2008 - Marketing Educators' Association

perhaps even relationships could change
dramatically (the notion of flexibility in programming);
however, at the outset, it is reasonable to assume
that students who know one another will most likely
attempt to engage in similar activities, i.e.,
aggregates are more likely to be the norm, as
opposed to individuals (Strange & Banning, 2001).
H4: The programming student satisfaction link will
be greater for those who know others in the
living/learning environment.
Gender
Considering again the influence of aggregates,
certain groups on college campuses tend to bond
more quickly and, as a result, become more visible
and/or vocal on campus. Some groups automatically
become the minority, not necessarily because such
groups have fewer numbers but because their
groups are not as visible or readily-accepted. With
regard to gender, female groups tend to be less
visible than male groups on campus (Strange &
Banning, 2001), even if the overall student
population is more female than male. With regard to
living/learning environments, it seems plausible then
to suggest that males will find greater satisfaction
from the community-building experiences that occur
in living/learning environments. Although females
might indeed welcome a living/learning environment
as an opportunity to more openly develop an
“aggregate,” males should have a greater
appreciation for this chance to increase their bonds
with one-another.
H5: The community-building student satisfaction
link will be greater for male students.
The Program
METHOD
Interview participants and survey respondents were
members of a living/learning program at a small,
private east coast school. The program, based in the
students’ sophomore year, infuses various
social/cultural/spiritual activities and mentoring
sessions (with both peers and adults) with academic
coursework and lectures. If accepted, students live
together in a designated space on the university
premises for the entire sophomore year. Many alums
of the program continue to participate as facilitators,
resident assistants, and peer mentors during their
junior and senior years. The entire programming and
formatting of this particular living/learning experience
revolve around three broad/thematic questions
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about the student’s “vocation” at this point in his/her
life.
The Sample
As part of an honors thesis research obligation, a
senior marketing student, who was also an alum of the
living/learning program, assisted with data collection.
In addition to identifying current and past participants
of the program to interview (see hypothesis section),
she also administered surveys to 35 students in each
of the classes that participated in the program (current
program students, junior alums, senior alums; N =
105). Although the commonly recommended minimum
sample size is 200 for LISREL models, acceptable
models have had as few as five to ten respondents per
item measure (Hair et al., 1998). Fifty-five percent of
the survey respondents were female, 68 percent of the
respondents were arts/sciences majors (other majors
include business, engineering and nursing), and
approximately 69 percent were involved with at least 3
other extracurricular activities in addition to the
living/learning program.
Measures
RESULTS
Due to a lack of empirical work on this topic, as well as
to the variety of collegiate living/learning programs
currently in existence (e.g., discipline-specific, faithbased,
extracurricular-oriented, etc.), items for the
programming and community-building constructs came
from the mission and purpose of this particular
program. The three satisfaction items were modified
from Oliver’s (1999) consumption satisfaction scale.
Concerning item reliability, all Cronbach alphas and
composite reliabilities (C.R.) were .78 or higher.
Discriminant validity was assessed via two methods.
The first method, recommended by Anderson and
Gerbing (1988), involves looking at each construct
pairing (e.g., programming and community-building),
constraining each pairing’s correlation to one, then
computing a Chi-square difference between a singlefactor
and a two-factor model. Discriminant validity is
achieved when the Chi-square difference is significant;
in the case of all three pairings in this dataset, the
differences were significant. The second method,
proposed by Fornell and Larcker (1981), involves
calculating the average variance extracted for each
construct and comparing this result to the squared
correlations of that construct with each of the other
constructs. Discriminant validity is achieved when a
construct’s average variance extracted exceeds all its
squared correlations with the other constructs. This
occurred for all three constructs.