Copyright © 2011, New Forums Press, Inc., P.O. Box 876, Stillwater, OK 74076. All Rights Reserved.
The Transfer Velocity Project: A Comprehensive Look at
the Transfer Function
Craig Hayward
Planning, Research, and Knowledge Systems, Cabrillo College
The 1999-2000 Transfer Velocity Project (TVP) cohort of 147,207 community college students is used to develop both a college-level
endogenous model, appropriate for applied research and guidance for campus action, and a student-level model. Survival analysis (Cox
regression) is employed to evaluate the relative contribution of 53 student-level characteristics to the transfer outcome. Results suggest
practical interventions for enhancing the transfer function such as encouraging the completion of the associate degree en route to transfer
as well as enhancing institutional transfer culture by, for example, fully staffing campus-based transfer centers. Discussion focuses on the
identification of transfer-facilitating characteristics, the strengths and weaknesses of the current research, suggestions for future research,
and practical approaches to facilitating transfer.
Introduction
Transfer rates are the bread and butter of transfer
studies, state accountability systems, and individual institutional research offices. They are accepted and familiar,
though they may not be as informative as commonly
believed. Transfer rates function well as key performance
indicators that shine a spotlight on trends over time, but
they are susceptible to misinterpretation due to the incomplete picture they provide.
One problem with transfer rates is that they ignore
any transfer activity past the time window used to define
the rate – potentially a great deal of transfer activity. Recently published research tracking high-risk cohorts that
began postsecondary education in the 1970s has shown
that transfer and postsecondary degree completion is actively occurring over multiple decades (Attewell, Lavin,
Domina & Levy, 2007). Researchers tracked nearly 2,000
non-traditional, low-income women through the integrated system of community colleges and universities in
the City University of New York (CUNY) system. These
students entered the system between 1970 and 1972 at a
time when the CUNY had just switched to an open admissions policy. Over a 30-year time frame more than 70% of
the women had graduated from CUNY. However, about
a quarter took more than 15 years to finish.
What, then, is the best time frame to use for calculating a transfer rate? Consider for a moment the variety
of transfer rates that are in use. The official Integrated
Postsecondary Educational Data System (IPEDS) rates
allow new community college students up to three years
to transfer, though that time frame will soon move to four
years – a nominal 200% of the “normal time to completion”
for two-year college students (IPEDS, 2010). Researchers,
state agencies, and policy analysts who make community
colleges their business often use five- or six- year transfer
rates (CCCCO, 2008; CPEC, 2007; Sengupta & Jepsen, 2006;
Shulock & Moore, 2007). Recently, some policy analysts
have begun to evaluate student outcomes using longer
time frames such as seven years (Offenstein, Moore, &
Shulock, 2010). However, the rationale for using a given
time frame is seldom explicitly evidence-based.
While transfer rates with longer time frames are better indicators of the true rate for a given cohort, reliance on
a single time frame and/or enrollment at a single institution still foreshortens our perspective and our understanding of transfer. College transfer rates or even system rates
cannot provide a complete picture of the transfer function.
Shifting focus to the individual student provides a different perspective; one that can be even more useful in terms
of yielding results that can be applied by practitioners. In
The Toolbox Revisited, Adelman (2006) followed a cohort
of students across all of their postsecondary enrollments,
allowing up to 8.5 years for students to complete their
postsecondary education. He found that nearly 60% of
his those in NELS:88/2000 cohort who attended college,
attended more than one postsecondary institution, calling
into question the validity of focusing on any one institution’s rates without reference to the greater tapestry of
student postsecondary engagement.
The current study provides both an institutional and
a student perspective. The student-level model of transfer
Vol. 18, No. 2, Spring 2011 / 21
utilizes survival analysis to evaluate how student coursework and behavior enhances or detracts from students’
transfer velocity. Combining the complementary, but
somewhat different perspectives of the college-level focus
on transfer rates and the student level focus on velocity
provides a richer, subtler and more fully dimensional
understanding of how our educational systems and our diverse student body interact and influence transfer rates.
In referring to transfer velocity, we borrow the term
“velocity” from physics. In the physical world an object’s
velocity is determined by its speed and orientation as it
travels along a pathway (or vector). Although analyses
of both transfer rates and transfer velocity require the
tracking of cohorts of similar students, the velocity analogy provides additional insight beyond the traditional
focus on a specific time window (e.g., the six-year transfer
rate). This is because transfer occurs as a more or less
continuous function over time. Transfer velocity, then,
is a way of understanding how the characteristics of a
postsecondary system interact with the characteristics
and behaviors of students to enhance or impede student
progression over time along a pathway toward a goal.
The key advantage of transfer velocity over transfer rates
is that more information – and more actionable information – is used and represented in the analysis of students’
transfer velocity.
Methods
When assessing transfer rates and transfer velocity,
determining which students to track and study is a key
issue. Each California Community College has a multipronged mission statement, reminding us that not all
students are transfer-oriented. As such, there is general
agreement in recent research that, students who are not
transfer-oriented, because they hold other goals and are
on other trajectories, should be excluded from the analysis, if possible. (Adelman, 2005; Banks, 1990; Cohen, 2005;
CPEC, 2007; Driscoll, 2007; Horn & Lew, 2007a; Bahr,
Hom & Perry, 2005; Shulock & Moore, 2007; Sengupta &
Jepsen, 2006; Spicer & Armstrong, 1996; Thompson, 2002).
Community colleges typically host multiple functions. For
instance, students may be seeking to acquire job skills and
enter directly into the workforce either with or without
a certificate of achievement or an associate’s degree. As
Thompson (2002) suggested, the cohort that is tracked in
any given study should be selected based on the goal of the
research. To this end, the TVP uses the “behavioral intent”
method established by Bahr, Hom & Perry (2005).
How did students qualify to be included in a TVP
cohort? The “behavioral intent” method involves tracking cohorts of first-time college students for six years to
determine if they show behavioral intent to transfer, also
referred to as “serious academic intent” (Bahr, Hom &
22 / Journal of Applied Research in the Community College
Perry, 2005; Woo, 2009). A student becomes potentially
eligible to enter a cohort by enrolling for the first time at
any California Community College (CCC). Students are
assigned a cohort year according to the academic year
in which they first enroll at a CCC. Students who have
completed at least 12 credit units and who have enrolled
in (not necessarily passed) a transfer-level math and/or
a transfer-level English course within six years of initial
enrollment enter into the TVP cohort for that year.
The analyses in this article focus on the cohort of
students that first entered the CCC system in the 1999-2000
academic year. The 1999-2000 TVP cohort was selected
for model building because it provides sufficient time to
track the vast majority of transfer activity; of all the transfer activity that occurred within 15 years of initial entry,
95% had occurred within 9 years of initial enrollment.
Additionally, major sources of data for the model building
(e.g., Census 2000) were gathered in the time period when
1999-2000 TVP cohort students were most active.
For purposes of calculating institutional transfer
rates, students were assigned to a home college according to where they first enrolled. In cases where students
simultaneously enrolled at more than one community
college in their first postsecondary semester (3% of cases),
the tie was broken by random assignment to a single
home college. Nine-year transfer rates were used for the
college-level model because they capture the vast majority
of TVP cohort transfer activity.
Cohort Description
There are 147,207 students included in the 1999-2000
TVP cohort, representing 24% of all first-time freshmen
for that academic year. The students in the 1999-2000 TVP
cohort had a mean age of 20 (median age of 18) upon initial
enrollment in the CCC. Forty-four percent of the cohort
is male, 25% had initially enrolled at a CCC while still in
high school, 40% had attended more than one CCC, 40%
had received some form of financial aid, 52% had transferred within nine years of initial entry, 29% earned an
associate’s degree within nine years of initial entry, and
6% had earned a certificate of achievement. The ethnic
breakdown of the 1999-2000 TVP cohort is 42% white, 24%
Latino, 14% Asian, 6% African American, 4% Filipino, 1%
Native American, 3% other, and 7% unreported.
Data Sources
The TVP utilized three major data sources. The first
was the CCC Chancellor’s Office (CCCCO) Management
Information Systems (MIS) data base, a central data repository for all CCCs which includes all student, college
and course information that has been submitted annually since 1992. The second data set comprised 45 sociodemographic variables that describe the social and envi-
ronmental context of each community college in California.
It was derived largely from Census 2000 data indexed to
individual college’s service areas and was also provided
by the CCCCO (van Ommeren, Liddicoat, & Hom, 2008).
The third data source was the transfer center surveys that
are required to be submitted to the Chancellor’s Office
annually by the transfer center director (or proxy) at each
CCC. The annual transfer center survey contains a variety
of information from the allocated budget and funding
sources, to the staffing levels, number of student contacts,
and ratings of areas of significant challenge. Data from
three years were aggregated by TVP staff to form a data set
describing how transfer center practices, staffing, student
contacts, priorities and more varied around the state for
the time period under examination (i.e., 1999-2008).
Results
An Endogenous Model of College Transfer
Rates
The endogenous college-level model focuses on those
factors that are likely to be under the control of community college personnel. This perspective on college-level
factors diverges from an accountability-based perspective
in that the ability of college staff to control or manipulate
the factors in the model is more important than the overall
explanatory power or fit of the model. We seek to understand those factors or levers that are close at hand and that
might be adjusted through a college’s policies and actions
to increase the transfer success of students
The Transfer Center survey data and Chancellor’s
Office (CO) MIS data about college schedules and multiple
community college attendance patterns were combined in
a backward linear regression analysis to determine which
factors best predicted nine-year college-level transfer
rates (all variables were included initially and variables
that did not provide a unique contribution to the model’s
explanatory power were removed in blocks). In the bestfitting endogenous model (adjusted R2 = 0.48), six factors
accounted for 48% of the variability in college-level transfer
Transfer Velocity Project
rates. While the R2 value is smaller than that of the exog-
enous accountability model created by the CO (CCCCO,
2008), the endogenous model can still be considered to be
robust and useful. Importantly, the factors in this model
provide guidance and can be used to galvanize a call to
action by campus personnel.
In Table 1, the standardized coefficients (i.e., Beta
values) show the direction of association as well as allowing for a comparison of the relative strength of the
variables included in the regression model. The Transfer
Center surveys provide three significant endogenous predictors of college-level transfer rate: 1) the number of CSUs
with which the college has established a TAA pathway; 2)
the average annual number of students who sign a TAA
with a UC; and 3) the Full Time Equivalency (FTE) of the
college’s Transfer Center Director. The signs of all three
of these factors’ Beta coefficients are positive, meaning
that as the number of TAAs and the FTE of the Transfer
Center director increase, so does a college’s transfer rate.
The TAA agreements, particularly the numbers of CSU
partners, have a strong relationship with transfer rates,
as the Beta coefficient of .212 indicates.
The college scheduling variables show that, perhaps
unsurprisingly, the percentage of transferable course sections at a college is positively associated with college’s
nine-year transfer rate. Nearly as strong, but in the opposite direction, is the relationship between the percentage
of Career Technical Education (CTE) sections offered by
a college and the college transfer rate. The final variable
in the model is the average level of college swirl, as indicated by the average number of colleges attended by the
student body; this variable is positively associated with
transfer indicating that colleges with higher proportions
of students attending multiple community colleges tend
to have higher transfer rates.
Student-Level Model of Transfer Velocity
Fifty-three student-level characteristics were analyzed with a form of survival analysis known as Cox regression. Cox regression is designed to properly handle
data where the ultimate disposition of all cases is not
known in regards to a key outcome. Originally developed
to analyze medical research, a
Cox regression analysis outTable
Endrogenous
college-level
transfer model coefficients
Table 1.
1. Endogenous
college-level
transfer model coefficients
puts risk statistics, which show
Unstandardized
Standardized
a
the relative degree of risk
Model
Coefficients
Coefficients
t
Sig.
(compared to an average coBeta
B
Std. Error
hort member) that is associ(Constant)
.144
.080
1.940
.075
ated with the presentation of
Pct. Transferrable Sections
.241
.219
.077
2.868
.000
a particular characteristic. A
Pct .CTE Sections
-.225
-.222
.086
-2.579
.005
Student TAAs signed (UC)
heightened “risk” of transfer
.201
.001
.000
2.551
.011
No. of CSU with TAAs
indicates that a certain charac.212
.041
.014
2.842
.012
FTE of Center Director
teristic or program is positively
.154
.055
.027
2.009
.047
Average swirl level
.279
.099
.026
3.766
.001
associated with transfer.
a. Dependent Variable: Nine-year College Transfer Rate
In the TVP the risk of the
Vol. 18, No. 2, Spring 2011 / 23
terminal outcome is actually a positive event – the transfer 2 reveals that there is a small negative effect of -5.6% asof a CCC student to a baccalaureate-granting institution. sociated with being male. That is, males are 5.6% less likely
Cases that do not demonstrate the terminal outcome of to successfully transfer than females, after controlling for
interest (i.e., transfer) by the end of the observation pe- the effects of the other variables in the model.
In addition to the simple binary variables, there are
riod are considered censored because
transfer could still
Table 2. Student-level transfer velocity model
occur, but it would occur outside our window of observa- three important student characteristics that are presented
tion. Cox regression is designed to properly model and as sets of binary variables. The three sets of variables are:
account for censored cases.
Table 2. Student-level transfer velocity model
Properly modeling censored
Incidence/
B
Standard
Sig.
Exp(B)
Relative risk of
observations and providing
average
Error (B)
transfer
discrete information about
Age
20.1
-0.0379
0.0008
0.000
0.963
-3.7%
a large number of student
Male
44.3%
-0.0579
0.0076
0.000
0.944
-5.6%
characteristics are strengths
of the Cox Regression proceAsian (reference group)
13.7%
dure. However, Cox RegresAfrican American
5.7%
-0.1662
0.0190
0.000
0.847
-15.3%
sion may not be as familiar
as Ordinary Least Squares
Filipino
3.7%
-0.1590
0.0212
0.000
0.853
-14.7%
regression to many readers
Latino
24.0%
-0.3083
0.0127
0.000
0.735
-26.5%
and it may require some
Native American
0.9%
-0.4368
0.0472
0.000
0.646
-35.4%
initial effort to interpret
Other
2.2%
-0.0762
0.0251
0.002
0.927
-7.3%
the results. Fortunately, the
Pacific Islander
0.7%
-0.1778
0.0455
0.000
0.837
-16.3%
Cox regression procedure
White
42.0%
-0.2020
0.0112
0.000
0.817
-18.3%
is capable of producing a
relatively intuitive statistic,
Unknown
7.0%
-0.2163
0.0165
0.000
0.805
-19.5%
namely the relative risk statistic which shows whether a
Transfer only
27.8%
0.3326
0.0083
0.000
1.395
39.5%
given student characteristic
Intellectual growth
10.7%
0.2589
0.0110
0.000
1.296
29.6%
enhances, reduces or has no
Basic
Skills
4.0%
0.1503
0.0183
0.000
1.162
16.2%
effect on the odds of successGED
9.1%
0.0529
0.0131
0.000
1.054
5.4%
ful vertical transfer.
With the exception of
Goal Unknown
1.2%
0.0332
0.0352
0.345
1.034
3.4%
age and GPA, the variables
Job Skills goal
15.8%
0.0130
0.0110
0.239
1.013
1.3%
in the student-level model
Undecided goal
38.6%
-0.0001
0.0082
0.992
1.000
0.0%
are expressed as binary opDegree and transfer
59.0%
-0.0158
0.0082
0.052
0.984
-1.6%
posites where 1 = “CharacDiscovery
goal
7.7%
-0.0286
0.0142
0.044
0.972
-2.8%
teristic is present” and 0 =
Certificate only
4.0%
-0.2118
0.0221
0.000
0.809
-19.1%
“Characteristic is absent.”
The average of a binary
Maintain license
2.9%
-0.2223
0.0255
0.000
0.801
-19.9%
variable across all students
Degree only
13.8%
-0.4419
0.0136
0.000
0.643
-35.7%
is equal to the proportion
of cases with a value of one.
GPA
2.92
0.3422
0.0072
0.000
1.408
40.8%
For example, the average of
<20% of grades are "W"
46.0%
0.4633
0.0084
0.000
1.589
58.9%
the binary variable “Male”
= .443, therefore 44.3% of
the 1999-2000 TVP cohort
<10% of units are CTE
49.6%
0.1296
0.0077
0.000
1.138
13.8%
is male. The relative risk of
Special Admit history
24.5%
0.1071
0.0108
0.000
1.113
11.3%
simple binary variables like
Summer enrollment
69.3%
0.3859
0.0090
0.000
1.471
47.1%
gender is straightforward;
Full Time student
21.8%
0.2732
0.0097
0.000
1.314
31.4%
when the characteristic is
Attended > 1 CCC
40.3%
0.2063
0.0085
0.000
1.229
22.9%
present (i.e., the student is
a male) the risk in the appropriate column of Table
First English, Transfer lvl.
50.4%
(reference group)
2 applies. Consulting the
First English, Degree lvl.
22.8%
-0.1103
0.0101
0.000
0.896
-10.4%
relative risk column in Table
24 / Journal of Applied Research in the Community College
1) Ethnicity; 2) First English course; and 3) First math
course. The interpretation of relative risk for the binary
variables in these sets needs to be in the context of the other
members of the set. This is because in order for the model
to produce meaningful output for these variable sets, it
is necessary to omit one member of the set; the omitted
variable serves as a reference category when interpreting
the remaining variables. For example, Asians were omitted from the ethnicity set; as the ethnic group with the
highest average transfer rate, Asian students constitute a
good reference group. The relative risks of the other ethnic
Table 2 (continued)
B
First English, Basic skills
Incidence/
average
15.7%
-0.1379
Standard
Error (B)
0.0127
First English, other
1.9%
-0.0704
0.0267
No English at CCC
9.3%
-0.1018
0.0145
First math, Transfer lvl.
(reference group)
First math, Degree lvl.
25.4%
36.3%
-0.3908
0.0097
First math, Basic skills
22.6%
-0.6406
0.0128
Math, other
2.8%
-0.4933
0.0235
No math at CCC
12.8%
-0.8198
0.0151
AA early
9.1%
0.7061
0.0122
AA mid
14.3%
0.3980
0.0101
AA late
5.7%
0.0242
0.0167
Certificate
6.4%
-0.5169
0.0184
Transfer Engl. in Year 1
18.7%
0.1692
0.0100
Transfer math in Year 1
6.7%
0.1583
0.0138
Postponed Engl. & math
15.2%
-0.3985
0.0119
Loan*
3.7%
0.1316
0.0211
Work Study*
3.2%
0.1067
0.0224
Grants (e.g., CalGrant)
16.2%
0.0648
0.0167
Scholarship
2.6%
0.0576
0.0220
Pell Grant
25.6%
-0.0228
0.0157
BOG
39.2%
-0.0514
0.0115
Skills course
1.8%
-0.0022
0.0279
Guidance course
15.6%
-0.0619
0.0104
Reading course
15.7%
-0.1733
0.0119
EOPS
11.9%
-0.0804
0.0158
DSPS
2.8%
-0.0380
0.0220
CalWorks
3.9%
-0.0894
0.0261
groups are expressed in relation to the Asian group and
are all negative (because members of other ethnic groups
have lower odds of transfer than Asian students). Whites,
for instance, are 21% less likely to transfer than Asians, all
other things in the model being equal. The omitted category for first course in math and first course in English is
the transfer level course. Thus, it is possible to know that
students whose first math class is a basic skills course are
49% less likely to transfer than students whose first math
class is transfer math, after controlling for the effects of
the other variables in the model.
In addition to the binary variables, there are two
variables that are expressed
Sig.
Exp(B)
Relative risk of
as a range of values: age and
transfer
GPA. These variables are in0.000
0.871
-12.9%
terpreted in a similar fashion
0.008
0.932
-6.8%
to the binary variables: for
0.000
0.903
-9.7%
each increment in age or GPA
the relative risk increases by
the given percentage. Therefore a student with an “A”
0.000
0.676
-32.4%
average (GPA = 4.0) is 42.3%
0.000
0.527
-47.3%
more likely to transfer than a
0.000
0.611
-38.9%
student with a 3.0 GPA and
0.000
0.441
-55.9%
85% more likely to transfer
than a student with “C”
average or 2.0 GPA (42.3% x
0.000
2.026
102.6%
[4.0-2.0] = 84.6%). Similarly
0.000
1.489
48.9%
a student who enters the co0.148
1.025
2.5%
hort at age 18 is 38% more
0.000
0.596
-40.4%
likely to transfer than a TVP
student who first enrolled at
community college at age 28
0.000
1.184
18.4%
(3.8% x [28-18] = 40%).
0.000
1.172
17.2%
The output of the stu0.000
0.671
-32.9%
dent-level transfer velocity
model is presented in Table
0.000
1.141
14.1%
2. The first column shows the
0.000
1.113
11.3%
student-level characteristic
in question while the second
0.000
1.067
6.7%
column shows the percent0.009
1.059
5.9%
age of the 1999-2000 TVP
0.146
0.977
-2.3%
possessing that characteristic.
0.000
0.950
-5.0%
In the cases of GPA and age,
the second column shows
0.936
0.998
-0.2%
the mean GPA or mean age
upon cohort entry. The third
0.000
0.940
-6.0%
column shows the Beta value
0.000
0.841
-15.9%
(“B”) of the characteristic; a
positive value indicates that
0.000
0.923
-7.7%
the characteristic is positively
0.085
0.963
-3.7%
associated with successful
0.001
0.915
-8.5%
transfer while a negative
Vol. 18, No. 2, Spring 2011 / 25
value means the characteristic is negatively associated with
transfer. The next column shows the standard error (“SE”) of
the Beta value. The standard error is used in calculating the
statistical significance. The next column gives the statistical
significance of the effect associated with the characteristic.
When the significance value is smaller than 0.01 (i.e., p <
.01) the associated effect is considered to be statistically
significant or not likely to have occurred by chance. The
column after the significance applies the exponential function, Exp(B), to the Beta value to derive the relative risk.
The final column simply makes the relative risk easier to
interpret by subtracting one from Exp(B) and then expressing the difference as a percentage. This last column is the
most important column for interpreting the effects of the
variables in the student-level transfer model.
How large does a relative risk have to be in order to
be meaningful? The size of the 1999-00 Transfer Cohort (n
=147,207) means that statistical significance is a low bar for
evaluating the importance of an effect. Indeed, the lack of
significance may itself be of interest in some cases, such as
in the case of CalWORKs and DSPS; the lack of significance
at p < .01 indicates that, all other things being equal, the
transfer rates of students in these programs are statistically indistinguishable from the transfer rates of students
who are not in the program; this could be considered to be
evidence of a successful intervention as students in those
programs are typically overcoming extra barriers on their
path to transfer. Determining whether an association is
large enough to be meaningful is essentially a subjective
judgment dependent upon a number of contextual factors. While others may prefer a different threshold, we
highlight in bold those factors that have a relative risk of
at least +/-10%.
Limitations of the Study
While the use of population data makes for a high
level of confidence in our findings, there are certain limitations inherent in the current approach. First, despite the
extensive set of information available for multivariate
analysis, there is a notable gap in the area of socioeconomic
information. While income and socioeconomic information
is available for those students who apply for financial, only
about half of the 1999-2000 TVP cohort completed a financial aid application. The lack of socioeconomic information
results in the confounding of socioeconomic status with
financial aid and ethnicity, making results for these areas
difficult to interpret. A second limitation of the current
analysis is that, despite the strongly multivariate nature
of the analyses, these are essentially correlational analyses.
Inferences about causation should be made cautiously and
with reference to other research and information sources.
Finally, because we are interested in understanding transfer behavior among those who clearly demonstrate an
26 / Journal of Applied Research in the Community College
intention to transfer, the current approach trades off some
generalizability for a more incisive look at a the population
of interest. The fact that the TVPs transfer models replicate
a wide array of prior research gives us confidence in their
general soundness and also in the soundness of the TVPs
more novel findings.
Discussion and Conclusion
The distinct elements found in the quantitative
analysis of the current study correspond to the factors
identified in the qualitative analysis of research on colleges
with higher than expected transfer rates. (Schiorring &
Mery, 2008). The six transfer-promoting factors identified
by Schiorring and Mery (2008) are: 1) Transfer culture;
2) Student-focused environment; 3) Commitment to the
institution; 4) Strong, strategic high school relationships;
5) Strong four-year college relationships; and 6) Effective
support services
Three of the six factors in particular are corroborated by the endogenous model: the importance of a
“transfer culture” (as indicated by a high proportion of
transfer sections in the class schedule); the importance of
“strong four-year college relationships” (as indicated by
the number of TAA pathways); and “effective support
services” (as indicated by the FTE of the Transfer Center
Director and the number of signed TAAs). The data that
were available for use in the endogenous model did not
sufficiently represent the other three factors, so the current
endogenous model is not able to speak to their relative
importance. These untested factors may be equally important, more important or less important, than the factors
that are included in the current model.
The endogenous model suggests that effective student support services (in this case, an adequately staffed
Transfer Center), strong relationships with four-year
schools (large number of TAAs), and a course schedule
that has a high proportion of transferrable coursework
(relative to CTE courses and non-transferable courses) are
transfer-promoting factors that are within the control of
campus personnel. A student body with a high proportion
of students who attend multiple community colleges is
also positively associated with college-level transfer rates.
This factor is picked up in the student-level model, as well,
and is discussed in more detail in that section.
Earlier research has also highlighted the importance
of some of the factors in the endogenous model. Poisel
and Stinard (2005) examined the importance of interinstitutional networks for transfer success. Of particular
note was the importance of the relationships between
faculty and staff at the university and those at the community college. Adelman (2005) found that students who
attended multiple community colleges were more likely
to transfer. A sustained, strategic focus on the six areas
highlighted by the endogenous model could result in
higher transfer rates over time.
It is worth noting that Schiorring and Mery (2008)
state that transfer culture extends beyond the college
Transfer Center; we agree with this point and do not feel
that an exclusive focus on Transfer Center characteristics
will provide the best understanding of inter-collegiate
differences in transfer rates. However, while the Transfer
Center may not be the totality of college’s transfer culture,
it is likely to be a meaningful barometer of it.
Discussion of the student-level transfer velocity
model, presented in Table 2, is broken into the following
areas: Demographic characteristics; Educational goals;
Academic performance; Enrollment patterns; Coursetaking patterns; Degrees and certificates; Financial aid;
and Special programs.
Asians have the greatest odds of transferring as a
group and thus are the reference group used to interpret
the relative risk of ethnic group membership. Native
Americans and Latinos have the greatest negative relative
risk statistics (-35.4% and -26.5%, respectively). These are
among the largest negative relative risk statistics in the
model. This outcome is particularly important as Latinos
are the second most populous ethnic group in the transfer
cohort and in the CCC system as a whole. Raising the
transfer success of Latinos and Native Americans should
be aspects of any plan to improve Baccalaureate attainment in California.
Educational Goals
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Student intention and motivation is an important
part of student-level model of transfer. Students’ goals
may change over time and thus a student may have
Demographic Characteristics
multiple goals over their postsecondary career. The
At the top of Table 2, one can see that increased age educational goal variables in the transfer velocity model
is negatively associated with transfer. While the effect for therefore indicate whether a student has ever expressed
a one-year difference is not large, students who are 10, 20, a given goal. For example, 59.0% of the 1999-2000 TVP
or 30 years older than is typical are much less likely to cohort students declared a goal of obtaining an associate’s
transfer (-37%,- 74% and -111%, respectively). This finding degree and also transferring to a four-year school (i.e.,
was present in the exogenous college-level model as well, “degree and transfer”) at least once in their academic cawhere colleges with older student bodies tended to have reer at the CCC, but some students who declared “degree
reliablyVelocity
lower transfer
Transfer
Project rates (model not shown). Gender and transfer” may have also declared another goal such as
was also found to have a small negative effect on the odds improving their basic skills (i.e., “basic skills”) in which
Figure
1. Relative
risk associated with ever declaring given educational goals
of transfer
(-5.6%).
case the relative risk of both goals would apply to them.
Relative risks are cumulative such
that the relative risk of a student
50%
who had declared a goal of “basic
39%
40%
skills” and “degree and transfer”
would be 14.6% (16.2% - 1.6% =
30%
30%
14.6%).
Table 2 and figure 1 show that
16%
20%
there are three educational goals,
which are relatively potent predic10%
5%
3%
1%
tors of successful transfer: Transfer
0%
0%
only (39%); Intellectual growth
-2%
-3%
(30%); and Basic skills (16%). Hav-10%
ing an educational goal that purely
focuses on transfer is, reasonably
-20%
-19% -20%
enough, positively associated with
-30%
successful transfer. Perhaps somewhat more surprising is the finding
-36%
-40%
that a goal of intellectual and cultural development is also positively
associated with transfer. One might
think that a goal of intellectual and
cultural development would be a
characteristic of lifelong learners
Figure 1. Relative risk associate with ever declaring given educational or students who already possess a
postsecondary degree and are regoals.
turning to the CCC for intellectual
Vol. 18, No. 2, Spring 2011 / 27
stimulation. Here it is important to remember that we
are analyzing a special cohort of students. Students who
already possess a postsecondary degree are excluded
from the transfer cohort by definition, and are therefore
not included in the model. When interpreting this finding we should be cognizant of this exclusion by phrasing
the finding thus: the students who demonstrate that they
intend to transfer and who declare the educational goal
of “intellectual and cultural development” are 30% more
likely to transfer than those TVP students who do not
declare that goal.
The 16% boost in the odds of transfer that is associated with a student indicating a goal of basic skills is intriguing. It suggests that a focus on improving basic skills
is congruent with successful transfer, at least for those
students who are able to progress far enough to complete
12 credit units and attempt a transfer level English and/
or math class.
The majority of students in the 1999-2000 TVP cohort
(59%) declared “Degree and transfer” as a goal at some
point. Therefore, the goal of “Degree and transfer” is not
strongly associated with successful transfer because it
does not distinguish among students. Those goals which
have strong relative risks associated with them are less
common; the six educational goals that have the largest
relative risk statistics are indicated by only about a quarter
of the cohort or less.
The three educational goals, which are negatively
associated with transfer are: Certificate only (-19.1%);
Maintain license (-19.9%); and Degree only (-35.7%). These
negative associations may indicate that there are some
students inappropriately included in the TVP cohort. That
is, the TVP cohort is behaviorally defined in such a way
as to focus on students who are likely pursuing transfer.
However, the cohort definition is not perfect. Students
with these three goals are less likely to transfer than other
TVP students and some of them, particularly the 13.8% of
the TVP cohort who indicate that they only want an associate’s degree, may be inappropriately included in the
TVP. Those students who are expressly seeking terminal
associate’s degrees are distinct from those who indicate
a goal of degree attainment and transfer. The negative
association between obtaining a certificate achievement
and transfer supports this interpretation, as well.
Academic Performance
A student’s competencies and course-level success
has a definite effect on successful transfer. An increase of
one grade point in GPA is associated with a 41% greater
chance of transfer. Thus, a straight “A” student is 41%
more likely to transfer than a student with a “B” average. Other grades are important, as well. Of particular
relevance is the “W” grade which is given when a student
withdraws in the middle of a class. Around half of the TVP
28 / Journal of Applied Research in the Community College
students (46%) had transcripts in which 20% or more of
their enrollments resulted in “W” grades (withdrawals).
The impact of receiving a large number of “W” grades
is quite negative. By contrast those TVP students with a
moderate to low level of “W” grades (fewer than 20% of
grades are “W” grades) were much more likely to successfully transfer (59%) than those students with more
“W” grades.
There is an inherent tension between “W” grades
and GPA. Students who withdraw from a course may
receive a “W” but those who stay even though they may
have missed a large number of classes or perhaps are
simply not “getting it” will receive an “F” or “D” grade
(in all likelihood). In other words, it is a dilemma for the
student and for those who would advise that student. In
most cases where a “W” grade is received the only other
possible option would have been an “F.” Neither are
desirable outcomes, but at least the “W” grade allows for
several opportunities to re-take the class with no negative
impact on the student’s cumulative GPA.
Used judiciously, the “W” option may help students,
but in excess, it is not going to facilitate student transfer.
It seems that in this case interventions aimed at keeping
a student focused on schoolwork and attending class
regularly may be the best hope of reducing the incidence
of both “W” grades and non-passing grades.
Enrollment Patterns
Several enrollment patterns are positively associated
with transfer: 1) attending a CCC while in high school
(i.e., concurrent enrollment); 2) attending more than one
community college; and 3) enrolling in summer sessions
within the first three years of college. Of these three, summer enrollment has the largest relative risk (47%) and it
also has the virtue of being a relatively straightforward
matter to encourage both through policy and through
direct action.
Concurrent enrollment is modestly transfer-enhancing factor, suggesting that encouraging high school
students to take college courses while they are still attending high school will predispose those students to be
more successful at the community college, or perhaps to
enter directly into a four-year school. In any case, students
who were concurrently enrolled at the high school and at
the community college were 11% more likely to transfer
than those students who did not experience concurrent
enrollment.
Enrollment at multiple colleges has a positive relative risk; students who attend multiple colleges are 15%
more likely to transfer than students who attend only a
single college. Forty percent of TVP students have enrollment records at more than one college. The reasons the
rate of multiple college enrollment is so high are many.
For example, students may change residences with the
result that another college is more proximate; one college may offer courses that are not full or not offered at
the student’s primary college; a different college may
have a better reputation or may offer different services,
and so on. Enrollment at multiple colleges is common in
the TVP cohort; but why does it have a positive effect on
transfer success?
Reasonable arguments can be made for anticipating either a positive or negative effect of attending multiple community colleges. On one hand, when a student
changes colleges it may be disruptive, requiring adaptation and extra effort, such as more time commuting, new
teachers, and so on. These disruptive effects might sap
resources and rob attention that could better be devoted
to study. On the other hand, attending multiple colleges
may allow students to expedite their progress. They can
pick up classes that are impacted or unavailable at their
primary institution and gain practice in navigating a new
institution. They may need to transfer transcripts, credits,
or test scores – activities that may be necessary to waive
pre-requisites or to take an advanced class. In addition to
the increase in transfer velocity that can be achieved by
picking up needed classes, navigating the bureaucracy of
another institution may prepare students for the transfer
experience with a four-year school. Multiple community
college attendance was predictive of transfer in the endogenous college-level model, as well, suggesting that
this finding is robust.
To further test the assumption that student swirl
is appropriate to consider as an endogenous factor, we
analyzed the distribution of student swirl to determine if
swirl were purely a function of geographic location. We
found that there was no association between college size
(a proxy for rural/urban) and the proportion of students
with multiple community college attendance. Breaking the
colleges into quartiles based on the average swirl level of
their student bodies showed that there were rural colleges
with low swirl levels, but that there were also rural colleges with intermediate and high swirl levels. Finally, we
observed a moderately strong correlation between average
swirl level and the BA Plus Index (r = .47) indicating that
lateral transfer or swirl may be a “proximal” variable that
partially explains how the BA Plus Index creates its effect.
In other words, lateral transfer may be a strategy that
students from areas with high average levels of education
use to advance their educational aspirations.
time frame. On the other hand, 15% of TVP students took
neither a math nor a reading class in their first year at the
community college. Students who showed this pattern
of postponing enrollment in both math and English were
one third less likely to transfer than those who took math
and/or English in their first year.
In general, students who start at transfer level enjoy better odds of successful transfer, than students who
start at the degree-applicable, non-transferrable level or
at the basic skills level. Students who start at the degreeapplicable, non-transferrable level of English are 10.4% less
likely to transfer than those who start at the transfer level;
those who start at the basic skills level are 12.9% less likely
to transfer than those who start at the transfer level.
The relative risk statistics for initial math level are
more pronounced than those for initial English level. Students who start at the degree-applicable, non-transferrable
level in math are 32.4% less likely to transfer than those
students whose initial math class is at the transfer level.
Those starting at the basic skills level in math are experience
a 48% reduction in their odds of transfer, relative to those
who start at the transfer level. Not taking any math at the
CCC resulted in a 51.4% reduction in the odds of transferring, indicating that math is an especially difficult bottleneck
for many students. Working closely with high schools to
improve the initial placement levels of incoming students
should be an important aspect of any plan to improve the
number of Bachelor’s degrees awarded in California.
Students’ enrollments in Career Technical Education (CTE) classes were also found to be associated with
transfer. If 10% or more of a student’s units are from Career Technical Education (CTE) classes, the likelihood of
transfer is decreased; students with transcripts containing
fewer than 10% of CTE enrollments were 14% more likely
to transfer than those students whose transcripts contained
more CTE enrollments.
The three categories of “extra assistance” classes
(Guidance, Reading, and Study Skills) all had low incidences of use among the TVP. They also had small
negative associations with transfer (except for study skills,
which had no significant effect), most likely because those
students who did seek out these classes were seeking to
overcome academic weaknesses. Another possibility is
that the guidance and/or reading classes were being used
disproportionately by job-seeking students who have a
lower incidence of transfer.
Course-Taking Patterns
Degrees and Certificates
Previous research (e.g., Adelman, 2005) has shown
that taking math and English courses immediately upon
college entry is correlated with degree completion and
transfer. In the TVP we found that 88% of students took
at least one math class within six years of enrolling and
that 91% of TVP students took an English class in the same
There is a very large positive effect of obtaining an
Associate’s degree (either an AA or AS) within either the
first six years of enrollment. Obtaining an AA/AS was
found to be a time variant factor in relation to transfer.
That is, the effect is not constant over time, it is more pronounced for those students who attain a degree within the
Vol. 18, No. 2, Spring 2011 / 29
first three years, or “early”; these students were 103% more
likely to transfer than students who did not receive an AA
within the first three years. Students who attain a degree
in the middle years (four through six) still enjoy a robust
positive effect (49% more likely to transfer). It may be that
California’s road to more Bachelor’s degrees is actually
paved with Associate’s degrees, particularly for African
American and Latino students who experienced a much
greater boost from attaining an Associate’s degree (193%
and 263%, respectively) than did whites and Asians.
By contrast, the 6% of TVP students who attain certificates are 40% less likely to transfer than those who do
not receive certificates. The effect does not vary over time;
it is negative regardless of the year in which the certificate
is attained. It is likely that many of the students who obtain certificates are “false positives” in terms of their entry
into the TVP cohort. That is, many of these students have
no intention to transfer but instead are seeking a terminal
certificate and employment in a related field.
Priorities to Consider
It is very important to realize that a large data mining
project such as this that is based on correlational methods
does not directly support causal inferences. It is difficult to
rule out competing hypotheses and draw truly causal conclusions. Still, efforts were made to include theoretically
sound variables and relationships which might suggest
action. Additional data sources, qualitative assessment
and future research will be able to tease out which relationships are truly causal. Action based on findings from the
current study could be evaluated to judge the causality of
the relationships suggested here.
While a great deal of information was analyzed in the
TVP, certain findings rise to the top in terms of the strength
of their associations and their accessibility to intervention.
We consider several of these now.
Class Withdrawal
Withdrawals are a significant issue that should be
tackled head-on by the system as a whole. The impact of
many “W” grades is clearly detrimental to transfer. While
there is evidence that excessive “W” grades are negatively
associated with transfer, this is an area where causal interpretations must be carefully considered. On campuses,
the “W” grade is often seen as a student-friendly way of
letting students who would otherwise fail have a chance
at being successful in their next attempt. While excessive
use of the “W” option is not associated with student success, the alternative grade is usually an “F” and a low
GPA does not promote transfer either. Both “F” and “W”
grades are likely to stem from the same persistent causes
(e.g., inadequate skills, disruptive personal issues, lack of
commitment, etc.). Limiting or prohibiting “W” grades
30 / Journal of Applied Research in the Community College
would be a bit like outlawing aspirin in an effort to stop
people from getting headaches. It would be an intervention aimed at the symptom, not the cause.
Proper intervention requires reflection upon root
causes. Effective actions to combat “W” grades are likely
to involve new resources and the reconfiguration of existing resources. For instance, students who are at risk for
failure could be identified at the time of enrollment based
on diagnostic information that is already available in their
transcript or from their placement testing. They could then
be routed to special class sections such as learning communities which provide greater support and guidance to
students who need more personal support in order to succeed. Such a proactive approach to heading off “W” grades
before they occur is analogous to the medical practice of
screening for risk factors. When risk factors are identified
(e.g., a student presents with a history of “W” grades)
then resources could be targeted to advise, track and if
necessary offer tutoring, special assistance, legal advice,
coaching, or remediation as the situation demands.
Summer Enrollment
The student-level model suggests that students
should be encouraged to enroll in summer terms, particularly in their first three years. The observed effect
may be partly due to the self-selection of more motivated
students choosing to enroll in summer school, but there is
good theoretical basis for expecting summer enrollment
to increase student engagement and retention, ultimately
facilitating transfer.
Math Support
In terms of supporting specific course work that
will enhance the odds of student transfer, the greatest
leverage may be gained by adding additional supports
and processes that will ensure that students are capable
of attaining and completing transfer-level math. To this
end, greater alignment and collaboration between high
schools and community colleges may increase the level
of initial student placement and thereby enhance entering
students’ chances of transfer success. Further pedagogical
innovations should be actively explored and supported as
the system seeks ways to improve math success.
Encourage Associate Degree Attainment
Finally, students should be encouraged to attain an
associate’s degree as quickly as possible upon entry to the
CCC even if they express a desire to transfer. Far from being a distraction, the attainment of an associate’s degree is
one of the most positive, transfer-facilitating factors in the
study. Attaining an AA or AS within the first three years of
enrollment increases the odds of transferring by 103%. Part
of the reason for the strong impact is that the qualifying for
an AA or AS within three years implies a fairly consistent
level of mostly full-time enrollment. But beyond the accumulation of units, qualifying for an associate’s degree
requires a certain transfer-conducive structure to the units
acquired. Thus, students’ with associate’s degrees may not
stray far from transfer requirements and have maintained
a good transfer velocity. Associate’s degrees do not fully
map to transfer, of course, and the requirements vary by
CCC and by transfer destination, but in many key respects
the AA/AS pathway provides guiderails for students to
maintain their transfer velocity.
Future Research
Future research should extend the current research
findings and method to an analysis of those factors that
lead first time freshmen to pass the threshold of entry into
a TVP cohort (or not). Students in the TVP had a relatively
high transfer rate, accounting for 67% of transfers from
a given cohort year of all entering first-time freshmen,
despite representing only 25% of the larger first-time
freshman cohort. It is possible that the return on investment by encouraging more students to get to the initial
momentum point of entering a TVP cohort (i.e., attempting
a transfer level English or math course and accumulating
12 for-credit units) will be larger than focusing efforts on
raising the transfer success of those students in the TVP,
an already largely successful group.
Applied research into the transfer function would
benefit from the development of a new transfer cohort
possessing characteristics that would enhance its usefulness to researchers, practitioners, and policy makers. Work
should begin to create a transfer cohort that can be identified at three years, rather than six. Our research indicates
that transfer patterns for a cohort of first-time freshmen
are well-set by the third year. A shorter time frame to set a
cohort would allow for more rapid analysis and feedback in
response to initiatives and programs that support transfer.
Transfer rates become reliable at three years and can be used
to predict six year and twelve year rates with a high degree
of accuracy. Any future transfer cohort definition should
have at least the same level of sensitivity and specificity
that is seen in the current TVP cohort definition.
A comprehensive study of the impacts of financial
aid on transfer velocity would be a complex but very
worthwhile undertaking. The apparent under-utilization
of financial aid by certain ethnic groups in the TVP cohort,
most notably Filipinos and Whites, provokes more questions, perhaps, than it answers. In an effort to cross-check
the pattern observed in the 1999-2000 Transfer Cohort, we
examined the entire system enrollment for the 2000-2001
academic year and found a pattern of financial aid usage
across the entire student population quite similar to that
observed in the TVP. The similar patterns of financial aid
usage suggests that more outreach should be done in order
to encourage greater use of grants and Board of Governor
(BOG) fee waivers, which do not have to be repaid. And
yet, there is still much that remains to be explored in terms
of the effectiveness of financial aid. Recently, a new data
element that assesses first-generation college status has
been added to the centralized CCC application program.
In the future this data element will be useful in providing a statistical control for all students. In the past, there
was little statistical information available about students
who did not choose to apply for financial aid. Can we
recommend greater use of loans and work study? Does the
relatively modest size of the positive relative risk warrant
widespread adoption of these forms of student aid?
There is some evidence that loans and work study
are particularly effective forms of financial aid for promoting student transfer and yet their utilization rates are
quite low. This finding is important because little work
has been done at the Community College on the relative
merits of student loans versus other types of aid. Some
researchers have reported finding a negative to neutral
impact student loans on student success at four-year colleges (Doud & Coury, 2006; Kim, 2007).
Prior research on the effects of the form of financial
aid known as work study has found that work study is
associated with higher student retention at college. This
effect is typically explained as a result of higher levels of
student engagement (e.g., Astin, 1999). The TVP found
that student participation in work-study is a positive,
transfer-facilitating factor that increases the odds of
transfer by 13%.
Kane (2003) found that CalGrants had a strong effect
on initial college-going behavior. The current study found
that receipt of non-Pell grants (i.e., “Other Grant”) – the
vast majority of which are CalGrants – increased the odds
of transfer by 8%. This is another important TVP finding
concerning the effects of financial aid. Because of the wide
ethnic differences in financial aid utilization observed in
this study and others (e.g., Kane, 2003) a more extensive
analysis of the impacts of financial aid across various ethnic and socioeconomic groups is called for. Studying the
combinatorial and interaction effects of different patterns
of aid utilization over time would deepen our understanding of the true impact of financial aid and allow for better
optimization of resources and results.
Another area worthy of additional research is multiple community college attendance. A more in-depth investigation of lateral transfer and its benefits for students
would shed more light on a common phenomenon that is
beginning to emerge as a way of empowering students to
advance their academic career and course-taking agenda.
How can multiple-college enrollment be managed to
enhance student success in larger numbers?
In closing, the goal of interventions targeting transfer
Vol. 18, No. 2, Spring 2011 / 31
success should be to increase the transfer velocity students
flowing from community colleges into institutions where
they can attain a Bachelor’s degree. The effect of increased
efficiencies resulting in reducing the average time to transfer for the TVP cohort by one year would result in overall
4% increase in the standard six-year transfer rate, representing 6,000 more transfers annually. The cascade effect
of having all six-year transfers complete in five years and
all five-year transfers complete in four years, etc. would
create a large swell in the transfer-ready population. No
one intervention is likely to result in such a potent effect,
but a series of well-targeted, well-resourced interventions,
calibrated by accurate assessment of the transfer function
would greatly increase the odds.
References
Adelman, C. (2006). The Toolbox Revisited: Paths to Degree Completion From
High School Through College. Washington, D.C.: U.S. Department
of Education.
Attewell, Paul, Lavin, David, Domina, Thurston, and Levey, Tania
(2007). Passing the Torch: Does Higher Education for the Disadvantaged
Pay Off Across the Generations? Russell Sage Foundation
Bailey, T., Jenkins, D., & Leinbach, D.T. (2006). Is Student Success Labeled
Institutional Failure? Student Goals and Graduation Rates in the
Accountability Debate at Community Colleges (Working Paper No.
1). New York: Community College Research Center, Teachers
College, Columbia University
California Community College Chancellor’s Office (2007). Transfer Rate
Study of California
Community Colleges (2005-06 Report), available at www.cccco.edu/
Portals/4/TRIS/research/reports/transfer_report.pdf.
California Department of Finance (2006). California Public Postsecondary
Enrollment Projections, 2006 Series, Sacramento, California.
California Postsecondary Education Commission (2007a). California
Higher Education Accountability: Goal—Student Success, Measure:
California Community College Students’ Degrees and Certificates
Awarded and Successful Transfers, available at www.cpec.ca.gov/
completereports/2007reports/07-06.pdf.
California Postsecondary Education Commission (2007b), College-Going
Rates: A Performance Measure in California’s Higher Education Accountability Framework, Commission Report 07-04, available at:
www.cpec.ca.gov/completereports/2007reports/07-04.pdf
California Postsecondary Education Commission (2008). Who Can Afford
It? How RisingCosts Are Making College Unaffordable for Working
Families, Sacramento, California.
Campaign for College Opportunity, Practices with Promise 2008: A Collection of Working Solutions for College Opportunity and Student
Success, available at www.collegecampaign.org/assets/docs/
pwp/pwp-full-report-2008-11-19.pdf.
Dowd, Alicia and Coury, Tarek (2006). “The Effect of Loans on the Persistence and Attainment of Community College Students”. Journal
Research in Higher Education (Vol. 47, No.1, pp. 33-62).
Driscoll, Anne K., “Beyond Access: How the First Semester Matters
for Community College Students’ Aspirations and Persistence,”
Policy Analysis for California Education, Policy Brief 07-2, Berkeley,
CA, August 2007.
Hill, Elizabeth G., Back to Basics: Improving College Readiness of Community College Students, Legislative Analyst’s Office, Sacramento, CA, 2008.
32 / Journal of Applied Research in the Community College
Hom, Willard (2009). “The denominator as the target,” Community College Review, 37 (2)
Horn, L., & Lew, S. (2007a). “California Community College transfer
rates: Who is counted makes a difference,” MPR Associates, Inc.,
Berkeley, CA.
Horn, L. & Lew, S. (2007b). “Unexpected pathways: Transfer patterns
of California community college students,” MPR Associates, Inc.,
Berkeley, CA
Hans, J. & Reed, D (2007). “Can California import enough college graduates to meet workforce needs?” California Counts, Vol. 8, No. 4, Public Policy Institute of California, San Francisco, CA, May 2007.
IPEDS (2010). Accessed on 8/7/10 https://surveys.nces.ed.gov/ipeds/
VisInstructions.aspx?survey=4&id=491&show=all
Kane, Thomas J. (1994). “College entry by Blacks since 1970: The role of
college costs, family background, and the returns to education,”
The Journal of Political Economy, 102 (5), pp. 878–911.
Kane, Thomas J. (2003). “A Quasi-Experimental Estimate of the Impact
of Financial Aid on College-Going,” National Bureau of Economic
Research Working Paper 9703, available at www.nber.org/papers/
w9703.pdf.
Kelly, Patrick J., As America Becomes More Diverse: The Impact of State
Higher Education Inequality, National Center for Higher Education
Management Systems, November 2005, available at www.nchems.
org/pubs/docs/Inequality%20Paper%20Jan2006.pdf.
Kim, D. (2007). “The Effect of Loans on Students’ Degree Attainment:
Differences by Student and Institutional Characteristics”. Harvard
Educational Review, 77 (1).
Mery, P. & Schiorring, E. (2008). “A Qualitative Study of Two-to-FourYear Transfer Practices in California Community Colleges,” RP
Group, available at: www.rpgroup.org/documents/TLC_Cross_
Case_Analysis.pdf.
National Center for Public Policy and Higher Education (2008). Measuring Up 2008: The National Report Card on Higher Education, San
Jose, CA.
Offenstein, J., Moore, C., & Shulock, N. (2010). Advancing by Degrees: A
Framework for Increasing College Completion, Institute for Higher
Education Leadership & Policy and The Education Trust, Sacramento, CA.
Provasnik, S., and Planty, M. (2008). Community Colleges: Special Supplement to The Condition of Education 2008 (NCES 2008-033). National
Center for Education Statistics, Institute of Education Sciences,
U.S. Department of Education. Washington, DC.
Reed, Deborah, California’s Future Workforce: Will There Be Enough College Graduates? Public Policy Institute of California, San Francisco,
CA, 2008.
Rostgaard, Klaus (2008). “Methods for stratification of person-time and
events – a prerequisite for Poisson regression and SIR estimation,”
Epidemiological Perspectives & Innovations, 5(7) available at: http://
www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2615420
Sengupta, R., and Jepsen, C. (2006). California’s Community College
Students, California Counts, Vol. 8, No. 2, Public Policy Institute
of California, San Francisco, CA.
Shulock, Nancy, and Colleen Moore, Rules of the Game: How State Policy
Creates Barriers to Degree Completion and Impedes Student Success in
the California Community Colleges, Institute for Higher Education
Leadership and Policy, Sacramento, CA, 2007.
Craig Hayward, Ph.D., is Director of Planning, Research,
and Knowledge Systems at Cabrillo College