Two part question for a research paper

Two part question: I have attached my first paper which is the synopsis and one of the references that is needed. Below is the other two part of the assignment

Part 1- you will need to submit a draft of your research paper with several components to ensure you are on the right track:

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an outline of your research and the intended direction of your argument,
at least five scholarly resources you have found that contribute to your topic, and
an abstract outlining your topic and your subsequent findings.

This research draft should be at least two pages, double spaced, with Times New Roman 12 point font, and use appropriate APA style writing. You should be thorough in your research so your professor (or a colleague) could adequately determine your intended research and the direction you are going with your paper.

Part 2- you are to submit the research paper you have been working on throughout the course. The research paper should be double spaced and at least four pages in length, not including the title, abstract, and reference pages, Times New Roman 12 point font, and with appropriate APA style. The research paper must contain a minimum of five scholarly references supporting your argument surrounding your topic, answering your research question(s), or supporting your points about the need for further research on this topic.

COMMUNICATIONS 4

Communications

Alex Blas

BCJ 4301

Columbia Southern University

Running head: COMMUNICATIONS 1

Communications

This synopsis will detail the importance of communication, how it affects the individual and teams at all level on their performance. Communication is an important aspect that revolves around everything that we do, from being a supervisor to leading a team. Communication is accomplished through many different forms such as verbal and non-verbal communication. Such as written words, speaking and not speaking. The decisions we make regards information through the means of communicating with one another. As a leader, it is important to have the ability to communicate openly and clearly.

A team cohesion and the dynamic process revolves around members communicating. A team must be able to successfully communicate its intent and goals. Such as a sport setting, for a team to win a game or be effective at what they do, they have to communicate. I will use my personal and professional experience to discuss this topic. From the personal experience, it will be from a sports team aspect when I used to be a coach for an outrigger canoe paddling team on Guam. For a team to successfully paddle a boat and be in harmony, all members have to have the same vision. That is done by communicating first of what the goals and objective are for that specific team. Understanding the vision will unite a team together to win and take first place.

From a professional experience, I will discuss my military experience with communication within a team. Leaders must communicate effectively to come up with a decision or a solution. Sometimes this is done by developing different strategies, procedures and trail and errors. Communication eventually establishes trust within a team as it is a two way street. Being able to clearly communicate and have your audience or team understand it will help clear any misconceptions. As a leader, we must be able to communicate our expectations up and down the chain of command. Finally, once communication is established we must maintain that effectiveness through listening and understanding the different elements of thoughts. The results of this research paper will explain the benefits of communication as it affects all individuals and teams at all level with their performance.

References

Whisenand, P. M., & McCain, E. D. (2015). Supervising police personnel: Strengths-based leadership (8th ed.). Upper Saddle River, NJ: Prentice Hall.

McLaren, C. D., & Spink, K. S. (2019). Examining the prospective relationship between communication network structure and task cohesion and team performance. Group Dynamics: Theory, Research, and Practice. https://doi-org.libraryresources.columbiasouthern.edu/10.1037/gdn0000110

Group Dynamics: Theory, Research, and
Practice
Examining the Prospective Relationship Between
Communication Network Structure and Task Cohesion
and Team Performance
Colin D. McLaren and Kevin S. Spink
Online First Publication, October

4, 2019. http://dx.doi.org/10.1037/gdn0000110

CITATION
McLaren, C. D., & Spink, K. S. (2019, October 14). Examining the Prospective Relationship Between
Communication Network Structure and Task Cohesion and Team Performance. Group Dynamics:
Theory, Research, and Practice. Advance online publication.
http://dx.doi.org/10.1037/gdn0000110

Examining the Prospective Relationship Between Communication
Network Structure and Task Cohesion and Team Performance

Colin D. McLaren and Kevin S. Spink
University of Saskatchewan

It has been reported in past research that information exchange at the individual and
team level (i.e., communication network structure) is associated with higher perceived
task cohesion and team performance. The current study extended these findings to
intact sport teams and tested these relationships across time. Competitive basketball
athletes (N � 133, k � 15; Mage � 27.4, SD � 7.5 years) completed measures of
information exchange with teammates during a game (peer nominations using social
network analysis) and task cohesion. Performance was collected using objective win-
ning percentage. A prospective design across the first half of a competitive season was
used. Controlling for early season perceptions of task cohesion, interacting with a
higher number of teammates, and higher collective information exchange at the team
level at early season significantly predicted later task cohesion perceptions (n � 70;
pseudo R2 � .49). Using a multilevel model, the overall variance accounted for was
captured at both the individual (42%) and team (7%) level. In a second analysis, a
hierarchical regression controlling for early season team performance found that
information exchange of the team as a whole at early season significantly predicted
team performance (n � 109; Radj2 � .48, p � .001). These results highlight a pattern of
relationships between information exchange and both task cohesion and team perfor-
mance consistent with past theorizing. In terms of uniqueness, specific aspects of
information exchange (i.e., individual vs. team level network structure) differed for
each dependent variable.

Keywords: information exchange, social network analysis, centrality, density, group
dynamics

Although support for the usefulness of co-
hesion in sport is well established (e.g., Car-
ron & Eys, 2012; Spink, 2016), the processes
by which individual members come to draw
cohesion perceptions receives less attention
(McLaren & Spink, 2018b; Spink, McLaren,
& Ulvick, 2018). If the long-term practical
goal of practitioners is to impact important

individual and team outcomes by altering
group cohesion perceptions, understanding
the possible sources of information that ath-
letes use to form these perceptions would be
instructive. In the context of sport, the ac-
cepted definition of group cohesion is “ . . . a
dynamic process which is reflected in the
tendency for a group to stick together and
remain united in pursuit of its instrumental
objectives and/or for the satisfaction of mem-
ber affective needs” (Carron, Brawley, &
Widmeyer, 1998, p. 213). Further, as this
definition is based on the assumption that
cohesion “ . . . develops as a function of the
socialization and interaction processes that oc-
cur within groups” (Carron & Brawley, 2000, p.
102), one variable that could serve as a potential
source of information for perceived cohesion is
team member communication.

Colin D. McLaren and Kevin S. Spink, College of Kine-
siology, University of Saskatchewan.

This research was supported by a doctoral scholarship to
Colin D. McLaren (Grant 752-2014-2655) from the Social
Sciences and Humanities Research Council of Canada.

Correspondence concerning this article should be ad-
dressed to Colin D. McLaren, College of Kinesiology, Uni-
versity of Saskatchewan, 87 Campus Drive, Saskatoon SK
S7N 5B2, Canada. E-mail: colin.mclaren@usask.ca

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Group Dynamics:
Theory, Research, and Practice

© 2019 American Psychological Association 2019, Vol. 1, No. 999, 000
ISSN: 1089-2699 http://dx.doi.org/10.1037/gdn0000110

mailto:colin.mclaren@usask.ca

http://dx.doi.org/10.1037/gdn0000110

In terms of nuance around communication, it
also has been noted that the sources of informa-
tion from which members of a sport team draw
their cohesion perceptions could span personal
experiences within the group as well as the
integration of information about the team as a
whole (Carron & Brawley, 2000; Carron, Braw-
ley, & Widmeyer, 2002), Together, these indi-
vidual and team aspects are selectively filtered
and interpreted in a way that informs the ath-
lete’s perception as to the unity of the team in its
pursuit of different team goals and objectives
(i.e., cohesion). This dual source informing co-
hesion maps well with team member communi-
cation as athletes have unique experiences com-
municating with teammates (individual level)
and share in the collective communication that
exists between all team members (team level).
As such, examining member communication
from these two different perspectives would be
of value to those interested in understanding
how perceptions of cohesion emerge in the sport
setting.

Acknowledging the importance of team
member communication as a structural compo-
nent of the team (i.e., individual and team level)
as it relates to group cohesion is not a new idea
(e.g., Leavitt, 1951; McGrath, 1984; Shaw,
1964). In a review of the group dynamics liter-
ature, Cartwright (1968) described group inter-
actions as one incentive property that members
consider in determining the attractiveness of a
group. First, Cartwright notes that at the team
level, individuals are more satisfied with their
membership in high task complexity groups like
sport teams (and hence more likely to remain)
when the structure of communication does not
rely on a small number of team members. At the
individual level, members are more satisfied
when they possess the capacity to be involved in
the various interactions of the team (i.e., com-
municate with many others; see also Shaw,
1964).

In the context of organizational psychology,
Balkundi and Harrison (2006) conducted a
meta-analysis to synthesize the communication
structure/attraction to the team (operationalized
as team viability) relationship in a sample of
over 3,000 intact organizational groups. Over-
all, the results supported the theorizing of early
group dynamics researchers that higher member
communication composed of task-related (i.e.,
instrumental) interactions between all members

of a group (who themselves also are well-
connected) was positively associated with team
viability (which included measures of team co-
hesiveness). It was argued that teams with more
instrumental connections would be those who
communicate often, which could serve to re-
duce the likelihood of fragmentation and in-
crease cohesiveness (Balkundi & Harrison,
2006).

As a multidimensional construct (Carron,
Widmeyer, & Brawley, 1985), cohesion can be
differentiated into four different subscales that
differ along two general dimensions: (a) per-
sonal motives/attractions (ATG) versus the
group as a totality (group integration [GI]), and
(b) task versus social orientations. Groups form
for different reasons and have different goals/
objectives (Carron & Brawley, 2000), so it is
important to recognize that information ex-
change will not serve to inform all dimensions
equally. Assessing team cohesion through a so-
cial lens (i.e., social cohesion) should be in-
formed by interpersonal or affective experi-
ences with other members (e.g., friendship;
Herbison, Benson, & Martin, 2017), whereas
either task or social cohesion along the individ-
ual attraction dimension (ATG) should be re-
lated to features of the group that a member
finds personally attractive (e.g., status rank;
Shanthi Jacob & Carron, 1998). Information
exchange, however, relates more to the way a
team comes to function as an integrated unit
around the task, which clearly aligns it with the
cohesion subscale of Group Integration-Task
(GI-Task; Carron et al., 1985), which is the
focus of this study.

Preliminary empirical research in sport has
supported this speculation to the extent that
athletes who reported that they communicated
with a higher number of team members also
reported higher task cohesion perceptions in
terms of the group as a totality (GI-Task;
McLaren & Spink, 2018a, 2019, Study 1). Con-
sidering the individual athlete and the sport
team together, research with hypothetical sport
teams (McLaren & Spink, 2019, Study 2) found
that when members personally engaged in
higher information exchange, and the team as a
whole engaged in more information exchange,
perceived task cohesiveness was higher. These
findings are consistent with the idea that engag-
ing in information exchange with teammates
should provide an athlete with a perceived win-

2 MCLAREN AND SPINK

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dow into how united the team is in pursuit of its
task goals and objectives (i.e., GI-Task). When
individuals exchange information with a higher
number of their teammates and the remaining
members of the team also exchange information
with most members, one plausible interpretation
by the individual is that teammates are “in it
together” in pursuing task goals (i.e., higher in
task cohesion; McLaren & Spink, 2019).

One other conclusion drawn in early group
dynamics research (e.g., Leavitt, 1951), and
confirmed with meta-analytic findings
(Balkundi & Harrison, 2006), is the importance
of the communication network for team perfor-
mance. Consistent with research from organiza-
tional group dynamics (e.g., Balkundi & Harri-
son, 2006; DeChurch & Mesmer-Magnus,
2010), groups in which there was more infor-
mation exchange between more members had
greater team performance. Results from a meta-
analysis by Balkundi and Harrison (2006) found
that higher information exchange at the individ-
ual and team level together would positively
predict team performance metrics for different
work teams. This has been extended to the
context of sport where it has been reported that
a more successful team (compared with the less
successful one in a head-to-head competition)
engaged in a greater amount of information
exchange during the game (McLaren & Spink,
2017). One potential interpretation is that when
more members are exchanging information with
teammates during a game, it is more likely that
the appropriate information is communicated at
the correct time. In this way, the likelihood of
possible process losses stemming from lack of
information would decrease, and inter alia, team
effectiveness would increase (e.g., Steiner,
1972).

Based on the preceding discussion, two im-
portant considerations are worth highlighting
with respect to the relations between informa-
tion exchange and both task cohesion and team
performance. First, member information ex-
change can be parceled into individual and team
structural components. A team is composed of a
number of different individuals, all of whom
may engage in information exchange to a dif-
ferent degree. While overlap exists, the number
of members who a specific individual ex-
changes information with does not necessarily
reflect the degree to which the remainder of the
team exchanges information. This has not re-

ceived consideration in the extant sport litera-
ture, suggesting that key information may be
lost.

Consider a hypothetical team member who
has less involvement in the information ex-
change patterns of the group. This member ex-
periences the social situation of the group in a
unique manner vis-à-vis a member who is very
central to information exchanges (i.e., ex-
changes with many others). In accordance with
this higher information exchange involvement,
this member also would perceive the team as
more cohesive. Support can be found in a recent
sport study where athletes who exchanged in-
formation with more of their teammates held
higher perceptions of task cohesion compared
with those who exchanged information with
fewer teammates (McLaren & Spink, 2019,
Study 1). These results, however, are based on
concurrent data collection making it difficult to
discern the flow of causality (e.g., does higher
information exchange lead to higher cohesion,
or vice versa?).

In addition, evidence for this relationship has
tested information exchange at the individual
level and ignored the collective information ex-
change of an entire team as a construct (e.g.,
Cranmer & Myers, 2015; McLaren & Spink,
2018a, 2018b). In one experimental study, the
information exchange patterns of an entire team
were matched with the individual exchange be-
haviors using hypothetical vignettes (McLaren
& Spink, 2019). Higher exchange patterns were
associated with higher anticipated task cohe-
sion. Bringing together the extant literature, it
follows that a positive relationship would exist
between information exchange at the individual
and team level and both perceived task cohesion
and objective team performance.

Reflecting on the direction of this relation-
ship, research in the organizational domain sup-
ports information exchange leading to per-
ceived task cohesion. For instance, past
experimental research in other group contexts
(e.g., organizational and military psychology)
supports the claim that under temporal urgency,
groups exposed to a high task cohesion manip-
ulation communicated more task-relevant in-
formation during a performance task than
those exposed to a low task cohesion manip-
ulation (Zaccaro, Gualtieri, & Minionis,
1995). However, sport teams are unique from
business, work, or student groups (Weinberg

3NETWORK STRUCTURE, COHESION, AND PERFORMANCE

& McDermott, 2002) in that members of a sport
team must execute their specific role responsi-
bilities against an opponent in real time. This
makes coordinating efforts more critical and
places a premium on communication. As team
performance in sport is not simply the sum of
individual achievements, it is possible that the
individual level of information exchange will be
less of a factor in predicting unique variance
compared to other organizational settings
(where tasks may be more divisible and differ in
interdependence structures; Steiner, 1972). To
date, individual and team components of mem-
ber interactions have not been examined in in-
tact teams.

In addition to this individual/team interface,
the second consideration is that these relation-
ships have not been tested across time in the
sport context. The research in sport to date is
based on cross-sectional field (e.g., Cranmer &
Myers, 2015; McLaren & Spink, 2018b) and
hypothetical vignette data (e.g., McLaren &
Spink, 2019, Study 2). Gathering concurrent
data might mask how the variables are associ-
ated with each other at different points in the
development of a group (Cronin, Weingart, &
Todorova, 2011). Further, a reliance on concurrent
data to inform research and practice has the po-
tential to overlook the role of method variance in
these relationships (Podsakoff, MacKenzie, Lee,
& Podsakoff, 2003), and undermine any chance of
creating a future case for causality. The use of a
prospective design is one way to begin to address
these potential limitations.

The purpose of the current study was to con-
structively replicate (Hüffmeier, Mazei, &
Schultze, 2016) the relationship between com-
munication as information exchange and the
outcomes of task cohesion and team perfor-
mance in intact sport teams. A constructive rep-
lication differs from exact replications in that it
seeks not only to add evidence for a relation-
ship, but also refines or extends what is already
known. To date, preliminary research has been
based on individual perceptions of typical team
communication patterns (McLaren & Spink,
2018b) and consideration of communication
structure from only the individual or from com-
bined individual and team communication using
hypothetical vignettes (McLaren & Spink,
2019). Collectively, these results suggested that
information exchange carried out between a
higher number of members (individual and

team levels) was associated with higher percep-
tions of task cohesion and overall team perfor-
mance. However, a next step in testing infor-
mation exchange as a potential cue for sport
group cohesiveness and an indicator of team
performance is needed. This involves using a
design in which the communication measure
precedes the assessment of cohesion and perfor-
mance in intact teams.

Given the first-generation nature of the re-
search question (Zanna & Fazio, 1982), it was
deemed important to test these relationships
during an appropriate stage of group develop-
ment. According to past meta-analytic research,
communication is most predictive of cohesion
and performance when it is examined earlier in
the life span of a group (Balkundi & Harrison,
2006). As such, these relationships were tested
in this study across the first half of the compet-
itive season of intact sport teams. Further, it was
important to adopt a methodology that allowed
for the individual/team interface to be appropri-
ately captured.

Social Network Analysis

This study used social network analysis (SNA),
which uses individual nominations of teammates
to model the social connections between members
of a group in the form of a network (a set of actors
and the social relations between these actors; Bor-
gatti, Everett, & Johnson, 2013; Katz, Lazer, Ar-
row, & Contractor, 2004; Wäsche, Dickson, Woll,
& Brandes, 2017). In this study, the exchange of
information between two athletes represents the
social connection. Portions of the network can be
isolated to examine an individual and his or her
connectedness, or all connections can be consid-
ered simultaneously as a team-level characteristic.

At the level of the individual athlete, an ap-
propriate metric that can be derived from the
network is individual centrality (Borgatti et al.,
2013). Typically used as an indicator of pres-
tige, importance, or social capital (Katz et al.,
2004), centrality can be further divided based
on the direction of a social connection: outde-
gree centrality is the number of group members
with whom a given individual reports having a
connection, and indegree centrality is the num-
ber of group members that report having ties
with a given individual. At the team level, net-
work density represents all of the connections
between all of the team members (Borgatti et

4 MCLAREN AND SPINK

al., 2013). In terms of this study, density would
generate an estimate of collective information
exchange by the team as a whole.

It is worth noting that there is likely some
conceptual overlap between network density
and cohesion. For instance, one of the items
used to assess task cohesion reflects an assess-
ment of communication network density. Fur-
ther, network density also has been called net-
work cohesion on occasion (e.g., Wise, 2014).
In the current study, we differentiate between
cohesion as a structural component of a network
(a term to describe density; Wise, 2014) and our
focus on task cohesion as a psychological con-
struct that encompasses perceptions of group-
level functioning.

Taken together, two hypotheses were posited
for the current study. In terms of cohesion, it
was hypothesized that an athlete who (a) re-
ported exchanging information with a higher
number of teammates, and (b) is a member of a
team that engaged in higher collective informa-
tion exchange at an early season measurement
point would be positively associated with later
perceptions of task cohesion with respect to
overall team functioning (GI-Task). Aligning
this hypothesis with specific SNA metrics, it
was predicted that athletes with higher outde-
gree centrality scores at early season would
report higher task cohesion (GI-Task) assessed
later in the season. In contrast, indegree central-
ity would not significantly predict later task
cohesion perceptions. The rationale for this fol-
lows. Returning to the formation of cohesion
perceptions (e.g., Carron & Brawley, 2000),
individual athletes are likely to recall who they
exchanged information with as part of their own
personal experiences. In contrast, the experi-
ences of other athletes identifying those with
whom they exchange information should not
directly serve to inform member perceptions in
the same way, as the member is not privy to the
fact that others have identified them as sharing
information. A null result between indegree
centrality and cohesion would offer validity to
the conceptualization of cohesion insofar as the
formation of cohesion perceptions. At the team
level, the second hypothesis would indicate that
higher network density (from the SNA perspec-
tive) early in the season would positively pre-
dict task cohesiveness later in the season (GI-
Task).

Second, team performance represents a team-
level variable that is likely influenced by the
overall ability of the team to integrate its com-
ponent parts. Therefore, the information ex-
change behaviors of each member on his or her
own (both personal experiences and other team-
mate reports) is less likely to predict overall
team effectiveness. Rather, the more likely out-
come is that team outcome would be associated
with the information exchange of all members
together. As such, it was hypothesized that the
team-level aspect of information exchange at an
early season measurement period would posi-
tively predict later team performance, while
early season individual-level information ex-
change would not be associated with team per-
formance.

Method

Participants

A total of 133 adult basketball athletes who
were members of competitive teams in two
adult sport leagues participated in this study.
Specifically, 109 athletes completed the study in
the early season and 102 athletes completed the
study during the later season measurement pe-
riods (78 participants completed both early and
later season measures). Male athletes were re-
cruited from teams in the top two divisions of a
six-division league (k � 11) and female athletes
were recruited from teams in the top division of
a four-division league (k � 4). Participants had
an average age of 27.4 (SD � 7.5 years, range
18 –53), and an average of 17.4 and 4.4 years of
experience competing in basketball, and on
their current team, respectively. With respect to
player status within the team, 95 athletes (71%)
self-identified as a starter.

Specific to the basketball leagues involved,
teams were required to have a team member as
a representative for administration purposes.
There were no coaches or official captains. As
such, decisions such as starting lineups, substi-
tutions, play calling, and weekly attendance
were at the discretion of the athletes. Players put
together a team, and then submitted the team to
the league. The submitted teams were placed
into specific competitive levels based upon past
season success (in order to achieve competitive
balance). Given the past histories of the teams
in this study, it was important to control for

5NETWORK STRUCTURE, COHESION, AND PERFORMANCE

perceived cohesion and team performance at
early season to control for any possible preex-
isting differences between teams.

Procedure

Following university research ethics ap-
proval, team representatives listed on the league
websites were contacted by the lead author with
an explanation of the research project. At this
time, permission also was requested from the
team representatives to recruit the remaining
members of the team at a weekly game early in
the competitive season. After providing in-
formed consent to participate in the research,
participants completed two separate question-
naires—the first on either the third or fourth
game of the season and the second on the sev-
enth or eighth game of the season (just before
the December break when the league shut down
for the holidays). All teams were scheduled to
compete in 15 regular season games.

At each measurement point, participants in-
dependently completed a paper-and-pencil
questionnaire package under the supervision of
the lead researcher. All participants present
agreed to participate. The questionnaire was
completed immediately following the game,
typically in the team bench area. All partici-
pants were informed that they could ask ques-
tions in the event that clarification was required,
and withdrawal from the study was possible at
any time. Survey completion lasted approxi-
mately 10 min at each measurement point. Fi-
nally, participants could enter a draw each time
they completed a questionnaire for a gift card to
a local restaurant as potential compensation for
their participation.

Measures

Information exchange. To measure the
exchange of information between athletes, par-
ticipants were asked to list those teammates
who were in attendance for the particular game
examined. This was done on a sheet of paper
that included spaces for names to be inserted.
Participants were asked to consider the mem-
bers with whom they exchanged information
during the game that just ended. To capture this,
participants were presented with the item “I
openly exchanged information with these team
members during the game,” and asked to place
a checkmark next to the name of each player

that applied to the statement. The item used was
adapted from Lee (1997), and has been used in
existing studies testing the association between
communication and group cohesion (e.g.,
McLaren & Spink, 2019).

Participant responses were translated into an
n � n adjacency matrix (where n was the num-
ber of team members present at that game)
using a binary 0 (did not exchange information)
or 1 (did exchange information). Rows (i) rep-
resented the members a specific individual
nominated as someone with whom they ex-
changed information during the game, while
columns (j) represented those from whom an
individual received nominations for exchanging
information during the game. Given that it was
possible to either be the sender or receiver of
information (without a need for reciprocity), the
network was directed rather than symmetrical
(cell ij may not be the same as cell ji). Diagonal
cells were left as missing values given that
athletes could not exchange information with
themselves. On average, 92.6% of eligible
group members were present at the early season
measurement period to complete the network
measure. The lowest total was 77.8% (7 of 9
members) with 8 of 15 teams having all mem-
bers present.

Using social network analysis (Borgatti et al.,
2013), it was possible to compute network met-
rics that directly corresponded to information
exchange at the individual level. Individual de-
gree centrality is the degree to which a specific
individual in the network is connected to others,
and can be divided into two separate metrics.
First, outdegree centrality is the number of
teammates with whom an individual reported
exchanging information during the game (cor-
respond to the rows in the matrix). Indegree
centrality, on the contrary, is the number of
teammates who reported exchanging informa-
tion with a given member during the game
(correspond to the columns in the matrix). Both
values were normalized as a proportion score to
control for minor differences in team size.
Based on the conceptualization of cohesion
(Carron et al., 1985), it is outdegree centrality
that would best capture personal experiences in
the team, whereas indegree centrality would
reflect the personal experiences of others. At the
team level of the information exchange network
structure, a metric of network density can be
calculated to estimate the number of exchanges

6 MCLAREN AND SPINK

that took place between all members of the team
as a whole (Borgatti et al., 2013).

Task cohesion. Perceptions of task cohe-
sion were measured with the positive wording
version (Eys, Carron, Bray, & Brawley, 2007)
of the Group Environment Questionnaire
(GEQ; Carron et al., 1985). The GEQ is an
18-item inventory that captures perceptions of
cohesion across two broad conceptual dimen-
sions: (a) task and social needs, and (b) per-
sonal attractions to the group and how the
group functions as a total unit. Given that the
exchange of information is situated at a group
level, five items that measured task cohesion
perceptions of the group as a totality were
assessed (GI-Task; e.g., “Our team is united
in trying to reach its goals for performance”).
Items were rated on a Likert-type scale an-
chored at 1 (strongly disagree) and 9
(strongly agree) with greater scores reflecting
greater task cohesion perceptions. Reliability
and validity for the original (Carron et al.,
1985) and positively worded (Eys et al.,
2007) versions of the GEQ have been dem-
onstrated in the context of sport. At both
measurement points, one item (“If members
of our team have problems in practice, every-
one wants to help them so we can get back
together again”) was removed for contextual
reasons (i.e., most teams did not have sepa-
rate practices). Internal consistency after re-
moving this item was found to be acceptable
at both time points (�early season � .83,
�later season � .85).

Team performance. An objective indica-
tor of a team’s overall performance was derived
from the winning percentage of a team. This
score was calculated based on the number of
games won as a proportion of the total number
of games played at the early and later season
measurement points. For example, a team with
three wins in four total games would have a
score of .750, where higher scores (ranging
from 0 to 1) reflected higher objective team
performance. This measure was similar to that
used in other sport team performance research
(e.g., Becker & Solomon, 2005; Benson, Siska,
Eys, Priklerova, & Slepicka, 2016).

Data Analysis

Prior to the main analysis, data were
screened for normality and multicollinearity.

Using a prospective design, the two study
hypotheses were tested in different ways. To
test the communication network/task cohe-
sion relationship, multilevel modeling using
hierarchical linear modeling (HLM Version
7.03; Raudenbush, Bryk, & Congdon, 2011)
was used. This was possible because later
season task cohesion was measured at the
individual level (a requirement of HLM),
which allowed for variance to be partitioned
into individual (Level 1) and team levels
(Level 2; Raudenbush & Bryk, 2002). The
deviance statistics were compared across two
nested models to determine if adding network
metrics in the full model predicted unique
variance in later season task cohesion beyond
that accounted for by early season task cohe-
sion perceptions alone (which was tested sep-
arately as the lone predictor in a smaller,
control Model 1; see Raudenbush & Bryk,
2002). For the full model (Model 2), early
season task cohesion and early season inde-
gree and outdegree centrality as Level 1 pre-
dictors and early season network density as a
Level 2 predictor were included as factors
informing later season task cohesion.1

To test the communication network/team per-
formance relationship, a hierarchical multiple
regression was used because team performance
was measured at the group level (i.e., Level 2)
and multilevel modeling requires a Level 1 out-
come measure. At Step 1 of the regression pre-
dicting later season team performance, early
season team performance was added as a con-
trol variable, while early season network den-
sity and both indegree and outdegree centrality
were added at Step 2. To determine the degree
to which the communication network structure
contributed to later season team performance,
the change in variance accounted for as well as
the semipartial (sr) correlations were examined
in the final model.

1 All data were collected following a weekly game. As
such, the current game outcome at the early season mea-
surement period also was entered as a control variable in
predicting later season task cohesion and team performance.
In both cases, the results were the same with the variables
entered or not entered. To maximize power, the game out-
come at early season was not entered as a second control
variable.

7NETWORK STRUCTURE, COHESION, AND PERFORMANCE

Results

Preliminary Analyses

Testing for the assumptions of multilevel and
hierarchical multiple regression analyses re-
vealed that multicollinearity between study
variables was not a concern. In terms of nor-
mality, GI-T perceptions both at early and later
season demonstrated a negative skew. Subse-
quent analyses revealed that the study results
were the same for raw and transformed (re-
flected and base-10 logarithm; Tabachnick &
Fidell, 2013) values. Thus, the raw scores were
retained for ease of interpretation. Eligible par-
ticipants in each analysis are described below.
Given that participants could participate at ei-
ther or both of the two measurement periods,
the number of participants potentially eligible
for the main analysis is included for each anal-
ysis.

In terms of descriptive statistics, participants
nominated (and were nominated by) approxi-
mately 85% of teammates for information ex-
change during the early season game. That is,
athletes exchanged information with roughly
seven of eight teammates during the game.
Also, participants reported moderate-high per-
ceptions of task cohesion at both early and later
season measurement periods, and objective
winning percentage was close to 50% across all
teams (Table 1).

Network metrics predicting task cohesion.
To check for the interdependence of responses
within teams, an intraclass correlation (ICC)
value was computed for later season task cohe-
sion (dependent variable). A null model (no
predictors) was tested to partition the variance
across Levels 1 and 2 (Raudenbush & Bryk,
2002). The resulting ICC for task cohesion (i.e.,
GI-Task) was 0.23. This suggests that a mean-
ingful amount of variance is housed at the team
level (23%). According to Schoemann,
Rhemtulla, and Little (2014), ICC values above
.05 may indicate that individuals’ responses are
likely to be more similar to others on their team
than that of athletes on another team, and a
multilevel model is appropriate. Models were
estimated using full maximum likelihood and
slopes were fixed. Level 1 predictor variables
were group-mean centered (Enders & Tofighi,
2007).

At the time of study, team representatives
reported an average team size of 7.7 members
(range � 7–9, median � 7). In terms of eligible
participants for this analysis, it was necessary
for participants to be present at both time points
to provide individual cohesion perceptions via
self-report. Two of the Level 2 units (teams)
had minimal participation at the later season
measure (2 and 3 participants, respectively), so
the HLM software only computed the analysis
with 13 of 15 teams. Two participants were
removed due to response bias at later season2

and one participant was removed as a univariate
outlier. In the end, 70 participants were eligible
for the analysis.

In Model 1, early season task cohesion (b �
.59, p � .001) significantly predicted later sea-
son task cohesion. A comparison of the devi-
ance statistic between the restricted null model
and the unrestricted Model 1 revealed a signif-
icant increase in model fit, �2(1) � 27.36, p �
.001, as it pertains to variance accounted for in
the dependent variable. In the full model
(Model 2), the addition of early season indegree
and outdegree centrality at Level 1 and early
season network density at Level 2 signifi-
cantly improved the model fit in predicting
later season task cohesion, �2(3) � 25.09,
p � .001. An overview of both models can be
found in Table 2.

Overall, a pseudo-R2 calculation (Kreft & De
Leeuw, 1998) revealed that 49% of the variance
was accounted for in later season task cohesion.
In terms of Level 1 and Level 2 variance com-
ponents, 42% of the variance could be attributed
to the individual level and 7% to the team level.
An examination of the specific predictors in
Model 2 revealed that early season outdegree
centrality (b � 2.17, p � .001) and early season
task cohesion (b � .41, p � .001) were signif-
icant indicators of later season task cohesion at
Level 1, and early season network density (b �
4.89, p � .01) was a significant predictor at
Level 2. Early season indegree centrality (b �
�.21, p � .74) was not significantly related to
later season task cohesion perceptions.

2 These two participants completed the later season ques-
tionnaire together and submitted the same responses. They
were removed from the main analysis.

8 MCLAREN AND SPINK

Network metrics predicting team
performance. Since team performance was
collected objectively, all participants present at
early season were eligible for this analysis (n �
109). Given that team performance is a Level 2
(group) variable as noted previously, the second
hypothesis was tested using hierarchical multi-
ple regression. At Step 1, early season team
performance (sr � .69) significantly predicted
later season team performance, accounting for
42% of the variance. The inclusion of early
season indegree and outdegree centrality and
early season network density at Step 2 signifi-
cantly increased the variance accounted for in
later season team performance, Rchange2 � .06,
p � .001. Standardized estimates revealed that
early season network density (sr � .23) and

early season team performance (sr � .65) were
significant predictors of later season team per-
formance (ps � .01), while indegree (sr � .00)
and outdegree centrality (sr � .00) were not
(ps � .95). In the final model, the variance
accounted for was 48%. A full overview of the
regression analysis can be found in Table 3.

Discussion

The importance of perceived group cohesion
in sport is evidenced by reported positive links
to key individual- (e.g., adherence; Spink, Wil-
son, & Odnokon, 2010) and team-level out-
comes (e.g., team performance; Carron, Col-
man, Wheeler, & Stevens, 2002). Further, it has
been argued recently that the sport group dy-

Table 1
Descriptive Statistics for Study Variables

Study variables Mean (SD) 1 2 3 4 5 6 7

1. ES indegree centrality .84 (.15) .39�� .59�� .23� .10 .25� .23�

2. ES outdegree centrality .85 (.22) .40�� .44�� .06 .66�� .15
3. ES network density .84 (.09) .32�� .14 .47�� .38��

4. ES task cohesion 7.74 (1.21) .12 .71�� .09
5. ES team performance .52 (.32) .11 .70��

6. LS task cohesion 7.83 (1.20) .21�

7. LS team performance .56 (.21)

Note. ES � early season measures; LS � later season measures.
� p � .05. �� p � .01. �� p � .001.

Table 2
Fixed Effects Estimates (Top) and Model Specification (Bottom) for Model
Predicting Later Season Task Cohesion

Parameter Null model Model 1 Model 2

Fixed effects
Intercept 7.92 (.18)�� 7.92 (.18)�� 7.92 (.14)��

Level 1
ES task cohesion .59 (.08)�� .41 (.10)��

ES indegree centrality �.21 (.61)
ES outdegree centrality 2.17 (.63)��

Level 2
ES network density 4.89 (1.65)�

Model specification
Deviance statistic 212.35 184.99 159.90
Parameters 3 4 7
Chi-square 34.42�� 54.19�� 44.23��

Note. N � 70. Model 1 is the smaller, control model with early season task cohesion as the
lone predictor of later season task cohesion. Model 2 is the full model with early season task
cohesion and early season network metrics as predictors of later season task cohesion. ES �
early season measure.
� p � .05. �� p � .01. �� p � .001.

9NETWORK STRUCTURE, COHESION, AND PERFORMANCE

namics knowledge base would benefit from a
better understanding of the sources of informa-
tion (i.e., cues) that athletes use to draw these
cohesion perceptions (e.g., McLaren & Spink,
2019; Spink et al., 2018). A basic component of
a group that could serve as one of these poten-
tial cues is the structure of member communi-
cation (e.g., Cartwright, 1968; McGrath, 1984).

The purpose of the current study was to con-
structively replicate (Hüffmeier et al., 2016) the
relationship between member communication
and task cohesion as well as test the relationship
between information exchange and perfor-
mance in sport teams. Based on preliminary
research in this area (McLaren & Spink, 2017,
2019), the constructive aspect of this replication
involved using a prospective field design with
intact sport teams. Further, generating whole
team communication networks allowed for an
examination of the specific exchanges between
members, and how these exchanges existed
within the context of a team (i.e., communica-
tion at the individual and team level as sources
of information for perceived task cohesion).
Also, perceived task cohesion is an individual-
level variable that reflects a personal belief
about the degree to which the team is united
around task goals and objectives (Carron et al.,
1985). As cohesion perceptions are drawn in
part by combining personal experiences within
the group and the reality of the group as a whole
(Carron & Brawley, 2000), it was hypothesized
that outdegree centrality and network density
early in the season would predict later season
task cohesion perceptions based in overall team
unit functioning (i.e., GI-Task).

Support for the first hypothesis was found.
Specifically, athletes who reported personally

exchanging information with a higher number
of their teammates early in the season (i.e.,
higher individual outdegree centrality) reported
higher task cohesion later in the season. In
addition, the collective information exchange of
an athlete’s team early in the season also was a
significant predictor of later task cohesion per-
ceptions. As expected, indegree centrality,
which is the number of teammates who reported
exchanging information with that same individ-
ual, was not a significant predictor of task co-
hesion.

The finding that both individual outdegree
centrality and network density predicted task
cohesion across time appears to be in line with
research from organizational psychology (e.g.,
Balkundi & Harrison, 2006). In spite of the
contextual differences between an organization
and the sport group setting, the pattern of results
that emerge in terms of predicting cohesiveness
are similar. Being a member who is highly
involved with information exchange and being
on a team that engages in a higher degree of
information exchange, collectively, appears to
predict task cohesion over and above earlier
task cohesion perceptions in the sport setting.

In support of the second hypothesis, the re-
sults of the current study revealed that informa-
tion exchange network density significantly pre-
dicted later season team performance while
indegree and outdegree centrality did not. The
findings revealed that higher objective team
performance scores (i.e., winning percentage)
were associated with teams where members col-
lectively engaged in a higher degree of infor-
mation exchange (i.e., higher network density).
One potential interpretation of this finding
could reflect the fact that when the communi-

Table 3
Early Season Communication Network Structure Predicting Later Season Team Performance

Variable Radjusted2 F (degrees of freedom) sr t

DV: LS team performance
Step 1 .42 98.59�� (1, 107)

ES team performance .69 .69 9.93��

Step 2 .48 33.04�� (4, 104)
ES team performance .65 .65 9.93��

ES indegree centrality �.01 �.00 �.06
ES outdegree centrality .00 .00 �.04
ES network density .29 .23 3.46��

Note. N � 109. DV � dependent variable; ES � early season measures; LS � later season measures; sr � semipartial
correlation.
�� p � .01. �� p � .001.

10 MCLAREN AND SPINK

cation network is higher in density (e.g., more
overall members exchanging information), the
potential effectiveness of the team is less likely
to be dampened by process losses (Steiner,
1972). This assumes, of course, that athletes are
exchanging and processing the appropriate
types of information at the appropriate times
(e.g., Hinsz, Tindale, & Vollrath, 1997). As this
was not examined in the current study, valida-
tion of this assumption warrants future consid-
eration.

Having just a few members who are not well
connected in a team sport such as basketball (as
used in this study) may be the difference be-
tween higher and lower team success because
the maximum number of possible connections
already are lower (compared with a soccer team
with a higher number of possible connections).
A potential explanation of these findings from a
methodological perspective is the operational
congruence between the predictor and outcome
variables (e.g., Cronin et al., 2011; McGrath,
Arrow, & Berdahl, 2000). In the same way that
sport team performance is not simply deter-
mined by one member, it may follow that a
team level of communication network structure
predicted objective team performance, which is
captured at the team level.

The current study had strengths worth recog-
nizing. First, the consideration of the individual
and team levels of the communication network
structure allowed for greater specificity insofar
as the structural properties differentially pre-
dicted task cohesion and team performance. In
past research, metrics of individual degree cen-
trality and network density have been consid-
ered together through the use of hypothetical
vignettes (McLaren & Spink, 2019). In the cur-
rent study examining all the variables together,
individual outdegree centrality and team net-
work density were associated with the individ-
ual-level outcome of perceived task cohesion,
while only whole team network density was
associated with the team-level outcome of ob-
jective team performance in intact sport teams.

From a measurement perspective, two
strengths emerge. First, the use of a prospective
design increased confidence in the directionality
proposed in past research (e.g., McLaren &
Spink, 2018a, 2019). It has been argued that
member communication may be one potential
cue for estimating perceptions of cohesion in
sport (McLaren & Spink, 2018a, 2018b); how-

ever, this research is based on concurrent re-
search designs. Although an experimental de-
sign is needed to verify this claim, consistently
replicating the relationship between greater in-
formation exchange among a higher number of
team members and higher task cohesion and
team performance offers credibility for this re-
lationship (Klein, Shepperd, Suls, Rothman, &
Croyle, 2015). Further, these results appear to
exist above and beyond the early season mea-
sures of each outcome variable that were con-
trolled in the analyses.

Second, although some past research sampled
athletes from intact sport teams, the goal was
not to compile whole network structures
(McLaren & Spink, 2019, Study 1) nor were
multilevel effects the focus. In the prediction of
later season task cohesion perceptions, the cur-
rent study captured and entered the network
structure metrics at the respective level, and
also captured the overall variance accounted for
by the individual and group. In line with initial
theorizing by Carron and colleagues (1998), it
appeared that later season task cohesion percep-
tions were significantly related to both personal
experiences of exchanging information with
other members early in the season and the col-
lective exchange behaviors of the team. From a
measurement perspective, the variance ac-
counted for in task cohesion also was associated
with both individual- and team-level effects. As
such, the nesting of athletes within intact teams
should be considered given that a portion of the
variance accounted for in task cohesion came
from the team level. As such, future research
designs using intact sport teams would be re-
miss to measure communication as information
exchange only at the individual level or aggre-
gated at the team level.

Despite these strengths, it also is important to
recognize the study’s limitations. First, the item
used to capture information exchange network
structure asked individual members to identify
those they exchanged information with during
the game. This captures a quantity-based metric
to identify how many teammates an athlete
communicated with as opposed to a more sen-
sitive measure of how the two athletes commu-
nicated or the quality of these interactions.
While this was intentional in order to replicate
past findings, future research may expand on
this in different ways. For instance, the measure
could be expanded to include different types of

11NETWORK STRUCTURE, COHESION, AND PERFORMANCE

information that can be exchanged (e.g., Hinsz
et al., 1997). The notion of type of information
exchanged may be important, as what consti-
tutes an important exchange in basketball (e.g.,
a point guard signifying a set play while drib-
bling the ball up court) may differ from that of
another sport (e.g., soccer—a goalkeeper direct-
ing a defensive player to open space to receive
the ball). Further, the item could be adapted to
include a scaled response as opposed to a binary
response (i.e., increase sensitivity), include
other types of communication mediums (e.g.,
verbal vs. nonverbal), or to generate evidence of
the quality of member interactions (i.e., com-
munication efficiency; Wittenbaum, Hollings-
head, & Botero, 2004). These considerations
would add value by establishing possible
boundary conditions (e.g., moderators) for the
relationships among communication, cohesion,
and team performance.

From a time perspective, it is possible that
exchanging information during a practice or
after a game, for instance, offers unique capac-
ities for comprehension (e.g., time, absence of a
competitor, access to video). Additions to future
study designs such as this will aid in under-
standing more specifically the relationship be-
tween information exchange and the outcomes
of cohesion and team performance.

A final limitation pertains to sample size.
While multilevel modeling is considered to be a
larger sample technique (e.g., Maas & Hox,
2005), the current study sampled teams that
were, by nature, (a) smaller in size, and (b) open
to fluctuations in member attendance over time.
Therefore, the overall sample size at Level 1
(athletes) and Level 2 (teams) was smaller than
typically suggested for a multilevel analysis but
representative of the reality of the groups under
study. A shortcoming of a smaller sample is the
reduction in power, which leads to the potential
for unstable estimates and associated standard
errors within the specified hierarchical model.
As a result, this increases the chances of reject-
ing a true null hypothesis (i.e., Type I error;
Maas & Hox, 2005). Further to this point, esti-
mates of within- and between-groups reliability
using a multilevel confirmatory factor analysis
are not possible with a smaller sample as the
number of parameters exceed the number of
clusters at Level 2 (i.e., teams), which leads to
nonidentification of the model (Geldhof,
Preacher, & Zyphur, 2014; see Whitton &

Fletcher, 2014 for an overview of group-level
reliability estimates for the GEQ). As such, it is
important for future research to increase the
number of teams when studying groups that, by
nature, may be smaller in size.

Overall, the current study makes a contribu-
tion to the sport communication research base
by constructively replicating (Hüffmeier et al.,
2016) the relationship between information ex-
change and the outcomes of task cohesion and
team performance using a field design with a
nested sample of athletes on intact teams (Klein
et al., 2015). The validity of these findings are
strengthened when coupled with similar results
reported across other studies employing differ-
ent samples and study designs (McLaren &
Spink, 2017, 2018a, 2019). The finding that
communication network structure was associ-
ated differentially with task cohesion and team
performance is a novel finding in sport, and
deserves further attention in future research.
Also, preliminary evidence now exists for sug-
gesting a potential indirect link (i.e., mediation)
between communication network structure and
objective team performance through task cohe-
sion. Whereas the team as a whole engaging in
higher information exchange is more directly
associated with team performance, the ex-
change behaviors of individual members may
inform a higher perception of task cohesiveness,
which has been linked to elevated performance
in past research (e.g., Benson et al., 2016; Car-
ron, Colman, et al., 2002). This suggests a pos-
sible indirect link and awaits future research.

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Received April 30, 2019
Revision received August 1, 2019

Accepted September 9, 2019 �

14 MCLAREN AND SPINK

http://dx.doi.org/10.1123/ijsc.2018-0004

http://dx.doi.org/10.1177/2167479516679412

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Examining the Prospective Relationship Between Communication Network Structure and Task Cohesion …

Social Network Analysis
Method
Participants
Procedure
Measures
Information exchange
Task cohesion
Team performance
Data Analysis
Results
Preliminary Analyses
Network metrics predicting task cohesion
Network metrics predicting team performance

Discussion
References

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