The Athletic Religious Faith Scale: Part II-Development and Initial Validation

Abstract

The Athletic Religious Faith Scale (ARFS) is an instrument measuring religious faith in a sporting context. This study validated the ARFS using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to reach a final version of the scale. Using the EFA, seven domains were classified: religious coping (9 items), religious psychological effects (5 items), dependence on faith (5 items), religious mental healing (6 items), flow (4 items), athletic identity (4 items), and religious dietary practices (7 items). Using CFA, all construct factor loadings were over 0.5. Composite reliability and Cronbach's alpha values indicated adequate reliability (0.81-0.94) for all ARFS domains. The convergent validity and discriminant validity of all constructs were also established. Overall, the ARFS is a reliable and valid assessment tool for measuring religious faith in athletes. The ARFS could potentially be used with athletes of various religious affiliations in different countries, cultures, and sporting contexts.

Full text

Original Research
SAGE Open
October-December 2023: 1–16
ÓThe Author(s) 2023
DOI: 10.1177/21582440231204682
journals.sagepub.com/home/sgo
The Athletic Religious Faith Scale:
Part II—Development and Initial
Validation
Yo u n g - E u n N o h
1
, Fariz Zaki
1
,EngWahTeo
1
, and
Mahmoud Danaee
1
Abstract
The Athletic Religious Faith Scale (ARFS) is an instrument measuring religious faith in a sporting context. This study validated
the ARFS using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to reach a final version of the scale.
Using the EFA, seven domains were classified: religious coping (9 items), religious psychological effects (5 items), dependence
on faith (5 items), religious mental healing (6 items), flow (4 items), athletic identity (4 items), and religious dietary practices
(7 items). Using CFA, all construct factor loadings were over 0.5. Composite reliability and Cronbach’s alpha values indicated
adequate reliability (0.81–0.94) for all ARFS domains. The convergent validity and discriminant validity of all constructs were
also established. Overall, the ARFS is a reliable and valid assessment tool for measuring religious faith in athletes. The ARFS
could potentially be used with athletes of various religious affiliations in different countries, cultures, and sporting contexts.
Keywords
religious faith, scale development, composite reliability, construct validity, athlete
Some athletes (e.g., tennis player Serena Williams, golfer
Bubba Watson, quarterback Tim Tebow and boxer
Muhammad Ali) generally express their religious faith in
public. When divers David Boudia and Steele Johnson
took the silver medal in the men’s synchronized platform
diving at the Rio Olympics, they mentioned how:
We both know our identity is in Christ .Going into this
event knowing that my identity is rooted in Christ and not
the result of this competition just gave me peace. And it let
me enjoy the contest. God’s given us a cool opportunity,
and I’m glad I could come away with an Olympic silver
medal. (Winston, 2016, para. 2).
A Jewish track athlete recited to himself softly before the
competition, ‘‘God, give me the courage to do the best I
can do’’ (Winston, 2016, A mental anchor, para. 5).
Kulsoom Abdullah, who was the first woman to com-
pete in weightlifting wearing a hijab in international
competition, said that ‘‘religion helped me with mental
benefits’’ (Winston, 2016, A strong believer, para. 6). As
seen in the examples above, it is often to watch some ath-
letes, at all levels, perform religious activities, such as
making the sign of the cross before stepping onto the
field or after scoring a touchdown.
Over the past two decades, the importance of religion
and spirituality in sport has entered the academic agenda.
Although it is challenging to establish a clear distinction
between spirituality and religion, these concepts are not
synonymous. Spirituality refers to beliefs that embrace
personal philosophy and an appreciation for the meaning
and purpose of life, whereas religion refers to belief in a
God or gods, organized rituals, and particular dogma
(Mauk, 2010). Religion is often viewed as worshiping
within a formally structured religious institution that
exists to organize the practices of religious faith (e.g.,
churches, synagogues, mosques, temples, and other
places of service). In this research, religion is defined as
the ‘‘beliefs, actions and institutions which assume the
existence of supernatural entities with powers of action,
or impersonal powers or processes possessed of moral
purpose’’ (Bruce, 2011, p. 112).
1
Universiti Malaya, Kuala Lumpur, Malaysia
Corresponding Author:
Young-Eun Noh, Faculty of Sports and Exercise Science, Universiti Malaya,
Administrative Office, Block B, Kuala Lumpur, Wilayah Persekutuan 50603,
Malaysia.
Email: youngeun@um.edu.my
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of
the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).

Studies have shown that religion and spirituality are
positively related to stress and anxiety, confidence, cop-
ing strategies, mental health and healing, and athlete
well-being in sport (Czech & Bullet, 2007; Czech et al.,
2004; Hoven, 2019; Hoven & Kuchera, 2016; Howe &
Parker, 2014; Maranise, 2013; McKnight, 2009;
McKnight & Juillerat, 2011; Noh & Shahdan, 2020;
Park, 2000; Roe & Parker, 2016; Roychowdhury, 2019;
Udermann et al., 2008; Watson & Czech, 2005; Wiese-
Bjornstal, 2000). Most research has been conducted to
examine the role of religion and spirituality in sport using
qualitative research methods (Czech et al., 2004; Egli
et al., 2014; Gamble et al., 2013; Howe & Parker, 2014;
Jules et al., 2018; Kretschmann & Benz, 2012; Mosley
et al., 2015; Park, 2000; Roe & Parker, 2016; Ronkainen
et al., 2015, 2020; Schroeder & Paredes Scribner, 2006;
Seitz et al., 2014; Wiggins et al., 2005). Crust (2006)
argued that the research on spirituality in sport depended
heavily on qualitative research approaches to gain
knowledge, even though they were needed to establish
conceptualizations and knowledge development in the
early stages. Crust emphasized that systematic and scien-
tific manner should be employed to unravel the knowl-
edge concerning spirituality in sport and integrate it into
current practice.
Due to the lack of instruments to measure the role of
religion and spirituality in sport, many researchers have
utilized assessment tools for the general population (e.g.,
Amrhein et al., 2016; Jackson & Wood, 2018; McKnight
& Juillerat, 2011; Najah et al., 2017; Proios, 2017; Storch
et al., 2001). For instance, researchers have examined
two items from the Brief COPE to assess the influence of
religious beliefs and practices on the mental health of
athletes with anterior cruciate ligament injuries (Najah
et al., 2017). Moreover, researchers have assessed the
strength of belief in a higher power using the Character
Strength Inventory-Spirit (CSI-Spirit; Isaacowitz et al.,
2003), a seven-item scale to examine the impact of reli-
gious belief on psychological factors (e.g., cognitive
appraisal, anxiety, self-efficacy, life satisfaction, and
achievement goals) in elite athletes (Jackson & Wood,
2018). Storch et al. (2001) evaluated the religiosity of
elite collegiate athletes using the five-item Duke Religion
Index (DRI; Koenig et al., 1997).
In sport, two measurement tools are used to access
religiosity in athletes: the Religious Behavior Survey
(RBS; Czech & Bullet, 2007) and the Spirituality in
Sport Test (SIST; Dillon & Tait, 2000; Spittle & Dillon,
2014). However, the RBS lacks validation and the SIST,
a unidimensional measurement consisting of 10 items,
insufficiently tests psychometric evaluation and lacks
information on item generation and content validity
(Dillon & Tait, 2000; Noh et al., 2022). To gain a deeper
understanding of the role of religion in sport, it is
necessary to develop a reliable and valid sport-specific
measurement for gauging athletes’ religious faith.
Recently, Noh and Shahdan (2022) have suggested a
religion and sport performance (RSP) model. Based on
the RSP model, sport performance is influenced by six
factors: mental health and healing, psychological effects,
coping strategy, performance outcomes, religious sup-
port, and religious dietary practices. A recent systematic
review of the literature also identified six psychological
factors that influence athletes’ performance: well-being
and healing, confidence, anxiety and depression, flow or
being in the zone, identity, and coping with adversity
(Noh & Shahdan, 2020). Previous studies have shown
that these religious-psychological elements have an effect
on each other and may aid religious athletes in enhancing
their athletic performance (Noh & Shahdan, 2020, 2022).
Thus, to gain a greater appreciation of the importance of
religion in sport, a multidimensional approach can be
used to help capture the various facets of the role of reli-
gion in this context, which the unidimensional approach
often does not discover.
To develop a new scale, it is important to follow the
systematic approach of scale construction (e.g., provid-
ing evidence regarding the content relevance of domains
and individual items, content validity, pilot study, reduc-
ing the number of items, and construct reliability and
validity). Noh et al. (2022) completed the process of item
generation and provided evidence for the content validity
of the Athletic Religious Faith Scale (ARFS). Based on
the RSP model and a systematic review, Noh et al.
(2022) identified eight relevant domains and the initial
version of ARFS consisted of 55 items. After assessing
the content validity of the ARFS, the second version was
designed to comprise 51 items across eight domains (i.e.,
coping strategies, religious support, psychological effects,
performance outcomes, religious dietary practices, men-
tal health and healing, identity, and flow). The aim of
this research is to evaluate the dimensionality of the
ARFS by checking its reliability (i.e., Cronbach’s alpha
and composite reliability) and validity (i.e., convergent
validity and discriminant validity) using EFA and CFA
to reach a final version of the scale.
Methods
Participants
The selection criteria for the participants included: (1)
athletes who were currently competing in sport events
(either individual or team sports, or both); (2) athletes
who were training in sports to increase their athletic
potential to perform at a high athletic level; (3) athletes
who were formally registered in local, regional, or
national sport federations; (4) athletes who were over
12 years old; and (5) athletes who had a particular
2SAGE Open

religion. The exclusion criteria were individuals who did
not respond to a survey completely. This assessment tool
was created for use with adolescents (ages 12218) and
adults. Based on the conceptual and empirical knowl-
edge of questionnaire research, standardized measure-
ment tools used with adults can be used in research with
adolescents due to the latter’s well-developed cognitive
ability and comprehension of logical operators and nega-
tions (De Leeuw, 2011).
The minimum sample size for the variance-based par-
tial least squares structural equation modeling (PLS-
SEM) was 177 (minimum sample size to detect effect)
and 264 (recommended minimum sample size). This was
based on power analysis using A-priori Sample Size
Calculator for Structural Equation Models (Soper,
2021), with eight latent variables and 51 observed vari-
ables, a medium effect size (ƒ
2
) of 0.3, an aof .05, and a
power of 0.80, which is most commonly used for social
sciences research (Hair et al., 2017).
In total, the authors recruited 675 athletes. However,
the authors excluded some participants (n= 10) who did
not have any particular religion and some participants
(n= 53) who did not answer the questionnaire com-
pletely. In total, 63 responses were excluded from the
study. The participants consisted of 612 athletes (310
males, 302 females), aged 12 to 70 years (mean age =
25.33; SD = 8.99), all of whom were competitors in
either team or individual sports, or both, and who were
involved in different types of sports and at different lev-
els. Participants’ demographic information is displayed
in Table 1.
Participants completed the measures in English. The
language known as Bahasa Melayu or Malay has been
the national or official language in Malaysia since 1968.
In 2003, the policy was changed to teach the subjects of
mathematics and science in English in schools at all lev-
els (Foo & Richards, 2004). Furthermore, English is the
medium of instruction in all private higher educational
institutions (Foo & Richards, 2004). Currently, English
is used widely in daily conversation and is encouraged at
all levels of education (from primary to tertiary).
Procedure
Ethics approval for the research was obtained from the
University of Malaya Research Ethics Committee
(UM.TNC2/UMREC-1025). The authors contacted the
executives of the sports council and sports federation
from each state (e.g., Selangor, Johor, Kedah, Kelantan,
Terenggangu, Pahang, Perak, Sarawak, Sabah, and
Kuala Lumpur), elite sports clubs (e.g., archery, badmin-
ton, hockey, diving, and squash), each state club (e.g.,
football, badminton, netball, martial arts, softball, and
cycling) and from sports associations (e.g., Lawn Tennis
Table 1. Demographic Characteristics of Participants (N= 612).
Demographics Value
Frequency (%)
n= 400 (EFA) n= 212 (CFA)
Gender Male 50.7 50.5
Female 49.3 49.5
Age 12–19 22.0 23.6
20–29 57.5 54.2
30–39 11.5 11.8
40–49 6.5 8.0
50–70 2.5 2.4
Religion Islam 74.3 76.4
Hinduism 9.8 7.5
Buddhism 9.3 6.1
Christianity 4.5 8.0
Catholicism 2.0 1.4
Other 0.3 0.5
Private religious activities
(e.g., prayer/meditation/Bible study)
Rarely or never 3.8 5.7
More than once a year 6.5 6.6
More than once a month 9.0 9.0
More than once a week 21.0 20.3
Once a day 12.5 11.8
More than once a day 47.3 46.7
Level of religious belief None 0.8 0.5
Low 3.3 2.8
Moderate 35.5 32.1
High 60.5 64.6
(continued)
Noh et al. 3

Table 1. (continued)
Demographics Value
Frequency (%)
n= 400 (EFA) n= 212 (CFA)
Sport Football 10.5 8.0
Badminton 8.8 8.0
Handball 5.8 5.7
Futsal 5.5 8.0
Volleyball 5.5 5.7
Basketball 4.8 4.2
Netball 4.5 9.9
Bowling 4.0 1.9
Running 4.0 0.9
Athletics 3.3 2.8
Cycling 3.0 3.3
Karate 3.0 1.9
Rugby 3.0 2.8
Sepak takraw 3.0 2.8
Hockey 2.5 2.8
Petanque 2.5 1.4
Floorball 2.0 1.9
Tennis 2.0 0.9
Softball 1.8 0.9
Swimming 1.8 1.4
Taekwondo 1.5 3.3
Track and field 1.5 1.9
Sailing 1.3 0.9
Table tennis 1.3 3.3
Gymnastics 1.0 3.3
Kabaddi 1.0 0
Dodgeball 0.8 0.5
Fencing 0.8 0.5
Pencak silat 0.8 0.9
Squash 0.8 0
Surfing 0.8 0.5
Archery 0.5 0
Marathon 0.5 0.9
Muaythai 0.5 0
Triathlon 0.5 1.4
Water sports 0.5 0
Bobsleigh 0.3 0
Boxing 0.3 0.5
Carrom ball 0.3 0
Chess 0.3 0
Discus 0.3 0
Diving 0.3 0
Field hockey 0.3 0
Frisbee 0.3 1.4
Golf 0.3 0
Hapkido 0.3 0
Jogging 0.3 0.5
Kayaking 0.3 0.9
Kick volleyball 0.3 0
Lawn bowls 0.6 1.4
Martial arts 0.3 0.9
Motorbiking 0.3 0
Shooting 0.3 0.5
Takraw 0.3 0
Trekking 0.3 0
Weightlifting 0.3 0
Baseball 0 0.5
(continued)
4SAGE Open

Association of Malaysia, Motor Sport Association of
Malaysia, Football Association of Malaysia, Malaysia
Rugby Association, and Malaysia Squash Organization).
Moreover, the authors invited around 50 head coaches
from local sports via email or telephone and got permis-
sion from coaches/directors/staff before the data collec-
tion began. The authors obtained a written consent form
from athletes who were volunteers and provided them
with an information sheet regarding the purpose and
procedure of the research including the confidentiality of
their individual information prior to data collection.
Due to the COVID-19 pandemic, the survey was con-
ducted through an online survey platform (e.g., Google
Form).
Measure
Athletic Religious Faith Scale (ARFS). Religious faith in the
sporting context was measured using the ARFS. This
scale had 51 items developed from the previous study
(Noh et al., 2022) and was used to measure eight differ-
ent domains. The ARFS included coping strategies
(6 items: e.g., dealing with sport-related pressure, emo-
tions and anxiety through religious faith before or during
competitions), religious support (8 items: e.g., perceiving
emotional support through religious faith when facing
hard times), psychological effects (6 items: e.g., motiva-
tion and confidence to achieve goals through religious
faith), performance outcomes (7 items: e.g., how athletes
accept their performance outcomes according to their
religious faith), religious dietary practices (7 items: e.g.,
items related to religious food restrictions before, during
and after competitions), mental health and healing
(7 items: e.g., depression and injury), identity (6 items:
e.g., finding the meaning in and purpose of athletes’ lives
through religious faith) and flow (4 items: e.g., the flow
state or being in the zone during competitions). The par-
ticipants responded to the items on a scale ranging from
1(strongly disagree)to5(strongly agree). After consider-
ing the age and information processing limitations of the
targeted respondents, a 5-point scale was developed to
offer the ideal number of anchors for answering
questions. A 5-point scale requires less cognitive effort
from respondents and maximizes the transfer of informa-
tion within the scale compared to 4-, 6-, 7-, 8-, and 9-
point scales (Chen et al., 2015). Furthermore, it is prefer-
able to include a midpoint to reduce potential acquies-
cence bias, non-response bias, and extreme response bias
(Chen et al., 2015; Chyung et al., 2017). From the pilot
study, the ARFS showed that the internal consistency
reliability (Cronbach’s alpha) of all the domains ranged
from 0.88 to 0.96 (Noh et al., 2022).
Data Analysis
The participants were randomly split into two groups
using SPSS (version 27) for separate analyses. EFA was
administered to Group 1 (n= 400) and CFA was tested
on Group 2 (n= 212). The recommended sample size is
at least 300 cases for EFA (Worthington & Whittaker,
2006) and the minimum sample sizes should be between
100 and 200 cases for CFA (Kline, 2016). Prior to per-
forming the EFA, the Kaiser–Meyer–Olkin (KMO) Test
and Bartlett’s Test of Sphericity (BTS) were done to
check the suitability of the data for structure detection in
the dataset from 400 participants using SPSS software
(version 27). The KMO value should be more than 0.60
and BTS ought to be significant at a\.05 to proceed to
the factor analysis (Hair et al., 2009).
Some researchers run CFA without taking into
account the merits of EFA. EFA is helpful for achieving
a parsimonious conceptual understanding of the latent
variables or factors underlying a measure by determining
the number and character of common factors required to
explain the pattern of correlations among the observed
variables (Fabrigar et al., 1999; Morgado et al., 2017).
Determining the number of factors is one of the most
important decisions in EFA. EFA attempts to identify
latent structures that can parsimoniously explain the
covariation underlying a set of measured variables
(Watkins, 2018).
In this study, the number of components was identi-
fied using maximum likelihood estimation. Maximum
likelihood estimation is utilized to estimate more robust
Table 1. (continued)
Demographics Value
Frequency (%)
n= 400 (EFA) n= 212 (CFA)
Current level of sport District level 13.8 11.8
Club level 19.0 24.5
University level 18.0 16.0
State level 29.5 27.8
National level 10.5 12.7
International level 9.3 7.1
Noh et al. 5

population parameters by sampling the observed correla-
tion matrix (Yong & Pearce, 2013). To obtain a better
interpretation, promax rotation was followed. Promax
rotation starts with an orthogonal rotation, which
assumes the factors are orthogonal, and then relaxes the
rotation with an oblique rotation, and the factors can
then be correlated to improve the fit to a simple structure
(Fabrigar et al., 1999; Russell, 2002). Promax rotation is
generally appropriate in the social sciences, because
almost everything assessed is correlated to some extent
(Watkins, 2018). The recommended minimum factor
loading cut-off point is 0.30 (Hair et al., 2009).
Next, factors extracted from EFA, CFA was per-
formed on another dataset including 212 participants
using SmartPLS (version 4), which is specially used for
structural equation modeling (SEM), CFA, and path
analysis. The internal consistency of the ARFS domains
was measured using Cronbach’s alpha and composite
reliability (CR). Cronbach’s alpha is equivalent to the
mean of all split-half estimates (Cronbach, 1951), which
is another measure of reliability when there is only one
test administered and it is the most frequently used to
compute internal consistency reliability (DeVon et al.,
2007). The CR is a reliability coefficient that is similar to
Cronbach’s alpha. When comparing Cronbach’s alpha
and CR, the CR provides more accurate estimates of
scale reliability than Cronbach’s alpha does because it
does not assume essential tau-equivalent that alpha
underestimates the reliability of the test (Hayes & Coutts,
2020; Tavakol & Dennick, 2011). As some researchers
are reluctant to adopt an alternative measure of reliabil-
ity that is less familiar, the authors cross-checked the
reliability with both methods to obtain accurate and reli-
able results. Like Cronbach’s alpha, threshold values
above .70 indicate acceptable internal consistency relia-
bility (Lance et al., 2006).
Finally, the convergent validity (based on factor load-
ings and the average variance extracted [AVE]) and dis-
criminant validity were tested. To assess the validity and
reliability of a measurement model in SEM, it is sug-
gested that the factor loading at the early stages of scale
development is acceptable at 0.50 or 0.60 when CR and
AVE are in the acceptable range (Chin, 1998).
Cronbach’s alpha should be ..70, the convergent valid-
ity (AVE) should be ..50 for each construct measure,
and the discriminant validity (HTMT) should be \.85
or .90 (Hair et al., 2019).
Results
Exploratory Factor Analysis
In order to explore the underlying factor structure of the
ARFS, EFA was applied to determine the factor struc-
ture with 51 items. The KMO measure for the sampling
adequacy was 0.961, where a desirable value is 0.80 or
higher, and BTS was significant (x
2(1,275)
= 15,324.16,
p\.001). The maximum likelihood with promax rota-
tions extracted eight factors that accounted for 67% of
the total variance (Table 2).
The components factor loadings explained by the
eight factors are shown in Table 3.
The threshold for factor loading was fixed at 0.40 and
11 items were eliminated due to cross-loading variables
or low factor loadings. As a result, 40 items were retained
with seven factors when rotating. The seven factors were
religious coping (9 items), religious dietary practices (7
items), religious mental healing (6 items), dependence on
faith (5 items), religious psychological effects (5 items),
athletic identity (4 items), and flow (4 items).
Confirmatory Factor Analysis (Measurement Model)
The ARFS was evaluated based on the measurement
model of evaluation. The assessment of the ARFS began
with an evaluation of the factor loadings, followed by a
determination of construct reliability and validity.
Construct Reliability and Convergent Validity of the
ARFS. According to the results of the measurement model,
all factor loadings of the construct were over 0.50, which
was still an acceptable value (Chin, 1998). Thus, no items
were further eliminated. The CR statistics ranged from
0.877 to 0.937, whereas Cronbach’s alpha values were
between .814 and .921, indicating adequate reliability for
all ARFS domains. The AVE values were over 0.50,
which showed that the convergent validity of all con-
structs was established. Factor loadings, Cronbach’s
alpha, CR, and AVE are presented in Table 4.
Discriminant Validity of the ARFS. To establish the discri-
minant validity, the hetrotrait–monotrait ratio of criter-
ion (HTMT) values were determined, which was part of
measurement model evaluation in variance-based SEM.
Table 5 shows the HTMT ratio of all the constructs was
Table 2. Total Variance Explained by Extracted Components
(51 Items).
Factor Total % of Variance Cumulative %
1 21.952 43.044 43.044
2 3.541 6.943 49.987
3 2.029 3.978 53.965
4 1.805 3.539 57.504
5 1.513 2.966 60.471
6 1.267 2.485 62.955
7 1.092 2.142 65.097
8 1.010 1.981 67.078
Note. Extraction method: Maximum likelihood with promax rotation.
6SAGE Open

less than the required threshold of 0.90, which indicated
that there was sufficient discriminant validity for the
domains. Moreover, the cross-loading for each construct
was very low, which indicated that there was a weak cor-
relation between items of a different construct.
Validating Higher-Order Construct of the ARFS. The higher-
order construct was validated as part of the assessment
of the measurement model. Sarstedt et al. (2019) have
advised that each of these constructs is needed to evalu-
ate reliability, convergent validity, and discriminant
validity with other lower-order constructs. The reliability
and convergent validity of all other constructs were
demonstrated, as the reliability value was greater than
0.70 and the AVE was greater than 0.50, which indicated
that both reliability and validity were established.
Additionally, the discriminant validity of the higher-
order and lower-order constructs was evaluated. HTMT
findings were also less than 0.90 (Table 6).
Since the overall ARFS was the second-order latent
variable that included seven sub-domains, it was investi-
gated to evaluate the significant contribution of all
lower-order latent variables using the bootstrap
approach. The results showed that all of the following
seven sub-domains statistically and significantly contrib-
uted to the ARFS (Table 7): religious coping, RC
Table 3. Factor Loadings Based on Maximum Likelihood Extraction With Promax Rotation.
Item 1 2 3 4 5 6 7
Q13 0.857
Q12 0.823
Q6 0.804
Q2 0.718
Q1 0.666
Q9 0.541
Q5 0.538
Q3 0.504
Q10 0.498
Q43 0.929
Q41 0.859
Q38 0.848
Q39 0.789
Q42 0.761
Q44 0.739
Q40 0.660
Q49 0.735
Q50 0.725
Q47 0.688
Q48 0.577
Q46 0.572
Q51 0.521
Q7 0.699
Q35 0.669
Q11 0.530
Q37 0.433
Q33 0.409
Q17 1.036
Q18 0.856
Q16 0.721
Q19 0.519
Q29 0.921
Q28 0.916
Q30 0.597
Q27 0.477
Q25 0.627
Q23 0.606
Q22 0.538
Q24 0.469
Q26 0.406
Reliability 0.879 0.920 0.865 0.832 0.900 0.814 0.874
Note. n = 400. Rotation method: Promax with Kaiser normalization.
Noh et al. 7

Table 4. Convergent Validity and Reliability of the ARFS Measurement Model (n= 212).
Construct Item Factor loading Cronbach’s aCR AVE
Religious coping COP1 0.760 .884 0.906 0.520
COP2 0.738
COP3 0.721
COP4 0.716
COP5 0.716
COP6 0.716
COP7 0.769
COP8 0.766
COP9 0.569
Religious dietary practices DIE1 0.882 .921 0.937 0.681
DIE2 0.798
DIE3 0.822
DIE4 0.831
DIE5 0.718
DIE6 0.867
DIE7 0.848
Religious mental healing HEA1 0.832 .872 0.904 0.615
HEA2 0.858
HEA3 0.849
HEA4 0.828
HEA5 0.714
HEA6 0.590
Dependence on faith DEP1 0.771 .833 0.882 0.599
DEP2 0.768
DEP3 0.809
DEP4 0.759
DEP5 0.762
Religious psychological effects PSY1 0.834 .876 0.910 0.669
PSY2 0.839
PSY3 0.791
PSY4 0.812
PSY5 0.812
Athletic identity ATH1 0.903 .900 0.931 0.771
ATH2 0.914
ATH3 0.883
ATH4 0.808
Flow FLOW1 0.787 .814 0.877 0.640
FLOW2 0.827
FLOW3 0.781
FLOW4 0.805
Note. CR = composite reliability; AVE = average variance extracted.
Table 5. Correlation of Latent Constructs and Discriminant Validity (HTMT Criterion).
RC DF RDP FLOW RMH AI RPE
RC
DF 0.805
RDP 0.395 0.430
FLOW 0.606 0.662 0.604
RMH 0.700 0.787 0.357 0.637
AI 0.645 0.686 0.420 0.705 0.587
RPE 0.851 0.722 0.409 0.769 0.734 0.676
Note. RC = religious coping; DF = dependence on faith; RDP = religious dietar y practices; FLOW = flow; RMH = religious mental healing; AI = athletic
identity; RPE = religious psychological effects.
8SAGE Open

(l= .860, p\.001); dependence on faith, DF (l= .825,
p\.001); religious dietary practices, RDP (l= .594, p
\.001); flow, FLOW (l= .785, p\.001); religious
mental healing, RMH (l= .800, p\.001); athletic iden-
tity, AI (l= .768, p\.001); and religious psychological
effects, RPE (l= .856, p\.001). The results indicated
that in the higher-order latent variables, the standardized
path coefficient (outer loadings) was above .50 and sig-
nificant. The highest contribution belonged to religious
coping, and the lowest contribution was observed for the
religious dietary practices sub-domain. Figure 1 shows
the lower-order construct measurement model of the
ARFS, and Figure 2 shows the higher-order construct
measurement model of the ARFS.
In CFA, the model fit describes the extent to which a
proposed model explains the correlations between vari-
ables in the dataset. Several statistical tests (e.g., the root
mean square error of approximation [RMSEA], the com-
parative fit index [CFI], and the standardized root mean
square residual [SRMR]) can be employed to evaluate a
hypothesized model’s fit with the data. However, a good
model fit between the proposed model and the data does
not necessarily indicate that the model is correct and
consistent with reality (Schermelleh-Engel et al., 2003);
rather, it indicates that the model is tenable
(Schermelleh-Engel et al., 2003). In PLS-SEM, research-
ers should report on and use the model fit with extreme
caution (Hair et al., 2017). Although some researchers
require that new model fit indices be reported for PLS-
SEM, there is a reason why the proposed criteria should
not be applied and reported in the assessment of PLS-
SEM results: the proposed criteria are in the initial stage
of research, which means the critical threshold values are
not fully understood (Ringle et al., 2022). For example,
some model fit indices presuppose a common factor
model, which necessitates outer residuals that are uncor-
related. However, it is not necessary for the outer resi-
duals of composite models to be uncorrelated in PLS-
SEM (Lohmo
¨ller, 1989). Thus, the criteria should not be
applicable to PLS-SEM (Ringle et al., 2022).
Discussion
Religion plays a crucial role in enhancing athletic perfor-
mance and promoting athletes’ psychological/mental
health. However, there are no valid and reliable sport-
Table 6. Higher-Order Construct of Reliability and Validity.
Cronbach’s alpha Composite reliability Average variance extracted (AVE)
RC DF RDP Flow RMH AI RPE
ARFS .897 0.920 0.624
Discriminant validity (HTMT)
RC
DF 0.702
RDP 0.358 0.378
Flow 0.535 0.561 0.524
RMH 0.631 0.680 0.322 0.558
AI 0.581 0.597 0.389 0.620 0.521
RPE 0.757 0.618 0.373 0.668 0.648 0.600
Note. RC = religious coping; DF = dependence on faith; RDP = religious dietar y practices; FLOW = flow; RMH = religious mental healing; AI = athletic
identity; RPE = religious psychological effects.
Table 7. Test of Second-Order Models Using Bootstrapping.
Sub-domain BSEtvalue pvalue
95% CI
[LL, UL]
ARFS -.RC .860 0.026 33.12 \.001 [0.812, 0.898]
ARFS -.DF .825 0.032 25.703 \.001 [0.767, 0.871]
ARFS -.RDP .594 0.064 9.326 \.001 [0.477, 0.688]
ARFS -.FLOW .785 0.034 22.875 \.001 [0.723, 0.834]
ARFS -.RMH .800 0.031 25.494 \.001 [0.747, 0.849]
ARFS -.AI .768 0.032 24.056 \.001 [0.714, 0.818]
ARFS -.RPE .856 0.024 36.298 \.001 [0.816, 0.892]
Note. RC = religious coping; DF = dependence on faith; RDP = religious dietar y practices; FLOW = flow; RMH = religious mental healing; AI = athletic
identity; RPE = religious psychological effects.
Noh et al. 9

specific instruments for measuring religious faith in
sport. The purpose of the present research was to develop
and provide evidence of the validity and reliability of a
new scale (the ARFS) to measure religious faith among
athletes. When assessing the results of the ARFS in this
study, based on the total variance, eight factors were
extracted. However, 11 items were eliminated due to
cross-loading variables or low factor loadings, and seven
factors were retained when rotating factors. When ana-
lyzing the CFA with information criteria, no items were
eliminated. Thus, the ARFS was finalized with 40 items,
with seven factors contributing most greatly to the
ARFS: religious coping (9 items), religious psychological
effects (5 items), dependence on faith (5 items), religious
mental healing (6 items), flow (4 items), athletic identity
(4 items), and religious dietary practices (7 items).
The first domain, religious coping (9 items), can be
described as when athletes who are feeling insecure or
handling uncertain circumstances gain comfort from
emotional support and encouragement through their reli-
gious faith. This domain explains most of the contribu-
tions of the ARFS, indicating that it is a key domain of
the ARFS. Frequently, two or more factors collapse into
a single factor, making it challenging to find a single uni-
fying theme among the measured variables (Fabrigar &
Wegener, 2012). This domain combined coping strategies
and religious support from eight relevant domains. In
fact, social support is a crucial coping strategy that helps
Figure 1. Lower-order constructs measurement model (CFA) for the ARFS.
Note. Coping = religious coping; Dependence = dependence on faith; Dietary = religious dietary practices; Healing = religious mental healing;
Identity = athletic identity; Psycho = religious psychological effects.
10 SAGE Open

individuals manage stress and make positive contribu-
tions to mental health (Aflakseir, 2010). Religious ath-
letes employ religious faith to enhance their coping skills
to handle their stress and anxiety before, during, and
after competitions (Czech & Bullet, 2007; Noh &
Shahdan, 2022; Watson & Czech, 2005). Specifically,
religious athletes frequently pray to relieve anxiety and
tension, control their emotions, and maintain calm under
pressure before and during competitions (Noh &
Shahdan, 2020, 2022). According to the RSP model, reli-
gious athletes manage their emotions through prayer,
especially during critical moments in competitions (Noh
& Shahdan, 2022). These results suggest that researchers
or coaches might need to consider how to provide emo-
tional support or sources for support (e.g., team players)
to reduce sport-related stress or anxiety through religious
faith. Religious faith provides a sense of structure and
offers a sense of connection to athletes with similar
beliefs. When developing programs to support religious
athletes, religious struggles where the athletes feel pun-
ished or abandoned by God are critical considerations.
Religious struggles are relatively common human experi-
ences leading to negative psychosocial and physical
health outcomes, such as lower levels of self-esteem (Wilt
et al., 2016). Those who are struggling are expected to
adapt their negative religious coping style when dealing
with critical circumstances (Exline et al., 2011). To the
best of our knowledge, however, no research has been
conducted regarding religious struggles among athletes.
Future research could therefore explore how to handle
religious struggles when athletes are facing hard times.
The second domain, religious psychological effects
(5 items), can be defined as the positive effects produced
by a person’s psychological aspects through religious
faith. This domain is the second main contribution of the
ARFS. Some researchers have found that adolescent
players gain both comfort and confidence through prayer
when confronting anxiety (Hoven & Kuchera, 2016).
Furthermore, it is believed that religious faith promotes
self-confidence, motivation, and a sense of security
(Shahdan et al., 2022). According to the RSP model, reli-
gious athletes rely on their religious faith to improve their
Figure 2. Higher-order constructs measurement model (CFA) for the ARFS.
Note. Coping = religious coping; Dependence = dependence on faith; Dietar y= religious dietary practices; Healing = religious mental healing;
Identity = athletic identity; Psycho = religious psychological effects.
Noh et al. 11

motivation so that they can accomplish the goals they
have set for themselves (Noh & Shahdan, 2022). Previous
research has shown that religious faith is considered to
positively affect such as alleviating anxiety levels, enhan-
cing motivation to achieve goals, and increasing self-
confidence to focus on competition (Noh & Shahdan,
2020).
The third domain, dependence on faith (5 items), can
be described as attitudes about accepting competition
outcomes that are wholly dependent on a higher power.
A higher power can be viewed as ultimate reality, a
supreme being, or a God/gods that is greater than the
individual who worships that power through their faith
(e.g., Buddhism, Hinduism, Christianity, or Islam).
Based on the RSP model, religious athletes accept per-
formance outcomes as God’s plan regardless of success
or failure (Noh & Shahdan, 2022). In fact, poor athletic
performance tends to coincide with a greater risk for
depression (Hammond et al., 2013; Wolanin et al., 2015).
However, religious athletes accept the performance out-
comes with hope and optimism when losing games and
prepare for their next competition (Noh & Shahdan,
2022).
The fourth domain, religious mental healing (6 items),
can be described as the process of alleviating emotional
distress through the power of religious faith. Athletes
face multiple stressors, including injuries, competitive
stress, overtraining, and poor performance outcomes
during training and competitions. In particular, sport-
related injuries lead to depression and long-lasting emo-
tional impacts (Wolanin et al., 2015). Injured players
have higher levels of depression and life stress scores
than uninjured players do (Brewer & Petrie, 1995).
According to the RSP model, religious athletes maintain
a positive mindset and try to eliminate negative or
depressed thoughts through religious faith (Noh &
Shahdan, 2022). It is necessary to take into account
incorporating religious faith into sport psychology con-
sultancy for athletes with different values and beliefs.
Sarkar et al. (2014) have also suggested that sport psy-
chology practitioners need to consider athletes with vari-
ous religious and spiritual perspectives and cultures
when working with them.
The fifth domain, flow (4 items), can be described as a
mental state in which an athlete is entirely concentrated
on their performance, generating momentum effortlessly,
and beyond the point of distraction. The mental states
experienced by religious athletes, such as being in the
zone or having a spiritual connection, are remarkably
comparable to flow (Spittle & Dillon, 2014). Because
religious athletes gain strength and comfort through
their religious faith, they may tend to experience flow.
However, there is little research exploring the relation-
ship between religion and flow, except for two studies
(Dillon & Tait, 2000; Spittle & Dillon, 2014). This corre-
lation research can be helpful in exploring the effects of
flow as well as designing interventions to enhance sport
performance.
The sixth domain, athletic identity (4 items), can be
described as how athletes perceive their existence in rela-
tion to the athletic role and talent bestowed upon them
by their religious faith. An individual with high athletic
identity, in particular, elite athletes, commit to develop-
ing their skills and achieving their goals by overcoming
obstacles through psychological and physical challenges.
Although two elite Christian athletes segregated their
religion from their athletic identity (Ronkainen et al.,
2020), many religious athletes seek to find their athletic
identity and meaning in their lives through their religious
faith (Hoven & Kuchera, 2016; Shafranske, 1996).
The seventh domain, religious dietary practices
(7 items), can be described as religious beliefs that affect
an individual’s dietary patterns and food selection. Many
religions have dietary restrictions that may or may not
be rigorously adhered to. For example, Hindus generally
avoid foods (e.g., garlic and onion) that they feel inhibit
spiritual growth (McCaffree, 2002) and certain foods are
forbidden during fasting. Seventh-Day Adventists adhere
to a stringent lacto-ovo vegetarian diet that excludes
poultry, meat, fish, tobacco, alcohol, and caffeine
(McCaffree, 2002). The Jewish religion permits the eating
of meat that has been prepared according to Jewish
rituals. Buddhist’s dietary practices are varied. While
many Buddhists are vegetarians, some Buddhists in Tibet
and Japan make individual choices (McCaffree, 2002).
Fasting is observed in various religions, such as the
Tenth of Tevet in Judaism, Fast of Esther, Christian
Lent, Tisha B’av, Yom Kippur, the Seventeenth of
Tamuz, Tzom Gedalia, and Ramadan fasting. There is
an issue about religious nutritional practice in sport, par-
ticularly regarding Ramadan fasting. Some studies found
that Ramadan fasting could negatively affect athletes
due to dehydration, accumulated sleep deficit and fati-
gue, while other studies reported no or minimal effects of
fasting on athletes’ performances (Chamari et al., 2019).
Unfortunately, there is very little research on the rela-
tionship between psychological factors and sport perfor-
mance during Ramadan fasting.
Practical Implications
Most research on religion and sport has been conducted
via qualitative research (Crust, 2006; Noh & Shahdan,
2020, 2022). Naturally, qualitative research is needed to
generate knowledge and gain a deep understanding of a
specific theory in the early stages. However, researchers
need to expand our knowledge and develop a wider
understanding of further phenomena regarding religion
12 SAGE Open

in sport based on scientific methods. One promising
approach to understanding these phenomena is through
validated tools, which involve quantitative measure-
ments. This new scale, the ARFS, can mainly be used
for two purposes. First, the scale will allow researchers
and/or sport psychologists to quantitatively assess
research hypotheses. Scales of measurement are impor-
tant instruments for assigning numerical values to
events that cannot be measured directly. They are col-
lections of elements that reflect levels of theoretical
variables that are otherwise directly unobservable.
Researchers could compare and quantify relationships
between two or more variables by observing different
groups or conditions or they could look at cause-and-
effect relationships to predict phenomena in natural or
designed experiments and generalize their findings for
an athletic population.
Second, the scale will help researchers/sport psycholo-
gists to design intervention programs to enhance sport
performance by using validated measures. Sport psychol-
ogists/coaches endeavor to help athletes to realize their
potential or increase their performance around a skill or
task through psychological skills training (e.g., goal set-
ting, motivation, imagery, relaxation, and concentra-
tion). As proven from the literature, religious faith plays
a significant role in achieving better performance among
religious athletes (Noh & Shahdan, 2020, 2022). When
predicting a religious–psychological factor’s impact on
sport performance, it helps to design effective interven-
tions to enhance sport performance for religious athletes.
For example, if religious coping is the main predictor
that influences sport performance, then it can be applied
by employing two methods (relaxation techniques and
prayer) to reduce sport-related anxiety and increase a
positive mindset in religious athletes. When increasing
the effectiveness of prayer, context-specific determinants
may need to be considered, such as culture and the degree
of belief.
Study Limitations
Even though this study has conducted following a sys-
tematic approach to developing the questionnaire, there
are several limitations to be considered. First, the
majority of participants were Muslims (76%). Malaysia
is a multi-ethnic and multi-religious country whose offi-
cial religion is Islam. To check content validity, four
different religious workers (i.e., one imam, one Thai
monk, one pastor, and one pujari) from the previous
study (Noh et al., 2022) identified each construct and
measurement item and provided evidence of good con-
tent validity. However, the results in the current study
do not represent the considerable diversity within vari-
ous religions. Therefore, a balanced number of
participants from diverse religious faiths should be
included in future research.
Second, a self-report questionnaire was tested for its
validity and reliability to develop a final questionnaire.
Hence, response bias may have arisen in this study. The
participants were required to recall a particular situation,
such as a competition, when answering the questionnaire.
Some participants might lack retrospective ability even
though they were attempting to be truthful and accurate.
Biased responses can be deliberate or accidental, but such
responses make results less informative and valuable.
Lastly, we recruited 612 participants in this study and
divided them into two groups for separate EFA and
CFA. Although the respondent sample size met the mini-
mum requirements of the analysis statistically, it is neces-
sary to employ larger sample sizes with sample
diversification to increase the credibility and generaliza-
tion of the results. The ARFS is designed to be used with
athletes of diverse religious faiths in various countries
and cultures. Thus, future studies with sufficiently large
samples from varied cultures and religious faiths are
needed to examine the psychometric properties of the
ARFS.
Conclusion
This study involved the development and validation of
the ARFS using factor analysis to reach a final version
of the scale. The ARFS is a reliable and valid assess-
ment tool for measuring religious faith in athletes. The
ARFS could be potentially utilized with athletes of vari-
ous religious affiliations in different countries, cultures,
and sporting contexts. Researchers can also use the
scale to test the relationship between the role of reli-
gious faith and sporting performance. Furthermore,
researchers and sport psychologists can design interven-
tion programs to enhance sport performance by gather-
ing statistical information using ARFS. The ARFS is a
multidimensional questionnaire that can measure which
factor is the most significant in influencing sport perfor-
mance. In addition, the ARFS can be a useful tool to
expand the knowledge of religious faith in sport litera-
ture. It is expected to stimulate further research on reli-
gion and sport performance.
Acknowledgments
The authors would like to thank all the athletes for their
participation.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Noh et al. 13

Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This work is supported financially by the Ministry of
Higher Education Malaysia via Fundamental Research Grant
Scheme (FRGS/1/2020/SS0/UM/02/2).
Ethical Approval
Approval for the research was obtained from the University of
Malaya Research Ethics Committee (UM.TNC2/UMREC-
1025).
ORCID iDs
Young-Eun Noh https://orcid.org/0000-0002-6266-8554
Eng Wah Teo https://orcid.org/0000-0002-1451-1720
Supplemental Material
Supplemental material for this article is available online.
Data Availability Statement
The data that support the findings of this study are available
from the corresponding author, [Y N], upon reasonable
request.
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