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Patience is the propensity to wait calmly in the face of frustration or adversity. The new 3-Factor Patience Scale (Study 1) measures three types of patience (interpersonal, life hardship, and daily hassles patience), which differentially relate to well-being and personality. In Study 2, goal pursuit and achievement mediated the relation between patience and well-being. Participants rated 10 personal goals on 15 dimensions of goal pursuit (e.g. patience enacted, difficulty, achievement satisfaction, effort). Patience facilitated goal pursuit and satisfaction especially in the face of obstacles. In Study 3, participants took part in a training program designed to increase trait patience. The program led to increased patience, decreased depression, and increased positive affect relative to a control condition, suggesting that patience may be modifiable.
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An examination of patience and well-being
Sarah A. Schnitker
a
a
School of Psychology, Fuller Theological Seminary, Pasadena, CA 91106, USA
Available online: 26 Jun 2012
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The Journal of Positive Psychology
Vol. 7, No. 4, July 2012, 263–280
An examination of patience and well-being
Sarah A. Schnitker
*
School of Psychology, Fuller Theological Seminary, Pasadena, CA 91106, USA
(Received 18 May 2012; final version received 22 May 2012)
Patience is the propensity to wait calmly in the face of frustration or adversity. The new 3-Factor Patience Scale
(Study 1) measures three types of patience (interpersonal, life hardship, and daily hassles patience), which
differentially relate to well-being and personality. In Study 2, goal pursuit and achievement mediated the relation
between patience and well-being. Participants rated 10 personal goals on 15 dimensions of goal pursuit (e.g.
patience enacted, difficulty, achievement satisfaction, effort). Patience facilitated goal pursuit and satisfaction
especially in the face of obstacles. In Study 3, participants took part in a training program designed to increase
trait patience. The program led to increased patience, decreased depression, and increased positive affect relative
to a control condition, suggesting that patience may be modifiable.
Keywords: patience; well-being; goals; virtue
Introduction
Patience has long been upheld as a character strength
and desirable personality trait that promotes human
flourishing and well-being. Familiar maxims such as
‘good things come to those who wait’ exemplify the
desirability of the trait, and moral philosophers and
religious writers have emphasized the importance of
developing patience to achieve the ‘good life’ (Harned,
1997). Although touted as an important trait for
millennia, patience is only recently receiving attention
as a topic of empirical investigation in psychology.
What is patience?
In the most basic sense, patience is the propensity of a
person to wait calmly in the face of frustration,
adversity, or suffering. Patience is enacted across a
wide range of circumstances and timeframes. It is
enacted (or not!) in mundane activities, such as waiting
in traffic, as well as in more significant and long-term
situations, such as parenting or dealing with a serious
illness. Although it often involves a temporal or
waiting component, patience is also called forth in
situations with no direct focus on time (e.g. dealing
with a difficult person).
Typically construed as a disposition (e.g. ‘She is a
patient person’), patience can also be viewed as a state
(e.g. ‘She waited patiently’). These two conceptualiza-
tions of patience are intertwined (people with high
dispositional patience will experience more discrete
states of patience), so it is important to consider both
of them as meaningful features. Demonstration of
genuine patience depends on both behavioral (i.e.
waiting) and emotional (i.e. low arousal positive affect
and notable absence of high arousal negative affect)
components.
Why study patience?
Situations that require patience are fundamental to
human experience. People regularly encounter frus-
trating circumstances where they may or may not
exhibit patience (e.g. parenting, working with a diffi-
cult boss). Waiting is an unavoidable part of life; a poll
reported in The Daily Telegraph found that the average
Briton spends 5 h and 35 min each month or six
months of an average lifetime standing in line
(Britons Spent Six Months Queuing, 2009). Research
shows that daily hassles and frustrations have a
negative impact on physical health and well-being,
perhaps of a greater magnitude than major life events
(DeLongis, Coyne, Dakof, Folkman, & Lazarus,
1982). Given the pervasiveness of waiting and frus-
trating stimuli in daily life, research examining indi-
vidual differences in how people react to such
circumstances is needed to maximize human function-
ing. Initial evidence shows that patience is positively
correlated with subjective well-being, positive coping,
virtues, and thriving (Schnitker & Emmons, 2007).
Research on personality and social processes underly-
ing the relation between patience and well-being has
*Email: sschnitker@fuller.edu
ISSN 1743–9760 print/ISSN 1743–9779 online
ß 2012 Taylor & Francis
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applications to improving quality of life in a plethora
of settings.
Patience and well-being
Patience is hypothesized to affect both hedonic and
eudaimonic well-being two long-established notions
representing distinct, but overlapping, paradigms to
understand ‘the good life’. Hedonism reflects the idea
that well-being consists of maximizing pleasure or
happiness and minimizing negative emotions (Ryan &
Deci, 2001). Patience directly influences hedonic well-
being as it buffers against emotions in stressful
situations, allows the person to cope more adaptively
with frustrations, and facilitates positive interpersonal
interactions. Though often misconstrued to concern
only physical pleasures, hedonism also comprises the
perspective that happiness arises from the achievement
of goals (Diener, Sapyta, & Suh, 1998). Thus, patience
may indirectly affect hedonic well-being by facilitating
goal attainment, which increases positive emotions and
life satisfaction.
A eudaimonic view of well-being maintains that the
‘good life’ lies in the realization of human potential
and the development of virtue (e.g. Ryff & Singer,
1998). Self-determination theory defines eudaimonic
well-being in terms of intrinsic motivation, integrity,
well-being, vitality, and self-congruence, which are
realized by the fulfillment of autonomy, competence,
and relatedness needs (Ryan & Deci, 2000). Patience is
proposed to increase eudaimonic well-being by facili-
tating the realization of goals that promote the
fulfillment of these basic needs.
Prior research related to trait patience
Patience was framed by Curry, Price, and Price (2008) as
low discounting rates across time. They operationally
define the patient person as one who is less likely to
discount the benefits of a reward if forced to wait for it,
and they found that patience positively correlated with
increased cooperation in a public-good game. This
conceptualization of patience highlights an important
distinction between patience and delay of gratification.
Although related to patience, delay of gratification is
distinguished as involving an overt choice between a
smaller reward now versus a larger reward later. In
contrast, patience is often demonstrated in situations
where there is no choice about whether or not to wait
the only choice is how one waits.
Impatience has been examined in research on the
Impatience/Irritability factor of the Type A personality.
Spence, Helmreich, and Pred (1987) found that those
scoring high on the Impatience/Irritability factor
reported a greater number of physical complaints,
but studies indicate that it is hostility and hard-driving
competitiveness, rather than impatience alone, that
drives the association between Type A personality and
cardiovascular disease (Miller, Smith, Turner,
Guijarro, & Hallet, 1996). Although findings on
impatience inform an understanding of patience, it is
important to note that impatience and patience are not
mirror opposites. Patience is better conceptualized as a
mean between two poles of behavior. According to
Aristotle (Rorty, 1980), a virtuous action is always an
intermediate state between two extremes of vice that
are reflected in excess and deficiency. In this case,
patience is a mean between the excess of recklessness
and the deficiency of sloth.
Prior research related to state patience
Researchers have uncovered several key aspects of
situations that affect momentary patience and impa-
tience. First, marketing researchers have examined
impatience in customer service ratings. An impatient
state will arise and service satisfaction will decrease if a
person perceives that the time spent waiting has
surpassed expectations for waiting (Groth &
Gilliland, 2006). Moreover, when a wait is attributed
to the service provider, customers exhibit even greater
impatience and dissatisfaction (Rose, Meuter, &
Curran, 2005).
People differ in their ‘wait-time construals’, or how
long they believe they should have to wait in a specific
situation. Several factors (e.g. situation factors, past
experiences, basic personality disposition, socio-cul-
tural norms, etc.) lead to the formation of these
construals. Roy, Cristenfeld, and McKenzie (2005)
found that people especially underestimate how long it
will take them to complete a task when they are highly
motivated to finish quickly. In studies using a word
game (Buehler, Griffin, & MacDonald, 1997) or
origami task (Byram, 1997), participants who offered
a monetary incentive to perform faster underestimated
how long it would take them to complete the task
compared to participants who were not offered an
incentive; however, the incentive had no effect on how
quickly the participants actually completed the task.
Thus, when a person wants an activity to occur
quickly, construals of how long the situation should
last shorten, and patience will most likely decrease.
Time estimation may be involved in state patience.
Situations that emphasize temporally salient informa-
tion will increase time estimates, which may decrease
patience. Situations that lead to absorption and
engagement of attention (i.e. increased mindfulness)
will decrease time estimates, thus increasing patience
(Glicksohn, 2001).
Even after people’s situation construals are violated
and they recognize that they are waiting or enduring a
frustration longer than expected, they may still
264 S.A. Schnitker
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respond patiently by utilizing emotion regulation
techniques. For example, patient people may utilize
reappraisals (Clore & Robinson, 2000) to change their
expectations and feel more patient. We hypothesize
that if people are taught such emotion regulation
strategies, they will be able to increase their ability and
propensity to wait patiently.
Preliminary understanding of patience: Correlates of
patience
Considering patience as a trait and character strength,
Schnitker and Emmons (2007) created The Patience
Scale-10 (PS-10) to measure a person’s self-evaluation
of patience (e.g. ‘Most people would say I am a patient
person’, or ‘Waiting in lines does not bother me’) and
attitudes about the importance of patience (e.g. ‘I
believe that when it comes to getting along with others,
patience is an important factor’). In response to claims
that patience is merely an amalgam of other virtues
(Peterson & Seligman, 2004), Schnitker and Emmons
(2007) established the discriminant validity of patience.
In a regression of patience on the 24 character
strengths of the Values in Action Inventory of
Strengths (VIA-IS; Peterson, Park, & Seligman,
2005), the 24 strengths combined accounted for only
26% of the variance in patience scores, indicating that
patience is not reducible to other virtues. In a
regression on the three virtues specifically hypothesized
by Peterson and Seligman (2004) to compose patience
(i.e. persistence, open-mindedness, and self-regulation),
only open-mindedness significantly predicted patience,
and the three strengths combined accounted for only
9% of the variance in patience scores.
Schnitker and Emmons (2007) distinguished
patience from basic personality traits while also pro-
viding evidence of convergent validity. In a regression
of patience on the Big Five (John, Donahue, & Kentle,
1991), the five factors accounted for 25% of the
variance in patience scores. Patience was correlated
with higher Agreeableness, higher Openness, and lower
Neuroticism. They also found a robust relation
between patience and well-being. Higher patience
correlated with decreased negative affect, decreased
depression, and lower incidence of health problems.
Patience positively correlated with prosocial traits (e.g.
empathy) and character strengths of the VIA-IS
(Peterson et al., 2005), namely strengths of
Temperance (Forgiveness r ¼ 0.38, Prudence r ¼ 0.29)
and Justice (Equity r ¼ 0.41, Leadership r ¼ 0.33).
However, the PS-10 suffered several weaknesses as
a measure of patience. First, the confirmatory factor
analysis of the PS-10 reported by Schnitker and
Emmons (2007) did not show good fit with a root
mean square error of approximation (RMSEA) of
0.098 (an RMSEA less than 0.10 is considered pass-
able, but good fit would be indicated with an RMSEA
at or less than 0.05; see Byrne, 1998). Second, the
PS-10 is limited to obtaining a person’s beliefs about
the importance of patience and a general self-evalua-
tion of patience. The scale does not capture theoretical
distinctions and nuances of patience that are important
for well-being outcomes. For example, patience in
dealing with chronic illness (life hardship patience)
differs considerably from patience when dealing with a
recalcitrant child (interpersonal patience) or when
stuck in traffic (daily hassles patience).
Research objectives
Given that the heretofore described research summa-
rizes nearly all published work on patience, many
opportunities for data collection and theory building
abound. To limit our scope of inquiry, we focus on
three main objectives in our research.
Objective 1 Establish construct validity and distinguish
different types of patience. As expounded by John and
Soto (2007), we seek to amass evidence of construct
validity for patience and build a nomological network
around the trait. In this vein, we present a more valid
and reliable new patience scale, capturing distinctive
dimensions of patience based on the nature of the
patience-eliciting stimuli. We hypothesize that the
antecedents and well-being outcomes from these three
types of patience will differ. We develop a new scale
measuring these three factors in Study 1, and we
construct a multitrait–multimethod matrix (Campbell
& Fiske, 1959) with the collection of informant reports
in Study 2.
Objective 2 Test the directionality and causality of the
patience-well-being relation. The second objective of the
present research is to better understand the relation
between patience and well-being. Previous findings on
the relation between patience and well-being (Schnitker
& Emmons, 2007) were ascertained from concurrent
correlational data and do not provide insight into the
directionality or causality of effects. Collection of
longitudinal data in Study 2 and experimental data in
Study 3 correct this deficit.
Addressing possible negative outcomes. Although
patience is theorized to engender beneficial effects, it
is possible that the trait may lead to negative outcomes.
It has been suggested that patience leads to passivity
and that too much patience can be injurious (Burke,
1769). Block (1996) found negative outcomes for
people at the extreme high end of the ability to delay
gratification, and Robins, John, Caspi, Moffitt, and
Stouthamer-Loeber (1996) found that boys with an
overcontrolled personality type (compared to those
with resilient and undercontrolled types) were prone to
internalizing problems. Like these positive traits, can
too much patience also have negative effects?
The Journal of Positive Psychology 265
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Variables that may point to the deleterious effects of
patience, such as assertiveness and passivity in goal
pursuit, are included in the succeeding studies to
specifically test for possible negative effects.
Objective 3 Examine mediation of the relation between
patience and well-being by goal pursuit and achievement.
We endeavor to understand the personality processes
mediating the relation between patience and well-
being, particularly looking at the processes of goal
pursuit and achievement. We hypothesize that patience
will increase goal achievement, which will increase
well-being.
The goal striving literature has traditionally con-
cerned itself with the relation between goals and well-
being outcomes. Palys and Little (1983) maintain that
goal enjoyability, difficulty, social support, and tem-
poral importance are associated with life satisfaction.
Sheldon, Ryan, Deci, and Kasser (2004) provide
evidence that goal content and motives affect quality
of life in a 1-year prospective study of change in well-
being. Within the goal framework, one way to conceive
of patience is as a coping tool in response to goal delay
or potential failure. Echteld, Elderen, and Kamp
(2001) highlight the adaptive function of avoidant
coping when a person has no sense of control over a
situation. Approach coping in an uncontrollable situ-
ation may cause high goal disturbance and high
perceptions of stress caused by that disturbance a
combination that leads to the lowest quality of life
scores. Although patience is not tantamount to
avoidant coping, this research illuminates possible
pathways from patience to beneficial outcomes.
Brandtstadter and Renner’s (1990) findings on
tenacious goal pursuit and flexible goal adjustment are
also informative. Tenacious goal pursuit refers to the
assimilative process of transforming external circum-
stances to fit personal preferences, and flexible goal
adjustment refers to the accommodative tendency to
adjust personal preferences and goal orientations to fit
situational constraints (akin to patience). Brandtstadter
and Renner report that both tenacious goal pursuit and
flexible goal adjustment predict high life satisfaction
and low depression, and both are positively correlated
with internalized control beliefs. Brandtstadter and
Rothermund (2002) findings support the importance of
utilizing accommodative strategies in uncontrollable
circumstances: the negative relation between measures
of control and depression disappear in circumstances
that are impervious to change (Wolk, 1976).
Study 1: Creating a three factor patience scale
The purpose of this study is to create and validate a
measure that distinguishes between interpersonal
patience, life hardship patience, and daily hassles
patience.
Method
Participants
Participants were 389 UC Davis undergraduates (296
females, 85 males; mean age ¼ 19.8 years) recruited
through the psychology subject pool who received
course credit. Participants were primarily Asian
American (43%) and Caucasian (24%), though other
ethnic minority groups were represented (11%
Hispanic, 3% African American, 2% Middle Eastern,
15% other).
Measures
Patience items. Participants were administered 40 items
hypothesized to measure interpersonal patience (e.g.
‘When someone is having difficulty learning something
new, I will be able to help them without getting
frustrated or annoyed’), life hardship patience (e.g. ‘I
am able to wait-out tough times’), and daily hassles
patience (e.g. ‘Although they’re annoying, I don’t get
too upset when stuck in a traffic jam’). Items were
rated on a 7-point Likert scale (1 ¼ very much unlike
me;7¼ very much like me).
PS-10. The Schnitker and Emmons (2007) PS-10 was
used to assess beliefs about the importance of patience
and self-evaluation of patience. The 10 items were
rated on a 7-point Likert scale (1 ¼ very much unlike
me;7¼ very much like me).
Well-being indicators
CES-D. The Center for Epidemiological Studies
Depression Scale (CES-D; Radloff, 1977) was used to
measure depression with 20 self-report items.
Participants rated (1 ¼ rarely or none of the time;
4 ¼ most or all of the time) how often they exhibited
depressive symptoms during the week prior to com-
pleting the questionnaire.
Rosenberg Self-Esteem Inventory. Ten items of the
Rosenberg Self-Esteem Inventory (Rosenberg, 1965)
were rated on a 1 ¼ strongly disagree to 4 ¼ strongly
agree scale.
Satisfaction with Life Scale. This 5-item scale (Diener,
Emmons, Larsen, & Griffin, 1985) was used to
measure global life satisfaction (1 ¼ strongly disagree;
7 ¼ strongly agree).
Hope. Snyder et al.’s (1991) 8-item Hope Scale was
used to measure the dispositional trait of hope
(1 ¼ definitely false;8¼ definitely true ).
R-UCLA Loneliness Scale (Short Form). This 3-item
scale of general loneliness (Hughes, Waite, Hawkley, &
Cacioppo, 2004) asks subjects to rate how often they
feel isolated, left out, and lacking companionship
(1 ¼ hardly ever,2¼ some of the time, and 3 ¼ often).
266 S.A. Schnitker
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Personality measures
Big Five Inventory (BFI). The 44-item BFI (John et al.,
1991) measured the personality factors of
Extraversion, Agreeableness, Conscientiousness,
Neuroticism, and Openness to Experience on a 5-
point Likert scale (1 ¼ disagree strongly;5¼ agree
strongly).
Assertiveness. The Rathus Assertiveness Schedule
(1973) was used to measure assertive behaviors. The
30 items of this measure are scored on a 3toþ3 scale
(3 ¼ very unlike me; þ 3 ¼ very like me). Example
items include ‘Anyone attempting to push ahead of me
in line is in for a good battle’ and ‘I have a hard time
saying no’ (reverse scored).
Mindful Attention Awareness Scale (MAAS). Brown
and Ryan’s (2003) 15-item scale was used to measure
present and mindful engagement (1 ¼ almost always;
6 ¼ almost never).
Attachment. The Experiences in Close Relationships
Scale (ECR; Brennan, Clark, & Shaver, 1998) is a
36-item measure of adult attachment (1 ¼ disagree
strongly;7¼ agree strongly) indicating secure, inse-
cure-anxious, or insecure-avoidant attachment in
relationships.
Procedure
Participants took the survey online using the
Concentus Assessment survey platform. They com-
pleted the preliminary patience items and then com-
pleted the other personality and well-being measures.
Two weeks later, participants were asked via e-mail to
take a second online survey. Participants (N ¼ 317)
again completed the preliminary patience items, as well
as other measures from the Time 1 survey, to establish
test–retest reliability.
Results
Initial analyses of scale construction: Exploratory
factor analyses
Exploratory factor analyses (EFAs, maximum-like-
lihood extraction/promax rotation) were run on the
initial pool of patience items. Examination of the
scree plot revealed one large factor and two or three
additional factors before the plot leveled off (Figure 1a).
Factor loadings for 1 -, 2 -, 3 -, and 4-factor solutions
were examined; the 3-factor solution demonstrated the
most simple structure and greatest interpretability.
Factors appeared to represent interpersonal, life hard-
ship, and daily hassles patience, respectively.
The pool of 40 items was reduced to 11. Items
loading less than 0.30 on a factor, with content loading
equally on multiple factors, or with content that did
not match the theoretical conceptualization of the
factors were eliminated. Reliability and item analyses
were employed to remove additional items that did not
correlate with other items or were redundant. An EFA
of these 11 items produced three factors (interpersonal,
life hardship, and daily hassles patience) with eigen-
values greater than one (4.23, 1.25, 1.08), accounting
for 38.48%, 11.35%, and 9.85% of the variance,
respectively. However, the scree plot supported a
1-factor solution (Figure 1b). Thus, the goodness-of-
fit for the 1 -, 2 -, and 3-factor models were tested.
Confirmatory factor analyses of EFA factor structure
Confirmatory factor analyses were run with 1 -, 2 -, and
3-factor models (both orthogonal and non-orthogo-
nal). Models were tested using maximum-likelihood
estimation in LISREL Version 8.54. Fit statistics for
all the models are shown in Table 1. As expected, the
non-orthogonal 3-factor model best fit the data, V
2
(41,
N ¼ 359) ¼ 107.30, p < 0.001. Akaike’s information
criterion (AIC) was 158.25, and the comparative fit
index (CFI) was 0.96. These indices and an RMSEA of
0.054 indicate fair to good fit (Byrne, 1998). The next
best-fitting model, the 3-factor orthogonal model, had
poorer indicators of fit (AIC ¼ 226.64,
RMSEA ¼ 0.094, CFI ¼ 0.90), so we adopted the
3-factor correlated model with confidence. Figure 2
depicts the adopted model and parameter estimates.
Reliability and scale descriptives
The appendix shows the final 3-Factor Patience Scale
(3-FPS). As can be seen in Table 2, the scale demon-
strates adequate internal reliability ( ¼ 0.66–0.80) and
an average 2-week test–retest reliability of 0.66. Scores
are normally distributed and do not systematically
differ by gender or ethnicity. Scores on the 3-FPS
correlate moderately to highly with the self-evaluation
factor of the PS-10 (r ¼ 0.66, 0.38, 0.66 for interpersonal ,
life hardship, and daily hassles patience, respectively)
and moderately with beliefs about the importance of
patience (r ¼ 0.43, 0.20, 0.33, respectively).
Patience and personality: External validity and
possible antecedents of patience
Big Five. The three patience factors have significant
zero-order correlations with four of the five Big Five
traits (Agreeableness, Conscientiousness, Neuroticism,
and Openness; p < 0.01). Of note, interpersonal patience
has a strong correlation with Agreeableness (r ¼ 0.60),
whereas life hardship and daily hassles patience have
moderate correlations (r ¼ 0.22, 0.30). Table 3 shows
each of the factors of the 3-FPS regressed on the Big
Five. When interpersonal patience is regressed on the
The Journal of Positive Psychology 267
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(a)
(b)
Figure 1. Scree plot of EFA of (a) the original 40 items and (b) the final 11 items.
Table 1. Model fit indices for 3-Factor Patience Questionnaire compared with alternate models.
Description Chi-sq. df AIC RMSEA CFI
Final model:
3-Factor Model (Correlated) 107.30 41 158.25 0.054 0.96
Alternate models:
3-Factor Model (Orthogonal) 227.49 44 226.63 0.094 0.90
2-Factor Model (Correlated) 186.84 43 246.37 0.100 0.92
2-Factor Model (Orthogonal) 294.07 44 309.42 0.120 0.86
Single-Factor Model 279.19 44 348.99 0.130 0.87
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Big Five, all five factors are significant predictors, but
Agreeableness is the strongest predictor ( ¼ 0.48) and
Neuroticism has a smaller effect ( ¼0.17). Only
Neuroticism ( ¼ 0.33) and Conscientiousness
( ¼ 0.33) are significant predictors of life hardship
patience. All the factors, except Openness, significantly
predict daily hassles patience.
Other antecedent traits. Avoidant attachment is nega-
tively correlated with interpersonal patience (r ¼0.26,
p < 0.01) and daily hassles patience (r ¼0.16, p < 0.01).
Anxious attachment is negatively correlated with all
three patience factors (r ¼0.22 to 0.27, p < 0.01).
Mindfulness is positively correlated with interpersonal
(r ¼ 0.27), daily hassles (r ¼ 0.34) and life hardship
patience (r ¼ 0.11). When mindfulness is added to a
regression equation after controlling for the Big Five, it
remains a predictor of only daily hassles patience,
¼ 0.22, p < 0.001; DR
2
¼ 0.04, F(1,374) ¼ 17.56,
p < 0.001. This supports the conceptualization of daily
hassles patience as enabling the person to endure
frustrations in the present moment.
Differential prediction of well-being outcomes by
patience factors
To assess discriminant validity of the three factors of
the 3-FPS, the well-being outcomes (life satisfaction,
hope, self-esteem, depression, and loneliness) were
regressed on the three factors in a series of five
regressions to test a priori expectations of differential
prediction (Table 4). Looking first at loneliness,
interpersonal patience was hypothesized as its primary
predictor since patience with others is integral to
maintaining relationships; interpersonal patience was
the only significant predictor of loneliness ( ¼0.15,
Item 1
Item 4
Item 7
Item 9
Item 2
Item 11
Item 5
Item 8
Item 3
Item 6
Item 10
1.00
1.00
1.00
1.10
–0.66
0.61
0.78
1.09
0.98
–0.77
0.82
Interpersonal
patience
Life hardships
patience
Daily hassles
patience
0.37
0.21
0.23
0.35
0.23
0.58
0.43
1.03
0.48
0.58
0.60
0.74
0.49
0.88
0.57
0.63
0.34
Figure 2. Parameter estimates from confirmatory factor analysis of final measurement model of the 3-FPS (first item loading on
each factor set constant to 1).
Table 2. Internal reliability, means, and SDs of the 3-FPS.
Cronbach’s alpha Test–retest reliability Mean SD
Patience factor Study 1 Study 2 Study 1 Study 2 Study 1 Study 2 Study 1 Study 2
Interpersonal patience 0.80 0.76 0.74 0.73 3.53 3.56 0.73 0.71
Life hardships patience 0.70 0.76 0.49 0.55 3.19 3.21 0.76 0.79
Daily hassles patience 0.66 0.58 0.75 0.69 3.24 3.32 0.86 0.79
Note: Test–retest reliability is over two weeks for Study 1 and eight weeks for Study 2.
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p ¼ 0.01). Second, we expected life hardship patience to
influence hope because of its role in creating a positive
future outlook. Life hardship patience predicted hope
( ¼0.22, p < 0.001), as did interpersonal patience
( ¼ 0.25, p < 0.001). Next, only interpersonal ( ¼ 0.13,
p ¼ 0.03) and daily hassles patience ( ¼ 0.13, p ¼ 0.02)
were significant predictors of life satisfaction, which
asks about satisfaction ‘so far’ in life. Interpersonal
patience was the strongest predictor of self-esteem
( ¼ 0.18, p ¼ 0.002). Finally, daily hassles patience was
the primary predictor of depression ( ¼0.19,
p ¼ 0.002).
Assertiveness and patience
In the prior discussion of the possible negative
outcomes of patience, assertiveness was presented as
a trait that may be lacking in individuals with ‘too
much’ patience. This was not supported. The zero-
order correlations between all three patience factors
and assertiveness were not significantly different
from zero at the p < 0.05 level (r ¼0.06, 0.04,
and 0.02), which indicates independence.
Moreover, a scatter plot of the data did not show
any curvilinear effects.
Discussion
Study 1 fulfilled its expressed research goals. A new
3-factor survey was created to measure interpersonal,
life hardship, and daily hassles patience. Considerable
support of construct validity was garnered under John
and Soto’s (2007) five forms of evidence. Looking at
content validity, the 3-FPS is face valid, and its items
are representative of trait patience as expressed across
different domains. Structural validity is supported by
good indicators of fit from the confirmatory factor
analyses. The 3-FPS demonstrates both internal reli-
ability and test–retest reliability. Evidence of external
validity both convergent and discriminant is
copious; the 3-FPS relates to other personality traits
and well-being outcomes in theoretically expected
ways.
Study 2: Patience, goal pursuit, and well-being
The main purpose of Study 2 is to examine patience
and well-being in the context of goal pursuit and
achievement. Traditionally, researchers examining goal
striving have tied certain goal characteristics and
content to both hedonic and eudaimonic well-being
(McGregor & Little, 1998). For example, goal achieve-
ment increases positive affect and hedonic happiness
Table 3. Regression of patience on the Big Five.
Patience factor
Beta
Personality
variable
Total
patience score
Beta
Regression 1:
interpersonal
patience
Regression 2:
daily hassles
patience
Regression 3:
daily hassles
patience
Extraversion 0.13** 0.11** 0.08 0.11*
Neuroticism 0.29** 0.17** 0.29** 0.22**
Agreeableness 0.33** 0.48** 0.07 0.18**
Conscientiousness 0.14** 0.09* 0.13* 0.12*
Openness 0.16** 0.19** 0.09 0.07
R 0.62 0.66 0.40 0.40
Note: *p < 0.05, **p < 0.01.
Table 4. Regressions predicting well-being indicators from three patience factors.
Well-being indicator
Beta
Patience factor
Regression 1:
loneliness
Regression 2:
hope
Regression 3:
life satisfaction
Regression 4:
self-esteem
Regression 5:
depression
Interpersonal patience 0.15* 0.25** 0.13* 0.18** 0.00
Life hardships patience 0.03 0.22** 0.08 0.13* 0.04
Daily hassles patience 0.08 0.06 0.13* 0.07 0.19**
R 0.22 0.37 0.27 0.31 0.20
Note: *p < 0.05, **p < 0.01.
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(Ryan & Deci, 2001). Study 2 elicits ten goals from
participants and then tracks goal achievement and
patience on those goals across time. It is hypothesized
that (a) patient people exert more effort in pursuit of
their goals and (b) pursue them with more patience
than less patient people. Previous research has shown
that goal progress across the course of the quarter
predicts well-being measured at the end of the quarter
and that satisfaction with goal progress directly affects
well-being (Sheldon & Elliot, 1999). Similarly, we
theorize that patience enacted on goals will increase
goal progress/satisfaction, which will then increase
well-being.
Study 2 also serves to provide additional evidence
of construct validity for patience and the 3-FPS.
Reliability and validity indices will replicate Study 1
results, and multimethod assessment with informant
reports will check for social-desirability effects.
Method
Participants
Participants were 259 (179 females, 58 males, 11
unknown) UC Davis undergraduates recruited through
the department subject pool. They received course
credit as remuneration. Data on ethnicity were not
collected in order to shorten the length of the surveys.
However, participants are assumed to match the
general UCD psychology subject pool, which is gen-
erally 40–45% Asian American, 40–45% Caucasian,
8–12% Hispanic, and 2–12% Other Ethnicity.
Design
Data were collected five times across the 10-week
academic quarter (at weeks 2, 4, 6, 8, and 10). At Times
1 and 5, participants completed a personal projects
assessment, as well as measures of patience, personality
traits, and well-being variables. At Times 2, 3, and 4,
participants completed only the personal projects
assessment and well-being measures.
Measures
Patience self-report measure administered at Times 1
and 5
3-Factor Patience Scale. See description under Study 1
Measures.
Personal projects measures
Self-reported projects. Little’s (1983) personal projects
assessment method was utilized to measure partici-
pants’ goals. Personal projects are a desirable unit to
analyze goals, as they are inherently temporal and
interactive (Palys & Little, 1983). At Time 1,
participants were given a definition of a personal
project and asked to list 10 projects they were working
on over the course of the academic quarter (e.g. ‘lose
ten pounds’ or ‘complete my thesis’).
Project ratings at Times 1–5. Participants were asked to
rate their projects on a number of dimensions. Using
the previous two weeks as the criterion, they were
asked at each time point to rate how much patience
each project required and their patience in the pursuit
of each project (‘This project requires me to be patient
as I strive for it’ and ‘I am patient in my pursuit of this
project’ 0 ¼ strongly disagree;10¼ strongly agree).
They were also asked to rate each project on variables
hypothesized to relate to patience, well-being, and goal
achievement. These variables included commitment,
control, effort, difficulty, obstacles, time adequacy,
progress, expected outcome, satisfaction, competence,
expected time frame, meaningfulness, and sanctifica-
tion. The collection of personal projects over time
provides a unique platform to view the effects of
patience on multiple levels of analysis.
Well-being measures administered at all time points
CES-D. Participants took the CES-D (see description
under Study 1 Measures) to report depressive symp-
toms for the past two weeks.
Satisfaction with Life Scale. See description under
Study 1 Measures.
Positive and Negative Affect Schedule. The PANAS
(Watson, Clark, & Tellegen, 1988) was used to assess
the extent to which subjects felt positive (i.e. interested,
enthusiastic, determined, excited, inspired, alert, active,
strong, proud, attentive) and negative (i.e. hostile,
irritable, guilty, ashamed, nervous, jittery, distressed,
upset, afraid, scared) emotions over the past two weeks
on a 1 ( very slightly or not at all)to5(extremely) scale.
The PANAS was revised to include more low-arousal
positive affect terms (e.g. calm, serene).
Personality measures administered at Times 1 and 5
BFI and self-regulation. See descriptions under Study 1
Measures.
Procedure
Participants were asked to fill out the Time 1 project
assessment measure online during the second week of
the academic quarter. Every two weeks (i.e. during
weeks 4, 6, and 8 of the quarter), they rated their 10
original projects on the rating dimensions and com-
pleted well-being questionnaires. During week 10 of
the quarter, participants completed the final survey
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packet, comprising project ratings, well-being mea-
sures, and personality questionnaires.
Informant reports. At Time 1, participants were asked
to provide the names and email addresses of three
close-others, including, but not limited to, ‘a best
friend, significant other, person who sees you daily,
and/or family member’. Informants were contacted via
e-mail and asked to fill out a survey about the
participant. The informant survey contained the same
personality and well-being measures taken by the
participant with the questions reformatted to reference
the participant.
Of the 857 informants asked to complete the
survey, 533 completed a report about the participant.
Of participants, 94% had at least one informant report
and 75% had two or more informant reports. The
informants were mostly friends (46%), but there were
also a substantial number of parents (23%), siblings
(16%), and significant others (10%).
Results
Construct validation for the 3-FPS
To provide evidence of generalizability, scale statistics
for the 3-FPS were calculated with Study 2 data to
replicate findings from Study 1. As can be seen in
Table 2, current data yielded analogous descriptive
statistics, internal reliability, and test–retest reliability
coefficients. Life hardship patience had a lower test–
retest correlation than the other factors in both studies.
Rather than instability in the measurement instrument,
we suspected that life hardship patience judgments are
more susceptible to state influences than other types of
patience in our college student samples. Additionally,
Cronbach’s alpha was slightly lower than typical cut-
off norms for daily hassles patience ( ¼ 0.58), but this
low alpha was not too alarming considering that the
factor contains just three items. Moreover, the factor
structure developed in Study 1 was replicated with the
new data, and the fit statistics for Study 2 show
good fit: RMSEA ¼ 0.046, CFI ¼ 0.98, and
V
2
(41, N ¼ 260) ¼ 62.67, p < 0.001. Factor loadings
and other parameter estimates closely resembled those
produced in the original sample.
Informant reports: Evidence of construct validity and
social-desirability check
Informant reports were used to formulate a multi-
method assessment matrix for patience. Beyond the
benefit of adding non-self-report data to the under-
standing of a trait (Vazire, 2005), informant reports are
a preferred way to check social desirability biases that
may appear in self-report measurement of positive
traits (Piedmont, McCrae, Riemann, & Angleitner,
2000).
Factor structure of informant ratings of patience. The
factor structure of the 3-FPS was replicated. Factor
loadings for informant reports were congruent to
previous samples, and the new fit statistics mirror
previous indices, denoting good/fair fit:
RMSEA ¼ 0.061, CFI ¼ 0.98, and V
2
(41,
N ¼ 533) ¼ 121.21, p < 0.001.
Consensus. Consensus among informants, or infor-
mant-informant agreement, was calculated from the
first two informant ratings collected of participants
who had more than one informant (N ¼ 192). As can
be seen in Table 5, consensus correlations were 0.24,
0.16, and 0.22 for daily hassles, life hardship, and
interpersonal patience, respectively. Informants varied
in their level of agreement depending on the person-
ality trait of interest. Consensus correlations on
patience were similar to those for low arousal positive
affect (r ¼ 0.21) and depression (r ¼ 0.24).
Informant–participant agreement correlations.
Agreement between participants’ self-reports and
informant ratings was examined. The informant rat-
ings for each participant were averaged to create a
mean informant rating score per participant for each
variable. These mean scores were correlated with the
participants’ self-reported scores (N ¼ 243), such that
each participant counted as a case in the agreement
calculations. Informant–participant agreement corre-
lations for patience and related variables can be viewed
in Table 5. Agreement was lowest for life hardship
patience and highest for interpersonal patience, which
Table 5. Participant self-report and informant rating
correlations.
Consensus
informant–
informant
agreement r
(N ¼ 192)
Mean
informant–
participant
agreement r
(N ¼243)
3-FPS
Daily hassles 0.24** 0.25**
Life hardship 0.16* 0.21**
Interpersonal 0.22** 0.35**
Other traits
Self-regulation 0.39** 0.36**
Well-being variables
Life satisfaction 0.28** 0.43**
Depression 0.24** 0.28**
Affect
Positive low arousal 0.21** 0.27**
Positive high arousal 0.29** 0.31**
Negative 0.24** 0.32**
Note: *p < 0.05, **p < 0.01.
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may reflect the fact that information relevant to
interpersonal patience is highly available to others.
Social desirability. Participants generally rated them-
selves as lower in patience ( M ¼ 3.40, standard devia-
tion (SD) ¼ 0.58) than they were rated by their
informants (M ¼ 3.68, SD ¼ 0.64), F(1, 790) ¼ 36.14,
p < 0.001. This self-diminishment bias is most likely due
to the informants’ limited accessibility to the partici-
pants’ internal negative emotionality (the expression of
which is often regulated), rather than an actual depres-
sion of participant self-ratings from a higher ‘true
score’.
Correlation between patience and well-being in infor-
mants. Informant ratings of participant patience were
correlated with informant ratings of participant life
satisfaction and depression (Table 6), and these corre-
lations were of equal or greater magnitude than those
observed in participant self-reports.
Patient people exert more patience and effort in
pursuit of their goals
The first step in looking at patience and goal pursuit
was to empirically demonstrate that people high in
trait patience pursue goals with more patience and
effort than people low in patience. This provides
evidence of substantive validity and shows that patient
people are not passively disengaged from goals.
We examined correlations between trait patience,
goal patience, and goal effort. Moderate correlations
were found between patience enacted on goals and
interpersonal patience (r ¼ 0.31, p < 0.01) as well as life
hardship patience (r ¼ 0.24, p < 0.01). There was only a
small correlation between daily hassles patience and
patience enacted on goals (r ¼ 0.14, p < 0.05). This
reflects the goal directed aspects of interpersonal and
life hardship patience versus the more reactive aspects
of daily hassles patience. A similar pattern of results
was found for effort correlated with interpersonal
(r ¼ 0.35, p < 0.01), life hardship (r ¼ 0.17, p < 0.01),
and daily hassles (r ¼ 0.13, p < 0.05) patience.
Likewise, we examined if trait patience predicts the
amount of effort enacted on a goal after controlling for
basic personality. In Step 1 of a hierarchical linear
regression predicting effort enacted on individual
goals, the Big Five accounted for 20% of the variance
in goal effort. At Step 2, trait patience (measured at
Time 1) was entered into the equation. Interpersonal
patience accounted for significant variance in predict-
ing goal effort after controlling for the Big Five,
¼ 0.27, DR
2
¼ 0.05, F(1, 255) ¼ 14.734, p < 0.001.
Neither life hardship nor daily hassles patience
accounted for significant variance in predicting effort
after controlling for the Big Five.
Patient people report higher goal achievement
satisfaction
Trait patience also predicts goal progress and goal
achievement satisfaction. We found moderate zero-
order correlations between interpersonal patience and
goal progress (r ¼ 0.30, p < 0.01) as well as goal
achievement satisfaction (r ¼ 0.34, p < 0.01; averaged
across goals and time). Correlations with goal progress
and satisfaction were also found for life hardship
patience (r ¼ 0.23, 0.28, p < 0.01). Small correlations
with goal progress and satisfaction were found for
daily hassles patience (r ¼ 0.13, 0.15, p < 0.05).
We also examined if trait patience predicts goal
progress and goal achievement satisfaction after con-
trolling for basic personality. In Step 1 of a hierarchical
linear regression predicting goal progress, the Big Five
accounted for 17% of the variance in progress. At Step
2, trait patience (measured at Time 1) was entered into
the equation. Interpersonal patience accounted for
significant variance in predicting goal progress after
controlling for the Big Five, ¼ 0.23, DR
2
¼ 0.03, F(1,
255) ¼ 9.554, p ¼ 0.002. Neither life hardship nor daily
hassles patience accounted for significant variance in
predicting progress after controlling for the Big Five.
Similarly, in Step 1 of a hierarchical linear regression
predicting goal achievement satisfaction, the Big Five
accounted for 20% of the variance in satisfaction. At
Table 6. Informant and participant correlations between patience factors and life satisfaction/
depression.
Informants
a
Participants
b
Life satisfaction Depression Life satisfaction Depression
Daily hassles patience 0.34** 0.25** 0.17** 0.13*
Life hardship patience 0.35** 0.27** 0.28** 0.27**
Interpersonal patience 0.40** 0.34** 0.26** 0.17**
Notes:
a
Individual informant ratings, N ¼ 533.
b
N ¼ 259.
*p < 0.05, **p < 0.01.
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Step 2, trait patience was entered into the equation.
Interpersonal patience accounted for significant vari-
ance in predicting goal satisfaction after controlling for
the Big Five, ¼ 0.22, DR
2
¼ 0.03, F(1, 255) ¼ 9.691,
p ¼ 0.002. Neither life hardship nor daily hassles
patience accounted for significant variance in predict-
ing satisfaction after controlling for the Big Five.
Goal satisfaction mediates the relation between
patience and well-being over time
To test the hypothesis that increased goal achievement
satisfaction mediates the effect of trait patience at Time
1 on life satisfaction measured at Time 5, nonpara-
metric bootstrapping analyses to compare indirect
effects (Preacher & Hayes, 2008) were performed
with a 99% bias corrected confidence interval (CI).
In bootstrapping, mediation is significant if the CIs do
not include zero. Results based on 1000 bootstrapped
samples indicated that there was a significant indirect
effect for goal satisfaction across time as a mediator of
the relation between patience at Time 1 and life
satisfaction at Time 5 (b ¼ 0.30, lower 99%
CI ¼ 0.1154, upper 99% CI ¼ 0.5057). In the mediation
model, patience predicted increased goal satisfaction
(b ¼ 0.80, standard error (SE) ¼ 0.13, p < 0.001) and
goal satisfaction predicted increased life satisfaction
(b ¼ 0.38, SE ¼ 0.07, p < 0.001).The initial direct effect
from patience to life satisfaction (b ¼ 0.50, SE ¼ 0.15,
p < 0.001) was reduced to a non-significant direct after
including goal achievement satisfaction in the regres-
sion equation (b ¼ 0.20, SE ¼ 0.15, p ¼ 0.192). Thus, we
can say that goal satisfaction nearly fully mediates the
relation between patience at Time 1 and life satisfac-
tion at Time 5.
Moderating effects of goal difficulty, obstacles, and
patience required
Patience was hypothesized to have a larger effect on
goal satisfaction when goals were more difficult and
obstacle-ridden, because patience is most essential
when people face frustrations. A hierarchical multiple
regression was run to test moderation of the relation
between patience enacted and goal satisfaction by goal
difficulty. In the first step, standardized values of goal
satisfaction (ratings entered as specific goals averaged
across time alone) were regressed on patience enacted
and goal difficulty. Both variables significantly pre-
dicted goal satisfaction (Patience Enacted: ¼ 0.51,
p < 0.001; Difficulty: ¼0.40, p < 0.001) and
accounted for 41% of the variance in goal satisfaction
scores (R ¼
0.64, p < 0.001). In the second step of the
regression, the interaction term of Patience
enacted Difficulty was significant, ¼ 0.10,
DR
2
¼ 0.01, F(1, 2,359) ¼ 40.90, p < 0.001. The effect
of patience on goal satisfaction is larger for difficult
goals. When goals are one SD above the mean on
difficulty, moving from low to high patience predicts a
1.18 SD increase in goal satisfaction, but when goals
are one SD below the mean on difficulty, moving from
low to high patience predicts only a 0.85 SD increase
in satisfaction. A similar pattern of results is seen for
Patience enacted Obstacles, ¼ 0.09, DR
2
¼ 0.01,
F(1, 2,359) ¼ 29.01, p < 0.001, and Patience
enacted Patience required, ¼ 0.18, DR
2
¼ 0.03, F(1,
2,359) ¼ 118.65, p < 0.001.
Discussion
Participant and informant data afforded further evi-
dence of construct validity of the 3-FPS. Informant
reports supported scale generalizability by demonstrat-
ing internal reliability and stable factor structure of the
3-FPS across multiple assessment methods.
Informant–participant agreement fell within the range
of agreement scores observed for other traits, and the
relation between patience and well-being was repli-
cated. Moreover, patient people, as measured by
3-FPS, effortfully pursued their personal projects and
enacted more patience as they worked toward their
goals. Patience facilitates goal pursuit and satisfaction
to improve well-being, and patience is especially crucial
for well-being when people are facing difficulties and
obstacles.
Study 3: Patience intervention increases well-being
The function of Study 3 is to examine if a patience
training intervention can increase trait patience and
well-being. The formulation of patience as a character
strength connotes that it is a somewhat malleable trait
that can be increased by specific practices or cognitive
strategies, and increases in the virtue should enhance
well-being. The experimental nature of Study 3 allows
for testing causality, similar to research designs that
have been utilized to examine the well-being effects of
other virtues (e.g. gratitude journaling in Emmons &
McCullough, 2003)
In this study, a patience training intervention was
developed to increase trait patience. Presented as a
stress reduction study, activities included group dis-
cussions, individual activities, and guided meditation.
This study aimed to test two hypotheses: (a) the
patience training program increases patience, and (b)
the patience training program increases well-being.
Method
Participants
Participants were 71 (61 females, 10 males) UC Davis
undergraduates recruited through the department
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subject pool. They received course credit for remuner-
ation. Participants were primarily Caucasian (40%)
and Asian American (34%), though other ethnic
minority groups were also represented (11%
Hispanic, 7% Middle Eastern, 3% African American,
4% other). Participants were 18–27 years old, with an
average age of 20.6 years (SD ¼ 1.8).
Measures
3-Factor Patience Scale. See description under Study 1
Measures.
Satisfaction with Life Scale. See description under
Study 1 Measures.
CES-D. Participants took the CES-D (see description
under Study 1 Measures) to report depressive symp-
toms for the past week.
PANAS. The PANAS (Watson, Clark, & Tellegen,
1988) was used to assess the extent to which subjects
felt positive (e.g. interested, enthusiastic, determined)
and negative (e.g. hostile, irritable, guilty) emotions
over the past week on a 1 (very slightly or not at all)to5
(extremely) scale.
Subjective Happiness Scale. Lyubomirsky and
Lepper’s (1999) four items, rated on a 7-point Likert
scale, were used to measure participant happiness. For
example, one items reads, ‘In general, I consider
myself ...not a very happy person (1) to a very happy
person (7)’.
Gratitude Questionnaire. The GQ-6 (McCullough,
Emmons, & Tsang, 2002) is a 6-item measure of
gratitude with items such as ‘If I had to list everything
that I feel grateful for, it would be a very long list’
rated on a 1 (strongly disagree)to7(strongly agree)
scale.
Emotion regulation. Gross and John’s (2003)
10-item Emotion Regulation Questionnaire assesses
individual differences in utilizing the two emotion
regulation strategies of cognitive reappraisal and
expressive suppression (1 ¼ strongly disagree;
7 ¼ strongly agree).
MAAS. See descriptions under Study 1 Measures.
Procedure
Upon agreement to participate in the study, which was
presented as a stress and personality study, partici-
pants attended a 45-minute introductory session and
completed the study measures. They were then ran-
domly assigned to the experimental or control
condition.
Participants in the experimental condition (i.e. the
patience training intervention) signed up for four
training sessions to be held across the following two
weeks. After the last training session, participants were
instructed to complete an online survey, retaking the
well-being and personality measures from Time 1.
Participants in the control group took the Time 1
survey at the initial training session and were sent a
link for the Time 2 survey when it was sent to the
experimental group. One month following the second
survey, all participants were e-mailed a link for a third
survey, also containing the original measures.
Experimental group patience training sessions. The
patience training was administered by the experimenter
to groups of three to six participants over four
30-minute sessions. The training consisted of teaching
and activity components, and it was formulated by
drawing upon interventions and methods utilized in
prior studies, specifically deriving insight from the
meditation, Type A, self-control, CBT, and character
strengths literatures.
Didactic component. The first session was focused on
increasing participants’ awareness of their positive and
negative emotions, as well as the precipitating causes
and triggers of those emotions. Meditative practice was
also introduced and discussed in the first session. The
second session focused on emotion regulatory strate-
gies, both preventive and reactionary. The third and
fourth sessions were focused on coping with interper-
sonal stress and fostering a sense of empathy and
compassion. Participants reflected upon past situations
in which they were frustrated with another person and
answered questions meant to encourage perspective-
taking. Participants also completed a reframing/reap-
praisal exercise. They were asked to consider a variety
of frustrating situations and write their typical negative
thoughts before writing a reappraisal of the situation.
For instance, participants were given the following
scenario,
You are catching a new release at the movies. The person
behind you keeps kicking your chair and chewing her
popcorn really loudly. As the movie progresses, she
proceeds to answer her phone twice,
and asked to describe their typical negative response
(e.g. People are so annoying! Can’t they understand
that I’m trying to watch a movie?’), as well as
the reframe (e.g. I’ve probably accidently kicked the
chair in front of me before, too, so maybe it was an
accident’).
Guided meditation. Each session contained a 10–15-
minute guided meditation. Meditations began with
instructions for participants to attend to their breath-
ing and posture in a non-judgmental manner. During
the first two training sessions, the meditation was
focused on replacing negative emotions with positive
emotions. The third and fourth sessions included a
loving-kindness meditation. Loving-kindness
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meditation involves directing warm and positive emo-
tions toward other people, beginning with the self and
extending gradually outward (i.e. to a benefactor, good
friend, neutral person, enemy, all beings of the world;
Carson et al., 2005; Fredrickson, Cohn, Coffery, Pek,
& Finkel, 2008; Kristeller & Johnson, 2005). The
loving-kindness meditation was included due to the
highly interpersonal nature of patience.
Results
One-way analyses of variance indicate there were no
differences between the experimental and control
groups at Time 1 on all study variables (at p < 0.05).
Effect of patience training on Time 2 variables
controlling for Time 1 scores
Patience training increases trait patience. Interpersonal
patience at Time 2 was regressed on interpersonal
patience at Time 1 and accounted for 54% of the
variance in Time 2 patience scores ( ¼ 0.733,
p < 0.001). At Step 2, treatment group (dummy
coded: 0 ¼ control, 1 ¼ experimental) accounted for
significant variance in Time 2 scores, ¼ 0.21,
DR
2
¼ 0.05, F(1, 54) ¼ 5.65, p ¼ 0.02. Participation in
the treatment intervention predicted higher interper-
sonal patience scores at Time 2, controlling for Time 1
patience. Treatment did not predict Time 2 life
hardship patience, ¼ 0.08, DR
2
¼ 0.01, F(1,
54) ¼ 0.54, p ¼ 0.47, nor did it predict daily hassles
patience, ¼ 0.16, DR
2
¼ 0.02, F(1, 54) ¼ 2.70, p ¼ 0.11.
Patience training predicts changes in positive, but not
negative, affect. Time 2 positive affect was regressed on
Time 1 positive affect, which accounted for 48% of the
variance in Time 2 scores ( ¼ 0.69, p < 0.001). At Step
2, treatment group accounted for significant variance,
¼ 0.20, DR
2
¼ 0.04, F(1, 54) ¼ 4.37, p ¼ 0.04, such
that participation in the intervention predicted higher
positive affect at Time 2. Treatment group did not
predict Time 2 negative affect controlling for Time 1
negative affect, ¼0.02, DR
2
< 0.001, F(1, 54) ¼ 0.02,
p ¼ 0.88.
Patience training decreases depression. Time 2 depres-
sion was regressed on Time 1 depression, which
accounted for 15% of the variance in Time 2 depres-
sion scores ( ¼ 0.39, p ¼ 0.004). At Step 2, treatment
group accounted for significant variance in scores,
¼0.26, DR
2
¼ 0.067, F(1, 54) ¼ 4.53, p ¼ 0.04, such
that patience training significantly decreased
depression.
Patience training and emotion regulation. Time 2
reappraisal was regressed on Time 1 reappraisal,
which accounted for 44% of the variance in Time 2
scores ( ¼ 0.66, p < 0.001). Treatment group then
accounted for marginally significant variance,
¼ 0.19, DR
2
¼ 0.03, F(1, 54) ¼ 3.41, p ¼ 0.07.
Patience training may increase participants’ use of
reappraisal. Treatment had no effect on suppression,
¼ 0.12, DR
2
¼ 0.012, F(1, 54) ¼ 1.03, p ¼ 0.31.
Life satisfaction, happiness, gratitude, and mindfulness.
Effects of the intervention at Time 2 did not reach
statistical significance for life satisfaction, ¼ 0.13,
DR
2
¼ 0.02, F(1, 54) ¼ 1.77, p ¼ 0.19; happiness,
¼ 0.10, DR
2
¼ 0.01, F(1, 54) ¼ 0.93, p ¼ 0.34; grati-
tude, ¼ 0.09, DR
2
¼ 0.01, F(1, 54) ¼ 0.86, p ¼ 0.36; or
mindfulness, ¼0.001, DR
2
< 0.001, F(1, 54) ¼ 0.001,
p ¼ 0.94.
Mediation effects
To test the hypothesis that increased patience mediates
the effect of the patience intervention on well-being
measured at Time 2, nonparametric bootstrapping
analyses to compare indirect effects with covariates
(Preacher & Hayes, 2008) were performed. Given the
small sample size and exploratory approach, 90% bias
corrected CIs were examined for the indirect effects
(mediation is significant if the CI does not include
zero).
There was partial support that interpersonal
patience mediates the effect of the patience training
intervention on T2 depression. Results based on 1000
bootstrapped samples indicated that there was a
significant indirect effect for interpersonal patience
(b ¼0.0193, lower 90% CI ¼0.0855, upper 90%
CI ¼0.0001), controlling for T1 depression.
Although there seems to be a significant indirect
effect, the path coefficients from the intervention to
interpersonal patience and from interpersonal patience
to depression do not reach statistical significance.
Given the small sample size of the mediation analysis
(N ¼ 55), this may be from a lack of power. Therefore,
we cannot conclude that interpersonal patience medi-
ates the effect of the patience training, but neither
should we reject the mediating effect. Instead, future
studies are needed to clarify the effect with more
statistical power.
Patience was also tested as a mediator of the
intervention’s effect on T2 positive affect. Results
based on 1000 bootstrapped samples indicated that
there was no significant indirect effect for interpersonal
patience (b ¼0.0193, lower 90% CI ¼0.0090, upper
90% CI ¼ 0.1235).
Effect of patience training on Time 3 variables
controlling for Time 1 scores
Time 3 trait patience. Interpersonal patience at Time 3
was regressed on interpersonal patience at Time 1,
which accounted for 39% of the variance in Time 3
276 S.A. Schnitker
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scores ( ¼ 0.622, p < 0.001). Treatment group was
then added to the equation, ¼ 0.230, R
2
change ¼ 0.049, F(1,42) ¼ 3.475, p ¼ 0.07. Even
though this effect only reaches marginal significance,
the beta coefficient is actually larger than the coeffi-
cient for predicting patience at Time 2 ( ¼ 0.21).
Although there was no effect of treatment on life
hardship patience at Time 2, there was a marginally
significant effect of treatment on life hardship patience
at Time 3. Life hardship patience at Time 1 accounted
for 27% of the variance in Time 3 life hardship patience
scores ( ¼ 0.52, p < 0.001). At Step 2, treatment group
accounted for a marginally significant variance not
explained by Time 1 scores, ¼ 0.24, DR
2
¼ 0.06, F(1,
42) ¼ 3.49, p ¼ 0.07. The beta is substantially larger
than was given for predicting life hardship patience at
Time 2 ( ¼ 0.08). This evidence of a delayed effect of
the patience training on life hardship patience fits a
priori hypotheses that participants will only become
aware of changes in their life hardship patience when
they have sufficient opportunity to observe themselves
reacting to long-term stressors over time.
There was no effect of the intervention on ratings
of daily hassles patience at Time 3, controlling for Time
1 scores, ¼0.05, DR
2
¼ 0.003, F(1, 42) ¼ 0.23,
p ¼ 0.64.
Time 3 depression. Time 3 depression was regressed on
Time 1 depression, which accounted for 6% of the
variance in Time 3 scores ( ¼ 0.25 p ¼ 0.11).
Treatment group was then added to the regression
equation, ¼0.19, DR
2
¼ 0.03, F(1, 42) ¼ 1.51,
p ¼ 0.23. Although treatment was not a significant
predictor of Time 3 depression, the beta coefficient was
similar in size to the beta for predicting Time 2
depression ( ¼0.26). Also, Time 1 depression scores
failed to reach statistical significance for predicting
depression at Time 3, which indicates a power issue.
Discussion
Study 3 provided evidence in partial support of its two
hypotheses. First, levels of trait patience were imme-
diately raised by the patience training intervention, and
results from a one month follow up point to a
sustained increase in patience (even though results do
not reach significance because of subject attrition).
These results support the conceptualization of patience
as a character strength that can be strengthened with
specific practices and activities.
The second hypothesis was also supported: the
patience training program increased well-being.
Controlling for Time 1 scores, participants who
partook in the patience training reported lower depres-
sion and higher positive affect at Time 2. Time 3 effects
were not significant, but the pattern of results suggests
they would most likely reach significance with higher
power.
As this was the first attempt to directly test if trait
patience could be increased through intentional activ-
ities, the patience training intervention was constructed
to minimize Type II errors by including a broad range
of exercises and activities. With initial results support-
ing the efficacy of the intervention, the separable
effects of the particular components (meditation,
cognitive reappraisal training, etc.) can now be exam-
ined to see which activities (or combination of them)
are driving the increase in patience. A limitation
resulting from the broad scope of the patience training
exercises is that we cannot exclusively attribute the
well-being effects of the manipulation to increased
patience. The intervention could also be enhancing
related psychological attributes, such as adaptive
coping, mindfulness, emotion regulation, and compas-
sion. However, the non-significant effects of the
intervention on Time 2 and Time 3 mindfulness and
gratitude suggest that the intervention is increasing
patience not other virtues!
While some may propose that our control condi-
tion does not rule out the possibility that our effects are
primarily the result of demand characteristics, such an
explanation is not supported by the pattern of results.
If demand characteristics were driving our effects, we
would expect the patience training to increase scores on
all of the well-being outcomes equally. Instead, we
observe that the training alleviates depression, but does
not significantly change life satisfaction or happiness.
Of these three well-being indicators, depression is least
susceptible to demand characteristics (with non-intui-
tive items such as ‘I did not feel like eating; my appetite
was poor’ and ‘My sleep was restless’), whereas the life
satisfaction and happiness are more susceptible to
demand characteristics (with transparent items such as
‘I am satisfied with my life’ and ‘I consider myself a
happy person’). Future studies with additional control
conditions (e.g. a control condition that teaches
organization skills) will further rule out this
alternative.
General discussion
Studies 1–3 fulfill their three research objectives: they
provide evidence of construct validity for the 3-FPS,
longitudinal and experimental data support that
patience increases well-being, and longitudinal data
support mediation of the patience–well-being relation
by goal pursuit and satisfaction.
Hedonic and eudaimonic well-being
Patience consistently relates to indicators of hedonic
well-being. Even after controlling for personality,
The Journal of Positive Psychology 277
Downloaded by [Sarah Schnitker] at 19:09 26 June 2012
patience predicts depression, life satisfaction, happi-
ness, and affect in both self- and informant-reports.
Goal achievement satisfaction mediates the relation
between patience and life satisfaction, supporting the
hypothesis that patience increases hedonic well-being
through increased goal achievement. Future studies
should be designed to collect more objective measures
of goal achievement (i.e. grades, relationship out-
comes, performance on a laboratory task, etc.) in
addition to participant ratings of goal achievement,
which are confounded with achievement satisfaction.
Objective measures will distinguish if patience increases
goal satisfaction because (a) it increases actual goal
progress/achievement, or (b) it changes the standards
by which people evaluate their goal progress and
construct satisfaction judgments.
Patience also relates to eudaimonic well-being.
Higher self-esteem in patient people points to higher
fulfillment of their competence and autonomy needs,
and lower loneliness points to the fulfillment of
relatedness needs. In addition, patient people score
higher on measures of other virtues that are a part of
the eudaimonic notion of ‘the good life’.
Patience does not lead to ill-being. In addition to
predicting well-being, patience does not predict nega-
tive outcomes, nor is there evidence that ‘too much’
patience is maladaptive. Instead, data support
Kierkegaard’s assertion that ‘patience is not resigna-
tion, passivity, or inaction; rather, it is the emergence
of freedom within the domain where necessity rules’
(Harned, 1997, p. 101). Study 1 results show that
patience and assertiveness are orthogonal, and Study 2
shows that patient people pursue goals with more
effort than less patient people.
State and trait patience
Patience was originally presented both as a trait and a
state. Although the trait formulation of the virtue
received the most attention in the three studies, a more
state-like form of patience was also examined: patience
enacted on specific goals (Study 2). Patience enacted
on goals was moderately correlated with trait patience,
but there was considerable unshared variance between
the two. Future studies could use a multileveling
modeling framework (of goals nested within persons)
to examine this state-trait connection.
Moderation of the patience–well-being relation
In Study 2, the difficulty of goals moderated the impact
of patience enacted on goal satisfaction, such that
patience played a more prominent role in predicting
satisfaction for goals that were difficult and obstacle-
ridden. Studies should further examine the moderating
role of situation characteristics. Patience, like
secondary control, may be most adaptive when the
person is unable to exert an influence on the environ-
ment and primary control efforts prove futile (Lam &
Zane, 2004). For example, Rothermund and
Brandtstadter (2003) found that switching to an
accommodative strategy of flexible goal adjustment
mitigated the negative impact of performance declines
on self-evaluations in older people.
On the flip side, are there certain situations in which
patience may lead to negative outcomes? Is patience a
maladaptive response when the person does have
control over the environment? If such situations are
found, the distinction between state and trait patience
may play into our understanding of the virtue. Moral
philosophers would argue that trait patience is always
beneficial and required for effective action, because only
when a person is dispositionally patient can he or she
clearly recognize when a situation requires an elevated
emotional response (low state patience) and when to
wait calmly (high state patience; Harned, 1997). From
this perspective, high state patience but not high trait
patience could lead to negative outcomes in particular
situations. Such an argument would apply to positive
traits and virtues more broadly, and research in this area
would deepen our understanding of person–situation
interactions.
Acknowledgments
I would like to express gratitude to Dr Robert Emmons for
his guidance in this research.
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Appendix: 3-Factor Patience Questionnaire
Instructions: For each of the statements below, please indicate how much the statement is like/unlike you.
1 ¼ Not like me at all
2 ¼ Unlike me
3 ¼ Neutral
4 ¼ Like me
5 ¼ Very much like me
___ 1. My friends would say I’m a very patient friend.
___ 2. I am able to wait-out tough times.
___ 3. Although they’re annoying, I don’t get too upset when stuck in traffic jams.
___ 4. I am patient with other people.
___ 5. I find it pretty easy to be patient with a difficult life problem or illness.
___ 6. In general waiting in lines does not bother me.
___ 7. I have trouble being patient with my close friends and family.
___ 8. I am patient during life hardships.
___ 9. When someone is having difficulty learning something new, I will be able to help them without getting frustrated or
annoyed.
___ 10. I get very annoyed at red lights.
___ 11. I find it easy to be patient with people.
Factor 1 Interpersonal patience: 1, 4, 7(r), 9, 11
Factor 2 Life hardship patience: 2, 5, 8
Factor 3 Daily hassles patience: 3, 6, 10(r)
280 S.A. Schnitker
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... Humility has been conceptualized as a virtue of caring (Gulliford & Roberts, 2018;McGrath, 2015), and patience a virtue of self-regulation (McGrath et al., 2018). Greater levels of humility and patience have each demonstrated associations with greater subjective and eudaimonic well-being (Jankowski et al., 2019;Schnitker, 2012;Schnitker et al., 2017). In fact, prior research has found that greater patience corresponded to greater happiness (Deng et al., 2020), and greater humility to greater happiness (Krause et al., 2016). ...
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... Humility and patience have each demonstrated positive associations with the tendency to utilize adaptive emotion regulation strategies (Schnitker, 2012;Schnitker et al., 2017). Emotion regulation is generally regarded as a multidimensional construct, with response modulation identified as one dimension and experiential avoidance a type of response modulation (Seligowski & Orcutt, 2015). ...
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Prior research on the religiousness/spirituality—well-being association has largely neglected the dimension of religious/spiritual exploration, and the recent trend examining virtues, religiousness/spirituality, and well-being has predominantly involved cross-sectional data. We expanded prior research by analyzing a longitudinal model consisting of three waves of data, approximately 6 months between waves, that explored the associations between experiential avoidance, humility, patience, religious/spiritual exploration, and distinct dimensions of well-being. We used joy as an indicator of the positive emotion dimension of subjective well-being, and presence of meaning in life as an indicator of eudaimonic well-being. We used a diverse sample of emerging religious leaders attending 18 graduate theological schools across North America (N = 283; Mage = 29.81; SD = 0.51; range = 19–62; 47.7% female; 61.8% White). We observed a negative influence for initial levels of exploration on later joy and meaning in life, when initial levels of experiential avoidance were high and humility was low. In contrast, we found a positive influence for initial levels of exploration on later joy and meaning in life, when initial levels of experiential avoidance remained high and humility was high. Initial levels of patience exhibited a positive influence on meaning in life 1 year later, indirectly via greater levels of exploration at time 2. Practical implications centered on providing opportunities for individuals to explore alternative beliefs, practices and experiences, and encouraging engagement in humility and patience self-cultivation practices, each of which could move them toward greater well-being.
... Sabrın bu iki kavramsallaştırması iç içe geçmiş durumdadır. Bu nedenle ikisini de anlamlı özellikler olarak değerlendirmek ve tanımlarken hem eğilim hem de durum olan sabrı dikkate almak gerekir (Schnitker, 2012). Çünkü sabır tek boyutlu bir kavram değildir. ...
... Geçerli ve güvenilir bir ölçme aracı geliştirmek için bazı kriterlere uyulması gerekmektedir. Öğretmen Sabır Ölçeği'nin (ÖSÖ) geliştirilmesi için öncelikle ilgili alan yazın tarandıktan sonra sabır konusunda yurt dışında geliştirilen iki ölçekten de (Dudley, 2003;Schnitker, 2012) yararlanılarak madde havuzu oluşturulmuştur. Madde havuzu oluşturulurken öğretmenlerin sabır düzeylerini belirlemeye ilişkin yarı yapılandırılmış bir görüşme formu hazırlanıp bu form kullanılarak eğitimin farklı kademelerinde görev yapan kişilerle (iki maarif müfettişi, iki okul müdürü, beş sınıf öğretmeni ve bir din kültürü ve ahlak bilgisi öğretmeni) görüşmeler yapılmıştır. ...
... ÖSÖ toplam puanı ile ölçeğin iki alt faktörü arasındaki ilişkiyi tespit etmek amacıyla yapılan korelâsyon analizine yönelik çıktılar ile aritmetik ortalama ve standart sapma değerleri Tablo 6'da gösterilmiştir. Alan yazında sabrın sınıflandırılmasıyla ilgili gerçekleştirilen çalışmalarda kavramı üç boyutta ele alan Mehrabian (1999) ve Schnitker (2012), sabrı bir kişilik özelliği olarak görmekle birlikte zamansal ve davranışsal yönünü de irdelemişlerdir. Sabrı, kararlı ve planlanmış bir eğilim olarak tanımlayan Mehrabian (1999) üç tip sabırdan bahsetmektedir: Kısa süreli sabır, günlük yaşamda sıkça karşılaşılan bekleme durumlarını; uzun süreli sabır, bireylerin herhangi bir zorlayıcı durum karşısındaki baş edebilme yeteneğini; kişiler arası sabır ise bireyin sosyal ilişkilerinde başkalarına gösterdiği tahammül durumunu ifade etmektedir. ...
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Bu araştırmanın amacı, öğretmenlerin sabır düzeylerini belirlemeye yönelik geçerli ve güvenilir bir ölçme aracı geliştirmektir. Araştırma iki farklı çalışma grubu ile gerçekleştirilmiştir. Araştırmanın birinci çalışma grubu Ordu ilinin Ünye ve Fatsa ilçelerinde basit tesadüfi örnekleme yöntemiyle belirlenen kamuya bağlı 20 ilkokulda görev yapan toplam 336 sınıf öğretmeninden; ikinci çalışma grubu ise Ordu ilinin Altınordu ilçesinde basit tesadüfi örnekleme yöntemiyle belirlenen kamuya bağlı 16 ilkokulda görev yapan toplam 257 sınıf öğretmeninden oluşturulmuştur. Veriler analiz edilirken geçerlik için açımlayıcı ve doğrulayıcı faktör analizi yapılmış, güvenirlik için Cronbach Alfa ve kompozit güvenirlik katsayıları ile madde toplam korelâsyonları hesaplanmıştır. Ölçek faktörlerinin birbirleriyle olan ilişkileri korelâsyon analiziyle test edilmiş ve ayrıca aritmetik ortalama ve standart sapma değerleri hesaplanmıştır. Araştırmanın sonucunda iki faktör altında toplanan 11 maddelik "Öğretmen Sabır Ölçeği" geliştirilmiştir. Ölçeğin faktörleri sırasıyla "Öğretim" ve "Etkileşim" olarak isimlendirilmiştir. Ölçekteki iki faktörün birlikte açıkladıkları toplam varyans %46.693'tür. Yapılan güvenirlik analizi sonucunda ölçeğin geneli için Cronbach Alfa katsayıları birinci ve ikinci çalışma gruplarında sırasıyla .81 ve .82 olarak hesaplanmıştır. Ölçeğin alt faktörleri için hesaplanan Cronbach Alfa ve kompozit güvenirlik katsayıları da genel olarak .70'in üzerindedir. DFA sonucunda elde edilen uyum iyiliği indeksleri modelin uyumlu olduğunu ortaya koymuştur (X 2 /sd=1.83, GFI=.95, AGFI=.92, CFI=.95, NFI=.91, TLI=.94, IFI=.95, RMSEA=.06, SRMR=.05, RMR=.02). Araştırmanın geçerlik ve güvenirlik analizlerinden elde edilen sonuçlar, ölçeğin Millî Eğitim Bakanlığına bağlı okullarda görev yapan öğretmenlerin sabır düzeylerinin belirlenmesinde kullanılabilecek geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir. Anahtar sözcükler: Öğretmen, sabır, öğretmen sabır ölçeği. ABSTRACT: The purpose of this research is to develop a valid and reliable measurement tool to determine the patience levels of teachers. The research was carried out with two different study groups. The first study group of the research consisted of 336 classroom teachers working in 20 public primary schools determined by simple random sampling method in Unye and Fatsa districts of Ordu province; the second study group consisted of 257 classroom teachers working in 16 public primary schools determined by simple random sampling method in Altınordu district of Ordu province. While analyzing the data, exploratory factor analysis and confirmatory factor analysis were performed for validity, and Cronbach Alpha and composite reliability coefficients and item total correlations were calculated for reliability. The relationships of the scale factors with each other were tested by correlation analysis and also the arithmetic mean and standard deviation values were calculated. "Teacher Patience Scale" consisting of 11 items under two factors was developed at the end of the study. The factors of the scale were named as "Teaching" and "Interaction" respectively. The total variance in the scale explained by two factors was 46.693%. As a result of the reliability analysis, the Cronbach's Alpha coefficients for the overall scale was calculated as .81 and .82 in the first and second study groups, respectively. The Cronbach Alpha and composite reliability coefficients calculated for the sub-factors of the scale were generally above .70. Goodness of fit indices obtained as a result of CFA revealed that the model was (X 2 /sd=1.83, GFI=.95, AGFI=.92, CFI=.95, NFI=.91, TLI=.94, IFI=.95, RMSEA=.06, SRMR=.05, RMR=.02). The results obtained from the validity and reliability analysis of the study suggested that the scale is a valid and reliable measurement tool that can be used to determine the patience levels of teachers working in schools affiliated with the Ministry of National Education. Keywords: Teacher, patience, teacher patience scale.
... Several empirical studies have demonstrated the potential relationships between caring ability factors (patience, knowing, and courage) and stress perception factors (perceived distress and coping). Regarding perceived distress, Schnitker (2012) found that interpersonal patience (responding to others' needs) was negatively correlated with depression. Knowing and courage also have been shown to have negative relationships with perceived distress (Coppetti et al., 2019). ...
... This means that patience has a significant effect on stress perception when controlling for its covariance with knowing and courage. However, The effects were the converse of our hypothesis and contradicted the findings of Schnitker (2012) and of Qodariah and Puspitasari (2016). Nonetheless, the different definitions of patience must be considered when interpreting the divergence. ...
... Thus, further empirical studies are necessary to understand the relationships among caring and other variables more rigorously. Second, the relationships established between patience of caring ability and two aspects of perceived stress through SEM failed to verify previous findings in Schnitker (2012) and contradicted the results of Qodariah and Puspitasari (2016), implying that future research should provide a more in-depth analysis of this relationship. Third, it is concerned that our interpretations toward the findings are limited by the unsatisfying RMSEA model fit index (in the CFA of CD-RISC, RMSEA = 0.07, in the mediating model, RMSEA = 0.09). ...
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While the literature has shown some positive effects of receiving social support, the benefits of offering support and helping others have also been emphasized recently. Based on this perspective, our research focuses on the effects of the ability to care for others on reducing stress perception. In addition, studies continue to suggest that factors comprising caring ability indispensably serve to build individual resilience. However, it is less clear how these factors contribute to individual resilience and relieve psychological stress. Thus, the present study aims to investigate the effect of caring ability on stress perception mediated by resilience at a factor level. A total of 295 Chinese graduate, undergraduate, and college students (221 females, 74 males; mean age = 21.67, SD = 1.91) completed the Caring Ability Inventory (CAI), the Connor-Davidson Resilience Scale (CD-RISC), and the Perceived Stress Scale-14 (PSS-14). A structural equation modeling (SEM) analysis with the maximum likelihood estimation procedure was used to examine the proposed model. Path coefficients indicated that knowing and courage in the CAI predicted less stress perception while patience in the CAI produced an opposite effect. A mediation analysis revealed that resilience successfully mediated the relationship between knowing as well as between courage and perceived stress. The results suggest that a higher degree of knowing and courage relate to a higher degree of resilience, which could reduce distressful feelings and enhance stress coping skills. Our findings provide specific insights into the roles of knowing, courage, and resilience in alleviating perceived stress and could inspire stress prevention or intervention practices in the future.
... Moreover, Harzer (2016) conducted an overview of several previous studies on character and well-being and found character strength to have a positive relationship with subjective and psychological well-being. Schnitker (2012) also found that patience has a positive relationship with well-being in emerging adults. Therefore, this research is the basis that character, meaning in life and well-being should be further studied in terms of their application in schools. ...
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Previous studies have revealed the benefits of character and meaning in life for individual well-being. However, little research has been conducted on elementary and junior high school teachers who teach students between the ages of 10-15 years (late childhood and early adolescence) in Indonesia. This study aims to explore teachers’ perceptions about character, meaning in life, and well-being. Interviews were conducted among 20 teachers. Our findings revealed that according to the teachers, the lessons and assessments in character education are well designed. However, coordination within the school community and with parents as well as specific roles between teachers and parents in building students’ character, meaning, and well-being needs to be improved. What teachers understood about meaning in life corresponds with the results of previous studies. Teachers’ understanding of well-being concept is still limited. They acknowledged family background, socioeconomic status, popularity, and academic achievement as important factors in students’ well-being. Keywords: character; meaning in life; well-being; teachers; children; adolescents
... In particular, the five domains of PTG identified by Tedeschi and Calhoun (1996)-improved relations with others, identification of new possibilities for one's life, increased personal strength, spiritual change, and enhanced appreciation of life-share substantial overlap with characteristics of specific character strengths (Jayawickreme, Infurna, et al., 2021;Linley & Joseph, 2004). For example, personal strength may overlap with the character strength of patience, which has been theoretically associated with increase in the wake of adversity (Schnitker, 2012). A number of theoretical perspectives have also posited that adversity may lead to positive changes in moral behavior (e.g., Staub & Vollhardt, 2008). ...
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Adversity has been assumed to foster positive personality change under certain conditions. In this article, we examine this assumption within the context of the three-tier personality framework integrating traits, characteristic adaptations, and narrative identity to provide a comprehensive understanding of personality growth. We first review findings on how adverse events affect personality on each of these three levels. Second, we summarize knowledge on event-based and person-based predictors of personality change in the face of adversity. Third, we specify affective, behavioral, and cognitive processes that explain personality change across levels of personality. Innovatively, our proposed process model addresses change at all three levels of personality, as well as similarities and differences in processes across the levels. We conclude by discussing unresolved issues, asking critical questions, and posing challenging hypotheses for testing this framework.
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