Music major,affects,and positive music listening experience

Abstract

The use of music in mood regulation has gained increasing attention in recent years. In this study, 199 college students (70 music majors, 126 non-music majors; 101 males, 94 females) responded to two measures: the Positive and Negative Affect Schedule (PANAS) and a 15-item Positive Music Listening Experience Scale we developed (Cronbach’s alpha = .90). It comprised 1 item on frequency of deliberate listening to music and 14 items concerning the effects of such listening on coping, solitude, and contemplative experience, comprising three subscales, respectively. Music majors scored significantly higher than non-music majors on overall Positive Music Listening Experience, as well as significantly higher in positive affect and lower in negative affect than non-music majors. Music majors scored significantly higher than non-music majors on the contemplation subscale; there were no significant differences on the coping and solitude subscales. In addition, the overall Positive Music Listening Experience score was linked significantly with positive affect and self-reported emotional intensity after the demographics were controlled. Contrary to our expectation, negative affect was not a significant predictor and was unrelated to all items of positive music listening experience. In contrast, positive affect was associated significantly with most items relating to positive music listening experience, particularly contemplative subscales items. We discuss the implications of these findings for better understanding the role of affect in influencing the effects of deliberate music listening.

Keywords

music major emotion regulation positive affect negative affect music listening contemplation

The use of music in mood regulation has gained increasing attention in recent years (Cook, Roy, & Welker, 2019; Saarikallio & Erkkila, 2007; Thoma, Ryf, Mohiyeddini, Ehlert, & Nater, 2012). It is generally recognized that listening to music helps individuals to regulate their emotions. Given that positive affect and emotion regulation are essential for the development of emotional intelligence and psychological well-being (Laptook et al., 2008; Laptook, Klein, Olino, Dyson, & Carlson, 2010; Park, Peterson, & Seligman, 2004; Rojas, Leen-Feldner, Blumenthal, Lewis, & Feldner, 2015; Sagone & Indiana, 2017; Zysberg & Raz, 2019), further exploration of the pathways between listening to music and affect regulation should be explored.

A growing number of studies have contributed to the literature focusing on the importance of music-related emotional experience. For example, Saarikallio (2010) investigated how the self-regulatory use of music develops across the lifespan. In this qualitative study, results indicated that the basic nature and strategies of music-related emotion regulation remained consistent throughout one’s lifespan; nevertheless, changes were still possible due to important life events such as retirement transition.

Similarly, Thoma et al. (2012) examined how individuals utilize music to induce specific emotional states for the purpose of emotion regulation. Their results revealed that music selection was usually emotionally congruent with one’s emotional states. In addition, one’s specific emotional-regulation style influenced the selection of music in everyday situations. For example, individuals who preferred a hedonistic means of emotion regulation (i.e., intensifying positive emotions) selected music differently than those preferring a distress-augmenting regulation (i.e., intensifying of negative emotions).

Related to emotion-regulation style, Chamorro-Premuzic and Furnham (2007) studied the relationship between individual differences (measured by general intelligence, big Five personality dimensions, and the tendency to engage in intellectual activities) and specific uses of music. Their results demonstrated that participants who were more open, intelligent, and actively engaged in intellectual activities (i.e., philosophical discussions or the arts) tended to use music in a rational way. For example, they enjoyed analyzing complex music composition and the techniques of the musicians. In contrast, those who were more non-conscientious, introverted, and neurotic tended to use music to change or enhance their mood. In the light of this, it may be interesting to investigate the personality traits of musicians playing particular instruments (e.g., string, brass and woodwind players, singers, or pianists). However, the literature in this domain is sparse, and some researchers have suggested that the purported differences in traits among musicians may be due to stereotypes instead of actual differences (Butković & Modrusan, 2019).

In addition to individual differences, the particular genre of music may also influence the effect of emotion regulation. Cook et al. (2019) studied music preferences among college students. They found that, in general, those who wished to increase their emotional arousal tended to listen to pop, rap/hip-hop, soul/funk, and electronic/dance music. More specifically, soul/funk music could be used to increase positive emotion and decrease negative emotion.

Although music is known to induce emotions, it is important to note that the induction of emotion by music may not be simple or direct. In reviewing the literature on how music induced emotion, Konečni (2008) noted that a sizable amount of studies in this area suffered from methodological weakness. In his view, the pathway between music and emotion is most likely not a direct pathway; basic emotions may be induced by music in low intensity, but probably through different mediators, such as specific cognitive interpretation, emotional memories, or physical dance. The most genuine, interesting, and memorable affective states that could be induced by music, according to this conceptualization, are often those of aesthetic awe or being emotionally moved. However, other more comprehensive models have been offered. For example, Juslin and his colleagues (see Juslin, 2013, 2019; Juslin, Barrados, & Eerola, 2015; Juslin, Liljestrom, Vastfjall, Barrados, & Silva, 2008) have presented a framework describing eight pathways by which music induces emotions. This model is referred to as BRECVEMA model and comprises the following pathways: (1) Brain Stem Reflex, (2) Rhythmic Entrainment, (3) Evaluative Conditioning, (4) Contagion, (5) Visual Imagery, (6) Episodic Memory, (7) Musical Expectancy, and (8) Aesthetic Judgment. As an example, aesthetic awe or typical appreciation emotion induced by music would be a part of the aesthetic judgment pathway.

Since music can induce genuine positive and meaningful experience, such as aesthetic awe, and individual differences (i.e., personality and emotion regulation style) may be important predictors of music listening experiences, the purpose of the present study was to investigate the relationship between trait affect (i.e., one’s characteristic degree of both positive and negative affect, both conceptually and neurologically distinct from one another) and positive music listening experiences. Music listening is one of the contemplative methods that can connect us to the purpose of life and quiet our minds in the present moment without opposition or conflict (Zajonc, 2016). To this end, we developed a scale that measures common positive music listening experiences, such as involving coping with stress, contemplation, and solitude. For example, who is likely to report that music improves relaxation and reduces worrying, enhances focusing on the present moment (mindfulness), provides a bigger picture to human existence, or boost one’s ability to be alone? In this study, we hypothesized that trait affect may be an important predictor of positive music listening experience. See Appendix 1 for specific items.

Given that music can potentially induce meaningful emotional states, it is important to examine the effects of music intervention or music education that promote positive music listening experiences. The evidence from several meta-analyses (Sala & Gobet, 2017; Standley, 1996) suggests that music education and intervention are generally effective for both emotion development and general education. For example, Guhn, Emerson, and Gouzouasis (2019) examined 112,000 student records in British Columbia. In controlling statistically for both the students’ cultural background and neighborhood socioeconomic status, the researchers found that those who were highly engaged in music had higher exam scores across all subjects such as math, science, and English. This relationship was even stronger for students who played an instrument compared to vocal music students, which was about one academic year ahead on average compared to non-music peers.

In view of the paucity of studies concerning college music majors and their positive music listening experience (Bergee, 1992), a second purpose of our study was to explore the differences between music and non-music majors in terms of their positive music listening experience and trait affect. We hypothesized that music majors would report more positive music listening experiences than non-majors. In addition, we hypothesized that there would be differences in trait affect among these two groups due to our assumption that individual emotional style would contribute both to one’s preference of music and college major selection.

Methods

Participants

One hundred and ninety-nine college students from two institutions of higher education in New York City were recruited to participate in a survey research. The vast majority (98%) of participants’ ages ranged from 18 to 30; 101 were male (51%), 94 were female (47%), and 2 (1%) were gender unknown. Approximately 29% were white, 24% were black, and 29% were of Asian/Pacific ancestry. Of the 196 students who provided usable data, there were 70 music majors (36%) and 126 non-music majors (64%). Although we did not elicit socio-economic data from participants, the institution of higher education which most of our participants attended has reported that more than 50% of its students come from families with annual incomes below $25,000 (Queensborough Community College of City University of New York, 2019). A substantial proportion of them (ranges from 42% to 80%) have received an income-based federal Pell grant intended for low-income students. As the average annual median household income in New York City is $57,789 (U.S. Census Bureau, 2019), it can be reasonably assumed that the majority of our participants came from lower to lower-middle socio-economic backgrounds.

It should be noted that within the United States, the major of music generally requires specific courses relating to major instrument/voice lessons (sometimes minor instrument/voice too), plus sight reading/singing, music theory, history, orchestra, choir, and chamber music ensemble. Students apply to enter the major with either a live audition or a video performance, and generally have devoted years to practice at home, weekly lessons at school and/or at home privately, and orchestral/ensemble participation in high school. However, in most community colleges (which award a 2-year rather than 4-year degree), no audition is typically required. In our sample, the non-music majors were students who were majoring in any subject other than music, such as biology, business, and psychology. Non-music majors are almost never required to take any courses in music; depending on their interest level, some may choose an elective course or two in “music introduction.” Table 1 presents the demographic variables for the total sample.

Table 1.

Demographic summary.

	n	Percent
Age
18–21	130	65.3
21–30	64	32.2
31–40	1	.5
41–50	1	.5
Over 50	2	1.0
Gender
Male	101	50.8
Female	94	47.2
Other	2	1.0
Race
White	57	28.6
Black	47	23.6
Asian/Pacific Islanders	58	29.1
Hispanic	8	4.0
American Indian/Alaska Native	2	1.0
Other	23	11.6
Education
No college	19	9.5
Some college	137	68.8
college degree	28	14.1
Advanced degree	5	2.5
Music major
Major	70	35.2
Non-music major	126	63.3

Note. Percentages do not sum to 100 due to missing data.

Measures

The main outcome variables for this study were the Positive Music Listening Experience Scale (PMLES) and its three subscales. The key predictors were the Positive and Negative Affect Schedule (PANAS) as well as a one-item emotional intensity measure.

PMLES

The PMLES is a 15-item self-report measure that we developed specifically to assess the positive impact of music listening on coping with stress, creativity, and contemplation. (see Appendix 1). The PMLES derives from empirical research indicating that music listening can enhance functioning in those areas (Alvarenga et al., 2017; Garrido, Baker, Davidson, Moore, & Wasserman, 2015; Hargreaves, Hargreaves, & North, 2012; Harrison & Loui, 2014; Hole, Hirsch, Ball, & Meads, 2015; Schäfer, 2016; Schäfer, Sedlmeier, Stadtler, & Huron, 2013).

To develop this instrument further, factor analysis was calculated to determine potential subscales. Using principal components analysis with a Varimax rotation, statistical results suggested a three-factor solution that accounted for 62.1% of the variance. The three factors are as follows: (1) Contemplation (Items 6, 7, 9, 10, 12, 13, and 14), (2) Coping (Items 3, 4, 8, and 11), and (3) Solitude (Item 2). In other words, our conceptualized factor of creativity turned out statistically to be part of the sub-scale for contemplation, and solitude emerged as a separate factor when we did the analysis.

In sum, PMLES comprised one item on frequency of deliberate listening to music and 14 items concerning the effects of such listening on (1) coping, (2) solitude, and (3) contemplative experience; these comprised three subscales, respectively. The total PMLES score is calculated by adding all 14 items. The PMLES has a high degree of internal consistency; Cronbach’s α for this scale is .895. The Cronbach’s α for the two multi-item subscales was also adequate (Contemplation = .895, Coping = .785). During the test construction phase, we did not perform test–retest reliability for this scale. We also did not perform convergent validity analysis because we were not aware of other scales that measures similar construct. Nevertheless, given that PMLES is positively correlated to Positive Affect subscale of the PANAS and has reasonable Cronbach’s α, it has demonstrated certain level of convergent validity with adequate reliability. Here is a sample item on contemplative experience: “Listening to music that I choose causes me to think more deeply about the meaning of life.” The score for the 14 items range on a five-point Likert-type scale from 1 (not at all or never) to 5 (very much or very often).

PANAS

The PANAS is a 20-item self-report measure comprised of two affect scales (10 items each), one measuring positive trait affect and the other measuring negative trait affect. Watson, Clark, and Tellegen (1988) reported that the mean population score for positive PANAS score was 33.3 (standard deviation [SD] = 7.2) and negative PANAS score was 17.4 (SD = 6.2). In that study, the Cronbach’s α was .898 for the Positive Affect subscale and .863 for the Negative Affect subscale. A sample item measuring positive affect is: “Indicate the extent you have felt enthusiastic over the past week,” and the response option is based on 5-point Likert-type Scale ranging from 1 (very slightly or not at all) to 5 (extremely).

Emotional intensity

We developed one self-report item on a four-point Likert-type scale to measure participants’ emotional intensity: “Do you consider yourself to have strong emotions compared to most people of your age?” Scores ranged from 0 (not at all) to 3 (to a large degree).

Procedure

The present study used a cross-sectional design. We did not intend to investigate the long-term correlations among the same participants; rather we examined the correlations among similar age groups at several urban universities. After receiving Institutional Review Board approval for our survey, we recruited college students from two institutions to participate. Prior to the distribution of surveys in class, participants were provided with an information sheet. In the information sheet, the purpose, procedures, risks and benefits of participation, and confidentiality were described, as well as the participants’ right to either complete or withdraw from the study at any time. To participate in this study, participants needed to be at least 18 years of age and studying at the undergraduate level. Extra credit was provided as an incentive for participation, and all students did so; they completed the survey anonymously.

The main aim of the current study was to examine the predictive power that measures of trait affect and emotional intensity might have on positive music listening experience. Important demographics such as music major and its effect on music listening experience subscales were also explored. All data were entered and analyzed by SPSS. Exploratory multivariate statistics were applied.

Results

Examination of frequency distributions, histograms, and tests of homogeneity of variance and of normality for the criterion measures indicated that the assumptions for the use of parametric statistics were met. All of the analyses presented were performed with the significance level (alpha) set at .05, two-tailed tests. Means and standard deviations for trait affect score and positive music listening scores are presented in Table 2.

Table 2.

Descriptive statistics on music major and key outcome measures.

	Music major
	Major (n = 62)		Non-major (n = 99)
Outcome variable	M	SD	M	SD
Positive music listening experience score*	61.5	8.85	57.9	10.9
Positive PANAS Score*	35.4	7.84	32.5	8.91
Negative PANAS Score**	21.6	6.84	25.2	9.21

PANAS: Positive and Negative Affect Schedule.

Note: *p < .05 (2-tailed). **p < .01 (2-tailed). Cases with missing data are not included.

As reported by Watson et al. (1988), the mean population score for the positive PANAS score is 33.3 (SD = 7.2) and the negative PANAS score is 17.4 (SD = 6.2). Our sample had a higher negative PANAS score compared to Watson et al.’s results but still within the normal range.

Music majors

Results in Table 2 suggested that music majors and non-music majors had different positive music listening experiences and PANAS scores. A one-way multivariate analysis of variance (MANOVA) indicated that there were significant differences in all three measures between the music and non-music majors, F(3, 157) = 4.74, p = .003, Wilks’s Λ = 0.917. The partial η² was .083, and the observed power was .893.

Table 2 indicated that music majors scored significantly higher than non-music majors on Positive Music Listening Experience, F(1, 159) = 4.989, p = .027, partial η² = .030, as well as significantly higher on Positive PANAS, F(1, 159) = 4.494, p = .036, partial η² = .027, and lower on Negative PANAS, F(1, 159) = 7.150, p = .008, partial η² = .043, than non-music majors.

Music major and music listening subscales

The differences between both music and non-music majors on the three music subscales (contemplation, coping, and solitude) were also examined. A one-way MANOVA indicated that there were significant differences in the subscale measures between the music and non-music majors, F(3, 181) = 4.727, p = .003, Wilks’s Λ = 0.927. The partial η² was .073, and the observed power was .893.

As presented in Table 3, music majors scored significantly higher than non-music majors on contemplation, F(1, 183) = 9.321, p = .003, partial η² = .048; there was no significant difference for the coping, F(1, 183) = .020, p = .889, partial η² = .001, and solitude, F(1, 183) = 2.186, p = .141, partial η² = .02.

Table 3.

Descriptive statistics on music major and positive music listening experience subscales.

	Music major
	Major (n = 67)		Non-major (n = 118)
Music Listening Experience Subscales	M	SD	M	SD
Contemplation**	28.27	5.04	25.3	6.92
Coping	13.1	1.99	13.1	2.03
Solitude	3.54	1.00	3.30	1.10

Note: *p < .05 (2-tailed). **p < .01 (2-tailed). Cases with missing data are not included.

Positive music listening experience

Overall experience

To determine which construct would be the best predictors of Positive Music Listening Experience Total Score, we conducted a sequential multiple regression analysis, in which we first entered the demographic predictors (age, gender, education, and music major), then the trait affect predictors (Positive PANAS score, Negative PANAS score and Emotional Intensity). In other words, we sought to investigate which trait affect constructs would be the best predictors after the demographics were controlled.

As shown in Table 4, the overall model for the Positive Music Listening Experience Total Score was significant, accounting for 13% of the variance. As a set, the demographics were not significant. However, within this set, participants who were music majors were more likely to report positive music listening experience compared to others.

Table 4.

Multiple regression analysis of positive music listening experience as a function of demographic variables and trait affects variables (N = 155).

	Model 1			Model 2
	B	SE B	β	B	SE B	β
Constant	61.1	4.99		39.6	6.7
Demographic
Gender	1.2	1.6	.06	1.1	1.6	.06
Age	1.0	1.3	.06	1.1	1.2	.07
Education	0.15	1.4	.01	−0.17	1.3	−.01
Music Major	4.0	1.7	.19*	3.4	1.7	.16*
Trait affects
Positive PANAS Score				0.30	0.09	.25**
Negative PANAS Score				0.13	0.10	.11
Emotional Intensity				3.6	1.4	.21**
F for model		1.94			4.31**
R ²		0.02			0.13
F for change in R ²		1.94			7.16**

PANAS: Positive and Negative Affect Schedule.

Note: *p < .05 (2-tailed). **p < .01 (2-tailed). Cases with missing data are not included.

After the demographics were controlled, the trait affect constructs as a set were significant, but individual t-tests indicated that the Negative PANAS score was not a significant construct in predicting Positive Music Listening Experience. The strongest predictors in the overall model were Positive PANAS score and Emotional Intensity.

Subscales of positive music listening experience

In addition to the total PMLES score, we expanded our analyses to the subscales. Means and standard deviations for each subscale are presented in Table 3. To investigate further the relationship between positive music listening experience and trait affect, Pearson product-moment correlation coefficients were calculated to determine whether trait affect and positive music listening subscales were related linearly.

Results indicated that Music Coping, r = .287, p = .001, and Music Contemplation, r = .230, p = .003, were correlated with positive PANAS, but not Music Solitude, r = -.080, p = .313. In contrast, the negative PANAS score was not correlated significantly with any of the music listening subscales. See Table 5 for the correlation results.

Table 5.

Correlations among key study variables.

	Contemplation	Coping	Solitude	Positive PANAS	Negative PANAS	Emotional Intensity	Music Listening Frequency	PMLES Total Score
Contemplation	1	0.582^**	0.155^*	0.230^**	0.077	0.256^**	0.284^**	0.953^**
Coping		1	0.114	0.287^**	0.006	0.195^*	0.275^**	0.750^**
Solitude			1	−0.080	−0.104	0.016	0.097	0.252^**
Positive PANAS				1	−0.219^**	0.068	−0.012	0.264^**
Negative PANAS					1	0.165^*	0.111	0.054
Emotional Intensity						1	0.071	0.248^**
Music Listening Frequency							1	0.310^**
PMLES Total Score								1

PANAS: Positive and Negative Affect Schedule; PMLES: Positive Music Listening Experience Scale.

Note: *p < .05 (2-tailed). **p < .01 (2-tailed). Cases with missing data are not included.

Individual items of positive music listening experience

Finally, we investigated the relationships between PANAS and all individual items. The Positive PANAS score was correlated positively with 10 out of 14 music items. In contrast, the negative PANAS score was not significantly correlated to any items of the positive music listening experience scale. See Table 6 for the correlation results.

Table 6.

Correlations among PMLES items and PANAS.

PMLES Items	Positive PANAS	Negative PANAS
Solitude: Prefer to do alone	−.082	−.102
Coping: Energizes me	.237^**	.063
Coping: Can get me out of a sad mood	.253^**	−.012
Coping: Helps me relax	.208^**	−.029
Coping: Helps me to better cope with worries	.101	.104
Contemplation: Gives me a sense of beauty that nothing else can	.214^**	.099
Contemplation: Causes me to think more deeply about the meaning in life	.018	.135
Contemplation: Spiritual experience	.157^*	.078
Contemplation: Enables me to know myself in ways that words cannot	.119	.095
Contemplation: Gives me a feeling of inner strength that nothing else can	.266^**	−.003
Contemplation: Increases my mindfulness	.254^**	−.024
Contemplation: Gives me glimpses of a bigger picture to human existence	.250^**	.034
Creativity: Enhances my creativity	.275^**	−.061
Relationship: Makes me feel closer to other people	.218^**	.077

PMLES: Positive Music Listening Experience Scale; PANAS: Positive and Negative Affect Schedule.

Note: *p < .05 (2-tailed). **p < .01 (2-tailed). Cases with missing data are not included.

Discussion

The purpose of the current study was to explore the relationship between trait affects and positive music listening experience. The differences in music listening experience between music and non-music majors were also investigated. First, the results indicated that music major students reported having much better overall positive music listening experiences. They also tend to have higher positive affect and less negative affect compared to the non-majors. In addition, music major students scored significantly higher than non-music majors on contemplation, but not on coping and solitude, which means that music major students were more likely to benefit from contemplative experience through music compared to others.

Given that there were significant differences between the music and non-music major students, to explore the relationship between trait affect and positive music listening experience we first controlled the effects of demographic variables such as major, gender, age, and education. The results indicated that the strongest predictors of total positive music listening experience were positive trait affect and emotional intensity. That is, participants with strong emotionality and positive trait affect were more likely to report positive music listening experience overall, regardless of their college major or other demographic factors. Also, those with strong negative trait affect did not report better positive music listening experience. Finally, participants with high positive trait affect were more likely to use music to cope with stress and to contemplate. In contrast, students who had high negative trait affect did not benefit from either of these aspects in their music listening experience. Although we did not directly measure the socioeconomic status of participants, the majority attended an institution of higher education which reported that students came from families whose annual income was considerably lower than average for New York City households (U.S. Census Bureau, 2019). Thus, it seems unlikely that the psychological differences evidenced between music majors and non-music majors reflected socioeconomic factors.

To our knowledge, no published studies have previously shown that music majors differ significantly from non-music majors in having both higher positive affect and lower negative affect. Since it appears unlikely that the specific college major of music influences one’s trait affect, the most reasonable explanation is that college students who choose to major in music are already higher in positive affect and lower in negative affect than their peers before declaring their major. Despite the paucity of research on the individual characteristics of music majors, it further seems reasonable to surmise that most studied a musical instrument or took vocal lessons in high-school or earlier, and thus from the outset, differed from other children. In our view, a self-selection process may be involved, in which children and adolescents low in positive affect and high on negative affect lack the emotional temperament necessary for regular, systematic instrumental, or vocal practice, with its inevitable frustrations and necessary self-discipline. In this light, positive affect has been linked to greater self-efficacy (i.e., confidence in one’s ability to deal with difficult or challenging situations successfully) among adolescents (Sagone & Indiana, 2017), and self-efficacy certainly seems crucial for the self-discipline involved in learning to play a musical instrument or perform vocally. Indeed, Rojas and Springer (2014) found that music performance majors had greater self-efficacy than non-music performance majors, supporting their hypothesis that the ability to maintain a rigorous practice schedule depends on a high level of self-efficacy. Concerning this domain, it should also be noted that in addition to self-efficacy, Butković, Ullen, and Mosing (2015) found that music practice was partially influenced by the Big Five trait of openness and proneness to music flow experience.

However, research has also demonstrated that music, especially music performance, can arouse positive emotional response through the generation of endorphin, as well as create bonding relationships among small communities (Dunbar, Kaskatis, MacDonald, & Barra, 2012). Similarly, there are studies demonstrating that a participant-preferred live music therapy session can be an effective intervention increasing positive affect and decreasing negative affect (Fredenburg & Silverman, 2014). Thus, it seems reasonable to hypothesize that positive music listening experience may foster the development of positive trait affect. In turn, this aspect would be likely to strengthen resilience, in view of studies linking high positive affectivity with greater psychological well-being (Hoffman et al., 2019). To demonstrate this point further, one randomized clinical trial study found that therapeutic music video intervention was an effective method to improve coping and resilience in young adults during a high-risk cancer treatment (Robb et al., 2014). Some researchers (see Costa-Giomi, 1999) have argued that studies comparing children who practice a musical instrument with their non-practicing peers have failed to take into account possible socioeconomic differences between these groups as an explanatory factor. In this light, it should be noted that both music majors and non-music majors in our sample predominantly attended a community college whose students come mainly from lower to lower-middle class backgrounds. In addition, our statistical analysis had controlled the effects of participant’s demographics, so the differences between the major and non-major were minimized when examined the relationship between trait affect and positive music listening experience.

Finally, our finding that enhanced contemplation was the key benefit from music listening among music majors suggests that integrating contemplative practice (e.g., mindfulness training such as Zen) into higher music education may be beneficial. When music majors learn to listen not just to the “outer voice,” but to the “inner voice,” they may begin to actualize who they are and live more fully through music education (Chang, Lin, & Seiden, 2019; Chang, Midlarsky, & Lin, 2003; Lin, Chang, Zemon, & Midlarsky, 2007; Shippee, 2010). Similarly, music contemplation may not just be beneficial to music majors but to college students in general as well as the broader adult population. For example, Işık and Üzbe (2015) found that adults with a greater sense of life meaning had higher positive affect.

Although our findings have important implications for understanding the relationship between trait affect and the self-reported benefits of music listening, generalizability is limited by the fact that our participants were exclusively undergraduates attending colleges in the northeast United States and the sample size was small. It is therefore not possible to assert that our findings are applicable for adolescents or adults in midlife or beyond. However, in view of the fact that trait affect seems to be developmentally fixed by mid-adolescence (Watson & Naragon-Gainey, 2014), we see little reason to doubt that positive affectivity significantly impacts the extent to which music listening enhances mood across the lifespan. Because our participants were predominantly born and raised in the United States, we are unable to apply our findings to their cohorts in other countries. For these reasons, we recommend that future research seeks to replicate our research design with both adolescents and adults past undergraduate age in other countries.

Footnotes

Appendix 1 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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr. Joanne Chang expresses gratitude for publication of this article to Fellowship/Sabbatical Award from Queensborough Community College of City University of New York. Dr. Peter Lin would also like to show his appreciation to the St. Joseph’s College New York’s Sabbatical Award.

ORCID iD

Peter Lin

References

Alvarenga

Leite

A. C. A.B.

Oliveira

M. S.

Nascimento

L. C.

Silva-Rodrigues

F. M.

Nunes

M. D. R.

de Carvalho

E. C.

(2017). The effect of music on the spirituality of patients: A systematic review. Journal of Holistic Nursing, 36, 192–204. doi.org/10.1177/0898010117710855

Bergee

M. J.

(1992). The relationship between music education majors’ personality profiles, other education majors’ profiles, and selected indicators of music teaching success. Bulletin of the Council for Research in Music Education, 112, 5–15.

Butković

Modrusan

(2019). Personality differences among musicians: Real differences or stereotypes? Psychology of Music. Advance online publication. doi:10.1177/0305735619849625

Butković

Ullen

Mosing

M. A.

(2015). Personality related traits as predictors of music practice: Underlying environmental and genetic influences. Personality and Individual Differences, 74, 133–138.

Chamorro-Premuzic

Furnham

(2007). Personality and music: Can traits explain how people use music in everyday life? British Journal of Psychology, 98, 175–185.

Chang

Lin

Seiden

(2019). Music performance and education: A case presentation of mindfulness approach in higher education. The Journal of Performance and Mindfulness, 2(2), 1–9. doi:10.5920/pam.617

Chang

Midlarsky

Lin

(2003). Effects of meditation on music performance anxiety. Medical Problems of Performing Artists, 19, 126–130.

Cook

Roy

A. R. K.

Welker

K. M.

(2019). Music as an emotion regulation strategy: An examination of genres of music and their roles in emotion regulation. Psychology of Music, 47, 144–154.

Costa-Giomi

(1999). The effects of three years of piano instruction on children’s cognitive development. Journal of Research in Music Education, 47, 198–212.

10.

Dunbar

R. I. M.

Kaskatis

MacDonald

Barra

(2012). Performance of music elevates pain threshold and positive affect: Implications for the evolutionary function of music. Evolutionary Psychology, 10, 688–702. doi.org/10.1177/147470491201000403

11.

Fredenburg

H. A.

Silverman

M. J.

(2014). Effects of music therapy on positive and negative affect and pain with hospitalized patients recovering from a blood and marrow transplant: A randomized effectiveness study. The Arts in Psychotherapy, 41, 174–180.

12.

Garrido

Baker

F. A.

Davidson

J. W.

Moore

Wasserman

(2015). Music and trauma: The relationship between music, personality, and coping style. Frontiers in Psychology, 6, 1–3. doi:10.3389/fpsych2015.00977

13.

Guhn

Emerson

S. D.

Gouzouasis

(2019). A population-level analysis of associations between school music participation and academic achievement. Journal of Educational Psychology. Advance online publication. doi:10.1037/edu0000376

14.

Hargreaves

D. J.

Hargreaves

J. J.

North

A. C.

(2012). Imagination and creativity in music listening. In Hargreaves

Miell

MacDonald

(Eds.), Musical imaginations: Multidisciplinary perspectives on creativity, performance, and perception. Oxford Scholarship Online. doi:10.1093.acprof:oso/9780199568086.003.0010

15.

Harrison

Loui

(2014). Thrills, chills, frissons, and skin orgasms: Toward an integrative model of transcendent psychophysiological experiences in music. Frontiers in Psychology, 5, 1–6. doi:10.3389/fpsych2014.00790

16.

Hoffman

Tran

Sasaki

Igarashi

(2019). Tears of joy among post-college-age Japanese adults: Implications for resilience. Asia Pacific Journal of Counselling and Psychotherapy, 10, 188–200.

17.

Hole

Hirsch

Ball

Meads

(2015). Music as an aid for postoperative recovery in adults: A systematic review and meta-analysis. The Lancet, 386, 1659–1671. doi:10.1016/S0140-6736(15)60169-6

18.

Işık

Üzbe

(2015). Personality traits and positive/negative affects: An analysis of meaning in life among adults. Educational Sciences: Theory & Practice, 15, 587–595.

19.

Juslin

P. N.

(2013). From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotion. Physics of Life Reviews, 10, 235–266.

20.

Juslin

P. N.

(2019). Musical emotions explained: Unlocking the secrets of musical affect. Oxford, UK: Oxford University Press.

21.

Juslin

P. N.

Barrados

Eerola

(2015). From sound to significance: Exploring the mechanisms underlying emotional reaction to music. American Journal of Psychology, 128, 281–304.

22.

Juslin

P. N.

Liljestrom

Vastfjall

Barrados

Silva

(2008). An experience sampling study of emotional reactions to music: Listener, music, and situation. Emotion, 8, 668–683.

23.

Konečni

V. J.

(2008). Does music induce emotion? A theoretical and methodological analysis. Psychology of Aesthetics, Creativity, and the Arts, 2, 115–129. doi:10.1037/1931-3896.2.2.115

24.

Laptook

R. S.

Klein

D. N.

Durbin

C. E.

Hayden

E. P.

Olino

T. M.

Carlson

(2008). Differentiation between low positive affectivity and behavioral inhibition in preschool-age children: A comparison of behavioral approach in novel and non-novel contexts. Personality and Individual Differences, 44, 758–767. doi:10.1016/j.paid.2007.10.010

25.

Laptook

R. S.

Klein

D. N.

Olino

T. M.

Dyson

M. W.

Carlson

(2010). Low positive affectivity and behavioral inhibition in preschool-age children: A replication and extension of previous findings. Personality and Individual Differences, 48, 547–551. doi:10.1016/j.paid.2009.12.003

26.

Lin

Chang

Zemon

Midlarsky

(2007). Silent illumination: A study on Chan (Zen) meditation, anxiety, and musical performance quality. Psychology of Music, 36, 139–155.

27.

Park

Peterson

Seligman

M. E. P.

(2004). Strengths of character and well-being. Journal of Social and Clinical Psychology, 23, 603–619.

28.

Queensborough Community College of City University of New York. (2019, October 25). Fast facts and economic value. Retrieved from http://www.qcc.cuny.edu/about/fast-facts.html

29.

Robb

S. L.

Burns

D. S.

Stegenga

K. A.

Haut

P. R.

Monahan

P. O.

Meza

. . .Haase

J. E.

(2014). Randomized clinical trial of therapeutic music video intervention for resilience outcomes in adolescents/young adults undergoing hematopoietic stem cell transplant: A report from the Children’s Oncology Group. Cancer, 120, 909–917. doi:10.1002/cncr.28355

30.

Rojas

Springer

D. G.

(2014). An exploratory study of musicians’ self-efficacy to maintain practice schedules. Bulletin of the Council for Research in Music Education, 199, 39–52.

31.

Rojas

S. M.

Leen-Feldner

E. W.

Blumenthal

Lewis

Feldner

(2015). Risk for suicide among treatment seeking adolescents: The role of positive and negative affect intensity. Cognitive Therapy and Research, 39, 100–109.

32.

Saarikallio

(2010). Music as emotional self-regulation throughout adulthood. Psychology of Music, 39, 307–327.

33.

Saarikallio

Erkkila

(2007). The role of music in adolescents’ mood regulation. Psychology of Music, 35, 88–109.

34.

Sagone

Indiana

M. L.

(2017). The relationship of positive affect with resilience and self-efficacy in life skills in Italian adolescents. Psychology, 8, 2226–2239.

35.

Sala

Gobet

(2017). When the music’s over. Does music skill transfer to children’s and young adolescents’ cognitive and academic skills? A meta-analysis. Educational Research Review, 20, 55–67.

36.

Schäfer

(2016). The goals and effects of music listening and their relationship to the strength of music preference. PLoS ONE, 11(3), e0151634. doi:10.1371/journal.pone.0151634

37.

Schäfer

Sedlmeier

Stadtler

Huron

(2013). The psychological functions of music listening. Frontiers of Psychology, 4, 1–33.

38.

Shippee

M. R.

(2010). The sound of starting where you are: Contemplative practice and music pedagogy. New Directions for Community Colleges, 151, 77–89.

39.

Standley

J. M.

(1996). A meta-analysis on the effects of music as reinforcement for education/therapy objectives. Journal of Research in Music Education, 44, 105–133.

40.

Thoma

M. V.

Ryf

Mohiyeddini

Ehlert

Nater

U. M.

(2012). Emotion regulation through listening to music in everyday situations. Cognition and Emotion, 26, 550–560.

41.

U.S. Census Bureau. (2019, October 25). Quick Facts of New York City. Retrieved from https://www.census.gov/quickfacts/fact/table/newyorkcitynewyork/PST040218

42.

Watson

Naragon-Gainey

(2014). Personality, emotions, and the emotional disorders. Clinical Psychological Science, 2, 422–442. doi:10.1177/2167702614536162

43.

Watson

Clark

L. A.

Tellegen

(1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070.

44.

Zajonc

(2016). Contemplation in education. In Schonert-Reichl

Roeser

(Eds.), Handbook of mindfulness in education (pp. 17–28). New York, NY: Springer.