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Pedagogical Research, 2018, 3(2), 08
ISSN: 2468-4929
Student Characteristics and Enrollment in a CTE Pathway Predict Transfer
Readiness
Renah Wolzinger 1*, Henry OLawrence 2
1 CSULB, 1250 Bellflower Blvd., CA, 90840 Long Beach, UNITED STATES
2 Saddleback College, CA, UNITED STATES
*Corresponding Author: rwolzinger@saddleback.edu
Citation: Wolzinger, R. and O’Lawrence, H. (2018). Student Characteristics and Enrollment in a CTE
Pathway Predict Transfer Readiness. Pedagogical Research, 3(2), 08. https://doi.org/10.20897/pr/91653
Published: June 14, 2018
ABSTRACT
This research predicts transfer readiness of students characteristics and enrollment in CTE pathway; and
most significantly addressed statewide CTE transfer students that meet the transfer requirements under
CTE Taxonomy of program (TOP) code for students who transfer to a California State University (CSU),
and Private or out-of-state university. There is a lack of adequate pathways for CTE students to prepare for
transfer to the California State University system. The CTE pathways include a high number of
underrepresented students, compounding the problem of equity in current transfer policy. Research
indicates that students in career and technical education pathways have a limited path toward a university
transfer. Policy has been enacted to improve transfer processes between California community colleges and
the CSU system, however the policy does not directly address barriers for students in CTE programs.
Keywords: scheduling, private schools, public schools, athletics, open spaces
INTRODUCTION
To address the transfer problem, in 2010 the California legislature enacted Senate Bill 1440 (Padilla). SB1440
requires the community colleges and the CSU to work together to develop Associate Degrees for Transfer (ADT).
Students completing the ADT with a minimum 2.0 GPA would receive registration priority at a CSU in their area
and in a major similar to their community college major, if it is available. They will enter with junior standing and
will complete 60 additional units to graduate with a bachelors degree. This law was aimed at reducing the number
for courses and units needed to transfer, and reducing the overall time it takes to complete a bachelors degree.
The law does not explain how this might be achieved, as it is left for key leadership in higher education to develop
a successful plan (Patton, 2012).
Faculty at the California community college and the CSU system have worked together to design the associate
degrees for transfer in over 30 majors, but have not incorporated many majors associated with Career and
Technical Education (CTE) courses (Patton, 2012). CTE refers to courses designated in the course outline of
record as occupational. These courses typically include applied learning simulating problem solving incorporating
equipment used in the workplace. CTE was formerly coined vocational education, and was an integral part of the
community college mission since its inception (Koos, 1921).
Students interested in a CTE area who wish to transfer may have to find an alternative pathway or complete a
transfer major outside of their area of interest. This problem may create extra time for students to pursue a transfer
degree, and may uncover an equity gap for underserved populations of students in CTE who are not able to
complete a pathway to a four-year institution.
Wolzinger and O’Lawrence / Transfer Readiness of Student’s Enrollment in CTE Pathway
2 / 9 © 2018 by Author/s
The design of a transfer degree between a community college and a four-year institution has several important
components. Kisker (2012) describes the elements of effective transfer degrees related to student persistence as
including common general education requirements, early pre-major pathways, transfer of credits, transfer as junior
status, priority admission, four-year degree credit limits, and acceptance into upper division courses. Each of these
elements contribute to increased transfer and degree completion when included in the design of a transfer
curriculum. Common general education requirements accepted as a block to a receiving public university regardless
of their own general education patterns simplifies lower division scheduling and eliminates many roadblocks.
Common pathways have been implemented in CA for many transfer majors, mostly non-technical. Transfer of
credits has been a barrier for many technical programs, as course design differs between community college and
universities. These elements emphasize the need to develop better pathways for students in career and technical
education. Each of these factors affect the ability to transfer as junior standing to a university (Kisker, 2012).
Research Purpose
The purpose of this research was to determine if students characteristics and enrollment in CTE pathway
predict transfer readiness. In California, the statewide data provides an analysis of the state transfer for CTE
students, however it does not provide findings to particular single institution. Therefore, this research addressed
statewide CTE transfer students that meet the transfer requirements under a CTE Taxonomy of Program (TOP)
code for students who transfer to a CSU, private or out-of-state university. For point of clarification, transfer
students from CTE areas as designated by the California Chancellors TOP code system are included in the analysis
using Taxonomy of Program (TOP) code to distinguish students in CTE pathways. The technical requirements
for jobs require more skills and the research has shown advantages to those who transfer and complete a bachelor
degree to be competitive in the job market. This study linked CTE pathways with enrollments and transfer data to
understand which types of students are enrolling in CTE pathways and if they are becoming transfer ready.
Problem Statement and Conceptual Framework
Pathways in CTE areas that lead to transfer to a CSU are far less prevalent than transfer pathways in other
academic areas (Moore, 2014). CTE pathways have many definitions, however for this study CTE pathways refers
to the courses identified by the same two-digit Taxonomy of program (TOP) code. The TOP code is a numerical
designation of a type of program offered. TOP code is used at the state level to gain information on programs that
have similar outcomes. There is an increase in the number of projected job openings in the next decade requiring
a four-year degree and technical skills, informing us that new practices are needed to grow the pathways for CTE
and transfer (Carnavale, 2013). Carnavale found that 24% of all jobs require a bachelor degree and are concentrated
in managerial, office and education clusters (Carnavale, 2012). As educators prepare the future workforce in
California, they need to design and implement clear transfer pathways for students (Moore, 2014). The
CTE/Transfer pathway addresses both providing pathways for learning technical skills leading to a four-year
degree, and training our diverse population in California to prepare for middle skill jobs, such as those found in
office and administrative services, construction, healthcare, and protective services.
REASEARCH METHODS
This quantitative study examined the pathways for CTE students that lead to an Associates Degree for Transfer
(ADT) to a California State University. The current development of ADTs in CTE areas and student outcomes
was examined. The equity gap between CTE student transfers and academic transfers was also explored. Data is
also available statewide by college and TOP code on CTE student outcomes, including student characteristics.
Utilizing the inferential statistics in a large data set enables the researcher to make generalizations about the greater
population (Morgan, 2012). The statewide study provides new information about CTE pathways and transfer when
looking at the two data sets.
Instrumentation
The California Community Colleges Chancellors Office Management Information Systems Data Mart is a
statewide data system that includes student data submitted by the 113 California Community Colleges
(http://datamart.cccco.edu/datamart.aspx). The Data Mart provides information about students, courses, student
services, and student outcomes. This database is available for public query to provide information sought by
researchers, practitioners or others to answer questions related to the California community colleges. Cal-PASS
Plus is a database that contains annual student level data from the California Department of Education K-12
CalPads data, California Community College MIS data, and many CSU and UC institutions
(https://www.calpassplus.org). The survey data from the California Employment Outcome Survey is also included
in the Cal-PASS Plus database. This database is used to answer questions about California community college
Pedagogical Research, 3(2), 08
© 2018 by Author/s 3 / 9
student outcomes, including the pathways and outcomes students experience moving from one system to another
and post college experiences (Table 1). Data Tools used in the Study to evaluate CTE Transfer Programs presents
a summary of these data tools and the study purpose of each tool.
Research question one addresses how student characteristics and enrollment in a CTE pathway predict transfer
readiness. To answer this question, a logistic regression was used. A logistic regression test was used to predict a
categorical variable from a set of predictor variables(Leech, 2015, p. 167). A logistic regression test is used to
predict a dichotomous outcome (Morgan, 2012). Logistic regression was conducted to assess whether eighteen
predictor variables (age, foster youth, financial aid, skills builder, female, other gender, African American, Asian,
Filipino, Pacific Islander, Other Ethnicity, Architecture, Business Management, Media and Communications,
Information Technology, Education, Engineering and Industrial Technology, and Commercial Services)
significantly predict whether a student became transfer ready. The model was checked for the assumptions that
variables were linearly related and the conditions were checked and met.
Research question two addresses how enrollment in a CTE pathway predicts time to transfer readiness. To
answer this question, a multiple regression was used. The dependent variable is time to transfer readiness. The
independent variables are age, gender, ethnicity, students with disabilities, first generation students, students who
received financial aid, basic skills students, and the top fifteen CTE pathways.
FINDINGS
The first research question in the study addressed how student characteristics and enrollment in a CTE pathway
predict transfer readiness. When all eighteen variables were considered together, they significantly predicted
whether a student becomes transfer ready (
χ
2 = 7136.34, df = 32. N = 65,535 p < .001). The log likelihood in Block
1 was 46070.251, and in Block 2 it was 45395.134, indicating that the second model is a better fitting model. The
analysis below begins with Model 2, which included student characteristics with the addition of CTE pathways
designated by Taxonomy of Program code.
The classification Table 1 below shows that overall there is a predictive capacity of 86%. This refers to the
participants that were predicted correctly as becoming transfer ready using the significant variables in the model,
the same number as found in Model 1. The table also shows a prediction of 8374 students becoming transfer ready,
and 56,050 students not becoming transfer ready. Therefore, adding variables between Model 1 and Model 2 did
not change the predictability of participants becoming transfer ready. The tables in the equation for Block 1 shows
that there is not an equal number of transfer ready and non-transfer ready students. There is an 84% greater
likelihood of a student not becoming transfer ready Exp(B) = .160. The omnibus model shows that when we
consider all eighteen predictors together, the model equation is significant (
χ
2 = 675.111, df = 16. N = 65,535, p <
.001).
Table 1. Predictive Model of Students Becoming Transfer Ready
Step 1
Observed
Predicted Transfer Ready
(Yes)
Predicted Transfer Ready
(No)
Percentage Correct
Not Transfer Ready
no
56050
460
99.2
Transfer Ready
yes
8374
651
86.5
Note. The cut value is .500
Table 2 below presents the odds ratios, which suggest that the odds of becoming transfer ready increase the
most if a student is enrolled in architecture, business management, or information technology. The odds of
becoming transfer ready also increase if the student is Asian, receiving financial aid, or is a skills builder (taking at
least 5 units in the same two-digit top code). The odds of not reaching transfer ready status are significant for older
students, foster youth, other gender, African American, Filipino, Pacific Islander, other ethnicity. The odds of not
reaching transfer readiness are also significant for students enrolled in engineering and industrial technology and
commercial services.
Table 2. Variance on Whether Students Became Transfer Ready
-2 Log likelihood
Cox & Snell R Square
45395.134a
.103
a Note. Estimation terminated at iteration number 20 because maximum iterations have been reached.
The odds of becoming transfer ready are denoted by Exp (B), and are highest for students who are in the
pathways for Architecture, Business Management or Information Technology. The student characteristics
Wolzinger and O’Lawrence / Transfer Readiness of Student’s Enrollment in CTE Pathway
4 / 9 © 2018 by Author/s
contributing the highest amount to becoming transfer ready include students who received financial aid, students
who were skills builders (returned to college to take additional courses related to a job), or were Asian. The
characteristics that predict that a student is less likely to persist in becoming transfer ready are older students, foster
youth, students who identify as other gender, African American, Filipino, Pacific Islander, or other ethnicity. There
is also a negative effect for prediction to becoming transfer ready in Engineering and Industry Technology and
Commercial Services pathways. The negative effect indicates there is a prediction that these groups take longer to
become transfer ready.
In the logistic regression model, whether a student became transfer ready during the five-year study period
between Fall 2009 through Spring 2015 (transfer ready) was the dependent variable, and student characteristics
and CTE pathway (two-digit top code) were the independent variables. The variables not in the equation table
show that ten of the sixteen variables (age, financial aid, first generation, skills builders, other genders, African
American, Asian, Filipino, two or more races, and Other Ethnicity) within the set of variable proposed were found
to be significant. These variables are individually significant predictors as to whether students became transfer
ready. This indicates that the subsequent models should yield significant results about predictors to transfer
readiness. The omnibus model shows that when we consider all ten predictors together, the model equation is
significant (
χ
2 = 6461.23, df = 16. N = 65,535, p < .001).
In block one of the linear regression model, there were eleven significant variables that predict a student
becoming transfer ready, including age, financial aid, disabled, skills builder, female, other gender, African
American, Asian, Filipino, Pacific Islander, or other ethnicity. The odds of becoming transfer ready are denoted
by Exp (B), and are highest for students who are skills builders or who are Asian. Skills Builders in this model are
students who took five units or more in the same pathway as denoted by two-digit top code. There were twelve
statistically significant variables found to predict length of time to transfer, F (28, 8996) = 45.73, p <.001. These
variables included age, ethnicity if identified as Asian, Filipino or two or more races, received financial aid, was
disabled, first-generation college students, or took basic skills courses. Pathways that predicted transfer readiness
F (15, 9009) = 82.70, p <.001 were Education, Health, Family and Consumer Science and Law. Significant
regression coefficients for all variable are represented in Table 3 below.
Table 3. Predictors to Becoming Transfer Ready
Step 1
B
S.E.
Wald
df
Sig.
Exp(B)
Age
-.156
.004
1491.638
1
.000
.855
Foster
-.365
.207
3.107
1
.078
.695
Fin Aid
.484
.026
344.385
1
.000
1.622
Disabled
-.141
.064
4.803
1
.028
.869
First Gen
-.028
.060
.222
1
.638
.972
Skills Builder
.920
.025
1391.015
1
.000
2.508
Basic Skills
.005
.025
.041
1
.840
1.005
Female
.052
.024
4.742
1
.029
1.054
Other Gender
-.721
.185
15.232
1
.000
.486
African Am
-1.114
.068
265.937
1
.000
.328
Amer. Indian
-.268
.175
2.339
1
.126
.765
Asian
.720
.036
390.808
1
.000
2.054
Filipino
-.409
.032
165.767
1
.000
.665
Two or More Races
.065
.071
.831
1
.362
1.067
Pacific Islander
-.394
.175
5.052
1
.025
.674
Other
-.124
.038
10.755
1
.001
.883
Constant
.813
.084
94.018
1
.000
2.255
The second research question addressed how long it took students to become transfer ready. To investigate
how student characteristics and enrollment in a CTE pathway predict time to transfer readiness, a multiple
regression was conducted using the number of years it took to become transfer ready (Transfer Year) as the
dependent variable, and studentscharacteristics and a set of dummy variables for each of the transfer pathways,
with students not in a CTE pathway as the omitted reference group. Top codes that had less than 50 students that
were transfer ready within the five-year study period were eliminated from the analysis. These excluded top codes
include Environmental Science (Code 03), Biological Science (Code 04), Humanities (Code 15), Physical Sciences
(Code 19), Social Sciences (Code 22), and Interdisciplinary Studies (Code 49). These areas of study typically do not
include courses designated as CTE, and therefore have a very low number of CTE enrollments in the database.
There were twelve statistically significant variables found to predict length of time to transfer, F (28, 8996) =
45.73, p <.001. These variables included age, ethnicity if identified as Asian, Filipino or two or more races, received
financial aid, was disabled, first-generation college students, or took basic skills courses. Pathways that predicted
Pedagogical Research, 3(2), 08
© 2018 by Author/s 5 / 9
transfer readiness F (15, 9009) = 82.70, p <.001 were Education, Health, Family and Consumer Science and Law.
Significant regression coefficients for all variable are represented in Table 4 below.
Table 4. Predictors to Becoming Transfer Ready
Step 1
B
S.E.
Wald
df
Sig.
Exp(B)
Age
-.156
.004
1491.638
1
.000
.855
Foster
-.365
.207
3.107
1
.078
.695
Fin Aid
.484
.026
344.385
1
.000
1.622
Disabled
-.141
.064
4.803
1
.028
.869
First Gen
-.028
.060
.222
1
.638
.972
Skills Builder
.920
.025
1391.015
1
.000
2.508
Basic Skills
.005
.025
.041
1
.840
1.005
Female
.052
.024
4.742
1
.029
1.054
Other Gender
-.721
.185
15.232
1
.000
.486
African Am
-1.114
.068
265.937
1
.000
.328
Amer. Indian
-.268
.175
2.339
1
.126
.765
Asian
.720
.036
390.808
1
.000
2.054
Filipino
-.409
.032
165.767
1
.000
.665
Two or More Races
.065
.071
.831
1
.362
1.067
Pacific Islander
-.394
.175
5.052
1
.025
.674
Other
-.124
.038
10.755
1
.001
.883
Constant
.813
.084
94.018
1
.000
2.255
Students who were Asian or were identified as two or more races became transfer ready faster than other
students did. The remaining significant indicators to that showed students becoming transfer ready as a slower rate
included age, ethnicity of Filipino, received financial aid, identified as disabled, first generation college student,
took basic skills courses, or were enrolled in the pathways for education, health, family and consumer sciences or
law. The most significant factor relating to a student becoming transfer ready was whether a student took basic
skills courses, with a beta of 0.185, p < .001, indicating the students in this category took longer to become transfer
ready. The adjusted R2 in Model 1 was .120, and the adjusted R2 in model 2 was .122. This indicated that the model
explained 12% of the variance in transfer ready status, and that .002% of the variance is explained by the addition
of pathways to the model.
Table 5.
Significant Predictors to a How Many Years It Takes to Become Transfer Ready
Variable
ß
p
t
Age
0.052
***
5.138
Asian
-0.106
***
-9.200
Filipino
0.087
***
7.222
Two or More Races
-0.026
*
-2.523
Financial Aid
0.149
***
14.621
Disabled
0.042
***
4.251
First Generation
0.040
***
3.989
Basic Skills
0.185
***
18.273
R2
0.120
F (15, 9009 )
82.70
***
TOP 08 Education
0.037
*
2.263
TOP 12 Health
0.071
*
2.344
TOP 13 Family and Cons. Sci.
0.077
*
2.237
TOP 14 Law
0.036
**
2.614
R2
0.122
R2
0.002
F (28, 8996)
45.73
***
*
p < .05, **p <.01, ***p < .001
Note. (Eliminated top codes <50, environmental science, biological science, foreign language, humanities, library science, math, physical
science, social science). Transfer year is the number of years it takes to becomes transfer ready.
To look at the number of students becoming transfer ready by CTE pathway and year, the following table was
constructed. This shows the quickest CTE pathways for students becoming transfer ready were Business
Management, Information Technology, Public and Protective Services, Family and Consumer Science, and Health
(see Table 5). The CTE pathways that showed the least likelihood of becoming transfer ready were
Interdisciplinary Studies, Commercial Services, Law, Education, Agriculture and Architecture. It should be noted
that Law refers to only the CTE designated courses in the Law pathway. To look at the number of students
Wolzinger and O’Lawrence / Transfer Readiness of Student’s Enrollment in CTE Pathway
6 / 9 © 2018 by Author/s
becoming transfer ready by CTE pathway and year, Table 6 was constructed. This shows the quickest CTE
pathways for students becoming transfer ready were Business Management, Information Technology, Public and
Protective Services, Family and Consumer Science, and Health (see Table 6). The CTE pathways that showed the
least likelihood of becoming transfer ready were Interdisciplinary Studies, Commercial Services, Law, Education,
Agriculture and Architecture. It should be noted that Law refers to only the CTE designated courses in the Law
pathway.
Table 6. Number of Years to Become Transfer Ready by Top Code
Time to Transfer Readiness
Pathway
Top
Code
2
Years
3
Years
4
Years
5
Years
6
Years
Total Trans.
Ready
Total
Students in
the Pathway
Percent
Trans.
Ready
Agriculture
01
7
27
21
14
6
75
433
17.32%
Architecture
02
7
28
2
16
7
60
204
29.41%
Business Management
05
230
543
370
237
129
1509
4625
32.63%
Media and Comm.
06
21
72
54
35
23
205
888
23.09%
Information Tech
07
76
172
123
84
49
504
1590
31.70%
Education
08
6
17
17
14
11
65
233
27.90%
Eng. & Ind. Tech
09
23
73
48
37
26
207
2142
9.66%
Fine & Applied Arts
10
15
56
41
31
10
153
739
20.70%
Health
12
30
100
83
74
47
334
1768
18.89%
Family & Cons Science
13
31
146
120
83
64
444
2521
17.61%
Law
14
6
8
4
11
9
38
211
18.01%
Pub & Prot. Services
21
33
136
122
96
58
445
2713
16.40%
Commercial Services
30
2
7
9
4
1
23
253
9.09%
Interdisciplinary Stud.
49
5
5
49
2
0
61
141
43.26%
Total
492
1390
1063
738
440
4123
18161
Note:
(eliminated top codes <
50, environmental science, biological science, foreign language, humanities, library science, math, physical
science, social science)
SUMMARY AND CONCLUSION
The analysis reported here showed that there several significant pathways and student characteristics that
predict transfer readiness and time to transfer readiness. The chi-squared analysis showed that underrepresented
students are enrolling at a higher rate than expected in CTE pathways that have a low number of students reaching
transfer readiness. The analysis also showed that CTE students from underrepresented populations are taking
longer to reach transfer readiness those other students.
There was heavy enrollment for CTE students in Business and Management (4625 students) compared to other
pathways. This was followed by Public and protective Services (2713 students), Family and Consumer Sciences
(2521 students), Engineering and Industrial Technologies (2142 students), Health (1768 students) and Information
Technology (1590 students). These top enrolled pathways account for 82% of the CTE students found in the
dataset. The remaining pathways in the top 15 were Media and Communications (888 students), Fine and Applied
Arts (739 students), Agriculture and Natural Resources (422 students), Commercial Services (253 students),
Education (233 students), Law (221 students), Architecture and Environmental Design (204 students),
Interdisciplinary Studies (141 students), and Biological Sciences (47 students). These findings show how that most
CTE students are enrolled in Business and Management, and the top six pathways by enrollment include most
CTE students. This finding is interesting as many California community colleges have large portfolios of CTE
programs including many certificate offerings. The literature review included that both students and employers are
confused on these various offerings and that they diminish the value of many CTE programs.
The student characteristics that were significant predictors to reaching transfer ready status were Asian,
receiving financial aid, or skills builders (students who took five or more units in the same TOP code). CTE
pathways that lead to transfer ready status were architecture, business management, or information technology.
These successful pathways in terms of transfer readiness encompass 34% of CTE students in the study. It is
consistent in the literature that Asian students are found to be achieving transfer readiness at a higher rate than
other students (Budd and Stowers, 2014). Students receiving financial aid would lose their financial aid eligibility
upon dropping below the required unit threshold, and are consistent with completing a program. Skills builders
are those students who take at least five units in the same top code, and are found to be continuing in their area
of concentration to transfer readiness. The strong pathways found here that lead to transfer readiness: architecture,
business management, or information technology, are all fields that have well-developed transfer pathways to the
CSU system leading to a Bachelors degree.
Pedagogical Research, 3(2), 08
© 2018 by Author/s 7 / 9
The student characteristics found not to be transfer readiness were older students, foster youth, other gender,
African American, Filipino, Pacific Islander, other ethnicity. The pathways found where students did not reach
transfer readiness were engineering and industrial technology and commercial services. The pathways that were
found to significantly predict a longer time to become transfer ready in the multiple regression analysis were
Education, Health, Family and Consumer Science and Law. Family and Consumer Science include programs in
Child Development, Nutrition Foods and Culinary Arts, and Hospitality. All of these program that take longer to
reach a transfer ready status involve some kind of license requirement in order to work in the field. The requirement
may be related to studentspersistence in completing the lower division program and/or completing the
requirements for transfer even if it takes extra years to complete the courses. No pathways were found that
significantly predicted a shorter time to transfer in this study.
Looking at how many years it took students to become transfer ready by CTE pathway, only Business
Management (230 students) and Information and Communication Technologies (76) students had over 50 students
that became transfer ready within two years. The multiple linear regression used to identify which pathways predict
time to degree found that found that the significant pathways were Education, Health, Family and Consumer
Science and Law. Although these were significant, predictors for how many years it takes to become transfer ready,
only pathways explained two% of the model. The most significant effect on the number of years to become transfer
ready was whether a student took basic skills courses (with a beta of 0.185, p <.001). This indicates that students
who took basic skills courses took longer to transfer than other students. The regression analysis indicated that SB
1440 (The Student Transfer Achievement Reform Act) as designed is not having the desired impact for most CTE
pathways.
This legislation stipulates that a student may transfer to a CSU if they meet a set of criteria including completion
of an associate degree for transfer offered at a maximum of 60 units and obtain a minimum 2.0 GPA. Several
pathways had more than 50 students becoming transfer ready after three years including Business Management
(543), Information Technology (172), Engineering and Industrial Technology (73), Fine and Applied Arts (56),
Health (100), Family and Consumer Science (146), and Public and Protective Services (122). The highest number
of students becoming transfer ready occurred in year three (1390) of the study when totaling all students who
became transfer ready. Year 4 was the next highest year of students becoming transfer ready (1063); with a drop
off in students becoming transfer ready in five years or six years.
The number of years it took CTE students to transfer could be related to students taking career programs over
a two-year period, and then completing GE requirements. CTE programs include lab time and are difficult to
schedule at the same time as completing other courses. In addition, CTE courses are many times taught when
faculty are available to teach, and not necessarily during the best time for students who are taking GE courses to
transfer. For this study, strong pathways were defined as more students becoming transfer ready. The analysis
yielded twelve significant pathways in the model when looking at how underrepresented students enrolled in CTE
pathways. Underrepresented students included Hispanic, African American, Pacific Islander, or Native
American/Alaska Native.
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Budd, D. and Stowers, G. N. (2014). Group differences in California community college transfers. Community College
Journal of Research and Practice, (ahead-of-print), 1-15.
California 2015-2016 State Budget. (2015). Available at: http://www.dof.ca.gov/documents/FullBudgetSummary-
2015.pdf
California Community Colleges Chancellors Office. (2014). California community colleges sets goal to increase student
completions by nearly a quarter of a million statewide (Press Release August 27, 2014). Sacramento, CA: Paige Marlatt
Dorr. Available at:
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Pedagogical Research, 3(2), 08
© 2018 by Author/s 9 / 9
APPENDIX A
LOGISTIC REGRESSION TABLE
Logistic Regression, All Variables Variables in the Equation
Step 1a
B
S.E.
Wald
df
Sig.
Exp(B)
Age
-.158
.004
1495.341
1
.000
.854
Foster
-.417
.211
3.898
1
.048
.659
Fin Aid
.503
.026
364.959
1
.000
1.653
Disabled
-.104
.065
2.598
1
.107
.901
First Gen
-.032
.061
.283
1
.595
.968
Skills Builder
.494
.184
7.228
1
.007
1.639
Basic Skills
.018
.026
.501
1
.479
1.018
Female
.054
.025
4.721
1
.030
1.056
Other Gender
-.720
.186
15.041
1
.000
.487
African American
-1.125
.069
268.484
1
.000
.325
American Indian
-.260
.176
2.175
1
.140
.771
Asian
.638
.037
295.345
1
.000
1.892
Filipino
-.398
.032
154.211
1
.000
.672
Two Or More Races
.054
.072
.578
1
.447
1.056
Pacific Islander
-.420
.177
5.630
1
.018
.657
Other Ethnicity
-.139
.038
13.238
1
.000
.870
TOP01 Agriculture
.126
.226
.309
1
.578
1.134
TOP02 Architecture
1.083
.239
20.540
1
.000
2.954
TOP05 Business Management
.975
.186
27.401
1
.000
2.652
TOP06 Media And Communications
.463
.201
5.292
1
.021
1.589
TOP07 Information Tech
.934
.193
23.558
1
.000
2.546
TOP08 Education
.658
.239
7.596
1
.006
1.930
TOP09 Eng. and Industrial Tech
-.464
.198
5.479
1
.019
.629
TOP10 Fine and Applied Arts
.222
.206
1.163
1
.281
1.249
TOP11 Foreign Lang
-14.055
40192.970
.000
1
1.000
.000
TOP12 Health
.208
.194
1.156
1
.282
1.232
TOP13 Family and Consumer Science
.124
.191
.421
1
.517
1.132
TOP14 Law
.372
.266
1.953
1
.162
1.451
TOP16 Library Sci
-19.549
8842.421
.000
1
.998
.000
TOP17 Math
-19.253
21458.198
.000
1
.999
.000
TOP21 Public and Protective Services
.025
.191
.018
1
.894
1.026
TOP30 Commercial Services
-.781
.288
7.353
1
.007
.458
Constant
.843
.085
99.300
1
.000
2.324
a. Variable(s) entered on step 1: TOP01Agriculture, TOP02Architecture, TOP05BusinessManage, TOP06MediaAndComm,
TOP07InformationTech, TOP08Education, TOP09EngandIndusTech, TOP10FineAndApplArts, TOP11ForeignLang, TOP12Health,
TOP13FamilyandConsSci, TOP14Law, TOP16LibrarySci, TOP17Math, TOP21PublicandProtServ, TOP30CommercialServices.
APPENDIX B
STUDENT ETCHNICITY TABLE
Student Ethnicity
Frequency
Percent
Valid%
Cumulative%
CCValid
African American
4898
7.5
7.5
7.5
American Indian/Alaska Native
351
.5
.5
8.0
Asian
7361
11.2
11.2
19.2
Filipino/a
1479
2.3
2.3
21.5
Hispanic
19636
30.0
30.0
51.5
Other
9974
15.2
15.2
66.7
Pacific Islander
358
.5
.5
67.2
Two or More Races
1681
2.6
2.6
69.8
White
19797
30.2
30.2
100.0
Total
65535
100.0
100.0
... The CSD lacks natural boundaries therefore does not have information on the inherent number of clusters. The criteria adopted in the discovery of the optimal number of clusters present in the CSD include; silhouette criterion [ information criterion (BIC) [44]. Elbow method considers the extent of variability as a function of the natural number of clusters. ...
... The elbow is depicted in a plot of the extent (percentage) of variability explained by clusters against the count of the clusters [43]. BIC is a criterion based on likelihood corrected by the model complexitythe number of parameters in the model [44]. Silhouette plots display the closeness measure of each point in one cluster to points in the neighboring clusters. ...
Article
This study explores the extent to which community colleges succeed in assisting students to transfer to four-year colleges. The study uses data from the California Community College system to test hypotheses about overall transfers and transfers of underrepresented students, It utilizes a framework based upon social reproduction theory (Bowles & Gintis, 1976) that also includes institutional factors. First, transfer rates differed significantly between groups, with African-American transfer rates being the lowest. Some of our hypotheses were supported, particularly those on the significance of communities with younger students and higher levels of education for transfer levels. A critical mass of students of underrepresented groups is also important for institutions that wish to transfer higher numbers of these students. Institutional effectiveness and level of funds spent on transfer programs did not appear to make any difference in transfer levels. One of the most important findings is that transfer dynamics are very different for each group, suggesting that administrators and policy-makers need to develop more detailed strategies to encourage higher rates of transfer.
Article
This chapter discusses the policy impetuses behind statewide pathways that simultaneously lead to an associate degree and transfer with junior status to a four-year college or university, then outlines the elements of effective transfer associate degrees.
Article
This chapter describes how California Community College and California State University faculty developed a system for implementing associate degrees for transfer that has the potential to simplify transfer and decrease unit accumulation. This intersegmental, faculty-led system provides a mechanism for maintaining local control of curriculum, while identifying curricular commonalities that can ultimately facilitate student movement within and between higher education segments in California.
From community college to university: Expectations for California's new transfer degrees
  • C Moore
  • N Shulock
Moore, C. and Shulock, N. (2014). From community college to university: Expectations for California's new transfer degrees. California State University, Sacramento. Sacramento, CA: Institute for Higher Education Leadership & Policy.