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2012 thesis correlation between undergraduate college students facebook use and co curicular...
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CORRELATION BETWEEN UNDERGRADUATE COLLEGE STUDENTS‟ FACEBOOK USE AND CO-CURRICULAR INVOLVEMENT A Thesis Submitted to the School of Graduate Studies and Research in Partial Fulfillment of the Requirements for the Degree Master of Arts Christopher Steven Weiss Indiana University of Pennsylvania May 2012
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CORRELATION BETWEEN UNDERGRADUATE COLLEGE STUDENTS‟
FACEBOOK USE AND CO-CURRICULAR INVOLVEMENT
A Thesis
Submitted to the School of Graduate Studies and Research
in Partial Fulfillment of the
Requirements for the Degree
Master of Arts
Christopher Steven Weiss
Indiana University of Pennsylvania
May 2012
ii
Indiana University of Pennsylvania
School of Graduate Studies and Research
Department of Student Affairs in Higher Education
We hereby approve the thesis of
Christopher Steven Weiss
Candidate for the degree of Master of Arts
_____________ _________________________________________________
John W. Lowery, Ph.D.
Associate Professor of Student Affairs in Higher Education, Advisor
_____________ _________________________________________________
Holley A. Belch, Ph.D.
Professor of Student Affairs in Higher Education
_____________ _________________________________________________
John A. Mueller, Ed.D.
Professor of Student Affairs in Higher Education
ACCEPTED
____________________________________________ _____________________
Timothy P. Mack, Ph.D.
Dean
School of Graduate Studies and Research
iii
Title: Correlation between Undergraduate College Students‟
Facebook Use and Co-Curricular Involvement
Author: Christopher Steven Weiss
Thesis Chair: Dr. John Wesley Lowery
Thesis Committee Members: Dr. John Mueller
Dr. Holley A. Belch
This study determined if a correlation existed between college students‟ Facebook
use and co-curricular involvement. While Facebook use has exploded in the past decade,
research on how this phenomenon affects college students and student affairs
professionals is limited. For the purpose of this study, Facebook use was quantified in
terms of minutes of use, frequency of logging in, and services utilized; and involvement
was measured by how much time and in what way students participated in co-curricular
activities and utilized campus resources. A statistically significant, but weak, positive
correlation was found between the amount of time participants‟ spent on Facebook the
previous day and the number of hours per week they participated in activities outside of
the classroom (r = .137, p < .05). Student affairs professionals should understand the
results of this study in order to effectively promote student involvement in an
environment dominated by Facebook use.
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ACKNOWLEDGMENTS
I am so thankful for those who have played a role in my work on this thesis over
the last 15 months. This has been an extremely meaningful experience and journey, and
my ability to complete this project would not have been possible without their support.
As my thesis advisor, Dr. John Wesley Lowery has played an immense role in my
work on this thesis. His countless hours of reading and revising, and ability to cut
through my manuscripts to get me to explain what I really meant, have been invaluable to
the work I have presented and my growth as a scholar. He has been an amazing teacher,
support system, and role model in my academic growth, which has instilled in me the
confidence and passion to continue to pursue my role as a scholar and researcher.
I would like to thank the rest of my faculty committee, Dr. Holley Belch and Dr.
John Mueller. They consistently pushed me to write clearer and more professionally. Dr.
Mueller played an integral role in the selection and narrowing of my research topic. Dr.
Belch provided her excellent detailed eye and high standards for implementing the style
guide used in this work.
I would also like to thank Dr. Reynol Junco for his support throughout my
research. Dr. Junco was very gracious in letting me use his latest instrument. I highly
valued his assistance with several questions that I had through my research process, and
his positive feedback and genuine interest in my work were very motivational. I also
must recognize the support of my friends and family, who have understood and supported
me in every way. While I certainly did not give them enough of my time and energy
while working on this thesis, I could not be more grateful to them for welcoming me back
after this work was complete.
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I am delighted to thank Hannah, the most wonderful partner any researcher could
ask for. Hannah has been so incredibly supportive, even though I usually chose to spend
time on this thesis rather than with her. She never made me feel guilty for the work I was
doing, and always showed that she valued it as much as I did. While she claims I would
have been able to complete this thesis without her support, I can say with absolute
certainty that without her I would have been miserable the entire time. For all the batches
of cookies and cups of tea she made during long nights of writing and researching, I truly
thank her for always standing by my side. I greatly anticipate our next adventure
together, and every day I am inspired by what the future holds for us.
Last, and perhaps most importantly, I would like to thank my supervisor, mentor,
and friend Julene Pinto-Dyczewski, who in so many ways has been the most influential
person to me over the last two years. Jules is by far the best student affairs professional
and supervisor I have ever met, and exemplifies the perfect model of integrating theory
into practice. Jules truly embodies the power of positive reinforcement and the critical
role it plays in human development, interpersonal relationships, and educating students,
and this thesis is an example of her success in encouraging me to complete this work.
Jules has affected my overall growth and development as a professional and a person
more than any other individual over the last two years. I am so grateful for her support in
being flexible with my assistantship around the needs of this thesis, and all around for
how much she has taught me these past two years, which I hope is reflected in this thesis.
From the bottom of my heart, I truly thank all of these individuals. Not a moment
goes by that I am not aware of how fortunate I am to have been surrounded by such
amazing people who have helped me to accomplish so much.
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TABLE OF CONTENTS
Chapter Page
One THE PROBLEM………………………………………………………..1
Statement of the Problem……………………………………………… 1
Facebook………………………………………………………………. 2
Involvement……………………………………………………………. 3
The Relationship between Involvement and Facebook Use……………4
Negative correlation…. .....………………………………………4
Positive correlation ………………………………………………5
Research Questions ...………………………………………………...7
Significance……………………………………………………………. 9
Summary………………………………………………………………11
Two LITERATURE REVIEW.……………………………………………..12
College Students……………….……………….…………………….. 12
Characteristics of the College Student Population……………….12
Millennial generation……………….………..……………… 13
Digital natives……………….……………….……………. ...13
Proliferation of Technology on Campus……………….………... 16
Mobile use……………….……………….……………….…. 16
Campus support for technology……………….…………….. 17
Summary……………….……………….……………….………. 18
College Student Involvement…............................…........................…19
Involvement Theory….….….….….….….….….….….….….….. 20
Involvement vs. Engagement….….….….….….….….….….…... 22
Benefits of Involvement….….….….….….….….….….….….…. 26
Satisfaction….….….….….….….….….….….….….….….… 26
Student development….….….….….….….….….….….….… 28
Persistence….….….….….….….….….….….….….….….….32
Measures of Involvement….….….….….….….….….….….…… 34
Hours of involvement….….….….….….….….….….….…... 35
National Study of Student Engagement (NSSE) scales….….. 36
Extracurricular Involvement Inventory (EII) ….….….….….. 37
College Student Experiences Questionnaire (CSEQ) ….…… 37
For the current study….….….….….….….….….….….….… 39
Summary….….….….….….….….….….….….….….….….…… 39
Facebook Use Among College Students….….….….….….….….…... 40
Definitions and Contextualization….….….….….….….….….… 40
Services offered by Facebook….….….….….….….….….…. 41
College students‟ use of Facebook….….….….….….….…... 42
Mobile Facebook use….….….….….….….….….….….…… 44
Negative Outcomes Associated with Facebook Use….…….…... 45
Facebook depression….….….….….….….….….….….……. 46
Narcissism….….….….….….….….….….….….….….….…. 47
Stress….….….….….….….….….….….….….….….….…… 49
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Chapter Page
Drinking and partying.…......….….….….….…........….….....49
Distraction................................................................................50
Positive Outcomes Associated with Facebook Use....................... 50
Sharing information and opportunities.................................... 51
Transition to college................................................................ 52
Diversity................................................................................... 53
Facebook therapy..................................................................... 54
Social capital............................................................................ 55
Offline social life..................................................................... 57
Measures of Facebook Use............................................................ 58
Facebook Intensity Scale......................................................... 58
Net.Generation survey............................................................. 58
Junco‟s (2012) Facebook instrument....................................... 59
For the current study................................................................ 60
Summary........................................................................................ 60
Facebook and Involvement....................................................................61
Negative Relationship.................................................................... 62
No Relationship............................................................................. 64
Foregger‟s (2008) study........................................................... 64
Ericson‟s (2011) study............................................................. 65
Positive Relationship..................................................................... 66
The Higher Education Research Institute‟s (HERI; 2007)
analysis..................................................................................... 66
Heiberger and Harper‟s (2008) study...................................... 67
Junco‟s (2012) study................................................................ 67
Summary........................................................................................ 68
Summary of Literature.......................................................................... 69
Conclusion............................................................................................. 71
Three METHOD ..............................................................................................72
Methodology..........................................................................................72
Sample................................................................................................... 72
Descriptive Statistics...................................................................... 73
Instrumentation...................................................................................... 76
Facebook Instrument...................................................................... 76
College Student Experiences Questionnaire.................................. 77
Demographics................................................................................ 80
Procedures............................................................................................. 80
Data Analysis.........................................................................................81
Conclusion............................................................................................. 82
Four RESULTS.............................................................................................. 83
Preliminary Analysis............................................................................. 83
Facebook Usage............................................................................. 83
Involvement................................................................................... 88
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Chapter Page
Primary Analysis................................................................................... 90
Correlations between Facebook Use and Involvement.................. 90
Factor analysis......................................................................... 94
Controlling for Demographics....................................................... 96
Conclusion............................................................................................. 97
Five DISCUSSION AND IMPLICATIONS................................................. 98
Discussion of the Findings.................................................................... 99
Preliminary Findings...................................................................... 99
Facebook use............................................................................ 99
Co-curricular involvement.................................................... .102
Primary Analysis.......................................................................... 104
Limitations........................................................................................... 109
Implications......................................................................................... 110
Implications for Theory ...............................................................110
Implications for Research ............................................................111
Implications for Practice .............................................................114
Summary and Conclusion ...................................................................118
REFERENCES................................................................................................................120
APPENDICES.................................................................................................................133
Appendix A - Junco‟s (2012) Facebook Instrument ....................................................133
Appendix B - College Student Experiences Questionnaire (CSEQ) ...........................138
Appendix C - Demographic Data ................................................................................141
Appendix D - INFORMED CONSENT FORM ..........................................................144
Appendix E - Email Inviting Students to Participate ...................................................145
Appendix F - Institution Review Board Approval .......................................................146
Appendix G - CSEQ Item Usage Agreement ..............................................................147
Appendix H - Spearman‟s rho Correlations between Facebook Items ( N = 207) ......149
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LIST OF TABLES
Table Page
1 Descriptive Statistics for Participants.....................................................................75
2 Time Spent on Facebook among Participants.........................................................84
3 Number of Times Facebook was Checked among Participants..............................85
4 Frequency of Performing Facebook Activities among Participants.......................86
5 Involvement in Activities Outside of the Classroom among Participants..............89
6 Involvement Response among Participants............................................................90
7 Pearson‟s r Correlations between Level of Facebook Use and Involvement
Measures..................................................................................................................90
8 Spearman‟s rho Correlations between Facebook Activities and Involvement
Measures.................................................................................................................. 91
9 Rotated Component Matrix.....................................................................................91
10 Correlations between Facebook Activity Scales and Involvement Measures........96
1
CHAPTER ONE
THE PROBLEM
This thesis describes a study to determine if a correlation exists between the level
and nature of undergraduate college students‟ Facebook use and co-curricular
involvement within the campus community. This chapter will provide an introduction of
the role that Facebook and co-curricular involvement play in the lives of undergraduate
college students, and will establish the key constructs and significance of the study for
stakeholders. The second chapter will discuss a review of the literature, and describe
previous research conducted on both variables. The third chapter will then discuss the
methodology used to conduct the study, including the instrumentation used, procedures
of determining the sample and administering the survey, and statistical analysis
techniques. That will be followed by a presentation of the results from the data collection
of the study in the fourth chapter. Finally, the fifth chapter will present a discussion of
the results, including potential meanings, limitations of the study, and implications for
future theory, research, and practice.
Statement of the Problem
College students have always found ways to connect with each other (Horowitz,
1988). For the past two decades, technology has provided some of the most popular new
ways for students to connect (Junco & Mastrodicasa, 2007). From the seemingly
limitless opportunities of what it allows students to do, to the appeal of experimenting
with new advanced devices, college students have always spent large amounts of time
using technology (Junco & Mastrodicasa, 2007; Strange & Banning, 2001). Throughout
this time, many professionals have worked to capitalize on this appeal and make these
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technologies educationally and developmentally purposeful; most often, these attempts
have not been as successful as desired (Strange & Banning, 2001).
The most recent evolution in technology that students have widely adopted is
social media. Social media refers to websites and applications that individuals can use
for social networking, where online communities of users develop interpersonal
relationships and share user-generated content through a technological medium (Ericson,
2011; Junco & Chickering, 2010; Junco, Heiberger, & Loken, 2010). The three most
common examples of social media services on college campuses are Facebook, Twitter,
and YouTube. Studies have found that as many as 90% of college students are frequent
users of social media (Junco, 2012; S. Smith & Caruso, 2010); this usage may play a role
in the ways in which students relate to and communicate with each other (Junco &
Mastrodicasa, 2007; Upcraft, 2007).
Facebook is a large social media service created in 2004, where users generate
profiles to connect and stay in touch with their friends and acquaintances. It began as a
small service limited to students at Harvard University, and shortly thereafter expanded
to Stanford, Columbia, and Yale, with nearly 1 million active users by the end of its first
year. As access expanded to all college and high school students by the end of 2005,
there were over 6 million active users. Facebook allowed anyone to join in 2006, and
reached more than 12 million active users (“Statistics,” 2012).
In 2011, there were over 845 million users, 50% of which logged in to the service
daily (“Statistics,” 2012). Facebook is by far the social media service of choice for most
college students: several studies have reported that over 85% of college students have an
3
active Facebook account (Junco, 2012; S. Smith & Caruso, 2010; A. Smith, Rainie, &
Zickuhr, 2011). In one study, college students on average used Facebook over 11.8 hours
per week (Junco, 2012).
Involvement
Involvement is “the physical and psychological time and energy that students
spend devoted to their college experience” (Astin, 1984, p. 235). When college students
spend their time in educationally or developmentally purposeful activities on campus,
they are spending their time productively involved. This definition focuses particularly
on factors that facilitate development rather than the developmental process itself.
Involvement theory is concerned solely with the behavior of involvement, and
intentionally excludes the impact of involvement or students‟ perception of how it makes
them feel (Astin, 1984; Evans, Forney, Guido, Patton, & Renn, 2010). A high intensity
of co-curricular involvement is evident when students are markedly committed enough to
a group or organization that they invest considerable time, psychic energy, and physical
activity, for the pursuit of furthering the group‟s purposes (Davis & Murrell, 1993;
Winston & Massaro, 1987).
Many studies have shown significant positive relationships between students‟
level of involvement and their overall personal development and persistence to
graduation (Astin, 1984, 1993; Kuh, 2009; Pascarella & Terenzini, 2005). The time and
energy of students is by far the most valuable resource within an institution of higher
education, even when compared to money, so it is important to understand the ways in
which students are using this resource (M. Wilson, 2004). Student affairs professionals‟
primary responsibility on campus is to increase student involvement in developmentally
4
purposeful activities (Evans et al., 2010).
The Relationship between Involvement and Facebook Use
Research has shown that students spend a significant amount of their time using
Facebook (Junco, 2012; Manago, Taylor, & Greenfield, 2012). Involvement theory has
stated that it is important for students to spend their time in educationally and
developmentally purposeful activities (Astin, 1984). With that in mind, evidence will
soon be presented to demonstrate the importance of determining if there is a correlation
between undergraduate college students‟ level and nature of Facebook use and co-
curricular involvement in these positive activities, and if a correlation exists, what its
direction is. Consensus does not currently exist on this question, though the following
studies have focused on similar constructs and offered insights into this specific
relationship.
Negative correlation. One common perception is that Facebook – like other
forms of technology that preceded it – isolates students (Barkhuus & Tashiro, 2010).
Althought it is in the very nature of Facebook to connect people online, technology
usually does keep students physically separated from each other, and has the potential to
limit face-to-face interaction (Coomes, 2004; Lowery, 2004). This could lead to a serious
degeneration in an entire population of adults‟ ability to develop effective interpersonal
skills, understand social cues, and confront interpersonal conflicts (Junco & Chickering,
2010).
One study (Junco, 2012) found an overall negative correlation between the
quantity of Facebook use, and student engagement and involvement in co-curricular
activities. Specific Facebook behaviors linked to this negative correlation were playing
5
games and checking up on friends. There was also a negative correlation between time
spent on Facebook Chat and time spent preparing for class. These results suggested that
Facebook use does require time and psychological energy, but overall that time and
energy does not translate positively to co-curricular involvement (Junco, 2012).
Positive correlation. On the other hand, authors (Heiberger & Harper, 2008;
Higher Education Research Institute [HERI], 2007; Manago et al., 2012) have noted that
students‟ use of Facebook can have a positive connection to involvement. One study
(Manago, et al., 2012) found results that imply that students who spend more time
involved in co-curricular activities come into contact with more individuals, who then
have the potential to become Facebook friends. If the size of one‟s social network is
linked to positive outcomes, and co-curricular activities increase the size of one‟s social
network, then Facebook use and co-curricular involvement may be positively correlated
(Manago et al., 2012).
There is evidence that online interactions through Facebook do not remove users
from the offline world, but indeed support and enhance offline relationships and social
capital, which therefore increases users‟ participation in offline settings (Ellison,
Steinfield, & Lampe, 2007; Manago et al., 2012). Students‟ involvement in this online
community connects them to far more people than could be provided within a physical
campus community. While earlier studies have stressed the importance of face-to-face
communication in personal development (Astin, 1993; Pascarella & Terenzini, 2005),
few have attempted to study the same developmental impact within the virtual landscape.
Additionally, the intentional nature of editing one‟s profile could mean that students have
the opportunity to self-select into various communities and migrate with ease, rather than
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being confined to their physical location or past placement (Martinez Aleman &
Wartman, 2009). This idea of technology providing an intentional and socially
reinforcing environment has existed before the invention of Facebook (Putnam, Feldstein,
& Cohen, as cited in Lowery, 2004), and studies on social capital have supported
Facebook‟s success in achieving this vision (Ellison et al., 2007; Manago et al., 2012).
It has also been suggested that Facebook is the new student union, and that
progressive leaders in higher education should shift their emphasis and resources from
the physical building where students used to be, to the community on Facebook where
the students of the 21st century spend their time (Heiberger & Harper, 2008). If Facebook
use is linked to increased participation in developmental activities, it would be difficult to
find the developmental difference between opportunities to participate in these activities
on campus compared to on Facebook (Martinez Aleman & Wartman, 2009). Along those
lines, some (Heiberger & Harper, 2008; Martinez Aleman & Wartman, 2009) have
suggested that Facebook is not intrinsically devoid of the capability to provide
developmental opportunities. Professionals should take advantage of opportunities to
turn virtual spaces into developmentally beneficial environments – as they already have
with physical spaces – in which students would want to participate (Heiberger & Harper,
2008; Junco, 2012; Martinez Aleman & Wartman, 2009).
Since students communicate frequently through Facebook (Junco, 2012; Junco &
Mastrodicasa, 2007), it is common for those who are already involved in campus
activities to utilize this online space to attract other students to join them in their pursuits
(Martinez Aleman & Wartman, 2009). Using Facebook as a campus bulletin board might
change the role of the service from being its own distinct activity, to becoming merely a
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medium for others to share their activities happening on the physical campus. Within this
online community, all student Facebook users have equal access to the information that
others share, unlike posters that students hang in residence halls that preclude outsiders
from ever becoming aware of certain opportunities, for example. In this way, Facebook
could serve as a vehicle for the democratization of opportunities for campus involvement.
Through increasing awareness of opportunities to participate in developmental activities,
Facebook has the potential to increase productive behaviors of involvement regardless of
how much time students spend on the service itself (Junco & Chickering, 2010; Martinez
Aleman & Wartman, 2009).
While these perspectives are enticing, existing research has not yet fully examined
these issues. The debate on the relationship between Facebook use and productive and
developmental levels of increased involvement continues (Junco & Chickering, 2010).
For the most part, the argument is highly polarized with a wide array of opinionated
speculations, with many positions lacking substantial supporting research (Junco, 2012).
Some professionals have recognized how much time students spend on Facebook, and
have therefore decided that they must incorporate it into their work of promoting students
to become involved in campus activities (Olson & Martin, 2010). This rationale is weak,
since the justification for using Facebook is unsubstantiated when the actual correlation
between college students‟ level and nature of Facebook use and co-curricular
involvement has not been fully established (Junco & Chickering, 2010).
Research Questions
Students‟ time is an invaluable resource (Astin, 1984). Many disagree about
whether Facebook is a productive use of this time and how it relates to involvement
8
(Cotton, 2008; Junco, 2012; Junco & Chickering, 2010). With that in mind, there is a
need for specific research on the relationship between student use of Facebook and their
co-curricular involvement. There are two research questions for this study: Is there a
correlation between the level of undergraduate college students‟ Facebook use and their
co-curricular involvement within the campus community? And is there a correlation
between the nature of undergraduate college students‟ Facebook use and their co-
curricular involvement within the campus community?
This study examined the extent and manner of Facebook use among college
students, their level and type of co-curricular involvement within the campus community,
and determined if there was a relationship between the two constructs. The researcher
accomplished this by measuring the level and specific activities of Facebook use and co-
curricular involvement. The instrument for this study measured Facebook use through
the number of minutes each day that students actively spent using the service, as well as
the number of times per day that they logged in to the service. To measure specific
Facebook behaviors, the survey asked 14 questions measuring the frequency with which
participants engaged in common Facebook activities.
The researcher measured involvement quantitatively by determining the average
amount of hours per week participants spent in co-curricular activities and organizations.
This was achieved by modifying a question on the College Student Experiences
Questionnaire (CSEQ) to ask how many hours per week participants typically spent
involved in activities outside of the classroom. The researcher then measured the
behavioral extent of that involvement by administering two scales of the CSEQ. The first
was the Campus Facilities scale, which measured the frequency and extent to which
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students utilized spaces on campus such as the student union and recreational facilities.
The second was the Clubs and Organizations scale, which measured the behavior and
level of involvement in student clubs and organizations. Based on previous research that
measured social media use and campus involvement to some extent (Heiberger & Harper,
2008; HERI, 2007; Junco, 2012; Junco, Heiberger & Loken, 2010; Martinez Aleman &
Wartman, 2009), this research study began with the hypothesis that there would be a
positive correlation between college students‟ level and nature of Facebook use and co-
curricular involvement on campus.
Significance
A greater understanding of the correlation between college students‟ level and
nature of Facebook use and co-curricular involvement will benefit many stakeholders.
First, college administrators would benefit from this study. Everything from creating
policy to campus design has an impact on student involvement (Astin, 1984; Braxton,
2003; Pascarella & Terenzini, 2005). In an environment where student time and energy
is the most valuable resource an institution has, it is essential to have a thorough
understanding of any phenomenon‟s impact on involvement (M. Wilson, 2004). This is
especially important given the significance of the relationship between involvement and
retention of students (Pascarella & Terenzini, 2005). Administrators would be able to use
this information in developing policies and making decisions, including the allocation of
resources to utilize or deter the usage of Facebook.
Second, students would benefit from this research. Professionals would be able to
create environments more suitable to increasing involvement among students,
characterized in part by heavy use of social media (Junco, 2012; S. Smith & Caruso,
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2010). Students will exhibit increased levels of learning and personal development, since
students‟ level of involvement is responsible for the most substantial amount of growth
from precollege characteristics (Astin, 1984; Davis & Murrell, 1993; Pace, 1982;
Pascarella & Terenzini, 2005). Therefore, this research could benefit students because
professionals would be able to better educate them about what Facebook does and does
not do, and help identify the most educationally and developmentally beneficial ways for
it to be used.
Third, student affairs professionals need to understand if a relationship exists
between Facebook use and student involvement. This understanding will help to inform
better practices for increasing involvement within campus communities, which is one of
the primary functions of student affairs professionals (Baird, 2003; Braxton, 2003; Evans
et al., 2010; Strange & Banning, 2001). As student affairs professionals continue to
educate students about how to have a safe and productive college experience, it is
becoming essential to include training about effective uses of Facebook, specifically with
the goal of promoting co-curricular involvement. Thus, in a continued effort to increase
involvement and therefore promote student learning and development, it is the
responsibility of student affairs professionals to gather empirical evidence to help guide
future interventions (Creamer, Winston, & Miller, as cited in Pope, Reynolds, & Mueller,
2004).
In an environment where Facebook is as ubiquitous as mobile phones (S. Smith &
Caruso, 2010), it is essential to understand the impacts that this technology has on student
involvement. By understanding if there is a correlation between college students‟ level
and nature of Facebook use and co-curricular involvement, higher education
11
administrators, students, and student affairs professionals will gain information that could
prove critical to making college environments supportive of current students.
Summary
Co-curricular involvement plays a crucial role in student learning and
development (Astin, 1984, 1993; Evans et al., 2010; Pascarella & Terenzini, 2005).
Social media, particularly Facebook, has become widely and frequently used on college
campuses (Junco, 2012; S. Smith & Caruso, 2010; Smith et al., 2011). In one single-
institution study (Junco, 2012), students spent over 11.8 hours per week using Facebook.
Given this amount of time students typically spend on Facebook – time that could be
spent participating in other activities – it is important to understand if there is a
correlation between undergraduate college students‟ Facebook use and co-curricular
involvement.
This chapter has introduced a research study that aims to determine if there is a
correlation between college students‟ level and nature of Facebook use and co-curricular
involvement within the campus community. This study is important to three main
stakeholders. It will assist college administrators in making decisions regarding
Facebook use on campus, it will be useful to students who are highly engaged in social
media and expect to experience maximum personal growth associated with attending
college, and it will help student affairs professionals in their goal of increasing co-
curricular involvement and fostering student learning and development. This chapter has
defined and clarified Facebook use and co-curricular involvement within the context of
this study. The following chapter will explore relevant research on these topics in
relation to the significance of this study.
12
CHAPTER TWO
LITERATURE REVIEW
The previous chapter introduced college student Facebook use and co-curricular
involvement, and the significance of a study on the relationship between the two. This
section will provide more insight into these two variables, specifically as they relate to
the proposed study. This chapter will begin with an introduction of the population of
interest for the proposed study, and then discuss co-curricular involvement, college
students‟ Facebook use, and studies that have measured the relationship between them to
some degree.
College Students
Before examining the background of the specific constructs of this study, it is
important to understand the population of interest. This section will provide a brief
overview of college students, and the proliferation of technology among this population
and on the college campus.
Characteristics of the College Student Population
As of 2010, there are over 17 million students enrolled in 4,291 institutions of
higher education across the United States (U.S. Department of Education, as cited in
“Profile of Undergraduate Students,” 2009). The demographics of these students vary
greatly, especially when considering age. Student enrollments represent almost every age
group of adults. This diversity contributes to the great richness in the higher education
community, yet is also a source of tension between the various generations (Coomes &
DeBard, 2004). For example, in the 2008-2009 academic year, 17.3% of students in
higher education were between the ages of 24-29, and 23% were 30 years old or older.
13
The largest group of students was in the 15-23 age group, which made up over 59% of
enrolled college students (U.S. Department of Education, as cited in “Profile of
Undergraduate Students,” 2009).
Millennial generation. As demonstrated by the breakdown of the student
population by age, the largest generation currently enrolled in higher education is the
Millennial generation, which includes individuals born between 1982 and 2002 (Coomes
& DeBard, 2004). There are important characteristics of this generation of students that
relate directly to higher education. They are larger than any previous generation, and are
estimated them to grow to over 33% larger than the Baby Boomer generation (Coomes &
DeBard, 2004). The Millennial generation is also more racially and ethnically diverse
than any previous generation. Over 39% of the generation belongs to a minority racial or
ethnic group, some of whom are first-generation Americans (Broido, 2004). Millennial
students are also typically more comfortable with technology, and have been referred to
as the Net generation (Junco & Mastrodicasa, 2007) and digital natives (Palfrey &
Gasser, 2008; Prensky, 2001; S. Smith & Caruso, 2010).
Digital natives. Prensky (2001) coined the term “digital native” (p. 1).
According to his definition, those born in 1980 or later fall within this category. Prensky
(2001) described these individuals as native speakers of the language of computers and
the Internet, where the development of digital technologies has created a dramatic
paradigm shift that makes them distinctively different from previous generations. This
generation of digital natives “think and process information fundamentally differently
than their predecessors” (Prensky, 2001, p. 1).
Palfrey and Gasser (2008) expanded on this definition, stating that the
14
distinguishing factor of digital natives is their high level of computer skill and
knowledge. The authors affirmed that a significant problem that directly results from this
is a lack of attention to protecting personal privacy. Digital natives publish a greater
amount of personal information in public spaces. This is particularly disturbing when
looking at how that published information could be detrimental years or decades later
(Palfrey & Gasser, 2008).
Scholars (boyd, 2007; Farquhar, 2009) have conducted studies that support these
characteristics of digital natives. In one case, at a large public Midwest institution,
Farquhar (2009) interviewed undergraduate students who then used Facebook while
being observed by the researcher. One of the conclusions of the study was that digital
natives had substantially different usages of technology and social media than older
generations. There were four specific findings: digital natives were successful at creating
an intricate and accurate representation of their personal identity online, reciprocity was a
key value in technologically mediated interpersonal relationships, differentiation between
online and offline identities did not exist, and digital natives appeared to have less-
developed offline social skills (Farquhar, 2009).
In seeking to understand college students‟ use of technology, Junco and
Mastrodicasa (2007) conducted a multi-institutional study to collect data on this
generation‟s use of technology and how it related to the college environment. The
authors said that the primary characteristic to take into account with digital natives – or
the Net generation, as the authors referred to them – is that students rely on technology to
communicate with each other. These college students have more interactions in a
technologically mediated space compared to previous generations, and will increasingly
15
expect their communications to occur online (Junco & Mastrodicasa, 2007). Junco and
Mastrodicasa (2007) asserted this is why it is important to consider this generation of
college students differently than previous generations.
On the other hand, some researchers have questioned the validity in making such
broad generalizations of the skills and characteristics of a single generation (Hargittai,
2009; Morgan & Bullen, 2011). Hargittai (2009) conducted a study of the online skills
and abilities of a diverse group of first-year college students at a private institution in the
Midwest. The study found a substantial degree of variance between the levels of skill
within the group of digital natives, with participants‟ abilities ranging from meeting the
expectation of digital natives, to a lower level of competency than expected of older
individuals. Overall, while digital natives are comfortable with technology, that comfort
does not translate to a high level of skill (Hargittai, 2009; Morgan & Bullen, 2011).
Additionally, Hargittai (2009) found statistically significant relationships between
technological skill level and socioeconomic standing, race, ethnicity, and gender.
Participants that displayed the lowest level of technological skill were predominantly
female, African American, Hispanic, and/or of low socioeconomic standing. Keeping in
mind the fact that the Millennial generation, or digital natives, are more racially and
ethnically diverse than any previous group (Broido, 2004), Hargittai‟s (2009) findings
suggest a modification to the differentiation between digital natives and other generations
to include recognition of the role of privilege in participation in the modern technological
society.
16
Proliferation of Technology on Campus
The EDUCAUSE Core Data Service report (Arroway, Davenport, Xu, &
Updegrove, 2010) provided results from a study on the information technology
environments and practices at over 875 institutions throughout the nation. These results
indicated that students are using technology more than ever, and administrators have
responded by making campuses more conducive to this evolution. Based on the
awareness that college students expect a larger integration of technology into every
aspect of their lives, and on the substantial advances in educational technology in the past
decade, technology has become present in almost every aspect of college campuses
(Arroway et al., 2010).
S. Smith and Caruso (2010) conducted the EDUCAUSE Center for Applied
Research (ECAR) Study of Undergraduate Students and Information Technology. The
participants were a nationally representative sample of almost 37,000 students. One
series of questions in the ECAR study sought to determine students‟ use of the Internet.
On average, students reported spending 21.2 hours per week online (S. Smith & Caruso,
2010). An analysis of this time compared to students‟ GPA showed no statistically
significant correlation. S. Smith and Caruso (2010) also reported that 98.6% of students
owned a computer, and that 33% of those students owned more than one computer. Over
89% of respondents said that they owned a netbook or laptop, while only half owned a
desktop computer.
Mobile use. A continuing theme among the ECAR (S. Smith & Caruso, 2010)
report was that students‟ technology use is becoming more mobile than ever before.
Almost all participants reported owning a mobile phone, and two-thirds of them said they
17
could access the Internet from their phone. Of those with internet access from their
phone, over 90% of respondents said they primarily use their mobile devices for text
messaging and accessing social networking sites, including Facebook, and that they
engage in these activities daily. Respondents also listed using their mobile phones to
check the news and weather, get sports updates and statistics, and send and receive email.
While studies (boyd, 2007; Farquhar, 2009; Junco & Mastrodicasa, 2007) have
reported that Millennial generation students are heavy users of technology, they are not
the only college students immersed in adapting it for educational purposes. Among the
ECAR (S. Smith & Caruso, 2010) study‟s participants, only about 78% belonged to the
Millennial generation. S. Smith and Caruso (2010) did not find a statistically significant
difference between the responses of Millennial students compared to older students,
which may suggest that the generations are not as different as previous definitions
(Palfrey & Gasser, 2008; Prensky, 2001) proposed. Therefore, even though higher
education is not composed only of Millennial students, this data suggests that most
college students share similar levels of technological skill and comfort as the digital
natives.
Campus support for technology. According to Arroway, Davenport, Xu, and
Updergrove‟s (2010) data, college and university administrators are expanding support
for students‟ use of technology, from smart classrooms to online web portals. The latest
trend is finding ways to support mobile devices on campus. One particular service that is
becoming standard is providing campus-wide wireless Internet access, or Wi-Fi.
Arroway et al. (2010) reported that most classrooms at institutions within the study were
equipped with Wi-Fi, and noted a similar trend within residence halls. Furthermore, the
18
authors stated that student unions provided Wi-Fi in over 95% of the participating
institutions (Arroway et al., 2010).
Student perceptions (S. Smith & Caruso, 2010) and institutional data (Arroway et
al., 2010) linked the integration of technology to increased levels of learning. Students
reported an overall acceptance and willingness to use technology for academic pursuits
both in and outside of the classroom. S. Smith and Caruso (2010) noted that over one-
third of students reported using technology in the classroom in some way, and most of
those students said that it was educationally beneficial. Over 66% of students who said
their professors posted class materials online reported that they did not use technology as
an excuse to skip class; some even reported that it helped improve their class experience
by easing the pressures of note-taking. Almost a quarter of the students surveyed said
they had used social networking sites as part of an assigned class responsibility or to
collaborate with peers on class work. Among those who did not, over one-third said that
they would appreciate the opportunity to do so (S. Smith & Caruso, 2010).
Summary
Prensky (2001), and Palfrey and Gasser (2008), described individuals within the
Millennial generation as digital natives. Hargittai (2009), on the other hand, found that
members of the Millennial generation were not a homogenous group who all possessed
the same level of technological skill. S. Smith and Caruso (2010) found that older
students answered questions regarding technology use in higher education similarly to
those of the Millennial generation. This distinction is relevant because it would
encourage higher education professionals to apply the same assumptions of students‟ use
of technology to all college students, regardless of age, and that technological comfort is
19
not equivalent to a high level of skill. Faculty, staff, and administrators have already
begun responding to this by increasing the technological integration of campus in many
ways (Arroway et al., 2010; S. Smith & Caruso, 2010).
This section provided an overview of the characteristics of college students,
examined how these students use technology, and discussed how most campuses support
the behavior. The student population is very diverse (Broido, 2004; “Profile of
Undergraduate Students,” 2009) and typically represented by high-users of technology
(Arroway et al., 2010; S. Smith & Caruso, 2010). This background information should
help to inform an understanding of college students‟ use of technology and social media,
and the substantial amount of time they dedicate to using it. The next section will look
more deeply at the importance of spending that time invested within the physical campus
community, specifically regarding co-curricular involvement.
College Student Involvement
According to Evans, Forney, Guido, Patton, and Renn (2010), one of the primary
goals of student affairs professionals is to increase involvement in order to expose
students to developmental activities and learning experiences outside of the classroom.
Astin (1984) defined student involvement as the “quantity and quality of the physical and
psychological energy that students invest in the college experience” (p. 235).
Specifically, this study will focus on students‟ level of involvement in co-curricular
activities, where the term co-curricular refers to activities which happen outside of the
classroom.
20
Involvement Theory
The theory of involvement originated from Pace‟s (1982) work (Astin, 1984;
Davis & Murrell, 1993; Kuh, 2009). Pace (1982) identified the two most important
features of student learning and development to be the frequency of time and quality of
effort students invest in the college environment. The college environment corresponds
to the facilities and opportunities that foster educative experiences within behavioral
settings, or places on campus that intentionally promote learning and development, such
as the student union or library. Pace (1982) verified this theory through a study of over
12,000 undergraduate students at 40 different institutions nationwide using the College
Student Experiences Questionnaire (CSEQ). The fundamental aspect of Pace‟s (1982)
theory was that the most substantial determining factor of student success in higher
education was the quality of effort students invested in the college experience, which was
far more predictive than precollege characteristics or the college environment. In other
words, Pace (1982) argued “what counts most is not who they are or where they are but
what they do” (p. 18).
In defining college student involvement, Astin (1984) identified five key
postulates of involvement theory: involvement requires physical and psychological
energy, involvement occurs along a continuum, involvement has both quantitative and
qualitative features, development is proportional to the quantity and quality of
involvement, and educational effectiveness is related to the capacity to increase
involvement. As Astin (1984) introduced, M. Wilson (2004) elaborated that students‟
finite time and energy is the single most significant resource an institution has.
Competing for this precious resource, the activities in which students spend their time are
21
in direct opposition with each other. The greatest benefits of involvement will only be
attained if students select the most educationally and developmentally purposeful
activities in which to participate (Davis & Murrell, 1993; Roberts, 2003). Davis and
Murrell (1993) warned that students might choose to become more involved in activities
that isolate them or take them away from their studies, and that professionals should
intervene to prevent these behaviors from commonly occurring.
Braxton (2003) echoed Astin‟s (1984) statement that everything an institution
does, from creation of policies to design and layout of campuses, significantly affects
how students spend their time and energy. Since the goal of higher education is student
learning and development, professionals should evaluate decisions and policies based on
their ability to increase involvement. Indeed, it has been repeatedly stated that
institutions could improve the overall quality of higher education simply by working to
increase student involvement on campus, both in and outside of the classroom (Astin,
1984; Braxton, 2003; Pascarella & Terenzini, 2005; Winston & Massaro, 1987).
Promoting involvement is the most effective way to make the most use of, and
build on, the strengths each student initially brings to the campus (Astin, 1984; Davis &
Murrell, 1993). One way of going about this would be creating communities that
effectively produce a culture of involvement, such as learning communities comprised of
highly engaged students. Baird (2003) suggested that a strategy for accomplishing this
would be for student affairs professionals to serve as negotiators between students and
their institutions, by creating the conditions for these communities to exist, and to
encourage students to become co-creators of the community. Braxton (2003) suggested
another perspective, that student affairs professionals should intentionally construct
22
opportunities for social interactions among students and with professionals, with a
specific emphasis on facilitating face-to-face discussions.
One of the main challenges facing student affairs professionals is finding ways to
make opportunities for involvement appealing to students (Astin, 1984; Baird, 2003;
Braxton, 2003; Evans et al., 2010). This is an even more challenging issue for large
institutions, where the likelihood of social involvement is significantly less than at
smaller institutions (Pascarella & Terenzini, 2005). While it is difficult to identify what
the most meaningful opportunities for involvement are (Winston & Massaro, 1987), they
include participating in diverse residential environments, joining student organizations,
taking responsibility within new leadership positions, participating in varsity or
intramural sports, and finding new peer groups. Finding ways to create more
opportunities for involvement, or determining what barriers might prevent students from
taking advantage of such opportunities, is a critical task for student affairs professionals
(Astin 1984, 1993; Chickering & Reisser, 1993; Pascarella & Terenzini, 2005; Strange &
Banning, 2001).
Involvement vs. Engagement
If Astin‟s (1984) definition of student involvement was an evolution of previous
research (Pace, 1982) on how students spend their time and energy on campus, student
engagement is the most recent embodiment of that idea. It is important then to clarify
why the current study will utilize the theory of involvement, which is less commonly
used than the current construct of engagement. This section will define engagement in
order to explain why the theory of involvement best meets the needs of this study.
Kuh (2009) defined engagement as “the time and effort students devote to
23
activities that are empirically linked to desired outcomes of college and what institutions
do to induce students to participate in these activities” (p. 683). This definition evolved
from Astin‟s (1984) theory of involvement, and Pace‟s (1982) earlier notion of the
quality of student effort and time on task (Kuh, 2009). It is important to note the
difference of Kuh‟s (2009) definition to these previous ideas, and contrast the
relationship to its evolution.
To start, there are obvious similarities that continue the tradition of involvement
in Kuh‟s (2009) definition of engagement. The emphasis remains on the time and effort
that students spend in activities believed to promote learning and development. Both
theory‟s core philosophy was introduced in the second “Student Personnel Point of
View” (American Council on Education, 1949/1994) which said that there is value in
understanding what activities students decide to invest their time and energy, and that
students are ultimately responsible for their own growth (Davis & Murrell, 1993).
The main tenets of engagement originated from Chickering and Gamson‟s (1987)
seven good practices in undergraduate education (Kuh, 2009). These principles are
student-faculty contact, active learning, prompt feedback, time on task, high expectations,
respect for diverse learning styles, and cooperation among students. This serves as the
foundation for the benchmarks of the National Survey of Student Engagement (NSSE),
which quantifies the student experience and measures engagement. The scope and reach
of these seven principles are indicators of the breadth of the definition of engagement
(Kuh, 2009).
A few key components of engagement differ from the definition of involvement.
Whereas Astin (1984) focused specifically on the behavior of involvement and not its
24
outcome, Kuh (2009) conceived of engagement as a broad construct that incorporates
most aspects of the collegiate experience and defined it by its impact on empirical
educational outcomes. This breadth of scope helps make NSSE data vital to most
stakeholders in higher education, and provides useful information for any party whose
goal is student learning and development. One of the most focal aspects of the definition
of engagement is the way in which students invest time and energy in their academic
pursuits. This is directly aligned with the goal of persistence and graduation, where
involvement in activities outside of the classroom is measured in terms of its ability to
impact academic and class-related outcomes (Kuh, 2009).
Kuh‟s (2009) definition of engagement provides another difference from Astin‟s
(1984) definition of involvement. Within engagement, there is an emphasis on how
strongly students feel connected to their institution because of what professionals within
the institution do to create environments conducive to engagement. It is logical that such
an evaluation would be necessary, based on the good practices introduced by Chickering
and Gamson (1987), specifically including student-faculty contact, prompt feedback, high
expectations, and respect for diverse learning styles. However, this approach to
engagement differs greatly from Astin‟s (1984) definition of involvement, which
intentionally does not include this feeling of connectedness.
The definition of engagement is highly concerned with developmental outcomes
and student response to their level of engagement. Due primarily to its substantial reach,
engagement is viewed partly as a behavior, and more significantly as an influence of
student learning and development. This is why one of the key aspects of measuring
engagement is to determine the likelihood of development resulting from satisfaction and
25
participation in a supportive campus environment (Kuh, 2009). On the other hand, the
definition of involvement – and therefore any of its measurements – focuses strictly on
the behavior of involvement. Astin (1984) stated that involvement does not focus on how
students feel resulting from their behavior, but simply the amount of time and energy
students devoted to intentionally educational and developmental activities. While this
behavior has become an important component of engagement, the definition of
involvement is highly focused in comparison. Involvement theory explicitly attempts to
understand students‟ behavior in regards to how they devote their time and energy in the
college experience.
Scholars (Baird, 2003; Ericson, 2011; Heiberger & Harper, 2008; Junco, 2012;
Kuh, 2009) have noted the similarities and differences between the definitions of
involvement and engagement. In some cases (Baird, 2003; Ericson, 2011), researchers
conflated the definitions to create an understanding of a specific interest in time and
effort, without sacrificing the broad developmental impacts of engagement. In other
studies, researchers (Heiberger & Harper, 2008; Junco, 2012) outlined the evolution of
involvement to engagement in order to show the significance of engagement in the
current understanding of student development, while highlighting the specific focus on
time on task.
To be sure, there are differences between the definitions of involvement and
engagement, which matches the perceptions of scholars and practitioners. Engagement is
the most recent evolution of the understanding of the impact of how students spend their
time and energy and its impact on their learning and development (Kuh, 2009). This
definition is much broader than Astin‟s (1984) definition of involvement. Involvement
26
theory is useful in providing specific focus, since it is conceptualized only as a student
behavior in how time and energy are spent, with a greater emphasis on experiences
outside of the classroom. It is also more clearly defined and articulated than Pace‟s
(1982) theory, from where involvement theory came. For that reason, involvement is
more directly relevant to this study, rather than the definition of engagement and its
emphasis on academic behavior, developmental outcomes, and feelings of connection to
institutional environments.
Benefits of Involvement
Roberts (2003) and Baird (2003) affirmed that students‟ overall satisfaction with
their college experience positively relates to the total time on task spent interacting with
one‟s peers and educational materials. Astin (1984) proposed that the amount of time
and energy students devote to developmental activities directly and positively affects
their ability to achieve desired developmental outcomes. Tinto (1975) and Kuh (1995,
2009) stated that involvement, satisfaction, and psychosocial development all relate to
student persistence. A brief discussion of studies on the benefits of student involvement
follows.
Satisfaction. Many studies have found a direct correlation between level of
involvement in co-curricular activities and degree of satisfaction with the overall college
experience. Since the theory of involvement originated from data on student satisfaction
and persistence, Astin (1984) specifically described the correlation between involvement
and satisfaction.
First, Astin (1984) reported that residential students, who are considered highly
involved based solely on the fact that they spend most, if not all, of their time on the
27
campus, reported higher levels of satisfaction than commuters. These students reported
the highest levels of satisfaction with the areas of student friendships, faculty-student
relationships, institutional reputation, and social life. Second, students involved with
athletic programs reported high levels of satisfaction compared to less-involved students.
The areas of satisfaction reported were with the institution‟s academic reputation,
intellectual environment, student friendships, and institutional administration. Third,
students involved with student government associations reported very high levels of
satisfaction with their college experience, specifically in greater than average satisfaction
with student friendships (Astin, 1984).
In addition to Astin‟s (1984) work, Kuh (1995) conducted a study of the value of
co-curricular experiences. In an attempt to measure the significant impact of experiences
outside of the classroom on student learning, Kuh (1995) carried out a multi-institutional
qualitative research study, where the overall goal was to determine the outcomes of out-
of-class experiences. In the end, the author compiled a 14-item taxonomy of significant
outcomes. This taxonomy included sense of purpose, social competence, confidence, and
application of knowledge. Kuh (1995) stated that the combination of these 14 items was
highly correlated with student satisfaction. Therefore, in addition to the specific
beneficial outcomes, Kuh (1995) concluded that student satisfaction in general increased
as a product of increased involvement.
Various institutional settings have reflected the relationship between involvement
and satisfaction. J. D. Wilson (1999) conducted a study of students involved in
recognized campus organizations at a mid-sized southern institution. The researcher
found a positive relationship between level of involvement and satisfaction with the
28
institution, which showed that students who belonged to an organization reported the
most satisfaction with their college experience. Elliott (2009) reported similar results
when focusing on community college students. When the researcher compared a group
of involved students to a similar group of their un-involved peers, students involved in
formal co-curricular activities reported significantly higher levels of satisfaction with
their college experience.
Overall, Astin (1993), and Pascarella and Terenzini (2005), reported similar
findings. In Astin‟s (1993) analysis of the Cooperative Institutional Research Program
(CIRP) data, the results showed that a perception of a strong student community had the
strongest direct effects on student satisfaction when compared to any other environmental
measure. Since involvement is a crucial component of community (Strange & Banning,
2001), Astin‟s (1993) analysis seems to show that involvement played a key role in
student satisfaction. Pascarella and Terenzini (2005) summarized the literature on the
impact that college has on students. They analyzed a remarkable number of studies, and
effectively summarized the overall impact that participation in higher education has on
college students. Pascarella and Terenzini (2005) echoed Astin‟s (1993) finding, where
students who perceived membership in an organized group of students who had similar
values and attitudes to their own showed higher levels of satisfaction with their college
experience.
Student development. A substantial number of studies have measured the
relationship and impact of involvement on student development. As stated explicitly in
Astin‟s (1984) theory of involvement, “The extent to which students can achieve
particular developmental goals is a direct function of the time and effort they devote to
29
activities designed to produce these gains” (p. 222). Involvement in co-curricular
activities has a direct impact on positive student development (Astin, 1984, 1993;
Chickering & Reisser, 1993; Elliott, 2009; Kuh, 1995; Pace, 1982; Pascarella &
Terenzini, 2005; Winston & Massaro, 1987). The small, single-institution studies that
determined links between specific aspects of involvement and development, and the large
and broad multi-institutional studies, have provided a thorough understanding of this
effect.
Referring back to Kuh‟s (1995) study, the qualitative analysis displayed
overwhelming benefits of involvement on student development, which supported the
theory of involvement. Of the 14 taxonomy items, 9 that were reported by Kuh (1995) to
be caused by high levels of involvement are also directly related to psychosocial and
cognitive development as identified by Chickering and Reisser (1993), and Love and
Guthrie (1999). Examples of these items include autonomy and self-directedness, social
competence, reflective thought, and application of knowledge.
Several scholars (Guardia & Evans, 2008; Guiffrida, 2003; Harper & Quaye,
2007; Renn & Bilodeau, 2005) have noted the impact that co-curricular involvement has
in the social identity development process. In their qualitative analysis, Harper and
Quaye (2007) found that Black students attending a predominantly White institution
reflected on their experiences involved with Black student organizations as being
beneficial to their sense of belonging and development of their racial identity. The
researchers placed specific emphasis on the fact that most students reported joining such
organizations while displaying characteristics symbolic of Cross‟ (1995) Immersion-
Emersion stage. Harper and Quaye (2007) noted that the more involved students they
30
interviewed showed signs of Cross‟ (1995) Internalization stage, the final stage after
Immersion-Emersion, and that the students directly related their development to their
involvement in these groups. Harper and Quaye (2007) concluded that co-curricular
involvement and participants‟ social identity development were directly related.
Guiffrida (2003) found similar results among African American students at another
predominantly White institution; Guardia and Evans (2008) reported similar findings
among Latino/a students at a Hispanic Serving Institution; and Renn and Bilodeau (2005)
identified a similar correlation among Lesbian, Bisexual, Gay, and Transgendered
(LBGT) students involved in a regional LGBT conference.
To study the relationship between involvement in clubs and organizations, and
overall student development, Foubert and Grainger (2006) administered the Student
Development Task and Lifestyle Inventory (SDTLI) and a simple measure of
involvement and leadership to a random sample of traditionally aged students at a mid-
sized southeast public institution, over the course of four years. The researchers found a
substantial positive statistically significant correlation between levels of involvement and
development, where the more intensely participants were involved, the higher they scored
on the SDTLI. Many researchers (Cooper, Healy, & Simpson, 1994; Elliott, 2009;
Martin, 2000; J. D. Wilson, 1999) have found results consistent with Foubert and
Grainger (2006).
Additional studies have examined the relationship between co-curricular
involvement, and critical thinking and cognitive development (Gellin, 2003; Terenzini,
Pascarella & Blimling, 1996; Whitt, Edison, Pascarella, Nora, & Terenzini, 1999). Gellin
(2003) conducted a meta-analysis of research presented between 1991 and 2000 on this
31
relationship, which showed an overall gain in critical thinking. The author stated that the
positive nature of involvement in multiple activities, which would provide various
perspectives and encourage reevaluation of preexisting attitudes and values, could help
these gains in critical thinking. Similar findings were reported by Terenzini, Pascarella,
and Blimling‟s (1996) review of previous literature on the relationship of involvement
and cognitive development, as well as Whitt, Edison, Pascarella, Nora, and Terenzini
(1999), whose results were measured with their Nonclassroom Peer Interaction Scale.
Moving to large multi-institutional studies on the impact of involvement on
student development, Astin‟s (1993) analysis of the CIRP data provided insights into the
effect of involvement on cognitive and affective outcomes of student learning and
development. Astin (1993) reported that there was statistically significant evidence that
greater time spent involved in both academic, and co-curricular activities had substantial
positive effects on learning and development. This significance was stronger than almost
every other measure of environmental aspects or precollege characteristics. Moreover,
Astin (1993) found the opposite to display similar significant results: strong negative
outcomes resulted from behaviors of un-involvement, including activities that isolated
students or removed them from the campus.
Dugan, Garland, Jacoby, and Gasiorski (2008) studied involvement among
commuter students, who are traditionally underrepresented in the literature. The
perception that commuter students are automatically less involved has led to negative,
generalized statements, such as commuter students being deficient in their involvement
and development, which is not accurate or supported by research studies (B. Jacoby,
personal communication, October 10, 2011). Using nationally representative data from
32
the Multi-Institutional Study on Leadership, Dugan et al. (2008) found that involvement
in specific peer interactions, leadership training, and employer relations had equal
impacts on self-efficacy and leadership development for commuter students as residential
students. This is relevant because it indicates that commuter students – which represent
over 85% of college students (Dugan, Garland, Jacoby, & Gasiorski, 2008) – who
participate in intentionally developmental activities receive similar benefits as residential
students.
Overall, one of Pascarella and Terenzini‟s (2005) conclusions was that
interpersonal involvement with peers and faculty directly increases psychosocial
development, and more specifically interpersonal development. Supporting Astin‟s
(1993) findings, Pascarella and Terenzini (2005) reported that overall, level of
involvement within the campus community is by far the single most predictive factor in
student development, especially when academic, co-curricular, and social involvement
are mutually reinforcing. Pascarella and Terenzini (2005) concluded “Students derive the
greatest developmental benefits from engagement in peer networks that expose them to
individuals different from themselves” (p. 615).
Persistence. One area of study that is of particular interest to a variety of groups
within higher education is student persistence, or the rate at which students successfully
continue enrollment at an institution through degree completion. Tinto‟s (1975) theory of
student persistence stated that precollege characteristics and social and academic
integration within the institution directly affect students‟ rate of commencement.
Braxton, Hirschy, and McClendon (2004) provided a summary and critique of this model.
The authors argued that a single theory could not explain a problem as complex as
33
student persistence across institutional types. However, the authors noted that social
integration, and therefore involvement, played an important role in understanding student
persistence and departure. One of Braxton, Hirschy, and McClendon‟s (2004) primary
conclusions was that “The greater the level of psychological energy a student invests in
various social interactions at his or her college or university, the greater the student‟s
degree of social integration” (p. 26).
Several researchers (Astin, 1984, 1993; Berger & Milem, 1999; Pascarella, 1982;
Tinto, 1975) have sought to understand the relationship between level of co-curricular
involvement and persistence. This is a logical connection when considering Tinto‟s
(1975) emphasis on social integration as one of the primary determining factors in
persistence, and since Astin‟s (1984) theory of involvement originated from research on
college student persistence. Pascarella (1982) conducted a multi-institutional study to
determine the predictive validity of theoretical models of student departure. The most
important result was the significant correlation between social and academic integration
from Tinto‟s (1975) model and departure decisions, even when taking into account a
wide variety in student precollege characteristics (Pascarella, 1982). A similar finding
was reported by Berger and Milem (1999) in their multi-institutional study, who stated
that involvement in co-curricular activities increased levels of social integration and the
perception that the institution was supportive to student needs and desires, which lead to
the decision to persist.
Elliott (2009) found a direct increase in persistence related to a student‟s level of
satisfaction and psychosocial development. In their summary of research within the
previous decade, Pascarella and Terenzini (2005) stated that the single most influential
34
factor of student persistence was level of involvement, specifically as it related to face-to-
face interaction among peers. Since involvement increases satisfaction with the college
experience, and increases the level of psychosocial development that occurs during
college, it becomes undeniable that involvement indeed affects persistence. According to
Pascarella and Terenzini (2005), “Interaction with peers is probably the most pervasive
and powerful force in student persistence and degree completion,” (p. 615). Additionally,
“extracurricular involvement had modest, positive effects on institutional persistence and
educational attainment” (Pascarella & Terenzini, 2005, p. 616).
Astin (1993) and Pascarella and Terenzini (2005), found that face-to-face
interaction with peers and faculty directly increased satisfaction, psychosocial and
interpersonal development, and likelihood of persistence. The emphasis is that student
satisfaction, learning and development, and persistence increases by providing access to
environments and opportunities for involvement; but caution must be taken to remember
that these benefits can only be realized if students are held individually responsible for
taking advantage of these opportunities (Davis & Murrell, 1993; Pascarella & Terenzini,
2005).
Measures of Involvement
The definition of involvement focuses particularly on factors that facilitate
development, rather than the developmental process itself. It is concerned solely with the
behavior of involvement, and intentionally excludes the impact of involvement or
students‟ perception of how it makes them feel (Astin, 1984; Davis & Murrell, 1993;
Evans et al., 2010). A high intensity of involvement is evident when students are
markedly committed enough to a group or organization that they invest considerable
35
time, psychic energy, and physical activity, for the pursuit of furthering the group‟s
purposes (Davis & Murrell, 1993; Winston & Massaro, 1987). This section will examine
four different measures of involvement, including a general understanding of an average
level of hours of involvement, select scales from NSSE, the Extracurricular Involvement
Inventory (EII), and the College Student Experiences Questionnaire (CSEQ).
Hours of involvement. The first measure of involvement is intentionally
simplistic. It is not limited to a specific instrument or scale, and often does not exist as
more than a single question. Quite simply, researchers ask participants to report how
many hours they spent involved in co-curricular activities in a given timeframe. This
type of measurement can range from one single number in which participants summarize
their total time of involvement, to a series of questions on the number of hours spent
participating in a list of activities. For example, in Junco‟s (2012) study of the
relationship between Facebook use and level of engagement, to measure students‟ level
of co-curricular involvement, one question was asked directing students to list the
number of hours per week that they spent involved in activities outside the classroom.
On the other hand, Pascarella (1982) created an instrument called the Student
Involvement Questionnaire, for a study that sought to measure level of involvement as it
related to social integration and persistence. The instrument was comprised of many
questions that asked participants to list how many hours each week they spent involved in
specific activities, such as intramural athletics, fraternity/sorority activities, and hobbies
or social clubs.
While this is a simplistic means of measuring involvement, it is useful when
trying to show that another activity does or does not inhibit the amount of hours that
36
participants can spend involved in co-curricular activities. In this way, this form of
measurement is the most common when used in combination with another measurement
to determine a relationship between involvement and another construct. Unfortunately,
this approach fails to measure the rich and complex definition of involvement as
summarized by Astin‟s (1984) five postulates. It does not provide the researcher with
enough information to draw conclusions of the actual relationship between a behavior
and actual level of involvement, since involvement is more complicated than simply the
number of hours students spend performing a behavior (Astin, 1984; Davis & Murrell,
1993).
National Study of Student Engagement (NSSE) scales. A second measure of
involvement utilized in multiple studies (Ericson, 2011; Junco, 2012; Junco, Heiberger,
& Loken, 2010) relied on scales from the NSSE instrument. Since involvement is one
component of engagement (Kuh, 2009), it is logical that part of the instrument used to
measure engagement must include a measurement of involvement. Ericson (2011)
created one instrument from selections of NSSE to measure involvement by extracting
the following scales: diversity within college activities, personal-social growth, non-
classroom experience, and miscellaneous student activities. Ericson (2011) scored this
instrument following the instructions from NSSE, and created an involvement score.
However, the original purpose for creating these scales was not to measure involvement,
and the researcher of this study broadened the definition of involvement to include the
items measured by these scales. The individual items in this instrument do not fall within
the scope of Astin‟s (1984) definition of involvement, since most seek to measure the
degree of students‟ development exhibited in their behaviors, rather than focusing
37
explicitly on the behavioral aspect of involvement.
Extracurricular Involvement Inventory (EII). The third measure of
involvement is the EII (Winston & Massaro, 1987). This instrument was derived by
expanding on Pace‟s (1983) Clubs and Organizations scale from the second edition of the
CSEQ, and added further detail implied by Astin‟s (1984) definition of involvement.
Winston and Massaro (1987) found that the EII was indeed more successful at measuring
high levels of involvement among students when compared to Pace‟s (1983) Clubs and
Organizations scale. Most frequently, researchers have used this instrument to study the
level of involvement among student leaders and to separate the moderately involved from
the highly involved, e.g. J. D. Wilson (1999) and Elliott (2009). However, the EII is not
as effective at measuring students who are less involved in co-curricular activities
(Winston & Massaro, 1987). Researchers have typically used the EII where the sample
was a specifically targeted group of student leaders.
College Student Experiences Questionnaire (CSEQ). The final measure of
involvement is the CSEQ, which various authors have continually updated since its
creation by Pace (1982) in 1978. The original function of the CSEQ was to measure the
quality of effort that students invest in their college experience, and understand its impact
on the level of achievement of college students (Davis & Murrell, 1993; Pace, 1982).
There have been four editions of the CSEQ, and this study will discuss and utilize the
most recent edition updated in by Pace and Kuh (1998). The main components of the
CSEQ are the scales that measure the frequency and quality of effort students invest in
their college experience. There are 14 scales in all. The first seven scales focus on the
use of campus resources, and the other seven focus on the extent to which participants
38
take advantage of personal and social opportunities. These scales measure topics
including campus facilities, clubs and organizations, residence hall involvement, athletic
and recreational facilities, and cultural activities (Pace, 1982).
The Campus Facilities and Clubs and Organizations scales, which remain
unchanged since the second edition, provide measures of involvement very closely
aligned to Astin‟s (1984) definition (Davis & Murrell, 1993). These scales from the
CSEQ ask how frequently students participate in activities such as using campus facilities
and participating in leadership activities within organizations, using a four-point Likert
scale. The response options are never, occasionally, often, and very often, where a
response of never scores one point and a score of very often scores four points. These
scales measure optional activities, that can be thought of as student initiative, since they
are not mandatory for college credit, and rank participation along a spectrum. This
means that the score goes beyond simple frequency of participation and is representative
of the quality of effort students exert in their behaviors (Pace, 1982), which is a
fundamental aspect of Astin‟s (1984) theory of involvement.
One specific study recently utilized the CSEQ, conducted by Pike, Kuh, and
Gonyea (2003). The authors sought to empirically link the influence of institutional
characteristics to student learning and intellectual development. Using the CSEQ, the
researchers were able to determine that student learning and development did in fact
occur in meaningful ways at a variety of nation-wide institutions, but that Carnegie
classification and institutional mission were not significantly correlated to the gains and
experiences of students enrolled at different institutions (Pike, Kuh, & Gonyea, 2003).
Pascarella and Terenzini (2005) recognized the importance of the CSEQ in the increase
39
in research conducted on involvement and the quality of student effort. Researchers
continue to use the CSEQ to measure the relationship between student involvement and
other constructs, with studies published as recently as 2010 (Murphy, 2010; J. R. Wilson,
2010).
An item missing from the CSEQ is a measure of how many hours in a given time
period students spend involved in co-curricular activities, though the instrument does ask
a similar question about academic involvement. A version of this question for co-
curricular involvement would be useful in providing a direct quantitative correlation
between hours of involvement and other constructs.
For the current study. It is important to consider these measures of
involvement, each with varying levels of detail and consistency with Astin‟s (1984)
original definition. For the purposes of this study, the CSEQ is the most appropriate
instrument. It effectively measures students‟ behavior of involvement, and their level of
involvement within those behaviors. This meets Astin‟s (1984) qualification of the
behavioral, or qualitative, aspect of involvement. The only addition to the CSEQ that
becomes necessary is a question asking for hours of co-curricular involvement within a
given timeframe, or the quantitative aspect of involvement. With this addition, the CSEQ
is by far the best choice to measure involvement as operationalized by this study, when
compared to the alternatives.
Summary
Involvement, as defined as the amount of physical and psychological time and
energy that students invest on campus (Astin, 1984), is a crucial element of the college
experience. Involvement directly influences student satisfaction, psychosocial and
40
cognitive development, and persistence to graduation. Student affairs professionals are
responsible for increasing student involvement on campus, and Astin (1984) suggested
evaluating the efforts of higher education administrators based on their ability to increase
such involvement. When looking at the programs, services, and facilities provided within
institutions of higher education it is essential to study their impact on student
involvement (Astin, 1984; Braxton, 2003; Evans et al., 2010). Likewise, it is essential to
understand new phenomena on campus in relation to this involvement, to determine
whether they are contributors or threats to student learning and development. This holds
particularly true for phenomena that could affect students‟ individual initiative in taking
advantage of these opportunities, since students are ultimately responsible for their own
participation (Davis & Murrell, 1993). The following section will discuss studies
conducted on one such phenomenon, Facebook and how it is used by college students.
Facebook Use Among College Students
As the social media service of choice, college students spend a great deal of their
time using Facebook (Junco, 2012; S. Smith & Caruso, 2010). In order to understand if a
relationship exists between campus involvement and Facebook use, it is important to
begin with a thorough definition of Facebook, the services it provides, and how it is used.
From there, a review of research studies will introduce the various negative and positive
outcomes linked to college students use of Facebook. Finally, this section will end with
measures of Facebook use, including the instrument that this study will employ.
Definitions and Contextualization
To define Facebook, first it is important to define its overarching category of
technology, social media. Social media refers to websites and applications that
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individuals can use for social networking, where online communities of users develop
interpersonal relationships and share user-generated content through a technological
medium (Ericson, 2011; Junco & Chickering, 2010; Junco, Heiberger, & Loken, 2010).
Some examples of social media platforms are Facebook, Twitter, YouTube, and
LinkedIn. Facebook allows users to generate profiles to connect and stay in touch with
their friends and acquaintances, while facilitating the creation and maintenance of large
social networks (Junco & Chickering, 2010; Manago et al., 2012). With over 845 million
users, Facebook is by far the largest social media service (“Statistics,” 2012).
Services offered by Facebook. There are many services that Facebook provides
to its users; Smock, Ellison, Lampe, and Wohn (2011) went as far as to call Facebook a
“toolkit” (p. 2326), composed of varying tools and services to meet diverse needs. In this
way, Facebook is not a single entity, but rather an umbrella service or suite of social
media tools. As demonstrated by its history, Facebook is very dynamic, and therefore the
list of these tools frequently changes based on the needs of its users – and more
commonly, based on the pressures of online competition.
In the fall of 2011, the tools that existed within this social media platform could
be grouped into three primary categories: staying connected, interacting with peers, and
gaming. The first category is staying connected with a Facebook user‟s friends, by
browsing the profiles and recent activities of peers whom they connect with through a
mutual approval process. Ways of doing this are through viewing others‟ status updates
answering the question of “What‟s on your mind,” creating and RSVPing to events, and
viewing photos and videos (Junco, 2012; Papacharissi & Mendelson, 2011; S. Smith &
Caruso, 2010; Smock, Ellison, Lampe, & Wohn, 2011).
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For the second category of interacting with peers, Facebook users can conduct
another group of activities according to Smock et al. (2011). The primary form of
interaction is broadcasting status updates to all users connected within one‟s social
network. Users can share links with their friends, most frequently to news stories or
various other websites. They can send private messages to each other, which are not
displayed publicly and typically represent a means of personal communication (Manago
et al., 2012). Users can comment on user-generated content, such as pictures, status
updates, and recent activities. Facebook has an instant messaging service, called
Facebook Chat. Facebook users can also post and tag photos and videos of themselves
and their friends (Junco, 2012; Papacharissi & Mendelson, 2011; S. Smith & Caruso,
2010; Smock et al., 2011).
For the last group of activities, Facebook users can play games created by third-
party application developers hosted within Facebook. Though this list is not exhaustive,
it does comprise the majority of Facebook activities in which college students most
frequently participate (Junco, 2012; Papacharissi & Mendelson, 2011; S. Smith &
Caruso, 2010; Smock et al., 2011).
College students’ use of Facebook. In the nationally representative ECAR Study
of Undergraduate Students and Information Technology, S. Smith and Caruso (2010)
reported that 90% of college students use social media services, and 97% of those
students use Facebook. Over 90% of students who use social media responded that they
logged in to Facebook daily. Similarly, in a nationally-representative study, A. Smith,
Rainie, and Zickuhr (2011) found that 86% of college students use Facebook. The
popularity of Facebook is also evident in the average number of minutes that students
43
report spending on the service every day. In a study conducted by Junco (2012) at a mid-
sized public northeastern institution, students who had active Facebook accounts reported
using the service an average of 101.9 minutes per day.
Furthermore, according to the nationally-representative 2011 CIRP Freshmen
Survey (Pryor, DeAngelo, Blake, Hurtado, & Tran, 2011), including responses from
203,967 incoming first-year students, only 5.2% of high school students reported not
spending any time on social networking sites. Of these incoming students, 51.3%
reported spending more than three hours per week using such services. This represents
an 11.5% increase over similar responses from 2007 (Pryor et al., 2011). Thus, it appears
that incoming students will continue to increase the currently observed levels of
Facebook use on campus.
Based on another single-institution study, the average number of Facebook
friends that existed within one‟s social network was 440 (Manago et al., 2012). These
networks were found to comprise several different types of relationships: close
connections, activity connections, acquaintances, maintained connections from previous
social groups, and strangers or online-only connections. The percentage of close friends
within one‟s network was 39%, compared to loose, or superficial, connections, which
was 61%. Moreover, the more Facebook friends an individual had, the higher the
percentage of loose connections that existed within their network. Manago, Taylor, and
Greenfield (2012) believed that these findings suggest that social networks expand
primarily through the addition of distant relationships, where increased participation in
activities led to a larger social network of loose connections.
There are many reasons that students use the tools offered by Facebook.
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Papacharissi and Mendelson (2011) conducted a study at an urban institution of students‟
motivations for using Facebook. The survey was administered online and promoted
through Facebook; the initial sample was snowballed, resulting in 15% of the population
not being current college students. Through a series of questions, the researchers asked
participants what they did when they logged on to Facebook. Nine distinct significant
motives of using Facebook were identified through a factor analysis: expressive
information sharing, habit, relaxing entertainment, passing time, cool and new trend,
companionship, professional advancement, escape, social interaction, and new
friendships (Papacharissi & Mendelson, 2011).
In a study conducted at a large Midwestern institution, Smock et al. (2011)
expanded on these nine motives (Papacharissi & Mendelson, 2011) to determine which of
these were reasons to use Facebook in general, in comparison to those that motivated use
of specific services within Facebook. They found a relation between general use and the
three motives of relaxing entertainment, expressive information sharing, and social
interaction. A more significant relationship existed between the other six (habit, passing
time, cool and new trend, companionship, professional advancement, escape, and new
friendships), and usage of specific Facebook features (Smock et al., 2011).
Mobile Facebook use. The newest trend with Facebook is accessing the service
on mobile devices, such as smartphones and Internet-enabled cell phones. Of the 62.7%
of students that reported owning such a mobile device in the ECAR study, 76.9% said
that they accessed Facebook from this device (S. Smith & Caruso, 2010). In Barkuus and
Tashiro‟s (2010) qualitative analysis of student Facebook use at a large public institution,
one main component of research examined mobile usage. Students who were able to
45
access Facebook on their mobile device reported an overall increase in usage of the
service, though they recognized a distinct change in the manner in which they used
Facebook.
On a cell phone, students said they were more likely to perform the following
activities: respond to messages, briefly check to see if anything new was happening with
their friends, or browse recently uploaded photos. Students reported that these behaviors
happened much more frequently than before using a mobile device, but they also said
they spent much less time on Facebook when using their phone compared to their
computer. The authors explained this evolution as students now using Facebook in “short
„bursts‟…remaining constantly „in touch‟ with a large set of friends and acquaintances”
(Barkhuus & Tashiro, 2010, p. 137). Students indeed reported that this communication
pattern was essential to maintaining their social life, particularly for scheduling ad-hoc
meetings in clubs or organizations, as well as study groups or class-related exchanges.
Negative Outcomes Associated with Facebook Use
When Facebook first gained notoriety, the popular media coverage seemed to
focus on the various negative outcomes of using Facebook, including privacy concerns,
potential harm from the existence of a sometimes-incongruent online persona, and users‟
misconception of their online audience (Ellison et al., 2007). There have been a number
of studies conducted to support claims of the relationship of negative outcomes to
Facebook use. Interestingly, students in one single-institution study were aware of a
majority of the detrimental outcomes associated with using Facebook, as demonstrated by
asking them to list these outcomes (Silverman, 2007). This section will highlight some of
these studies, including negative linkages to psychosocial, behavioral, and academic
46
issues.
Facebook depression. One concept that has appeared frequently is “Facebook
depression” (O‟Keeffe, Clarke-Pearson, & Council on Communications and Media,
2011, p. 800). This term has been discussed in clinical journals, primarily written about
adolescents, but can affect anyone ranging from pre-teen children to adults. There are
many components to Facebook depression, but the main idea is that using Facebook
causes symptoms identical to clinical depression – which should be treated similarly –
but has a tangible, solvable cause (Aboujaoude, 2011).
One aspect of Facebook depression was contextualized in a study of college
students at a medium-sized West Coast university by Jordan, Monin, Dweck, Lovett,
John, and Gross (2011), where sadness was found to be caused by false perceptions about
friends‟ level of happiness. The authors found that this came from the fact that most
users publish the best, or even over-glorified, version of themselves and their lives. This
results in a misconception when students view others‟ profiles and perceive how happy
they must be. When compared to their own lives, students‟ friends seem to be doing
much better than they are. Due to this error in perception, users reported feeling
depressed (Jordan et al., 2011).
Another contributor to Facebook depression is when a Facebook user receives
negative or no feedback on content that she or he shares. In this way, attention seeking
leads to Facebook depression. The trigger for this depression usually occurs when a user
spends a great deal of time and energy creating content that they believe is particularly
outstanding. If the post does not receive the desired feedback, students have reported
feelings of depression, caused by thinking that people do not care about their posts or that
47
their contributions are not significant (Aboujaoude, 2011).
Narcissism. The previously mentioned behaviors can result in users who are
more interested in their outward image and persona than the relationships they have with
others, which is part of the definition of narcissistic behavior. A Facebook profile is a
user‟s online persona, according to Aboujaoude (2011), a psychiatrist who discussed case
studies from college students and members of the local community that were treated at
the Stanford University Impulse Control Disorders Clinic. One‟s offline persona is
different from this online persona, which individuals use to connect and interact with
other users through a technological medium. This causes a great opportunity to present
the best side of oneself, but also a challenge to ensure that the profile properly reflects the
individual. Moreover, students may now be establishing their social identity through this
public performance on Facebook. Many students spend a great deal of time grooming
their online persona to ensure the presentation of their absolute best image, regardless of
accuracy (Aboujaoude, 2011; Manago et al., 2012).
Users interact through commenting and liking the content shared by their peers.
This means that many students will share content, with the primary motivation of seeking
the approval and acceptance of their friends through comments and likes. The intentional
effort to create content that maximizes attention from peers is very narcissistic, especially
when coupled with the desire to consistently edit, or groom, one‟s profile and public
image (Aboujaoude, 2011; Bergman, Fearrington, Davenport, & Bergman, 2010; Manago
et al., 2012).
Buffardi and Campbell (2008) conducted a single-institution study that sought to
understand how Facebook manifests narcissism in users. The authors stated that
48
narcissism is more likely, and perhaps even caused, by social networking sites like
Facebook. This is due to the a heightened level of control over almost every aspect of the
environment, and the emphasis on shallow relationships with a great number of peers.
Their most significant finding was that students were surprisingly effective at recognizing
narcissistic students simply by viewing their Facebook profile, as confirmed through
scores of the Narcissistic Personality Inventory (NPI). Buffardi and Campbell (2008)
asserted that this is problematic for college students, because when users perceive an
individual to be narcissistic they do not pay attention to the content of the individual‟s
profile, which in turn decreases the individual‟s social capital and offline social life.
Since Facebook could increase the likelihood of narcissistic behavior, to which other
users would respond negatively, simply using this social media service could be
damaging to an individual‟s online persona and ability to remain involved in offline
social activities on campus (Buffardi & Campbell, 2008).
Further research from a Southeastern university has supported this hypothesis
(Saculla & Derryberry, 2011). The authors stated that students in their sample who
reported higher levels of Facebook use also scored higher on measures of narcissism.
The instrument used to measures narcissism was the Facebook as a Vehicle for
Popularity scale, which measured the extent to which participants used Facebook to
portray themselves as being popular. The results of this research are relevant because
they showed that an increased usage of Facebook could lead to increased levels of
narcissism, which the authors observed could be linked to decreased likelihood of moral
judgment development. Therefore, students should be educated in ways to use Facebook
that do not lead to narcissistic behaviors, or eliminate the need for face-to-face
49
communication, which would also decrease opportunities for moral judgment
development (Saculla & Derryberry, 2011).
Stress. Long before the current prominence of Facebook, scholars (Lowery,
2004; Roberts, 2003) noted that socially interactive technologies have the potential to
increase stress in the lives of college students. A study conducted at a mid-Atlantic
university by Gemmill and Peterson (2006) showed that keeping up with online requests
through Facebook can be a daunting task, and it caused a great deal of additional stress
for a quarter of their participants. The authors noted that a significant amount of
participants reported using Facebook to receive social support from friends and family
during stressful times, which S. Smith and Caruso‟s (2010) data supported. Gemmill and
Peterson (2006) found that some students had reported using Facebook as an effective
way to escape from the academic stresses of college life.
However, Gemmill and Peterson (2006) clarified that this is a bi-directional form
of stress relief, and while Facebook could provide a medium to receive support, it
therefore becomes a way that one‟s peers might seek social support. In this way,
Facebook use could lead to increased stress levels for students through their friends
seeking more social support then they are able to provide, or at a time when they were
already experiencing high levels of stress (Gemmill & Peterson, 2006).
Drinking and partying. In an analysis of CIRP‟s Your First College Year
(YFCY) survey data, conducted by the Higher Education Research Institute (HERI,
2007), high usage of Facebook (defined as spending more than six hours per week on the
service) was highly correlated to partying and drinking alcohol. All users of Facebook
reported an overall greater degree of social interactions to some degree. The specific data
50
regarding alcohol consumption showed a significant increase in the behavior by any
participants who spent greater than one hour per week using the service.
Distraction. It is easy to associate the distraction reported from technology use
throughout the last two decades with Facebook (Barkhuus & Tashiro, 2010). In a
quantitative and qualitative study including multiple focus groups, Silverman (2007)
found a consistent theme that Facebook was perceived as a waste of time and served as a
large distraction. The ECAR study (S. Smith & Caruso, 2010) reinforced this emphasis
on distraction. Not only did respondents report concerns that Facebook was distracting
them from academic and social endeavors, but some students even stated that Facebook
served as a distraction during class through its use on mobile phones.
Junco and Cotten (2011) linked this information with data supporting
educationally damaging effects of Facebook as a distraction, in a study conducted at a
small public Northeastern institution. Their data showed a negative relationship between
Facebook use, particularly when studying, and overall GPA. The authors took into
account previous literature which provided a framework for understanding that
multitasking in any way could negatively affect the learning process. When compared to
the relationship between general distractions and GPA, Junco and Cotten‟s (2011) data
demonstrated that paying attention specifically to Facebook while studying had a
markedly more significant relationship to poorer academic outcomes.
Positive Outcomes Associated with Facebook Use
Despite the list of negative outcomes associated with Facebook use, there are also
positives. While the students in Silverman‟s (2007) study reported that they were aware
of many of the drawbacks to Facebook use, a majority stated that the overall benefits
51
outweighed these risks. Results from the ECAR (S. Smith & Caruso, 2010) study echoed
this finding. The research that follows will show the linkages between Facebook use and
positive outcomes. This section will introduce research studies that have shown
communicational, psychosocial, and offline benefits connected to Facebook use.
Sharing information and opportunities. One of the most commonly cited
reasons that Facebook is beneficial on college campuses is that it dramatically increases
exposure to opportunities. As has already been established, most college students use
this service. It is logical that providing information through Facebook gives the greatest
number of students the greatest opportunity to receive that information (Martinez Aleman
& Wartman, 2009; Silverman, 2007).
Through the qualitative data analysis of the ECAR study, S. Smith and Caruso
(2010) found that many students reported being able to organize or discover study
sessions through Facebook. Between scheduled Facebook events and informal status
updates, students reported the ability to create study groups, organize meeting times and
locations, and invite other students, regardless of how strong preexisting social
connections were. Silverman (2007) found through qualitative and quantitative data that
students discovered many opportunities for involvement through Facebook. S. Smith and
Caruso (2010) also reported that students utilized the various aspects of the service to
find out about and join on-campus organizations, and that the online context of signing
up greatly reduced the social anxiety and barriers to entry that are typically associated
with the fear of joining a new organization.
The ease of sharing opportunities for co-curricular involvement, access to faculty
and staff members, and awareness of events happening within the community are
52
valuable services that Facebook provides (Martinez Aleman & Wartman, 2009). This
creates a possible opportunity to accomplish the primary goal of student affairs
professionals, in reaching out and encouraging the greatest number of students to become
involved in purposefully developmental activities (Heiberger & Harper, 2008).
Since such a substantial number of college students use Facebook (S. Smith &
Caruso, 2010), a single social media service connects the majority of them. Access to
this group of individuals can be useful in mobilizing students for a cause, spreading
awareness of an issue, or sharing an opportunity to get involved in an opportunity larger
than any single campus (Silverman, 2007). Even more significantly, within Facebook,
students are the ultimate authority when promoting their ideas. Therefore, students can
start campus initiatives, make the information available to a significant number of users
across the world, and receive more respect than if an administrator were to encourage a
group of students to act on the topic (Martinez Aleman & Wartman, 2009; Silverman,
2007).
Transition to college. Facebook can contribute to easing first-year students‟
transition to the college environment (Ellison et al., 2007; Heiberger & Harper, 2008;
Martinez Aleman & Wartman; Silverman, 2007). New students are able to connect with
their college network before they arrive on campus. This allows them sufficient time to
create online relationships with their new peers, and establish a group of friends to
support each other when they arrive on campus. Many orientation staffs and student
leaders attempt to reach out to new students during the summer before their arrival. The
focus is often to promote joining their clubs or organizations, but the overall goal is to
promote positive involvement and make new students feel comfortable about their
53
transition before they officially begin their first year (Heiberger & Harper, 2008;
Martinez Aleman & Wartman; Silverman, 2007). The difficult process of leaving a
social circle, such as a high school, and starting to form a new one from the ground up
can also be stressful, and there is evidence that first-year students utilize Facebook to
maintain their old ties as a support system to decrease the stress in creating a new
network (Ellison et al., 2007; Manago et al., 2012).
Diversity. A core aspect of students‟ development in college is exposure to and
acceptance of diversity (Pope, Reynolds, & Mueller, 2004). When students come from
less diverse environments, they are less likely to participate in multicultural activities in a
new college environment (Pascarella & Terenzini, 2005). Fortunately, studies (Ericson,
2011; Silverman, 2007) have found that Facebook helps facilitate students‟ participation
in diverse peer groups and organizations, and promote opportunities for involvement in
multicultural activities.
On a private campus with very little visible diversity, Ericson (2011) found that
traditionally underrepresented students were among the highest users of Facebook, and
that they were effectively able to promote involvement in their activities to members of
their racial or ethnic group as well as the general community. On a highly diverse
campus, Silverman (2007) found that Facebook use increased exposure to opportunities
for involvement with groups of traditionally underrepresented students. Overall, research
has shown that Facebook is a useful platform to equalize opportunities for involvement in
a variety of multicultural activities (Ellison et al., 2007; Martinez Aleman & Wartman,
2009; Silverman, 2007).
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Facebook therapy. Facebook therapy is when individuals who experience
emotional instability seek social support by posting self-relevant information online
through Facebook, in search for fulfillment of psychological and social needs (Buechel &
Berger, 2011; Ferrell, 2011). A study conducted at a Midwestern university by Ferrell
(2011) found that one function of Facebook use was to maintain a balanced lifestyle
between students‟ psychological needs, social needs, and social interactions. The author
described Facebook as serving a homeostatic function, where use of the service reduced
social pain for those who might have felt excluded, and assisted them in their return to a
state of equilibrium. Ferrell‟s (2011) results suggested that Facebook helped students
deal with perceptions of social exclusion through the finding that participants who
reported feeling left out spent more time using the service.
In their study at a mid-Atlantic university, Buechel and Berger (2011) found that
social sharing of emotions on Facebook was related to an increase in psychological well-
being. The researchers stated that sharing self-relevant content through Facebook acted
as a therapeutic source of assistance for students in need. Specifically, Buechel and
Berger (2011) found that students‟ perception that close friends and family would read
the content that they published boosted their well-being through increasing their
perception of social support. Since Buechel and Berger‟s (2011) data showed that
emotionally unstable individuals were more likely to share content about negative
experiences, students who were in the greatest need of social support were also the ones
to receive the most positive outcomes from sharing personal information through
Facebook. Manago, Taylor, and Greenfield (2012) found a significant positive
relationship between the overall size of one‟s social network and their perception of the
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level of social support it provided, regardless of how close the relationships within that
network were. The results were consistent with previous findings, in that larger social
network sizes were correlated with higher self-reports of self-esteem (Manago et al.,
2012).
Social capital. Ellison, Steinfield, and Lampe (2007) conducted a study to
measure the relationship between Facebook use and the bonding, bridging, and
maintaining of social capital at a large Midwestern university. Social capital refers to the
resources acquired from relationships with people, which allow individuals to draw on
resources from members of their network. This is relevant because social capital relates
to increased levels of commitment, community, and collective actions. Bridging social
capital is utilizing weak ties within individuals to expand and connect new individuals;
bonding social capital is the deeper relationship of a close group of friends and family;
and maintained social capital is the ability to stay in touch with a social network after
physically disconnecting from it (Ellison et al., 2007). The researchers found that
Facebook use alone was not a significant predictor of social capital, but that intensity of
Facebook use was a highly positive and statistically significant predictor.
Overall, there was a correlation between Facebook use and the maintenance and
creation of social capital. Ellison et al. (2007) found that Facebook played a role in the
process of forming bonding and bridging social capital, as well as maintaining it. The
authors noted that Facebook use seemed to lower the barriers to participation in social
activities and relationships, and made it easier to initiate and maintain communication,
especially among shy students who might be reluctant to do so in person. Bridging social
capital, and the establishment of weak ties, was found to be assisted by Facebook use,
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which resulted in wide and expanding social groups, willingness to support the campus
community, and an increased sense of wanting to get involved on campus (Ellison et al.,
2007).
Moreover, Ellison et al. (2007) found that Facebook use helped students to
maintain their social capital, particularly in regards to social networks from participants‟
high schools. The authors argued that this maintained social network is responsible for
eliminating the difficulties of losing connection with old friends, and therefore eases the
transition into the college social network. Facebook use made it easier for participants to
convert latent ties – or social connections that were technically possible but did not yet
exist – into actual relationships (Ellison et al., 2007).
According to the researchers, Facebook use also assisted students with low
satisfaction and low self-esteem. Bridging capital assisted students by providing
increased opportunities and information, which allowed them to get more out of their
college experience without needing to be highly satisfied, engaged, outgoing, or assertive
as prerequisite conditions. Facebook acts as a useful tool in crystallizing relationships
that may have started weak, where the individuals did not have access to the time or
location to develop them further, and allowed them to build and expand the relationships
online (Ellison et al., 2007).
Manago et al.‟s (2012) results, when controlled for self-esteem, suggested that
those with larger social networks reported that they were overall happier with their lives,
implying that the social capital gained through Facebook use has direct applications
beyond the realm of the social media service. One of Ellison et al.‟s (2007) closing
remarks summarized their findings, “Online interactions do not necessarily remove
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people from their offline world but may indeed be used to support relationships and keep
people in contact, even when life changes move them away from each other” (p. 1165).
This explanation of Facebook‟s role in increasing social capital summarized how
Facebook use could translate to increased social and co-curricular involvement.
Offline social life. Several studies (Barkhuus & Tashiro, 2010; Ellison et al.,
2007; HERI, 2007; Martinez Aleman & Wartman, 2009; Silverman, 2007) have found
that increased levels of Facebook use relate to greater levels of offline social interactions.
Silverman (2007) found that students reported using Facebook to establish ways to get
together with friends offline, and then to stay in touch with their peers during periods
where these meetings were not possible. The results of the HERI (2007) analysis showed
that students who reported using Facebook more often also reported spending more time
socializing with peers. This finding was later supported by Manago et al. (2012) in that
students‟ social networks primarily contained friends gained from participation in co-
curricular activities. This finding was potentially explained by the idea that increased
time spent offline in various activities introduced students to more potential Facebook
friends, which then resulted in a larger amount of time spent using the service (Manago et
al., 2012).
In Barkhuus and Tashiro‟s (2010) study, participants reported that using
Facebook increased their offline social lives because it was so easy to connect online to
create and maintain their relationships. Ellison et al. (2007) found evidence to suggest
that participation in an online Facebook community did not preclude students‟ from
involvement in the offline world, but in fact was useful in promoting that involvement
both within the campus community, as well as with relationships to individuals in
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previously inhabited communities.
Measures of Facebook Use
Researchers have developed multiple measures of Facebook use. Understanding
how people use Facebook is relevant for marketers, psychologists, and educators alike.
The type of measurement a researcher chooses to use is a direct result of the primary
goals for which they intend to use the information, and therefore the types of
measurements vary greatly. The most prominent measures of Facebook use are the
Facebook Intensity Scale, the Net.Generation survey, and Junco‟s (2012) Facebook
instrument.
Facebook Intensity Scale. In the field of marketing and communications media,
the most prominent measurement of Facebook use is the Facebook Intensity Scale
(Ellison et al., 2007). It focused on the motivations and gratifications of users of
Facebook, and included specific scales for each of Facebook‟s various services. It is
composed of a list of activities in which participants indicate how frequently they use the
service, and then determines the benefits and social results of that participation. It is a
thorough and long instrument, and has been proven to be effective in gauging how and
why participants use the service (Ellison et al., 2007).
Net.Generation survey. Junco and Mastrodicasa (2007) created an instrument
for their study of how Millennials, or in their words the Net generation, use modern
social technologies. This instrument focused on what services students use, how they
interact with them, and how much time they spent using them. It broadly measured how
students used online instant messaging, cell phones, Internet websites, and social media.
The instrument has not been updated since the researchers created it, and most of the
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technologies that it measures are no longer indicative of what students use, though it was
very effective in laying a foundation for researchers to measure this type of technology
use.
Junco’s (2012) Facebook instrument. Most recently, Junco (2012) studied
college students‟ use of Facebook and their overall educational engagement. He sought
to measure both the quantity of Facebook use, but also the way in which participants used
the service. This instrument measures the quantity of Facebook use in two ways. It asks
how many minutes participants spend using the service in an average day, and then how
many minutes they spent yesterday. Next, it asks how many times the participant checks
Facebook in an average day, and how many times they checked yesterday.
In order to measure the behaviors in which students participate while using
Facebook, Junco (2012) created a 14-item questionnaire measuring specific Facebook
behaviors. The author created the instrument by asking for recommendations from
current students, Facebook friends, and a network of expert professionals, on how
students currently use Facebook. Junco (2012) then compiled the list and presented it to
two classes of undergraduate students. After making suggested modifications, colleagues
and students made final recommendations through Facebook in regards to wording and
content structure. Current students who use Facebook, and a group of expert
professionals knowledgeable on the topic verified the questions of the final instrument
(Junco, 2012), and the behaviors that the instrument measures reflect the findings of
previous research (Papacharissi & Mendelson, 2011; Smock et al., 2011) on the ways in
which individuals use Facebook. This 14-item questionnaire seeks to measure what
percentage of time users spend participating in these specific Facebook activities.
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For the current study. While the Facebook Intensity Scale is highly regarded
within the field that has produced the most literature on Facebook, it is very long and
detailed, specifically in regards to the motivations and outcomes of Facebook use.
Therefore, it is beyond the scope of the purposes of this study. As for the Net.Generation
survey, only Facebook use is of interest for this study, which makes this survey is too
broad to meet the needs of this research, and it has not been updated to reflect recent
changes in technology. Junco‟s (2012) instrument measures the quantity of Facebook
use, through minutes of use per day and number of times checked per day, and behavior
of Facebook use, by measuring how frequently students participate in specific Facebook
activities. This instrument meets the specific needs of the current study in that it
succinctly provides detailed information about Facebook activities, as well as level of
use. Junco‟s (2012) instrument is designed to measure the information that this study is
seeking to collect, and does so better than other previously mentioned instruments.
Summary
Facebook is by far the preferred social media service of choice for college
students; many students report spending a great deal of time utilizing the service (Junco,
2012; S. Smith & Caruso, 2010). The research has simultaneously identified positive and
negative outcomes associated with this use. However, based on the different activities
that can be performed through Facebook, and viewing it as a collection of unique tools
rather than a single entity, it is more likely that the service itself has no intrinsically
positive or negative influence (Junco, 2012). Having looked at the issues of Facebook
use among college students, and with an effective tool to measure specific Facebook
activities in relation to the level to which students use the service, data will be provided
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that will help answer the research questions for this study. The next section will look
more specifically at the psychosocial development of college students, with an emphasis
on co-curricular involvement in relation to Facebook use.
Facebook and Involvement
The definitions of Facebook use and co-curricular involvement have now been
presented. It is necessary to understand Facebook use since college students use the
service so heavily (Junco, 2012; S. Smith & Caruso, 2010); it is equally essential to
understand co-curricular involvement due to its impact on student development and
persistence (Astin, 1984; Pascarella & Terenzini, 2005). Despite the importance of these
concepts, the relationship between college students‟ level and nature of Facebook use and
co-curricular involvement is still not well understood (Junco, 2012).
Before the existence of Facebook, scholars have speculated that social
technologies would have an impact on co-curricular involvement. Strange and Banning
(2001) noted the dual potential of the introduction of computer-mediated communications
on campus. Depending on the viewpoint, technology could potentially be either
responsible for destroying vital aspects of the current definition of community on
campus, or for assisting in creating an even more dynamic and accessible community.
Lowery (2004) echoed that perspective by speculating that online social relationships
could prove beneficial in strengthening offline connections, but that care must be taken to
avoid the potential downside of eliminating the priority of face-to-face communication.
In a study of a selection of data from the 2003 National Survey of Student Engagement
(NSSE), Nelson Laird and Kuh (2005) found that students who were frequent users of
technology also displayed higher levels of educational achievement and engagement.
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The studies (Heiberger & Harper, 2008; HERI, 2007; Junco, 2012) that
researchers have conducted to date on Facebook use have helped illuminate potentially
meaningful relationships with involvement. Most of these studies have focused on
broader measurements of student engagement rather than the specific definition of
involvement used for this study. Since interest in Facebook for its developmental and
educational outcomes is a recent development on college campuses, relevant research is
limited, and therefore this section will provide an overview of all of this literature.
Negative Relationship
Of the studies that have included objectives to test for a relationship between
Facebook use and level of involvement, one found an overall negative correlation. Junco
(2012) conducted a study to measure the relationship between level of engagement within
the institution, and level and behavior of Facebook use. He sampled all students through
a campus-wide email invitation at a mid-sized, Northeastern public institution, and
received an overall response rate of 44%. The purpose of the study stemmed from a
belief that a link existed between Facebook and real-world engagement in meaningful
ways.
The first construct that Junco (2012) based this study on was engagement, defined
by Kuh (2009). The researcher measured engagement through an instrument written by
Junco, Heiberger, and Loken (2010) for a similar study of Twitter use and engagement.
The researchers created this instrument by adapting 19 items from NSSE, with
permission, that specifically targeted the researchers‟ definition of engagement. As part
of engagement, the instrument measured students‟ level of involvement through one item,
which asked how many minutes per week students typically spent involved in activities
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outside of the classroom.
This understanding of engagement is similar to the definition of involvement used
for this study, but differs in a few ways. As stated earlier, involvement relates to
behavioral decisions. While research has shown a direct link between involvement and
personal development (Astin, 1984, 1993; Pascarella & Terenzini, 2005), the definition
for this study did not seek to understand that outcome. Junco‟s (2012) definition of
engagement, on the other hand, intentionally sought to understand the developmental
outcomes of engagement. Moreover, engagement as used for Junco‟s (2012) study was
primarily focused on academic outcomes. It included a limited emphasis on co-curricular
involvement as a key aspect of overall engagement, but emphasized academic measures
and outcomes of engagement. This was displayed by the fact that the researcher only
measured involvement through a simple scale of minutes per week. To measure level
and nature of Facebook use, Junco (2012) asked how many minutes each day participants
spent using the service, how many times each day they checked the service, and how
frequently they participated in specific activities within the service.
Junco (2012) found a statistically significant correlation between overall
Facebook use and decreased levels of student engagement. Specifically, time spent on
Facebook in non-communicative behaviors were negative predictors of engagement,
where these behaviors were defined as playing games, checking up on friends, and
posting photos. The author concluded that there was a negative relationship between the
investment of time in Facebook, and engagement and co-curricular involvement. It is
also worth noting that Junco (2012) did find statistically significant positive correlations
between specific behaviors of Facebook use, which will be discussed in the section on
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positive relationships.
No Relationship
In addition to Junco‟s (2012) study that found a negative relationship between
degree of Facebook use and level of engagement, two studies found no statistically
significant relationship between Facebook use and co-curricular involvement. The first
study (Foregger, 2008) sought to measure students‟ intentions for using Facebook. The
second (Ericson, 2011), measured the relationship between socially interactive
technologies, including Facebook use, and campus involvement.
Foregger’s (2008) study. One of Foregger‟s hypotheses was that students who
spent more time involved in campus activities would spend proportionately less time on
Facebook. Foregger (2008) administered a survey to students registered for a
communication course at a large public Midwestern university, and received 340
responses that met the criteria for the study. First, participants completed a survey of
potential uses of Facebook, including how much time they spent on the service. Second,
Foregger (2008) asked participants to list the number of student organizations within
which they were involved during the previous semester. With that data, Foregger (2008)
split participants into two groups based upon level of involvement in campus activities.
To qualify for the low activity group, participants reported involvement in no activities;
for the high activity group, students were involved in greater than four activities.
Foregger (2008) found that whether students reported high or low levels of
involvement in campus activities, they reported similar amounts of time spent on
Facebook. The author noted that this finding suggested that Facebook is potentially
unrelated to campus involvement altogether, in that it neither hinders nor promotes
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involvement in campus activities.
Ericson’s (2011) study. More recently, Ericson (2011) conducted a study to
determine if a correlation existed between student use of socially interactive technologies
and involvement on campus. Through the researcher‟s definition of socially interactive
technologies, the study measured student use of cell phones, text messaging, email,
instant messaging, and Facebook. For the construct of involvement, Ericson (2011)
based the study‟s definition on a combination of Kuh (2009) and Astin‟s (1984) work, as
explained in detail earlier.
Ericson‟s (2011) survey combined selections from two instruments. The first
instrument the researcher used was a compilation of four subscales of NSSE, which
sought to measure student involvement as one specific aspect of engagement. These
subscales were diversity within college activities, personal-social growth, non-classroom
experience, and miscellaneous student activities. The second instrument was the
Net.Generation Survey created by Junco and Mastrodicasa (2007).
Ericson (2011) sampled a population of students at a small, private, Northeastern
religiously affiliated institution. The response rate was 15.4%, totaling 154 students who
were demographically representative of the student population. In the analysis of the
social media piece, Ericson (2011) found no statistically significant correlation between
Facebook use and measures of involvement in campus activities. Similar to Foregger
(2008), Ericson‟s results showed that participants with varying levels of campus
involvement reported equally high and low levels of social media use.
Ericson (2011) concluded that these findings suggested that students were not
disengaged on campus because they are using Facebook, nor were they using Facebook
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to find new ways to become involved. Rather, Ericson (2011) suggested that the
participants had found ways to simultaneously use Facebook and continue to be involved
in campus activities. Essentially, Ericson‟s (2011) point was that students who were
more involved but who reported similarly high levels of Facebook use must have been
multitasking in some way. Due do the high numbers of students using mobile phone to
access Facebook, Ericson (2011) concluded that participants had not needed to sacrifice
participation in order to achieve high levels of involvement and Facebook use.
Positive Relationship
On the other hand, three studies have found a positive relationship between use of
Facebook, and level of engagement and campus involvement. One was an analysis of
data of a nationally representative sample of college students conducted by HERI (2007),
while the other two studies (Heiberger & Harper, 2008; Junco, 2012) were single-
institution studies. All three discuss the positive relationship between Facebook use and
co-curricular involvement.
The Higher Education Research Institute’s (HERI; 2007) analysis. HERI
(2007) analyzed results of CIRP‟s YFCY survey. The analysis found several statistically
significant results that students who spent time on Facebook also spent more time
involved socially, and in campus activities, which were measured separately. The HERI
(2007) analysis indicated that students who spent more time on Facebook did not
consequently spend less time than their peers on academic activities such as homework
and preparing for class. Moreover, “First-year students who spent more time on social
networking sites were much more likely to report that they interacted daily with close
friends at their institution and those not at their institution” (HERI, 2007, p. 2). The
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authors stated that their results seemed to suggest that students used social media as an
extension of their social lives, where students who spent more time involved with their
peers also spent more time interacting on social media.
Heiberger and Harper’s (2008) study. Seeking to build upon the HERI (2007)
analysis, Heiberger and Harper (2008) conducted a survey of 377 students at a medium-
sized, public Midwestern university. The goal of the study was to measure students‟
level of engagement in relation to their level of Facebook use. The researchers used the
same definitions of Facebook use and engagement as described by Junco and Cole-Avent
(2008), which influenced Junco‟s (2012) definition that was discussed earlier in this
chapter. Heiberger and Harper (2008) found that students who spent more than an hour a
day on Facebook responded that they felt a stronger connection to their friends within
their university, and spent more time involved in campus activities.
Heiberger and Harper (2008) found that Facebook use did not interfere with
campus involvement, and actually related to higher levels of engagement and
involvement. In fact, participants who reported spending greater than one hour per day
on Facebook were almost 20% more likely to be involved in one or more student
organizations. The students were almost twice as likely to spend six or more hours
involved in a student organization, compared to students who spent less than one hour per
day on Facebook (Heiberger & Harper, 2008).
Junco’s (2012) study. Finally, although Junco (2012) found an overall negative
relationship between Facebook use and student engagement, the results showed different
relationships between individual behaviors of Facebook use. By breaking down
individual Facebook behaviors for Junco‟s (2012) Facebook instrument, it became more
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clear that a positive correlation existed between some activities, and engagement and
involvement. First, there was a positive relationship between time spent on Facebook
and time spent participating in co-curricular activities. Second, Junco (2012) found that
communicative activities, defined as commenting on content, creating or RSVP‟ing to
events, and viewing pictures, were statistically significant and strongly positively related
to level of engagement. Junco (2012) stated that there was a direct link between these
communicative behaviors and real-world experiences, and therefore is logically related to
increased levels of engagement. These results indicated that while certain Facebook
activities were potentially negative predictors of engagement, others were positive
predictors (Junco, 2012).
Summary
These conflicting results illuminate the fact that there is still a limited
understanding of the relationship between college student Facebook use and co-curricular
involvement, despite observing that Facebook use and involvement independently have a
large impact on students. There has not yet been a study specifically conducted to
measure the constructs of Facebook use and co-curricular involvement as defined by
Astin (1984). In addition to conflicting results, previous studies have had varying
structures, primarily in regards to instrumentation. The results of these studies have
shown limited measurement of one or more of the constructs of interest in the current
study.
Some studies (Ericson, 2011; Foregger, 2008; Heiberger & Harper, 2008; HERI,
2007) have not focused on or measured Facebook use to as nuanced a degree as Junco
(2012), where the instrument measured behavior and level of use. Some (Ericson, 2011;
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Foregger, 2008; Junco, 2012) have also differed in their measurement of involvement. In
one case (Foregger, 2008), a minimal measure of involvement in campus activities
existed, where the only data collected was the number of organizations in which
participants reported being previously involved. Alternatively, the tool to measure co-
curricular involvement in another case (Ericson, 2011) was extracted from a larger
instrument intended to measure engagement, which is a much broader construct.
This lack of understanding restrains student affairs professionals‟ work with
students, particularly in promoting their co-curricular involvement and development.
Therefore, it is essential to specifically measure, with as much detail as possible, the
relationship between level and behavior of Facebook use and involvement on campus.
Summary of Literature
College students overall are heavy users of technology (Junco, 2012; S. Smith &
Caruso, 2010). This has created an expectation that every aspect of the college
environment should support this behavior (Arroway et al., 2010). Moreover, the way in
which college students connect with their world, information, and peers has changed to
fit within this technological environment (Ellison et al., 2007; Junco & Mastrodicasa,
2007; Manago et al., 2012; S. Smith & Caruso, 2010). Regardless of age or generation,
the majority of college students prefer to interact through technological mediums, and the
modern college campus has grown a great deal to support this trend (Arroway et al.,
2010; S. Smith & Caruso, 2010). This is relevant because many aspects of the college
experience are impacted by this communication evolution (Junco & Mastrodicasa, 2007),
including the understanding of student learning and development that has always guided
the work of higher education professionals (Junco, 2012).
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Students‟ co-curricular involvement is an important aspect of the college
experience (Astin, 1984; Evans et al., 2010). How students decide to spend their time
and energy is a critical component in understanding the success of institutions of higher
education in achieving their educational mission and goals (Davis & Murrell, 1993; Pace,
1982). As the most valuable resource an institution has at its disposal, it is important to
determine where students are investing their time and energy to ensure that they are
participating in educationally and developmentally beneficial activities (M. Wilson,
2004). Since time and energy are finite resources, it is the role of every member of the
institution to verify that positive student behaviors are promoted, because every activity
that does not fall within these guidelines is in direct competition with positive levels of
involvement (Braxton, 2003). Indeed, Astin (1984) and Baird (2003) called for an
evaluation of everything that professionals do within an institution based on their ability
to increase involvement. The positive outcomes associated with increasing this
involvement are substantial: student satisfaction, psychosocial development, and
persistence to graduation (Astin, 1993; Kuh, 1995; Pascarella & Terenzini, 2005;
Winston & Massaro, 1987), which scholars stated are essential in a high quality college
experience (Astin, 1984; Baird, 2003; Braxton, 2003; Evans et al., 2010; Kuh, 2009;
Roberts, 2003; Tinto, 1975; M. Wilson, 2004).
More than 85% of college students use Facebook (S. Smith & Caruso, 2010;
Smith et al., 2011), and one recent study reported that students spend over 101.9 minutes
each day on the social media platform (Junco, 2012). There are many negative and
positive outcomes linked with this usage. The negatives include decreased focus on
educationally purposeful tasks (Junco & Cotten, 2011), increased stress (Gemmill &
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Peterson, 2006), and psychological detriments including narcissism (Buffardi &
Campbell, 2008; Manago et al., 2012; Saculla & Derryberry, 2011) and depression
(Jordan et al., 2011). Positives include increased levels of engagement (Heiberger &
Harper, 2008), social capital (Ellison et al., 2007; Manago et al., 2012), and offline social
activities (Manago et al., 2012; Martinez Aleman & Wartman, 2009). Students have
reported an awareness of most of these negatives and positives, but may not be effective
decision-makers in choosing the most beneficial ways of using Facebook (Silverman,
2007). Most importantly, studies determining concrete outcomes of Facebook use are
still limited (Junco, 2012). This means that a firm understanding of the ways in which
Facebook affects college students is still lacking, especially in regards to its role in the
vital task of increasing co-curricular involvement.
Conclusion
This chapter described characteristics of college students and their technologically
connected lives; the importance of co-curricular involvement in student satisfaction,
development, and persistence; and college students‟ use of Facebook and the relationship
to positive and negative developmental outcomes. It has described many research studies
that sought to understand each of these ideas in detail. This chapter ended with a
summary and synthesis of these studies, and connected them to highlight the importance
of this research study. The following chapter will discuss the proposed methods of
conducting this research study.
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CHAPTER THREE
METHOD
The purpose of this research study was to determine whether a relationship
existed between college students‟ level and nature of Facebook use and co-curricular
involvement within the campus community. With the understanding of the significance
of this study and relevant literature on these constructs in mind, this chapter will describe
the means by which the researcher conducted this study, including the methodology,
sample, instrumentation, procedures, and data analysis.
Methodology
The quantitative research approach for this study employed a correlational design.
It aimed to determine if a correlation existed between both the level and nature of college
students‟ Facebook use and their co-curricular involvement. This type of study involves
determining if a relationship exists between two or more variables, and to what degree,
with an attempt to discover a connection (Gay, Mills, & Airasian, 2009). This approach
was appropriate because the variables of Facebook use and campus involvement are scale
variables, and the study seeks to understand the relationship between the two.
Sample
The subjects of interest for this study were undergraduate college students who
use Facebook and have an active account with the service. Graduate students were not of
interest to the researcher because the definition of involvement used for this study
pertains particularly to undergraduate students (Astin, 1984). This study was conducted
at a mid-sized public doctoral-granting institution in the mid-Atlantic region, which
enrolls a fairly diverse population of over 15,000 students. Those invited to participate in
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this study were selected through a random sample of all current undergraduate students at
the institution, with a sample size of 1,000 students. Staff from the research assistance
center generated the random sample and provided a list of email addresses to the
researcher. Invited study participants were asked to complete an online survey that
measured their level and nature of Facebook use and co-curricular involvement, and then
collected their demographic data.
Descriptive Statistics
Of the 1,000 undergraduate students in the random sample, 225 participants
responded for an overall response rate of 22.5%. Upon initial analysis, there were eight
students who did not report having an active Facebook account, and were removed from
further analyses. Ten participants did not complete the entire survey and were also
removed from analysis. Therefore, all analyses are based on the 207 respondents with
active Facebook accounts who completed the survey. Table 1 provides the responses to
the demographic section of the survey.
Women represented 73.3% of the sample, and 26.7% were men. The mean age of
the sample was 20.7 years of age and the standard deviation was 4.2 years. The age of
participants ranged from 18 to 57, although over 89% were between the ages of 18 and
22. First-year students represented the largest group when broken down by years of
attendance, at 36.8%. In terms of race and ethnicity, the sample was predominantly
Caucasian, with 85.7% of respondents identifying as White. Most categories used in the
demographic section of the questionnaire reflected the admission data collected for the
institution, to be used in establishing generalizability to the overall campus population.
These demographic responses were closely related to the overall undergraduate student
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population, except for a slight over-representation of women and Caucasians within the
response group.
Additional demographic data collected from the sample included place of
residence, major, GPA, and device used to complete the survey. Among respondents, the
largest group of students lived off campus within a five-mile radius of the university, at
43.9%, followed closely by those who lived on campus at 42.9%. A wide range of
academic majors were represented in the sample, with the highest percentage of students
majoring in the College of Health and Human Services at 38.2%, followed by the College
of Natural Sciences and Mathematics at 19.8%. Reported GPA from the Fall 2011
semester ranged from 1.0 or lower to 3.6 of higher, with 3.35 being the mean GPA of
participants. The largest group of participants reporting 3.6 or higher at 40%, with only
12.9% reporting lower than a 2.6 GPA. Participants by far used a laptop computer to
complete the survey, at 65.7% of responses. The remainder of the respondents used
either a desktop computer, or mobile device (defined by the survey as „smart phone, iPod
touch, etc.‟), and no respondents reported using a tablet computer.
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Table 1
Descriptive Statistics for Participants
Variable n %
Gender Identity ( N = 206)
Male 55 26.7
Female 151 73.3
Age ( N = 205)
18 40 19.5
19 43 20.9
20 40 19.5
21 46 22.4
22 15 7.3
23 7 3.4
24 2 0.9
25 2 0.9
26 – 30 5 2.4
31 – 40 3 1.4
41 – 50 1 0.4
50 and older 1 0.4
Race or Ethnicity ( N = 204)
American Indian 0 0.0
Asian 6 2.9
Black 12 5.8
Hispanic 3 1.4
International 3 1.4
Multiracial 5 2.4
Pacific Islander 0 0.0
White 175 85.7
Years Enrolled ( N = 206)
1 or less 76 36.8
2 48 23.3
3 40 19.4
4 36 17.4
5 or more 6 2.9
Current Residence ( N = 205)
On Campus, Residence Hall 88 42.9
Off Campus, < 5 miles away 90 43.9
Off Campus, > 5 miles away 27 13.1
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Variable n %
GPA, as of Fall 2011 ( N = 205)
1.0 or lower 1 0.4
1.1 – 1.5 1 0.4
1.6 – 2.0 9 4.3
2.1 – 2.5 16 7.8
2.6 – 3.0 29 14.1
3.1 – 3.5 67 32.6
3.6 or higher 82 40.0
College of Enrollment ( N = 207)
Business 21 10.1
Education and Educational Technology 24 11.6
Fine Arts 10 4.8
Health and Human Services 79 38.2
Humanities and Social Sciences 31 15.0
Natural Sciences and Mathematics 47 19.8
Device Used for Survey ( N = 207)
Laptop Computer 136 65.7
Desktop Computer 36 17.4
Mobile Device (Smart Phone, iPod Touch, etc.) 34 16.4
Tablet Computer (iPad, Kindle Fire, etc.) 0 0.0
Note. Totals of percentages are not 100 for every characteristic because of rounding.
Instrumentation
The survey for this study was composed of two previously created instruments,
and a researcher-created section that collected basic demographic data. The purpose of
these individual instruments, their construction, and psychometric properties will be
reported.
Facebook Instrument
Junco (2012) created an instrument to measure participants‟ level and nature of
Facebook use. This instrument assesses the participants‟ level of use by asking, on
average, how many minutes per day they spend on Facebook. Level of use is also
assessed in a question that asks how many minutes participants spent on Facebook
yesterday, which refers to the day before the participant completed the survey. It also
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measures the frequency with which participants log in to Facebook, by asking on average
how many times per day the participant logs in, as well as how many times the
participant logged in the previous day. Finally, the instrument measures the nature of
Facebook use by asking 14 questions related to how likely participants are to engage in
specific Facebook behaviors when they log in to the service, along a five-point scale
ranging from „very frequently (nearly 100% of the time)‟ to „never‟ (Junco, 2012; see
Appendix A).
To score the results, the researcher first determined the quantity of Facebook use
through the results of the hours of use and the frequency of checking Facebook, and
compared the individual scores to the distribution of all participants. Second, the
researcher coded the results of the 14 items that measured quality of use, where a
response of „very frequently‟ received a score of five and „never‟ received a score of one.
Each response generated an individual score, and the researcher analyzed each score
independently. The instrument‟s author stated that the process of creating the
questionnaire discussed in Chapter two established content validity, by including the
most current feedback from the target population of Facebook users and relevant
authorities in the field (R. Junco, personal communication, June 20, 2011). Reliability
was not reported by the author.
College Student Experiences Questionnaire
Pace (1982) created the CSEQ in 1978, in order to measure the quality of student
effort in the college experience and its impact on achievement. Since its original
creation, the CSEQ has been updated three times. The fourth edition, updated by Pace
and Kuh in 1998, was used for this study. In measuring the quality of student effort, the
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CSEQ utilizes 14 scales, seven of which ask students about their use of various campus
resources, and seven which ask about the social and academic opportunities in which they
have participated. These scales report the quality of effort students invest in their college
experience, or their level of initiative, which relates directly to the behavioral component
of Astin‟s (1984) definition of involvement.
The two scales that were used for this study are the Campus Facilities scale,
which measured the extent to which students took advantage of the physical campus, and
the Clubs and Organizations scale, which measured the extent to which students
participated in campus events and organizations. Some questions from the Campus
Facilities scale include “How often have you met other students at some campus location
(campus center, etc.) for a discussion,” “How often have you played a team sport
(intramural, club, intercollegiate),” and “How often have you attended a lecture or panel
discussion.” Questions from the Clubs and Organizations scale include “How often have
you attended a meeting of a campus club, organization, or student government club,”
“How often have you managed or provided leadership for a club or organization,” and
“How often have you met with a faculty member or staff advisor to discuss the activities
of a group or organization” (see Appendix B).
Responses were measured along a four-point Likert scale, with options ranging
from „very often‟ to „never‟. Each choice received a score, with „very often‟ receiving
four points, and „never‟ receiving one point. The researcher averaged the score of all
items of the questionnaire, where the mean was the quality of effort, or the students‟
behavioral level of co-curricular involvement. Scores were analyzed by individual scale
and collectively as a total CSEQ score.
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To measure the quantity of involvement, the researcher modified a preexisting
question on the CSEQ related to academic involvement. The original question asked for
hours of academic involvement outside of the classroom each week. The modification
for this study simply changed the academic portion to ask about co-curricular
involvement. The modified question reads “During the time school is in session, about
how many hours a week do you usually spend outside of class on activities related to co-
curricular involvement, such as using campus recreational facilities, participating in
organizations, campus publications, student government, fraternity or sorority,
intercollegiate or intramural sports, attending a lecture or panel discussion, etc.?” The
response options were a checklist of the number of hours at five-hour intervals, ranging
from zero to more than 30, which were the response options from the original question.
Reliability and validity for the CSEQ were estimated through procedures
conducted by the Indiana University Center for Postsecondary Research and Planning,
based on a nationally representative sample of undergraduate students. A high level of
reliability was established, with Cronbach‟s Alpha scores ranging from .74 to .92 among
the different scales. Each scale correlated with one another, and the items of the scales
were significantly correlated based on their scores. Specifically for the current study, the
Cronbach‟s Alpha score for the Clubs and Organizations scale was .83, and the
Cronbach‟s Alpha score for the Campus Facilities scale was .74 (Gonyea, Kish, Kuh,
Muthiah & Thomas, 2003).
Validity for the CSEQ was calculated in several ways. Construct validity was
shown through correlations among the activity scales, supported by a factor analysis,
which resulted in personal-social and intellectual-academic factors. Content validity was
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established through inter- and intra-scale cluster correlations, where alpha factors for the
Clubs and Organizations scale and Campus Facilities scale were .47-.82, and .74,
respectively. Overall, the Buros Mental Measurements stated that the items on the CSEQ
are clear, well-defined, and have face validity (Gonyea, Kish, Kuh, Muthiah & Thomas,
2003).
Demographics
In addition to these two instruments, the survey obtained basic demographic
information. Participants were asked to identify their place of residence (on-campus, off-
campus residing within five miles of campus, and off-campus residing greater than five
miles away from campus), years of attending the institution, gender, age, race and
ethnicity, GPA, and academic major (see Appendix C). These demographic
characteristics have been shown to directly relate to co-curricular involvement (Astin,
1984; Winston, Miller, & Cooper, 1999) or Facebook use (Ericson, 2011; Hargittai,
2009; HERI, 2007; Junco, 2012), and could affect the results of the correlation between
the two. By administering the two previously discussed instruments, the survey for this
study measured the behavior and quantity of co-curricular involvement through the
CSEQ and the level and nature of Facebook use through Junco‟s (2012) instrument.
Procedures
R. Junco (personal communication, June 20, 2011) granted approval to use the
Facebook instrument. The Indiana University Center for Postsecondary Research and
Planning approved the usage of the CSEQ items through a licensing contract. The
researcher submitted a proposal to the human subjects review board of the host institution
and received permission to conduct a study of live participants. With the aid of the staff
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in the research assistance center, the researcher established the random sample.
The researcher administered the instrument as an online questionnaire through
Qualtrics. Qualtrics is an online survey research suite that allowed the researcher to host,
distribute, and administer the survey, and from where the results were downloaded at the
end of the study. The survey included Junco‟s (2012) Facebook instrument, the two
CSEQ scales and question about the number of hours spent in activities outside of the
classroom, and demographic questions. The researcher sent an email to the sample
inviting them to participate, which described the research study and provided a link to the
Qualtrics survey (See Appendix D). The first question of the survey asked participants if
they had a Facebook account, and if they had logged in within the last 30 days, based on
Facebook‟s definition of an active user. Those who did not meet the criteria for
participating were forwarded to the end of the survey, thanked for their participation, and
offered the opportunity to enter into a raffle. As an incentive to participate in the study,
students were informed that upon completion of the survey they had the option of
entering into a raffle drawing to receive one of 10 iTunes gift cards valued at $10 each.
Through Qualtrics, the researcher sent two reminder emails to those who had not
responded. The researcher then sent a thank you email to all participants after the survey
closed.
Data Analysis
The data of this survey were used to determine if a correlation existed between
Facebook use and co-curricular involvement on campus. The researcher downloaded the
results of the survey from Qualtrics into the Statistical Package for the Social Sciences
(SPSS), and scored them. In the preliminary analysis, the means, standard deviations,
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and reliabilities were reported for each scale and subscale. Then a correlation was run
between the quantity of Facebook use responses and hours per week of involvement,
Campus Facilities CSEQ responses, Clubs and Organizations CSEQ responses, and total
CSEQ score. Next, a correlation matrix through SPSS was run to identify co-variates
among the construct variables and demographic variables.
Conclusion
This chapter has described the methodology of a research study on the correlation
between college students‟ level and nature of Facebook use and co-curricular
involvement within the campus community, and explained how Facebook use and
involvement were quantified. It has also described the process of analyzing the data
collected by the researcher. The following chapter will describe the analysis of the
results of this data collection.
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CHAPTER FOUR
RESULTS
The research study for this thesis sought to determine whether a relationship
existed between undergraduate college student Facebook use and co-curricular
involvement on campus. The researcher collected data through an online survey, which
administered Junco‟s (2012) Facebook instrument, items from the CSEQ (Pace & Kuh,
1998), and a series of demographic questions. Following the previously introduced
methodology, this chapter will report the responses of the data collection of 207
participants in the preliminary analysis, and the correlational findings in the primary
analysis.
Preliminary Analysis
The following sections will report the responses of the 207 participants who had
an active Facebook account at the time of taking the survey and completed the survey in
its entirety. The first section will describe the reported quantity and level of Facebook
usage, followed by a description of the participants‟ level and type of co-curricular
involvement.
Facebook Usage
To determine average levels for Facebook use, the researcher assigned the mean
of the range as the score of each nominal response option. Based on the mean of the
range, the mean amount of time respondents reported spending on Facebook on average
was 61.26 minutes per day, where the most common response was an average of 31 to 40
minutes per day, as reported in Table 2. As for the amount of time participants reported
spending on Facebook yesterday, using the same method, the mean time spent on
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Facebook yesterday was 50.27 minutes, with the largest group of respondents reporting
spending 10 or less minutes on Facebook yesterday. There was a strong positive
correlation (r = .809, p < .01) between responses to the time spent using Facebook on
average and yesterday questions.
Table 2
Time Spent on Facebook among Participants
Average Facebook Use
( N = 207) Facebook Use Yesterday
( N = 206)
Minutes n % n %
10 or less 21 10.1 36 17.4
11 to 20 19 9.2 27 13.0
21 to 30 19 9.2 29 14.0
31 to 40 30 14.5 23 11.1
41 to 50 14 6.8 14 6.8
51 to 60 21 10.1 19 9.2
61 to 70 13 6.3 8 3.9
71 to 80 6 2.9 6 2.9
81 to 90 18 8.7 10 4.8
91 to 100 5 2.4 6 2.9
101 to 110 8 3.9 4 1.9
111 to 120 10 4.8 3 1.4
121 to 130 7 3.4 8 3.9
131 to 140 3 1.4 2 1.0
141 to 150 2 1.0 0 0.0
151 to 160 2 1.0 0 0.0
161 to 170 1 0.5 1 0.5
171 to 180 4 1.9 3 1.4
More than 180 4 1.9 7 3.4 Note. Totals of percentages are not 100 for every characteristic because of rounding.
For number of times Facebook was checked on average, based on the mean of the
range, participants reported checking Facebook an average of 8.59 times per day, and the
highest number of respondents reported checking Facebook between three and four times
per day. As reported in Table 3, the mean of participants reported checking Facebook
7.48 times yesterday, with the largest group of respondents reporting checking Facebook
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three to four times yesterday. There was a strong positive correlation between responses
of number of times participants checked Facebook on average and yesterday (r = .841, p
< .01). There was a moderate correlation between the average amount of time
respondents spent on Facebook and the average number of times they checked Facebook
(r = .483, p < .01). A moderate positive correlation existed between amount of time
participants spent on Facebook yesterday and number of times they checked Facebook
yesterday (r = .571, p < .01).
Table 3
Number of Times Facebook was Checked among Participants ( N = 207)
Average Facebook Checks Facebook Checks Yesterday
Times n % n %
Less than 1 5 2.4 14 6.8
1 to 2 30 14.5 36 17.4
3 to 4 38 18.4 42 20.3
5 to 6 31 15.0 34 16.4
7 to 8 25 12.1 16 7.7
9 to 10 23 11.2 17 8.2
11 to 12 12 5.8 10 4.8
13 to 14 5 2.4 10 4.8
15 to 16 10 4.9 4 1.9
17 to 18 2 1.0 3 1.4
19 to 20 9 4.4 9 4.3
21 to 22 3 1.5 2 1.0
23 to 24 2 1.0 0 0.0
25 to 26 4 1.9 2 1.0
27 to 28 2 1.0 1 0.5
29 to 30 0 0.0 2 1.0
More than 30 6 2.9 5 2.4 Note. Totals of percentages are not 100 for every characteristic because of rounding.
For the behavioral measurement of Facebook use, the online survey included 14
questions regarding the frequency of conducting different individual activities.
Responses ranged from „very frequently,‟ or 100% of the time the participant logged in to
Facebook, which received a score of five, to „never,‟ which received a score of one. This
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section reports the participants‟ responses to these questions, to describe the individual
behavior of Facebook use among respondents. The most common activities that
participants conducted were: commenting on content, checking to see what their friends
were up to, and viewing photos. Nearly half of the respondents reported that they
performed these activities somewhat or very frequently when they logged in to Facebook.
The least common Facebook activities among participants were creating or RSVPing to
events, playing games, posting videos, and tagging videos. More than 77% of
respondents reported rarely or never performing these activities. Table 4 reports the
responses to all Facebook activities.
Table 4
Frequency of Performing Facebook Activities among Participants Variable M SD n %
Posting Status Updates ( N = 207) 2.62 0.93
Very Frequently (100% of the time) 6 2.9
Somewhat Frequently (75% of the time) 27 13.0
Sometimes (50% of the time) 78 37.7
Rarely (25% of the time) 75 36.2
Never 21 10.1
Sharing Links ( N = 206) 2.33 1.01
Very Frequently (100% of the time) 4 1.9 Somewhat Frequently (75% of the time) 24 11.6
Sometimes (50% of the time) 54 26.1
Rarely (25% of the time) 79 38.2
Never 45 21.7
Sending Private Messages ( N = 206) 2.70 1.02
Very Frequently (100% of the time) 13 6.3 Somewhat Frequently (75% of the time) 31 15.0
Sometimes (50% of the time) 58 28.0
Rarely (25% of the time) 89 43.0
Never 15 7.2
Commenting on Content ( N = 207) 3.35 1.03
Very Frequently (100% of the time) 29 14.0
Somewhat Frequently (75% of the time) 62 30.0
Sometimes (50% of the time) 78 37.7
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Variable M SD n %
Commenting on Content ( N = 207) 3.35 1.03
Rarely (25% of the time) 29 14.0
Never 9 4.3
Chatting on Facebook Chat ( N = 203) 2.67 1.12
Very Frequently (100% of the time) 10 4.8 Somewhat Frequently (75% of the time) 43 20.8
Sometimes (50% of the time) 52 25.1
Rarely (25% of the time) 67 32.4
Never 31 15.0
Checking to See What Somebody is Up To ( N = 205) 3.51 1.17
Very Frequently (100% of the time) 50 24.2
Somewhat Frequently (75% of the time) 59 28.5 Sometimes (50% of the time) 53 25.6
Rarely (25% of the time) 32 15.5
Never 11 5.3
Creating or RSVPing to Events ( N = 205) 2.02 0.91
Very Frequently (100% of the time) 3 1.4 Somewhat Frequently (75% of the time) 12 5.8
Sometimes (50% of the time) 32 15.5
Rarely (25% of the time) 97 46.9
Never 61 29.5
Playing Games ( N = 205) 1.57 1.03
Very Frequently (100% of the time) 7 3.4 Somewhat Frequently (75% of the time) 10 4.8
Sometimes (50% of the time) 12 5.8
Rarely (25% of the time) 35 16.9
Never 141 68.1
Posting Photos ( N = 206) 2.79 1.07
Very Frequently (100% of the time) 15 7.2 Somewhat Frequently (75% of the time) 36 17.4
Sometimes (50% of the time) 64 30.9
Rarely (25% of the time) 72 34.8
Never 19 9.2
Tagging Photos ( N = 205) 2.51 1.12
Very Frequently (100% of the time) 14 6.8
Somewhat Frequently (75% of the time) 25 12.1
Sometimes (50% of the time) 49 23.7
Rarely (25% of the time) 81 39.1
Never 36 17.4
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Variable M SD n %
Viewing Photos ( N = 205) 3.58 1.06
Very Frequently (100% of the time) 39 18.8 Somewhat Frequently (75% of the time) 82 39.6
Sometimes (50% of the time) 51 24.6
Rarely (25% of the time) 24 11.6
Never 9 4.3
Posting Videos ( N = 204) 1.80 0.92
Very Frequently (100% of the time) 4 1.9 Somewhat Frequently (75% of the time) 5 2.4
Sometimes (50% of the time) 30 14.5
Rarely (25% of the time) 73 35.3
Never 92 44.4
Tagging Videos ( N = 204) 1.63 0.88
Very Frequently (100% of the time) 4 1.9 Somewhat Frequently (75% of the time) 4 1.9
Sometimes (50% of the time) 19 9.2
Rarely (25% of the time) 63 30.4
Never 114 55.1
Viewing Videos ( N = 205) 2.34 1.04
Very Frequently (100% of the time) 7 3.4 Somewhat Frequently (75% of the time) 19 9.2
Sometimes (50% of the time) 58 28.0
Rarely (25% of the time) 73 35.3
Never 48 23.2
Note. Totals of percentages are not 100 for every characteristic because of rounding.
Involvement
For the first measure of co-curricular involvement, the researcher asked
respondents to report how many hours per week they typically spent involved in activities
outside of the classroom. This section reports the participants‟ responses to demonstrate
the overall amount of time reported participating in co-curricular activities. Table 5
reports that almost half of participants reported spending between 1-5 hours per week
involved in co-curricular activities, with zero hours per week being the second-highest
ranking response. Based on the mean of the range of response options, the average
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amount of time that participants spent involved in co-curricular activities was 6.27 hours
per week.
Table 5
Involvement in Activities Outside of the Classroom among Participants ( N = 207) Variable n %
0 Hours per week 33 15.9
1 – 5 Hours per week 96 46.4
6 – 10 Hours per week 31 15.0
11 – 15 Hours per week 27 13.0
16 – 20 Hours per week 12 5.8
21 – 25 Hours per week 6 2.9
26 – 30 Hours per week 1 0.5
More than 30 Hours per week 1 0.5
Note. Totals of percentages are not 100 for every characteristic because of rounding.
Participants answered questions from two scales of the CSEQ to report their
behavior of co-curricular involvement. All CSEQ response options ranged from „very
often,‟ which received four points, to „never,‟ which received one point. To determine
the total CSEQ score and scores on each of the two subscales, the researcher averaged the
total CSEQ scores, and the presented score represents the mean of the responses.
Therefore, possible scores for the CSEQ scales and total score could range from one to
four. Scores on the Campus Facilities scale ranged from one to 3.5, the Clubs and
Organizations scale scores ranged from one to four, and the total CSEQ scores ranged
from one to 3.54. Overall, the average response to CSEQ items was 1.99, which is
equivalent to a response of „occasionally,‟ or that the mean of respondents occasionally
used campus resources and participated in campus activities.
The correlation between hours per week involved in co-curricular activities and
total CSEQ score was r = .536 (p < .01), the Campus Facilities scale was r = .454 (p <
.01), and the Clubs and Organizations scale was r = .458 (p < .01). The Campus
Facilities and Clubs and Organizations scales were also moderately correlated at r = .444
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(p < .01). Additionally, there was a strong internal correlation within each of the scales
and among all items, demonstrated by Cronbach‟s Alpha scores of .73 to .84, showing a
strong consistency within the measures of the behavior of involvement. The most
common CSEQ behaviors that participants reported performing were meeting students at
a campus location, using campus recreational facilities, and attending a meeting of a
campus club or organization. Over 40% of the participants reported that they performed
these activities often or very often. Table 6 reports means, standard deviations, and alpha
coefficients of the CSEQ measures.
Table 6
Involvement Response among Participants ( N = 207)
Alpha Coefficient Mean Response Standard Deviation
Campus Facilities 0.73 2.07 0.57
Clubs & Organizations 0.84 1.88 0.77
CSEQ 0.82 1.99 0.55
Primary Analysis
The primary analyses for this study employed various correlations. The
researcher conducted analyses starting from the broadest constructs of the correlation
between the quantity of involvement and Facebook use. Then correlations were
calculated between more specific items, such as Facebook behaviors and the CSEQ
scores. This section will report these findings, and highlight the statistically significant
and noteworthy relationships.
Correlations between Facebook Use and Involvement
The first correlations that the researcher calculated answered the first research
question of the relationship between the level of Facebook use and co-curricular
involvement. Level of Facebook use variables included the amount of time spent using
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Facebook on average and yesterday, and the number of times participants checked
Facebook on average and yesterday. Involvement variables included hours per week
spent involved in co-curricular activities, Campus Facilities scale score, Clubs and
Organizations scale score, and total CSEQ score. Table 7 reports this correlation matrix.
The most noteworthy correlation this analysis found was a relationship between hours per
week of co-curricular involvement and amount of time spent on Facebook yesterday,
which had a weak, but statistically significant correlation of r = .137 (p < .05).
Table 7
Pearson’s r Correlations between Level of Facebook Use and Involvement Measures
( N = 207) Variables
1 2 3 4
1. Hours of Involvement –
2. CSEQ Total Score 0.536** –
3. CF Score 0.454** 0.876** –
4. C&O Score 0.458** 0.821** 0.444** –
5. FBAvg 0.097 -0.007 -0.016 0.005
6. FBYest 0.137* -0.010 -0.061 0.053
7. FBCheckAvg 0.006 -0.011 -0.016 -0.002
8. FBCheckYest 0.055 0.014 -0.023 0.053
Note. CF = Campus Facilities Scale; C&O = Clubs and Organizations Scale; FBAvg = amount of time
spent on Facebook on average; FBYest = amount of time spent on Facebook yesterday; FBCheckAvg =
number of times Facebook was checked on average; FBChecYest = number of times Facebook was
checked yesterday.
* p < .05. ** p < .01.
The next set of correlations answered the second research question of the
relationship between the nature of Facebook use and co-curricular involvement. Since
the Facebook activity scales are non-intervallic and the responses were not normally
distributed, a Spearman‟s rho correlation was calculated between the four involvement
scores and each of the Facebook activities, reported in Table 8. There were several
statistically significant, but weak correlations. The activities that did not have a
statistically significant correlation with an involvement score were posting status updates,
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sharing links, commenting on content, checking to see what someone was up to, playing
Facebook games, and posting videos. The only negative correlation was between
viewing videos and the Clubs and Organizations scale score.
There was a positive correlation between creating or RSVPing to events and all
measures of involvement: hours per week of involvement (rs = .232, p < .01), total CSEQ
score (rs = .242, p < .01), Campus Facilities scale (rs = .164, p < .05), and Clubs and
Organizations scale (rs = .223, p < .01). A correlation also existed between sending
private messages and the total CSEQ score (rs = .148, p < .05). There was a relationship
between chatting on Facebook Chat and hours per week of involvement (rs = .182, p <
.01), total CSEQ score (rs = .217, p < .01), and the Campus Facilities scale (rs = .214, p <
.01). The correlation between posting photos and the Campus Facilities scale was rs =
.141 (p < .05). Tagging photos was correlated to the total CSEQ score (rs = .195, p <
.01) and the Campus Facilities scale (rs = .188, p < .01). There was a correlation between
tagging videos and the total CSEQ score (rs = .170, p < .05) and the Campus Facilities
scale (rs = .157, p < .05). Finally, viewing videos was positively correlated with the
Campus Facilities scale (rs = .186, p < .01).
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Table 8
Spearman’s rho Correlations between Facebook Activities and Involvement Measures
(N = 207)
Hours CSEQ CF C&O
1. FB Status 0.016 0.083 0.064 0.106
2. FB Share 0.087 0.101 0.062 0.131
3. FB Message 0.127 0.148* 0.124 0.129
4. FB Comment 0.111 0.112 0.128 0.069
5. FB Chat 0.182** 0.217** 0.214** 0.118
6. FB Checking Up 0.051 0.068 0.046 0.050
7. FB Event 0.232** 0.242** 0.164* 0.223**
8. FB Game 0.060 0.072 0.037 0.110
9. FB Photo-Post 0.019 0.128 0.141* 0.025
10. FB Photo-Tag 0.104 0.195** 0.188** 0.114
11. FB Photo-View 0.120 0.114 0.119 0.048
12. FB Video-Post 0.090 0.140 0.137 0.114
13. FB Video-Tag 0.100 0.170* 0.157* 0.140*
14. FB Video-View 0.060 0.120 0.186** -0.005
Note. Hours = hours of involvement; CSEQ = total CSEQ score; CF = Campus Facilities Scale; C&O =
Clubs and Organizations Scale; Status = posting status updates; Share = sharing links; Message = sending
private messages; Comment = commenting (on statuses, wall posts, pictures, etc.); Chat = chatting on
Facebook Chat; Checking Up = checking to see what someone is up to; Event = creating or RSVPing to
events; Game = playing games (FarmVille, MafiaWars, etc.); Photo-Post = posting photos; Photo-Tag =
tagging photos; Photo-View = viewing photos; Video-Post = posting videos; Video-Tag = tagging videos;
Video-View = viewing videos.
In an attempt to meaningfully group the Facebook activity items and identify
relationships, the researcher used factor analysis to create groupings of items. This
grouping was suggested by Junco‟s (2012) study, where two different sets of activities
were found to have opposite effects on the dependent variable. Junco‟s (2012) groupings
were not created statistically through factor analysis, but separated by the author based on
whether the activity was positively or negatively related to the dependent variable. The
groupings separated some activities into either the communicative or non-communicative
category. The specific approach of conducting factor analysis for the current study was
suggested by R. Junco (personal communication, March 2, 2012) to determine
appropriate groupings for the results of this sample, since no prior scales had been
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established statistically by Junco (2012). The following section will report the procedure
of conducting factor analysis to create scales for this study.
Factor analysis. The 14 Facebook items were subjected to principal component
analysis (PCA) using SPSS version 19. Prior to performing PCA, the suitability of data
for factor analysis was assessed. Inspection of the correlation matrix revealed the
presence of many coefficients of .3 and above (see Appendix H for correlation matrix).
The Kaiser-Meyer-Olkin value was .788, exceeding the recommended value of .6 and
Bartlett‟s Test of Sphericity reached statistical significance, supporting the factorability
of the correlation matrix.
Principal component analysis revealed the presence of four components with
eigenvalues exceeding 1, explaining 36.14%, 11.69%, 9.6%, and 8.15% of the variance
respectively. An inspection of the screeplot revealed a clear break after the third
component. Using Catell‟s scree test, it was decided to retain three components for
further investigation. This was further supported by the results of Parallel Analysis,
which showed only three components with eigenvalues exceeding the corresponding
criterion values for a randomly generated data matrix of the same size. Additionally,
based on the items grouped in each component, the three groupings were the most
conceptually meaningful to the researcher.
The three-component solution explained a total of 57.45% of the variance, with
Component 1 contributing 36.14%, Component 2 contributing 11.69%, and Component 3
contributing 9.6%. To aid in the interpretation of these three components, oblimin
rotation was performed. The rotated solution revealed the presence of simple structure,
with all three components showing a number of strong loadings and all variables loading
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substantially on only one component, as displayed in Table 9. There were moderate
positive correlations between the three factors. The correlations for Factors 1 and 2 was r
= .444 (p < .01), Factors 1 and 3 was r = .469 (p < .01), and Factors 2 and 3 was r = .476
(p < .01). The results of this analysis support the use of the three factors as separate
scales.
Table 9
Rotated Component Matrix
Component
Variable 1 2 3
Posting status updated .216 .652* .106
Sharing links .026 .583* .341
Sending private messages .007 .743* .151
Commenting on content .571* .554 -.069
Chatting on Facebook Chat .213 .680* .120
Checking to see what someone is up to .646* .036 .124
Creating or RSVPing to events .204 .508* .128
Playing games -.221 .323 .266
Posting photos .810* .138 .217
Tagging photos .774* .171 .267
Viewing photos .786* .181 .147
Posting videos .223 .163 .878*
Tagging videos .209 .258 .843*
Viewing videos .311 .196 .718*
Note. * = Item grouped in this component
The first scale, named by the researcher as the Facebook Interactive Scale,
contained the following items: posting status updates, sharing links, sending private
messages, chatting of Facebook Chat, and creating or RSVPing to events, and had a
reliability of α = .68. The second scale, named the Facebook Passive Scale, contained
commenting on content, checking to see what someone was up to, posting photos,
tagging photos, and viewing photos, and had a reliability of α = .82. The third scale,
named the Facebook Video Scale, contained posting videos, tagging videos, and
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watching videos, and had a reliability of α = .86.
After creating these three scales, the researcher analyzed them using a similar
method to the individual Facebook activities. Since intervallic scale scores were
produced, Pearson‟s r was used to correlate each scale to the four measures of
involvement. There were positive correlations between all scales and measures of
involvement, and most correlations were statistically significant, as reported in Table 10.
Table 10
Correlations between Facebook Activity Scales and Involvement Measures ( N = 207)
Hours CSEQ CF C&O
FB Interactive Scale 0.199** 0.214** 0.173** 0.190**
FB Passive Scale 0.095 0.161* 0.162* 0.107
FB Video Scale 0.106 0.155* 0.184** 0.065
Note. Hours = hours of involvement; CSEQ = total CSEQ score; CF = Campus Facilities Scale; C&O =
Clubs and Organizations Scale.
* p < .05. ** p < .01.
Controlling for Demographics
The researcher sought to determine if any demographic variables were influential
to the correlations described in the primary analysis. This was done by calculating the
correlations between the demographic variables, each of the four measures of
involvement, time spent on Facebook yesterday and on average, number of times
Facebook was checked yesterday and on average, and each of the three Facebook scales.
The strongest correlation found in this analysis was r = .292 (p < .01) between gender
identity and the Facebook Passive scale. Due to this correlation, a partial correlation was
run between the Facebook Passive scale and each of the four measures of involvement,
controlling for gender. No meaningful differences in the correlations were found, so
there was no evidence to suggest a need to control for gender. As a result, it was
determined that it was not necessary to control for any demographic variables in the
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primary analysis.
Conclusion
This chapter presented the findings of a quantitative research study on the
correlation between undergraduate college student Facebook use and co-curricular
involvement. Data from 207 participants who had active Facebook accounts and
completed an online survey were analyzed. The online survey contained a Facebook
instrument created by Junco (2012), items from the CSEQ (Pace & Kuh, 1998), and a
series of demographic questions. This chapter presented descriptive responses of the
participants, as well as the correlation between the constructs of Facebook use and co-
curricular involvement. In the next chapter, a discussion of these findings will be
presented, as well as limitations of the study and implications for theory, practice, and
future research.
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CHAPTER FIVE
DISCUSSION AND IMPLICATIONS
The past four chapters of this thesis have described a study of the correlation
between undergraduate college student Facebook use and co-curricular involvement
within the campus community. Facebook is the largest social media service, and is an
online tool where users generate profiles to connect and stay in touch with friends and
acquaintances, among other things. A vast majority of college students use Facebook
(Junco, 2012; S. Smith & Caruso, 2010; Smith et al., 2011), and they spend a great deal
of time using the service (Junco, 2012).
Astin (1984) argued that student affairs professionals should work to increase the
amount of physical and psychological time and energy that students invest in
intentionally educational and developmental activities. Within the context of
involvement theory, the amount of time and energy that students spend using Facebook
may be negatively correlated to the developmental outcomes associated with
involvement. Previous research has identified many negative (Gemmill & Peterson,
2006; Jodan et al., 2011; Saculla & Derryberry, 2011) and positive (Ellison et al., 2007;
Manago et al., 2012; Martinez Aleman & Wartman, 2009) outcomes directly associated
with Facebook use among college students.
This chapter will begin with a discussion of the findings, including an answer to
the research questions of this study. The first research question of this study was: Is there
a correlation between the level of undergraduate college students‟ Facebook use and their
co-curricular involvement within the campus community? And the second research
question was: Is there a correlation between the nature of undergraduate college students‟
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Facebook use and their co-curricular involvement within the campus community? Then,
this chapter will provide an interpretation of the findings described in the previous
chapter, limitations of the study, and implications for theory, research, and practice.
Discussion of the Findings
This section will discuss the findings and important relationships in response to
the research questions for this study. It will begin with an introduction and interpretation
of the preliminary and descriptive findings, including the level and nature of Facebook
use and co-curricular involvement among participants. The primary analysis will discuss
the statistically significant relationships between Facebook use and co-curricular
involvement, as well as potential meanings for the results.
Preliminary Findings
The initial descriptive analysis of the study revealed insights about the nature and
level of Facebook use and co-curricular involvement within the participants of this study.
This section will examine the noteworthy findings from both instruments. The researcher
will analyze the scores and responses of the participants, and provide a summary of
potential behaviors of respondents.
Facebook use. As Chapter Three introduced, the participants completed Junco‟s
(2012) Facebook instrument as part of the study. This instrument is composed of two
sections. The first measured overall level of Facebook use, and the second measured the
frequency of conducting specific Facebook behaviors. To score the first half, the
researcher used the mean of the range of each option to create the mean score for each
response, and represented participants through the mean of all responses. Since each of
the Facebook activities listed in the second half of the instrument represent individual
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behaviors, this section reports responses individually based on option response rate to
demonstrate the types of behaviors exhibited among respondents.
Starting with average amount of time spent on Facebook, the mean response that
participants spent an average of 61.26 minutes per day is notably lower than the most
recent reporting of another single-institution study, which found an average usage of
101.9 minutes per day (Junco, 2012). As for time spent on Facebook yesterday, the mean
of the responses was 50.27 minutes. For the number of times that participants checked
Facebook each day on average, the mean of the responses was 8.59, compared with 7.48
times Facebook was checked yesterday.
When considering these responses, one possible pattern that emerges is that on
average students spent approximately six to seven minutes using the service each time
they logged on to Facebook. Another possible pattern is that participants used Facebook
in great depth only once or twice each day, and then checked in for updates for a few
moments at a time throughout the rest of the day. These possibilities are especially likely
if participants used Facebook from a mobile device during these shorter periods, which is
supported by Barkhuus and Tashiro‟s (2010) finding that students used Facebook in short
bursts when on a mobile device.
There were three Facebook activities that most participants reported engaging in
often or very often: commenting on content, checking to see what friends were up to, and
viewing photos. In light of how Facebook works, this is not surprising since these three
activities can all be conducted from the Facebook News Feed, which is the home page
that users arrive on when logging in to the service. This page is a constantly updating
feed where users can see all of their friends‟ most current activities, including embedded
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photos, while having the ability to comment on information without leaving the feed. By
simply logging in to Facebook, users are automatically checking to see what their friends
are up to and viewing their photos, and do not have to take additional steps to comment
on the content they are viewing. These three activities are also easily accessed from most
mobile platforms, including cell phones. While this study did not distinguish between the
device on which Facebook was used, the high response rate to these three activities could
result from the fact that respondents can perform these activities very quickly from any
location, and are therefore the most common ways they use Facebook.
Among participants of this study, 21.3% and 25.6% reported sending private
messages or chatting on Facebook Chat somewhat to very frequently, respectively.
Manago et al. (2012) proposed that students who spend more time sending private
messages and chatting on Facebook Chat – activities with statistically significant positive
correlations among respondents of the current study (rs = .515, p < .01) – have a social
network composed of closer, more intimate friends. Fifteen percent of the respondents of
the study reported posting status updates somewhat to very frequently. Manago et al.
(2012) reported that students who post status updates more frequently believe that they
have a large committed audience who read and follow their updates. In their study, there
was a link between this perception and higher levels of narcissism, but also increased
levels of life satisfaction, perceived social support, and self-esteem.
Among respondents in the current study, over 76% reported that they rarely or
never created or RSVPed to events. According to Martinez Aleman and Wartman
(2009), one of the most effective ways to increase likelihood of student involvement in
co-curricular activities through Facebook use is by encouraging them to create and RSVP
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to events. Of participants, 85% reported rarely or never playing Facebook games, an
activity that Junco (2012) stated was one of the most physically and socially isolating
Facebook behaviors. Thus, of the activities that previous researchers identified as having
the most substantial positive or negative impact on involvement, most participants of the
current study did not report performing these activities.
Co-curricular involvement. As introduced in Chapter Three, two scales and one
modified question from the CSEQ were used to measure co-curricular involvement in this
study. The two scales used in the survey were the Campus Facilities scale and the Clubs
and Organizations scale. To score the scales, the average of all responses were taken,
where a response of „very often‟ received four points, and a response of „never‟ received
one point. The modified question from the original CSEQ measured the overall hours per
week of co-curricular involvement. The responses to this question were reported to
demonstrate participants‟ amount of time spent involved in co-curricular activities each
week.
The moderate positive correlations between measures of involvement reinforce
the tools utilized to quantify the construct of involvement for this study. These
correlations suggest that those who spent more time involved in activities outside of the
classroom were equally more likely to spend more time taking advantage of the physical
campus as well as opportunities to participate in campus activities. It should be noted
that over 86% of participants lived on campus or within 5 miles of campus. There were
three behaviors on the CSEQ that 40% or more of the participants reported performing
often or very often: meeting students at a campus location, using campus recreational
facilities, and attending a meeting of a campus club or organization. These three most
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common activities represent physically spending time on campus, but do not represent
higher levels of involvement such as holding a leadership role, meeting with a faculty or
staff advisor, or maintaining a regular workout routine.
Among the participants of this study, 7.7% received a total CSEQ score which
reflected being involved on campus often or very often, and 15.5% of participants of the
current study reported playing a team sport often or very often. Over 27% of respondents
reported working on a campus committee, student organization, or project often or very
often; and 21.4% reported providing leadership for a campus organization often or very
often. Other noteworthy activities of co-curricular involvement, including the percentage
of students who reported performing them often or very often, are: meeting another
student at a campus location, 44.9%; attending a cultural or social event, 22.2%; going to
a lecture or panel discussion, 12.1%; and all activities from the Clubs and Organization
scale, 13%. Many studies have described important positive developmental outcomes
including direct links with student satisfaction, psychosocial and cognitive development,
and persistence to graduation (Astin, 1993; Kuh, 1995; Pascarella & Terenzini, 2005),
associated with co-curricular involvement, which is measured by these CSEQ items.
The representation of this population‟s co-curricular involvement is skewed
toward the lower end of the involvement spectrum. Moreover, these scores represent a
lower level of involvement than the norms determined by a multi-institutional study of
the fourth edition of the CSEQ (Gonyea et al., 2003). The national norm for the Campus
Facilities scale is an average response of 2.65, compared to 2.07 in the current study; for
the Clubs and Organization scale is a 2.42, compared to 1.88 in the current study; and the
overall average score for the two scales is 2.54, compared to 1.99 in the current study.
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However, based on the amount of hours per week that participants reported being
involved in activities outside of the classroom, respondents of this study reported
spending more time involved than students in the campus population as indicated by the
2011 NSSE results at the host institution. In response to a very similar question on the
NSSE survey with the exact same response options, 71% of first year participants and
70% of senior participants of the 2011 study reported spending 0-5 hours per week
involved in activities outside of the classroom, compared to only 62.3% of participants in
the current study (Indiana University of Pennsylvania Institutional Research, Planning,
and Assessment, 2011). The participants of this study reported being less involved in co-
curricular activities than the national norm for the two CSEQ scales used in this study,
but reported spending more time involved in co-curricular activities each week than the
general student population at the institution.
Primary Analysis
This section will discuss the correlations between the constructs of Facebook use
and co-curricular involvement and attempt to provide an answer to the research questions
of this thesis. The researcher will answer the first research question through a discussion
of the correlations between the level of Facebook use and co-curricular involvement,
followed by answering the second research question through a discussion of the
relationship between the nature of Facebook use, or scores of the Facebook activity
scales, and co-curricular involvement. Statistically significant findings will be
highlighted, and potential sources of the relationships will be offered.
In response to the first research question for this study, weak correlations were
found between the level of Facebook use and co-curricular involvement among
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respondents. The results showed a statistically significant, but weak, positive correlation
between time spent on Facebook yesterday and hours per week involved in co-curricular
activities (r = .137, p < .01). There were no other statistically significant relationships
between the level of Facebook use and co-curricular involvement.
There are several potential meanings for this relationship, which are evident from
the descriptive analysis of the findings as well as the environment of the host institution
of the study. The first explanation of the statistically significant, weak positive
correlation between time spent on Facebook yesterday and hours per week of co-
curricular involvement is that the two constructs are not correlated in any meaningful
way. These results could indicate that Facebook use and co-curricular involvement are
unrelated, with neither construct having an effect on the other.
This low correlation could also be due to the nature of time spent on Facebook
among respondents, primarily two of the patterns that emerged when considering the
relationship between time spent on Facebook and number of times Facebook was
checked. If participants spent no more than several minutes at a time using the service
multiple times throughout the day, or for one large period of time followed by many
small periods of time, the low correlation could be a result of students using Facebook as
a way to fill spare windows of time. Participants could have been using Facebook from a
mobile device in these ways, perhaps as a way to pass time in short bursts, as described
by Barkhuus and Tashiro (2010). Examples of these types of time periods are the several
minutes waiting before a meeting begins, between classes, or immediately before or after
a meal. In this way, neither Facebook nor involvement would act as mutually exclusive
or influencing activities, they would simply occur in different blocks of available time.
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While up to one hour each day dedicated to using Facebook is a considerable time
commitment, these potential patterns of Facebook use could be less perilous within the
context of involvement theory.
Another explanation could come from the low variance in the level of
involvement among participants, with most grouped around the lower levels of
involvement. If overall responses represented a broader range of involvement, then the
findings might have potentially revealed a stronger correlation between the constructs.
Since the involvement instrument measured a variety of behaviors, from using a study lab
to serving on a committee, opportunities for participation are not limited since these
activities are simultaneously equally available to all students within the campus.
Another important aspect of the correlation between Facebook use and co-
curricular involvement is the environment of the host institution of the study. At the time
of the study, there was no formal intentional approach to using Facebook within most
levels of the institution. This lack of intentionality could have some responsibility for the
weak correlations of this study.
Despite the fact that most students who participated in this study were generally
uninvolved, most used Facebook for a considerable amount of time. Since Facebook
does not have a negative or positive role in student involvement, one way that Facebook
could be playing a role in students‟ college experience is as a shared unifying experience
and common area to discuss daily happenings with their peers. In other words, Facebook
could be serving as the social backchannel of the college experience. A backchannel is a
real-time online space where individuals can document, discuss, and add to something
occurring in a physical space. In this way, Facebook may be acting as the online space
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for college students to keep track of and share their day-to-day lives. Facebook provides
the means to connect and stay in touch with hundreds of individuals across the world, so
college students may be using it as the platform for the social commentary of their
college experience with their Facebook friends. For the purpose of this study, this
conclusion would support the finding that Facebook use is not directly related to physical
involvement within the campus, but rather that it is the online background where students
go to discuss those things that happen within the physical campus.
Another set of correlations were run to answer the second research question, or if
there is a correlation between the nature of Facebook use and co-curricular involvement
in campus activities. There were correlations found between the scores of each of the
Facebook scales created by factor analysis described in Chapter Four, and co-curricular
involvement. All relationships were positive, and most were statistically significant. The
Interactive scale had the strongest statistically significant, though still relatively weak,
relationship with measures of involvement, with its relationship to the total CSEQ score
being the highest (r = .214, p < .01). The Interactive and Passive scales are similar to
those identified by Junco (2012) in his communicative and non-communicative items,
though Junco (2012) did not determine these groupings through factor analysis and there
are some items from the current study that do not follow that grouping. Also, while
Junco‟s (2012) items were either positively or negatively related to the measure of
engagement, items in the Interactive or Passive scale of the current study varied only in
the strength of their positive correlation.
Items on the Passive scale include commenting on content, checking to see what
friends are up to, and posting, tagging, and viewing photos. The first two, commenting
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on content and checking to see what friends are up to, are activities that can be conducted
from the News Feed with no additional steps necessary (Junco, 2012). There is also no
indication in previous research that there is a connection between these activities and
social relationships or offline involvement. The last three items, posting, tagging, and
viewing photos, are typically both physically and socially isolating behaviors. While the
photos themselves may imply some social connections – being with people to take the
pictures, including friends in them, and reminiscing about an event – the behavior of
uploading and tagging photos typically occurs alone from a laptop or desktop computer
(Junco, 2012).
The items of the Interactive scale, on the other hand, are activities that typically
involve more intentional effort and interaction with others (Junco, 2012). Items on this
scale are posting status updates, sharing links, creating or RSVPing to events, sending
private messages, and chatting on Facebook Chat. Previous research has indicated that
Facebook Events is the most effective way to use Facebook to increase co-curricular
involvement (Martinez Aleman & Wartman, 2009). In fact, in the current study creating
or RSVPing to events was the only individual Facebook activity that had a statistically
significant positive relationship with all four measures of involvement. These
correlations were among the strongest throughout this study, with the correlation between
creating or RSVPing to events and total CSEQ score (rs = .242, p < .01) representing this
study‟s strongest relationship between Facebook use and co-curricular involvement.
Additional research has identified that sending private messages and chatting on
Facebook Chat can indicate intimate interpersonal relationships and increased offline
social activities (Manago et al., 2012). Results of the current study are consistent with
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previous research, but suggest a weaker level of correlation, in that those who engage in
these Facebook activities are also somewhat more likely to display a higher level of
involvement within the campus community.
Another approach to the correlation between the items of the Facebook Passive
and Interactive scales to co-curricular involvement can be taken from the perspective of
Strange and Banning‟s (2001) explanation of community. Facebook users who spend
more time performing activities from the Interactive scale can use Facebook more
effectively to invite others to interact with them, or use Facebook as a jumping off point
to get involved within the campus community (Junco, 2012; Manago et al., 2012;
Martinez Aleman & Wartman, 2009). Strange and Banning (2001) introduced the idea of
technology serving as the front porch of the college experience. In this case, Facebook
acts as the front porch, or the place where students connect around the college experience
to reflect, process, and discuss their experiences. Individuals on the porch have the
opportunity to invite others to join them or use the porch as the way in which to join the
outside social world, similar to activities on the Interactive scale. The porch also allows
individuals to choose behaviors more associated with „lurking‟ on the social world
beyond (Strange & Banning, 2001), similar to those who spend more time conducting
behaviors from the Passive scale (Junco, 2012).
Limitations
While this study provided results that revealed several statistically significant
findings, when considering these results the reader should acknowledge a few limitations.
By design, this was a single-institution study. Though each of the instruments has been
used separately in other studies, the results of the participants in this study may not be
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representative of all college students. In some ways, the demographic breakdown of
participants is not representative of the host institution‟s student population as a whole.
While it is close, and representation can potentially be assumed, the group of respondents
is overly representative of women and Caucasians.
A limitation of the measure of involvement is that there are many factors that
influence student involvement on campus. These factors include studying and having a
job, among many others (Astin, 1984; Pace, 1982), though these potentially influential
factors were not measured in the current study. Junco (2012) based part of the Facebook
instrument on measuring individual Facebook activities, but each activity is its own scale
item. This makes scoring difficult since a cumulative score is not generated. While
factor analysis was used for this study to aid in scoring and interpretation of the results, it
did not create the same groupings of activities as identified in Junco‟s (2012) original
study. Additionally, Junco‟s (2012) Facebook instrument is new and lacks a formal
verification of its psychometric properties.
Implications
Considering the findings of this study, as well as potential interpretations of their
meaning, there are a number of implications for the future. From the weak but positive
and statistically significant correlations that existed among respondents, to the different
levels of correlation based on various Facebook activities, potential new directions
emerge. This section will identify implications for future theory, research, and practice.
Implications for Theory
The primary theory that this study utilized was Astin‟s (1984) theory of
involvement. Astin (1985) identified five postulates, and stated that involvement is the
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amount of physical and psychological time and energy that students invest in their
college experience. One aspect of the theory noted that student time and energy are finite
resources, so all of the options for how to spend this time and energy are in direct
competition with each other. Therefore, it is the role of student affairs professionals to
assist college students in choosing to participate in the most intentionally developmental
and educational activities in order to maximize the positive outcomes associated with
involvement (Astin, 1984; Braxton, 2003; Evans et al., 2010).
The findings of the current study suggest that time spent on Facebook is unrelated
to co-curricular involvement. Involvement theory should be re-examined in light of
students‟ online presence through social media, which did not exist during the creation of
the theory. Furthermore, there is the potential that students simultaneously engage in
multiple activities while using Facebook, especially if they are accessing the service from
a mobile device as speculated by Ericson (2011). If this is the case, the ways in which
students spend their time would not necessarily be mutually exclusive, which would
indicate a modification to involvement theory to account for the possibility of
simultaneous activities or multi-tasking. With these findings in mind, it may also be
appropriate to reconsider the characterization of Facebook as a negative use of time
(Foregger, 2008; Gemmill & Peterson, 2006; Junco & Cotten, 2011; Silverman, 2007)
within the context of involvement theory
Implications for Research
In order to determine more generalizable answers to the author‟s research
questions, this study should be conducted at institutions with a more diverse student
population, and among different types of institutions. It is important to measure the
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correlation between Facebook use and co-curricular involvement among diverse student
populations, especially since the participants of this study are overly representative of
Caucasians compared to the general student population, so the findings could vary if
studies specifically target traditionally underrepresented groups of students. When
exploring the effect of demographic variables, it will be useful to conduct a logistical
hierarchical regression in order to gain an understanding of the predictive power of these
variables on the constructs and their correlations.
It is also important to measure this correlation within various institutional settings,
since this relationship may be different based on institutional type as well as the
environment of the institution in regards to how Facebook is utilized and how involved
the student population is in co-curricular activities. As mentioned in the interpretations
of the findings, one aspect that could potentially affect these relationships is the
environment in which the study occurs, specifically how intentional different groups of
the community are in using Facebook to increase involvement. In order to understand
what impact the environment could have, if any, another approach would be to conduct
an environmental assessment among institutions with different levels and types of
intentional Facebook use in addition to administering the instrument used for this study.
One way to increase the effectiveness of the measure of Facebook use could be to
include a way to determine the differences between mobile use and desktop use. While it
is clear that many students use Facebook on their mobile device (S. Smith & Caruso,
2010), only qualitative studies have sought to understand this use as it differs from
traditional Facebook use (Barkhuus & Tashiro, 2010). Another way to improve the
measure of Facebook use would be to create scales to understand how certain groups of
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Facebook activities are related, and create scoring methods for those scales. Moreover,
future research could further examine the distinction between the nature of Facebook use
into the Interactive and Passive scales, and examine the difference between the outcomes
associated with use of items from these different scales.
The strongest determining element of the correlation between Facebook use and
co-curricular involvement in the current study was the way in which students chose to use
Facebook. This is highlighted by the finding that the strongest positive correlation with
measures of involvement was creating or RSVPing to events, followed by the Facebook
Interactive scale, compared to lower positive correlations among items of the Passive and
Video scales. This supports recommendations by Smock et al. (2011), and Junco (2012),
that Facebook should be viewed as a collection of social media tools rather than a single
entity. While Facebook as a whole was largely neutral in the current study, some of its
services were associated with varying levels of positive outcomes. Moving forward,
researchers could focus more on understanding how certain Facebook activities have
varying effects on involvement and developmental outcomes, and how these positive
outcomes could be enhanced.
If Facebook use has a similar quantitative and qualitative definition as
involvement, it would be beneficial to have an instrument that measures and scores
Facebook use as comprehensively as the CSEQ measures involvement. Finally, typical
of most social media platforms, Facebook is constantly changing in dynamic ways, so it
is essential that measures of Facebook use continue to adapt. As more social media
services become popular and intertwined, it will be important to move beyond measuring
Facebook and find a way to comprehensively measure social media usage as a whole.
114
In order to provide more rich and accurate results, it would be beneficial to add a
qualitative component to the study. Ericson (2011) utilized this approach, and simply
asked the research question for the study as an open-ended question at the end of the
survey, which was useful in providing a comprehensive way to measure the relationship
of these variables from the perspective of the participant. Finally, if the goal of co-
curricular involvement is student development, then future research should attempt to
measure the direct relationship between Facebook use and student development. This
can be approached through a similar methodology to that of this study, but using a
different instrument than the CSEQ, such as the Student Development Task and Lifestyle
Assessment (Winston et al., 1999) or similar instruments.
Implications for Practice
The findings from this study indicate that there is no meaningful correlation
between Facebook use and co-curricular involvement. This contradicts recent studies
(Heiberger & Harper, 2008; HERI, 2007; Junco, 2012; Martinez Aleman & Wartman,
2009) in that involvement does not necessarily increase Facebook use, that Facebook
does not decrease involvement, and that Facebook is not intrinsically responsible for an
increase in involvement (Olson & Martin, 2010). This middle ground sends a message to
professionals on both sides of the argument on whether to use Facebook in student
affairs: Among these participants, Facebook use was unrelated to involvement in co-
curricular activities.
The findings from this study do not support the justification of using Facebook to
meet students where they are to increase participation in intentionally developmental
opportunities. Previous research (Heiberger & Harper, 2008; HERI, 2007; Junco, 2012;
115
Martinez Aleman & Wartman, 2009) suggested that student affairs professionals could
potentially look more carefully at the ways in which Facebook is used to interact with
students, and the current study provides a recommendation that specific attention could
be paid to using activities from the Interactive scale. If Facebook is to be used,
intentional time and resources may be dedicated to identify the ways in which to create a
developmentally positive environment within Facebook use, rather than simply using the
service and expecting positive outcomes.
There has been a call to dedicate more resources to professionals‟ use of
Facebook (Junco, 2012; Junco & Chickering, 2010; Olson & Martin, 2010). The finding
that Facebook activities from the Interactive scale are correlated with slightly higher
levels of co-curricular involvement than other activities could be useful in meeting this
call, in order to turn an inherently neutral service into one that promotes involvement,
meeting the calls of Astin (1984), Strange and Banning (2001), and Evans et al. (2010).
Since student affairs professionals have been responsible for increasing co-curricular
involvement for decades (Astin, 1984; Evans et al., 2010) the remaining area for future
improvement would be the ways in which they use Facebook. If there is a low level of
intentional Facebook use, the weak correlation of this study could potentially indicate the
baseline, or intrinsic level of Facebook‟s relationship to involvement. This idea of
Facebook as intrinsically neutral in relation to developmental outcomes is supported by
this author‟s summary of the literature. This summary suggested that many potential
outcomes are primarily guided by the ways in which Facebook is used by individuals,
and not a reflection of the innate qualities of Facebook itself.
If the members of a community use Facebook intentionally and strategically to
116
increase involvement, the relationship between the two might be stronger. When
considering the possible conclusion that no correlation exists between Facebook use and
involvement because of limited intentional use of Facebook to promote co-curricular
involvement, the following perspective emerges as still being relevant. Strange and
Banning (2001) suggested that it should be possible to use technology to promote and
maintain involvement within the community, as well as “improve the effectiveness of
college and university campus learning environments” (p. 198). Since so much time and
energy is already dedicated to increasing involvement, it seems to make sense to match
that level of dedication by using the most current and popular form of communication
technology to do so.
As those within institutions begin developing and implementing these intentional
Facebook strategies to increase involvement, it will be essential to assess the results of
their efforts. A similar approach to the methodology of this study should be used to
evaluate the effect of new interventions of Facebook use. This evaluation is essential to
determine whether Facebook played a role in any outcomes related to involvement, and
can assist in decision-making about the continued allocation of personnel and resources
to such interventions.
Several steps of education have been suggested by previous literature (Junco,
2012; Junco & Chickering, 2010; Olson & Martin, 2010) and have not been discredited
by the results of this study. College students should be educated on the potential positive
and negative outcomes associated with Facebook use, and in ways to use Facebook that
are relatively more productive at increasing involvement and developmental outcomes
when compared to other types of activities. Educational programming can focus on
117
emphasizing ways to engage in activities from the Interactive scale to achieve higher
positive outcomes. As educators, student affairs professionals should potentially also be
informed about these topics. It is not enough to simply understand what Facebook is. A
more sophisticated understanding of Facebook‟s pieces could be beneficial, including
how they function, and the ways in which they are related to involvement and potentially
developmental outcomes in different ways. This will help them become more effective
teachers and role models to their students, and will also assist them in using Facebook in
slightly more effective ways to encourage co-curricular involvement.
One way to begin this process of educating student affairs professionals could be
for graduate preparation programs to include formal or informal training on Facebook
use. This may achieve the previous goal of educating student affairs professionals, and
could also help graduate students as they transition from their roles of using Facebook as
their own social backchannel to finding ways to use it in a professional setting. It should
not be assumed that graduate students would already know how to become positive role
models and productive users of Facebook. This training is also supported by the
ACPA/NASPA professional competency areas for student affairs practitioners (2010) that
identified technology as an underlying thread across all professional competences, which
would include Facebook-specific training to intentionally target graduate students as they
undergo the transition to becoming emerging new professionals.
Even those outside of student affairs could benefit from being educated on this
neutral relationship between Facebook use and co-curricular involvement. Higher
education administrators throughout the institutional community may be included. Astin
(1984) and Braxton (2003) proposed everything that every professional within higher
118
education does has an effect on involvement. These professionals should be trained on
the importance of promoting co-curricular involvement, as well as ways to use Facebook
that are relatively more likely than others to achieve this goal. This will help ensure that
all individuals within the institution who may be responsible for coordinating Facebook
use will understand that there is likely a neutral relationship between the two, with some
activities that are comparatively more highly correlated to positive outcomes than others.
Summary and Conclusion
This chapter discussed the findings of a research study on the relationship
between undergraduate college student Facebook use and co-curricular involvement. The
results showed that there is a statistically significant, but weak, correlation between the
two constructs. Therefore, Facebook use and co-curricular involvement are not strongly
correlated, so neither construct is inherently positive or negative for the other. This
chapter discussed the limitations of this study, as well as implications for theory,
research, and practice based on these findings. This thesis has stated the problem and
significance in understanding if there is a correlation between undergraduate college
students‟ Facebook use and their level of co-curricular involvement within the campus
community. A review of the literature provided a background on college students, the
theory of involvement, and Facebook use among college students. The section that
followed introduced the methodology of the research study used to understand this
relationship. The researcher described and discussed findings from the study, and found
an answer to the research questions among participants of the current study.
These findings showed a neutral relationship between Facebook use and co-
curricular involvement. Increasing co-curricular involvement has been a core aspect of
119
effective higher education for decades (Astin, 1984; Evans et al., 2010), and Facebook
use is highly debated within higher education (Junco & Chickering, 2010; Olson &
Martin, 2010). Higher education administrators benefit from understanding the finding in
the current study that there is no fixed relationship between Facebook use and co-
curricular involvement. It is helpful to understand that the current study suggests that
Facebook use is not a significant barrier to involvement. This will assist in making
policies and decisions, as well as providing justification for ways to reallocate resources
for using Facebook to involve students.
Student affairs professionals benefit from these findings in several ways. As
educators, they will be able to help students understand the importance of the difference
between the ways in which they use Facebook, and highlight the activities that have been
found to have a relatively higher level of positive impact on involvement compared to
other types of activities. As practitioners guided by the goal to increase co-curricular
involvement, this study provides specific activities that are slightly more likely to
potentially increase this level of involvement. The findings from this study also can be
useful in creating strategies grounded in research to justify the reallocation of resources
for Facebook use with students, and assess the effect of those strategies after they have
been implemented. Most importantly, these findings are significant because it is only
when student affairs professionals can effectively utilize the modern forms of
communication used by their students that they will achieve the goal originally provided
by Astin (1984) of promoting student development by increasing co-curricular
involvement.
120
References
Aboujaoude, E. (2011). Virtually you: The dangerous powers of the e-personality. New
York, NY: W. W. Norton.
ACPA College Student Educators International, & NASPA Student Affairs
Administrators in Higher Education (2010). ACPA/NASPA Professional Competency
Areas for Student Affairs Practitioners. Retrieved from
http://www.naspa.org/about/boarddocs/710/competencies.pdf
American Council on Education. (1994). The student personnel point of view. In A. L.
Rentz (Ed.), Student affairs: A profession’s heritage (2nd ed., pp. 108-123).
Lanham, MD: University Press of America. (Originally published 1949.)
Arroway, P., Davenport, E., Xu, G., & Updegrove, D. (2010). EDUCAUSE core data
service: Fiscal year 2009 summary report. Boulder, CO: EDUCAUSE Center for
Applied Research. Retrieved October 14, 2011, from:
http://www.educause.edu/coredata/reports/2009
Astin, A. W. (1984). Student involvement: A developmental theory for higher education.
Journal of College Student Personnel, 25(4), 518-529.
Astin, A. W. (1993). What matters in college: Four critical years revisited. San
Francisco, CA: Jossey-Bass.
Baird, L. L. (2003). New lessons from research on student outcomes. In S. R. Komives
& D. B. Woodard, Jr. (Eds.), Student services: A handbook for the profession (4th
ed., pp. 595-617). San Francisco, CA: Jossey-Bass.
Barkhuus, L., & Tashiro, J. (2010, April). Student socialization in the age of Facebook.
CHI 2010 Proceedings of the 28th international conference on human factors in
121
computing systems: Organizations and Communities, USA, 133-142.
doi:10.1145/1753326.1753347
Berger, J. B., & Milem, J. F. (1999). The role of student involvement and perceptions of
integration in a causal model of student persistence. Research in Higher
Education, 40, 641-664.
Bergman, S. M., Fearrington, M. E., Davenport, S. W., & Bergman, J. Z. (2010).
Millennials, narcissism, and social networking: What narcissists do on social
networking sites and why. Personal and Individual Differences, 50, 706-711.
doi:10.1016/j.paid.2010.12.022
boyd, d. (2007). Why youth heart social network sites: The role of networked publics in
teenage social life. In D. Buckingham (Ed.), Youth, identity, and digital media
(pp. 119-142). Cambridge, MA: The MIT Press.
Braxton, J. M. (2003). Student success. In S. R. Komives & D. B. Woodard, Jr. (Eds.),
Student services: A handbook for the profession (4th ed., pp. 317-335). San
Francisco, CA: Jossey-Bass.
Braxton, J. M., Hirschy, A. S., & McClendon, S. A. (2004). Understanding and
reducing college student departure. Retrieved from ERIC database. (ED501184)
Broido, E. M. (2004). Understanding diversity in Millennial students. In M. D. Coomes
& R. DeBard (Eds.), Serving the Millennial generation: New directions for
student services, No. 106 (pp. 73-85). San Francisco, CA: Jossey-Bass.
Buechel, E., & Berger, J. (2011). Facebook Therapy? Why do people share self-relevant
content online? Unpublished manuscript, Wharton Marketing Department,
University of Pennsylvania, Philadelphia, Pennsylvania.
122
Buffardi, L. E., & Campbell, W. K. (2008). Narcissism and social networking web sites.
Personality and Social Psychology Bulletin, 34, 1303-1314.
Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in
undergraduate education. American Association of Higher Education, 39(7), 3-7.
Chickering, A. W., & Reisser, L. (1993). Education and identity (2nd ed.). San Francisco,
CA: Jossey-Bass.
Coomes, M. D. (2004). Understanding the historical and cultural influences that shape
generations. In M. D. Coomes & R. DeBard (Eds.), Serving the Millennial
generation: New directions for student services, no. 106 (pp. 17-32). San
Francisco, CA: Jossey-Bass.
Coomes, M. D., & DeBard, R. (2004). A generational approach to understanding
students. In M. D. Coomes & R. DeBard (Eds.) Serving the Millennial
generation: New directions for student services, no. 106 (pp. 5-15). San
Francisco, CA: Jossey-Bass.
Cooper, D. L., Healy, M. A., & Simpson, J. (1994). Student development through
involvement: Specific changes over time. Journal of College Student
Development, 35, 98-102.
Cotton, S. R. (2008). Students‟ technology use and the impacts on well-being. In R.
Junco & D. M. Timm, (Eds.), Using emerging technologies to enhance student
engagement: New directions for student services, no. 124 (pp. 55-70). San
Francisco, CA: Jossey-Bass.
Cross, W. E., Jr. (1995). The psychology of Nigrescence: Revising the Cross model. In
J. G. Ponterotto, J. M. Casas, L. A. Suzuki, & C. M. Alexander (Eds.), Handbook
123
of multicultural counseling (pp. 93-122). Thousand Oaks, CA: Sage.
Davis, T. M., & Murrell, P. H. (1993). Turning teaching into learning: The role of
student responsibility in the collegiate experience. Retrieved from ERIC
database. (ED372703)
Dugan, J. P., Garland, J. L., Jacoby, B., & Gasiorski, A. (2008). Understanding
commuter student self-efficacy for leadership: A within-group analysis. NASPA
Journal, 45, 288-310.
Ellison, N. E., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook „Friends:‟
Social capital and college students‟ use of online social network sites. Journal of
Computer-Mediated Communication, 12, 1143-1168.
Elliott, J. R. (2009). The relationship of involvement in co-curricular programs on
community college student success and development. (Doctoral dissertation).
Retrieved from ProQuest Dissertations and Theses database. (Publication No.
AAT 3369325)
Ericson, B. E. (2011). The relationship between student use of socially interactive
technology and engagement and involvement in the undergraduate experience.
(Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses
database. (Publication No. AAT 3457230)
Evans, N. J., Forney, D. S., Guido, F. M., Patton, L. D., & Renn, K. A. (2010). Student
development in college: Theory, research, and practice (2nd ed.). San Francisco,
CA: Jossey-Bass.
Farquhar, L. K. (2009). Identity negotiation on Facebook.com. (Doctoral dissertation).
Retrieved from ProQuest Dissertations and Theses database. (Publication No.
124
AAT 3368949)
Ferrell, J. D. (2011). A functional role of Facebook: Psychological and social needs.
(Master‟s thesis). Retrieved from ProQuest Dissertations and Theses database.
(Publication No. AAT 1492220)
Foregger, S. K. (2008). Uses and gratifications of Facebook.com. (Doctoral
dissertation). Retrieved from ProQuest Dissertations and Theses database.
(Publication No. AAT 3331906)
Foubert, J. D., & Grainger, L. U. (2006). Effects of involvement in clubs and
organizations on the psychosocial development of first-year and senior college
students. NASPA Journal, 43, 166-182.
Gay, L. R., Mills, G. E., & Airasian, P. (2009). Educational research: Competencies for
analysis and applications (9th ed.). Columbus, OH: Merrill-Prentice Hall.
Gellin, A. (2003). The effect of undergraduate student involvement on critical thinking:
A meta-analysis of the literature 1991-2000. Journal of College Student
Development, 44, 746-762.
Gemmill, E. L., & Peterson, M. (2006). Technology use among college students:
Implications for student affairs professionals. NASPA Journal, 43(2), 280-300.
Gonyea, R. M., Kish, K. A., Kuh, G. D., Muthiah, R. N., & Thomas, A. D. (2003).
College Student Experiences Questionnaire: Norms for the fourth edition.
Bloomington, IN: Indiana University Center for Postsecondary Research, Policy,
and Planning.
Guardia, J. R., & Evans, N. J. (2008). Factors influencing the ethnic identity
development of Latino fraternity members at a Hispanic Serving Institution.
125
Journal of College Student Development, 49, 163-181.
Guiffrida, D. A. (2003). African American student organizations as agents of social
integration. Journal of College Student Development, 44, 304-319.
Hargittai, E. (2009). Digital na(t)ives? Variation in Internet skills and uses among
members of the “Net Generation.” Sociological Inquiry, 80(1), 92-113.
Harper, S. R., & Quaye, S. J. (2007). Student organizations as venues for Black identitty
expression and development among African American male student leaders.
Journal of College Student Development, 48, 127-144.
Heiberger, G., & Harper, R. (2008). Have you Facebooked Astin lately? Using
technology to increase student involvement. In R. Junco & D. M. Timm, (Eds.),
Using emerging technologies to enhance student engagement: New directions for
student services, no. 124 (pp. 19-35). San Francisco, CA: Jossey-Bass.
Higher Education Research Institute (HERI) (2007). College freshmen and online social
networking sites. Retrieved from Higher Education Research Institute‟s website:
http://gseis.ucla.edu/heri/PDFs/pubs/briefs/brief-091107-SocialNetworking.pdf
Horowitz, H. L. (1988). Campus life: Undergraduate cultures from the end of the
eighteenth century to the present. Chicago, IL: University of Chicago Press.
Indiana University of Pennsylvania Institutional Research, Planning, and Assessment.
(2011). National Survey of Student Engagement Frequency Distributions.
Retrieved from: http://www.iup.edu/WorkArea/DownloadAsset.aspx?id=122232
Jordan, A. H., Monin, B., Dweck, C. S., Lovett, B. J., John, O. P., & Gross, J. J. (2011).
Misery has more company than people think: Underestimating the prevalence of
others‟ negative emotions. Personality and Social Psychology Bulletin, 37(1),
126
120-135. doi:10.1177/0146167210390822
Junco, R. (2012). The relationship between frequency of Facebook use, participation in
Facebook activities, and student engagement. Computers and Education, 58(1),
162-171.
Junco, R., & Chickering, A. W. (2010, September/Otober). Civil discourse in the age of
social media. About Campus, 15(4), 12-18.
Junco, R., & Cole-Avent, G. A. (2008). An introduction to technologies commonly used
by college students. In R. Junco & D. M. Timm, (Eds.), Using emerging
technologies to enhance student engagement: New directions for student services,
no. 124 (pp. 3-17). San Francisco, CA: Jossey-Bass.
Junco, R., & Cotton, S. (2011). A decade of distraction? How multitasking affects
student outcomes. Paper presented at A Decade in Internet Time Symposium on
the Dynamics of the Internet and Society, Oxford, United Kingdom. Abstract
retrieved from http://ssrn.com/abstract=1927049
Junco, R., Heiberger, G., & Loken, E. (2010). The effect of Twitter on college student
engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-
132. doi:10.1111/j.1365-2729.2010.00387.x
Junco, R., & Mastrodicasa, J. (2007). Connecting to the net.generation: What higher
education professionals need to know about today’s students. Washington, DC:
National Association of Student Personnel Administrators.
Kuh, G. D. (1995). The other curriculum: Out-of-class experiences associated with
student learning and personal development. The Journal of Higher Education,
66(2), 123-155.
127
Kuh, G. D. (2009). What student affairs professionals need to know about student
engagement. Journal of College Student Development, 50, 683-706.
Love, P. G., & Guthrie, V. L. (1999). Synthesis, assessment, and application. In P. G.
Love & V. L. Guthrie (Eds.), Understanding and applying cognitive development
theory: New directions for student services, no. 88 (pp. 77-93). San Francisco,
CA: Jossey-Bass.
Lowery, J. W. (2004). Student affairs for a new generation. In M. D. Coomes & R.
DeBard (Eds.), Serving the Millennial generation: New directions for student
services, no. 106 (pp. 87-99). San Francisco, CA: Jossey-Bass.
Manago, A. M., Taylor, T., & Greenfield, P. M. (2012). Me and my 400 friends: The
anatomy of college students' Facebook networks, their communication patterns,
and well-being. Developmental Psychology, Advance online publication.
doi:10.1037/a0026338
Martin, L. M. (2000). The relationship of college experiences to psychosocial outcomes
in students. Journal of College Student Development, 41, 294-303.
Martinez Aleman, A. M., & Wartman, K. L. (2009). Online social networking on
campus: Understanding what matters in student culture. New York, NY:
Routledge.
Morgan, T., & Bullen, M. (2011). Digital learners in higher education: A research
project update. Journal of Distance Education, 25(1), n.p. Retrieved November
5, 2011, from http://www.jofde.ca/index.php/jde/article/view/730/1202
Murphy, C. (2010). Residential learning outcomes: Analysis using the College Student
Experiences Questionnaire at a large public research university (Doctoral
128
dissertation). Retrieved from ProQuest Dissertations and Theses database.
(Publication No. AAT 3425639)
Nelson Laird, T. F., & Kuh, G. D. (2005). Student experiences with information
technology and their relationship to other aspects of student engagement.
Research in Higher Education, 46(2), 211-233. doi: 10.1007/s 11162-004-1600-y
O‟Keeffe, G. S., Clarke-Pearson, K., & Council on Communications and Media. (2011).
The impact of social media on children, adolescents, and families. Pediatrics,
127(4), 800-804. doi: 10.1542/peds.2011-0054
Olson, D., & Martin, Q. (2010). Engaging college students through online social
networks. The Journal of Technology in Student Affairs, 11(1), 1-2. Retrieved
from:
http://studentaffairs.com/ejournal/Winter_2010/EngagingCollegeStudents.pdf
Pace, C. R. (1982). Achievement and the quality of student effort (Report No.
ED227101). Washington, DC: National Commission on Excellence in Education.
Pace, C. R. (1983). College student experiences (2nd ed.). Los Angeles, CA: University
of California, Higher Education Research Institute.
Pace, C. R., & Kuh, G. (1998). College Student Experiences Questionnaire, Fourth
Edition. Bloomington, IN: Center for Postsecondary Research and Planning,
Indiana University.
Palfrey, J., & Gasser, U. (2008). Born digital: Understanding the first generation of
digital natives. New York, NY: Basic Books.
Papacharissi, Z., & Mendelson, A. (2011). Toward a new(er) sociability: Uses,
gratifications, and social capital on Facebook. In S. Papathanassopoulos (Ed.)
129
Media perspectives for the 21st century (pp. 212-230). New York, NY:
Routledge.
Pascarella, E. T. (1982). Validation of a theoretical model of college dropouts (Report
No. ED221130). Washington, DC: National Institution of Education.
Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade
of research (2nd ed.). San Francisco, CA: Jossey-Bass.
Pike, G. R., Kuh, G. D., & Gonyea, R. M. (2003). The relationship between institutional
mission and students‟ involvement and educational outcomes. Research in
Higher Education, 44(2), 241-261.
Prensky, M. (2001). Digital natives, digital immigrants. On The Horizon, 9(5), 1-6.
Pope, R. L., Reynolds, A. L., & Mueller, J. A. (2004). Multicultural competence in
student affairs. San Francisco, CA: Jossey-Bass.
Profile of Undergraduate Students, 2007-8 – Almanac of Higher Education – The
Chronicle of Higher Education (2009, August 24). Retrieved from
http://chronicle.com/article/Profile-of-Undergraduate/48078/
Pryor, J. H., DeAngelo, L., Palucki Blake, L., Hurtado, S., & Tran, S. (2011). The
American freshman: National norms fall 2011. Los Angeles: Higher Education
Research Institute, UCLA. Retrieved April 12, 2012 from
http://heri.ucla.edu/PDFs/pubs/TFS/Norms/Monographs/TheAmericanFreshman2
011.pdf
Renn, K. A., & Bilodeau, B. L. (2005). Queer student leaders: An exploratory case study
of identity development and LGBT student involvement at a Midwestern research
university. Journal of Gay and Lesbian Issues in Education, 2(4), 49-71.
130
Roberts, C. D. (2003). Community building and programming. In S. R. Komives & D.
B. Woodard, Jr. (Eds.) Student services: A handbook for the profession (4th
ed., pp. 539-554). San Francisco, CA: Jossey-Bass.
Saculla, M. M., & Derryberry, W. P. (2011). Addressing relationships among moral
judgment development, narcissism, and electronic media and communication
devices. Unpublished manuscript, Department of Psychology, Western Kentucky
University, Bowling Green, Kentucky.
Silverman, S. C. (2007). Creating community online: The effects of online social
networking communities on college students’ experiences. How can student
affairs professionals best respond to this emergent phenomenon. (Doctoral
dissertation). Retrieved from ProQuest Dissertations and Theses database.
(Publication No. AAT 3291809)
S. Smith D., & Caruso, J. B. (2010). ECAR study of undergraduate students and
information technology. (Research Study, Vol. 6). Boulder, CO: EDUCAUSE
Center for Applied Research. Retrieved March 1, 2011, from
http://www.educause.edu/Resources/ECARStudyofUndergraduateStuden/217333
A. Smith, A., Rainie, L., & Zickuhr, K. (2011). College students and technology.
Retrieved from Pew Research Center‟s Internet & American Life Project
website: http://www.pewinternet.org/Reports/2011/College-students-and-
technology/Report.aspx
Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit:
A uses and gratification approach to unbundling feature use. Computers in
Human Behavior, 27, 2322-2329.
131
Statistics (2012, March 25). Retrieved from
http://newsroom.fb.com/content/default.aspx?NewsAreaId=20
Strange, C. C., & Banning, J. H. (2001). Educating by design: Creating campus learning
environments that work. San Francisco, CA: Jossey-Bass.
Terenzini, P. T., Pascarella, E. T., & Blimling, G. S. (1996). Students‟ out-of-class
experiences and their influence on learning and cognitive development: A
literature review. Journal of College Student Development, 37, 149-162.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent
research. Review of Educational Research, 45, 89-125.
Upcraft, M. L. (2007). Foreword. In R. Junco & J. Mastrodicasa (Eds.), Connecting to
the net.generation: What higher education professionals need to know about
today’s students (pp. xi-xii). Washington, DC: National Association of Student
Personnel Administrators.
Whitt, E. J., Edison, M., Pascarella, E. T., Nora, A., & Terenzini, P. T. (1999).
Interactions with peers and objective and self-reported cognitive outcomes across
three years of college. Journal of College Student Development, 40, 61-78.
Wilson, M. E. (2004). Teaching, learning, and Millennial students. In M. D. Coomes &
R. DeBard (Eds.), Serving the Millennial generation: New directions for student
services, no. 106 (pp. 59-72). San Francisco, CA: Jossey-Bass.
Wilson, J. D. (1999). The impact of involvement in campus organizations on student
satisfaction, skill development, and perceived personal change. (Doctoral
dissertation). Retrieved from ProQuest Dissertations and Theses database.
(Publication No. AAT 9975119)
132
Wilson, J. R. (2010). Understanding nontraditionally aged college students an
exploratory study (Doctoral dissertation). Retrieved from ProQuest Dissertations
and Theses database. (Publication No. AAT 3391349)
Winston, R. B., Jr., & Massaro, A. V. (1987). Extracurricular involvement inventory: An
instrument for assessing intensity of student involvement. Journal of College
Student Personnel, 28(2), 169-175.
Winston, R. B., Jr., Miller, T. K., & Cooper, D. L. (1999). Preliminary technical manual
for the Student Development Task and Lifestyle Assessment. Athens, GA: Student
Development Associates.
133
APPENDICES
Appendix A
Junco’s (2012) Facebook Instrument
134
135
136
137
138
Appendix B
College Student Experiences Questionnaire (CSEQ)
139
140
141
Appendix C
Demographic Data
142
143
144
Appendix D
INFORMED CONSENT FORM
Correlation Between Undergraduate College Students’ Facebook Use
and Co-Curricular Involvement.
The following information is provided in order to help you make an informed decision
whether or not to participate in this research study.
The purpose of this study is to gain a better understanding of the relationship between
Facebook use and co-curricular involvement in campus activities. This research will be
used to assist campus administrators in educating students about Facebook more
effectively, and finding ways to increase opportunities for co-curricular involvement on
campus. This online survey should take no longer than five (5) minutes to complete. The
survey will focus on your level and behavior of Facebook use, as well as your quantity
and nature of co-curricular involvement on campus.
Your responses are important to us and we hope you will participate. However, your
participation is completely voluntary, and you are under no obligation to participate.
There are no known risks or discomforts associated with this research, and due to the
nature of the online survey software it is impossible to link your responses to your actual
identity. You may discontinue participation at any time by closing the browser window.
Your responses will be considered only in combination with those from other
participants. The information obtained in the study may be published in journals or
presented at professional conferences but your identity will never be revealed. Due to the
nature of this online survey software, it will be impossible for even the researcher to link
your responses to your name or email address.
All participants who complete the survey will be offered the opportunity to enter into a
raffle to win 1 of 10 $10 iTunes gift cards. After completing the survey you will be
redirected to an entry form, which is not linked in any way to your responses of this
survey.
If you have any questions regarding this study or would like a summary of the findings,
please contact Christopher Weiss at [email protected]. The faculty advisor for this
research is Dr. John Wesley Lowery. This research has been approved by the Indiana
University of Pennsylvania Institutional Review Board for the Protection of Human
Subjects (724-357-7730). If you have questions about the research, research subjects‟
rights or research results, contact the following person:
Christopher S. Weiss
Graduate Student, Student Affairs in Higher Education
G-37 Ruddock Hall
724-357-5506
145
Appendix E
Email Inviting Students to Participate
Hello ${m://FirstName},
I‟m Chris Weiss, a student in the Student Affairs in Higher Education graduate program
here at IUP. I‟m hoping you can help me in a research study I‟m conducting for my
Master‟s Thesis on the relationship between college student Facebook use and co-
curricular involvement.
You have been randomly selected to participate in this study. Your participation is
completely voluntary, and if you decide to participate you have the opportunity to enter a
raffle to win 1 of 10 $10 iTunes gift cards. The online survey should take approximately
five (5) minutes of your time, and your responses and identity will be completely
confidential. I sincerely hope you will take the time to let me know how you use
Facebook and how involved you are on campus.
More information on the study, including an informed consent form, can be found at the
first page on the link below. If you would like to participate just follow the link below to
the online survey. After completing the survey, you will be provided the opportunity to
enter your email address to win one of the iTunes gift cards.
Follow this link to the Survey:
${l://SurveyLink?d=Take the Survey}
Or copy and paste the URL below into your internet browser:
${l://SurveyURL}
If you have any questions, please email me at [email protected].
Thank you so much for considering participating in my study, I hope your semester is off
to a great start!
Christopher Weiss
Graduate Assistant for Training and Student Leadership
Office of Housing, Residential Living, and Dining
Indiana University of Pennsylvania
724-357-2628
146
Appendix F
Institution Review Board Approval
147
Appendix G
CSEQ Item Usage Agreement
148
149
Appendix H
Spearman’s rho Correlations between Facebook Items ( N = 207) Variables
FBAvg FBYest FBCheckAvg FBCheckYest
1. FB Status 0.347**
0.365**
0.355**
0.340**
2. FB Share 0.349**
0.292**
0.263**
0.246**
3. FB Message 0.176**
0.169**
0.167**
0.185**
4. FB Comment 0.364**
0.291**
0.350**
0.310**
5. FB Chat 0.297**
0.322**
0.295**
0.300**
6. FB Checking Up 0.247**
0.142* 0.361
** 0.291
**
7. FB Event 0.188**
0.164* 0.071 0.047
8. FB Game 0.107 0.116 0.028 -0.007
9. FB Photo-Post 0.244**
0.116 0.321**
0.182**
10. FB Photo-Tag 0.262**
0.118 0.294**
0.162*
11. FB Photo-View 0.324**
0.188**
0.331**
0.226**
12. FB Video-Post 0.222**
0.199**
0.107 0.143*
13. FB Video-Tag 0.238**
0.188**
0.124 0.147*
14. FB Video-View 0.262**
0.221**
0.218**
0.153*
1 2 3 4
1. FB Status –
2. FB Share 0.493**
–
3. FB Message 0.237**
0.248**
–
4. FB Comment 0.468**
0.282**
0.244**
–
5. FB Chat 0.252**
0.216**
0.515**
0.384**
6. FB Checking Up 0.156* 0.046 -0.045 0.383
**
7. FB Event 0.143* 0.100 0.259
** 0.229
**
1 2 3 4
8. FB Game 0.047 0.121 0.212**
-0.032
9. FB Photo-Post 0.309**
0.172* 0.138
* 0.389
**
10. FB Photo-Tag 0.282**
0.206**
0.168* 0.416
**
11. FB Photo-View 0.233**
0.128 0.116 0.452**
12. FB Video-Post 0.279**
0.349**
0.199**
0.201**
13. FB Video-Tag 0.318**
0.326**
0.339**
0.212**
14. FB Video-View 0.237**
0.324**
0.134 0.254**
5 6 7 8
6. FB Checking Up 0.181**
–
7. FB Event 0.321**
0.070 –
8. FB Game 0.109 -0.009 0.183**
–
9. FB Photo-Post 0.223**
0.311**
0.248**
-0.084
150
Variables
5 6 7
10. FB Photo-Tag 0.277**
0.312**
0.243**
11. FB Photo-View 0.206**
0.494** 0.139
*
12. FB Video-Post 0.222**
0.172* 0.173
*
13. FB Video-Tag 0.325**
0.168* 0.209
**
14. FB Video-View 0.285**
0.242**
0.143*
9 10 11
10. FB Photo-Tag 0.827**
–
11. FB Photo-View 0.593**
0.487**
–
12. FB Video-Post 0.288**
0.305**
0.181**
13. FB Video-Tag 0.269**
0.345**
0.203**
14. FB Video-View 0.301**
0.297**
0.335**
13 14
14. FB Video-View 0.547**
–
Note. FBAvg = amount of time spent on Facebook on average; FBYest = amount of time spent on
Facebook yesterday; FBCheckAvg = number of times Facebook was checked on average; FBChecYest =
number of times Facebook was checked yesterday; Status = posting status updates; Share = sharing links;
Message = sending private messages; Comment = commenting (on statuses, wall posts, pictures, etc.); Chat
= chatting on Facebook Chat; Checking Up = checking to see what someone is up to; Event = creating or
RSVPing to events; Game = playing games (FarmVille, MafiaWars, etc.); Photo-Post = posting photos;
Photo-Tag = tagging photos; Photo-View = viewing photos; Video-Post = posting videos; Video-Tag =
tagging videos; Video-View = viewing videos.
* p < .05. ** p < .01.
