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Abstract

While much research focuses on single-instrument performance, less is known about the unique challenges faced by multi-instrumentalists. This study examined the relationship between music performance anxiety (MPA), perfectionism, flow experiences, resilience, and trait anxiety among musicians who play both a primary and a secondary instrument. A sample of 91 musicians (60% female, M = 35.2 years, SD = 12.6) completed the MPA and flow questionnaires for both the primary and secondary instruments, while the measures for trait anxiety, resilience, and perfectionism were administered only once. Females reported higher levels of trait anxiety and MPA related to their secondary instrument. Interestingly, while flow experiences were negatively correlated with MPA for both instruments, resilience was linked, weakly, only to flow on the primary instrument. In multiple regression analysis, primary instrument MPA and primary instrument flow positively predicted secondary instrument MPA, while secondary instrument flow was a negative predictor. These findings reveal the complex dynamics of MPA in multi-instrumentalists, emphasizing how individual traits shape the musical experience.

Keywords

music performance anxiety (MPA)perfectionism flow resilience secondary instrument

Research suggests that learning multiple instruments is associated with greater versatility and a deeper understanding of music, particularly in terms of harmony, melody, and rhythm (Koh, 2021). While some skills can transfer between instruments (Forrester, 2018), musicians may still encounter initial difficulties, such as tone production, hand coordination, and playing in tune, as each instrument requires its own unique motor skills (Koh, 2021). Despite these aspects, existing literature predominantly focuses on single-instrument proficiency, overlooking the experiences of multi-instrumentalists. This study seeks to bridge this gap by examining the relationship between music performance anxiety (MPA), perfectionism, flow experiences, resilience, and trait anxiety in musicians who play both primary and secondary instruments. This is the first study to explore these variables in the context of secondary instruments.

While mastering a secondary instrument offers significant rewards, it also comes with specific challenges, especially in performance contexts. These challenges can be particularly intense for musicians, who often face high expectations and critical self-assessment when performing on an instrument with which they are less confident. This can lead to MPA, a well-documented phenomenon that affects musicians across various levels of expertise. MPA is characterized by persistent apprehension tied to musical performance, stemming from biological, psychological, and social factors (Kenny, 2009). It manifests through cognitive, affective, somatic, and behavioral symptoms, which vary across solo and group settings and diverse music genres (Papageorgi et al., 2007; Robson & Kenny, 2017). The prevalence of MPA is high, with estimates ranging from 16% to 83% in higher education settings (Fernholz et al., 2019; Miller & Chesky, 2004), with female musicians having more anxiety than males (Ma, 2023). Recognizing gender differences in MPA provides valuable insights for music educators. It is crucial to address the early signs of anxiety, particularly for female musicians, by implementing strategies to help them manage anxiety. Understanding the role of gender and personality traits offers deeper insights into the multifaceted nature of MPA across various developmental stages and levels of musical experience.

It can significantly affect performance quality, occupational health, and mental well-being (Araújo et al., 2017). Several factors have been associated with MPA, including low self-esteem (Sickert et al., 2022) and perfectionism (Dobos et al., 2019). Furthermore, MPA often coexists with conditions like social anxiety (Dobos et al., 2019). Performing on a secondary instrument can increase anxiety, as musicians often feel less skilled compared with their proficiency on their primary instrument. This increases the fear of making mistakes or the inability to meet personal or external expectations, which adds to performance stress (Casanova et al., 2018).

Exploring MPA in the context of secondary instruments is important for several reasons, above all, for a well-rounded musical education through enhancing cognitive skills, fostering adaptability, and ultimately leading to a more versatile and creative musician. Researching the differences in experiences between playing primary and secondary instruments sheds light on individual differences among musicians, helping with a better understanding of their nature, character, and behavior. Knowing these differences might be useful in decision-making between instruments. It is well known that different instruments attract musicians with different personalities (Kemp, 1981). For example, some musicians prefer melodic instruments like flute, while some prefer rhythmic instruments such as drums. By understanding the strengths and nuances of each instrument, one can better appreciate its unique qualities and bring out its individuality when performing (Mateos-Moreno & Hoglert, 2024). The process of choosing a secondary instrument can also be influenced by external factors, such as culture or one’s social network, for example, family members or teachers. In certain institutions of music education, playing a secondary instrument may be mandatory, most commonly the piano, which is often used as an accompaniment. By learning to play piano and thus playing the accompaniment, a musician can deepen musical skills such as hearing or a sense of rhythm (Hayes, 2023). In relation to the performance context, there is a difference between solo and orchestral instruments in technical skills and abilities to collaborate with other musicians. Understanding these differences can help musicians choose instruments that align with their performance intentions. In addition, experiences of different instruments enhance creativity and the opportunity to interpret the same piece of music from various perspectives (Alperson, 2008).

This fear of failure can be intensified in individuals with high trait anxiety, a stable personality trait that predisposes them to perceive situations as more threatening, making it particularly associated with MPA (Kenny, 2004). Highly trait-anxious individuals are more likely to perform better with a well-rehearsed piece in a familiar environment, while those with low trait anxiety may thrive when presented with a more difficult piece and a higher-pressure setting, such as an exam or competition (Osborne et al., 2005). Learning or playing a secondary instrument may make musicians with higher levels of trait anxiety more anxious about their performance on both primary and secondary instruments, particularly in evaluative contexts. The pre-existing anxiety and negative experiences on the primary instrument may influence performance on other instruments, as the musician is used to playing with anxiety. In terms of the secondary instrument, the new challenges may amplify the overall anxiety levels or lead to the development of new patterns of anxiety. This may affect performance on both instruments.

Perfectionism is a striving for flawlessness accompanied by critical self-evaluations (Frost et al., 1990). In the context of music, the presence of perfectionism has been associated with higher MPA, influencing expectations and creating pressure to excel (Dobos et al., 2019). While musicians are often motivated to improve their performance, over time excessive perfectionism has been associated with maladaptive outcomes (Arbinaga, 2023). Maladaptive perfectionism, marked by preoccupation with mistakes and excessive self-criticism, may become particularly debilitating when musicians perform on secondary instruments, as inexperience with them can intensify feelings of self-doubt (Frost et al., 1993; Ryan et al., 2024). This is reinforced by external pressure from evaluative audiences, such as teachers and peers, which sustain perfectionistic tendencies (Paetz, 2024; Ryan et al., 2024). In contrast, adaptive perfectionism is a positive striving for excellence. This form of perfectionism can enhance performance, as it encourages musicians to develop regular practice, refine their skills, and strive for continuous improvement. Adaptive perfectionism helps musicians maintain focus without becoming overwhelmed by anxiety (Dobos et al., 2019).

Based on the positive association between MPA and perfectionism (Arbinaga, 2023; Dobos et al., 2019; Paetz, 2024; Ryan et al., 2024), we may hypothesize that perfectionism will also be present for those who play a secondary instrument.

While anxiety and maladaptive perfectionism can hinder musical performance, it is equally important to identify protective factors that can counteract these challenges. One such factor is flow, a psychological state of complete immersion and enjoyment in an activity, which has increasingly been linked to peak performance (Cohen & Bodner, 2019). Defined as focused engagement in challenging yet rewarding activities, flow emerges when perceived challenges align with one’s skills, fostering intrinsic motivation and skill development (Nakamura & Csikszentmihalyi, 2002). While research suggests flow experience is consistent across genders, with no significant correlation found between gender and flow states (Cohen & Bodner, 2019), so far, there are no studies regarding gender differences in flow experiences among those who play a secondary instrument. Investigating the role of gender in flow for a secondary instrument may provide useful insights into how this context might shape musical experiences differently for men and women.

Cohen and Bodner (2019) found a strong, negative correlation between flow experiences and MPA, indicating that higher levels of flow were associated with lower levels of MPA. Some studies suggest that classical musicians experience lower flow compared with those playing other styles; it has been suggested that the structured learning environment of classical music studies may hinder performers’ ability to achieve a higher flow state (Moral-Bofill et al., 2023). This may prove a greater challenge for mastering a secondary instrument, which could potentially hinder the achievement of a higher flow state. The relationship between flow experience and secondary instruments remains unexplored, highlighting a significant gap in current research.

Another key factor in overcoming performance challenges is resilience—the ability to recover from significant stressors or adversity (Windle, 2011). Research has shown that music students with high resilience scores have lower scores in MPA and maladaptive perfectionism (Arbinaga, 2023; Osborne et al., 2014). Musicians’ deep engagement with music-making is essential to their well-being, and resilience strategies, for example, maintaining a disciplined practice routine, developing positive self-talk to handle the difficult moments in their practice or performance, are particularly beneficial in music education, where resilience can be a key factor in fostering self-confidence (Arbinaga, 2023). Osborne et al. (2014) suggested that performance resilience is a key psychological trait that protects against the negative effects of MPA. Additionally, Kegelaers et al. (2021) found that higher levels of resilience and physical health are negatively associated with mental health issues in professional musicians. However, research on resilience in musicians remains limited, despite the results that flow experiences can contribute to student well-being through building resilience and self-esteem (Mao et al., 2024). Despite its protective role, resilience remains underexplored in music psychology, and further research is needed to understand how resilience, MPA, and flow affect overall musician performance, how these factors may differ between primary and secondary instruments, and how resilience may promote optimal performance and well-being in musicians.

Study aim

In this study, we analyzed associations between MPA, trait anxiety, perfectionism, resilience, and flow, in a sample of music students and musicians who play primary and secondary instruments. The objectives of this study were the following: (1) to collect data about primary and secondary instrument MPA and flow as well as other variables and detect potential gender differences for them; (2) to examine bivariate associations between these psychological variables, music-related variables (genre, performance frequency and type, years of music training, hours of weekly practice), and sociodemographics (age, gender) in connection with primary and secondary instruments; and (3) to determine the most relevant predictors of secondary instrument MPA, including associations of primary instrument MPA/secondary instrument MPA and primary instrument flow/secondary instrument flow.

Based on these research questions, the following hypotheses were formulated:

Hypothesis 1 (H1): There will be higher levels of MPA among musicians when performing on their secondary instruments.

Hypothesis 2 (H2): We expect that there will be higher levels of MPA among women compared with men, based on previous research on female musicians having more anxiety symptoms than men (Ma, 2023).

Hypothesis 3 (H3): The levels of flow will be lower for secondary compared with the primary instruments, due to potential discrepancy between the challenges and skill levels of musicians on each of their instruments (Moral-Bofill et al., 2023; Nakamura & Csikszentmihalyi, 2002).

Hypothesis 4 (H4): MPA for primary and secondary instruments will be positively correlated with trait anxiety and (maladaptive) perfectionism, and negatively with flow experience and resilience.

Hypothesis 5 (H5): Trait anxiety and maladaptive perfectionism will be significant contributors to MPA for secondary instruments in multivariate analysis, based on previous results (Kenny, 2004).

Hypothesis 6 (H6): Flow and resilience will be protective in MPA for the secondary instruments in multivariate analysis. Flow is linked to peak performance and enjoyment (Cohen & Bodner, 2019). Likewise, both resilience and flow contribute to well-being (Mao et al., 2024). Secondary instrument flow may provide protection for MPA. Flow experiences with the primary instrument may also reduce MPA for the secondary instrument.

Method

Participants

Participants were recruited through an online advertisement on Facebook, inviting musicians and music teachers to participate voluntarily in the study. The sample consisted of university music students and music teachers (N = 91; 36 men and 55 women) from various universities across Hungary, who were still actively performing. All participants were Hungarian, from different regions of Hungary. Ethical approval for the study was granted by the Institutional Review Board at the University of Szeged (reference number: 6/2017). Participants were aged between 18 and 70 years, with a mean age of 35.2 years (SD = 12.6). Regarding education levels, 20% of respondents had a high school diploma, 17% had a bachelor’s degree, 59% had a master’s degree, and 4% had a PhD. On average, they had a professional experience of 26.3 years (SD = 12.1) and practiced for 12.2 hr per week (SD = 12.1) for both instruments combined. The characteristics of the primary and secondary instruments were also assessed. Individuals were also asked to answer questions about the type of instrument they played, the genre of music, their performance frequency, and the nature of their performances. The primary instrument is defined as the one that musicians have played the longest, typically the first instrument they start with when they begin music school. In Hungarian music education, the secondary instrument is mainly the piano, which is introduced during high school education.

Table 1 presents descriptive statistics for the sample characteristics related to the participants’ primary and secondary instruments. For instrument type, participants primarily played woodwind (30%) and string (23%) instruments as their primary instrument, whereas for their secondary instruments, a higher percentage reported playing keyboard (46%) and woodwind (21%) instruments. Classical music was the dominant genre both for primary (91%) and secondary instruments (83%), with a minimal representation for pop, jazz, and folk music. Performance frequency varied between primary and secondary instruments. Combining weekly and monthly performances, 65% of participants performed regularly with their primary instruments, while for secondary instruments, this rate was 25%. Less frequent performance with the primary instrument was reported by 35% of the participants, and by 75% with secondary instruments. Regarding the type of performance, the majority of primary instrument players (40%) participated in both solo and chamber music settings, whereas for secondary instruments, chamber music only performances were reported by a larger percentage (30%). Solo only performances were more common for secondary instruments (22%), compared to only 3% of primary instrument players.

Table 1.

The characteristics of the primary and secondary instruments (n = 91).

	Primary instrument (%)	Secondary instrument (%)
Instrument type
Woodwind	30	21
String	23	13
Keyboard	20	46
Brass	19	9
Vocal	7	8
Percussion	1	2
Genre of music
Classical	91	83
Pop	7	12
Jazz	1	2
Folk	1	3
Performance frequency
Once weekly	29	16
Once a month	36	9
Four times a year	23	23
Two times a year	7	21
Once a year	2	7
Once in a few years	3	24
Type of performance
Solo only	3	22
Mostly solo	22	13
Solo and chamber music	40	12
Mostly chamber music	25	23
Chamber music only	10	30

Measures

Participants completed the measures outlined below, all in Hungarian. Respondents completed the MPA and flow questionnaires for both the primary and secondary instruments. Measures for trait anxiety, resilience, and perfectionism were administered only once at pretest.

MPA

We assessed MPA using the Music Performance Anxiety Inventory (MPAI-A) developed by Kenny and Osborne (2006). While this was originally developed for adolescent musicians, the language level of the test is suitable and easy to understand for musicians of all ages. It includes 15 items that assess somatic (e.g., “When I perform in front of an audience I get sweaty hands”), cognitive (e.g., “I often worry about my ability to perform”), and behavioral (e.g., “I try to avoid playing on my own at a school concert”) characteristics of anxiety, with responses measured on a 7-point Likert-type scale from (0 = not at all to 6 = all of the time). Total scores on this inventory range from 0 to 90, where higher scores indicate higher MPA. The translated version in this study showed high internal consistency. Cronbach’s α value of reliability of the composite measurement was .88 for the primary instrument and .90 for the secondary instrument.

Flow

In a previous study, Cohen and Bodner (2019) used the 9-item Short Flow Scale (Martin & Jackson, 2008), which may have psychometric limitations for measuring musicians’ flow experiences, given its low internal consistency (Cronbach’s α = .68). Accordingly, the current study used the Flow State Questionnaire (PPL-FSQ, Magyaródi et al., 2013), which has stronger psychometric properties, to assess flow in musicians. This questionnaire was developed using various flow measurement tools and identified two main factors: (1) The balance between challenges and skills (11 items, e.g., “I was able to keep up with the challenges”) and (2) absorption in reality (9 items, e.g., “It completely captured my attention”). Participants responded to items on a 5-point scale (1 = this does not apply to me, 5 = this applies to me). Higher scores indicate higher levels of flow. Cronbach’s alpha for our sample was excellent, with values of .92 for the primary instrument and .93 for the secondary instrument.

Trait anxiety

We used the 20-item State-Trait Anxiety Inventory Trait version (STAI-T) to measure anxiety as a personality trait (Spielberger et al., 1970; Hungarian version by Sipos & Sipos, 1978). Participants rated how they generally feel on a 4-point Likert scale (1 = not at all, 4 = very/completely), producing scores of between 20 and 80 points. Items include statements such as “I worry too much over something that really doesn’t matter.” The reverse-scored items on the questionnaire are: 1, 6, 7, 10, 13, 16, and 19. Higher scores indicate greater levels of trait anxiety. Cronbach’s alpha was .88.

Perfectionism

An eight-item Frost Multidimensional Perfectionism Scale—Brief (F-MPS-Brief, Burgess et al., 2016; Hungarian version by Dobos et al., 2019) was used as a self-report measure to capture two dimensions of perfectionism: maladaptive perfectionism (4 items, e.g., “The fewer mistakes I make, the more people will like me”) and adaptive perfectionism (4 items, e.g., “I set higher goals for myself than most people”). Participants responded to each item on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), with scores derived by summing the responses. This scale showed acceptable internal consistency for maladaptive perfectionism (Cronbach’s alpha = .75) and good internal consistency for adaptive perfectionism (Cronbach’s alpha = .85).

Resilience

The 10-item Resilience Questionnaire is a tool developed to assess psychological resilience (Campbell-Sills & Stein, 2007). The items are adapted from the 25-item version of the Connor-Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003). We applied the Hungarian validated version (Járai et al., 2015). Participants responded to each item using a 5-point Likert scale, where 0 = not true at all and 4 = almost always true. Total scores fall within a range of 0 to 100, with higher scores indicating higher resilience. Higher summary scores reflect higher resilience. This 10-item version of the CD-RISC demonstrated good internal consistency (Cronbach’s alpha = .81).

Statistical analyses

Data were analyzed using SPSS Version 25.0. Descriptive statistics were calculated, and bivariate correlations were assessed between sociodemographic variables (gender and age) and other study variables for both primary and secondary instruments. Additionally, four hierarchical regression models were performed to explore the predictive value of various factors related to secondary instrument MPA. Collinearity diagnostics were conducted to ensure the reliability of the regression models.

Results

Descriptive statistics for the scales—including minimum, maximum, mean, and standard deviation values—are shown in Table 2. Analyses revealed that, compared to man, women reported significantly higher levels of trait anxiety, t(89) = 2.77, p < .01), and MPA related to their secondary instrument, t(89) = 2.13, p < .05). Specifically, female musicians reported slightly higher levels of MPA for their secondary instrument, while males’ scores were lower. For flow associated with the secondary instrument, both genders reported lower scores compared to those for the primary instrument, t(89) = 0.78, p = .43). The differences using paired t-test (p > .05) between primary instrument MPA/secondary instrument MPA, and primary instrument flow/secondary instrument flow were not significant.

Table 2.

Descriptive statistics for music performance anxiety, trait anxiety, dimensions of perfectionism, resilience, and flow by gender (n = 91).

	Minimum	Maximum	Mean ± SD	Females (n = 55)M (SD)	Males (n = 36)M (SD)
Primary instrument MPAI-A	8	81	38.9 ± 16.9	40.9 (16.1)	35.9 (17.7)	^ap = .158
Secondary instrument MPAI-A	7	84	38.7 ± 20.0	42.3 (19.5)	33.3 (19.8)	p = .035
Trait anxiety	22	73	46.1 ± 11.6	48.8 (11.6)	42.1 (10.6)	p = .007
Adaptive perfectionism	4	19	9.5 ± 4.0	9.8 (4.0)	8.9 (3.9)	p = .288
Maladaptive perfectionism	4	20	14.6 ± 4.0	15.3 (3.7)	13.6 (4.3)	p = .056
Resilience	11	40	30.5 ± 5.9	31.0 (6.2)	29.6 (5.2)	p = .275
FLOW primary instrument	32	98	80.0 ± 13.0	81.1 (12.4)	78.2 (13.9)	p = .300
FLOW secondary instrument	21	100	77.1 ± 15.3	78.2 (14.6)	75.6 (16.3)	p = .435

Notes. MPAI-A = Music Performance Anxiety Inventory; FLOW = Flow State Questionnaire.

Two-sample t-test.

Table 3 presents the means, standard deviations, and correlation coefficients for bivariate relationships among measures related to the primary instrument. Music performance anxiety (MPAI-A) showed a significant negative correlation with flow (r = −.54; p < .01) and resilience (r = −.22; p < .05), and positive correlations with trait anxiety (r = .40; p < .01) and evaluative concerns (r = .25; p < .05). Flow was positively correlated with resilience (r = .27; p < .01), years of music training (r = .27; p < .01), and age (r = .21; p < .05), and a negative association with trait anxiety (r = −.25; p < .05). Evaluative concerns correlated positively with adaptive perfectionism (r = .23; p < .05) and trait anxiety (r = .59; p < .01) and negatively associated with resilience (r = −.37; p < .01). Other notable correlations include a strong positive correlation between years of music training and hours of weekly practice (r = .91; p < .01). Age (r = −.20; p < .05) and gender (r = −.28; p < .01) displayed weak negative associations with trait anxiety.

Table 3.

Pearson correlations for the primary instrument (n = 91).

	2	3	4	5	6	7	8	9	10	11	12	13
1. Primary instrument MPAI-A	^a −.54**	.25*	.06	−.22*	.40**	−.10	.25*	.02	−.17	.04	−.13	−.14
2. Primary instrument FLOW		−.10	.13	.27**	−.25*	−.10	−.17	.08	.27**	−.04	.21*	−.11
3. Maladaptive perfectionism			.23*	−.37**	.59**	−.04	−.11	.07	−.12	.13	−.17	−.11
4. Adaptive perfectionism				.28**	.14	−.16	.10	−.17	−.09	−.02	−.16	−.20
5. Resilience					−.46**	−.01	−.15	−.05	.10	−.06	.02	−.11
6. Trait anxiety						.04	.15	−.01	−.19	.12	−.20*	−.28**
7. Genre of music (1 = classical, 2 = other)							−.10	−.23*	.06	−.05	.07	.10
8. Performance frequency								−.03	−.04	−.19	.00	−.16
9. Type of performance (1 = solo, 2 = chamber music)									.17	−.09	.14	.16
10. Years of music training										−.15	.91**	.03
11. Hours of weekly practice											−.20	−.07
12. Age												.02
13. Gender (1 = female, 2 = male)

Notes. MPAI-A = Music Performance Anxiety Inventory; FLOW = Flow State Questionnaire.

Correlation coefficients (r).

p < .05. **p < .01.

Table 4 presents the means, standard deviations, and correlation coefficients for bivariate relationships among measures related to the secondary instrument. MPAI-A was negatively correlated with flow (r = −.57; p < .01), while positively associated with trait anxiety (r = .34; p < .01) and evaluative concerns (r = .25; p < .05). MPAI-A was also positively correlated with performance frequency (r = .22; p < .05). Flow showed positive correlations with type of performance (r = .39; p < .01) and years of music training (r = .21; p < .05) but it was negatively related to both trait anxiety (r = −.22; p < .05) and performance frequency (r = −.21; p < .05). Evaluative concerns correlated positively with trait anxiety (r = .59; p < .01) and negatively with resilience (r = −.37; p < .01) and type of performance (r = −.21; p < .05). Adaptive perfectionism was positively associated with resilience (r = .28; p < .01) but showed no significant relationships with other variables. Trait anxiety negatively correlated with resilience (r = −.46; p < .01), and age (r = −.20; p < .05). Gender was weakly but negatively correlated with MPAI-A (r = −.22; p < .05) and trait anxiety (r = −.28; p < .01).

Table 4.

Pearson correlations for the secondary instrument (n = 91).

	2	3	4	5	6	7	8	9	10	11	12	13
1. Secondary instrument MPAI-A	^a −.57**	.25*	.08	−.12	.34**	−.28*	.22*	−.34**	−.22*	.08	−.15	−.22*
2. Secondary instrument FLOW		−.15	.12	.14	−.22*	.07	−.21*	.39**	.21*	−.07	.15	−.08
3. Maladaptive perfectionism			.23*	−.37**	.59**	−.27**	.01	−.21*	−.12	.13	−.17	−.11
4. Adaptive perfectionism				.28**	.14	−.02	.13	.02	−.09	−.02	−.16	−.20
5. Resilience					−.46**	−.01	−.14	−.03	.10	−.06	.02	−.11
6. Trait anxiety						−.10	.01	−.11	−.19	.12	−.20*	−.28**
7. Genre of music (1 = classical, 2 = other)							−.33**	.15	.07	−.13	.05	.33**
8. Performance frequency								−.24*	−.28**	−.02	−.22*	−.05
9. Type of performance (1 = solo, 2 = chamber music)									.33**	−.07	.31**	−.05
10. Years of music training										−.15	.91**	.03
11. Hours of weekly practice											−.20	−.07
12. Age												.02
13. Gender (1 = female, 2 = male)

Notes. MPAI-A = Music Performance Anxiety Inventory; FLOW = Flow State Questionnaire.

Correlation coefficients (r).

p < .05. **p < .01.

Table 5 displays the bivariate associations between the measures of flow and MPA, in relation to the primary and secondary instruments. A significant positive relationship was detected between MPA1 and MPA2 (r = .62; p < .01), while the association between the flow measures was non-significant.

Table 5.

Bivariate associations between primary instrument MPA/secondary instrument MPA and primary instrument flow/secondary instrument flow (n = 91).

	2	3	4
1. Primary instrument MPAI-A	^a 0.62*	–0.54*	–0.10
2. Secondary instrument MPAI-A		–0.16	–0.057*
3. Primary instrument FLOW			0.16
4. Secondary instrument FLOW

Notes. MPAI-A = Music Performance Anxiety Inventory; FLOW = Flow State Questionnaire.

Correlation coefficients (r).

p < .01.

Table 6 presents the multiple (hierarchical) regression models predicting MPA for the secondary instrument, including MPA1, flow1 and flow2, and gender. Perfectionism, resilience, and age were excluded, giving no valuable effect to the model. Likewise, trait anxiety was excluded due to high level of multicollinearity.

Table 6.

Hierarchical regression models for the secondary instrument music performance anxiety (MPAI-A) (n = 91).

Variables	Model 1β (SE)	Model 2β (SE)	Model 3β (SE)	Model 4β (SE)	VIF (tolerance)
Primary instrument MPAI-A	^a0.62 (0.09)***	0.56 (0.07)***	0.74 (0.08)***	0.70 (0.08)***	1.50 (0.67)
Secondary instrument FLOW		–0.50 (0.08)***	–0.54 (0.07)***	–0.55 (0.07)***	1.03 (0.97)
Primary instrument FLOW			0.33 (0.10)***	0.29 (0.10)***	1.50 (0.67)
Gender (1 = female, 2 = male)				–0.13 (2.37)*	1.08 (0.92)
Constant	10.06*	64.01***	19.23	33.39*
R ²	0.38	0.64	0.72	0.73
F value	55.51***	78.41***	73.18***	58.64**

Notes. MPAI-A = Music Performance Anxiety Inventory; FLOW = Flow State Questionnaire.

β = Standardized regression coefficient, SE = Standard error.

p < .01. **p < .001.

In Model 1, primary instrument MPAI-A was positively associated with secondary instrument MPAI-A (β = 0.62; p < .001), explaining 38% of the variance (R² = 0.38). In Model 2, the variance explained by primary instrument MPAI-A scores (β = 0.56; p < .001) was reduced by the inclusion of secondary instrument flow (β = –0.50; p < .001). This model explained 64% of the variance (R² = 0.64). In Model 3, primary instrument flow was added, showing a positive association (β = –0.32; p < .001), while primary instrument MPAI-A (β = 0.74; p < .001) and secondary instrument flow (β = –0.54; p < .001) remained significant. The variance explained increased to 72% (R² = 0.72). Finally, Model 4 included gender as a predictor. While gender showed a small negative association (β = –0.13; p < .05), primary instrument MPAI-A (β = 0.70; p < .001), secondary instrument flow (β = 0.55; p < .001), and primary instrument flow (β = 0.29; p < .001) remained significant. This final model explained 73% of the variance (R² = 0.73).

Discussion

This study is the first to examine the relationships between MPA, perfectionism, flow, resilience, and trait anxiety in musicians playing both primary and secondary instruments. Thus, our findings aim to address the gap in music psychology, offering valuable insights into how these factors influence performances and affect musicians’ well-being.

Gender differences in MPA

Our findings revealed significant gender differences in MPA for secondary instruments, with females reporting higher anxiety than men. These findings align with previous research (Dobos et al., 2019; Kenny, 2009; Ma, 2023). Notably, our results indicate that females showed higher MPA scores for secondary instruments compared to their primary ones, while men showed lower scores across both instruments. This pattern suggests a complex interaction between gender and instrument proficiency that needs further exploration. The heightened anxiety and self-doubt associated with secondary instruments, often attributed to reduced familiarity and skill levels (Casanova et al., 2018; Robson & Kenny, 2017), may primarily apply to females. These results reflect the need for music educators to use gender specific approaches to help female students manage performance anxiety, particularly when it is associated with a secondary instrument.

Flow differences between primary and secondary instruments

Although there was a trend toward lower flow states for secondary instruments, this difference was not statistically significant. This finding may suggest that flow occurs when perceived challenges align with one’s skills (Nakamura & Csikszentmihalyi, 2002). The potential discrepancy between challenges and skill levels for secondary instruments may still influence flow states (Moral-Bofill et al., 2023). Additionally, our study replicated previous research, showing no significant gender differences in flow experiences (Cohen & Bodner, 2019). While both males and females experienced lower flow with secondary instruments, this effect was consistent across genders. The lack of difference in flow between primary and secondary instruments suggests that musicians may adapt to the challenges of secondary instruments over time, despite initial discrepancies.

Risk and protective factors for MPA

Our findings revealed significant correlations between MPA and the psychological scales. Both primary and secondary instrument MPA showed positive correlations with trait anxiety and maladaptive perfectionism, aligning with previous research (Dobos et al., 2019; Kenny, 2009). The current study expands on this, suggesting that secondary instrument performance is particularly challenging for trait-anxious musicians, especially females. Musicians with high trait anxiety tend to perceive performance situations as threatening, heightening their susceptibility to performance stress (Kenny et al., 2004). This association is strengthened by the positive correlation between MPA and maladaptive perfectionism. The interaction of these two factors creates a cycle of heightened performance pressure (Dobos et al., 2019; Ryan et al., 2024).

The finding that primary instrument MPA was negatively correlated with resilience, while there was no association between secondary instrument MPA and resilience, is unique and suggests that the relationship between MPA and resilience may differ depending on the level of emotional investment. Musicians are often more emotionally invested and experience higher performance pressure with their primary instrument, which they have likely been learning for a longer period. This extended engagement may contribute to the development of greater resilience and self-confidence, which in turn helps reduce MPA (Arbinaga, 2023).

Predictors of the secondary instrument MPA

Multiple regression models revealed that primary instrument MPA is a strong predictor of secondary instrument MPA, suggesting that the anxiety experienced in one context (e.g., on primary instrument) is not isolated but rather extends to other aspects of musical performance (e.g., playing a secondary instrument). Primary instrument flow was a positive predictor of secondary instrument MPA, while secondary instrument flow was a negative predictor. These findings suggest that when musicians experience flow in a familiar context (playing their primary instrument), it may amplify their expectations for the secondary instrument, even though they may not have the same level of proficiency on it. In contrast, secondary instrument flow appears to directly reduce anxiety, which aligns with research demonstrating flow’s role in enhancing well-being (Mao et al., 2024). The significant gap in research regarding flow experiences with secondary instruments highlights an important area for future study. Further research is needed to explore how flow in one domain influences anxiety in another one, and what factors contribute to this effect.

Hypothesis evaluation

First, MPA did not vary across primary and secondary instruments; thus, our first hypothesis (H1) was not supported. Females reported significantly higher MPA scores for secondary instruments compared with men, providing support for the second hypothesis (H2). Our third hypothesis (H3) was partially true, as the level of flow was lower (but not significantly) for the secondary instrument. The correlation analysis confirmed our fourth hypothesis (H4). Both primary and secondary instrument MPAs correlated positively with trait anxiety and maladaptive perfectionism. Primary instrument MPA correlated negatively with flow and resilience, while secondary instrument MPA correlated negatively with flow experience only. As to the fifth hypothesis (H5), we assumed that maladaptive perfectionism and trait anxiety might be significant contributors to MPA for the secondary instruments in multivariate analysis. However, these variables were excluded from the regression due to high multicollinearity. Finally, our sixth hypothesis (H6) was partially supported: while secondary instrument flow acted as a protective factor, significantly reducing MPA for secondary instruments, primary instrument flow unexpectedly served as a positive predictor. Resilience was excluded due to high multicollinearity.

Strengths and limitations

Limitations of this study include its cross-sectional design and small sample size, which limited our ability to perform subgroup analyses, such as instrument and genre-specific comparisons. Additionally, using a convenience sampling method may reduce the generalizability of findings. Thus, the current sample has been too heterogeneous, including both music teachers and students with a wide age range and different performance opportunities. Another limitation is that the data obtained for all the scales are based on self-reports. Therefore, the results reflect participants’ own evaluation of these constructs.

One important limitation of our study is that we did not provide a measure of participants’ years of music training, nor the relative competence/level of achievement on each instrument, or the hours of weekly practice separately for each instrument; instead reporting a combined measure across both instruments. This limits our ability to draw conclusions about the effect of practice time for each instrument separately. Future studies could address this limitation by measuring practice time for primary and secondary instruments separately for a more detailed understanding of their impact. Additionally, this limited our findings regarding how experience on each instrument contributes to anxiety, perfectionism, flow, and resilience. While we measured performance frequency and type of performance, our study did not detail the evaluative context of performance due to questionnaire length constraints.

Despite these limitations, the novelty of our study lies in its focus on secondary instruments, specifically examining the roles of gender, perfectionism, resilience, and performance context. In terms of strengths, this study expands research on MPA in secondary instruments, filling a gap in the field of music psychology. This research field is still underinvestigated. Furthermore, the analysis of risk and protective factors offers valuable insights for musicians’ mental health and performance optimization. The online data collection method enabled us to access a diverse sample of musicians, overcoming recruitment challenges. In the future, we should further explore these complex relationships, how psychological factors such as MPA, flow, and resilience evolve over time in multi-instrumentalists. Expanding the sample to include musicians from diverse genres and cultural backgrounds would help assess the generalizability of the findings.

Conclusion

This study offers new insights into the factors associated with MPA in musicians playing both primary and secondary instruments. Our findings highlight significant gender differences in MPA, with women reporting higher anxiety on secondary instruments. This suggests that anxiety related to secondary instruments, often linked to reduced familiarity and skill levels, may be more pronounced in female musicians. Our findings identify high trait anxiety and maladaptive perfectionism as key risk factors, contributing to increased performance pressure. Interestingly, resilience serves as a protective factor for primary instrument MPA but does not correlate with secondary instrument anxiety, suggesting that emotional investment and experience play a role in how resilience impacts anxiety. Flow experiences were crucial; primary instrument flow predicted higher MPA, while secondary instrument flow reduced anxiety, possibly due to lower performance pressure or more enjoyment.

Footnotes

ORCID iDs

Bianka Dobos

Dianna T. Kenny

Bettina F. Piko

Ethical considerations

This study received ethical approval from the University of Szeged Ethics Committee (ref. no.: 6/2017).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received no external funding. The Article Processing Charges were covered by the University of Szeged Open Access Fund, Grant number: 7986.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data are available from the corresponding author upon reasonable request.

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Music performance anxiety: Insights into primary and secondary instruments

Abstract

Keywords

Study aim

Method

Participants

Measures

MPA

Flow

Trait anxiety

Perfectionism

Resilience

Statistical analyses

Results

Discussion

Gender differences in MPA

Flow differences between primary and secondary instruments

Risk and protective factors for MPA

Predictors of the secondary instrument MPA

Hypothesis evaluation

Strengths and limitations

Conclusion

Footnotes

ORCID iDs

Ethical considerations

Funding

Declaration of conflicting interests

Data availability statement

References