Journal of Sports Sciences
ISSN: 0264-0414 (Print) 1466-447X (Online) Journal homepage: http://www.tandfonline.com/loi/rjsp20
Examining multidimensional sport-confidence in
athletes and non-athlete sport performers
Moe Machida, Mark Otten, T. Michelle Magyar, Robin S. Vealey & Rose Marie
Ward
To cite this article: Moe Machida, Mark Otten, T. Michelle Magyar, Robin S. Vealey & Rose
Marie Ward (2016): Examining multidimensional sport-confidence in athletes and non-athlete
sport performers, Journal of Sports Sciences, DOI: 10.1080/02640414.2016.1167934
To link to this article: http://dx.doi.org/10.1080/02640414.2016.1167934
Published online: 18 Apr 2016.
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Date: 18 April 2016, At: 07:58
JOURNAL OF SPORTS SCIENCES, 2016
http://dx.doi.org/10.1080/02640414.2016.1167934
Examining multidimensional sport-confidence in athletes and non-athlete sport
performers
Moe Machidaa, Mark Ottenb, T. Michelle Magyarc, Robin S. Vealeyd and Rose Marie Wardd
School of Physical Education, Osaka University of Health and Sport Sciences, Osaka, Japan; bDepartment of Psychology, California State University,
Northridge, CA, USA; cThe Office of the Governor, California State Board of Education, Sacramento, CA, USA; dDepartment of Kinesiology and
Health, Miami University, Oxford, OH, USA
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a
ABSTRACT
ARTICLE HISTORY
Sport-confidence is considered a critical success factor for sport performers at all levels. Researchers
have suggested that sport-confidence is a multidimensional rather than a unidimensional construct,
and the sport-confidence model identified three types of sport-confidence (i.e., physical skills and
training, cognitive efficiency, and resilience) that are important for success in sport. However, such
multidimensionality of sport-confidence and its measurement have not been fully examined. On a large
sample of sport performers with varied skill levels and characteristics, the purpose of the present study
was to examine the three-factor model of sport-confidence. We tested the measurement invariance of
the Sport-Confidence Inventory across 512 athletes and 1170 non-athlete sport performers. Results from
the multiple group model analysis showed that the three-factor model of sport-confidence fit better for
the athlete sample than for the non-athlete sample. The results implicate that the three-factor model of
sport-confidence model is suitable to athletes, though sport-confidence may appear more unidimensional for non-athletes. The use of the Sport-Confidence Inventory for non-athlete sport performers
demands further consideration; however, the findings implicate that it could be a useful tool to assess
sport-confidence of sport performers at any levels.
Accepted 14 March 2016
Confidence is one of the most influential and extensively
studied psychological constructs in sports. Confidence has
been related to many positive attributes and outcomes,
including lower cognitive and somatic anxiety (e.g., Cresswell
& Hodge, 2004; Vealey, Hayashi, Garner-Holman, & Giacobbi,
1998), adaptive goal orientation (e.g., Hall & Kerr, 1997; Vosloo,
Ostrow, & Watson, 2009), the success of Olympic champions
(Gould, Dieffenbach, & Moffett, 2002), a mediator in flow state
(Koehn, Pearce, & Morris, 2013), and ultimately better performance (e.g., Moritz, Feltz, Fahrbach, & Mack, 2000). Many sport
psychology practitioners consider increasing confidence as
one of the main goals of interventions with athletes (e.g.,
Beaumont, Maynard, & Butt, 2015; Hanton & Jones, 1999;
Mamassis & Doganis, 2004).
Perhaps the most studied conceptual approach to confidence in sport is the sport-confidence model (Vealey, 1986;
Vealey & Chase, 2008, see Figure 1). Sport-confidence can be
defined as the belief or degree of certainty that individuals
possess about their ability to be successful in sport (Vealey,
2001). The most recent sport-confidence model posits that
sport-confidence is a multidimensional construct and there
are multiple types of sport-confidence specific to the task
KEYWORDS
Self-efficacy; learning;
psychological assessment;
sport psychology
demands (Vealey & Chase, 2008; Vealey & Knight, 2002).
Vealey and Knight identified three areas of abilities that are
most critical for success in sport and developed the SportConfidence Inventory (SCI) to measure sport-confidence for
each area of ability. In their study, they also established its
preliminary validity and reliability with high school and college
varsity athletes. However, studies of multidimensionality in
sport-confidence are still limited (Hays, Maynard, Thomas, &
Bawden, 2007; Vealey & Chase, 2008).
In addition, studies of the sport-confidence model have traditionally focused on athletes who participate in competitive
sport. However, the sport-confidence model could also be a
useful framework for understanding the learning processes of
sport skills in non-athlete sport participants. Nonetheless, studying sport-confidence among non-athletes has been a challenge
because there is no measurement tool of multidimensional
sport-confidence established for non-athlete population. Thus,
the purposes of the present study were to examine the applicability of the sport-confidence model both to athletes and nonathletes, and to compare the model across these two groups to
test the measurement invariance of the SCI (Vealey & Knight,
2002; see Figure 2 for the hypothesised model).
CONTACT Moe Machida
machidam@ouhs.ac.jp
School of Physical Education, Osaka University of Health and Sport Sciences, 1-1 Asashirodai, Kumatoricho, Sennan-gun, Osaka 590-0496, Japan
Part of the data is from the first author’s master’s thesis which was supported by Master’s Thesis Support Grant from Miami University. The first author published a
paper from her master’s thesis (Machida, M., Ward, R.M., & Vealey, R.S. (2012). Predictors of sources of self-confidence in collegiate athletes. International Journal of
Sport and Exercise Psychology, 10, 172–185). However, this paper examined completely different variables and research questions. Thus, there is no overlap of
variables examined in these two papers. About 15% of data from Sample 4 (n = 201) has been published in a past paper (Otten, M. (2009). Choking vs. clutch
performance: A study of sport performance under pressure. Journal of Sport and Exercise Psychology, 31, 583–601) but it examined completely different research
questions from the current paper.
© 2016 Informa UK Limited, trading as Taylor & Francis
2
M. MACHIDA ET AL.
Demographic and personality
characteristics (i.e. personal
factors)
Organizational culture (i.e. social
factors)
Sources of Sport-Confidence
Demonstration of
ability
Mastery
Physical/Mental
preparation
Physical selfpresentation
Social Support
Vicarious
Experience
Coach's leadership
Environmental
Comfort
Situational
favorableness
Types of Sport-Confidence
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Cognitive efficiency
Physical skills and
training
Resilience
Affect
Behavior
Cognition
Uncontrollable external
factors
Performance
Physical skills and
characteristics
Figure 1. Sport-confidence model (Vealey, 1986; Vealey & Chase, 2008). Adapted, with permission, from R.S. Vealey and M.A. Chase, 2008, Self-confidence in sport. In
Advances in sport psychology, 3rd ed., edited by T.S. Horn (Champaign, IL: Human Kinetics), 71.
Multidimensionality indicated in the sport-confidence
model
The conceptual foundation of sport-confidence was developed from self-efficacy theory (Bandura, 1997) in response
to the need for a conceptualisation of confidence specific to
competitive sport (Vealey, 1986). After a few revisions, the
most recent sport-confidence model (Vealey, 1986; Vealey &
Chase, 2008, see Figure 1) was developed to supplement
the theoretical limitations of the original model. This revised
model is more consistent with Bandura’s self-efficacy theory
and was developed in a social cognitive theoretical framework (Vealey & Chase, 2008). First, instead of taking a traitand state- dichotomous approach (Vealey, 1986), it contends that levels of sport-confidence can fluctuate depending on the situation. It is consistent with Bandura’s selfefficacy theory in that perceived efficacy is a dynamic fluctuating property and not a dispositional trait. Second,
though the original model considered sport-confidence as
a unidimensional construct, the revised model views sportconfidence as a multidimensional property, and therefore
includes multiple types of sport-confidence. As Bandura
indicated in self-efficacy theory, self-efficacy is a multidimensional construct that is specific to the task. While limitations of a unidimensional approach to measuring
psychological constructs have been discussed in other constructs (e.g., self-concept, Marsh & Craven, 2006), Bandura
also argues that self-efficacy is “not a contextless global
disposition” (p. 42). To achieve its predictive ability of an
outcome, self-efficacy and its measurement should be
domain specific. Similarly, taking a unidimensional approach
to measuring sport-confidence could limit our understanding of its effect on specific sport outcomes. Thus, the
revised sport-confidence model argues that sport-confidence should be understood and measured multidimensionally for both theoretical and practical purposes.
The model includes the antecedents (i.e., athlete and
organisational characteristics) and sources of athletes’
sport-confidence, and specifies the influences of three
types of sport-confidence on athletes’ affect, behaviour,
and cognition (see Figure 1, Vealey, 1986; Vealey & Chase,
2008). The first type of sport-confidence is sport-confidence
about physical skills and training (SC-Physical Skills and
Training), which refers to athletes’ confidence about their
training and their capability to perform the physical skills
necessary for success in sport (Vealey & Chase, 2008). The
second type of sport-confidence is sport-confidence about
cognitive efficiency (SC-Cognitive Efficiency), which is defined
as athletes’ confidence about their ability to maintain optimal focus and concentration and to make critical decisions
necessary for successful performance (Vealey & Chase,
2008). Finally, sport-confidence about resilience (SCResilience) is conceptualised as athletes’ confidence about
their ability to refocus and bounce back from performance
errors and mistakes, and overcome adversities and setbacks
to be successful in sport (Vealey & Chase, 2008). Among
college and high school varsity athletes, Vealey and Knight
found that these three types of sport-confidence were
differently related to athletes’ cognition, affect, and performance, supporting the multidimensionality of sportconfidence.
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JOURNAL OF SPORTS SCIENCES
3
Figure 2. Hypothesised confirmatory factor analysis. Ovals indicate proposed latent variables (factors). Rectangles indicate proposed measured variables. Straight
arrows represent predicted regressions. Straight arrows leading to dependent variables represent predicted residual variances (errors). Double arrows represent
predicted correlations.
Measurements of sport-confidence
Perhaps the most popular confidence measure in sport has
been the Trait and State Sport-Confidence Inventory (TSCI,
SSCI: Vealey, 1986). A number of studies were conducted
using these scales and found their links to various psychosocial factors, such as anxiety, coping skills, flow states,
achievement goal orientation, and performance (e.g.,
Cresswell & Hodge, 2004; Koehn et al., 2013; Magyar &
Duda, 2000; Moritz et al., 2000). However, these two scales
treat sport-confidence as a unidimensional construct, and
adopt a dichotomous approach claiming that there are dispositional sport-confidence and state sport-confidence.
Thus, these scales do not capture the multidimensionality
and fluctuating properties of sport-confidence as proposed
in the revised sport-confidence model (Vealey, 1986; Vealey
& Chase, 2008). Responding to the need for a scale that
enables an assessment of the multidimensionality in sportconfidence, Vealey and Knight (2002) conducted a fourphase study and developed the SCI to measure the three
types of sport-confidence identified as important by athletes (i.e., SC-Physical Skills and Training, SC-Cognitive
Efficiency, and SC-Resilience). In this original study with
high school and college varsity athletes, they found preliminary reliability and validity of the SCI.
There is limited research on the multidimensionality of
sport-confidence. Hays et al. (2007) attempted to capture
the multidimensionality of world-class athletes’ confidence
in their qualitative study and identified six types of sportconfidence necessary for their success in sport (i.e., skill
execution, achievement, physical factors, psychological factors, superiority to opposition, and tactical awareness). This
study identifies more than three types of sport-confidence
that are important to athletes; however, these study
findings were specific to world-class athletes. Also the
study did not use a quantitative measurement to assess
these different types of confidence; thus, their interviewbased approach was not suited to contrasting confidence
in multiple dimensions. Otten (2009) utilised the SCI in a
study of clutch performance of basketball free throw shooting, with 201 undergraduate students of varied basketball
experience, and found that sport-confidence was related to
performance through its effect on perceived control under a
pressured situation. Though this study provided some predictive validity of the SCI, he tallied the SCI scores to
indicate a participant’s overall sport-confidence and treated
sport-confidence as a unidimensional construct.
Sport-confidence among non-athlete sport performers
Besides Otten’s (2009) study, studies of sport-confidence and
measures such as the TSCI, SSCI, and SCI have most often been
based on athletes who participate in competitive sport, while
only a select few studies tested the use of the TSCI and SSCI in
non-athlete sport performers. Such studies on non-athlete sport
performers include Mills and Mitchell (1996) which investigated
the validity of the TSCI and SSCI with middle school aged physical education students. Also Boyd and Yin (1999) studied the TSCI
in its relationships with self-schemata in sport among undergraduate students. However, there is no study to date which
examined the multidimensionality of sport-confidence and utilised the SCI in their studies with non-athlete sport performers.
The use of the sport-confidence model (Vealey, 1986; Vealey &
Chase, 2008) and the SCI should not be limited to assessing
sport-confidence of athletes who participate in competitive
sport and should be utilised for studies on non-athlete sport
performers. As Bandura (1997) indicates, self-efficacy is a critical
4
M. MACHIDA ET AL.
psychological factor in maintaining adaptive cognitive functioning during a learning process and improving skills. Thus, for the
present study, with large samples of athletes and non-athlete
sport performers, we aimed to test the applicability of the multidimensional perspectives of sport-confidence, compare the
model between these two groups to examine the measurement
invariance of the SCI (Vealey & Knight, 2002; see Figure 2 for the
hypothesised model), and establish its construct validity and
reliability.
Method
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Participants and data collection procedure
Data was collected from four samples at four different time
frames.1 Each data collection was preceded by obtaining permission from institutional review boards at authors’ institutions.
All data collections were preceded by obtaining consents (and
assents if participants were under 18) from participants.
Sample characteristics were summarised in Table 1. Sample
1 represents 17 teams from two competitive volleyball clubs
competing in the western region of the United States. The
majority of this sample described themselves as White
(n = 93); others described themselves as Black (n = 7),
Mexican (n = 6), Asian (n = 5), Native American (n = 2), and
Bi-racial (n = 15); 33 participants did not report their ethnic
origin. Participants in Sample 2 were from universities in the
United States. There were 42 club2 and 163 varsity athletes.
The majority of participants were from NCAA Division I
(n = 111), and three were from NCAA Division III programmes.
The participants represented 13 sports: ice skating (n = 50),
track and field (n = 45), softball (n = 13), golf (n = 12), soccer (n
= 11), swimming (n = 11), basketball (n = 10), field hockey (n =
10), diving (n = 4), American football (n = 2), and volleyball
(n = 2). Participants were predominantly White (n = 177), while
there were seven Asian, seven Black, and seven Hispanic athletes, with one participant indicating other.
Participants in the third sample were from a junior college
(i.e., two-year university) in the United States. The participants
were derived from five sports teams: American football
(n = 53), soccer (n = 33), volleyball (n = 19), softball (n = 15),
and basketball (n = 7); three participants chose not to disclose
their team affiliation. Participants reflected an ethnically
diverse sample: 50 were White, 32 were Black, 21 were of
mixed ethnicity, 16 were Hispanic, and 11 either indicated
“other” ethnicity or did not disclose the information.
Participants in the fourth sample were undergraduate students from two universities in the United States. Students
participated in the study as part of each university’s psychology department subject pool, and thus were able to sign up
online and receive course credit in exchange for their efforts.
Participants first filled out the questionnaire and then proceeded with a round of 15 basketball free throws. Thus, the
items in the assessment referred to participants’ sport-confidence specifically with regard to their free throw shooting
ability. These participants also reflected an ethnically diverse
sample: 413 were Hispanic, 269 were White, 253 were of Asian
descent, 113 were Black, and 192 were either of mixed or
“other” ethnicity, or did not record this information.
Final sample for analyses
Pooling all four samples together, we categorised participants
into athlete and non-athlete categories; criteria were as follows. We defined “athlete” sport performers as those who
currently participate and have deliberate training in competitive sport or who demonstrate competence in a sport skill
tested (i.e., basketball free throw). Thus, those in Samples 1,
2, and 3 were designated as athletes; within Sample 4, 70
participants were categorised as athletes, because they made
at least 10 out of their 15 free throw attempts (with no prior
warm-up; 66% of success rate), which is considered a reasonable standard for basketball players in high school and college
levels (Plubinskas, 2012). Average years of experience in competitive basketball for the athlete sample in Sample 4 were
compatible with Samples 1, 2, and 3 which further confirmed
our inclusion of these participants in the athlete sample.
The remaining 1170 participants from Sample 4 were categorised as non-athletes. We operationalised “non-athletes” as
Table 1. Sample characteristics.
N
Sample
1
2
Total Female Male
163 163
0
206 139
67
3
4
Athletes
Nonathletes
130
1240
70
1170
52
611
23
588
75
621
45
576
Age: M (SD)
Sport
14.71 (1.56) Volleyball (High School Varsity)
19.62 (1.25) Various Sport (College Club and
Varsity)
18.98 (1.49) Various Sport (Junior College Varsity)
19.84 (3.21) Basketball (Free Throw)
20.10 (2.88) Basketball (Free Throw)
19.83 (2.81) Basketball (Free Throw)
Years of exeperience in sport: M (SD)
3.0 (1.68)
10.38 (1.25)
9.64
5.91
8.46
5.71
(3.97)
(4.18)
(4.64)
(4.44)
Likert
scale
0 to 10
1 to 7
0 to 10
0 to 100
0 to 100
0 to 100
Survey type
Paper
Online (n = 156)
paper (n = 50)
Paper
Paper
Paper
Paper
Note: Samples 1–3 were categorised into the “athlete” sample.
1
Present data was collected from different projects. To serve our purposes of the study, we decided to combine their SCI data which made a large
enough sample to allow for more sophisticated analysis – the analysis we could never complete in each of our original projects. Sample 1 and Sample 3’s
data have not been published elsewhere. Sample 2’s data is from the first author’s master’s thesis and the first author published a paper from the project
(see acknowledgment for publication details). However, this paper examined completely different variables and research questions so there is no overlap
of variables between this paper and the present paper. Also, approximately 15% of data from Sample 4 has been published in a past paper by the
second author (see acknowledgement for publication details) which examined completely different research questions.
2
Club athletes in this study were athletes who played their respective sports in university-approved clubs. They were not recreational or intramural sport
participants.
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performers who did not show enough competence in a sport
skill tested (i.e., basketball free throw). Free throw skill is
considered a fundamental and the only skill in basketball
that is performed in a closed manner. Though these participants indicated having an average of more than 5 years of
experience in basketball, performance below the standard on
this fundamental skill can be an indication of their lack of
deliberate training in basketball; thus, they could not be considered as “athletes”. Also, mean years of experience in basketball for the non-athlete sample in Sample 4 were significantly
lesser than the athlete sample in Sample 4. Therefore, the final
non-athlete sample consisted of these 1170; adding the 70
Sample 4 participants to those from the other three samples
led us to a total of 512 participants within the athlete sample.
Measures
Participants in all four samples filled out a demographic questionnaire that assessed sex, age, years of experience in sport,
and ethnicity. To assess levels of sport-confidence, the SportConfidence Inventory (SCI; Vealey & Knight, 2002) was
employed. The SCI measures three types of sport-confidence
(SC): SC-Physical Skills and Training (5 items: e.g., “Your physical fitness level will allow you to complete successfully”), SCCognitive Efficiency (5 items: e.g., “You can successfully make
critical decisions during competition”), and SC-Resilience (5
items: e.g., “You can regain your mental focus after a performance error”).
In the present study, the SCI was employed to the four
samples as follows (also see Table 1). For Samples 1 and 3,
athletes responded to the following stem “How confident are
you that. . .” and then rated each item on an 11-point Likert
scale ranging from 0 (not at all confident) to 10 (very confident). For Sample 2, participants were asked to rate how they
typically feel about their ability on a 7-point Likert scale, with 7
being “totally certain” and 1 being “can’t do it at all.” For
Sample 4, items in the SCI were slightly modified to assess
participants’ sport-confidence about basketball free throw
shooting (e.g., “you can successfully manage your emotions
during competition” was modified to “you can successfully
manage your emotions while shooting free throws”).
Participants were asked to rate how they feel about their
abilities in basketball free throw shooting on a 11-point
Likert scale ranging from 0 (not at all confident) to 100 (completely confident).
To convert values from each of the four samples to the
same scale, responses from Sample 1 and Sample 3 were each
multiplied by 10 to match the scale of the Sample 4. For
Sample 2, a “7” was switched to “100,” “6” to “83.33,” “5” to
“66.67,” and so on at equal intervals until “1” became “0.” To
verify the latter conversion, variances across items were compared between Samples 2 and 3, which had compatible participants’ characteristics. Of the 15 items, the variances of 8
items were greater in Sample 2, while the variances of the
other 7 items were larger within Sample 3. This suggested that
analyses combining these samples and Likert scales may be
reasonable going forward.
5
Data analyses
Reliability of the SCI subscales, means, and standard deviations
were calculated using SPSS version 22. EQS version 6.2 (Bentler,
2014) was employed to perform the current confirmatory factor
analyses through structural equation modeling (SEM).
We first tested the hypothesised three-factor model proposed by Vealey and Knight (2002, see Figure 2) with the
whole sample, allowing three factors to correlate. Then, we
conducted a multiple group model analysis to compare and
test for the measurement invariance between the athlete and
non-athlete samples. This analysis follows an incremental process (Muthén & Muthén, 2009): (a) fitting the model separately
in each group (i.e., athletes and non-athletes) to assess the fit of
each; (b) fitting the model in both groups simultaneously with
all parameters to be freed; (c) fitting the model in both groups
with constraining factor loadings equal, to test the equivalence
of these across groups. If equivalent, further constraints are
then considered (e.g., factor variances and covariances) to arrive
at a final multiple group model to represent the data.
For the SEM, goodness-of-fit is assessed between the data
collected and the data that would be implied by the hypothesised model, by way of the maximum-likelihood chi-square
(χ2) statistic (using α = .05), the Comparative Fit Index (CFI),
the root mean squared error of approximation (RMSEA), and
the standardised root mean-square residual (SRMR; Preacher &
MacCallum, 2003). CFI values fall between 0 and 1, with values
greater than .95 indicate best model fit; RMSEA values less
than .06 and SRMR values below .08 suggest likewise.
Results
Missing data existed for 59 athletes and 45 non-athletes; after
listwise deletion of these cases, n = 510 athletes and n = 1125
non-athletes remained. All remaining responses fell within the
possible 0-to-100 range of the SCI, and no outliers were found
(beyond a standardised score of ±3.29). Means and standard
deviations of each item for these athlete and non-athlete
samples are presented in Table 2. Scores of every item were
significantly higher among the athlete sample than among the
non-athlete sample (see Table 1). Effect sizes for these
Table 2. Summary statistics for athletes and non-athletes.
Mean (SD) on the Sport-Confidence Inventory (0-to-100 scale)
Variable
Item 1
Item 2
Item 3
Item 4
Item 5
Item 6
Item 7
Item 8
Item 9
Item 10
Item 11
Item 12
Item 13
Item 14
Item 15
81.49
82.00
80.61
81.92
80.46
78.88
84.37
81.81
78.69
85.70
84.14
79.68
82.15
83.78
79.57
Athletes
(14.64)
(14.64)
(14.79)
(16.37)
(14.95)
(15.97)
(16.20)
(13.89)
(16.40)
(13.91)
(14.04)
(15.84)
(14.79)
(14.46)
(16.41)
Note: df = 1585, p < .001 for all tests.
Non-athletes
68.80
(21.37)
74.29
(19.12)
73.65
(19.72)
66.20
(24.56)
68.04
(21.10)
68.54
(20.86)
69.26
(23.70)
72.66
(20.26)
69.34
(21.13)
71.82
(22.04)
74.73
(20.20)
74.11
(20.06)
71.24
(20.24)
66.96
(23.62)
68.46
(20.88)
t
11.84
7.88
6.93
12.82
11.67
9.68
12.71
9.01
8.62
12.72
9.27
5.39
10.69
14.44
10.34
6
M. MACHIDA ET AL.
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differences ranged from relatively small (item 12; Cohen’s
d = .32) to large (item 14; Cohen’s d = .74). Meanwhile, each
item’s standard deviation was greater for the non-athlete
sample than for the athlete sample. For athletes, Cronbach’s
α reliabilities of each subscale were .89 (SC-Physical Skills and
Training), .85 (SC-Cognitive Efficiency), and .89 (SC-Resilience).
For non-athletes, these Cronbach’s α values were .89, .89, and
.92 respectively.
Fit of the three-factor SEM for the sample as a whole
(n = 1635) was adequate: χ287 = 1632.88, p < .001; CFI = .92;
RMSEA = .10; SRMR = .05. For the athlete sample, statistics
indicated slightly better model fit (χ287 = 447.45, p < .001;
CFI = .93; RMSEA = .09; SRMR = .06) than for the non-athlete
sample (χ287 = 1381.46, p < .001; CFI = .91; RMSEA = .12;
SRMR = .06). For each model, results of the Lagrange multiplier
(LM) and Wald tests were explored, but did not yield any
helpful post hoc modifications. Standardised factor loadings,
covariances, and residual variances are displayed for the athlete and non-athlete samples in Figures 3 and 4, respectively.
To further contrast the athlete and non-athlete samples, a
multiple group SEM with a mean structure was run. A model
with no parameters constrained to be equal across the two
Figure 3. Final confirmatory factor analysis, athletes only. Ovals indicate latent variables (factors). Rectangles indicate measured variables. Straight arrows represent
regressions. Straight arrows leading to dependent variables represent residual variance (error) estimates. Double arrows represent correlations. Parameter estimates
are standardised regression coefficients; all values shown are significantly different than zero (p < .001).
Figure 4. Final confirmatory factor analysis, non-athletes only. Ovals indicate latent variables (factors). Rectangles indicate measured variables. Straight arrows
represent regressions. Straight arrows leading to dependent variables represent residual variance (error) estimates. Double arrows represent correlations. Parameter
estimates are standardised regression coefficients; all values shown are significantly different than zero (p < .001).
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groups was tested first, to serve as a baseline or benchmark
for comparison. The fit of this model was moderate:
χ2198 = 2132.42, p < .001; CFI = .90; RMSEA = .12;
SRMR = .07. Factor loadings were then constrained across
groups, resulting in a significant decrement in the model fit
(χ2 difference = 87.48; df = 12). In particular, the LM test
indicated that the loadings for items 5, 6, 9, 12, 13, 14 and
15 were significantly different between athletes and non-athletes. This suggested disparities across groups in the make-up
of each of the three model factors; thus, the process of constraining additional parameters between groups was halted
here. Factor means for non-athletes were 67.33 (SC-Physical
Skills and Training), 72.90 (SC-Cognitive Efficiency) and 71.93
(SC-Resilience). For athletes, factor means were 80.67, 81.58,
and 79.78, respectively. Athletes were higher in all SCI latent
means than non-athletes.
Discussion
The purposes of the present study were to test the applicability of the multidimensional perspectives of sport-confidence (Vealey & Chase, 2008; Vealey & Knight, 2002) to
athletes and non-athlete sport performers, and to examine
the measurement invariance of the SCI to establish its construct validity and reliability across groups. Combining four
different samples allowed us to obtain large samples of
athletes and non-athlete sport performers and conduct the
multiple group model analysis to directly compare the
model fit between these two groups. The original study by
Vealey and Knight (2002) only included athlete samples. To
extend their findings, our results showed that the model fits
slightly better for athletes as compared with non-athletes.
Also, loadings of several items were significantly different
between athletes and non-athletes, though the loadings
were still high in both samples. Our athlete sample varied
in their characteristics (e.g., different age, ethnicity, sex,
sport, skill level), which may allow our results to be generalised across diverse athlete populations.
The results suggest that the three-factor model and the
SCI are feasible for athletes. It seems that athletes were able
to distinguish these different types of sport-confidence (i.e.,
SC-Physical Skills and Training, SC-Cognitive Efficiency, and
SC-Resilience). This serves to replicate the results of the
preliminary study by Vealey and Knight (2002), which examined the factorial structure and the use of the SCI in high
school varsity and college varsity athletes. While the threefactor model may suit athletes, a one-factor model rather
than a three-factor model may be more feasible for nonathlete sport performers. The results show that correlations
among three sport-confidence factors were especially high
among non-athletes, which indicates that non-athlete sport
performers were less able to distinguish the three dimensions of sport-confidence described in the inventory, perhaps due to their lack of experience and understanding of
the various abilities necessary to succeed in sport. Also,
variances in responses to the items were greater among
non-athletes as compared with athletes. The present study
was the first to test the utility of the SCI to non-athletes.
The results indicate that the current SCI may not be feasible
7
for assessing multidimensional sport-confidence towards
executing new skills, and/or may be better treated as a
unidimensional construct for those who do not participate
and are not training in competitive sport.
Limitations and future directions of the study
Though we were able to test a model with a measurement
that is grounded in a well-established theory to a large sample
of sport performers with different characteristics and experiences, there are a few limitations to be addressed. We combined the samples from different projects1 to obtain a large
enough sample to conduct a multiple group model analysis.
For the present study, participants completed different versions of the stem and rating scale in assessing sport-confidence across different subsamples to fit to the sample
characteristics and situations at the time of each data collection. Though secondary analysis of data has some limitations
and warrants concerns, Fischman (2011) indicated that it could
make a substantial contribution to the literature. In this study,
we were able to statistically correct for differences in response
ratings in the analyses to achieve our aim of the study.
However, it is also important to note that scholars (Dawes,
2008; Wakita, Ueshima, & Noguchi, 2012) caution against contrasting responses collected using different Likert scales.
Future studies should clarify the most appropriate rating
scale to measure sport-confidence to ensure consistency in
the results, for example, by using an Item Response Theory
framework (see an example by Myers, Feltz, & Wolfe, 2008).
Also, researchers may pursue creating a new scale for nonathlete sport performers by deleting some items. A shorter
version of the scale may work better for non-athletes who are
less able to distinguish the multiple dimensions of sportconfidence.
In addition, while our athlete sample was varied in types
of sport participation, our non-athlete sample was composed only of those sport performers who lack competence
in basketball free throw. Thus, the results from the multiple
group comparison in our study may be due to the differences between sport-confidence about completing broader
sport skills and completing a specific sport skill (i.e., basketball free throw). This possibility cannot be ruled out.
Though the present study showed a feasibility of modifying
the items for the specific sport skill in assessing sport-specific confidence which has an important implication for
future studies in this area (especially for studies using
experimental designs), future researchers may retest the
model for non-athletes in additional sports or using more
diverse and interesting tasks. Also the original study by
Vealey and Knight (2002) indicated that three types of
sport-confidence are differently related to personal factors
of athletes (e.g., anxiety, coping, motivation). To further test
the model, examining the influences of athletes’ characteristic and organisational culture (e.g., age, sex, personality,
cognitive orientation, coaches’ leadership) on three types of
sport-confidence could offer important insights into the
multidimensionality of sport-confidence.
The SCI was developed based on multiple phases of studies
that identified three areas of abilities required for success in
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8
M. MACHIDA ET AL.
sport (Vealey & Knight, 2002). However, findings from a study
on world-class athletes by Hays et al. (2007) indicated that
these athletes identified six types of sport-confidence. In our
study, we used very limited categorisation of sport performers
(athletes vs. non-athletes) to allow for an analysis that requires
a large sample. It is possible that as expertise of sport performers develop, the more they distinguish and categorise their
capacity into specific abilities necessary for their “success” at
their competitive levels. Scholars should further investigate
the types of sport-confidence required for various competitive
levels of sport performers (e.g., novices, youth, high school,
college, elite) and an applicability of the SCI to these subpopulations. Alternative forms of analysis (e.g., bi-factor analysis,
Myers, Martin, Ntoumanis, Celimli, & Bartholomew, 2014) may
also be useful for examining the multidimensionality (vs. unidimensionality) of sport-confidence in sport participants.
Also, being consistent with Bandura’s (1997) self-efficacy
theory, Vealey and Chase (2008) argue that sport-confidence
fluctuates over time. The preliminary study by Vealey and
Knight (2002) indicated fluctuations of the SCI scores across
time among collegiate male swimmers. Meanwhile, this
study is based on data collected only once per participant.
Recently, the importance of stable confidence or “robust
confidence” has been explored in studies (e.g., Beaumont
et al., 2015; Thomas, Lane, & Kingston, 2011). Conducting a
longitudinal study using the SCI to examine the fluctuating
properties and “robustness” of sport-confidence may be
another feasible follow-up topic for future researchers to
explore.
The study is also reliant on self-report. Further, objective
evidence of validity (e.g., predictive validity) of the SCI is
warranted. For example, Otten (2009) showed that the total
SCI score was predictive of performance under pressure
among novice and expert performers in free throw shooting. Further understanding of how the different dimensions
of sport-confidence are related to performance would have
significant implications for practice.
Conclusion
In this study, we examined the applicability of a multidimensional perspective of sport-confidence (Vealey &
Chase, 2008; Vealey & Knight, 2002) and the SCI to assess
three types of sport-confidence in athletes and non-athlete
sport performers. Multiple group model analysis results
suggest that the three-factor model of sport-confidence
suits better to athletes and the SCI is more feasible for
assessing the nature of sport-confidence for athletes than
non-athletes. Capacity to distinguish different abilities
necessary for success in sport could be an indication of
experiences, and further study is warranted to examine an
appropriate measure of sport-confidence for non-athletes.
Disclosure statement
No potential conflict of interest was reported by the authors.
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