Michigan Journal of Community Service Learning
Winter 2003, pp. 15-24
Can Service-Learning Help Students Appreciate
an Unpopular Course? A Theoretical Framework
Nicholas Evangelopoulos
University of North Texas
Anna Sidorova
University at Albany, SUNY
Laura Riolli
California State University, Sacramento
A theoretical model is presented that proposes a mechanism through which service-learning can
improve students’ attitudes toward a course. The model suggests that service-learning projects
have an effect on students’ perception of the course material’s usefulness, and that higher
perceived usefulness, along with higher perceived ease of subject, leads to more favorable
attitudes toward the course and stronger intentions for future use of its material. In order to test
the model, a longitudinal study involving undergraduate students enrolled in a business statistics
course was conducted. The results of the study provided full support for the proposed model and
suggested that students involved in service-learning projects experienced significantly higher
increase in their perception of the course material’s usefulness relative to a comparison group,
which resulted in improved attitudes toward the course and in stronger intentions for future use
of the course material.
Over the last few years, significant effort has been extended to bring service-learning
opportunities to classroom, and extensive research has been conducted, examining impacts of
service-learning on a wide range of outcomes (Eyler, 2000). Among social and attitudinal
outcomes, Osborne, Hemmerich, and Hensley (1998) reported a positive impact of servicelearning on student social development, including social competency and perceived ability to
work with adverse others. Kendrick (1996) demonstrated that involvement in service-learning
resulted in increased social responsibility and personal efficacy. Other studies examined servicelearning’s impact on the sense of commitment to future service, and found that projects with a
substantive link between course work and service had a stronger impact on students (Gray et al.,
1999).
With the increased interest in cognitive outcomes of service-learning (Steinke & Buresh,
2002), a number of studies examined the impact of participation in service-learning projects on
students’ analytical skills and academic performance. Kendrick (1996) found that students
involved in service-learning demonstrated a higher ability to apply course concepts to new realworld situations. Other studies suggest that participation in service-learning is associated with
better self-reported learning outcomes (Eyler & Giles, 1999), higher academic performance
(Strage 2000), and higher satisfaction with the course.
Two important outcomes of learning are the students’ attitude to the subject matter after
completing a course, and their willingness to apply the skills and the knowledge acquired in their
future life and career. These outcomes are particularly important in the case of courses required
by the curriculum, since students often perceive such courses as not directly related to their area
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Evangelopoulos, Sidorova, and Riolli
of interest or academic major. Examples of such courses might include general education
science courses for non-science majors, or math and statistics courses for business students.
In spite of the growing number of research studies in service-learning, there is still
insufficient knowledge—both theoretical and empirical—about service-learning’s ability to
produce more positive attitudes toward subject matter and to motivate use of acquired skills and
knowledge in one’s future life and career (Eyler, 2000). This paper attempts to shed some light
on these issues. Because research in psychology suggests that intentions for future behavior are
strong predictors of actual future behavior (Fishbein & Ajzen, 1975), we focus on intention for
future use of course material rather than the actual future use. Hence, the purpose of this paper is
to develop and empirically test a theoretical model that explains the relationship between
participation in service-learning projects and such learning-related outcomes as perceived
usefulness of course material, attitude toward the course, and intention for future use of course
material.
The rest of the paper is organized as follows: First a theoretical model is developed based on
the Technology Acceptance Model and prior research on service-learning. Then, a longitudinal
study used to test the theoretical propositions is described and the study results are presented.
Finally, implications of the study, as well as its limitations and conclusions are discussed.
Theoretical Model
Existing Models Explaining Behavioral Intentions
A number of theoretical models exist that explain behavioral intentions as well as future
behavior, e.g. the Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980; Fishbein & Ajzen
1975), the Theory of Planned Behavior (TPB) (Ajzen 1985), and the Technology Acceptance
Model (TAM) (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). As the starting point in our
theory development we will use the TAM, which was developed to explain individual acceptance
of information technology.
In short, the TAM suggests that individuals’ beliefs about the usefulness of an information
system and the ease of use of this information system affect the attitudes toward the information
system, with perceived ease of use of an information system positively affecting the perceived
usefulness of the information system. Further, the model suggests that attitudes toward an
information system influence individuals’ intentions for future use of the system; in addition,
believes about the usefulness of an information system have a direct effect on the intention to use
the information system. Finally, intentions to use an information system fully mediate the effect
of other variables on the actual use of the system. A number of empirical studies provide support
for the TAM (Davis et al., 1989; Taylor & Todd, 1995).
Recently, the TAM was expanded (TAM2) to include a number of contextual factors
(Venkatesh & Davis, 2000). In particular, it was proposed that perceptions of usefulness of an
information system are affected by subjective norms, result demonstrability, output quality,
image associated with the use of an information system, and job relevance. Subjective norms are
defined as a “person’s perception that most people who are important to him or her think that he
should or should not perform the behavior in question” (Fishbein & Aizen, 1975, p. 302). Result
demonstrability is experiencing positive outcomes from an activity. In TAM2, Venkatesh and
Davis suggest that output quality goes “over and above considerations of what tasks a system is
capable of performing and the degree to which those tasks match job goals (job relevance)” (p.
191), and define it as a perception of how well the system performs those tasks. In addition,
2
Can Service-Learning Help Students Appreciate an Unpopular Course?
such factors as voluntariness and experience were proposed to moderate the relationship between
subjective norms, perceived usefulness and intention for future use. Propositions of the TAM2
are presented in Figure 1.
The Course Acceptance Model
The TAM is particularly appropriate for providing insights into students’ attitudes toward
course material and intentions to use it in the future, due to several commonalities between
information technology, and techniques and knowledge learned in class. First, similar to
information technology, knowledge, skills and techniques taught in universities, are intended to
help individuals to make more effective and well-informed decisions in their professional and
personal life. Further, as with information technology, individuals are free to rely on these skills
and this knowledge, or to ignore them. For example, in making a personal investment decision, a
recent university graduate may use their knowledge about time value of money once acquired in
a finance class, or to ignore this knowledge. Secondly, and most importantly, there exist
knowledge barriers that an individual needs to overcome in order to be able to successfully apply
skills and techniques taught in universities, such as with information technology (Attwell, 1992).
In other words, in order to use skills or techniques, or to apply theoretical knowledge, one first
needs to invest time and effort in learning these skills and techniques, and acquiring knowledge.
Considering these important similarities, students’ attitudes toward a course and the learning
in the course—and their intentions to use acquired skills and knowledge—are likely to be
influenced by similar factors that affect individual attitudes toward, and intentions to use,
information technology. Therefore, drawing on the TAM, we introduce a Course Acceptance
Model (CAM), which is described in the following propositions:
Proposition 1. Perceived ease of course material will have a direct positive effect on the
perceived usefulness of course material.
Proposition 2. Perceived usefulness and perceived ease of course material will have a direct
positive effect on favorable attitudes towards the course.
Proposition 3. Favorable attitude towards the course will have a direct positive effect on
intentions to use the course material in the future.
Proposition 4. Perceived usefulness of course material will have a direct positive effect on
intentions to use the course material in the future.
Service-Learning and Perceived Usefulness of the Course
The CAM proposes that perceived usefulness of the course material leads to more positive
attitudes towards the course and stronger intentions to use the course material in the future. This
suggests that improving perceived usefulness of the course is an important educational objective.
Drawing on the theoretical base of the TAM2 (Venkatesh & Davis, 2000), we propose that
participation in service-learning projects can improve perceived usefulness of course material in
two ways: through subjective norms and through result demonstrability.
One of the critical components of service-learning is building a close relationship with
community partners and working together with them on identifying problems (Porter & Monard,
2001). Such close reciprocal collaboration ensures that community partners benefit from student
projects, and therefore, students receive a positive feedback on the application of the course
3
Evangelopoulos, Sidorova, and Riolli
material. Such positive feedback (subjective norms) is likely to lead to increased student
perceptions of usefulness of the course material.
In addition, by participating in service-learning, students get a chance to help their
community partners solve existing problems. Seeing positive outcomes of applying course
material to real life (result demonstrability) is also likely to increase perceived usefulness of
course material. This leads us to the following proposition.
Proposition 5. Participation in service-learning projects will have a positive effect on the
perceived usefulness of course material.
Because research related to acceptance of information technology showed that subjective
norms have a direct effect on intentions only in case of involuntary adoption, and because
application of course material in the future is voluntary, we do not hypothesize a direct effect of
participation in service-learning projects on intentions. The CAM and the effect of servicelearning are graphically presented in Figure 2.
Method
In order to test the theoretical model described above, a longitudinal study was conducted,
which involved undergraduate students at a medium size public university in the Western part of
the U.S. The students were enrolled in six sections of a business statistics course taught by three
instructors. The course was mandatory for business administration majors and the syllabus
included common statistical techniques, such as statistical quality control, simple regression,
multiple regression, and analysis of variance.
In order to investigate the effect of service-learning on their perceptions of usefulness,
students were given an opportunity to either participate in a service-learning project, or to do a
research-oriented project on a subject of interest to the students. Projects were classified as
service-learning projects if a community partner, such as a non-profit organization, a State or
City office, or a local small business, offered an existing situation, problem, or issue of interest,
together with data and if students helped to address the problem by applying statistical analysis.
Projects were defined as research projects if they involved investigation of a current socioeconomic issue that interested and excited the curiosity of the group members, without the
involvement of a community partner. In such cases, students analyzed data available from public
sources.
The business statistics course used for this study was traditionally offered without any
service-learning component. After a pilot introduction of service-learning during the semester of
Fall 2000, the course was offered with a service-learning component in Spring 2001, when the
present study took place. It is important to note that the major goal of this study was to provide a
valid comparison between service-learning and other types of projects involving application of
the course material. In order to allow for such valid comparison, we wanted to ensure equal
treatment of service-learning and research projects. At the risk of weakening the effect of
service-learning, course instructors avoided emphasizing the benefits of one type of project over
the other. Workload and format of all graded project components were also kept equal for both
types of projects. While our goal of ensuring the comparability between the two project types
imposed certain constraints on our ability to literally comply with service-learning guidelines, we
made an effort to incorporate the most important characteristics usually associated with servicelearning: high quality service, high quality learning, reciprocity (Furco, 1996; Porter & Monard,
4
Can Service-Learning Help Students Appreciate an Unpopular Course?
2001), and reflection (Hill & Pope, 1997; Kendall, 1990). To that end, the instructors made
contact with community partners before the beginning of the semester, and a number of possible
projects were identified as a result of a series of meetings. During the first half of the semester,
two community partners were invited to give brief (15-minute) presentations. During the second
half of the semester, all students taking the course, regardless of the type of project they were
working on, had to turn in a written proposal stating, among other things, why they find their
suggested topic interesting. Regarding the issue of reflection, no weekly journal entries were
made. On the other hand, since the students were working in small groups, team members
communicated frequently their thoughts on the project to each other, mostly over e-mail, and we
believe that this served as a reflection mechanism similar to journal-keeping. Towards the end of
the semester, students were required to make a 10-minute class presentation, during which the
group members had the opportunity to share their findings and their experience with their
classmates. About a week after the oral presentations, the students were required to turn in a
written project report, similar in size to a term paper. All these items were graded separately,
with the written report carrying the heaviest weight.
Examples of service-learning projects included a project involving the local branch of the
American Society for the Prevention of Cruelty to Animals (ASPCA). The students would have
to develop a time series model that predicts the number of dogs adopted every month, so that the
Society can better plan and manage the sheltered animals. Another service-learning project
involved the local Community Services Planning Council (CSPC). The students would have to
develop a regression model to explain average home prices of the various city neighborhoods,
using socio-economic factors as explanatory variables. Such a model would be helpful in the
city’s community planning and budget prioritization efforts. A few cases of service-learning
projects, were actually proposed by the students, such as a project related to the City’s Waste
Management Plan.
In order to overcome threats to internal validity generally associated with quasi-experimental
designs, a control group design with a pre-test and a post-test was used (Cook & Campbell,
1979), with the students working on research projects acting as a control group. Perceived
usefulness was measured using a part of the Course Acceptance Survey described below before
the commencement of the project and after the completion of the project among students
working on service-learning and research projects. While one may argue that students working
on research projects cannot be technically considered a no-treatment control group because they
are exposed to an alternate treatment, such design allows to isolate effects of service-learning
and to separate them form effects of other types of projects requiring application of course
material.
In order to test propositions of the CAM (Propositions 1 through 4), perceived usefulness and
ease of the course material, as well as attitudes towards the course and the intentions for future
use were measured using a Course Acceptance Survey developed on the basis of the survey used
to measure corresponding constructs in the TAM (Davis, 1989). The questions of the
questionnaire were tailored to the specifics of the course and included references to particular
techniques taught in the course. The full text of survey questions is presented in Appendix A. In
order to capture the change in the students’ perceptions and attitudes over time, the survey was
administered twice, in the middle of the semester (before the commencement of the group
project) and at the end of the semester (after the completion of the project). A passage of a
significant period of time between a pre-test and a post-test allowed to minimize threats to
internal validity due to testing effect (Cook & Campbell, 1979). To ensure privacy and
5
Evangelopoulos, Sidorova, and Riolli
confidentiality, persons other than course instructors administered the surveys, and the results
were shared with the instructors only after the semester was over and the grades were turned in.
Results
The measurement scales of the Course Acceptance Survey exhibited high reliabilities, with
Chronbach’s alpha values ranging between 0.89 and 0.94 (see Table 1). The Course Acceptance
Model was tested using Structural Equation Modeling (SEM), a methodology lately proliferating
in behavioral science research, which allows for the combination of factor analysis, survey
instrument reliability tests, and a number of simultaneous Analyses of Covariance. (The reader
is encouraged to read an excellent SEM tutorial by Gefen, Straub, & Boudreau, 2000, for a
comprehensive coverage of many technical details.)
Two SEM models were fitted for the middle-of-semester and end-of-semester data. The
theoretical constructs were modeled as the latent (unobservable) variables Ease, Usefulness,
Attitude, and Future Use. The students’ responses to the survey questions were modeled as the
measurable variables EA1-3 (Ease), US1-4 (Usefulness), A1-2 (Attitude), and FU1-3 (Future
Use). For each model, several goodness-of-fit indicators were obtained, including the Goodness
of Fit Index (GFI), Adjusted Good of Fit Index (AGFI), Comparative Fit Index (CFI), etc.
Goodness-of-fit indicators for each of the two runs of the model (middle-of-semester and end-ofsemester), as well as recommended values for these indices according to the SEM literature
(Gefen et al., 2000), are reported in Table 2. Goodness-of-fit indicators (GFI > 0.90, CFI > 0.90,
etc.) suggest a very good fit of the proposed model for the middle-of-semester, as well as end-ofsemester data. These indicators assess the entire hypothesized structure, eliminating the need for
separate factor analysis for validating the measurement instruments.
In addition to the overall goodness-of-fit indicators, individual path coefficients were
calculated for each of the relationships described in Propositions 1 through 4. Path coefficients
for the middle-of-the-semester data are presented in Figure 3. The analysis suggested that all the
relationships (paths) are significant at the 0.01 level. Similar results were obtained for the endof-semester data. Thus, the SEM analysis provides full support for the CAM.
In order to test the effect of participation in the service-learning projects on perceived
usefulness, an ANCOVA model was tested, with perceived usefulness at the end-of-semester
(post-test) as the response variable, project type and section as factors, and perceived usefulness
in the middle-of-semester (pre-test) as a covariate. A total of 110 usable responses were
analyzed. Results of the ANCOVA are presented in Table 3. The results suggest that servicelearning has a significant positive effect (b-coefficient = 0.580, p-value = 0.032) on perceived
usefulness when the effects of the pre-test usefulness perceptions, as well as the effects of
section, are accounted for. Effects of section and pre-test perceived usefulness were also
significant.
Because the students were not randomly assigned to service-learning vs. research projects,
selection bias might be a legitimate concern. The inclusion of the pre-test perceived usefulness
as a covariate in the ANCOVA model discussed above statistically accounts for pre-existing
individual differences among students, regardless of project type. However, one might argue
that students who initially perceived the course as more useful chose to participate in servicelearning projects, and their service-learning experience simply intensified their pre-existing
believes. In order to investigate such possibility, we compared pre-test perceived usefulness of
students who later chose to participate in service-learning projects, to that of students in research
6
Can Service-Learning Help Students Appreciate an Unpopular Course?
projects. Results of an ANOVA model, with pre-test perceived usefulness as the response
variable, project type and section as factors, are presented in Table 4. These results suggest that
there were no significant differences in usefulness perceptions across sections (p-value = 0.976)
or project type (p-value = 0.793).
Discussion
The results of the study provided support for the theoretical model proposed in this paper. In
particular, all propositions of the CAM were supported. Students who believe that material
taught in a particular course is useful or easy have more positive attitudes towards that course
and intend to use the course material in the future. According to prior research in psychology,
such intentions for future use are likely to result in actual use. One should not consider
establishing a link between “usefulness” and “using” course material a tautology, because in a
number of situations people choose not to use knowledge or skills that they generally consider
useful. For example, a student majoring in business administration who is required to take a
course in the Java Programming Language may believe that knowing Java is useful because it
would increase their job security. However, this student may not intend to use Java in the future
because they find programming very difficult.
Interestingly, our results suggest that students who believe that course material is easy also
tend to find it more useful. While future research is needed to establish a causal relationship
between ease and usefulness, this finding has an important practical implication for pedagogy.
Teachers may want to consider reducing the difficulty of course material, especially when it
comes to general education or supporting courses. This would lead their students to perceive the
course as more useful, and to be more likely to use the course material more in the future.
Our results also suggest that service-learning can significantly improve students’ perceptions
of the usefulness of the course. We theorized that such effect would occur due to better result
demonstrability and because students feel the appreciation community partners have for their
projects (subjective norms). Our study, however, did not examine whether subjective norms and
result demonstrability actually mediate the relationship between service-learning and perceived
usefulness. We believe that establishing these relationships through an empirical study would be
beneficial for service-learning theory and practice. It would not only provide an explanation of
why service-learning has an effect on perceived usefulness, but also suggests that practitioners
can enhance the impact of service-learning by encouraging community partners to provide
positive feedback to the students and emphasizing the project results.
The observed effect of service-learning on perceived usefulness in combination with the
CAM suggests that students participating in service-learning projects will have more positive
attitudes toward the course, and will be more likely to apply course material in their future lives.
A longitudinal study monitoring the actual future application of course material by former
service-learning students might be an interesting direction for future research.
It is important to note several limitations of this study. First, the students were not randomly
assigned to service-learning vs. research project, which raises concerns about selection bias.
While we tried to address these concerns by using appropriate statistical techniques, we believe
that it would be beneficial to replicate our study by randomly assigning students to project type,
or at least randomly assigning different sections of the same course taught by the same instructor
during the same semester to either service-learning or research projects.
7
Evangelopoulos, Sidorova, and Riolli
Another limitation of the study is related to the degree to which our service-learning projects
complied with widely accepted guidelines for service-learning. Due to practical difficulties, our
approach was not to offer our students a pure form of service-learning, but to simulate it in the
best possible way so that valid comparisons between the effects of service-learning and research
projects could be made. We believe that with the advanced preparation of most service-learning
projects by the community partners and the instructors, with the on-campus Office of
Community Collaboration acting as a liaison, the community partner in-class presentations, and
the student-partner interaction, a fair degree of reciprocity was reached. We also believe that the
written proposals, the frequent written communication between group members over e-mail, the
oral presentations, as well as the final written reports contributed to a fair representation of
reflection, especially if we consider that our approach treated as a student body unit not the entire
class, but the group working on the same project. Still, future studies should employ servicelearning projects designed in a way that would conform more rigorously to service-learning
standards.
Although a number of authors have studied the impact of service-learning projects alone, it is
hard to find studies that put service-learning to hard testing, by mixing service-learning and
research projects in the same section, and using control groups which are so similar in time, task
difficulty, promotion, reward, and other characteristics to their treatment counterparts. During
the academic semester that our study took place, we did not aim at offering our students a
complete service-learning experience, but rather at uncovering the defining characteristics of a
service-learning project, when a research project option kept as many project attributes as
possible at an equivalent level. Because our projects were very structured and their requirements
were very specific, we observed a similar level of academic outcome across the two project types
and believe that both groups of students learned about the same amount of course material.
However, the fact that somebody cared to receive outcomes of their course material in a realworld setting made a difference for the service-learning participants, and convinced them that the
course material was more useful that what they would otherwise think. Through the mechanism
presented in our CAM, we expect them to use this material in the future more than they would
had they been involved in a research project.
In view of these findings, we believe that service-learning could increase the educational
value of unpopular courses that have a stereotypical reputation of being “unpleasant” or
“useless”, courses where students cannot see themselves “ever using their material again”. If it
worked for business statistics, it could also work for business accounting, critical analysis of
documents in communication, introduction to biology, and introduction to computer
programming in engineering. We believe that introduction of service-learning to such courses
should be promoted.
Note
This study was partially supported by a Spring 2001 Community Service Learning
Award, made available by the Office of Community Collaboration, California State University,
Sacramento (CSUS). The authors would like to thank Dr. Charlotte Cook and the Service
Learning Scholars Group at CSUS for their inspiration, as well as two anonymous external
reviewers for their detailed and constructive comments on an earlier draft of this article.
8
Can Service-Learning Help Students Appreciate an Unpopular Course?
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Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to
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Porter, M. & Monard, K. (2001). Ayni in the global village: Building relationships of reciprocity
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Authors
NICHOLAS EVANGELOPOULOS is currently an Assistant Professor in the Department of
Business Computer Information Science at the University of North Texas (UNT). Before joining
UNT, he worked for California State University, Sacramento (CSUS) and was an active member
of the Service-Learning Scholars Group at CSUS.
ANNA SIDOROVA is currently an Assistant Professor in the Department of Management
Science and Information Systems at the University at Albany, SUNY.
LAURA RIOLLI is an Assistant Professor of Organizational Behavior and Human Resources
at California State University, Sacramento. She is an active member of the CSUS ServiceLearning Scholars Group.
10
Can Service-Learning Help Students Appreciate an Unpopular Course?
Figure 1
Extended Technology Acceptance Model (TAM2), adapted from Venkatesh & Davis (2000)
Experience
Voluntariness
Subjective
Norms
Image
Job
Relevance
Perceived
Usefulness
Intention
to Use
Usage
Behavior
Perceived
Ease of Use
Output
Quality
Technology Acceptance Model
Result
Demonstrability
Figure 2
Course Acceptance Model (CAM) as Applied to Statistics and the Effect of Service-Learning.
Service-Learning
Use of
Statistics
Usefulness of
Statistical Techniques
Ease of Use of
Statistical Techniques
Attitudes
towards
Statistics
Intentions to
Use Statistics
in the Future
11
Evangelopoulos, Sidorova, and Riolli
Figure 3
Structural Equation Modeling: Path Analysis (Pre-Test)
0.67
US1
1.42
0.32
US2
1.43
0.67
US3
0.52
0.28
0.68
FU1
FU2
FU3
1.60
Usefulness
1.35
0.50
EA1
1.23
0.65
EA2
1.26
0.34
EA3
1.41
Future
Use
0.39
0.62
0.59
1.50
Ease
0.36
0.39
1.34
Attitude
1.67
1.50
A1
A2
0.32
0.76
Note: All Paths are significant at the 0.01 level (all p-values < 0.01).
Table 1
Reliability Testing for the Four Survey Constructs
Construct
Ease
Usefulness
Attitude
Future Use
Survey Items
Pre
Post
3
3
3
4
2
2
3
3
Reliability
Cronbach's Alpha
Pre
Post
0.9033
0.8881
0.9111
0.9440
0.8989
0.9113
0.9291
0.9291
Note: Pre-test constructs (N = 180) as well as post-test constructs (N = 159) are listed. Pre-survey and post survey items were
identical, with the exception of a fourth usefulness item (US4) added to the post-survey, which was a repetition of US2. Full text
and descriptive statistics for all survey items are provided in the Appendix.
12
Can Service-Learning Help Students Appreciate an Unpopular Course?
Table 2
Structural Equation Modeling (SEM) Statistics: Pre-Test Fit, Post-Test Fit, and Recommended Values
SEM Statistic
Pre-test fit
180
Sample size (N)
69.65
Normal Weighted LS Chi-Square
39
Degrees of Freedom
1.79
Chi-Square / Degrees of Freedom
0.93
Goodness of Fit Index (GFI)
0.89
Adjusted Goodness of Fit Index (AGFI)
0.97
Non-Normed Fit Index (NNFI)
0.98
Comparative Fit Index (CFI)
0.036
Standardized Root Mean Square Residual (SRMR)
Post-test fit
159
84.43
49
1.72
0.92
0.87
0.97
0.98
0.035
Recom. Value
≥ 100-150
≤
≥
≥
≥
≥
≤
3.00
0.90
0.80
0.90
0.90
0.10
Table 3
Analysis of Covariance (ANCOVA) Table for the Effects of Project and Section on the Post-Test Course Usefulness,
Using the Pre-Test Usefulness as a Covariate
Source
Corrected Model
Intercept
Project
Section
Useful1
Error
Total
Corrected Total
Type III Sum of
Squares
90.157
54.090
4.992
14.010
67.771
107.119
2981.549
197.276
df
7
1
1
5
1
102
110
109
Mean Square
12.88
54.09
4.992
2.802
67.771
1.05
F
12.262
51.505
4.754
2.668
64.532
p
0.000
0.000
0.032
0.026
0.000
Note: The Total Variance explained by this model (R-squared) is 0.457.
Table 4
Analysis of Variance Table for the Pre-Test Validation of the Quasi-Experimental Design
Source
Corrected Model
Intercept
Section
Project
Section × Project
Error
Total
Corrected Total
Type III Sum of
Squares
20.141
1164.544
1.844
0.160
11.328
225.661
2877.111
245.802
df
11
1
5
1
5
98
110
109
Mean Square
1.831
1164.544
0.369
0.16
2.266
2.303
F
0.795
505.739
0.16
0.069
0.984
p
0.644
0.000
0.976
0.793
0.432
13
Evangelopoulos, Sidorova, and Riolli
Appendix
The Pre-Test (N = 180) and Post-Test (N = 159) Survey
All Items were Measured on a 7-point Likert Scale, where 1 = Strongly Disagree, and 7 = Strongly Agree. Means
and Standard Deviations (in Parentheses) for Each Item are Shown.
Construct
Measure
Ease of Statistics (EA)
Learning the Multiple Regression technique was easy for me
EA1
It was easy for me to become skillful at using Statgraphics and
EA2
performing statistical analyses
Learning a number of Statistical techniques was easy for me
EA3
Usefulness of Statistics(US)
Using statistical software and Statistical techniques would increase
US1
my performance in a business organization
Using Statistical techniques would enhance my decision-making
US2
skills as a manager
US3
I would find Multiple Regression a useful tool that would enhance
my problem-solving capabilities as a business consultant
US4
Using Statistical techniques would enhance my decision-making
skills as a manager
Attitude towards Statistics (A)
Do you enjoy performing statistical analysis?
A1
Do you like statistics?
A2
Intention for Future Use (FU)
How likely is it that you will be using statistics in the future?
FU1
How frequently do you intend to use statistical software and
FU2
statistical techniques in the future?
FU3
Are you going to use data analysis and statistical modeling to
support the future decisions of yourself or your organization?
* See note for Table 1.
14
Pre-test
Post-test
Mean (SD) Mean (SD)
3.97
(1.45)
4.73
(1.35)
4.74
4.27
(1.50)
(1.46)
4.94
4.47
(1.46)
(1.38)
4.97
(1.64)
5.12
(1.54)
5.13
(1.54)
5.13
(1.38)
4.62
(1.58)
5.04
(1.48)
*
*
5.08
(1.34)
3.58
3.65
(1.77)
(1.74)
4.18
4.00
(1.59)
(1.66)
4.18
(1.76)
4.42
(1.64)
3.73
(1.59)
3.96
(1.59)
4.08
(1.63)
4.16
(1.71)