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Can perseverance of effort become maladaptive? Study addiction moderates the relationship between this component of grit and well-being among music academy students

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Grit, defined as perseverance and passion for long-term goals, is investigated as a predictor of academic success and well-being. This trait may have special importance for musicians’ functioning as their lives revolve around practice routines and mastering their craft for years. However, there is a growing recognition that extreme perseverance may be maladaptive in some cases. Persistent overinvolvement in goal-oriented activities is related to compulsive overworking, conceptualized within the behavioral addiction framework as work and study addiction. A previous study showed that study addiction is relatively highly prevalent among young musicians and has a clearly negative effect on their functioning. The aim of this study was to investigate the relationships between grit, study addiction, and psychosocial functioning among music academy students. It was hypothesized that perseverance of effort is related to well-being, grade point average (GPA), and study addiction, and that it becomes maladaptive for individuals addicted to studying. A cross-sectional correlational study was conducted among 213 music academy students in Poland. Perseverance of effort was positively related to GPA and study addiction. The relationships between perseverance of effort and self-rated general health, and between perseverance of effort and quality of life, were moderated by study addiction. The results suggest that grit may become maladaptive perseverance in the cases of individuals at risk of study addiction. Based on these findings, further investigations of grit among musicians, as well as further studies of the negative aspects of grit in general, are warranted. Implications for prevention and intervention programs are discussed.
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Citation: Czerwiński, S. K., Lawendowski, R., Kierzkowski, M., & Atroszko, P. A. (2022).
Can perseverance of effort become maladaptive? study addiction moderates the relationship
between this component of grit and well-being among music academy students. Musicae
Scientiae, 102986492210951. https://doi.org/10.1177/10298649221095135
Copyright © [2022] (SAGE Publisshing)
Can perseverance of effort become maladaptive? Study addiction moderates the
relationship between this component of grit and well-being among music academy
students
Stanisław K. Czerwiński, University of Gdańsk, Bażyńskiego 4, 80-309 Gdańsk, Poland, e-
mail: stanislaw.czerwinski@phdstud.ug.edu.pl, ORCID number: 0000-0002-4245-046X
Rafał Lawendowski, University of Gdańsk, Bażyńskiego 4, 80-309 Gdańsk, Poland, e-mail:
rafal.lawendowski@ug.edu.pl ORCID number: 0000-0002-7966-4293
Michał Kierzkowski, the Stanisław Moniuszko Academy of Music in Gdańsk, Łąkowa 1-2,
80-743, Gdańsk, Poland, e-mail: m.kierzkowski@amuz.gda.pl
Paweł A. Atroszko, University of Gdańsk, Bażyńskiego 4, 80-309 Gdańsk, Poland, e-mail:
p.atroszko@ug.edu.pl, ORCID number: 0000-0001-5707-3882
Corresponding author:
Stanisław Czerwiński
University of Gdańsk
Street address Bażyńskiego 4
80-309 Gdańsk
Poland
Phone: +48 512 578 260
E-mail: stanislaw.czerwinski@phdstud.ug.edu.pl
Ethics: The study was carried out in accordance with the Declaration of Helsinki. All gathered
data was anonymous, and participants were informed about all the proper details about the
study and their role in it, including that they can withdraw at any point. Attaining formal and
written informed consent was not regarded as necessary, as voluntary completion of the
questionnaires was regarded as providing consent, and no medical information was gathered.
Authors’ contribution: SKC assisted with literature search, study design and concept, data
collection, statistical analyses, data interpretation, generation of the initial draft of the
manuscript, manuscript preparation and editing, and final editing; RL assisted with literature
search, study design and concept, data interpretation, generation of the initial draft of the
manuscript, manuscript preparation and editing, and final editing; MK with study design, data
collection, and final editing; PAA assisted with literature search, study design and concept,
data interpretation, manuscript preparation and editing, and final editing. All authors have
approved the final manuscript.
Funding: This research received no specific grant from any funding agency in the public,
commercial, or not-for-profit sectors.
Acknowledgments: We would like to thank Wiktoria Braun, Bartłomiej Czarny, and
Aleksandra Leniec who helped in the realization of the study.
1
Can perseverance of effort become maladaptive? Study addiction moderates the
relationship between this component of grit and well-being among music academy
students
Abstract
Grit, defined as perseverance and passion for long-term goals, is investigated as a predictor of
academic success and well-being. This trait may have special importance for musicians'
functioning as their lives revolve around practice routines and mastering their craft for years.
However, there is a growing recognition that extreme perseverance may be maladaptive in
some cases. Persistent over-involvement in goal-oriented activities is related to compulsive
overworking, conceptualized within the behavioural addiction framework as work and study
addiction. A previous study showed that study addiction is relatively highly prevalent among
young musicians and has a clearly negative effect on their functioning. The aim of this study
was to investigate the relationships between grit, study addiction, and psychosocial
functioning among music academy students. It was hypothesized that perseverance of effort is
related to well-being, grade point average (GPA), and study addiction, and that it becomes
maladaptive for individuals addicted to studying. A cross-sectional correlational study was
conducted among 213 music academy students in Poland. Perseverance of effort was
positively related to GPA and study addiction. The relationships between perseverance of
effort and self-rated general health, and between perseverance of effort and quality of life,
were moderated by study addiction. The results suggest that grit may become maladaptive
perseverance in the cases of individuals at risk of study addiction. Based on these findings,
further investigations of grit among musicians, as well as further studies of the negative
aspects of grit in general, are warranted. Implications for prevention and intervention
programmes are discussed.
2
Keywords: academic performance, maladaptive perseverance, music education,
workaholism, work addiction
“If I don't practice one day, I know it; two days, the critics know it; three days, the public knows it.” Often
quoted by musicians, attributed to various authors.
There are no two words in the English language more harmful than ‘good job.’” Fletcher, in the movie
Whiplash.
Research on the predictors of academic success often points to the importance of skills
such as self-control and the ability to delay gratification (Mischel, 2015), perseverance,
curiosity (Tough, 2012), strong growth mindset and effort (Dweck, 2006), deliberate practice
and hours of training (Gladwell, 2011), and passion and grit (Duckworth, 2016). Hard and
sustained effort seems to be the basis of success in many areas of life, including arts education.
After all, achieving excellence in the musical arts, especially as a performer, entails regular
practice and determination in reaching a previously set goal.
A lack of tolerance for wasting time and procrastination, as well as thousands of hours
of deliberate practice (see Ericsson et al., 1993), are typically perceived as necessary
requirements for achieving success. However, goals that stimulate intense determination,
engagement, and perseverance can also have a darker side and provoke dangerous
psychological and health consequences. Recent years have seen the emergence of research
progressively revealing the differences between harmonious engagement in work or study,
leading to better results, engagement, health, and happiness (cf., Ascenso et al., 2017; Croom,
2015), and obsessive and compulsive engagement leading to burnout, lower productivity,
worsening of physical and mental health (cf., Kreutz et al., 2008; Kegelaers et al., 2020; Matei
et al., 2018; Matei & Ginsborg, 2020), a sense of meaninglessness, and a lack of satisfaction
3
with life (Vallerand et al., 2003). Cursory observations indicate that some of the people who
are convinced that only determination and hard work can bring them happiness in the form of
future success also believe that they can overcome any challenges and failures simply with
increased effort and perseverance. However, being overburdened with work or study can lead
to an addictive behavioural pattern, which is related to this set of beliefs (Atroszko, 2015;
Atroszko, Andreassen et al., 2015). Consequently, this pattern not only decreases the chances
of developing one's artistic potential but also threatens the health and sometimes even the life
of the musician (Gembris et al., 2018).
Grit
Grit was defined by Duckworth et al. (2007) as [p]erseverance and passion for long-
term goals (p. 1087), and has been shown to be a good predictor of academic success beyond
intelligence (Credé et al., 2017; Duckworth et al., 2010; Duckworth et al., 2007; Eskreis-
Winkler et al., 2014). This construct has been receiving increasing attention from educational
researchers worldwide to the point of gaining significant attention from the Departments of
Education of the USA and UK and inspiring the development of programmes aimed at
training grit (KIPP, 2020; UK Government, 2014, 2015; SRI International, 2018).
Grit has also been found to be related to a number of indicators of well-being, such as
(reduced) depression, life satisfaction, meaning in life, and positive affect across a range of
cultures (Credé et al., 2017; Datu, King, et al., 2018; Datu, Yuen, et al., 2018; Disabato et al.,
2018; Hill et al., 2014), as grittier individuals are more motivated and capable of pushing
through adversity on their paths to reach life goals, more successful in achieving their goals,
and more often engage in activities that give them a sense of purpose. Moreover, they report
better overall physical health because they have better skills for managing their health
(Sharkey et al., 2017). However, it is not always optimal to remain committed to a goal. A
4
series of experiments by Lucas et al. (2015) showed, for example, that individuals with higher
levels of grit were less likely to give up on tasks when facing certain failure, even when their
persistence came at the cost of a potential monetary loss. Another study found that the
relationship between grit and progress towards work goals changes over time, producing an
inverted U-shape (Khan et al., 2020).
It is important to note that, despite the undeniable popularity of the concept of grit
among social scientists, its conceptualization and measurement have raised many
controversies. The higher-order structure originally proposed, with two lower-order factors of
consistency of interest, representing passion for long-term goals, and perseverance of effort,
representing persistence in efforts to achieve long-term goals, has little empirical support
(Credé, 2018; Credé et al., 2017). Moreover, the majority of studies suggest that most of the
predictive power of grit comes from the perseverance of effort dimension and not consistency
of interest (Bowman et al., 2015; Credé et al., 2017; Datu et al., 2015; Disabato et al., 2018;
Goodman et al., 2016). For overviews of issues related to the concept and proposals for
refining it, see Credé (2018), Datu (2021), and Tynan (2021).
Perseverance and passion in the lives of musicians
From the beginning of their formal musical education, young adepts of the musical arts
are met with high standards (McPherson, 2005), psychological pressure (Gembris et al., 2018),
abusive teachers, unsupportive environments, social comparison, competition, disillusionment
after entering the profession, social isolation, identity foreclosure, burnout, injury,
perfectionism, and endless hours of practice (Pecen et al., 2016; 2018). Although many classical
musicians derive positive emotions from their music-making and find their profession
meaningful, they also risk negative consequences such as hearing loss, music performance
anxiety (MPA), and performance-related musculoskeletal disorders (Matei et al., 2018).
5
Sacrifice and self-discipline are needed in order to practise for the amount of time
required for an individual to become a professional musician (Jørgensen, 2009). Perfecting
one's skill set is a constant element of a musician's work, founded on sustained and hard
effort. Performing a composition at the highest level of skill involves realizing both short-
(see Hallam, 1997) and long-term goals based on regular, deliberate practice (Krampe &
Ericsson; 1996). Kemp (1996) stated that the most talented musicians were driven by a form
of motivation bordering on obsession. Other studies have shown that the highest-achieving
students exhibit long-term engagement in playing their instruments and practise a great deal
(McPherson & McCormick; 2000; Sloboda et al., 1996). As such, musicians’ high
perseverance of effort should be indicative of their higher chance of achieving academic and
professional success, as well as higher levels of well-being, due to their being more suited to
face the challenges inherent in the profession. According to young, pre-elite music graduates
interviewed by Pecen et al. (2018, p. 11), perseverance, resilience, commitment, and focus
were the qualities most vital to learning and teaching. One research participant summarised
their view as follows:
Practice, practice, practice. If you feel insecure, you just haven't practised hard
enough. . . . there's still that idea that somehow you have to practise all the time. If I
did what I thought was the “right” thing, I would practise all the time.
Although the components of grit, perseverance and passion seem to be very important
in the lives of musicians, grit has rarely been studied among musicians, to the authors'
knowledge; and even in the published literature it is rarely the main focus (Miksza & Tan,
2015; Miksza at el., 2016; Tan et al., 2021; Tan & Miksza, 2017a, 2017b). These studies have
shown that grit is positively associated with achievement motivation, flow, and practice
efficiency, and negatively associated with MPA. Moreover, perseverance of effort has been
shown to influence performance positively, and to facilitate progress and artistic growth
6
(Ericsson et al., 1993; Krampe & Ericsson, 1996). However, it can also have consequences
that are clearly negative. Large numbers of hours of practice, involving repetition, making
corrections, and striving for precision, corresponds with perfectionism, especially of a
dysfunctional nature (Butković et al., 2021; Stoeber & Eismann, 2007).
Study addiction
Study addiction has been conceptualized as a potential early form of, or precursor to,
work addiction (Atroszko, 2015; Atroszko, Andreassen et al., 2015). It has been defined by
analogy to a recently proposed general definition of work addiction as
characterized by a compulsion to study and preoccupation with study activities leading
to significant harm and distress of a functionally impairing nature to the individual
and/or other significantly relevant relationships (friends, family). The behaviour is
characterized by the loss of control over the studying activity and persists over a
significant period of time. This problematic study-related behaviour can have varying
intensity from mild to severe (Atroszko, Sawicki, et al., 2019, p. 326; for a definition
of work addiction see Atroszko, Demetrovics, et al., 2019, p. 9).
To date, the published data strongly support the notion that study addiction and work
addiction reflect the same pathological addictive process. They are both i) stable over time
and ii) longitudinally related (Atroszko et al., 2016a, b); and iii) evident from seven core
symptoms: salience, changes in mood, tolerance, withdrawal, conflict, relapse, and problems
(Atroszko, Andreassen et al., 2015). They are related to iv) more engagement in
studying/working and more time devoted to studying/working; v) high scores on measures of
neuroticism and conscientiousness; vi) lower levels of performance; and vii) impaired general
health, decreased quality of life, poor sleep, and higher levels of perceived stress. Finally, they
have similar prevalence rates in populations including high-school and undergraduate students
7
(Atroszko, 2018, 2019; Wróbel, 2020). Indeed 16% of young musicians were diagnosed as
suffering from study addiction in a recent study of music academy students (Lawendowski et
al., 2020) using the diagnostic criteria for behavioural addiction.
Work (Atroszko, 2019b; Atroszko, Demetrovics et al., 2019) and study addiction
(Atroszko, 2015; Atroszko & Lawendowski, 2020) are conceptualized as a process of coping
ineffectively with stress or other underlying problems. Specifically, compulsive engagement
in work-related activities may serve as means of avoiding negative emotions and personal
problems such as loneliness. Being immersed in study or work helps the individual to forget
about unresolved sources of stress and makes them feel they are being productive. This, in
turn, may create the illusion that one day these unattended problems will solve themselves
automatically when the individual achieves success in study or work. For example, congruent
with this model, social anxiety was found to mediate emotional stability/extraversion and
study addiction, which shows that particular personality traits may increase the risk of social
anxiety and that this, in turn, may affect study addiction as a means of coping with it
(Lawendowski et al., 2020). These results indicate that individual risk factors related to
personality and social anxiety, and related constructs such as MPA, should be included in
analyses of the specific effects of study addiction on psychosocial functioning.
Lawendowski et al.’s (2020) study also confirmed that an assessment based on the
common components of addiction is a valid measure of study addiction that can be used in
research involving the participation of music students. Study addiction was found to be
positively associated with learning engagement, at least to some extent; it was also positively
associated with other variables including low extraversion and high social anxiety, specific
aspects of studying (longer learning time and lower academic performance), and indicators of
decreased well-being (impaired general health, decreased quality of life and sleep quality, and
higher perceived stress). These findings are similar to those of Atroszko (2015), Atroszko and
8
Atroszko (2019), Atroszko, Andreassen et al. (2015), and Wróbel (2020) in suggesting that
study addiction is pathological. These and the results of other studies on study and work
addiction support the notion that intensive, prolonged over-involvement in work-related
activities may exact high costs in terms of health, quality of life, productivity, and social
relations (Atroszko, 2018, 2019; Griffiths et al., 2018). Recent analyses suggest that such
costs may pose a considerable challenge for health care on a global level (Atroszko,
Demetrovics et al., 2020), a systemic emphasis on studying/working and studying/working-
related goals being ever present in modern society. As such, individuals with a natural
inclination towards persevering in any efforts they undertake might spend more time studying
or working and are less likely to stop even when this is ineffective and harmful, which makes
them susceptible to study/work addiction.
Maladaptive perseverance
It is plausible that there exists a phenomenon akin to maladaptive perfectionism,
which occurs when an individual sets too high a standard for themselves, leading to decreased
well-being and other negative consequences (Enns et al., 2001; Hamachek, 1978). Similarly,
stubbornly pursuing an unobtainable goal or one that requires unhealthy habits could prove to
be harmful and might be considered maladaptive perseverance (Grant & Schwartz, 2011;
Niemiec, 2019; Smith et al., 2017). A substantial body of research on work addiction strongly
indicates that a relentless focus on productivity may lead to clinically significant harm (e.g.,
health problems, stress) and, paradoxically, a loss of productivity. It is possible that for
perseverant individuals who are addicted to studying, their perseverance becomes maladaptive
as it reinforces and exacerbates the harmfulness of their already unhealthy behaviours of
excess and unproductive studying. In consequence, the normally positive influence of
perseverance of effort on well-being and achievement might become negative.
9
It should be emphasized, however, that maladaptive perseverance and study/work
addiction are not the same constructs. While study/work addiction may be driven by the
realization of long-term goals, some study/work addicts may engage excessively in activities
regardless of their function in achieving these goals. In other words, ad hoc work- and study-
related tasks may serve the purpose of mood regulation just as well as those that are part of
greater ambitions. Maladaptive perseverance, by contrast, does not always lead to addictive
behaviour. For example, perseverance in a particular activity may cause health problems or
even loss of life, but the individual does not suffer typical addictive symptoms such as
withdrawal. Evidence to support a relationship between maladaptive perseverance and
addictive behaviour is currently limited, so more research is needed to explore the function of
perseverance in study/work addiction and the role of compulsion in perseverance.
Another construct that may seem similar to maladaptive perseverance is obsessive
passion, a component of the Dualistic Model of Passion (Vallerand et al., 2003; Vallerand,
2015). However, passion refers to strong engagement in a particular form of activity whereas
grit refers to general determination to stick to any long-term plan. Nevertheless it is plausible
that when individuals who score high on measures of perseverance of effort engage in
activities about which they are obsessively passionate, their perseverance may become
maladaptive.
The present study
The present study aimed to examine the relationships between measures of
perseverance of effort and variables representing the psychosocial functioning of music
academy students, and the moderating role of study addiction in these relationships. Based on
previous research and the theoretical frameworks described above, the following hypotheses
were tested:
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1. Perseverance of effort is related positively to GPA;
2. Perseverance of effort is related to indicators of well-being (i.e., negatively to
perceived stress and positively to general health, sleep quality, and quality of life);
3. Perseverance of effort is related positively to study addiction; and
4. Study addiction moderates the relationship between perseverance of effort and
perceived stress, general health, sleep quality, quality of life, and GPA such that, for
students scoring high on measures of study addiction, the relationship between
perseverance and i) perceived stress is positive (Hypothesis 4a), ii) general health,
sleep quality, and quality of life is negative (Hypothesis 4b); and iii) GPA is negative
(Hypothesis 4c).
The purpose of testing these hypotheses was to clarify the role of perseverance of effort,
based on the mechanism of pressing on towards ones goals regardless of cost, in predicting
well-being and academic performance (Bowman et al., 2015; Credé et al., 2017; Disabato et
al., 2018). However, to encompass the entirety of the original concept of grit, all analyses
were performed for both of its components, that is, passion as well as perseverance of effort.
The results of the analyses using the composite grit score are presented in the supplementary
materials.
A number of covariates were included in the analyses on the basis of previous
findings. Learning engagement was included because confounding may occur if it is not
controlled for (Lawendowski et al., 2019). There is a partial overlap between the components
of high time and effort involvement in learning engagement and study addiction, so they
should always be measured together and their mutual effects controlled for in the statistical
analyses (Atroszko & Atroszko, 2019). Otherwise, the negative effects of study addiction may
bias the results of research on learning engagement, and the positive effects of learning
engagement may bias the results of research on study addiction. The Big Five personality
11
traits were used as covariates because personality is a robust predictor of health and well-
being (Czerwiński & Atroszko, 2020; Pinon, 2019). MPA was used as a covariate because of
its considerable impact on musicians' well-being (Biasutti & Concina, 2014; Kenny, 2011;
Kenny et al., 2004).
Methods
Participants
A convenience sample was recruited, consisting of 213 music academy students: 135
(63.4%) were female, 73 (34.4%) were male and 5 (2.3%) individuals did not report their
gender. The participants’ mean age was 22.74 years (SD = 3.31). They had attained a mean of
11.25 years (SD = 4.76) of formal musical education. They participants studied at three music
academies in Poland: the Stanisław Moniuszko Academy of Music in Gdańsk, the Feliks
Nowowiejski Academy of Music in Bydgoszcz, and the Department of the Fryderyk Chopin
University of Music in Białystok. They were taking different courses in different faculties,
and were in different year groups. A total of 85 participants were studying instrumental
performance (39.9%), 58 music education (27.2%), 20 solo singing (9.4%), 10 musical theatre
(4.7%), 9 studied conducting (4.2%), 6 church music (2.8%), 6 sound engineering (2.8%), 6
music theory (2.8%) and the remaining 13 were taking a variety of other courses (6.1%). In
Poland, all music academy students take courses closely related to performance, such as
classical piano, their own choice of one or more orchestral instruments, piano improvisation,
accompaniment, or vocal studies. These courses usually end with a formal examination or
public performance. As such, all participants can be considered students of music
performance regardless of their main specialization. There were very few missing data (less
than 1% overall); these were imputed when necessary using the expectationmaximization
(EM) algorithm in SPSS 25.0, which provides unbiased estimates of parameters (Enders,
12
2001; Scheffer, 2002). The dataset can be made available from the corresponding author on
request.
Instruments
Grit. The Short Grit Scale was used to assess grit (Duckworth & Quinn, 2009). The
scale consists of two 4-item subscales: perseverance of effort (e.g., "I finish whatever I
begin") and consistency of interest (e.g., "I often set a goal but later choose to pursue a
different one"). Participants indicate how well each statement describes them using a 5-point
Likert-type scale ranging from 1 (not like me at all) to 5 (very much like me). It showed good
psychometric qualities in previous research (Wyszyńska et al., 2017). The Cronbach's alpha
reliability coefficients in the current sample were .75 for perseverance of effort and .72 for
consistency of interest.
Study Addiction. Study addiction was measured using the Bergen Study Addiction
Scale (BStAS; Atroszko, Andreassen et al., 2015). It consists of seven items pertaining to
experiences during the past 12 months (e.g., "Studied in order to reduce feelings of guilt,
anxiety, helplessness and depression?"), with a Likert-type response scale ranging from 1
(never) to 5 (always). It showed good psychometric qualities in previous research
(Lawendowski et al., 2020). The Cronbach's alpha reliability coefficient in the current sample
was .76. Based on the polythetic cut-off score, 16% of students fulfilled the criteria for study
addiction, which is identical to a previous study (Lawendowski et al., 2020).
Learning Engagement. Learning engagement was measured using a single item, the
question "How engaged in learning are you?" (Atroszko 2014), with responses ranging from 1
(I am not at all engaged) to 7 (I am completely engaged). It showed good validity and test
retest reliability in previous research with an intraclass correlation coefficient of .77 for test-
retest reliability (Atroszko 2014; Atroszko, Czerwiński et al., 2019; Łukowicz et al., 2017).
13
Big Five personality traits. The mini-IPIP (Donnellan et al., 2006) was used to
measure the Big Five personality traits. It consists of a 20-item inventory with four items
measuring each of the Big Five personality factors: extraversion (e.g., "Talk to a lot of
different people at parties"), agreeableness (e.g., "Sympathize with others' feelings"),
conscientiousness (e.g., "Like order"), neuroticism (e.g., "Get upset easily") and intellect (e.g.,
"Have a vivid imagination"). Participants indicate how well each statement described them
using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The
scale showed good psychometric qualities in previous research (Czerwiński & Atroszko,
2020; Czerwiński et al., 2019). The Cronbach’s alpha reliability coefficients in the current
study were the following: .74 for extraversion, .61 for agreeableness, .77 for
conscientiousness, .71 for neuroticism and .64 for intellect.
MPA. The Kenny Music Performance Anxiety Inventory Revised (K-MPAI-R;
Kenny, 2009) was used to measure MPA. The instrument consists of 40 items (e.g., "My
worry and nervousness about my performance interferes with my focus and concentration")
with a 7-point Likert-type response format, ranging from 0 (strongly disagree) to 6 (strongly
agree). Although originally intended as a multidimensional tool, recent research indicates the
unidimensional approach to be superior (Chang-Arana et al., 2017). The scale showed good
psychometric qualities in previous research (Kantor-Martynuska & Kenny, 2018). The
Cronbach's alpha reliability coefficient in the current sample was .93.
Perceived stress. The short version of the Perceived Stress Scale (PSS-4; Cohen et al.,
1983) was used as a measure of perceived stress, with four items referring to the previous
month (e.g., "In the last month, how often have you felt difficulties were piling up so high that
you could not overcome them?"). Response options are 0 (never), 1 (almost never), 2
(sometimes), 3 (fairly often), and 4 (very often). The scale showed good validity and reliability
14
in previous research (Atroszko, 2015; Czerwiński et al., 2020). The Cronbach's alpha
reliability coefficient in the current sample was .78.
General quality of life, general health and quality of sleep. Three single-item
measures of different aspects of quality of life, developed on the basis of the WHOQOL-
BREF (Skevington et al., 2004), were used. General quality of life was measured by the
question "How would you rate your quality of life?" with a 9-point Likert-type scale ranging
from very poor (1) to very good (9). General health was measured by the question "How
satisfied are you with your health?" with a 9-point Likert scale ranging from 1 (very
dissatisfied) to 9 (very satisfied). Sleep quality was measured by the question "How satisfied
are you with your sleep?" with a 9-point Likert scale ranging from 1 (very dissatisfied) to 9
(very satisfied). This instrument showed good validity and testretest reliability in previous
research with intraclass correlation coefficients of .86 for general quality of life, .72 for
general health and .81 for sleep quality (Atroszko, Bagińska et al., 2015; Czerwiński et al.,
2020).
GPA. The students were asked to provide information about their GPA from the
semester prior to the study as accurately as possible. All universities used a scale ranging
from 1 to 25. However, some courses at the Stanisław Moniuszko Academy of Music in
Gdańsk used a scale ranging from 2 to 6. In these cases, the GPA was recalculated using the
conversion rate provided in the official rulebook of the academy.
Procedure
Data were collected in May 2019 using a paper-and-pencil cross-sectional survey.
Students were invited to participate anonymously, on a voluntary basis, during lectures or
classes. No monetary or other material rewards were offered. Completion of the survey was
15
regarded as proof that the participant had given their informed consent. The estimated
response rate was above 95%.
Statistical analyses
Means, standard deviations, percentages, and correlation coefficients were calculated
using SPSS 25.0. Six hierarchical regression analyses were conducted in which study
addiction, stress, general health, sleep quality, quality of life, and GPA were the dependent
variables. The independent variables introduced in subsequent steps can be found in Tables 2
and 3. For all linear regression analyses, preliminary analyses were conducted to ensure that
the assumptions of normality, linearity, multicollinearity, and homoscedasticity were not
violated. Five moderation analyses were performed in which perseverance of effort was the
independent variable; study addiction was the moderator; stress, general health, sleep quality,
quality of life and GPA were the dependent variables; and consistency of interest, learning
engagement, gender, age, Big Five and MPA were covariates. In addition, five parallel
moderation analyses were performed for consistency of interest as the independent variable
and perseverance of effort as a covariate. The PROCESS 3.3 macro was used for moderation
analysis (Hayes, 2017). All tests were two-tailed, and the significance level was set to α = .05.
Additionally, the same analyses but using the composite score of grit can be found in the
supplemental materials.
Because the study was confirmatory in nature and the analysis was driven by clear
hypotheses, no adjustments for multiple testing were applied. It should also be noted that
correcting for multiple comparisons is controversial, some researchers arguing that it is
incorrect (see Gelman et al., 2012; Perneger, 1998; Rothman, 1990).
Three benchmarks were set for study addiction based on the mean score and standard
deviation. Tests of the significance of regression slopes at these benchmarks were conducted.
16
The bootstrap method, with bias-corrected 95% confidence intervals and 5,000 bootstrap
samples, was used. The moderation plots were prepared using the interactions 1.0.3 package
(Long, 2020) and the R language in version 4.0.3 (R Core Team, 2020).
Ethics statement
The study was carried out in accordance with the Declaration of Helsinki and the
guidelines of the Ethics Committee at the Institute of Psychology of the University of Gdańsk.
It did not include ethically doubtful procedures, sensitive data or vulnerable populations.
Participants were given detailed information about the study and their role in it, assured that
they would be providing data anonymously, and told that they could withdraw at any point.
Results
The mean scores, standard deviations, percentages, and correlation coefficients of the
study variables are presented in Table 1. As found in previous studies (Nedelcut et al., 2018;
Vaag et al., 2015) participants scored significantly higher on the measures of stress (t = 3.05, p
< .01) and lower on sleep quality (t = 2.84, p < .01) than a sample of university (non-music)
students who took part in Atroszko et al.’s (2018) using the same measures.
[INSERT TABLE 1 ABOUT HERE]
The regression analysis for study addiction (F11,191 = 7.84, p < .001) showed that the
significant independent variables in Step 5 were perseverance of effort, agreeableness, MPA,
and learning engagement (see Table 2).
[INSERT TABLE 2 ABOUT HERE]
The regression analysis for stress (F12,190 = 7.98, p < .001) showed that the significant
17
independent variables in Step 5 were study addiction, conscientiousness, and neuroticism.
Neither the interaction between perseverance of effort and study addiction nor the interaction
between consistency of interest and study addiction, added in Step 6, were significant.
The regression analysis for general health (F12,190 = 3.18) showed that the significant
independent variables in Step 5 were age and neuroticism, while study addiction and MPA were
only on the verge of reaching statistical significance. There was a significant interaction
between perseverance of effort and study addiction, added in Step 6. The conditional effects of
the focal predictor values of the moderator showed that the relationship between perseverance
of effort and general health was negative for participants who scored high on study addiction
and non-significant for participants whose scores were average or low (see Figure 1). The
significant covariates were age, neuroticism, and MPA. The interaction between consistency of
interest and study addiction, added in Step 6, was not significant.
The regression analysis for sleep quality (F12,190 = 2.58, p < .001) showed that the
significant independent variable in Step 5 was study addiction, while learning engagement, age,
and neuroticism were on the verge of reaching statistical significance. Neither the interaction
between perseverance of effort and study addiction nor the interaction between consistency of
interest and study addiction, added in Step 6, were significant.
The regression analysis for quality of life (F12,190 = 6.07, p < .001) showed that the
significant independent variables in Step 5 were study addiction, learning engagement, and
neuroticism, while consistency of interest and MPA were on the verge of reaching statistical
significance. There was a significant interaction between perseverance of effort and study
addiction, added in Step 6. The conditional effects of the focal predictor values of the moderator
showed that the relationship between perseverance of effort and quality of life was positive for
participants who scored low on study addiction and non-significant in participants whose scores
were average or high (see Figure 2). The significant covariates were learning engagement and
18
neuroticism, while consistency of interest and MPA were on the verge of statistical significance.
The interaction between consistency of interest and study addiction, added in Step 6, was not
statistically significant.
The regression analysis for GPA (F12,177 = 4.15, p < .001) showed that the significant
independent variables in Step 5 were perseverance of effort, consistency of interest, and
learning engagement, while gender, agreeableness, and intellect were on the verge of reaching
statistical significance. Neither the interaction between perseverance of effort and study
addiction nor the interaction between consistency of interest and study addiction, added in Step
6, were significant (see Tables 3, 4, and 5).
[INSERT TABLE 3 ABOUT HERE]
[INSERT TABLE 4 ABOUT HERE]
[INSERT FIGURE 1 ABOUT HERE]
Figure 1. Plot for the moderating effects of study addiction on the relationship between perseverance of effort and
general health.
[INSERT FIGURE 2 ABOUT HERE]
Figure 2. Plot for the moderating effects of study addiction on the relationship between perseverance of effort and
quality of life.
[INSERT TABLE 5 ABOUT HERE]
Discussion
The aim of this study was to investigate the moderating role of study addiction in the
relationships between measures of perseverance of effort, one of the two components of grit,
and variables representing the psychosocial functioning of music academy students including
performance and well-being. Hypothesis 1 was supported in that perseverance of effort was
related positively to GPA, even when controlling for variables such as personality, MPA, and
learning engagement. This finding confirms the important role of grit in musicians’ lives and
19
justifies the argument that grit should be a focus of music education research. However,
Hypothesis 2 was not supported, as perseverance of effort was not related to any of the well-
being indicators. Hypothesis 3 was supported, as perseverance of effort was positively related
to study addiction, even when controlling for variables such as personality, MPA and learning
engagement. These results suggest that perseverance and well-being may not always be
related to each other, as the musician’s profession can be highly stressful and pose substantial
health risks (Kreutz et al., 2008; Matei & Ginsborg, 2020; Matei et al., 2018). The likelihood
that this suggestion is correct was confirmed by subsequent analyses revealing that study
addiction moderated the relationships between perseverance of effort and general health and
quality of life. This shows how young musicians may experience maladaptive perseverance.
Hypothesis 4 was only partially supported, however, as study addiction was found to
moderate the relationship between perseverance of effort and only, among the five variables
tested, general health and quality of life.
The results of the present study are congruent with findings in the wider research and
educational context (see Macnamara et al., 2014; Manturzewska, 1974; McPherson, 2000;
Lehmann et al., 2007; Stumpf, 1890), which show that the path to musical achievement
involves intensive, goal-oriented work, training, and practice. Young musicians face
requirements and expectations such that they are encouraged to commit themselves to
engagement in education for musical excellence, demonstrating extreme perseverance of
effort. However, greater perseverance of effort can not only help musicians achieve better
results but can also cause them to experience specific psychological difficulties. Comparisons
with other artists and with the general population (Kapsetaki & Easmon, 2019; Kenny et al.,
2014; Vaag et al., 2016) show that musicians experience certain psychological difficulties
more frequently and more severely. There is a strong link between study addiction and
performance anxiety (Lawendowski et al., 2019), and many music teachers suggest that
20
performance anxiety is the result of inadequate practice and thus recommend longer periods
of more intense practice. Extending practice time is not always the optimal course of action
(see Mornell et al., 2018) as it can increase the individual’s perfectionist tendencies and cause
strong, negative emotions related to lack of perfection and social pressure (Stoeber &
Eismann, 2007). This is also congruent with the findings of research testing the dualistic
model of passion among musicians showing that musical engagement can be beneficial as
well as detrimental to the functioning of an individual (Bonneville-Roussy et al., 2010;
Bonneville-Roussy & Vallerand, 2020).
The present results support to some extent concerns as to the potential for individuals
with high levels of grit to become dysfunctional (Credé et al., 2017; Datu, 2021; Khan et al.,
2020; Lucas et al., 2015). They have important implications for developing educational
policies and planning interventions designed to foster perseverance of effort, as this may
reinforce the harmful behavioural patterns of some individuals. Many higher education
institutions for music, for example in Poland, have not yet established robust, appropriate, and
accessible support for students in the areas of psychological self-management, health, and
well-being, despite the obvious need for it (Nogaj, 2020). As Pecen et al. (2018) note, this is
particularly important because musical success is determined not solely by technical and
musical proficiency but also by various environmental and psychosocial factors.
The moderating effects of study addiction
The results of the analyses revealing the moderating effect of study addiction on the
relationships between perseverance of effort and measures of well-being differed according to
the measure of well-being. Interactions were present for general health and quality of life, but
not for sleep quality and perceived stress. To explain these differences, three factors need to
be considered. First, measures of stress and sleep quality indicate well-being from day to day,
while measures of general health and quality of life indicate well-being in the long term. In
21
other words, increased stress and reduced sleep signal compromised wellbeing immediately,
while problematic behaviours may take longer to affect an individual’s view of their general
health and quality of life. Other studies have shown similar differences in the relationships
between these indicators and those of study and other forms of behavioural addiction in young
people (Atroszko, Andreassen et al., 2015; Atroszko et al., 2018; Uzarska et al., 2021). Music
academy students are already heavily committed to reaching goals related to their future lives
as professional musicians (Ericsson et al., 1993; McPherson, 2000) leading, for many of them,
to additional worries and less time for sleep or anxiety-driven sleep disturbance (Nedelcut et
al., 2018; Vaag et al., 2015). The results of the research cited above suggest that while many
music academy students experience high levels of stress associated with sleep problems, it is
only in some extreme cases, related to study addiction, that they have noticeable
consequences for general health and quality of life. This moderating effect is congruent with
the results of previous research indicating the high stress levels of young musicians (see
overview in Lawendowski et al., 2020; Atroszko, Wróbel et al., 2019).
Second, perseverance of effort is strongly related to the performance indicators
included in this study, such as GPA. It has been argued that when analysing data on work and
study addiction, it is necessary to consider a proper framework for trade-offs (see Atroszko et
al., 2019). In the present study, greater effort yields superior performance for the individuals
who are most obsessed (i.e., who score highest for perseverance and study addiction), but at
the cost of compromised general health and quality of life. This result is consistent with those
of studies of highly successful performing musicians who face a substantially increased risk
of death as the result of addiction and health problems (Bellis et al., 2007; Breitenfeld et al.,
2014).
Third, for participants who scored low on study addiction, perseverance of effort was
significantly and positively related to quality of life, despite the non-significant results of the
22
correlation and regression analyses. The absence of a significant relationship between grit and
well-being may be attributable to the high prevalence of study addiction among musicians
(Lawendowski et al., 2020). It is also worth noting that even though the interaction effects for
stress and sleep quality were not significant, the non-significant relationship between
perseverance of effort and stress was positive for participants who scored low on study
addiction but negative for those with average and high scores. Similarly, the relationship
between perseverance of effort and sleep quality neared significance for participants with
average and high scores for study addiction. It may therefore be that moderation effects do
exist for these indicators of well-being but are too small to be detected in a relatively small
sample or were diminished by the relatively minor impact of perseverance of effort on well-
being found in the present study compared to that found in studies of different populations.
Although the relationship between grit and general well-being is well established, few studies
have specifically targeted stress and sleep quality. As for GPA, the relationship between study
addiction and GPA was not found to be significant, contrary to the findings of previous
studies (Lawendowski et al., 2020). This suggests that further research on this topic is
warranted.
Consistency of interest and the conceptual clarity of grit
Consistency of interest plays a much smaller role than perseverance of effort, a finding
congruent with those of previous studies (Bowman et al., 2015; Credé et al., 2017; Disabato et
al., 2018). It was not related to study addiction and was even negatively related to GPA. It
was positively related to sleep quality and negatively related to stress, but only in the first few
steps of the regression analyses. Also, there was no interaction between consistency of interest
and study addiction. As agreed by many other researchers in this area, further refinements in
the concept and measurement of grit are warranted. Its components, particularly consistency
of interest, are vague and discrepant and need to be clarified, perhaps by reference to better
23
established psychological constructs such as temperament.
Potential prevention and intervention programmes
Study and work addiction are greatly under-recognized problems among the general
population and particularly among musicians. One reason for this is that addiction is related to
denial, and the overwhelming majority of affected individuals either do not recognize it as a
problem or, if they do, fail to seek help. It would be helpful if there were more public
initiatives and campaigns to raise awareness of work and study addiction, stimulating research
and the development and implementation of prevention and intervention programmes. The
legendary Alanis Morissette is one famous musician who has spoken in public about her own
work addiction and taken part in interviews, with others, with a work addiction researcher
who has carried out seminal work in this area, Bryan E. Robinson (2014; Alanis Morissette,
2018). Similar public disclosure by Lindsey Stirling has also proved useful for raising
awareness of eating disorders (EDs) in young musicians. These are highly comorbid with
work addiction among working women (five out of six young working women with EDs were
found to meet the criteria for work addiction; Atroszko, Mytlewska et al., 2020). Widespread
social recognition of the clinically relevant negative consequences of compulsive
overworking is perhaps among the most important challenges to its proper acknowledgement,
prevention, and treatment. There is no lack of empirical evidence (Atroszko, Demetrovics et
al., 2020), only a lack of public awareness of problem behaviours associated with work/study
addiction.
Interventions promoting flow (Cohen & Bodner, 2021) and mindfulness, for example,
are potentially useful for coping with music performance anxiety (Czajkowski et al., 2020),
reducing stress, and preventing study and work addiction (Khoury et al., 2013; Van Gordon et
al., 2014). However, the relationships between flow and grit (Miksza & Tan, 2015; Tan et al.,
2021), and study addiction, need to be investigated cautiously as the absorption component of
24
engagement, which is related to flow (Mesurado et al., 2016), may be a gateway to addiction
(Atroszko & Atroszko, 2019; Bereznowski et al., 2021). The results of previous studies
revealing links between study addiction and lower social competencies suggest that it may be
useful to develop programmes for improving these competencies and thus the functioning of
introverted and highly socially anxious young musicians (Lawendowski et al., 2020). In many
cases, therapeutic interventions based on a cognitive-behavioural approach and motivational
interviewing may be effective in reducing compulsive study-related behaviours.
Strengths and limitations
To the authors' knowledge, this is one of the first studies to examine grit among
musicians thoroughly, and the first to show that higher levels of grit, a construct hitherto
associated by many scientists, policy makers and the media solely with a positive impact on
human functioning, could for some individuals be related to lower levels of well-being. We
therefore propose the construct of maladaptive perseverance. All the psychometric tools we
used were valid and reliable. However, the sample was relatively small, and all the data were
self-reported. We used convenience sampling, so we recommend that the results of the study
are generalized to other populations with caution.
Future research directions
The present study focused on a population of music academy students, because
attending a music academy represents a comparatively advanced stage of musical education.
Most participants had already experienced many years of musical training and showed
passion and perseverance in pursuing their long-term goal of becoming a professional
musician. It is therefore possible that grit also has an impact on the lives of those who are just
beginning their musical training, so future studies could focus on examining grit at earlieror
indeed the earlieststages of musical education. It would also be worth exploring the
25
stability of the results of such research over time, using longitudinal methods, and how
individuals function in a variety of educational settings.
The results of the present study could also be complemented by analyses of
phenomenological data. Such analyses would improve our understanding not only of the
meaning attributed by individuals to perseverance of effort, and their reasons for
perseverance, but also how they themselves understand their experiences and the mechanisms
underlying engagement in studying and realizing goals. Importantly, this research would
make a contribution to the literature on the negative consequences of music making.
The study of grit and its components is rooted in the paradigm of positive psychology,
yet this paradigm has been criticised on the grounds that it is binary and reductive, (Grant &
Schwartz, 2011; Held, 2002, 2004; Ivtzan, et al., 2016; Wong, 2011; Wong & Roy, 2017).
According to this view, certain nuances of psychological phenomena are disregarded by
positive psychologists, undermining the development of psychology as a science and the
implementation of psychological findings in everyday lives. By contrast, a recent trend in
personality psychology is to show that personality traits traditionally perceived as positive can
also have negative consequences for the lives of individuals (Carter et al., 2015; Smith et al.,
2017). The finding of the present research that potential conditional effects can turn positive
influences into negative ones is part of this trend and should be explored further. Another
promising area for future research is the study of the potential negative consequences of grit
and the occurrence maladaptive perseverance in other populations.
Conclusions
The findings of the present study show that perseverance of effort is crucial during the
course of an individual’s musical education, but if it drives study addiction it can lead to
persistent overinvolvement in harmful and unhealthy behaviours. We have called this
phenomenon maladaptive perseverance. Identifying perseverance of effort as a contributor
26
only to positive outcomes such as objectively and subjectively measured success and treating
it as a typical or necessary condition of musical education, could have negative outcomes for
beginner musicians.
Monitoring problems associated with perseverance of effort among students at music
schools and conservatories may enable potential risks to be identified more effectively and
sooner, adequate health behaviours to be promoted via the use of behavioural change
techniques and the teaching of life skills, and improved well-being. These suggestions echo
those made by other researchers in the same field (e.g., Araujo et al., 2017; Leech-Wilkinson,
2020; Matei & Ginsborg, 2020), drawing attention, first, to the need for further developments
in the provision of health and psychological education for musicians and, second, criticisms of
the rigid norms of Western classical music and the long-established but nevertheless
questionable techniques for educating and training musicians, which assumeincorrectly, as
we have seenthat musicians necessarily become stronger, and more resilient and
perseverant in their behaviours.
Conflicts of interest:
The authors state that there are no conflicts of interest.
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Table 1
Mean scores and standard deviations (SD), percentages, and correlation coefficients (Pearson product-moment/point-biserial) between study variables.
Variable
Mean (SD)/
Percentages
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
1. Perseverance of effort
15.54 (2.87)
2. Consistency of interest
11.62 (3.17)
.36**
3. Study addiction
18.18 (5.27)
.29**
.01
4. Learning engagement
4.87 (5.27)
.51**
.30**
.42**
5. Gender a
63.4% females
.20**
.02
.11
.07
6. Age
22.74 (1.27)
.03
.01
.11
.01
.26**
7. Extraversion
13.34 (3.65)
.01
.11
.14*
.11
.03
.07
8. Agreeableness
16.63 (2.43)
.17*
.04
.10
.07
.21**
.01
.27**
9. Conscientiousness
12.99 (3.93)
.37**
.17*
.12
.20**
.03
.07
.20**
.01
10. Neuroticism
13.92 (3.52)
.12
.17*
.21**
.09
.35**
.19**
.11
.03
.01
11. Intellect
15.96 (2.81)
.13
.01
.04
.04
.05
.00
.19**
.18**
.03
.14*
12. MPA
110.76 (35.76)
.08
.33**
.27**
.06
.24**
.13
.22**
.04
.12
.49**
.17*
13. Stress
11.48 (3.07)
.03
.19**
.22**
.03
.17*
.13
.06
.01
.16*
.52**
.03
.39**
14. General health
5.71 (2.06)
.04
.04
.16*
.04
.15*
.08
.04
.02
.10
.27**
.09
.27**
.37**
15. Sleep quality
4.77 (2.26)
.06
.10
.18**
.04
.06
.07
.07
.04
.10
.19**
.04
.20**
.36**
.54**
16. Quality of life
6.86 (1.49)
.08
.08
.24**
.18**
.02
.00
.10
.07
.03
.31**
.14*
.34**
.40**
.43**
.28**
17. GPA
19.57 (1.90)
.34**
.02
.14*
.32**
.15*
.02
.09
.03
.16*
.05
.18*
.11
.11
.00
.05
.08
a Point-biserial correlation coefficient (0 = female, 1 = male)
p < .10 *p < .05 **p < .01
Table 2
Results of hierarchical multiple regression analyses in which grit, age, gender, big five personality traits and MPA were regressed upon study
addiction.
Predictor
β
R2
Step 1
.103**
Perseverance of effort
.34**
Consistency of interest
.10
Step 2
.008
Perseverance of effort
.34**
Consistency of interest
.10
Gender a
.01
Age
.09
Step 3
.063*
Perseverance of effort
.34**
Consistency of interest
.08
Gender a
.00
Age
.05
Extraversion
.09
Agreeableness
.14*
Conscientiousness
.00
Neuroticism
.16*
Intellect
.04
Step 4
.038**
Perseverance of effort
.34**
Consistency of interest
.02
Gender a
.02
Age
.05
Extraversion
.03
Agreeableness
.17*
Conscientiousness
.04
Neuroticism
.05
Intellect
.02
MPA
.26**
Step 5
.098**
Perseverance of effort
.16*
Consistency of interest
.06
Gender a
.01
Age
.06
Extraversion
.01
Agreeableness
.17**
Conscientiousness
.04
Neuroticism
.03
Intellect
.02
MPA
.27**
Learning engagement
.37**
Total R2
.311**
a 0 = female, 1 = male
p < .10 *p < .05 **p < .01
Table 3
Results of hierarchical multiple regression analyses in which grit, study addiction, learning engagement, gender, age, Big Five personality
traits, MPA were regressed upon the scores for stress, general health, sleep quality, quality of life, and GPA.
Stress
General health
Sleep quality
Quality of life
GPA
Predictor
β
R2
β
R2
β
R2
β
R2
β
R2
Step 1
.040*
.009
.029
.011
.129**
Perseverance of effort
.06
.09
.15*
.08
.38*
Consistency of interest
.21**
.08
.15*
.05
.13
Step 2
.062**
.040*
.039*
.141**
.033*
Perseverance of effort
.02
.09
.15
.06
.29**
Consistency of interest
.17*
.04
.11
.03
.16*
Study addiction
.28**
.21**
.21**
.39**
.01
Learning engagement
.10
.16
.16
.31**
.21*
Step 3
.024
.036*
.017
.002
.007
Perseverance of effort
.02
.06
.14
.06
.27**
Consistency of interest
.16*
.02
.10
.03
.15*
Study addiction
.26**
.22**
.22**
.40**
.00
Learning engagement
.09
.16
.16
.32**
.21*
Gender a
.13
.17*
.07
.02
.04
Age
.07
.15*
.13
.05
.04
Step 4
.204**
.066*
.051
.110**
.046
Perseverance of effort
.00
.07
.15
.11
.25**
Consistency of interest
.09
.04
.04
.09
.14
Study addiction
.19**
.18*
.21**
.34**
.00
Learning engagement
.09
.15
.16
.32**
.22**
Gender a
.02
.10
.00
.08
.13
Age
.03
.16*
.14
.09
.05
Extraversion
.01
.07
.10
.01
.11
Agreeableness
.03
.06
.02
.05
.16*
Conscientiousness
.15*
.10
.11
.07
.06
Neuroticism
.48**
.25**
.20*
.35**
.05
Intellect
.11
.00
.04
.14*
.14
Step 5
.004
.016
.004
.013
.005
Perseverance of effort
.00
.07
.15
.10
.25**
Consistency of interest
.04
.08
.02
.13
.17*
Study addiction
.17**
.15
.19*
.31**
.02
Learning engagement
.08
.14
.15
.31**
.21*
Gender a
.02
.09
.01
.09
.14
Age
.03
.17*
.14
.09
.05
Extraversion
.03
.11
.12
.03
.09
Agreeableness
.02
.08
.01
.03
.14
Conscientiousness
.14*
.07
.10
.09
.05
Neuroticism
.44**
.19*
.17
.29**
.02
Intellect
.09
.04
.02
.11
.12
MPA
.09
.17
.09
.16
.09
Total R2
.335**
.168**
.140**
.277**
.220**
a 0 = female, 1 = male
p < .10 *p < .05 **p < .01
Table 4
Results of adding the interaction between perseverance of effort and study addiction, and consistency of interest and study addiction in step 6
to the regression models from Table 3.
Stress
General health
Sleep quality
Quality of life
GPA
Predictor
β
R2
β
R2
β
R2
β
R2
β
R2
Step 6 for perseverance of
effort
. 001
.030**
.002
.017*
.016
Perseverance of effort
.01
.13
.17
.06
.20**
Consistency of interest
.05
.09
.02
.14
.16*
Study addiction
.18*
.13
.19*
.29**
.01
Learning engagement
.08
.12
.14
.29**
.23*
Gender a
.02
.09
.01
.09
.14
Age
.02
.14*
.13
.08
.03
Extraversion
.04
.08
.11
.01
.07
Agreeableness
.02
.10
.00
.01
.16*
Conscientiousness
.13
.10
.10
.07
.03
Neuroticism
.45**
.17*
.16
.27**
.03
Intellect
.10
.05
.01
.10
.13
MPA
.09
.18*
.09
.16
.08
Interaction
(perseverance of
effort*study addiction)
.00
.01**
.00
.01*
.01
Total R2
. 336**
.198**
.142**
.294**
.236**
Step 6 for consistency of
interest
. 000
.004
.000
.002
.008
Perseverance of effort
.00
.09
.15
.09
.28**
Consistency of interest
.04
.08
.02
.16
.17*
Study addiction
.17*
.15
.19*
.31**
.02
Learning engagement
.08
.13
.15
.30**
.22*
Gender a
.02
.08
.00
.10
.13
Age
.03
.17*
.14
.10
.05
Extraversion
.03
.11
.12
.02
.08
Agreeableness
.02
.09
.01
.02
.16*
Conscientiousness
.14
.08
.10
.09
.04
Neuroticism
.45**
.18*
.17
.28**
.03
Intellect
.09
.03
.02
.11
.11
MPA
.09
.18*
.09
.16
.08
Interaction (consistency
of interest*study
addiction)
.00
.00
.00
.00
.01
Total R2
. 335**
.172**
.140**
.279**
.228**
a 0 = female, 1 = male.
p < .10 *p < .05 **p < .01
Figure 1
Figure 2
Table 5
Conditional effects of the focal predictor values of the moderator with 95% confidence intervals (unstandardized values).
Moderation
Group
Stress
General health
Sleep quality
Quality of life
GPA
Perseverance of
effort*study addiction
Low study addiction
.02 [.16; .21]
.03 [.11; .17]
.10 [.26; .06]
.10* [.00; .19]
.11 [.02; .24]
Average study addiction
.01 [.19; .16]
.10 [.22; .03]
.13 [.28; .01]
.03 [.06; .12]
.20** [.08; .32]
High study addiction
.05 [.29; .19]
.22* [−.40; −.05]
.17 [.37; .03]
.04 [.16; .08]
.29** [.12; .45]
Consistency of
effort*study addiction
Low study addiction
.04 [.21; .14]
.01 [.14; .12]
.00 [.14; .15]
.04 [.13; .05]
.15* [−.27; −.03]
Average study addiction
.04 [.18; .09]
.05 [.15; .05]
.02 [.10; .13]
.06 [.13; .01]
.10* [.19; .01]
High study addiction
.05 [.22; .13]
.10 [.23; .04]
.03 [.12; .17]
.08 [.17; .03]
.05 [.17; .07]
p < .10. *p < .05 **p < .01
Supplemental material
Table S1
Results of hierarchical multiple regression analyses in which grit, age, gender, big five personality traits and MPA were regressed upon study
addiction.
Predictor
β
R2
Step 1
.035**
Grit
.19**
Step 2
.013
Grit
.18**
Gender a
.06
Age
.08
Step 3
.074**
Grit
.18*
Gender a
.03
Age
.04
Extraversion
.06
Agreeableness
.12
Conscientiousness
.06
Neuroticism
.22**
Intellect
.02
Step 4
.053**
Grit
.25**
Gender a
.00
Age
.04
Extraversion
.00
Agreeableness
.16*
Conscientiousness
.09
Neuroticism
.08
Intellect
.04
MPA
.30**
Step 5
.122**
Grit
.07
Gender a
.00
Age
.05
Extraversion
.03
Agreeableness
.17*
Conscientiousness
.07
Neuroticism
.04
Intellect
.03
MPA
.30**
Learning engagement
.40**
Total R2
.298**
a 0 = female, 1 = male
p < .10 *p < .05 **p < .01
Table S2
Results of hierarchical multiple regression analyses in which grit, study addiction, learning engagement, gender, age, Big Five personality
traits, MPA and the interaction between grit and study addiction were regressed upon the scores on stress, general health, sleep quality,
quality of life and GPA.
Stress
General health
Sleep quality
Quality of life
GPA
Predictor
β
R2
β
R2
β
R2
β
R2
β
R2
Step 1
.017
.000
.000
.011
.038**
Grit
.13
.00
.01
.11
.20*
Step 2
.076**
.044*
.048**
.139**
.063*
Grit
.15
.03
.01
.02
.07
Study addiction
.30**
.23**
.24**
.38**
.04
Learning engagement
.08
.14
.13
.32**
.26**
Step 3
.028*
.039*
.020
.002
.016
Grit
.16
.02
.01
.02
.06
Study addiction
.28**
.23**
.25**
.39**
.03
Learning engagement
.07
.15
.14
.33**
.26**
Gender a
.14*
.18*
.09
.01
.13
Age
.07
.15*
.13
.05
.04
Step 4
.21**
.068*
.058*
.102**
.057*
Grit
.06
.08
.06
.01
.04
Study addiction
.20**
.19*
.23**
.32**
.03
Learning engagement
.08
.15
.14
.34**
.26**
Gender a
.01
.11
.01
.09
.16*
Age
.03
.17*
.14*
.09
.06
Extraversion
.01
.08
.11
.02
.14
Agreeableness
.03
.05
.03
.04
.14
Conscientiousness
.14*
.09
.09
.05
.12
Neuroticism
.49**
.26**
.22**
.33**
.01
Intellect
.11
.00
.03
.15*
.15*
Step 5
.005
.016
.006
.010
.002
Grit
.04
.12
.09
.04
.03
Study addiction
.17*
.15
.21*
.29**
.04
Learning engagement
.07
.14
.13
.33**
.26**
Gender a
.02
.09
.01
.10
.16*
Age
.03
.17*
.14*
.08
.06
Extraversion
.03
.11
.13
.01
.13
Agreeableness
.02
.08
.02
.02
.13
Conscientiousness
.13*
.07
.07
.06
.11
Neuroticism
.45**
.19*
.18
.27**
.01
Intellect
.09
.04
.01
.12
.14
MPA
.10
.17
.11
.14
.05
Step 6
.001
.022*
.000
.017
.009
Grit
.05
.17*
.09
.09
.06
Study addiction
.17*
.14
.21*
.28**
.04
Learning engagement
.08
.11
.13
.31**
.27**
Gender a
.02
.08
.00
.11
.15*
Age
.03
.16*
.14*
.08
.06
Extraversion
.04
.10
.13
.01
.12
Agreeableness
.02
.11
.02
.00
.14
Conscientiousness
.13
.09
.07
.05
.10
Neuroticism
.45**
.17*
.18*
.26**
.01
Intellect
.09
.04
.01
.12
.14
MPA
.09
.20*
.11
.16
.04
Interaction (grit*study
addiction)
.03
.16*
.00
.14*
.10
Total R2
.335**
.189**
.132**
.281**
.220**
a 0 = female, 1 = male
p < .10 *p < .05 **p < .01
Table S3
Conditional effects of the focal predictor values of the moderator with 95% confidence intervals (unstandardized values).
Moderation
Group
Stress
General health
Sleep quality
Quality of life
GPA
Grit*study addiction
Low study addiction
.02 [.12; .09]
.01 [.09; .07]
.04 [.13; .05]
.01 [.04; .07]
.01 [.09; .06]
Average study addiction
.03 [.12; .06]
.07* [.14; .00]
.04 [.12; .04]
.02 [.07; .02]
.02 [.04; .09]
High study addiction
.05 [.19; .09]
.14** [.25; −.04]
.04 [.16; .08]
.07 [.14; .00]
.06 [.03; .16]
p < .10 *p < .05 **p < .01
Experiment Findings
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Tracking citations to my work and connecting research: +1,260 citations on Google Scholar as of June 29 2022. https://scholar.google.com/citations?user=jEGe8qAAAAAJ https://www.academia.edu/43694236/Dr_Adam_M_Croom_citations
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Thesis
Study addiction has been recently proposed as a behavioral addiction and conceptualized within the framework of the theory and research on work addiction. This pattern of excessive overinvolvement into studying has been validated across different countries and cultures; however, only among university students. There remains the question of whether students prior to being at the stage of university education experience the compulsive need to study and, if so, are the antecedents and consequences of this behavior similar for high school students and undergraduate and graduate students. The aim of this study was to investigate the validity of the construct of study addiction among high school students, including its conceptualization as an ineffective coping strategy leading to higher stress and its consequences. A cross-sectional correlation study was conducted among 1720 high school students in the Pomeranian region in Poland. The Bergen Study Addiction Scale (BStAS), together with commonly used measures of personality and well-being, as well as some indicators of learning engagement, school achievement, ADHD, and rigid perfectionism, were applied in the study. A one-factor solution for the BStAS was found to have an acceptable fit to the data. The prevalence of study addiction in high school students was 15.2% using the polythetic approach; however, the discrepancy between genders was major (6.2% males and 21.2% females). As expected, study addiction was positively related to learning engagement, neuroticism, conscientiousness, social anxiety, and rigid perfectionism, and negatively to well-being and school achievement. However, it was negatively associated with ADHD, which constitutes a difference in comparison to work addiction studies consistently linking this phenomenon with ADHD among the adult working population. Moreover, study addiction has been found to mediate the relationship between potential risk factors in terms of underlying psychopathology or personality (rigid perfectionism, ADHD, social anxiety, neuroticism), and perceived stress. This suggests that individuals with particular vulnerabilities may experience additional stress because of their study addiction. The results of this study are mostly in line with previous research and confirm that study addiction is a severe problem in education, especially for females. Based on these results, it is suggested that future research should concentrate on estimating the age of onset of the study addiction and investigating the gender differences in prevalence and severity of study addiction.
Chapter
Current measures of grit misrepresent its original conceptualization. This chapter aims to outline the flaws of extant grit measures, particularly in light of recent updated recommendations for maximizing construct validity during scale development. After reviewing empirical findings regarding grit’s construct validity, structure, and association with success outcomes, recommendations for the future development of grit will be proposed. Grit was developed as a higher-order construct consisting of two facets: perseverance of effort and consistency of interests. However, the higher order construct of “overall grit” is not supported by item response theory, factor analytic, or structural equation modeling approaches. Grit is currently better interpreted as two separate constructs, possibly within a bifactor model in which “overall grit” does not consist of perseverance and consistency. After controlling for conscientiousness, perseverance explains little incremental variance in success outcomes. However, perseverance may be related to, but distinct from, lower-order facets of conscientiousness. Consistency is much less associated with success outcomes, and its predictive utility is unclear.
Article
Music performance anxiety (MPA) is part of every musician’s life. Individual differences in MPA have been associated with individual differences in perfectionism, especially maladaptive perfectionism. The aim of this study was to examine MPA and its association with perfectionism in a combined sample of music students studying at the Academy of Music and members of the professional orchestras in Zagreb. Based on the previous studies, we hypothesized that gender, age, and maladaptive perfectionism would predict higher MPA. Data were collected for 239 musicians (152 music students, 87 orchestral musicians, 50.2% female), who filled in Kenny Music Performance Anxiety Inventory–Revised (K-MPAI-R) and Almost Perfect Scale–Revised (APS-R). In our sample, 28% ( n = 67) of musicians had a clinically significant MPA level. In the regression analysis with gender, age, and dimensions of adaptive and maladaptive perfectionism as predictors, 46% of the MPA variance was explained with gender (β = .14, p = .007), age (β = −.22, p < .001), and discrepancy (β = .62, p < .001) as significant predictors. Higher MPA was predicted by being female, a younger musician, and having a higher maladaptive perfectionism. This indicates it would be important to deal with maladaptive perfectionism to effectively manage MPA.