Journal of Marketing for Higher Education
Vol. 21, No. 1, January –June 2011, 47 –60
Reaching the non-traditional stopout population: a segmentation
approach
Kim Schatzel, Thomas Callahan, Crystal J. Scott∗ and Timothy Davis
College of Business, The University of Michigan-Dearborn, Dearborn, USA
Downloaded By: [Davis, Timothy] At: 14:02 16 June 2011
(Received 25 August 2010; Accepted 19 February 2011)
An estimated 21% of 25–34-year-olds in the United States, about eight
million individuals, have attended college and quit before completing a
degree. These non-traditional students may or may not return to college.
Those who return to college are referred to as stopouts, whereas those
who do not return are referred to as stayouts. In the face of declining
pools of traditional students, colleges and universities have attempted to
induce these students to return to higher education. Regrettably, little is
known about the intentions and attitudes of these young adults after they
have left higher education. This paper uses segmentation and targeting to
identify those students who intend to return to college and those who do
not. Using demographic and psychographic variables, five unique
segments are identified. The study recommends strategies for reaching
those segments which are most likely to return to higher education.
Keywords: adult learner; stopouts; segmentation; nontraditional learner
Introduction
As two-year and four-year institutions of higher learning face shrinking
numbers of their largest pool of potential students, demographic projections
indicate that the number of high school graduates will not equal its 2008
peak until after 2018 (College Board, 2008). At the same time, in the United
States, about eight million individuals between the ages of 25 and 34 have
attended college but left before completing degree requirements (US Census
Bureau, 2007). Some of these students will never return to college, whereas
others will reenroll. Those who withdraw for a semester or more and then reenroll in college are referred to as stopouts (Carnegie Commission on Higher Education, 1973). O’Toole, Stratton, and Wetzel (2003), estimate that during the
first year of college, stopouts represent about 40% of all students who withdraw.
Overall, Stratton, O’Toole, and Wetzel (2008) report that more than 30% of all
college students stopout from college for at least a semester. For institutions of
higher learning, stopouts represent a large and unique pool of potential students:
∗
Corresponding author. E-mail: cjscott@umd.umich.edu
ISSN 0884-1241 print/ISSN 1540-7144 online
# 2011 Taylor & Francis
DOI: 10.1080/08841241.2011.569590
http://www.informaworld.com
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48
K. Schatzel et al.
those who have attended college in the past and now have the intention to return
to higher education.
The characteristics of stopouts have been studied with data from national
databases, such as those created by the National Center for Education Statistics
(NCES) (Horn, 1998; Horn & Berger, 2004; O’Toole et al., 2003). For example,
Stratton, O’Toole, and Wetzel (2007) and Stratton et al. (2008) have employed
the NCES’s Beginning Postsecondary Students (BPS) database to study
stopouts from a national sample of institutions of higher education. The
demographic information in these databases allows researchers to identify
factors such as age, academic performance, gender, and ethnicity that explain
stopouts’ behaviors. A second line of research analyses data from a single or
small number of educational institutions (e. g. Ahson, Gentemann, & Phelps,
1998; Bynum & Thompson, 1983; Daubman, Williams, Johnson, & Crump,
1985; Grossett, 1993; Herzog, 2005; Hoyt & Wynn, 2004; Johnson, 2006;
Okun, Benin, & Brandt-Williams, 1996; Pascarella, Duby, Miller, &
Rasher, 1981; Stokes & Zusman, 1992; Woosley, 2003, 2004; Woosley, Slabaugh, Sadler, & Mason, 2005). These studies often rely on exit surveys
from which measures such as attitudes toward education and intentions to
return can be collected.
Specifically in the area of market segmentation, researchers (e.g., Blasco &
Saura, 2006; Shank, Winchell, & Myers, 2001; Broekemier, 2002) have sought
to define this potential student pool, but have not specifically researched the
intentions of nontraditional students who have stopped out of education.
Researchers have agreed that more empirical research regarding the motivations
of adult learners to attend colleges/universities is needed (Broekemier, 2002;
Fujita-Starck, 1996; Morstain & Smart, 1974). Given the size and importance
of this population as a potential enrollment input, the identification of demographic and psychographic characteristics of these nontraditional stopouts,
would result in more efficient and cost-effective recruitment programs directed
toward them.
To our knowledge, no studies have been specifically designed to identify the
demographic and psychographic factors that differentiate reenrollment intentions among nontraditional former college students using a representative
demographic population. For example, studies using national databases have
not included a measure of intention to reenroll. Studies that have included
this or a similar variable have utilised data from a single institution. Therefore,
the purpose of this study is to overcome this gap in the study of adult learners
and stopouts. To accomplish this purpose, we chose a segmentation methodology to identify characteristics of stopouts from a large metropolitan area to
aid colleges and universities to target and better serve this population. The
specific research goals of this study are to: (1) describe the nontraditional
stopout and stayout populations based on their intentions to return; (2) identify
distinct segments of the stopout and stayout populations relying on demographic and psychographic variables from previous models of nontraditional
Journal of Marketing for Higher Education
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students’ attrition and reenrollment intentions; and (3) recommend strategies
that colleges and universities might use to reach nontraditional stopouts.
Theoretical background
Researchers have relied on theoretical models of student retention and withdrawal behaviors for guidance when studying stopouts from higher education
because these models explain two important stopout decisions: (1) decisions
to leave college; and (2) decisions to return to college. Providing guidance
for this line of research, Tinto’s (1975, 1993) models of student retention
and withdrawal explain students’ behaviors as resulting from commitments
to both higher education and a particular institution. These commitments influence their academic performances and social integrations. These integrations
determine the fit between a student and an institution and motivate decisions
to stay or leave. An expanded model of student retention proposed by Bean
and Eaton (2000) states that cognitions, behaviors, and attitudes lead to
decisions to stay or leave. The Bean and Eaton (2000) model recognises the
importance of concepts such as approach-avoidance, locus of control, and
self-efficacy and maintains that these strongly influence students’ intentions
to withdraw. Studies have extended the psychological and sociological findings of research based on these models to the study of the stopout population
(e. g. Grossett, 1993; Johnson, 2006; Okun et al., 1996; Stokes & Zusman,
1992; Woosley, 2003, 2004; Woosley et al., 2005). The development of
research variables for this study flows from both the general theoretical
models of withdrawal and the research findings from the study of stopouts’
intentions and behaviors.
Demographics
Demographic variables have been used to explain participation in adult education in several previous studies (Aslanian Group, 2006; O’Donnell &
Tobbell, 2007). In fact, the national demographic profile of the undergraduate
adult learner has remained fairly consistent over the last 20 years with the
only difference being that these students are getting older (Aslanian Group,
2006). Several studies have determined that age plays a role in adult learners’
intention to return to higher education (e.g. Benshoff, 1991; Richter-Antion,
1986; Spanard, 1990; Terrell, 1990). Older students face barriers related to
limited social support systems, lack of peer group classmates, and other life
obligations (Klein, 1990). From a motivational perspective, Broekeimer
(2002) researched motivations to pursue education among adult students
aged 25 to 34. He found that baccalaureate students were more likely to cite
the pursuit of general knowledge and job advancement with their current
employers, whereas community-college students gave reasons such as new
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K. Schatzel et al.
career, health/injury issues, and family considerations. After withdrawal from
higher education, Horn (1998) reports that older stopouts are less likely to reenroll after leaving. Hoyt and Winn (2004), Desjardins, Ahlburg, and McCall
(2006), and Grosset (1993) have found that stopouts are likely to be older
than continuing students.
Minority status has been shown to explain withdrawal behaviors even when
Caucasians represent the minority (Bynum & Thompson, 1983). Lower minority participation has been linked to feelings of isolation and dissimilarity
within majority dominated institutions (Thomas, 2001). Pascarella et al.
(1981) and Johnson (2006) have shown that African-Americans are more
likely to stopout of and less likely to reenroll in college than are majority
group members. Woosley et al. (2005) studied both intentions and actual reenrollment behaviors at one institution. Their results indicate that minorities do not
differ from nonminorities in actual reenrollment, despite the fact that they are
less likely to state the intention to reenroll.
In general, research has shown that female adult learners have a particularly
difficult time returning to school because of factors including finances, work
schedules, and child care (Blais, Duqueite, & Painchaud, 1989; Lockhardt,
2003; Johnson, Schwartz, & Bower, 2000). In support of this premise,
Johnson (2006) has shown that men are more likely to reenroll after stopping
out than are women. In contrast, Woosley et al. (2005) indicate that women
are less likely to state the intention to reenroll, but have found no differences
between the actual reenrollment behaviors of men and women.
Barriers to enrollment
The most frequently described barriers to enrollment include time constraints,
costs, family responsibilities, inconvenient class schedules, transportation, and
employment problems (Gustafson & Sorgaman, 1983; Osborne, Cope, &
Johnstone, 1994; Levine & Cureton, 1998; Harris & Brooks, 1998). Many
of these problems are interrelated and it is often impossible to separate the
effects factors such as family responsibilities, time constraints, and costs
(Chao, DeRocco, & Flynn, 2007). These factors have also been found to be
related to stopout intentions and behaviors. For example, Light (1995) has
found that workers who experience unemployment are more likely to reenroll
in higher education. Johnson (2006) has found that stopouts who have lower
income are less likely to reenroll that are higher income stopouts. St. John,
Cabrera, Nora, and Asker (2000) have established that concerns about
costs have a negative influence on college persistence behaviors, including
reenrollment. Likewise, students who have significant family responsibilities
are more likely to permanently withdraw from college rather than temporarily
stopout (Ahson et al., 1998; Horn, 1998; Hoyt & Winn, 2004; Stratton et al.,
2008).
Journal of Marketing for Higher Education
51
Educational experiences and values
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Previous academic success and past educational attainment have been shown to
be positively related to participation in adult education (Kleiner, Carver, Hagedorn, & Chapman, 2005; Creighton & Hudson, 2002). For stopouts, research
finding by Johnson (2006), Berkovitz and O’Quin (2006, 2007), and DesJardins
et al. (2006) indicate a positive relationship between previous academic performance and the likelihood of reenrollment.
An understanding of the importance of education and its effect on life goals
are strong predictors of attraction behaviors (Tinto, 1993). Specifically for
stopouts, Ahson et al. (1998), Berkovitz and O’Quin (2006, 2007), Hammer
(2003), and Woosley et al. (2005) have all found that levels of previous educational attainment contribute to an intention to reenroll. Similarly, stopouts
who value education and recognise its importance in society are more likely
to state the intention to reenroll (Woosley et al., 2005).
Segmentation studies in higher education
Segmentation has been used to aid decision making in corporate settings, but
has rarely been applied in higher education. An early approach was taken by
Goodnow (1982) who applied benefits segmentation, based on student motivations, to a population of enrolled community college students. She determined five distinct segments exist with different motivational orientations
ranging from a social/improvement motivation to one centered on learning/
career. Her findings were used to identify target markets and design
program offerings in response to the needs and interests of members in
selected benefit segments. Although a causal result is not implied in Goodnow’s research, she reports that enrollment increased about 20% a year
following the study. Recently Ghosh, Javalgi, and Whipple (2007) took a
service provider view of higher education when segmenting undergraduate
business students from a large, urban, public university. The authors used
student registration data to uncover five distinct segments and suggest appropriate service strategies for each one. As an example, one such segment was a
domestic non-traditional segment in which the students were older, mainly
female, and about 40% were married. Services recommended for this group
included orientation and advising services, counseling, tutoring, and financial
aid advice. Blasco and Saura (2006) also considered higher education organizations as a service provider and segmented students based on their expectations and perceptions of service quality.
A study by Shank et al. (2001) segmented the nontraditional market of students over 25 years of age and looked specifically at the adult female learner.
The authors uncovered three distinct market segments for the non-traditional
female learner. The biggest discriminator of these three segments was ‘services
sought’. One segment required academic support such as advising, a second
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K. Schatzel et al.
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segment needed a variety of support services from financial assistance to academic advising, and the third segment consisted of young women in need of
financial assistance and childcare.
Although we know a bit more about the adult learner in terms of their enrollments, we know little about the adult learner who withdraws from education.
Prior research has found that stopouts return to school and graduate at rates
similar to students who have never withdrawn (Stokes & Zusman, 1992;
Woosley et al., 2005). Previous research has treated the stopout population as
a homogenous group. Segmentation allows us to identify the underlying heterogeneity in these subpopulations of stopouts and stayouts.
Methodology and results
Respondents
Respondents were identified from current voter registration data representing an
metropolitan statistical area with a population of over four million. They were
selected to participate in telephone interviews if they had previous college
credits but no degree, were not currently enrolled in college, and were
between the ages of 25–34. Many recent studies suggest that students aged
25– 34 define the adult learner age demographic (Broekemier, 2002; Saunders
& Bauer 1998; Harris & Brooks, 1998; Poppenga & Prisbell, 1996). Of 33,468
contacts, 9269 agreed to participate. This indicated a response rate of 28%. Individuals who agreed to participate and met all of the above criteria numbered
646. Of these, 599 answered all the survey items.
The mean age for the entire sample was just under 30 years old. Females
comprised about 52% of the sample. More than 80% of the sample described
their race/ethnicity as Caucasian, whereas almost 13% of the sample described
their race/ethnicity as African-American. The remainder of the sample included
individuals who described their race/ethnicity as Hispanic, Native American,
Asian, or multiracial. Individuals who were employed full or part-time represented 73% of the respondents. The percentages of minorities in this
sample were roughly equivalent to those reported in census data for this metropolitan area (US Census Bureau, 2006).
Instrument
The survey contained both demographic and psychographic items that previous
research had established were related to intentions to return to college. Two versions of the survey were administered, one designed for adults who stated an
intention to return to college and the other designed for those who did not
intend to return. Verb tenses constituted the only differences between these
surveys.
Journal of Marketing for Higher Education
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Procedure
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A commercial survey company administered the interviews by telephone.
Potential respondents were asked their ages, previous college experiences,
and current college enrollment. Each of these three items served as filters for
the selection of respondents. As an example, respondents not in the appropriate
age groups were thanked for their time and not interviewed. If the respondents
were in the appropriate age group, they were asked whether they had attended
college without completing a degree. If the respondents had previously attended
college without completing a degree, the interview continued. Next, respondents were asked if they were presently enrolled in college. If respondents provided a negative response to this question, the interview continued. Table 1
presents the background variables and latent constructs for the analysis.
Data analysis
Confirmatory factor analysis assessed the measurement model for the four latent
variables associated with the Likert scale items. The four latent variables
measured two perceived barriers to reenrollment: finances and time, as well as perceptions of the importance of education, and previous college success. The CFI
(Comparative Fit Index) was 93.9 and the RMSEA (Root Mean Square Error
of Approximation) was .056. Both of these indices indicate a reasonable, but
not strong fit of the model to the data (Browne & Cudeck, 1993; Hu & Bentler,
1999). Average variance extracted among the variables ranged from .58 to .78,
which is above the acceptable .50 minimum value, suggesting adequate convergent validity (Bagozzi, Yi, & Phillips, 1991). The scale composite reliabilities
for these latent constructs range from .70 to .80, which is equal to or greater
than the .70 recommended minimum value (Bagozzi et al., 1991). Nested
models of the correlations among pairs of variables were compared to test discriminant validity. A chi-square difference test determined whether the constrained
and unconstrained pairs of constructs significantly differed (Anderson & Gerbing,
1988). For all pairs, the unconstrained model, less one degree of freedom, indicated a chi-square of at least 3.84 lower than that of the constrained model, indicating discriminant validities for all pairs (Anderson & Gerbing, 1988).
To explore the issue of sample heterogeneity, we employed cluster analysis
to identify homogeneous groups of stopouts with similar needs and wants from
an institution of higher learning. Here, we submitted the intentions to reenroll
and latent psychographic variables to the cluster procedure. Intentions to
reenroll were measured with two questions that asked about interest in and likelihood of reenrollment. Respondents used a 5-point Likert scale to indicate their
interest in reenrolling (1-Not interested at all, 2-Not Interested, 3-Somewhat
Interested, 4- Interested, 5-Very Interested). Respondents also used a 5-point
Likert scale to gauge their likelihood of returning (1-Very unlikely, 2-Unlikely,
3-Somewhat likely, 4-Likely, 5-Very Likely). We also employed the latent
54
K. Schatzel et al.
Table 1. Background variable measures and latent constructs (composite reliabilities).
College Credits:
Gender:
Ethnicity:
Income:
Laid Off:
Marital Status:
Financial Supporter:
Primary Caregiver:
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Children:
Finances: (.70)
You said you have previously earned college credits, would you say you
completed ,4 ranges of years of college completed given.
Your gender is?
You would best identify your race as?
Which of the following best describes your annual household income?
,five ranges of income given.?
Have you been laid off in the past two years?
Your marital status is?
You are the primary financial support for your family?
,5 levels of agreement.
You are the primary caregiver for your family?
,5 levels of agreement.
How many children under the age of 10?
You left college for financial reasons
Family finances prevent you from returning to college
You do not believe you could receive the financial aid you would need to
return to college
Time (.73)
You could not fit college classes around your work schedule
You do not have the time to return to college
Important (.80)
A college education is important for a person’s success
To make a good living you need a college education
Success (.74)
You performed as well academically as you expected
You were academically successful at the last college you attended
You left college because you did not have the study skills necessary for
college
variables measuring financial problems, importance of education, time issues,
and previous academic success.
Using the hierarchical based Ward’s Method (Ward, 1963), we identified a
five-cluster solution based on inspection of the overall diagnostics. The cubic
clustering criterion was 4.319 with a Pseudo F-Statistic ¼ 154.67 and an
overall R-Squared of .484. Additionally, inspection of the dendrogram identified a five cluster solution as well. This method of hierarchical clustering
was recommended by Punj and Stewart (1983) given that it minimizes an explicit loss function, the error sums-of-squares, and typically results in compact and
well-separated clusters.
The five-cluster solution reveals two segments that have no intention of
reenrolling, two segments that do intend to reenroll, and one segment that is
interested in but not likely to return to school. Each group is substantial in
size with the smallest group representing 13% of the sample and the
largest group representing 27%. The two segments with intentions of reenrolling make up 40% of respondents. The five distinct segments revealed here
are driven by different demographics, family issues, and financial circumstances. An ANOVA (Analysis of Varience) was conducted on the various
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Table 2. Profile of segment characteristics.
Segment
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Name and Size (Proportion)
1. Interested in Returning
2. Likely to Return
3. Women
4. Married
5. Average Age
6. Non-Caucasian
7. Completed at least 2 years of
college?
8. Work Full-Time
9. Make less than $25K a year
10. Make $50K or more a year
11. Primary Financial Support
for Family
12. Children under 10 yrs. old
13. Primary Care Giver
14. Family Responsibilities
Prevent Return
15. Don’t have time to Return
16. Left college because:
a) Financial Reasons
b) Family Reasons
c) Would not benefit career
1
2
3
4
5
Family
Ties
(19%)
Married and
Comfortable
(20%)
Financially
Strapped
Singles
(13%)
Progressors
(27%)
Time
Bound
(21%)
45%
7%
51%
57%
30.4
20%
36%
0%
1%
54%
70%
30.0
13%
43%
76%
89%
44%
34%
28.7
37%
35%
72%
79%
54%
48%
29.5
22%
50%
1%
0%
52%
69%
30.2
9%
38%
61%
15%
50%
50%
59%
5%
68%
45%
51%
35%
45%
49%
61%
18%
51%
48%
61%
6%
59%
45%
46%
60%
70%
49%
54%
46%
26%
49%
41%
43%
65%
69%
48%
67%
69%
61%
46%
22%
89%
95%
65%
47%
21%
18%
25%
44%
40%
28%
11%
42%
37%
16%
43%
34%
35%
attributes and variables to determine how the five segments differed in their
intentions. The segments, along with their means and percentages on the
various attributes are given in Table 2. We label these resulting five segment
as: (1) Family Ties; (2) Married and Comfortable; (3) Financially Strapped
Singles; (4) Progressors; and (5) Time Bound.
The first segment, ‘Family Ties’, are stayouts and represent one of the
smaller clusters, approximately one-fifth of the total sample. This group is
51% female, slightly older than the other groups with an average age of 30.4
years old and 57% are married. This group was most likely to indicate that
they left college for financial reasons and that family responsibility was a deterrent to returning. Family responsibilities and having children are characteristics
that distinguish stayouts from stopouts. Almost half of this group expressed an
interest in returning to school, although they believe that it is unlikely.
The second stayout segment is labeled, ‘Married and Comfortable.’ This
group represents 20% of the sample, with an average age of 30 years old. A
little over half (54%) are women, and 70% are married. This segment has the
highest income with over two-thirds stating a household income of at least
$50,000 and over 40% indicating that a college education would not benefit
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56
K. Schatzel et al.
their careers. Financial reasons were not a factor in their decisions to leave
school.
The third segment, ‘Financially Strapped Singles’, are stoputs and express
the greatest intention to reenroll. This is the youngest group with an average
age of 28.7 years old. Less than half are women (44%), 60% are single and
only about a quarter of these segment members have children. They have the
lowest income of all the segments, with over a third making under $25,000.
This group contains significantly more African-American members than the
other four groups. Time and family responsibilities do not prevent them from
returning to school. However, they have completed fewer years of college
than the other segments.
‘Progressors’, who are stopouts, represent 24% of the sample. The average
age for this group is 29.5. Fifty-four percent (54%) are female, over half are
married (54%), and over half have children. This group has the most college
experience as half report completing at least two years of college. Over twothirds have children and time to return to school is an issue for almost 90%
of this group. Despite their time issues, this group believes that college is
important to a person’s success.
The fifth and last segment, ‘Time Bounds’, are stayouts representing 21% of
the sample. Less than 1% express an interest or likelihood of returning to
school. Over two-thirds are married and 95% indicate time as a deterrent to
returning. Similar to the Married and Comfortable segment, they do not
believe finishing school would benefit their careers, possibly because they
have relatively high incomes.
Conclusion
Although the focus of this paper was on the stopout population, the sample contained those who intend to stayout of higher education. Literature comparing
stopouts and stayouts find that stayouts are older, more likely to have children
at home, and cite family responsibilities as a major deterrent to returning to
school (Hoyt & Winn, 2004). Our results confirm these findings. We recommend colleges and universities not ignore these groups of stayouts. As an
example ‘Family Ties’ have stated an interest in reenrolling but state they are
unlikely to renenroll due to time constraints. Colleges might target this
segment with creative ways to attend classes overcoming constraints of their
family responsibilities. One way might be to provide cooperative child-care
programs with parents/students acting as volunteers.
Our study confirms that stopouts are not a homogeneous group of adult learners. We uncovered two distinct stopout segments in this research, representing
40% of the sample. The first stopout subpopulation represents young singles
with financial concerns and the second stopout segment represents those who
are closest to finishing their education but have little time in their schedule to
do so. To target the Financially Strapped Singles segment, colleges might
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Journal of Marketing for Higher Education
57
educate individuals from this segment on the sources of financial aid or create
specific programs for this younger and single group. The Financially Strapped
Singles segment also contains significantly more minority students. Existing literature cites feelings of isolation, dissimilarity, and integration difficulties
experienced by minority students (Johnson, 2006). Colleges considering reaching out to this segment not only need to educate potential students about financial aid but also about ways to socially integrate students such as mentoring or
networking programs for minority students (Tinto, 1993; Johnson, 2006).
The non-traditional student represents an important target market for
colleges and universities. Their sheer numbers alone, approximately eight
million individuals between 25 and 34 years old, and the predicted growth of
this market make it very important for universities to recognise the best way
to serve them and the strategies they should use to reach them. From 2006 to
2017, NCES projects a rise of 10% in enrollments of people under 25 years
of age, and a rise of 19% in enrollments of people 25 and over. The current
study looked at the population of individuals 25–34 years old who have
stopped out of college who may or may not state intentions of reenrolling.
The research uncovered five distinct segments of former non-traditional
students with differing levels of intentions to reenroll.
We recommend universities and colleges with evening day care programs
redirect any advertising they are currently doing with traditional segments
and target ‘Progressors’, which is the largest segment identified. A school offering online courses and weekend classes might benefit this segment. Additionally, very early morning classes, before typical work hours might be an
option for this segment as well. Colleges looking to target the younger stopouts,
Financially Strapped Singles, with fewer family responsibilities, but with financial issues might develop creative low-interest loans, scholarships, work-study,
and flexible payment plans for this segment. Online counseling and advising
would benefit both groups.
As with any study, this research has limitations. First of all, the data were
collected from a metropolitan area in the Midwest where unemployment is
higher than the rest of the country. High unemployment rates in this area
may have affected our results. The study of stopouts represents a relatively
small area of research. More studies should define the study of stopouts as
the main research objective. A study of stopouts in other parts of the county
and perhaps with a wider representation of ethnic groups might be one fruitful
area to pursue. Further qualitative and quantitative work using university students, 25– 34 years old and who have reenrolled could confirm or disconfirm
the segments identified in our work.
Acknowledgements
The authors would like to thank the Department of Labor and the Detroit Regional Chamber for
funding this study through the Workforce Innovation in Regional Economic Development
58
K. Schatzel et al.
(WIRED) Program. Additionally, the authors want to recognise iLabs, The Center for Innovation
Research at the University of Michigan-Dearborn, Mr. Greg Handel, Senior Director of
Workforce Development-Detroit Regional Chamber, and Dr. Daniel Little, Chancellor of the University of Michigan –Dearborn, for their support of this research.
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References
Ahson, J., Gentemann, K., & Phelps, L. (1998). Do stop-outs return? A longitudinal study of reenrollment, attrition, and graduation. AIR Annual Forum Paper: Retrieved June 23, 2010
from http://www.eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/
80/17/07/7c.pdf
Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A Review
and recommended two-step approach. Psychological Bulletin, 103, 411–423.
Aslanian Group. (2006). Trends in adult learning: A 2006 snapshot. New York: Author.
Bagozzi, R.P., Yi, Y., & Phillips, L.W. (1991). Assessing construct validity in organizational
research. Administrative Science Quarterly, 36(3), 421–458.
Bean, J.P., & Eaton, S.B. (2000). A psychological model of college student retention. In M. John
Braxton (Ed.), In Rethinking the departure puzzle: New theory and research on college
student retention. Nashville, TN: Vanderbilt University Press.
Benshoff, J. (1991). Nontraditional college students: A developmental look at the needs of
women and men returning to school. Journal of Young Adulthood and Middle Age, 3, 47–61.
Berkovitz, R.A., & O’Quin, K. (2006/2007). Predictors of graduation of readmitted “at risk”
college students. Journal of College Student Retention, 8(2), 199–214.
Blais, J., Duqueite, A., & Painchaud, G. (1989). Deterrents to women’s participation in workrelated educational activities. Adult Education Quarterly, 39(4), 224–34.
Blasco, M.F., & Saura, I.G. (2006). Segmenting university students on the basis of their expectations. Journal of Marketing for Higher Education, 16(1), 25–45.
Broekemier, G.G. (2002). A comparison of two-year and four-year adult students: Motivations
to attend college and importance of choice criteria. Journal of Marketing for Higher
Education, 12(1), 3–48.
Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen &
J.S. Long (Eds.), Testing structural equation models (pp. 136– 162). Newbury Park, CA:
Sage.
Bynum, J.E., & Thompson, W.E. (1983). Stayouts, stop-outs and persisters: The effects of race
and sex composition of college classes. College and University, 59(1), 39–48.
Carnegie Commission on Higher Education. (1973). The Carnegie classification of institutions
of higher education, Carnegie Commission for the Advancement of Teaching. Retrieved
June 23, 2010 from http://www.carnegiefoundation.org/classifications/.
Chao, E.L., DeRocco, E.S., & Flynn, M.K. (2007). Adult learners in higher education: Barriers
to success and strategies to improve results. Washington, DC: Employment and Training
Administration, Retrieved June 8, 2010 from http://wdr.doleta.gov/research/FullText_
Documents/Adult%20Learners%20in%20Higher%20Education1.pdf.
College Board. (2008). Current demographic trends in higher education, Retrieved May 20, 2010
from http://professionals.collegeboard.com/data-reports-research/trends/higher-ed-landscape.
Creighton, S., & Hudson, L. (2002). Participation Trends and Patterns in Adult Education:
1991 – 1999, National Center for Education Statistics (ED) Washington, DC. Retrieved
from http://nces.ed.gov/PUBSEARCH/pubsinfo.asp?pubid=20021199.
Daubman, K.A., Williams, V.B., Johnson, D.H., & Crump, D. (1985). Time of withdrawal and
academic performance: Implications for withdrawal policies. Journal of College Personnel,
26(6), 518–524.
DesJardins, S.L., Ahlburg, D.A., & McCall, B.P. (2006). The effects of interrupted enrollment
on graduation from college: Racial, income, and ability differences. Economics of Education
Review, 25, 575–590.
Fujita-Starck, P.J. (1996). Motivations and characteristics of adult students: Factor stability and
construct validity of the educational participation scale. Adult Education Quarterly, 47(1),
29–40.
Downloaded By: [Davis, Timothy] At: 14:02 16 June 2011
Journal of Marketing for Higher Education
59
Ghosh, A.K., Javalgi, R., & Whipple, T.W. (2007). Service strategies for higher educational
institutions based on student segmentation. Journal of Marketing for Higher Education,
17(2), 238–255.
Goodnow, B. (1982). Increasing enrollment through benefit segmentation. Adult Education,
32(2), 89–103.
Grosset, J.M. (1993). A profile of college stop-outs. Community College Review, 20(4), 51–58.
Gustafson, M., & Sorgman, M. (1983). The Degree of Satisfaction with Institutional Climate as
Perceived by Female Students in Teacher Education: An Exploratory Study, Retrieved from
http://eric.ed.gov/ERICWebPortal/custom/portlets/recordDetails/detailmini.jsp?_nfpb=
trueand_andERICExtSearch_SearchValue_0=ED241484andERICExtSearch_SearchType_
0=noandaccno=ED241484.
Hammer, T. (2003). The probability for unemployed young people to re-enter education or
employment: A comparative study in six northern European countries. British Journal of
the Sociology of Education, 24(2), 209–223.
Harris, M.B., & Brooks, J. (1998). Challenges for older students in higher education. Journal of
Research and Development in Education, 31(4), 226–235.
Herzog, S. (2005). Measuring determinants of student return vs. stayout/stop-out vs. transfer:
A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8),
883–928.
Horn, L. (1998). Stopouts or stayouts? Undergraduates who leave college in their first year
(NCES 1999 – 087), U.S. Department of Education, Washington, DC. Retrieved from
http://www.eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/17/
1f/d5.pdf
Horn, L., & Berger, R. (2004). College persistence on the rise? Changes in 5-year degree corpcompletion and postsecondary persistence rates between 1994 and 2000 (NCES 2005-156),
U.S. Department of Education Washington, DC. Retrieved from http://nces.ed.gov/
pubs2005/2005156.pdf.
Hoyt, J.E., & Winn, B.A. (2004). Understanding retention and college student bodies:
Differences between drop-outs, stop-outs, opt-outs, and transfer-outs. NASPA Journal,
41(3), 395–417.
Hu, L.T., & Bentler, P.M. (1999). Cut-off criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6,
1–55.
Johnson, I.Y. (2006). Analysis of stopout behavior at a public research university: The multispell discrete-time approach. Research in Higher Education, 47(8), 905–934.
Johnson, L.G., Schwartz, R.A., & Bower, B.L. (2000). Managing stress among adult women
students in community colleges. Community College Journal of Research and Practice,
24(4), 289–300.
Klein, J. (1990). An analysis of the motivational characteristics of college reentry students.
College. Student Journal, 24(3), 281–286.
Kleiner, B., Carver, P., Hagedorn, M., & Chapman, C. (2005). Participation in adult education
for work-related reasons: 2002 – 2003. U. S. Department of Education: Institute of Education
Sciences NCES 2006 –040.
Levine, A., & Cureton, J.S. (1998). What we know about today’s college students. About
Campus, 3(1), 4–9.
Light, A. (1995). Hazard model estimates of the decision to reenroll in school. Labour
Economics, 2, 381–406.
Lockhardt, M. (2003). Community collaboration to support reentry adult women. Academic
Exchange Quarterly, 7(2), 170–174.
Morstain, B.R., & Smart, J.C. (1974). Reasons for participation in adult education courses: A
Multi-Variate Analysis of Group Differences. Adult Education, 24, 83098.
O’Donnell, V., & Tobbell, J. (2007). The transition of adult students to higher education:
Legitimate peripheral participation in a community of practice? Adult Education
Quarterly, 57(4), 312–328.
Okun, M.A., Benin, M., & Brandt-Williams, A. (1996). Staying in college: Moderators of the
relation between intention and institutional departure. Journal of Higher Education,
73(2), 189–236.
Downloaded By: [Davis, Timothy] At: 14:02 16 June 2011
60
K. Schatzel et al.
Osborne, M.J., Cope, P., & Johnstone, R. (1994). The backgrounds and experiences of adult
returners to an Access to Secondary Teaching scheme. Continuing Higher Education
Review, 58(1/2), 41–63.
O’Toole, D.M., Stratton, L.S., & Wetzel, A. (2003). A longitudinal analysis of the frequency of
part-time enrollment and the persistence of students who enroll part-time. Research in
Higher Education, 44(5), 519–537.
Pascarella, E.T., Duby, P.B., Miller, V.A., & Rasher, S.P. (1981). Pre-enrollment variables and
academic performance as predictors of freshman year persistence, early withdrawal, and
stopout behavior in an urban, nonresidential university. Research in Higher Education,
15, 329–349.
Poppenga, J.G., & Prisbell, M. (1996). Differences in apprehensions about communicating in
the classroom between traditional and adult students. Psychological Reports, 78, 802.
Punj, G., & Stewart, D.W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20(2), 134–148.
Richter-Antion, D. (1986). Qualitative differences between adult and younger students. NASPA
Journal, 23, 58–62.
St. John, E.P., Cabrera, A.F., Nora, A., & Asker, E. (2000). Economic influences on persistence
reconsidered: How can finance research inform the reconceptualization of persistence
models? In J.M. Braxton (Ed.), Reworking the Student Departure Puzzle (pp. 29– 47).
Nashville, TN: Vanderbilt University Press.
Saunders, L.E., & Bauer, K.W. (1998). Adult Students in Higher Education. Journal of Higher
Education, 69(1), 65–88.
Shank, M.D., Winchell, M.H., & Myers, M. (2001). Appreciating the needs of non-traditional
students: Women as a growing market for colleges and universities. Journal of Marketing
for Higher Education, 11(1), 63–72.
Spanard, J. (1990). Beyond intent: Reentering college to complete the degree. Review of
Educational Research, 1990, 60(3), 309–344.
Stokes, J.P., & Zusman, B.J. (1992). A study of stop-outs at an urban commuter university.
NASPA Journal, 29(4), 283–289.
Stratton, L.S., O’Toole, D.M., & Wetzel, J.W. (2007). Are the factors affecting stayout behavior
related to initial enrollment intensity for college undergraduates? Research in Higher
Education, 48(7), 453–485.
Stratton, L.S., O’Toole, D.M., & Wetzel, J.W. (2008). A multi-nominal logit model of college
stopout and stayout behaviors. Economics of Education Review, 27(3), 319–331.
Terrell, P. (1990). Adapting institutions of higher ed. to serve adult students’ needs. NASPA
Journal, 27, 241 –247.
Thomas, V. (2001). Educational experiences and transitions of reentry women: Special considerations for African American female students. The Journal of Negro Education, 70(3),
139–155.
Tinto, V. (1975). Stayout from higher education: A theoretical synthesis of recent research.
Review of Educational Research, 45, 89–127.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student retention.
Chicago: The University of Chicago Press.
US Census Bureau. (2006). State and County Quick Facts._Retrieved from http://quickfacts.
census.gov/qfd/states/26/2622000.html.
US Census Bureau. (2007). American Community Survey Estimates: 2007. Retrieved from
http://factfinder.census.gov/servlet/DatasetMainPageServlet_program=ACS.
Ward, J.E. (1963). Hierarchical grouping to optimize an objective function. Journal of American
Statistical Association, 59, (1963), 236 –244.
Woosley, S. (2003/2004). Stop-out or drop-out? An examination of college withdrawals and reenrollments. Journal of College Student Retention, 5(3), 293–303.
Woosley, S., Slabaugh, K., Sadler, A.E., & Mason, G.W. (2005). The mystery of Stop-outs: Do
commitment and intentions predict reenrollment? NASPA Journal, 42(2), 188–201.