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The relationship between small ensemble experiences, empathy, and emotional self-regulation skills in music students

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The relationship between small ensemble experiences, empathy, and emotional self-regulation skills in music students

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Content THE RELATIONSHIP BETWEEN SMALL ENSEMBLE EXPERIENCES, EMPATHY, AND EMOTIONAL SELF-REGULATION SKILLS IN MUSIC STUDENTS Eun Cho A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF MUSICAL ARTS MUSIC EDUCATION University of Southern California May 2018 Advisory Committee: Dr. Beatriz Ilari, Chair Dr. Peter Webster Dr. Mary Helen Immordino-Yang ii Copyright Page iii ABSTRACT Small music ensembles represent a unique form of human social activity, involving a highly complex set of interpersonal communicative skills. In order to achieve joint musical goals, ensemble performers strive to reach out to the “other,” by sensitively attending to whilst aligning their emotions with those of their co-performers. It is equally crucial for performers to effectively regulate their emotions and behave in ways appropriate to the given musical context for successful ensemble engagement. Thus, participation in small music ensembles may be a fruitful means to cultivate the habit of empathizing as well as effective emotional self-regulation. To support this notion, this study explored the relationships between music students’ small ensemble experiences and their empathy and emotional self-regulation skills. Undergraduate music performance majors in their senior year (N = 165) voluntarily completed an online survey that included questions about their background and participation in and attitudes toward small ensembles. They also completed self-assessment questionnaires that measured their dispositional empathy levels, as well as their tendencies to regulate emotions using cognitive reappraisal and expressive suppression. Hierarchical regression analysis indicated that students’ levels of participation in various small ensemble activities significantly predicted their empathy skills, even after controlling for the effect of personal factors. Yet, no association between music students’ small ensemble experiences and their tendencies to regulate their emotions using either cognitive reappraisal or expressive suppression was found. Meanwhile, several personal factors, such as personality traits, ethnicity, and primary instrument, appeared to play important roles in predicting students’ empathy and emotional self-regulation skills. Although this work is correlational in nature, its finding hint at the possible effects of small ensemble as a way to cultivate empathy. Extensive iv literature suggests that small music ensembles hold the potential power of not only promoting musical development but also enhancing various social-emotional skills in students, and findings from this study provide evidence to support the notion that small music ensembles could be an effective educational domain to cultivate empathy. Keywords: small music ensemble, empathy, emotional-self regulation, non-musical effects of music, social-emotional development v ACKNOWLEDGEMENTS It has been such a long journey to finish this dissertation, and I am profoundly aware that it is in no way a solo endeavor. I am very grateful to the wonderful people in my life who have supported me in the creation and development of this work. I express my deepest gratitude to my advisor, Dr. Beatriz Ilari, for her patience, guidance, encouragement, and continuous support throughout the journey in attaining my doctoral degree. Beyond her significant help with this dissertation, she is a model of teaching and scholarship that I seek to emulate, and I treasure her relentless mentorship and our friendship. I would not be where I am today without her. I will be forever indebted to Dr. Peter Webster, whose infectious enthusiasm and vision have been a great inspiration to me. His guidance, encouragement, and support gave me great strength and made me believe in myself. I appreciate his wisdom and knowledge, which I will take wherever my career may lead me. I would also like to thank Dr. Mary Helen Immordino-Yang for her support and added insight. I really appreciate the chance to learn from her and share my passion. Unending gratitude goes to my family. I am especially grateful to my loving parents who are always a rock of support. Without their dedicated support and love, this journey would not have been possible. To Min Joon, my incredible husband and my best friend, thank you for believing in me and constantly lifting me up. I am truly thankful for having you in my life. I look forward to our next adventure together with our precious children, Elliana and Caleb. Above all, God you made it all happen. I love you. vi TABLE OF CONTENTS Abstract………………………………………………………………………………. iii Acknowledgements…………………………………………………………………... v List of Tables…………………………………………………..……………….......... ix List of Figures……...…………………………………...……………………………. xi Chapter ONE Overview of the Study……………………………………………… Background…………………………………………………………... Purpose of the Study……………………………………………......... Research Questions…………………………………………………... Terms and Acronyms………………………………………………… Overview of Chapters………………………………………………... 1 3 5 6 6 9 TWO Review of Related Literature Empathy and Emotional Self-Regulation……………………………. Empathy: Experiencing Emotional States of Others…………… Development of empathy in childhood…………………... Empathy in emerging adulthood…………………………. Factors associated with empathy………………………… Emotional Self-Regulation: Flexibly Regulating Emotional Responses………………………………………………………. Development of emotional self-regulation in childhood….. Emotional self-regulation in emerging adulthood………… Factors associated with emotional self-regulation………… Small Ensembles in Music…………………………………………... Co-performer Communication in the Rehearsal Phase………… Co-performer Communication in the Performance Phase……… Previous Empirical Research on Music and Social-Emotional Skills……………………………………………………………. Chapter Summary……………..……………………………………... 10 10 10 14 15 20 23 28 30 33 37 42 46 52 61 THREE Method……………………………………………………………..... The Current Study…………………………………………………… Participants…………………………………………………………... Data Collection Instrument…………………………………………... Part 1: Background Information………………………………... Part 2: Musical Experiences Before Entering College………..... 62 62 63 65 66 67 vii Part 3: Small Ensemble Experiences in College………...……… Part 4: Personality and Emotional Life…………………………. Pilot Study…………………………………………………………… Data Collection Procedure…………………………………………… Participant Recruitment………………………………………… Data Storage and Confidentiality……………………………………. Variables……………………………………………………………... Data Analysis Procedure…………………………………………….. Data Scoring……………………………………………………. Statistical Analysis……………………………………………... Chapter Summary……………..……………………………………... 67 68 74 74 76 77 77 80 80 81 83 FOUR Results……………………………………………………………….. Descriptive Analysis…………………………………………………. Participation in Small Ensemble....……………………………... Attitudes Toward Small Ensemble……………………………... Empathy………………………………………………………… Emotional Self-Regulation……………………………………... Research Question One……………………………………………… Research Question Two……………………………………………… Gender………………………………………………………….. Ethnicity………………………………………………………… Primary Instrument……………………………………………... Primary Area of Study………………………………………….. Age at Commencement of Music Training…………………….. Personality Traits……………………………………………….. Research Question Three…………………………………………….. Empathy………………………………………………………… Emotional Self-Regulation – Cognitive Reappraisal…………… Emotional Self-Regulation – Expressive Suppression…………. Chapter Summary……………..……………………………………... 84 84 84 92 94 95 99 103 103 104 105 106 107 108 110 113 116 118 122 FIVE Discussion Relationships Between Personal Factors and Empathy and Emotional Self-Regulation…………………………………………... Gender………………………………………………………….. Ethnicity………………………………………………………… Primary Instrument……………………………………………... Primary Area of Study………………………………………….. Age at Commencement of Music Training…………………….. Relationships Between Participation in and Attitudes Toward Small Ensemble……………………………………………………………... Relationships Between Attitudes Toward Small Ensemble, Empathy, and Emotional Self-Regulation……….……………………………… 125 126 126 127 129 130 132 133 135 viii Empathy………………………………………………………… Emotional Self-Regulation……………………………………... Relationships Between Participation in Small Ensemble, Empathy, and Emotional Self-Regulation………………………………………. Empathy………………………………………………………… Emotional Self-Regulation……………………………………... Roles of Personality Traits on Empathy and Emotional Self- Regulation……………………………………………………………. Empathy………………………………………………………… Emotional Self-Regulation……………………………………... Summary……………………………………………………………... Limitations of the Study……………………………………………... Implications for Music Teaching and Learning……………………… Recommendations for Future Study…………………………………. Conclusion…………………………………………………………… 135 136 137 137 141 144 144 148 150 152 154 156 157 References……………………………………………………………………………. 160 Appendices A: Small Ensemble Experience and Social-Emotional Competence Questionnaire (SEESEC)………………………. B: Institution Review Board (IRB) Approval Form…………….. C: Informed Consent Form……………………………………… D: Electronic Invitation to the Survey…………………………... 199 214 215 216 ix List of Tables Table 3.1: Distribution of Participants by Musical Background……………………... Table 3.2: Distribution of Participants by Demographic Information………………... Table 3.3: The Small Ensemble Experience and Social-Emotional Competence Questionnaire (SEESEC) ……………..……………..……………..………………… Table 3.4: Variables used in the Current Study……………..………………………... Table 4.1: Participation in Formal Small Ensemble in the College Years by Personal Factors…….……………..……………..…………………...………..……………..… Table 4.2: Distribution of Participation in Informal Small Ensemble in the College Years by Demographic Factors……..…...…………………….……………..………. Table 4.3: Percentage of Participants Who Had Engaged in Small Ensemble Before College by Personal Factors………..……….…..……………..………..……………. Table 4.4: Descriptive Statistics for Participation in Small Ensemble and the Big Five Personality Traits……………..………………………..……………...………… Table 4.5: Descriptive Statistics for Attitudes Toward Small Ensemble and Personal Factors…….……………..……………..…………………..…………………...…….. Table 4.6: Correlations Among the Big Five Personality Traits and Attitudes Toward Small Ensemble.……………..……………..……………..…………………. Table 4.7: Descriptive Statistics for EQ Scores by Personal Factors………………… Table 4.8: Descriptive Statistics for the ERQ and Demographic Factors……………. Table 4.9: Correlations Among EQ, ERQ, and Big Five Personality Traits…………. Table 4.10: Correlations Among Attitudes Toward Small Ensemble, EQ, and ERQ... Table 4.11: Descriptive Statistics for Attitudes Toward Small Ensemble and Participation in Small Ensemble….……………..……………..……………………... Table 4.12: One-Way ANOVA: Attitudes Toward Small Ensemble by Participation in Formal Small Ensemble....……………..……………..…………………...……….. Table 4.13: One-Way ANOVA: Attitudes Toward Small Ensemble by Participation in Informal Small Ensemble.……………..…………..……………..………………... 64 65 66 79 85 87 89 91 93 94 95 97 99 100 101 101 102 x Table 4.14: Regression Analysis of Gender on EQ and ERQ-ES……………………. Table 4.15: Regression Analysis of Ethnicity on EQ and ERQ-CR………………….. Table 4.16: Regression Analysis of Primary Instrument on ERQ-ES………………... Table 4.17: Regression Analysis of Primary Area of Study on EQ………………….. Table 4.18 Regression Analysis of Age at Commencement of Music Training on EQ Table 4.19: Regression Analysis of the Big Five Personality Traits and EQ, ERQ- CR, and ERQ-ES……………..……………..……………..……………..…………... Table 4.20: Descriptive Statistics for Participation in Small Ensemble and EQ/ERQ.. Table 4.21: Regression Analysis of Participation in Small Ensemble on EQ………... Table 4.22: Regression Analysis of Participation in Small Ensemble on the ERQ-CR Table 4.23: Regression Analysis of Participation in Small Ensemble on ERQ-ES….. 104 105 106 107 108 110 111 115 118 121 xi List of Figures Figure 2.1: Input – Process – Output Model of Small Ensembles……………………. 38 1 CHAPTER ONE: OVERVIEW OF THE STUDY Bullying, dropouts, smartphone/video game addictions, substance abuse, juvenile delinquency, and suicide: the list of issues facing youth today is rather daunting. It is no longer surprising to hear news that young students who suffered from bullying commit suicide or fire a gun at schoolmates. Extensive research has indicated that social maladjustment in the adolescent years is likely to lead to depression, violence, alcohol or drug abuse, addition, self-harms, and engagement in other reckless behaviors (Laye-Gindhu & Schonert-Reichl, 2005; Rolison & Scherman, 2002; Villani, 2001). Also, particularly in East Asian countries, there has been a significant increase in the number of young people who choose to withdraw from social life, seeking extreme degrees of isolation like never leaving their houses—a phenomenon described as hikikomori (Furlong, 2008). Other youngsters simply dissociate themselves from reality and live in a fantasy world like animations and mangas, known as otaku (Hills, 2002). These are growing social concerns that industrialized countries all over the world can hardly avoid. Traditionally, educational school systems focus on developing students’ intellectual or cognitive abilities. Such tendency is clearly reflected in the school curriculum today and across the world. Many schools have eliminated “non-academic” subjects, such as music, drama, visual arts, and physical education, from their curricula in order to increase time spent on “academic” subjects, including mathematics and language arts. Although recent educational policies like the Every Students Succeeds Act (ESSA) marks a positive step forward by embracing the intrinsic value of a “well-rounded education,” in reality, students continue to be pushed to focus on academic subjects. However, it is still questionable whether this knowledge-based education can really prepare young people to become successful citizens in today’s society. Immordino-Yang and Damasio (2007) claimed that there is a significant connection between cognitive and 2 emotional functions. They argued that “[w]ithout adequate access to emotional, social, and moral feedback… learning cannot inform real-world functioning as effectively” (p. 6). In other words, cognition, learning, attention, and memory are profoundly connected with the processing of emotion. Thus, factual knowledge acquired from school education can effectively transfer to real-world situations only in conjunction with emotional thought (Immordino-Yang & Damasio, 2007). This implies that social and emotional development are as important as (or, even more important than) intellectual development. Even if students acquire extensive knowledge in their minds, their learning becomes meaningless if they do not know how to wisely use it in the real world. Imagine if a student who gained substantial knowledge from school uses it to create a weapon to kill hundreds of thousands of people. With a desire to cultivate not only academically but also socially and emotionally competent students, many educators and researchers strive to find interventions and teaching methods to promote social-emotional skills in students. One example is the approach known as Social and Emotional Learning (SEL) (Cohen, 2001). While the traditional school curriculum mainly focuses on students’ knowledge acquisition (Zins & Elias, 2006), SEL emphasizes the cultivation of well-balanced students who are not only proficient in academic subjects but also “able to work well with others from diverse backgrounds in socially and emotionally skilled ways, practice healthy behaviors, and behave responsibly and respectfully” (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011, p. 406). Previous research has demonstrated that some forms of SEL have resulted not only in improvements in students’ academic achievement, but also in the enhancement of peer relationships, positive attitudes, and adaptive social-emotional behaviors. In addition, conduct problems, antisocial and maladaptive behaviors, drug problems, and emotional distress significantly decreased (Cohen, 2001; Durlak et al., 2011; Wang et al., 3 2012). The current study proposes the inclusion of small music ensemble in a curriculum as a possible SEL strategy by exploring the relationships between small ensemble experiences and two types of social-emotional skills, namely empathy and emotional self-regulation. Background Music is a profound means of social and emotional activities. Over time and in most societies across the world, people have used music to represent and express their inner selves, reach out to the divine, woo love, foster social cohesion, experience aesthetic enjoyment, inspire political movements, and lull babies to sleep, to name a few (Turino, 2011). Music not only helps to convey and share emotions, values, and meanings that language cannot, but it also induces powerful emotional, physical, and behavioral responses. For this reason, music making is recognized as an effective interpersonal communicative tool. Making music with others by means of singing, clapping, dancing, and drumming has long been observed over the course of human history. Koelsch (2013) noted that engagement in group music making enables individuals to “have contact with other individuals, engage in social cognition, participate in co- pathy [the social function of empathy], communicate, coordinate their actions, and cooperate with each other, leading to increased social cohesion” (p. 204). Thus, music can be viewed as a useful channel of social interaction and communication, which is “something we do with and for other people” (Hargreaves, MacDonald, & Miell, 2005, p.1). When making music with others in a socially enjoyable environment, we naturally experience a state of “togetherness” (Cross, Laurence, & Rabinowitch, 2012). We also become sensitive to our inner states as well as of the states of our co-performers, continuously paying attention to the sounds that they produce in an attempt to anticipate their musical intentions. At 4 the same time, we constantly try to align our own emotional states with those of our co- performers, with the end goal of achieving musical cohesion. Malcolm Martineau (2008, as cited in Myers & White, 2012), a well-known accompanist, described this process of the interactive musical communication as a “circle of energy” (p. 259). As this energy is continually circulated among performers, the performance gains momentum, extending and deepening each performer’s aesthetic experiences, and ultimately increasing ensemble cohesion (Myers & White, 2012). The moment-by-moment process of co-performer communication in a small ensemble seems potentially relevant to the psychological mechanisms that shape the dynamics of everyday conversations that humans have with partners, families, friends, and others. In fact, some researchers have drawn an analogy between these two social contexts (Davidson & Good, 2002; Keller, 2014b; King, 2013). To understand this analogy, it is useful to imagine the following two scenarios: a) a teenage girl having a conversation with two of her best friends about failing an exam; and b) four string performers rehearsing a piece for a string quartet. During the conversation, the two friends may constantly make inferences concerning the girl’s mental state, trying to predict how she might feel and think (Astington, 2003). Similarly, in the string quartet context, each performer deliberately attends to the inner states of their co-performers and tries to predict their expressive intentions in the moment (Keller, 2014a). Furthermore, in the conversation context, the friends try to comprehend and share the girl’s emotional state while modulating their responses accordingly in order to effectively comfort and cheer her up (Sonnentag & Barnett, 2011). Similarly, performers in the string quartet delicately try to align their emotional states with those of their co-performers by inhibiting and modulating their expressions. This is done to achieve musical cohesion in an effective manner (Keller, 2014a). 5 Just like highly competent ensemble performers are able to deliberately modulate their sound production to attune to their co-performers in real-time performance, socially and emotionally competent people easily adapt to various social situations. They do so by accurately capturing the mental states of others during a conversation and, consequently, regulating their responses. It is thus clear that many aspects of the psychological processes taking place in small ensemble mirror the social interaction and interpersonal coordination experienced in everyday life. In this respect, I believe that the small ensemble can be viewed as a microcosm of human social interaction. Therefore, I speculate that the small ensemble can be a fruitful domain in which to cultivate individuals’ social-emotional skills, bearing resemblances to effective SEL strategies. In order to lend support this view, it is necessary to investigate the potential effect of small music ensembles on the development of social-emotional skills. By shedding some light on the potential associations between small ensemble experience and music students’ empathy and emotional self-regulation skills, I hope to contribute with valuable insights into the effectiveness of small ensemble as a way to cultivate students’ social-emotional skills. Purpose of the Study The primary purpose of this study was to explore the relationships between small music ensemble experiences of college music students and their empathy and emotional self-regulation skills. A secondary purpose of this study was to investigate whether personal factors, such as gender, ethnicity, personality, primary performance medium, primary study area, and age at commencement of music training, play significant roles in predicting music students’ empathy and emotional self-regulation skills. 6 Research Questions The following research questions guided this inquiry: (1) What are the relationships among music students’ small ensemble experiences, and their empathy and emotional self-regulation skills? (2) To what extent do personal factors, including gender, ethnicity, primary performance medium, primary study area, age at commencement of music training, and personality, contribute to music students’ empathy and emotional self-regulation skills? (3) To what extent do music students’ small ensemble experiences contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factors? Terms and Acronyms In this study, the following terms and acronyms were adopted: Empathy: Due to the lack of consensus in the definition of empathy, the definition proposed by Decety and Jackson (2004) is adopted here. For them, empathy is, “at a phenomenological level of description, a sense of similarity between the feelings one experiences and those expressed by others” (p. 71). Simply put, empathy refers to understanding and sharing the affective states of other people, which allows one to feel connected to others, make sense of their behavior, and respond appropriately to their emotional experiences (Baron-Cohen & Wheelwright, 2004). In this study, empathy is viewed as a multidimensional construct, involving three specific dimensions (Decety, 2015): affective sharing, or “the capacity to share or become affectively aroused by others’ emotional valence and relative intensity without confusion between self and other” (p. 1); empathic concern, or “the motivation to caring for another’s welfare” (p. 1); and 7 perspective taking, or the ability to attribute mental states (e.g., beliefs, intentions, desires) to self and others. Emotional Self-Regulation: “The complex process of initiating, inhibiting, and modulating the conscious aspects of emotion” (Sonnentag & Barnett, 2011, p. 577) to attain desired affective states and adaptive outcomes. Emotional self-regulation is the capacity to effectively manage one’s emotions, like inhibiting emotional responses perceived as inappropriate in a given context and transforming them in ways that are socially expected, even if this process may not be pleasant (Whitebread & Basilio, 2012). Because emotion is a multidimensional construct that encompasses cognitive, experiential, behavioral, and physiological responses, emotional self- regulation is associated with changes in one or more of these components (Gross, 1998). Among a wide variety of strategies to regulate one’s emotions, two specific emotional self-regulation strategies were considered in this study: cognitive reappraisal and expressive suppression, as proposed by Gross (1998). Cognitive Reappraisal (CR): A form of cognitive change that involves modulating the trajectory of emotional responses by reinterpreting the meaning of the emotional situation (Gross & John, 2003). Specifically, this regulatory process occurs in two steps: (a) recognition of one’s emotional response to the situation (appraisal), and then (b) re-evaluating the emotional situation in a more neutral or positive way (re-appraisal). Cognitive Reappraisal is considered an antecedent-focused emotion regulation strategy because, as re-appraisal occurs early in the emotional situation, it can modify the entire emotional sequence before emotion-response tendencies become fully generated (Gross, 2001). Expressive Suppression (ES): A form of response modulation that involves inhibiting the overt signs of emotions one feels with the aim of hiding current emotional states through facial 8 expressions, behaviors, and ways of talking (Gross, 1998). Expressive suppression is considered a response-focused emotion regulation strategy because the act of inhibiting and modifying emotional responses takes place after emotional response tendencies have been generated (Gross, 2001). Small Ensemble (SE): As a form of group music making and a microcosm of human social interactions (Davidsons & Good, 2002; Keller, 2014b; King, 2013), a small ensemble refers to “a group of performers who pursue a joint musical goal through collective action” (Keller, 2014b, p. 396). In a small ensemble, each performer is entitled with autonomy to make musical decisions, which distinguishes it from traditional large ensembles, such as orchestras and big bands, where a conductor or leader is officially in charge of decision making. Therefore, in small ensembles, there is “an active striving to reach out to the other” (Cross, Laurence, & Rabinowitch, 2012, p. 340) and to engage in democratic relationships. The followings are brain-related terms and acronyms: Amygdala: An almond-shaped nucleus in the anterior temporal lobe of the brain, known to be involved in emotion and certain types of learning and memory (Bear, Connors, & Paradiso, 2007). Anterior Cingulate Cortex (ACC): A brain region located towards the front of the corpus callosum in the medial frontal lobe. It is thought to be involved in decision making and emotional regulation as well as regulation of physiological processes, including heart rate and blood pressure (Stevens, Hurley, & Taber, 2011). Insula: A small region of the cerebral cortex located deep within the lateral sulcus and generally known to share reciprocal functional and structural connections with linguistic, motor, limbic, and sensory brain areas (Oh, Duerden, & Pang, 2016). 9 Medial Prefrontal Cortex (mPFC): A part of the brain located at the front of the frontal lobe, known to be implicated in complex cognitive human behaviors, such as decision making, executive functions, and various social behaviors (Bear, Connors, & Paradiso, 2007). Prefrontal Cortex (PFC): A cortical area at the rostral end of the frontal lobe that receives input from the dorso-medial nucleus of the thalamus (Bear, Connors, & Paradiso, 2007). Overview of Chapters Chapter one provided an overview of the study, including background, purpose of the study, specific research questions, and definitions of terms used in this study. Chapter two presents an in-depth review of relevant literature to situate the current study through a discussion of scholarly resources pertaining to the relationships between small ensemble experiences, empathy, and emotional self-regulation. Chapter three describes the methodology of the study with a detailed description of the study participants, study instrument, and data collection and analysis procedure. The results of the statistical analyses for the research questions are presented in Chapter four. Finally, Chapter five offers the discussion and interpretation of the findings, along with limitations of the study, implications for music teaching and learning, and recommendations for future work. 10 CHAPTER TWO: REVIEW OF RELATED LITERATURE This chapter provides a review of a broad array of existing literature in order to establish a conceptual framework for the relationships between small music ensemble experiences, empathy, and emotional self-regulation skills. Since this study is primarily concerned with empathy and emotional self-regulation skills in young musicians, the first part of the chapter examines previous research associated with these two psychological constructs. Specifically, the concepts of empathy and emotional-self regulation, the development of these social-emotional skills in childhood as well as in emerging adulthood, and various factors associated with them are discussed. The second part focuses on scholarly work concerning small ensembles in music. Extensive research on various types of interactive, group-based, musical activities is reviewed in depth, with a special focus on co-performer communication in rehearsal and performance phases. The chapter concludes with a review of previous empirical research on interactive musical activities and various social-emotional skills. Empathy and Emotional Self-Regulation Empathy: Experiencing Emotional States of Others Unlike other species, humans can empathize with others, that is, understand and share affective states of and with others (Decety & Jackson, 2004). Specifically, empathizing allows us to imagine how others might feel or think, make sense of their behavior, and predict what they might do next. Thus, empathizing enables us to feel connected to others’ experience and respond appropriately to them (Baron-Cohen & Wheelwright, 2004). Empathy is a motivating power for moral behaviors, particularly as it relates to prosocial and altruistic behaviors, as well as the internalization of social rules (Eisenberg, 2007). Conversely, not only is the lack of empathic 11 skill related to aggression and antisocial behaviors (Eisenberg, 2007), but deficits in this skill have been implicated in pathologies such as autism spectrum disorder, antisocial personality disorder, and nonverbal learning disorder (Goldstein & Winner, 2012). In short, empathy is a crucial ability that enables one to effectively interact with others in the social world. Considering empathy as a multidimensional construct, Decety (2015) suggested that empathy involves three specific dimensions: (a) affective sharing, or “the capacity to share or become affectively aroused by others’ emotional valence and relative intensity without confusion between self and other” (p. 1); (b) empathic concern, or “the motivation to caring for another’s welfare” (p. 1); and (c) perspective taking or the cognitive aspect of empathy. The first two aspects relate to an individual having an appropriate affective response to the mental state of others. The latter largely overlaps with the concepts of “theory of mind” or “mindreading” (Baron-Cohen, Campbell, Karmiloff-Smith, Grant, & Walker, 1995), or the ability to attribute mental states (e.g., beliefs, intentions, desires) to self and others. Sometimes the cognitive component is distinguished from the concept of empathy because cognitive understanding of others’ minds can exist independently, that is, without affective empathy. For instance, many children involved in bullying understand what their victims may feel, but are unable to feel their suffering (Goldstein & Winner, 2012). Nonetheless, there is growing consensus that “both cognitive and affective components are aspects of the same complex construct of empathy, and they are not conceived as independent” (Knafo, Zahn-Waxler, Hulle, Robinson, & Rhee, 2008, p. 737). Given that empathy is a critical aspect of social-emotional development that enables people to comprehend and share affective states with others and, therefore, helps to promote social bonds (Decety & Jackson, 2004), the neural bases of empathy have been a major topic of 12 cognitive psychologists’ investigation for many years. With recent technological breakthroughs, social cognitive neuroscientists have been using observable brain activities to empirically investigate the neural mechanisms that mediate empathy in the brain (e.g., Decety & Jackson, 2004; Decety & Moriguchi, 2007; Zaki, Weber, Bolger, & Ochsner, 2009). Evidence suggests that the cognitive aspect of empathy, and theory of mind more specifically, is closely associated with a set of brain regions, including the medial prefrontal cortex (mPFC), temporoparietal junction, and posterior superior temporal sulcus (Decety, 2010). Yet, there are no specific cortical areas that directly relate to the affective aspect of empathy. Rather, the neural underpinnings related to affective empathy tend to be widely distributed, and the pattern of activation varies depending on the specific emotion being aroused in each particular situation (Decety & Jackson, 2004). For example, in a study by Jackson, Meltzoff, and Decety (2005), healthy, right-handed participants (N = 15) were seen a series of photographs that described situations causing physical pain in hands and feet, and asked to assess the level of pain experienced by the person in the photographs. The fMRI results revealed that perceiving and assessing painful situations experienced by others was associated with significant bilateral changes in activity in several brain regions, including the anterior cingulate cortex (ACC) and the anterior insula. Similar findings were also reported from another study (Wicker et al., 2003). When healthy, right- handed male participants (N = 14) were asked to smell disgusting odors and then asked to watch video clips showing the emotional facial expression of disgust, the fMRI results demonstrated that feeling disgust and observing such faces activated the same brain regions, the anterior insula and the ACC. 13 Several studies indicated that the amygdala is also closely relevant to specific negatively- valenced emotions, such as fear, pain, and sadness. As an example, patients whose amygdala was bilaterally impaired showed deficits in experiencing fear expressed by faces (Adolphs, Tranel, Damasio, & Damasio, 1995). Increased activity in the amygdala, in addition to the parieto- frontal area, was also found when participants watched videos depicting a person telling sad stories in the first-person tense (Decety & Chaminade, 2003). In this study, while undergoing Positron emission tomography (PET) scans, healthy, right-handed male participants (N = 12) watched a series of video clips that actors tell sad and neutral stories as if they had personally experienced them. At the end of each clip, participants were asked to rate the mood of the person in the video and how likeable they found that person. Results showed that listening to sad stories, compared to listening to neutral stories, activated neural structures known to be involved in emotional processing, including the amygdala and its adjacent cortices in the temporal poles (Decety & Chaminade, 2003). On the other hand, greater activation in the PFC was found when participants were engaged in cognitively judging and assessing emphatic responses to others’ emotional experiences (Farrow et al., 2001; Rameson, Morelli, & Lieberman, 2012). For example, healthy, right-handed undergraduate students (N = 32) were asked to watch sad images under three different conditions: watching naturally, intentionally empathizing, and under cognitive load (memorizing an eight-digit number), and rate their empathic reaction to the images. The fMRI results indicated that, across conditions, higher levels of self-reported empathy were closely associated with greater activity in the mPFC and that self-report of empathic experience and activity in the mPFC were higher in the empathize condition. This suggests that, unlike the affective component of empathy, cognitive empathy consistently involves the PFC associated 14 with higher-order cognitive functions (Blakemore, 2012) as well as the right temporoparietal junction (Saxe, 2010). Therefore, the affective and cognitive components of empathy may have different developmental trajectories. Development of empathy in childhood. Very young children, even newborns as young as 18-hour-olds, can display some kinds of empathy-related responses, such as reflexive crying and early mimicry (Davidov, Zahn-Waxler, Hanania, & Knafo, 2013). During infancy, infants respond to a variety of distress cues (e.g., crying) produced by others (e.g., Sagi & Hoffman, 1976), which is evidence of a rudimentary form of empathic responding. At around the age of 2, children begin to show empathic concern for others—for example, when attempting to comfort another person who is in a stressful situation (Vaish, Carpenter, & Tomasello, 2009—and engage in prosocial behaviors, such as helping and sharing, even at some cost to themselves (Brownell, Svetlova, & Nichols, 2009). Despite these signs of affective sharing and empathic concern from the early years of life, children are not yet able to take others’ perspectives into account until around the age of 4 (McDonald & Messinger, 2011). As children’s language capacities and social-cognitive abilities dramatically increase during the preschool age, impressive gains in cognitive empathy ability are also seen. Preschoolers become capable of differentiating between their own emotions and those of others as well as understanding others’ perspectives (McDonald & Messinger, 2011). School-aged children expand their empathic abilities in a wide spectrum of emotions, even in the physical absence of a “victim.” In this stage, children’s capacity for top-down emotional regulation also develops, leading them to show more advanced adaptive responses in various social situations (Decety & Michalska, 2010). For example, while younger children tend to respond to others in emotionally distressed situations with self-oriented personal distress, older children usually 15 respond with other-oriented sympathetic concern. This gradual transformation from empathic distress to sympathetic concern is considered an important milestone for subsequent social development because it allows children to carry out more effective helping strategies (McDonald & Messinger, 2011). In fact, children with higher empathetic and sympathetic abilities tend to be more highly nominated by their peers (Eisenberg, Huerta, & Edwards, 2012). While literature on the development of empathy beyond childhood is scarce, recent neurological studies have opened up a new perspective toward empathy development across the lifespan. Greimel et al. (2010) explored developmental changes in the neural mechanisms underlying empathy from childhood to early adulthood. Participants, aged 8 to 27, viewed faces displaying happiness and sadness, and were asked to infer the emotional states from the faces (cognitive empathy) as well as judge their own emotional response to them (affective empathy). No age-related difference was found in the behavioral results. Yet, fMRI data suggested that there were some age-related changes in brain activation pattern. In terms of the affective aspect, neural activity in the left inferior frontal gyrus increased with age when participants tried to evaluate their own emotional responses to the emotional faces, whereas the right precuneus and right intraparietal sulcus showed greater activation in younger participants. Since the right parietal structures are known to be related to self-referential processing (Lou et al., 2004), these results suggest that younger people tend to rely on their own self-concept to interpret incoming information about others (Uddin et al., 2005). Thus, younger and older people use different cognitive strategies to process their own response to emotional states of others (Greimel et al., 2010). Empathy in emerging adulthood. As a special developmental period between adolescence and adulthood, emerging adulthood—typically defined as the period between ages 16 18 to 25 in industrialized countries (Arnett, 2000)—is a particularly interesting time for social and emotional development. Despite wide variations across cultures, most emerging adults in industrialized countries undergo dramatic individual, social, and contextual transitions (e.g., college entrance, entering a workplace). As they step out of their social comfort zone, emerging adults begin to explore a wide variety of social relationships in everyday life, such as romantic relationships and varied interactions in the workplace (Galambos, Barker, & Krahn, 2006). Still, literature on empathy development beyond adolescence is not only scarce, but tends to provide a mixed pattern of findings, making it challenging for one to understand developmental changes during this very particular period. Although some researchers have suggested empathy to be a relatively stable disposition (Gruhn et al., 2008), extensive evidence from social behavioral studies supports the idea that social contexts and genetic factors continue to exert substantial influence on the development of empathic reactions elicited in social situations across the lifespan (Knafo, Waxler, van Hulle, Robinson, & Rhee, 2008). For example, in a longitudinal study that examined stability and change in empathy during late adolescence, Davis and Franzoi (1991) assessed high school students’ empathic reactions in the forms of perspective-taking, personal distress, and empathic concern using the Interpersonal Reactivity Index (a multidimensional instrument measuring individual difference in empathic tendencies, based on perspective taking, empathic concern, personal distress, and fantasy; Davis, 1980). Measurements were taken at 1-year intervals for 4 consecutive years. The results indicated that both perspective taking and empathic concern increased over time, whereas personal distress exhibited a gradual decline. Also, perspective taking was positively correlated with empathic concern and negatively correlated with personal distress, suggesting that an increasing capacity for perspective taking may enable late 17 adolescents to transform their personal distress reactions into other-oriented reactions of sympathy and compassion (Davis & Franzoi, 1991). In addition, a longitudinal study that examined age-related changes in empathy-related responding and prosocial disposition from mid-adolescence (aged 15-16) to emerging adulthood (aged 25-26) showed a similar pattern of findings (Eisenberg et al., 2005). Participants completed self-report measures of prosocial responding (rating their response to items, such as how frequently they engaged in behaviors such as giving money to charity, on a 5-point scale), empathy-related responding (sympathy, perspective taking, and personal distress subscales on the Interpersonal Reactivity Index; Davis, 1980) and prosocial moral reasoning (discussing about their reasoning of helping the needy other in five stories which involves moral dilemmas) at 2- year interval for 10 years. Results showed that, from mid-adolescence to emerging adulthood, perspective taking increased and personal distress declined with age, whereas sympathy did not show a specific pattern of age-related change. While Davis and Franzoi (1991) suggested that the decline in personal distress may result from an increasing capacity for perspective taking, Eisenberg et al. (2005) suggested that personal distress is dependent on age-related changes in regulatory skills. Although it is unclear what factors contribute to developmental changes in empathic abilities during the transition from adolescence to emerging adulthood, these findings support the notion that the abilities to share and experience the emotional states of others are still malleable after childhood. Recent neuroimaging studies offer new and promising perspectives to the understanding of the pattern of individual’s empathy over time. In a fMRI study on neurodevelopmental changes in the circuits underlying empathy from childhood and adulthood, participants (aged 7- 40) were asked to engage in a task that involved short animated videos depicting various types of 18 painful situations (Decety & Michalska, 2010). While study participants, irrespective of age, responded similarly when they were asked to rate how painful each situation was, their neural responses showed different age-related patterns of activation. Specifically, when they were exposed to visual stimuli of painful situations, younger participants had stronger activations of the amygdala and posterior insula, which are known to appear early in life (Alcauter, Lin, Smith, Gilmore, & Gao, 2015). On the other hand, older participants showed greater activities in the dorsolateral PFC and inferior frontal gyrus, which are known to be related to cognitive control and response inhibition. These areas continue to mature into adulthood (Swick, Ashley, & Turken, 2008). Similar neural patterns were also found when participants (aged 8-27) viewed emotional faces involving happiness and sadness, and were asked to infer the emotional states from the faces and judge their own emotional responses to them (Greimel et al., 2010). Findings suggested that there is a gradual shift in the neural pattern of empathic responses during childhood into adulthood, with children relying more on limbic affect processing systems, and adults relying more on prefrontal systems (Decety & Michalska, 2010). Empirical research studies from the fields of education and clinical psychology have also demonstrated improved empathy-related responding among student and adult populations through various interventions. Empathy, a way of understanding and sharing others’ emotional experiences, is considered to be particularly important in some professions, such as teaching, counseling, and the medical fields. For example, Boker, Shapiro, and Morrison (2004) examined changes in empathic abilities of medical school students who participated in empathy enhancement intervention sessions involving reading, discussing, and reflecting on literature regarding issues of illness and distress. Before and after participating in the intervention, the students (N = 22) participated in a group interview and completed two empathy measures (a self- 19 assessment of ability to listen carefully and accurately paraphrase the feelings of other people; and a self-report questionnaire on how participants feel others’ suffering or take pleasure in their happiness) as well as one instrument assessing attitudes toward the humanities. The post-test results revealed significant improvements in empathy and in attitudes toward humanity. Also, students’ understandings of patients’ perspectives became more detailed and sophisticated. Similarly, Lyons and Hazler (2002) explored the effects of a counselor education program on counseling major students’ empathy development by comparing the empathic abilities of first- and second-year students. Participating students (N = 162) completed two measures of empathy, the Questionnaire Measure of Emotional Empathy (a self-report questionnaire designed to assess emotional empathy; Mehrabian & Estein, 1972) and the Empathic Understanding Scale (a self-assessment measuring counseling students’ abilities to accurately discriminate between various levels of empathic responses; Carkhuff, 1969). Results indicated significant group differences, with second-year students clearly outperforming their first-year peers on both cognitive and affective empathy. Based on these results, the authors suggested that empathy could be learned or developed in counselor education classrooms. Goldstein and Winner (2011) examined whether participation in acting training could increase young students’ empathy, as acting is an activity where people realistically pretend to be another person without any intent to deceive. At the beginning and end of the school year, participating students completed a set of empathy-related measures. For elementary school-aged students, two self-reported cognitive and one perceptual measures of theory of mind (the Faux Pas, Baron-Cohen, O’Riordan, Stone, Jones, & Plaisted, 1999; the Strange Stories Test, Happe, 1994; and the Reading the Mind in the Eyes for children, Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001), along with two empathy measures (the Index of Empathy for Children, 20 Bryant, 1982; the Fiction Emotion-Matching task, Goldstein & Winner, 2011) were utilized. In the case of high school students, two measures of theory of mind (the Reading the Mind in the Eyes and the Empathic Accuracy Paradigm, Ickes, 2001) as well as two empathy measure (the Basic Empathy Scale for adolescents, Jolliffe & Farrington, 2006; and the the Fiction Emotion- Matching task) were used to assess their theory of mind and empathic abilities. Results indicated that, following 1 year of acting classes, elementary school-aged students (n = 35) and high school students (n = 28) showed significant gains in empathy scores, implying that empathy was enhanced by role-playing. In conclusion, empathy, or the ability to share and experience other people’s emotional states, is still malleable beyond childhood and adolescence and open to further improvement when appropriate stimuli and interventions are provided. Factors associated with empathy. A wealth of research has examined the factors that possibly influence in shaping individual’s empathic abilities using a variety of instruments. While self-report measures, in which participants respond to a list of questions using Likert scales (e.g., The Questionnaire Measure of Emotional Empathy, Mehrabian & Epstein, 1972; The Interpersonal Reactivity Scale, Davis, 1980; The Empathy Quotient, Baron-Cohen & Wheelwright, 2004) are widely employed, other researchers have used observation measures where verbal and non-verbal behaviors of the target can be examined (e.g., Long, Angera, & Hakoyama, 2006; Moran & Diamond, 2008). Still others have relied on physiological measures that assess participants’ heart rate, skin conductance, and general somatic activities in certain emotional situations (e.g., Levenson & Ruef, 1992; Marci, Ham, Moran, & Orr, 2007). These instruments have been helpful to uncover some of the factors impacting empathic responses, including gender, culture, ethnicity, and personality. 21 Gender, culture and ethnicity. Consistent with commonly-held stereotypes in social- emotional functioning, extensive research has shown women displaying more empathetic responses than men (Baron-Cohen & Wheelwright, 2004; Barrio, Aluja, & Garcia, 2004; Kataoka et al., 2009; Toussaint & Webb, 2005; Wen et al., 2013). Neurological studies have also revealed distinct neural networks in females and males. Whereas females recruited more emotion-related regions, including the amygdala, males showed more activation in cognitive- relation areas, such as the temporo-parietal junction when engaged in emotion recognition, emotional perspective taking, and affective responsiveness tasks (e.g., Derntl et al., 2010; Schulte-Rüther et al., 2008). While no clear explanation is available to date, gender differences have been explained in relation to various factors, such as motivation (Klein & Hodges, 2001), evolutionary-biological gender characteristics (Kataoka et al., 2009), interpersonal style in caring, socialization, and gender role expectation (Eisenberg & Lennon, 1983; Hojat et al., 2005). Group differences in empathy have also been found among different ethnic and cultural groups. Previous studies have shown that, regardless of age, participants from East Asian backgrounds reported less empathic concern but greater personal distress when compared to those from Western cultural backgrounds (Cassels, Chan, Chung, & Birch, 2010; Frieldmeier & Trommsdorff, 1999; Trommsdorff, Frieldmeier, & Mayer, 2007). For example, Cassels, Chan, Chung, and Birch (2010) measured levels of affective empathy in adolescents from different cultural backgrounds using Davis’s Interpersonal Reactivity Index. While East Asian adolescents reported greater personal distress and less empathic concern than their Western counterparts, many bi-cultural adolescents, who were born and raised in a Western country but self-identified with an East Asian ethnicity, fell in between the cultural groups. Yet, the pattern of bi-cultural 22 adolescents’ empathic responding was, overall, more similar to that of their East Asian peers, even if the relationship between affective empathy and social-emotional health was closer to that of their Western peers. This suggests that there are significant influences of environmental factors, such as family and community, on the development of affective empathy (Cassels, Chan, Chung, & Birch, 2010). Personality. The possible association between personality traits and empathy has long been an interesting topic in psychology. One of the dominant paradigms in personality research, the Five Factor model consisting of Extraversion, Neuroticism (also known as Emotional stability), Openness to experience, Agreeableness, and Conscientiousness, is often used to examine potential associations between personality traits and empathic abilities. Several studies revealed a significant correlation between Agreeableness and empathy (Barrio, Aluja, & Garcia, 2004; Chauhan & Rai, 2013; Mooradian et al., 2011). This association makes sense because Agreeableness is often conceptualized as “feeling with another” (Eisenberg & Strayer, 1987, p. 5), overlapping with the concept of empathic concern. Agreeableness is also considered an important social trait that reflects the quality of an individual’s social relationships, specifically how well a person gets along with others (Barrio, Aluja, & Garcia, 2004), being strongly implicated in the prediction of prosocial and aggressive behaviors (Graziano, Habashi, Sheese, & Tobin, 2007). However, the relationships between empathy and other personality traits appears less clear. For example, while some studies showed significant positive association between Neuroticism and empathy (Eysenck & Eysenck, 1991), other studies found negative (Shiner & Caspi, 2003) or insignificant associations (Chauhan & Rai, 2013; Barrio, Aluja, & Garcia, 2004). Interestingly, Neuroticism is often strongly associated with personal distress (Mooradian, 23 Matzler, & Szykman, 2008; Mooradian, Davis, & Matzler, 2011), reflecting the experience of negative emotion in both constructs. Other personality traits also showed inconsistent patterns in the findings. Conscientiousness, for example, is often expected to demonstrate close associations with empathic abilities because not only does this trait negatively correlate with Eysenck’s dimension of Psychoticism reflecting a lack of empathy (Barrio, Aluja, & Garcia, 2004), but high scores on this trait are also known to be associated with the inhibition of aggressive behaviors (John, Caspi, Robins, Moffitt, & Stouthamer-Loeber, 1994). Nonetheless, only a handful of studies (e.g., Aluja, Garcia, & Garcia, 2002; Chauhan & Rai, 2013) have found a link between Conscientiousness and empathy. Similarly, only one study revealed a positive correlation between Extraversion and altruism (Tait, 2009). In summary, empathy is a complex psychological construct, involving the affective experience of actual or inferred emotional experiences of others as well as some minimal cognitive recognition and understanding of them (Decety, 2015). Although it is unclear which factors and in what ways they affect individual differences in empathic abilities, extensive literature supports that both environmental and genetic influences play important roles in shaping individual’s empathy skills. This suggests that the ability to experience and share the emotional states of others is a malleable skill, and thus, that empathy can be developed when appropriate social and emotional experiences are exerted (Lyons & Hazler, 2002). Emotional Self-Regulation: Flexibly Regulating Emotional Responses Emotional regulation refers to “the complex process of initiating, inhibiting, and modulating the conscious aspects of emotion” (Sonnentag & Barnett, 2011, p. 577) in order to attain desired affective states and adaptive outcomes. Simply put, it is the capacity to effectively manage one’s emotions by inhibiting emotional responses perceived as inappropriate in a given 24 context and transforming them in ways that are socially expected, even when the process is not pleasant (Whitebread & Basilio, 2012). Given the multidimensional nature of emotions—which encompasses cognitive, experiential, behavioral, and physiological dimensions—emotional self- regulation is associated with changes in one or more of these emotional response components (Gross, 1998). Thus, the ability to regulate emotions affect not only people’s emotional experience, but also broader emotional, cognitive, and interpersonal functioning (Gross & John 2003; Kashdan, Barrios, Forsyth, & Steger, 2006). Although emotional regulation is generally considered as decreasing or downregulating negative emotions, it actually refers to all strategies that people may use to increase, maintain, and decrease both negative and positive emotions (Gross, 2001). Emotional self-regulation often occurs in conscious and effortful manners (e.g., suppressing anger by taking a big breath, trying to feel better in a sad situation by thinking about a happy moment). It also occurs without conscious awareness (e.g., exaggerating joy upon receiving an unattractive gift with no awareness of such reaction; Gross & Thompson, 2007). Emotional self-regulation is a crucial skill that allows one to effectively live in the social world because it significantly influences the quality of social interactions (Lopes, Salovey, Cote, & Beers, 2005), as well as the development of healthy patterns of affect, social functioning and wellbeing (Cutuli, 2014). In addition, ineffective and inappropriate emotion regulation is closely associated with the development of psychological disorders, including depression, social anxiety, and mood disorders (Campbell- Sills, Barlow, Brown, & Hoffman, 2006; Kashdan & Steger, 2006; Mennin, 2006). Among a variety of regulatory strategies in managing one’s emotional responses, Gross (1998) paid attention to two specific strategies that people commonly use in everyday life: expressive suppression and cognitive reappraisal. Cognitive reappraisal is the process by which 25 individuals cognitively re-evaluate a potentially emotion-eliciting situation to decrease its emotional impact before their emotional responses are fully activated (Gross, 1998). For instance, imagine a car abruptly cutting in front of your car while driving on the highway. Your first reaction may be to get upset, appraising the driver by thinking, “What a terrible driver he is!” However, you soon revisit your emotional response to the situation and re-think that “he may have not seen my car” or “he may have had an emergency” in order to calm yourself down. This regulatory process involves two steps: a) recognizing one’s negative emotional response to the situation (appraisal) and then b) re-evaluating the situation in a more neutral or positive way to reduce the intensity of the negative emotion (reappraisal). Such adaptive regulatory process is considered an antecedent-focused emotion regulation strategy because, as reappraisal occurs early in the emotional situation, it can modify an entire emotional sequence before emotion- response tendencies become fully generated (Gross, 2002). Expressive suppression refers to the processes by which individuals try to inhibit and modify the overt signs of an emotion that they are feeling after their emotional response tendencies have already been generated (Gross, 1998; Tackman & Srivastava, 2015). Expressive suppression is a response-focused emotion regulation strategy because it targets expressive behaviors, such as facial expressions, behaviors, and ways of talking that are readily observable by others and ordinarily function as social signals. Going back to the earlier example of a car abruptly cutting in, you may get upset of the driver and feel an impulse to honk the horn or yell at the driver as a way to express your anger. However, you decide to suppress your anger and stay calm because your children are sitting in the back seat watching you. In this case, although you were successful in hiding your anger in the moment by inhibiting overt actions (e.g., yelling or honking), the subjective and physiological responses generated by the negative experience are 26 already activated and the negative emotions are likely to continue to linger and accumulate unresolved. Because emotion suppression comes late in the emotion-eliciting situation (Gross, 1998), meaning that emotional response tendencies are already fully generated, it requires individuals to continually monitor and correct their behavioral responses throughout the emotional event, as the negative emotions are likely to occur constantly (Cutuli, 2014). Research on these two regulation strategies has typically favored the former because cognitive reappraisal often results in more positive consequences (Cutuli, 2014; Gross, 2001; 2002; Gross & John, 2003; Moore, Zoellner, & Mollenholt, 2008; Ochsner & Gross, 2005). For example, although expressive suppression is effective to decrease negative behavioral responses, it often fails to decrease negative emotions and even increases physiological responses due to the continual efforts to inhibit ongoing emotional expressions (Gross, 2001; 2002; Jackson, Malmstadt, Larson, & Davidson, 2000). In the situation described earlier, the driver’s heart may keep beating fast and the feeling of anger may continue to linger even after the car leaves and is out of sight. By contrast, cognitive reappraisal helps to effectively alter the overall emotional response tendencies, leading to lesser negative emotional, behavioral, and physiological responses. From a cognitive perspective, expressive suppression tends to be costly because it requires continual self-monitoring efforts throughout the emotional event. For example, one should keep self-instructing, “I need to keep my face still” (Gross, 2001; 2002) throughout an entire emotional situation, which depletes the cognitive resources available for processing other aspects of the situation (Cutuli, 2014). On the other hand, cognitive appraisal does not require such regulatory efforts because reappraisal occurs early in the emotion-eliciting situation (Gross, 2001). That is, there is no need to consciously alter one’s responses, as reappraisal has already 27 successfully reduced the experiential and behavioral components of negative emotion (Gross & John, 2003). Therefore, individuals who habitually use suppression strategies are, likely, less sensitive to the emotional cues of others in social situations, which may hamper the quality of their social interaction (Gross, 2002). In contrast, individuals who reported using the reappraisal strategy more frequently in everyday life showed superior functioning in interpersonal domains across self and peer-reports (i.e., being liked more by others and receiving more social supports from others; Gross & John, 2003). In addition, they showed a tendency to experience less negative affect and fewer depressive symptoms in everyday life (Gross & John, 2003). The effectiveness of cognitive appraisal on regulating emotions, especially for down- regulating negative affect, has also been confirmed by neurological research (Goldin, McRae, Ramel, & Gross, 2009; McRae, Ramel, & Gross, 2008; Ochsner & Gross, 2005). Activities in the PFC regions, closely associated with cognitive control and strategy selection and monitoring (MacDonald, Cohen, Stenger, & Carter, 2000), become active at the early stage of the regulatory process (Goldin, McRae, Ramel, & Gross, 2009; Kim & Hamann, 2007; Levesque et al., 2003; Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner & Gross, 2004). Early responses in the PFC appear to be related to decreased activities in the emotion-responsive brain regions, including the amygdala and insula. This implies that the activation of the PFC regions may play a significant role to modulate neural processing of emotional intensity and salience in limbic brain regions (Goldin et al, 2009). On the other hand, even when expressive suppression led to decreased negative behavioral responses, neural processing showed elevated activities in the amygdala and insula, providing evidence of continual physiological responses during the emotional situation (Gross, 2001; 2002). Activities in the PFC regions occurred in the late stage of the regulatory process, which may reflect one’s increasing effort to sustain cognitive control to inhibit negative 28 expressions throughout the emotional event (Goldin et al., 2009). These provide evidence of the effectiveness of cognitive appraisal on regulating emotion. Development of emotional self-regulation in childhood. In the examinations of the developmental trajectories of emotional self-regulation, two general developmental trends have been commonly discussed. The first trend refers to the transition from total reliance on adults to an independent self-regulation of emotions (Calkins, 2011). As infants hold very limited capacities to regulate their emotions, they rely completely on caregivers to attain desirable affective states. For example, when babies display distress or frustration through facial expressions or cries, caregivers promptly help them regulate their arousals states by soothing, feeding, or distracting them (Grolnick, McMenamy, & Kurowski, 2006). By the end of the first year, infants become aware of various affective states and realize that their emotions can be influenced by their own reactions as well as by the actions of others (Eisenberg & Morris, 2002). The emergence of self-originating regulation is consistent with the development of the key brain region for emotion regulation, which is the ACC. While the ACC involves spindle- shaped neurons, allowing for widespread connections with other brain regions, the neurons become functionally mature during the first year of life (Perlman & Pelphrey, 2010), coinciding with the development of the capacities to self-soothe and divert attention (Grolnick, McMenamy, & Kurowski, 2006). At the ages of 3 to 4, young children begin to use language to express their affective states and share their feelings with others (Kopp & Neufeld, 2003). Moreover, the development of executive attention or the ability to effortfully control attention between conflicting cues (Rothbart, Ellis, & Posner, 2004), as well as inhibitory control or the ability to voluntarily withhold prepotent reactions (Kopp & Neufeld, 2003) significantly contribute to improvements in emotional self-regulation in early childhood. 29 The second developmental trend is the transition from the use of behavioral strategies to the use of cognitive coping strategies (Eisenberg & Morris, 2002). With age, children’s abilities to monitor and modulate their emotional reactions become increasingly sophisticated, along with advances in cognitive complexity and emotional understanding (Zimmermann & Iwanski, 2014). Compared to younger children, who tend to undertake behavioral strategies such as physically diverting themselves from the stressors to regulate their emotions, older children are better at regulating their affective states by using cognitive strategies like trying to think positively or mentally distracting themselves from the stressors. In a neuroimaging study that investigated the development of ACC in 5- to 11-year-olds, older children appeared to recruit the dorsal or “cognitive” areas of the ACC more when trying to regulate their emotional responses, whereas younger children engaged more of the ventral or “emotional” areas (Perlman & Pelphrey, 2010). Similarly, Killgore, Oki, and Yurgelun-Todd (2001) showed progressive, age-related differences in the left prefrontal region and left amygdala over the adolescent years. When 9- to 17-year-olds viewed photographs of faces expressing fearful affect, younger participants used more limbic- related structures, including the left amygdala, to regulate their emotional responses, whereas adolescents showed greater activation within the left prefrontal regions and decreased activation within the left amygdala (Killgore, Oki, & Yurgelun-Todd, 2001). These studies suggest that adolescents, compared to younger children, engage in more cognitive strategies for greater self- regulation over emotional behaviors. Likewise, children develop greater abilities to effectively manage emotions and select appropriate strategies to deal with emotion-eliciting situations, and these regulatory abilities continue to develop in late childhood and adolescence (Eisenberg & Morris, 2002). 30 Emotional self-regulation in emerging adulthood. The ability to effectively regulate emotional states is particularly crucial in emerging adulthood because this is a specific period in time when young adults are constantly confronted with socially challenging situations, perhaps more frequently than ever before. Research has indicated that emotional self-regulation is closely associated with the quality of young adults’ social life (e.g., Eisenberg, Fabes, Guthrie, & Reiser, 2000; Lopes, Salovey, & Straus, 2003). For example, college students with greater ability to regulate emotions are likely to have more positive relationships with others, less conflict in peer relationships, and greater affection and support in their relationships with parents (Lopes, Salovey, & Straus, 2003). Also, individuals with greater emotional regulation abilities were rated more favorably by their peers and viewed themselves as being more interpersonally sensitive (Lopes et al., 2005). John and Gross (2004) suggested that, as people mature and gain more extensive life experiences, they increasingly learn to make greater use of healthy emotional regulation strategies (i.e., cognitive reappraisal) while using unhealthy strategies (i.e., expressive suppression) less often. Although no consistent pattern in terms of the normative development of emotion regulation has been reported, many researchers consider emotional regulation “not as immutable traits but as socially acquired strategies that are sensitive to individual development” (John & Gross, 2004, p. 1320). A study by Srivastava, Tamir, McGonigal, John, and Gross (2009) on college students’ use of expressive suppression and its relation to their social functioning suggested that emotional reactions in response to diverse social-emotional situations were likely to depend on how the individuals perceive and interpret the social contexts, even if individual temperament, personality, and early learning experiences play important roles in forming the basis of emotion regulation abilities. 31 Furthermore, many neuroimaging studies have suggested that the PFC, closely associated with self-regulatory function (Banfield, Wyland, Macrae, Munte, & Heatherton, 2004), undergoes continuous changes in emerging adulthood. Specifically, literature indicated that while the PFC is relevant to higher-order cognitive functions, including theory of mind and executive function (Heathertone, 2011), the PFC, along with some other regions, is also implicated in emotion-related self-regulatory processes (Decety & Michalska, 2010; Immordino- Yang & Damasio, 2007). The PFC develops relatively slower than other brain regions, reaching maturation later in life; thus, this region is open for continuous development into adulthood (Pitskel et al., 2011). Unsurprisingly, cognitive reappraisal has been associated with various cognitive abilities, such as executive attention, working memory, and response selection, which, in turn, are related to the PFC (John & Gross, 2004). In a study that investigated the use of cognitive reappraisal from childhood to emerging adulthood (McRea et al., 2012), groups of older children (aged 10-13), adolescents (aged 14-17), and young adults (aged 18-22) participated in an emotion regulation task. They were instructed to decrease their negative emotions in response to aversive images using reappraisal ability. While no age-related differences were found in self-reported emotional reactivity, fMRI data indicated linear, age-related increases in activation in the left ventrolateral PFC and a quadratic, age-related pattern of activation in regions associated with social cognitive processes, including the mPFC, posterior cingulated cortex, and anterior temporal cortex, supporting increased engagement of the cognitive control components of reappraisal with age (McRea et al., 2012). In addition, maturation of the PFC regions is related to the abilities that allows one to selectively attend to goal-relevant information in emotional situations, manage and regulate affective states, and exercise inhibitory control over one’s prepotent reactions (Decety & 32 Michalska, 2010). For example, Beauregard, Levesque, and Bourgouin (2001) examined the neural correlates of conscious emotion regulation among healthy male participants (aged 20-42). They were asked to inhibit their emotional reactions to the stimuli in the inhibition condition and normally react in the arousal condition while watching a series of erotic films. fMRI data showed that, in the arousal condition, greater activation was produced in “limbic” and paralimbic structures, including the right amygdala, right anterior temporal pole, and hypothalamus. In the inhibition condition, however, activation was mostly found in the right dorsolateral PFC and ACC while no significant activation was produced in the limbic areas (Beauregard, Levesque, & Bourgouin, 2001). In a similar experiment (Ochsner, Bunge, Gross, & Gabrieli, 2002), young female adults (aged 18-30) were asked to normally attend to feelings elicited by stimuli (attend condition) and try to re-interpret their feelings to inhibit negative responses (reappraisal condition) while viewing pictures that arouses negative emotions. While the participants successfully diminished the negative emotions elicited by the pictures using the reappraisal strategy, effective reappraisal was associated with increased activation in the lateral and medial PFC, as well as decreased activation in the amygdala (Ochsner et al., 2002). Social behavioral research has also provided evidence of age-related increases in the emotion-regulatory abilities from childhood through emerging adulthood. Tottenham, Hare, and Casey (2011) conducted a behavioral assessment of emotion discrimination, emotion regulation, and cognitive control in children, adolescents, and emerging adults. The emotional go/nogo task was employed to assess their abilities to recognize the emotional significance of perceived stimuli—both positive (happiness) and negative emotions (fear, anger, and sadness)—and regulate impulsive reactions to the emotional information. Not only did emotion discrimination 33 skills increase steadily with age, but emotional regulation, including cognitive control, also improved with age in a linear manner. A similar finding was also reported in a cross-sectional study that compared the emotion regulation strategies of early adolescents (aged 12-15), late adolescents (aged 16-18), adults (aged 18-65), and elderly people (aged 66-97) (Garnefski & Kraaij, 2006). Participants were asked to complete the self-report Cognitive Emotion Regulation Questionnaire (Garnefski, Kraaij, & Spinhoven, 2002) that assesses the specific cognitive emotion regulation strategies used in response to the experience of stressful life events. Among nine cognitive regulation strategies, namely, rumination, catastrophizing, self-blame, other-blame, acceptance, positive reappraisal, putting into perspective, positive refocusing, and planning, the adolescents reported using cognitive regulation strategies significantly less often than adults. In other words, although the adolescents seemed to use all cognitive strategies in everyday life, the extent to which they used the strategies was significantly lower than the adults, particularly in the case of positive reappraisal (Garnefski & Kraaij, 2006). Taken together, the literature reviewed here seems to support that while the PFC continues to undergo maturation during emerging adulthood, the ability to regulate emotional states can be developed when appropriate social and emotional experiences are exerted. Factors associated with emotional self-regulation. Researchers have long been interested the factors that may impact individual’s skills to effectively regulate emotions using different regulatory strategies. Among these factors are gender, culture, ethnicity, and personality traits, which are discussed here. Gender, culture and ethnicity. It is widely believed that men and women differ in the ways they express and regulate their emotions; yet, very limited research is available on sex 34 differences on this issue. Some studies have shown that males tend to show less emotional expressions and employ expressive suppression more often than females (Flynn, Hollenstein, & Mackey, 2010; Gross & John, 2003; Haga, Kraft, & Corby, 2009; Kring & Gordon, 1998), possibly due to social norms learned from early ages; for example, boys implicitly being taught not to cry (Buck, 2003). Yet, no consistent sex differences have been found in terms of the use of cognitive reappraisal (Gross & John, 2003; Haga, Kraft, & Corby, 2009). However, a neurological study by McRae, Ochsner, Mauss, Gabrieli, & Gross (2008) suggested that although both men and women showed comparable decreases in negative affect behaviorally when using the cognitive reappraisal strategy, the regulatory strategy may operate differently in men and women at a neurological level. fMRI data showed that, compared to men who recruited more activities in the PFC regions (associated with cognitive control), women showed greater activities in the amygdala (i.e., associated with emotional responding) and ventral striatal regions (i.e., associated with reward processing). This suggests that men may be able to process cognitive reappraisal with less effort than women and that women may use positive emotions in down-regulating negative affect to a greater extent than men (Goldin, McRae, Ramel, & Gross, 2009; McRae, Ochsner, Mauss, Gabrieli, & Gross, 2008). Culture may also greatly influence ways people regulate their emotions because cultural values and norms play an important role in defining what emotions are appropriate and socially acceptable and, thus, in the ways people should regulate their emotions (Campos, Frankel, & Camras, 2004). Cross-cultural studies have typically explored the differences between Eastern and Western cultures, frequently showing that Asians’ tendency to report less emotional responses to emotion-eliciting experiments (Mauss & Butler, 2010; Gross & John, 2003) and to endorse more frequent use of expressive suppression in both positive and negative emotions, 35 compared to Western cultural groups (Butler, Lee, & Gross, 2007; Gross & John, 2003; Soto, Perez, Kim, Lee, & Minnick, 2011). On the other hand, individuals from a Western cultural background tended to favor the use of cognitive reappraisal to expressive suppression (Gross & John, 2003; Butler, Lee, & Gross, 2007) and valued outwardly expressing positive emotions; yet they also tended to suppress negative emotions (Soto, Perez, Kim, Lee, & Minnick, 2011). Interestingly, in individuals from Eastern cultural backgrounds where emotion inhibition is more normative, habitual suppression was not strongly associated with negative social and emotional outcomes, including low quality social interaction and adverse psychological functioning (Butler, Lee, & Gross, 2007; Soto, Perez, Kim, Lee, & Minnick, 2011). Conversely, those from Western backgrounds showed greater negative socio-emotional outcomes with the habitual use of suppression, which appeared to negatively affect their wellbeing and life satisfaction (Butler, Lee, & Gross, 2007). These findings suggest that the expressions of emotion are deeply embedded in social and cultural contexts (Haga, Kraft, & Corby, 2007); therefore, what defines “appropriate” emotion regulation strategies may be highly context-specific. Personality. In terms of the association between the Big Five personality traits and emotional self-regulation strategies, two affectively-based predispositions, Neuroticism and Extraversion, often appeared to be related to individuals’ uses of emotion regulation. More specifically, a negative correlation was found between cognitive reappraisal and Neuroticism and a positive correlation was found between cognitive reappraisal and Extraversion (Gross & John, 2003; John & Gross, 2004; Wang, Shi, & Li, 2009). These findings suggest that individuals who tend to experience negative feelings like anxiety, fear, frustration less (low Neuroticism) and who tend to be enthusiastic and action-oriented (high Extraversion) are less likely to feel overwhelmed by negative affect and, thus, more likely to use cognitive reappraisal in order to 36 effectively reduce the intensity of negative affect when facing an emotional situation (John & Gross, 2004). In addition, Gresham and Gullone (2012) reported that higher levels of Extraversion and Openness to Experience are associated with greater use of cognitive reappraisal, suggesting that individuals, who tend to behave more assertively and confidently as well as those who exhibit a greater creativity and breadth of interests, are more likely to use the cognitive reappraisal strategy as a way to regulate emotions. On the other hand, expressive suppression appeared to be associated with low Extraversion, specifically with shyness. John and Gross (2004) noted that “self-conscious in social situations and keenly aware of looming rejection, shy people may be particular sensitive to potential rejection cues from others. This sensitivity may lead individuals to employ suppression to distance potentially rejecting others” (p. 1322). Also, some research has reported that lower levels of Openness to Experience and Agreeableness were associated with greater use of expressive suppression, suggesting that individuals whose ways of perceiving the world are more conservative and those who are less likely to extend themselves for other people may be more likely to use expressive suppression often (Gresham & Gullone, 2012). In summary, emotional self-regulation is a multidimensional construct that involves the physiological, behavioral, and cognitive processes by which individuals inhibit and modulate emotional expressions to effectively attain adaptive social outcomes (Sonnentag & Barnett, 2011). Cognitive reappraisal and expressive suppression are important emotional regulation strategies that people commonly use in their everyday lives. Although individual differences exist, research studies reviewed here support that individual’s ability to effectively regulate emotions is open to further improvement even in adulthood if appropriate stimuli and interventions are provided. 37 Small Ensembles in Music In this section, previous scholarly work and research studies in the field of music education concerning small music ensembles are reviewed. First, the concept of small music ensembles and their important characteristics is described. Then, I focus on is co-performer communication in the rehearsal and performance phases of various types of small ensemble activities. This section ends with a review of empirical research studies on small group-based, interactive, musical activities and the development of various social-emotional skills. Throughout history, humans in nearly every society of the world have enjoyed making music together for various purposes—to foster social bonds, convey emotions and values, represent identities, reach out to the divine, and inspire political movements (Turino, 2008). The discovery of ancient musical instruments, such as whistles, flutes, and drums, along with images of music making activities depicted in mural paintings at archaeological sites, implies the existence of collective forms of music making from thousands of years ago (Keller, 2014a). Evolutionary theorists posit that group music making may have functioned for adaptation purposes, as music facilitates social cohesion (Dunbar, 2012; Huron, 2001). There is some evidence to suggest that about 40,000 years ago human beings engaged in forms of collective music making to transmit their emotional states to a greater number of individuals as a means to keep the group united. This was possibly useful to ward off threats from other groups as well as predatory animals (Barras, 2014). Some have even argued that making music together may be one of the most ancient forms of human communication and cooperation (Schulkin & Raglan, 2014). As one of the traditional forms of group music making, the small ensemble can be understood as a self-governing group that performers collaboratively work together, pursuing 38 shared musical goals (Keller, 2014b). Based on Guzzo & Shea’s (1992) model of group performance, Marotto, Roos, and Victor (2007) described small ensemble work as an input – process – output model. In small ensembles, the individual performer’s musicianship, including musical knowledge and skills, group composition, and shared goals (input), along with the interpersonal communication among performers (process), explain the ensemble’s performance quality, viability and well-being as well as those of the individual performers (output) (see Figure 2.1). Particularly, the “process” of small ensemble is often highlighted in research because it entails a complex interpersonal coordination. According to Young and Colman (1979), the delicate nature of the small ensemble demands each performer to have an extraordinarily high level of coordination, because “mis-timings even of a fraction of a second, minute hesitations, slight differences in intonation, tiny misjudgments of dynamics and so on are regarded as monumental blunders” (p. 12). Thus, performers in a small ensemble strive to coordinate their cognitive, motor, and social skills to effectively communicate information about musical structure and expressive intentions to co-performers moment-by-moment (Keller, 2014a; Tan, Pfordresher, & Harré, 2010). Figure 2.1: Input – Process – Output Model of Small Ensembles INPUT •individual musicianship (musical knowledge and skills) •group composition •shared goals PROCESS •interpersonal coordination and communication OUTPUT •performance quality •group viability •wellbeing of individual performers 39 In a small ensemble, it is crucial for performers to be in close contact with one another in order to effectively coordinate their performance in real time (Keller, 2014a). Physical proximity allows performers to be aware of each other and to easily take into account each other’s actions. Schutz (1951) called this a “mutual tuning-in relationship,” meaning that “the ‘I’ and the ‘Thou’ are experienced by participants as a ‘We’ in vivid presence” (p. 79). Considering the high degree of intimacy and subtlety involved in small ensemble, some researchers consider musicians of two to 10 as an ideal size (Keller, 2014; Lim, 2014; Sicca, 2010). Small ensembles are traditionally operated without the lead of a conductor or director. Each performer in the group is entitled with the autonomy to make decisions and, thus, their shared musical goals are achieved through each other’s contributions. Therefore, performers in a small ensemble are directly and collectively responsible for their joint musical venture. Simply put, in a small ensemble, “there is an active striving to reach out to the other” (Cross, Laurence, & Rabinowitch, 2012, p. 340) and to engage in democratic relationships in which ensemble works are animated by the capacities, endowments and desires of all individuals in the group (Allsup, 2012). At a musical level, two characteristics make the small ensemble distinct from larger-sized ensembles. First, unlike larger-sized ensembles in which a distinct hierarchy exists among the instrument groups and within each group of instruments, each performer in a small ensemble has its own independent and distinctive role in the performance. Imagine that a violinist in a string quartet, or a vocal in a rock band, momentarily steps into the spotlight and performs a beautiful melody while the other performers step back, jointly supporting the soloist. The lead is then passed on to another and the performance is done in such a democratic manner that no one dominates the entire performance. Thus, “each performer is considered being equal in importance, as the execution of each part is equally consequential to the overall performance” 40 (Lim, 2014, p. 308). Second, the way to keep time represents a unique characteristic of the small ensemble. Because no conductor is present in small ensembles, synchronization is often a considerable challenge for many performers. To solve this issue, usually one performer takes on the role of “time-keeper,” for example, the violinist or keyboard player in Western classical music, or the drummer or bass player in pulse-based forms of music, such as jazz, rock, and pop. Some ensemble groups also employ a “rhythm section” to solve the issue of time keeping (Tan, Pfordresher, & Harré, 2010). A more interesting dimension of the small ensemble comes from its social aspect, which is the focus of the current study. Every ensemble group is, of course, unique since each group consists of different individuals, is bound by different contexts, and is formed to serve different functions. Nonetheless, striking similarities are apparent, particularly in relation to issues concerning interpersonal communication (Lim, 2014). As the final product of ensemble work is not simply an aggregate of each performer’s musical competences, but also significantly determined by the sociocultural factors of the group like the interpersonal dynamics among performers (Gilboa & Tal-Shmotkin, 2010), a highly complex set of personal and interpersonal skills is required of musicians in small ensembles. At a personal level, striking the perfect balance between individual and group identities is an essential element. Each performer should hold back his or her personal identity and “not hog the limelight, but be discreet and retiring” (Adorno, 1962, cited in Sicca, 2000, p. 155). Similarly, Murnighan and Conlon (1991) highlighted having “a soloist’s skills but not a soloist’s temperament” (p. 167) as a crucial element that ensemble performers should possess. Oftentimes, there are moments when musical interpretations and desires of an individual performer conflict with those of his/her co-performers. This implies that working in a small 41 ensemble involves endless negotiation between individual and group identities (Jensen & Marchetti, 2010). Yet, opportunities for the expression of individuality are not entirely absent, as repertoire choices and music arrangements are typically made in ways that each performer is granted with solo roles (Lim, 2014). At the interpersonal level, interpersonal awareness and mutual sensitivity are necessary for the effective communication within small ensemble groups. Each performer is expected not only to flawlessly execute his or her own part, but also to be attuned to the spontaneous performance of the entire group (Brandler & Peynircioglu, 2015). This requires them to draw tremendous awareness to listen to themselves as well as to their co-performers in the succession of microseconds and simultaneously coordinate their actions to adapt the expressive features (e.g., tempo, dynamics, phrasing, and sonorities). In addition, they must be ready to serve as a leader or follower at any given moment, with these roles being solely communicated through implicit modes, such as the act of listening, seeing, and responding to each other (Keller, 2014a; Schutz, 1951). Such role-changing process leads performers to share a collective experience, which may generate a sense of togetherness. When this occurs, such positive experience produces positive emotions toward their musical experiences and eventually strengthens the levels of engagement among performers, thereby enhancing the social bonds within the group (Myers & White, 2012). Small ensemble work generally involves two distinct phases: rehearsals and performance. While both phases share a common overarching goal, that is, to accomplish a unified, compelling performance, each phase differs in terms of the ways performers communicate (Seddon & Biasutti, 2009). Typically, the main objective of the rehearsal phase is to get musicians familiarized with the music, find consensus on the interpretations of expressive features, and 42 establish musical cohesion (Seddon & Biasutti, 2009). To do so, performers may attempt various musical experimentations, negotiate interpretations, work on technical issues, and practice complicated excerpts repeatedly (Davidson & King, 2004). These processes require both non- verbal and verbal communication modes. In the performance phase, performers exclusively use implicit communication strategies to make time-critical decisions such as keeping the tempo, balancing the sound quality, and making articulatory adjustments. Auditory communication and body language prevail. In fact, the performance phase remains “mysterious” even to the performers themselves because, various external factors like the presence of the audience, acoustics and temperature of the performance venue, as well as internal factors, such as performance anxiety and an individual performer’s mood, significantly affect the overall quality of ensemble performance. In addition, despite a thorough preparation in the rehearsal phase, countless “spontaneous variations” occur in the performance phase. As Murnighan and Conlon (1991) revealed, “even after considerable rehearsal, members can surprise each other or their audience with spontaneous flourishes” (p. 166). In-depth examination of the interpersonal coordination and communication in the rehearsal and performance phases is followed in next section. Co-performer Communication in the Rehearsal Phase The small ensemble can be considered a socially-skilled practice because achieving a compelling and cohesive performance is the result of consistent group efforts (Jensen & Marchetti, 2010). There is a growing body of literature on the interpersonal aspects of small ensembles in various musical genres and styles, including Western classical groups (e.g., Blank & Davidson, 2007; Davidson & Good, 2002; Gilboa & Tal-Shmotkin, 2010; Goodman, 2002; King, 2006; Seddon & Biasutti, 2009; Williamon & Davidson, 2002; Young & Colman, 1979), 43 jazz ensembles (e.g., MacDonald & Wilson, 2005; Sawyer, 2008; Seddon, 2005; Seddon & Biasutti, 2009), popular music ensembles (e.g., Jaffurs, 2004), and ethnic, folk music ensembles (e.g., Maduell & Wing, 2007). Various issues have emerged from these studies, including the interpersonal relationships among ensemble performers (Davidson & Good, 2002; Dobson & Gaunt, 2013), the role of leadership (Young & Colman, 1979), the complex social dynamics within ensemble groups (Murnighan & Conlon, 1991), different communication modes used by ensemble performers (Seddon, 2005; Seddon & Biasutti, 2009), and the roles of individual performers (King, 2006). Previous work commonly highlighted the collaborative and communicative skills among performers as a key to a successful performance because, as Goodman (2002) put, “[e]nsemble performance is about teamwork: half the battle of making music together (and ultimately staying together as an ensemble) is fought on social grounds” (p. 163). Unlike larger-sized ensembles, in which a conductor, or a director, is officially in charge of decision making, each performer in a small ensemble holds some degree of autonomy to make musical decisions and, accordingly, is naturally given a sense of collective responsibility for the outcome of their work (King, 2006). This makes the small ensemble display a unique culture of collaborative music making. An interview with a professional ensemble performer in Dobson and Gaunt’s study (2015) clearly articulated this notion: I think it’s the sense of creating something with a group of other people, that everybody adds their contribution, and the finished result is a joint effort, and the spirit of cooperation and ... well, to an extent, comradeship, because you really do need to get on with the people you work with, even if it’s just a sort of working relationship, it doesn’t need to be a deep friendship, but you really need to try and understand people (p. 34). 44 Of course, an appropriate level of each individual performer’s musical competences, such as technical skill, is the main determinant of the quality of a performance. Still, it is evident that performers’ inclination “to listen, communicate, and respond within the group” is equally critical in a small ensemble (Dobson and Gaunt, 2013, p. 32). In a study on the social and musical coordination within a string quartet, Davidson and Good (2002) undertook an exploratory observation of a string quartet consisting of undergraduate students in a British music college. They also interviewed the ensemble members. The authors concluded that while there are many factors that influence the functioning of an ensemble, a sensitive awareness to co-performers, specifically in relation to their styles of playing, musical gestures, and communicative behaviors, particularly led to a successful performance. Through a year-long observation of a postgraduate orchestral conducting program and interviews of the musicians, Marotto, Roos, and Victor (2007) also found that ensemble peak performance, or, “a transformational experience for group members, suspending their perception of time, binding them together and being a source of great joy and inspiration” (p. 388), was significantly affected by positive interactions and engagement among performers. While the concept of peak performance resonates with “group flow,” the ten key flow-enabling conditions, proposed by Sawyer (2007) based on Csikszentmihalyi’s notion of “flow” (1990), are worthy of attention. The ten keys include the presence of a shared goal, close and responsive listening, complete concentration, being in control, the blending of egos through listening and reacting, equal participation, familiarity with tacit rules of a given context, constant communication, keeping it moving forward, and the potential for failure. This suggests that creating a healthy working relationship within an ensemble is a necessary prerequisite for the optimal experience of group music making, as “open channels of communication can facilitate 45 the responsiveness and adaptation required for innovative progress” (Dobson & Gaunt, 2013, p. 26). In fact, small ensembles are notably vulnerable to disagreements and conflicts (Young & Colman, 1979). In typical ensemble rehearsals, multiple performers with different personalities, preferences, working styles, and musical backgrounds come together to make music. They also bring different opinions concerning the interpretations of the repertoire. These differences almost inevitably give rise to disagreements and even conflicts because, in most cases, the opinions involved are mostly simple differences in taste, which are inherently subjective in nature (Young & Colman, 1979). Therefore, these disparities need to be managed to minimize tensions and conflicts. How an ensemble achieves and maintains a high degree of agreement usually reflects the viability and wellbeing of the ensemble group. As an example, Lim (2014) conducted a case study of a professional vocal ensemble to explore what factors underlie the excellence of chamber music ensembles as work groups. Interview data of eight professional singers in a a capella vocal ensemble in London revealed that the mutual trust and respect for each other’s voices as well as the expressed appreciation for each other’s talents and strengths were critical in maintaining a healthy working relationship. In addition, recognizing the diversity of personalities and preferences within the group, learning to accept each other, and making mutual accommodations for each other appeared to be vital in nurturing a high level of harmony in their working relationship in the rehearsal phase (Lim, 2014). Horizontal relationships within a group further determine the level of conflict or disagreement in the small ensemble. Although there is usually no “official leader” in a small ensemble, one or a few members often gets to play a leadership role. Young and Colman (1979) discussed that in ensemble groups with a democratic leader, who makes group decisions on the 46 basis of consensual ideas and decisions of group members, there is a tendency towards higher levels of member satisfaction, engagement, and work quality. Similarly, in a study of performers’ roles in a string quartet, King (2006) observed three quartet ensembles consisting of undergraduate music students over a period of four weeks and interviewed the ensemble members. The qualitative data indicated eight roles played by performers across the different ensemble groups: leader, deputy-leader, contributor, inquirer, fidget, distractor, joker, and ‘quiet one.’ Although the roles of individual performers were potentially flexible across rehearsals, most performers tended to stick with a single role across a series of rehearsals, though some switched their roles easily. King (2006; 2013) also found that ensembles with stable team roles tended to be more successful in making better progress in rehearsals, maintaining higher engagement, and achieving a better performance. Taken together, mutual trust and respect, along with competent collaboration and communication skills, are crucial in maintaining positive group dynamics in the rehearsal phase in a small ensemble. Co-performer Communication in the Performance Phase As noted earlier, the small ensemble can be perceived as a unique form of human social activity involving “non-verbal communication that is achieved through specialized and codified forms of social interaction” (D’Ausilio, Novembre, Fadiga, and Keller, 2015, p. 111). In an ensemble performance, performers continually try to relate to and communicate with each other and share emotions with each other. Musical cohesion achieved through the successful interpersonal communication ultimately brings a sense of togetherness, which is aroused from “the actualization of empathic processes and states in the course of collective engagement in music-making” (Cross, Laurence, & Rabinowitch, 2012, p. 338). Thus, engaging in such collaborative music making allows individuals “to spend time meaningfully with another person, 47 regardless of age, and to allow this time to be shaped into mutually satisfying narratives of interaction” (Trevarthen & Malloch, 2002, p. 12). To some extent, the nature of “communicative musicality” (Malloch & Trevarthen, 2009) found in small ensemble performance corresponds with a preliminary form of musically coordinated interpersonal interaction, the infant-caregiver interaction. When caregivers talk to their infants, their musical expressions in the forms of raised pitch with glissando variations, slowed tempo, wide pitch contours, hyper-articulations, and repeated phrases, along with exaggerated bodily gestures and facial expressions, are generated to capture infants’ attention (Krueger, 2013). These exaggerated features often enable infants to access feelings and intentions of the adult, which are likely to draw out the infants’ responses, such as smiley faces, cooing, eye contact, or crying. While caregivers continually monitor infant’s responses and maintain or modify their reactions accordingly, infants often mirror and synchronize with salient moments of the adults’ responses by bodily gestures or vocalization (Trevarthen & Malloch, 2002). This sensitive “two-way mirroring” enhances affective sharing between the caregiver and infant and, ultimately, leads them into “a state of felt attunement” (Malloch, 2000, p. 31). Krueger (2013) suggested that this early mode of communicative musicality facilitates infants gaining access to the social world around them and cultivating social relationships. These, in turn, are crucial in shaping their subsequent social and emotional development (Ilari, 2016). As an advanced form of communicative musicality, the small ensemble brings individual performers into a more complex, intense form of social interaction. Although aesthetic and musical goals of ensemble performance may vary depending on the musical genres, styles, pieces, and social-cultural contexts of the group (Keller, 2014b), performers in small ensembles strive to deliberately communicate information about musical structures and expressive 48 intentions to each other. This social process takes place in sophisticated, formalized ways “that are constrained by the tools they use (musical instruments), conventions (genre-specific performance styles and leader-follower roles), and often a script (the musical score)” (D’Ausilio, Novembre, Fadiga, & Keller, 2015, p. 111). In a classical string quartet performance, for example, performers engage with one another by expressing their own musical intentions while continually tracking those of co- performers. At the same time, all monitor the overall ensemble balance in order to achieve musical cohesion in terms of musical parameters (e.g., rhythm, melody, timbre, harmony, dynamics) indicated in the musical score, as well as expressive intentions derived from their inner expressions and aesthetic goals. Such process of interpersonal coordination requires an exquisite balance between temporal precision and flexibility in interpersonal coordination (Keller, 2013). Here, various types of cues are employed, including auditory (e.g., timing, intensity) and visual cues (e.g., bow movements, eye contact, facial expressions, and whole body sway), to regulate the interpersonal coordination and facilitate the sharing of artful expressions (Wing et al., 2014). In the case of non-classical music ensembles, such as jazz and traditional folk music ensembles, the absence of notated music demands, arguably, even more sensitive awareness and responsiveness. The interpersonal dynamics in a flamenco ensemble, for example, typically consists of at least one dancer, singer, and instrumentalist (Maduell & Wing, 2007). While the overall flamenco performance is structured in a pre-determined pattern, performers take turns to be the leader in charge of the musical interaction, who is at the center of the audience’s attention. When the dancer becomes the leading performer, she or he may modify the tempo and the quality of rhythm through expressive movements of the head, arms, and torso, hand clapping, 49 and feet stumping. The singer and instrumentalists watch and listen with close attention to the dancer and quickly adjust to any changes set by the dancer and then hold it as a stable base, so that the dancer can freely indulge in counter-time or other off-beat variations and concentrate on other aspects of the performance (Maduell & Wing, 2007). Sicca (2000) claimed that the quality of the interpersonal communication in the ensemble performance is determined by the “listening ability” possessed by each performer. The ability that performers listen to themselves and simultaneously to their co-performers to be perfectly in tune with each other is an essential element in the production of a compelling, artistic ensemble performance. This notion resonates with Schutz’s (1951) concept of “mutual tuning-in relationship,” which suggests that performers are reciprocally and sensitively oriented with each other. It also aligns with Seddon’s (2005) notion of “empathetic attunement,” which is realized through a sensitive attunement and adaptation among performers. This view has been clearly articulated by Lim (2014): Beyond interpreting and executing their own parts, musicians must simultaneously anticipate co-performers’ interpretations of their parts, and others’ anticipations of their own execution. They must draw on tremendous awareness to hear themselves and co- performers in the succession of microseconds, mutually adapting their pitch, tempo, phrasing, and sonorities instantly, and must be prepared to be a leader or follower at any time with no communication other than the act of listening, seeing, and responding to each other and the music (p. 309). Additionally, an interviewee in a study by Dobson and Gaunt (2015) also highlighted this notion: ... no matter how good you are at playing yourself, you have to be playing in tune and in time and in the same way as your colleagues. So it’s really, really important to be 50 listening the whole time to everyone else and responding to what everyone else does. And that ... leads on to the next thing, which is adaptability, because when you’re playing with a hundred other people you’re not always going to be agreeing with absolutely everything, on the first play through certainly. And so you have to be able to adapt what you think is the way of playing something, in order to fit with everyone else (p. 30). Attempting to construct a theoretical framework on the interpersonal coordination in small ensemble performance, Keller (2008) proposed a psychological mechanism underlying the quality of the real-time interpersonal communication. The mechanism involves three cognitive- motor skills: anticipation, attention, and adaptation. The first skill relates to anticipatory mechanisms that enable performers to plan the production of their own sounds and predict upcoming sounds of co-performers through mental imagery or automatic expectancies (Keller, 2014b). Anticipation can take place effortlessly and automatically, as performers instantly sense auditory and visual cues associated with the upcoming sounds during performance. Or, the deliberate use of mental imagery where performers run in an internal simulation of the upcoming sounds can also facilitate the anticipatory mechanisms (Keller, 2014b). The second cognitive- motor skill concerns the cognitive process of dividing attention during the performance of the ensemble, which involves paying attention to one’s own sounds and those of co-performers while concurrently monitoring the overall ensemble input (Keller, 2008). This selective attention to self and others “facilitates ensemble cohesion by allowing co-performers to adjust their actions based on the online comparison of the ideal ensemble sound and incoming perceptual information about the actual sound” (p. 3). The last cognitive-motor skill is based on the adaptive mechanisms that help performers modulate their sound production in the face of errors or discrepancies in musical parameters (e.g., tempo, rhythm, intensity, articulation, intonation). 51 While ensemble performers aim to achieve joint musical goals by coordinating their sounds, each performer should be motivated to share and empathetically respond to each other’s expressive intentions, not stubbornly persist on their own. Even one performer’s inability to regulate her or his own sounds, for example, by not keeping a unified sense of tempo or not aligning detailed emotional interpretations, will likely lead to the failure of overall musical cohesion. In addition to real-time coordination skills, Keller (2014a) suggested that shared performance goals and familiarity with co-performers’ stylistic tendencies, as well as various social-psychological factors, including personality, communication styles, motivation, and gender can affect the effectiveness of musical communication in ensemble performance. For example, if performers in an ensemble group are already familiar with each other, it will be easier for them to anticipate upcoming sound productions and accommodate performance plans accordingly. That is, each performer’s personality, particularly in terms of locus of control and social competence, may influence the adaptive tendencies of the ensemble as well (Keller, 2014b). However, even though such social and psychological factors can potentially impose constraints on effective musical communication in ensemble performance, “the degree to which constraints on coordination can be overcome, and the use of strategies to enhance the communication of musical expression, presumably depend upon the performers’ levels of musical skills and experience” (Keller, 2014b, p. 274). In summary, while small ensembles are often viewed as a unique form of human social activity that involves complex interpersonal communication, research studies reviewed here suggest that the moment-by-moment process of co-performer communication in a small ensemble may be potentially relevant to the psychological mechanisms of empathy and emotional self-regulation. During an ensemble performance, performers strive to reach out to the 52 other by sensitively attending to whilst aligning their emotional experiences with each other to achieve musical cohesion. It is equally crucial for them to effectively regulate their emotions at any moments and behave in ways appropriate to the given musical context for successful ensemble performance. These ideas provide a theoretical framework to make a claim that continuous engagement in small ensembles may act as a scaffold that can help to acquire the habit of empathizing as well as effective emotional self-regulation. Previous Empirical Research on Music and Social-Emotional Skills Previous literature on co-performer communication taking place in small ensembles suggests that engagement in a small ensemble may act as a scaffold that helps individuals to cultivate various social-emotional skills. In line with this view, many music education researchers and psychologists have examined if participation in group-based interactive musical activities have positive impacts on these skills. In one of the early studies, Kalliopuska and Tiitinen (1991) investigated the effect of two group-based music programs on empathy and prosocial skill development among preschool children. While the programs focused primarily on teaching issues regarding emotions, each program incorporated different types of musical activities, such as physical exercise and drawing with music (Program I) and role-play, acting, and storytelling with music (Program II). After four months of participation, children in both programs (N = 62, aged 6–7) showed significant gains in both empathy and prosocial skill, compared to those in the control group who did not participate in any of the music programs. Similar findings were also reported in recent experimental studies. Preschool children aged 4 to 6 years (N = 93) were assigned to one of two different groups: a music group that underwent an intervention program that facilitated various interactive activities using musical components and a control group (Brand & Bar-Gil, 2010). While children in both groups 53 received the same number of weekly hours of music instruction, children in the music program were exposed to a musically rich environment, along with an extended weekly music lesson that emphasized the connections between musical activities and interpersonal communication. Results showed that children in both groups showed improvements in various musical skills (e.g., creative expression in singing, moving, and improvising) and in interpersonal communication skills (e.g., verbal expression of emotions, problem solving skills, cooperation in learning and play) in the post-tests, but the gains were considerably greater for children in the music intervention group (Brand & Bar-Gil, 2010). In another study (Ritblatt, Longstrech, Hokoda, Cannon, & Weston, 2013), preschool children aged 3 to 5 years (N = 102) were engaged in a music-based curriculum designed to promote the learning of social-emotional skills while others participated in a similar curriculum but without the use of musical elements. Between 4 and 8 months later, children in the music group showed considerable gains in those skills, as measured by the Preschool and Kindergarten Behavioral Scale, a behavioral rating instrument for evaluating social skills and problem behavior patterns completed by parents and teachers. Specific improvements were found particularly for items related to social cooperation (e.g., “Shows self-control,” “Takes turns with toys and other objects”), social interaction (e.g., “Makes friends easily,” “Accepts decisions made by adults”), and social independence (e.g., “Is able to separate from parents without extreme distress,” “Adapts well to different environments”) scales. A study with infants also showed evidence of the potential effect of interactive musical activities (Gerry, Unrau, & Trainor, 2012). Six-month-olds (N = 52) and their caregivers were assigned to one of two different groups, an active or a passive training group. In the active training class, infants and their caregivers engaged in active interactive music making activities 54 using a variety of musical repertoires, which were designed to develop musical skills through participation and observation. In the passive training class, infants and their caregivers were encouraged to freely play at five play stations, including art, books, balls, blocks, and action games, while recorded music played by synthesized musical instruments with no musical expression was heard in the back. After 6 months of participation in these classes, infants in the active training group showed not only accelerated acquisition of Western tonality but also superior development of pre-linguistic communicative gesture and social behaviors in comparison to the infants in the passive training group (Gerry, Unrau, & Trainor, 2012). Although these study results are promising, it is unsure whether these positive effects resulted from children’s engagement in music making activities or were due to the content of the activities. While the music programs described in these studies incorporated various musical components, these programs were explicitly tailored to facilitate learning of specific social- emotional skills. Also, given that children participants in these studies took part in their music program only for a few months, the positive effects of these musical activities on social- emotional development are open to scrutiny. Engagement in music for a few months seemed insufficient to make meaningful changes in the participants’ social-emotional skills. One cannot rule out the possibility that the positive consequences found in the aforementioned studies were a temporary effect, and not long-term. With these concerns in mind, Rabinowitch, Cross, and Burnard (2012) conducted a study with a music program that focused on the musical interaction itself with neither explicit nor implicit reference to specific social-emotional skills. Also, they had their participants engaged in the program for an entire school year. In this study (Rabinowitch, Cross, & Burnard, 2012), 52 school-aged children (aged 8-11) were randomly assigned in music (incorporating a variety of 55 interactive musical activities), game (incorporating similar activities of the music group, but without the use of music), or general control (no special program) groups. Following a full academic year, children’s empathy was measured using the Matched Faces test (selecting a matched facial expression of the protagonist after watching a short video), Bryant’s (1982) Index of Empathy questionnaire, and Memory Task (selecting the facial expression that they remembered having seen among the faces presented prior to the presentation). Children in the music group exhibited significant increases in the Index of Empathy questionnaire and also showed an advantage in remembering the emotions, measured by the Memory Task, compared to those in the game and control groups. Similar findings emerged even when children participated in existing music programs that were specifically designed for the purpose of research. Schellenberg, Corrigall, Dys, and Malti (2015) compared the level of sympathy and prosocial behavior between 8- and 9-year-old children who attended a group music training program in public schools and those who did not (N = 84). This program involved learning to play the ukulele and singing with an emphasis of interaction and cooperation among students. Following 10 months of participation, children in the music group, particularly those who began the program with poor prosocial skills, exhibited greater improvements in these social-emotional skills than those in the control group. In another study with adolescent participants (aged 13–16) who were attending a specialized high school for the arts, Goldstein and Winner (2012) examined the effects of involvement in an extensive school-based acting training on empathy-related abilities. Students engaged in extensive school-based music or visual art training were included as the control group (n = 25). Following 10 months of participation, students in the control group (music or visual art training) showed some gains in a theory of mind measure (Reading the Mind in the Eyes) and 56 two empathy measures (Empathic Accuracy & Fiction Emotion-Match). These findings suggest the association between continuous engagement in group music making activities and an individual’s social-emotional skill development. The social process taking place in group music making activities may facilitate the sharing of emotional experiences and promote self-other merging and, thus, foster social cohesion, cooperation, and prosocial orientation (Schellenberg, Corrigall, Dys, & Malti, 2015). Interestingly, Kirschner and Tomasello (2010) showed that even one-time participation in joint music making can increase empathic connection and social bonding among 4-year-old children (N = 96). Their study involved two brief sessions of pretend play in a laboratory setting. In the first, the musical condition, a pair of children and an experimenter engaged in interactive musical games in the form of joint singing and dancing. In the second session, the non-musical condition, a pair of children and the same experimenter experienced the same level of interactive play, but with no music. After playing the interactive games with their partners for some minutes, significantly more children in the musical condition exhibited spontaneous helping behaviors (i.e., when one of the children accidentally spilled her set of marbles) and chose to cooperatively solve a challenging task, compared to those in the non-musical condition. The authors interpreted these results through the lens of shared intentionality. Because music is “a collectively intended activity… that [satisfies the] human desire to share emotions, experiences, and activities with others” (p. 362), the joint music making activities in which young children engaged encouraged them to naturally experience each other as “co-active, similar, and cooperative members of a group” (p. 362) which, in turn, motivated them to spontaneously help or cooperate with each other. 57 The potential benefits of both short- and long-term engagements in group-based musical activities in enhancing social-cognitive and emotional skills shed some light into the field of special education, particularly for children with Autism Spectrum Disorder (ASD). LaGasse (2014) conducted an experimental study of two groups of children with ASD (N = 17), ages 6 to 9, where one group participated in a music class and the other participated in a no-music class. Both classes facilitated activities to boost various social skills, such as eye gaze, joint attention, and communication. Over a period of 5 weeks, significant group differences were found between the two groups; yet, children who participated in the music class showed greatly increased occurrences of eye gaze towards others and joint attention with peers. Similarly, another study of older children with ASD (aged 11-17) also showed improvement in social engagement and interaction after participating in inclusive music ensembles, in which students with ASD were engaged in group music making with typically developing peers (Cardella, 2014). The author pointed out that the inherently social nature of group music making afforded students with ASD, who are often socially isolated, with an ideal place to interact with other peers in a non-threating, enjoyable environment. In addition, since children with ASD tend to relate more effectively to objects than to people, musical instruments can be an effective tool to facilitate their social experience (Cardella, 2014). Despite these interesting and promising study findings on the possible effect of interactive musical activities on social-emotional skill development, most of the studies seem to have neglected a significant aspect of musical experience. That is, most studies reviewed here simply compared participants who participated in a music program with those who did not, but without taking into account the qualitative aspects of participants’ musical experience (e.g., levels of engagement, enjoyment, and motivation). Because the nature of interactive music 58 making is participatory, it is hard to imagine people gaining positive outcomes if their musical experiences are not engaging, motivating, and pleasurable. In addition, these studies did not consider the quality of their treatment and control programs—what kinds of teaching approach did the instructors use? How engaging were the programs? Was the teaching content relevant to the participants’ interests and experiences? Considering that “real” learning can take place when learners are motivated to, engage in, and enjoy their learning experiences (Wills, 2007), neglect of these aspects may be a limitation of these studies. Still, considering positive effects of musical engagement found in these earlier studies, the relationships between engagement in interactive musical activities and increased social- cognitive and emotional skills are evident. At this point, a question arises. What are the underlying mechanisms that would lead these activities to promote those skills? Two possibilities can be considered: the socially-stimulating nature of music making activities in general and synchrony among peers in particular. The inherent socializing force of music often allows individuals to experience the exhilaration of enabling each other to belong, grow, and learn. When people are engaged in music making with others in a safe, democratic environment where each individual has freedom to explore and the act of discovery is honored as the foundation of collective work, they relate to one another in the dynamic interplay and eventually experience a sense of togetherness (St. John, 2005). Dissanayake (2000) offered that the sense of belonging generated from a positive social experience is fundamental to finding and making meaning, developing competence and confidence, and enhancing engagement and collaboration. Thus, group music making has the potential to promote optimal social experiences, therefore, plays a critical role in the development of social-emotional skills. 59 Another underlying mechanism may be found in one of the fundamental musical elements of group music making, synchrony, which refers to “the temporal coordination of biological events, social behavior, or affective states” (Schellenberg, Corrigall, Dys, & Malti, 2015, p. 10). Throughout history, social groups, including armies, churches, and communities, benefited, intentionally or unintentionally, from “cultural practices that draw on ‘muscular bonding’ or physical synchrony, to solidify ties between members” (Wiltermuth & Heath, 2009, p. 1). Synchrony is also known to effectively weaken the boundaries between the self and the group, which eventually leads to a sense of collective bond that makes groups cohesive (Hove & Risen, 2009). Indeed, several recent empirical studies provide evidence of a causal link between rhythmic synchronic activities and increased social skills. For example, in an experimental study by Cirelli, Einarson, and Trainor (2012), 14-month-old infants (N = 40) were held by an experimenter in a baby carrier facing outward and bounced to the beat of a melody for a few minutes. At the same time, another experimenter faced the infant while hearing the same music via headphones and bounced either synchronously or asynchronously to the infant’s movements. After the “joint dancing,” the infants who were bounced with music in synchrony with the experimenter showed more helping behaviors towards the experimenter, compared to those who were bounced in an asynchronous way. Similar findings were also found in studies with adult participants. Hove and Risen (2009) conducted a series of experiments to examine the effect of interpersonal synchrony on affiliation to co-actors. In the first experiment, affiliation ratings were examined based on the extent to which participants tapped in synchrony with the experimenter. In the second experiment, while synchrony was manipulated, participants’ affiliation ratings were compared 60 for an experimenter who (a) tapped in synchrony, (b) tapped out of synchrony, and (c) did not tap with the participants. In the third experiment, participants tapped either in synchrony or out of synchrony with a metronome, not the experimenter. Results showed that interpersonal synchrony rather than a general effect of synchrony was a critical factor that contributed to participants’ affiliation to co-actors. Specifically, the degree of interpersonal synchrony between participants and the experimenter predicted how much the participants liked the experimenter. Also, participants who tapped in synchrony with the experimenter liked the experimenter more than those who tapped out of synchrony or alone (Hove & Risen, 2009). In addition, Wiltermuth and Heath (2009) showed that acting in synchrony (e.g., walking in synchrony, singing and moving in synchrony) with other people subsequently led to more helpful and cooperative responses when playing an interactive game with group members. These findings support the idea that the inherently social and communicative nature of group music making provides an ideal opportunity to promote the coordination of action, affect, and mental states of performers, and lead to enhanced connectedness, which, in turn, promotes social- emotional skill development. Taken together, despite some limitations implicated in the studies, recent empirical work suggests that engagement in group-based, interactive, musical activities hold a strong potential for enhancing various social-emotional skills, including empathy and emotional regulation skills. These findings resonate with previous literature on co-performer communication taking place in small ensemble contexts reviewed earlier. Taken together, a broad array of literature provides a strong conceptual framework for the current study, whereby the relationships between small ensemble experiences, empathy, and emotional self-regulation skills are examined. 61 Chapter Summary This chapter presented a review of relevant literature to build a conceptual framework for the relationships between music students’ small music ensemble experiences and their empathy and emotional self-regulation skills. Because the population of interest for this study was college students, it was necessary to find evidence that the social-emotional skills of emerging adults, whose physical maturation is almost completed, can be improved and enhanced if appropriate social and emotional experiences are exerted. Extensive literature reviewed in this chapter seem to support this notion. Also, a broad array of literature on small group-based, interactive, musical activities have highlighted small music ensembles to be a unique human social activity, as they involve a highly complex set of interpersonal coordination, cooperation, and collaboration skills (Davidson & Good, 2002). In addition, previous empirical studies have demonstrated positive effects of interactive musical activities on social-emotional skill development across a wide range of age groups—from infants and children, to adolescents and adults. While this dissertation study is primarily concerned with the relationships between small music ensemble experiences of college music students and their empathy and emotional self-regulation, the scholarly work and research studies reviewed here suggest that small music ensembles could be fruitful domains in which to cultivate the habit of empathizing as well as to develop effective emotional self-regulation. To seek evidence to support this notion, this study explored the possible relationships between music students’ small music ensemble experiences and their empathy and emotional self- regulation skills, as seen in the subsequent chapters. 62 CHAPTER THREE: METHOD This chapter presents the research methodology utilized in this study. It begins with the methodological background of the current study. Next, descriptions of the sample, data collection instrument and procedure, data storage and confidentiality, and variables used in this study are presented. The chapter ends with a description of the data analysis procedure. The Current Study This study was designed as a predictive correlational study to examine whether music students’ small ensemble (SE) experiences predict their levels of empathy and emotional self- regulation skills. Using a snowball technique, an anonymous online questionnaire was widely distributed to undergraduate music performance majors in their senior year across the country. The questionnaire surveyed participants’ background information and SE experiences, and also measured personality, empathy, and emotional self-regulation skills using standardized psychological assessments. This study conformed to the tenets of postpositivism, which claims that understanding of truth should be based on probability rather than certainty and also acknowledges that theories, knowledge, and values of the researcher can influence what is being studied (Phillips & Burbules, 2000). The primary goal of the current study was to explore the relationships between music students’ SE experiences, empathy, and emotional self-regulation skills. A secondary goal was to identify whether demographic factors, such as gender, ethnicity, personality, primary instrument, primary area of study, and age at commencement of music training, played significant roles in predicting their empathy and emotional self-regulation skills. The following research questions were addressed in the study: 63 (1) What are the relationships among music students’ participation in small ensemble, attitudes toward small ensemble, and empathy and emotional self-regulation skills? (2) To what extent do personal factors, including gender, ethnicity, primary instrument, primary primary area of study, age at commencement of music training, and personality, contribute to music students’ empathy and emotional self-regulation skills? (3) To what extent do music students’ participation in and attitudes toward small ensemble together contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factors? Through an examination of extant research on individual’s empathy and emotional self- regulation skills, ten sets of variables were identified for this study: (a) gender; (b) ethnicity; (c) primary instrument; (d) primary area of study; (e) age at commencement of music training; (f) personality traits; (g) participation in SE; (h) attitudes toward SE; (i) empathy; and (j) two of emotional self-regulation strategies, namely cognitive reappraisal (CR) and expressive suppression (ES). Participants The population of interest for this study was undergraduate music performance majors in their senior year in four-year music colleges or universities in the United States. Because students’ SE experiences were the primary concern of this study, it was necessary to ensure that potential study participants had an adequate level of ensemble experiences. Thus, while music performance majors specializing in various musical genres and styles were eligible to participate, non-performance music majors, such as music education, music technology, composition, and music therapy, were excluded from the study. In total, 176 students voluntarily participated in 64 the study; however, 11 participants who failed to complete the survey were excluded from the final sample, yielding a final sample size of 165. Participants (N = 165) were music performance majors enrolled in 28 music colleges and universities located in the Northeast (n = 33), Midwest (n = 43), West (n = 53), and South (n = 31) regions of the United States (unknown, n = 5). Table 3.1 shows the distribution of participants by musical background. Specifically, 25 keyboard, 45 string, 31 woodwind, 25 brass, 8 percussion, and 31 voice majors comprised the study population. A large number of the participants (78%) reported their primary area of study to be classical music while approximately 13% reported primarily studying jazz and/or popular music. The remaining participants indicated that they studied both classical and non-classical music. Table 3.1 Distribution of Participants by Musical Background Factor n % Primary Instrument Keyboard (e.g., piano, organ, electronic keyboard) 25 15.2 String (e.g., violin, viola, cello, bass, guitar, harp) 45 27.3 Woodwind (e.g., flute, clarinet, saxophone, oboe, bassoon) 31 18.8 Brass (e.g., trumpet, French horn, trombone, tuba) 25 15.2 Percussion (e.g., drum, marimba, xylophone) 8 4.8 Voice 31 18.8 Primary Area of Study Classical music 128 77.6 Non-classical music (Jazz, n = 17; Popular music, n = 3) 21 12.7 Both classical and non-classical music 16 9.7 Age at Commencement of Music Training Under 5 33 20.0 5 – 9 87 52.7 10 – 14 35 21.2 15 and Over 10 6.1 65 In terms of demographic information, 53% of the participants were females and 45% male, with the remaining participants indicating “other.” Over 70% of the participants were between 21 and 23 years of age; participants younger than 21 and older than 23 years of age accounted for 14% and 13%, respectively, of the entire sample. Approximately 50% of participants self-identified as Caucasian, 31% as Asian, and 19% with other racial/ethnic groups. Table 3.2 presents demographic information of the study participants. Table 3.2. Distribution of Participants by Demographic Information Factor n % Gender Male 72 43.6 Female 90 54.5 Other 3 1.8 Age 20 and under 21 12.7 21 – 23 122 74.0 Over 23 22 13.3 Ethnicity African American 10 6.1 Asian 51 30.9 Hispanic 17 10.3 Caucasian 83 50.3 Other (Persian, n = 1; Mixed, n = 3) 4 2.4 Data Collection Instrument The Small Ensemble Experience and Social-Emotional Competence Questionnaire (SEESEC, see Appendix A) was constructed for the purpose of this study. The SEESEC is divided into four sections (see Table 3.3). The first sections centers on demographic data, including information about participants’ musical background. The second section is comprised 66 of questions about participants’ musical experience before college and the following section gathers data on SE experiences during their undergraduate studies. The last section consists of three standardized psychological assessments that measure personality traits, empathy, and emotional self-regulation skills of study participants. The SEESEC was administered in a user- friendly online survey using the online software SurveyMonkey. Table 3.3. Contents of the Small Ensemble Experience and Social-Emotional Competence Questionnaire Part Content Part I Demographic information Age Gender Ethnicity Part II Musical experiences before college Age at commencement of music training Types of musical activities (i.e., lessons, small ensembles, large ensembles) Part III SE experiences in the college years Attitudes toward SE Levels of participation in various SE activities Part IV Psychological assessments Personality traits (TIPI) Empathy (EQ) Emotional self-regulation (ERQ) Part 1: Background Information As noted, part 1 of the SEESEC gathered demographic information about the participants, including age, gender (i.e., female, male, and other), and racial/ethnic background (i.e., African American, Native American, Asian, Hispanic, Pacific Islander, Caucasian, and other). Participants were also asked about their musical background, including primary instrument (i.e., keyboards, strings, woodwinds, brass, percussion, voice, and other) and primary 67 area of study (i.e., classical music, jazz, popular music, world music, and other). They were required to answer these questions using multiple choice. Part 2: Musical Experiences Before Entering College Part 2 of the SEESEC collected information on participants’ musical experience prior to college, specifically, the age at commencement of formal music training and types of musical activities (i.e., individual music lesson, small ensemble, and large ensemble) they participated in before starting their music degree at college (i.e., elementary years, junior high years, and high school years). Part 3: Small Ensemble Experiences in College. Part 3 of the SEESEC consisted of two sub-sections: a scale that measured participants’ attitudes toward SE and a report on levels of participation in various SE activities during college. In the first section, participants were asked to rate 13 items related to their SE experiences on a five-point scale ranging from 1 (not at all) to 5 (completely). This attitudinal scale was devised to measure music students’ predisposition to think, feel, perceive, and behave (Kerlinger, 2000) toward SE activities. All questionnaire items began with the common stem, “When I perform/work in small ensembles,” and some examples of items are “I gain more confidence in performance,” “I feel that I’m important and useful,” “I experience uplifting and motivating feelings,” and “I feel pride in relation to the group’s success.” Based on Desselle’s suggestions (2005) in constructing and implementing attitude scales, questionnaire items were designed through a careful review of previous literature on teamwork in musical contexts and consultation with two experts in music education research. A pilot test of the attitude scale was conducted with 26 music majors—undergraduate and graduate students— to evaluate its feasibility and establish the reliability and validity. A Cronbach’s alpha coefficient 68 of 0.90 indicated a high internal consistency for the scale. Initially, the questionnaire consisted of 20 items, but seven items that loaded the least in the factors (< .30) were eliminated, yielding a total of 13 items in the final version. After removing these seven items, an exploratory factor analysis yielded three factors which accounted for 71.8% of the variance. A correlation between the remaining items and each factor was greater than 0.40, suggesting that the attitude scale effectively captured music students’ attitudes toward SE. The second section of Part 3 required participants to report their levels of participation in formal and informal SE activities, in and out of their college curricula. Multiple choice questions included: “As part of your school curriculum, how many small ensemble courses did/do you take?”; “In how many small ensemble performances did/do you participate that were not related to the school curriculum?”; “In how many concerts/recitals, competitions, master classes, and/or auditions did/do you participate as part of a small ensemble?”; and “How often did/do you engage in informal small ensembles?” Participants answered each question separately for each academic year (i.e., freshman year, sophomore year, junior year, and senior year). Part 4: Personality and Emotional Life The last part of the SEESEC consisted of three standardized psychological measures, which assessed participants’ personality traits, empathy, and emotional self-regulation skills. Detailed information about each measure is presented below. Ten Item Personality Inventory (TIPI). The TIPI measures personality characteristics based on the “Big Five” dimensions: Extraversion, Agreeableness, Conscientiousness, Emotional Stability (or Neuroticism), and Openness to Experience. The Big Five personality framework is supported by many contemporary personality psychologists, who believe that “most individual differences in human personality can be classified into five broad, empirically derived domains” 69 (Gosling, Rentfrow, & Swann, 2003, p. 506). Although many comprehensive Big Five personality instruments exist, they tend to require extensive time to complete. As an alternative, Gosling, Rentfrow, and Swann (2003) developed the TIPI, a short measure of the Big Five personality traits. Given that the TIPI was originally developed for situations where personality is not the primary topic of interest, but where short personality measures are needed (Gosling, Rentfrow, & Swann, 2003). This particular instrument was found to be suitable for use in this study. The TIPI contains 10 items and each consists of a set of two descriptive words, separated by a comma, using the common stem, “I see myself as.” The two words are either descriptive of, or the opposite of, the personality traits. Examples are “extraverted, enthusiastic” and “reserved, quite” for Extraversion, “dependable, self-disciplined” and “disorganized, careless” for Conscientiousness, and “anxious, easily upset” and “calm, emotionally stable” for Emotional Stability. Participants rate how much they see themselves as possessing the traits using a 7-point Likert rating scale ranging from 1 (strongly disagree) to 7 (strongly agree). Extensive studies (e.g., Ehrhart et al., 2009; Gosling, Rentfrow, & Swann, 2003; Romero, Villar, Gomez-Fraguela, & Lopez-Romero, 2012) have examined the psychometric properties of the TIPI. In Gosling et al.’s (2003) study, the reliability of each domain was reported with alpha coefficients of .68 (Extraversion), .40 (Agreeableness), .50 (Conscientiousness), .73 (Emotional Stability), and .45 (Openness to Experience). Ehrhart et al.’s (2009) study reported coefficient alpha values of .71 (Extraversion), .34 (Agreeableness), .56 (Conscientiousness), .65 (Emotional Stability), and .52 (Openness to Experience). Although the internal consistency of the TIPI is relatively low because each dimension includes only two items, the test-retest reliability appears to be strong with an average correlation of .72 for the five dimensions in a six-week time span 70 (ranging from .62 to .77 in Gosling, Rentfrow, & Swann, 2003 and from .52 to .83 in Romero, Villar, Gomez-Fraguela, & Lopez-Romero, 2012). In addition, the TIPI appears to have good convergent and discriminant validity. Gosling, Rentfrow, and Swann (2003) reported convergent correlations with the Big-Five Inventory (John & Srivastava, 1999), one of the most widely used Big Five instruments, as follows: .82 for Extraversion, .77 for Agreeableness, .80 for Conscientiousness, .76 for Emotional Stability, and .70 for Openness to Experience, on average. No discriminant correlation was greater than .36, with an average of .20 (Gosling, Rentfrow, & Swann, 2003). Convergences between the TIPI and the Revised NEO Personality Inventory, another personality assessment inventory based on the Big Five dimensions (Costa & McCrae, 1992), are also strong (ranging from .56 to .68 in Gosling, Rentfrow, & Swann, 2003 and from .36 to .64 in Romero, Villar, Gomez- Fraguela, & Lopez-Romero, 2012). Overall, the TIPI has reasonably acceptable psychometric properties to warrant use as a personality measure when the use of longer instruments is not convenient. Empathy Quotient-short version (EQ-short). The EQ-short was employed to measure participants’ empathy skills. While diverse methods to measure empathy exist, the EQ-short was chosen for this study because it is explicitly designed to assess levels of both cognitive and affective empathy in adults (Baron-Cohen & Wheelwright, 2004). Other widely used empathy measures tend to take different views in terms of their operationalization of empathy. For example, Hogan’s empathy scale (1969) focused on the cognitive aspect of empathy while Mehrabian and Epstein’s (1972) Questionnaire Measure of Emotional Empathy considered empathy as an exclusively affective phenomenon. In addition, Davis’s (1980) Interpersonal 71 Reactivity Scale tended to embrace broader dimensions in defining empathy, including perspective taking, fantasy, empathic concern, and personal distress. The EQ-short is a 40-item self-report measure (Baron-Cohen & Wheelwright, 2004). The full version of the EQ contains 60 items, 20 of which are meant to distract from the measurement of empathy. In the EA-short, the 20 distractor items are omitted. Participants were asked to rate their responses to 40 statements using a 4-point Likert scale, from “1 = strongly agree” to “4 = strongly disagree.” Examples of the statements are “I can pick up quickly if someone says one thing but means another,” “Seeing people cry doesn’t really upset me,” and “It upsets me to see an animal in pain.” (Baron-Cohen & Wheelwright, 2004). The EQ is a valid and reliable self-report measure of empathy (e.g., Allison, Baron- Cohen, Wheelwright, Stone, & Muncer, 2011; Baron-Cohen & Wheelwright, 2004; Lawrence, Shaw, Baker, Baron-Cohen, & David, 2004). In multiple studies of adults with high-functioning autism (HFA), Asperger’s Syndrome (AS), and age-matched controls from a general population (e.g., Baron-Cohen & Wheelwright, 2004; Wheelwright et al., 2006), the EQ appeared to reliably distinguish between clinical and control groups, as individuals with HFA/AS showed significantly lower levels of empathy than controls. Also, gender differences among healthy control adults were found for females, who scored slightly but significantly higher than males. These findings have been replicated in cross-cultural studies in different parts of the world, including Japan (Wakabayashi et al., 2007), Italy (Preti et al., 2011), and France (Berthoz et al., 2008). The EQ also demonstrates high test-retest reliability over a period of 12 months, r = .97, p < .001 (Baron-Cohen & Wheelwright, 2004). In terms of construct validity, Baron-Cohen and Wheelwright (2004) reported that the EQ inversely correlated with the Autism Spectrum 72 Quotient (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) and positively correlated with the Friendship Questionnaire, an assessment of reciprocity and intimacy in relationships (Baron-Cohen & Wheelwright, 2003), as well as the Reading the Mind in the Eyes, an assessment of theory of mind (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001). Additionally, the EQ showed moderate correlations with the empathic concern and perspective- taking subscales of the Interpersonal Reactivity Index, a self-report instrument designed to assess empathic tendencies (Davis, 1983). This illustrates high concurrent validity of the EQ (Lawrence, Shaw, Baker, Baron-Cohen, & David, 2004). In addition, the EQ is correlated with neural activities during emotion perception in an fMRI study (Chakrabarti, Bullmore, & Baron- Cohen, 2006). Emotion Regulation Questionnaire (ERQ). The ERQ assesses emotional regulation skills, specifically two types of regulatory strategies, cognitive reappraisal and expressive suppression. These strategies were chosen for this study because they are considered to be important for SE activities (Glowinski, Bracco, Chiorri, & Grandjean, 2016; Lim, 2014; Murnighan & Conlon, 1991). For example, when performers’ musical interpretations and desires conflict while rehearsing in a SE context, individual performers need to regulate their emotions by reinterpreting the meaning of the emotional situation (i.e., cognitive reappraisal) to minimize conflicts. In addition, when a SE group performs on stage, there are times when individual performers need to mask their emotional state by inhibiting ongoing emotion-expressive responses (i.e., expressive suppression), for instance, when one makes a mistake. As the ERQ was designed to measure these two specific emotional regulation strategies, this measure seemed to be an appropriate tool to use in this study. 73 Developed by Gross and John (2003), the ERQ is a 10-item self-report questionnaire to assess individual differences in the habitual use of two emotion regulation strategies: CR and ES. The CR scale has six items (e.g., “When I want to feel less negative emotion, I change the way I’m thinking about the situation”) and the ES has four items (e.g., “I keep my emotions to myself”). In addition to items related to emotions in general, the CR and ES sub-scales include at least one item on the regulation of negative emotions (e.g., “When I want to feel less negative emotion (such as sadness or anger), I change what I’m thinking about”) and positive emotions (“When I want to feel more positive emotion (such as joy or amusement), I change what I’m thinking about”). Study participants were asked to rate each item using a 7-point Likert scale, from 1 (strongly disagree) to 7 (strongly agree). Studies conducted in different parts of the world have shown that the ERQ has adequate internal consistency among different age and cultural groups (Abler & Kessler, 2009; Balzarotti, John, & Gross, 2010; Gross & John, 2003; Matsumoto et al., 2009). For example, data drawn from four different cultural groups (N = 1,483) indicated high alpha coefficients (< .75 for CR and < .68 for ES; Gross & John, 2003). Gross and John (2003) also reported good test-retest reliability over a three-month period (r = .69 for both sub-scales). Convergent and discriminant validity also appear to be adequate, as the ERQ is positively or negatively correlated with many conceptually relevant constructs. Specifically, CR is positively correlated to coping through positive reinterpretation, measured by the COPE inventory, an instrument that measures coping styles and strategies (Carver, Scheier, & Weintraub, 1989). The CR is also correlated to positive affect measured by the Positive and Negative Affect Schedule (PANAS) an assessment of positive and negative affect (Watson, Clark, & Tellegen, 1988), and negatively correlated with negative affect measured by the same 74 instrument (Balzarotti, John, & Gross, 2010; Gross & John, 2003). The ES, by contrast, is positively correlated to the Inauthenticity scale, an instrument that measures attempts to mask one’s true inner self (Gross & John, 1998), as well as negatively correlated with coping through venting, as measured by the COPE inventory, and with positive affect, as measured by the PANAS (Balzarotti, John, & Gross, 2010; Gross & John, 2003). Taken together, these studies point to the ERQ’s reliability, validity, and utility to evaluate individuals’ strategies of emotion regulation in the adult population. Pilot Study As noted, a preliminary version of the SEESEC was pilot-tested with 13 music performance major undergraduate and graduate students to (a) assess the feasibility of the questionnaire, (b) identify any problems with administration of the questionnaire, and (c) assess the reliability of the rating scale measures (i.e., attitude scale). Several issues identified from the pilot test, such as redundant options for multiple choice questions, vague expressions, ambiguous word choices, and misspellings, were investigated and resolved for the final version. Data Collection Procedure To collect data, SurveyMonkey was employed as the platform for distributing the online survey as well as collecting and storing responses. As a widely used method for data collection in the social and behavioral sciences (Lyons, Cude, Lawrence, & Gutter, 2005), an online survey provides a relatively inexpensive, quick, and efficient way to obtain large amounts of information from a large sample of people. It is convenient for participants, as they can access and complete the survey at any time and place at their convenience. It is also advantageous when 75 the survey is administered anonymously because an interviewer is not present asking questions directly, which leads to a decreased likelihood of response bias and an increased response rate (Skitka & Sargis, 2006). Sampling bias concerning representativeness is often discussed as one of the weaknesses of online surveys (Szolnoki & Hoffmann, 2013) because online surveys are confronted with limited access to certain demographic groups. However, because the targeted population for this study was college students, this was not considered to be a major concern. Another issue concerning online survey is related to truthfulness, as the researcher can never be sure about the identity of the person who actually fills out the questionnaire. Although it was impossible to control who actually took the survey, this concern was handled by disseminating the online survey through a private channel (i.e., personal email). Considering the strengths and weaknesses of online surveys, it was concluded that an online questionnaire was an effective, reliable method for data collection in this study. Upon approval from the Institutional Review Board of University of Southern California (see Appendix B), the online survey became available through a web link provided by the abovementioned platform. The first page of the survey (see Appendix C) displayed a brief description of the research, inclusion and exclusion criteria for participation, and informed consent and participants’ right. Participants verified that they had thoroughly reviewed the information and agreed to participate by clicking ‘Next’ at the bottom of the first page. After successfully completing the questionnaires, the last page of the survey asked participants to leave an email address in order to receive a $5 Amazon gift card as compensation. The compensation was electronically sent to the participants within 24 hours after completion of the survey. Data collection occurred in a span of 6 months, between April and September of 2016. 76 Participant Recruitment In order to recruit participants, I created an electronic invitation that contained the URL of the survey, along with a brief description of the study, including information about qualifications and disqualifications of study participation and the compensation (see Appendix D). The recruitment process was as follows. First, I searched “best colleges for music performance in America” on google and chose the first 50 music colleges/universities appearing on three different websites (niche.com; hollywoodreporter.com; usnew.com), which yielded a total of 78 music colleges/universities. Then, I searched websites of these schools to obtain contact information of administrators (i.e., associate dean of academic/student affairs, undergraduate academic advisor, undergraduate program director) and professors (i.e., department chair) who may have access to their student email lists. Based on the information collected, I sent out an email to 93 individuals, asking their help to disseminate the electronic invitation to their senior students. Sixteen people responded that they had sent out the invitation to their students. Second, since it was the graduation season at the time of data collection and the targeted participants were graduating seniors, I searched graduation booklets that universities disseminated on commencement day, as these contained lists of names of graduating students and their majors. While a few universities made the booklet available to access through online, I was able to obtain booklets from nine music colleges. Using the information of music performance major students listed in the booklets, I obtained students’ email addresses from the directory of their university websites (many universities allow people to obtain students’ email address if they know the students’ full names and majors). An electronic invitation was then sent out to the individual students via email. 77 To enlarge the participant pool, a message was sent to all study participants, asking them to disseminate the invitation to their peers. This message was appended to the electronic compensation that was sent out to participants once they completed the questionnaire. Through these recruitment processes, the survey invitation was widely disseminated and a desirable sample size was effectively attained. Data Storage and Confidentiality Participants’ anonymity was assured at the outset of the survey. To ensure the anonymity and confidentiality of the participants, participants’ responses were automatically sent to a secure database at surveymonkey.com upon completion of the survey where the researcher was not able to trace or identify the participants. Study participants were asked to leave their email addresses solely for the purpose of receiving compensation (i.e., the electronic gift certificate), but their email addresses and responses to the questionnaires were never linked. The SurveyMonkey platform did not identify participants beyond the demographic information that was provided in the survey, nor did it directly identify participants via electronic means, such as through computer IP addresses 1 . All data were stored in a computer, with a password-protected hard drive within a locked office. Variables Ten sets of variables were used to explore the relationships between music students’ small music ensemble experiences and their empathy and emotional self-regulation skills. Table 1 However, SurveyMonkey does use de-identified IP address data to prevent the same participant from completing the survey more than once. 78 3.4 presents details of the variables used in this study. These variables can be largely categorized into three main groups: control variables, predictor variables, and criterion variables. Control variables consisted of personal factors, including gender, ethnicity, primary instrument, primary area of study, age at commencement of music training, and personality traits measured by the TIPI. All variables except for personality were categorical, consisting of two or more independent groups. Predictor variables consisted largely of attitudes toward SE and participation in SE. Attitudes toward SE referred to scores from the attitudinal scale that I designed. Participation in SE comprised of five specific variables, (a) participation in formal SE and (b) participation in informal SE in the college years, along with SE participation prior to college, specifically (c) in the elementary school years, (d) in the junior high school years, and (e) in the high school years. Lastly, criterion variables included empathy and emotional self-regulation measured by EQ and ERQ, respectively. Since the emotional self-regulation variable consisted of two sub- scales, cognitive reappraisal and expressive suppression, the criterion variables yielded a total of three separate variables. In sum, variables used in this study were six control variables (gender, ethnicity, primary instrument, primary area of study, age at commencement of music training, and personality traits), two predictor variables (attitudes toward SE, participation in SE), and three criterion variables (empathy, emotional self-regulation: cognitive reappraisal and expressive suppression). 79 Table 3.4. Variables used in the Current Study Control Variables Gender Female Male Other Ethnicity African American Asian Hispanic Caucasian Other Primary instrument Keyboard String Woodwind Brass Percussion Voice Primary area of study Classical Non-classical (i.e., jazz, popular music) Both classical and non-classical Age at commencement of music training Under 5 5 – 9 10 – 14 15 and over Personality traits Extraversion Agreeableness Conscientiousness Emotional Stability Openness to Experience Predictor Variables Attitudes toward SE Participation in SE Formal SE in the college years Never Rarely Sometimes Often Informal SE in the college years Never Seldom Infrequently Occasionally Frequently in the elementary school years in the junior high school years in the high school years Criterion Variables Empathy Emotional self-regulation Cognitive reappraisal Expressive suppression 80 Data Analysis Procedure The analysis of data occurred in two stages. First, a codebook was created to aid in assigning scores to participants’ responses and the data were then coded following the codebook. Next, data were analyzed using various descriptive and inferential statistics using Statistical Package for the Social Sciences (SPSS) Version 24. Data Scoring Background data, including gender, ethnicity, primary instrument, primary area of study, and age at commencement of music training, were categorically coded. The attitudinal scale for SE experiences was scored by averaging the responses to the 13 items. The final score ranged from 1 (most negative) to 5 (most positive). In terms of participation in SE, participation in formal SE was scored by averaging the responses to 3 items concerning the number of SE courses taken as well as the frequency of students’ participation in SE gigs and in concerts/recitals, competitions, masterclasses, and/or auditions as part of a SE over the course of their undergraduate studies. “1 (never)” indicated that no course was taken and that the student did not participate in gigs, concerts/recitals, competitions, masterclasses, and/or auditions during his or her entire undergraduate program. “2 (rarely)” indicated that one course was taken and that the student took part in gig one or two times, and in one concert/recital, competition, masterclass, or audition per academic year, on average. “3 (sometimes)” indicated that two courses were taken and that the student took part in gigs three or four times and in two concerts/recitals, competitions, masterclasses, auditions per academic year, on average. Lastly, “4 (often)” indicated that three or more courses were taken and that the student took part in gigs more than five times, and in three or more concerts/recitals, competitions, masterclasses, and/or auditions per academic year, on average. For participation in 81 informal SE, participants’ response to the item concerning how often they engaged in informal group music making during their undergraduate studies was scored with a possible range of 1 (never) to 5 (frequently). Scoring for the three standardized assessments followed the instructions provided by the authors for each assessment. In the TIPI (Gosling, Rentrow, Swann, 2003), each personality trait involved two questions, one positively and one negatively scored items (e.g., for Extraversion, “extraverted and enthusiastic” and “reserved and quiet”). Negatively scored items were reversely coded first and the scores were then averaged with scores for positively scored items to find a single composite score for each of the five traits. The highest score (7) resulted in a stronger response to a trait and the lowest score (1) indicated the weakest response to a trait. Each item for the EQ was rated on a 4-point Likert scale (Baron-Cohen & Wheelwright, 2004). “Strongly agree/disagree” responses scored 2 points and “slightly agree/disagree” responses scored 1 point. Individual participant’s responses to all items were summed following the instruction and the final score ranged from 0 (being the least empathetic possible) to 80 (being the most empathetic possible). Lastly, the ERQ consisted of 10 items (Gross & John, 2003), 6 items concerning cognitive reappraisal and the remaining items related to expressive suppression. Individual participant’s responses to these items were averaged separately with a possible score raning from 1 (the weakest response to each facet) to 7 (the strongest response to each facet). Statistical Analyses Prior to addressing the research questions, four sets of variables were first analyzed descriptively to determine meaningful patterns emerging from the data. These sets of variables included (a) participation in SE, (b) attitudes toward SE, (c) empathy, and (d) emotional self- 82 regulation, which were the primary interest of this study. Inferential statistics, such as one-way analyses of variance (ANOVAs), and Pearson’s product-moment correlations, were also performed when appropriate to determine whether there were statistically significant differences between the means of the different groups and to understand if there were linear relationships among different variables. To answer the first research question, a Pearson’s product-moment correlation was first utilized to determine the relationship between attitude toward SE, empathy, and emotional self- regulation skills. Cohen (1988)’s guideline was used in the interpretation of correlation results: correlations of .10 to .30 are considered small, between .30 and .50 as a moderate relationship, and .50 and greater considered to be strong. Since variables for participation in SE were categorical, their relationships with empathy skill and emotional self-regulation skills were analyzed using an independent sample t-test and one-way ANOVA. All assumptions, including outlier, normal distribution, homogeneity of variances, were tested prior to the analysis of the data. The second research question was investigated as a preliminary step towards the answer to research question 3, which was the primary interest of this study. A series of regression procedures were performed to gain an understanding of the existing relationships between personal factors and empathy and emotional self-regulation skills. A series of dummy variable regression and linear regression analyses were performed to answer the research question. Finally, a series of hierarchical multiple regressions were conducted to answer the third research question. Here, the predictor variables were participation in and attitudes toward SE and the criterion variables were empathy and two emotional self-regulation skill subscales – cognitive reappraisal and expressive suppression. In order to retain statistical power, the number 83 of variables was limited by selectively entering control variables in the regression models. Only variables shown to have significant bivariate relationships with each criterion variable in the analysis of research question 2 were entered as covariates in the regression analyses. To control the effects of personal factors in predicting participants’ empathy and emotional self-regulation skills, control variables were entered first into the model with predictor variables being added in subsequent models (Hoffman & Shafer, 2015). Prior to the regression analyses, all assumptions for multiple regression, including linearity, independence of residuals, homoscedasticity of residuals, multicollinearity, normality, outliers, were also tested. Chapter Summary This chapter offered an overview of the methodology, including a description of study participants, the instrument, and data collection and analysis procedure. Undergraduate music performance majors in their senior year (N = 165) voluntarily completed the SEESEC, which involved questions about their background and small ensemble experiences, and self-assessment questionnaires that measured their personality traits, dispositional empathy levels as well as tendency to regulate their emotions using cognitive reappraisal and expressive suppression. Various statistical tests and procedures, such as Pearson’s product-moment correlations, one-way analyses of variance, and multiple regression analyses, were utilized to analyze these data. Results of the data analysis are presented in in the next chapter. 84 CHAPTER FOUR: RESULTS This chapter presents the statistical analysis of the data collected for this study. The chapter begins with descriptive analyses of four sets of variables: (a) levels of participation in SE, (b) attitudes toward SE, (c) empathy, and (d) emotional self-regulation. This section primarily provides a descriptive overview of these variables by personal factors. Next, results for each research question are presented. Results contained in this section are organized by research question. Pearson’s product-moment correlations and a series of multiple regressions were primarily used to answer the research questions. Descriptive Analysis Participation in Small Ensemble Participation in SE consisted of five specific variables: (a) participation in formal SE and (b) participation in informal SE in the college years, along with SE participations prior to college, specifically (c) in the elementary school years, (d) in the junior high school years, and (e) in the high school years. Table 4.1 presents the distribution of participants by participation in formal SE in the college years and personal factors. Over 90% of participants had taken at least one SE course and had played at least one or two SE gigs as well as at least one SE concert/recital, competition, masterclass, or audition, on average, in each academic year. Examining the distribution by each personal factor separately, a general trend was found that the largest number of participants in most groups fell into either “rarely” or “sometimes.” Exceptions were African-Americans, non-classical music majors, and students who commenced music training before the age of five, who marked the largest number of “often” responses for small ensemble participation. Particularly, the non-classical music group displayed the highest level of 85 participation in formal SE across the whole sample, with over 90% of participants responding that they had taken over two SE courses and had done over three to four SE gigs as well as over two SE concerts/recitals, competitions, masterclasses, or auditions, on average, in each academic year. Table 4.1. Participation in Formal Small Ensemble in the College Years by Personal Factors Never Rarely Sometimes Often n % n % n % n % Gender Male 0 0 17 23.6 29 40.3 26 36.1 Female 2 2.2 31 34.4 39 43.3 18 20.0 Other 0 0.0 1 33.3 1 33.3 1 33.3 Ethnicity African American 0 0.0 3 30.0 3 30.0 4 40.0 Asian 1 2.0 19 37.3 18 35.3 13 25.5 Hispanic 0 0.0 2 11.8 10 58.8 5 29.4 Caucasian 0 0.0 24 28.9 37 44.6 22 26.5 Other 1 25.0 1 25.0 1 25.0 1 25.0 Primary instrument Keyboards 1 4.0 8 32.0 10 40.0 6 24.0 Strings 1 2.2 11 24.4 18 40.0 15 33.3 Woodwinds 0 0.0 8 25.8 14 45.2 9 29.0 Brass 0 0.0 6 24.0 11 44.0 8 32.0 Percussions 0 0.0 5 62.5 2 25.0 1 12.5 Voice 0 0.0 11 35.5 14 45.2 6 19.4 Primary Area of Study Classical 2 1.6 43 33.6 56 43.8 27 21.1 Non-classical 0 0.0 2 9.5 5 23.8 14 66.7 Both 0 0.0 4 25.0 8 50.0 4 25.0 Age at Commence- ment of Music Training Under 5 1 3.0 7 21.2 11 33.3 14 42.4 5 – 9 1 1.1 23 26.4 40 46.0 23 26.4 10 – 14 0 0.0 16 45.7 12 34.3 7 20.0 15 and Over 0 0.0 3 30.0 6 60.0 1 10.0 Note. Never = no SE course, no SE gig, no SE concert, competition, masterclass, or audition; Rarely = 1 SE course, 1-2 SE gigs, 1 SE concert, competition, masterclass, or audition; Sometimes = 2 SE courses, 3-4 SE gigs, 2 SE concerts, competitions, masterclasses, or auditions; Often = over 3 SE courses, over 5 SE gigs, over 3 SE concerts, competitions, masterclasses, or auditions. 86 Similarly, the distribution of participation in informal SE (see Table 4.2) indicated that the largest number of participants in most groups had “occasionally” engaged in informal group music making over the course of their undergraduate studies. Meanwhile, males (38.9%), African Americans (50%), brass majors (44%), and non-classical music majors (71.4%) reported that they had “frequently” engaged in informal group music making. Interestingly, the non- classical music group marked the highest level of participation in informal SE with nearly 90% of participants responding that they had engaged in informal group music making either “occasionally” or “frequently.” Considering that the non-classical music group ranked the highest in level of participation in both formal and informal SE, it can be assumed that non- classical music majors tended to have more opportunities to engage in SE activities in and out of their college curricula. 87 Table 4.2. Distribution of Participation in Informal Small Ensemble in the College Years by Demographic Factors Never Seldom Infrequently Occasionally Frequently n % n % n % n % n % Gender Male 1 1.4 16 22.2 13 18.1 14 19.4 28 38.9 Female 8 8.9 17 18.9 12 13.3 37 41.1 16 17.8 Other 0 0.0 0 0.0 2 66.7 0 0.0 1 33.3 Ethnicity African American 0 0.0 1 10.0 2 20.0 2 20.0 5 50.0 Asian 2 3.9 14 27.5 10 19.6 15 29.4 10 19.6 Hispanic 0 0.0 3 17.6 4 23.5 4 23.5 6 35.3 Caucasian 7 8.4 14 16.9 10 12.0 29 34.9 23 27.7 Other 0 0.0 1 25.0 1 25.0 1 25.0 1 25.0 Primary instrument Keyboards 2 8.0 3 12.0 7 28.0 7 28.0 6 24.0 Strings 2 4.4 9 20.0 7 15.6 13 28.9 14 31.1 Woodwinds 2 6.5 7 22.6 5 16.1 12 38.7 5 16.1 Brass 0 0.0 4 16.0 6 24.0 4 16.0 11 44.0 Percussions 0 0.0 3 37.5 0 0.0 3 37.5 2 25.0 Voice 3 9.7 7 22.6 2 6.5 12 38.7 7 22.6 Primary Area of Study Classical 9 7.0 28 21.9 22 17.2 43 33.6 26 20.3 Non-classical 0 0.0 2 9.5 1 4.8 4 19.0 15 71.4 Both 0 0.0 3 18.8 4 25.0 4 25.0 5 31.3 Age at Commencement of Music Training Under 5 1 3.0 6 18.2 4 12.1 12 36.4 10 30.3 5 – 9 7 8.0 17 19.5 15 17.2 28 32.2 20 23.0 10 – 14 1 2.9 7 20.0 6 17.1 10 28.6 11 31.4 15 and Over 0 0.0 3 30.0 2 20.0 1 10.0 4 40.0 88 Table 4.3 presents the number of participants who engaged in SE activities prior to college by personal factors. Across the entire study population, 29.1% of the participants (n = 48) reported that they had engaged in SE activities in their elementary school years, and 47.9% (n = 79) and 71.5% (n = 118) of the participants did so in their junior high and high school years, respectively. In terms of participation in SE activities during elementary and junior high school years, a relatively higher number of participants was found among African Americans (40% in elementary, 60% in junior high), string majors (35%, 60%), non-classical music majors (42.9%, 71.4%), and those who commenced music training before the age of five (57.6%, 72.1%). While over 50% of participants in most group reported that they engaged in some type of SE activity in high school years, a relatively higher number of participants was found among Caucasians (83.1%), woodwind majors (90.3%), non-classical music majors (90.5%), and those who commenced music training before the age of five (81.8%). 89 Table 4.3 Percentage of Participants Who Had Engaged in Small Ensemble Before College by Personal Factors Elementary (n = 48) Junior High (n = 79) High (n = 118) n % n % n % Gender Male 21 29.2 35 48.6 53 73.6 Female 25 27.8 42 46.7 64 71.1 Other 2 66.7 2 66.7 1 33.3 Ethnicity African American 4 40.0 6 60.0 7 70.0 Asian 15 29.4 20 39.2 29 56.9 Hispanic 7 41.2 7 41.2 11 64.7 Caucasian 22 26.5 43 51.8 69 83.1 Other 0 0.0 3 75.0 2 50.0 Primary instrument Keyboards 7 28.0 8 32.0 16 64.0 Strings 16 35.6 27 60.0 34 75.6 Woodwinds 5 16.1 14 45.2 28 90.3 Brass 9 36.0 12 48.0 17 68.0 Percussions 2 25.0 5 62.5 5 62.5 Voice 9 29.0 13 41.9 18 58.1 Primary Area of Study Classical 37 28.9 56 43.8 87 68.0 Non-classical 9 42.9 15 71.4 19 90.5 Both 2 12.5 8 50.0 12 75.0 Age at Commence- ment of Music Training Under 5 19 57.6 24 72.7 27 81.8 5 – 9 21 24.1 37 42.5 60 69.0 10 – 14 6 17.1 16 45.7 25 71.4 15 and Over 2 20.0 2 20.0 6 60.0 Table 4.4 presents descriptive statistics for personality traits by participation in SE in the college years. One-way ANOVAs were utilized to determine whether the mean scores for each personality trait were significantly different among groups of participants with different levels of SE participation. Although mean scores for each personality trait varied among groups with different levels of formal SE participation, group differences were not statistically significant, 90 Extraversion: F(3, 161) = 2.28, p = .063; Agreeableness: F(3, 161) = 1.93, p = .126; Conscientiousness, F(3, 161) = 0.56, p = .640; Emotional Stability: F(3, 161) = 1.53, p = .207; Openness to Experience: F(3, 161) = 0.41, p = .746. Also, no significant group differences were found among participants with different levels of informal SE participation, Extraversion: F(4, 160) = 1.77, p = .137; Agreeableness: F(4, 160) = 0.28, p = .890; Conscientiousness, F(4, 160) = 0.47, p = .756; Emotional Stability: F(4, 160) = 1.05, p = .385; Openness to Experience: F(4, 160) = 1.89, p = .114. 91 Table 4.4. Descriptive Statistics for Participation in Small Ensemble and the Big Five Personality Traits Big Five Personality Traits Extraversion Agreeableness Conscientiousness Emotional Stability Openness to Experience M SD M SD M SD M SD M SD Overall 4.23 1.54 4.83 1.25 5.49 1.14 4.64 1.21 5.41 1.17 Participation in Formal SE Never 3.00 1.41 6.25 1.06 5.00 2.12 5.50 2.12 6.00 1.41 Rarely 4.27 1.44 4.71 1.19 5.64 1.03 4.43 1.26 5.33 1.18 Sometimes 3.93 1.44 5.01 1.16 5.48 1.08 4.83 1.02 5.37 1.20 Often 4.70 1.69 4.62 1.41 5.38 1.30 4.54 1.36 5.52 1.14 Participation in Informal SE Never 4.61 1.40 4.89 1.11 5.72 .97 4.44 1.36 5.00 1.03 Seldom 4.12 1.76 4.83 1.40 5.60 1.16 4.89 1.24 4.98 1.25 Infrequently 3.74 1.45 4.22 1.21 5.26 1.10 4.89 .97 5.52 1.16 Occasionally 4.13 1.55 4.97 1.11 5.47 1.16 4.46 1.12 5.50 1.11 Frequently 4.64 1.35 4.74 1.38 5.53 1.17 4.56 1.37 5.62 1.17 92 Attitudes Toward Small Ensemble Participants’ attitudes toward SE were measured by an attitudinal scale designed for this study. Internal consistency was checked using Cronbach’s alpha, obtaining a value of 0.87. This value is considered to be high, indicating good internal consistency (Kline, 2011). A factor analysis extracted three factors with an eigenvalue greater than 1. Examination of the items in each factor suggested that these were not meaningful clusters and, given the high Cronbach’s alpha value, it was thought more appropriate to analyze the attitudinal scale as a single scale without any specific subscales. While the attitudinal scale yielded scores with a range between 1 and 5, the mean score across all participants was 3.95 (SD = 0.59), with the distribution of scores appearing to approximate to a normal distribution (skewness = -0.64; kurtosis = 0.28). Table 4.5 presents descriptive statistics for participants’ attitude scores by personal factors. 93 Table 4.5. Descriptive Statistics for Attitudes Toward Small Ensemble and Personal Factors M SD Skewness Kurtosis Overall 3.95 .59 -0.64 0.28 Gender Male 3.89 .65 -0.61 0.28 Female 3.99 .54 -0.57 0.20 Other 4.04 .47 -1.60 1.23 Ethnicity African American 3.98 .47 -0.66 -1.19 Asian 3.85 .62 -0.78 1.18 Hispanic 4.07 .58 -0.39 0.31 Caucasian 4.00 .58 -0.64 -0.21 Other 3.59 .68 0.77 1.22 Primary Instrument Keyboards 3.93 .51 0.09 -0.53 Strings 4.01 .42 -0.21 -0.87 Woodwinds 3.90 .60 -0.46 0.00 Brass 3.82 .86 -0.58 -0.73 Percussions 4.28 .48 -1.68 3.14 Voice 3.96 .59 -0.44 -0.25 Primary Area of Study Classical 3.95 .60 -0.64 0.40 Non-classical 4.10 .46 -1.37 1.77 Both 3.73 .52 0.00 -1.66 Age at Commence- ment of Music Training Under 5 4.06 .38 0.00 -0.51 5 – 9 3.96 .59 -0.76 0.43 10 – 14 3.87 .70 -0.18 -0.59 15 and Over 3.78 .69 -0.86 0.07 One-way ANOVAs were performed to determine whether mean scores for the attitudinal scale was statistically different among each personal factor group. The results indicated no significant differences by gender: F(22, 142) = 0.66, p = .875; ethnicity: F(22, 142) = 1.16, p = .293; primary instrument: F(22, 142) = 0.75, p = .786; primary area of study: F(22, 142) = 1.24, p = .226; age at commencement of music training: F(22, 142) = 1.00, p = .470. 94 In order to examine the relationships between attitudes toward SE and each of the Big Five personality traits, Pearson’s product-moment correlations were used (see Table 4.6). There was a small but significant positive relationship between attitudes toward SE and Agreeableness (r = .181, p = .020), as well as a moderate positive correlation between attitudes toward SE and Openness to Experience (r = .317, p < .001). These findings suggest that agreeable individuals, who are usually trusting, generous, sympathetic, and cooperative (see Gosling, Rentfrow, & Swann Jr., 2003), and that those who are open to experience—are curious, reflective, creative, and open-minded—tend to have a more positive attitude toward SE. Table 4.6. Correlations Among the Big Five Personality Traits and Attitudes Toward Small Ensemble Extraversion Agreeableness Conscientious- ness Emotional Stability Openness to Experience Attitudes toward SE -.023 .181* .148 -.008 .317*** Note. * p < .05. ** p < .01. *** p < .001. Empathy Participants’ levels of empathy were measured using the EQ (Baron-Cohen & Wheelwright, 2004). Descriptive statistics for EQ scores by personal factors are presented in Table 4.7. The mean scores for the EQ across the entire sample was 43.65 (SD = 14.4). Compared with Wheelwright et al.’s study (2006) of college students (N = 1,761, M age = 21.0, SD = 2.58), the mean score of music performance majors in the current study appeared to be close to that of students majoring in other domains of study (M = 44.3, SD = 12.2). However, when the factors of gender and study domain were considered together, female participants in the current study scored relatively lower than female students in other domains of study (physical science = 44.7; biological science = 48.5; social science = 48.7; humanities = 48.7), while male 95 participants scored similarly (physical science = 35.9; biological science = 41.6; social science = 41,4; humanities = 40.5). Table 4.7. Descriptive Statistics for EQ Scores by Personal Factors M SD Skewness Kurtosis Overall 43.65 14.40 -0.05 0.48 Gender Male 41.78 14.90 0.18 -0.58 Female 45.70 13.11 -0.02 -0.39 Other 27.00 26.89 1.71 - Ethnicity African American 51.30 16.20 -0.08 -.1.56 Asian 38.69 15.13 0.30 -0.55 Hispanic 44.06 12.95 0.26 -0.52 Caucasian 45.85 13.33 -0.29 0.16 Other 40.50 14.43 0.00 0.91 Primary Instrument Keyboards 41.44 16.43 -0.11 -0.18 Strings 43.40 12.76 0.12 0.15 Woodwinds 43.87 14.03 0.09 -0.99 Brass 42.12 16.65 0.32 -0.92 Percussions 43.13 10.40 -0.03 -1.92 Voice 46.94 14.72 -0.55 -0.06 Primary Area of Study Classical 42.78 12.87 -0.15 -0.29 Non-classical 51.57 18.65 -0.64 -0.71 Both 40.19 17.07 0.29 -0.70 Age at Commence- ment of Music Training Under 5 49.48 14.47 -0.08 0.01 5 – 9 42.40 13.63 -0.17 -0.54 10 – 14 40.77 14.15 0.03 -0.48 15 and Over 45.30 18.00 0.00 -1.49 Emotional Self-Regulation Participants’ scores on two subscales of emotional self-regulation, CR and ES, were measured using the ERQ (Gross & John, 2003). Mean scores for the ERQ subscales across all participants were 4.84 (SD = 1.07) for CR and 3.70 (SD = 1.31) for ES (See Table 4.8). 96 Compared with Gross and John’s (2003) study of college students (N = 1,483), both male and female participants in the current study scored slightly higher in both the CR and ES subscales [in Gross and John (2003)’s study, male college students’ mean scores were 4.60 (SD = 0.94) for CR and 3.64 (SD = 1.11) for ES, and female college students’ mean scores were 4.61 (SD = 1.02) for CR and 3.14 (SD = 1.18) for ES]. Another research study conducted with female college students (N = 301) reported mean scores of 4.53 (SD = 1.01) for CR and 3.50 (SD = 1.18) for ES (Eftekhari, Zoellner, & Vigil, 2009), which also suggests a relatively higher mean scores for female participants in the current study. 97 Table 4.8. Descriptive Statistics for the ERQ and Demographic Factors ERQ-CR ERQ-ES M SD Skewness Kurtosis M SD Skewness Kurtosis Overall 4.84 1.07 -0.53 0.38 3.71 1.31 0.70 -0.67 Gender Male 4.70 1.21 -0.68 1.55 3.89 1.29 0.27 -0.60 Female 4.97 1.01 -0.34 -0.40 3.54 1.32 -0.20 -0.50 Other 4.61 1.42 -1.49 - 4.25 1.52 1.68 - Ethnicity African American 4.92 0.85 -1.33 2.21 3.65 1.48 -0.02 -1.67 Asian 4.77 0.99 -0.26 -0.47 4.11 1.24 -0.15 -0.49 Hispanic 4.98 1.06 -0.57 1.36 4.13 1.40 0.21 -0.49 Caucasian 4.87 1.51 -0.64 0.71 3.40 1.25 0.23 -0.63 Other 4.50 1.38 -0.15 -4.61 3.19 1.46 -1.96 3.86 Primary Instrument Keyboards 5.04 0.84 -0.11 -0.18 4.28 1.18 -0.52 -0.17 Strings 4.61 1.07 0.12 0.15 3.81 1.37 -0.03 -0.49 Woodwinds 4.84 1.15 0.09 -0.99 3.62 1.32 0.28 -0.55 Brass 4.80 0.92 0.32 -0.92 3.74 1.37 -0.05 -0.11 Percussions 4.96 1.09 -0.03 -1.92 3.84 1.43 0.08 -2.14 Voice 5.02 1.27 -0.55 -0.06 3.12 1.09 0.45 -0.05 Primary Area of Study Classical 4.86 1.09 -0.49 0.37 3.73 1.30 -0.15 -0.29 Non-classical 4.86 1.01 -0.83 0.99 3.49 1.54 -0.64 -0.71 Both 4.69 1.02 -0.76 0.75 3.78 1.21 0.29 -0.70 Age at Commence- ment Under 5 4.65 1.26 -0.08 0.01 3.84 1.33 0.33 -0.16 5 – 9 4.84 1.01 -0.17 -0.54 3.69 1.26 0.07 -0.74 10 – 14 4.98 1.05 0.03 -0.48 3.81 1.42 -0.18 -0.77 15 and Over 5.07 1.07 0.00 -1.50 3.08 1.30 0.20 -1.42 98 Additionally, a Pearson’s product-moment correlation assessed the relationships between the EQ and ERQ subscales and each of the personality traits (see Table 4.9). While previous research has shown a mixed pattern of findings regarding the relationships between the Big Five personality traits and empathy, significant correlations were detected between the EQ and most personality traits. Specifically, there were moderate positive correlations between the EQ and Extraversion, r = 0.311 (p < .001) as well as Openness to Experience, r = 0.446 (p < .001). Also, small correlations were found between the EQ and Agreeableness, r = 0.232 (p = .003), and Conscientiousness, r = 0.238 (p = .002). No significant relationship was found between the EQ and Emotional Stability. These results suggest that music students who are more extraverted, agreeable, conscientious, and open to experience tend to be more empathetic. In terms of the relationships between the Big Five personality traits and the ERQ subscales, several correlations—positive and negative—were found. Consistent with previous research (Gross & John, 2003; John & Gross, 2004), Emotional Stability was positively correlated with CR (r = 0.364, p < .001) and Extraversion was negatively correlated with ES (r = -0.489, p < .001). Additionally, CR had small but significant correlations with Agreeableness (r = 0.262, p = .001), Conscientiousness (r = 0.222, p = .004), and Openness to Experience (r = 0.171, p = .028) while ES was correlated with Emotional Stability (r = 0.215, p = .006) and negatively correlated with Openness to Experience (r = -0.157, p = .044). These findings suggest that music students who are more agreeable, conscientious, open to experience, and emotionally stable tend to use the cognitive reappraisal strategy more often in everyday life. They further suggest that music students, who are less extroverted and open to experience, as well as those who are emotionally more stable, tend to use the expressive suppression strategy to regulate their emotions more often. 99 Table 4.9. Correlations Among EQ, ERQ, and Big Five Personality Traits Extraversion Agreeableness Conscientious- ness Emotional Stability Openness to Experience EQ .311*** .232** .238** -.093 .446*** ERQ-CR .064 .262** .222** .364*** .171* ERQ-ES -.489*** -.059 -.042 .215** -.157* Note. * p < .05. ** p < .01. *** p < .001. In sum, while descriptive statistics and analyses showed some meaningful patterns among variables, the personality traits indicated multiple correlations with various variables. Specifically, two personality traits, Agreeableness and Openness to Experience, had small to moderate correlations with participants’ attitudes toward SE. Also, participants’ levels of empathy were positively correlated with Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. In addition, the subscales of the emotional self-regulation showed some correlations with personality traits: CR positively correlated with Agreeableness, Conscientiousness, Emotional Stability, and ES negatively correlated with Extraversion as well as positively correlated with Emotional Stability and Openness to Experience. Next, results for the three research questions are presented. Research Question One To answer the first research question, What are the relationships among music students’ participation in SE, attitudes toward SE, empathy, and emotional self-regulation skills?, a series of statistical procedures were utilized. First, a Pearson’s product-moment correlation was conducted to determine the strength and direction of the relationships between attitude toward 100 SE and the EQ and ERQ subscales. Since variables for participation in SE were categorical, they were not included in this analysis. Significant correlations were detected among these variables (see Table 4.10). For example, small but significant correlations were found between attitudes toward SE and EQ, r = .271, p < .001, as well as ERQ-CR, r = .166, p = .033. This suggests that students who have more positive attitudes toward SE tend to be more empathetic and tend to use cognitive reappraisal more often as a way to regulate their emotions. In addition, EQ was positively correlated with ERQ-CR (r = .186, p = .017) and also negatively correlated to ERQ-ES (r = -.367, p < .001). This hints to the idea that people who are more empathetic tend to use cognitive reappraisal more often and expressive suppression less often as their emotion regulation strategies. Consistent with previous literature, no significant correlations were detected between ERQ-CR and ERQ- ES, suggesting an independent relationship between these two emotion regulation strategies (Gross & John, 2003; Moore, Zoellner, & Mollenholt, 2008). Table 4.10. Correlations Among Attitudes Toward Small Ensemble, EQ, and ERQ EQ ERQ-CR ERQ-ES Attitudes toward SE .271*** .166* .063 EQ .186* -.367*** ERQ-CR .098 Note. * p < .05. ** p < .01. *** p < .001. Next, a one-way ANOVA and an independent sample t-test were conducted to determine whether mean scores for the attitudinal scale differed among groups with different levels of participation in SE. Table 4.11 presents descriptive statistics for attitudes toward SE and participation in SE. 101 Table 4.11. Descriptive Statistics for Attitudes Toward Small Ensemble and Participation in Small Ensemble M SD Skewness Kurtosis Formal SE Never 4.19 .44 - - Rarely 3.75 .69 -.44 -.18 Sometimes 3.95 .56 -.28 -.28 Often 4.15 .44 -.84 .89 Informal SE Never 3.58 .51 -.30 .68 Seldom 3.82 .69 -.86 .39 Infrequently 3.78 .61 -.49 .15 Occasionally 3.95 .55 -.04 -.52 Frequently 4.22 .45 -.89 .81 SE before College Elementary (yes) 3.97 .64 -1.19 1.53 (no) 3.94 .57 -.35 -.39 Junior High (yes) 4.07 .55 -.57 -.28 (no) 3.83 .61 -.66 .47 High School (yes) 4.03 .54 -.50 -.16 (no) 3.74 .65 -.66 .25 Participants’ attitudes toward SE were significantly different among groups of participants with different levels of formal SE participation, F(3, 161) = 3.99, p = .009 (see Table 4.12). Tukey’s HSD procedures indicated that participants in the “often” group scored significantly higher in the attitude scale than those in the “rarely” group (0.40, 95% CI [0.10 to 0.71], p = .002) and “sometimes” group (0.20, 95% CI [0.09 to 0.71], p = .005). Table 4.12. One-Way ANOVA: Attitudes Toward Small Ensemble by Participation in Formal Small Ensemble df SS MS F P Between Groups 3 3.93 1.31 3.99 .009 Within Groups 161 52.79 0.33 Total 164 56.72 102 Furthermore, a significant group difference was found among groups of participants with different levels of participation in informal SE, F(4, 160) = 4.63, p = .001 (see Table 4.13). Tukey’s HSD procedures showed that participants who were in the “frequently” group scored significantly higher than the “never” group (0.64, 95% CI [0.07 to 1.21], p = .019), the “seldom” group (0.40, 95% CI [0.04 to 0.76], p = .047), and the “infrequently” group (0.44, 95% CI [0.07 to 0.82], p = .031). A trend toward significance was also found between the “occasionally” group and “frequently” group, 0.28, 95% CI [0.26 to 0.58], p = .069. Table 4.13. One-Way ANOVA: Attitudes Toward Small Ensemble by Participation in Informal Small Ensemble df SS MS F P Between Groups 4 5.89 1.47 4.63 .001 Within Groups 160 50.83 0.32 Total 164 56.72 Additional independent-sample t-tests were performed to determine if there were differences in the attitude scores between groups of participants who had engaged in SE prior to their college years and those who had not. Scores were higher for those who had engaged in SE in the junior high years (M = 4.07, SD = 0.55) than for those who had not (M = 3.83, SD = 0.61), which was a statistically significant difference, M = 0.24, 95% CI [0.06, 0.42], t(163) = 2.67, p = .008. In addition, students who engaged in SE during the high school years (M = 4.03, SD = 0.54) also scored higher than those who had not (M = 3.74, SD = 0.65), which was a statistically significant difference, M = 0.29, 95% CI [0.09, 0.49], t(163) = 2.92, p = .004. No significant difference was found in participation in SE during the elementary school years, M = 0.03, 95% CI [-0.17, 0.23], t(163) = 0.31, p = .811. 103 Research Question Two The next research question, To what extent do personal factors, including gender, ethnicity, primary instrument, primary area of study, age at commencement of music training, and Big Five personality traits, contribute to music students’ empathy and emotional self- regulation skills?, was examined as a preliminary step towards addressing research question 3, which was the primary interest of this study. A series of regression procedures were performed in order to gain an understanding of the existing relationships between personal factors and empathy and emotional self-regulation skills. Categorical variables (gender, ethnicity, personality, primary instrument, primary area of study, age at commencement of music training) were dummy coded prior to entering them into the regression analyses (Vogt, 2007). As a first step, a series of dummy variable regression analyses examined to what extent each personal factor predicts music students’ scores on the EQ and ERQ subscales. Next, a series of linear regression analyses investigated if the Big Five personality traits can predict music students’ EQ and ERQ scores. Gender Dummy variables were created which dichotomized gender into male (coded “0”) and female (coded “1”) groups. To ease the interpretation, three participants who marked ‘Other’ were excluded in this analysis, yielding a sample size of 162. The relationships between gender and EQ as well as ERQ-ES did not reach significance (p = .077 and p = .083, respectively). Compared to male students, female students scored slightly higher in EQ (t = 1.78, p = .077). In the case of the ERQ-ES, female students scored slightly lower than male students (t = 1.75, p = .083) (see Table 4.14). Although extensive literature, including neurological studies, has shown distinct gender differences in empathy (Baron-Cohen & Wheelwright, 2004; Barrio, Aluja, & 104 Garcia, 2004; Kataoka et al., 2009; Toussaint & Webb, 2005; Wen et al., 2013) and expressive suppression (Flynn, Hollenstein, & Mackey, 2010; Gross & John, 2002; 2003; Haga, Kraft, & Corby, 2009; Kring & Gordon, 1998), the differences were not strong in this study population. Consistent with previous research (Gross & John, 2003; Haga, Kraft, & Corby, 2009), no significant relationship was found between gender and ERQ-CR, F(1, 160) = 2.37, p = .126. Table 4.14. Regression Analysis of Gender on EQ and ERQ-ES (N = 162) EQ ERQ-ES B SE B B SE B (Constant) 41.78*** 1.64 3.90*** .15 Female = 1 3.92 2.20 -.36 .20 Adjusted R 2 .013 .012 Note. *** p < .001. Ethnicity Ethnicity was coded into five dummy variables (African-American, Asian, Hispanic, Caucasian, and Other). A series of simultaneous multiple regression were performed, with each of the five variables being set as a comparison group (Vogt, 2007). Ethnicity significantly predicted scores in EQ, F(4, 160) = 2.88, p = .024, accounting for 6.7% of the variation, with adjusted R 2 = 4.4%, as well as ERQ-ES, F(4, 160) = 3.06, p = .018, accounting for 7.1% of the variation, with adjusted R 2 = 4.8%. However, ethnicity did not significantly predict scores in ERQ-CR, F(4, 160) = 0.25, p = .907. Asian students scored less than African-American students by 12.61points (t = 2.59, p = .010) and Caucasian students by 7.16 points (t = 2.86, p = .005) (see Table 4.15). These findings correspond with previous literature (e.g., Cassels, Chan, Chung, & Birch, 2010; Friedlmeier & Trommsdorff, 1999; Trommsdorff, Friedlmeier, & Mayer, 2007), which showed that Asian individuals tend to score lower in various empathy measures when 105 compared to other ethnicity groups. In terms of ERQ-ES, self-identifying as Caucasian led to a statistically significant decrease by 0.70 than self-identifying as Asian (t = 3.09, p = .002) and by 0.73 as Hispanic (t = 2.14, p = .034). Consistent with previous research (Butler et al., 2007; Gross & John, 2003; Soto et al., 2011), data from the current study suggests that Caucasian participants used expressive suppression strategies less often than other ethnic groups as a way to regulate their emotions in the everyday life. Table 4.15. Regression Analysis of Ethnicity on EQ and ERQ-CR (N = 165) EQ 1 ERQ-ES 2 B SE B B SE B (Constant) 38.69 1.97 (Constant) 3.40*** .14 African American 12.61** 4.87 African American .25 .43 Hispanic 5.37 3.94 Asian .70** .23 Caucasian 7.16** 2.50 Hispanic .73* .34 Other 1.81 7.31 Other -.22 .66 Adjusted R 2 .044 Adjusted R 2 .048 Note. 1 Asian is the excluded/comparison group; 2 Caucasian is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Primary Instrument Primary instrument was coded into six dummy variables: keyboards, strings, woodwinds, brass, percussion, and voice. A series of simultaneous multiple regression analyses were performed with each variable being set as a comparison group. Results showed that primary instrument significantly predicted scores in ERQ-ES, F(5, 159) = 2.39, p = .040, accounting for 7.0% of the variation, with adjusted R 2 = 4.1%, but not for scores in EQ and ERQ-CR (see Table 4.16). Voice majors scored 1.16 points less than keyboard majors (t = 3.36, p = .001) and 0.69 points less than string majors (t = 2.28, p = .024) in ERQ-ES. In addition, a trend toward 106 significance was found in the brass group, as brass majors outscored voice majors by 0.62 points (t = 1.79, p = .075) in ERQ-ES. Table 4.16. Regression Analysis of Primary Instrument on ERQ-ES (N = 165) ERQ-ES B SE B (Constant) 3.12*** .23 Keyboards 1.16** .35 Strings .69* .30 Woodwinds .50 .33 Brass .62 .35 Percussion .72 .51 Adjusted R 2 .041 Note. Voice is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Primary Area of Study Primary area of study was coded into three dummy variables (classical music, non- classical music, and both) and a series of simultaneous multiple regressions were performed with each variable being set as a comparison group. Regression results showed that primary area of study significantly predicted EQ, F(2, 162) = 4.02, p = .020, accounting for 4.7% of the variation, with adjusted R 2 = 3.6%, but not in any of the ERQ subscales (see Table 4.17). It was found that studying non-classical music led to a statistically significant increase in EQ by 8.79 points compared to studying classical music (t = -2.65, p = .009) and by 11.38 points compared to studying both classical and non-classical music (t = -2.43, p = .016). 107 Table 4.17. Regression Analysis of Primary Area of Study on EQ (N = 165) EQ B SE B (Constant) 42.78 1.25 Classical -8.79** 3.33 Both -11.38* 4.69 Adjusted R 2 .036 Note. Non-classical is the excluded/comparison group. * p < .05. ** p < .01. Age at Commencement of Music Training The age at commencement of music training was coded into four dummy variables: under 5, 5–9, 10–14, and 15 and over. A series of simultaneous multiple regressions were performed with each variable being set as a comparison group. The regression analysis showed a trend toward significance of age at commencement of music training in predicting EQ scores, adj. R 2 = .029, F(3, 161) = 2.61, p = .053, but it did not significantly predict any scores in the ERQ subscales (see Table 4.18). Students who began music training before the age of five outscored those who began between the ages of 5 and 9 by 7.08 points (t = 2.44, p = .016) and those who began between the ages of 10 and 14 by 8.71 points (t = -2.53, p = .012). 108 Table 4.18. Regression Analysis of Age at Commencement of Music Training on EQ EQ B SE B (Constant) 49.49*** 2.47 5 – 9 -7.08* 2.90 10 – 14 -8.71* 3.44 15 and Over -4.19 5.12 Adjusted R 2 .029 Note. Under 5 is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Big Five Personality Traits To examine the extent to which the Big Five personality traits predicted scores in the EQ and ERQ subscales, a simultaneous multiple regression was performed for each criterion. There was linearity as assessed by partial regression plots and a plot of studentized residuals against the predicted values. There was independence of residuals, as assessed by the Durbin-Watson statistic of 1.87 (EQ), 2.18 (ERQ-CR), and 1.81 (ERQ-ES). There was homoscedasticity, as assessed by visual inspection of a plot of studentized residuals versus unstandardized predicted values. There was no evidence of multicollinearity, as assessed by tolerance values greater than 0.1. Also, there were no studentized deleted residuals greater than ± 3 standard deviations, no leverage values greater than 0.2, and values for Cook’s distance above 1. The assumption of normality was met, as assessed by a Q-Q Plot. Results are displayed in Table 4.19. Regression analysis showed the Big Five personality traits to account for significant variance in all criterion variables, specifically, 33.1% in EQ scores, adj. R 2 = .31, F(5, 159) = 15.74, p < .001; 19.7% in ERQ-CR scores, adj. R 2 = .17, F(5, 159) = 7.80, p < .001; and 30.4% in ERQ-ES scores, adj. R 2 = .28, F(5, 159) = 13.89, p < .001. 109 All personality traits significantly predicted the EQ, indicating that, for every one point increase in the TIPI score for each personality trait, EQ scores increased by 2.29 points (Extraversion: t = 3.70, p < .001); by 2.17 points (Agreeableness: t = 2.78, p = .006); by 1.80 points (Conscientiousness: t = 1.10, p = .037); and by 4.39 points (Openness to Experience: t = 5.22, p < .001) while decreasing by 2.29 points (Emotional Stability: t = -2.87, p = .005). These results suggest that music students who were more extraverted, agreeable, conscientious, and open to experience but less emotionally stable tended to score higher in the EQ. In terms of ERQ-CR, the unstandardized coefficients for Agreeableness, Conscientiousness, and Emotional Stability were 0.14, 0.12, and 0.27, respectively, suggesting that a one-point increase in each personality trait leads to an increase of ERQ-CR by 0.14 points (Agreeableness: t = 2.22, p = .028), 0.12 points (Conscientiousness: t = 1.98, p = .051), and 0.27 points (Emotional Stability: t = 4.11, p < .000) in ERQ-CR. These results suggest that music students who were more agreeable, conscientious, and emotionally stable tended to use the cognitive reappraisal strategy more often as a way to regulate their emotions in their everyday lives. In the analysis of ERQ-ES, Extraversion (B = -0.41, t = -7.05, p < .000) and Emotional Stability (B = 0.26, t = 3.53, p = .001) emerged as significant predictors, implying that students who were more introverted and emotionally stable showed a tendency to use the expressive suppression strategy more often. 110 Table 4.19. Regression Analysis of the Big Five Personality Traits and EQ, ERQ-CR, and ERQ-ES EQ ERQ-CR ERQ-ES B SE B B SE B B SE B (Constant) .53 6.89 1.75** .56 5.32*** .64 Extraversion 2.29*** .62 .04 .05 -.41*** .06 Agreeableness 2.17** .78 .14* .06 -.12 .07 Conscientiousness 1.80* .86 .12 .07 -.01 .08 Emotional Stability -2.29** .80 .27*** .07 .26** .07 Openness to Experience 4.39*** .84 .06 .07 -.09 .08 Adjusted R 2 .310 .172 .282 Note. * p < .05. ** p < .01. *** p < .001. Research Question Three As the primary interest of the current study, the last research question asked, To what extent music students’ participation in and attitudes toward SEs together contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factors? To address this question, an examination of the extent to which participants’ participation in SE as well as their attitudes toward SE predict their scores on the EQ and ERQ subscales was carried out. Thus, the predictor variables in this analysis were participation in SE—participation in formal SE and informal SE in the college years and SE participation in the elementary, junior high, and high school years— and attitudes toward SE. The criterion variables were scores for EQ, ERQ-CR, and ERQ-ES. Table 4.20 presents the descriptive statistics for participation in SE and the EQ and ERQ subscales. 111 Table 4.20. Descriptive Statistics for Participation in Small Ensemble and EQ/ERQ EQ ERQ-CR ERQ-ES M SD M SD M SD Formal SE 0 39.00 1.41 5.33 .70 3.88 .18 1 39.84 13.65 4.80 1.08 3.52 1.37 2 41.46 13.37 4.83 1.04 3.96 1.26 3 51.36 14.40 4.89 1.15 3.51 1.32 Informal SE 0 42.56 8.16 4.35 1.07 3.78 1.63 1 38.70 14.10 4.91 1.20 3.58 1.29 2 36.44 14.48 4.77 .96 4.06 1.32 3 44.70 12.71 4.83 1.08 3.75 1.14 4 50.62 14.37 4.95 1.04 3.52 1.44 SE before College Elementary (yes) 46.96 16.57 4.63 1.20 3.60 1.38 (no) 42.29 13.24 4.93 1.01 3.75 1.29 Junior High (yes) 48.32 13.99 4.99 1.06 3.64 1.34 (no) 39.36 13.46 4.71 1.05 3.77 1.29 High School (yes) 45.56 14.40 4.92 1.05 3.73 1.37 (no) 38.85 13.36 4.66 1.11 3.65 1.16 A series of hierarchical multiple regressions were conducted to determine the extent to which the predictor variables predict the EQ and ERQ subscales, after controlling for the effect of personal factors. Because sample size is important when analyzing power of effect among variables in a multiple regression analysis (Abu-Bader, Pryce, Shackelford, & Pryce, 2006), the number of variables was limited by selectively entering control variables in the regression models. That is, variables that showed significant bivariate relationships with each criterion variable in the analysis of research question 2 were entered as covariates. Therefore, ethnicity, primary area of study, age at commencement of music training, and all personality traits were included as control variables for EQ. For ERQ-CR, only three of the personality traits, Agreeableness, Conscientiousness, and Emotional Stability, were utilized as control variables. 112 Ethnicity, primary instrument, and the personality traits of Extraversion and Emotional Stability were included in the analysis of ERQ-ES. In order to control the effects of personal factors, they were entered into the model first. Predictor variables were added in subsequent models (Hoffman & Shafer, 2015). More specifically, personality traits were put into the regression model first because they were shown to have the greatest bivariate relationship with EQ (R 2 = .331, p < .001), as well as with the ERQ subscales (CR: R 2 = .197, p < .001; ES: R 2 = .304, p < .001). Next, selected control variables that reported associations with the criterion variable in the previous literature were added to model 2. In the following model, the remaining personal factors were entered. Finally, predictor variables were entered into the last model. Prior to the regression analyses, assumptions for multiple regression were tested. There was linearity as assessed by partial regression plots and a plot of studentized residuals against the predicted values. There was independence of residuals, as the value of the Durbin-Watson statistic were very close to 2 in all analyses. There was homoscedasticity, as assessed by visual inspection of a plot of studentized residuals versus unstandardized predicted values. There was no evidence of multicollinearity, as assessed by tolerance values greater than 0.1. The assumption of normality was met, as assessed by a Q-Q Plot. In terms of unusual cases, SPSS Casewise Diagnostics detected one outlier whose standardized residual was slightly greater than 3 standard deviations in the analyses of EQ; however, the case was kept in the final analysis, as the outlier did not seem to have an appreciable effect on the analysis when results of the regression analyses were compared with and without it. There were two cases in which leverage values were greater than 0.2, but these cases were also kept in the final analyses as they did not have an appreciable effect. There was no value for Cook’s distance above 1. 113 Empathy A hierarchical multiple regression determined the extent to which participation in and attitudes toward SE together predicted EQ scores, after controlling for the effect of personal factors. Table 4.21 summarizes the findings of the hierarchical regression. In light of the significant effect of all Big Five personality traits on EQ shown in the research question 2 (see Table 22), they were entered in model 1, which accounted for 33.1% of the variance in EQ, F(5, 159) = 15.74, p < .001, adj R 2 = .310. Music students would attain a 2.29 decrease in the EQ for every one point increase in Emotional Stability (t = -0.19, p = .005) while attaining 2.29, 2.17, 1.80, and 4.39 increases for every one-point increase in Extraversion (t = 0.24, p < .001), Agreeableness (t = 0.19, p = .006), Conscientiousness (t = 0.14, p = .037), and Openness of Experience (t = 0.36, p < .001), respectively. Ethnicity was then added in model 2, with Asian being the comparison group. While model 2 explained only 0.1% more of the variance in EQ, F(9, 155) = 9.23, p < .001, adj. R 2 = .311, no ethnicity variables showed significant B weights. All personality traits remained as significant predictors of EQ. In model 3, the addition of variables for primary area of study (non- classical music as the comparison group) and age at commencement of music training (before the age of five as the comparison group) led to a significant increase in R 2 of .078, F(14, 150) = 7.98, p < .001, adj. R 2 = .373. Significant effects of primary area of study and age of commencement of music training predicted that individuals who studied non-classical music outscored those who studied classical music by 7.97 points (t = -.23, p = .010) and those who studied both classical and non-classical music by 12.71 points (t = -.26, p = .002). Also, individuals who began music training before the age of five outscored those who began between the ages of 5 and 9 by 5.78 114 points (t = -.20, p = .018) and those who began between the ages of 10 and 14 by 6.61 points (t = -.19, p = .021). Yet, all variables for ethnicity remained as insignificant predictors. Finally, when levels of participation in SE and attitudes toward SE were entered in model 4, a significant amount (8.7%) of the variance was increased in predicting EQ, yielding R 2 = .514, F(25, 139) = 5.87, p < .001, adj. R 2 = .426. Interestingly, all personal factors, except for the Big Five personality traits, became insignificant predictors in this model. In terms of students’ SE experiences, although participants’ attitudes toward SE did not significantly predict EQ, several variables for students’ levels of participation in SE during the college years emerged as significant predictors of EQ. Regression results indicated that students who participated in formal SE “often” outscored those who “rarely” participated by 7.79 points (t = -2.76, p = .007) and those who “sometimes” participated by 5.91 points (t = -2.31, p = .022). In addition, it also predicted that students who participated in informal SE “frequently” outscored those who “infrequently” participated by 6.43 points (t = -2.02, p = .045). These findings suggest that, even after controlling for the effect of personal factors, participation in formal and informal SE in the college years significantly predicted participants’ EQ scores, but their attitudes toward SE did not. Yet, considering the significant effect of personality traits on EQ, it becomes evident that personality should be carefully taken into account when studying empathy and musical engagement. 115 Table 4.21. Regression Analysis of Participation in Small Ensemble on EQ Model 1 TIPI Model 2 + Ethnicity Model 3 + Primary area of study & Age at Commencement Model 4 + Participation in & Attitudes toward SE (Constant) 0.53 [6.89] 9.07 [6.91] 7.97 [7.12] 13.42 [9.20] Extraversion 2.29*** [0.62] 1.99** [0.60] 2.03** [0.61] 1.55* [0.60] Agreeableness 2.17** [0.78] 2.88*** [0.76] 3.10*** [0.78] 2.93*** [0.77] Conscientiousness 1.80* [0.86] 2.11* [0.82] 2.04* [0.87] 2.06* [0.86] Emotional Stability -2.29** [0.80] -2.00* [0.77] -1.93* [0.78] -1.68* [0.77] Openness to Experience 4.39*** [0.84] 4.14*** [0.81] 3.91*** [0.84] 3.41*** [0.90] African-American 1 7.94 [4.32] 4.88 [4.35] 4.94 [4.18] Hispanic 1 2.18 [3.41] 1.94 [3.32] 1.34 [3.25] Caucasian 1 0.97 [2.31] 0.84 [2.29] 0.08 [2.27] Other Ethnic Groups 1 -3.98 [6.27] -6.12 [6.08] -4.76 [6.30] Classical 2 -7.97* [3.06] -2.90 [3.18] Classical + Non- Classical 2 -12.71** [4.09] -7.65 [4.16] Commenced at age 5 – 9 3 -5.78* [2.41] -3.34 [2.49] Commenced at age 10 – 14 3 -6.61* [2.83] -3.80 [2.98] Commenced at age 15 and Over 3 -2.94 [4.28] 1.17 [4.43] SE in Elementary (yes = 1) -0.923 [1.76] SE in Junior High (yes = 1) 2.15 [2.16] SE in High (yes = 1) 2.96 [2.04] Formal SE Never 4 -7.19 [9.12] Formal SE Rarely 4 -7.79** [2.82] Formal SE Sometimes 4 -5.91* [2.56] 116 Informal SE Never 5 -0.33 [4.55] Informal SE Seldom 5 -3.83 [2.96] Informal SE Infrequently 5 -6.43* [3.18] Informal SE Occasionally 5 -1.22 [2.59] Attitudes toward SE 1.96 [2.19] Adj. R 2 .310 .311 .373 .426 Note. B = unstandardized coefficients; Standard errors are in parentheses. 1 Asian is the excluded/comparison group; 2 Non-Classical is the excluded/comparison group; 3 Commenced under 5 is the excluded/comparison group; 4 Formal SE Often is the excluded/comparison group; 5 Informal SE Frequently is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Emotional Self-Regulation – Cognitive Reappraisal A hierarchical multiple regression was performed to determine the extent to which participation in and attitudes toward SE together would predict participants’ scores on the ERQ- CR, after controlling for the effect of personal factors. Table 4.22 presents the summary of the regression analysis. Because the analysis of the research question 2 indicated that only three personality traits (i.e., Agreeableness, Conscientiousness, and Emotional Stability) were significant predictors of ERQ-CR (see Table 21), these were entered in model 1 to partial out the effect of personal factors. Model 1 explained a significant portion of the variance (18.9%) in the ERQ-CR, F(3, 161) = 12.49, p < .001, adj. R 2 = .174. All three personality trait variables contributed significantly to this model (Agreeableness: B = 0.15, p = .019; Conscientiousness: B = 0.14, p = .043; Emotional Stability: B = 0.27, p < .001). The addition of variables for students’ SE experiences in model 2, specifically their attitudes toward SE as well as levels of participation in SE, resulted in the addition of 6.4% of the variance, F(14, 150) = 3.63, p < .001; adj. R 2 = .183. Despite the overall significant effect, none of the variables for participation in and attitudes toward SE noted significant B weights. 117 Yet, two personality traits, Agreeableness and Emotional Stability, still remained as significant predictors, assuming that music students attained 0.14 point and 0.29 point increases in the ERQ- CR for every one-point increase in Agreeableness (t = 2.18, p = .031) and Emotional Stability (t = 4.38, p < .001), respectively. Taken together, it appears that music students’ personality traits rather than their SE experiences are a better predictor of their use of the cognitive reappraisal strategy as a way to regulate one’s emotions. 118 Table 4.22. Regression Analysis of Participation in Small Ensemble on the ERQ-CR Model 1 TIPI Model 2 + Participation in & Attitudes toward SE (Constant) 2.11*** [0.49] 1.61* [0.72] Agreeableness 0.15* [0.06] 0.14* [0.07] Conscientiousness 0.14* [0.07] 0.10 [0.07] Emotional Stability 0.27*** [0.07] 0.29*** [0.07] SE in Elementary (yes = 1) 0.14 [0.15] SE in Junior High (yes = 1) -0.34 [0.18] SE in High (yes = 1) 0.26 [0.17] Formal SE Never 1 0.03 [0.75] Formal SE Rarely 1 0.17 [0.24] Formal SE Sometimes 1 -0.04 [0.22] Informal SE Never 2 -0.53 [0.38] Informal SE Seldom 2 -0.14 [0.25] Informal SE Infrequently 2 -0.12 [0.27] Informal SE Occasionally 2 -0.05 [0.22] Attitudes toward SE 0.17 [0.19] Adj. R 2 .174 .183 Note. B = unstandardized coefficients; Standard errors are in parentheses. 1 Formal SE 3 is the excluded/comparison group; 2 Informal SE 4 is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Emotional Self-Regulation – Expressive Suppression A hierarchical multiple regression was run to examine the extent to which students’ participation in SE and attitudes toward SE together predict their scores on the ERQ-ES, after 119 controlling for the effect of personal factors. Table 4.23 summarizes the findings from the regression analysis. In model 1, two personality trait variables, Extraversion and Emotional Stability, that emerged as significant predictors of the ERQ-ES from the previous analysis (see Table 21) were entered. The regression model was significant, accounting for 28.2% of the variance in the ERQ-ES, F(2, 162) = 31.82, p < .001, adj. R 2 = .273. The significant effect of the personality traits predicted that music students attained a 0.42 decrease in the ERQ-ES for every one point increase in Extraversion (t = -7.30, p < .001) while attaining a 0.22 increase for every one-point increase in Emotional Stability (t = 3.11, p = .002). In the following model, the addition of ethnicity led to a significant increase in R 2 of .049, F(6, 158) = 13.02, p < .001, adj. R 2 = .305. Caucasian students outscored Asian students by 0.58 points (t = 2.44, p = .004) and Hispanic students by 0.61 points (t = 2.07, p = .039) in the ERQ-ES. These two personality traits also remained as significant predictors of the ERQ-ES in this model. When primary instrument was added in model 3, a small but significant amount of the variance (3.6%) increased, F(11, 153) = 8.06, p < .001, adj. R 2 = 321. While the two personality traits and two ethnicity groups remained significant predictors of ERQ-ES, the significant effect of primary instrument suggested that voice major students outscored keyboard majors by 0.72 points (t = 2.37, p = .019). In the final model, variables for students’ SE experiences (levels of participation in SE and attitudes toward SE) were entered as predictor variables. The regression model showed a significant addition of the variance (4.1%), yielding R 2 = .408, F(22, 142) = 4.45, p < .001, adj. R 2 = .317; however, none of the variables showed significant B weights. Meanwhile, two personality traits, two ethnic groups and one primary instrument group continued to approach significance as predictors of ERQ-ES. Specifically, significant effects of personal factors 120 indicated that, for every one-point increase in Extraversion and Emotional Stability, the ERQ-ES will decrease by 0.39 points (t = -6.57, p < .001) and increase by 0.25 points (t = 3.39, p = .001); Asian and Hispanic students outscored Caucasian students by 0.69 points (t = 3.21, p = .002) and 0.73 points (t = 2.40, p = .018), respectively; and keyboard majors outscored voice majors by 0.65 points (t = 2.01, p = .046) in the ERQ-ES. In fact, despite the increase in R 2 in the final model, adjusted R 2 had actually shrunk to .320. Thus, these findings suggested that it is personal factors, specifically personality traits, ethnicity, and primary instrument, and not their SE experiences that will predict music students’ use of expressive suppression as a way for emotional self-regulation. 121 Table 4.23. Regression Analysis of Participation in Small Ensemble on ERQ-ES Model 1 TIPI Model 2 + Ethnicity Model 3 + Primary instrument Model 4 + Participation in & Attitudes toward SE (Constant) 4.42*** [0.43] 4.00*** [0.44] 3.64*** [0.49] 3.02** [0.93] Extraversion -0.42*** [0.06] -0.39*** [0.06] -0.39*** [0.06] -0.39*** [0.06] Emotional Stability 0.22** [0.07] 0.24** [0.07] 0.24** [0.07] 0.25** [0.07] African-American 1 0.45 [0.37] 0.52 [0.38] 0.67 [0.39] Asian 1 0.58** [0.20] 0.49* [0.20] 0.69** [0.22] Hispanic 1 0.61* [0.29] 0.61* [0.30] 0.73* [0.31] Other Ethnic Groups 1 -0.21 [0.56] -0.27 [0.57] 0.01 [0.62] Keyboard 2 0.72* [0.31] 0.65* [0.32] String 2 0.49 [0.26] 0.42 [0.27] Woodwind 2 0.15 [0.28] -0.04 [0.30] Brass 2 0.27 [0.31] 0.23 [0.32] Percussion 2 0.71 [0.43] 0.80 [0.45] SE in Elementary (yes = 1) 0.07 [0.17] SE in Junior High (yes = 1) -0.20 [0.20] SE in High (yes = 1) 0.03 [0.20] Formal SE Never 3 -0.52 [0.90] Formal SE Rarely 3 -0.17 [0.27] Formal SE Sometimes 3 0.07 [0.25] Informal SE Never 4 0.66 [0.44] Informal SE Seldom 4 -0.05 [0.29] 122 Informal SE Infrequently 4 0.12 [0.31] Informal SE Occasionally 4 0.16 [0.25] Attitudes toward SE 0.35 [0.27] Adj. R 2 .273 .305 .321 .317 Note. B = unstandardized coefficients; Standard errors are in parentheses. 1 Caucasian is the excluded/comparison group; 2 Voice is the excluded/comparison group; 3 Formal SE 3 is the excluded/comparison group; 4 Informal SE 4 is the excluded/comparison group. * p < .05. ** p < .01. *** p < .001. Chapter Summary The primary purpose of this study was to explore the relationships between music students’ small music ensemble experiences and their empathy and emotional self-regulation skills. A secondary purpose was to investigate whether personal factors play significant roles in predicting music students’ empathy and emotional self-regulation skills. Specific research questions guiding this study were: (1) What are the relationships among music students’ small ensemble experiences, and their empathy and emotional self-regulation skills? (2) To what extent do personal factors, including gender, ethnicity, primary performance medium, primary study area, age at commencement of music training, and personality, contribute to music students’ empathy and emotional self-regulation skills? (3) To what extent do music students’ small ensemble experiences contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factors? In answering the first research question, data showed that students’ EQ scores were positively correlated to the ERQ-CR scores and also negatively correlated to the ERQ-ES scores. 123 Consistent with previous literature, no significant correlation was found between ERQ-CR and ERQ-ES, implying their independent relationships. Data also revealed significant relationships between music students’ attitudes toward SE and their levels of participation in different types of SE activities. For example, participants who often engaged in formal SE activities during their college years had more positive attitudes toward SE, compared to those who rarely engaged. Also, participants who frequently engaged in informal SE activities had significantly more positive attitudes toward SE than those who never, seldom, and infrequently engaged in the SE activities. Participants who engaged in SE activities in their junior high school and high school years also had more positive attitudes toward SE. In answering the second question, several personal factors, including ethnicity, primary area of study, age at the commencement of music training, and personality traits, appeared to be significantly related to music students’ empathy skills. Specifically, Asian students tended to show a relatively lower level of empathy than African-American and Caucasian students. Also, while non-classical music majors, who studied popular music and jazz, showed a higher level of empathy than classical music majors as well as those who studied both, students who commenced music training before the age of 5 had a higher level of empathy than those who commenced training at the age of 5 to 14. Lastly, students who had a tendency to be extraverted, agreeable, conscientious, and open to experience and less emotionally stable appeared to have a higher level of empathy. In terms of emotional self-regulation, only three personality traits showed significant associations with cognitive reappraisal while other personal factors did not have any associations. Specifically, students who had a tendency to be agreeable, conscientious, and emotionally stable used cognitive reappraisal more often to regulate their emotions. In the case 124 of expressive suppression, ethnicity, primary instrument, and two personality traits were significantly associated with music students’ use of expressive suppression. While Caucasian students used expressive suppression significantly less than Asian and Hispanic students, voice majors used it significantly less than keyboard and string majors. Also, students who tended to be less extraverted and more emotionally stable appeared to use expressive suppression more often as ways to regulate their emotions in their everyday lives. In answering the last research question, this study found that students’ levels of participation in various SE activities during their college years, but not their attitudes toward SE, significantly predicted their empathy skills. Yet, personality traits also appeared to play a significant role in predicting empathy, suggesting that personality should be taken into account when one studies the effects of musical engagements on empathy-related skills. Meanwhile, study results showed no association between music students’ SE experiences and their tendency to regulate their emotions using either cognitive reappraisal or expressive suppression. 125 CHAPTER FIVE: DISCUSSION The purpose of this study was to explore the relationships between small music ensemble experiences of college music students and their empathy and emotional self-regulation skills. A secondary purpose was to investigate whether personal factors play significant roles in predicting music students’ empathy and emotional self-regulation skills. Specifically, the following research questions guided this inquiry: (1) What are the relationships among music students’ small ensemble experiences, and their empathy and emotional self-regulation skills? (2) To what extent do personal factors, including gender, ethnicity, primary performance medium, primary study area, age at commencement of music training, and personality, contribute to music students’ empathy and emotional self-regulation skills? (3) To what extent do music students’ small ensemble experiences contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factors? This chapter presents the discussion of results. They are organized into the following categories: (a) relationships between personal factors and empathy and emotional self-regulation; (b) relationship between participation in and attitudes toward small ensemble; (c) relationships between attitudes toward small ensemble and empathy and emotional self-regulation; (d) relationships between participation in small ensemble and empathy and emotional self- regulation; and (e) roles of personality traits on empathy and emotional self-regulation. 126 Relationships Between Personal Factors and Empathy and Emotional Self-Regulation Gender A linear regression analysis indicated that the relationship between gender and empathy, although trending, was not significant (p = .077). Female students in this study showed slightly higher levels of empathy than male students, yet the mean difference in EQ scores was less than 4 points. Previous research, however, reported women being more empathic than men (Baron- Cohen & Wheelwright, 2004; Barrio, Aluja, & Garcia, 2004; Kataoka et al., 2009; Toussaint & Webb, 2005; Wen et al., 2013). One possible explanation to the relatively weak gender differences may be due to female students’ relatively low EQ scores in the studied sample. According to a large-scale study of 1,761 college students (Wheelwright et al., 2006), the mean scores for EQ among female students in different domains of study were as follows: physical science: 44.7; biological science: 48.5; social science: 48.7; and humanities: 48.7. Meanwhile, the mean score for female music students in the current study was 45.7 (SD = 13.11), which is only slightly higher than the lowest empathy group, physical science majors. Yet, the mean score for male music students in the current study did not considerably differ from that of male students in the abovementioned study (Wheelwright et al., 2006). In terms of emotional self-regulation, neither CR (p = .126) nor ES (p = .083) were significantly associated with gender. In the case of CR, this finding is consistent with previous literature that found no association between gender and individual’s tendency to use CR for emotional regulation (Gross & John, 2003; Haga, Kraft, & Corby, 2009). For ES, data from the current study indicate that, although this was not statistically significant, male students tended to use ES slightly more frequently than female students. This resonates with previous studies that report males generally showing less emotional expressions and employing greater use of ES than 127 females (Flynn, Hollenstein, & Mackey, 2010; Gross & John, 2002; 2003; Haga, Kraft, & Corby, 2009; Kring & Gordon, 1998). Perhaps, as some scholars have pointed out (Haga, Kraft, & Corby, 2009), males’ tendency to regulate emotions using ES may be related to the general gender expectancy of emotion expression learned from an early age, for instance, boys implicitly being taught not to cry (Buck, 2003). However, considering that the gender differences in both empathy and ES were not statistically significant in this study, further research is needed to examine the reasons behind these findings. Ethnicity Regression results showed that ethnicity significantly predicted music students’ empathy skills, with Asian students showing lower levels of empathy than their African-American and Caucasian counterparts. This finding corresponds with previous literature, which showed that, regardless of age, people from Asian backgrounds tend to show less empathic responses when compared to people from Western cultural backgrounds (Atkins, Uskul, & Cooper, 2016; Cassels, Chan, Chung, & Birch, 2010; Friedlmeier & Trommsdorff, 1999; Trommsdorff, Friedlmeier, & Mayer, 2007). Although the reasons behind these cultural differences are unclear, some researchers have attempted to explain them through different perceptions of self and others between Eastern and Western cultural contexts (Atkins, Uskul, & Cooper, 2016). For example, in Western cultural contexts, the self is typically perceived as an independent entity. That is, the focus is often on one’s internal attributes, such as preferences, traits, and desires. On the other hand, in Eastern cultural contexts, the self is typically experienced as an interdependent and interpersonally-connected entity (Kitayama, Duffy, & Uchida, 2007). Therefore, it is reasonable to posit that cultural differences in the construal of the self and interpersonal relationships may 128 play central roles in shaping an individual’s empathic responses to the emotional experiences of others. The relationships between ethnicity and music students’ tendency to use CR and ES for emotional self-regulation were also examined. Ethnicity significantly predicted music students’ uses of ES, but not CR. Specifically, Asian and Hispanic students tended to use ES more often to regulate their emotions than Caucasian students. Previous work examining cultural differences in ES use for emotional regulation (Butler et al., 2007; Gross & John, 2003; Mauss & Butler, 2010; Soto et al., 2011) commonly reported a tendency of Asian individuals to report less emotional responses to emotion-eliciting experiments and to endorse greater use of ES (in both positive and negative emotions) in their daily lives, compared to the Western counterparts (Butler et al., 2007; Gross & John, 2003; Soto et al., 2011). This may be understandable when one considers that cultural values and norms play important roles in defining what the appropriate and socially acceptable ways of emotional expression are (Campos, Frankel, & Camras, 2004). Generally, affective inhibition is considered to be normative in Asian culture (Butler et al, 2007; Soto et al, 2011). Yet, although previous research showed that emotional openness is often valued in Hispanic cultures and that Hispanic individuals tend to be more emotionally expressive than Asian and Anglo individuals (Soto, Levenson, & Ebling, 2005), it is ironic that Hispanic students in this study showed greater use of ES. Possibly, because Hispanic individuals’ high emotional openness is often related to positive emotions but not negative emotions (Triandis, Marin, Lisansky, & Betancourt, 1984), their ES use may be particularly related to negative emotional situations. Taken together, the significant role of culture in individuals’ empathy and emotional self-regulation skills suggest that participants’ cultural backgrounds should be taken into account when studying the effects of musical engagement on these social-emotional skills. 129 Primary Instrument Regression analyses showed that the types of instrument that students primarily played significantly predicted their tendency to use ES for emotional regulation. On a more specific note, voice majors used ES significantly less often than keyboard and string majors. Also, a trend toward significance was found between voice and brass majors, with the latter majors using ES more frequently than voice majors. Although there is, to my knowledge, no literature that directly examined the relationships between musicians’ uses of emotional regulation strategies and their primary instrument, research on musicians’ personality traits provide some explanation to these findings. In a discussion of singers’ personality, Kemp (1996) stated that “singers have no visual or tangible instrument on which to focus their attentions, or towards which to direct the attention of an audience. They cannot ‘hide’ behind an instrument that offers a personality for them to project…” (p. 173). In addition, there is a general expectation for singers to be expressive, particularly in certain genres like opera and Broadway. These particular genres require singers to act while singing. Given a study of actors that showed actors’ tendency to be more emotionally expressive than the general population (Nettle, 2006), it is plausible that these unique characteristics lead singers to form a habit of directly expressing, rather than trying to hide, their emotions. Thus, singers may exhibit less of a tendency to suppress their emotions than instrumentalists in their everyday lives. In addition, and according to research on the relationship between emotional self- regulation and personality traits (Gresham & Gullone, 2012; John & Gross, 2004), individuals who use ES less as ways to regulate their emotions tended to be more extraverted, agreeable, and openness to experience. Interestingly, data from the current study found that voice majors obtained the highest score in Agreeableness and second highest scores in Extraversion and 130 Openness to Experience, following percussion majors, although these differences were not statistically significant. This is also consistent with previous research that revealed singers in the classical music as well as in popular music domains tend to be much more extraverted than instrumentalists (Kemp, 1996; Cameron, Duffy, & Glenwright, 2015). The relationships between personality traits and ES are also useful to explain why keyboard majors appeared to use ES most often, when compared to other instrument majors. In this study, keyboard majors had the lowest scores in Extraversion. This suggests that keyboard majors were the most introverted group in the sample, and also had the second lowest score in Agreeableness, following string majors. Previous literature often reported pianists as being introverted (Ben-Tovim & Boyd, 1990; Blank & Davidson, 2007; Chmurzynska, 2012) and less open to experience or conservative (Chmurzynska, 2012; Kemp, 1996). For example, Blank and Davidson (2007) described pianists as “natural loners” (p. 234) due to their tendencies to be separate from other musicians, and their instrument is arguably more musically self-contained (Kemp, 1996). Therefore, it can be assumed that personality traits of musicians may play a role in shaping their emotion regulation in various emotion-eliciting situations. Primary Area of Study Regression results revealed that primary area of study significantly predicted music students’ empathy skills in the current study. Non-classical music students had significantly higher levels of empathy than classical music students as well as those who reported studying both classical and non-classical music. Compared to the mean EQ score of the general college student population (M = 44.3, SD = 12.20, see Wheelwright et al., 2006), non-classical music students in this study scored considerably higher on the same empathy measure (M = 51.57, SD = 4.07). In addition, given that previous research commonly reported females typically showing 131 higher levels of empathy than males, this is even more interesting as males comprised 76% of the non-classical music group in this study. One possible explanation for the higher levels of empathy among non-classical music students is that, compared to classical musicians, non-classical musicians do not spend as many hours in secluded practice routines. Rather, they tend to develop their musical skills through group projects, interacting constantly with other musicians (Creech et al., 2008; Kemp, 1996). Extensive group work experiences may have enhanced their empathy skills. Group music making embraces various social and emotional qualities, as it requires performers to sensitively listen, communicate, and respond with their co-performers (Dobson & Gaunt, 2013). As non- classical musicians frequently engage in various modes of implicit communication within group music making contexts, they may have cultivated social skills to relate to and communicate with each other, developing awareness of and feelings towards each other. In fact, data from the current study supports this explanation, as non-classical music students tended to participate in various SE activities more often than classical music students and those who studied both. Therefore, it is plausible to assume that one factor for the higher levels of empathy among non- classical music students is their extensive ensemble experiences. However, and perhaps ironically, the relationship between personality traits and empathy seems to contradict the findings from the current study. Agreeableness is often reported to be related to communal behaviors, prosocial motivation, and empathy (Graziano, Habashi, Sheese, & Tobin, 2007). Agreeableness also represents the tendency to be altruistic, tender-minded, cooperative, trusting, forgiving, helpful, and sympathetic (Graziano & Eisenberg, 1997), reflecting some of the essential characteristics of empathy. Although the differences were not statistically significant, non-classical music students in this study appeared to be considerably 132 less agreeable than classical music students, as well as those who studied both classical and non- classical music. Previous research on the personalities of non-classical musicians’ (i.e., rock musicians, pop musicians) also characterized these musicians as displaying relatively lower levels of Agreeableness, compared to normative values (Cameron, Duffy, & Glenwright, 2014; Gillespie & Myors, 2000; Kemp, 1996). Given that the sample size of non-classical music majors in this study was actually small, a replication of the current study with a larger number of these musicians may explain this contradiction. Age at Commencement of Music Training Regression results indicated a trend toward significance between the age at commencement of music training and empathy (p = .053). Specifically, students who began music training early in their life (i.e., under the age of 5) tended to show higher levels of empathy than those who began later in their life (i.e., age 5 to 14). While no literature, to my knowledge, has directly investigated how the age that musicians began music training is related to their empathy skills, previous works on brain plasticity and early music training provide hints to understand this finding. It is possible that non-musical effects of music learning, including empathy, are more robust in the early years of life when the brain is more plastic. This idea is consistent with previous studies that showed various non-musical effects of music training in the early years through changes of brain structures and functions (e.g., Hyde, et al., 2009; Moreno et al., 2008; Schlaug, Norton, Overy, & Winner, 2005), as well as improvements in various behavioral tests (e.g., Bilhartz, Bruhn, & Olson, 2000; Schellenberg, 2005). As discussed in Chapter 2, language capacities and social-cognitive abilities dramatically increase at about the age of 5, which coincides with an acceleration of children’s cognitive empathy development (McDonald & Messinger, 2011). Therefore, early musical experiences involving complex 133 multimodal skills (Luo et al., 2012) as well as creative, joyful, and social engagement (Hallam, 2010) may effectively facilitate various aspects of child development, including empathy. In addition, data from the current study also suggests that students who began music training under the age of 5 tended to participate in SE activities more often, not only in their college years but also during their junior high and high school years, when compared to those who began music training after the age of 5. Therefore, another possible explanation is that the relationship between age at commencement of music training and empathy is mediated by students’ SE experiences. This is a question for future research. Relationships Between Participation in and Attitudes Toward Small Ensemble The relationship between music students’ levels of participation in SE and their attitudes toward SE was also examined. Music students who frequently participated in various SE activities tended to show significantly more positive attitudes toward SE, compared to those who participated in SE less often. In addition, students who engaged in SE activities prior to their college years, specifically during their junior high and high school years, had significantly more positive attitudes toward SE. These findings suggest a strong association between music students’ levels of participation in SE and their attitudes toward SE. Previous literature on motivation, self-concept, and engagement in academics as well as in music learning contexts helps to explain this relationship. In a review of studies on college choir students’ motivation to continue music participation, Sichivitsa (2003 & 2007) found that, in addition to a supportive family environment, self-concept in music, or students’ self- evaluation of their musical ability (Green et al., 2012), is an important factor in influencing students’ decision to persist in their music studies. Particularly, students who had more positive 134 previous musical experiences tended to develop a better self-concept in music, value music more, and, as a result, be more motivated to continue music participation in the future. In fact, many items that constituted the attitude scale used to measure participants’ attitudes toward SE in the current study correspond with self-concept in music. For example, the items: When I perform/work in small ensembles, “I do better quality work,” “I gain more confidence in performance,” and “I feel that I’m important and useful.” Similarly, academic self-concept positively predicted university students’ attitudes toward school in earlier research. Attitudes toward school also positively predicted students’ class participation (Green et al., 2012; Valiente, Lemery-Chalfant, Swanson, & Resier, 2008). Furthermore, active participation in learning at school resulted in enhanced academic performance, which, in turn, reinforced students’ academic self-concept (Dishman et al., 2005; Green et al., 2012; Valiente, Lemery-Chalfant, Swanson, & Resier, 2008). Based on these findings, it can be assumed that, as music students have positive experiences in SE, their self- concept in ensemble engagement is enhanced. Such enhanced self-concept, then, builds positive attitudes toward ensemble participation, motivating students to continue to participate in ensemble activities more often. Although data from the current study does not indicate what mediates the relationship between students’ levels of participation in SE and their attitudes toward SE, related literature suggests that positive attitudes toward ensemble experiences may enhance music students’ engagement in SE. 135 Relationships Between Attitudes Toward Small Ensemble, Empathy, and Emotional Self-Regulation Empathy In the current study, music students’ attitudes toward SE did not predict their empathy skills, after controlling for the effects of personal factors. This result is somewhat surprising, considering that students who participated in diverse SE activities more frequently appeared to also have more positive attitudes toward SE; and that levels of participation in SE significantly predicted students’ empathy skill. However, when each item in the attitudinal scale used to measure participants’ attitudes toward SE and participants’ EQ scores were examined independently 2 , several items were significantly associated with students’ empathy skills. Specifically, items such as (When I perform/work in small ensembles), “I enjoy the performance more,” “I experience uplifting and motivating feelings,” “I put a lot of effort to reach group goals,” and “I feel pride in relation to the group’s success” appeared to be positively related to students’ empathy skills (r = .21 to .45, p < .001). These correlations suggest that music students, who have positive affective experiences and make effort to reach shared goals when working in a SE, are more likely to have higher levels of empathy. Meanwhile, other items shown to be unrelated to empathy seemed to correspond to self-concept in ensemble performance; for instance, (When I perform/work in small ensembles), “I do better quality work,” “I gain more confidence in performance,” and “I feel that I’m important and useful.” Considering the discussion presented earlier (refer to Relationships Between Participation in and Attitudes Toward Small Ensemble, p. 139), it is possible to speculate that while self-concept in ensemble 2 Note that the value of students’ attitudes toward SE used in the regression analysis was the sum of eight separate items 136 performance may influence students’ motivation to continue their engagement in SE activities, positive affective experiences when working in SE may be more directly related to student empathy skills. Emotional Self-Regulation The associations between music students’ attitudes toward SE and their tendencies to use two of the emotional self-regulation strategies, CR and ES, were also examined. Students’ attitudes toward SE did not predict their use of any of the regulatory strategies, after controlling for the effects of personal factors. In addition, even when the relationships between each item of the attitudinal scale and CR and ES were examined independently, none of the items appeared to relate to students’ use of CR and ES. Initially, it was anticipated that students’ positive attitudes toward SE may help to predict their emotional self-regulation skills because SE activities involve complex social interactions through extensive coordination, collaborative, and cooperative processes, allowing individual performers’ skills to effectively regulate their emotions and behave in ways appropriate to the given contexts (Cross, Laurence, & Rabinowitch, 2012). However, study findings did not support this hypothesis. Although the reasons are unclear, it can be assumed that students’ attitudes toward SE do not necessarily have a direct impact on their emotional self-regulation skills used in their everyday lives. In other words, even if one has a positive attitude toward SE and this positive attitude has positive impacts on their interpersonal skills within a SE, for instance, helping the individual to effectively regulate his or her emotions in situations of conflict, there is no guarantee that this effective interpersonal skill within a SE will transfer to other domains in daily life. Another possibility is that the scale used to measure students’ attitudes toward SE was not sensitive enough to capture how they think, feel, perceive, behave toward SE experiences. 137 Relationships Between Participation in Small Ensemble, Empathy, and Emotional Self-Regulation Empathy Regression results revealed that music students’ levels of participation in various SE activities during their college years significantly predicted their empathy skill, even after controlling for the effect of personal factors. In other words, students who participated in SE activities more frequently showed higher levels of empathy than those who participated less often. These findings support previous works that indicated possible links between engagements in various interactive musical activities and empathy-related skills in children (e.g., Brand & Bar-Gil, 2010; Gerry, Unrau, & Trainor, 2012; Kalliopuska & Tiitinen,1991; Kirchner & Tomasello, 2010; Rabinowitch, Cross, & Burnard, 2012; Ritblatt, Longstrech, Hokoda, Cannon, & Weston, 2013; Schellenberg, Corrigall, Dys, & Malti, 2015) and adults (e.g., Hove & Risen, 2009 Wiltermuth & Heath, 2009). As discussed earlier (see Chapter 2), extensive literature suggests a close relationship between engagement in SE and empathy, because, as King and Waddington (2017) put it, empathy is “essential for performers to be able to work together at the highest level (p. 2).” While SE is viewed as a unique human social activity in which performers should rely on empathic relationships for successful performance (Babilioni et al., 2012; Haddon & Hutchinson, 2015; Waddington, 2017), prior studies provided evidence that empathy is fundamental to creating cooperation and collaboration both in the performance and rehearsal phases. For example, Haddon and Hutchinson (2015) discussed empathy as an important facilitative tool in SE that contributes towards the construction of shared musical concerns, facilitates socio- emotional connections for creative “flow,” and aids performers to negotiate and resolve possible 138 conflicts. Keller (2014) suggested that the fundamental vehicle to drive performers in a SE to “achieve precise interpersonal coordination in fundamental musical parameters (timing, intensity, and intonation), while simultaneously displaying the flexibility required to match artful stylistic expression” (p. 2) is empathy. Sicca (2000) further claimed that the quality of the interpersonal communication within a SE is primarily determined by performers’ ability to tune with each other, which corresponds to Schutz’s (1951) concept of “mutual tuning-in relationship” and Seddon’s (2005) notion of “empathetic attunement.” Although the relationship between SE engagements and empathy seem clear, the mechanisms by which this relationship occurs are often not discussed. Yet, some earlier works offer clues into the ways engagement in SE may contribute to enhancing performers’ empathy. First, because music is based on an underlying, external rhythmic framework that facilitates all performers to naturally engage in synchrony, performing in a SE often causes some blurring of self and other (Tarr, Launay, & Dunbar, 2014). Rhythm may provide external, predictable scaffolding that aids performers to attune to each other, share emotional experiences, and thus achieve social cohesion—all central to the development of empathy. This can be also explained by neural pathways that code for both action and perception (Overy & Molnar-Szakacs, 2009). Many experimental studies on the positive effects of synchronization on prosocial tendencies and positive social feelings toward others (e.g., Cirelli, Einarson, & Trainor, 2014; Hove & Risen, 2009; Wiltermuth & Heath, 2009) support this notion. Tarr, Launay, and Dunbar (2014) further discussed the role of endorphins, a type of neurohormones, to support the idea of synchronization as a mediating effect of empathy in interactive group music making. Endorphins are known to be closely involved in several human social behaviors (e.g., Machin & Dunbar, 2011; Nelson & Panksepp, 1998), including group music activities (e.g., Cohen, Ejsmond-Frey, Knight, & 139 Dunbar, 2010; Sullivan, Rickers, & Gammage, 2014). For example, synchronized group dancing to music resulted in an increase of social bonding and an elevated pain threshold that is widely regarded as a proxy for endorphin activation (Tarr, Launay, Cohen, & Dunbar, 2015). Therefore, one possible explanation to the mechanisms by which engagement in SE promotes empathy may be interpersonal synchrony that constitutes the foundation of ensemble performance. Synchronization in ensemble performance is likely to release endorphin (Sullivan & Rickers, 2013; Sullivan, Rickers, & Gammage, 2014), which, in turn, strengthens non-sexual, non-kinship social bonding (Machin & Dunbar, 2011), and consequently enhancing an individual’s skills to understand and share the emotional experiences of others. Similarly, the role of oxytocin, commonly known to be a significant neurohormone associated with social affiliation (Feldman, 2012) is discussed in other research (Clarke, DeNora, & Vuoskoski, 2015; Grape, Sandgren, Hansson, Ericson, & Theorell, 2002; Schellenberg, Corrigall, Dys, & Malti, 2015; Tarr, Launay, & Dunbar, 2014). The underlying idea is that engagement in interactive music making leads to self-other merging due to the release of neurohormones, particularly oxytocin. In fact, oxytocin has been shown to increase empathy- related skills (e.g., empathic accuracy, Bartz et al., 2010; emotional empathy, Hurlemann et al., 2010; generosity toward strangers, Barraza & Zak, 2009; Zak, Stanton, & Ahmadi, 2007; empathy to pain, Shamay-Tsoory et al., 2013). For example, Domes, Heinrichs, Michel, Berger, and Herpertz (2007) found that inhalation of oxytocin from a nasal spray increased participants’ success in a ‘mind-reading’ task, which can be considered, in broad terms, as cognitive empathy. Since performing in a SE often involves sensory overload, physical activity, strong emotional arousal and social behaviors, it is possible that interactive musical activities may be particularly conducive for oxytocin release (Tarr, Launay, & Dunbar; 2014). In a recent study, group singing 140 led to increased subjective measures of well-being as well as elevated levels of oxytocin in saliva sample (Kreutz, 2014). Thus, another possible explanation to the relationship between engagement in SE and empathy is that elevated oxytocin release during ensemble performance may actually strengthen performers’ experiences of social affiliation and bonding. The latter arise “from the actualization of empathic processes and states in the course of collective engagement in music-making” (Cross, Laurence, & Rabinowitch, 2012, p. 338). Lastly, while these speculations focus primarily on the performance aspect, extensive literature also brings attention to the preparation or rehearsal phase in SE activities. In rehearsals, students are likely to acquire the habit of empathizing because rehearsing in a SE involves complex social interactions and interpersonal coordination with co-performers (Cross, Laurence, & Rabinowitch, 2012). Typically, the main objective of a rehearsal stage is to get performers familiar with the music, find consensus on the interpretations of musical features, and establish ensemble cohesion (Seddon & Biasutti, 2009). This preparatory process requires performers to experiment countless times with music and sound and to undergo many conflicts and negotiations through both non-verbal and verbal communication. As Goodman (2002) put, “[e]nsemble performance is about teamwork: half the battle of making music together… is fought on social grounds” (p. 163). Therefore, performers’ inclination “to listen, communicate, and respond within the group” (Dobson & Gaunt, 2013, p. 32) is a critical component for the success of any ensemble. It follows that, as music students continuously engage in various SE activities, they are “naturally” placed in situations in which sensitive interpersonal awareness and mutual sensitivity are required. Their continual efforts to actively strive to reach out to other players and align their own emotional states with those of their co-performers within an ensemble may effectively cultivate the habit of empathizing with others. 141 Alternatively, it is possible that more empathetic people are simply more likely to seek out SE experiences than less empathic people. Because many empirical studies on the relationship between interactive musical activities and empathy-related skills are correlational (e.g., by Cirelli, Einarson, & Trainor; 2012, Hove & Risen, 2009; Wiltermuth & Heath, 2009), including the current one, causal links cannot be established. Thus, caution should be exercised when interpreting results from such studies. Even though regression results from the current study suggest that music students’ levels of participation in SE predict their empathy skills, one cannot conclude that students’ empathy skills were enhanced through their engagement in SE. It is also possible that as more empathetic people tend to be more liked by other people (Lakin & Chartrand, 2003), they were simply invited to join in a SE more often than less empathetic people, leading to higher levels of participation in SE. Alternatively, students’ higher levels of empathy and levels of participation in SE could actually stem from a common cause, like perceived social self-efficacy (Giunta, Eisenberg, Kupfer, Steca, Tramontano, & Vittorio, 2010) or executive function skills. Further investigation will be needed to establish the causal links between students’ engagement in SE and empathy. Emotional Self-Regulation In the current study, the relationships between music students’ levels of participation in SE and their tendency to use two of the emotional self-regulation strategies were examined. Regression analyses showed that students’ levels of participation in various SE activities did not predict any of the emotional self-regulation skills, even after controlling for the effect of personal factors. These findings suggest that music students’ use of CR and ES may have no association with how often they engaged in various SE activities. 142 Initially, the hypothesis that there would be a relationship between students’ levels of participation in SE and their emotional self-regulation skills was posed based on the idea that as SE activities involve shared intentionality (Ilari, 2016; Reddish, Fischer, & Bulbulia, 2013), they actively collaborate to produce joint actions. Joint actions include performers intentionally modifying their emotional responses and trying to behave in ways appropriate to the given context (Cross, Laurence, & Rabinowitch, 2012). In fact, performers in a SE spend considerable time to reach consensus on their musical interpretation during the rehearsal phase. This process requires navigating through conflicts and negotiations, oftentimes evoking intense and even intolerable emotional experiences. At such moments, individual performers’ skills to effectively regulate their emotions and appropriately respond to the situation may be crucial for maintaining positive group dynamics. While research suggests CR to be more effective at decreasing negative emotional experiences in intergroup conflicts than ES (Gross, Halperin, & Porat, 2013; Halperin, Sharvit, & Gross, 2011), this may be true in most emotion-eliciting situations during the rehearsal phase. On the other hand, the performance phase is a unique context that may require a different strategy for emotional regulation. While performers strive for an extraordinarily high level of coordination during performance, there are often moments when performers are required to regulate their emotions instantaneously, for example, when someone makes a mistake. Considering that the skill to effectively cover one’s emotion and stay calm is considered to be a crucial virtue in addressing unexpected circumstances, CR may not be the best regulatory strategy because it is typically demanding attention for cognitive control (Morris, Leclerc, & Kensinger, 2014). During performance, “mis-timings even of a fraction of a second, minute hesitations, slight differences in intonation, tiny misjudgments of dynamics and so on are 143 regarded as monumental blunders” (Young & Colman, 1979, p. 12). Thus, there may be no room for performers to spend their attention to cognitively reappraise the situation. In such cases, it may be more effective to suppress the overt signs of the negative emotion and move on, although potential negative influences may be hard to avoid (Cutuli, 2014). Based on these ideas, it was initially hypothesized that, as music students constantly engage in various SE activities, their skills to effectively use both CR and ES strategies would become a natural part of their emotional experiences. In fact, data from this study indicate that, compared to the general college student population (Eftekhari, Zoellner, & Vigil, 2009; Gross & John, 2003), music students’ mean scores for both ERQ subscales appeared to be slightly higher, suggesting that they may habitually use both CR and ES strategies more frequently in their daily lives than the general college student population. However, the regression results showed that it is not the level of participation in SE that contributes to students’ emotional regulation skills. Perhaps, particular aspects of music may influence their emotional regulation skills. For example, some unique music learning environments, such as an apprenticeship model of music training (Burwell, 2012) and hierarchical structure of large ensembles (e.g., conductor vs. performers; hierarchy among the instrument groups and within each group of instruments) (Lim, 2014) may provide situations where students have to regulate their emotions using distinct regulatory strategies. It should be clarified that the scale used to measure participants’ habitual uses of the emotional regulation strategies (ERQ) did not measure the effectiveness of participants’ use of regulation strategies; rather, it measured their tendency to regulate their emotions using CR and ES. Therefore, even if the data revealed no association between music students’ levels of participation in SE and their emotional regulation skills, future research is 144 needed to examine whether participation in SE is related to the effectiveness of students’ emotional self-regulation skills. Roles of Personality Traits on Empathy and Emotional Self-Regulation Empathy As discussed earlier, music students’ levels of participation in various SE activities appeared to significantly predict their empathy skills, even after controlling for the effect of personal factors. However, to complicate matters, regression results showed that all Big Five personality traits remained significant predictors of students’ empathy skills in the last regression model (see Table 4.21). Although music students’ SE experiences played a significant role in predicting empathy skills, their personality traits were also closely related to empathy. In fact, 33.1% of the variance in empathy was accounted for by personality traits, implying that personality traits are a strong predictor of empathy. Although no direct associations between music students’ levels of participation in SE and their personality traits were found, the data suggest some associations between students’ attitudes toward SE and their personality traits, particularly Agreeableness and Openness to Experience. Interestingly, these two personality traits showed the highest B weights in predicting students’ empathy, with the relationships reaching significance at the .001 level. Given that music students with more positive attitudes toward SE appeared to have higher levels of participation in SE, it is reasonable to assume that music students who tend to be more agreeable and open to experience are more likely to have positive attitudes toward SE and, thus, to be more inclined to engage in SE activities than those who tend to be less agreeable and open to experience. 145 Another interesting point is that findings regarding the relationship between personality traits and empathy differed somewhat from previous literature. While study results showed that all five personality traits were significantly associated with empathy, either positively or negatively, previous studies on the relationships between the Big Five personality traits and empathy have reported a mixed pattern of findings. As an example, Agreeableness was the only trait that research commonly found a close association with empathy (Barrio, Aluja, & Garcia, 2004; Chauhan & Rai, 2013; Melchers et al., 2016; Mooradian et al., 2011; Nettle, 2007; Wakabayashi & Kawashima, 2015). Perhaps this makes sense because Agreeableness is primarily a dimension of interpersonal behaviors that are known to represent the quality of one’s social life (Melchers et al., 2016), specifically how well a person gets along with others (Barrio, Aluja, & Garcia, 2004). This trait also overlaps with the concept of empathic concern (Eisenberg & Strayer, 1987). In addition, research showed that individuals with low levels of Agreeableness tended to lack prosocial motivation and empathic affect because they lacked skills to shift the focus of these reactions to others (Graziano, Habashi, Sheese, & Tobin, 2007). Yet, in the case of Conscientiousness and Emotional Stability, previous research found no consensus on their relationship with empathy. While some studies showed positive association between Neuroticism (or the opposite of Emotional Stability) and empathy (Ashton, Paunonen, Helmes, & Jackson, 1998; Eysenck & Eysenck, 1991), other studies found negative (Shiner & Caspi, 2003) or non-significant associations (Chauhan & Rai, 2013; Barrio, Aluja, & Garcia, 2004). In the case of Conscientiousness, some researchers expected it to be positively correlated with empathy (Melchers et al., 2016) because not only did high scores in this trait inhibit aggressive behaviors (John, Caspi, Robins, Moffitt, & Stouthamer-Loeber, 1994), but also were negatively associated with Eysenck’s dimension of Psychoticism, which reflects a lack of 146 empathy (Barrio, Aluja, & Garcia, 2004). Yet, the link between Conscientiousness and empathy has not been clearly demonstrated. Meanwhile, both Extraversion and Openness to Experience are often found to be unrelated to empathy (Chauhan & Rai, 2013; Moordadian et al., 2011), although a handful of studies (Nettle, 2007; Wakabayashi & Kawashima, 2015) indicated a small correlation between Extraversion and empathy. However, this study demonstrated all five personality traits closely relating to empathy. Although no literature supports this finding at the time of writing, some speculations can be made. First, in a study of professional actors (N = 191), Nettle (2006) reported actors scoring higher in Extraversion and also having higher levels of empathy, compared to the general population. According to the author, because high Extraversion is related to actors’ orientation towards social attention and reward (Hill & Yousey, 1998), actors’ extraverted personality presumably helps them to sensitively responsive “towards the strong interpersonal rewards of being the center of… an audience’s attention” (p. 381). This seems to also correspond to the case of musicians. It is very important for musicians, whose performances are often directly affected by the audience’s responses, to be able to sensitively recognize and appropriately respond to the audience’s emotional reactions to make them deeply engage in their performance. Interestingly, data from the current study indicate that, consistent with previous research, non-classical music majors tended to be more extraverted (Butkovic & Dopudj, 2017; Dyce & O’Connor, 1994) and also had higher levels of empathy. Therefore, one possible explanation to the relationship between Extraversion and empathy could be made through the importance of sensitive responsiveness in the nature of music performance. Also, previous research suggested that relationships between several personality traits, specifically, Emotional Stability, Openness to Experience, and Conscientiousness, and empathy 147 may be mediated by creativity. For example, increased Neuroticism often appeared to be a feature of individuals in creative disciplines, including in the performing arts (Nowakowska, Strong, Santosa, Wang, & Ketter, 2005; Strong et al., 2007). On that note, Strong et al (2007) discussed that high Neuroticism, or low Emotional Stability, may provide a creative advantage by increasing access to a range of intense affective experiences, particularly negative affect, which may propel innovative ideas. Also, Openness to Experience, often taken as a distinctive personality trait of musicians (Cameron, Duffy, & Glenwright, 2015; Gillespie & Myors, 2000), has been described as fundamental to creativity (e.g., Li et al, 2015; George & Zhou, 2001; Griffin & McDermott, 1998; Williamon, Thomson, Lisboa, & Wiffen, 2006). According to this view, people who tend to be imaginative, curious, independent thinkers, and amenable to new perspective (George & Zhou, 2001) are likely to have greater access to a variety of feelings, thoughts, ideas, and perspectives, which, in turn, may stimulate them to come up with novel ideas and behaviors to change the status quo (McCrae & Costa, 1997). In addition, while Conscientiousness is closely linked to high job performance across a variety of occupations (Barrick & Mount, 1993; Tett, Jackson, & Rothstein, 1991), some researchers have argued that high Conscientiousness may discourage performance in creative activities (George & Zhou, 2001; Reiter-Palmon, Illies, & Kobe-Cross, 2009). The underlying idea here is that distinct characteristics of Conscientiousness, including impulse control, conformity, and determination (Costa & McCrae, 1992), may hinder the potential for one to come up with new perspectives and seek to change the status quo. Since previous literature has established the associations between creativity and empathy (e.g., Carlozzi, Bull, Eells, & Hurlburt, 1995; Rego, Sousa, Cunha, Correia, & Amaral, 2007), it can be assumed that the relationship between empathy and Emotional Stability, Openness to Experience, and Conscientiousness may have been mediated by 148 music students’ creativity. While the reason behind the close associations between all five personality traits and empathy is still unclear, findings from this study suggest that personality traits should be carefully taken into account when we study the relationships between empathy and musical engagement. This is another question for future research to examine. Emotional Self-Regulation In the analysis of the relationship between music students’ SE experiences and their tendency to use CR for emotional regulation, only two personality traits, Agreeableness and Emotional Stability, emerged as significant predictors for students’ CR use. Perhaps, this is not surprising as previous studies have rarely found the factors that influence individual’s use of CR. While some works revealed the influences of culture (Gross & John, 2003; Butler, Lee, & Gross, 2007) and age (Garnefski & Kraaij, 2006) on individual’s use of CR, other factors, including gender, are often found to be unrelated to CR. Yet, it is important to recall that culture did not emerge as a significant predictor of CR in this study. In terms of personality traits, Extraversion and Neuroticism, are often associated with CR. Specifically, a negative correlation was reported between Neuroticism and CR, while a positive correlation was found between Extraversion and CR (Gross & John, 2003; John & Gross, 2004; Wang et al., 2009). Taken together, these findings suggest that individuals who are emotionally more stable and extraverted tend to use CR more often when regulating their emotions. This is partly consistent with findings from this study, as Emotional Stability appeared to be a strong predictor of music students’ CR use. This makes sense given that individuals who tend to be emotionally stable, calm, and self-confident are less likely to feel overwhelmed by negative affect. This possibly affords them greater opportunities to reappraise the emotion- 149 eliciting situation in order to effectively reduce the intensity of negative emotional experiences (John & Gross, 2004). Yet, Extraversion did not show a significant relationship with CR use in this study; instead, Agreeableness emerged as a significant predictor. Although literature supporting this relationship was not retrieved at the time of writing, a study by Haas, Omura, Constable, and Canli (2007) of individual differences in the lateral prefrontal cortex (LPFC) activation during the processing of negative affects suggests that there is a possible association between Agreeableness and individual’s use of CR. While the LPFC is known to be related to cognitive control (Tully, Lincoln, & Hooker, 2013) and likely to be activated when one cognitively reappraises emotional situations, Omura et al. found that more agreeable individuals showed greater activation in the right LPFC in response to negatively-valenced stimuli, suggesting that more agreeable individuals tend to automatically engage in emotional regulation using a cognitive strategy. While this finding may support the relationship between Agreeableness and CR use, further investigation will be necessary to fully explain why Agreeableness but not Extraversion appeared to be a significant predictor for music students’ tendencies to use CR in this study. Various factors, including gender, culture, age, and personality traits, are known to influence individual’s use of ES (e.g., Gross & John, 2003; Haga, Kraft, & Corby, 2009; Mauss & Butler, 2010; Wang et al., 2009). In terms of music students’ tendencies to use ES for emotional regulation, two personality traits, Extraversion and Emotional Stability, along with ethnicity and primary instrument, emerged as significant predictors of ES use. In fact, the correlation between low Extraversion, particularly shyness, and ES use is well established (e.g., Gresham & Gullone, 2012; John & Gross, 2004; Wang et al., 2009; Xia, Gao, Wang, & Hollon, 150 2014). Because introverted individuals are more likely to feel self-conscious in social situations, they may prefer to use ES to distance themselves from potential rejection in emotion-eliciting situations (John & Gross, 2004). In the case of Emotional Stability, only a handful of studies (e.g., Gresham & Gullone, 2012) have found a link with ES use, where individuals with high Neuroticism, or low Emotional Stability, use greater ES. This finding is in stark contrast with findings from the current study. Study results showed that Emotional Stability was positively related to music students’ use of ES, suggesting emotionally more stable students using greater ES. Nonetheless, considering that ES was positively related to impulse control, or the ability to keep calm, maintain control, and avoid impulsive behaviors (Balzarotti, John, & Gross, 2010), it is reasonable to expect that music students who have a tendency to be emotionally stable and calm are likely to keep better control of their own emotional expressions by inhibiting their display of emotions in emotion-eliciting situations. Summary The discussion part discussed five interesting issues that emerged from the results of this study. Regarding the first research question, What are the relationships among music students’ small ensemble experiences, and their empathy and emotional self-regulation skills? the study results revealed a strong association between music students’ levels of participation in SE and their attitudes toward SE. This association may be explained by the relationships between motivation, self-concept, and engagements. Previous research studies suggested that students with a better self-concept tend to build positive attitudes toward the given activity, and thus be more motivated to participate in the activity more often. In terms of the second research question, To what extent do personal factors contribute to music students’ empathy and emotional self-regulation skills? study results indicated empathy 151 being closely related to ethnicity, primary area of study, the age at commencement of music training, and all five personality traits. Particularly, the relationship between empathy and primary area of study may be explained by the fact that the nature of jazz and popular music practice is group enterprise, so non-classical music majors’ extensive group work experiences may have influenced to enhance their empathy skills. Similarly, a higher level of empathy among students who commenced music training early in life could be explained by the fact that they tended to have a higher level of SE participation not only in college years but also prior to college years. Therefore, one possible explanation to these relationships might be students’ SE experiences. In the case of emotional self-regulation, ES was significantly related to ethnicity, primary instrument, and two personality traits (Extraversion and Emotional Stability), while CR was only related with three personality traits (Agreeableness, Conscientiousness, and Emotional Stability). Particularly, the relationships between primary instrument and ES were understood through the literature on the personality traits of musicians. Regarding the last research question, To what extent do music students’ small ensemble experiences contribute to their empathy and emotional self-regulation skills, after controlling for the effect of personal factor? the study findings revealed that music students’ SE experiences, particularly how often they engaged in various types of SE activities, were closely associated with their empathy skills, but not their emotional self-regulation skills. In other words, students who participated in SE activities more often tended to have a higher level of empathy than those who participated less often. Although the mechanisms by which this relationship occurs are unclear, particular neurohormones, including endorphin and oxytocin, known to be released during interpersonal synchrony may hint at this relationship. Also, as SE activities involve inevitable conflicts and negotiations, performers naturally learn to listen, communicate, and 152 appropriately respond within the group as they engaged in ensemble works, which may all contribute to the development of empathic abilities. Although findings of this study indicated that personality traits played a significant role in predicting students’ empathy, this study also supported the notion that small music ensembles hold the potential to promote students’ skills to understand and share emotional experiences of others. Limitations of the Study This research study has limitations that must be acknowledged when considering the study results in relation to the effects of students’ SE experiences on empathy and emotional self-regulation skills in the general population. First, this study aimed to explore the relationships between SE experiences and two types of social-emotional skills, namely empathy and emotional self-regulation, in order to find evidence to support SE as a possible SEL strategy. Although findings from this study suggest that there are possible relationships between music students’ SE experiences and their empathy skills, the sample population of the study involved senior music performance majors attending four-year colleges and universities. The participants were likely accomplished young musicians who received intensive music training for a fair amount of time, and who were studying in strong programs and centers of musical excellence in the United States. Therefore, generalization of the results to the general student population, such as K-12 students, could be misleading and study findings cannot generalize beyond the sample. Since the target of SEL generally involves K-12 students, replication of this study to the K-12 student population is necessary to ensure a greater degree of generalizability of study findings. 153 Second, in order to propose SE activities as a possible SEL strategy, it is necessary to present evidence that students’ social-emotional skills can actually be enhanced through SE experiences. Although many interesting results were revealed in this study, conclusions regarding the nature of the relationships between students’ SE experiences and their empathy and emotional self-regulation skills are limited to correlational interpretations. Controlled experimental studies are necessary to determine whether the regression results are indeed at all causal in nature. Third, while various personal factors, including gender, ethnicity, primary performance medium, primary study area, age at commencement of music training, and personality, were considered as control variables based on literature reviewed, some potential confounding factors were missing in this study. For example, extensive research has shown various aspects of family environment, such as parenting style, parental attachment, family structure, and family engagements, to be important factors influencing an individual’s empathy (e.g., Schaffer, Clark, & Jeglic, 2009; Tisot, 2003) and emotional self-regulation skills (e.g., Field, 1994; Morris, Silk, Myers, & Robinson, 2007). Also, religion and, more specifically, how one processes religious and spiritual issues, is also known to be closely related to an individual’s empathy skill (Duriez, Soenens, & Beyers, 2004). Since these factors were not considered in this study, it is possible that different results would have emerged if these factors had been included in the analysis. Lastly, this study employed three standardized psychological measures to assess participants’ personality traits as well as empathy and emotional self-regulation skills. Although the validity and reliability of these measures were assessed in the study design stage, it is possible that some of these measurements were not exactly true-to-life or sensitive enough. Also, in the case of emotional self-regulation, because ERQ was designed to measure participants’ 154 tendency to regulate their emotions using CR and ES, it is unclear to understand the effectiveness of students’ use of the regulatory strategies in various emotion-eliciting situations. In addition, while students’ attitudes toward SE appeared to be unrelated to both empathy and emotional self- regulation, more detailed examination of the psychometric properties of the attitudinal scale used in this study is warranted. With these limitations in mind, the current study offers a broad picture of how students’ SE experiences are related to their empathy and emotional self-regulation skills. Implications for Music Teaching and Learning Small music ensembles hold myriad opportunities for the development of various music- related skills as well as social-emotional skills, yet such potential for students’ growth inherent in SE activities has often been neglected in school music programs. Although innovative music programs have increasingly emerged in the last decade, most school music programs still focus on traditional large ensembles, such as orchestra, band, and choir. As discussed earlier, young students in today’s society are faced with many social and emotional issues that earlier generations may not be able to imagine. Traditional ways of teaching and learning can no longer prepare young people to become successful citizens in this society (Williams, 2011; Webster, 2016). While cultivating competent students, not only academically but also socially and emotionally is in great demand, the small music ensemble can be a fruitful domain to meet the needs of our society. One of the main concerns regarding traditional large ensembles is that while a distinct hierarchy exists between the teacher and students, the teacher primarily plays the leader role, making decisions regarding music making. As a result, students are rarely afforded with 155 opportunities to make contributions to their ensemble works (Goolsby, 1994). Meanwhile, small music ensembles grant more technical and musical responsibility on individual performer, as shared goals are achieved only through a collection of individual performer’s contributions. This suggests that performers in a SE are collectively and directly responsible for their joint musical venture (Lim, 2014). Such responsibility is likely to motivate students to pay attention to minute differences of “intonation, balance, blend, style, attacks, releases, articulations, tempo, and even tone quality—the very stuff of good music” (Goolsby, 1994, p. 28). Also, in SE performance, each performer’s sound production directly affects the overall ensemble and small errors and mistake become immediately apparent. The nature of SE greatly helps students to develop their skills to listen to each other and sensitively attune to their co-performers. In addition, as students engage in the authentic musical collaboration, they also learn to think like professional musicians (Berg, 2008). Moreover, small music ensembles are a great place to develop important social-emotional skills, including empathy and emotional self-regulation. For example, as students engage in SE activities, they learn how to work with others in socially and emotionally skilled ways, practice healthy behaviors, and behave responsibly and respectfully (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011). They also develop leadership skills, learn to make value judgments, and develop critical thinking (Goolsby, 1994). As the nature of SE involves collaboration and cooperation in non-hierarchical power relationships, students naturally engage in collective decision making through risk-taking, experiments, conflicts, and negotiations, which offers them to learn how to maintain their self-identity while adopting the other’s perspective (Haddon & Hutchinson, 2015). Likewise, SE, as a powerful means of social interaction, affords students immense opportunities to develop various social-emotional skills 156 that are required to be successful citizens in current society. The close association between students’ levels of participation in SE and their empathy skills found in this study reveals the great potential of SE for various personal growth and improvement. Music teachers in K-12 schools are highly encouraged to consider including SE activities in their music programs as a means to achieve contemporary educational demands (practical information on how to incorporate SE in school music programs can be found in Berg, 2008 and Goolsby, 1994). Recommendations for Future Study Findings from this study suggest many possible directions for future research. First and foremost, future researchers could replicate this study with different sampling frames, such as the K-12 student population, to ensure a greater degree of generalizability of the findings. In addition, a replication of this study with a larger sample of non-classical music majors may be necessary to clarify inconsistent findings with previous literature. For example, data from the current study regarding the relationships between personality traits and empathy and gender differences in empathy appear to contradict findings from previous literature. Considering the small sample size of non-classical music majors (13% of the entire sample), a replication of the study with a larger sample size will help to clarify this discrepancy. Second, given the inherent limitations of correlational research, longitudinal experimental research is necessary to determine whether the regression results from this study can establish causal links. By now, it is impossible to tell whether students’ empathy skills were enhanced through their engagement in various SE activities. Controlled experimental research is needed if generalizations regarding the effectiveness of SE experiences on various social-emotional skills are to be made. Given that the findings of this study shed light on the possible potential for SE to 157 promote students’ empathy skills, longitudinal experimental studies may yield valuable findings for music education practitioners. Lastly, multiple regression analyses like those featured in this study only explain what variables may predict an outcome, but such analyses cannot explain why and how these variables affect the outcome. Specifically, while findings from this study suggested a significance of students’ levels of participation in various SE activities in predicting their empathy skills, the data itself could not explain why and how their SE experiences were related to empathy. Answers to “why?” and “how?” questions may be better addressed through qualitative investigations because the nature of musical experiences is highly subjective and personal; and, therefore, hard to be quantified. Individual performer’s voices about their SE engagement would provide a deeper understanding of the relationships between SE experiences and social- emotional skills. Conclusion Assertions about musical engagement and their benefits abound. The effects of active engagement with music has claimed to promote cognitive, intellectual, personal, and social development of children and young people (see Hallam, 2010). Among a variety of types of music making activities, this study paid particular attention to the small music ensemble. This type of group work is often considered a unique form of human social activity in which multiple performers make music together through a highly complex set of coordination, collaboration, and cooperation (Davidson & Good, 2002; Keller, 2012). Extensive literature suggests small music ensembles “as a process that requires us to be sensitive to the inner states of others; as an environment that may allow us to experience feelings that are congruent with the feelings of 158 others; and as a manifestation of a state of shared intentionality” (Cross, Laurence, & Rabinowitch, 2012, p. 340). Thus, the small music ensemble is thought to be a fruitful domain to cultivate the habit of empathizing and effective emotional self-regulation. To support this notion, this study explored the relationships between music students’ small music ensemble experiences and their empathy and emotional self-regulation skills. While several personal factors played roles in predicting music students’ empathy, their levels of participation in various small ensemble activities were closely associated with their empathy skills. Although these findings are not definitively conclusive, they do more than hint at the possibility of the effectiveness of small ensemble as a way to nurture crucial social-emotional skills for successful social life. It should be acknowledged that empathy and emotional self- regulation are perhaps much more complex psychological constructs than they appear to be. It is possible that the relationships between small ensemble experiences and these social-emotional skills and the multiplicity of individuals’ experiences are actually difficult to capture because there are too many factors that influence them. Yet, the present findings also shed light on the power of small ensembles. Future study should continue to investigate the effects of small music ensemble on empathy and emotional self-regulation skills using different research methods, including experimental design and qualitative study. Lastly, it should be highlighted that although engagement in a small ensemble is likely to enhance social-emotional skills in students, this is possible only if it provides positive music making experiences. This means that engagement in small ensembles should be pleasurable, motivating, and rewarding. 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(Choose one) African American/Black American Indian/Native American Asian Hispanic/Latino Pacific Islander White/Caucasian Other (please specify) 4. What is your primary performance medium? (Choose one) Keyboards (Piano, Organ, Harpsichord) Strings (Violin, Viola, Cello, Bass, Guitar) Woodwinds (Flutes, Clarinet, Saxophone, Oboe, Bassoon) Brass (Trumpet, French Horn, Trombone, Tuba) Percussion (Drums, Marimbas, Xylophones) Voice Other (please specify) 5. In your music performance study, which of the areas below do you consider your primary? (Choose all applicable) Classical music Jazz Popular music World music Other (please specify) 4 201 Part 2 Musical Experience Before College Small Music Ensemble Experience and Social Emotional Competences 1. How old were you when you first commenced formal music training (it doesn’t have to be your major instrument)? Under 5 5 6 7 8 9 10 11 12 13 14 15 and over Elementary years Junior high years High school years Individual music lessons Small ensembles (less than 10 performers) Large ensembles (over 10 performers) 2. Think of your individual music lessons and ensemble works before college. Check the box below that describes your experiences. 5 202 In this part, we are interested in your small ensemble experiences in college. We define "small ensemble" as any music groups composed of two to ten performers and that functioned primarily without a conductor or couch. The group could be both in and outside of the school context. Part 3 Musical Experience in College Small Music Ensemble Experience and Social Emotional Competences not at all slightly moderately mostly completely 1. I do better quality work. 2. I enjoy the performance more. 3. I gain more confidence in performance. 4. I feel that I am part of what is going on in the group. 5. I become friends with the co-performers. 6. I like to help my group members with what I am good at. 7. I feel that I’m important and useful. Think about the times when you worked or performed in small ensembles. Please rate your agreement with the following statements. When I perform/work in small ensembles, 6 203 not at all slightly moderately mostly completely 8. I am willing to take risks by exploring something new to me. 9. I do my best to prepare my parts in advance. 10. I experience uplifting and motivating feelings. 11. I put a lot of effort to reach group goals. 12. I feel pride in relation to the group’s success. 13. I am quite an easy- going person to work with. --- 7 204 In this part, we are interested in your small ensemble experiences in college. We define "small ensemble" as any music groups composed of two to ten performers and that functioned primarily without a conductor or couch. The group could be both in and outside of the school context. Part 3 Musical Experience in College Small Music Ensemble Experience and Social Emotional Competences None 1 course 2 courses over 3 courses Freshman year Sophomore year Junior year Senior year 1. As part of your school curriculum, how many small ensemble courses did/do you take? (Check all boxes that apply) None 1 - 2 times 3- 4 times over 5 times Freshman year Sophomore year Junior year Senior year 2. In how many small ensemble performances did/do you participate that were not related to the school curriculum (including gigs)? (Check all boxes that apply) None 1 time 2 times over 3 times Freshman year Sophomore year Junior year Senior year 3. In how many concerts/recitals, competitions, masterclasses, and/or auditions did/do you participate as part of a small ensemble? (Check all boxes that apply) 8 205 Never Seldom Infrequently Occasionally Frequently Freshman year Sophomore year Junior year Senior year 4. How often did/do you engage in informal small ensembles (e.g., music making with friends for fun)? (Check all boxes that apply) 9 206 Part 4 Personality and Emotional Life Small Music Ensemble Experience and Social Emotional Competences strongly disagree disagree somewhat disagree neither disagree nor agree somewhat agree agree strongly agree 1. Extraverted, enthusiastic. 2. Critical, quarrelsome. 3. Dependable, self- disciplined. 4. Anxious, easily upset. 5. Open to new experiences, complex. 6. Reserved, quiet. 7. Sympathetic, warm. 8. Disorganized, careless. 9. Calm, emotionally stable. 10. Conventional, uncreative. Here are a number of personality traits that may or may not apply to you. Please indicate the extent to which you agree or disagree with the following statements. I see myself as: 10 207 Part 4 Personality and Emotional Life Small Music Ensemble Experience and Social Emotional Competences strongly disagree disagree somewhat disagree neutral somewhat agree agree strongly agree 1. When I want to feel more positive emotion (such as joy or amusement), I change what I’m thinking about. 2. I keep my emotions to myself. 3. When I want to feel less negative emotion (such as sadness or anger), I change what I’m thinking about. 4. When I am feeling positive emotions, I am careful not to express them. 5. When I’m faced with a stressful situation, I make myself think about it in a way that helps me stay calm. Below is a list of statements about your emotional life, in particular, how you control your emotions. The questions involve two distinct aspects of your emotional life. One is your emotional experience, or what you feel like inside. The other is your emotional expression, or how you show your emotions in the way you talk, gesture, or behave. Although some of the following questions may seem similar to one another, they differ in important ways. Please indicate the extent to which you agree or disagree with that statement. 11 208 strongly disagree disagree somewhat disagree neutral somewhat agree agree strongly agree 6. I control my emotions by not expressing them. 7. When I want to feel more positive emotion, I change the way I’m thinking about the situation. 8. I control my emotions by changing the way I think about the situation I’m in. 9. When I am feeling negative emotions, I make sure not to express them. 10. When I want to feel less negative emotion, I change the way I’m thinking about the situation. -- 12 209 Part 5 Empathy Small Music Ensemble Experience and Social Emotional Competences strongly disagree slightly disagree slightly agree strongly agree 1. I can easily tell if someone else wants to enter a conversation. 2. I find it difficult to explain to others things that I understand easily, when they don’t understand it first time. 3. I really enjoy caring for other people. 4. I find it hard to know what to do in a social situation. 5. People often tell me that I went too far in driving my point home in a discussion. 6. It doesn’t bother me too much if I am late meeting a friend. 7. Friendships and relationships are just too difficult, so I tend not to bother with them. 8. I often find it difficult to judge if something is rude or polite. Please read the following statements carefully and rate how strongly you agree or disagree with them. There are no right or wrong answers, or trick questions. 13 210 strongly disagree slightly disagree slightly agree strongly agree 9. In a conversation, I tend to focus on my own thoughts rather than on what my listener might be thinking. 10. When I was a child, I enjoyed cutting up worms to see what would happen. 11. I can pick up quickly if someone says one thing but means another. 12. It is hard for me to see why some things upset people so much. 13. I find it easy to put myself in somebody else’s shoes. 14. I am good at predicting how someone will feel. 15. I am quick to spot when someone in a group is feeling awkward or uncomfortable. 16. If I say something that someone else is offended by, I think that that’s their problem, not mine. - 14 211 strongly disagree slightly disagree slightly agree strongly agree 17. If anyone asked me if I liked their haircut, I would reply truthfully, even if I didn’t like it. 18. I can’t always see why someone should have felt offended by a remark. 19. Seeing people cry doesn’t really upset me. 20. I am very blunt, which some people take to be rudeness, even though this is unintentional. 21. I don’t tend to find social situations confusing. 22. Other people tell me I am good at understanding how they are feeling and what they are thinking. 23. When I talk to people, I tend to talk about their experiences rather than my own. 24. It upsets me to see an animal in pain. -- 15 212 strongly disagree slightly disagree slightly agree strongly agree 25. I am able to make decisions without being influenced by people’s feelings. 26. I can easily tell if someone else is interested or bored with what I am saying. 27. I get upset if I see people suffering on news programs. 28. Friends usually talk to me about their problems as they say that I am very understanding. 29. I can sense if I am intruding, even if other person doesn’t tell me. 30. People sometimes tell me that I have gone too far with teasing. 31. Other people often say that I am insensitive, though I don’t always see why. 32. If I see a stranger in a group, I think that it is up to them to make an effort to join in. - 16 213 strongly disagree slightly disagree slightly agree strongly agree 33. I usually stay emotionally detached when watching a film. 34. I can tune into how someone else feels rapidly and intuitively. 35. I can easily work out what another person might want to talk about. 36. I can tell if someone is masking their true emotion. 37. I don’t consciously work out the rules of social situations. 38. I am good at predicting what someone will do. 39. I tend to get emotionally involved with a friend’s problems. 40. I can usually appreciate the other person’s viewpoint, even if I don’t agree with it. - 17 214 Appendix B: Institution Review Board (IRB) Approval Form Eun Cho <eunc@usc.edu> Study Approval Notice Sent istar@istar.usc.edu <istar@istar.usc.edu> Fri, Apr 15, 2016 at 12:58 PM Reply-To: istar@istar.usc.edu To: eunc@usc.edu, ilari@usc.edu University of Southern California University Park Institutional Review Board 3720 South Flower Street Credit Union Building (CUB) #301 Los Angeles, CA 90089-0702 Phone: 213-821-5272 Fax: 213-821-5276 upirb@usc.edu Date: Apr 15, 2016, 12:58pm Action Taken: Approve Principal Investigator: Eun Cho THORNTON SCHOOL OF MUSIC Faculty Advisor: Beatriz Ilari THORNTON SCHOOL OF MUSIC Co- Investigator(s): Project Title: Small Music Ensemble Experience and Social Emotional Competences Study ID: UP-16-00223 Funding Types: No Funding The University Park Institutional Review Board (UPIRB) designee determined that your project meets the requirements outlined in 45 CFR 46.101(b) (2) and qualifies for exemption from IRB review. This study was approved on 04/15/2016 and is not subject to further IRB review. Minor revisions were made to the information sheet, recruitment script and sections 24.2, 24.4, 26.2, 26.5 and 35.1 by the IRB Administrator (IRBA). The following materials were reviewed and approved: -- Certified Recruitment Script UP-16-00223 4.15.16 -- Certified Information Sheet UP-16-00223 4.15.16 To access IRB-approved documents, click on the “Approved Documents” link in the study workspace. These are also available under the “Documents” tab. Attachments: Certified Information Sheet UP-16-00223 4.15.16.docx Certified Recruitment Script UP-16-00223 4.15.16.docx Social-behavioral health-related interventions or health-outcome studies must register with clinicaltrials.gov or other International Community of Medical Journal Editors (ICMJE) approved registries in order to be published in an ICJME journal. The ICMJE will not accept studies for publication unless the studies are registered prior to enrollment, despite the fact that these studies are not applicable “clinical trials” as defined by the Food and Drug Administration (FDA). For support with registration, go to www.clinicaltrials.gov or contact Jean Chan ( jeanbcha@usc.edu, 323-442-2825). Approved Documents: view This is an auto-generated email. Please do not respond directly to this message using the "reply" address. A response sent in this manner cannot be answered. If you have further questions, please contact iStar Support at (323) 276-2238 or istar@usc.edu. The contents of this email are confidential and intended for the specified recipients only. If you have received this email in error, please notify istar@usc.edu and delete this message. 215 Appendix C: Informed Consent Form This was attached in the first page of the survey. You are being invited to participate in a dissertation study titled “The Relationship Between Small Music Ensemble Experience and Social Emotional Competences of Music Students,” conducted by Eun Cho at University of Southern California. You are being invited because you are a music major performance major attending a senior year in a four-year college or university. Survey questions will center on general background information, small ensemble experiences, and three psychological measures, specifically personality, empathy, and emotional self-regulation. This survey will take approximately 20-25 minutes to complete. Your participation in this research study is completely voluntary and your answers will remain anonymous. You may choose not to participate. If you decide to participate in this research survey, you may withdraw your participation at any time without any penalties. All information collected for the purpose of this study will be kept strictly confidential. Data will be stored in a password protected electronic format. All identifiable data will be destroyed upon the completion of the study. The results of this study will be used for scholarly purposes only. Once you successfully complete the survey, you will be provided with a compensation of $5 Amazon gift card. You will receive it within 24 hours of survey completion via email. Your email address will be kept in a separate file from the survey. If you have any questions about the research study being conducted, please contact Eun Cho, Principal Investigator at eunc@usc.edu. This research has been reviewed by the University of Southern California IRB. It is being supervised by Dr. Beatriz Ilari (ilari@usc.edu). By clicking START SURVEY you are verifying that you have read and understood the explanation of the study, and that you agree to participate. 216 Appendix D: Electronic Invitation to the Survey <Invitation to a Research Study> My name is Eun Cho and I am a doctoral candidate in music education at University of Southern California. I would like to invite you to take part in my dissertation study titled “The relationship between small music ensemble experience and social emotional competences of music students.” The goal of this study is to explore possible relationships between various personal/musical factors and music students’ empathy/emotional self-regulation abilities. You are invited because you are a music performance major in your senior year in a four-year college or university. All types of music performance majors are eligible to participate, regardless of musical genres and styles. Excluded from this study are non-performance majors or, those majoring in areas such as composition, music education, music technology, and music therapy. If you are majoring in more than one area and one of them includes music performance, you are eligible to participate in the study. Participation in this study includes completing an online questionnaire (link below) that will take approximately 20-25 minutes. Your participation is voluntary and all answers will remain confidential and anonymous. There are no known risks associated with this research. All identifiable data will be destroyed upon the completion of the study. Once you complete the survey, you will receive a $5 Amazon gift card as a token of my appreciation for your time and effort. Also, I would be grateful if you would share this invitation with other potential participants. If you agree to participate, please follow this link to the Survey: Take the Survey Or copy and paste the URL below into your internet browser: https://www.surveymonkey.com/r/XDFF556 Questions about the study can be directed to the principal investigator, Eun Cho, via email at eunc@usc.edu. If you have questions about your rights as a research participant, you may contact the University of Southern California Institutional Review Board by phone at (213)-821-5272, or by email at upirb@usc.edu. Thank you very much for your time.

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Asset Metadata

Creator Cho, Eun (author)

Core Title The relationship between small ensemble experiences, empathy, and emotional self-regulation skills in music students

School Thornton School of Music

Degree Doctor of Musical Arts

Degree Program Music Education

Publication Date 02/08/2018

Defense Date 11/09/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag emotional-self regulation,Empathy,non-musical effects of music,OAI-PMH Harvest,small music ensemble,social-emotional development

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ilari, Beatriz (committee chair), Immordino-Yang, Mary Helen (committee member), Webster, Peter (committee member)

Creator Email eunc@usc.edu,euncho0227@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-472002

Unique identifier UC11266718

Identifier etd-ChoEun-6020.pdf (filename),usctheses-c40-472002 (legacy record id)

Legacy Identifier etd-ChoEun-6020.pdf

Dmrecord 472002

Document Type Dissertation

Rights Cho, Eun

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA