Creative Education
2013. Vol.4, No.11, 694-699
Published Online November 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.411098
Open Access
694
Community College Funding: Legislators’ Attitudes
Debby Lindsey-Taliefero1, LaDonna Tucker2
1School of Business, Howard University, Washington DC, USA
2School of Education and Urban Studies, Morgan State University, Baltimore, USA
Received August 20th, 2013; revised September 20th, 2013; accepted September 27th, 2013
Copyright © 2013 Debby Lindsey-Taliefero, LaDonna Tucker. This is an open access article distributed under
the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
The purpose of this article is to examine the impact of the Obama Administration’s Community College
Initiative (CCI) on state legislators’ attitude toward economic funding for community colleges. Data on
legislators’ attitude toward community colleges funding were collected using a customized Community
College Goals Inventory (CCGI) survey developed by the Educational Testing Service (ETS), the Ameri-
can Association of Community and Junior Colleges. Data were analyzed using descriptive and inferential
statistics including measures of central tendency and dispersion as well as ANOVA, regression analysis,
t-test or F-test. The results indicated that President Obama’s Community College Initiative has had a posi-
tive and statistically significant influence on state legislators’ attitude toward community college funding.
Additionally, demographic characteristics and information sources, that is, where legislators obtain their
knowledge to make decision about educational policies both had a positive and statistically significant
impact on legislators’ attitude toward community college funding. The article provides insight into fund-
ing-attitude markers, that can be used as capital by community college presidents to shape funding poli-
cies affecting their institutions.
Keywords: Community College; Legislators Opinions & Attitudes; Higher Education Funding
Introduction
Attaining a post-secondary degree or credential is no longer
just a pathway to opportunity for a few talented people; rather,
it is a prerequisite for the growing jobs of the new economy.
Over this decade, employment in jobs requiring education be-
yond a high school diploma will grow more rapidly than em-
ployment in jobs that do not; of the 30 fastest growing occupa-
tions, more than half require postsecondary education. With the
average earnings of college graduates at a level that is twice as
high as that of workers with only a high school diploma, higher
education is now the clearest pathway into the middle class
(National Center for Education Statistics, 2013).
In higher education, the United States (US) has been out-
paced internationally. In 2009, the US ranks ninth in the world
in the proportion of young adults enrolled in college, and we’ve
fallen to 12th in the world in our share of certificates and de-
grees awarded to adults aged 25 - 34—lagging behind Korea,
Canada, Japan, United Kingdom and other nations (Organiza-
tion for Economic Co-operation and Development, 2013). We
also suffer from a college attainment gap, as high school gradu-
ates from the wealthiest families in our nation are almost cer-
tain to continue on to higher education, while just over half of
our high school graduates in the poorest quarter of families
attend college (The White House, 2013).
To close the attainment gap, the US is looking forward our
community colleges. Today, the nation’s community colleges
enroll nearly 7 million undergraduates, or nearly 4 million full-
time equivalent (FTE) students (about 35 percent of all students
in higher education). This is up from 3 million FTE students in
2000 (Snyder & Dillow, 2011). The graduation rate is 3 years
for 2-year degrees and 6 years for 4-year degrees. Using this
rate, community colleges have a 22-percent graduation rate. In
comparison, non-selective four-year public institutions have a
29 percent graduation rate (Horn, 2010).
Graduation rates don’t tell whole story, and according to a
the National Student Clearinghouse, 15 percent of students who
started at two-year institutions in 2006 completed a degree at a
four-year institution within six years. Nearly two-thirds of these
students (63%) did so without first obtaining a two-year degree.
Traditional graduation rates that focus on completions at the
starting institution do not account for this type of outcome.
Thus, community colleges often do not receive credit for many
students who go on to complete a four-year degree (Shapiro,
Dundar, Chen, Ziskin, Park, Torres, & Chiang, 2012).
The 2008 Great Recession has made community colleges
more than an every vital link in the educational chain and work
force preparedness. However, community colleges are highly
dependent on state funding, since unlike four-year public
schools, they do not have diversified revenue sources such as
hospitals, endowments, or research grants. While enrollments
have been increasing, state funding per student has remained
relatively flat (see Figure 1). In 2009, community colleges
received approximately $6450 per FTE student, only slightly
higher than the $6210 in 1999 (Desrochers & Wellman, 2011).
Acknowledging these issues early in his Administration,
President Obama challenged every American to commit to at
least one year of higher education or post-secondary training.
The President has also set a new goal for the country: that by

D. LINDSEY-TALIEFERO, L. TUCKER
Figure 1.
Community college enrollment and state funding. United States de-
partment of education, the economics of higher education, December
2012.
2020, America would once again have the highest proportion of
college graduates in the world (The White House, 2009). The
Obama Administration has been working to make college more
accessible, affordable, and attainable for all American families.
In so doing, the President is expanding his commitment to the
Community College Initiative by promoting industry partner-
ships to foster career readiness and jobs creation for trained
workers. In the 2013 budget request, President Obama proposed
the Community College to Career Fund, an $8 billion invest-
ment in community colleges and states over three years to
partner with businesses to train workers in a range of high-
growth and in-demand areas, such as health care, logistics,
transportation, and advanced manufacturing (US Department of
Education, 2012). In the 2014 budget request, $4 billion in
mandatory funds, beginning in fiscal year 2015, are for a Com-
munity College to Career Fund that would support community
college-based training programs and other activities that help to
prepare workers for jobs in high-growth and high-demand sec-
tors (US Department of Education, 2013). These should help
America’s students and workers receive the education and
training needed for the jobs of today and tomorrow, and pro-
vide greater security for the middle class.
Aim
The purpose of this article is to examine the impact of the
Obama Administration’s Community College Initiative (CCI)
on state legislator’s attitude toward funding for community
colleges.
Method
The survey method was used to investigate the impact of
demographic characteristics, information sources and the
Obama’s Administration Community College Initiative on state
legislators’ attitude toward funding for community colleges.
Survey
The survey instrument was a modification of the original
Community College Goals Inventory (CCGI) as developed by
the Educational Testing Service (ETS) and the American Asso-
ciation of Community and Junior Colleges (Peterson & Uhi,
1979). The survey asked respondents to use a five-point Likert
scale to capture their attitudes toward community college mis-
sions and functional areas. The modified 33-item questionnaire
comprised of closed-ended questions was designed to help
community colleges define their educational goals, establish
priorities among those goals, and give direction to their present
and future planning.
Reliability and validity for the CCGI are 0.87 and 0.88, re-
spectively. The CCGI has been well vetted; validity has been
tested by nineteen specialists familiar with California’s four
year colleges and universities, and community colleges (Peter-
son, 2002).
The survey design is longitudinal. Data was collected in
2007 and 2012. This time period reflects the pre and post
President Obama Administration’s Community College Initia-
tives. The survey captured data on legislators’ attitudes toward
the missions and functional area of the community college. The
missions and functions are those defined by Cohen and Brawer
(2008) as academic transfer/general education, globalization,
community service, continuing education, developmental edu-
cation, open access, student services, vocational-technical train-
ing, and funding.
Sample
The sample was drawn from the Maryland general assembly
roster and list of committees for 2006 and 2011 sessions (De-
partment of Legislative Services, 2006 & 2011). The study’s
sample size is 111 legislators, which was determined by Krejcie
and Morgan formula with a finite population (N) of 188 Mary-
land state legislators, a 95-percent level of confidence, and a
sample proportion (p) that would be within a margin of error
value of 0.06 of the population proportion (p) value of 0.5
(Krejcie & Morgan, 1970).
Statistical Analysis
Data was analyses included descriptive and inferential statis-
tics. For each survey question descriptive statistics were calcu-
lated including measures of central tendency (means, modes, or
percentages) and measures of dispersion (variances or standard
deviations). The descriptive data was then used to help narrow
the focus of the inferential statistics employed to capture the
influence of national educational policy on legislators’ attitude
toward community college funding. Analyses used for statisti-
cal inference included one or more of the following statistical
procedures and tests where appropriate: ANOVA, regression
analysis, t-test or F-test.
Results and Discussion
The dependent variable is the attitude of legislator’s towards
community college funding (see Figure 2). Funding is one
community college element function defined by Cohen and
Brawer (2008) that is a part of identifying the mission of com-
munity college. Attitude toward funding is measured as percent
score. Three of the 33 questions asked on the attitude assess-
ment survey captured funding. Each question was based on a
five point scale. The maximum score for funding attitude is 15
points, if a respondent answered every question as 5 (extremely
important). To make the data quantitative, each respondent’s
attitude score is converted into a percent based on 100 point
scale. For example, if a respondent’s attitude score for commu-
nity college funding summed to 13, then the percent score
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D. LINDSEY-TALIEFERO, L. TUCKER
Attitude toward
Community College
Funding
Va l u e s vary by demographics, race,
education, gender, occupation, political
affiliation, and years of service.
Beliefs vary by awareness, knowledge, or
sources of information, state government,
media federal government, etc.
Figure 2.
Applied Rosenberg’s structure attitude theory dynamics.
would equal 86.7% [i.e., (13/15) * 100 = 86.7]. This implies
that respondent had an above-average favorable attitude toward
community college funding.
The independent variables are classified into two distinct
groups, values and beliefs (see Figure 2). The first group, val-
ues measures demographic characteristics of the legislators
such as, educational attainment, age, gender, race, income,
political affiliation, occupation and years of service. Payne
(1984) sanctions the importance of these characteristics in par-
ticular to politicians. He describes politicians as complex hu-
man being, each with idiosyncratic traits, attitudes, and abilities,
which has a bearing on their choices. The second group, beliefs
measure awareness or knowledge. It captures information
sources or where legislators’ collect information from to make
their decisions about educational policy. These sources in-
cluded obtaining information from the governor’s office, state
higher education agency, constituents/taxpayers, business lead-
ers, other legislators, advisors or experts, media, federal gov-
ernment, national or regional organizations, the state legislative
audit, research, & review board, faculty unions as well as
awareness of the Obama Administration’s Community College
Initiative.
Obama Administration’s CCI on CCF At titude
To captured how national educational policy influences
Maryland legislators’ attitude toward community college fund-
ing, an independent sample t-test was conducted testing the
difference between 2007 and 2012 mean values attitude score
towards community college funding. The shift in national pol-
icy is reflected in the cohorts 2007 and 2012, capturing Presi-
dent Obama’s Community College Initiative. Table 1 presents
the results of the t-test. The Levene’s test for homogeneity of
variances is F(1,108) = .048, p = .827. Accordingly, the t-test
for equal variances not assumed should be used. In that case,
the t-test indicates a significant difference in attitude toward
community college funding between groups, t(103) = −3.124, p
= .002. This result suggests that Maryland state legislators Pre-
Obama Community College Initiative (M = 66.67; SD = 15.65)
have less favorable attitudes toward community college funding
than Maryland state legislators Post-Obama Community Col-
lege Initiative (M = 75.89; SD = 15.14). The mean difference
(MD) is −9.22. Using Cohen’s d (1992), the size of this effect
−.60, which exceeds the convention of a medium effect size (d
= 50). In other words, the Obama’s Community College Initia-
tive has had a positive and statistically significant influence on
Maryland state legislators’ attitude toward community college
funding.
Demographic Ch ar acteristics
Each regression model was estimated using the ordinary least
squares SPSS stepwise method. In stepwise regression not all
independent variables end up in the equation. In Table 2, the
stepwise regression focuses on determining the best combina-
tion of demographic characteristics along with President’s
Obama’s Community College Initiative (CCI) that predicts
legislators’ attitudes toward Community college funding. The
demographic characteristics for the legislators comprised of
educational attainment (56% had masters, doctorate or profes-
sional degrees), years of services (47% serviced more than
seven years in the state legislation), age (47% were 51 years or
older), gender (46% were female), income (75% earned
$75,000 or more), race (62% were minorities), political party
(84% were democratic) and occupation (86% work outside of
the education field). More than half (55%) of the legislators
have serviced since President Obama’s Community College
Initiative. Legislators as a whole had an average attitude (M =
71.70; SD = 15.981).
Table 2, Model 5 presents the best combination of demo-
graphic characteristics that predicts legislators’ attitudes toward
Community College Funding. The results indicate a R2 = .925,
which implies that 92.5% of legislators’ attitude toward com-
munity college funding is explained by the regression model
and that percent share explained is statistically significant
[(F(5,106) = 261.83, p < .001]. Four of the seven demographic
variables are statistically significant and include occupation [β
= 23.199, t(106) = 4.555, p < .01], political party [β = 26.357,
t(106) = 5.581, p < .01], age [β = 11.489, t(106) = 2.950, p
< .01], and income [β = 17.486, t(106) = 2.980, p < .01] Presi-
dent Obama’s Community and income College Initiative is also
significant [β = 14.341, t(106) = 3.579, p < .01]. The results
point out that if legislators are Democrats then their attitude
toward community college funding is 26.4 points higher than if
they are Republicans or Independents. Similarly, if legislators
work in non-educational professions, earn 75,000 or plus, ser-
vice more than seven years, or are older 51 than their attitude
toward community college funding are 23.2, 17.5, or 11.5
points higher than if legislators work in the educational field,
earn less $75,000, or are 50 years old or less, respectively.
Bandura (1996) also found demographic characteristics such as
ethnicity, age, income level and education (or class), and gen-
der as major determinants of politicians behavior. As a final
point, President Obama’s Community College Initiative in-
creases attitude toward community college funding by 14.3
points.
Information Sources
In Table 3, the stepwise regression focuses on determining
the best combination of information sources that can be used to
predict legislators’ attitudes toward community college funding.
To help make decision about educational policy, legislators
used information from a variety of places including the gover-
nor’s office (36% of the time), state higher education agency
(62%), constituents & taxpayers (70%), business leaders (42%),
other legislators (53%), advisors or experts (70%), media (33%),
federal government (25%), national or regional organizations
(58%), the state legislative audit, research & review board
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D. LINDSEY-TALIEFERO, L. TUCKER
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Table 1.
Independent samples t-test for equality of mean funding attitudes—pre and post Obama Community College Initiative.
Group Stat is tics
Year N Mean Std. Deviation Std. Error Mean
Pre-Obama’s Community College Initiative 50 66.67 15.649 2.213
Attitude toward Community
College Funding (CCF) Post-Obama’s Community College Initiative60 75.89 15.137 1.954
Independent Samples Test
Levene’s Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)Mean Difference Std. Error Difference
Attitude toward Community
College Funding (CCF)
Equal variances
assumed .048 .827 −3.133108.002 −9.222 2.943
Equal variances
not assumed −3.124103.002 −9.222 2.952
Table 2.
Predicting attitudes toward community college funding with demo-
graphic characteristicsa,b.
Unstandardized
Coefficients
Model β Std. Error t Sig.
1 Occupationc 72.404 3.100 23.358 .000
2 Occupationd 39.923 4.515 8.843 .000
Political Party 38.880 4.567 8.513 .000
3 Occupatione 37.360 4.331 8.626 .000
Political Party 33.998 4.523 7.516 .000
Age 15.069 4.091 3.683 .000
4 Occupationf 30.441 4.639 6.562 .000
Political Party 32.853 4.341 7.568 .000
Age 14.206 3.923 3.622 .000
Obama_CCI 13.760 4.147 3.318 .001
5 Occupationg 23.199 5.093 4.555 .000
Political Party 26.357 4.722 5.581 .000
Age 11.489 3.894 2.950 .004
Obama_CCI 14.341 4.007 3.579 .001
Income 17.486 5.867 2.980 .004
Note: aDependent variable: attitude toward community college funding. bLinear
regression through the origin. cR2 = .832, SEE = 30.213, F(1,110) = 545.59, p
< .001. dR2 = .899, SEE = 23.522, F(2,109) = 486.28, p < .001. eR2 = .910, SEE =
22.274, F(3,108) = 366.08, p < .001. fR2 = .919, SEE = 21.308, F(4,107) = 302.76,
p < .001. gR2 = .925, SEE = 20.564, F(5,106) = 261.83, p < .001.
(65%) and faculty unions (24%).
Table 3, Model 5 presents the best blend of information
sources that can be used to predict legislators’ attitudes toward
community college funding. The results indicate that R2 = .879
which implies that 87.9% of legislators’ attitude toward com-
munity college funding is explained by the regression model
and that percent share explained is statistically significant
[(F(5,106) = 153.723, p < .001]. Four of the eleven information
sources are statistically significant and include advisors or ex-
perts [β = 26.333, t(106) = 5.172, p < .01], state higher educa-
tion agency [β = 16.287, t(106) = 2.882, p < .01], media [β =
Table 3.
Predicting attitudes toward community college funding with informa-
tion sourcesa,b.
Unstandardized
Coefficients
Model β Std. Error t Sig.
1Advisors or Expertsc 73.336 4.827 15.194.000
2Advisors or Expertsd 51.359 4.361 11.777.000
Obama_CCI 44.445 4.847 9.169 .000
3Advisors or Expertse 35.280 4.868 7.247 .000
Obama_CCI 38.948 4.426 8.799 .000
State Higher_Ed Agency27.847 5.098 5.463 .000
4Advisors or Expertsf 32.093 4.759 6.744 .000
Obama_CCI 37.963 4.247 8.939 .000
State Higher_Ed Agency24.032 5.014 4.793 .000
Media 18.052 5.470 3.300 .001
5Advisors or Expertsg 26.333 5.091 5.172 .000
Obama_CCI 37.472 4.131 9.071 .000
State Higher_Ed Agency16.287 5.652 2.882 .005
Media 17.986 5.316 3.384 .001
State Legislative Audit,
Research or Review Board15.405 5.696 2.705 .008
Note: aDependent variable: attitude toward community college funding. bLinear
regression through the origin. cR2 = .667, SEE = 41.901, F(1,110) = 230.853, p
< .001. dR2 = .818, SEE = 31.627, F(2,109) = 244.643, p < .001. eR2 = .857, SEE
= 28.124, F(3,108) = 216.196, p < .001. fR2 = .870, SEE = 26.918, F(4,107) =
179.717, p < .001. gR2 = .879, SEE = 26.158, F(5,106) = 153.723, p < .001.
17.989, t(106) = 3.384, p < .01] and the state legislative audit,
research & review board [β = 15.405, t(106) = 2.705, p < .01].
President Obama’s Community College Initiative is also sig-
nificant [β = 37.472, t(106) = 9.071, p < .01]. The results show
that legislators who obtain their information data from their
advisor or experts in the field of education have more favorable
attitudes toward community college funding by 26.3 points.
Similarly, if legislators used information sources from the state
higher education agency, the media or the state legislative audit,
the research & review board increases their attitude toward
community college funding by 16.287, 17.986, and 15.405

D. LINDSEY-TALIEFERO, L. TUCKER
points, respectively. Along with the joint interaction of infor-
mation sources, the Obama’s Community College Initiative
increases attitude toward community college funding by 37.472
points.
CCI, Demographics, & Information Sources
Table 4, Model 7 presents the best mix of demographic
characteristics and information sources along with President’s
Obama’s Community College Initiative that predicts legisla-
tors’ attitudes toward Community College Funding. The results
indicate that R2 = .933 which implies that 93.3% of legislators’
attitude toward community college funding is explained by the
regression model and that percent share explained is statisti-
cally significant [(F(7,104) = 205.973, p < .001]. Four of the
seven demographic variables are statistically significant and
include occupation [β = 15.709, t(104) = 2.925, p < .01], po-
litical party [β = 21.719, t(104) = 4.546, p < .01], age [β =
11.116, t(104) = 2.976, p < .01], and income [β = 15.810, t(104)
= 2.795, p < .01]. Two of the eleven information sources are
statistically significant. They include obtaining information
from advisors or experts [β = 10.985, t(104) = 2.603, p < .05]
and the media [β = 9.007, t(104) = 2.167, p < .05]. President
Obama’s Community College Initiative is also significant [β =
17.344, t(104) = 4.408, p < .01]. The results indicate that if
legislators obtain their information from advisors or experts, the
media, work in non-educational fields, are 51 or older, are de-
mocratic or have incomes $75,000 & plus have more favorable
attitudes toward community college funding. Along with the
joint impact of information sources and demographics, the
Obama’s Community College Initiative increases attitude to-
ward community college funding by 17.344 points.
Conclusion
This study adds to the literature on legislators’ attitudes to-
ward community college funding. As the United States pursues
the national goals to add 5 million graduates by 2020, commu-
nity college presidents, higher education groups, and govern-
ment agencies must learn how to equip legislators with valuable
and pertinent information to make sound decisions about com-
munity college funding. The study’s outcome clearly suggests
at the state level an upward shift in attitude toward community
college funding once community colleges were made a priority
at the federal government level. In short, President Obama’s
Community College Initiative has significantly increases legis-
lators’ attitude toward community college funding. The best
demographic predictors of legislators attitudes we learned are
occupation, political affiliation, age, and income. For informa-
tion sources, the best predictors are obtaining materials from
advisors or experts, the state higher education agency, media,
and the state legislative audit, research and review board.
These key predictors of funding attitudes are markers that
community college presidents can capitalize on to shape fund-
ing policies that impact their institutions. This proactive ap-
proach shared by Boswell (2004) in his study indicates that
state policymakers must become better informed and base pol-
icy decisions on data rather than parochial political interests.
Community college presidents can use their faculty to study
issues unique to their institutions and inform legislators by
writing white papers, holding webinars or seminars to address
the gaps where low-level attitudes toward community college
Table 4.
Predicting attitudes toward community college funding with demo-
graphic characteristics and information sourcesa,b.
Unstandardized
Coefficients
Model B Std. Error t Sig.
1 Occupationc 72.404 3.100 23.358.000
2 Occupationd 39.923 4.515 8.843.000
Political Party 38.880 4.567 8.513.000
3 Occupatione 37.360 4.331 8.626.000
Political Party 33.998 4.523 7.516.000
Age 15.069 4.091 3.683.000
4 Occupationf 30.441 4.639 6.562.000
Political Party 32.853 4.341 7.568.000
Age 14.206 3.923 3.622.000
Obama_CCI 13.760 4.147 3.318.001
5 Occupationg 23.199 5.093 4.555.000
Political Party 26.357 4.722 5.581.000
Age 11.489 3.894 2.950.004
Obama_CCI 14.341 4.007 3.579.001
Income 17.486 5.867 2.980.004
6 Occupationh 17.730 5.381 3.295.001
Political Party 22.281 4.854 4.590.000
Age 10.845 3.799 2.855.005
Obama_CCI 16.376 3.978 4.117.000
Income 17.310 5.712 3.031.003
Advisors or Experts 11.235 4.293 2.617.010
7 Occupationi 15.709 5.370 2.925.004
Political Party 21.719 4.778 4.546.000
Age 11.116 3.736 2.976.004
Obama_CCI 17.344 3.935 4.408.000
Income 15.810 5.656 2.795.006
Advisors or Experts10.985 4.221 2.603.011
Media 9.007 4.156 2.167.032
Note: aDependent variable: attitude toward community college funding. bLinear
regression through the origin. cR2 = .832, SEE = 30.213, F(1,110) = 545.588, p
< .001. dR2 = 899, SEE = 23.522, F(2,109) = 486.284, p < .001. eR2 = .910, SEE =
22.274, F(3,108) = 366.081, p < .001. fR2 = .919, SEE = 21.308, F(4,107) =
302.759, p < .001. gR2 =. 925, SEE = 20.564, F(5,106) = 261.828, p < .001. hR2
= .930, SEE = 20.019, F(6,105) = 231.371, p < .001. iR2 = .933, SEE = 19.676,
F(7,104) = 205.973, p < .001.
funding exist. Katsinsa, Tollefson & Reamey (2007) document
low-level attitudes toward community college funding by state.
They reported that 20 of 34 states with funding formulas did
not fully fund their community college. Sixteen states indicated
a lack of capacity to serve the current and projected needs of
high school graduates, and 14 states indicated a lack of capacity
to serve older, returning adult students. Lack of appropriate
funding is a major challenge for community colleges and ad-
dressing this problem must be done in a collaborative effort
between college administrators, public officials, faculty, staff,
and legislative liaisons to design data-driven strategies that
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D. LINDSEY-TALIEFERO, L. TUCKER
Open Access 699
effectively target key attitude predictors to achieve required
funding from their respective state legislators.
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