01.10.2020 INSTITUTIONAL RESEARCH & ACADEMIC PLANNING ucal.us/irap 27
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 in
prior 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 Relationship
between Demographic Characteristics and SAT/ACT Scores.”)
Two sets of models with the sum of SAT Reading/Math and SAT Writing (Model 4) or the sum
of ACT Composite and ACT Writing (Model 9) were developed to examine how much
additional 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 (ACT
Composite) and SAT Writing (ACT Writing) are highly correlated (e.g., r=.85 for the freshman
entering cohort in 2015). Therefore, there would be a collinearity issue if two measures were
entered 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., in
2015) 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 and
SATRM or ACT Composite models (difference in variance between SAT Models 5 and 6, and
ACT Models 10 and 11). Similar to what has been found previously, it is concluded that writing
scores 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 coefficients
in 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 these
three 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 and
freshman GPA after controlling for student demographics. We ran regression models adding in
student 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