NAME: ELIZABETH COPELAND
STUDENT NUMBER: ST08002705
SCHOOL OF SPORT: SPORT AND PHYSICAL EDUCATION
UNIVERSITY OF WALES INSTITUTE CARDIFF
THE EFFECT OF GROUP SIZE AND PERFORMANCE ON TEAM COHESION
DECLARATION FORM
TABLE OF CONTENTS
PAGE
ACKNOWLEDGEMENTS
i.
ABSTRACT
ii.
CHAPTER ONE
INTRODUCTION
1.
CHAPTER TWO
LITERATURE REVIEW
3.
CHAPTER THREE
METHOD
Participants
12.
Instruments
12.
Procedure
12.
Data Analysis
13.
CHAPTER FOUR
RESULTS
Scale Reliabilities
15.
Descriptive Statistics
15.
Group Size and Cohesion
15.
Time and Cohesion
16.
Assumption Tests
17.
Relationship Between Performance and Cohesion
17.
CHAPTER FIVE
DISCUSSION
18.
CHAPTER SIX
CONCLUSION
27.
CHAPTER SEVEN
REFERENCES
28.
APPENDICES
A. Consent Form
34.
B. Participant Information Sheet
35.
C. Group Environment Questionnaire
36.
D. Multivariate Tests
38.
E. Univariate Tests
39.
LIST OF TABLES
PAGE
1. Mean and standard deviations for scores given on each of the
four subscales over time for each group.
16.
ACKNOWLEDGEMENTS
I would like to take this opportunity to thank Kieran Kingston for his much needed
support during this process. I would also like to thank everyone who took part in
my study, especially as it was a five week process requiring thinking.
i
ABSTRACT
The main aim of the present study was to establish whether group size effects
team cohesion, with the secondary aim to check whether cohesion was affected
by performance. To investigate the two aims stated, participants (n=42) were
recruited from university netball, hockey, and basketball teams, completing the
group environment questionnaire over five consecutive weeks. To measure the
effect of performance on cohesion, player ratings were collected the after the
game for the same five consecutive weeks, then GEQ’s were handed out at the
next training session. Results were analysed using SPSS (statistical package for
the social sciences). A MANOVA was conducted to find the differences in
cohesion for sport, and the effect of time on cohesion. Differences in sport was
found to be significant, Wilk’s lambda = .025, F(8,72) = 2.377, p < .03, along with
the effect of time on cohesion, Wilk’s lambda = .055, F(16,24) = 2.045, p < 0.06. A
correlational analysis found that overall, performance did not affect cohesion. In
conclusion, sport type did have an effect on cohesion; however this was not due to
group size, warranting further research. The main practical implication from the
study suggests that to stop cohesion decreasing over time, attraction to group
should be focused on, and when trying to increase cohesion between teams,
group integration needs to be focused on. Future research should concentrate on
the differences found in the results from a within subject design and a between
subjects design. This will show whether group size only affects cohesion within
sports.
ii
CHAPTER I
INTRODUCTION
“The way a team plays as a whole determines its success. You may have the
greatest bunch of individual stars in the world, but if they don't play together, the
club won't be worth a dime.” (Ruth, 1940). This quote was taken from George
Ruth, a famous baseball player, expressing the importance of cohesion in sport.
Even though this quote was taken from over seventy years ago, it establishes that
cohesion has always been an important factor within sport in order to be
successful. A more recent quote by Mark Jones shows how cohesion is still a
major factor within sport. “It has been disappointing this time around, in terms of
both results and performances, and we haven't looked anywhere near as cohesive
as we were two years ago”, referring to the Wales performance in the 2010 six
nations (Galea, 2010).
As shown in the quotes above, cohesion is a major aspect within sport which is
recognised by athletes, coaches, and psychologists. Team cohesion is a vastly
important area within sport as it has been linked with a variety of other variables
that can affect performance, such as coach behaviour and goal setting. When
reviewing research on group behaviour within sport, team cohesion is one of the
most commonly investigated areas (Horn, 2008). Carron (1982) established four
factors that influence team cohesion; leadership, environmental, team, and
personal factors. Research investigating cohesion in sport has focused on these
factors on cohesion, which the mostly widely and commonly research area being
performance and its affect on cohesion. Performance is usually taken into account
when measuring other variables and their affect on cohesion within sport as
success is the main method in which athletes are judged.
As mentioned above, there are four major factors that influence cohesion, and the
current study focuses on one of the major environmental factors, which is group
size. Therefore the main aim of this study is to investigate whether group size
affects team cohesion. As stated above, team cohesion has many affects on
different areas within sport; therefore all variables in the field of sport should be
investigated to ascertain if there is a link between them and cohesion. Currently,
there are a small number of studies investigating the link between group size and
cohesion. Currently there is only one major study that is recognised as looking into
1
the effect of group size on cohesion; ‘the effect of group size in sport’, conducted
by Widmeyer et al. (1990). By researching this area, the findings may allow certain
sports with a small/large amount of team members to focus on increasing group
cohesion, which in turn can increase success (Boone et al, 1997; Rudder and Gill,
1982).
The secondary aim of the study is to establish whether cohesion is affected by
performance. Investigating this area will show whether any low cohesion scores
from a specific time were affected by the player ratings they have given
themselves. This aim will help support/contradict Widmeyer et al. (1985) cohesionsuccess-cohesion relationship, stating that cohesion leads to success, which then
results in the growth of a greater cohesiveness.
As shown above, there is a gap within the research area of cohesion and group
size, giving a rationale for the study. By researching into this area, increased
knowledge of the effect of group size on cohesion will be found, as there are
currently no studies looking at this effect between sports. Therefore to investigate
further into the area, the research statement that has been developed is ‘The
effect of group size and performance on cohesion.’
2
CHAPTER II
LITERATURE REVIEW
According to Moran (2004, p.185), cohesion is “the extent to which a group of
athletes or players is united by a common purpose and bonds together in pursuit
of that objective.” A squad can be more effective when the individuals value
working as a group/team more than working as an individual (Estabrooks and
Dennis, 2003).
Cohesion is said to be multidimensional, dynamic (changes over time),
instrumental (replicates why teams come together), and affective (gives a positive
influence) (Singer et al., 2001). Supporting this view that cohesion is a
multidimensional phenomenon, Salminen (1987) conducted a study which found
that many different types of cohesion affected the results between the winning and
losing teams. When looking further into cohesion, Carron and Chelladurai (1981)
investigated the dynamics of group cohesion in sport and found that there were
two specific factors; individual-to-group cohesiveness and group-to-a-unit
cohesiveness. A further two factors were also established. These were task and
social cohesion. Task orientation represents a general enthusiasm heading
towards accomplishing the group’s instrumental objectives, whereas social
orientation represents a general enthusiasm heading towards developing and
sustaining social relationships and activities within the group (Widmeyer et al.,
1985). Anderson (1975) found that social cohesion was higher when similar
values were shared within a group, and that task cohesion was higher when goals
were set and achieved.
Along with the factors of cohesion, Carron (1982) came up with a general
conceptual system for cohesiveness in sports teams. This system describes the
factors that influence cohesion in teams and the outcomes that occur due to
cohesion. Four main factors affecting cohesion are leadership (for example,
leadership style), environmental (for example, contractual responsibility), personal
(for example individual differences), and team factors (for example, desire for
group success). These four factors feed into cohesion, which then affects two
outcomes; group outcomes (for example, team stability), and individual outcomes
(for example, behavioural consequences). This conceptual model of cohesion is
widely used when grouping the factors that affect cohesion.
3
The main sections of this literature review look further into cohesion and the main
factors that facilitate it. Following on from the definition of cohesion, and the
description of its multidimensional construct, research into how cohesion can be
measured will be shown. The main body of the literature review focuses on
different factors that affect team cohesion. As cohesion is affected by more than
one factor at any given time, several factors need to be investigated so that they
may all be taken into consideration when looking into cohesion. The literature
review will conclude with a rationale as to why the study should be carried out,
focusing on the gap within the research.
As stated previously, cohesion is known to be a multidimensional concept that can
have a positive effect on teams. In order for coaches and players to enhance
cohesion and maintain it at an optimum level, a form of measurement for cohesion
is needed.
One of the earliest studies producing such a measurement was carried out by
Martens et al. (1972), resulting in the Sport Cohesiveness Questionnaire (SCQ)
being created. ‘The SCQ contains seven items that yield ratings of friendship
(interpersonal attraction), personal power or influence, enjoyment, closeness,
teamwork, sense of belonging, and perceived value of membership’ (Carron et al.,
2005, p234). Salminen (1987) used the SCQ with ice hockey players to show that
success increases cohesion. Other measurements that have been created to
assess cohesion include the Multidimensional Sport Cohesion Instrument
(Yukelson et al., 1984), and the Team Cohesion Questionnaire (Gruber and Gray,
1982).
Carron et al. (1985) had an alternative conceptualisation of the dimensions of
cohesion, and from this developed the Group Environment Questionnaire (GEQ),
which is now the most widely used way to assess and measure cohesion. Carron
et al. (1985) began by creating a conceptual model of cohesion which identified
four constructs of cohesion; group integration-task (GI-T), group integration-social
(GI-S), individual attractions to group-task (ATG-T), and individual attractions to
group-social (ATG-S). The GEQ had eighteen items, grouped into the four scales
stated above. This questionnaire has subsequently been the measurement scale
4
of choice in studies assessing group cohesion (Nezhad and Keshtan, 2010;
Senecal et al., 2008; Van Raalte et al., 2007). A strength of the study carried out
by Carron et al. (1985) when creating the GEQ was that four studies were used to
finalise the questionnaire and ensure its reliability and validity.
Eys et al. (2009) went on to create a sport environment questionnaire for youth’s.
They used four phases to ensure that the questionnaire was researched properly
and that it would provide researchers with correct and reliable results. The final
questionnaire contained 16 items that assessed task and social cohesion, and two
items that were negatively worded. A strength of this study is that it uses youth’s
from a range of different sports, giving it wider range of use and increased
population validity.
The next sections of this literature review outline some of the areas that team
cohesion is linked to within sport. One example was shown by Eys et al. (2003),
finding that cohesion was linked to competitive state anxiety. They discovered that
if anxiety is seen as positive, cohesion of the team is higher than if anxiety is seen
as negative. Links have also been made with the type of leadership style (Callow
et al., 2009; Huang et al., 2003), mental skills (Senecal et al., 2008; Hardy et al.,
2003), and performance (Tziner et al., 2003; Carron et al., 2002b).
One area that has not been widely investigated is cohesion’s link with group size.
Widmeyer et al. (1990) conducted two studies to look at this, using different sized
basketball teams for study one, and different sized volleyball teams in study two.
In the first study, their aim was to look at the social and performance effects of
changing the team size. The participants were either in a team of three, a team of
six, or a team of nine, however only three members of each team would be playing
at any one time. The GEQ was used, finding that the initial AGT was higher in the
team of three than the team of nine. They also found that for the larger teams,
there were low levels of all types of cohesion and in the smallest team there were
only low levels of social cohesion. The aim of the second study was to look at the
teams’ enjoyment and how it affected cohesion. Volunteer University students
were used in a within-subjects design, and results suggested a negative
5
relationship between group size and enjoyment. The bigger teams found the
competition less enjoyable than the smaller teams. An earlier study carried out by
Widmeyer et al. (1986) also established similar results to of both Widmeyer et al.
(1990) studies. It was found that low levels of cohesion are linked with high levels
of drop out, which is also linked to larger group sizes. Along with this, it was also
found that higher drop out levels are influenced by low player satisfaction from an
individual’s performance. Widmeyer et al. (1986) results is linked to Widmeyer et
al. (1990) findings as they all show that larger group size decreases cohesion
levels, along with satisfaction, which can cause a greater likelihood of drop out. All
these studies show no gender bias, however they all only looked at one sport,
causing population bias as the findings can only be generalized to one sport.
Unlike group size, performance and its link with cohesion has been researched in
great depth. Widmeyer et al. (1985, p.72) said that ‘cohesion leads to success;
which, in turn, results in the development of a greater cohesiveness’ which shows
a cohesion-success-cohesion relationship. Boone et al. (1997) conducted a study
on sixty-five basketball players to look at whether winning or losing affected the
cohesion of the team. The GEQ was used to measure cohesion, and was
completed three times (one week before announcement of who was travelling on
the spring training trip, when returning from the trip, and at the end of the season).
The results showed that members of the losing teams decreased in their attraction
to group task, group integration task, and group integration-social. When looking
at the winning teams, task cohesion was not enhanced, and no positive or
negative effect was found with group integration-social. There were no differences
in ATG-S between the winning and losing teams. One criticism of this study is that
does not look at a team with an equal win/loss record. This would enhance the
results as it would allow for more comparison and clarity of the results. Rudder
and Gill (1982) also studied the link between cohesion and performance by using
a win/loss record. They too found that the losing teams decreased in levels of
cohesion, supporting Boone et al. (1997) and that perceptions of cohesion were
immediately affected by the result of the game.
6
As shown previously, performance has been measured using a win/lose record,
however other measurements of performance can be used. According to
Cashmore (2008) individuals who define success as a matter of improving their
own performance, rather than an outright win/loss result, are likely to prepare
themselves with greater exertion, believing that fate is in their own hands.
Cashmore (2008) also supposed that success or failure does not always equate to
a win or loss. This shows that a win/loss record may not actually show how a
player is feeling about a match that they have just played in, which affects
cohesion according to the cohesion-success-cohesion relationship. Balaguar et al.
(2002) previously adopted the belief that a win/loss record may not be an effective
in measuring performance, as stated by Cashmore (2008). Therefore Balaguar et
al. (2002) used player ratings to look at participant’s satisfaction with their own and
their teams’ performance and its link to a task involving climate, instead of a
win/loss record. Increasing satisfaction has also been linked to team cohesion.
Bromley (2000) looked at male and female basketball players, and found that
satisfaction was strongly linked to social cohesion. However, again, as only one
sport was used in the study, external validity is decreased as it can only be
generalised to the basketball population. Lowther and Lane (2002) also used
player ratings in their study, looking at the effect of mood on performance. They
found that by using player ratings instead of win/loss records, mood affected
performance greater. They believed that this is due to different positions on the
pitch. For example a losing team who’s Forwards had scored goals, may not feel
as if they had played badly. Even though this study does not investigate cohesion,
it shows how player ratings can be a successful and enhanced way to measure
performance.
Goal setting is a widely used coping skill, which can also be used in a team setting
when trying to modify cohesion. This variable on cohesion is an important factor in
the current study as following the results, coaches may need to implement a
programme to increase cohesion, and goal setting is one method of achieving this.
According to Widmeyer and Ducharme (1997), team goal setting can directly
impact team cohesion by setting a team focus, and indirectly impact it by
7
increasing team performance. Senecal et al. (2008) used 86 female basketball
players to see whether a season-long goal setting intervention would affect
cohesion, measured using the GEQ. By allocating participants into either an
experimental group or a control group, they found that at the start of the season,
both groups cohesion levels did not vary. However at the end of the season, the
experimental group had higher perceptions of cohesion, compared to the control
group. Brawley et al. (1993) looked further into how goal setting can affect
cohesion and how you can increase goal setting effects on cohesion. They found
that the link between cohesion and goals increases when there is team
satisfaction with the goals and when individuals believe that their team mates are
also engaging in the set group goals. However goal setting should be
implemented correctly and taken cautiously, as for an individual to consider the
goals as theirs and view them as important, the whole team needs to contribute to
the goals set (McMorris and Hale, 2006). According to Estabrooks and Dennis
(2003) as levels of cohesion increase in teams, motivation in the athletes to reach
their goals will also increase. Both the Brawley et al. (1993) and the Senecal et al.
(2008) studies support the link that goal setting increases cohesion levels,
however Brawley et al. (1993) used four different sports in his study, making it
easier to generalise, unlike Senecal et al. (2008) who only used basketball
players.
As highlighted in the research previously, some studies use just one sport, and
some use a variety. This should be taken into consideration when reviewing
findings from different studies as results from a study just using footballers for
example, may not be the same as results from a study just using basketball
players, even when the exact same methodology is used. The findings can only
be generalized to that specific population, as individuals from a variety of sports
may yield different results, decreasing the findings reliability.
Sports
can
be
categorized
into
two
types;
co-acting/individual
and
interacting/team. A co-acting sport is where the main focus is upon the individual
performing, and depending on how the team or coach focus, the team can either
8
be equal to the individual or at times a secondary focus, for example, athletics and
swimming (Murphy, 2005). An interacting sport is where two or more individuals
who hold a common characteristic, have common goals and objectives, share a
common fate, communicate and cooperate together in certain patterns, possess
the same opinion on the group formation, are personally and instrumentally
interdependent, can see qualities in others from the group, and consider
themselves to be a group (Carron et al., 2005). Boone et al. (1997) looked at
success in basketball players (an interacting/team sport) and found that cohesion
remained stable in winning teams throughout the season. However in the losing
teams, cohesion decreased in three out of the four sub-categories for cohesion.
They concluded that in interacting/team sports, success does not show a positive
effect, but keeps cohesion stable. Williams and Widmeyer (1991) looked at
success with eighty-seven golfers (a co-acting/individual sport). They found that
when looking at the cohesion-performance outcome relationship, cohesion was a
significant predictor of performance, with task cohesion being the biggest
predictor. These studies show that cohesion has an influence on both co-acting
and interacting sports; however they both only consider one type of sport.
One study that overcomes this issue and compares the two types of sports,
dealing with the weakness in Boone et al. (1997) and Williams and Widmeyer
(1991) studies, was carried out by Matheson et al. (1997), who looked at
swimming, gymnastics, lacrosse, and basketball. They found that the co-acting
teams (swimming and gymnastics) had greater cohesion than interacting teams
(lacrosse and basketball) in AGT, GIT and GIS after winning, losing and in preseason. The interacting teams had greater cohesion in AGS than co-acting teams.
The difference in the results between the first two studies and the Matheson et al.
(1997) study showed that by using different sports, different results are found. This
means that cohesion will depend on the type of sport being used in the sample.
For example, Turman (2003), and Nezhad and Keshtan (2010) both researched
into coaching and leadership effects on cohesion, however Turman (2003) used a
wide range of sports in his study, giving greater external validity as it can be
9
generalised across sports teams, whereas Nezhad and Keshtan (2010) only used
football players.
Another aspect of the current study is group size. According to Horn (2008), most
research that looks into group size is based on Steiner’s theory of group
productivity, which outlines three relationships. The first relationship is that as
group size increases, so does potential productivity, however it does reach a
plateau. The second relationship is that as group size increases, group processes
decrease. Group processes include communicating about task and social worries
and planning and coordinating the individuals within the group activities. The third
and final relationship states that as group size increases, so does actual
productivity, until a certain number, where after actual productivity starts to
decrease.
Carron et al. (1990) conducted two studies into group size and its effects within
exercise. In the first study they looked at the relationship between group size and
behaviour, using classes varying from 5 to 45 members. They found that attention
and retention of the members was higher in small and large classes and lower in
medium and moderately large classes. The second study also looked at group
size but its relationship with specific social psychological factors. The findings
showed that both small and large classes were more favourable than the medium
classes when comparing the social psychological factors measured. It also
showed that the perceptions of the instructor to class size had a negative linear
trend, in that as class size increased, perceptions of the instructor decreased.
Carron et al. (2005, p.42) said that “a sufficient number of individuals should be
retained in order to practice efficiently and effectively.” This suggests that group
size is dependent on the sport, as different sports have different numbers of
participants permitted on in team at any one time.
From the literature and research outlined in this literature review it is clear that
cohesion has been widely researched in many areas, with the main section
investigating cohesion’s link with performance. However, it is also clear that there
has been little research undertaken into cohesion levels and its link to group size
10
in different sports. Usually each sport has a set team size, therefore research to
see if different sports will always suffer with reduced cohesion compared to other
sports due to the size of the team required to perform correctly is needed. This
then provides a rationale for the current study as there is a gap in the research
that needs to be filled. Player ratings were also taken to provide insight into any
anomalies that occurred in the findings. Considering the gap in the research and
investigations that have been carried out previously, the statement explored was
‘The effect of group size and performance on team cohesion.’ From looking at the
previous research into this area, especially the investigation carried out by
Widmeyer et al. (1990), it is expected that as group size increases, cohesion will
decrease, showing a negative linear relationship. When examining the effect of
player ratings on cohesion, it is expected that cohesion will reduce when player
ratings are low, as in the research mentioned earlier, higher satisfaction has been
linked with higher cohesion levels (Bromley, 2000).
11
CHAPTER III
METHOD
Participants
Between 38-50 female university netball, hockey, and basketball participants aged
18-24 were used in a repeated measures design. UWIC ladies second team
hockey squad, UWIC ladies second team netball squad, and UWIC ladies second
team basketball squad was used as there were three different sports that have
different sized squads, and second teams will be used to reduce the bias of
having teams with a big difference in ability. Three teams have been used so that
the results can be applied to more sports, reducing the risk of population bias.
Only females have been used as a mixture of different gendered teams could bias
the study.
Instruments
The instrument that was used in this study is the Group Environment
Questionnaire (GEQ) (Carron et al., 1985). This was used as it has been shown
and used by many researchers (Eys et al., 2007; Brawley et al., 1987) to be a
reliable and valid way of collecting data on team cohesion. Li and Harmer (1996)
found that many sections of the GEQ are internally consistent, reliable, and
converged, and Nezhad and Keshtan (2010) found cronbach alpha scores of 0.72
for social cohesion and 0.71 for task cohesion.
It is an 18item questionnaire that has four main scales; group integration-task (GIT, five items), group integration-social (GI-S, four items), individual attractions to
group-task (ATG-T, four items), and individual attractions to group-social (ATG-S,
five items). Brawley et al. (1987) assessed the validity of all three studies used to
formulate the GEQ. They found that the GEQ successfully distinguished between
team and individual sports, and also which athletes were new to the team and
which were longstanding members. In conclusion, they found that the GEQ was
valid in its construct. A player rating system was also used, ranking from one (‘I
played really badly’) to 10 (‘I played one of the best games I ever have’), as
adopted by Balaguar et al. (2002).
Procedure
When recruiting the participants, the coach for each team was approached. They
were told about the study and given an information sheet to read, and then asked
12
whether they would give permission for their team to be approached. Once
approval had been given, the participants were then approached and asked if they
were happy to volunteer for the study. This method of volunteers were used as in
a team setting, more participants are likely to be induced to engage as everyone
else in their team is. Both the coach and the participants were given a few days to
decide, in order not to pressure them and force them to participate, which would
be unethical. Participants were first asked to read the information sheet that they
were given, and then asked to sign the consent form.
To begin data collection, a player rating between one and ten was taken from
each player after the match. To receive this player rating, a block text was sent out
to everyone who played, asking them to send back a text message rating
themselves on how they thought they played as an individual. A text message was
used so that no one else on their team would know what score they had given.
Player ratings were used as win or loss records don’t necessarily reflect the game
and how the individual felt about them game. According to Cashmore (2008,
p.250) “in sport, failure itself matters far less than how athlete’s interpret that
failure”. The GEQ was then filled out at their next training session. The first player
rating and questionnaire wasn’t taken until five weeks into the season so that the
team is sorted out and the final squads had been formed (as done by Widmeyer et
al., 1990). The questionnaires were filled out individually, in a group setting with
the team around them but not close enough to talk to one another, so that the
participants would feel more comfortable when filling them out. Each team filled
out the questionnaire in the same week each time so that the teams had spent the
same amount of time together, and to reduce the likelihood of bias. This
procedure was carried out for five weeks, allowing for a reliable conclusion to be
made. The participants were also informed verbally that they had the right to
withdraw at any time, and that their data was kept confidential.
Data Analysis
The mean and standard deviation for each teams cohesion level, each time they
complete the questionnaire will be calculated (as shown by Matheson et al.,
1997), along with the mean and standard deviation for the player ratings for each
game. To see if there was a significant difference between the three teams
13
cohesion levels, a repeated measures ANOVA test was used. The statistical
analysis software for windows (SPSS) was used to work out the significance
levels. According to Gratton and Jones (2004), using significance levels is a useful
and good way of represneting data.
14
CHAPTER IV
RESULTS
To investigate the effect of group size on cohesion, three sports teams from
different sports were used and examined over time. To measure performance and
its affect on cohesion, player ratings were collected straight after each game, and
then the GEQ’s were filled out at the next training session. This process was
carried out for five weeks. As time was a variable taken into account, along with
different sports, both cohesion between groups and cohesion over time were
examined. To analyse the data collected, a repeated measures MANOVA was
practised on SPSS, using sport type, the four subscales of cohesion, and time
across five periods. The dependant variable in this study was cohesion, and the
independent variables were the sport groups and performance.
Scale reliabilities
Internal reliability scores using Cronbach’s alpha coefficient for the four subscales
of the GEQ were taken using the scale reliability analysis on SPSS. Cronbach
alpha coefficients were used to test the internal consistency of each of the four
subscales. Showing Cronbach alpha coefficients is important when looking into a
number of items and it states whether each question within the four subscales are
measuring the same aspect. The four coefficients were .69 (AGT), .63 (AGS), .75
(GIT), and .72 (GIS).
Descriptive statistics
The mean and standard deviations of each group over the five time periods for
the four subscales of cohesion are shown in table one. The Bonferroni correction
was used to establish whether there was a significant difference between the
means for the four subscales when comparing the sport groups. Findings showed
that the difference between hockey and netball for GIS was the only comparison
that had a significant difference, .036.
Group size and cohesion
For the effect of group size on team cohesion, a between subjects MANOVA
showed a significant main effect, Wilk’s lambda = .025, F(8,72) = 2.377, p < .03.
This illustrates that there were changes in cohesion across the different sports.
15
When looking at the effect of group size of the four subscales of cohesion, tests of
between subjects using a follow up univariate main effects test found that GIS was
significant, p = .038, GIT was found to approach significance, p = .058, and AGT
and AGS were found to be insignificant. This shows that these aspects of
cohesion had significant changes across the sports.
Table one:
Mean and standard deviations for scores given on each of the four
subscales over time for each group.
HOCKEY
BASKETBALL
NETBALL
WEEK 1
WEEK 2
WEEK 3
WEEK 4
WEEK 5
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
AGT
8.33
.49
8.08
.69
7.58
.80
7.58
.71
7.69
.91
AGS
7.25
1.44
7.00
1.47
6.93
1.24
6.60
1.27
6.60
1.07
GIT
7.02
1.39
6.83
1.42
6.82
1.44
7.02
1.09
7.22
1.11
GIS
6.56
1.32
6.15
.89
6.35
1.29
6.06
1.38
6.25
1.19
AGT
7.39
1.30
6.96
1.06
7.21
1.11
7.02
.96
6.98
1.34
AGS
7.19
1.24
6.63
1.45
7.10
1.40
6.87
1.12
6.53
1.50
GIT
5.84
1.64
5.60
1.17
6.39
1.41
6.24
1.17
6.23
1.20
GIS
6.96
1.21
6.77
1.16
7.14
.99
7.00
.96
6.98
.82
AGT
7.83
.96
7.81
1.52
7.63
1.17
7.69
1.31
7.42
1.47
AGS
7.49
1.31
7.98
1.05
7.64
1.37
7.70
1.28
7.38
1.42
GIT
6.74
.73
7.04
.99
6.76
1.22
6.80
1.15
6.58
1.08
GIS
7.39
1.19
7.14
1.23
6.94
1.47
7.44
1.20
7.42
1.01
Note. 1 equals low cohesion, and 9 equals high cohesion.
Time and cohesion
The MANOVA for group and team cohesion also produced a within subjects main
effect value for the effect of time on cohesion, where a significance was found,
Wilk’s lambda = .055, F(16,24) = 2.045, p < 0.06, with partial eta squared = .577,
showing that there were changes of cohesion levels over time. The interaction
between time and group size had no significance, with Wilk’s lambda = .541. A
follow up ANOVA was also performed, showing AGT and AGS to have a
16
significant effect, .005 and .047 respectively, and GIT and GIS to have an
insignificant effect. This shows that for AGT and AGS had significant changes in
cohesion levels across all sport over time.
Assumption Tests
Mauchly’s test of Sphericity found that AGT and GIT had a significant effect, .000
and .027 respectively, concluding that there are significant differences between
the variance of differences, so the assumption of sphericity is not met in the two
subscales. This therefore assumes that results regarding AGS and GIS cannot be
trusted, so should consequently be taken with caution.
Relationship between performance and cohesion
Correlations were taken to see the effects of performance scores on each of the
four subscales of cohesion, using a within subjects design. A significant effect was
only found between performance and AGT in week one, p=.041. Levene’s test of
equality of error variances showed that only week two’s performance scores had a
significant variance between the groups, F (2, 9) = 6.14, p<.03, concluding that
there were differences between groups in week two only.
17
CHAPTER V
DISCUSSION
The two main aims of this study were to (a) assess the effect of group size on
cohesion, and (b) establish whether there is a correlation between performance
and team cohesion. Findings show that there was an effect of group size on
cohesion, with group integration being the cause. When comparing sports,
significance was only found in GIS, with hockey having significantly greater
cohesion than netball. The findings also highlighted a significant effect of time on
cohesion, with attraction to group causing this change over time. Results on
performance and cohesion found that only AGT at one point in time showed a
correlation, concluding that overall, performance does not have an effect on
cohesion.
The findings illustrated that group size did have an effect on cohesion as a whole,
however did not support the hypothesis that as group size increases, cohesion
decreases. Widmeyer et al. (1990) used just one sport and found that as group
size increased, cohesion decreased. This suggests that when looking at group
size within one sport, the prediction of group size and cohesion will be true,
however results from this current study demonstrate that when making
comparisons between the three sports, it is found to be false. When cohesion was
split into its four sub categories, findings showed that GIT and GIS had significant
changes between the sports. This implies that when comparing different sports,
the aspect that is mostly affected by the size of a team is group integration
(perception of the group as a whole). Overall, the findings associated with
cohesion in different sports are not varied due to group size, but by what sport is
being played.
Matheson et al. (1997) investigated co-acting and interacting sports and the level
of cohesion that two sports from each type possess. Matheson et al. (1997)
findings indicated that co-acting sports had greater cohesion levels following a
loss than interacting sports, and that this is due to athletes’ perceptions of control
and responsibility. Linking the current study to the findings found by Matheson et
al. (1997), it is suggested that cohesion is linked to the type of sport played, and
18
that certain sports will not have reduced cohesion due to a particular sport
requiring a larger number of team members.
As stated previously, the hypothesis that an increase in group size will cause a
decrease in cohesion was not supported when comparing different sports,
suggesting that there may be other factors that affect cohesion between sports.
One aspect that may have had an effect on cohesion is leadership, as it has been
shown that the way an individual/team acts within a sport can be due to their
coach. According to Jowett (2007, p.63), “Coaches are key people who attempt to
influence athlete’s in many important ways”.
Turman (2003) looked into the different coaching techniques that can affect team
cohesion. He found that techniques such as embarrassment, inequality and
ridicule, cause cohesion to decrease, whereas motivational speeches, quality of
opponent, athlete directed techniques and dedication, increase cohesion. Nezhad
and Keshtan (2010) then looked at the types of leadership styles coaches’ use,
and how they affect cohesion. Using football players, they found that at the end of
the season, teams had a higher perception of cohesion when the coach offered
social support, positive feedback and a more democratic style, in comparison to
an autocratic style. They also found that successful football teams tended to have
greater levels of cohesion and that their coaches showed higher levels of
democratic behaviours. A strength of the study carried out by Nezhad and
Keshtan (2010) is that the GEQ was used to assess cohesion, which has been
shown as a reliable way to measure cohesion, unlike Turman (2003) who just
used surveys.
Another external variable that may have affected this study is team building.
According to Kornspan (2009), many coaches believe that team building to
enhance cohesion is influential towards success and Carron et al. (2007)
recommended team building as a method for increasing team cohesiveness.
These statements demonstrate the importance of team building and the following
research are offered in support. Stevens and Bloom (2003) used the GEQ five
19
times throughout a season to look at the effects of a team building intervention on
cohesion. Results showed that the intervention group had greater levels of
cohesion when compared to the control group. Hoigaard et al. (2006) also
investigated team building and cohesion and in addition to this, researched into
the effectiveness of team building on social loafing. Supporting the findings shown
by Stevens and Bloom (2003), Hoigaard et al. (2006) found that a team building
intervention increased cohesion levels, and lowered the willingness to engage in
social loafing.
In conclusion, the research on team building makes it clear that team building
activities athletes participate in, outside of performance in their sport, may affect
the team cohesion levels recorded. When linking this back to the current study, it
is clear that the results would have been affected as some teams may have
participated in more team building activities than others. This would cause internal
validity to reduce as external variables that may have affected the results were not
being controlled and accounted for.
The results relating to time and cohesion found a weak significance, showing that
time did have a slight effect on cohesion. This demonstrates that cohesion for all
groups changes over time, suggesting that there are factors other than group size
that effect cohesion. Cohesion was split into its four sub categories, according to
the GEQ, to establish which of the four contributed to the significance shown. The
univariate test found that AGT and AGS were the sub categories that significantly
contributed to the effect of time on cohesion, showing that attraction to group
(individuals’ attraction to the group) affected the changes of cohesion over time,
for all three sports. These findings contradict what Carron and Brawley (2000)
stated when referring to which types of cohesion would change over time.
According to them, having focus on the task in hand to begin with, would be more
important in order to become successful, however once the team have become
fixed in routine, there would be time for social cohesion to be improved. They
suggested that AGS and GIS would predominately change more over time
because of the social aspect involved with them. To compare between sports, in
20
the current study each sport’s cohesion levels were also compared to see if the
changes over time were different, however differences between the sports were
not found. This implies that although cohesion levels do change over time, all
three sports were affected by changes over time. Matheson et al. (1996) however,
did find a difference between sports when investigating cohesion over a whole
competitive season. This suggests that diversity of cohesion levels between the
sports may have been found if a longer time period was investigated.
Another aim that was investigated by this study was performances’ effect on
cohesion. It was hypothesized that when player ratings were low, cohesion would
decrease. A correlational analysis found that out of the four subscales across the
five weeks, a significance was only found for AGT in week one. This shows that
for AGT in week one, player ratings and cohesion levels had a positive
relationship, indicating that cohesion and performance were at the same level.
However as only one relationship was found for all four subscales over the five
time periods, it is concluded that individuals’ perceptions of performance does not
have an overall effect on cohesion, contradicting studies that looked at the effect
of a win/loss record on cohesion (Rudder and Gill, 1982; Boone et al., 1997;
Carron et al., 2002a). Other studies carried out investigating the cohesionperformance relationship however also found there to be no relationship,
supporting the findings from this study. Melnick and Chemers (1974) looked into
cohesion and success in basketball teams, finding that team performance had no
affect on cohesion, therefore supporting the findings from the current study. Earlier
studies investigating the cohesion-performance relationship have also found a
negative relationship, showing that as one factor increases, the other factor
decreases. Grace (1954) used basketball teams as a sample, finding that
cohesion was inversely related to performance, however he described cohesion
as conformance, which according to Gill (1977) is a questionable measure of
cohesion.
From an applied perspective, the results showed that cohesion over time changes
due to attraction to group. This suggests that if coaches wish to keep cohesion
21
high or increase cohesion over time, then attraction to group should be focused
on. The GEQ highlighted that attraction to group includes how the players feel
individually, therefore to be able to increase cohesion, coaches and players must
communicate thoughts and feelings to facilitate the change. Results also found
that group size had an effect on cohesion, with group integration causing this
effect. Coaches are therefore recommended that if cohesion is low due to
changes in group size, the focus should be on increasing group integration. The
GEQ shows that group integration involves the unity of the teams’ thoughts and
feelings, therefore players and coaches should aim to work towards the same
goals and aim to act as a unit.
The main practical implication that is present from the findings stated is that group
size is not a variable that affects cohesion between sports, suggesting that there
are other variables, such as team building and leadership, which have caused the
differences in cohesion between the sports. From a researcher’s perspective, this
implies that further research needs to be carried out to find out what aspects are
different in a diverse number of sports, causing the differences found between the
sports in the current study. From a coach’s perspective, this finding suggests that
the way a sport is led and coached will affect the cohesion of the team, implying
different sports may have reduced cohesion due to this factor. The two aspects of
leadership and team building should be investigated by using a sample of both
interacting and co-acting sports, looking into variables individually. It is suggested
that one study researches into the affect of team building on cohesion, and a
second study researches the affect of leadership (both coach and team) on
cohesion. The results from these two studies would give greater insight into the
differences between sports and reasons for the differences in cohesion. The
implications from these studies would provide valuable information to coaches,
players and researchers on methods to successfully increase cohesion.
When reviewing the present study, there are a number of limitations that need to
be addressed. The first is the use of only females in the study, causing gender
bias. By only using females, external validity is reduced as the results can only be
22
generalised to females. Carron et al. (2002b) looked into the differences between
genders on the cohesion-performance relationship, finding that relationship was
higher in female athletes. They concluded that reduced cohesion in females is
more likely to be detrimental to success when compared with males. Wrisberg and
Draper (1988) also showed that female teams had higher levels of overall
cohesion compared to males. However, Paiement and Bischoff (2007), when
investigating the affect of gender on cohesion and success, found that there were
no gender differences. As all research done has found different results and does
not have a specific direction for all findings, a mixed gender sample could be used
to ensure that the study can be generalised to both males and females. Another
option to increase generalisation is by using a two group design, keeping males
and females separate, however still allowing comparisons to be made. Therefore,
one area that could be investigated in the future is the differences in gender when
researching the effect of group size on cohesion. This will develop knowledge, as
it may highlight differences between genders in the sub categories of cohesion
that are affected by group size and also the affect of group size on cohesion as a
whole. To maximise external validity and generalisation, three types of team
should be researched; male, female, and mixed gender teams, giving the greatest
comparison. A hypothesis cannot be made for the differences in mixed gender
and single sexed teams as there is currently no research on this particular
comparison. When comparing males and females, no hypothesis can be made as
research has found both differences (Wrisberg and Draper, 1988) and no
differences (Paiement and Bischoff, 2007) in cohesion. This implies that gender
differences may be found due to the method used to conduct the study.
As with gender, the type of sport used in the sample gives a limitation to the study
as only interactive sports were used, and therefore cannot be generalised to coacting sports. However, Carron et al. (2002b) found that when investigating the
cohesion-performance relationship, no significant differences was found between
co-acting and interacting sports, suggesting that even though only co-acting teams
were used, the results found can be generalised to both types of sports.
23
As cohesion was also one of the main areas researched, differences in cohesion
should also be taken into account when generalising the findings. Matheson et al.
(1997), who looked at swimming, gymnastics, lacrosse, and basketball, found that
the co-acting teams (swimming and gymnastics) had higher cohesion than
interacting teams (lacrosse and basketball) in AGT, GIT, and GIS after winning,
losing and in preseason. This suggests that although performance findings from
Matheson et al. (1997) can be generalized to all sports, cohesion between
different sports and group size cannot, as differences found may have varied if
both co-acting and interacting teams were used. These are two improvements that
can be carried out to increase the appropriateness of the findings to a wider range
of athletes. Nevertheless, it is also important to take into account homogeneous
groups. By using both genders and a variety of sports, the ability of the groups will
become more varied, possibly causing a wider range of results.
One methodological limitation that is clear in the present study is the set period of
time when the questionnaires were requested to be completed. As they were
completed the instance before training, individuals may have been distracted and
rushing, in order to get to training on time. A way to overcome this particular
limitation would be for the questionnaire to be completed one hour prior to training,
allowing the participants more time, possibly causing the answers given to further
accuracy. However, a strength of the method chosen was that as the
questionnaires were completed in the presence of the researcher, a good
response rate was seen, and any queries the participants had about the
questionnaires could be answered, which according to Cohen et al.(2007) are
strengths of self administered questionnaires.
As stated, improvements could be made with the administration of the
questionnaires, however there are also be limitations with the GEQ itself. The
GEQ uses both positively and negatively worded questions on a likurt scale, and
according to Babbie (2010), negatively worded questions can lead to
misconceptions on what the question is asking, causing the participant to mark
oppositely to what they actually believe. Eys et al. (2007) researched this area,
24
finding that by using a positively worded questionnaire instead of a mix of both
positive and negative, internal reliability of the scale increased. To overcome this
limitation of the GEQ, Estabrooks and Carron (2000) just used positive worded
items when creating a variant of the GEQ that could be used in an exercise
setting. They found that when compared to the original GEQ, participants found it
easier to complete and understand. This suggests that in order to both increase
the internal reliability of the GEQ, and ensure that it is easy for the participants to
complete the questionnaire, a modified version of the GEQ should be used, with
all items being positively worded.
A second methodological limitation of this study was the performance variable.
The results found showed that performance did not have an effect on cohesion,
however this may have been due to the amount of performance scores collected.
As squad size was used instead of team size, not every participant played every
week. This may have affected the results as there would have been different types
of players rating themselves; those with a large amount of playing time and those
who had a small amount of playing time allocated. “I’m not happy with the amount
of playing time I get” is one of the items on the GEQ, showing that playing time will
affect the cohesion level given, and research has also shown this to be true
(Grubber and Gray, 1982). To overcome this limitation team size instead of squad
size could be used, obtaining player ratings from the same participants every
week. However, the team used is not always the same every week, making it hard
for this limitation to be corrected. Along with the way the performance scores were
collected, the type of performance data obtained can also have flaws. The current
study collected the athlete’s perceptions on how they thought they had played,
however the most common form of performance measurement used in studies is a
win/loss record (Boone et al., 1997; Rudder and Gill, 1982). By using a win/loss
record instead of performance ratings, data collection would become simpler as
only one score will be needed per team every week, plus results would not be
reliant on participants giving a rating.
25
One of the main areas for future research is the comparison between a ‘within
sports’ design and a ‘between sports’ design, looking into the effect of group size
on cohesion. As results have shown, differences were found in cohesion between
the groups, however group size wasn’t the variable that caused this difference.
Little research has been carried out into the area of group size and cohesion,
which is something that sport researchers should address to ensure better
understanding. Before the current study, Widmeyer et al. (1990) was the only
major study that investigated this area. Carron et al. (2007) stated that group size
was one of the two major situational factors related to cohesion, warranting that
further investigation should be examined. Supporting the need for further
research, Lavallee et al. (2004) stated that squad size (a situational factor) is one
of the few areas that has limited support for its role in affecting cohesion.
When carrying out further research, investigators should take into account other
variables that may affect cohesion along with group size, as the current study
found a comparison between sports that was not due to group size. An example of
a variable that could be measured along with group size is team building
exercises, due to the research shown above that team building has a positive
effect on cohesion (Stevens and Bloom, 2003; Hoigaard et al., 2006).
In conclusion, it has been shown that there is a lack of research into the affects of
group size on cohesion, warranting the purpose of this study. Findings showed
that different sports do have an effect on cohesion, however this is not due to
group size. In addition, results showed that performance did not have an effect on
cohesion, though this may have been due to the method used. The current study
has established that future research needs to be carried out into the effect of
different sports on cohesion, and the variables that may cause this difference.
26
CHAPTER VI
CONCLUSION
In conclusion, the results from this study present information on the effect of group
size on cohesion, specifically the differences between sports, which was an area
identified as having limited research. The main finding from the current study
established that group size did have an effect on cohesion, however did not
support the hypothesis that as group size increases, cohesion will decrease.
Instead, it was found that there were differences between the groups, causing
there to be a significant affect of group size on cohesion, however this may have
been due to other factors such as team building and leadership, not the size of the
group. When cohesion was split into the four categories, results found that the
difference between the groups was most apparent in group integration.
Findings also established that there was an effect of time on cohesion, however
no correlation between player ratings given and cohesion. This implies that
cohesion changes over time, however this change is not subject to performance.
When splitting cohesion into the four categories, results showed that the change
over time was due to attraction to group decreasing. This change over time was
not different between sports therefore all teams were affected by this change.
The main practical implication from the current study was that when attempting to
increase cohesion between groups, coaches/practitioners should focus on
increasing group integration, through methods such as team goal setting. When
attempting to stop cohesion decreasing over time, coaches/practitioners should
focus on keeping attraction to group stable, which can be achieved by way of
communication between athlete and coach.
Future research should concentrate on finding reasons for the differences
between results found using a within subjects design and a between subjects
design when investigating group size and its effect on cohesion. The results found
may show that group size effect is only present when comparing teams from one
sport, along with reasons why some sports have higher cohesion levels than
others.
27
CHAPTER VII
REFERENCES
Anderson, A. (1975). Combined effects of interpersonal attraction and goal-path
clarity on the cohesiveness of task oriented groups. Journal of Personality
and Social Psychology. 33(1), p.68-75.
Babbie, E. (2010). The Practise of Social Research (ed.12). Belmont: Wadsworth.
Balaguer, I., Duda, J., Atienza, F., & Mayo, C. (2002).
Situational and
dispositional goals as predictors of perceptions of individual and team
improvement, satisfaction and coach ratings among elite female handball
teams. Psychology of Sport and Exercise. 3(4), p.293-308.
Boone, K. S., Beitel, P., & Kuhlman, J. S. (1997). The Effect of the Win/Loss
Record on Cohesion. Journal of Sport Behavior. 20 (2), p.125-134.
Brawley, L., Carron, A., & Widmeyer, Y. (1987). Assessing the cohesion of teams:
validity of the group environment questionnaire. Journal of Sport Psychology.
9(3), p.275-294.
Brawley, L., Carron, A., & Widmeyer, W. (1993). The Influence of the Group and
its Cohesiveness on Perceptions of Group Goal-Related Variables. Journal
of Sport and Exercise Psychology. 15(3), p.245-260.
Bromley, S. (2000). The relationship of the congruence of perceived and preferred
cohesion to sport performance and satisfaction. Microform Publications.
Callow, N., Smith, M. J., Hardy, L., Arthur, C. A., & Hardy, J. (2009). Measurement
of Transformational Leadership and its Relationship with Team Cohesion
and Performance Level. Journal of Applied Sport Psychology. 21(4), p.395412.
Carron, A., Bradley, L., & Widmeyer, W. (1990). The Effects of Group Size in an
Exercise Setting. Journal of Sport and Exercise Psychology. 12(4), p.376387.
Carron, A., & Brawley, L. (2000). Cohesion: Conceptual and Measurement Issues.
Small Group Research. 31(1), p.89-106.
28
Carron, A., Bray, S., & Eys, M. (2002a). Team cohesion and Team success in
Sport. Journal of Sports Sciences. 20(), p.119-126.
Carron, A., & Chelladurai, P. (1981). The Dynamics of Group Cohesion in Sport.
Journal of Sport Psychology. 3, p.123-139.
Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002b). Cohesion and
Performance in Sport: A Meta Analysis. Journal of Sport and Exercise
Psychology. 24(2), p.168-188.
Carron, A., Eys, M., & Burke, S. (2007). Team cohesion: nature, correlates, and
development. In: S. Jowett, & D. Lavallee, ed.(2007). Social Psychology in
Sport. Leeds: Human Kinetics. Ch. 7.
Carron, A., Hausenblas, H, & Eys, M. (2005). Group Dynamics in Sport (3rd Ed.).
Morgantown: Fitness Information Technology.
Carron, A. V., Widmeyer, W. N., & Brawley, L. R. (1985). The Development of an
Instrument to Assess Cohesion in Sport Teams: The Group Environment
Questionnaire. Journal of Sport Psychology. 7(3), p.244-266.
Cashmore, E. (2008). Sport and Exercise Psychology; The Key Concepts (2 nd
ed.). Oxon: Routledge.
Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education.
New York: Taylor and Francis.
Estabrooks, P., & Carron, A. (2000). The Physical Activity Group Environment
Questionnaire: An Instrument for the Assessment of Cohesion in Exercise
Classes. Group Dynamics: Theory, Research, and Practise. 4(3), p.230-243.
Estabrooks, P., & Dennis, P. (2003). The Principles of Team Building and Their
Applications to Sport Teams. In: R. Lidor, & K. Henschen, ed.(2003).
Psychology of Team Sports. Morgantown: Fitness Information Technology.
Ch5.
29
Eys, M. A., Carron, A. V., Bray, S. R., & Brawley, L. R. (2007). Item Wording and
Internal Consistency of a Measure of Cohesion: The Group Environment
Questionnaire. Journal of Sport and Exercise Psychology. 29(3), p.395-402.
Eys, M. A., Hardy, J., Carron, A. V., & Beauchamp, M. R. (2003). The Relationship
between task cohesion and Competitive State Anxiety. Journal of Sport and
Exercise Psychology. 25(1), p.66-76.
Eys, M., Loughead, T., Bray, S.R., & Carron, A. V. (2009). Development of a
Cohesion
Questionnaire
for
Youth:
The
Youth
Sport
Environment
Questionnaire. Journal of Sport and Exercise Psychology. 31(3), p.390-408.
Galea, C. (2010). Jones: We lost Cohesion. Sky Sports [internet] 19 March.
Available
at:
http://www.skysports.com/story/0,19528,12040_6036676,00.html [Accessed
6 March 2011]
Gill, D. (1977). Cohesiveness and Performance in Sport Groups. In: D. Smith, &
M. Bar-Eli, ed.(2007). Essential Readings in Sport and Exercise Psychology.
Leeds: Human Kinetics. Ch.18.
Grace, H. (1954). Conformance and Performance. Journal of Social Psychology.
40(2), p.333-335.
Gratton, C., & Jones, I. (2004). Research Methods for Sport. New York: Taylor
and Francis.
Gruber, J., & Gray, G. (1982). Response to forces influencing cohesion as a
function of player status and level of male varsity basketball competition.
Research Quarterly for Sport and Exercise. 53(1), p.27-36.
Hardy, J., Hall, C. R., & Carron, A. V. (2003). Perceptions of Team Cohesion and
Athletes’ Use of Imagery. International Journal of Sport Psychology. 34(2),
p.151-167.
30
Hoigaard, R., Tofteland, I., & Ommundsen, Y. (2006). The effect of Team
Cohesion on Social Loafing in Relay Teams. International Journal of Applied
Sports Sciences. 18(1), p.59-73.
Horn, T. (2008). Advances in Sport Psychology (3rd ed.). Leeds: Human Kinetics.
Huang, J. M., Chen, S., Chen, C. W., & Chiu, T. C. (2003). A study of perceived
leadership styles, preferred leadership styles, and team cohesion of high
school basketball teams in East Taiwan. Missouri Journal of Health, Physical
Education, Recreation & Dance. 13, p.38-46.
Kornspan, A. (2009). Fundamentals of Sport and Exercise Psychology. Leeds:
Human Kinetics.
Lavallee, D., Kremer, J., Moran, A., & Williams, M. (2004). Sport Psychology:
Contemporary Themes. Basingstoke: Palgrave Macmillan.
Li, F., & Harmer, P. (1996). Confirmatory Factor Analysis of the Group
Environment Questionnaire with an Intercollegiate Sample. Journal of Sport
and Exercise Psychology. 18(), p.49-63.
Lowther, J., & Lane, A. (2002). Relationship between Mood, Cohesion and
Satisfaction with Performance among Soccer Players. Athletic Insight. 4(3),
p.57-69.
Martens, R., Landers, D., & Loy, J. (1972). Sport Cohesiveness Questionnaire.
Washington: AAHPERD publications.
Matheson, H., Mathes, S., & Murray, M. (1997).The Effect of Winning and Losing
on Female Interactive and Coactive Team Cohesion. Journal of Sport
Behavior. 20(3), p.284-298.
McMorris, T., & Hale, T. (2006). Coaching Science: Theory into Practice.
Chichester: John Wiley and Sons Ltd.
Melnick, M., & Chemers, M. (1974). Effects of group social structure on the
success of basketball teams. Research Quarterly. 45(1), p.1-8.
31
Moran, A. (2004). Sport and Exercise Psychology: A Critical Introduction. Sussex:
Routledge.
Murphy, S. (2005). Sport Psych Handbook: A complete Guide to Today’s Best
Mental Training Techniques. Leeds: Human Kinetics.
Nezhad, R. R., & Keshtan, M. H. (2010). The coach's leadership styles team
cohesion and team success in Iran football clubs professional league.
International Journal of Fitness. 6(1), p.53-61.
Paiement, C., & Bischoff, D. (2007). Effect of interdependence and gender on
team cohesion and performance. Journal of Sport and Exercise Psychology.
29, p.196-199.
Rudder, K., & Gill, D. (1982). Immediate Effects of Win-Loss on Perceptions of
Cohesion in Intramural and Intercollegiate Volleyball Teams. Journal of Sport
Psychology. 4(3), p.227-234.
Ruth, G. (1940). Bat it out. The Rotarian, July, p.13c.
Salminen, S. (1987). Relationships between cohesion and success in ice hockey
teams. Scandinavian Journal of Sports Sciences. 9(1), p.25-31.
Senecal, J., Loughead, T. M., & Bloom, G. A. (2008). A Season-Long TeamBuilding Intervention: Examining the Effect of Team Goal Setting on
Cohesion. Journal of Sport and Exercise Psychology. 30(2), p.186-199.
Singer, R. N., Hausenblas, N. A., & Janelle, C. (2001). Handbook of Sport
Psychology. New York: John Wiley & Sons.
Stevens, D., & Bloom, G. (2003). The effect of Team Building on Cohesion.
AVANTE. 9(2), p.43-54.
Turman, P. (2003). Coaches and cohesion: the impact of coaching techniques on
team cohesion in the small group sport setting. Journal of Sport Behaviour.
26(1), p.86-104.
32
Tziner, A., Nicola, N., & Rizac, A. (2003). Relation between social cohesion and
team performance in soccer teams. Perceptual and Motor Skills. 96(1),
p.145-148.
Van Raalte, J. L., Cornelius, A. E., Linder, D. E., & Brewer, B. W. (2007). The
Relationship between Hazing and Team Cohesion. Journal of Sport
Behavior. 30(4), p.491-507.
Widmeyer, W. N., Brawley, L. R., & Carron, A. V. (1985). The Measurement of
Cohesion in Sport Teams: The Group Environment Questionnaire. London:
Sports Dynamics.
Widmeyer, W. N., Brawley, L. R., & Carron, A. V. (1990). The Effect of Group Size
in Sport. Journal of Sport and Exercise Psychology. 12(2), p.17-190.
Widmeyer, W. N., & Ducharme, K. (1997). Team Building through Team Goal
Setting. Journal of Applied Sport Psychology. 9(1), p.97-113.
Williams, J., & Widmeyer, W. (1991). The cohesion-performance outcome
relationship in a co-acting sport. Journal of Sport and Exercise Psychology.
13(4), p.364-371.
Wrisberg, C., & Draper, M. (1988). Sex, sex role orientation, and the cohesion of
intercollegiate basketball teams. Journal of Sport Behaviour. 11(1), p.45-54.
Yukelson, D., Weinberg, R., & Jackson, A. (1984). A Multidimensional group
cohesion instrument for intercollegiate basketball.
Psychology. 6(1), p.103-117.
33
Journal of Sport
APPENDICES
APPENDIX A
PARTICIPANT INFORMATION SHEET
Title of Project:
The Effect of Group Size and Player Ratings on
Team Cohesion.
Name of Researcher :
Elizabeth Copeland
UREC Reference no.:
Background
This study is examining the psychology of teams. The aim is to explore how
squad size affects this. These results will also explore the link between
cohesion and performance.
What will it entail you doing?
You will need to give yourself a player rating between one (I played really
badly) and 10 (I played one of the best games I ever have) five times, once
every week. You will also be required to complete a single questionnaire, once
every week for five weeks. Completing the questionnaire should take no more
than 20mins.
Your rights
In this project no names will be used, only codes. This will make sure that noone apart from me or my supervisor may know whose results they are. If in
any part of the study you wish to withdraw from the study, you may do so with
no consequences.
Further information
If you have any questions or wish to know more, please don’t hesitate to
contact me.
Elizabeth Copeland

[email protected]
34
APPENDIX B
PARTICIPANT CONSENT FORM
Title of Project:
The Effect of Group Size and Player Ratings on
Team Cohesion.
Name of Researcher:
Elizabeth Copeland
UREC Reference no.:
Participant to complete this section:
Please initial each box.
1. I confirm that I have read and understand the information
sheet dated ……….........for this study. I have had the
opportunity to consider the information, ask questions and
have had these answered satisfactorily.
2. I understand that my participation of is voluntary and that it is
possible to stop taking part at any time, without giving a
reason.
3. I also understand that if this happens, my relationship with
UWIC, or my legal rights, will not be affected.
4. I understand that information from the study may be used for
reporting purposes, but that I will not be identified.
Name of Participant
Signature of Participant
Date
35
APPENDIX C
Group Environment Questionnaire
Title of Project:
The Effect of Group Size and Player Ratings on
Team Cohesion.
Name of Researcher:
Elizabeth Copeland
UREC Reference no.:
Date: ……………………
Age: …………
Mobile no: ……………………………
Name: ……………………………………….
Sport: ………………………………...
The following questions are designed to assess your feelings about your personal
involvement with this team. Please circle a number from 1 to 9 to indicate your level of
agreement with each of the statements.
Strongly
Disagree
Strongly
Agree
1. I do not enjoy being part of the
social activities of this team.
1
2
3
4
5
6
7
8
9
2. I’m not happy with the amount of
playing time I get.
1
2
3
4
5
6
7
8
9
3. I am not going to miss the
members of this team when the
season ends.
1
2
3
4
5
6
7
8
9
4. I’m unhappy with my team’s level of
desire to win.
1
2
3
4
5
6
7
8
9
5. Some of my best friends are on this
team.
1
2
3
4
5
6
7
8
9
6. This team does not give me
enough opportunities to improve my
personal performance.
1
2
3
4
5
6
7
8
9
7. I enjoy other parties more than
team parties.
1
2
3
4
5
6
7
8
9
8. I do not like the style of play on this
team.
1
2
3
4
5
6
7
8
9
9. For me this team is one of the most
important social groups to which I
belong.
1
2
3
4
5
6
7
8
9
36
The following questions are designed to assess your perceptions of your team as a
whole. Please circle a number from 1 to 9 to indicate your level of agreement with each of
the statements.
10. Our team is united in trying to
reach its goals for performance.
Strongly
Disagree
1
2
3
4
5
6
7
Strongly
Agree
8
9
11. Members of our team would
rather go out on their own than go
together as a team.
1
2
3
4
5
6
7
8
9
12. We all take responsibility for any
loss or poor performance by our
team.
1
2
3
4
5
6
7
8
9
13. Our team members rarely party
together.
1
2
3
4
5
6
7
8
9
14. Our team members have
conflicting aspirations for the team’s
performance.
1
2
3
4
5
6
7
8
9
15. Our team would like to spend time
together in the off season.
1
2
3
4
5
6
7
8
9
16. If our team have problems in
practise, everyone wants to help them
so we can get back together again.
1
2
3
4
5
6
7
8
9
17. Members of our team do not stick
together outside of practices and
games.
1
2
3
4
5
6
7
8
9
18. Our team members do not
communicate freely about each
athlete’s responsibilities during
competition or practice.
1
2
3
4
5
6
7
8
9
37
APPENDIX D
Multivariate Testsd
Hypothesis
Effect
Between
Value
Intercept
Subjects
F
df
Partial Eta
Error df
Sig.
Squared
Noncent. Observed
Parameter
Powerb
Pillai's Trace
.988 773.197a
4.000 36.000
.000
.988
3092.789
1.000
Wilks' Lambda
.012 773.197a
4.000 36.000
.000
.988
3092.789
1.000
Hotelling's Trace 85.911 773.197a
4.000 36.000
.000
.988
3092.789
1.000
85.911 773.197a
4.000 36.000
.000
.988
3092.789
1.000
Roy's Largest
Root
Sport
Pillai's Trace
.406
2.354
8.000 74.000
.026
.203
18.835
.856
Wilks' Lambda
.626
2.377a
8.000 72.000
.025
.209
19.014
.859
Hotelling's Trace
.547
2.395
8.000 70.000
.024
.215
19.159
.860
Roy's Largest
.430
3.977c
4.000 37.000
.009
.301
15.908
.866
Pillai's Trace
.577
2.045a
16.000 24.000
.055
.577
32.714
.834
Wilks' Lambda
.423
2.045a
16.000 24.000
.055
.577
32.714
.834
Hotelling's Trace
1.363
2.045a
16.000 24.000
.055
.577
32.714
.834
Roy's Largest
1.363
2.045a
16.000 24.000
.055
.577
32.714
.834
Root
Within Subjects time
Root
time *
Pillai's Trace
.764
.965
32.000 50.000
.535
.382
30.875
.702
Sport
Wilks' Lambda
.372
.960a
32.000 48.000
.541
.390
30.722
.691
1.326
.953
32.000 46.000
.551
.399
30.493
.679
.938
1.465c
16.000 25.000
.191
.484
23.441
.668
Hotelling's Trace
Roy's Largest
Root
a. Exact statistic
b. Computed using alpha = .05
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
d. Design: Intercept + Sport
Within Subjects Design: time
38
APPENDIX E
Univariate Tests
Source
Measure
time
AGT
AGS
GIT
GIS
time * Sport
AGT
AGS
GIT
GIS
Type III
Sum of
Squares
Mean
Square
df
F
Sig.
Partial
Eta
Noncent. Observed
Squared Parameter Powera
Sphericity Assumed
6.241
4
1.560 3.846
.005
.090
15.384
.889
GreenhouseGeisser
6.241 2.737
2.280 3.846
.014
.090
10.526
.782
Huynh-Feldt
6.241 3.115
2.004 3.846
.010
.090
11.979
.821
Lower-bound
6.241 1.000
6.241 3.846
.057
.090
3.846
.481
Sphericity Assumed
5.713
4
1.428 2.464
.047
.059
9.857
.694
GreenhouseGeisser
5.713 3.463
1.649 2.464
.056
.059
8.535
.646
Huynh-Feldt
5.713 4.000
1.428 2.464
.047
.059
9.857
.694
Lower-bound
5.713 1.000
5.713 2.464
.125
.059
2.464
.334
Sphericity Assumed
1.335
4
.334
.588
.672
.015
2.350
.191
GreenhouseGeisser
1.335 3.352
.398
.588
.643
.015
1.969
.177
Huynh-Feldt
1.335 3.893
.343
.588
.668
.015
2.287
.189
Lower-bound
1.335 1.000
1.335
.588
.448
.015
.588
.116
Sphericity Assumed
1.842
4
.460
.930
.448
.023
3.721
.290
GreenhouseGeisser
1.842 3.495
.527
.930
.439
.023
3.251
.270
Huynh-Feldt
1.842 4.000
.460
.930
.448
.023
3.721
.290
Lower-bound
1.842 1.000
1.842
.930
.341
.023
.930
.156
Sphericity Assumed
3.347
8
.418 1.031
.415
.050
8.251
.468
GreenhouseGeisser
3.347 5.474
.611 1.031
.406
.050
5.645
.373
Huynh-Feldt
3.347 6.230
.537 1.031
.409
.050
6.425
.403
Lower-bound
3.347 2.000
1.674 1.031
.366
.050
2.063
.217
Sphericity Assumed
6.273
8
.784 1.353
.221
.065
10.824
.604
GreenhouseGeisser
6.273 6.927
.906 1.353
.231
.065
9.372
.557
Huynh-Feldt
6.273 8.000
.784 1.353
.221
.065
10.824
.604
Lower-bound
6.273 2.000
3.136 1.353
.270
.065
2.706
.274
Sphericity Assumed
7.899
8
.987 1.738
.094
.082
13.905
.737
GreenhouseGeisser
7.899 6.704
1.178 1.738
.109
.082
11.652
.676
Huynh-Feldt
7.899 7.786
1.014 1.738
.096
.082
13.533
.727
Lower-bound
7.899 2.000
3.949 1.738
.189
.082
3.476
.342
Sphericity Assumed
4.025
8
.503 1.017
.426
.050
8.132
.462
GreenhouseGeisser
4.025 6.990
.576 1.017
.422
.050
7.105
.426
Huynh-Feldt
4.025 8.000
.503 1.017
.426
.050
8.132
.462
4.025 2.000
2.013 1.017
.371
.050
2.033
.214
Lower-bound
a. Computed using alpha = .05
39
Tests of Between-Subjects Effects
Transformed Variable:Average
Type III Sum
Square
F
AGT
11799.720
1 11799.720 2539.014
.000
.985
2539.014
1.000
AGS
10514.275
1 10514.275 1666.567
.000
.977
1666.567
1.000
GIT
9042.807
1
9042.807 1750.295
.000
.978
1750.295
1.000
GIS
9682.883
1
9682.883 1999.925
.000
.981
1999.925
1.000
AGT
20.066
2
10.033
2.159
.129
.100
4.318
.415
AGS
29.047
2
14.524
2.302
.113
.106
4.604
.439
GIT
31.740
2
15.870
3.072
.058
.136
6.143
.560
GIS
34.439
2
17.219
3.557
.038
.154
7.113
.627
AGT
181.247
39
4.647
AGS
246.049
39
6.309
GIT
201.491
39
5.166
GIS
188.823
39
4.842
40
Squared
Parameter
Powera
Intercept
a. Computed using alpha = .05
Sig.
Noncent. Observed
Measure
Error
df
Partial Eta
Source
Sport
of Squares
Mean