Social Networking, 2012, 1, 1-12
http://dx.doi.org/10.4236/sn.2012.11001 Published Online July 2012 (http://www.SciRP.org/journal/sn)
“Good Vibrations”: The Social Networks of Optimists
and Alter-Optimists
Miguel Pereira Lopes
School of Social and Political Sciences, Lisbon Tech University, Lisboa, Portugal
Received May 8, 2012; revised June 14, 2012; accepted July 7, 2012
ABSTRACT
This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in
one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The results provide evidence that
optimism trait is independent from the way social networks of personal-issue sharing, advice-seeking, problem-solving,
and innovation, are structured. In contrary, the alter-optimism capability does provide a good explanation of one’s so-
cial network position. Implications of these findings are discussed at the end.
Keywords: Optimism; Alter-Optimists; Homophily; Social Network Analysis
1. Introduction
Research on social networks is increasingly becoming a
relevant topic in social sciences given its ability to si-
multaneously stress both individual attributes and social
factors to explain human behavior. However, most of the
research conducted on this ground refers mainly to socio-
demographic variables such as gender, race, age, or job
level and seniority [1-5]. Although psychological attri-
butes have already been researched (e.g. [6]), the study
of personality and social networks is still underdeveloped
and results are often contradictory.
In the same way, research conducted on the topic of
social networks usually accentuates the influence that the
position an individual occupies in a social network can
have on shaping the individual’s beliefs and personality
characteristics, such as attitudes [7,8]. With some excep-
tions (e.g. [9]), research has seldom focused on how in-
dividual differences lead people to seek and develop dif-
ferent kinds of social networks.
In this study, I relate optimism to the social networks.
The study of optimism has generally focused on the in-
fluence of optimism on personal outcomes. Empirical
research has shown that optimistic people have better
psychological and physical well being, improved health
protective behavior [10], and higher work performance
[11]. Although there are already some research relating
optimism to social network characteristics (e.g. [12]),
this study adds to these studies in at least two ways: 1) I
have opted to collect social network and optimism mea-
sures on different sources, and thus the results can not be
deemed as the effect of common-source bias, which is
particularly problematic when studying optimism, given
the well known biases that optimists are prone to [13,14];
2) I introduce the concept of alter-optimism to refer to
this capacity to generate optimistic psychological states
in others through interpersonal relationships, asserting
alter-optimism as a particular type of positive social rela-
tionship [15]. I did this by collecting data on the capabi-
lity of each individual in the network to generate opti-
mism in his/her alters, in addition to a self-rating trait
measure of optimism.
The purpose of this study is thus to understand how
optimism and alter-optimism relate to an individual’s
social network position. Research on social networks has
continuously emphasized the proclivity of individuals to
interact with similar others, supporting the popular say-
ing that “birds of a feather, flock together”. This effect
has been termed the homophily principle, and refers to
the fact that contacts between similar people occurs at
higher rates than contacts among dissimilar people [16].
If optimism and alter-optimism relate to social network
position, the homophily principle would lead us to hy-
pothesize that highly optimistic and highly alter-opti-
mistic individuals have a preference to interact with indi-
viduals having similar levels of optimism and alter-op-
timism. The same is true for those who are low in opti-
mism and alter-optimism.
However, because there is a rationale for thinking that
optimists are more attractive to initiate a relationship due
to being more liked [17] and having lower levels of
negative moods [18], we are able to devise alternative
hypothesis that go against homophily in that they predict
higher network centrality for optimists and alter-opti-
C
opyright © 2012 SciRes. SN

M. P. LOPES 2
mists, resulting in heterophilic relations instead.
As such, the goal of this article is to understand how
high and low optimists and alter-optimists “flock to-
gether” in social networks. Do high and low optimists
and alter-optimists tend to relate apart with each other
(homophily) or are high optimists and alter-optimists
more prone to occupy central roles in the social network,
thus being searched by both high and low optimists and
alter-optimists (heterophily)? To study how do high and
low optimists and alter-optimists “flock together” I col-
lected network data from a sample of undergraduate stu-
dents concerning a variety of social networks regarding
sharing of a personal-issue, advice seeking, problem-
solving, and innovation.
In the reminding of the paper I present the hypothesis
and their theoretical backup. I then present the metho-
dology used to test these hypothesis and the results I got.
I finish by discussing the major conclusions from our
findings, stressing the limitations of the study, and point-
ing further directions.
2. Birds of a Feather Flock Together, or Do
They?
2.1. The Homophily Principle.
There is extensive research evidencing that people tend
to establish personal relationships that are more homo-
philous than chance would suggest [5]. Louch [19], for
example, has found that when alters share a socio-
demographic attribute such as race, education, or religion,
the likelihood of contact between them increases signifi-
cantly. In the same vein, Hays and Oxley [20] found
evidence that network members are increasingly per-
ceived as similar in the course of the development of a
social network within a community of freshmen under-
graduates.
McPerson et al. [16] have made an extensive review
on the literature of homophily in social networks, report-
ing a large body of evidence that similar individuals are
more likely to be connected than those who are different.
These authors also distinguish two distinct kinds of ho-
mophily: status homophily and value homophily. While
status homophily refers to similarity based on informal,
formal, or ascribed status, the second is based on values,
attitudes, and beliefs. In addition, homophily has also
been found regarding personality traits such as intelli-
gence which has long been pointed as a crucial factor to
explain the selection of associates [16].
Given the consistence and robustness of the homophily
principle, I reasoned that other personality traits, such as
optimism, can be deemed to follow the same pattern. If
this is true, a high optimistic individual is more prone to
interact with another high optimist in the same way a low
optimistic is to contact another low optimist.
As such, I hypothesize that:
H1a: Both high and low op timists establish h omoph ilic
relationships in social networks.
2.2. The Network Centrality Effect.
Burt et al. [6] have stated that there is reason to boldly
believe that individuals have a personality as a function
of the history of network positions they have occupied. In
opposition, Kalish and Robins [9] consider that research
should instead strive to understand how “psychological
traits or predispositions of an individual actor may result
in a particular structuring of his or her immediate net-
work environment” (p. 57). I will not discuss here these
aspects of causal order on the agency-structure issue, but
instead follow the recommendation of Burt et al. [6] that
this is “a story of personality correlates” of structural
features.
Still, the truth is that people do seem to come to oc-
cupy different positions in a given social network [21].
The importance of holding different positions in a social
network comes from the fact that certain positions func-
tion more than others as an asset for individuals. One of
such advantaged positions has been extensively studied
by Burt [22,23], and refers to a position whereby an indi-
vidual controls the connection between other individuals
or groups by bridging the links between them [24]. These
network positions represent “structural holes” and give
rise to the emergence of brokerage opportunities for in-
dividuals in a network to improve their social capital
[22,24]. Empirical research has shown that some person-
ality characteristics associate with structural hole posi-
tioning. This is the case for the entrepreneurial, the au-
thority searching, and the advocacy and change thriving
personality-types, which have been found to positively
correlate with occupying a brokering position [6].
More optimistic individuals have higher probability to
get into environments where positive things can and do
happen and even when conditions do not guarantee a
certain result, the positive beliefs of optimists can make a
difference through the effects of a self-fulfilling pro-
phecy [25]. Also, optimistic individuals have been shown
to possess higher aspiration levels and to set more ambi-
tious goals [26], characteristics that resemble the entre-
preneurial brokers proposed by [6]. As such, I hypothe-
sized that high optimistic individuals tend to occupy
more brokerage network positions than less optimistic
individuals, similar to what Burt’s entrepreneurs do.
In the same way, we also have reasons to expect high
optimists to occupy more central positions in the social
tissue (i.e. to interact more with others). Optimism leads
individuals to believe that further effort can be useful to
attain one’s goals, thus engaging in more proactive be-
havior as they perceive those efforts to be productive

M. P. LOPES 3
[27,28]. That would ultimately lead these individuals to
interact more with others and to occupy more central
roles in their social networks. This hypothesis has been
supported by empirical research concluding that greater
optimism is significantly associated with reports of
greater friendship network size [12].
Although I build on this research to derive the hy-
pothesis, I extend their work by including a wide set of
network centrality measures including ego out-degree,
ego in-degree, and ego betweenness. The distinctions be-
tween these different measures of network centrality will
be made ahead in this article, but I advance that they can
give us a more accurate picture of an individual’s posi-
tion [29] than the network size measure used by Brissette
and collaborators. I also considered brokering measure as
a network centrality indicator, given the assertion of [30]
that “an individual who is central is more important than
the less central in the sense that a central individual is in
a position to control or influence the network and its
members” (p. 339), which is precisely what happens with
those occupying a structural hole.
For all this, I hypothesize that:
H2: High optimists occupy more central positions in
social networks than low optimists.
In the case one accepts that high and low optimistic
individuals do not equivalently occupy similar network
positions, but that instead high optimists are searched
more by others and play an increased role in the network
flows, one must also acknowledge that relationships are
not prone to homophily regarding the optimistic trait. On
contrary, high and low optimists will tend to establish
heterophilic relationships because high optimists will be
sought more by the others, including those who are low
in optimism.
As such, and as an alternative and concurrent hypothe-
sis to H1a, we can hypothesize that:
H1b: Both high and low optimists establish hetero-
philic relationship s in social networks.
3. Developing the Concept of
Alter-Optimism
Optimism refers to the generalized belief that good
things will happen in the future [31]. Sometimes re-
garded as a personality trait [31,32], some authors have
come to consider optimism as also incorporating a state-
like character [25], in the sense that it is difficult to deny
that even a very optimistic person might feel less opti-
mistic sometimes. The acceptance of optimism as a state-
like psychological variable is important as it implies the
possibility of temporarily influencing the state of mind of
a person regarding optimism [26].
I focus on one of these possibilities, which is to en-
hance optimistic states through positive relationships at
work. Positive work relationships, such as high-quality
connections [33], have been confirmed to constitute a
main driver of positive psychological states. [15] define
positive work relationships as “a reoccurring connection
between two people that takes place within the context of
work and careers and is experienced as mutually benefi-
cial, where beneficial is defined broadly to include any
kind of positive state, process, or outcome in the rela-
tionship” (p. 9). Though dispersed through several litera-
tures, positive work relationships’ studies have greatly
benefited from research on high-quality connections [33,
34]. A connection is the dynamic that exists between two
people involving mutual awareness and social interaction
[35]. Based on the concept of connection, [34] have de-
fined high-quality connections as those between two
people that are marked by vitality, mutuality and positive
regard. Thus, high-quality connections are a particular
kind of connection focused on the improvement of indi-
vidual positive states.
The capability to enhance positive psychological states
through high-quality connections may include the posi-
tive psychological states of optimism. This means that
while interacting with others, individuals can generate
optimistic states of mind in their counterparts (or “alters”,
to use social network research terminology), through the
establishment of positive work relationships. This is also
similar to what has been researched in the field of ener-
gizing relationships [36]. Research on energizing rela-
tionships has shown that employees may vary regarding
to how much they are able to get others to act or, as they
say, to feel energized [37]. In the context of leadership,
these authors have distinguished energizing leaders from
de-energizing leaders, by evaluating how they typically
affected others’ energy levels. They found that energizers
were better at getting others to act on their ideas, such as
garnering support for initiatives and persuading clients to
purchase a service or product. De-energizers—those
rated as making others’ energy levels drop—had an en-
ergy-depleting effect on the social networks, thus adver-
sely affecting positive organizational outcomes.
Based on this work on positive relationships, I define
alter-optimism as the capacity of an individual to estab-
lish a positive work relationship with another person in a
way that will increase that other person’s psychological
states of optimism. I distinguish between individuals
high and low in alter-optimism. Research has distin-
guished energizers from de-energizers based on how
much psychological energy they can trigger in others
[37,38]. In a similar vein, individuals can also be deemed
as high or low in how much they are able to generate
optimistic states of mind in others.
There is a well established literature on psychology
concerning the emotional contagion that occurs when
people interact with one another [39,40]. This research

M. P. LOPES 4
has provided strong evidence that people tend to display
and experience other people’s emotions. Several expla-
nations have been advanced to explain this phenomenon.
Research run within the “feeling good-doing good”
framework [41] has found evidence that personality traits
such as positive affect are positively associated with cor-
responding affective tones in social interactions [42],
which in turn enact similar feelings in their alters.
George [42], for instance, has asserted that those “who
feel excited, enthusiastic, and energetic themselves are
likely to similarly energize their followers” (p. 84).
Those who have continued this line of research have also
stressed the ability of high energetic individuals to spread
their positive energy throughout their social networks
[37,38].
Extending the contagion effect to the study of opti-
mism in social networks, leads us to hypothesize a simi-
lar association to that I have made for optimists above. It
follows that one should expect to find a tendency for
homophilic relationships between those who are high and
low in inducting optimism in others because by con-
taminating alters with one’s own behavior would lead to
an increased homogeneity in optimism levels. In short,
high alter-optimists would be more prone to interact with
other high alter-optimists, and low alter-optimists to re-
late to other low alter-optimists.
Given so, we hypothesize that:
H3a: Both high and low alter-optimists establish ho-
mophilic relationships in social ne tworks.
Despite literature pointing to the homophilic thesis, it
is also possible to make beforehand a very different set
of conjectures on the social positions of high and low
alter-optimists. Just like those who are able to energize
others are better at getting them to act on their ideas re-
gardless of being high or low energizers [38], we can
likewise expect high alter-optimists to attract others more
than low alter-optimists, regardless of their alters’ alter-
optimistic type. As supported by recent studies (e.g.
[43,44]), optimism positively relates to life satisfaction
and, as such, individuals are expected to privilege to in-
teract with those who make them feel more optimistic. If
this is true, high alter-optimists should occupy more cen-
tral roles in social networks in the same way as high en-
ergizers do [37].
We thus can alternatively hypothesize that:
H4: High alter-optimists occupy more central posi-
tions in social networ ks than low alter-optimists.
If we assume that high and low alter-optimists do
occupy different positions in social networks, we can
no longer sustain that homophily rules the network of
these individuals regarding their alter-optimism effect.
Instead, low alter-optimists will be prone to search high
alter- optimists more often, making the relationship be-
tween individuals to be marked by a heterophilic cha-
racter.
As such, we can also hypothesize that:
H3b: Both high and low alter-optimists establish he-
terophilic relationships in social networks.
4. Method
4.1. Sample and Procedure
Participants were 41 undergraduate students (31 female
and 10 male) attending the same class. The data was col-
lected three weeks after the beginning of the semester, in
order to allow the students to meet each other but not to
establish a strong tie relationship. Participants first com-
pleted a personality questionnaire measuring optimism
and hope, after which a social network measure was ad-
ministered. The average age of the sample was 22.2
(range from 21 to 26).
4.2. Measures
Dispositional Optimism. Optimism was measured with
the revised version of the Life Orientation Test (LOT-R)
developed by [32]. LOT-R comprises 6 coded items and
4 filler distracter items. Three of the nonfiller items are
reverse-coded (negative). To compose a global measure
of optimism, the three items (negative items) are thus
reversed and added to the other nonfillers (positive
items). LOT-R items on our questionnaire ranged from
“Strongly Disagree” (1) to “Strongly Agree” (7). Sub-
stantive evidence has been collected both for discrimi-
nant validity of the LOT-R with closer constructs like
anxiety, self-mastery and self-esteem, and for its reliabi-
lity [28,32].
Alter-optimism. The measure of alter-optimism was
assessed with a single item asking participants “Please,
indicate the colleagues who make you feel more optimis-
tic when interacting with you”. After pointing the names
of those colleagues, participants also rated how much
each of the nominated colleagues have made them feel
more optimistic, in a scale ranging from “Enough” (1) to
“Very Much” (7). The alter-optimism index for each in-
dividual is the mean value of the ratings of those who
choose the individual.
4.3. Network Measures
Network measures were collected by asking participants
to nominate up to 5 same-class colleagues whom they
would turn to: 1) when having to talk about a personal
issue (personal issues network); 2) when having to make
an important or hard decision (advice-seeking network);
3) when having a new study-related problem to solve
(problem-solving network); and 4) to discuss study-re-
lated innovative ideas (innovation network). These four
social networks were taken from the recommendation of

M. P. LOPES 5
Cross and Parker (2004) and are widely used in social
network research. In addition, for each of the nomina-
tions, participants also had to rate from “Enough” (1) to
“Very Much” (7) how much they really turn to the
nominees, for each of the network questions. Based on
these ratings I was able to construct a valued graph
which provided information on the differential of inten-
sity of each of their choices. I finally asked participants
to identify themselves in order to match the attribute
measure we collected (i.e. dispositional optimism). How-
ever, I guaranteed total confidentiality of the data and
appealed for their sincerity.
4.4. Data Analysis Strategy
To test the hypotheses I begun by assigning each indi-
vidual to the group of high or low optimism and high or
low alter-optimism, based on the median split of the
sample. To test hypotheses H1a, H1b, H3a and H3b, I
first analysed the clique structure of our sample regard-
ing both optimism and alter-optimism. All these analysis
were made using UCINET 6 [45]. In social network
analysis jargon, a clique is an informal association of
people among whom there is a degree of group feeling
and intimacy. Technically, a clique is a sub-set of points
[elements] in which every possible pair of elements is
directly connected by a line and the clique is not con-
tained in any other clique [46]. In practical terms, this
means that, to form a clique, anybody in the clique must
have a direct link to anybody else in the same clique.
This criteria of total maximally connection can be re-
laxed to include elements who are related only to some
of the elements of the clique (e.g. someone who is a
friend of three out of four elements of a clique). However,
since I was mainly interested in the relationships of
naturally developing groups, cliques are a more pure ob-
ject to analyse the homophily/heterophily character of
their members [47]. Looking at how much the groups are
homophilic/heterophilic allows us to understand the role
of optimism and alter-optimism on group formation and
development.
In addition, I also tested H1a and H1b at the dyadic
relationships level. Because the attributes (optimism and
alter-optimism) were measured as continuous variables, I
was able to use a powerful statistical tool to analyze ho-
mophily—the Moran Statistics. The Moran Statistics of
autocorrelation was originally developed in geography
studies and is based on the spatial distances between the
network elements. When applied to measure attribute
similarity and social distances, it allows us to answer
questions of the type “is there a tendency for actors who
have more similar attributes to be located closer to one
another in a network?” [48]. In the context of the present
study, the Moran Statistics allows us to answer the ques-
tions: do high optimists tend to relate to another high
optimists and low optimists to relate to relate to low op-
timists above the chance? Similarly, do high alter-opti-
mists tend to relate to another high alter-optimists and
low alter-optimists to relate to low alter-optimists above
the chance? This Statistics is a measure of network auto-
correlation ranging from −1.0 to 1.0 and is constructed
similarly to the regular correlation coefficient.1 The in-
terpretation of the Moran Statistics is also quite straight-
forward with negative values indicating a tendency for
individuals who are adjacent in the data matrix (i.e. who
were chosen by the other) to differ on a given attribute,
and positive values indicating the tendency for adjacent
individuals to be similar on the attribute [48]. The sig-
nificance of the autocorrelation can be accessed by gen-
erating a sample distribution through the use of permuta-
tion trials. Before computing the Moran Statistics of
autocorrelations, I have built a closeness matrix which
entered as the input for the statistic calculation.
To test H2 and H4, I first computed four social net-
work indices of network centrality—out-degree, in-de-
gree, betweenness, and brokerage—for each of the four
social networks we considered. These analyses were also
made by using UCINET 6 [45]. Out-degree and in-de-
gree represent basic individual measures of network local
centrality. Generally, a central position in a network is a
position with a great many direct contacts with other
points [46]. Whereas out-degree centrality refers to the
number of out-going ties a person has in a given network,
in-degree represents the number of incoming ties [37].
Discriminating out-degree from in-degree is only possi-
ble for directed graphs, where one can distinguish the
direction of the relationship (i.e. who chooses whom).
Betweenness is another network measure also relating to
the centrality of an individual on a network. It respects
the extent to which an individual lies “between” the other
individuals in a social network [49,50]. Betweenness has
a very different nature from degree centrality measures.
A person with a low degree centrality might keep playing
a central role on a network if laying between many others.
Even if that person has just a few ties (low degree cen-
trality), others relating to that person’s alters might de-
cide to search that person to reach more individuals in
the network (high betweenness centrality). Our between-
ness measure is also different from brokerage because
whereas I measured betweenness as the probability that
the ego lies on the shortest directed path between two
other individuals, brokerage was measured by analyzing
the number of pairs not directly connected in the ego
networks (cf. [48]).
1The Moran statistic is constructed much like a regular correlation
coefficient. It indexes the product of the differences between the scores
of two actors and the mean, weighted by the actor’s similarity (or
closeness of the actors). In a second step, this sum is taken in ratio to
variance in the scores of all actors for the mean [48].

M. P. LOPES 6
After computing these indices of network centrality I
directly tested H2 and H4 with a t-test analysis between
high and low optimists and between high and low alter-
optimists. These analyses were also run with UCINET 6
[45], giving a more accurate assessment of the differ-
ences for network data. Traditional statistical packages
are not much appropriate for treating network data be-
cause rather than describing distributions of actors, net-
work analysis concerns describing distributions of rela-
tions among actors. This is particularly important be-
cause “observations” in network data are not independent
samplings from populations and, thus, the standard for-
mulas for computing standard errors and tests on attrib-
utes generally assuming independent relationships are
not appropriate [48]. The t-tests computed by UCINET
are interpreted in the same way as those found in other
statistical packages.
5. Results
5.1. Homophily and Heterophily in Optimistic
Social Networks
The clique structure for high and low optimists and for
high and low alter-optimists is presented in Tables 1 and
2, respectively. The results showed a low tendency for
the existence of homophilic cliques, with the ratio of
these kinds of cliques for each of the networks ranging
from 20.00% to 33.33% for optimism, and from 30.00%
to 42.86% for alter-optimism. At the group level, these
are empirical evidences in support of heterophilic rela-
tionships (H1b and H3b) and against homophily (H1a
and H3a).
The results for the dyadic relationships give a more
specific and accurate picture of the character of the rela-
tionships. Results from the Moran Statistics of autocor-
relation are presented in Table 3. For all the four net-
works we analyzed, the results showed that the autocor-
relation did not reach statistical significance for the op-
timism trait, evidencing that the homophilic/heterophilic
character of these networks is independent from the op-
timism trait of individuals (i.e. from being high or low
optimist). In contrary, the results regarding alter-opti-
mism were quite different. In fact, concerning alter-op-
timism, there was a significant tendency for heterophilic
dyadic relationships for all the four networks. In the
whole, I have not found support for the existence of an
Table 1. Clique structure for the social networks of high and low optimists.
Clique Members*
Clique
Personal Issues Network Advice-Seeking Network Problem-Solving Network Innovation Network
1 19, 20, 23, 26 19, 20, 23, 26, 32 4, 8, 9 1, 33, 41
2 19, 20, 26, 32 3, 41 4, 10, 35 1, 3, 41
3 19, 20, 22, 32 4, 10, 35 4, 15, 28 4, 10, 35
4 1, 3, 41 5, 16, 28 3, 13, 41 6, 12, 18
5 4, 10, 35 6, 12, 18 1, 3, 41 1, 13, 41
6 5, 16, 28 22, 33, 34 1, 33, 41 14, 21, 39
7 6, 12, 18 29, 38, 40 6, 12, 17 5, 16, 28
8 6, 12, 37 6, 12, 18 19, 20, 23, 26, 32
9 13, 37, 41 12, 17, 37 25, 29, 38
10 29, 38, 40 13, 37, 41 25, 29, 30
11 14, 21, 39
12 19, 20, 23, 26, 32
13 20, 32, 37
14 22, 33, 34
15 25, 29, 38, 40
“Uncliqued” 2, 7, 8, 9, 11, 14, 15, 17, 21, 24,
25, 27, 28, 30, 31, 33, 34, 36, 39
1, 2, 7, 8, 9, 11, 13, 14, 15, 17, 21,
24, 25, 27, 30, 31, 36, 37, 39
2, 5, 7, 11, 16, 24,
27, 30, 31, 36
2, 7, 8, 9, 11, 15, 17, 22,
24, 27, 31, 34, 36, 37
% Homophilic Cliques 20.00 28.57 33.33 20.00
*High optimists are bolded.

M. P. LOPES 7
Table 2. Clique structure for the social networks of high and low alter-optimists.
Clique Members*
Clique
Personal Issues Network Advice-Seeking Network Problem-Solving Network Innovation Network
1 19, 20, 23, 26 19, 20, 23, 26, 32 4, 8, 9 1, 33, 41
2 19, 20, 26, 32 3, 41 4, 10, 35 1, 3, 41
3 19, 20, 22, 32 4, 10, 35 4, 15, 28 4, 10, 35
4 1, 3, 41 5, 16, 28 3, 13, 41 6, 12, 18
5 4, 10, 35 6, 12, 18 1, 3, 41 1, 13, 41
6 5, 16, 28 22, 33, 34 1, 33, 41 14, 21, 39
7 6, 12, 18 29, 38, 40 6, 12, 17 5, 16, 28
8 6, 12, 37 6, 12, 18 19, 20, 23, 26, 32
9 13, 37, 41 12, 17, 37 25, 29, 38
10 29, 38, 40 13, 37, 41 25, 29, 30
11 14, 21, 39
12 19, 20, 23, 26, 32
13 20, 32, 37
14 22, 33, 34
15 25, 29, 38, 40
“Uncliqued” 2, 7, 8, 9, 11, 14, 15, 17, 21, 24,
25, 27, 28, 30, 31, 33, 34, 36, 39
1, 2, 7, 8, 9, 11, 13, 14, 15, 17,
21, 24, 25, 27, 30, 31, 36, 37, 39
2, 5, 7, 11, 16, 24,
27, 30, 31, 36
2, 7, 8, 9, 11, 15, 17, 22,
24, 27, 31, 34, 36, 37
% Homophilic Cliques 30.00 42.86 33.33 30.00
*High alter-optimists are bolded.
Table 3. Moran statistics for the social networks.
Moran Statistics*
Network
Optimism Trait Alter-Optimism
Autocorrelation −0.027 −0.054
Significance 0.358 0.001
Permutation Average−0.025 −0.025
Who do you turn to when you have to talk about a personal issue?
Standard Error 0.007 0.008
Autocorrelation −0.025 −0.053
Significance 0.481 0.001
Permutation Average−0.026 −0.025
Who do you turn to for advice when you have to make an important or hard decision?
Standard Error 0.009 0.007
Autocorrelation −0.028 −0.051
Significance 0.280 0.010
Permutation Average−0.025 −0.025
Who do you turn to when you have a new study-related problem to solve?
Standard Error 0.007 0.009
Autocorrelation −0.028 −0.052
Significance 0.328 0.002
Permutation Average−0.025 −0.025
Who do you turn to discuss study-related innovative ideas?
Standard Error 0.008 0.009
*Significance based on a permutation approach with the number of permutations set at 1000.

M. P. LOPES 8
homophilic (H1a) or heterophilic (H1b) tendency ac-
cording to optimism trait, nor for the existence of homo-
phily according to alter-optimism (H3a), but I did find
support for an heterophilic bias regarding alter-optimism
(H3b).
5.2. Network Centrality in Optimistic Social
Networks
Given the independence of the social network structure
from the individual’s optimism trait, it is not surprising
that I have not found differences between high and low
optimists in network centrality. This was true for all the
networks we studied and for all the network measures we
considered (Table 4).
For alter-optimism, though, the picture was very dif-
ferent. The differences were generally significant with
high alter-optimists revealing higher values for network
centrality measures (Table 5). As can be seen from Ta-
ble 5, the differences were even higher for the work-
related networks (problem solving network and innova-
tion network) than for personal-related networks (per-
sonal issue network and advice-seeking network). Over-
all, the results provide clear supporting evidence for H4,
but not for H2.
Table 4. Centrality measures for high and low optimists.
Network Centrality Measures High OptimistsLow Optimists t-Test (Difference)
Mean9.00 5.82
Out-degree SD 6.56 4.83 3.176
Mean8.42 6.65
In-degree SD 6.75 4.42 1.77
Mean7.56 2.78
Betweenness SD 10.05 5.78 4.71
Mean2.63 1.56
Who do you turn to when you have to
talk about a personal issue?
Brokerage SD 3.38 2.90 1.07
Mean6.58 6.88
Out-degree SD 5.66 5.35 0.30
Mean6.71 6.71
In-degree SD 5.32 5.10 0.002
Mean3.81 1.21
Betweenness SD 7.74 3.88 2.61
Mean1.40 0.85
Who do you turn to for advice when you have to
make an important or hard decision?
Brokerage SD2.34 1.51 0.54
Mean10.29 10.00
Out-degree SD5.78 8.02 0.29
Mean10.17 10.18
In-degree SD9.16 10.41 0.01
Mean30.78 22.20
Betweenness SD35.66 27.60 8.58
Mean4.00 2.56
Who do you turn to when you have a
new study-related problem to solve?
Brokerage SD5.97 3.63 1.44
Mean8.63 9.65
Out-degree SD6.01 6.30 1.02
Mean8.96 9.18
In-degree SD5.85 6.30 0.22
Mean24.77 30.50
Betweenness SD30.90 46.19 5.73
Mean0.85 1.24
Who do you turn to discuss study-related innovative ideas?
Brokerage SD1.92 1.62 0.38

M. P. LOPES 9
Table 5. Centrality measures for high and low alter-optimists.
Network Centrality Measures High Alter-OptimistsLow Alter-Optimists t-Test (Difference)
Mean7.90 7.48
Out-degree SD6.47 5.74
0.42
Mean11.00 4.52
In-degree SD 5.80 4.11 6.48***
Mean7.30 4.00
Betweenness SD 9.04 8.36 3.30
Mean3.28 1.14
Who do you turn to when you have
to talk about a personal issue?
Brokerage SD 3.86 1.98 2.13*
Mean7.50 5.95
Out-degree SD5.56 5.40 1.55
Mean9.50 4.05
In-degree SD 4.70 4.23 5.45***
Mean4.35 1.19
Betweenness SD 7.74 4.68 3.16*
Mean3.28 1.14
Who do you turn to for advice when you
have to make an important or hard decision?
Brokerage SD3.86 1.98 2.13*
Mean12.10 8.33
Out-degree SD6.80 6.27 3.77
Mean15.35 5.24
In-degree SD11.24 3.66 10.11***
Mean37.06 17.85
Betweenness SD33.21 29.56 19.22*
Mean5.63 1.29
Who do you turn to when you have a
new study-related problem to solve?
Brokerage SD6.45 1.73 4.34**
Mean11.05 7.14
Out-degree SD6.26 5.38 3.91*
Mean12.70 5.57
In-degree SD5.18 12.7 7.13***
Mean43.03 12.02
Betweenness SD44.75 21.39 31.00**
Mean1.83 0.24
Who do you turn to discuss study-related
innovative ideas?
Brokerage SD2.22 0.68 1.59***
*p < 0.05; **p < 0.01; ***p < 0.001.
6. Discussion
In this paper I studied how optimism trait and al-
ter-optimism effect relate to the social network positions
of individuals. Contrary to previous research that found a
positive association between optimism and network de-
gree/centrality [12] I found no relationship between the
levels of optimism and network position. This is possibly
due to the methodological option to rely on a hetero-
rating evaluation to measure network degree. Given that I
used sociometric data to access an individual’s network
centrality I was able to avoid common-source bias,
contrary to the study of Brissete et al. [12] where indi-
viduals provided their own network data and potentially
biasing their real position on the social network. When
controlling for this bias, it seems that being optimistic or
not, does not make a difference.
More generally, these results are thus in contrast to the
literature stating a relationship between personality traits
and social network positions. With few exceptions (e.g.
[51]), in most of these studies (e.g. [6,9]), as in Brissete
et al.’s [12], both social network and personality mea-
sures have been collected from the same source, even if

M. P. LOPES 10
there is now substantive research evidencing that infor-
mation concerning to whom an ego is tied is highly sub-
ject to bias [52,53]. In this line, the results of the present
study question the accuracy of previous research in ac-
cessing an individual’s social network position and its
relationship with personality, and points to a need to re-
vise the interpretation of those studies’ conclusions as the
effect of a personality assessment bias.
The results also disconfirm the existence of a relation-
ship between personality and social structure tendencies
for homophily and heterophily. Although homophily has
been claimed to constitute a general phenomena includ-
ing for personality [16], I found no evidence for such a
structuring effect concerning the optimistic personality
trait. This result seems intriguing because common-sense
or personal experience has many times advised that peo-
ple are often inclined to contact with people who are in
high spirit for help, as is the case of a depressive person
seeking support or counseling.2 However, being in a
“high spirit for help” does not require that the helping
person is feeling positive. It just requires that she is able
to generate positive feelings in the depressed person (i.e.,
being alter-optimist), which entails a clear distinction
between the psychological state and the alter-capability
of the helper.
This is reinforced by the results that were obtained for
alter-optimism. In sharp contrast to a person’s optimism
level, whether one is an alter-optimist or not, does seems
to make a difference concerning social structures. The
results show that, because high alter-optimism people are
sought more on their social networks (including being
sought by low alter-optimists), social relationships have a
proclivity to be heterogeneous. As such, high alter-opti-
mists, regardless of being optimistic or not, do seem to
play a major role in promoting optimism in social net-
works.
Despite these contributions, the conclusions of the pre-
sent study are not without its limitations. One of the
limitations of this study is the fact that our data is cross-
sectional and, as such, it does not allow us to analyze the
dynamics of the relationships between high and low op-
timists and alter-optimists. For example, when interact-
ing with a low alter-optimism person, a high alter-opti-
mism individual might change his behavior and become
lower alter-optimist. The same may happen to his/her
counterpart. The study of the dynamics of this kind of
relations is a promising field of research and will take us
further to another step in understanding how optimism
can be generated in social relations.
Another limitation of this study is that I did not collect
a hetero-rating measure of the optimistic trait. Specifi-
cally, I could have asked how individuals would have
rate their colleagues regarding their optimism levels. In
fact, I did not ask respondents to rate how much they
think their colleagues were optimists, but instead how
they felt after interacting with each one of them. Asking
them additionally to rate their colleagues’ optimism trait
would have allowed me to directly access if respondents
have really over-inflated their personality trait.
Notwithstanding these issues, tough, this study consti-
tutes a first step in trying to understand how alter-opti-
mism relationships helps to explain the social network
characteristics regarding advice-seeking, problem-solv-
ing and innovation in organizations, and this finding has
important theoretical and methodological implications
for researchers on optimism and for practitioners as well.
It is usually assumed that expressed behavior is equi-
valent to what individuals are feeling and that positive
psychological states will be behaviorally expressed in
such positive ways. This study shows that this is not al-
ways true. I found that some low optimistic individuals
are able to generate more optimistic states in others, the
same way that some high optimistic individuals can in-
duct low optimistic states. This builds on emotional con-
tagion theories which have taken for granted the assump-
tion that there is equivalence between expressed behavior
and inducted emotion. Optimism has been shown to be
associated to emotional states, and thus the conclusions
of the present study might be extensive to emotional
contagion. Future studies should better analyze this hy-
pothesis and search for the conditions that trigger and the
processes that explain such asymmetric relationships.
A major theoretical contribution of the present study is
that research on optimism and positive psychological
states should move their focus from positive psychologi-
cal characteristics into the study of positive relationships
and behaviors. I found evidence that, to create positive
optimistic environments, one does not necessarily need
optimistic individuals but “mere” alter-optimism indi-
viduals.
Indeed, these conclusions are in synchrony with work
taking social relationships as the ontological level into
which we should start looking to enhance positive rela-
tions and psychological states (e.g. [34]). The results of
this study support the need to direct research on opti-
mism and social networks from individual traits and
states to interpersonal relationships as well as the ur-
gency to establish an explicit agenda to deeply under-
stand what high alter-optimists actually do to generate
such optimistic states of mind in those with whom they
interact with.
7. Conclusion
The aim of this study was to gain a deep understanding
of how optimism relates to social network position. In
the overall, our results provide evidence that optimism
2I thank an anonymous reviewer for noticing this.

M. P. LOPES 11
trait is independent from the way social networks of per-
sonal-issue sharing, advice-seeking, problem-solving, and
innovation, are structured, suggesting that optimism does
not seem to relate to the position that a person occupies.
In contrary, the ability to generate optimism in others—
i.e., alter-optimism—does provide a good explanation of
one’s social network position. Those who were rated by
their colleagues as high alter-optimists tended to occupy
more central social networks. I hope this work inspires
future research to increase our understanding of the
mechanisms through which this occurs.
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