Frightened by the stage or by the public? Exploring the multidimensionality of music performance anxiety

Abstract

The multidimensionality of Music Performance Anxiety (MPA) was examined in this study. Three related dimensions were identified: Somatic and Cognitive Features, Performance Context and Performance Evaluation. Although MPA has been widely studied in the last 20 years, it has been regarded mainly as unidimensional. A sample of 134 high school music students was tested using the Music Performance Anxiety Inventory for Adolescents (Osborne & Kenny, 2005), and The International Personality Item Pool (Goldberg, 1992). Confirmatory factor analysis supported the existence of three correlated MPA factors. Hierarchical regression analyses showed that both the pattern of predictors and the variance explained was different in the three MPA dimensions. Analysis of covariance (ANCOVA) revealed that girls scored higher only on Somatic and Cognitive Features. Overall, our results support the multidimensionality of MPA, pointing out that our understanding of this disorder could be enhanced by treating it as multi-, rather than unidimensional.

Keywords

five-factor model multidimensionality music performance anxiety personality

Music performance anxiety (MPA) represents a serious problem that musicians have to face during their entire career as performers (Yoshie, Kudo, Murakoshi, & Ohtsuki, 2009). Regardless of age and level of experience, MPA tends to manifest from childhood (Kenny & Osborne, 2006; Ryan, 2005) to adolescence (Fehm & Schmidt, 2006), and also in professional life (Kenny, Davis, & Oates, 2004). Consequently, it was found to be the most frequent non-musculoskeletal medical problem reported by musicians (Fishbein, Middlestadt, Ottati, Straus, & Ellis, 1988). Its consequences are often devastating, because high levels of MPA can lead to the deterioration of performance skills, thus threatening a musician’s career (Yoshie, Shigemasu, Kudo, & Ohtsuki, 2009).

One of the most widely-used definition for MPA is “the experience of marked and persistent anxious apprehension related to musical performance that has arisen through specific anxiety conditioning experiences, and which is manifested through combinations of affective, cognitive, somatic and behavioural symptom” (Kenny, 2011, p. 433). Several aspects concerning MPA have been covered so far by studies related to this disorder. Firstly, women generally report higher levels of MPA than men (Kokotsaki & Davidson, 2003; Osborne & Kenny, 2008; Steiner, 1998). Secondly, the main personality dimensions that are positively related to MPA are neuroticism, introversion and trait anxiety (Sadler & Miller, 2010; Salmon, 1990; Smith & Rickard, 2004). Thirdly, perfectionism seems to be positively related to MPA as well (Kenny et al., 2004; Kobori, Yoshie, Kudo, & Ohtsuki, 2011; Stoeber & Eissman, 2007). Fourthly, performance aspects such as context and frequency appear to be related to MPA (Fehm & Schmidt, 2006). Also, although MPA manifests regardless of age, it appears to reach a peak in adolescence (Fehm & Schmidt, 2006; Osborne & Kenny, 2005).

Usually, MPA is regarded as a unidimensional construct. However, several authors have provided evidence for the existence of three factors that are believed to comprise MPA (and other anxiety disorders): cognitive, physiological and behavioural (Hardy & Parfitt, 1991; Lang, Davis, & Öhman, 2000). Furthermore, some authors identified three related factors that comprise MPA: Somatic and Cognitive Features, Performance Context and Performance Evaluation (Osborne & Kenny, 2005).While the first factor aligns with the cognitive and physiological aspects mentioned before, the last two factors focus on behavioural and circumstantial aspects of performance. Somatic and Cognitive Features mainly describes the physical manifestations of performance anxiety immediately prior to and during a performance. Performance Context mainly reflects the performers’ preference for either solo or group contexts, being concerned with anxiety as a consequence of more or less performance isolation and the nature of an audience. Performance Evaluation reflects the consequences of being able to keep focused while performing, and of the evaluation that both the audience and the performer make of a performance, being concerned with the anxiety stemming from these evaluations. To our knowledge, no study so far has tried to analyze MPA as a multidimensional construct, or to verify if its component factors can be explained by different predictors. Even the authors who identified the three factors still used only one general score in the analyses, probably because it was more appropriate for the objectives of their research to treat MPA unidimensionally.

Although MPA has been the focus of research for the past three decades, several authors have pointed out that the instruments used for measuring it are far from being psychometrically reliable (Fehm & Schmidt, 2006; Osborne & Kenny, 2005). While some scales measure general performance anxiety, others were developed for specific samples (e.g., pianists) and have only been used once. Furthermore, many of these scales were developed using small samples (n < 50), or have failed to demonstrate internal consistency and test–retest reliability. One of the most reliable and psychometrically sound instruments used for measuring MPA is the Music Performance Anxiety Inventory for Adolescents (MPAI-A; Osborne & Kenny, 2005). Beside optimal psychometric properties, this instrument measures the three dimensions of MPA described earlier. Thus, it represents a suitable choice for our research.

The main purpose of this study is to verify the multidimensionality of MPA (i.e., the existence of three related MPA factors). Secondly, we aimed at determining whether the three dimensions of MPA can be explained by different predictors, from among performance aspects (time practiced, performing frequency and performing pattern) and personality (the five-factor model). Thirdly, we checked for possible gender differences in the three dimensions of MPA. Because, as stated earlier, MPA reaches a peak in adolescence, we decided to verify our objectives on a sample of adolescent musicians.

Method

Participants

The research sample consisted of 134 high school students (66.4% female), aged between 14 and 19 years (M = 16.41, SD = 1.21), from the “Ion Vidu” National Art College in Timişoara, Romania. 63.4% were instrumental players, while the others were vocalists. The participants had been studying their current instrument for an average of 6.02 years (SD = 3.76). Regarding their stage performance in the past school year, 21.6% performed only as soloists, 24.6% performed mostly solo with some group work, 23.1% indicated half solo–half group performances, 18.7% performed mostly in ensembles with some solo work, while 11.9% performed only in ensembles.

Although the research sample is somewhat small, the a-priori analysis using PowerStaTim (Sava & Maricuţoiu, 2008) showed optimal statistical power (between .80 and .99) for the expected effect size (R² between .10 and .25) in the hierarchical regression analyses. Also, the sample is adequate for verifying the multidimensionality of MPA via confirmatory factor analysis, because it ensures a participants-item ratio larger than 5 to1 (Gorsuch, 1997; MacCallum, Widaman, Zhang, & Hong, 1999).

Instruments

The Music Performance Anxiety Inventory for Adolescents (MPAI-A).

The original MPAI-A (Osborne & Kenny, 2005) contains 15 items scored on a 7-point Likert scale (0 = Not at all, 6 = All of the time), conceptualized to measure the three dimensions of MPA: Somatic and Cognitive Features, Performance Context and Performance Evaluation. The following modifications were made to the original scale:

1) The original authors had removed the item “It is easier to play in front of my family and friends than in front of strangers,” in order to increase Cronbach’s α of the total scale from .88 to .91. Because this also resulted in a decrease of α for the Performance Context scale, we found this decision questionable, and decided to keep this item. We considered this modification necessary because a) maximizing the internal consistency for each subscale of the MPAI-A is beneficial for running the hierarchical regression analyses with the three subscales as criterion variables, and b) overall, an α greater than .80 is already at an optimal level, and does not require further improvement.

2) Item 10 (“When I finish performing, I usually feel happy with my performance”) is the only positively coded item (for all the other items, a high score reflects a high level of anxiety). Although the authors did not discuss this aspect because the item seemed to work well, in our sample it was the only item which correlated negatively (−.15) with the total score. The initial α for the Performance Evaluation scale was .44, and after the removal of item 10, it increased to .68. Therefore, this item was removed from all further analyses.

3) The original response scale was a 7-point Likert scale (0 = Not at all, 6 = All of the time), which did not contain a description for each scale point. We believe that, mainly because the scale was developed to be used with adolescents, a somewhat clearer response scale (such as a 5-point Likert scale with a description for each scale point) would be more appropriate. Therefore, the response scale was changed into a 5-point Likert scale. The modified version of the scale (with all the modifications described earlier) is provided in the Appendix.

The translation of the MPAI-A was accomplished using the back-translation method. Specifically, the items were translated into Romanian by a psychologist proficient in both languages. Afterwards, they were translated back into English by a professional translator, and compared with the original MPAI-A. No major differences emerged.

The International Personality Item Pool (IPIP)

The IPIP (Goldberg 1992) contains 50 items scored on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree), and measures the dimensions of the five-factor personality model: Extraversion (E), Openness to Experience (O), Emotional Stability (S), Conscientiousness (C) and Agreeableness (A). The IPIP has been recently adapted for research use in Romania (Rusu, Maricuţoiu, Macsinga, Vîrgă, & Sava, 2012). The internal consistency alphas (in our sample) ranged from .71 (for the A scale) to .83 (for the S scale).

Demographics

The following data were collected: age, gender (0 = Male, 1 = Female), principal instrument played, age first performed in front of audience, time practiced each week (1 = Less than 3 hours, 5 = More than 21 hours), performing frequency (1 = Less than once a month, 5 = Over 6 times a month) and performing pattern (1 = Only as soloist, 5 = Only in ensembles).

Procedure

The students completed the questionnaires at school, in groups of 16–23, during class time. No information about their identity was requested. Permission for conducting this research was obtained from the school principal.

Statistical analysis

Confirmatory factor analysis (CFA), as implemented by AMOS 16 (Arbuckle, 2007), was used to examine the factor structure of the MPAI-A (implicitly aiming at verifying the multidimensionality of MPA). The CFA was performed over the covariance matrix of the items using the maximum likelihood estimation method. The goodness-of-fit of the tested models was evaluated using both absolute and relative indices. The absolute goodness-of-fit indices calculated were the χ² goodness-of-fit statistic, the Goodness of Fit Index (GFI) and the Root Mean Square Error of Approximation (RMSEA). The relative goodness-of-fit indices computed were the Incremental Fit Index (IFI) and the Comparative Fit Index (CFI). For GFI, IFI and CFI, values greater than .90 indicate a good fit, while values greater than .85 indicate an acceptable fit (Byrne, 2001). For RMSEA, values less than .05 indicate a good fit, and values less than .08 indicate an acceptable fit (Browne & Cudeck, 1993). Also, a non-significant χ² usually reflects an acceptable model fit. However, χ² is highly sensitive to sample size, and so the probability of obtaining a significant χ² increases as the sample size increases. Therefore, the χ² / df ratio is used as a more trustful indicator. Generally, a ratio close to 1 indicates a good fit, while a ratio greater than 2 represents an inadequate fit (Byrne, 1989).

The hierarchical multiple regression analyses were conducted using the three dimensions of MPA as criterion variables, and age, gender, time practice, performing frequency, performing context and Emotional Stability as predictors. Emotional Stability was the only personality factor included in the hierarchical regression analyses because it was the only one that correlated with at least one form of MPA. In the first step, the predictors were age and gender. In the second step, performing aspects (time practiced, performing frequency and performing pattern) were added as predictors, while in the third step, Emotional Stability was also included in the model.

It is worth mentioning that some variables included as predictors in the hierarchical multiple regression analysis are ordinal variables. Although this might seem unusual, several authors have argued that these kinds of variables can be treated as continuous when there are at least five response categories (Aiken, West, & Pitts, 2003; Cohen, Cohen, West, & Aiken, 2003). Because all our predictors meet this criterion, we concluded that they are suitable for this analysis.

Results

Confirmatory factor analysis

The factor structure of the MPAI-A was verified through CFA. Three models were tested: the first model assumes that all items load on one common MPA factor; the second model implies the existence of three uncorrelated MPA factors, while the third model assumes that the three MPA factors are correlated with each other. The CFA results are presented in Table 1.

Table 1.

Goodness of fit statistics for the three models of MPAI-A.

Model	χ²	df	χ²/df	GFI	RMSEA	IFI	CFI
One factor	189.15**	90	2.10	.82	.091 [.073–.109]	.87	.86
Three uncorrelated factors	293.88**	90	3.27	.79	.131 [.114–.147]	.73	.72
Three correlated factors	137.62**	87	1.58	.88	.066 [.044–.086]	.93	.93

Note. ** p < .01

The first model showed almost acceptable fit, with the relative goodness-of-fit indices reaching acceptable values, but with all the absolute goodness-of-fit indices showing inadequate values. The second model achieved the worst fit, with all indices showing inadequate values. The third model achieved the best fit, with all indices showing adequate values. Therefore, these results support the existence of three correlated MPA factors (see Figure 1).

Figure 1.

Confirmatory factor analysis of the MPAI-A (mp1 to mp16 represent the items of MPAI-A).

Table 2 presents the correlations between the research variables, and the Cronbach α values. The only α falling below the .70 threshold was for the Performance Evaluation scale (.68). Because none of the variables’ distributions deviated substantially from normality, r (Pearson) was used for calculating the correlation matrix. Emotional Stability was the only personality factor that correlated with at least one form of MPA. Therefore, it was the only personality factor included in the hierarchical regression analyses. The three MPA factors moderately correlated with each other (.56–.62)

Table 2.

Correlation matrix (2-tailed).

Scale	M	SD	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.
1. Age	16.41	1.21	–
2. Gender	1.66	.47	−.05	–
3. Time.prac	2.87	1.16	−.20*	−.10	–
4. Perf.freq	2.11	1.02	−.19*	−.09	.27**	–
5. Perf.patt	2.75	1.31	.13	−.20*	−.05	−.06	–
6. O	3.55	.53	−.11	.28**	.19*	.04	.01	(.73)
7. E	3.41	.67	−.08	−.04	.18*	−.05	−.11	.17	(.80)
8. S	3.09	.76	.04	−.30**	.15	.13	.06	.14	.06	(.83)
9. C	3.56	.63	−.06	.09	.06	.06	−.02	.23**	.09	.08	(.75)
10. A	3.81	.50	.11	.07	−.04	.16	.20*	.06	.40**	.31**	.06	(.71)
11. Som&Cog	2.75	.93	−.21*	.24**	−.15	−.28**	.00	.10	.02	−.42**	.02	−.13	(.85)
12. Perf.cont	2.74	1.09	.00	−.03	−.21*	−.40**	.19*	−.05	−.04	−.13	−.07	−.11	.56**	(.72)
13. Perf.eval	2.20	.97	−.10	−.06	−.13	−.22**	.13	.05	.00	−.26**	.01	−.14	.62**	.60**	(.68)

Notes. * p < .05; ** p < .01. Internal consistency alphas are displayed in the diagonal (where they were calculated), Time.prac = Time practiced, Perf.freq = Performance frequency, Perf.patt = Performance pattern, O = Openness to Experience, E = Extraversion, S = Emotional Stability, C = Conscientiousness, A = Agreeableness, Som&Cog = Somatic and cognitive features, Perf.cont = Performance context, Perf.eval = Performance evaluation.

Hierarchical regression analyses

The results of the hierarchical multiple regressions are presented in Table 3. Somatic and Cognitive Features were explained by the model composed of age and gender at a rate of 9.8% (R² = .098), with both predictors showing significant values. In the second step, the only significant predictor was performing frequency (β = −.29). Performing aspects added an extra 10.8% (ΔR² = .108) to the model’s explanatory potential. In the third step, Emotional Stability added an extra 10.3% (ΔR² = .103) to the model’s explanatory potential, becoming the strongest predictor in this model (β = −.34). Thus, Somatic and Cognitive Features can be explained by age, performing frequency and Emotional Stability at a rate of 30.9% (R² = .309).

Table 3.

Hierarchical multiple regression results.

Variables	Somatic and cognitive features		Performance context		Performance evaluation
	R²/ ΔR²	β	R²/ ΔR²	β	R²/ ΔR²	β
Step 1	.098**		.001		.013
Age		−.20*		.00		−.10
Gender		.23**		−.03		−.06
Step 2	.206/.108**		.215/.214**		.099/.086**
Age		−.29**		−.12		−.18*
Gender		.20*		−.05		−.07
Time practiced		−.10		−.13		−.10
Performing frequency		−.29**		−.39**		−.23*
Performing pattern		.07		.17*		.12
Step 3	.309/.103**		.223/.007		.164/.065**
Age		−.27**		−.11		−.16*
Gender		.11		−.07		−.14
Time practiced		−.06		−.12		−.07
Performing frequency		−.26**		−.38**		−.20*
Performing pattern		.07		.17*		.13
Emotional stability		−.34**		−.09		−.27**

Notes. * p < .05; ** p < .01.

For Performance Context, the model composed of age and gender had almost no explanatory potential (R² = .001), with neither of the two predictors being significant. In the second step, performing aspects added an extra 21.4% (ΔR² = .214) to the model’s explanatory potential, with both performing frequency (β = −.39) and performing pattern (β = .17) having significant values. Finally, Emotional Stability added a nonsignificant 0.7% (ΔR² = .007) to the model’s explanatory potential. Therefore, performance context can be explained only by performing frequency and performing context at a rate of 22.3% (R² = .223).

Lastly, Performance Evaluation was explained by the model composed of age and gender at a reduced rate of 1.3% (R² = .013). In the second step, the only significant predictor was performing frequency (β = −.23). Performing aspects added an extra 8.6% (ΔR² = .086) to the model’s explanatory potential. In the third step, Emotional Stability added an extra 6.5% (ΔR² = .065) to the model’s explanatory potential, showing the strongest relation with the criterion variable (β = −.27). Thus, performance evaluation can be explained by age, performing frequency and Emotional Stability at a rate of 16.4% (R² = .164).

Gender differences

An analysis of covariance (ANCOVA) was used for verifying gender differences in MPA. Performing frequency was included as a covariate because of its negative correlations with the three dimensions of MPA. The results (Table 4) show that girls scored higher than boys on Somatic and Cognitive Features, with moderate effect size (.05). No significant differences were identified on Performance Context and Performance Evaluation. Performing frequency had a significant effect on all three dimensions, with moderate to high effect size (.05–.16).

Table 4.

ANCOVA results for gender differences.

Scale	Group						F	Partial η²
	Boys (n = 45)			Girls (n = 89)
	EM	M	SD	EM	M	SD
Som&Cog	19.73	19.49	7.49	23.09	23.21	7.09	6.88**	.05
Perf.freq							10.13**	.07
Perf.cont	11.35	11.13	4.15	10.77	10.88	4.49	.63	.01
Perf.freq							25.77**	.16
Perf.eval	6.90	6.82	3.14	6.44	6.48	2.80	.77	.01
Perf.freq							6.95**	.05

Notes. ** p < .01. EM = Estimated Mean, Som&Cog = Somatic and cognitive features, Perf.cont = Performance context, Perf.eval = Performance evaluation, Perf.freq = Performance frequency.

Discussion

The main objective of this study was to verify the multidimensionality of MPA. Confirmatory factor analysis provided support for three correlated dimensions of MPA: Somatic and Cognitive Features, Performance Context and Performance Evaluation. Internal consistency alphas were satisfactory, ranging from .68 (for Performance Evaluation) to .85 (for Somatic and Cognitive Features). The moderate correlations identified between the three dimensions suggest that they share both mutual and distinct variance.

The hierarchical regression analyses showed that a similar pattern of predictors was identified for Somatic and Cognitive Features and Performance Evaluation. Specifically, both were explained by age, performing frequency and Emotional Stability. However, while those predictors accounted for approximately 31% of the variance in Somatic and Cognitive Features, they only accounted for about 16% of the variance in Performance Evaluation. Notably, the pattern of predictors was different for Performance Context, because its only significant predictors were performing frequency and performing pattern. While the explanatory potential of Emotional Stability is in line with previous research (Sadler & Miller, 2010; Smith & Rickard, 2004), its absence as a significant predictor in the analysis concerning Performance Context seems to suggest that this dimension is somewhat different from the other two.

Overall, the results of the hierarchical regression analyses tell a story that is in line with the theoretical background. Specifically, younger students with lower levels of emotional stability and lower performance frequency report higher levels of MPA (this pattern was identified for Somatic and Cognitive Features and Performance Evaluation). However, for Performance Context, students with lower performance frequency who tend to play more in ensembles report higher anxiety about contexts requiring solo performances and/or unfamiliar audiences. A possible explanation is that solo settings elicit the highest degree of performance anxiety, mainly because players draw emotional support from group settings (Cox & Kenardy, 1993).

Gender differences are, to some extent, similar to those already identified (Osborne & Kenny, 2005; Steiner, 1998). Girls scored higher on Somatic and Cognitive Features, while no significant differences were revealed on the other two dimensions. This seems to point out that the pattern of gender differences could be more sophisticated than previous research has shown.

Taken together, all our results suggest that the three dimensions of MPA have both similar and different characteristics. Thus, the main finding of our research is that MPA appears to be a multidimensional construct, rather than a unidimensional one.

Several future research directions emerge from our results. Firstly, as perfectionism was identified as being positively correlated with MPA (Kenny et al., 2004; Kobori et al., 2011), one could verify the way different facets of perfectionism are related to different dimensions of MPA. Secondly, the identified MPA factors could be replicated in samples of different age (e.g., children, college students). Also, gender differences should be further investigated.

Our research has some limitations that must be considered when interpreting its results. Firstly, the sample size is somewhat small (n = 134), although it was adequate for accomplishing our objectives. Future studies should use larger samples (n > 200) whenever possible. Secondly, the sample consists only of adolescents. Therefore, in order to draw more definite conclusions about the multidimensionality of MPA, it should also be tested in musicians of different age (e.g., college students, adults). Thirdly, the dimensionality of MPA depends on the item pool used. Hence, other item pools should be considered as well, in order to clearly establish the multidimensionality of MPA.

Overall, our research highlights the existence of three related factors forming MPA: Somatic and Cognitive Features, Performance Context and Performance Evaluation. These results expand the classical view of MPA, suggesting that while different predictors exist for these dimensions, different treatments could also exist. The possibility of contributing to enhance the treatment options for MPA, by understanding the particularities of its composing dimensions, is a very exciting proposal.

Footnotes

Appendix

The Music Performance Anxiety Inventory for Adolescents (modified)

Instructions: Several statements are listed below, which describe various reactions or feelings that people can have when they perform on stage. Read each statement and then mark an “X” on its right side to indicate to what extent it suits you. There are no “right” or “wrong” answers. Please work quickly and do not waste too much time on a statement.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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