1. Introduction
There
is a huge emphasis on the role of non-cognitive characteristics along with
cognitive qualities in shaping university students’ productive academic and
personal endeavors (Alhadabi & Karpinski, 2020; Duckworth et al., 2007;
Stadler et al., 2016). One of the principal affective qualities is self-control
which has paramount influences in determining the quality of decisions,
behaviors, and approaches adopted by university students in academic and
personal contexts (Duckworth et al., 2019; Milyavskaya & Inzlicht, 2018;
Tangney et al., 2004). Milyavskaya and Inzlicht (2018) define self-control as
“the effortful inhibition of an immediately gratifying behavior or impulse,” meaning,
a person exerts effort when deciding to enjoy an immediate hedonic behavior
(e.g., watching TV) versus completing less pleasurable tasks (e.g., studying)
that facilitate accomplishing long-term goals (e.g., completing an academic
degree). Self-control requires a conscious, deliberate, and effortful act,
which according to Werner and Milyavskaya (2019) not necessarily results in achieving long-term goals. Rather, it
associates with pessimistic outcomes like ego depletion (Friese et al., 2018).
Nevertheless,
an influential line of research has emphasized the positive role of
self-control. For instance, Tangney et al. (2004) revealed that self-control is
positively associated with higher academic achievement, superior self-esteem, a
healthier diet, optimal emotional response, and quality relationships. Several
studies have echoed the productive impact of self-control on academic attainment,
getting higher grades, autonomous learning, influential psychological traits
such as grit, self-efficacy, doing academic tasks, well-being, and dealing with
ambiguous academic circumstances (Duckworth et al., 2019; Opelt &
Schwinger, 2020; Salazar & Meador, 2023; Tangney et al., 2004). Taha et al.
(2014) confirmed the negative relationship between self-control and ambiguity
intolerance, that is, individuals with higher intolerance to ambiguous
situations were likelier to report lower self-control levels, particularly in
complicated and unclear circumstances.
A
comprehensive review of psychometric literature revealed numerous observations.
First, several
scales were found to assess self-control, including the self-control behavior
inventory (Fagen et al., 1975), the self-control rating scale (Kendall &
Wilcox, 1979), the self-control schedule (Rosenbaum,1980), Eysenck
impulsiveness scale (Eysenck et al., 1984),
the self-control questionnaire (Brandon et al., 1990), the bonding self-control
scale (Gottfredson, 1990), the Grasmick’s self-control scale (Grasmick et al.,
1993), and the long and the brief self-control scale (Tangney et al., 2004).
Nevertheless, these scales were heterogeneous regarding validating samples,
item content, factorial structures, and scoring methods. Four scales (i.e., the
self-control rating scale, the self-control schedule, the bonding self-control
scale, and Grasmick’s self-control scale) were utilized to assess self-control
among irregular samples including clinical sample (Rosenbaum,1980; Tangney et
al., 2004) and criminal sample (e.g., children and juvenile delinquents;
Gottfredson, 1990; Grasmick et al., 1993; Fung et al., 2020). Eysenck
Impulsiveness Scale has mainly been used with children (Eysenck
et al., 1984). Only Tangney’s et al. (2004) long and brief
versions of the self-control scale (LSCS and BSCS) were validated and used
among university students, which concurs with the current study scope.
The
LSCS is a 36-item scale that consists of five factors, which are (1) General
self-discipline (i.e., eleven items), (2) Inclination toward non-impulsive
action (i.e., ten items), (3) Health habits (i.e., seven items), (4) Work
ethics (i.e., five items), and (5) Reliability (i.e., five items). The BSCS is
a 13-item scale that has the same factorial structure (i.e., Dimension 1 with 5
items, Dimension 2 with 3 items, Dimension 3 with 2 items, Dimension 4 with 2
items, and Dimension 5 with 1 item). Nevertheless, Tangney et al. (2004) found
that the five factors in BSCS did not enhance the prediction of several related
outcomes (e.g., psychological adjustment and academic achievement), which
motivates other researchers to consider BSCS as a unidimensional scale (Brevers
et al., 2017; Manapat et al., 2021). Such a lack of clarity raises a red flag.
In addition, psychometric literature has emphasized a rule about the minimum
number of items (i.e., three) per factor to maintain a reasonable number of
representative items and consider the factor as a valid dimension (Fabrigar
& Wegener, 2012; Watkins, 2018). This rule raises an additional concern
about the validity of factors 3, 4, and 5 that were offered by (Tangney et al.,
2004). The remaining discussion is oriented to the brief self-control scale
(BSCS) that is the scope of the current paper.
Second,
the BSCS has been adopted, translated, and validated in numerous cultures. This
prompted several versions, which are: Italian (Gürdere et al., 2022), Chinese
(Fung et al., 2020), French (Brevers et al., 2017), Turkish (Nebioglu et al.,
2012), German (Bertrams & Dickhäuser, 2009), and several US samples (De
Ridder et al., 2011; Ferrari et al., 2009; Maloney et al., 2012; Manapat et
al., 2021; Morean et al., 2014). None of the
above-mentioned versions assessed the BSCS in the Arabic context, specifically
in the Middle East, which highlights a research gap that the current study
wanted to fill. Only one Arabic study used the LSCS (i.e., 36 items) to explore
the association between emotional stability and self-control among university
students (Alrabie & Eatia, 2016). As far as the author knows, no
psychometric assessment of the scale factorial structure was conducted in the
Arabic context.
Third,
subsequent psychometric studies of BSCS appear to propose that one dimension
may not be sufficiently representative. Despite two studies confirming the
unidimensionality in the French (Brevers et al., 2017) and Italian samples
(Gürdere et al., 2022), the remaining studies demonstrated alternative
factorial structures. Preserving 13 items and keeping the scale intact, the two-factor
solution was supported by Ferrari et al. (2009) in the U.S. sample (i.e., self-discipline
and impulse control) and by Nebioglu et al. (2012) in the Turkish sample (i.e.,
Self-discipline and impulsivity). The two factors were obtained after
eliminating three items (i.e., initiatory self-control and inhibitory
self-control; De Ridder et al., 2011), five items (i.e., impulsivity and
restraint; Maloney et al., 2012), and six items (i.e., impulse control and
self-discipline; Morean et al., 2014). Manapat et al. (2021) found that one-and
two-factor structures were the most plausible in the two samples (heterogeneous
sample of U.S. adults and undergraduates). A four-factor solution was supported
in the Chinese sample (i.e., self-discipline, impulsivity, healthy habits, and
self-regulation; Fung et al., 2020). There is a dearth of sound-validated
scales of self-control. In addition, previous studies on the brief scale have
not reached an agreed-upon factor structure.
Therefore,
the prime objectives of this research were: (1) Adopting and translating BSCS
and assessing its factorial structure among Arab universities students using
exploratory factor analysis (EFA), (2) Verifying the obtained factorial
solution utilizing confirmatory factor analysis (CFA), and lastly, (3)
Obtaining evidence of the construct validity by investigating the correlation
between self-control and ambiguity tolerance. Such an attempt can be
illuminating and informative, establishing new psychometric evidence about the
Arabic BSCS form. The next section reviews self-control and ambiguity
tolerance.
2. Literature Review
2.1 Self-Control
Tangney
et al. (2004) defined self-control as “the ability to override or change one’s
inner responses, as well as to interrupt undesired behavioral tendencies and
refrain from acting on them.” In other words, self-control expresses a
dispositional quality that enables students to achieve desirable long-term
goals by practicing effortful inhibition and avoidance of immediately
gratifying behaviors, impulses, or expected rewards. Nevertheless, the
definitions of self-control might vary considerably depending on the
overarching theory (e.g., psychodynamic, biological process, and cognitive
self-regulation; Carver, 2005). Duckworth et al. (2019) discuss the jingle
jangle problem when defining self-control. In other words, there is an overlap
between several psychological and cognitive neuroscience concepts and
self-control such as self-regulation (i.e., a set of psychological and
motivational processes including self-efficacy, learning strategies, Zimmerman,
1990), conscientiousness (i.e., a personality trait that includes the facet of
orderliness, grit, complying to social norms; Park et al., 2017), and core
executive function (i.e., top-down inhibitory control, working memory, and the
cognitive flexibility that is required to accomplish a task; Zhou et al.,
2012).
Correspondingly,
a long list of scales has been used (e.g., the self-control behavior inventory,
the self-control schedule, the self-control questionnaire, the bonding
self-control scale, the Grasmick’s self-control scale, LSCS, and BSCS;
Duckworth & Kern, 2011; Tangney et al., 2004). A couple of scholars raised
a valid question of whether or not these scales are measuring the same
underlying construct (Duckworth & Kern, 2011). The literature demonstrated
that these scales were heterogeneous regarding validating samples, responding
and scoring methods, and the items about content, quantity, and factorial
structure.
In
respect of validating samples, some scales were used to assess self-control
among irregular samples including clinical samples, participants with eating
irregularity, and criminal samples (Maloney et al., 2012), whereas a limited
number of scales were validated among university students (e.g., LSCS and BSCS;
Tangney at al., 2004). Related to responding and scoring methods, a
meta-analysis study of 282 studies showed paramount diversity in tools
measuring self-control that can be categorized into four distinct approaches
(i.e., executive function tasks, self-report questionnaires, informant-report
questionnaires, and hindering of gratification experiments; Duckworth &
Kern, 2011). Overall, the results articulated that convergent validity was
moderate in these scales, in which the correlation coefficients were the
strongest among self-report and informant-report surveys and the weakest among
executive function tasks. For the third category (i.e., scale length and
factorial structure), there is a striking inconsistency between scales. For
instance, the Grasmick’s et al. (1993) scale is a 24-item scale, which has six
factors (i.e., impulsivity, simple tasks, self-centered, physical activities,
temper, and risk-seeking. In contrast, the Eysenck impulsiveness scale has
three factors (i.e., impulsiveness, venturesomeness, and empathy) that are
assessed by 63 items (Eysenck et al., 1984).
For university students, the LSCS is a 36-item and five-factor scale whereas
BSCS is a 13-item and unidimensional scale (Tangney et al., 2004).
Notwithstanding
the dilemma of definitions and the diversity of measurement tools, self-control
has essential influences on students’ academic outcomes. Duckworth et al.
(2019) emphasized that self-control has positive effects on academic
achievement, academic attainment, course grades, standardized tests,
accomplishing academic goal-congruent tasks, and dampening academic goal-incongruent
tasks. Stadler et al. (2016) found that self-control accounted for a
substantial amount of variance in grade point average and subjective academic
achievement even when controlling cognitive ability among university students.
Salazar and Meador (2023) provided additional support about the mediating role
of self-control in the academic context (i.e., the association between grit and
autonomous learning) among undergraduates. Self-control also mediated the
relationship in the personal context (i.e., the relationship between grit and
well-being) among undergraduates. Recent studies have substantiated a positive
moderate correlation between self-control and well-being, implying that highly
self-controlled students are more likely to express having well-being and
making better decisions (Hofmann et al., 2014; Opelt & Schwinger, 2020). A
meta-analysis of 104 studies revealed a significant positive relationship
between self-control and a set of desired behaviors surrounding school, work,
eating habits, weight, interpersonal functioning, well-being, and adjustment
with small to medium effect sizes (de Ridder et al., 2012). In addition, this
study found a significant correlation between low self-control and a set of
undesired behaviors, which are deviant behaviors (e.g., nonviolent crime,
cheating, over-speed while driving), unhealthy lifestyle (e.g., eating disorder
symptoms, unsafe sexual behavior), and addictive behaviors (e.g., smoking and
marijuana use), reflecting medium effect sizes.
2.2 Ambiguity
Tolerance (AT)
Budner
(1962) articulates ambiguity tolerance as a propensity to perceive ambiguous
and demanding situations as appealing and not a source of threat. For instance,
students who tolerate ambiguity tend with greater extent to engage effectively
in challenging learning experiences; they are more open to exploring new and
complicated learning tasks, and practice more creative and critical thinking in
such ambiguous learning experiences. Related to the current study’s scope,
students who tolerate ambiguity show a greater likelihood to achieve long-term
goals, adopting future orientation, practicing self-control, and delaying
impulsive gratifying of momentary rewards. Yang et al. (2021) demonstrated that
self-control mediated the relationship between the lack of AT and future time
perspective among college students. Tangney et al. (2004) articulated that
individuals who show higher self-discipline are better at tolerating ambiguity
and successfully overcoming unproductive affective responses (e.g., anxiety and
distress). A recent neuroimaging study has shown a significant positive
correlation between AT and GMV in DLPFC (i.e., the brain area that relates to
the executive function of evaluating choices and making decisions during
cognitive control processing), which necessitates practicing self-control (Tong
et al., 2015).
Various
studies have indicated that AT is correlated with novelty (Weissenstein et al.,
2018), higher academic motivation (Tapanes et al., 2009), positive feelings and
life satisfaction (Bardi et al., 2009), students’ engagement (Yu et al., 2022),
and intellectual curiosity and assertiveness (Jack & Smillie, 2019) among
university students. Another study revealed that students with moderate levels
of AT had higher reading comprehension scores compared to students with high
and low AT, suggesting the presence of a relationship between AT and learning
strategies (El-Koumy, 2003).
Conversely,
lack of AT is associated with higher levels of anxiety when receiving
unstructured learning material (DeRoma et al., 2003), less motivation to
participate in online learning (Tapanes et al., 2009), negative personality
traits (e.g., lower mental flexibility, higher conformity, and prejudice;
Tatzel, 1980), higher obsessive-compulsive response (Tolin et al., 2003), direct
effects on negative emotions and anxiety as well as indirect effects on life
satisfaction and positive emotions that are mediated by challenge and threat
appraisals (Bardi et al., 2009).
3. Study Aim
The
current study sought to adopt and evaluate the factorial
structure and reliability of the BSCS scale among Arab
university students by implementing EFA and CFA. Furthermore, it aimed to investigate
the construct validity by estimating the association between self-control and
ambiguity tolerance. Therefore, this research sought to answer the following
three questions:
1- What
are the psychometric properties (i.e., the exploratory factorial solution and
reliability coefficient) of BSCS among Arab
university students?
2- Can
the estimated factorial structure estimated in question 1 be validated among a
different sample of university students?
3- Is
there supporting evidence of BSCS’s construct validity?
4. Method
4.1 Participants
University
students were invited to participate online in the study via emails and social media
posts after Institutional Review Board’s (IRB) approval, particularly from two
countries (i.e., Oman and Egypt). A sample of 525 university students was
obtained. There were 133 males (25.3%) and 392 females (74.7%). Considering the
country, 28% of the students were Omani (n = 148) and 72% of students
were Egyptian (n = 377). The mean age of the sample was 22.64 (SD
= 4.89). Many students studied in scientific colleges (n = 372, 70.9%)
and remained in art/humanities colleges (n = 153, 29.1%). Related to
study level, 78% of students were undergraduates (n = 411) and 13% were
graduates (n = 114).
4.2 Measures
The
data was collected during the academic year of 2021/2022 using an online
questionnaire, which consists of: (1) Demographic information, (2) Brief Self-Control
Scale (BSCS; Tangney et al., 2004), and (3) Short Multiple Stimulus Types
Ambiguity Tolerance (MSTAT-II; McLain, 2009). In the first section, several
demographic variables were obtained, including gender, country, college, and
study level.
The
main scale in the second section, the BSCS (Tangney et al., 2004), has 13 items
reflecting the overall capability of self-discipline. Nine items are negatively
coded (i.e., 2, 3, 4, 5, 7, 9, 10, 12, 13). Examples of items include: “People
would say that I have iron self-discipline” and “I am good at resisting
temptation.” All items were rated on a 5-point scale, which ranges from
“Strongly Disagree” (Coded 1) to “Strongly Agree” (Coded 5). In the current
paper, the scale had good internal consistency reliability (α = .79),
which is consistent with prior studies (i.e., .83 and .80; Fung et al., 2020;
Tangney et al., 2004).
The
MSTAT-II (McLain, 2009) in the third section is a 13-item scale that assesses
ambiguity tolerance toward several stimuli including ambiguous stimuli in
general, novel stimuli, uncertain stimuli, complex stimuli, and insoluble
stimuli. Like BSCS, all items were rated on a 5-point scale. “I don’t tolerate
ambiguous situations very well” is an example of the MSTAT-II item. The
Cronbach’s 𝛼 of the scale was .70, implying an
acceptable reliability coefficient, which was to some extent similar to the
reliability coefficient presented by (McLain, 2009).
4.3 Data
Analysis
THE
dataset was inspected for missing data, normality, and outliers using SPSS
Version 24.0 (Tabachnick & Fidell, 2007). The clean data was randomly split
into two data sets. The first dataset (n = 200) was analyzed using EFA.
In particular, principal axis factoring (PAF) was used. Costello and Osborne
(2005) recommend the use of an oblique rotation when there are high
inter-correlation coefficients (i.e., r > .32) between dimensions. In
contrast, when the inter-correlation
coefficients between dimensions are smaller than .32, Varimax
rotation is implemented.
Several
assumptions were evaluated (i.e., multicollinearity,
sampling adequacy, existence of identity matrix, and singularity).
The multicollinearity is avoided when correlation coefficients are
within the acceptable range (i.e., .08 > r > .03).
In addition, the size of KMO determines the sampling adequacy. That is, “Good
to Great” sampling adequacy is obtained when values of KMO are ≥ .80,
fair sample adequacy is inferred when KMO equals .70, and moderate to bad
sample adequacy when KMO values equal .60 to .50 (Pett et al., 2003).
Furthermore, an identity matrix does not exist when Bartlett’s Test is
significant (Thompson, 2004). Lastly, singularity is prevented when the
determinant is small but greater than zero (i.e., > .00001; Tabachnick &
Fidell, 2007). Three criteria were investigated to decide the optimal factorial
structure, which are: eigenvalues greater than one according to Kaiser’s rule,
the Parallel Test, and scree plots (Patil et al., 2008). Also, item loadings
greater than .40 on the relevant factor are acceptable (Field, 2009).
Nevertheless, factor loadings of .32 were adequate too (Costello & Osborne,
2005). A minimum of three items per dimension is advised (Watkins, 2018). The
coefficient (Cronbach’s) Alpha was calculated to verify the internal
consistency reliability. Pearson correlation coefficient between self-control
and ambiguity tolerance was obtained to authenticate construct validity.
The
second dataset (n = 325) was utilized to verify the obtained factorial
structure by fitting the CFA model using Mplus (Muthén, & Muthén, 2017). Schumacker
and Lomax (2016) summarized several fit indices that
should be examined including Chi-Square, Root-Mean-Square-Error of
Approximation (RMSEA), Standardized Root-Mean-Residual (SRMR), Comparative Fit
Index (CFI), and Tucker-Lewis Index (TLI). A non-significant χ2 is
suggestive of good model fit. RMSEA and SRMR ≤ 0.05 indicate optimal fit.
RMSEA within a range (0.05- 0.08) implies an acceptable fit. The fit is
considered marginal when RMSEA values range from 0.08 to 0.10. Models with CFI
and TLI ≥ 0.90 and ≥ 0.95 infer acceptable and good fit,
respectively.
5. Results
5.1 Study
1, Findings:
Descriptive
Statistics and Correlation Matrix-Means, standard
deviations, skewness, Kurtosis, outliers (z ± 2.58), and minimum and
maximum values were examined, as summarized in Table 1. Findings demonstrated
no alarming floor and ceiling effects nor the violation of normality.
Table 1: Descriptive
Statistics of the Brief Self-Control Scale Items
|
Variables
|
M
|
SD
|
Skewness
|
Kurtosis
|
|
1.
I am good at resisting temptation
|
3.52
|
1.19
|
-0.59
|
-0.55
|
|
2.
I have a hard time breaking bad habits
|
3.57
|
1.18
|
-0.48
|
-0.65
|
|
3.
I am lazy
|
3.68
|
1.16
|
-0.52
|
-0.54
|
|
4.
I say inappropriate things
|
4.00
|
1.12
|
-0.86
|
-0.26
|
|
5.
I do certain things that are bad for me, if they are fun
|
4.35
|
.94
|
-1.47
|
1.71
|
|
6.
I refuse things that are bad for me
|
4.11
|
1.10
|
-1.32
|
1.13
|
|
7.
I wish I had more self-discipline
|
2.56
|
1.34
|
0.36
|
-1.08
|
|
8.
People would say that I have iron self-discipline
|
3.59
|
1.05
|
-0.53
|
-0.19
|
|
9.
Pleasure and fun sometimes keep me from getting work done
|
3.31
|
1.27
|
-0.20
|
-1.01
|
|
10.
I have trouble concentrating
|
3.29
|
1.13
|
-0.28
|
-0.59
|
|
11.
I am able to work effectively toward long-term goals
|
3.42
|
1.18
|
-0.46
|
-0.61
|
|
12.
Sometimes I can’t stop myself from doing something, even if I know it is
wrong
|
3.61
|
1.18
|
-0.53
|
-0.61
|
|
13.
I often act without thinking through all the alternatives
|
3.58
|
1.21
|
-0.35
|
-0.94
|
Note:
The Max = 5.00 and Min = 1.00 for all items.
The
results of the preliminary investigation of EFA indicated all assumptions were
met. The inter-items relationship coefficients were under .80, implying
multicollinearity is prevented, as presented in Table 2.
In addition, the sample adequacy was good because KMO test = 0.80 (Pett et al.,
2003). Bartlett’s Test of Sphericity (χ2[78] = 564.54, p
< 0.001) was significant, implying no concern about an identity matrix. The
determinant value was 0.05, indicating no issue with singularity.
Table 2: Correlation
Matrix of the BSCS Items (N = 13)
|
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
13
|
|
1
|
-
|
0.20**
|
0.29***
|
0.23***
|
0.40***
|
0.14**
|
0.07
|
0.34***
|
0.27***
|
0.18**
|
0.37***
|
0.20**
|
0.13*
|
|
2
|
|
-
|
0.33***
|
0.30***
|
0.30***
|
-0.01
|
0.26***
|
0.06
|
0.13*
|
0.27***
|
0.07
|
0.39***
|
0.25***
|
|
3
|
|
|
-
|
0.37***
|
0.37***
|
0.05
|
0.25***
|
0.30***
|
0.27***
|
0.41***
|
0.34***
|
0.28***
|
0.31***
|
|
4
|
|
|
|
-
|
0.34***
|
0.03
|
0.27***
|
0.22**
|
0.19**
|
0.23**
|
0.44***
|
0.40***
|
0.41***
|
|
5
|
|
|
|
|
-
|
0.12
|
0.15*
|
0.27***
|
0.33***
|
0.23**
|
0.16*
|
0.48***
|
0.26***
|
|
6
|
|
|
|
|
|
-
|
-0.16*
|
0.14*
|
0.13*
|
0.09
|
0.17**
|
0.07
|
-0.02
|
|
7
|
|
|
|
|
|
|
-
|
0.04
|
0.28***
|
0.26***
|
0.02
|
0.20**
|
0.19**
|
|
8
|
|
|
|
|
|
|
|
-
|
0.21**
|
0.13*
|
0.35***
|
0.15*
|
0.09
|
|
9
|
|
|
|
|
|
|
|
|
-
|
0.31***
|
0.15*
|
0.22**
|
0.14*
|
|
10
|
|
|
|
|
|
|
|
|
|
-
|
0.16*
|
0.26***
|
0.38***
|
|
11
|
|
|
|
|
|
|
|
|
|
|
-
|
0.19**
|
0.19**
|
|
12
|
|
|
|
|
|
|
|
|
|
|
|
-
|
0.41***
|
|
13
|
|
|
|
|
|
|
|
|
|
|
|
|
-
|
Note: *p
<0.05, **p < 0.01, ***p <0.001.
EFA
Finding-At the start, principal axis factoring (PAF) with
Direct Oblimin rotation was run to assess the magnitude of association between
the extracted dimensions. Inter-correlation coefficients
between dimensions were small (i.e., < 0.32), necessitating changing the
rotation method from Direct Oblimin to Varimax.
Thus, EFA
was rerun, specifically PAF with varimax rotation, as advised by Thompson
(2004). The findings showed four factors with eigenvalues greater than 1.
Nevertheless, the parallel test and scree
plot showed greater support as the structure with two factors (see Figure 1).
The rotated factor matrix demonstrated no cross-loading of items in the two
factors that are above 0.35 (Costello & Osborne, 2005). All
items had adequate factor loadings (i.e., > 0.32; Tabachnick
& Fidell, 2007).
Fig. 1: Scree Plot of Extracted
Factors
Factor
1 had nine items, explaining 24.59% of the variance (see Table 3). The items
with the highest two loadings are: “I often act without thinking through all
the alternatives” and “Sometimes I can’t stop myself from doing something, even
if I know it is wrong.” This factor was named “impulsive behavior” because it
describes a set of value behaviors and emotions reflecting poor self-control.
Factor 2 had four items, accounting for 6.34% of the variance. This factor was
named “self-discipline,” reflecting a set of behaviors that students do to
demonstrate self-control (e.g., I am good at resisting temptation”). The two
factors had high Cronbach Alpha, implying good reliability. On the whole, the
author determined that a two-factor structure with a total of 13 items was the
finest factorial solution.
Table
3:
Exploratory Factor Analysis Results and Factor Loadings with PAF with Varimax
Rotation for MSTAT-II Scale
|
Items
|
Factor1: Impulsivity
|
Factor2:Self-discipline
|
|
12
|
Sometimes
I can’t stop myself from doing something, even if I know it is wrong
|
0.61
|
0.17
|
|
13
|
I
often act without thinking through all the alternatives
|
0.59
|
0.07
|
|
2
|
I
have a hard time breaking bad habits
|
0.56
|
0.04
|
|
4
|
I
say inappropriate things
|
0.53
|
-0.05
|
|
10
|
I
have trouble concentrating
|
0.51
|
0.14
|
|
3
|
I
am lazy
|
0.50
|
0.30
|
|
7
|
I
wish I had more self-discipline
|
0.50
|
-0.07
|
|
5
|
I
do certain things that are bad for me, if they are fun
|
0.46
|
0.04
|
|
9
|
Pleasure
and fun sometimes keep me from getting work done
|
0.32
|
0.27
|
|
8
|
People
would say that I have iron self-discipline
|
0.11
|
0.56
|
|
11
|
I
am able to work effectively toward long-term goals
|
0.18
|
0.55
|
|
1
|
I
am good at resisting temptation
|
0.21
|
0.54
|
|
6
|
I
refuse things that are bad for me
|
-0.06
|
0.34
|
|
Cronbach’s
Alpha
|
0.78
|
0.62
|
|
Eigenvalue
|
3.86
|
1.53
|
|
Variance
explained
|
24.59%
|
6.34%
|
CFA Findings. In
the initial step, preliminary investigation of CFA showed a poor model fit (see
Table 4.A). The chi-square test was significant. The RMSEA value was 0.09 and
SRMR values were ≥ 0.05, indicating an improper fit. GFI was relatively
acceptable (i.e., ≥ 0.90) whereas AGFI was below 0.90, indicating poor
fit. These findings entailed modifying the model by adding six error covariance
terms, as shown in Figure 2. The modified model showed a better fit (see Table
4). The chi-square test was significant, which commonly occurs due to sample
size. However, SRMR was 0.05, illustrating an acceptable fit. Concerning The
RMSEA, the value indicated an acceptable fit because RMSEA (0.06) is within the
range (0.05-0.08). CFI and TLI implied an acceptable model fit.
Table
4:
Confirmatory Factor Analysis Results and Comparison of Standardized Estimates
of the Initial and Modified Models
|
Fit Statistics
|
A.
Initial Model
|
B.
Modified Model
|
|
Factor 1: Impulsivity
|
Factor 2: Self-discipline
|
Factor 1: Impulsivity
|
Factor 2: Self-discipline
|
|
Factor
loadings
|
|
|
|
|
|
SC_12
|
0.71
|
|
0.73
|
|
|
SC_13
|
0.64
|
|
0.60
|
|
|
SC_2
|
0.52
|
|
0.54
|
|
|
SC_4
|
0.60
|
|
0.52
|
|
|
SC_10
|
0.51
|
|
0.48
|
|
|
SC_3
|
0.50
|
|
0.47
|
|
|
SC_7
|
0.50
|
|
0.52
|
|
|
SC_5
|
0.63
|
|
0.62
|
|
|
SC_9
|
0.51
|
|
0.52
|
|
|
SC_8
|
|
0.59
|
|
0.32
|
|
SC_11
|
|
0.53
|
|
0.84
|
|
SC_1
|
|
0.49
|
|
0.89
|
|
SC_6
|
|
0.49
|
|
0.29
|
|
Fit
Indices
|
|
|
|
|
|
χ2(df)
|
237.61(64)
|
123.59(58)
|
|
p value
|
0.00
|
0.00
|
|
RMSEA
|
0.09
|
0.06
|
|
SRMR
|
0.07
|
0.05
|
|
CFI
|
0.82
|
0.93
|
|
TLI
|
0.76
|
0.91
|
Fig. 2: Confirmatory Factor
Analysis of the Modified Model with Standardized Values
Validity
Evidence based on Relations to Other Variables Findings.
The newest standards for educational and psychological testing (American
Educational Research Association, 2014) stated that “the construct should be
related to some other variables, and, as a result, analyses of the relationship
of test scores to variables external to the test provide another important
source of validity evidence” (p. 16). Underpinning the literature, the present
study hypothesized that self-control is positively associated with ambiguity
tolerance. Pearson correlation coefficient showed that self-control had a
moderate positive relationship with ambiguity tolerance (r = 0.32, p
< 0.001).
6. Discussion
Self-control
is one of the affective dispositional qualities that had significant influences
on students’ academic and personal pathways. The self-control scale, BSCS, is
considered the most efficient, the most valid, and the top reliable measure of
self-control among university students. BSCS has been originally constructed
and validated in the U.S. background and was adopted by manifold cultures
(e.g., Turkish, French, Italian, Chinese, and German; Bertrams
& Dickhäuser, 2009; Brevers et al., 2017; Fung et al., 2020; Gürdere et
al., 2022; Nebioglu et al., 2012). This scale, though, has
not been embraced in the Arabic context. In addition, prior psychological
measurement studies echoed several concerns related to the inconsistency concerning
the BSCS factorial solution in the U.S. context (De Ridder et al.,
2011; Maloney et al., 2012; Manapat et al., 2021; Morean
et al., 2014) and other cultures (Nebioglu et al.,
2012).
Thus, this study had three goals: (1) Adopting BSCS and evaluating
its psychometric properties among Arab students using EFA, (2) Validating BSCS’s
factorial structure by fitting the CFA model, and (3) Investigating the
construct validity of BSCS by estimating
Pearson Correlation Coefficient between self-control and ambiguity tolerance.
EFA
findings found the two-factor structure (i.e., impulsivity and
self-discipline) in the Arab sample. Factor 1 has nine items
(i.e., Items
2, 3, 4, 5, 7, 9, 10, 12, 13) whereas Factor 2 has four items
(i.e., Items 1, 6, 8, 11). This factorial solution is consistent with the
structure that was estimated by Ferrari et al. (2009) in the U.S. sample and
Nebioglu et al. (2012) in the Turkish sample. The two factors
had good reliability. However, impulsivity explained more variance (i.e.,
24.59%), whereas self-discipline explained a smaller amount of variability
(i.e., 6.34%). The current study’s finding echoes the idea of rethinking the
unidimensionality of BSCS. In contrast, this factorial solution was inconsistent
with several studies in the U.S. samples (Maloney et al., 2012;
Morean et al., 2014) and the Chinese sample (Fung et al., 2020). This
divergence can be attributed to differences in the scale length. The current
study retained and preserved 13 items, aligning with the original scale by
Tangney et al. (2004), whereas these studies eliminated three items (De Ridder
et al., 2011), five items (Maloney et al., 2012), and six items (Morean et al.,
2014).
The findings of the initial CFA displayed
poor model fit. Modifying the model by adding several reasonable error
covariance terms leads to a good model fit. For instance, several sensible
error variances were added between Item 4 (i.e., I say inappropriate things)
and two items, which include: Item 13 (i.e., I often act without thinking
through all the alternatives), Item 5 (i.e., I do certain things that are bad
for me, if they are fun), meaning, students might say inappropriate things
because they have not thought carefully about a specific topic or because they
perceive it as a funny thing, which increases the likelihood of doing unethical
academic behaviors (e.g., cheating and plagiarism; Curtis et al., 2018). In
addition, two error variances were added between Item 11 (i.e., I am able to
work effectively toward long-term goals) and Item 1 (i.e., I am good at
resisting temptation), implying resisting frequent and spontaneous temptations
enable students to focus and achieve long-term goals (Milyavskaya
et al., 2015). One error variance was inserted between Item 8
(i.e., People would say that I have iron self-discipline) and Item 6 (i.e., I
refuse things that are bad for me), which is a reasonable judgment by others
when the individual firmly refuses to do inappropriate things. Another error
variance was included between Items 3 (i.e., I am lazy) and 10 (i.e., I have
trouble concentrating), implying that lazy students are more likely to face
academic problems (e.g., difficulty concentrating and more likely to
procrastinate; Kumaraswamy, 2013; Ugwuanyi et al., 2020).
According to Kline (2010), specifying correlation among error terms (i.e.,
error covariance) is common, given the nature of measured outcomes in the
social sciences, particularly when there are substantive and justifiable
reasons for the error covariance. Sensible sources of such covariance that aim
to explain construct-irrelevant variance include item format, item wording,
item order, and systematically different item response formats. Failing to
specify error covariance might result in biased model estimates (Cole et al.,
2007).
Validity
was substantiated by a significant positive moderate correlation between
self-control and ambiguity tolerance (r = 0.32, p < .001).
Findings are coherent with prior studies (e.g., Tangney et al.,
2004); Tong et al., 2015; Yang et al., 2021) results, which boost the
construct validity of BSCS. Given that, students who had higher self-control
have lower intolerance to uncertainty and are more eager to pursue future
orientation and long-term goals that are surrounded by higher levels of
ambiguity (Yang et al., 2021). In addition, disciplined students are better at
overriding obsessive-compulsive responses (Tolin et al., 2003)
and excessive emotional responses (Tangney et al., 2004).
They are more likely to use the brain area that relates to the
executive function of evaluating choices and making decisions during cognitive
control processing (i.e., GMV) when they are faced with ambiguous situations
(Tong et al., 2015).
7. Implications and Limitations
The
present study revealed several theoretically valuable results. First, it
validated BSCS to the Arabic culture, which makes it a useful tool that can be
administered in all countries in the Middle East. It demonstrated that BSCS has
a decent factorial structure. The Arabic BSCS can be a baseline for forthcoming
studies that can widen the investigation of the influence of self-control on
academic and personal outcomes among students in higher education institutions.
The present study, also, confirms alarming indicators related to the BSCS
unidimensionality, encouraging future psychometric and educational studies to
reconsider the two-factor solution in their analyses. As well, the present
study findings have applied implications. Establishing a brief version of the
self-control scale (i.e., BSCS) that is sound and solid psychometrically would
be a beneficial contribution to instructors’ toolbox, facilitating the
assessment of students’ traits that may result in a better learning environment
and overriding the overwhelming workload. It decreases any concerns that
preclude students from answering the scale because of scale length and
preserves the respondents’ cognitive resources and time (Burisch,
1984; Arquero
& McLain, 2010). Acquisition of vibrant comprehension of
the influences of self-control would empower students’ performance and permit
instructors to identify fruitful interventions.
Nevertheless,
the present study has a couple of limitations. The four levels of measurement
invariance (configural, metric, scalar, and strict invariance) were not tested
in the current study. The generalizability of this study’s findings is limited
because the sample consisted of traditional college students from public
universities in Oman and Egypt only. Therefore, this study recommends: (1) Testing
measurement invariance across several demographic variables (i.e., gender,
school type, colleges, and countries), (2) Gathering more samples, covering
public and private universities, and (3) Conducting experimental studies
assessing self-control on students’ learning.
In
conclusion, the BSCS scale is a valid and reliable scale to measure two
dimensions of self-control (i.e., impulsivity and self-discipline) in the
Arabic culture, mainly university students. Although this study raised a red
flag about the unidimensionality of BSCS, aligning with several prior studies.
Additional psychometric investigation of the scale, particularly measurement
invariance, is demanded.
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Final declarations:
-
The author/s declare that they got the required voluntary human participants’ consent to participate in the study
as well as the necessary institutional approvals.
-
The datasets generated and/or analyzed during the current study are
available from the corresponding author upon reasonable request.
تصريحات
ختامية:
-
يصرح
المؤلف/المؤلفون
بالحصول على
موافقة
الأشخاص
المتطوعين للمشاركة
في الدراسة
وعلى
الموافقات
المؤسسية
اللازمة.
-
تتوفر
البيانات
الناتجة و/أو
المحلَّلة
المتصلة بهذه
الدراسة من
المؤلف
المراسل عند
الطلب.
Journal
of Educational Sciences, Qatar University 
مجلة
العلوم
التربوية