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Tolerance on Facebook: Exploring
Network Diversity and Social Distance
Lee Farquhar (bios)
Samford University
Theresa Davidson
Samford University
Abstract
This
study examines Facebook usage, network composition, and desired social distance
from groups often perceived as the “other”.
Specifically, we examine attitudes toward Atheists, Muslims, and Gays.
Findings indicate that social network composition (ie
- network diversity, number of unique groups, number of Facebook friends) plays a significant role in participants’ desired
social distance from said groups. Generally, these findings suggest that
increasing diversity in a Facebook network may lead to a decrease in prejudice.
Introduction
Facebook’s
number of users has grown to over 1.39 billion users as of December 2014 (Facebook newsroom).
Certainly, it is a worldwide company, allowing one’s social network to
experience potential expansion that has never before been possible with such
ease. With very few degrees of separation, and with the relative ease of a few
mouse-clicks, a Facebooker could potentially become
Facebook friends with strangers from other lands, from unfamiliar cultures, and
who speak different languages. Facebook’s social world is now filled with a
broad range of demographics. With all of this potential diversity in one’s social network, we seek to examine the impact of
Facebook on the Facebooker’s general tolerance and
acceptance levels. Essentially, is Facebook making us more tolerant of
difference?
Facebook’s meteoric rise in popularity has brought with it a
slew of studies. Many have focused on audience, identity, and the tensions that
come with Facebook activity (Binder, Howes, &
Sutcliffe, 2009; boyd, 2010;
Hogan, 2010; Lampinen et al, 2011; Lampinen et al, 2009; Marwick & boyd,
2011; Ozenc & Farnham, 2011;
Stutzman et al, 2012). However, most of these studies
focus on privacy, disclosure levels and other boundary regulation, and tensions
that arise from family and friends co-mingling on the site. Generally, they emphasize
what the individual can do to manage their experiences. The present study,
however, is focused on what the Facebook experience does to the user. A gap in the literature is present with regard to
social distance, contact theory, and the diversity in one’s social network.
Most
examinations of diversity in a social network have leaned toward positive
outcomes, particularly regarding social capital (Binder et al, 2009; Ellison, Steinfield, Lampe, 2007; Steinfield,
Ellison, Lampe, 2008; Valenzuela, Park, and Kee,
2009). Generally, the discussion on this side stems from Granovetter’s
(1977, 1983) canonic strength of weak
ties argument. Having more individuals with unique backgrounds and assets
raises one’s social capital.
On the other side of the coin, there are
some (though few) dystopianists that argue Facebook –
and other aspects of digital life – are pulling society into social isolation.
Concepts such as the interpersonal
divide
(Bugeja, 2005), alone together (Turkle,
2011) and the daily me (Negroponte, 1996, explained in a 2009 NY
Times op-ed piece)
suggest that individuals might have unprecedented access to others and
information, but we are losing personal and physical contact, are demanding
more of others but not willing to give of ourselves, and are becoming engrossed
in our own solipsistic worlds. Further, we are becoming unsympathetic to
others, viewing them merely as resources at our disposal (Wellman studies).
Clearly, this debate is larger than what
can be settled by a single study. However, the present research seeks to
explore further the role of network diversity with regard to a Facebooker’s tolerance of others, and several other
identity-management concepts.
Conceptual
Framework
Facebook
Intensity and number of unique groups
To
begin the examination of social distance and network diversity, we are first
including some baseline measurements that speak to one’s Facebook usage and
network makeup. Research on online communities now reaches back decades (Boydand Ellison, 2007), and Facebook specifically has garnered much
scholarly attention. Further, the sizeable set of research on Facebook, in
particular, has resulted in a consistent measure to examine Facebook usage and
involvement level (Binder et al, 2009; Ellison, Steinfield,
Lampe, 2007; Steinfield, Ellison, Lampe, 2008;
Valenzuela, Park, and Kee, 2009).
The Facebook
Intensity Scale focuses on how important Facebook is
to an individual by combining number of Facebook friends, time spent on the
site, and with a series of Likert statements such as
“Facebook has become a part of my daily routine.” In sum, having a bigger
network of Facebook friends, lengthier sessions spent on the site, and deeper
personal involvement on Facebook leads to higher Facebook Intensity scores. For
this study, given the importance of connections with individuals, we will also
examine the number of Facebook friends as a standalone variable.
Past research has typically
associated higher Facebook Intensity with social benefits, particularly in
terms of bridging and bonding social capital (see especially Ellison, et al,
2007), and, generally, we anticipated the benefit of increased network
diversity in the present study (along with other, related variables). Network DiversityNetwork diversity requires the
inclusion of multiple sub-groups within a network. It is quite logical, then,
that a larger social network on Facebook would contain more sub-groups as well as more members of each subgroup. To
examine the number of unique social subgroups present within one’s Facebook
network, we rely on a measure used by McCarty et al (2001) to determine network
size. The measure effectively asks a participant to identify the number of
unique groups present in their network. In addition to Number of Unique Groups,
we include Network Diversity as a standalone variable.
For the purposes of this study, we
separate diversity into four potential categories of “others” (these include
sexual orientation, social class, religion, and race). For each of these areas
we asked participations to assess how much of their Facebook network was
similar to them. For example, a question about racial diversity read, “About
how many of the people in your Facebook network do you feel are the same race
as you?” This produced both a measure of perceived diversity based on the single item (race, in the example) and,
when combined with the other categories, an overall
diversity score.
Using the same categories that were
applied in the Network Diversity metric, we assessed change in network diversity over the past five years. Participants
were asked how their current network compares to five years ago on each of the
four diversity measures. Five years was selected as the period of change
because our sample is made up of college students who have, in that time, made
the transition from high school to college.
Ten years seemed too large of a span and less than five years would
result in an examination of college-years-only for a portion of our sample.
This measure was intended to assess the perceived
change in diversity over time.
Given their similarities, it is very
likely that the number of unique groups and Facebook Intensity will have a
strong correlation. Additionally, it is expected that Network Diversity and
change would also be positively associated with Facebook Intensity and the
Number of Unique Groups within a given network. Lastly, we anticipated that
Diversity Change and Network Diversity would be positively associated.
H1: Facebook intensity, the number of
Facebook Friends, and the Time Spent on Facebook are positively associated with
Number of Unique Groups
H2: Facebook intensity, the number of
Facebook Friends, and the Time Spent on Facebook are positively associated with
Network Diversity
H3: Facebook intensity, the number of
Facebook Friends, and the Time Spent on Facebook are positively associated with
Diversity Change
H4: Number of Unique Groups is
positively associated with Network Diversity
H5: Number of Unique Groups is
positively associated with Diversity Change
H6: Diversity Change is positively
associated with Network Diversity
Social Distance and Contact Theory
Social distance is defined as “the
degree of sympathetic understanding that exists between two persons or between
a person and a group (personal distance or personal-group distance)” (Bogardus, 1933). The social distance
measure, originally developed by Emory Bogardus
(1933), has been used in various forms as a way to capture negative sentiment
toward members of different racial or ethnic groups (Durrheim,
2011; Brocket et al., 2010; Lee et al., 1996; Odell et al., 2005). The measure
is designed to capture the amount of physical or social distance desired from
someone who possesses a group membership different from one’s own. Social
distance measures should represent an underlying bias in favor of one’s own
group, thus, a bias against those perceived to be members of an out-group.
Indeed, the Bogardus scale has been used consistently
since its development and has been useful in tracking the decrease in prejudice
against various ethnic groups over time in the U.S. (Owen, Eisner, & McFaul,
1977; Parillo & Donaghue,
2005).
The Bogardus scale has also been used to assess attitudes
toward other perceived out-groups. Social distance has gauged desired distance
from religious groups (Brinkerhoff et al., 1991; Brockett et al., 2009), those
with mental illness or some type of disability (Adewuya
& Makanjuola, 2008; Ouellette-Kuntz et al., 2010;
Pescosolido et al., 2013), and homosexuals (Maurer,
2013). As one measure of prejudice, the Bogardus
Scale has demonstrated its usefulness in delineating the contours of negative
sentiment toward key groups in society.
This concept
further implies that individuals who desire more
social distance from those they define as different, subsequently construct
their social spheres to be comprised predominantly of people like themselves.
Indeed, research has shown that individuals’ networks tend to be remarkably
homogenous regarding characteristics like race, social class, and religion
(McPherson et al., 2001). The level of homophily of
social networks may suggest preference for one’s own group and, consequently,
prejudice or even hostility toward out-group members. Nonetheless, this
wariness of others can be diminished through contact with out-group members. We
anticipate the Number of Unique Groups will thus be inversely related to Social
Distance. That is, those whose networks have fewer unique groups will desire
more social distance than those with a high number of unique groups.
Contact
Theory (Allport, 1954) asserts that contact with
those who are different from oneself (in terms of race, ethnicity, social
class, and the like) can mediate the negative attitudes held by in-group
members. Indeed, much research has
established that prejudice reduction does occur with positive interactions
between members of different groups. For example, Aberson
et al. (2004) demonstrated that study subjects who had close friends who were
members of an out-group scored lower on an implicit bias measure. Using national data, Dixon & Rosenbaum
(2004) found that Whites who had contact with Blacks and Hispanics were less
likely to endorse stereotypes about those groups. Similarly, Ellison et al.
(2011) showed that friendship contacts with Hispanics predicted more empathetic
attitudes and less restrictive public policies toward that group. O’Neil & Tienda (2010) concluded similarly in their study of
attitudes toward immigrants.
While not
necessarily challenging the findings previously mentioned, other studies
suggest complexity regarding contact and prejudice reduction. In a study of
South African college students (Shrieff et al.,
2010), researchers found that social distance measures increased based on
perceived comfortability with those of a different
race, and that these perceptions inhibited intergroup contact. Their study was
conducted via observations in a dining hall, which should have provided ample
opportunity for close intergroup contact and decreased social distance.
Likewise, Ouellette-Kuntz et al. (2010) found that respondents’ desired social
distance from those with intellectual disabilities was partly contingent on the
perceived severity of the disability.
In light of
the previous research, it is clear that intergroup contact can play a role in
reducing desired social distance from an “other”. However, there is complexity
in how the process of contact occurs, and the outcomes and implications for
prejudice reduction. Indeed, according to Allport,
key conditions must exist for that contact to be effective in reducing
prejudice. These four conditions include
cooperation, common goals, equal social status, and institutional support (Allport, 1954). The
present study will add to this literature by considering the role of Facebook
to bring one into contact with the “other”, thereby decreasing network homophily, and, potentially reducing social distance and
increasing tolerance and acceptance. It is important to note that while the
four conditions outlined by Allport may not be
present in the Facebook sphere, recent research
suggests this may not be necessary. Crisp and Turner (2009) contend that
simulated social contact can be effective in reducing fear and prejudice. They identify a “continuum of contact”
ranging from actual, sustained contact, like that articulated by Allport (1954), to imagined positive contact with the “other”. Facebook interactions may lie somewhere on
this continuum between actual and imagined contact. Certainly, time spent on
Facebook can be thought of as time spent with
others in an imagined community of sorts (boyd, 2010). Further, since Facebook is so
widespread, it is highly likely that at least a portion of those “others” will be of different backgrounds and with
diverse perspectives. In conclusion, we anticipate that higher Facebook Intensity will be associated with a lower desire
for social distance than those with low Facebook Intensity.
H7: Facebook
intensity, the number of Facebook Friends, and the Time Spent on Facebook predict a decrease in desired Social Distance
H8: Number of
Unique Groups predicts a decrease in desired Social Distance
H9: Network
Diversity predicts a decreased in desired Social Distance
H10:
Diversity Change predicts a decreased in desired Social Distance
Sample
A total of
400 students completed the survey for this study. Their participation was
solicited via emails, containing a survey-linked URL address, from instructors
teaching sociology and communication courses in three different institutions.
These included two universities in the Southeastern United States (including
one private, religiously-based institution), and one large university in the
Midwest. The URL address linked respondents to the online survey, which took
approximately 20 minutes to complete. The sample was largely white (78%) and
female (71%), both of which are proportional to sociology and communication
course enrollment at the three universities.
Concept
Measurement: Dependent Variable
Social
Distance was measured by using a modified version of the Bogardus (1933) Social Distance Scale wherein participants’
acceptance of an “other” was measured by their desired social distance from
said other. For example, each participant was asked how they would feel about
having a Muslim “as a relative by marriage”, “as a personal friend”, “as a
neighbor”, and so on, with the highest desired distance being “I’d exclude them
from my country”. For social distance measures, we focused on three primary
“others”, 1) Muslims, 2) Atheists, and 3) Gays.
Concept Measurement:
Independent Variables
We include
four measures that tap into ones level of engagement with Facebook: number of
unique groups, a Facebook Intensity scale, the number of friends one has on
Facebook, and the amount of time spent per day on Facebook. The variable number of unique groups was created by
adding the total number of groups identified by the respondent as part of their
network. The average number of unique groups reported was 8.58, with a minimum
of 0 and maximum of 13 possible groups (such as “Family”, “Coworkers”, and
“Friends through religious organizations”).
Facebook Intensity scale combined responses of six questions
tapping into use and intensity of engagement with Facebook such as “Facebook
has become part of my daily routine” and “I feel out of touch if I haven’t
logged onto Facebook for a while”.
Response categories were Likert Scales ranging
from “strongly agree” to “strongly disagree”.
The Cronbach’s Alpha measure of internal
consistency yielded a score of.822.
A third measure of Facebook
Engagement is the number of friends respondents
report. This is a categorical measure ranging from “50 or less” to “more than
900”.
Finally, we ask respondents how much
time in the past week, on average, they have spent on Facebook. The response
categories range from “less than 10 minutes” to “more than 3 hours”.
Network
Diversity was comprised of five survey items and asked each participants what portion theirnetwork was similar to them in the following areas: Religious Preference,
Race, Social Class, Sexual Orientation, and Political Views. Answers ranged
from “Almost All” to “None”. These items were then combined for an overall
measure of Network Diversity.
Change in
Network Diversity (over 5 years) was measured in five
survey items that mirrored the categories of Network Diversity. Here, however,
participants were asked how their networks had changed over the past five years
in each of the five categories (Religious diversity, Racial
diversity, etc). Answers ranged from “Much more
diverse” to “Much less diverse”.
Control
measures included sex, race, parent’s education level, class standing,
political views (measured on a 7-item continuum ranging from extremely
conservative to extremely liberal), church attendance (measured by seven
categories ranging from “more than once a week” to “never”), prayer (measured
by seven categories ranging from “several times a day” to “never”), and whether
the respondent reveals their religious views on Facebook.
Results
Table 1
presents the sample descriptives. Regarding our key
dependent variables, desired social distance from Muslims, Atheists, and Gays
is fairly low. The most social distance is desired from Atheists (1.33) and the
least from Gays (.85), with Muslims (1.12) falling in-between.
Our measures
for Facebook Engagement show moderate to high levels of Facebook Intensity
(19.11) and an average number of 8.5 unique groups per respondent. About 80
percent of respondents report having more than 300 Facebook friends, and
roughly 30 percent spend between 10 – 30 minutes per day on Facebook. Regarding overall Network Diversity,
respondents indicate that most to about half of their
network is the same religion, race, social class, sexual orientation, and
political views (2.41). Similarly, most report that their network is the same
on those characteristics as it was 5 years earlier (3.42), edging toward
slightly more diversity.
For our control measures, about 72 percent of
the sample report a parent with a Bachelor’s degree or higher and 38 percent
are Freshmen in college. About 35 percent report
being extremely to slightly conservative. On our religious control
measures, about 25 percent attend church services weekly or more, 41 percent
pray daily or more, and 54 percent reveal their religious preference on
Facebook.
Table 2 shows
the correlations pertaining to Hypotheses 1-6. H1 was supported in that FB
Intensity had a significant (p<.001), positive association with Number of FB
Friends (.225), Time Spent on FB (.596) and the Number of Unique Groups (.201).
Thus, higher levels of Facebook intensity is associated with more Facebook
friends, more time spent on Facebook, and more unique groups among one’s
network.
Meanwhile, H2
was not supported. Actually, Network Diversity had a significant (p<.001) negative association with FB Intensity
(-.189) and Number of FB Friends (-.182). This means that higher
levels of diversity in one’s network is associated with less Facebook
intensity and fewer Facebook friends.
H3 received some support in that FB
Intensity was positively associated with Diversity Change (.103, p<.05) at a
minimal level, meaning greater engagement with Facebook is related to increases
in network diversity. However, associations with Time Spent on FB and Number of
FB Friends were not significant.
H4 was not
supported in that Number of Unique groups was not positively associated with
Network Diversity (-.201, p<.001). Instead, a greater number of unique
groups is associated with less diversity of one’s
network.
H5 was
supported. Number of Unique Groups was positively associated with Diversity
Change though at only a minimal level (.101, p<05). In other words, a
greater number of unique groups is related to greater
diversity over time of one’s network.
H6 was not
supported in that Network Diversity and Change in Diversity were not
significantly linked. Interestingly, no relationship exists between total
network diversity and change in diversity over time of one’s network.
Table 3
reports the linear regression results for social distance desired from
Muslims. Only one measure of Facebook
Engagement is predictive. The more
unique groups in one’s network, the less social distance desired from Muslims.
One measure of Network Diversity is predictive. The more change one perceives
in their network over the past five years, the less social distance desired
from Muslims. Finally, political views predict distance such that the more
conservative one is, the more social distance desired from Muslims.
Table 4 presents
the linear regression results for social distance from Gays. The higher the
number of unique groups in one’s network, the less social distance desired from
gays.
However, the
more Facebook friends reported, the more social distance from gays is desired.
None of our diversity measures show predictive power. However, females and the
more politically liberal tend to desire less social distance from gays.
Table 5
presents the linear regression results for social distance from Atheists. Regarding Facebook engagement, those with
more Facebook friends desire more social distance from Atheists. None of our
network diversity measures are related to attitudes toward atheists. For our
controls, Black respondents are more likely than those of other races or ethnicities
to desire distance from atheists. In addition, those who engage in more
frequent prayer also desire more distance. However, those with higher class
standing, and, those with more liberal political views tend to desire less
distance from atheists than their counterparts.
Discussion
In sum, our
analyses show that Facebook’s potential impact on desired social distance is
nuanced. First, desired social distance was most strongly predicted by the
Number of Unique Groups in one’s network (decrease in desired social distance).
This, we believe, is an important component in generating tolerance and
acceptance of others. The Facebooker’s network is
largely made up of offline connections, so those that have a higher number of
unique groups online are likely to have interacted with these same groups
offline. Facebook interactions might serve as a reaffirmation of positive,
offline interactions, thus lending general support to Contact Theory and a
reduction in desired Social Distance.
Second, the
number of friends in one’s network predicts an increase in desired social distance. This finding is important
because we move beyond simplistic thinking that having more relationships
equates to increased acceptance of others. Here, interacting with a largely
homogenous group results in a polarizing effect
regarding (negative) attitudes toward others. The key takeaway from this and
the preceding paragraph is that increased tolerance and acceptance comes from
interacting with members of unique
groups, not merely interacting more frequently with like-minded friends.
Third,
perceptions of network diversity change over the past five years predict a
decrease in desired social distance specifically from Muslims. This finding
directly supports a basic tenet of Contact Theory in that increased experiences
with individuals who are different from oneself yields higher tolerance and
acceptance of others.
We feel that
these three findings lend support to two key theoretical arguments. First, a
higher number of unique groups predicts decreases in
social distance, suggesting that online contact may indeed be reducing
prejudice, as Intergroup Contact Theory asserts. This is potentially due to the
unique groups bringing a variety of ideas and perspectives to the individual,
thus leading to a more-open-minded stance to others in general. Second, those who report an increase in diversity of their
networks tend to desire less social distance. This finding suggests that
diverse social networks may, in fact, be a way to build social capital by increasing
trust and decreasing distrust of the Other. This
argument may be indirectly strengthened by our finding that more friends in
one’s Facebook network tends to predict increased
social distance. It may be that if one is building an online network that is
largely homogeneous, they are not exposing themselves to individuals and groups
who are different from themselves, thus not building social capital and
intergroup trust.
Additionally,
our findings may align with Crisp and Turner’s (2009) notion of a “continuum of
contact”. They argue that actual contact with the Other
may not be necessary for prejudice reduction. In that regard, if one is
building an online network where “friends of friends” are a different race,
religion, class, political persuasion, or sexual orientation, this may be
enough to promote tolerance. Actual sustained interaction may not be necessary,
but, rather, the knowledge that a good friend likes and trusts an Other could be sufficient to
challenge stereotypes. Further, Crisp and Turner found that simply imagining a positive interaction with
someone who was different reduced prejudice. One could speculate that
individuals with more diverse online networks, while not actually interacting
with those who are different, may imagine positive interactions, thus reducing
the desire for social distance.
It should be noted that Network
Diversity did not predict Desired Social Distance.
We suspect
that this finding is due to the fact that so many respondents reported a
relatively homogeneous network. In our view, this supports Allport’s
Intergroup Contact Theory in that individuals tend to surround themselves with
others they perceive as very similar to themselves, reducing the opportunity
for contact with anyone who may be different.
Given Facebook’s relatively recent
population shift toward older participants, our findings might indicate a
potential avenue for decreasing desired social distance among those with
perhaps more entrenched perspectives about the Other.
However, given our sample demographics, it might also be that our findings
pertain mostly to younger participants, who are perhaps still forming their
opinions about members of groups different from their own.
To
restate, Facebook’s relationship to desired social distance appears to be
nuanced. On one hand, a Facebooker’s network is
typically made of offline connections, and their network diversity therefore is
merely a mirror of their offline diversity experience. On the other hand, it
appears that having a significant number of unique groups within a FB network –
again, mirroring offline interactions – may complement or even embolden tendencies toward acceptance of Others.
Limitations
and Future Research
There are
several limiting factors regarding this study’s generalizability. First, our
sample was a convenience sample of college students. Additionally, it skewed
toward female and white. Facebook, of course, has nearly saturated the college
demographic, so the sample in many ways represents the bulk of Facebook’s early
adopters. However, with Facebook’s current growth outside college students, the
study’s findings are limited in scope. Other populations are certainly fruitful
ground for future research.
An added
concern is with the direction of causality. Because our study design is
cross-sectional, we cannot be sure if respondents’ increase in tolerance is a
result of changes to their network. It may be that more tolerant individuals
simply build more diverse online networks. However, respondents were asked
about their perceived change in their networks, suggesting that network change
may influence decreased desired social distance. Further research should
incorporate a panel design to be more confident about causality.
Another
possible concern is with the validity of our dependent measures. We gauged social distance desired from
Muslims, Gays, and Atheists. However, it
is important to note that since the terrorist attacks of 9-11 respondents may have
been exposed to messages of tolerance of Muslims from media, educational institutions,
family, and peers. It is possible that
respondents reported less social distance simply due to the effects of this
tolerance socialization. Similarly, the
past few years has witnessed a sea change in visibility of gays and lesbians,
messages of tolerance and acceptance, and even major legislative changes in
support of gay-friendly public policy.
While it is likely that attitudes have genuinely changed among many
young people, it is also possible that their responses reflect a concern with
negative social sentiment for revealing what are now seen as prejudiced
attitudes. It may be helpful for future
researchers to include a “social desirability scale” to control for this
possibility. However, we suspect that
given the anonymity afforded with this survey, responses are likely
authentic.
Related, the
greatest social distance desired from the three groups is from atheists. Atheists have not had the level of exposure
in media like Muslims or gays, nor have concerns about atheists reached the
level of national discourse. This may be
one of the reasons why respondents report relatively high levels of distance
desired. This could be a fruitful area
for future research.
Lastly, our
sample – for perhaps a variety of reasons – did not score highly in Network
Diversity or Change in Network Diversity (over the past 5 years). This may be
due to the largely homophilous populations found at
our three Universities. Also, it might be that our instrument needs further
testing; this is particularly likely for our measure of Change in Network
Diversity. Five years may simply have not been enough time for significant
changes to occur.
About the Authors
Dr. Lee Farquhar, assistant professor in the Journalism and Mass Communication Department, joined Samford in 2009. Prior to arriving at Samford, he completed his doctoral work at the University of Iowa.Dr. Farquhar received his bachelor’s degree in communication with an emphasis in electronic media at the University of Northern Iowa. He received a master’s degree in Mass Communication from Kansas State University. His professional experience is primarily in radio and television broadcasting. He has also worked as a freelance videographer, shooting video for Kansas State University and MTV.
Dr. Farquhar teaches video production courses at Samford, with particular emphases in producing television news for the Samford News Network and the Department’s Film Production program. His research interests are social media, identity, and social psychological theories. He current research projects focus on the relationships between Facebook use, social anxiety, self-monitoring levels, and religiosity.
Theresa Davidson came to Samford’s sociology department in 2005. She was attracted to Samford because of the close relationships she could have with her students and colleagues. She says that Samford’s small class sizes allow her to have more one-on-one time with her students and colleagues.
Both of Davidson's parents were teachers and she found in graduate school the same passion for teaching. She currently teaches Samford's Introduction to Sociology, Research Methods, Race Relations, Gender Studies, and Social Class and Inequality courses. She says she most enjoys teaching these classes because they challenge students to look at the world in a different way create social change.
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Tables
Table 1 Descriptives
|
|
Mean or
Proportion
|
Standard
Deviation
|
Range
|
Social
Distance Measures
|
|
|
|
Distance
from Muslims
|
1.12
|
1.82
|
0.0-7.0
|
Distance
from Atheists
|
1.33
|
2.02
|
0.0-7.0
|
Distance
from Gays
|
.85
|
1.50
|
0.0-7.0
|
Facebook
Engagement
|
|
|
|
Facebook
Intensity
|
19.11
|
4.53
|
6.0 - 29.0
|
Number of
Unique Groups
|
8.58
|
2.46
|
0.0 – 13.0
|
More than
300 FB Friends
|
80%
|
|
|
Spend 10-30
Minutes per Day on FB
|
30%
|
|
|
Network
Diversity
|
|
|
|
Total
Network Diversity
|
2.41
|
.49
|
1.0 – 5.0
|
Network
Diversity Change Over 5 Years
|
3.42
|
.49
|
1.4 – 5.0
|
Controls
|
|
|
|
Female
|
71%
|
|
|
White
|
78%
|
|
|
Black
|
11%
|
|
|
Parent has
Bachelor’s Degree or More
|
72%
|
|
|
Freshman
|
38%
|
|
|
Politically
Conservative
|
35%
|
|
|
Attend
Church Weekly or More
|
25%
|
|
|
Pray Daily
or More
|
41%
|
|
|
Reveal
Religion on Facebook
|
54%
|
|
|
Table 2
Correlation Matrix
|
|
FBintense
|
Nugtotal
|
FBFriends
|
FBtime
|
DivTot
|
Div5chng
|
Facebook
Intensity
|
1
|
.201***
|
.225***
|
.596***
|
-.189***
|
.103*
|
Number of
Unique Groups
|
|
1
|
.360***
|
.104*
|
-.201***
|
.101*
|
Facebook
Friends
|
|
|
1
|
.115*
|
-.182***
|
.040
|
Facebook
Time
|
|
|
|
1
|
-.045
|
.063
|
Total
Network Diversity
|
|
|
|
|
1
|
.048
|
Network
Diversity Change over 5 years
|
|
|
|
|
|
1
|
p<.001***
p<.01** p<.05*
Table 3
Predicting Social Distance from Muslims
|
|
Beta (standardized
coefficients)
|
Standard
Error
|
Facebook
Engagement
|
|
|
Facebook
Intensity
|
.034
|
.611
|
Number of
Unique Groups
|
-.116*
|
.047
|
Number of
FB Friends
|
.080
|
.158
|
Time per
Day on FB
|
-.006
|
.083
|
Network
Diversity
|
|
|
Total
Network Diversity
|
-.026
|
.204
|
Network
Diversity Change Over 5 Years
|
-.101*
|
.201
|
Controls
|
|
|
Female
|
-.007
|
.224
|
White
|
-.094
|
.336
|
Black
|
.086
|
.443
|
Parent
Education
|
-.063
|
.073
|
Class
Standing
|
.022
|
.090
|
Political
Views
|
-.241***
|
.074
|
Church
Attendance
|
.028
|
.056
|
Prayer
|
-.004
|
.064
|
Reveal
Religion on Facebook
|
.003
|
.221
|
|
p<.05*
p<.01** p<.001***
|
r2 =.064
|
Table 4
Predicting Social Distance from Gays
|
|
Beta
(standardized coefficients)
|
Standard
Error
|
Facebook
Engagement
|
|
|
Facebook
Intensity
|
-.099
|
.022
|
Number of
Unique Groups
|
-.135*
|
.035
|
Number of
FB Friends
|
.154**
|
.048
|
Time per
Day on FB
|
.014
|
.064
|
Network
Diversity
|
|
|
Total
Network Diversity
|
-.065
|
.158
|
Network
Diversity Change Over 5 Years
|
-.019
|
.155
|
Controls
|
|
|
Female
|
-.189***
|
.174
|
White
|
-.026
|
.259
|
Black
|
.077
|
.343
|
Parent
Education
|
-.034
|
.057
|
Class
Standing
|
-.087
|
.070
|
Political
Views
|
-.296***
|
.057
|
Church
Attendance
|
-.073
|
.043
|
Prayer
|
-.003
|
.049
|
Reveal
Religion on Facebook
|
-.015
|
.171
|
|
p<.05*
p<.01** p<.001***
|
r2 =.148
|
Table 5
Predicting Social Distance from Atheists
|
|
Beta
(standardized coefficients)
|
Standard
Error
|
Facebook
Engagement
|
|
|
Facebook
Intensity
|
.054
|
.029
|
Number of
Unique Groups
|
-.033
|
.047
|
Number of
FB Friends
|
.119*
|
.064
|
Time per
Day on FB
|
.000
|
.085
|
Network
Diversity
|
|
|
Total
Network Diversity
|
-.061
|
.210
|
Network
Diversity Change Over 5 Years
|
-.076
|
.207
|
Controls
|
|
|
Female
|
-.031
|
.230
|
White
|
-.001
|
.345
|
Black
|
.279***
|
.458
|
Parent
Education
|
-.045
|
.076
|
Class
Standing
|
-.110*
|
.093
|
Political
Views
|
-.188**
|
.077
|
Church
Attendance
|
-.020
|
.057
|
Prayer
|
-.154*
|
.065
|
Reveal
Religion on Facebook
|
-.025
|
.227
|
|
p<.05*
p<.01** p<.001***
|
r2 =.162
|
|
|