend is driven by changes in the composition of youths in
the estimation sample, most likely as a result of changing UC admissions policies. Expanding
admissions policies that favor disadvantaged applicants, for example, could increase theexplanatory power of demographic characteristics among admits by enlarging the pool of lowSAT high-disadvantage students in the sample, which would mechanically increase
demographics’ correlation independent of the exam’s bias or changes in applicants’ averagelatent aptitude.
Arbitrating between these alternative explanations for the “Demographic Testing Trend”, orDTT, is crucial to understanding how the SAT’s informativeness has evolved over the past 25
years. Advocates in favor of the first explanation (the “Bias Explanation”) include Saul Geiser,
who argues that the DTT invalidates use of the SAT in the absense of race-based affirmative
Omitting these samples may mechanically decrease the correlation between income and SAT, since they
represent the two extremes of income where the covariance with test scores may be highest. In order tomaintain these samples, this analysis includes three measures of parental income in each regression model:log CPI-adjusted parental income (replaced as 0 when missing or infinite), an indicator for missing
income, and an indicator for zero income. This change likely explains the higher proportion of SAT
variation explained by the presented estimates.
2. Parental education: Our earlier version included only an indicator for whether one parent has a college
degree, while Geiser may have included an ordered integer measuring the more-educated parent’s highest
level of education. Both of these measures simplify a high-dimensional student feature—the educational
level of their parent—into a highly-parametric summary. This analysis includes indicator variables foreach combination of educational background held by the applicant’s parents, using the full available
information set. This change may partly account for the aggregate increase in explanatory power ofdemographics for applicants’ SAT score.
3. Ethnicity: Our earlier version and Geiser include only an indicator for whether the applicant is from anunderrepresented group, including Black, Chicano/Latinx, or Native American. This analysis includes
indicators for every observed ethnicity, or 15 in all. This may also contribute to the general increase indemographics’ explanatory power for SAT scores.
The added value of including these multi-dimensional measures of students’ background characteristics is that they
more fully specify each student’s background, leading to more explanatory power and avoiding possibly-importantmodel restrictions that could challenge interpretation (especially in the case of parental income). The disadvantage
of using multi-dimensional measures is that there is no longer a single standardized regression coefficient associatedwith each measure, making it impossible to directly compare the degree to which each contributes to their mutual
absorption of SAT variation. As a result, rather than presenting regression coefficients, we show the degree to which
each individual characteristic (as measured multi-dimensionally) alone can explain variation in applicants’ SAT
scores.
Finally, all three analyses use the same definition of applicants’ SAT score: the sum of the mathematics and reading
components of the SAT exam.

STTF Report Appendix I

action because it increasingly favors White and Asian applicants (Geiser, 2015). Advocates forthe second explanation (the “Real-Disparities Explanation”) include the College Board, whichargued in its recent meeting with the Task Force that “performance on the SAT differs acrosssubgroups, which largely reflects educational differences in high schools”. The third explanation(the “Compositional Explanation”) has drawn less vocal support.
This section presents evidence that approximately one-third of the DTT is explained the RealDisparities Explanation, with two-thirds explained by the Compositional Explanation. Figure 1
shows that the Real-Disparities Explanation explains 31 percent of the DTT. Figures 2 and 3
motivate the Compositional Explanation, showing the magnitude of UC compositional changes
over the past 25 years, and Figure 4 shows that the Compositional Explanation fully explains theremaining net DTT trend. Figure 4 directly challenges the Bias Explanation as even playing asecondary role in explaining the DTT.
First, we rerun the regressions that estimate the cross-student DTT including high school fixed
effects, which eliminate cross-high-school variation that likely reflects increasing educational
disparities in California secondary schools. We show analysis for both the population of UC
applicants and the population of UC enrollees, restricting the sample to California-resident
freshman applicants. If the Real-Disparities Explanation were to be accurate in the strong sense(that is, if the real disparities were exclusively across high school, without increasing disparities
within high school as well), then the results would show no trend in demographics’ explanatory
power for applicants’ or enrollees’ SAT scores.
Figure 2: Within High School DTT Trend

Note: R2 (excluding fixed effects) from annual OLS regressions of applicants’ or enrollees’ SAT score or highschool GPA on comprehensive parental education indicators, ethnicity indicators, and family income (and an
indicator whether family income is reported), including fixed effects by origin high school. Sample restricted to
California-resident freshmen.

Figure 2 above shows that this is not the case. While far less variation in SAT scores can be
explaned by demographics when only comparing students to others from their same high schools,
there is still a notable upward trend in demographics’ explanatory power, from 11.3 percent at its
trough in 1999 to 16.4 percent in 2016 among UC applicants (representing a 31 percent decline in
the proportional increase in explanatory power since 1996). Demographics’ explanatory power forSTTF Report Appendix I

HSGPA increased from 4.9 to 7.4 percent in the same period, suggesting even faster proportional
growth (a 51 percent increase, relative to 45 percent). A similar trend holds among UC enrollees.48
One important feature that these figures share with the original DTT chart is the decline indemographics’ explanatory power between 1995 and 1999. These declines likely reflect that
period’s phasing out of UC’s affirmative action program, which decreased underrepresented
minorities’ UC enrollment by at least 700 students per year across all campuses (Bleemer, 2019)and led to parallel declines in UC URM applications. As a result, the Compositional Explanation
clarifies that demographics’ explanatory power in the period declined as the affirmative-acted
students—who tended to have lower SAT scores that could be explained by their demographic
characteristics—ceased enrolling at UC. This observation provides the first circumstantial
evidence supporting the role of student composition in regulating the correlational relationshipbetween demographics and SAT performance.
In order to further motivate the Compositional Explanation, we next plot the annual variance in
SAT scores and HSGPAs across all UC campuses. Figure 3 shows that the amount of variance in
SAT scores among UC applicants has been swiftly rising since about 2005, while the amount ofvariance in HSGPA fell in the late 1990s and early 2000s and has persisted at the lower level.
The trends among UC enrollees are even more pronounced; variation in SAT scores among UC
applicants has increased by more than 10 percent since 1996, while variation in HSGPAs has
fallen by more than 20 percent. These trends likely reflect two important admissions policies—
Eligibility in the Local Context and Holistic Review—that have substantially replaced
affirmative action since the 1990s in enrolling disadvantaged applicants. As various UC
campuses increase their numbers of low-SAT high-HSGPA students (the latter of which ismeasured relative to the lower-preparedness high schools from which the disadvantaged
applicants are pulled), SAT scores are increasingly varying across the campuses’ student bodies.
Figure 3: Change in Annual SAT and HSGPA Standard Deviations Since 1995

Note: Annual standard deviation in SAT and HSGPA of UC applicants and enrollees. SAT and HSGPA are
normed to have standard deviation 1 on average across all years. Plot shows two-year moving averages.
Figures 2 and 5 present ‘projected R2” measures from the relevant annual linear regression estimates of SAT on
demographic characteristics. To be more specific, these models include high school fixed effects, which themselves(importantly) absorb some cross-school variation in SAT performance, and there’s no reason to include that
explanatory power in the reported R2. Instead, I merely report the R2 of projected SAT scores, after differencing out
the high-school-specific averages, regressed on demographic characteristics. This is a standard techniqueimplemented using the fixed effect linear regression package felm in R.
48

STTF Report Appendix I

In other words, the increased variance is by design, an artifact of admissions policies which
intentionally target lower-SAT applicants. We will return below to another explanation for theincrease in SAT variation over time: the expansion of the Riverside and Merced campuses.
The next plot visualizes the proportion of California high school students who apply to at least
one University of California campus. One of the chief successes—and political challenges—ofUC’s Comprehensive and Holistic Review admissions programs has been their encouragementof applications from high school graduates who would previously have not applied to UC
because of their poor perceived likelihood of admission. Figure 4, pulled from Douglass andBleemer (2018), shows that the proportion of 18-year-olds in California who apply to at least one
UC campus has doubled since 1995, from about 10 percent to about 20 percent. Some of thisFigure 4: Proportion of California Residents Who Apply to UC
0.3
0.25
0.2
0.15
0.1
0.05
0
1993

1998

2003

Prop. CA 18-Year-Olds Apply

2008

2013

2018

Prop. CA HS Grads Apply

Note: The proportion of California 18-year-olds and California high school
graduates who apply to at least one UC campus in each year since 1994. The annual
number of California 18-year-olds is as estimated by the California Department of
Finance, which also reports the annual number of high school graduates in the state.

increase comes from increasing high school graduation rates, but even among graduates the
proportion of applicants has increased by about 8 percentage points, to almost 25 percent. Thischange in application behavior has surely dramatically altered the composition of UC applicants,
and is also reflected in UC’s students as a result of changing admissions policies. These charts
showing increasing SAT variation and increasing broad application behavior by California
youths strongly suggest that compositional changes in UC applicants and enrollees are central
factors in explaining the DTT: after all, UC has spent the past 20 years bolstering admissions
policies that favor the lower-SAT disadvantaged applicants who would mechanically increase
the SAT-demographics correlation.
Finally, and perhaps most consequentially, we replicate Figure 2 by campus (for applicants).
Under either the Bias Explanation or the Real-Disparities Explanation, we would expect that thepredictive power of the SAT has increased consistently at every UC campus, either because of
consistent bias or consistently-varying disparities across applicants. In fact, Figure 5 shows a

STTF Report Appendix Ivery different pattern. Only a single campus, Santa Barbara, manifests any increase indemographics-SAT correlation, and even there the increase in demographics’ explanatory power
for the SAT is slight (from 14.8 to 15.2 percent). Most other campuses have faced almost nochange in demographics’ explanatory power since the end of affirmative action in the late 1990s(like Irvine, Davis, and UCLA) or have actually experienced declines in demographics’
explanatory power (San Diego, Riverside, and most notably Merced). What would cause thesepatterns?
Figure 5: Within High School DTT Trend by Campus

Note: R2 (excluding fixed effects) from annual OLS regressions of applicants’ or enrollees’ SAT scores oncomprehensive parental education indicators, ethnicity indicators, and family income (and an indicator whether
family income is reported), including fixed effects by origin high school. Estimated separately for each UC
campus. Sample restricted to California-resident freshmen.

The answer is the Compositional Explanation. Campuses with high explanatory power—
especially Merced, which in the late-2000s had demographic explanatory power in the 30-40
percent range—have grown, in enrollees but especially in applicants (who wouldn’t otherwise
have applied to UC campuses). The end of affirmative action pushed demographics’ explanatory
power down, especially at the Berkeley and UCLA campuses where that program was mosteffective, and the end of the old ELC program in 2011 also appears to have compressed
demographics’ explanatory power at the campuses where that program was most effective (San
Diego, Davis, and Irvine). Meanwhile, all of the campuses were growing more selective on
average, compressing their HSGPA distributions, but also instituting disadvantaged-focused
admissions programs that purposely admitted students whose low test scores were offset bymeasures of disadvantage that mechanically strengthened the correlation between demographics
and the SAT, both among enrollees and the applicants the the programs encouraged. These results

STTF Report Appendix I

are very difficult to reconcile with the Bias Explanation, and strongly suggest that the net DTT
after the Real-Disparities Explanation can be fully explained by the Compositional Explanation.
As a result, we conclude that about one third of the DTT can be explained by the Real-Disparities
Explanation, since only comparing students who enrolled at the same high school explains 31
percent of demographics’ explanatory power for SAT scores. The remainder appears to be
explained by the Compositional Explanation, which is motivated by measured compositionalchanges within and across UC campuses in their applicant and student bodies and confirmed by
the absense of a DTT trend within 7 out of 8 UC campuses, which nevertheless show patterns
strongly consistent with the implementation of disadvantage-favoring admissions programs.
Indeed, the recently-increasing DTT-demographics correlation at many campuses is best
understood as a manifestation of their admissions policies’ successes in attracting thedisadvantaged lower-SAT students that they are designed to attract.
Standardized Tests and Academic Preparation
This section examines the correlations between SAT/ACT scores and high school GPA as wellas the relationship between SAT/ACT scores and UC’s Analytical Writing Placement Exam
(AWPE) score. SAT Writing refers to the SAT II writing for 2001 through 2005, SAT Writingfor 2006 through 2016, and SAT Essay for 2017 and 2018. SAT Reading/Math refers to SAT I
Verbal and Math for 2001 to 2005, SAT Critical Reading and Math for 2006 to 2016, and SAT
Evidence-Based Reading and Writing (EBRW) and Math for 2017 and 2018. ACT Writing refers
to the ACT Combined English/Writing (CEW) scores for 2006 through 2016 and the writing(essay) subscore for 2017 and 2018. ACT Composite combines the English, Reading, Math, andScience multiple choice sections. CEW combined the English multiple choice section with thewriting (essay) section. On the new ACT, the writing subscore is combined with the English andReading multiple choice sections to produce an English Language Arts (ELA) score.
Table 1 shows the across and within school correlations between SAT/ACT and high school
GPA controlling for socioeconomic status (family income and parental education). Generally,
the within school correlations between SAT scores and high school GPA are slightly higher than
the across school correlations. This finding is consistent with what Zwick and Green (2007)
found in their analysis.49 They concluded that one of the primary reasons is that grading
stringency varies across both high schools and courses. This can potentially result in small
between-school variability in high school grades. Their study also indicates that between-school
variance in mean test scores is likely to be substantial than within-school variance. For bothacross and within correlations between SAT and HSGPA, we see an increasing trend from 2005
to 2018, except that those between SAT writing and high school GPA dropped in 2018, whichmight be due to the new SAT essay (starting in 2017) with a different scoring scale. Across theyears, the correlations between SAT Writing/Essay and HSGPA are consistently lower thanthose between SAT Reading/Math and HSGPA. Overall, the correlations between SAT and
HSGPA are moderate at around .50.

Zwick, R. and Green, J.G. (2007). New Perspectives on the Correlation of SAT Scores, High School Grades, andSocioeconomic Factors. Journal of Educational Measurement. Retrieved fromhttps://onlinelibrary.wiley.com/doi/full/10.1111/j.1745-3984.2007.00025.x, May 3,2019
49

STTF Report Appendix I

Similarly, the within school correlations between ACT and high school GPA are higher than theacross school correlations. Generally, there is an upward trend of correlations between ACT and
HSGPA across years, except for 2018 when there is a drop from 2015. ACT Writing is also lesscorrelated with HSGPA than ACT Composite.
Table 1. Partial Correlation of Standard Test Scores and High School GPA
SAT Reading/Math
SAT Writing (SAT Essayfor 2018)
Sum of SAT
Reading/Math and SAT
Writing
ACT Composite
ACT Writing (ACT ELA
for 2018)
Sum of ACT Composite
and ACT Writing

Across schools
Within schools
Across schools
Within schools
Across schools

2001
0.47
0.53
0.44
0.49
0.48

2005
0.45
0.51
0.44
0.49
0.47

2007
0.48
0.53
0.46
0.50
0.50

2015
0.50
0.55
0.48
0.52
0.52

2018
0.51
0.56
0.39
0.41
n/a

Within schools

0.54

0.54

0.56

0.57

n/a

Across schools
Within schools

0.47
0.51

0.46
0.51

0.46
0.51

0.50
0.55

0.47
0.53

Across schools
Within schools
Across schools
Within schools

n/a
n/a
n/a
n/a

n/a
n/a
n/a
n/a

0.43
0.47
0.47
0.52

0.46
0.49
0.50
0.55

0.30
0.31
n/a
n/a

Note: All correlations control for family income and parental education.

A previous analysis also looked at the relationship between the new SAT and new ACT and
UC’s Analytical Writing Placement Exam (AWPE) for the Fall 2017 incoming class.50 The
analysis found that 76 percent of those at or above a threshold of 690 for SAT Evidence Based
Reading and Writing (EBRW) passed the AWPE (Table 2). On the old SAT, more than 75
percent of those meeting the old threshold of 680 on SAT Writing would have passed the
AWPE. On the SAT Essay, even at the top score of 24, only 69 percent passed the AWPE, andonly two percent of test-takers achieved that score.51 For ACT, 75 percent or more of students
scoring 30 or higher on ACT English Language Arts (ELA) would have passed the AWPE. Thisis similar to the pattern for the old threshold using the old ACT Combined English/Writingscores. The report recommended setting a threshold for passing the Entry Level Writing
Requirement (ELWR) of 690 on SAT EBRW, leaving the threshold of 30 on the ACT ELA inplace, and not setting a new threshold using SAT Essay.

University of California Office of the President, Institutional Research and Academic Planning. (2017). New SAT
and ACT tests and the Entry Level Writing Requirement.
51
A later analysis looked at the SAT Essay sub-scores, showing that in Fall 2017, 65% of those with a top Writingscore of eight passed the AWPE and only 5% of test-takers achieved this score; 73% of those with a top Analysis
score of eight passed the AWPE and only 2% of test-takers achieved this score; 65% of those with a top Readingscore of eight passed the AWPE and only 5% of test-takers achieved this score.
50

STTF Report Appendix I

Table 2. Number of test-takers by AWPE score status and SAT EBRW, SAT Essay, or ACT
ELA score, Fall 2017
SAT EBRW score

Failed AWPE

Passed AWPE>= 690
< 690
Total

185
6,754
6,939

572
4,778
5,350

SAT Essay score

Failed AWPE

Passed AWPE

= 24
< 24
Total

15
6,924
6,939

34
5,316
5,350

ACT ELA score

Failed AWPE

Passed AWPE

>= 30
< 30
Total

n/a
6,939
6,939

n/a
5,350
5,350

24%
59%
56%

31%
57%
56%

n/a
56%
56%

76%
41%
44%

69%
43%
44%

n/a
44%
44%

Total
757
11,532
12,289

100%
100%
100%

Total
49
12,24012,289

100%
100%
100%

Total
n/a
12,28912,289

n/a
100%
100%

Note: This table includes all incoming freshmen who submitted a statement of intent to register (SIR) and had both an AWPE
score and an SAT EBRW, SAT Essay, or ACT ELA score (as applicable). Students with a 30 or higher on the ACT ELA did not haveto take the AWPE.

On the basis of the data analysis described above, the University Committee on Preparatory
Education (UCOPE), a committee of UC Academic Senate, approved a new threshold of 680 on
SAT EBRW for passing the Entry Level Writing Requirement (ELWR), left the threshold of 30
on ACT ELA in place, and did not set a new threshold using SAT Essay. Note that SAT EBRW
is based on multiple choice sections of the test while ACT ELA is based on multiple choicesections and the writing/essay section. SAT Essay is a stand-alone essay section.
In summary, the partial correlations between test scores and HSGPA after controlling for SES
are moderate. This means that standardized test scores and HSGPA can both measure part ofstudents’ knowledge and skills but there are different aspects of students’ academic preparations
that can only be measured by test scores and HSGPA separately. In addition, the writing tests areless correlated with HSGPA than Reading/Math or composite tests, which means SAT/ACT
writing tests may provide additional information about students’ qualifications that HSGPA
cannot provide. High scores on the SAT EBRW and ACT ELA are associated with passing the
UC’s AWPE writing exam, but this is not necessarily true for the SAT Essay.
Standardized Tests and Student SuccessThis section examines the relationship between the SAT/ACT and college success at UC throughfour outcome measures: (1) student first-year college GPA; (2) first-year retention; (3) four-year
graduation; and (4) college graduation GPA. The section first presents results of descriptive
analysis and then inferential statistics to show how well standardized test scores predict college
success.

STTF Report Appendix I

Figure 6 shows mean first-year GPA, first year retention rates, four-year graduation rates andmean graduation GPA of four-year graduates by HSGPA and SAT Reading/Math or ACT
Composite quartile. Not surprisingly, students with a higher HSGPA and a higher SAT
Reading/Math score or ACT Composite score (i.e., both in the highest quartiles) tend to havebetter performance on all four measures than students in the lowest test score and HSGPA
quartiles. On average, their first-year GPA is higher than the GPA of those in the lowest HSGPA
and test score quartiles by one point, or about one third; their first year retention rate is almost
100 percent, compared to about 90 percent for those in the lowest quartiles. The difference ingraduation GPA between those in the highest and the lowest quartiles is smaller, but it is still
about a half point. On average, students in the highest quartiles are twice as likely to graduate
within four years.
In addition, Figure 6 shows that students in the same HSGPA quartile, but in a higher test scorequartile are more likely to perform better, especially in terms of freshman GPA, first year
retention rates, and four-year graduation rates than those in a lower test score quartile. For
example, 83 percent of students in the highest HSGPA (4.23-4.61) and the highest SAT (13501600) quartiles graduate within four years, compared to 57 percent of those in the same HSGPA
quartile, but with a lower SAT score (620-1070). This indicates that test scores do provide
additional value beyond HSGPA to explain differences in students’ performance at UC.
Results further show that students with a higher test score (1350-1600) and a lower HSGPA(3.00-3.78) seem to have a better freshman GPA, but a low first year retention rate whencompared to students with a lower SAT/ACT score (620-1070) regardless of their HSGPA
quartile. However, the first year retention rate graphs show the opposite, indicating that students
in the highest HSGPA quartile and the lowest test score quartile are retained at a rate of 95
percent compared to 92 percent for those in the lowest HSGPA quartile and the highest
SAT/ACT quartile. Similar to what was found for first-year GPA and retention rates, on average,
students with higher test scores, but lower HSGPA, tend to have a higher graduation GPA and alower four-year graduation rate, compared with those with a higher GPA, but a lower score on
SAT Reading/Math or ACT Composite. This finding suggests that HSGPA may be slightly morepredictive of first-year retention and four-year graduation, while standardized test scores may beslightly more predictive of both first-year and graduation GPA. Regression models will be
developed in the following sections to further examine if this conclusion based on descriptive
statistics holds and how HSGPA and test scores are related to college success.

STTF Report Appendix I

Figure 6. First-Year GPA, First-Year Retention Rates, Four-Year Graduation GPA, and FourYear-Graduation Rate by HSGPA and SAT Reading/Math or ACT Composite Quartile

2.98
3.12
3.19
3.18

3.05
3.20
3.23
3.31

3.14
3.25
3.35
3.43

3.12
3.32
3.45
3.57

2.83
3.05
3.24
3.40

2.92
3.16
3.38
3.54

4.05-4.22

4.23-4.61

86%

96%
97%
97%
98%

95%
97%
97%
98%
4.23-4.61

3.79-4.04

91%
91%

93%
95%
96%
98%
4.05-4.22

3.12
3.29
3.43
3.53

3.12
3.22
3.34
3.42

3.02
3.16
3.22
3.33

2.96
3.11
3.16
3.20

57%
70%
79%
83%
4.23-4.61
1350-1600

3.00-3.78

3.79-4.04
9-23
24-27

56%
70%
77%
82%

54%
67%
73%
72%
4.05-4.22
1220-1340

53%
66%
74%
73%

63%
65%
62%
47%
3.79-4.04
1080-1210

43%

41%
50%
52%
49%
3.00-3.78
620-1070

62%
66%
65%

Four Year Graduation Rates

Four Year Graduation Rates

HSGPA/SAT Quartile

Graduation GPA

41%
51%
56%
49%

90%
93%
95%
96%
3.79-4.04

87%
90%
91%
92%
3.00-3.78

Graduation GPA

3.00-3.78

First Year Retetion

93%
95%
97%
97%

4.23-4.61

2.72
2.98
3.14
3.30

2.94
3.15
3.34
3.55

4.05-4.22

90%
94%
96%
97%

2.82
3.04
3.22
3.42

3.79-4.04

96%

2.71
2.97
3.14
3.25

3.00-3.78

First Year Retetion

By High School GPA and ACT Quartile
First-Year GPA
2.56
2.86
2.97
3.12

2.58
2.80
2.98
3.06

By High School GPA and SAT Quartile
First-Year GPA

4.05-4.22
4.23-4.61
28-31
32-36

HSGPA/ACT Quartile

Standardized Tests and Academic Preparation: Aggregate First-Year GPA

Table 3 shows the percent of variance in freshman GPA accounted for by HSGPA and test scoresbased on a series of regression models. Results indicate that HSGPA, SAT Reading/Math, SAT
Writing, ACT Composite, and ACT Writing are all moderate predictors of freshman GPA at UC.
HSGPA accounted for 17 to 20 percent of variance in the freshman GPA prior to 2007, and then15 percent in 2012 and 13 percent in 2015, the lowest ever while the variance accounted for by
test scores has increased over the time from 13 percent in 2001 to 20 percent in 2015.

STTF Report Appendix I

Table 3: Percent of Variance in Freshman GPA Accounted for by HS GPA and the SAT/ACT
Model(1) HSGPA

2001
17%

2005
18%

2007
20%

2012
15%

2015
13%

(2) SATRM(3) SAT Writing(4) SAT TOTAL (SATRM + Writing)(5) HSGPA + SATRM

13%
15%
17%
22%

17%
18%
19%
26%

19%
19%
21%
27%

21%
20%
22%
26%

20%
19%
22%
25%

Standardized Coefficients: HSGPA
SATRM

0.31
0.25

0.32
0.29

0.29
0.31

0.24
0.36

0.23
0.36

(6) HSGPA + SATTOTAL (SATRM + Writing)

24%

27%

28%

27%

26%

Standardized Coefficients: HSGPASATTOTAL

0.30
0.28

0.30
0.32

0.30
0.33

0.22
0.38

0.22
0.38

(7) ACT Composite(8) ACT Writing(9) ACT Total (Composite + Writing)(10) HSGPA + ACT Composite

16%
n/a
n/a
22%

21%
n/a
n/a
27%

20%
19%
22%
27%

23%
18%
23%
27%

22%
19%
22%
26%

0.28
0.29
n/a

0.31
0.30
n/a

0.29
0.33
29%

0.22
0.38
27%

0.23
0.39
26%

n/a
n/a

n/a
n/a

0.28
0.35

0.22
0.38

0.22
0.38

(12) HSGPA + SAT Total + Demographics

30%

32%

34%

32%

32%

Standardized Coefficients: HSGPAStandardized Coefficients: SATTOTAL

0.39
0.25

0.39
0.26

0.38
0.28

0.34
0.34

0.34
0.34

Standardized Coefficients: HSGPAACT CMP(11) HSGPA + ACT Total (ACT CMP and ACT Writing)Standardized Coefficients: HSGPAACT Total

There are multiple reasons for the declining trend in explanatory power of HSGPA. Since 2001,
more and more campuses have adopted a comprehensive review process in admissions, including
several that have adopted holistic review.52 Using this process, campuses look beyond grades andtest scores to evaluate students’ qualifications for admission. However, several aspects of UC
admissions policy still emphasize high school grades and test scores. The minimum requirementfor UC admissions eligibility is to complete a minimum of 15 college-preparatory courses with aletter grade of C or better; the Eligibility in the Local Context (ELC) program purely replies on
HSGPA in UC-approved coursework completed in the 10th and 11th grades; the statewide
eligibility indices were created based on test scores and HSGPA; UC Regents increased theminimum HSGPA required for UC freshman eligibility from 2.80 to 3.00, effective for the fall2007 entering class. Therefore, HSGPA still plays a significant role in eligibility in admissions at
All campuses have used comprehensive review since 2002 and six campuses have adopted holistic review as their
method of implementing comprehensive review: Berkeley (starting 2002), Los Angeles (2007), Irvine (2011), San
Diego (2011), Davis (2012), Santa Cruz (2012).
52

STTF Report Appendix I

UC. In addition, rapid growth of the qualified applicant pool pushed up the overall selectivity at
all UC campuses. Analyses showed that recent UC admits have higher HSGPA than admits inprior years. All these together may have led to less variability in HSGPA of enrolled students.
This change along with more variability in test scores caused by educational disparity among
California K-12 schools, and constant variability in the freshman GPA at UC over the years may
be some of many reasons that may have contributed to the change in the explanatory power of
HSGPA and test scores in the freshman GPA at UC. (See the previous section “The Relationshipbetween Demographic Characteristics and SAT/ACT Scores.”)
Two sets of models with the sum of SAT Reading/Math and SAT Writing (Model 4) or the sumof ACT Composite and ACT Writing (Model 9) were developed to examine how muchadditional variance writing scores accounted for beyond SAT Reading/Math or ACT Composite.
The reason to use the sum of scores on two tests is because SAT Reading/Math (ACTComposite) and SAT Writing (ACT Writing) are highly correlated (e.g., r=.85 for the freshmanentering cohort in 2015). Therefore, there would be a collinearity issue if two measures wereentered in the same regression model. Results indicate that in 2001, adding SAT Writing to
SATRM increased the variance explained from 13 percent to 17 percent, or by four percentage
points, but since 2005, it has only increased the explained variance by about two percentage
points (difference in the explained variance between Model 2 and Model 4). Similarly, adding
ACT Writing scores to ACT Composite scores does not increase the explanatory power at all(e.g., the difference in variance between Models 7 and 9). It is concluded that in the most recent
year, adding writing scores to reading/math or composite scores does increase the explanatory
power in explaining variation of freshman GPA, but the increase is not substantial.
In addition, four models were developed to examine how much additional variance in the
freshman GPA standardized test scores account for beyond HSGPA. As showed by Models 5, 6,
10, and 11, adding SAT Reading/Math or ACT Composite to the model in recent years (e.g., in2015) doubled the variance accounted for by HSGPA alone. However, adding SAT Total or
ACT Total to the HSGPA models hardly changed the variance accounted for by the HSGPA andSATRM or ACT Composite models (difference in variance between SAT Models 5 and 6, andACT Models 10 and 11). Similar to what has been found previously, it is concluded that writingscores do not add any additional value in predicting student’s freshman GPA beyond HSGPA
and SAT Reading and Math tests or the ACT composite test. Also, the standardized coefficientsin these multivariate regression models indicate that test scores are stronger predictors for
freshman GPA than HSGPA, especially for the 2012 and 2015 entering cohorts.
The analysis in previous sections of this report indicates that student characteristics (parental
education, family income and race/ethnicity) account for 26 percent variation in applicants’ SAT
scores in the late 1990s and more than 40 percent in recent years. The explanatory power of thesethree factors in HSGPA has also increased from five percent in 2000 to 11 percent in recent
years. Thus, it is helpful to examine the relationship between HSGPA and/or SAT Total andfreshman GPA after controlling for student demographics. We ran regression models adding instudent demographics such as campus affiliation, major discipline, first-generation status, family
income, and high school API quintile (Model 12 in Table 3). Results show that controlling for
demographics increased explained variation of freshman GPA by six percentage points(difference between Model 6 and Model 12). Results further show that after controlling for

STTF Report Appendix I

demographics, the explanatory power of HSGPA increased from 0.22 to 0.34 in terms ofstandardized coefficients in the 2015 model. The standardized coefficients of the SAT total
decreased from 0.38 to 0.34 in the 2015 model, which indicates a decrease of the explanatory
power of the SAT total in freshman GPA. This implies that SAT scores are more associated withthe demographics than HSGPA, consistent with earlier findings.
The influence of varying student characteristics on the relationship between HSGPA andSAT/ACT scores on first-year GPA is further examined based on 2015 entering students.
Detailed model coefficients are presented in Appendix C. Results are generally consistent withthose from the overall models (Table 3), indicating that standardized test scores were generally
the stronger predictor of first-year GPA than high school GPA by campus, discipline,
race/ethnicity, first generation, family income, and CA API ranking. For some groups, HSGPA
predicted first year GPA better. For example, HSGPA appeared to be a stronger predictor thantest scores for African American students, White students, students with family income over
161k, and etc.
We also examined the explanatory power of the SAT Essay score and subscores on each of threecomponents (Reading, Analysis, and Writing), using the most recent 2017 freshman enteringcohort data. Results (Table 4) show that the Essay score only accounts for nine percent ofvariation in freshman GPA, which is much less explanatory power than SATRM (21 percent)and the SAT Writing score (20 percent) in earlier years. Each of the three components accounts
for six to eight percent of variation in freshman GPA with the Essay Analysis being the strongest
predictor. Adding the Essay Total to HSGPA only increased the explanatory power by one
percentage point. With limited explanatory power added by the Essay scores, it is worth
considering the social costs of additional writing tests. It is not ideal that students spend lots oftime preparing for various tests while they could be focusing their energy on more important
academic and social activities that could benefit them in the future.
Table 4: Percent of Variance in Freshman GPA Accounted for by HSGPA and the SAT
Model
2017
(1) HSGPA
16%(2) SATRM
21%(3) Essay Total
9%(4) Essay Reading
6%(5) Essay Analysis
8%(6) Essay Writing
7%(7) HSGPA + Essay Total
19%
Standardized Tests and Academic Preparation: First-Year Course-Level Performance

The previous analyses of the relationship between application components and freshman GPA
are limited by the substantial variation in average grades across campuses, departments, and evenindividual professors. For example, students enrolled in first-year courses in Engineering
departments tend to have higher-than-average SAT scores but earn lower-than-average grades,

STTF Report Appendix I

not as a result of poor performance but rather due to the higher standards and lower grade curve
enforced by Engineering departments nationwide. As a result, Engineering departments decrease
the positive correlation between SAT scores and freshman grades, though the decrease isuninformative about actual student performance.
In order to “control for” students’ freshman course selections, we disaggregate our analysis fromthe level of freshman GPAs to the individual-course level. The resulting data contains thenumerical grades (from 0 to 4) received by each student in each course. Courses are weighted bytheir number of units, and each student is given equal (total) weight. Unlike the analysis above,these “within-course” estimates condition on the specific courses taken by each student (that is,each linear regression is estimated using course-semester fixed effects). The resulting estimates
can be interpreted as the amount of variation within each course a student completes in their firstyear—that is, only comparing the student’s performance to that of the other students in theirsame class—that can be explained by each application component. As a result of data
availability, we estimate course-level results from 2001 to 2016 for students at only three UC
campuses: Berkeley, Davis, and Riverside. These regressions do not control for any fixed student
characteristics, though controlling for characteristics like race and gender hardly changes thefindings.
Table 5: Percent of Within-Course Freshman GPA Explained by HS GPA and SAT (3 Campuses)

Note: Reported R2 from the projected performance outcome net course-specific fixed effects, which also capture
campus effects. Courses are weighted by units earned and then normalized to give each student equal weight.

Table 5 shows the percent of within-course freshman grade variation that can be explained byhigh school GPA (HSGPA) and SAT scores. In 2001, HSGPA and SAT each explained about 89 percent of within-course performance variation, but reflecting a similar trend in the aggregate
GPA results, the two slowly diverge over the following years; by 2016, HSGPA only explains
about 5 percent of variation (likely reflecting the decline in cross-student HSGPA variation as
UC becomes more selective), while SAT explains 12 percent of variation. Moreover, HSGPA
and SAT strongly complement each other; the inclusion of both nearly sums to two R2 values,
implying that they explain independent components of students’ first-year course performance(see Model 6 compared to sum of Model 1 and Model 4). The SAT Writing exam has lower
explanatory power that has only slightly increased in the past 15 years. Estimates of standardized

STTF Report Appendix I

coefficients show that both HSGPA and SAT explain separate components of first-year course
performance, with a one-standard-deviation increase in SAT scores correlating to a 0.3 point
increase (almost a full grade step, e.g., A- to A) in each course taken by first-year students.
We conclude from this analysis that both HSGPA and SAT scores are independently-important
explanatory factors for first-year students’ course performance relative to their peers in similarcourses, with the latter growing in relative importance as SAT predictive validity improves and
HSGPA variation declines.
Disaggregating freshman GPAs to the course level also enables more precise analysis of the
explanatory power for student performance in different academic disciplines, which mightprovide further insight into the specific values of each measure of students’ academic
preparedness. We identify first-year courses taught in four areas—Humanities, Social Sciences,
Natural Sciences, and Engineering—and estimate the proportion of variation in course
performance across areas that can be explained by SAT and HSGPA.
Table 6: Percent of Within-Course 2016 GPA Explained by HSGPA and SAT by General Area

Note: Reported R2 from the projected performance outcome net course-specific fixed effects, which also capture
campus effects. Courses are weighted by units earned and then normalized to give each student equal weight.

Table 6 shows expected explanatory patterns. Disaggregating the SAT into its Reading andMathematics components, we find that the Reading component explains more variation in
Humanities and Social Science course performance (7-13%) while the Mathematics component
explains more variation in Natural Science and Engineering course performance (7-14%). SAT
scores explain substantially more variation than HSGPA alone; in Engineering, for example,
high school GPA explains 2.3 percent of performance, but its combination with the SAT
components explains more than 12 percent of performance. The SAT Writing out-performs
HSGPA in all four areas, explaining between 5 and 12 percent of variation, but its addition to
HSGPA and the two primary SAT components only explains substantial variation (almost 1%) in
the Humanities.

STTF Report Appendix I

The scaled coefficients reported in Table 6 show that a one standard deviation increase in
HSGPA is associated with increases in students’ performance in each of their first-year courses
by 0.19 grade points in the Humanities and 0.30 grade points in the Natural Sciences. A one
standard deviation increase across students’ SAT scores yields even stronger correlations whenholding HSGPA fixed, by 0.22 points in the Humanities and 0.45 points in the Natural Sciences,with non-negligible contributions by each SAT component in the areas of their respective
strengths. We conclude that both HSGPA and SAT are independently-important explanatory
factors for first-year students’ course performance across academic disciplines, with SAT
particularly important in cases like Engineering courses where HSGPA alone has lower
explanatory power.
Finally, we identify two course types of particular interest: freshman writing courses, which arerequired for most enrollees (unless they satisfy their Entry Level Writing Requirement via
examination), and Organic Chemistry, the key gatekeeper course for the popular pre-medical
track at each campus. Data for these courses is available at all campuses for 2016, though noisein the nine-campus course data results in lower R2 values across the board.
Table 7: Percent of Within-Course 2016 GPA Explained by HSGPA and SAT

Note: Reported R2 from the projected performance outcome net course-specific fixed effects,
which also capture campus effects. Courses are weighted by units earned and then normalized togive each student equal weight.

Table 7 shows that, unsurprisingly, the SAT Reading and Writing components each explain
substantial variation in freshman writing course performance, while the SAT Math component
explains the largest share of variation in Organic Chemistry performance. High school GPA also
explains a significant share of performance variation in each course type, and when both
admissions components are included simultaneously, once again high school GPA and the SAT
scores explain similar amounts of variation in course performance. As in the full course analysis,
these results suggest that each of the four admissions components—HSGPA, SAT Math, SAT
Reading, and SAT Writing—provide valuable and differentiated information about expected UC
student performance in their chosen first-year coursework.
Standardized Tests and Academic Preparation: Longer-Run OutcomesSTTF Report Appendix I

Table 8 presents the percent of variance in graduation GPA accounted for by HSGPA and testscores. Results show that HSGPA and test scores are weak predictors of graduation GPA. They
have almost equal explanatory power, each accounting for less than 20 percent of variance ingraduation GPA. Adding SAT or ACT writing scores to SAT Math/Reading scores does notincrease predictive value of Graduation GPA beyond SAT Reading/Math or ACT Composite
scores. For example, for 2010 entering cohort, SAT Reading/Math accounts for 16 percent ofvariance in graduation GPA, while the sum of SAT Reading/Math and Writing scores (SAT
Total) accounts for 18 percent, up by only two percentage points.
When HSGPA and SAT Reading/Math or ACT Composite scores were entered into the samemodels, the percent of variance accounted for by the two predictors together increased about five
percentage points or one third of explanatory power by the HSGPA model alone. However,replacing SAT Reading/Math or ACT Composite scores with the sum of SAT Reading/Math and
SAT Writing scores (SAT Total) or the sum of ACT Composite and ACT Writing scores (ACT
Total) in these models does not change explanatory power. For example, for the 2010 enteringcohort, the HSGPA and SATRM model accounts for 23 percent of variance in graduation GPA,
while the HSGPA and SAT Total model accounts for 24 percent of variance. The two models arebasically the same. This means that again writing scores do not contribute to predictive value of
graduation GPA beyond HSGPA and SAT Math/Reading or ACT Composite. This is largely due
to a high correlation between SAT Reading/Math and SAT Writing scores or ACT Composite
and ACT Writing scores.
After controlling for campus and student characteristics including campus affiliation, major
discipline, first-generation status, family income, and high school API quintile, HSGPA has
more explanatory power of UC graduation GPA than the SAT Total in terms of standardized
coefficients (Model 12 in Table 8). This means that SAT scores are more associated with thedemographics than HSGPA, consistent with previous findings.
The influence of varying student characteristics on the relationship between HSGPA andSAT/ACT scores on graduation GPA is also examined based on 2010 entering students. Detailed
model coefficients are also presented in Appendix D. Similar results were found thatstandardized test scores were the stronger predictor of graduation GPA than high school GPA bycampus, discipline, race/ethnicity, first generation, family income, and CA API ranking.
However, the difference in the explanatory power between HSGPA and SAT/ACT scoresbecame smaller for graduation GPA than for first year GPA. For some groups, HSGPA predictedgraduation GPA better. For example, HSGPA appeared to be a stronger predictor of graduation
GPA than test scores for students at Davis, students majored in Arts, White students, not first
generation students, students with family income over 107k, and etc.

STTF Report Appendix I

Table 8. Percent of Variance in UC Graduation GPA Accounted for by HSGPA and the SAT/ACT
Model(1) HSGPA

2001
16%

2005
17%

2007
18%

2010
16%

(2) SATRM(3) SAT Writing(4) SAT Total (SATRM + Writing)(5) HSGPA + SATRM

15%
18%
18%
22%

15%
19%
18%
24%

17%
19%
19%
25%

16%
18%
18%
23%

Standardized Coefficients: HSGPA
SATRM

0.29
0.27

0.32
0.28

0.31
0.28

0.27
0.29

(6) HSGPA + SATTOTAL (SATRM + Writing)

27%

26%

26%

24%

Standardized Coefficients: HSGPASATTOTAL

0.27
0.31

0.29
0.32

0.29
0.31

0.26
0.32

(7) ACT Composite(8) ACT Writing(9) ACT Total (Composite + Writing)(10) HSGPA + ACT Composite

18%
n/a
n/a
23%

19%
n/a
n/a
26%

18%
20%
19%
25%

17%
17%
23%
22%

Standardized Coefficients: HSGPAACT CMP(11) HSGPA + ACT Total (ACT CMP and ACT Writing)

0.24
0.33
n/a

0.27
0.34
n/a

0.28
0.31
26%

0.25
0.31
23%

Standardized Coefficients: HSGPAACT Total

n/a
n/a

n/a
n/a

0.26
0.34

0.24
0.33

(12) HSGPA + SAT Total + Demographics

32%

33%

34%

31%

Standardized Coefficients: HSGPAStandardized Coefficients: SATTOTAL

0.38
0.31

0.40
0.29

0.40
0.29

0.37
0.32

As shown in Table 9, both HSGPA and test scores are very weak predictors of first year retention
and four year graduation. The models with HSGPA and test scores only account for about 2.5
percent of variance in first year retention and eight percent of variance in four year graduation. In
addition, the standardized coefficients show that there is no significant difference in explanatory
power of predicting first year retention and four year graduation between HSGPA and testscores. The percent of variance accounted for by these predictors has remained about the same
since 2001, so the results from the logistic regression models based on 2015 entering cohort for
first year retention and 2010 for four year graduation are presented here in the table.
Again after controlling for student demographics, HSGPA is still a stronger predictor for firstyear retention and four-year graduation, with more explanatory power than SAT scores in termsof standardized coefficients (Model 5 in Table 9). This finding further indicates that SAT scoresare more associated with the demographics than HSGPA, consistent with previous findings.

STTF Report Appendix I

Table 9. Statistics of Logistic Regression Models Predicting First Year Retention and Four Year
Graduation by HSGPA and SAT/ACT ScoresFirst Year
Retention (2015Entering Cohort)
2.3%
0.22
0.22

Four Year
Graduation (2010Entering Cohort)
7.5%
0.23
0.17

2.4%
0.20

7.9%
0.21

SATTOTAL

0.22

0.19

(3) HSGPA + ACT Composite (R-squared)Standardized Coefficients: HSGPAACT CMP

2.5%
0.20
0.23

7.7%
0.19
0.20

(4) HSGPA + ACT Total (ACT CMP and ACT Writing, R-squared)Standardized Coefficients: HSGPAACT Total

2.4%
0.20
0.22

8.1%
0.18
0.22

(5) HSGPA + SAT Total + Demographics

3.0%
0.19
0.14

12.0%
0.25
0.16

Model
(1) HSGPA + SATRM (R-squared)Standardized Coefficients: HSGPA
SATRM(2) HSGPA + SATTOTAL (SATRM + Writing, R-squared)Standardized Coefficients: HSGPAStandardized Coefficients: HSGPAStandardized Coefficients: SATTOTAL

Conclusion
This study reviewed literature about the validity of the SAT and ACT and examined therelationship between these tests and college preparation of UC California resident applicants andcollege performance of enrolled students at UC.
The following conclusions emerged from the literature review:High school GPA and test scores are moderately correlated. Previous research also
indicates that socioeconomic status is associated with SAT and ACT scores.



SAT/ACT test scores are positively associated with college success in terms of freshman
GPA, graduation GPA, first-year retention, and graduation. In addition, researchdemonstrates the increment in the predictive validity afforded by the SAT/ACT over
HSGPA. On the other hand, evidence also suggests that standardized test scores are lesspredictive across certain subgroups.

Analysis based on UC data shows that:STTF Report Appendix IThe majority of UC applicants submit SAT scores, while more and more students submit
ACT scores. The average test scores changed slightly over years. SAT/ACT scores are
more associated with demographic factors than HSGPA. The explanatory power ofparental education, the URG status, and parents’ income has been growing with parental
education continuing to be the strongest predictor of SAT/ACT scores. This possibly
could be a problem as the UC seeks to find race neutral ways to achieve racial diversity at
selective campuses.53 SAT/ACT scores may have a disproportionate effect on admit rates
for URG students.



Similar to what previous research findings show, test scores are moderately correlated tohigh school GPA. Apparently, there is some overlap between what the tests measure andwhat HSGPA measures, but not that much. The College Board and ACT hold that SAT
and ACT scores are designed to measure knowledge and skills in high school subject
areas and readiness for first year college courses. High school GPA likely represents bothknowledge and skills and also behavior (such as completing homework).



The analysis of the relationship between the new SAT and new ACT and UC’s Analytical
Writing Placement Exam (AWPE) shows that high scores on the SAT EBRW and ACT
ELA are associated with passing the AWPE. However, on the SAT Essay, even at the top
score of 24, only 69 percent passed the AWPE. Based on the results of data analysis,
UCOPE has accepted the new ACT ELA and SAT EBRW scores but not the new SAT
Essay scores as methods of meeting UC’s Entry Level Writing Requirement (ELWR).

Regarding college success, findings from UC’s data analysis are generally consistent withthose from previous research. SAT scores and HSGPA are both moderate predictors ofstudent first-year GPA and graduation GPA. They are weak predictors of student
retention and graduation. HSGPA predicted first-year GPA slightly better than SAT
scores from 2001 to 2005 before controlling for student characteristics. Over the years,
the explanatory power of HSGPA has been decreasing. For graduation GPA, HSGPA andtest scores have the same explanatory power. HSGPA and test scores are not strong
predictors of first-year retention and four-year graduation. However, after controlling forstudent characteristics, HSGPA and test scores have the same explanatory power offreshman GPA, but HSGPA is a stronger predictor of the first year retention, graduation
GPA and four-year graduation.

Models that combine HSGPA along with standardized test scores predict student successbetter than ones that only use one or the other. In contrast, the addition of the SAT/ACT
writing tests did little to improve the prediction of student success above and beyond
HSGPA and SAT Reading/Math and ACT composite scores.

UC Office of the President. (2015). UC Files Amicus Brief in Affirmative Action Case. Retrieved fromhttps://www.universityofcalifornia.edu/press-room/uc-files-amicus-brief-affirmative-action-case on October 16,
2018.
53

STTF Report Appendix I

In summary, this report presents what literature and UC data show about the relationshipbetween standardized test scores and high school GPA, and predictive value of HSGPA and testscores in student success as measured by freshman GPA, first year retention, graduation GPA
and four-year graduation. IRAP will do a series of simulations to further examine what eligibility
pool and admissions outcomes would look like if UC used different measures to determine
eligibility and admit students.

STTF Report Appendix I

Appendix A: Test Takers and Average Test Scores of UC California Applicants from 2001 to 2018
Table A1: Test Takers and Average Test Scores, UC California Applicants, 2001 to 2018

CA Freshman Applicants% Submitted Scores
SAT I Verbal MathSAT II Writing
SAT I & SATII Writing
SAT Verbal Math
SAT Writing
SAT Reading Math Writing

2001
59,747

2002
62,903

2003
66,774

2004
63,852

2005
65,851

2006
71,011

2007
74,5092008
80,029

2009
81,113

2010
82,341

2011
85,187

2012
93,460

2013
2014
2015
2016
2017
2018
99,447 100,077 103,259 105,547 111,869 120,026

98.4%
96.9%
96.0%

98.4%
96.8%
95.8%

98.4%
96.1%
95.0%

98.3%
96.0%
94.9%

98.3%
94.8%
93.8%

88.4%
48.5%
48.1%
98.3%
98.5%
98.1%

0.8%
0.2%
0.1%
97.9%
97.9%
97.9%

97.5%
97.5%
97.5%

97.0%
97.0%
97.0%

94.6%
94.6%
94.6%

94.0%
94.0%
94.0%

93.2%
93.2%
93.2%

91.5%
91.5%
91.5%

89.3%
89.3%
89.3%

86.7%
86.7%
86.7%

84.6%
84.6%
84.6%

SAT Math and EBRW (EvidenceBased Reading and Writing)
SAT Essay
ACT Composite
ACT English with Writing
ACT ELA (English Language Arts)
ACT Writing subscore
Average Scores
SAT I Verbal MathSAT II Writing
SAT I & SATII Writing
SAT Verbal Math
SAT Writing
SAT Reading Math Writing

0.8%
0.8%

20.4%

0.8%

73.2%

86.4%

73.2%

86.4%

27.6%

27.7%

32.7%

32.9%

33.2%

34.9%
29.5%

35.1%
32.8%

39.4%
37.8%

44.0%
43.1%

46.2%
45.4%

47.3%
46.9%

45.5%
45.0%

46.4%
45.9%

49.1%
48.7%

52.2%
51.7%

53.4%
50.2%

52.5%
0.2%
45.5%
27.9%

44.3%
0.0%
37.6%
37.4%

1171
575
1751

1163
578
1746

1164
573
1744

1172
581
1760

1177
579
1766

1162
649
1816
1161
572
1734

1144
559
1727
1159
570
1729

1153
569
1722

1154
571
1725

1157
573
1729

1153
572
1725

1137
566
1703

1135
560
1695

1136
562
1698

1128
556
1684

1117
550
1667

1241
610

1327
643

SAT Math and EBRW (EvidenceBased Reading and Writing)
SAT Essay
ACT Composite
ACT English with Writing
ACT ELA (English Language Arts)
ACT Writing subscore

19.5%
20.4%

24

24

24

24

24

24
24

24
24

25
24

25
24

25
24

25
24

25
23

25
23

25
24

25
24

25
24

1845

1952

1167

1203

16

16

26
29
25
8

25
27
25
8

STTF Report Appendix I

Appendix B: Average Test Scores of UC California Applicants by Demographic
Characteristics, Selected Years
Table B1: SAT Reading/Math and Writing by Parents’ Highest Level of Education
No High
SchoolSome HighSchoolHigh School
Graduate

Some
College

2 YearCollege

4 YearCollege

Post
Graduate
Study

Unknown

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

SAT
RM

SAT
W

2001

978

479

1008

492

1079

526

1105

542

1125

550

1182

577

1249

617

1221

602

2005

981

483

1008

491

1082

528

1107

543

1135

555

1191

582

1264

625

1229

604

2007

967

472

998

485

1069

522

1087

533

1107

542

1177

578

1253

620

1210

594

2012

949

471

977

485

1049

519

1068

528

1104

544

1183

588

1271

637

1118

549

2015

952

468

973

479

1040

510

1061

522

1091

536

1181

583

1279

635

1114

544

2018

1052

15

1069

15

1126

15

1145

15

1179

16

1260

17

1342

17

1192

16

Year

Table B2: SAT Reading/Math and Writing by Parents’ Income (2001 dollars)Year$0-$56k$56k up to $112k$112k up to $169k$169k+

Unknown

SATRM

SATW

SATRM

SATW

SATRM

SATW

SATRM

SATW

SATRM

SATW

2001

1140

559

1180

578

1222

601

1251

617

n/a

n/a

2005

1084

531

1184

581

1236

608

1255

619

1248

616

2007

1068

521

1167

573

1213

599

1243

617

1244

616

2012

1049

520

1185

588

1243

620

1280

644

1208

607

2015

1042

512

1184

583

1249

619

1297

647

1205

601

2018

1127

15

1251

17

1311

17

1358

18

1256

16

Table B3: SAT Reading/Math and Writing by Race/Ethnicity
Year

African American

American Indian

Hispanic/Latinx

Asian/Pacific Islander

White

SATRM

SATW

SATRM

SATW

SATRM

SATW

SATRM

SATW

SATRM

SATW

2001

1035

520

1169

570

1047

521

1182

566

1215

603

2005

1042

526

1184

587

1049

524

1198

575

1230

610

2007

1027

516

1160

576

1032

514

1187

570

1213

603

2012

1015

509

1164

581

1013

507

1206

594

1215

607

2015

1025

512

1149

569

1008

499

1214

595

1217

602

2018

1120

15

1225

16

1099

15

1298

17

1288

17

Note: SAT Writing refers to SATII Writing for 2001 and 2005 and SAT Writing for 2007, 2012 and 2015,
and SAT Essay for 2018. International students are included in each racial/ethnic category based on theirself-reported race/ethnicity.

STTF Report Appendix I

Table B4: ACT Composite and Writing by Parents’ Highest Level of Education

ACT
CMP

ACT
W

ACT
CMP

ACT
W

ACT
CMP

ACT
W

ACT
CMP

ACT
W

ACT
CMP

ACT
W

ACT
CMP

ACT
W

Post
GraduateStudy
ACT ACT
CMP
W

2001

19

n/a

20

n/a

22

n/a

23

n/a

23

n/a

25

n/a

26

n/a

25

n/a

2005

20

n/a

20

n/a

22

n/a

23

n/a

24

n/a

25

n/a

26

n/a

26

n/a

2007

20

19

20

20

22

22

23

23

23

23

25

25

27

26

26

25

2012

20

19

21

19

22

21

23

22

24

23

26

25

28

26

25

23

2015

20

19

21

19

23

21

23

22

24

23

27

25

29

27

25

23

2018

20

19

21

20

23

21

24

22

25

23

28

26

30

28

26

24

Year

No High
SchoolSome HighSchoolHigh School
Graduate

Some
College

2 YearCollege

4 YearCollegeUnknown
ACT
CMP

ACT
W

Table B5: ACT Composite and Writing by Parents’ Income (2001 dollars)Year$0-$56k$56k up to $112k$112k up to $169k$169k+

Unknown

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

2001

23

n/a

24

n/a

25

n/a

26

n/a

n/a

n/a

2005

22

n/a

25

n/a

26

n/a

26

n/a

26

n/a

2007

22

21

25

24

26

25

27

26

27

26

2012

22

21

26

25

28

26

29

27

27

25

2015

22

21

27

25

28

27

29

28

27

26

2018

23

22

27

26

29

27

30

29

29

27

Table B6: ACT Composite and Writing by Race/Ethnicity
Year

Hispanic/Latinx

Asian/PacificIslanderAfrican American

American IndianWhite

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

ACT CMP

ACT W

2001

21

n/a

25

n/a

21

n/a

24

n/a

26

n/a

2005

21

n/a

25

n/a

21

n/a

24

n/a

26

n/a

2007

21

21

25

25

21

21

25

24

26

26

2012

22

21

26

25

22

20

26

24

27

26

2015

22

22

26

25

22

21

27

25

28

27

2018

23

22

27

26

22

21

29

27

29

27

Notes: ACT Writing refers to ACT English with Writing for 2007, 2012, and 2015 and ACT English
Language Arts for 2018. International students are included in each racial/ethnic category based on theirself-reported race/ethnicity.

STTF Report Appendix I

Appendix C: Standardized Regression Coefficients for HSGPA and SAT/ACT
Scores in UC Freshman GPA Models, 2015 Freshman Entering Cohort
SAT Models
UC System
By Campus
Berkeley
Davis
IrvineLos Angeles
MercedRiverside
San Diego
Santa Barbara
Santa Cruz
By Enrolled Majors
Arts
Engineering/Computer Sciences
Humanities
Life Sciences
Physical Sciences/MathProfessional Fields
Social Sciences/Psychology
Undeclared/All Others/Unknown
By Race/Ethnicity
African American
Asian
Latino(a)White
By First Generation Status
Not First Generation
First Generation
By Family Income$0-$53,999$54,000-$106,999$107,000- $160,999$161,000 or higher
Missing
By High School API Quintile (2013)
1 through 3
4 through 7
8 and higher
PrivatePublic Missing
Missing OtherHSGPA SAT_RM
0.23
0.36

ACT Models% Variance Explained
25%

HSGPA
0.22

ACTCMP
0.38% Variance Explained
26%

0.20
0.18
0.31
0.16
0.29
0.24
0.24
0.29
0.22

0.36
0.44
0.36
0.39
0.29
0.25
0.38
0.32
0.31

22%
27%
28%
23%
18%
11%
21%
21%
16%

0.22
0.17
0.32
0.17
0.29
0.22
0.22
0.28
0.22

0.32
0.44
0.38
0.41
0.28
0.28
0.39
0.36
0.34

19%
27%
30%
25%
18%
12%
21%
24%
18%

0.21
0.16
0.30
0.25
0.28
0.24
0.29
0.25

0.28
0.42
0.35
0.42
0.36
0.38
0.34
0.31

15%
26%
31%
31%
27%
26%
26%
20%

0.15
0.17
0.32
0.23
0.26
0.22
0.26
0.25

0.40
0.40
0.35
0.44
0.37
0.39
0.39
0.33

22%
25%
33%
31%
28%
25%
29%
21%

0.27
0.22
0.23
0.28

0.25
0.35
0.24
0.23

18%
22%
14%
18%

0.28
0.19
0.23
0.28

0.26
0.38
0.25
0.22

20%
22%
15%
28%

0.26
0.22

0.29
0.29

21%
22%

0.24
0.22

0.29
0.30

20%
17%

0.22
0.22
0.26
0.27
0.24

0.30
0.31
0.28
0.26
0.27

18%
19%
20%
20%
19%

0.22
0.21
0.24
0.27
0.07

0.32
0.32
0.28
0.25
0.01

19%
20%
19%
19%
16%

0.22
0.25
0.30
0.27
0.21
0.17

0.20
0.26
0.26
0.26
0.34
0.25

12%
19%
22%
21%
22%
11%

0.25
0.25
0.26
0.30
0.23
0.18

0.20
0.27
0.28
0.23
0.36
0.15

13%
19%
21%
20%
24%
8%

STTF Report Appendix I

Appendix D: Standardized Regression Coefficients for HSGPA and SAT/ACT
Scores in UC Graduation GPA Models, 2010 Freshman Entering Cohort
SAT Models
UC System
By Campus
Berkeley
Davis
IrvineLos Angeles
MercedRiverside
San Diego
Santa Barbara
Santa Cruz
By Enrolled Majors
Arts
Engineering/Computer Sciences
Humanities
Life Sciences
Physical Sciences/MathProfessional Fields
Social Sciences/Psychology
Undeclared/All Others/Unknown
By Race/Ethnicity
African American
Asian
Latino(a)White
By First Generation Status
Not First Generation
First Generation
By Family Income$0-$53,999$54,000-$106,999$107,000- $160,999$161,000 or higher
Missing
By High School API Quintile (2013)
1 through 3
4 through 7
8 and higher
PrivatePublic Missing
Missing OtherACT Models

HSGPA SAT_RM % Variance Explained
0.27
0.29
23%

HSGPA
0.25

ACTCMP
0.31% Variance Explained
22%

0.20
0.34
0.24
0.20
0.26
0.25
0.29
0.27
0.31

0.30
0.30
0.27
0.32
0.33
0.25
0.32
0.28
0.30

18%
23%
14%
18%
17%
13%
20%
17%
21%

0.20
0.33
0.22
0.17
0.34
0.21
0.25
0.24
0.29

0.33
0.32
0.26
0.31
0.32
0.27
0.34
0.32
0.34

20%
24%
12%
16%
23%
13%
20%
18%
23%

0.35
0.30
0.29
0.30
0.30
0.29
0.30
0.24

0.19
0.28
0.30
0.35
0.33
0.27
0.29
0.32

20%
24%
26%
30%
26%
23%
25%
22%

0.29
0.27
0.28
0.27
0.28
0.30
0.26
0.24

0.22
0.31
0.34
0.36
0.36
0.31
0.32
0.31

17%
24%
28%
27%
27%
27%
24%
21%

0.22
0.29
0.24
0.30

0.25
0.26
0.27
0.19

16%
21%
18%
17%

0.26
0.26
0.24
0.27

0.22
0.26
0.28
0.20

16%
18%
18%
15%

0.31
0.24

0.20
0.27

19%
18%

0.29
0.23

0.21
0.28

18%
17%

0.25
0.27
0.30
0.36
0.32

0.28
0.25
0.19
0.16
0.21

19%
18%
17%
20%
21%

0.23
0.25
0.29
0.31
0.31

0.29
0.25
0.21
0.17
0.20

18%
18%
18%
17%
19%

0.24
0.26
0.34
0.31
0.21
0.31

0.23
0.26
0.18
0.25
0.39
0.20

15%
19%
20%
24%
26%
19%

0.24
0.23
0.32
0.29
0.12
0.34

0.21
0.28
0.19
0.29
0.40
0.26

14%
19%
20%
24%
22%
25%

STTF Report Appendix I

Appendix E: Explanation for Change of Variance in Freshman GPA Accounted for
by High School GPA and Test ScoresOver the past 10 years, the amount of freshman GPA variation explained by high school GPA(HSGPA) has fallen significantly. Table 3 shows that, in terms of aggregate freshman GPA,HSGPA’s explanatory power fell from 17% in 2001 and 20% in 2007 to 13% in 2015. There area large number of possible explanations for this decline. This section provides the results of somepreliminary analysis conducted to examine the change.
Change of the Admit Pool
The change of variation in HSGPA and test scores may be related to the admit pool. Whilealways skewed toward the top quartiles of SAT/ACT and HSGPA, the distribution of admits haschanged over time (Figures E1 and E2). In particular, admits became more concentrated in the
top quintiles of HSGPA between 2001 and 2015. This change has definitely resulted in lessvariation in HSGPA and may be related to the diminishing power of HSGPA to predict UC
freshman GPA during this time period. Almost all applicants with high HSGPA are admitted, but
the admit rates of those with high SAT/ACT depend on HSGPA. This also suggests that HSGPA
is given more weight in the admission process than SAT/ACT.
Figure C1. Admit Rate by HSGPA and SAT/ACT Quartiles
100%
80%
60%
40%
20%
0%
2001

2015

<3.35
< 10702001

2015

3.35-< 3.71
1070- < 1190

2001

2015

3.71-< 4.02
1190- <1310

2001

2015

>= 4.02>= 1310

Note: Thresholds for HSGPA and SAT/ACT quartiles were constructed from2001 data and used for all years for consistency.

STTF Report Appendix I

Figure C2. Share of Admits by HSGPA and SAT/ACT Quartiles

27%
46%

Share of admits by
HSGPA quartile>= 4.02

28%

27%

27%

3.35 - < 3.71

23%

19%

17%

6%

2001

2015

< 3.3525%

Share of admits by
SAT/ACT quartile>= 131027%

3.71 - < 4.02
29%

37%

21%

21%

19%

2001

2015

1190 - < 1310
1070 - < 1190
< 1070Note: Thresholds for HSGPA and SAT/ACT quartiles were constructed from2001 data and used for all years for consistency.

High School Grade Inflation, Enrollment in Low-Grade and Larger Freshman Courses
There are a large number of possible explanations for the decline in the explanatory power of
HSGPA over time, many of which can be adjudicated between using course-level grade
performance.
Table C1 shows that at the freshman course level, HSGPA explanatory power has fallen by 18.8
percent since 2012, from 4.3 percent to 3.5 percent (note that the SAT and HSGPA explain less
Table C1: Changes in Explanatory Power of HSGPA for Freshman Grades

Note: "FE R2" measures the percent of variation explained by each additional group of fixed
effects (FE), like FE's for every high school or every SAT score. "GPA R2" measures the percent
of remaining variation explained by HS GPA. "Δ" shows the percent change from 2012 to 2016.
Valid explanations for the decline in HS GPA explanatory power for freshman grades should
decrease the GPA R2, since some of the decline would be explained away by the FEs.

STTF Report Appendix I

course-specific variation than they do freshman aggregate GPA variation, since the latter are
averaged over many courses and thus present a smoother and more-easily-modeled distributionof student performance). This baseline decline is conditional on gender, ethnicity, and campus.
Each row of Table C1 adds an additional set of controls, removing one dimension of grade
variation and testing how that information’s removal impacts the degree to which HSGPA’s
explanatory power has declined since 2012. Controls that lead to a decline in the change in
HSGPA’s R2 are interpreted as playing a role in HSGPA’s explanatory decline.
The second row of Table C1 tests the hypothesis that HSGPA’s explanatory power is declining
because UC is enrolling students from a wider range of high schools with varying GPA
standards, muddying the informational content of HSGPA. Indeed, Figure C3 shows that theconcentration of high schools from which UC has enrolled students (as measured by aNormalized Herfindahl Index) has indeed been declining since about 2007 after a period of
relative stability, implying that UC has been taking students from increasingly-dispersed highschools, likely as a result of its Eligibility in the Local Context program and the expansion of its
Merced campus. However, when the relationship between HSGPA and freshman GPA ismeasured conditional on high school fixed effects—that is, only comparing students who
attended the same high school—the decline in the explanatory power of HSGPA grows to 25.0
percent. We conclude that the wider distribution of UC students’ high schools plays no role inthe decline in HSGPA’s explanatory power, since the within-high-school decline is even larger
than the across-high-school decline.
The third row tests the hypothesis that HSGPA has less variance among students with the same
SAT score, which would likely arise if the distribution of HSGPAs was increasingly compressed
as a result of high schools’ grade inflation. Controlling for fixed effects for every SAT score, theFigure C3: High School Concentration of First-Year UC Students
0.00140.00130.00120.00110.001

0.00090.0008
1990

1995

2000

2005

2010

2015

2020

Note: Annual Normalized Herfindahl Index of high schools attended by freshman California-resident UC students,
measuring the degree to which students come from a concentrated set of schools.

STTF Report Appendix I

decline in HSGPA explanatory power falls slightly to 15.3 percent. We conclude that a smallpart of HSGPA’s decline in explanatory power results from its diminished relationship with SAT
score, likely resulting from high schools’ grade inflation.
The fourth row of Table C1 tests whether the decline in HSGPA explanatory power is the resultof which departments freshmen choose to enroll in. If high-HSGPA students are increasingly
enrolling in low-grade courses like Engineering and Economics, then HSGPA’s explanatory power
would decline (since it would appear that high-HSGPA students were performing poorly, when infact they’re merely taking courses that award lower grades). Indeed, this appears to be the largest
identified factor in the decline in HSGPA’s explanatory power, explaining about 1/3 of the decline.
Moreover, a full 10 percent of freshman grade variation can be explained by the department inwhich each course was taken, and the explanatory power of departments has increased by 25
percent since 2012. Cross-department variation is a key explanation for changes in freshman GPAevaluation in the past five years.
Finally, if students have tended towards taking larger freshman courses with more formal testbased examination (like multiple choice exams), then HSGPA would lose explanatory power
because student performance would increasingly resemble SAT examination as opposed to themulti-modal examinations provided in high school. The UC Accountability Report shows that theavailability of small classes at UC has fallen dramatically over the past 5 years, limiting freshmancourse options. Indeed, controlling for course size shows that size explains a small part of thedecline in HSGPA explanatory power.
Overall, we conclude that almost half of the decline in HSGPA’s ability to explain freshmancourse performance can be attributed to changes in freshman course enrollment acrossdepartments (the largest observed factor), HSGPA compression as a result of grade inflation andincreased UC selectivity, and increases in the average size of freshman courses. While about halfof the explanatory decline remains unexplained, we conclude that increased dispersion in thehigh schools sending students to UC does not play a role in HSGPA’s explanatory decline, since
within-high-school explanatory power has fallen even faster than overall explanatory power(leaving no room for a decline across high schools).
In summary, the change of admit pool and thus enrollment may result in less variability of
HSGPA and a slightly greater variability of test scores. This change may be related to increase ofeligible applicants, admission process, and student enrollment behavior. Further analyses need tobe conducted to test this hypothesis. Regardless of how it happened, it definitely explains part of
HSGPA’s decline and test scores’ increase in explanatory power. In addition, high school grade
inflation and enrollment in low-grade course and large freshman courses also explain part of thedecline in HSGPA explanatory power in course performance.

01.10.2020 INSTITUTIONAL RESEARCH & ACADEMIC PLANNING ucal.us/irap

45

STTF Report Appendix II

HSGPA versus SAT as a predictor of UC freshman grades
across demographic groups, 2010-2012
CategoryN

GPA-only models

SAT-only models

All

90504

Slope
0.39

R^2
0.16

Slope
0.46

R^2
0.21

<$30K
$30-$60K$60-$120K$120K+

20178
19232
19307
21339

0.34
0.36
0.36
0.41

0.12
0.13
0.13
0.17

0.41
0.42
0.39
0.35

0.17
0.18
0.15
0.12

Asian
Black
Hispanic
White

37899
3652
22522
23046

0.38
0.29
0.33
0.38

0.15
0.09
0.11
0.14

0.42
0.37
0.39
0.34

0.18
0.14
0.15
0.11

Not 1stGen
First Gen

47626
40405

0.39
0.34

0.15
0.12

0.37
0.4

0.14
0.16

Table 1. High school GPA (HSGPA) compared to SAT as a predictor of
freshman grades at UC for different demographic groups, 2010-2012.
SAT is calculated as SATRR + SATRM. Both SAT and HSGPA are
normalized (z-scored) prior to ordinary least-squares regression.

STTF Report Appendix II

HSGPA plus SAT to predict UC freshman grades
across demographic groups, 2010-2012
Category

N

Combined models

All

90504

GPA
slope
0.25

SAT
slope
0.36

R^2
0.26

<$30K
$30-$60K$60-$120K$120K+

20178
19232
19307
21339

0.23
0.23
0.25
0.32

0.33
0.34
0.29
0.21

0.22
0.23
0.20
0.20

Asian
Black
Hispanic
White

37899
3652
22522
23046

0.26
0.19
0.22
0.29

0.32
0.30
0.31
0.22

0.24
0.17
0.20
0.18

Not 1stGen
First Gen

47626
40405

0.29
0.23

0.26
0.32

0.21
0.21

Table 2. High school GPA (HSGPA) combined with SAT to predict
freshman grades at UC for different demographic groups, 20102012. SAT is calculated as SATRR + SATRM. Both SAT and HSGPA
are normalized (z-scored) prior to ordinary least-squares
regression.

STTF Report Appendix II

HSGPA versus SAT as predictors of graduation GPAs at UC
across demographic groups, 2010
CategoryN

GPA-only models

SAT-only models

All

25523

Slope
0.39

R^2
0.15

Slope
0.4

R^2
0.16

<$30K
$30-$60K$60-$120K$120K+

5154
5040
5457
5519

0.34
0.36
0.37
0.41

0.12
0.13
0.14
0.16

0.36
0.36
0.34
0.32

0.13
0.13
0.11
0.1

Asian
Black
Hispanic
White

11077
869
5239
6917

0.39
0.32
0.33
0.38

0.15
0.1
0.11
0.14

0.38
0.34
0.36
0.31

0.14
0.12
0.13
0.09

Not 1stGen
First Gen

13876
10273

0.4
0.34

0.16
0.11

0.33
0.35

0.11
0.12

Table 3. High school GPA (HSGPA) compared to SAT as a predictor of GPA
at graduation for different demographic groups, 2010. SAT is calculated as
SATRR + SATRM. Both SAT and HSGPA are normalized (z-scored) prior to
ordinary least-squares regression.

STTF Report Appendix II

HSGPA plus SAT to predict graduation GPAs at UC
across demographic groups, 2010
Category

N

Combined models

All

25523

GPA
slope
0.28

SAT
slope
0.29

R^2
0.22

<$30K
$30-$60K$60-$120K$120K+

5154
5040
5457
5519

0.25
0.26
0.28
0.33

0.28
0.27
0.23
0.18

0.19
0.19
0.18
0.19

Asian
Black
Hispanic
White

11077
869
5239
6917

0.29
0.23
0.23
0.3

0.26
0.26
0.27
0.19

0.21
0.16
0.17
0.17

Not 1stGen
First Gen

13876
10273

0.31
0.24

0.2
0.27

0.19
0.17

Table 4. High school GPA (HSGPA) combined with SAT topredict GPAs at graduation for different demographic groups,
2010. SAT is calculated as SATRR + SATRM. Both SAT and
HSGPA are normalized (z-scored) prior to ordinary least-squares
regression.

STTF Report Appendix III

Percentage of Applicants Reporting SAT Scores Who Were Admitted by Campus, High School
GPA (HSGPA) Range and SAT Range. Fall 2018 California Resident Applicants.
••••

The SAT used is the maximum sum of the reading and math sections. No concordance from ACT scores
was used.
The means and standard deviations used were computed for each campus or the system as a whole.
Cells corresponding to fewer than 30 students are blank.
In the tables below:
o “High”= more than one standard deviation above the meano “Mid-High” = between the mean and one standard deviation above the meano “Mid-Low” = between the mean and one standard deviation below the meano “Low”= more than one standard deviation below the mean
SystemHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
95%
95%
94%
85%
75%
73%
65%
51%
43%
41%
25%
17%
86%
68%
52%

Low SAT
84%
64%
28%
9%
27%

Net
95%
75%
43%
16%
58%

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
54%
42%
43%
36%
20%
18%
2%
2%

Low SAT
30%
14%
2%

Net
47%
23%
2%
1%
16%

Berkeley

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

36%

17%

11%

4%

Davis

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
88%
91%
94%
48%
42%
45%
3%
3%
5%
58%

38%

27%

Low SAT
92%
50%
6%
15%

Net
90%
45%
4%
1%
34%

IrvineHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
80%
68%
65%
40%
33%
26%
9%
4%
2%
53%

30%

16%

Low SAT
42%
13%
1%
4%

Net
73%
30%
3%
1%
25%

STTF Report Appendix III
Los Angeles

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
59%
34%
30%
19%
8%
9%
1%
2%
31%

10%

7%

Low SAT
5%
1%
2%

Net
45%
11%
2%
1%
12%

MercedHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
99%
99%
98%
98%
97%
90%
83%
74%
62%
16%
15%
10%
91%
82%
66%

Low SAT
91%
57%
19%
25%

Net
99%
90%
59%
9%
68%

Riverside

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
97%
96%
91%
94%
84%
57%
77%
48%
11%
27%
7%
90%
69%
33%

Low SAT
61%
13%
1%
5%

Net
94%
70%
25%
3%
49%

San Diego

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
75%
75%
73%
38%
33%
31%
13%
8%
6%
50%

32%

19%

Low SAT
57%
20%
2%
6%

Net
74%
32%
6%
1%
27%

STTF Report Appendix III
Santa Barbara

High HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
90%
74%
69%
55%
32%
31%
11%
7%
8%
63%

31%

19%

Low SAT
42%
17%
2%
5%

Net
80%
35%
6%
1%
29%

Santa CruzHigh HSGPA
Mid-High HSGPA
Mid-Low HSGPA
Low HSGPA
Net

Percent of Applicants Admitted
High SAT
Mid-High SAT
Mid-Low SAT
97%
93%
61%
93%
57%
35%
36%
18%
13%
84%

48%

22%

Low SAT
30%
15%
1%
4%

Net
89%
54%
14%
1%
39%

STTF Report Appendix IV

Interpreting R2 as Percent Explained Variance
In regression, 𝑅𝑅 2 is a measure of how well a model fits a set of data. Sometimes, it is referred
to as the “proportion of explained variation.” If the data is perfectly fit by the model, then𝑅𝑅 2 = 1; if the model doesn’t’ fit the data at all then 𝑅𝑅 2 = 0; This brief describes where thelanguage of “explained variation” comes from and hopefully will provide a better
understanding of how to interpret 𝑅𝑅 2 .

Consider a very simple set of data 𝑦𝑦 = {0, 5, 5, 5, 10}. If we know nothing about where this data
came from, it would be natural to at least calculate the mean, which is just 𝑦𝑦� = 5. The“residual” is the difference between each data point and the mean, which in this case is
𝑟𝑟 = {-5, 0, 0, 0, 5}. The “variance” of the data from the mean is called the “null variance” (nomodel, just calculated from the mean) and is the sum of the square of the residuals. In thiscase, the null variance 𝑣𝑣𝑣𝑣𝑣𝑣0 = 50.
Now suppose that we can associate anothervariable 𝑥𝑥 with 𝑦𝑦 in a way that we can express 𝑦𝑦
as a function of 𝑥𝑥: 𝑦𝑦 = 𝑓𝑓(𝑥𝑥). For our data,
suppose that 𝑥𝑥 = {0, 1, 2, 3, 4}. We can examine
the relationship between the two variables byplotting one against the other, and let’s now fit
this data with a straight line that minimizes thevariance between the predicted 𝑦𝑦’s at each 𝑥𝑥 andthe actual 𝑦𝑦’s.

Doing so gives 𝑓𝑓(𝑥𝑥) = 1 + 2𝑥𝑥, which gives
predicted 𝑦𝑦’s of 𝑦𝑦 ′ ={1, 3, 5, 7, 9}. The residuals
for this model are then 𝑟𝑟 ′ ={-1, 2, 0, -2, 1}. The variance is again the sum of the squares of theresiduals, which in this case is model variance 𝑣𝑣𝑣𝑣𝑣𝑣𝑚𝑚 = 10. This is obviously smaller than the nullvariance of 50, and as a percentage, the model reduces the variance by 80%, or we might saythat the model “explains” 80% of the originally observed variation.
In general for this sort of linear regression, 𝑅𝑅 2 can be computed as the normalized difference
between the null and the model variances,

For our example, 𝑅𝑅 2 = 0.80.

𝑅𝑅 2 =

𝑣𝑣𝑣𝑣𝑣𝑣0 −𝑣𝑣𝑣𝑣𝑣𝑣𝑚𝑚
𝑣𝑣𝑣𝑣𝑣𝑣0

𝑣𝑣𝑣𝑣𝑣𝑣= 1 − 𝑣𝑣𝑣𝑣𝑣𝑣𝑚𝑚 .
0

STTF Report Appendix IV

Now let’s consider some larger sets of data in order to get a visual sense of how much scatter isassociated with varying levels of 𝑅𝑅 2 . Below are three simulations created by adding random“noise” of increasing magnitude to the same straight line. In all three cases, a scatter plot and a
box-and-wire plot are shown, along with the fitted line. The vertical scales are different due tothe different amounts of scatter in the y data.

Noise introduced into 𝑦𝑦 at the level of ±2.5.

Noise introduced into 𝑦𝑦 at the level of ±5.

Noise introduced into 𝑦𝑦 at the level of ±10.

STTF Report Appendix IV

As a final example, we consider data that may be a function of two independent variables:𝑧𝑧 = 𝑓𝑓(𝑥𝑥, 𝑦𝑦). Noise is again introduced and various functional fits are examined against thedata. First consider 𝑧𝑧 with only 𝑥𝑥 as an independent variable:zz

Now consider 𝑧𝑧 with only 𝑦𝑦 as the independent variable:zz

If we can only use one independent variable to predict 𝑧𝑧, it is clear that the fit using 𝑦𝑦(𝑅𝑅 2 = 0.82; maximum residual ≅ 5) does a better job than the fit using 𝑥𝑥 (𝑅𝑅 2 = 0.21; maximum
residual ≅ 10).

STTF Report Appendix IV

If we fit 𝑧𝑧 using a function that is linear in both 𝑥𝑥 and 𝑦𝑦, we find that the residual is zero and
𝑅𝑅 2 = 1. This is because the noise was introduced into 𝑥𝑥 and 𝑦𝑦 not into 𝑧𝑧, but we couldn’t tellthat by examining either of the 1D models. The second figure below shows that all of thepoints lie on a single plane.

STTF Report Appendix V

Appendix V. Campus by campus study of relationships between HSGPA, test scores,
and demographics in admissions.


Figures 1-10: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
racial/ethnic subgroup.



Figures 11-20: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
family educational history.



Figures 21-30: Applicant and admittee HSGPAs, test scores, and admissions rates for all
campuses together (Figure 1) and each campus individually (Figures 2-10), broken down by
family income category.



Tables 1-3: Applicant and admittee HSGPAs and test scores, by demographic group andcampus.



Tables 4-6: Applicant and admittee HSGPAs and test scores with respect to highest-scoringgroups, by demographic group and campus.

Legend for Figures 1-30
Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into
13 equal bands from 2.1 to 4.5. Y-axis shows the percent of applicants in that demographic group who
had a HSGPA in that range. Means and standard deviations are plotted above each group. Colorcorresponds to demographic group.
Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is thepercent of applicants in each demographic group who had HSGPA in that range (light color) or who had
HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of
accepted students are plotted above each group.
Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA.
To preserve student privacy, combinations with fewer than 30 accepted students are blanked out. Groups
that UC considers disadvantaged or underrepresented are indicated in red or orange.
Tables 1-3: Applicant and admittee HSGPAs and test scores, by demographic group and campus.
Legend for Tables 1-3: Left: Each row shows, for one campus and one demographic group, the mean
HSGPAs for applicants and for admitted students, the change in mean grades between the pool ofapplicants and the pool of accepted students, and the size of the change normalized to units of standard
deviations (calculated across the entire applicant pool) – a measure of how strongly the campus selects
based on that qualification. Normalized changes are color coded (red = strongest selection, white = noselection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically
substantially higher than the HSGPAs of applicants, 2) the SATs of admitted students are also typicallyhigher than the SATs of applicants, 3) measured selectivity based on test scores is generally much smaller
than the selectivity based on grades, across different demographic groups and campuses, and 4) for thegroups most underrepresented at UC, the contribution of selection on HSGPA exceeds the contribution of
selection on test scores.
Tables 4-6: Applicant and admittee HSGPAs and test scores with respect to highest-scoring groups,
by demographic group and campus.
Legend for Tables 4-6: Left: Each row shows, for one campus and one demographic group, mean
HSGPAs for applicants, the difference in mean HSGPA (green) between applicants in that group and thegroup with the highest test scores, mean HSGPAs for admitted students, the difference in mean HSGPA
between admitted students in that group and the group with the highest test scores (green), and the ratio of
the Accepted difference vs the Applicant difference. If the difference in scores between two groups ofaccepted students is exactly the same as the difference in scores between applicants in those groups, (i.e.,
uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions processeffectively compensates for the mean differences between groups. Right: As Left, but for SAT(differences in blue). Note that 1) the remaining, uncompensated differences in HSGPAs are typicallymuch larger than uncompensated differences in test scores (i.e., the left green columns are darker than theright green columns, but the left blue columns are similar to the right blue columns), 2) the
uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Figure 1. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for all UC campuses combined.

00AppliedAccepted0

Hispan:3.64+-0.45
10 n=44628

Hispan:3.88+-0.33
10 n=23324

Percent

0

Applied0White :
20 3.88+-0.38
n=26680

White :
20 4.05+-0.28
n=15658

3

3.5

4

0

4.5

AppliedAccepted

2.5

HSGPA

b

d

Percent

Percent

Hispan

3.5

4

4.5

3.5

4

Percent

AppliedAccepted

20 Asian :101270+-160
n=21496
AppliedAccepted

20
10

Hispan:1080+-155
n=21876
AppliedAccepted

00White :
20 1214+-156
n=20079

20 1262+-142

10White :

Applied1000

n=11774

10

AppliedAccepted

0
500

1500

1000

20

d

Hispan10 White

HSGPA

Accepted(All Campuses)
20

Asian

0
500

1000

1500

Af-Amer
Asian

10 Hispan
White

0
500

SAT

D
100

80

80

Af-Amer
Hispan
White

1500

SAT

Af-Amer

60 Asian
40

20

1000

Acceptance Percent(All Campuses)

100

60 Asian

1500

SAT

Applicants(All Campuses)

4.5

Percent

Percent3

Acceptance Percent(All Campuses)

40

Applied30 Af-Amer

Hispan

HSGPA

B

10

b

10 White
2.5

10

Af-Amer:
1120+-154
n=3033SAT

20 Asian

0

20

0

0
500

Af-Amer

10 White
3

AppliedHispan:20
1022+-167
n=41005

4.5

30

Asian

2.5

4

Accepted(All Campuses)

30 Af-Amer0

3.5

1221+-178
n=31143HSGPA

Applicants(All Campuses)

203

20 Asian :10

Accepted(All Campuses)

0

Percent

2.5

10Percent

0

AppliedApplied

0AppliedAccepted

010Percent10n=25452

Percent

Applied20 3.99+-0.29

Percent

Percent

n=36655

10

Af-Amer:
1043+-173
n=63740

Asian :

Percent

Percent

Percent

Percent

Asian :

20 3.85+-0.40
10AppliedAccepted

20

Percent

Applied

Af-Amer:
3.86+-0.33
10 n=3397

cApplicants(All Campuses)

Percent

Af-Amer:
3.55+-0.49
10 n=7354

Ca

Accepted(All Campuses)

Percentc

Percent

Applicants(All Campuses)

Percent

Percent

Aa

Hispan
White

20
2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 2. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Berkeley.

20 Hispan:
4.21+-0.15
n=1891

Applied

10

AppliedAccepted

00White :
4.01+-0.33
20 n=11867

White :
4.23+-0.16
20 n=2109

Applied

2.5

3

3.5

4

0

4.5

2.5

3

HSGPA

b

d

Percent

Percent

Asian

20 Hispan
White

3

3.5

4

4.5

10
5
0

HSGPA

B

Hispan
White

4

10

Applied10

1195+-155
n=1723
AppliedAccepted

00White :
20 1277+-148
n=846120 1362+-125

10White :

Applied1000

n=1456

10

AppliedAccepted

0
500

1500

1000

20

d

Accepted(Berkeley)

8

Asian
Hispan10 White
1000

1500

6 Af-Amer
4

Asian
Hispan2 White
0
500

SAT

D

1500

SAT

Applicants(Berkeley)

HSGPA

1000

1500

SAT

Acceptance Percent(Berkeley)

50

50

40

40

Af-Amer

Percent

Percent

20 Hispan:

0
500

4.5

Acceptance Percent(Berkeley)

30 Asian
20

0

b

Asian

3.5

AppliedAccepted

20 Hispan:
1088+-176
n=1287030 Af-Amer3

n=3063SAT

Af-Amer

2.5

10

0

0
500

4.5

Accepted(Berkeley)

40 Af-Amer

2.5

4

AppliedHSGPA

Applicants(Berkeley)0

3.5

Percent

0

AppliedAccepted

n=16593

20 1390+-137

Percent

10

Percent

0

20 Hispan:
3.82+-0.42
n=14215

AppliedAccepted
Asian :

20 1294+-165
101199+-157
n=319

0Asian :

AppliedAccepted0100Percent

AppliedApplied

Percent

Asian :
4.24+-0.12
20 n=3860

1020 Af-Amer:1088+-186
n=2729Percent

Asian :
3.99+-0.33
20 n=20174

Percent0

Accepted(Berkeley)

Percent

AppliedAccepted

0cApplicants(Berkeley)

20 Af-Amer:

Percent

Af-Amer:
4.13+-0.25
n=367
10

Percent

Percent

AppliedCa

Accepted(Berkeley)20

Af-Amer:
3.67+-0.48
10 n=3183

Percent

Percent

Percent

Percent

Percent

20cPercent

Applicants(Berkeley)

Percent

Aa

HispanWhite

Af-Amer
Asian
Hispan
20
White
30

10

10
2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 3. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Davis.

n=21356
Applied10

Hispan:4.10+-0.19
10 n=5203

Percent

0

Applied0White :
20 3.91+-0.36
n=12873

White :
20 4.19+-0.16
n=4920

2.5

3

3.5

4

10
0

4.5

AppliedAccepted

2.5

3

HSGPA

b

Asian
Hispan10 White
2.5

3

3.5

4

4.5

20
10
0

HSGPA

B

Asian
Hispan
White

3

3.5

Percent

101136+-162
n=620
AppliedAccepted

20
10

Asian :
1310+-155
n=6373
AppliedAccepted0

Hispan:20
1036+-169
n=15688

10

Applied20
10

Hispan:1108+-162
n=4874
AppliedAccepted

00White :
20 1228+-150
n=9773

White :
20 1290+-135
n=3710

10

Applied

b

1000

10

AppliedAccepted

0
500

1500

1000

4

Hispan10 White

HSGPA

Accepted(Davis)
Af-Amer

Asian

0
500

4.5

d

30 Af-Amer
20

1000

1500

10 Asian5

Hispan
White

0
500

SAT

D

Acceptance Percent(Davis)1500

SAT

Applicants(Davis)

Af-Amer

2.5

20 Af-Amer:

SAT

1000

1500

SAT

Acceptance Percent(Davis)
80

100
80

60

Af-Amer60
Asian
Hispan40
White

Percent

Percent

Applied

0
500

4.5

Accepted(Davis)Percent

Percentd

30 Af-Amer

0

4

10

Asian :
1236+-174n=17884

HSGPA

Applicants(Davis)20

3.5

Percent

0

Applied20

Accepted(Davis)00AppliedAccepted

010Percent

AppliedAccepted

Hispan:3.69+-0.44
10 n=17092

Applied0n=76710

101055+-176
n=2614

Percent

10

20 Af-Amer:

Percent

Asian :

20 4.18+-0.14

Percent

Asian :

20 3.87+-0.38

cApplicants(Davis)Percent0

Percent

Percent

Percent

Percent

0

AppliedAccepted

Percent

Applied

Af-Amer:
4.08+-0.21
10 n=707

Percent

Af-Amer:
3.59+-0.47
10 n=3040

Ca

Accepted(Davis)Percentc

Percent

Applicants(Davis)Percent

Percent

Aa

20

Af-Amer
Asian
40
Hispan
White
20

2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 4. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Irvine.

00n=8616
AppliedAccepted0

Hispan:3.65+-0.44
10 n=25957

Hispan:4.12+-0.19
10 n=4671

Percent

0

AppliedAppliedAccepted

00White :
20 3.86+-0.38
n=11169

White :
20 4.17+-0.19
n=2945

2.5

3

3.5

4

10
0

4.5

AppliedAccepted

2.5

3

HSGPA

b

20

Af-Amer
Hispan10 White
2.5

3

3.5

4

4.5

Percent

AppliedAccepted

20
10

Asian :
1325+-142
n=7368
AppliedAccepted0

Hispan:20 1022+-162
n=24254

20 1137+-149

10

Hispan:Applied10n=4444
AppliedAccepted

0White :
20 1207+-153
n=8977

10

White :

Applied1000

20 1289+-137
n=2419

10

AppliedAccepted

0
500

1500

1000

1500

SAT

d

Applicants(Irvine)Accepted(Irvine)1030 Af-Amer

Af-Amer

White

2.5

3

3.5

4

20

Af-Amer

Asian
Hispan10 White
0
500

4.5

HSGPA

1000

1500

Asian

5 Hispan
White

0
500

SAT

D

Acceptance Percent(Irvine)

1000

1500

SAT

Acceptance Percent(Irvine)
80

80

60

60

Af-Amer
Asian
40 HispanWhite

Percent

Percent

10

Af-Amer:
1178+-141
n=760

0

b

10 Hispan

0

20

SAT

Asian

HSGPA

B

Applied

0
500

4.5

Accepted(Irvine)Percent

Percentd

20 Asian

04

10Asian :
1227+-170
n=23759

HSGPA

Applicants(Irvine)
30

3.5

20

Accepted(Irvine)0

Percent

0

AppliedApplied0

Percent

10

Percent

10

Percent

Applied20 4.17+-0.17

Percent

Percent

n=2777110

Af-Amer:
1047+-170
n=33580Asian :

Percent

Percent

Percent

Percent

Asian :

20 3.86+-0.38
10AppliedAccepted

20

Percent

Applied

Af-Amer:
3.98+-0.25
10 n=845c

Applicants(Irvine)Percent

Af-Amer:
3.56+-0.48
10 n=3763

Ca

Accepted(Irvine)Percentc

Percent

Applicants(Irvine)Percent

Percent

Aa

20

Af-Amer
Asian
40
Hispan
White
20

2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 5. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Merced.

0

Hispan:3.46+-0.44
10 n=12939

Hispan:3.67+-0.33
10 n=8199

Percent

0

AppliedAppliedAccepted

00White :
3.63+-0.44
10 n=2273

White :
3.78+-0.32
10 n=1740

Applied

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

3

HSGPA

b

10 Hispan
White

3.5

4

4.5

10 Hispan
White

0

2.5

3

3.5

4

Percent

n=813
AppliedAccepted

00
20 Asian :1140+-179
10 n=5188
Applied1176+-165
10 n=3991
AppliedAccepted00

Hispan:20 954+-144
n=12068

Hispan:20 999+-132
n=7830AppliedAppliedAccepted

00White :
20 1141+-155
n=191620 1168+-145

10White :

Applied1000

10n=1485
AppliedAccepted

0
500

1500

1000

d

30 Af-Amer
20

Hispan10 White

HSGPA

Accepted(Merced)
Af-Amer

Asian

0
500

1500

SAT

Applicants(Merced)

4.5

1000

1500

20 Asian10

Hispan
White

0
500

SAT

D

1000

1500

SAT

Acceptance Percent(Merced)

100

100

80

80

Percent

Percent

10

20 Asian :b

Acceptance Percent(Merced)

Af-Amer
60 Asian

Hispan40

Applied

SAT

Asian

HSGPA

B

n=14290500

20 Af-Amer

Asian

3

10

4.5

Accepted(Merced)

Percent

Percentd

20 Af-Amer

2.5

4

Af-Amer:

20 1041+-140

HSGPA

Applicants(Merced)0

3.5

Percent0

Percent

AppliedAccepted

Af-Amer:

Percent

AppliedAsian :
3.76+-0.32
10 n=4522

Accepted(Merced)20 978+-159

Percent

Percent

Asian :
3.61+-0.43
10 n=5895

c

Applicants(Merced)

Percent0

Percent

Percent

Percent

Percent

0

AppliedAccepted

Percent

Applied

Af-Amer:
3.65+-0.32
10 n=874

Percent

Af-Amer:
3.37+-0.46
10 n=1592

Ca

Accepted(Merced)

Percentc

Percent

Applicants(Merced)

Percent

Percent

Aa

60
40

White

Af-Amer
Asian
Hispan
White

20
2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 6. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Riverside.

Hispan:3.89+-0.28
10 n=7572

Percent

0

AppliedAppliedAccepted

00White :
3.70+-0.41
10 n=4620

White :
3.90+-0.29
10 n=2942

Applied

3.5

4

0

4.5

2.5

3

HSGPA

b

0

d

Asian
Hispan
White

2.5

3

3.5

4

4.5

3

3.5

4

10

Asian :
1245+-141
n=8287
AppliedAccepted

Percent

Hispan:20 1080+-126
n=7232

10

Applied

10

AppliedAccepted

00White :
20 1153+-152
n=382420 1213+-125

10White :

Applied1000

10n=2404
AppliedAccepted

0
500

1500

1000

20

d

Asian
Hispan10 White
0
500

HSGPA

1000

1500

Accepted(Riverside)20
10

Af-Amer
Asian
Hispan
White

0
500

SAT

D
100

80

80

Percent

Af-Amer
Hispan
White

1000

1500

SAT

Acceptance Percent(Riverside)

100

60 Asian

1500

SAT

Applicants(Riverside)

4.5

Acceptance Percent(Riverside)

40

20

0

30 Af-Amer

White

2.5

AppliedAccepted

Hispan:
20 977+-149
n=19389b

10 Hispan
0

n=984

SAT

Asian

HSGPA

B

Applied0500

20 Af-Amer

Af-Amer

10

0

4.5

Accepted(Riverside)

Percent

Percent

10

4

10Asian :
1183+-169
n=12380

HSGPA

Applicants(Riverside)
20

3.5

20

20 1123+-131

0Percent

3

AppliedAccepted

Percent

2.5

Percent

AppliedAccepted

Hispan:3.52+-0.44
10 n=20646

Applied03.88+-0.29
n=9714100n=2705Percent

Applied10

Accepted(Riverside)
Af-Amer:

20 1006+-161

Percent

Percent

3.71+-0.41
n=14248
100Percent

AppliedAccepted

0
20 Asian :Percent

Percent

Percent

Percent

020 Asian :cApplicants(Riverside)
Af-Amer:

Percent

Applied

Af-Amer:
3.85+-0.30
10 n=1080

Percent

Af-Amer:
3.44+-0.47
10 n=3032

Ca

Accepted(Riverside)

Percentc

Percent

Applicants(Riverside)

Percent

Percent

Aa

Af-Amer

60 Asian40

Hispan
White

20

20
2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 7. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UCLA.

Asian :
4.26+-0.13
20 n=3604

0
20 Hispan:

3.75+-0.44
n=2299610

4.19+-0.20
n=200810

Percent

0

AppliedAppliedAccepted

00White :
3.98+-0.34
20 n=15464

White :
4.24+-0.19
20 n=2043

Applied

3

3.5

4

0

4.5

2.5

3

HSGPA

b

20 Hispan
White

3

3.5

4

4.5

Applied2 White
2.5

20
10

3

3.5

Asian :
1411+-129
n=2863
AppliedAccepted

00

20 Hispan:
1056+-175
n=21030

20 Hispan:

10

Applied101203+-157
n=1834
AppliedAccepted

00White :
20 1250+-153
n=11284

20 1369+-133

10White :

Applied1000

n=1390

10

AppliedAccepted

0
500

1500

1000

d

Applicants(UCLA)

4

20

Af-Amer

Hispan10 White

HSGPA

Accepted(UCLA)
6

Asian

0
500

4.5

1000

1500

4 Asian2

Hispan
White

0
500

SAT

D

Acceptance Percent(UCLA)
60

1500

SAT

30 Af-Amer

4 Hispan0

Percent

Asian :
1268+-169
n=21305b

6 Asian

HSGPA

B

10

AppliedAcceptedSAT

8 Af-Amer

Af-Amer

2.5

20

0
500

4.5

Accepted(UCLA)

Percent

Percent

d

Asian

0

4

10

1200+-168
n=460

0

HSGPA

Applicants(UCLA)
40

3.5

Applied20 Af-Amer:

Percent

2.5

AppliedAccepted

Percent

0101070+-179
n=3866

0

AppliedAccepted

20 Hispan:

20 Af-Amer:

Accepted(UCLA)

Percent

Asian :
3.95+-0.35
20 n=25553

cApplicants(UCLA)

Percent0

Percent

0

Applied

Percent

AppliedAccepted

Percent

Applied

Af-Amer:
4.12+-0.27
10 n=512

Percent

Percent

Af-Amer:
3.64+-0.48
10 n=4453

Ca

Accepted(UCLA)

Percentc

Percent

Applicants(UCLA)

Percent

Percent

Percent

Percent

Percent

Aa

1000

1500

SAT

Acceptance Percent(UCLA)
50
40

40 Af-Amer

Percent

Percent

50

Asian
30 Hispan
20 White
10

Af-Amer
30 Asian
Hispan20 White
10

2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 8. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Santa Barbara.

10

n=7597
AppliedAccepted

Hispan:4.08+-0.23
10 n=5164

Percent

0

Hispan:3.67+-0.44
10 n=21599

0White :
20 3.90+-0.36n=16796

White :
20 4.18+-0.18n=5126

2.5

3

3.5

4

10
0

4.5

AppliedAccepted

2.5

3

HSGPA

b

d

Percent

Percent

Hispan10 White
3

3.5

4

4.5

10 Hispan
White

HSGPA

B

2.5

3

3.5

4

10

Percent

Applied20 1352+-138
10

n=6043
AppliedAccepted

20
10

Hispan:1159+-143
n=4792
AppliedAccepted

00White :
20 1228+-148
n=12277

White :
20 1324+-130
n=3636

10

Applied

1000

10

AppliedAccepted

0
500

1500

1000

d

30 Af-Amer
20

Asian
Hispan10 White

HSGPA

1000

1500

Accepted(UCSB)
10 Af-Amer
Asian

5 Hispan
White

0
500

SAT

D

Acceptance Percent(UCSB)

1500

SAT

Applicants(UCSB)

0
500

4.5

1000

1500

SAT

Acceptance Percent(UCSB)
80

80
60 Af-Amer

Percent

Percent

AppliedAccepted0

Hispan:20
1040+-168
n=19725

b

Asian

0

n=684

SAT

20 Af-Amer

Asian

2.5

Applied

0
500

4.5

Accepted(UCSB)

30 Af-Amer04n=18079

HSGPA

Applicants(UCSB)

20

3.5

Percent

0

Applied

10

Asian :

20 1248+-167
10

20 1211+-144

00AppliedAccepted010

0Asian :

0

AppliedApplied

Percent

Appliedn=3015Percent

n=21959

10

Accepted(UCSB)
Af-Amer:

20 1064+-174

Percent

Asian :

20 4.16+-0.16

Percent

Asian :

20 3.87+-0.38
10Percent

AppliedAccepted0

Percent

Percent

Percent

Percent

0cApplicants(UCSB)
Af-Amer:

Percent

Applied

Af-Amer:
4.06+-0.22
10 n=813

Percent

Af-Amer:
3.59+-0.47
10 n=3495

Ca

Accepted(UCSB)

Percentc

Percent

Applicants(UCSB)

Percent

Percent

Aa

Asian
40 HispanWhite

60 Af-Amer

Asian
40 Hispan
White
20

20

2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 9. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC Santa Cruz

Hispan:3.98+-0.25
10 n=4299

Percent

0

Hispan:3.56+-0.45
10 n=16798

AppliedAccepted00

20 White :3.81+-0.39
n=1071320 White :4.05+-0.23
n=5491

0

Applied

2.5

3

3.5

4

10
0

4.5

AppliedAccepted

2.5

3

HSGPA

b

d

Percent

Percent

20 Asian0

Hispan
White

2.5

3

3.5

4

4.5

10 Hispan
White

2.5

3

3.5

Percent
Percent

Asian :
1207+-173
n=11829
Appliedn=669
AppliedAccepted

20
10

Asian :
1301+-144
n=5766
AppliedAccepted00

Hispan:20 1007+-161
n=15375

20 1144+-139

10

Hispan:Applied10n=3908
AppliedAccepted

00White :
20 1205+-148
n=8125White :
20 1279+-124
n=4013

10

Applied

b

Asian

0

10

1000

10

AppliedAccepted

0
500

1500

1000

SAT

4

d

Asian
Hispan10 White
0
500

HSGPA

Accepted(UCSC)
20

30 Af-Amer
20

1000

1500

Af-Amer
Asian

10 Hispan
White

0
500

SAT

D

Acceptance Percent(UCSC)

100

80

80

Af-Amer
Asian
Hispan
40
White

1000

1500

SAT

Acceptance Percent(UCSC)

100

60

1500

SAT

Applicants(UCSC)

4.5

Percent

Percent

10

20 1146+-141

0

0
500

20 Af-Amer

HSGPA

B

20

4.5

Accepted(UCSC)

Af-Amer

104AppliedHSGPA

Applicants(UCSC)

30

3.5

Percent10n=2026Percent

AppliedAccepted0

Applied

Percent

10

4.02+-0.23
n=7152

Percent

Percent

Applied10

Accepted(UCSC)
Af-Amer:

0

20 Asian :3.75+-0.41
n=14178

Percent

Percent
Percent
Percent

10

AppliedAccepted

0

20 Asian :Af-Amer:

20 1043+-166

Percent

0

Af-Amer:
3.85+-0.29
10 n=769c

Applicants(UCSC)

Percent

AppliedCa

Accepted(UCSC)

20

Af-Amer:
3.50+-0.46
10 n=2361

Percent

Percent

20cPercent

Applicants(UCSC)

Percent

Aa

Af-Amer
Asian
Hispan
40
White
60

20

20
2.5

3

3.5

HSGPA

4

4.5

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 10. Race/ethnicity, HSGPAs, and test scores in the applicant and admit
pools for UC San Diego.

00n=7516

AppliedAccepted

020

Percent

Hispan:3.71+-0.45
10 n=19370
AppliedAppliedAccepted

0White :
20 3.93+-0.36
n=14489

White :
20 4.19+-0.17
n=3948

Applied

3

3.5

4

0

4.5

AppliedAccepted

2.5

3

HSGPA

b

d

Percent

Percent

Asian
Hispan
White

0

2.5

3

3.5

4

4.5

HSGPA

B

Asian
Hispan2.5

Percent

Applied3

3.5

AppliedAccepted

20
10

Asian :
1346+-136
n=6379AppliedAccepted

20
10

Hispan:1177+-144
n=4334
AppliedAccepted

0White :
20 1241+-153
n=10917

10

White :

Applied1000

20 1324+-126
n=3035

10

AppliedAccepted

0
500

1500

1000

d

Applicants(UCSD)

4

20

Hispan10 White

HSGPA

Accepted(UCSD)
10 Af-Amer

Asian

0
500

4.5

1000

1500

Asian

5 Hispan
White

0
500

SAT

D

Acceptance Percent(UCSD)

1500

SAT

30 Af-Amer

80

1000

1500

SAT

Acceptance Percent(UCSD)
60

60 Af-Amer

Percent

Percent

10b

5 White0101201+-149
n=504

SAT

15 Af-Amer
10

20 Af-Amer:0

Hispan:20
1058+-173
n=17700

0
500

4.5

Accepted(UCSD)

Af-Amer

20

4AppliedHSGPA

Applicants(UCSD)

40

3.5

10

Asian :
1262+-167
n=21411Percent

2.5

20

Accepted(UCSD)

0

0

Percent

0

Applied0

Hispan:4.12+-0.20
10 n=4609

0Percent

Percent

020

Percent

Applied

Percent

n=25599

20 4.19+-0.16

Percent

20 3.91+-0.37

101067+-177n=30420

Asian :

Percent

Percent

Asian :

Percent

AppliedAccepted

20 Af-Amer:

Percent

Applied

Af-Amer:
4.12+-0.20
10 n=551c

Applicants(UCSD)

Percent

Af-Amer:
3.61+-0.48
10 n=3479

Ca

Accepted(UCSD)

Percentc

Percent

Applicants(UCSD)

Percent

Percent

Aa

Asian
40 Hispan

White

20

20

2.5

Af-Amer
40 Asian

3

3.5

HSGPA

4

4.5

Hispan
White

600

800

1000

SAT

1200

1400

1600

Appendix V. Figure 11. Family income, HSGPAs, and test scores in theapplicant and admit pools for all UC campuses combined.

CacAppliedAccepted00

1stGen:3.64+-0.46
10 n=55655

1stGen:3.89+-0.32
10 n=30467

Applied

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

HSGPA

b

d

4

Percent20
Not 1stGen

10 1stGen
3

3.5

4

4.5

0

HSGPA

B

Not 1stGen
1stGen

2.5

3

3.5

4

AppliedAccepted

20 1stGen:
101097+-158
n=28875
AppliedAccepted

0
500

1500

1000

1500

SAT

d

Applicants(All Campuses)

Accepted(All Campuses)
20

20
Not 1stGen

10 1stGen
0
500

4.5

HSGPA

1000

1500

10 Not 1stGen
1stGen

0
500

SAT

D

1000

1500

SAT

Acceptance Percent(All Campuses)

100

100

80

Percent

80

Percent

1000

30

Acceptance Percent(All Campuses)

60
40

Applied

b

20
10

10

Not 1stGen:
1281+-151
n=30317

SAT

30

2.5

1036+-171
n=51640

0
500

4.5

Accepted(All Campuses)

30

Percent

3.5

20 1stGen:
10

20

0

HSGPA

Applicants(All Campuses)

03

Applied0

Percent010

Not 1stGen:
1226+-170
n=48251

Percent

Appliedn=38837

10

Percent

n=61523

10

20

Accepted(All Campuses)

Percent

Not 1stGen:

20 4.02+-0.29

Percent

Not 1stGen:

20 3.87+-0.40

Applicants(All Campuses)

Percent

Accepted(All Campuses)

Percent

Percent

Percent

Applicants(All Campuses)cPercent

Aa

Not 1stGen
1stGen20

60
40

Not 1stGen
1stGen

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 12. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Berkeley.

CacAccepted(Berkeley)0Applied

2.5

3

3.5

4

20
10
0

4.5

AppliedAccepted

HSGPA

b

d

20 Not 1stGen
1stGen

10

2.5

3

3.5

4

4.5

5
0

Percent

20 1stGen:

1103+-180
n=17559
Appliedb

B

1stGen3

3.5

AppliedAccepted

1000

101205+-156
n=2335
AppliedAccepted

0
500

1500

1000

SAT

Not 1stGen

2.5

n=4500

0

0
500

4.5

10

HSGPA

4

d

Accepted(Berkeley)820
Not 1stGen

10 1stGen

HSGPA

1000

1500

6
4 Not 1stGen
2 1stGen
0
500

SAT

D

40

40

Percent50

Not 1stGen
1stGen

10

1000

1500

SAT

Acceptance Percent(Berkeley)

50

30

1500

SAT

Applicants(Berkeley)

0
500

4.5

Acceptance Percent(Berkeley)

20

10

30

Percent

Percent

4

Accepted(Berkeley)

40

Percent

3.5

AppliedHSGPA

Applicants(Berkeley)03n=23935

20 1stGen:

1stGen:4.20+-0.16n=2526

2.5

10

20 1390+-130

Percent

10

1stGen:3.82+-0.43
n=19051

Not 1stGen:

20 1297+-159

0Percent

20

Accepted(Berkeley)Not 1stGen:

AppliedAccepted0

Percent

Percent0

Not 1stGen:
4.24+-0.14
20 n=6071

Percent

AppliedApplicants(Berkeley)

Percent

Not 1stGen:
4.00+-0.33
20 n=31606

Percent

Percent

Applicants(Berkeley)cPercentAa

30
20

Not 1stGen
1stGen

10
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 13. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Davis.

Cac
Accepted(Davis)3.70+-0.44
n=23522
10
Applied

2.5

3

3.5

4

4.10+-0.19
10 n=7213

0

4.5

AppliedAccepted

2.5

HSGPA

b

4

n=9113
AppliedAccepted0

20 1stGen:
10101055+-173
n=21831
Applied1000

20 1stGen:
101127+-162
n=6860AppliedAccepted

0
500

1500

1000

SAT

b

1500

SAT

d

Applicants(Davis)

2.5

3

3.5

4

4.5

10
0

B

Not 1stGen
1stGen

2.5

3

3.5

4

20
Not 1stGen

10 1stGen
0
500

4.5

Percent

Not 1stGen

20

HSGPA

HSGPA

Accepted(Davis)

1000

1500

10
5

Not 1stGen
1stGen

0
500

SAT

D

Acceptance Percent(Davis)

1000

1500

SAT

Acceptance Percent(Davis)
80

100
80

60

Percent

Percent

Applied20 1321+-14330

10 1stGen

60
40

n=24884

0
500

4.5

Accepted(Davis)Percent

Percent

d

30

0

3.5

10

HSGPA

Applicants(Davis)20

3

Not 1stGen:

20 1250+-163

0Percent0

Not 1stGen:

AppliedAccepted0
20 1stGen:

Percent

Percent

0
20 1stGen:

10

Accepted(Davis)Percent

AppliedNot 1stGen:
20 4.19+-0.15
n=11851

Percent

10

Applicants(Davis)Percent

Not 1stGen:
20 3.89+-0.37
n=31939

Percent

Percent

Applicants(Davis)c

Percent

Aa

Not 1stGen
1stGen20

40

Not 1stGen
1stGen

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 14. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Irvine.

Cac
Accepted(Irvine)20
10

AppliedAccepted0
1stGen:1047+-170
n=32499

Percent

Percent0n=8399Applied

0
500

1000

20
10

1stGen:1169+-151
n=6961
AppliedAccepted

0
500

1500

1000

SAT

b

d

Percent

Percent20
Not 1stGen

10 1stGen
1000

1500

1stGen1000

AppliedAccepted

1stGen:3.67+-0.44
10 n=34584

1stGen:4.12+-0.19
10 n=7294

Applied

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

3.5

4

4.5

d

Accepted(Irvine)20

20
Not 1stGen

10 1stGen
2.5

SAT

3

3.5

4

4.5

10 Not 1stGen
1stGen

0

2.5

HSGPA

D

Acceptance Percent(Irvine)3

HSGPA

Applicants(Irvine)

0

1500

80

3

3.5

4

4.5

HSGPA

Acceptance Percent(Irvine)80

Percent

60

Percent

n=102710b

5 Not 1stGen

0
500

10

0

30

SAT

40

Applied20 4.17+-0.18

HSGPA

Accepted(Irvine)

10

30

B

n=35051

SAT

Applicants(Irvine)

0
500

10

0

1500

Not 1stGen:

20 3.86+-0.39

Percent

Applied10

Percent

n=28544

Percent

10

Accepted(Irvine)Not 1stGen:

20 1333+-141

Percent

Not 1stGen:

20 1233+-167

Percent

Percent

Not 1stGen:

Applicants(Irvine)Percent

Applicants(Irvine)c

Percent

Aa

Not 1stGen
1stGen60
40 Not 1stGen

1stGen

20

20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 15. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Merced.

Cac
Accepted(Merced)00

20 967+-151
n=14826

10

Applied

0
500

1000

20 1013+-138
n=9702

10

AppliedAccepted

0
500

1500

1000

SAT

b

d

Percent

Percent

Not 1stGen

10 1stGen
1000

1500

1stGen

1000

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

d

3.5

4

4.5

Accepted(Merced)20

10 Not 1stGen
1stGen

0

3

HSGPA

Applicants(Merced)

1500

2.5

SAT

3

3.5

4

4.5

10 Not 1stGen
1stGen

0

2.5

HSGPA

D

3

3.5

4

4.5

HSGPA

Acceptance Percent(Merced)

100

100

80

80

Percent

Percent

Appliedb

Acceptance Percent(Merced)60

Not 1stGen40

1stGen:3.68+-0.33
10 n=10132

HSGPA

Not 1stGen

SATB

1stGen:3.47+-0.44
10 n=15876

20

0
500

AppliedAccepted

0

0

20
10

Not 1stGen:
3.77+-0.32
10 n=5340

01500

Accepted(Merced)

30

0
500

AppliedSAT

Applicants(Merced)20

Percent

1stGen:Percent

1stGen:Percent

AppliedAccepted

Not 1stGen:
3.61+-0.44
10 n=6994

Percent

101194+-158
n=4513

Accepted(Merced)

Percent

Applied

20 Not 1stGen:

Percent

101157+-173
n=5901

Applicants(Merced)

Percent

20 Not 1stGen:

Percent

Percent

Applicants(Merced)c

Percent

Aa

60
40

1stGenNot 1stGen
1stGen

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 16. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Riverside.

AppliedAccepted

1stGen:3.53+-0.44
10 n=26253

1stGen:3.88+-0.28
10 n=10699

2.5

3

3.5

4

0

4.5

HSGPA

b

Not 1stGen
1stGen

2.5

3

3.5

B

4

4.5

b

1000

1stGen

2.5

3

3.5

20 1105+-13510n=10218
AppliedAccepted

0
500

1500

1000

4

d

Applicants(Riverside)20
Not 1stGen

10 1stGen
0
500

4.5

HSGPA

1000

1500

Accepted(Riverside)
20
10 Not 1stGen
1stGen

0
500

SAT

D

80

80

Percent100

Not 1stGen
1stGen

20

1000

1500

SAT

Acceptance Percent(Riverside)

100

60

1500

SAT

30

10 Not 1stGen

0

AppliedAccepted

SAT

Acceptance Percent(Riverside)

40

Applied

0
500

4.5

20

HSGPA

Percent

4

Accepted(Riverside)

Percent

Percent

d

20

0

3.5

n=24654HSGPA

Applicants(Riverside)

10

3

10

Not 1stGen:
1251+-138
n=8895

1stGen:20 998+-160
10

20

0
1stGen:AppliedAccepted

2.5

Applied0

Percent010

Not 1stGen:
1182+-169
n=13907

Percent

020

Accepted(Riverside)

Percent

Not 1stGen:
3.89+-0.29
n=10923
10

0

AppliedApplicants(Riverside)

Percent

Percent

Applied

Percent

Percent20

Not 1stGen:
3.70+-0.42n=16660
10c

Accepted(Riverside)

Percent

Applicants(Riverside)

20

Percent

Cac

PercentAa

60

Not 1stGen

40 1stGen
20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 17. Family income, HSGPAs, and test scores in theapplicant and admit pools for UCLA.

Ca

c
Accepted(UCLA)

0

Applied

3

3.5

4

0

4.5

4.19+-0.21
n=2550AppliedAccepted

2.5

HSGPA

b

d

Percent

Percent

20 Not 1stGen
1stGen

3

3.5

4

4.5

5

0

Percent

20
10

10Applied

1000

10

3.5

1209+-159
n=2393
AppliedAccepted

0
500

1500

1000

SAT

b

Not 1stGen

3

AppliedAccepted

20 1stGen:

1072+-180
n=27171

4dApplicants(UCLA)

Accepted(UCLA)
6

20
Not 1stGen

10 1stGen
0
500

4.5

HSGPA

1000

1500

4
Not 1stGen

2 1stGen
0
500

SAT

D

Acceptance Percent(UCLA)

50

40

40

Not 1stGen

20 1stGen

1000

1500

SAT

Acceptance Percent(UCLA)

50

30

1500

SAT

30

2.5

Not 1stGen:
1400+-132
n=4400

0

0
500

4.5

Percent

Percent

4

1stGen

HSGPA

B

3.5

Accepted(UCLA)

10

40

2.5

AppliedHSGPA

Applicants(UCLA)

03

Percent

2.5

10

10

Not 1stGen:
1268+-165
n=31270

20 1stGen:

Percent

3.76+-0.44
n=29265200

20 1stGen:

Percent

Percent

0

AppliedAccepted0

20 1stGen:10

Not 1stGen:
4.25+-0.16
20 n=5978

Accepted(UCLA)

Percent

AppliedApplicants(UCLA)

Percent

Not 1stGen:
3.96+-0.35
20 n=40613

Percent

Percent

Applicants(UCLA)c

PercentAa

30

Not 1stGen

20 1stGen
10

10
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 18. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Santa Barbara.

Cac
Accepted(UCSB)

1stGen:3.68+-0.44
10 n=26704
Applied

2.5

3

3.5

4

1stGen:4.08+-0.21
10 n=7398

0

4.5

AppliedAccepted

2.5

HSGPA

b

d

Not 1stGen

10 1stGen
2.5

3

3.5

1stGen:1054+-172
n=24706
Applied

1000

4

4.5

1stGen

0

2.5

3

3.5

n=8775
AppliedAccepted

20
10

1stGen:1184+-144
n=6863
AppliedAccepted

0
500

1500

1000

4

d

Applicants(UCSB)

Accepted(UCSB)
10

20
Not 1stGen

10 1stGen
0
500

4.5

1500

SAT

30

HSGPA

1000

1500

5 Not 1stGen
1stGen

0
500

SAT

D

1000

1500

SAT

Acceptance Percent(UCSB)
80

Percent

80

Percent

10

10
0

b

Acceptance Percent(UCSB)

100

20

20 1356+-134

SAT

10 Not 1stGen

HSGPA

B

Applied

0
500

4.5

20

Percent

Percent

4

Accepted(UCSB)

30

03.5

n=29280

HSGPA

Applicants(UCSB)

20

3

10
0

Percent

0

AppliedAccepted

020

Percent

Percent020

n=1199610

Not 1stGen:

20 1246+-161

Percent

AppliedNot 1stGen:

Percent

n=38453

10

Accepted(UCSB)

Percent

Not 1stGen:

20 4.18+-0.17

Applicants(UCSB)

Percent

Not 1stGen:

20 3.89+-0.37

Percent

Percent

Applicants(UCSB)c

Percent

Aa

60
40 Not 1stGen

60
40 Not 1stGen

1stGen1stGen

20

20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 19. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Santa Cruz

Cac
Accepted(UCSC)

0

1stGen:3.57+-0.45
10 n=20920

1stGen:3.97+-0.25
10 n=5594

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

HSGPA

b

0

Not 1stGen
1stGen

2.5

3

3.5

10

4

4.5

10

n=9530AppliedAccepted0
1stGen:1020+-164
n=19314
Applied

b

1000

20
10

1stGen:1147+-139
n=5218
AppliedAccepted

0
500

1500

1000

1500

SAT

d

Applicants(UCSC)

Accepted(UCSC)

2030

10 Not 1stGen
1stGen0

20 1302+-133

SAT

2.5

3

3.5

4

20
Not 1stGen

10 1stGen
0
500

4.5

HSGPA

1000

1500

10

Not 1stGen
1stGen

0
500

SAT

D

Acceptance Percent(UCSC)

100

80

80

60

Not 1stGen
1stGen

20

1000

1500

SAT

Acceptance Percent(UCSC)

100

40

Applied0500

Percent

Percent

20

4.5

20

HSGPA

B

4

Accepted(UCSC)

Percent

Percent

d

20
10

3.5

n=18568

HSGPA

Applicants(UCSC)

30

3

10
0

Percent0

Not 1stGen:

20 1219+-162

Percent

AppliedAccepted0

Applied

Percent

Applied10

Accepted(UCSC)

Not 1stGen:

4.03+-0.23
n=12708

Percent

Percent

1020 Not 1stGen:

3.79+-0.40
n=23916

Percent

Percent

Percent

20 Not 1stGen:

Applicants(UCSC)

Percent

Applicants(UCSC)c

PercentAa

60
40

Not 1stGen
1stGen

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 20. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC San Diego.

CacAccepted(UCSD)

Applied

2.5

3

3.5

4

AppliedAccepted

2.5

HSGPA

b

d

3.5

4

Not 1stGen

2.5

3

3.5

4

4.5

5 1stGen
3

3.5

4

1000

Percent

AppliedAccepted

101205+-147
n=6466
AppliedAccepted

0
500

1500

1000

d

Accepted(UCSD)
10

20
Not 1stGen

10 1stGen

HSGPA

1000

1500

5 Not 1stGen
1stGen

0
500

SAT

D

Acceptance Percent(UCSD)

1500

SAT

Applicants(UCSD)

0
500

4.5

1000

1500

SAT

Acceptance Percent(UCSD)
80

80
60

Percent

Percent

Applied30

2.5

n=817520 1stGen:

1077+-177
n=23964b

Not 1stGen

0

10

SAT

10

HSGPA

B

10

20 1352+-132

0

0
500

4.5

15

1stGen03

Accepted(UCSD)

Percent

Percent

20

AppliedHSGPA

Applicants(UCSD)

40

n=30030

20 1stGen:

4.13+-0.19
n=6812
10

0

4.5

10
0

Percent0

Not 1stGen:

20 1264+-164

Percent

3.72+-0.44
n=25833
10

Accepted(UCSD)

Not 1stGen:

AppliedAccepted0
20 1stGen:

Percent

Percent

0
20 1stGen:

Not 1stGen:
20 4.20+-0.17
n=10321

Percent

AppliedApplicants(UCSD)

Percent

Not 1stGen:
20 3.92+-0.37
n=38411Percent

Percent

Applicants(UCSD)c

Percent

Aa

40 Not 1stGen

60
40 Not 1stGen

1stGen1stGen

20

20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 21. Family income, HSGPAs, and test scores in theapplicant and admit pools for all UC campuses combined.

CacAccepted(All Campuses)

10

AppliedAccepted00

Low Income:
3.63+-0.46
10 n=45610

Low Income:
3.88+-0.32
10 n=25405

Applied

0

2.5

3

3.5

4

0

4.5

2.5

3

3.5

4

d

4

4.5

0

HSGPA

B

Not Low Income
Low Income

2.5

3

3.5

4

1000

AppliedAccepted

20 Low Income:10

1100+-165
n=23947

AppliedAccepted

0
500

1500

1000

10

1500

SAT

d

Applicants(All Campuses)

Accepted(All Campuses)
20

20
Not Low Income
Low Income

0
500

4.5

HSGPA

1000

1500

10 Not Low Income
Low Income

0
500

SAT

D

1000

1500

SAT

Acceptance Percent(All Campuses)

100

100

80

Percent

80

Percent

Applied30

Acceptance Percent(All Campuses)

60
40

1035+-178
n=42088

b

20
10

10

1251+-161
n=3313900500Percent

Not Low Income

10 Low Income
3.5

10

20 Not Low Income:SAT

Accepted(All Campuses)

Percent

Percent

20

3

Applied20 Low Income:

4.5

30

2.5

10

1194+-176
n=54092

HSGPA

Applicants(All Campuses)

0

20 Not Low Income:0

AppliedAccepted

HSGPAbPercent

Appliedn=40623

Percent

Percent10

n=65977

20 4.01+-0.29

Accepted(All Campuses)

Percent

Not Low Income:20 3.84+-0.41

Percent

Percent

Percent

Not Low Income:Applicants(All Campuses)

Percent

Applicants(All Campuses)cPercent

Aa

Not Low Income
Low Income

60
40

Not Low Income
Low Income

20

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 22. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Berkeley.

Cac

Not Low Income:
4.23+-0.14
20 n=5586

0Applied

2.5

3

3.5

4

20
10
0

4.5

Low Income:
4.20+-0.15
n=2336
AppliedAccepted

2.5

HSGPA

b

d

1110+-188
Applied

20 Not Low Income
Low Income

2.5

3

3.5

4

4.5

5
0

b

B

Not Low Income
Low Income

2.5

3

3.5

AppliedAccepted

1225+-164

10 n=2126
AppliedAccepted

0
500

1500

1000

SAT

10

HSGPA

4

d

Accepted(Berkeley)
620
Not Low Income

10 Low Income

HSGPA

1000

1500

4
Not Low Income

2 Low Income
0
500

SAT

D

40

40

Percent50

Not Low Income
Low Income

1000

1500

SAT

Acceptance Percent(Berkeley)

50

30

1500

SAT

Applicants(Berkeley)

0
500

4.5

Acceptance Percent(Berkeley)

20

1000

10

Not Low Income:
1369+-143
n=4286

0
20 Low Income:

10 n=15422
0
500

4.5

20

30

Percent

Percent

4

Accepted(Berkeley)

40

Percent

3.5

AppliedHSGPA

Applicants(Berkeley)

0

3

10

Not Low Income:
1274+-166
n=24098

Percent

10

Low Income:
3.81+-0.43
n=1690920

Accepted(Berkeley)0
20 Low Income:

Percent

20

0Percent

Percent

0

AppliedAccepted

Percent

AppliedApplicants(Berkeley)

Percent

Not Low Income:
3.98+-0.34
20 n=30598c

Percent

Accepted(Berkeley)

Percent

Applicants(Berkeley)

Percent

Percent

Aa

30
20

Not Low Income
Low Income

10

10
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 23. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Davis.

Cac

Accepted(Davis)
Not Low Income:

n=32874
Applied3.68+-0.44
10 n=19754
Applied

2.5

3

3.5

4

AppliedAccepted

2.5

HSGPA

b

d

Applied1000

101132+-171
n=5577
AppliedAccepted

0
500

1500

1000

SAT

b

2.5

3

3.5

4

4.5

20
10
0

HSGPA

B

Not Low Income
Low Income

2.5

3

3.5

4

1500

SAT

d

Applicants(Davis)20
Not Low Income

10 Low Income
0
500

4.5

HSGPA

Accepted(Davis)

1000

1500

10
5 Not Low Income
Low Income

0
500

SAT

D

Acceptance Percent(Davis)

1000

1500

SAT

Acceptance Percent(Davis)
80

100
80

60

Percent

Percent

AppliedAccepted

20 Low Income:

1056+-180n=18197

0
500

4.5

10

Not Low Income:
1291+-155
n=97090Percent

Not Low Income

10 Low Income

60
40

10

20

30

Percent

Percent

4

Accepted(Davis)

30

0

3.5

AppliedHSGPA

Applicants(Davis)20

3

10

Not Low Income:1222+-170
n=2661220 Low Income:

4.10+-0.19
10 n=5914

0

4.5

20

Accepted(Davis)0

Percent

0

AppliedAccepted

0
20 Low Income:

Percent

Percent

0
20 Low Income:n=12065

10

Percent

10

20 4.18+-0.16

Percent

20 3.88+-0.38

Percent

Percent

Not Low Income:Applicants(Davis)Percent

Applicants(Davis)c

Percent

Aa

Not Low Income
Low Income

20

40

Not Low Income
Low Income

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 24. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Irvine.

Cac

Accepted(Irvine)Applied10n=10973
AppliedAccepted00

Low Income:
3.66+-0.45
10 n=29386

Low Income:
4.12+-0.19
10 n=5855

Applied

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

HSGPA

b

d

Percent

Percent20
Not Low Income

10 Low Income
3

3.5

4

4.5

20 Low Income:
101045+-176
n=27414

2.5

3

3.5

AppliedAccepted

Applied1000

20 Low Income:
10

1178+-159
n=5536

AppliedAccepted

0
500

1500

1000

d

Applicants(Irvine)4

Not Low Income
Low Income

0
500

4.5

Accepted(Irvine)
8

20
10

HSGPA

1000

1500

6
4 Not Low Income
2 Low Income
0
500

SAT

D
80

60

60

20

1000

1500

SAT

Acceptance Percent(Irvine)80

Not Low Income
Low Income

1500

SAT

30

Percent

Percent10

Not Low Income:
1301+-152
n=92810b

Low Income

0

20

SAT

Acceptance Percent(Irvine)40

Applied

0
500

4.5

10 Not Low Income

HSGPA

B

4

Accepted(Irvine)

20

30

2.5

3.5

10

Not Low Income:
1205+-172
n=31685

HSGPA

Applicants(Irvine)

0

3

20

0

Percent0

Percent

n=37570

Percent

Percent

10

20 4.16+-0.18

Accepted(Irvine)Percent

Not Low Income:20 3.85+-0.39

Percent

Percent

Percent

Not Low Income:Applicants(Irvine)Percent

Applicants(Irvine)c

Percent

Aa

40

Not Low Income
Low Income

20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 25. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Merced.

Cac

Not Low Income:
3.60+-0.43
10 n=8793

Not Low Income:
3.76+-0.32
10 n=6695

20 Not Low Income:20 Not Low Income:

1114+-178
n=76711150+-166
n=5882

AppliedAccepted

Low Income:
3.46+-0.45
10 n=13492

Low Income:
3.68+-0.33
10 n=8448

Applied

2.5

3

3.5

4

0

4.5

HSGPA

b

d

Low Income:

3.5

4

3

3.5

b

4

4.5

10 Not Low Income
Low Income

0

HSGPA

B

2.5

3

3.5

4

1000

n=8079

10

AppliedAccepted

0
500

1500

1000

d

30
20

Not Low Income

10 Low Income
0
500

HSGPA

1000

1500

Accepted(Merced)20
10

Not Low Income
Low Income

0
500

SAT

D

1500

SAT

Applicants(Merced)

4.5

Acceptance Percent(Merced)

1000

1500

SAT

Acceptance Percent(Merced)

100

100

80

Percent

80
60

Not Low Income
40

Applied20 1010+-140

SAT

Percent

Percent

Low Income

2.5

n=12594

10
0
500

20

10 Not Low Income

AppliedAccepted
Low Income:20 963+-153

4.5

Accepted(Merced)20

Percent

3

10
0

HSGPA

Applicants(Merced)00AppliedAccepted

2.5

Applied

Percent

010Percent0

Percent

Applied

Percent

Accepted(Merced)

Percent

Applicants(Merced)

Percent

Accepted(Merced)0

Percentc

Applicants(Merced)

Percent

Percent

Percent

Aa

60
40

Low Income

Not Low Income
Low Income

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 26. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Riverside.

Cac

Accepted(Riverside)

Not Low Income:
3.69+-0.42
10 n=19142

Not Low Income:
3.89+-0.29
10 n=11924

20 Not Low Income:20 Not Low Income:0

Low Income:
3.53+-0.45
10 n=22511

Low Income:
3.87+-0.28
10 n=9034

2.5

3

3.5

4

0

4.5

2.5

d

4

b

Low Income

3.5

4

4.5

Low Income

HSGPA

B

2.5

3

3.5

4

d

AppliedAccepted

1000

Not Low Income

10 Low Income
0
500

HSGPA

Accepted(Riverside)

1000

1500

10 Not Low Income
Low Income

0
500

SAT

D

80

80

Percent100

Not Low Income
Low Income

20

1000

1500

SAT

Acceptance Percent(Riverside)

100

60

1500

20

20

4.5

Acceptance Percent(Riverside)40

n=8577SAT

30

10 Not Low Income

0

10

0
500

1500

Applicants(Riverside)

Percent

10 Not Low Income

3

1000

20 1105+-138

SAT

20

20

2.5

Applied

0
500

4.5

Accepted(Riverside)

Percent

Percent

3.5

n=21033HSGPA

Applicants(Riverside)

0

3

AppliedAccepted
Low Income:20 995+-163
10101226+-144
n=10076

0
Low Income:AppliedAccepted

HSGPA

b

0

Percent

AppliedApplied

Percent

0101149+-175
n=16551

Percent

AppliedAccepted

Percent

Applied

Percent

Applicants(Riverside)

Percent

Accepted(Riverside)

0

Percentc

Applicants(Riverside)

Percent

Percent

Percent

Aa

60
40

Not Low Income
Low Income

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 27. Family income, HSGPAs, and test scores in theapplicant and admit pools for UCLA.

Ca

c

Not Low Income:
3.95+-0.3520n=40458

Not Low Income:
4.25+-0.1620n=5507

0

2.5

3

3.5

4

10
0

4.5

AppliedAccepted

2.5

HSGPA

b

d

3.5

4

Percent

Low Income

2.5

3

3.5

1000

4

4.5

4 Not Low Income
2 Low Income
0101223+-170
n=2171
AppliedAccepted

0
500

1500

1000

1500

SAT

d

Applicants(UCLA)

Accepted(UCLA)

30

2.5

3

3.5

4

20
Not Low Income

10 Low Income
0
500

4.5

HSGPA

1000

1500

4
2

Not Low Income
Low Income

0
500

SAT

D

Acceptance Percent(UCLA)
50

1000

1500

SAT

Acceptance Percent(UCLA)
50

40

40

Percent

Percent

Applied

b

6

HSGPA

B

10

AppliedAccepted

SAT

8

20 Not Low Income

10

Not Low Income:
1380+-142
n=421420 Low Income:

1073+-186
n=23502

0
500

4.5

Accepted(UCLA)

40

Percent

3

20

0

HSGPA

Applicants(UCLA)

0

Percent

AppliedApplied20 Low Income:

4.19+-0.20
n=2342

Percent

03.75+-0.44
n=25543

10

Not Low Income:
1244+-170
n=32449

0

20 Low Income:

Percent

Percent

20 Low Income:10

AppliedAccepted20

Accepted(UCLA)

Percent

0

Applicants(UCLA)

Percent

Appliedc

Percent

Accepted(UCLA)

Percent

Applicants(UCLA)

Percent

Percent

Aa

30

Not Low Income
20
Low Income

30

Not Low Income
20 Low Income

10

10
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 28. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Santa Barbara.

Cac

Accepted(UCSB)

Applied10

n=11812
AppliedAccepted00

Low Income:
3.67+-0.45
10 n=22913

Low Income:
4.08+-0.21
10 n=6294

Applied

2.5

3

3.5

4

0

4.5

AppliedAccepted

2.5

HSGPA

b

d

4

Percent20
Not Low Income

10 Low Income
3

3.5

4

4.5

1056+-179
n=21039

Applied

1000

2.5

3

3.5

n=9091
AppliedAccepted

20 Low Income:
10

1192+-152
n=5787

AppliedAccepted

0
500

1500

1000

4

d

Applicants(UCSB)

Accepted(UCSB)
10

20
Not Low Income

10 Low Income
0
500

4.5

HSGPA

1000

1500

5 Not Low Income
Low Income

0
500

SAT

D

Acceptance Percent(UCSB)

1500

SAT

30

Low Income

0

10
0

b

1000

1500

SAT

Acceptance Percent(UCSB)

100

80

80

60

Percent

Percent

10

20 1328+-145

SAT

10 Not Low Income

HSGPA

B

Applied

0
500

20

2.5

n=30564

20 Low Income:4.5

Accepted(UCSB)

30

Percent

3.5

10

HSGPA

Applicants(UCSB)

03

Not Low Income:20 1221+-167

0Percent0

Percent

n=38525

Not Low Income:20 4.17+-0.18

Percent

Percent

10

Accepted(UCSB)

Percent

Not Low Income:20 3.87+-0.38

Percent

Percent

Percent

Not Low Income:Applicants(UCSB)

Percent

Applicants(UCSB)c

Percent

Aa

40 Not Low Income

Low Income

20

60
40

Not Low Income
Low Income

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 29. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC Santa CruzCac

4.02+-0.24
n=12391

10

AppliedAccepted00

Low Income:
3.56+-0.45
10 n=17768

Low Income:
3.97+-0.25
10 n=4750

Applied

0

2.5

3

3.5

4

0

4.5

HSGPA

b

3

3.5

4

d

Low Income

3

3.5

4

4.5

Low Income

0

2.5

3

3.5

4

AppliedAccepted

20
10

Low Income:
1155+-145
n=4394
AppliedAccepted

0
500

1500

1000

d

Accepted(UCSC)
15

20
Not Low Income

10 Low Income
0
500

HSGPA

1000

1500

10

Not Low Income

5 Low Income
0
500

SAT

D
100

80

80

Not Low Income
Low Income

20

1000

1500

SAT

Acceptance Percent(UCSC)

100

60

1500

SAT

Applicants(UCSC)

4.5

Percent

Percent

1000

30

Acceptance Percent(UCSC)

40

Appliedb

10 Not Low Income

HSGPA

B

Low Income:
1019+-169
n=163170500

Percent

Not Low Income

10

Not Low Income:
1283+-140
n=9637

SAT

Accepted(UCSC)

Percent

Percent

20

2.5

10

4.5

20

0

20

20

0

HSGPA

Applicants(UCSC)

10

Applied

0AppliedAccepted

2.5

10

Not Low Income:
1190+-169
n=20088

Percent

Applied

Percent

10

20

Percent

20 Not Low Income:

3.76+-0.40
n=24883

Accepted(UCSC)

Percent

20 Not Low Income:cApplicants(UCSC)

Percent

Accepted(UCSC)

Percent

Applicants(UCSC)

Percent

Percent

Percent

Aa

60
40

Not Low Income
Low Income

20
2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Figure 30. Family income, HSGPAs, and test scores in theapplicant and admit pools for UC San Diego.

CacApplicants(UCSD)

Accepted(UCSD)

Not Low Income:20 3.91+-0.37
n=38436

Not Low Income:20 4.19+-0.17
n=10505

2.5

3

3.5

4

HSGPA

d

Applicants(UCSD)

2.5

3

3.5

4

4.5

b

10
Not Low Income

5 Low Income

HSGPA

B

Percent

Applied

1000

3

3.5

AppliedAccepted

10

1211+-153
n=5378
AppliedAccepted

0
500

1500

1000

4

d

Applicants(UCSD)

Accepted(UCSD)
10

20
Not Low Income

10 Low Income
0
500

4.5

HSGPA

1000

1500

5 Not Low Income
Low Income

0
500

SAT

D

Acceptance Percent(UCSD)

1500

SAT

30

2.5

n=8668SAT

Accepted(UCSD)

0

10

20 Low Income:

1081+-185
n=20521

0
500

4.5

Percent

Not Low Income

10 Low Income
0

4

15

30
20

3.5

10

20 1329+-140

0

HSGPA

Percent

Percentb

3

Applied20 Low Income:AppliedAccepted

2.5

n=311280

4.13+-0.19
10 n=5721

0

4.5

10

Percent

AppliedNot Low Income:20 1241+-168

Percent

3.72+-0.45
10 n=22362

0AppliedAccepted

0
20 Low Income:

Percent

Percent

0
20 Low Income:10

Accepted(UCSD)

Not Low Income:

Percent

Appliedc

Applicants(UCSD)

Percent

10

Percent

Percent

Aa

1000

1500

SAT

Acceptance Percent(UCSD)
80

80
60

Percent

Percent

60

40 Not Low Income

40

Low Income

Not Low Income
Low Income

20

20

2.5

3

3.5

4

4.5

600

HSGPA

800

1000

1200

1400

1600

SAT

Aa: Rows show HSGPAs for applicants from different demographic groups. HSGPAs are divided into 13 equal bands from 2.1 to 4.5. Y-axis shows the percent
of applicants in that demographic group who had a HSGPA in that range. Means and standard deviations are plotted above each group. Color corresponds todemographic group. Ab: HSGPAs for all applicant demographic groups are overlaid.
Ac: HSGPA distributions for students applying (lighter color) and accepted (darker color). Y axis is the percent of applicants in each demographic group who had
HSGPA in that range (light color) or who had HSGPA in that range and were admitted (dark color). Means and standard deviations for HSGPAs of accepted students
are plotted above each group. Ad: HSGPAs for accepted students from all demographic groups are overlaid.
B: Percent of applicants in each demographic group and HSGPA combination who were admitted.
C-D: As A-B but for SAT scores instead of HSGPA. To preserve student privacy, combinations with fewer than 30 accepted students are blanked out.

Appendix V. Table 1: HSGPA and Score Differences By Race/Ethnicity, Each Campus
All
Campuses

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.55
3.86
0.31
0.7
Af-Amer
3.85
3.99
0.14
0.32
Asian
3.64
3.88
0.25
0.56
Hispan
3.88
4.05
0.16
0.37
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1043
1120
77
0.39
Af-Amer
1221
1270
49
0.25
Asian
1022
1080
57
0.29
Hispan
1214
1262
48
0.24
White

Berkeley

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.67
4.13
0.46
1.19
Af-Amer
3.99
4.24
0.25
0.64
Asian
3.82
4.21
0.39
1.02
Hispan
4.01
4.23
0.23
0.59
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1088
1199
110
0.57
Af-Amer
1294
1390
96
0.5
Asian
1088
1195
107
0.55
Hispan
1277
1362
85
0.44
White

Davis

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.59
4.08
0.49
1.2
Af-Amer
3.87
4.18
0.31
0.75
Asian
3.69
4.1
0.41
0.99
Hispan
3.91
4.19
0.28
0.69
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1055
1136
81
0.42
Af-Amer
1236
1310
74
0.38
Asian
1036
1108
72
0.37
Hispan
1228
1290
62
0.32
White

UCLA

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.64
4.12
0.47
1.17
Af-Amer
3.95
4.26
0.31
0.76
Asian
3.75
4.19
0.44
1.09
Hispan
3.98
4.24
0.26
0.65
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1070
1200
130
0.66
Af-Amer
1268
1411
143
0.72
Asian
1056
1203
147
0.74
Hispan
1250
1369
119
0.6
White

Appendix V. Table 1. Left: Each row shows, for one campus and one demographic group, the mean HSGPAs for applicantsand for admitted students, the change in mean grades between the pool of applicants and the pool of accepted students, and thesize of the change normalized to units of standard deviations (calculated across the entire applicant pool) – a measure of how
strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest selection, white =no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically substantially higher
than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of applicants, 3)
measured selectivity based on test scores is generally much smaller than the selectivity based on grades, across different
demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection on
HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 1

Appendix V. Table 1 (continued): HSGPA and Score Differences By Race/Ethnicity, Each Campus
Riverside

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.44
3.85
0.4
0.91
Af-Amer
3.71
3.88
0.17
0.39
Asian
3.52
3.89
0.37
0.83
Hispan
3.7
3.9
0.2
0.44
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1006
1123
116
0.62
Af-Amer
1183
1245
62
0.33
Asian
977
1080
103
0.55
Hispan
1153
1213
60
0.32
White

UCSD

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.61
4.12
0.51
1.23
Af-Amer
3.91
4.19
0.27
0.66
Asian
3.71
4.12
0.41
1
Hispan
3.93
4.19
0.27
0.65
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1067
1201
134
0.69
Af-Amer
1262
1346
84
0.43
Asian
1058
1177
120
0.62
Hispan
1241
1324
82
0.42
White

UCSC

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.5
3.85
0.36
0.82
Af-Amer
3.75
4.02
0.26
0.61
Asian
3.56
3.98
0.41
0.95
Hispan
3.81
4.05
0.24
0.54
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1043
1146
103
0.54
Af-Amer
1207
1301
94
0.49
Asian
1007
1144
137
0.72
Hispan
1205
1279
75
0.39
White

UCSB

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.59
4.06
0.47
1.14
Af-Amer
3.87
4.16
0.3
0.71
Asian
3.67
4.08
0.41
0.98
Hispan
3.9
4.18
0.28
0.68
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1064
1211
147
0.77
Af-Amer
1248
1352
104
0.54
Asian
1040
1159
119
0.62
Hispan
1228
1324
96
0.5
White

Appendix V., Table 1 (continued). Left: Each row shows, for one campus and one demographic group, the mean HSGPAs forapplicants and for admitted students, the change in mean grades between the pool of applicants and the pool of accepted
students, and the size of the change normalized to units of standard deviations (calculated across the entire applicant pool) – ameasure of how strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest
selection, white = no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically
substantially higher than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of
applicants, 3) measured selectivity based on test scores is generally much smaller than the selectivity based on grades, acrossdifferent demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection
on HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 2

Appendix V. Table 1 (continued): HSGPA and Score Differences By Race/Ethnicity, Each Campus
IrvineHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.56
3.98
0.42
0.98
Af-Amer
3.86
4.17
0.31
0.73
Asian
3.65
4.12
0.47
1.1
Hispan
3.86
4.17
0.31
0.73
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1047
1178
131
0.68
Af-Amer
1227
1325
98
0.51
Asian
1022
1137
115
0.59
Hispan
1207
1289
82
0.43
White

MercedHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.37
3.65
0.28
0.62
Af-Amer
3.61
3.76
0.15
0.34
Asian
3.46
3.67
0.21
0.47
Hispan
3.63
3.78
0.15
0.34
White

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
978
1041
62
0.35
Af-Amer
1140
1176
36
0.2
Asian
954
999
45
0.25
Hispan
1141
1168
27
0.15
White

Appendix V. Table 1 (continued). Left: Each row shows, for one campus and one demographic group, the mean HSGPAs forapplicants and for admitted students, the change in mean grades between the pool of applicants and the pool of accepted
students, and the size of the change normalized to units of standard deviations (calculated across the entire applicant pool) – ameasure of how strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest
selection, white = no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically
substantially higher than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of
applicants, 3) measured selectivity based on test scores is generally much smaller than the selectivity based on grades, acrossdifferent demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection
on HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 3

Appendix V. Table 2: HSGPA and Score Differences By Family Income, Each Campus
All
Campuses

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.84
4.01
0.17
0.38
Not Low
Income
3.63
3.88
0.25
0.56
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1194
1251
57
0.29
Not Low
Income
1035
1100
65
0.33
Low
IncomeBerkeley

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.98
4.23
0.25
0.66
Not Low
Income
3.81
4.2
0.39
1.01
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1274
1369
95
0.49
Not Low
Income
1110
1225
116
0.59
Low
IncomeDavis

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.88
4.18
0.3
0.74
Not Low
Income
3.68
4.1
0.41
1
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1222
1291
68
0.35
Not Low
Income
1056
1132
76
0.39
Low
IncomeUCLAHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.95
4.25
0.3
0.74
Not Low
Income
3.75
4.19
0.44
1.09
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1244
1380
136
0.69
Not Low
Income
1073
1223
151
0.76
Low
IncomeAppendix V. Table 2. Left: Each row shows, for one campus and one demographic group, the mean HSGPAs for applicantsand for admitted students, the change in mean grades between the pool of applicants and the pool of accepted students, and thesize of the change normalized to units of standard deviations (calculated across the entire applicant pool) – a measure of how
strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest selection, white =no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically substantially higher
than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of applicants, 3)
measured selectivity based on test scores is generally much smaller than the selectivity based on grades, across different
demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection on
HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 4

Appendix V. Table 2 (continued): HSGPA and Score Differences By Family Income, Each Campus
Riverside

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.69
3.89
0.21
0.47
Not Low
Income
3.53
3.87
0.35
0.78
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1149
1226
78
0.42
Not Low
Income
995
1105
110
0.59
Low
IncomeUCSDHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.91
4.19
0.28
0.68
Not Low
Income
3.72
4.13
0.41
1
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1241
1329
88
0.45
Not Low
Income
1081
1211
131
0.68
Low
IncomeUCSCHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.76
4.02
0.26
0.6
Not Low
Income
3.56
3.97
0.41
0.94
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1190
1283
93
0.49
Not Low
Income
1019
1155
136
0.71
Low
IncomeUCSBHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.87
4.17
0.3
0.73
Not Low
Income
3.67
4.08
0.41
0.99
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1221
1328
107
0.56
Not Low
Income
1056
1192
136
0.71
Low
IncomeAppendix V. Table 2 (continued). Left: Each row shows, for one campus and one demographic group, the mean HSGPAs forapplicants and for admitted students, the change in mean grades between the pool of applicants and the pool of accepted
students, and the size of the change normalized to units of standard deviations (calculated across the entire applicant pool) – ameasure of how strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest
selection, white = no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically
substantially higher than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of
applicants, 3) measured selectivity based on test scores is generally much smaller than the selectivity based on grades, acrossdifferent demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection
on HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 5

Appendix V. Table 2 (continued): HSGPA and Score Differences By Family Income, Each Campus
IrvineHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.85
4.16
0.31
0.74
Not Low
Income
3.66
4.12
0.46
1.08
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1205
1301
96
0.5
Not Low
Income
1045
1178
133
0.69
Low
IncomeMercedHSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
3.6
3.76
0.16
0.35
Not Low
Income
3.46
3.68
0.22
0.48
Low
IncomeSAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units
1114
1150
36
0.2
Not Low
Income
963
1010
47
0.26
Low
IncomeAppendix V. Table 2 (continued). Left: Each row shows, for one campus and one demographic group, the mean HSGPAs forapplicants and for admitted students, the change in mean grades between the pool of applicants and the pool of accepted
students, and the size of the change normalized to units of standard deviations (calculated across the entire applicant pool) – ameasure of how strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest
selection, white = no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically
substantially higher than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATsof applicants, 3) measured selectivity based on test scores is generally much smaller than the selectivity based on grades, acrossdifferent demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection
on HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 6

Appendix V. Table 3: HSGPA and Score Differences By Family Educational History, Each Campus
All
Campuses

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen 3.87

4.02

0.16

0.36

Not 1stGen 1226

1281

55

0.28

3.64

3.89

0.24

0.55

1stGen

1036

1097

62

0.31

1stGenBerkeley

Davis

UCLA

HSGPA differences between applicant andaccepted pools
Appl. Accepted Change ChangeGroup
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

4

4.24

0.24

0.63

Not 1stGen 1297

1390

93

0.48

1stGen

3.82

4.2

0.38

1.01

1stGen

1103

1205

102

0.53

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

3.89

4.19

0.3

0.72

Not 1stGen 1250

1321

71

0.37

1stGen

3.7

4.1

0.41

0.98

1stGen

1055

1127

72

0.37

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

3.96

4.25

0.29

0.72

Not 1stGen 1268

1400

132

0.67

1stGen

3.76

4.19

0.43

1.06

1stGen

1072

1209

137

0.69

Appendix V., Table 3. Left: Each row shows, for one campus and one demographic group, the mean HSGPAs for applicantsand for admitted students, the change in mean grades between the pool of applicants and the pool of accepted students, and thesize of the change normalized to units of standard deviations (calculated across the entire applicant pool) – a measure of how
strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest selection, white =no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically substantially higher
than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of applicants, 3)
measured selectivity based on test scores is generally much smaller than the selectivity based on grades, across different
demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection on
HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 7

Appendix V. Table 3 (continued): HSGPA and Score Differences By Family Educational
History, Each Campus
Riverside

UCSD

UCSC

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STD
units
Not1stGen

3.7

3.89

0.19

0.43

Not 1stGen 1182

1251

69

0.37

1stGen

3.53

3.88

0.34

0.77

1stGen

998

1105

108

0.58

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

3.92

4.2

0.27

0.66

Not 1stGen 1264

1352

88

0.46

1stGen

3.72

4.13

0.41

0.98

1stGen

1077

1205

128

0.66

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen 3.79

4.03

0.24

0.55

Not 1stGen 1219

1302

83

0.44

3.57

3.97

0.4

0.92

1stGen

1020

1147

127

0.67

1stGenUCSB

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

3.89

4.18

0.3

0.72

Not 1stGen 1246

1356

111

0.58

1stGen

3.68

4.08

0.4

0.97

1stGen

1054

1184

130

0.68

Appendix V, Table 3. Left: Each row shows, for one campus and one demographic group, the mean HSGPAs for applicantsand for admitted students, the change in mean grades between the pool of applicants and the pool of accepted students, and thesize of the change normalized to units of standard deviations (calculated across the entire applicant pool) – a measure of how
strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest selection, white =no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically substantially higher
than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of applicants, 3)
measured selectivity based on test scores is generally much smaller than the selectivity based on grades, across different
demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection on
HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 8

Appendix V. Table 3 (continued): HSGPA and Score Differences By Family Educational
History, Each Campus
IrvineMercedHSGPA differences between applicant andaccepted pools
Appl. Accepted Change ChangeGroup
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen

3.86

4.17

0.31

0.72

Not 1stGen 1233

1333

99

0.52

1stGen

3.67

4.12

0.45

1.05

1stGen

1047

1169

122

0.63

HSGPA differences between applicant andaccepted pools
Group
Appl. Accepted Change Change
in STDunits

SAT differences between applicant and accepted
pools
Group
Appl. Accepted Change Change
in STD
units

Not1stGen 3.61

3.77

0.16

0.36

Not 1stGen 1157

1194

37

0.2

3.47

3.68

0.21

0.47

1stGen

967

1013

47

0.26

1stGenAppendix V, Table 3. Left: Each row shows, for one campus and one demographic group, the mean HSGPAs for applicantsand for admitted students, the change in mean grades between the pool of applicants and the pool of accepted students, and thesize of the change normalized to units of standard deviations (calculated across the entire applicant pool) – a measure of how
strongly the campus selects based on that qualification. Normalized changes are color coded (red = strongest selection, white =no selection). Right: As Left, but for SAT. Note that 1) the HSGPAs of admitted students are typically substantially higher
than the HSGPAs of applicants, 2) the SATs of admitted students are also typically higher than the SATs of applicants, 3)
measured selectivity based on test scores is generally much smaller than the selectivity based on grades, across different
demographic groups and campuses, and 4) for the groups most underrepresented at UC, the contribution of selection on
HSGPA exceeds the contribution of selection on test scores.

Appendix Y ‐ Page 9

Appendix V. Table 4: Uncompensated GPA and Score Differences By Race/Ethnicity, Each Campus
All
Campuses

Berkeley

Davis

UCLA

HSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.55
3.85
3.64
3.88

0.31
--0.22
-0.03

3.86
3.99
3.88
4.05

Percent
Uncompensated0.14
55%----0.11
48%-0.05 -73%

HSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.67
3.99
3.82
4.01

0.32
--0.18
-0.01

4.13
4.24
4.21
4.23

Percent
Uncompensated0.11
66%----0.03
81%
0.01
135%

HSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.59
3.87
3.69
3.91

0.28
--0.18
-0.04

4.08
4.18
4.1
4.19

Percent
Uncompensated0.09
66%----0.08
56%
-0.01
67%

HSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.64
3.95
3.75
3.98

0.31
--0.20
-0.02

4.12
4.26
4.19
4.24

Percent
Uncompensated0.14
54%----0.07
67%
0.02
179%

SAT: Appl. vs Asian / Accepted vs AsianGroup

Appl. Diff Accepted Diff Percent
Uncompensated
1120
150
16%
Af-Amer 1043 177
1221 --1270----Asian1022 198
1080
190
4%
Hispan
1214
6
1262
8
-23%
White
SAT: Appl. vs Asian / Accepted vs AsianGroup

Appl. Diff Accepted Diff Percent
Uncompensated1199
191
7%
Af-Amer 1088 205
1294 --1390----Asian1088 206
1195
195
5%
Hispan1277 16
1362
28
-70%
White
SAT: Appl. vs Asian / Accepted vs AsianGroup

Appl. Diff Accepted Diff Percent
Uncompensated1136
174
4%
Af-Amer 1055 182
1236 --1310----Asian1036 200
1108
202
-1%
Hispan
1228
9
1290
20
-136%
White
SAT: Appl. vs Asian / Accepted vs AsianGroup

Appl. Diff Accepted Diff Percent
Uncompensated
1200
211
-6%
Af-Amer 1070 199
1268 --1411----Asian1056 212
1203
208
2%
Hispan1250 18
1369
42
-129%
White

Appendix V. Table 4. Left: Each row shows, for one campus and one demographic group, mean HSGPAs for applicants,
the difference in mean HSGPA (green) between applicants in that group and the group with the highest test scores, mean
HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and the group withthe highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If the difference inscores between two groups of accepted students is exactly the same as the difference in scores between applicants in thosegroups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions processeffectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue). Notethat 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differences intest scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 4 (continued): Uncompensated GPA and Score Differences By Race/
Ethnicity, Each Campus
Riverside

UCSD

UCSC

UCSB

HSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.44
3.71
3.52
3.7

0.27
--0.190.01

Af-Amer
Asian
Hispan
White

1006
1183
977
1153

3.85
3.88
3.89
3.9

Percent
Uncompensated0.03
87%-----0.01 104%-0.02 280%

SAT: Appl. vs Asian / Accepted vs AsianHSGPA: Appl. vs Asian / Accepted vs Asian

1123
1245
1080
1213

122
--165
32

SAT: Appl. vs Asian / Accepted vs Asian

Group

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.61
3.91
3.71
3.93

0.30
--0.20-0.01

Af-Amer
Asian
Hispan
White

1067
1262
1058
1241

4.12
4.19
4.12
4.19

Percent
Uncompensated0.07
78%----0.06
69%
-0.01
41%

177
--207
30

HSGPA: Appl. vs Asian / Accepted vs Asian

195
--204
21

1201
1346
1177
1324

144
--168
22

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.5
3.75
3.56
3.81

0.26
--0.19-0.06

Af-Amer
Asian
Hispan
White

1043
1207
1007
1205

HSGPA: Appl. vs Asian / Accepted vs Asian

164
--199
2

1146
1301
1144
1279

156
--157
22

Percent
Uncompensated
5%
--21%
---

SAT: Appl. vs Asian / Accepted vs Asian

Group

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.59
3.87
3.67
3.9

0.28
--0.19-0.03

Af-Amer
Asian
Hispan
White

1064
1248
1040
1228

4.06
4.16
4.08
4.18

Percent
Uncompensated0.10
64%----0.08
57%
-0.02
39%

Percent
Uncompensated
26%
--18%
-7%

SAT: Appl. vs Asian / Accepted vs Asian

Group

3.85
4.02
3.98
4.05

Percent
Uncompensated0.17
36%----0.04
77%
-0.03
52%

Percent
Uncompensated
31%
--20%
-5%

184
--208
20

1211
1352
1159
1324

140
--193
28

Percent
Uncompensated
24%
--7%
-38%

Appendix V. Table 4 (continued). Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 4 (continued): Uncompensated GPA and Score Differences By Race/
Ethnicity, Each Campus
IrvineMercedHSGPA: Appl. vs Asian / Accepted vs Asian
Group

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.56
3.86
3.65
3.86

0.29
--0.21---

Af-Amer
Asian
Hispan
White

1047
1227
1022
1207

3.98
4.17
4.12
4.17

Percent
Uncompensated0.19
36%----0.05
76%--42%

SAT: Appl. vs Asian / Accepted vs AsianHSGPA: Appl. vs Asian / Accepted vs Asian

1178
1325
1137
1289

147
--188
36

SAT: Appl. vs Asian / Accepted vs Asian

Group

Appl.

Diff Accepted Diff

Group

Appl. Diff Accepted Diff

Af-Amer
Asian
Hispan
White

3.37
3.61
3.46
3.63

0.24
--0.15-0.01

Af-Amer
Asian
Hispan
White

978
1140
954
1141

3.65
3.76
3.67
3.78

Percent
Uncompensated0.12
52%----0.09
40%
-0.02
-19%

180
--205
20

Percent
Uncompensated
18%
--8%
-79%

161
--186
-1

1041
1176
999
1168

Percent
Uncompensated136
16%----177
4%
8---

Appendix V. Table 4 (continued). Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 5: Uncompensated HSGPA and Score Differences By Family Income,Each Campus
All
Campuses

HSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.84--4.01----Income
3.63 0.20
3.88
0.13
38%
Low
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1251----Not Low 1194 --Income
1035 159
1100
151
6%
Low
IncomeBerkeley

HSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.98--4.23----IncomeLow
3.81 0.17
4.2
0.03
80%
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1369----Not Low 1274 --Income
1110 164
1225
144
12%
Low
IncomeDavis

HSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.88--4.18----Income
3.68 0.19
4.1
0.08
58%
Low
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1291----Not Low 1222 --Income
1056 167
1132
159
5%
Low
IncomeUCLAHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated--4.25----Not Low 3.95
IncomeLow
3.75 0.20
4.19
0.06
69%
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1380----Not Low 1244 --Income
1073 172
1223
157
9%
Low
IncomeAppendix V. Table 5. Left: Each row shows, for one campus and one demographic group, mean HSGPAs for applicants,
the difference in mean HSGPA (green) between applicants in that group and the group with the highest test scores, mean
HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and the group withthe highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If the difference inscores between two groups of accepted students is exactly the same as the difference in scores between applicants in thosegroups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions processeffectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue). Notethat 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differences intest scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 5 (continued): Uncompensated HSGPA and Score Differences By
Family Income, Each Campus
Riverside

HSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.69--3.89----Income
3.53 0.16
3.87
0.02
85%
Low
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1226----Not Low 1149 --Income
995 154
1105
121
21%
Low
IncomeUCSDHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low Income
Appl. Diff Accepted Diff Percent
Group
Uncompensated
--4.19----Not Low 3.91
Income3.72 0.20
4.13
0.06
69%
Low
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1329----Not Low 1241 --Income
1081 160
1211
117
27%
Low
IncomeUCSCHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated
--4.02----Not Low 3.76
IncomeLow
3.56 0.20
3.97
0.05
74%
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1283----Not Low 1190 --Income
1019 171
1155
128
25%
Low
IncomeUCSBHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated
--4.17----Not Low 3.87
IncomeLow
3.67 0.20
4.08
0.09
54%
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1328----Not Low 1221 --Income
1056 165
1192
136
18%
Low
IncomeAppendix V. Table 5, continued. Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 5 (continued): Uncompensated HSGPA and Score Differences By
Family Income, Each Campus
IrvineHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.85--4.16----Income
3.66 0.18
4.12
0.04
78%
Low
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1301----Not Low 1205 --Income
1045 160
1178
123
23%
Low
IncomeMercedHSGPA: Appl. vs Not Low Income / Accepted vs
Not Low IncomeGroupAppl. Diff Accepted Diff Percent
UncompensatedNot Low 3.6--3.76----IncomeLow
3.46 0.14
3.68
0.08
43%
IncomeSAT: Appl. vs Not Low Income / Accepted vs Not
Low IncomeGroupAppl. Diff Accepted Diff Percent
Uncompensated1150----Not Low 1114 --Income
963 151
1010
139
8%
Low
IncomeAppendix V. Table 5, continued. Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 6: Uncompensated GPA and Score Differences By Family
Educational History, Each Campus
All
Campuses

HSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated
3.87--4.02----Not
1stGen3.64 0.22
3.89
0.14
38%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1226 --1281----Not
1stGen1036 191
1097
184
3%
1stGenBerkeley

HSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated4--4.24----Not
1stGen3.82 0.18
4.2
0.04
80%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1297 --1390----Not
1stGen1103 194
1205
185
5%
1stGenDavis

HSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenAppl. Diff Accepted Diff Percent
Group
Uncompensated
3.89--4.19----Not
1stGen
3.7
0.20
4.1
0.09
56%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1250 --1321----Not
1stGen1055 195
1127
194
1%
1stGenUCLAHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated
3.96--4.25----Not
1stGen3.76 0.20
4.19
0.06
69%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1268 --1400----Not
1stGen1072 196
1209
191
2%
1stGenAppendix V, Table 6. Left: Each row shows, for one campus and one demographic group, mean HSGPAs for applicants,
the difference in mean HSGPA (green) between applicants in that group and the group with the highest test scores, mean
HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and the group withthe highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If the difference inscores between two groups of accepted students is exactly the same as the difference in scores between applicants in thosegroups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions processeffectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue). Notethat 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differences intest scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 6 (continued): Uncompensated GPA and Score Differences By
Family Educational History, Each Campus
Riverside

HSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
UncompensatedNot
3.7--3.89----1stGen
3.53 0.17
3.88
0.02
90%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1182 --1251----Not
1stGen998 184
1105
145
21%
1stGenUCSDHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated
Not
3.92
--4.2
----1stGen
3.72 0.20
4.13
0.07
66%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1264 --1352----Not
1stGen1077 186
1205
147
21%
1stGenUCSCHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated
3.79--4.03----Not
1stGen3.57 0.22
3.97
0.06
73%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1219 --1302----Not
1stGen1020 199
1147
155
22%
1stGenUCSBHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
UncompensatedNot
3.89--4.18----1stGen
3.68 0.21
4.08
0.11
50%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1246 --1356----Not
1stGen1054 192
1184
173
10%
1stGenAppendix V. Table 6, continued. Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.

Appendix V. Table 6 (continued): Uncompensated GPA and Score Differences By
Family Educational History, Each Campus
IrvineHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenAppl. Diff Accepted Diff Percent
Group
Uncompensated3.86--4.17----Not
1stGen3.67 0.19
4.12
0.05
73%
1stGenSAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1233 --1333----Not
1stGen1047 187
1169
164
12%
1stGenMercedHSGPA: Appl. vs Not 1stGen / Accepted vs Not
1stGenAppl. Diff Accepted Diff Percent
Group
UncompensatedNot
3.61--3.77----1stGen
1stGen3.47 0.13
3.68
0.08
38%

SAT: Appl. vs Not 1stGen / Accepted vs Not
1stGenGroup
Appl. Diff Accepted Diff Percent
Uncompensated1157 --1194----Not
1stGen967 1911013
181
5%
1stGenAppendix V. Table 6, continued. Left: Each row shows, for one campus and one demographic group, mean HSGPAs forapplicants, the difference in mean HSGPA (green) between applicants in that group and the group with the highest testscores, mean HSGPAs for admitted students, the difference in mean HSGPA between admitted students in that group and thegroup with the highest test scores (green), and the ratio of the Accepted difference vs the Applicant difference. If thedifference in scores between two groups of accepted students is exactly the same as the difference in scores between
applicants in those groups, (i.e., uncompensated difference of 0), that is consistent with the hypothesis that the UC admissions
process effectively compensates for the mean differences between groups. Right: As Left, but for SAT (differences in blue).
Note that 1) the remaining, uncompensated differences in HSGPAs are typically much larger than uncompensated differencesin test scores (i.e., the left green columns are darker than the right green columns, but the left blue columns are similar to theright blue columns), 2) the uncompensated differences in HSGPA are largest for underrepresented groups.