Kenny Music Performance Anxiety Inventory: Exploratory Factor Analysis of the Romanian version

Abstract

Kenny Music Performance Anxiety Inventory (K-MPAI) is one of the most widely used instruments in the research of music performance anxiety. The aim of this study was to investigate the factor structure of the Romanian version of K-MPAI. A sample of 420 (aged 18–66, M = 24.46, SD = 7.36; 48% women and 52% men) musicians completed the K-MPAI. Exploratory factor analysis with principal axis factoring and oblimin rotation method indicated eight factors which explained 49.16% of variance. Due to the overestimation of the number of factors by the Kaiser’s criterion of 1, parallel analysis with the syntax provided by O’Connor was implemented. Four factors were extracted which explained 41.37% of variance. They were named “music performance anxiety symptoms,” “depression and hopelessness,” “parental support,” and “memory self-efficacy.” Results partially support the theoretical model which sustained the development of K-MPAI, and further clinical implications for the Romanian musician population are discussed.

Keywords

performance anxiety anxiety inventory K-MPAI factor structure musicians

Music performance anxiety (MPA) affects musicians of all ages all around the world (Kenny, 2011). Prevalence rates vary across studies (Kaspersen & Gotestam, 2002; Lockwood, 1989; Ryan, 2005; Steptoe, 2001; Van Kemenade, Van Son, & Van Heesch, 1995; Wang, 2001; Wesner, Noyes, & Davis, 1990). These differences can be accounted by individual factors (e.g., gender, age, high trait anxiety) as well as by methodological factors (e.g., the use of several instruments, many of them not being validated, or lack of clear cutoff points and lack of a unified definition of MPA).

Although the scientific study of MPA has begun a few decades ago, there is still debate in the literature regarding its definition and implications (Kenny, 2011). According to DSM-V (American Psychiatric Association, 2013), MPA is a form of the performance-only sub-type of Social Anxiety Disorder, as long as the musician meets all diagnostic criteria, including distress or professional impairment. Salmon (1991) defined MPA as “the experience of persisting, distressful apprehension 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). Kenny (2011) criticizes this definition because it restricts the diagnosis for well-accomplished musicians and MPA can be found at each level of musical development. Instead, she offers a broader definition. Accordingly, MPA is the:

experience of marked and persisted anxious apprehension related to musical performance that has arisen through underlying biological and/or psychological vulnerabilities and/or specific anxiety conditioning experiences. It is manifested through combinations of affective, cognitive, somatic and behavioral symptoms. It may occur in a range of performance settings, but it is usually more severe in settings involving high ego investment, evaluative threat (audience) and fear of failure. It affects musicians across the lifespan and is at least partially independent of years of training, practice and level of musical accomplishment. It may or may not impair the quality of musical performance. (Kenny, 2011, p. 61)

As the above definition mentions, MPA symptoms can be grouped into four categories (e.g., affective, cognitive, somatic, and behavioral). Affective symptoms include anxious apprehension about one’s musical performance. Cognitive symptoms include memory lapses, concentration difficulties, along with negative self-talk, as well as narrowed attention onto perceived threats, both internal (e.g., one’s personal evaluation of the performance) and external threats (e.g., the evaluation of the audience). Among the somatic symptoms, musicians mention hyperventilation, trembling hands and legs, sweaty hands, dry mouth, increased heart rate, and so on. Behavioral symptoms can be grouped as follows: (1) overt avoidance behaviors (e.g., avoiding auditions, solos, practicing) and (2) covert avoidance behaviors (e.g., avoiding eye contact with jurors and audience members, avoiding expressing one’s self, avoiding more challenging repertoire [Juncos et al., 2017]). The intensity of these symptoms as well as their impact on the musician’s functionality may require psychological interventions (Spahn, 2006; Taborsky, 2007).

To efficiently address MPA, there is a need for valid theories that explain mechanisms involved in the development and maintenance of MPA. These theories should sustain the development of appropriate instruments to assess this concept. Kenny (2009) developed an instrument based on an adaptation of Barlow’s model of anxiety, the Kenny Music Performance Anxiety Inventory (K-MPAI). Barlow’s model proposes three sources of vulnerability which can generate dysfunctional anxiety. The first source of vulnerability is a generalized biological vulnerability which is heritable. The second one is a generalized psychological vulnerability whose source is the early uncontrollable or adverse experiences. The third vulnerability source is a specific psychological vulnerability which is determined by specific environmental stimuli and can be reinforced through conditioned or vicarious learning (Barlow, 2000). Kenny’s (2009) adaptation of Barlow’s (2000) model operationalizes three interacting factors which sustain the development of MPA. The first factor describes the early relationship context. The second factor is related to a depressive thinking pattern which constitutes psychological vulnerability. The third factor reflects specific concerns regarding different performance situations.

K-MPAI has two published versions, one of 26 items (Kenny, Davis, & Oates, 2004) and one of 40 items (Kenny, 2009). There is also a 15-item MPAI-A for adolescents, which has been adapted for the Romanian population (Sârbescu & Dorgo, 2014). The psychometric properties of the 26-item version of K-MPAI for adults were investigated in a Spanish sample too (Zarza Alzugaray, Hernández, López, & Gil, 2015). The psychometric properties of the adult 40-item version of K-MPAI were investigated on professional musicians, amateur musicians, and samples of tertiary-level student musicians from Australia, Peru, Germany, and Brazil (Barbar, Souza, & Osório, 2015; Chang-Arana, Kenny, & Burga-León, 2018; Kenny, 2009; Peschke & von Georg, 2015). Different factorial structures were obtained for the 40-item version of K-MPAI. For example, 12 factors were obtained for a sample of tertiary-level music students (N = 159; Kenny, 2009) and eight factors for a sample of professional musicians (N = 357; Kenny, unpublished), so Kenny (2011) suggested to further investigate its structure on larger samples. The adult version of K-MPAI was not translated or adapted on Romanian population, which prevents us from a deeper understanding of MPA in the Romanian context. Thus, the aim of this article is to translate and investigate the internal factor structure of K-MPAI on a sample of Romanian musicians.

The translation procedure followed the guidelines recommended by Chavez and Canino (2005) and can be briefly found in Figure 1.

Figure 1.

Cross-validation of the K-MPAI, from English to Romanian.

Method

Participants

A total of 420 musicians (aged 18–66, M = 24.46, SD = 7.36; 48% women and 52% men) filled in the Romanian version of the K-MPAI in two steps. A first step included the completion of many questionnaires, which resulted in a number of 134 participants. The second step implied the filling in of K-MPAI and demographic information regarding the instrument they played, gender, age, and the music genre the participants played. It took less than 15 min and we had another 286 responses. Participants gave their written consent in both stages. Musicians played different instruments and the sample included vocalists as well. The instruments were grouped according to other research articles classifications and are presented in the “Results” section.

Instruments

K-MPAI (Kenny, 2009). The 40-item instrument was developed to assess anxiety symptoms and other associated constructs (e.g., parental empathy/support, depression) in the context of music performance. Its original version included 26 items and the present one is a revised and extended version of it (Kenny, 2009; Kenny et al., 2004). Questions are answered on a 7-point Likert-type scale (0 = strongly disagree to 6 = strongly agree) with higher scores being indicators of greater anxiety and psychological distress. The internal reliability of the scale is excellent, the value of Cronbach’s alpha for the entire scale is .94 (Kenny, 2009).

Data analysis

For the purpose of this research, we decided to conduct an exploratory factor analysis (EFA). This was preferable to a confirmatory factor analysis (CFA), for three main reasons: (1) a new instrument translated from a different language emerged (Sava, 2004), (2) the number of studies on this particular inventory is rather small, and (3) other studies which investigated the factor structure of K-MPAI reported different number of factors for it (Barbar et al., 2015; Chang-Arana et al., 2018; Kenny, 2009; Peschke & von Georg, 2015). Data analysis was performed using SPSS Statistics 20.

Results

Descriptives

Means and standard deviations for the 40 items of the K-MPAI are presented in Table 1.

Table 1.

Means and standard deviations of the 40-item version of the K-MPAI.

Item	M	SD	Item	M	SD	Item	M	SD	Item	M	SD
1	1.72	1.35	11	2.76	1.85	21	2.25	1.95	31	1.64	1.86
2	3.02	1.60	12	2.65	1.85	22	3.67	1.86	32	2.74	1.92
3	2.59	1.96	13	2.30	1.87	23	1.85	1.78	33	2.05	1.91
4	2.73	1.71	14	2.06	1.80	24	1.86	1.94	34	2.24	1.90
5	2.27	1.93	15	3.21	1.84	25	3.37	1.84	35	1.40	1.63
6	1.42	1.65	16	2.02	1.87	26	2.55	1.80	36	2.93	1.90
7	3.80	1.78	17	2.11	1.61	27	1.89	1.85	37	1.61	1.70
8	3.80	1.72	18	2.75	1.84	28	1.55	1.65	38	3.32	1.87
9	2.04	1.73	19	2.37	1.93	29	1.72	1.77	39	3.72	1.75
10	2.07	1.87	20	2.33	1.93	30	1.99	1.90	40	4.32	1.84

K-MPAI: Kenny Music Performance Anxiety Inventory; SD: standard deviation.

Information regarding the type of music, instrument category representation, and number of years of playing the principal instrument is represented in Table 2.

Table 2.

Information regarding music experience.

Years playing the instrument		M (SD)
		11.05 (8.24)
Main music genre		Percentage (%)
Classical		44
Rock		15.71
Pop		9.52
Traditional music		9
Folk		5.71
Religious		3.33
Jazz		2.85
Other		9.88
Instrument category	n	Percentage (%)
Strings	173	42.19
Woodwinds	49	11.66
Percussion	101	24.04
Brass	12	2.85
Voice	67	15.95
Other	18	4.28

SD: standard deviation.

EFA

EFA with principal axis factoring with oblimin rotation was computed. This rotation method was chosen due to the expected correlation between factors. Tabachnick and Fidell (2013) argue that if the correlations between the factors exceed .32, it shows that there is at least 10% overlap variance of factors, which would indicate the appropriate use of an oblique rotation.

One of the first measures calculated in the EFA was the Kaiser–Meyer–Olkin index (KMO), which measures the adequacy of the sample to the entire population. The KMO value is recommended to be at least .5 and our value was .92, thus proving an excellent adequacy. With Bartlett’s test, we measured if the correlation coefficients are different from zero, thus justifying the use of a factor model (Table 3).

Table 3.

KMO and Bartlett’s test.

KMO measure of sampling adequacy	.926
Bartlett’s test of sphericity
Approximate chi-square	7,584.3
df	780
Sig.	<.001

KMO: Kaiser–Meyer–Olkin.

An anti-image correlation matrix (see Appendix 1) was computed to double check the sample size adequacy and noted that the diagonal values (in bold letters) are over .5 (Field, 2000), while the rest of the values were as low as possible.

We identified eight factors which had eigenvalues greater than Kaiser’s criterion of 1. They explained 49.16% of variance. It is known that the number of the variables influences the meaning of the eigenvalues. Field (2013) and Tabachnick and Fidell (2013) recommend using this criterion when (1) there are less than 30 variables, (2) the commonalities values are greater than .7, (3) there are more than 250 participants, and (4) commonalities mean is higher than .6. Of the four criteria, only one was met by our sample, the number of participants. Due to this fact, we considered the Kaiser criterion was not effective enough to choose the number of the factors.

Zwick and Velicer (1986) tested The Scree plot test, Horn’s parallel test, and Velicer’s MAP (Mininum Average Partial) test in simulation studies which included a data set with a clear factor structure. The parallel test and the minimum average partial test were reliable tools for factor extraction (Tabachnick & Fidell, 2013, p. 698).

Parallel analysis (Horn, 1965; Tabachnick & Fidell, 2013) is a three-step analysis. First, using the same number of variables and cases as the original database, randomly generated databases are developed. Second, each database is analyzed for its factor structure and the eigenvalues obtained for each analysis are registered. In the third step, comparisons are made between the eigenvalues of the randomly generated databases and the ones of the collected data. Factors with eigenvalues higher than the ones obtained in the generated data should be retained. Parallel analysis with the syntax provided by O’Connor (2000) was implemented. We obtained a solution of four factors with eigenvalues higher than the eigenvalues of the factors in the generated data sets.

The four-factor solution explained 41.37% of variance. Table 4 includes the pattern matrix with the item loading on the four factors, using the .4 loading criterion. Tabachnick and Fidell (2013) recommend the interpretation of items with factor loading higher than .32. Comrey and Lee (1992) offer the following criteria: loadings higher than .71 (50% overlapping variance) are considered excellent, .63 (40% overlapping variance) very good, .55 (30% overlapping variance) good, .45 (20% overlapping variance) fair, and .32 (10% overlapping variance) poor. Tabachnick and Fidell (2013) also recommend selecting a cutoff that helps the researcher better interpret the factor structure. We chose a value of .4 for it sustained a robust factorial structure. The items which did not meet the loading criterion were deleted.

Table 4.

K-MPAI pattern matrix with item loadings.

Pattern matrix
Item	Factor
Item	1	2	3	4
26 My worry and nervousness about my performance interferes with my focus and concentration	.778
22 Prior to, or during a performance, I experience increased heart rate like pounding in my chest	.751
36 Prior to, or during a performance, I experience shaking or trembling or tremor	.740
30 Prior to, or during a performance, I have increased muscle tension	.728
16 Prior to, or during a performance, I feel sick or faint or have a churning in my stomach	.681
10 Prior to, or during a performance, I get feelings akin to panic	.656
14 During a performance, I find myself thinking about whether I’ll get through it	.654
38 I am concerned about being scrutinized by others	.651
34 I worry so much before a performance, I cannot sleep	.651
25 After the performance, I worry about whether I played well enough	.650
24 I give up worthwhile performance opportunities due to anxiety	.616
15 Thinking about the evaluation, I may get interferes with my performance	.591
28 I often prepare for a concert with a sense of dread and impending disaster	.591
21 I worry that one bad performance may ruin my career	.585
20 From early in my music studies, I remember being anxious about performing	.566
11 I never know before a concert whether I will perform well	.557
18 I am often concerned about a negative reaction from the audience	.527
12 Prior to, or during a performance, I experience dry mouth	.499
23 My parents always listened to me		.803
33 My parents encouraged me to try new things		.726
9 My parents were mostly responsive to my needs		.654
6 I often feel that life has not much to offer me			.845
31 I often feel that I have nothing to look forward to			.771
4 I often find it difficult to work up the energy to do things			.699
3 Sometimes I feel depressed without knowing why			.695
19 Sometimes I feel anxious for no particular reason			.558
13 I often feel that I am not worth much as a person			.520
27 As a child, I often felt sad			.418
35 When performing without music, my memory is reliable				−.762
37 I am confident playing from memory				−.610

K-MPAI: Kenny Music Performance Anxiety Inventory.

The first factor was named “music performance anxiety symptoms” and included 18 items which had loadings higher than .4. It included items which described somatic symptoms (e.g., “Prior to, or during a performance, I experience dry mouth”; “Prior to, or during a performance, I experience increased heart rate like pounding in my chest”) or worries regarding the performance or other scrutiny (e.g., “I worry that one bad performance may ruin my career”; “I am concerned about being scrutinized by others”). The second factor was named “parental support” and it included three items with factor loading higher than .4 regarding the relationship between the musician and his or her parents (“My parents were mostly responsive to my needs”; “My parents always listened to me”; “My parents encouraged me to try new things”). The third factor was named depression and hopelessness and included seven items with factor loadings higher than .4 which described the psychological vulnerability dimension (e.g., “I often feel that life has not much to offer me”; “Sometimes I feel depressed without knowing why”). The fourth factor included just two items regarding memory self-efficacy with high factor loading (“When performing without music, my memory is reliable” and “I am confident playing from memory”). After determining the four factors of the inventory, internal consistency values were computed for each sub-scale as well as for the entire scale. “Music performance anxiety-related symptoms” sub-scale had a Cronbach’s α = .93, “early parental relationship” had a Cronbach’s α = .77, “depression and hopelessness” had a Cronbach’s α = .86 and “memory” had a Cronbach’s α = .81. The total scale had a Cronbach’s α = .91.

Discussion

The objective of this study was to adapt the K-MPAI for the Romanian population. From our knowledge, this is the first study which investigated the factorial structure of K-MPAI on the Romanian adult musician population.

We chose K-MPAI because it is one of the few instruments in the MPA research which was developed using a theoretical framework, the adaptation made by Dianna Kenny of Barlow’s theory of the vulnerability factors that contribute to the development and maintenance of MPA (Barlow, 2000; Kenny, 2009). Kenny (2009) proposes three vulnerability sources. Different sub-scales of K-MPAI can be grouped onto them. Kenny (2009) using principal axis factoring of K-MPAI in a sample of music and dance students (N = 151) obtained a 12-factor solution and grouped them as following: early relationship context (Sub-scale 7: generational transmission of anxiety and Sub-scale 4: parental empathy), psychological vulnerability (Sub-scale 1: depression, Sub-scale 9: controllability, Sub-scale 11: trust, Sub-scale 12: pervasive performance anxiety), proximal performance concerns (Sub-scale 3: proximal somatic anxiety, Sub-scale 2: worry dread, Sub-scale 6: pre- or post-performance rumination, Sub-scale 8: self/other scrutiny, Sub-scale 10: opportunity cost, Sub-scale 5: memory reliability). Also, Kenny (unpublished article, Kenny, 2011, pp. 104–105) obtained an eight-factor structure on a sample of professional musicians, members of eight state orchestras in Australia: proximal somatic anxiety and worry about performance, worry/dread focused on self/other scrutiny, depression, parental empathy, memory, generational transmission of anxiety, anxious apprehension, and biological vulnerability. Three sub-scales are common for the two samples (generational transmission of anxiety, depression, and parental empathy). The items regarding several anxiety symptoms had different factor loadings in these samples.

The factors which resulted after the analysis in our sample can be grouped in the categories as it follows: early relationship context (Sub-scale 2: parental support), psychological vulnerability (Sub-scale 3: depression and hopelessness), and proximal performance concerns (Sub-scale 1: MPA-related symptoms and Sub-scale 4: memory self-efficacy). When Kenny (2009) adapted Barlow’s model of anxiety, the heritable component in Barlow’s model was named early relationship context and included two sub-scales, generational transmission of anxiety which referred to the heritable aspect and parental empathy which reflected a more environmental influence of the early relationship context. Two items regarding generational transmission of anxiety were deleted from our sample due to their small factor loading. One of them “As a child, I often felt sad” loaded on our depression and hopelessness factor. This item describes a past disposition toward sadness and it appears that musicians in our sample associated it more to depression. The items which loaded on the parental support factor (Kenny’s parental empathy sub-scale) are related to one’s early relationship context, referring to the way parents responded to the musician’s needs in childhood. Thus, the factor structure we obtained can be integrated in the adapted model of MPA by Kenny (2009), but the heritable aspect of Barlow’s model in the case of Romanian musicians should be measured using other instruments (e.g., trait anxiety measures or biological markers) to assess the biological vulnerability component.

More recent studies obtained factorial structures with fewer factors. For example, Peschke and von Georg (2015) obtained a three-factor model in a sample of 130 professionals, students, and amateurs: Factor 1—MPA symptoms related to the performance, Factor 2—general depression; and Factor 3—early relationship context—parental empathy and generational transmission of anxiety. Chang-Arana et al. (2018) obtained similar results on a sample of Peruvian and Australian musicians. They initially performed a first-order factor analysis which described the same four-factor structure we first obtained: “proximal performance concerns” (20 items, Cronbach’s α = .91), “psychological vulnerabilities” (seven items, Cronbach’s α = .80), “confidence in memory” (two items, Cronbach’s α = .82), and “early parental relationship context” (three items, Cronbach’s α = .7). Furthermore, they performed a second-order factor analysis which resulted in a high order factor which was named “negative affectivity in relation to music performance anxiety” and the two first-order factors had been named “music performance anxiety” and “depression” for both samples. This factorial structure was interpreted by Chang-Arana et al. (2018), in the context of the tripartite model of anxiety and depression and they suggested to use the inventory in a bi-dimensional way. Higher order factor analysis is recommended if and only if there are inter-correlations between first-order factors (Navruz, Capraro, Bicer, & Capraro, 2015). In our sample, we obtained high correlations between the first order factors only for Factor 1 and Factor 2, had high factor correlations between Factors 1 and 3, specifically, MPA symptoms related symptoms sub-scale and depression and hopelessness. Due to this fact, we did not use higher order factor analysis on our sample.

Our study had several limitations. First, though we had a pretty large sample with a good gender balance, most of the musicians were representative for the classical music genre (44%) and the other 56% were musicians which represented other music genres (e.g., rock, pop, traditional music, folk, jazz), the percentages varying from 2.85% to 15.71%. Future studies could include more musicians for each category to have a more representative sample, from the point of view of the music genre. Second, to have access to a big number of respondents, we collected data through various Internet channels and though we did offer them an e-mail for questions regarding the questionnaire, we do not have the certainty that the respondents did not have questions regarding the meaning of different questions and chose not to send them to us. Third, the topic of the questionnaire discusses MPA which is not an easy subject for the Romanian musicians and some answers might have been influenced by social desirability. However, we tried to reduce this potential influence by not asking for any personal information.

Future studies could further investigate the convergent and divergent validity of K-MPAI on the Romanian population. Also, the determination of cutoff points regarding clinical and non-clinical levels of MPA would help clinicians as well as researchers in their endeavor of assisting musicians with MPA.

Having theory-grounded and adapted psychological instruments is very important for both qualitative research and professional clinical practice. This study was a first step to reach this aim for the Romanian music population.

Conclusion

Clinical practice and research requires validated instruments if we are looking to effectively measure changes in different psychological phenomena. Moreover, to be able to generalize conclusions, research on many populations is mandatory. Our research is important for it was the first attempt to validate one of the most used instruments in the MPA domain on the Romanian population. The statistical methods we implemented helped us to obtain a robust factorial structure of the instrument. It can be a base for further research regarding specific aspects of MPA of the Romanian musicians.

Footnotes

Appendix

Appendix 1.

Anti-image correlation matrix of K-MPAI.

	1	2	3	4	5	6	7	8	9	10		11	12	13	14	15	16	17	18	19	20
1	.90	−.06	−.08	−.03	−.04	−.05	.05	.00	.01	−.15	11	.93	−.19	−.01	−.19	.05	−.05	−.05	−.08	−.05	−.13
2	−.06	.50	.10	.02	.02	−.05	−.09	−.16	−.13	.04	12	−.19	.93	.08	−.02	−.08	−.03	.06	.10	.04	−.05
3	−.08	.10	.91	−.19	−.14	−.09	−.09	.02	.02	.05	13	−.01	.08	.94	−.10	.01	−.02	−.09	−.08	−.03	.03
4	−.03	.02	−.19	.94	−.07	−.22	−.07	−.07	−.01	−.07	14	−.19	−.02	−.10	.95	−.16	−.01	−.13	−.06	.09	.01
5	−.04	.02	−.14	−.07	.90	−.08	−.08	−.10	.05	.02	15	.05	−.08	.01	−.16	.94	−.05	−.04	−.14	−.10	−.02
6	−.05	−.05	−.09	−.22	−.08	.88	−.01	−.11	−.02	−.08	16	−.05	−.03	−.02	−.01	−.05	.95	−.13	−.01	−.10	−.02
7	.05	−.09	−.09	−.07	−.08	−.01	.88	−.04	.01	−.09	17	−.05	.06	−.09	−.13	−.04	−.13	.93	−.00	−.01	−.01
8	.00	−.16	.02	−.07	−.10	−.11	−.04	.80	.03	.11	18	−.08	.10	−.08	−.06	−.14	−.01	−.00	.94	−.09	.05
9	.01	−.13	.02	−.01	.05	−.02	.01	.03	.71	−.02	19	−.05	.04	−.03	.09	−.10	−.10	−.01	−.09	.93	−.09
10	−.15	.04	.05	−.07	.02	−.08	−.09	.11	−.02	.93	20	−.13	−.05	.03	.01	−.02	−.02	−.01	.05	−.09	.95
	21	22	23	24	25	26	27	28	29	30		31	32	33	34	35	36	37	38	39	40
21	.96	−.06	−.10	.00	−.11	−.02	−.01	−.19	−.09	.02	31	.90	−.02	−.03	.05	−.06	−.03	.02	−.00	−.14	−.03
22	−.06	.93	−.02	−.12	−.05	−.10	.01	.07	.03	−.18	32	−.02	.91	.08	−.07	.07	−.06	.01	−.05	−.10	−.02
23	−.10	−.02	.75	−.00	.02	.00	−.05	.03	−.03	.01	33	−.03	.08	.76	.05	−.00	.08	−.02	−.02	.05	.01
24	.00	−.12	−.00	.95	−.06	−.10	.01	−.16	−.10	.00	34	.05	−.07	.05	.95	.00	.01	−.04	−.08	−.09	.02
25	−.11	−.05	.02	−.06	.96	−.14	.01	.06	.05	.06	35	−.06	.07	−.00	.00	.68	−.06	−.62	.03	.12	.12
26	−.02	−.10	.00	−.10	−.14	.96	−.05	−.14	.01	−.10	36	−.03	−.06	.08	.01	−.06	.93	.07	−.19	.04	−.01
27	−.01	.01	−.05	.01	.01	−.05	.95	−.06	−.08	−.08	37	.02	.01	−.02	−.04	−.62	.07	.69	.03	−.05	−.03
28	−.19	.07	.03	−.16	.06	−.14	−.06	.96	−.09	−.14	38	−.00	−.05	−.02	−.08	.03	−.19	.03	.94	−.17	−.09
29	−.09	.03	−.03	−.10	.05	.01	−.08	−.09	.90	−.05	39	−.14	−.10	.05	−.09	.12	.04	−.05	−.17	.90	−.03
30	.02	−.18	.01	.00	.06	−.10	−.08	−.14	−.05	.95	40	−.03	−.02	.01	.02	.12	−.01	−.03	−.09	−.03	.82

K-MPAI: Kenny Music Performance Anxiety Inventory.

Acknowledgements

We would like to thank Dianna Kenny, PhD, author of the Kenny Music Performance Anxiety Inventory, for granting her approval to translate the scale. Also, we wish to acknowledge the participants for their help with filling in the data, the experts who sustained the translation process, and our colleague, PhD candidate Ionuț Mone, for the help regarding the statistical analysis. This research is part of a doctoral thesis within the Applied Cognitive Sciences Doctoral School at the Faculty of Psychology and Educational Sciences, Babeș-Bolyai University.

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Andrada Lavinia Faur

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