The relation of music performance anxiety (MPA) to optimism,self-efficacy,and sensitivity to reward and punishment: Testing Barlow’s theory of personal vulnerability on a sample of Spanish music students

Abstract

Music performance anxiety (MPA) is a phenomenon often encountered among professionals and students who make public appearances. This article presents the results of a study carried out on a sample of music students in superior music conservatories in Spain (N = 434). Our goal was to analyze MPA on the basis of Barlow’s (2000) anxiety theory, supplementing it with further personality constructs such as dispositional optimism, general auto-efficacy, and sensitivity to reward and punishment. Our structural equation modeling (SEM) results reveal that several of those constructs exert their effect via the helplessness factor – the central construct in Barlow’s theory – and that they likewise exert a further series of direct effects on MPA. All in all, the variables taken into consideration account for 45.6% of variance in MPA in males and of 52.1% thereof in females. This study thus upholds Barlow’s theory of anxiety, while broadening it with further explanatory mechanisms.

Keywords

conservatoires Music Performance Anxiety optimism personality self-efficacy sensitivity to punishment

Salmon (1990) defined musical performance anxiety (MPA) as “the experience of persisting, distressful apprehension about and/or actual impairment of performance skills in a public context, to a degree unwarranted given the individual’s musical aptitude, training and level of preparation” (p. 3). It is one of the psychological conditions that most affect performance (Papageorgi, Creech, & Welch, 2013; Yoshie, Kudo, Murakoshi, & Ohtsuki, 2009). Several studies find different degrees of MPA in music students and active professional musicians (Papageorgi et al., 2013). One could suppose that MPA is one of the factors responsible for music student dropout; nevertheless, 25% of music professionals still have to deal with serious anxiety problems (Fishbein, Middlestadt, Attati, Strauss, & Ellis, 1988).

As Brodsky (1996) affirms, MPA spans a continuum of different manifestations. In mild cases it can be regarded as an integral element of the musical profession; taken to the other extreme, however, it can cause severe, debilitating symptoms akin to panic which entirely impede performance. In the case of musicians with a high level of mastery, however, it is possible that the activation of such states might not necessarily be associated with a decrease in performance capacity but in its improvement; such anxiety could be designated as adaptive instead of maladaptive (Papageorgi et al., 2013). In the population we are researching here, it would nevertheless be unlikely to encounter such high levels of mastery.

One of the goals of music education should be to prepare musicians for public performance while teaching them how to avoid phenomena such as MPA. In order to approach this problem from an educational and clinical point of view, it is necessary to have a theoretical framework that helps us target the problem’s origin and understand the reasons for its persistence. Barlow’s theory of anxiety (Barlow, 2000; Brown, White, Forsyth, & Barlow, 2004) stands out in this field by offering a plausible explanation for the interaction between personal and contextual variables (Kenny, Davis, & Oates, 2004; Papageorgi et al., 2013; Rae & McCambridge, 2004). The former are essential in explaining inter-individual differences, the latter much more relevant in explaining intra-individual variation.

Barlow (2000) postulates that the origin of different problems associated with anxiety can be ascribed to a triple set of vulnerabilities: a generalized biological vulnerability, a generalized psychological vulnerability and a more specific psychological vulnerability. The first two are associated with a series of personal and contextual characteristics, all of which tend to generate personal profiles of helplessness and a perceived lack of control of events (Barlow, 2000; Brown et al., 2004; Gallagher, Bentley, & Barlow, 2014), whereas specific psychological vulnerability would determine the concrete type of problem the individual would subsequently develop. Biological vulnerability refers to personality traits such as neuroticism, negative affect or behavioral inhibition (Barlow, 2000). Psychological vulnerability is determined by experiences of perceived situational uncontrollability in the first years of life, giving rise to a belief system in which negative thoughts and an overall impression of uncontrollability prevail (Chorpita & Barlow, 1998). Once the vulnerability pattern has installed itself and the first symptoms of anxiety have begun to appear, lack of control over them retrofeeds the process and the difficulties tend to persist.

Kenny (Kenny et al., 2004; Kenny, Fortune, & Ackermann, 2009) has applied Barlow’s anxiety model to MPA by developing K-MPAI, the Kenny Music Performance Anxiety Inventory, based on three theoretical components. One of them associates MPA with specific cognitions, while the other two are designed to take psychological vulnerability into account – early family context and helplessness, respectively. Even if the theory of Barlow (2000) suggests three factors, as K-MPAI also does, there is no correspondence between the two, since K-MPAI factors do not evaluate the biological vulnerability or specific vulnerability. It rather introduces a specific anxiety indicator (cognitions), a factor about early context near to Barlow’s psychological vulnerability factor (Barlow, 2000; Chorpita & Barlow, 1998) and a third factor of helplessness, which would be the result of the interaction between biological and psychological vulnerabilities described by Barlow (2000). An approach to this last factor has been planned and evaluated through the Control Anxiety Questionnaire (Brown et al., 2004). The K-MPAI evaluation tool has been most recently adapted to other European contexts in a Portuguese version (Figueiredo, Dias-Neto, & Gattaz, 2011) and a Spanish version (Zarza, Orejudo, Casanova, & Mazas, 2016).

In this paper we intend to provide new evidence upholding the usefulness of Barlow’s model in predicting MPA by providing and analyzing data collected in a country – e.g. Spain – where no study has hitherto addressed the subject within this theoretical framework. The specific objective of this study is to compare, using structural equation modeling, the mediating role of different personal variables that have been linked to performance anxiety, such as helplessness, and according to Barlow’s model development carried out by Kenny et al. (2004), that is, sensitivity to reward and punishment (Corr, 2002; Gray, 1982), which are also considered in Barlow’s model (2000). Furthermore, we are taking up an idea from Gallagher et al. (2014), who have pointed out the importance of continuing to investigate different dimensions of perceived control as related to problems of anxiety by adopting further constructs such as dispositional optimism (Carver & Scheier, 2005; Carver, Scheier, & Segerstrom, 2010). In this study we also include general auto-efficacy (another control construct highlighted in the literature) and sensitivity to reward and punishment (an element of Barlow’s anxiety theory which has hardly been investigated elsewhere). Most of these constructs share a common theoretical approach: in the area of social cognition they are featured in theories of self-regulation of conduct (Umstattd, McAuley, Motl, & Rosengren, 2007). In addition, this comparison will be analyzed independently for groups of men and women. Including such a distinction in our analysis is justified by differences in performance anxiety previously found for males and females (Osborne & Kenny, 2005, 2008; Papageorgi et al., 2013; Rae & McCambridge, 2004; Ryan, 2004), as well as the possible differential role in men and women attributed to development of these problems, according to Barlow (2000).

Dispositional optimism is defined as a personality trait implying that positive expectations are associated with future events, including the confidence that goals will be achieved (Carver & Scheier, 2005; Carver et al., 2010). Empirical evidence has associated dispositional optimism with a number of psychological adjustment indicators – well-being, even anxiety and depression – as well as with the impact of a series of stressors in specific contexts. The protective role of dispositional optimism has been demonstrated in studies with students as subjects (Brissette, Scheier, & Carver, 2002; Hirsch, Wolford, LaLonde, Brunk, & Parker Morris, 2007; Mazé & Verlhiac, 2013; Schulz, Vögele, & Meyer, 2009; Vickers & Vogeltanz, 2000).

Self-efficacy has been described as the belief in one’s own capacity to plan and carry out the actions required to cope with future situations (Bandura, 1977). Social cognitive theory (SCT) holds that self-efficacy is associated with goals and with the perseverance required to achieve them, as well as with the impact of stressors. In the specific area of appearances in public (Bandura, 2006; Brown & Barlow, 2009; Gallagher et al., 2013), self-efficacy serves as the fundamental measure of the genesis of anxiety.

As a specific coping strategy, self-efficacy has also been associated with musical performance (McPherson & McCormick, 2006; Papageorgi et al., 2013; Ritchie & Williamon, 2010). As a general psychological construct (Padilla, Acosta, Guevara, Gómez, & González, 2006), it has not been associated with MPA, but its occurrence correlates significantly with other specific problems (Shal, Sharbaf, Abdekhodaee, Masoleh, & Salehi, 2011; Skaret, Kvale, & Raadal, 2003). Other authors affirm, along the same lines, that the self-efficacy construct can mediate the effect of other variables, such as personality and stress (Ebstrup, Eplov, Pisinger, & Jørgensen, 2011).

Behavioral inhibition systems (BIS) and behavioral activation systems (BAS) have a longstanding tradition in the study of human behavior in the field of psychopathology (Corr, 2002, 2004; O’Connor, Colder, & Hawk, 2004; Pinto-Meza, Suárez, Caseras, Haro, Serrano-Blanco, & Torrubia, 2009; Torrubia, Ávila, Moltó, & Caseras, 2001). Such behavioral systems underlie two dimensions of personality: anxiety and impulsivity, as described in Gray’s psychobiological theory of personality (Gray, 1982). Sensitivity to reward and punishment is associated with the stress experienced by students prior to exams (test anxiety) and awaiting test feedback (Krupić & Corr, 2014).

Methods

Participants

The sample was composed of 434 participants enrolled in six higher music academies in Spain, studying for a bachelor’s degree and aiming to become professional music performers.

Two hundred and thirty-three participants (54%) were male and 201 (46%) were female. Ages ranged from 16 to 51, with a mean age of 22.56 years (SD = 4.59). Participants between 16 and 19 years of age comprised 23.6% of the sample; 26.1% were aged 20–21, 22.9% were aged 22–23 and 27.5% were 24 and older. Compared to women (M = 21.82, SD = 4.02), male subjects were slightly older (M = 23.19, SD = 4.96); F(1, 431) = 9.721, p = .002, η² = .022.

Subdivided into families of instruments, the majority of participants played a bowed string instrument as main instrument (28.8%, n = 125), closely followed by woodwinds (26.5%, n = 115), brass (15%, n = 65), and keyboard (15%, n = 65). Less frequent instruments were plucked strings (6.5%, n = 28), percussion (3.0%, n = 13), and voice (4.1%, n = 18). The remaining five participants (1.2%) played other instruments.

Variables and instruments

The variables analyzed in this study were MPA, dispositional optimism, general self-efficacy, and sensitivity to reward and punishment. The tools we applied to collect data are explained in the following paragraphs.

In order to evaluate MPA we used the Spanish version (Zarza et al., 2016) of K-MPAI. Elaborated by Kenny et al. (2004) on the basis of Barlow’s theory of anxiety (Barlow, 2000), it deals with three different aspects of MPA: one which attempts to express specific cognitions associated with anxiety (11 items, α = .868), and two others expressing global vulnerabilities associated with anxiety: a factor of psychological helplessness (10 items, α = .786) and another one stemming from family experiences at an early age (3 items, α = .568), both of which can contribute toward generating a psychological profile of helplessness. In the Spanish version, all three scales obtain results with internal consistency; confirmatory factorial analysis shows good adjustment to the original version (Zarza et al., 2016). Participants assessed the items on a 7-point Likert scale. According to the aim of the research, which purports to compare personality characteristics and performance anxiety, we will only use the first two scales of this questionnaire.

To evaluate dispositional optimism we used the Spanish version (Ferrando, Chico, & Tous, 2002) of the Life Orientation Test (LOT-R) designed by Scheier, Carver, and Bridges (1994). It features 10 items on a 5-point Likert scale: three items measure optimism, the three next ones pessimism, and the four remaining ones are fillers (not scored). The psychometric properties of the LOT-R test in its Spanish version are quite similar to the original (Ferrando et al., 2002). Thus, it reproduces a two-factor structure for optimism and pessimism items and a differential value for both. The reliability study provides values “around 0.58 at the ends of both traits and 0.70 for the remaining levels” (Ferrando et al., 2002, p. 677).

General self-efficacy was evaluated using the Spanish version (Sanjuán Suárez, Pérez García, & Bermúdez Moreno, 2000) of the General Self-Efficacy Scale originally designed by Baessler and Schwarcer (1996). It consists of 10 items on a 10-point Likert scale, measuring beliefs that people display about themselves and about the way they cope with stressful elements in their daily lives. The validation study in a Spanish population shows a high internal consistency (α = .87) as well as convergent and divergent validity and predictive capacity on performance (Sanjuán Suárez et al., 2000).

In order to evaluate sensitivity to reward and punishment, we used the questionnaire authored by Torrubia et al. (2001), known as the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), and designed to evaluate the two motivational systems described by Gray (1982): the Behavioral Inhibition System (BIS) and the Behavioral Activation System (BAS). It presents two scales of 24 dichotomic items, one for each of the behavioral systems as described in the theoretical model: the avoidance of punishment and the seeking of reward. The study of the Spanish adaptation of the instrument indicates good internal consistency of both scales, 0.83 for punishment and 0.76 for reward, temporal stability (r = .89 and r = .87 three months later for men and women), a bifactorial structure consistent with both scales, and convergent and divergent validity. For more details see Torrubia et al. (2001).

Procedure

Our research team contacted all higher academies of musical learning in Spain, requesting their collaboration on this project. To those who responded affirmatively, we explained the study’s objective in detail and agreed upon the data collection procedure, including the date when one of our research team members would visit the conservatory and administer the questionnaire to all students present that day on the premises. Thus, from September to December 2011, members of our team traveled to the six towns where participating conservatories were located and collected data from a total of 434 participants. In the different classrooms in which the students were present, the questionnaire was completed with pencil and paper in sessions lasting 15 to 20 minutes. Students were informed that anonymity was guaranteed and that participation in the study was on a voluntary basis. Apart from three students, all agreed to participate in the investigation and giving consent.

Statistical procedure

We used SPSS 22 to conduct variance analysis on the above-described variables in order to ascertain differences between males and females. After having verified these results, we posited a series of structural equation models and used AMOS 17.0 to analyze these differences in greater detail. On MPA we tested five mediation models, each one with the corresponding variables: helplessness, optimism, pessimism, self-efficacy, and sensitivity to punishment. In each model, the rest of the variables are considered potential predictors of the mediating variable and of performance anxiety. This comparison of models was done, in this first step, with the two groups of men and women together and establishing the restriction of equal regression weights for both groups in the model. The estimation method used is the maximum likelihood, which offers appropriate estimations under the multivariate normality assumption, a condition that occurs in this case (multivariate kurtosis = 4.121, accepting values lower than 5; Byrne, 2010, p. 104).

To compare these models we use different rates of adjustment with the same number of variables, all of them not nested. For example, Batista and Coenders (2000, p. 99) indicate that, for this purpose, adjustment indicators such as Comparative Fit Index (CFI), Akaike Information Criterion (AIC), Consistent Akaike Information Criterion CAICA, Normen Fit Index (NNFI), and root mean square error of approximation (RMSA) can be used. Meanwhile, Brown (2006, p. 175) points to rates such Akaike Information Criterion (AIC) and the Cross-Validation Expected Index (ECVI). In the latter two cases, lower values indicate a better adjustment (Byrne, 2010, p. 82).

After this phase of model comparison, we initiated a second phase which is aimed at improving the models by setting to zero non-significant estimates in both the men’s and women’s groups. Finally, there was a third block of models that tested the mediation models in both genders. Specifically, if we find non-significant regression weights and we allow different regression weights, then we hypothesize that: there are differences in the mediation models between men and women.

For these second and third steps, being nested models, comparisons were made following the usual procedures in these cases through the Δχ² (Byrne, 2010; McDonald & Ho, 2002).

Results

Table 1 displays descriptive results from the analyzed music student sample. For MPA conditions we find an average of 40.27 points (12.65), thereby confirming that music students indeed often display worries about public performance. The variable is normally distributed (Kolmogorov-Smirnov Z = 1.025, p = .244). Women, however, score significantly higher than men, F(3, 429) = 33.33, p < .001, and the difference is of a certain magnitude, with a partial η² value of 0.076. Table 1 also features the other descriptive variables: for all of them, significant differences between men and women can be observed, except for the case of pessimism. However, these differences are not of great magnitude – except in the case of self-efficacy, where men score higher than women (Table 1).

Table 1.

ANOVA by sex.

Criterion variable	Sex	N	Min–Max	Mean	SD	F	df	p	η²
K-MPAI (Specific cognitions)	Males	233	11–73	37.04	12.55	33.33	433	< .001	0.076
	Females	201	15–75	44.01	11.72
	Total	434	11–75	40.27	12.65
K-MPAI(Helplessness)	Males	233	10–60	31.12	9.59	7.18	433	.008	0.016
	Females	201	12–64	33.57	9.40
	Total	434	10–64	32.25	9.57
Optimism	Males	222	4–15	10.79	2.33	4.56	433	.033	0.010
	Females	201	3–15	10.29	2.52
	Total	434	3–15	10.56	2.44
Pessimism	Males	233	3–15	7.44	2.24	2.38	433	.123	0.005
	Females	201	3–15	7.80	2.55
	Total	434	3–15	7.61	2.39
Self-Efficacy	Males	233	27–100	71.83	12.81	15.01	433	< .001	0.034
	Females	201	17–100	66.96	13.37
	Total	434	17–100	69.57	13.29
Sensitivity to reward	Males	233	1–24	10.69	4.28	29.954*	433	< .001	0.063
	Females	201	1–20	8.62	3.59
	Total	434	1–24	9.74	4.11
Sensitivity to punishment	Males	233	0–23	9.81	5.65	10.771	433	.001	0.024
	Females	201	1–23	11.56	5.37
	Total	434	0–23	10.62	5.58

Brown-Forsythe (N = 434; Levene’s = 8.067; ρ = 0.005).

Grouped according to age bracket (ages 16–19 vs. 22–23 and 24 and older), significant differences neither appeared between the age variable and specific cognitions associated with MPA, F(3, 429) = 1.638, p = .180, nor with respect to the rest of variables: helplessness, F(3, 429) = 0.551, p = .648, self-efficacy, F(3, 429) = 0.355, p = .785, sensitivity to rewards, F(3, 429) = 0.981, p = .401, sensitivity to punishment, F(3, 429) = 1.750, p = .176, optimism, F(3, 429) = 0.676, p = .648, or pessimism, F(3, 429) = 0.064, p = .979.

Featuring relations among variables, Table 2 plots the correlations for males and females respectively. It shows that in both males and females, MPA correlates significantly with helplessness, optimism, pessimism, self-efficacy, and sensitivity to punishment. The correlations in all cases lie between 0.3 and 0.6. Values for males are greater, but the male–female difference never attains statistical significance. Sensitivity to reward does not correlate significantly with any of the other variables.

Table 2.

Correlations (males and females).

		Specific cognitions	Helplessness	Optimism	Pessimism	Self-efficacy	Sensitivity to reward	Sensitivity to punishment
		Females
Specific cognitions	Males	1	.535**	−.375**	.443**	−.426**	−.018	.474**
Helplessness		.611**	1	−.535**	.592**	−.454**	.100	.556**
Optimismo		−.502**	−.510**	1	−.544**	.476**	.071	−.400**
Pessimism		.507**	.502**	−.523**	1	−.361**	.011	.426**
Self-efficacy		−.479**	−.498**	.569**	−.461**	1	.110	−.441**
Sensitivity to reward		.047	.159*	.005	.102	−.057	1	.047
Sensitivity to punishment		.570**	.597**	−.489**	.476**	−.564**	.106	1

p ≤ .05. **p ≤ .01.

Finally, Table 3 displays the results of the five proposed structural equation models. Each one of them has a different variable as mediator: helplessness, self-efficacy, sensitivity to punishment, pessimism, and optimism. As can be ascertained, all five models display good adjustment; however, the adjustment index is more suitable in the case of model 1, where the mediating variable is helplessness. This model is the only one where χ² value leads to no rejection of the null hypothesis; i.e., the model does fit the data properly. Similarly, the values of AIC and ECVI are the lowest in this case. The rest of the models also exhibit good adjustment indicators.

Table 3.

Model comparison.

	Chi-square	df	p	CMIN/DF	RMSEA	CFI	Akaike	ECIV
Step A – Mediation model with all predictors
Model 1a – Helplessness	11.202	8	.190	1.400	.030	.997	135.202	.313
Model 2a – Self-efficacy	16.045	8	.042	2.006	.048	.992	140.045	.324
Model 3a – Punishment	19.638	8	.012	2.455	.058	.988	143.638	.332
Model 4a – Pessimism	19.397	8	.013	2.425	.057	.989	143.397	.332
Model 5a – Optimism	15.372	8	.052	1.921	.046	.993	139.372	.323
Step B – Setting to zero non-significant regression weights
Model 1b – Helplessness	14,380	10	.156	1.438	.032	.996	134.380	.311
Model 2b – Self-efficacy	23,630	14	.051	1.688	.040	.990	135.630	.314
Model 3b – Punishment	38,475	16	.001	2.405	.057	.977	146.475	.339
Model 4b – Pessimism	27, 852	16	.033	1.741	.041	.988	135.852	.314
Model 5b – Optimism	27,206	16	.039	1.700	.040	.989	135.206	.313
Step C – Differentiated by gender
Model 1c – Helplessness	16.282	13	.234	1.252	.024	.997	130.282	.302
Model 2c – Self-efficacy	30.581	17	.022	1.799	.043	.986	136.581	.316
Model 3c – Punishment	27,251	17	.054	1,603	.037	.990	133.251	.308
Model 4c – Pessimism	27.134	16	.040	1.696	.040	.989	135.134	.313
Model 5c – Optimism	33.954	18	.013	1.886	.045	.984	137.954	.319

However, these models offer parameter estimations equal to zero, for both men and women. This is the case in model 1, in which the regression coefficients of reward and optimism about cognitions are similar to 0. In the other models, even more situations like this are found. Therefore, we compared the models again in a second step (step B), setting to zero such coefficients. The new results (Table 3, Step 2) show, again, that the model in which helplessness mediates (model 1b) is the one which indicates the best adjustment of the five. Thus, now the differences in the value of AIC and ECVI are confirmed, different values for CFI or RMSEA are found. Comparisons between models through Δχ² show significant differences for the 1b model and for the 3b models (∆χ² = 24.095, df = 6, p = .001), 4b (∆χ² = 13.472, df = 6, p = .036), and 5c (∆χ² = 12.826, df = 6, p = .046), being non-statistically different for 1a and 1b models (∆χ² = 9.250, df = 4, p = .055).

However, these models again present regression coefficients with no estimates different from zero in any of the samples. Therefore, we adjusted the models again considering these differences between men and women. The results are shown in Table 3, Step C. As in previous cases, model 1c, helplessness, is the one which fits the best (Table 3, Step C), with all the differences now unlike according to Δχ² (model 1c vs 2c, Δχ² = 14.299, df = 4, p = .006; model 1c vs 3c, Δχ² = 10.969, df = 4, p = .027; model 1c vs 4c, Δχ² = 10.852, df = 5, p = .013; model 1c vs 5c, Δχ2 = 17.672, df = 5, p = .003).

After checking that model 1 is the one which fits best in all cases, we must compare this model along the three steps. Thus, from the perspective of global adjustment, the three models are similar by χ² value, as there are no differences between them (model 1a vs 1b, ∆χ² = 3.178, df = 2, p = .204, model 1a vs. 1c, ∆χ² = 5.080, df = 5, p = .406). However, the third model has a lower value of AIC or RMSEA. Besides, it shows significant estimates in all regression coefficients, accounting for some differences between men and women.

Thus, model 1c explains 48.9% of the variance of specific cognitions in males, and 36.9% thereof in females. Helplessness accounts for 45.0% of variance among males and 52.1% in females. Figures 1 and 2 display the standardized regression weights in males and females. As can be observed therein, all variables have weights that are capable of significantly predicting helplessness (except in the case of self-efficacy in males). Regarding specific cognitions, the main predictor is helplessness (β = .316 for males and β = .268 for females); however, other variables likewise have direct effects: sensitivity to punishment (β = .235 for males and β = .190 for females) and pessimism (β = .163 for males and β = .143 for females). Two variables have a protector function: self-efficacy (only in the case of females: β = –.169) and optimism (only for males: β = –.140).

Figure 1.

Path diagram for males.

Figure 2.

Path diagram for females.

Evaluating the global effects of these variables on specific cognitions and combining the direct effects observed and displayed in Table 3 as well as indirect effects, our model shows that the variable with the greatest amount of predictive weight for males is sensitivity to punishment (direct effect = .235 and indirect effect = .123), followed by helplessness (direct effect = .316). Optimism and pessimism, on the other hand, result in a total effect of .224 and –.211, representing the same result of the previously mentioned direct effect and of indirect effects mediated by helplessness, .061 and -.070, respectively. Regarding sensitivity to reward, a small indirect effect of .032 appears, mediated by helplessness: it thus has a lower impact on MPA.

For females we find that the best predictor is helplessness with a direct effect of .268, followed by sensitivity to punishment, with a direct effect of .190 and an indirect effect of .074. These are followed by pessimism (total effect = .228) and self-efficacy (total effect = –.206) with the direct effects pointed out in Table 3 and indirect effects amounting to .085 and –.037, respectively. As opposed to males, optimism in females only has an indirect effect of –.052, similarly to sensitivity to reward: .030.

Conclusions

This study has attempted to shed light on the relation between MPA and other psychological variables in a group of subjects studying music at superior music academies. While respecting the general framework of the model of personal vulnerability expounded by Barlow (2000) our theoretical proposal attempts to investigate the role that other variables, apart from those in Barlow’s model, could play in the origin and persistence of the problem, as certain authors have suggested (Gallagher et al., 2014). Our study was conducted on Spanish teenagers and young adults training to become professionals in the music field. The results reveal that MPA is certainly present among them, and to a greater degree in females than in males – as found, independent of subjects’ age, in previous studies (Osborne & Kenny, 2008; Papageorgi, Hallam, & Welch, 2007).

Except for sensitivity to reward, all other variables associated with the personal vulnerability profile correlate significantly with MPA and with one another. The applied structural equations reveal that the model best adjusted to the data postulates that the variables’ effects are mediated by helplessness, in accordance with the description of psychological vulnerability provided by Barlow’s model (Kenny et al., 2004). In men and women, helplessness acquires the role of best mediator and best predictor of MPA. The data thus reveal that 45% of the variance of helplessness in males and 52.1% in females can be predicted by their scores in optimism, pessimism, sensitivity to reward and punishment, and self-efficacy. Other studies have revealed connections with those variables in the context of the study of anxiety. In a sample of adults, for example, Umstattd et al. (2007) revealed connections between pessimism and self-efficacy, and suggested that self-efficacy mediates the relation between pessimism and fear of aging. Fernández-Castro, Rovira, Doval, and Edo (2009) analyzed the connections between optimism and perceived competence, and identified two underlying structures: one of them was associated with the aspect of control and served as a link between the two constructs, whereby the authors also point out further differential elements with specific content. Nevertheless, the relations among those constructs are apparently not always linear. Thus, Sanz Ruiz, Villamarín Cid, Álvarez Moleiro, and Torrubia Beltri (2007) found an interaction between sensitivity to punishment and self-efficacy in a laboratory context where they measured blood pressure and cardiovascular reactions while asking subjects to perform a cognitive task.

While confirming the role of the shared component associated with helplessness, our study has also revealed specific effects of the other featured constructs on MPA. Vickers and Vogeltanz (2000) obtained a similar result, although they were not studying anxiety, but symptoms of depression. Testing a group of 190 university-level students, they found that dispositional optimism had measurable effects that were independent from negative affect. Along with the aforementioned studies, our data reveals the need to postulate further theoretical constructs apart from helplessness in order to explain the causes of anxiety. Dispositional optimism and self-efficacy (Ebstrup et al., 2011) thus open up entire new areas within which we can pinpoint motivational mechanisms to reduce a person’s predisposition to suffer from anxiety. Such mechanisms could include the lowered assessment of threat in stimuli, the use of coping strategies that focus on the problem, greater perseverance in the face of difficulties, and the individual’s implication in a greater number of projects and experiences that would help him/her acquire greater control in the context of public appearances. Strategies such as these could help students with the above-described characteristics to eventually experience lower levels of anxiety. For example, Kenny, Fortune, and Ackermann (2011) point out that flutists who undergo an increased amount of weekly training eventually experience lower levels of anxiety. At any rate, further studies would be required in order to investigate such possible mechanisms. The absence of previous studies along these lines reinforces the value of these findings, which reveal new elements awaiting investigation. In future MPA research it would likewise be of great interest to investigate the connections between these constructs and a role which they possibly play independently of helplessness.

It is also necessary to point out some of our study’s limitations. Although we have only evaluated the factor of personal vulnerability in Barlow’s model (Barlow, 2000), he also postulates the element of specific vulnerability, stemming from a person’s past encounters with a series of potentially phobic stimuli. Since such factors were not analyzed in this study, they could be mediating the relations between anxiety and personal characteristics. In fact, such relations could even differ and tend to vary in the course of a musician’s successive stages of training. Certain data could help us probe further along these lines. For example, we could consider the different levels of anxiety experienced by students who strongly intend to pursue a professional musical career, as well as variance according to type of musical instrument, and the different types of expectation to which these future professionals are often subjected (Fehm & Schmidt, 2006; Kenny et al., 2004; Osborne & Kenny, 2008; Papageorgi et al., 2013). Moreover, by analyzing this kind of context, we could finally take another thoroughly significant factor into account: the crucial role played by music teachers (Kobori, Yoshie, Kazutoshi, & Ohtsuki, 2011) or families. Good reviews on this topic can be found in Lehmann and Kristensen (2014) and in Moore, Burland, and Davidson (2003).

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Programa Operativo FEDER Aragón 2014-2020 “Construyendo Europa desde Aragón”.

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