A LATENT PROFILE ANALYSIS OF SECONDARY STUDENTS’ MUSIC
PARTICIPATION IN THREE SALT LAKE CITY SCHOOLS:
AN EXPLORATORY STUDY
by
Seth Pendergast
A dissertation submitted to the faculty of
The University of Utah
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
School of Music
The University of Utah
August 2018
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Copyright © Seth Pendergast 2018
All Rights Reserved
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The University of Utah Graduate School
STATEMENT OF DISSERTATION APPROVAL
The dissertation of
Seth Pendergast
has been approved by the following supervisory committee members:
Jared Rawlings
, Chair
May 29, 2018
Date Approved
Nicole R. Robinson
, Member
May 29, 2018
Date Approved
Mark Ely
, Member
May 29, 2018
Date Approved
Jessica Nápoles
, Member
Date Approved
Elisabeth Conradt
, Member
May 29, 2018
Date Approved
and by
Miguel Chuaqui
the Department/College/School of
, Chair/Dean of
Music
and by David B. Kieda, Dean of The Graduate School.
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ABSTRACT
This study was conducted to better understand the musical and personal
characteristics of students inside and outside school music programs. Therefore, the
purpose of this study was two-fold: (1) to identify patterns of musical activity from an
adolescent school population; and (2) to examine the demographic, environmental, and
personal beliefs associated with different patterns of musical activity. Participants for this
study were students from two high schools and one middle school in Salt Lake City, Utah
(N = 855). Individuals completed a researcher-designed music participation index to
measure levels of musical activity. The musical activities were categorized by three
separate domains: formal (school/private lessons), nonformal (community music), and
informal (home music).
Data from the music participation index were analyzed using latent profile
analysis, which is a quantitative technique that enabled the researcher to identify hidden
or unobserved (latent) patterns (profiles) of musical activity. Results revealed six distinct
profiles of musical activity. Students in Profile 1 (21%) reported below average rates of
musical activity in each domain (i.e., formal, nonformal, informal). Students in Profile 2
(24%) listened to music at average rates but showed below average rates of participation
in almost every other domain. Students in Profile 3 (22%) reported above average desire
for music participation but did not actually participate in very many musical activities.
Students in Profile 4 (8%) showed above average rates of informal musical activity while
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demonstrating little participation in formal or nonformal activities. Students in Profile 5
(17%) reported the most involvement in formal and nonformal activities. Finally,
students in Profile 6 showed above average involvement in every informal music domain
and relatively high rates of formal and nonformal music participation. The majority of
students from this sample (67%) reported average or below average rates of music
listening and little performing or creating musical activities in any domain (i.e., formal,
nonformal, informal). These findings may have implications regarding the amount of
students outside of school music programs who are interested in engaging in schoolbased music activities. The music participation profiles were also compared on the basis
of several demographic, environmental, and personal belief variables.
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TABLE OF CONTENTS
ABSTRACT .................................................................................................................. iii
LIST OF TABLES ....................................................................................................... viii
LIST OF FIGURES......................................................................................................... x
ACKNOWLEDGMENTS .............................................................................................. xi
Chapters
1. INTRODUCTION....................................................................................................... 1
Conceptualizing Participation ................................................................................. 3
Adolescent Activity Participation ................................................................... 4
Adolescent Music Participation ...................................................................... 5
A Framework for Adolescent Music Participation .......................................... 7
Summary of Adolescent Music Participation.................................................. 9
Need for the Study.................................................................................................. 9
Music Participation Profiles ........................................................................... 9
Music Participation Profiles and Related Factors.......................................... 11
Purpose of the Study............................................................................................. 14
Definition of Terms .............................................................................................. 15
Conclusion ........................................................................................................... 18
2. REVIEW OF LITERATURE .................................................................................... 19
Introduction .......................................................................................................... 19
Adolescent Music Participation ............................................................................ 20
Formal Music Participation .......................................................................... 23
Nonformal Music Participation .................................................................... 31
Informal Music Participation........................................................................ 35
Summary of Music Participation .................................................................. 41
Demographic, Environmental, and Personal Belief Factors ................................... 43
Student Attitudes Toward Music .................................................................. 43
Perceived Musical Ability Beliefs ................................................................ 44
Parent and Peer Influence............................................................................. 45
Comprehensive Studies of Music Course Enrollment ................................... 47
Chapter Summary ................................................................................................. 48
3. METHOD ................................................................................................................. 52
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Purpose of the Study............................................................................................. 53
Research Setting ................................................................................................... 54
Sample and Participants........................................................................................ 54
Description of the Instrument and Measures ......................................................... 56
Demographic Variables................................................................................ 56
Pilot Study – Development of the Music Participation Index ........................ 56
Primary Study – Environmental Factors ....................................................... 63
Primary Study – Personal Belief Scales ........................................................ 64
Procedures ............................................................................................................ 66
IRB .............................................................................................................. 66
Survey Administration ................................................................................. 66
Pilot Study - Data Analysis .......................................................................... 67
Primary Study - Data Analysis ..................................................................... 70
4. RESULTS ................................................................................................................. 77
Pilot Study............................................................................................................ 78
Sample Demographics ................................................................................. 78
Missing Data ................................................................................................ 78
Pilot Research Study Question One .............................................................. 81
Pilot Research Study Question Two ............................................................. 83
Pilot Study Summary ................................................................................... 92
Primary Study....................................................................................................... 93
Sample Demographics ................................................................................. 93
Variables ..................................................................................................... 93
Missing Data ................................................................................................ 96
Differences Between Adolescents Enrolled in HS A, HS B, and MS ............ 97
Informal Music Participation – MIMIC CFA Models ................................... 97
Primary Study – Research Question One .................................................... 104
Primary Study – Research Question Two ................................................... 116
Primary Study - Research Question Three .................................................. 117
Primary Study - Research Question Four.................................................... 119
Conclusion ......................................................................................................... 123
5. SUMMARY AND CONCLUSIONS ....................................................................... 125
Purpose and Research Questions......................................................................... 125
Pilot Study.......................................................................................................... 126
Summary of Pilot Study Results ................................................................. 126
Explanation and Alignment With Past Research ......................................... 128
Research Question One....................................................................................... 129
Explanation and Alignment With Past Research ......................................... 131
Research Question Two ...................................................................................... 132
Research Question Three .................................................................................... 134
Explanation and Alignment With Past Research ......................................... 135
Research Question Four ...................................................................................... 136
Explanation and Alignment With Past Research ......................................... 137
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Implications........................................................................................................ 139
Theoretical Implications............................................................................. 139
Methodological Implications ...................................................................... 142
Implications for Music Education Pedagogy .............................................. 146
Implications for Music Teacher Education ................................................. 150
Suggestions for Future Research ......................................................................... 151
Limitations ......................................................................................................... 152
Conclusion ......................................................................................................... 153
APPENDIX ........................................................................................................ 155
REFERENCES................................................................................................... 168
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LIST OF TABLES
Tables
2.1 Formal, Nonformal, and Informal Music Learning Domains .................................. 22
3.1 Demographic Data of Participating Schools............................................................ 55
4.1 Demographic Characteristics of Primary Study Participants and HS A Population . 79
4.2 Correlations for Formal CFA Analysis .................................................................... 86
4.3 Comparison of Fit Statistics for Each Stage of the Formal, Nonformal, and Informal
models ........................................................................................................................... 87
4.4 Estimates for the Formal and Nonformal Music Participation MIMIC CFA Models 88
4.5 Correlations for Nonformal CFA Analysis ............................................................... 90
4.6 Correlations for Informal CFA Analysis .................................................................. 92
4.7 Demographic Characteristics of Primary Study Participants ................................... 94
4.8 Differences in Adolescents for HS A, HS B, and MS................................................. 98
4.9 Correlations for Informal Performing CFA Analysis ............................................... 99
4.10 Comparison of Fit Statistics for the Informal Performing, Informal Creating, and
Informal Responding MIMIC CFA Models .................................................................. 100
4.11 Estimates for the Informal Performing, Informal Creating, and Informal Responding
MIMIC CFA Models ................................................................................................... 101
4.12 Correlations for Informal Creating CFA Analysis ............................................... 103
4.13 Correlations for Informal Responding CFA Analysis ........................................... 104
4.14 Means and Standard Deviations for Model Variables .......................................... 106
4.15 Profile Fit Indices................................................................................................ 109
4.16 Six-Profile Model - Unconditional Probabilities, Conditional Probabilities, and
Means ......................................................................................................................... 110
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4.17 Means and Standard Deviations for Peer and Parent Norms ............................... 118
4.18 Percentages of Long-Term Musical Identity Within Music Participation Latent
Profiles ....................................................................................................................... 120
4.19 Means and Standard Deviations for Music Ability Beliefs .................................... 121
4.20 Means and Standard Deviations for Secondary Music Courses ............................ 122
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LIST OF FIGURES
Figures
3.1. Visual representation of the Music Participation Index framework. ........................ 59
3.2. Reflective vs. formative indicator (Brown, 2015; Streiner et al., 2015). .................. 60
3.3. Model for formal, nonformal, and informal Music Participation Indices. Each
formative indicator (bottom) represents the domain (F, N, I; formal, nonformal, informal)
and the mode (P, C, R; perform, create, respond)........................................................... 63
3.4. Latent profile of music participation model ............................................................ 74
4.1. Model for formal, nonformal, and informal music participation indices. Each
formative indicator (bottom) represents the domain (F, N, I; formal, nonformal, informal)
and the mode (P, C, R; perform, create, respond)........................................................... 84
4.2. Final MIMIC CFA model for formal music participation. ....................................... 89
4.3. Final MIMIC CFA model for nonformal music participation. ................................. 91
4.4. Final MIMIC CFA model for informal performing music participation................. 102
4.6. Final MIMIC CFA model for informal responding music participation. ................ 105
4.7. Final music participation latent profile model. Each formative indicator (bottom)
represents the domain (F, N, I; formal, nonformal, informal) and the mode (PS – perform
school; PL – perform lesson; C – create; R – respond; Su – desire).............................. 106
4.8. Graphic representations of each profile in the 6-profile LPA model. ..................... 113
4.9. Mean attitudes for secondary music courses according to Music Participation Latent
Profile. ........................................................................................................................ 122
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ACKNOWLEDGMENTS
Dr. Jared Rawlings: Thank you for investing in me for the past 2 years. You gave
your time, support, and encouragement without hesitation. You pushed me to explore, to
think, to write, and to teach. I will forever be a better scholar, a better teacher, and a
better person because of your guidance. I will never forget your generosity, support, and
grace – thank you.
Dr. Jessica Nápoles, Dr. Nicole R. Robinson, and Dr. Mark Ely: My time at the
University of Utah has changed my life. You taught me what it means to care for a
colleague, to work as a team, and to do what’s best for students. You made me think. You
helped me grow. Thank you for your mentorship. Whatever good I do in the future, it is
because I am standing on your shoulders.
Dr. Elisabeth Conradt: I sincerely appreciate your participation in this project.
Your insights and guidance were invaluable. Thank you for all the advice and direction
you offered.
Monica: It has always bothered me that, at the end of this journey, I will receive
letters by my name and friends and family will congratulate me on accomplishing the
doctorate while, in so many ways, you have made more sacrifices than I. Thank you for
every night you stayed home with our son while I worked to finish this document. Thank
you for changing the course of your career so that I could accomplish this degree. Thank
you for your reassurance as I stressed over each class, paper, and project. You have given
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so much – I look forward to spending the rest of my life paying you back. We went
“dancing in a minefield” but we are through now, and I can’t wait to see what the future
has in store for us. Thank you, my love.
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CHAPTER 1
INTRODUCTION
Many young people participate in multiple musical activities characterized by
distinctive contexts, media, artistic expression, style, and modes of learning (Elpus, 2014;
Green, 2002; Hallam, Creech, & Varvarigou, 2017; Higgins & Willingham, 2017; Moir,
2017). For example, young people may listen to music regularly (Rideout, 2015), perform
in school-based music groups (Elpus & Abril, 2011), compose their own music (Tobias,
2015), or participate in community music activities (Higgins, 2016). Yet, it is unclear
how often, to what degree, and in what combination adolescents choose to engage in the
variety of musical activities available to them.
Recently, music educators from a number of disciplines have raised concerns that
many students are unable or choose not to participate in school-based music programs.
Currently, 34% of public school students participate in secondary public-school music
programs, which typically consist of large performing ensembles and/or other music
courses such as general music, music appreciation, guitar, and piano (Elpus, 2014;
Stewart, 1991). Many music education stakeholders have expressed their commitment to
ensuring secondary music programs serve all students, not just the students currently
enrolled in music programs (Miksza, 2013; Odegaard, 2018; Randles, 2015; Shuler,
2011b). This commitment has encouraged music education scholars to investigate the
1
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factors that influence music participation, especially in secondary music programs.
A number of factors seem to influence school music participation. First, lack of
access to music instruction may serve as a barrier to school music participation for some
students, especially those from low socioeconomic backgrounds (Parsad & Spiegelman,
2012). Second, some scholars discuss strengthening the relevance of the secondary music
curriculum to ensure all students find the curriculum interesting and compelling. Several
scholars have encouraged innovative and creative adaptations of current course offerings
as way to boost participation from all students in secondary public schools (Allsup, 2016;
Fonder, 2014; Miksza, 2013; Shuler, 2011a). Other scholars recommend wholesale
changes to secondary curricula such as reductions in large ensemble course offerings and
increases in alternative music courses and/or methodologies to encourage school music
participation (Kratus, 2007; Randles, 2015; D. A. Williams, 2011; D.B. Williams, 2011).
Finally, researchers have elucidated several other psychosocial factors that seem to
influence participation in music such as perceived musical ability (Hawkinson, 2015;
Ruybalid, 2016), personal attitudes towards music participation (McPherson &
Hendricks, 2010), and the social norms of parents and/or peers (McPherson, Davidson, &
Faulkner, 2012).
As music education researchers attempt to understand how to increase schoolbased music participation, one relevant issue that requires further investigation is the
manner and degree to which adolescents currently participate in musical activities across
different life environments (e.g., school, home, community, etc.). It is important to
evaluate the type, frequency, and breadth of adolescent musical activity as well as the
factors associated with different types of musical activity in order to better understand
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how adolescent musical activity is related to school-based music participation and the
school music curricula. Developing a comprehensive understanding of adolescent music
participation may enable researchers to identify groups of students that participate in
music in their home or community and might also enroll in school-based music courses.
Researchers might also be able to identify students who do not engage in musical
activities or engage only in certain manners. These different groups may be compared on
the basis of demographics, environmental factors, and personal beliefs to better
understand how patterns of adolescent musical participation emerge. In light of scholars’
limited understanding of the quantity and breadth of musical activity among adolescents,
it is important to study the ways in which students participate in music across their lives
and the psychosocial factors associated with their involvement.
Conceptualizing Participation
The World Health Organization (2007) defines participation as “involvement in a
life situation” (p. xvi). The nature and settings of involvement tend to vary depending
upon human development, life situations, and relationships (World Health Organization,
2007). Chang and Coster (2014) extend this definition by stressing the social and
contextual nature of participation. They define participation as “active involvement in
activities that are intrinsically social and occur in a societally-defined context” (Chang &
Coster, 2014, p. 1792).
When examining individuals’ participation in societally-defined contexts,
researchers consider indicators such as enrollment in activities, attendance, breadth of
involvement, persistence over time and physical/social context (Eccles & Barber, 1999;
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Mahoney, Vandell, Simpkins, & Zarrett, 2009; Whiteneck & Dijkers, 2009).
Furthermore, researchers draw a distinction between participation and other
conceptualizations of involvement, specifically, engagement. Engagement concerns
several factors relating to an individuals’ involvement with an activity. Researchers
studying engagement may consider behavioral, emotional (interest, enjoyment), cognitive
(learning strategies), and agentic factors (preferences, creating new directions) (Reeve,
2015). While participation does include behavioral and, sometimes, emotional factors of
engagement, it does not contain cognitive or agentic factors (Chang & Coster, 2014).
Participation also does not include expertise. Instead, researchers studying participation
consider what a child does and not how well a child does it (King et al., 2007). In
summary, the construct of participation encapsulates an individual’s involvement in
activities (frequency, breadth, persistence) in a variety of societally defined contexts.
Adolescent Activity Participation
For adolescents, participation in extracurricular activities varies widely and is
influenced by many factors at different stages of development. Much of the research
literature on adolescent participation involves participation in extracurricular or out-ofschool activities (e.g., employment, organized activities). Most adolescents participate in
at least one organized activity during adolescence (Mahoney, Harris, & Eccles, 2006).
Adolescent participation varies according to timing, duration, breadth of involvement,
and the types of activities adolescents choose to participate (Darling, 2005; Pedersen,
2005). Demographic factors such as socioeconomic status and race/ethnicity seem to
influence both the availability of activities and parents’ abilities to help their children
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access extracurricular activities (Mahoney et al., 2009). In general, adolescent
involvement in activities seems to increase from elementary school to early high school
and then decline (Darling, 2005). This trend may be because adolescents have new
demands on their time (e.g., activities, homework, etc.). They may also spend more time
socializing with friends and have access to a wider-variety of activities where
participation in activities becomes contingent upon ability level (Borden et al., 2005;
Mahoney et al., 2009; McNeal, 1998). Psychosocial factors such as interest, motivation,
values, and competencies also play a role in determining if youth begin and continue
participation in an activity (Mahoney et al., 2009). Finally, peer relationships and parental
values seem to be some of the central factors for beginning and continuing to participate
in an activity (Borden et al., 2005; Persson et al., 2007; Simpkins et al., 2009; Wimer et
al., 2008). This study will focus exclusively on adolescent music participation.
Adolescent Music Participation
Music education researchers have often used music participation as a grouping
variable when comparing adolescents on a range of characteristics such as academic
achievement, literacy, social-benefits of music-making, music perception, and
neurocognitive processes (Elpus, 2017). Researchers have categorized adolescent music
participants in several manners. Often, music education researchers distinguish between
school music students and those students not enrolled in school music classes based upon
class enrollment data from school transcripts (Butzlaff, 2000; Elpus, 2013, 2014; Elpus &
Abril, 2011; Fitzpatrick, 2006; Kinney, 2008; Stewart, 1991; Wallick, 1998). Other
researchers have classified adolescents based on their music training: formal music
6
training or no formal music training (Butzlaff, 2000; Chobert, Marie, François, Schön, &
Besson, 2011; Corrigall & Trainor, 2011; Costa-Giomi, 1999, 2004; Kraus &
Chandrasekaran, 2010; Schlaug, Norton, Overy, & Winner, 2005; Wong, Skoe, Russo,
Dees, & Kraus, 2007). Self-report measures of music participation are also prevalent
among music education scholars. For example, some self-report measures address arts
participation as a whole (Bergonzi & Smith, 1996; Catterall, Dumais, & HampdenThompson, 2012) while others only address participation in school-based music or formal
music lessons (Darling, 2005; Elpus & Abril, 2011; McPherson & O’Neill, 2010; Miksza,
2007, 2010; Rawlings, 2017). Three of the more detailed measures of music participation
encapsulated instrumental music study, instrumental style, years playing an instrument,
and group or individual study (Hille & Schupp, 2015; Rawlings, 2016; Rawlings &
Stoddard, 2017). In summary, researchers often conceptualize music participation in
terms of individuals’ training or formal music experiences.
While many researchers have found substantive differences between participant
groups using the methods of measurement cited above, there is some evidence that
classifying adolescents as participants/nonparticipants in the arts, trained/untrained
musicians or enrolled/not enrolled in school music may not adequately describe
adolescents’ involvement in music activities. In How Popular Musicians Learn, Green
(2002) explains many adolescents participate in a duality of musical activities both
formal (e.g., school-based, private lessons, etc.) and informal (e.g., alone at home, with
friends, community music groups, etc.). Folkestad (2006) stresses the importance of
considering musical involvement on a continuum of formal to informal musical
experiences, not on a binary. Some scholars have noted the prevalence of both formal and
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informal learning practices in adolescents’ lives (Allsup, 2002; Higgins & Willingham,
2017; Lamont, Hargreaves, Marshall & Tarrant, 2003; Moir, 2017). Indeed, there is some
evidence that students within the United States participate in a variety of musical
activities such as school-based music courses (Elpus, 2014), community music programs
(Higgins & Willingham, 2017), church/religious music groups (Ingalls & Yong, 2015;
Miller & Miller, 2012), songwriting (Tobias, 2013a, 2015), DJ/computer music
(Rambarran, 2017; Rideout, 2015), popular music ensembles (Moir, 2017), musicmaking with video games/multimedia (Cayari, 2017; O’Leary & Tobias, 2017; Tobias,
2013b), and a high degree of music listening (Rideout, 2015). Music education scholars
who consider adolescent music participation in their research may benefit from
definitions, frameworks, and/or measurements of music participation that address the
many ways youth participate in performing music, creating music, and listening to music.
A Framework for Adolescent Music Participation
A framework for music participation may assist scholars in fully conceptualizing
the multiple ways adolescents engage in musical activities. Veblen (2012), citing several
other researchers but primarily drawing upon Folkestad (2006), placed music learning
practices on a spectrum ranging from formal to informal with nonformal falling between
them. Each learning practice is qualified by context, learning style, ownership (i.e., the
person in control of the learning), intentionality of participants (i.e., to play music or to
learn music), and modes for transmission (i.e., notation, aural, etc.). Formal music
instruction is characterized by school or institutional contexts led by a teacher or leader
with planned and sequenced instruction. The musical learning that occurs in formal
8
contexts is intentional because participants focus their attention on learning how to play
or sing instead of just playing or singing for fun (Veblen, 2012). Nonformal music
experiences often have identifiable learning goals established through a collaborative
process between the teacher/mentor and the participants. The learning process is flexible,
contextualized, and tailor made with a teacher/mentor acting as a guide (Higgins, 2016).
This process can occur in a variety of contexts including community, religious, and
school-based music programs (Veblen, 2012). Finally, informal music participation is
characterized by experiential learning (Veblen, 2012). It typically occurs in unofficial,
casual settings and the learning process happens through the interactions of the
participants (Folkestad, 2006). The individuals control the learning process and learning
may be incidental or even accidental (Green, 2008). Folkestad (2006) characterizes the
process as playing music instead of learning how to play music.
Veblen’s (2012) synthesis of music learning practices is a useful framework for
conceptualizing music participation. For the purpose of this investigation, I will adopt
Veblen’s (2012) classification of learning practices as a framework for categorizing
music participation. Adolescent musical activities may be categorized into one of the
three music learning practices (formal, nonformal, informal) based upon the various
features of each musical activity. For example, an adolescent’s musical involvement in a
church setting (physical context) is often led by a director/mentor (learning style) with a
focus on playing or preparing music for the benefit of others (intentionality). Music
participants in a religious setting may use notation or aural means to learn music (modes
for transmission) and typically have the freedom to stay, leave or request a new direction
for the ensemble (ownership). This example would be classified as a nonformal music
9
experience. More specific classifications might be made based upon other features of the
activity (i.e., performance task, creative task, listening task). Further details about this
framework are available in Chapter 2 and Chapter 3.
Summary of Adolescent Music Participation
Chang and Coster (2014) defined participation as “active involvement in activities
that are intrinsically social and occur in a societally-defined context” (Chang & Coster,
2014, p. 1792). In the research literature, music scholars have conceptualized music
participation in a number of ways (Elpus, 2017) but due to the multifaceted nature of
adolescents’ musical participation (Green, 2002), robust categorizations have proven
difficult to achieve. Several researchers have conceptualized frameworks for
understanding and studying the nature of adolescent involvement in music through all life
contexts (Folkestad, 2006; Green, 2002; Veblen, 2012). Formal, nonformal, and informal
music learning practices are one way to conceptualize music participation.
Need for the Study
Descriptive research is needed to explore the various potential music participation
profiles among adolescents and how particular profiles of music participation are
associated with demographic, environmental, and psychological factors.
Music Participation Profiles
Researchers who consider factors of musical involvement often classify music
participants dichotomously (e.g., in-school/out-of-school, trained musician/untrained
10
musician). This type of classification, while not without theoretical justification, may
shroud meaningful variations of music participation within a study population. For
example, Elpus and Abril (2011) conducted a demographic analysis of high-school
seniors in band, chorus, and orchestra in the United States during the 2004 school year
using a National Center for Education Statistics database. Participants in the database
were classified as music students or nonmusic students based on their self-reported
enrollment in a band, chorus or orchestra class. The results indicated that 21% (n =
621,895) of the sample participated in music classes while 79% (n = 2,337,850) did not.
It may be that individuals within these two large groups participate in a variety of music
activities that extend beyond school-based music participation. One might hypothesize
that if the students were subdivided into profiles of music participation along a spectrum
of formal, nonformal, and informal music practices, new and significant results may
emerge. Some qualitative researchers do gather rich and detailed data concerning music
participation but, as is the nature of qualitative research, most do so with relatively small
sample sizes when compared to quantitative researchers (Allsup, 2002; Hallam, Creech &
Varvarigou, 2017; O’Neil, 2017; Tobias, 2015). Researchers may benefit from additional
empirical studies testing methodologies for capturing, quantifying, and comparing
detailed music participation profiles. Such measures may be useful for researchers who
wish to compare groups of music participants on a range of relevant research topics (e.g.,
motivation, interest in music programs, life-long engagement in music, social/health
outcomes of music-making, music identity, self-concept, etc.).
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Music Participation Profiles and Related Factors
In recent years, the relationship between adolescents’ musical lives and the
secondary school-based music participation has been a central point of conversation in
music education scholarship. Researchers have shown that, since 1982, U.S. students
have participated in secondary music courses at an average rate of 34% (Elpus, 2014;
Elpus & Abril, 2018). Still, researchers also stress that there are groups of students that
remain underrepresented in secondary music courses, namely: Hispanic students, males,
children of parents holding a high school diploma or less, children from low
socioeconomic backgrounds, and students with individualized education plans (Elpus,
2014; Elpus & Abril, 2011, 2018). These findings have encouraged conversations among
music education scholars regarding why some students participate in school-based music
programs and others do not (Fonder, 2014; D. A. Williams, 2011).
Some scholars suggest underrepresented groups of adolescents do not participate
in secondary school music classes because the current secondary music curricula are very
different from their own musical lives and interests (Cavicchi, 2009; Kratus, 2007;
Reimer, 2015; D.B. Williams, 2011). They argue music education programs do not offer
modes of music-making that are reflective of the technological environment, listening
habits, or musical interests of young people (Kratus, 2007; Regelski, 2014; D. A.
Williams, 2011; D.B. Williams, 2011). In an effort to address broader types of music
activities, music education scholars have developed alternative music education programs
to provide spaces for students to engage in nonformal and informal learning practices
(Clements, 2010; Hallam et al., 2008; Powell, Krikun, Pignato, 2015; Randles, 2015).
Still, other music education stakeholders do not feel alternative music programs
12
must be offered to ensure music programs are relevant to secondary students. They
believe that innovative and creative adaptations to current course offerings may be a way
to reach underserved populations of students (Allsup, 2016; Fonder, 2014; Miksza, 2013;
Shuler, 2011a). Miksza (2013) articulates this viewpoint when he explains:
It is much more important that we gather our collective energy and apply it
toward maximizing what is possible by discovering innovative ways for
increasing the meaning and depth of students’ music experiences in many
curricular models. We also need to continue to break down barriers for students
all walks of life by… engaging students in a variety of music that taps into our
common humanity. (p. 49)
Scholars who subscribe to this approach suggest there are multiple ways to meaningfully
engage secondary students.
Access to music instruction, especially for underserved populations, may also
influence school-based music participation. Several national and regional reports indicate
that the majority of children do have access to music instruction but there are differences
along racial, socio-economic and academic lines (Morrison, 2010; Parsad & Spiegelman,
2012; Salvador & Allegood, 2014; Woodworth, Gallagher & Guha, 2007). For example,
a report from the National Center of Education Statistics indicates that while access to
arts instruction increased from 2000 to 2009 in schools with the highest socioeconomic
status (89% to 96%), it decreased during the same period of time in schools with the
lowest socioeconomic status (89% to 81%) (Parsad & Spiegelman, 2012). Indeed, if
school-based music programs are not available to students, they will be unable to
participate in formal music instruction.
Finally, there is evidence that suggests other factors may also influence a child’s
ability or decision to engage in musical activities. As cited above, some scholars have
noted demographic disparities in music participation (Elpus & Abril, 2011). Other studies
13
have indicated that adolescents’ positive or negative beliefs about their own musical
abilities may be another determinant for musical involvement (Hawkinson, 2015;
Ruybalid, 2016). Several researchers have found that social influences such as
parents/peers perceived value for musical activity as well as structural barriers like
involvement in other school activities may serve as obstacles to school music enrollment
(Hawkinson, 2015; Horne, 2007; McPherson, Davidson, & Faulkner, 2012).
Undoubtedly, adolescent personal attitudes towards music-making also play a role in
music participation. For example, in a sample of over 3,000 students in the United States,
McPherson and Hendricks (2010) found student interest in school music tended to be
ranked lowest among all secondary grades while student interest in music outside of
school tended to rank at or near the top for all secondary grades.
If music education scholars are to unravel the intersecting factors that influence
adolescent participation in school-based music courses, researchers may need to develop
a comprehensive and quantitative view of the breadth of adolescent music participation
(i.e., formal, nonformal, and informal music participation), the varying degrees of
participation, and the unique combinations of type and frequency of music participation
among adolescent populations. This line of inquiry may hold several benefits for
understanding school-based music participation. First, as different typologies or groups of
music participants are identified, researchers may be able to ascertain which students
might participate in school-based music programs, which will not, and why. Second,
groups of music participants might be compared on a number of relevant factors such as
demographics, environmental factors, and psychosocial variables to better understand
why adolescents develop different patterns of musical participation. Finally, music
14
education researchers might examine how patterns of music participation are related to
prevalent arguments surrounding school-based participation, namely access to music
instruction and the relevance of the music education curriculum. In summary, additional
descriptive research is needed to better understand the connection between adolescents’
musical lives (i.e., formal, nonformal, and informal music participation) and various
demographic, environmental, and personal belief factors related to music participation.
The nature of the relationship between music participation profiles and the factors listed
above may begin to reveal some of the reasons music participation disparities exist
among the secondary school student population.
Purpose of the Study
The purpose of this study was two-fold: (1) to identify profiles of music
participation from an adolescent school population; and (2) to examine the demographic,
environmental and personal belief differences of adolescents according to music
participation profile. The four research questions were:
1. What music participation profiles exist from the sampled population?
2. What is the relationship between music participation profiles and demographic
variables such as gender, grade, GPA, and parent education?
3. To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life
activity involvement, and past experiences with school music classes?
4. To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity,
15
music ability beliefs, and attitudes towards secondary school music classes?
To answer these questions, I will utilize quantitative procedures associated with latent
profile analysis (LPA) and analysis of variance. The dataset includes observed variables,
self-reported by the participants, related to music participation (i.e., music participation
profile) and demographic, environmental, and personal belief variables. Participants are
from two high schools and one middle school in the Salt Lake City metro area. Further
details regarding the methodology are presented in Chapter 3.
Definition of Terms
For the purposes of this study, the following terms will be defined as:
Participation – “…involvement in a life situation” (The World Health
Organization, 2007, p. xvi).
Music participation – This term refers to an individual’s involvement in
performing, creating, or responding to music, both alone or with others (Shuler,
Norgaard, & Blakeslee, 2014). Music participation may vary based on context, learning
style, ownership (i.e., the person in control of the learning), intentionality of participants
(e.g., to play music or to learn music), and modes for transmission (i.e., notation, aural,
etc.) (Veblen, 2012).
Formal music participation – Formal music participation is teacher led, directed,
and sequenced in a formal or institutional setting (e.g., school, private lessons, etc.).
Formal music instruction is characterized by school or institutional contexts led by a
teacher or leader with planned and sequenced instruction. The musical learning that
occurs in formal contexts is intentional because participants focus their attention on
16
learning how to play or sing instead of just playing or singing for fun (Veblen, 2012).
Nonformal music participation – Nonformal music participation is directed by a
facilitator or guide with only loosely established goals and an emphasis on
playing/singing solely for enjoyment, not to accomplish specific learning objectives (e.g.,
church choir, community music ensemble, etc.). Nonformal music experiences may have
identifiable goals but they are adaptable and established through a collaborative process
between the facilitator and participants. The learning process is flexible, contextualized,
and tailor made with a facilitator acting as a guide (Higgins, 2016). This process can
occur in a variety of contexts including community, religious, and after-school music
programs (Veblen, 2012).
Informal music participation – Informal music participation is self-driven. It
occurs independent of any teacher or guide. It typically occurs in unofficial, casual
settings and begins or ends when the participants wish (Folkestad, 2006). The individuals
control the learning process, learn from each other, and any learning that does occur is
often incidental or even accidental (Green, 2008). Folkestad (2006) characterizes the
process as playing music instead of learning how to play music.
Traditional music courses – This term refers to the most common types of music
courses in U.S. public high schools, both currently and historically. Choir,
band/orchestra, and secondary general (e.g., music appreciation) courses are among the
most common music courses currently and historically (Elpus, 2014).
Emerging music courses – This term refers to secondary music courses that are
less common than traditional music courses in U.S. public school music programs but are
currently growing in popularity and curricular definition. This broad array of music
17
courses includes but is not limited to: composing and arranging music with technology
(Freedman, 2013); popular music ensembles (Powell, Krikun, Pignato, 2015); informal
music learning courses (Green, 2008); piano/guitar courses (Shuler, 2011b); world music
courses (Clements, 2010); and mixed-media (O’Leary, 2017).
Attitude – An individual’s “…positive or negative evaluations of [a specific]
behavior and its outcomes” (McDermott et. al, 2015, p. 161). For this study, attitudes are
measured for large ensemble class, piano/guitar class, music composition with
technology class, popular music group, and past experiences with school music.
Subjective norms – The construct of subjective norms refers to perceived social
pressure to perform or not to perform a behavior (Armitage & Connor, 2001). Parent and
peer subjective norms are measured as part of this study.
Ability beliefs – Ability beliefs are the degree to which one believes they are
effective in skill, ability and interaction in a social environment (Evans, 2015).
Long-term musical identity – Long-term musical identity concerns an individual’s
perception of what their musical involvement may be like in the future (Evans &
McPherson, 2015).
Adolescent – Adolescence is the gradual transition period from childhood to
adulthood. In the broadest biological terms, an adolescent is an individual between 10
and 24 years old (Patton et al., 2018). For the purposes of this study, the term adolescent
refers to secondary school students (7th –12th grade) between the ages of 12 and 18.
18
Conclusion
This chapter began by defining the concept of participation as “active
involvement in activities that are intrinsically social and occur in a societally-defined
context” (Chang & Coster, 2014, p. 1792). Adolescent music participation often varies
based on artistic processes (i.e., perform, create, respond), physical contexts, learning
styles, intentions, modes of transmission, and ownership (Shuler et al., 2014; Veblen,
2012). There is a need to capture and quantify adolescents’ music participation profiles in
a manner consistent with the many ways adolescents are involved with music. Currently,
there is little consensus among music education researchers concerning which factors
drive adolescent involvement in musical activities, especially school-based music
programs. Various scholars propose that access to music instruction, the relevance of
secondary music curricula, parent/peer influence, attitudes toward school-music classes,
and music ability-beliefs may contribute to adolescent involvement in school music
programs in different ways. The purpose of this study is to better understand the
relationship between the demographic, environmental, and personal belief factors and the
musical lives (music participation profiles) of an adolescent population.
19
CHAPTER 2
REVIEW OF LITERATURE
Introduction
Chapter 1 provided a rationale for examining the relationship between
adolescents’ music participation and associated demographic, environmental, and
personal belief variables. Extant research indicates that adolescents participate in music
in different ways and with varying breadth and frequency (Chin & Rickard, 2012;
Hallam, Creech, & Varvarigou, 2017; O’Neil, 2017; Rideout, 2015; Rideout, Foehr, &
Roberts, 2010). The relationship between these mixed forms of music participation and
the secondary music curriculum is a source of contention among music education
stakeholders (Allsup, 2016; Fonder, 2014; Kratus, 2007; Miksza, 2013). The central issue
often concerns how students’ musical lives outside of school, among other environmental
and psychosocial factors, affect their decision to participate in school-based music
programs (Cavicchi, 2009; McPherson, Davidson, & Faulkner, 2012; Reimer, 2015; D.
A. Williams, 2011). In light of this discussion in the field of music education, empirical
research is needed to better understand how types and amount of adolescent music
participation relate to demographic, environmental, and personal belief factors that have
shown associations with school music participation in past research. Therefore, the
purpose of this study was two-fold: (1) to identify profiles of music participation from an
20
adolescent school population; and (2) to examine the demographic, environmental and
personal belief differences of adolescents according to music participation profile.
In order to address the purpose and research questions for this study, I review
empirical studies in this chapter on the following topics: (1) adolescent music
participation in formal, nonformal, and informal domains, (2) environmental factors that
affect music participation, and (3) personal belief constructs that affect music
participation. The first section on adolescent music participation will address
characteristics of each domain of music participation (i.e., formal, nonformal, informal).
Following this discussion, I will address the frequency of adolescent participation within
each domain where data is available. I will also use illustrative examples of music
participation within each domain to demonstrate the breadth of adolescent musical
involvement. In the second section, I will review the empirical literature surrounding the
factors that can influence adolescent music participation.
Adolescent Music Participation
For the purposes of this study, music participation is defined as an individual’s
involvement in performing music, creating music, or responding to music, both alone or
with others (Chang & Coster, 2014; Shuler et al., 2014). The three domains of music
participation are formal, nonformal, and informal music participation. Green (2008),
Folkestad (2006), and Veblen (2012) have conceptualized formal, nonformal, and
informal music learning in two primary ways. First, music learning practices may vary
based on context, learning style, ownership (i.e., the person in control of the learning),
intentionality of participants (i.e., to play music or to learn music), and modes of
21
transmission (i.e., notation, aural, etc.) (Folkestad, 2006; Smart & Green, 2017; Veblen,
2012) (see Table 2.1). Different contexts, learning styles, ownership, intentionality, and
modes of transmission collectively serve to differentiate the three domains of music
learning practices (i.e., formal, nonformal, informal). Second, the domains of music
learning practices are a continuum ranging from formal to informal (with nonformal in
between) (Folkestad, 2006; Green, 2008). In summary, music participation may be
described as an individuals’ involvement in music activities. Each musical activity may
be characterized by the norms associated with formal, nonformal, and informal music
learning practices.
While formal, nonformal, and informal music-making were originally conceived
as music learning practices, they may also serve as a framework to outline different types
of music participation. That is, an adolescent may participate in musical activities that are
typified by formal, nonformal, or informal music learning characteristics (see Table 2.1).
Using the three domains of music participation to categorize different musical activities
enables researchers to profile the multidimensional nature of an individual’s music
participation. For example, an adolescent may take private music lessons for violin at a
local music conservatory (formal), play in a community orchestra (nonformal), and play
fiddle for fun in a blue-grass band with some family members (informal). The nature of
their musical participation includes formal, nonformal, and informal music.
This section of Chapter 2 reviews the research literature related to adolescents’
frequency and breadth of musical involvement within each music participation domain:
formal, nonformal, informal. Each subsection will explicate the full definition of a
particular music participation domain (i.e., formal, nonformal, informal) and the ways in
22
Table 2.1
Formal, Nonformal, and Informal Music Learning Domains
Formal learning
Nonformal learning
Informal learning
Physical context
School, institution,
classroom
Institution or unregulated settings
Unofficial, casual,
unregulated settings
Learning style
Activity planned and
sequenced by teacher
or other who
prepares and leads
teaching activity
Process may be led
by a director, leader
or teacher, or may
happen by group
interaction
Process happens
through interaction
of participant, not
sequenced
beforehand
Ownership
Focus on teaching
and how to teach.
Teacher plans and
guides activities.
Focus on learning.
Student usually
controls learning or
goes along with
teacher or group
choice, but has
ultimate control.
Focus on learning
how to learn (student
perspective). Student
chooses voluntarily
and controls
learning. Learning
takes place, intended
or not.
Intentionality
Focus on how to play Focus on playing
Focus on playing
music. Intentional
music. Social aspects music. Incidental or
learning.
and personal benefits accidental learning.
intertwined.
Intentional or
incidental learning.
Modes for
transmission
Often has a
notational
component.
May use aural and/or Variety – by ear,
notation components, cyberspace – many
tablature, or other
uncharted processes
systems.
Note. Adapted from “Adult music learning in formal, nonformal, and informal contexts,” by K. Veblen
(2012), The Oxford Handbook of Music Education, p. 246.
23
which adolescents participate within that domain.
Formal Music Participation
This section of the chapter reviews research related to adolescent formal music
participation. I will begin with a brief description of the characteristics of formal music
participation. Following this overview, I will discuss the number of youth who participate
in school-music courses and the common characteristics of these courses. This section of
the chapter will end with a discussion on formal music instruction outside of school.
Characteristics of formal music participation. Smart and Green (2017) explain
“‘formal’ learning tends to be defined as the kind of learning that takes place in
institutions such as schools and universities, as a direct result of teaching” (p. 108).
Formal music learning is often graded and hierarchical with a teacher who “controls and
guides materials, pacing, and interactions in a structured environment,” such as private
lessons or school contexts (Veblen, 2012, p. 247). Skills and concepts are taught
methodically and with increasing complexity (Veblen, 2012). The learner understands the
teacher’s instructional design and typically makes an attempt to learn (Folkestad, 2006;
Green, 2002). Regarding musical style, the characteristics of formal music learning
described above should be viewed as a pedagogical approach that is not linked to any
musical style. Several scholars stress that formal music learning does not necessarily
imply Western classical music but is simply a type of music learning practice (Folkestad,
2006; Smart & Green, 2017). In summary, formal music activities are typified by teacher
led instruction with a focus on specific music learning goals for the learner.
Among the three domains of music participation, formal music participation is
24
perhaps the most readily quantifiable because most formal music participation occurs
within a school context. That is, most school music courses may be generally classified as
a formal learning experience as U.S. school music courses typically include teacher-led
instruction, curriculum, and national learning goals/standards (Shuler et al., 2014). One
way of quantifying the amount of formal music participation is through school enrollment
data.
National school music enrollment. It can be difficult to construct an exact
picture of secondary music course enrollment because there is no single database that
readily offers all the relevant information a researcher may require when investigating
how many students are registered for music courses on any given year across the United
States. Researchers must use data from a variety of sources such as national transcript
databases (Elpus, 2014), other national education databases (Elpus & Abril 2011;
Stewart, 1991; United States Government Accountability Office [GAO], 2009) and data
from a variety of national surveys conducted by the government, other organizations,
advocacy groups and corporations (Child Trends, 2015; Parsed & Spiegelman, 2012;
Rabkin & Hedberg, 2011; Sparks, Zhang, & Barr, 2015). With most of the data sources
consisting of surveys, variations in results can occur because each survey may ask
different questions to different population samples in different ways. Therefore, several
data sources must be combined to offer a summary of the probable music course
enrollment in the United States and the demographic characteristics of this population.
This section of the chapter will use a range of sources to accurately conceptualize the
current enrollment in secondary music courses nationally.
There are only a few recent studies that summarize national enrollment in
25
secondary music courses. In a study published in 2014, Elpus constructed a data set from
“ten separate large-scale high school transcript studies conducted by the U.S. Department
of Education’s NCES.” Elpus (2014) found from 1982 to 2009 an average of 34% of all
students enrolled in at least one music course in high school. Results also revealed an
increase in the number of students who persist in music courses through the course of
their 4 years in high school (5.42% in 1982, 9.43% in 2009). In a separate study, Elpus
and Abril (2011) found that approximately 21% of high school seniors were enrolled in
music courses in 2004. Elpus acknowledges the differing results between the 2014 and
2011 study and suggests the inconsistency is because the 2011 study only considered
high school seniors enrolled in ensemble classes in 2004 and used a different data source.
Elpus (2014) will serve as the national average for the remainder of this review.
There are additional sources useful in constructing a complete picture regarding
national music enrollment. However, caution is encouraged when using these data
because it is difficult to verify the methodology and/or results in studies that have not
been peer reviewed. The nonprofit research organization Child Trends analyzed data
from the “Monitoring the Future” survey and found that in 2013, 48.2% of 8th graders,
35.7% of 10th graders and 36.7% of 12th graders were involved in music or other
performing arts nationally. In a 2007 Harris Poll, 75% of American adults indicated that
they were involved in school music programs as children (The Harris Poll, 2007). While
this number most likely includes elementary school music experiences as well as private
music instruction and should not be cited with too much authority as the methodology
and results have not been peer-reviewed, it does indicate that music instruction in school
has been a significant part of many American lives. In summary, Elpus (2014) states: “…
26
it should be heartening for most music teachers to learn that a core group of just over one
third of all U.S. high school students, for nearly 30 years, has consistently chosen to
enroll in a music class.”
The studies cited above also indicate that some demographic groups are
underrepresented in the national music student population (Child Trends, 2015; Elpus,
2014; Elpus & Abril, 2011; The Harris Poll, 2007). Students who are Hispanic, male,
children of parents holding a high school diploma or less, of low-socioeconomic status
and students with individualized education plans were all underrepresented in music
classes while native English speakers, children of parents with advanced degrees and
students with high test scores and GPAs are overrepresented in music programs (Elpus &
Abril, 2011; Elpus, 2014). Stewart (1991) reported females and students from more
affluent backgrounds were more likely to take music classes. He also cited high
academics as a predictor of enrollment.
Types of music courses in secondary schools. Elpus (2014) also investigated
high-school students’ enrollment in specific music courses. Nationwide transcript data
from 2009 showed the following high-school student enrollments according to class:
choir – 14.61%; band – 12.33%; orchestra – 2.39%; secondary general – 5.99%; other
music courses – 6.54% (Elpus, 2014). Results from this study show high-school music
students tend to participate in predominantly large performing ensembles like band,
chorus and orchestra (29.33%). At least 12.53% of high-school music students
participated in alternative music courses such as secondary general music, piano, guitar,
music theory, music technology and IB music (Elpus, 2014).
Most school music participants are involved in school-based large performing
27
ensembles, which represent the majority of course offerings in secondary school music
programs (Abril & Gault, 2008; Elpus, 2014; Elpus & Abril, 2011; Williams, 2007).
Adolescents often begin their musical instruction in large performing ensemble settings
(McPherson, Davidson, & Evans, 2016; Welch, 2016). Large ensembles typically feature
instruction that is teacher led with a focus on musical development, personal artistry,
performance, and sometimes composition (Allen, 2001; Holt, 2008; Kerchner & Strand,
2016; Lautzenheiser, 2000; Randles & Stringham, 2013). Many school-based large
ensemble programs include a leveled course structure based on musical ability, different
types of classes (e.g., jazz band, wind band, mariachi, etc.) and extracurricular groups
(e.g., jazz choir, marching band, etc.) (Hoffer, 2017; Phillips, 2004). The size of
secondary music programs often requires music teachers to manage funds, uniforms,
equipment, and trip planning (Townsend, 2011). Secondary school students report many
musical and personal benefits from participation in large ensembles such as: (1)
meaningful and enjoyable music experiences (Abril, 2013; Berg, 2009); (2) developing
musical and artistic confidence (Adderly, 2009; Parker, 2018); (3) social-identity
development (Parker, 2014, 2018); (4) and a strong sense of community and connection
to others (Abril, 2013; Adderley, 2009; Adderley, Kennedy, & Berz, 2003; Parker, 2014).
While the school-based large ensembles represent the majority of music courses
in secondary schools, alternative music courses are also a common way for adolescents to
participate in school music instruction. In a nationally representative study of high-school
students’ transcripts, Elpus (2014) found 12.53% of high-school students participate in
alternative music courses. Abril and Gault (2008) surveyed a stratified random sample of
1,000 secondary principals about the music courses offered in their schools. While band
28
(93%) and chorus (88%) lead the list, results also showed alternative music courses such
as general music (45%), orchestra (42%), theory (40%), guitar (19%), piano/keyboard
(13%), music technology (10%), and mariachi (5%) classes were also represented. In a
similar study of U.S. high school principals, Dammers (2012) found 14% of high schools
offered technology-based music courses. Finally, popular music programs such as Music
Makes Us (Metro Nashville Public Schools), Amp Up NYC (New York City Public
Schools), and Little Kids Rock (a national popular music curriculum) are growing in
popularity (Powell, Krikun, & Pignato, 2015; Weiss, Abeles, & Powell, 2017). In
summary, extant research suggests alternative music courses in secondary schools remain
far less common than large ensembles but do represent a significant portion of many
adolescents’ formal school music education.
While alternative music courses are an increasing part of secondary students’
school music involvement, the nature of these courses is more diverse than the largeensemble because of the wide array of potential alternative music courses. Performancebased alternative music courses may include, but are not limited to mariachi ensembles
(Fitzpatrick-Harnish, 2015); piano/guitar courses (Abramo, 2010; Rescsanszky, 2017;
Shuler, 2011b); samba ensembles (Higgins & Willingham, 2017); steel pan ensembles
(Resch, 2010); modern band/rock band ensembles (Powell & Burstein, 2017; Weiss et al.,
2017); and electronic music ensembles (Williams, 2014). Some of the performance
classes listed above are teacher-led (e.g., piano/guitar courses, mariachi ensembles, steel
pan ensembles, etc.) while instruction in others involves project-based learning and
student-led groups (e.g., samba ensembles, modern band/rock band, electronic music
ensembles). Some alternative performance courses utilize traditional musical notation
29
(e.g., piano/guitar courses, mariachi ensemble, steel pan ensemble); others use simplified
notation such as chord charts (e.g., modern/rock band), and others are generally
conducted without notation (e.g., samba ensemble). Composition or creative alternative
music courses may include, but are not limited to songwriting (Kratus, 2016);
technology-based music composition (Freedman, 2013; Pendergast, 2016); electronicdance music courses (Manzo & Kuhn, 2015); and informal music learning courses
(D’Amore & Smith, 2017; Green, 2008). These courses are designed around projectbased instruction in individual or small learning groups. Finally, some secondary
programs include general music courses that address music listening and appreciation
(Kratus, 2017; Menard, 2013; Shuler, 2011b).
Formal music participation outside school. Outside of secondary music
programs, adolescents may engage in formal music activities through private music
lessons. Research regarding the number of U.S. youth participating in music lessons is
less precise than enrollment numbers in secondary music courses. In the process of
quantifying participation, some researchers group participation in music lessons with
other arts participation or extracurricular activities, obscuring precise counts of formal
music participation outside school (Moore, Hatcher, Vandivere, & Brown, 2000; NEA,
2012; Rabkin & Hedberg, 2011). Still, two research reports offer a general sense of youth
participation in music lessons. In a 2012 survey, the researchers from the National
Endowment for the Arts (NEA) surveyed adults about music participation during their
childhood. Approximately 16% participated in school music instruction, 24% participated
in outside private instruction, and 36% were involved in music instruction inside and
outside school (NEA, 2013). In the United Kingdom, researchers from the Associated
30
Board of the Royal Schools of Music (ABRSM) conducted a nation-wide survey
concerning adolescents’ involvement in music (ABRSM, 2014). Results indicated 36% of
U.K. youth report taking instrumental music lessons. Both the NEA study and ABRSM
study cited above do not directly measure U.S. youth participation in music lessons
outside of school. Still, they demonstrate private music lessons are one significant way
adolescents become involved in formal music instruction.
Children and adolescents begin private music instruction for an array of reasons.
Some seem to have an intrinsic attraction to a particular instrument (McPherson et al.,
2016). Others begin for extrinsic reasons. For example, a child may wish to keep up with
their friends and siblings or their parents may insist they begin music lessons
(McPherson, Davidson, & Faulkner, 2012). They may also wish to emulate a musical
model they admire (McPherson et al., 2012). Researchers have found adolescents are
likely to continue music lessons if they find personal satisfaction in playing, ascribe
importance to the instrument, see the lessons as useful, and are not overwhelmed by the
difficulty of the instrument (McPherson et al., 2016). Parents and teachers also play an
integral role in supporting children’s progress throughout their lessons (McPherson et al.,
2012). In summary, while adolescents may begin private music instruction for many
different reasons, they must experience a supportive environment from parents and
teachers if they are to continue.
In the U.S., many students engage in formal music activities through school-based
music instruction and private lessons (Elpus, 2014; NEA, 2013; Powell et al., 2015).
Formal music participation encompasses a diverse field with variations based upon
discipline (e.g., instrumental, vocal, composition, etc.), content or learning style (e.g.,
31
teacher-led instruction, project-based instruction, student-led groups, etc.), and context
(e.g., school-based instruction, private instruction). Still, the fundamental characteristics
of formal music learning practice unite the various iterations. The characteristics of
formal music participation include: (1) school or private lesson contexts, (2) teacherdirected activities, curriculum, and instruction, (3) and intentional music learning
(Veblen, 2012). While formal music participation is prevalent among adolescents, many
are involved in music activities outside of formal contexts.
Nonformal Music Participation
This section of the chapter reviews research related to adolescent nonformal
music participation. I will begin with a brief description of the characteristics of
nonformal music participation. Following this overview, I will discuss examples of
adolescent nonformal music participation.
Characteristics of nonformal music participation. Coffman (2002) describes
nonformal music learning activities as systematic and intentional but less regulated than
formal music activities. Oftentimes, such musical activities are designed for a specific
learning community interested in making music together, like community music, church
music, or after-school music groups (Smart & Green, 2017). Unlike formal music
contexts, members of nonformal music learning communities have ultimate democratic
control over the group (Veblen, 2012). While many nonformal music activities are indeed
led by a director or teacher, those involved in the activity have fundamental collective
control over the musical direction of the endeavor (Higgins & Willingham, 2017; Veblen,
2012). Nonformal music learning environments also require participants to apply or
32
acquire knowledge on the spot instead of developing skills across a long-period of time as
in a formal learning sequence (Mak, 2006). Mak (2006) describes this process as
“…learning by doing” instead of “learning from books or instructions” (p. 7). Therefore,
the learning process is often incidental as individuals immediately develop and apply
their knowledge to new contexts (Veblen, 2012). Overall, nonformal music activities are
usually located in institutions (e.g., community music school, after-school music
program, religious organization, etc.) and the focus is directed towards active musicmaking and community building instead of sequential learning.
The complexity of nonformal music involvement is difficult to quantify and
challenging to capture in broad summative descriptions (Higgins & Willingham, 2017;
Schipper, 2018). Mantie and Smith (2017) use the metaphor of “grasping a jellyfish”
when referring to exhaustive attempts to describe nonformal musical involvement (p. 3).
Comprehensive descriptions may be problematic because nonformal musical involvement
tends to occur within community music settings such as after-school music groups,
religious organizations, community music schools, and other public or civic organizations
(Higgins & Willingham, 2017; Mak, 2006; Smart & Green, 2017). These activities are
diverse, fluid, and intimately woven into the nature of the community (Higgins &
Willingham, 2017). The overwhelming “diversity and scope” of community music
programs make them difficult to define (Schipper, 2018, p. 26). Therefore, scholarship
concerning nonformal music involvement (e.g., community music, religious music, etc.)
tends away from broad participation profiles and centers instead on many smaller casestudies from specific communities because most nonformal music participation is highly
contingent upon the people, culture, and resources of a community (Higgins &
33
Willingham, 2017; Schipper, 2018). This section will review several case-studies that
may be indicative of adolescents’ nonformal music involvement in the United States.
Community music involvement. Community music centers serve many areas
throughout the U.S. In most community music centers, children, adolescents, adults, and
seniors participate in musical activities for a fee or supported by external funding
(Higgins & Willingham, 2017). The purpose, structure, and scope of community music
centers vary. For example, McPhail Center for Music (MCM) is a national community
music organization that serves areas in Minnesota, Texas, Tennessee, and others (Music
for everyone, 2018). MCM specializes in location-based and online programs for children
and youth including group/individual music lessons, group music classes, and large
music ensembles (Music for everyone, 2018). Other community organizations operate
within just one local area. For example, South Shore Conservatory (SSC) primarily
serves local communities south of Boston (Welcome, 2018). Additionally, SSC utilizes a
model serving all age-ranges (children to seniors) and offers a host of arts disciplines
(e.g., music, dance, drama, etc.). Further still, some community music organizations work
to support a very specific population within their community. For example, the Music
Resource Center in Charlottesville, VA serves only adolescents using only popular music
activities such as songwriting, music and dance, music lessons, performance, and
recording (Davis, 2007). Administration and staff at the Music Resource Program take
pride in their ability to connect responsively to students’ musical interests (Cohen, Silber,
Snagiorgio, & Iadeluca, 2012). Overall, community music programs for youth differ from
one community to the next employing distinctive models with the goal of serving
different populations.
34
Each example above typifies nonformal musical involvement in several ways.
First, the youth involved in community music programs have ultimate control of whether
they continue to participate or not. They may also influence the direction of programs
either directly (e.g., through various forms of feedback) or indirectly (e.g., staying in the
program or withdrawing their tuition dollars and leaving the program). The democratic
control of group members is a staple of nonformal music activities (Veblen, 2012).
Second, while teachers and leaders direct many of the ensembles or music classes,
“…those who work within this domain refer to themselves as music facilitators rather
than teachers” (Higgins, 2016, p. 597). That is, the music leader or facilitator coconstructs learning with the student as they strive for responsive instruction that fits the
needs and interests of the group. Therefore, the democratic nature of group involvement
in community music programs as well as the role of music facilitators in group activities
signifies youth involvement in community music centers as nonformal music
participation.
Religious music involvement. Involvement in church and religious musical
groups may also be classified as nonformal music participation. For youth involved in
religious life, music can be an integral part of their spiritual experience (Ingalls, 2008;
Ingalls & Yong, 2015). Some may participate in choirs while others may engage in small
instrumental ensembles (Ingalls & Yong, 2015; Miller & Miller, 2013). Several aspects
of religious music involvement are characteristic of nonformal music practices. First,
choir and band leaders often assume the role of a musical facilitator, drawing upon the
musical strengths, abilities, and interests of parishioners to accomplish musical goals
(Higgins, 2016; Miller & Miller, 2013). Second, sparse practice schedules require
35
participants to learn and refine music immediately instead of through a regular learning
process (Ingalls & Yong, 2015; Mak, 2006). Third, religious musical groups often form
close personal connections with older or more experienced musicians mentoring younger
musicians (Miller & Miller, 2013; Smart & Green, 2017). It is likely that adolescents
involved in religious musical activities experience musical environments with many
nonformal music learning practices.
Nonformal music activities tend to embody a musical collaboration between
group members and music facilitators in institutions and organizations outside school or
the private studio (Coffman, 2002; Higgins, 2016; Veblen, 2012). Music facilitators and
music participants work together to co-construct knowledge (Higgins, 2016). The musicmaking process is driven by immediate application of knowledge and skills with new
learning emerging from an organic process of successes and failures (Mak, 2006).
Nonformal music participation represents a semi-structured model for adolescent music
participation. Still, many adolescents are informally involved in musical activities outside
the boundaries of any schools or institutions.
Informal Music Participation
This section of the chapter reviews research related to adolescent informal music
participation. I will begin with a brief description of the characteristics of informal music
participation. Following this overview, I will discuss examples of adolescent informal
music participation. Empirical studies regarding youth music listening habits and
manners of informal music participation will be reviewed.
Characteristics of informal music participation. Informal music activities
36
cover a wide-range of adolescent music participation including passive (listening) and
active music-making. Informal music experiences generally occur in unofficial, informal
or unregulated settings (Smart & Green, 2017). These settings might include playing,
singing, composing, or listening to music casually with friends, family members, or other
familiar people groups. In contrast to formal and nonformal musical involvement, the
music learning process for informal music activities is not structured beforehand in any
manner (Folkestad, 2006). The musical activity is directed solely through the interactions
of the participants (Folkestad, 2006; Green, 2002). As participants play and sing together
they may move between performing, composing, and improvising, learning about each
domain incidentally or accidentally as participation occurs (Green, 2002; Wright, 2016).
Veblen (2012) describes the focus of informal music activities as actively playing or
singing instead of learning how to play or sing. It is important to note that informal music
learning practices may also be applied in contexts more closely related to formal or
nonformal music participation (Green, 2008; Wright, 2016). Smart and Green (2017)
explain “…informal learning finds its fullest expression outside of [formal and
nonformal] contexts, although it may take place within them” (p. 109). The remainder of
this section will address some common ways adolescents participate in informal music
activities.
Passive informal music activities (listening) among adolescents. Extant
research suggests listening to music plays an important role in the lives of young people
(McFerran, 2010). Results from a recent nationally representative survey of U.S.
adolescents (age 13–18) indicated music listening is the top media activity among teens,
with 66% of youth listening to music every day compared with only 45% using social
37
media everyday (Rideout, 2015). The study also showed adolescents spend 1 hour and 54
minutes listening to music each day (Rideout, 2015). Other scholars have shown youth
listen to music significantly more than adults and in a wider array of contexts
(Bonneville-Roussy, Rentfrow, Xu, & Potter, 2013). Adolescents’ typically use the radio,
smartphones, or iPods to listen to music (Rideout, 2015). Common listening activities
may include, but are not limited to (1) listening to music while doing homework (Hallam
et al., 2017; Rideout, 2015); (2) listening to music socially (Hallam et al., 2017); (3)
discussing recorded or original musical works using social media (Tobias, 2013a); (4)
and listening to music while traveling from one location to another (Heye & Lamont,
2010). For adolescents, listening to music is an important part of forming personal
identities, managing moods, coping with stressful situations, and managing relationships
(McFerran. 2010; McFerran & Saarikallio. 2014). Altogether, these results confirm
findings from the previous decade that suggest music listening is a highly preferred and
meaningful informal music activity among adolescents (Lamont, Hargreaves, Marshall,
2003; North, Hargreaves, & O’Neill, 2000).
Active informal music activities among adolescents. While extant research
provides a reasonably clear picture regarding the frequency and manners of youth
listening habits (Bonneville-Roussy et al., 2013; Rideout, 2015), the amount of active
informal music participation among adolescents in the United States is far less clear.
Many students participate in school music programs or lessons (formal) (Elpus, 2014;
NEA, 2013) and many participate in community-based music organizations (nonformal)
(Higgins & Willingham, 2017). Yet, the precise amount of active informal music
participation is largely unknown.
38
The frequency of active informal music participation among youth is unclear.
Some studies seem to indicate there is interest in active informal music participation
outside of school while others cast doubt on the frequency with which these activities
occur. A study comparing U.S. secondary students’ interests towards a variety of school
subjects showed students had the least interest in school music when compared to other
school subjects (McPherson, 2010). However, students also showed the strongest interest
in music as a topic outside of school (McPherson & Hendricks, 2010). These results may
indicate some youth have a greater interest in active informal music involvement than
formal, school-based music participation. D.B. Williams (2011) surveyed students
involved in secondary technology-based music programs to better understand their
musical lives outside of school. Most of the students were not involved in formal music
activities so the researchers hypothesized the students would likely engage in active
informal music-making outside of school. However, results showed only 28% of the
students reported an active musical life outside of school. Similarly, Rideout (2015)
conducted a national survey of adolescents’ media habits, including their use of computer
and tablet technology. Results showed that, even with advancement of creative
technology on computers and tablets, only 3% of young people spend time creating music
or art on tablets and computers (Rideout, 2015). Extant research does not provide a clear
explanation on the frequency of active informal music-making among adolescents.
Further investigation is needed to better understand the frequency of adolescents’ active
informal music participation outside-of-school.
Even without a clear picture of the frequency of informal music participation,
several studies indicate the breadth and diversity of active informal music-making. For
39
most adolescents, informal music participation is closely connected to the content and
techniques of popular music because popular music embodies most adolescents’ socialcontext (Green, 2002). The studies cited below demonstrate some of the ways in which
youth engage in active informal music activities.
Some youth engage in active informal music making by forming performancebased popular music groups with friends (MacDonald, Miell, & Wilson, 2005; Miell &
Littleton, 2008). While members may have some formal music education, the experience
of playing together outside of any institutional context is entirely an informal music
experience (Moir, 2017). The first learning process that may occur alone or with the
entire group is learning repertoire aurally by playing along with recordings or watching
online tutorials (Moir, 2017). This aural and visual learning process is typical of how
many popular musicians learn their repertoire (Green, 2002). Next, the group participants
rehearse the repertoire using a model of sustained evaluation, appraisal, and criticism
(Miell & Littleton, 2008). The members of the group critique the musical performance of
themselves, the other members, and the group as a whole. In turn, the music is rearranged
and attempted again to improve the performance (Miell & Littleton, 2008). This iterative
process often repeats over and over until a performance is refined. For many young
informal musicians, the final step is performing and publishing videos online (Moir,
2017). This step seems to validate the performance process and solidifies the identity of
the group members as real musicians (Moir, 2017). Engagement in active informal music
performance is one way adolescents participate outside of musical institutions or
frameworks.
Informal music activities are often a blend of composition, performance, and
40
production (Green, 2002). Some adolescents, for example, write or arrange music
(Tobias, 2015), record their performance (Tobias, 2013b), and upload the performance to
video hosting websites like YouTube (Cayari, 2017). Websites like YouTube are
transformed into a performance space as young people write, record, produce, and
perform their own musical material (Cayari, 2017; Tobias, 2013b). Tobias (2013b)
explains adolescents may produce music for these platforms using a variety of
techniques: cover songs, satire, parody, multitracking (recording each part yourself),
remixing, sample-based producing, creating mash-ups, creating tutorials, etc. Each
example requires both performance and composition. Adolescents may also use
technology, production, and composition as a part of live performance. For example,
DJing is an increasingly popular mode of informal music participation that requires live
performance mixed with musical production (Rambarran, 2017). Finally, adolescents
may also use recording technology to produce their own music for films and video games
(O’Leary & Tobias, 2017). In summary, adolescents who engage in active informal
music-making may utilize technology to create, perform, and share musical content live
and online.
Video games have come to represent a substantial portion of adolescents’ leisure
time. Rideout (2015) reports “…on any given day…56 percent of teens play games, and
those who play spend an average of 2:00 [hours:minutes] and 2:25 doing so,
respectively” (p. 43). O’Leary and Tobias (2017) explain video games represent a unique
participatory culture for adolescents’ (and adults) to engage in musical activity. Several
games, such as Guitar Hero or Rock Band, represent rhythmic-based musical games
where individuals trigger sounds when the correct button is pressed in time with the
41
music (O’Leary & Tobias, 2017). Other games such as Mario Paint or FRACT OSC
allow users to actively compose music within the game interface (O’Leary & Tobias,
2017). In summary, it is possible that one of the key modes of informal music
participation among adolescents is through a video game interface.
Informal music activities include a wide-array of passive and active musical
activities conducted outside the confines of schools or other institutions. Passive musical
activities might include listening to and discussing music with friends and family
members (Hallam et al., 2017; Rideout, 2015). Youth may engage in active informal
music activities such as learning an instrument by ear, playing music with friends,
writing/recording/producing original music, uploading original music/media content
online, or even playing musical video games (Cayari, 2017; Green. 2002; Moir, 2017;
O’Leary & Tobias, 2017; Rambarran, 2017). Regardless of the specific activity, informal
music involvement is always driven entirely by the participants, without a learning plan
or musical facilitator.
Summary of Music Participation
Music participation is involvement in performing music, creating music, or
responding to music, both alone or with others (Chang & Coster, 2014; Shuler et al.,
2014). Individuals participate in music through a wide-array of music activities. To
further clarify the nature of participation, each activity may be classified as a
predominantly formal, nonformal or informal music experience (Veblen, 2012). Formal
music participation typically occurs in schools or institutions where a teacher plans,
sequences, and directs music learning activities and students focus on how to best
42
perform or compose music (Smart & Green, 2017). Adolescent formal music
participation usually consists of school music involvement and music lessons (Abril &
Gault, 2008; Elpus, 2014; McPherson et al., 2016). Nonformal music participation tends
to occur in community organizations or religious institutions where a facilitator provides
loosely structured activities and students focus on the enjoyment and social benefits of
participation rather than specific learning goals (Coffman, 2002; Higgins, 2016; Veblen,
2012). For adolescents, nonformal music participation frequently consists of involvement
in community, religious, or after school music groups (Higgins & Willingham, 2017;
Ingalls, 2008; Ingalls & Yong, 2015; Miller & Miller, 2013). Informal music
participation occurs in casual environments with friends, family, and other personally
connected groups (Folkestad, 2006). The participants have complete control over the
direction of the musical activity and any learning that occurs is typically incidental or
accidental (Veblen, 2012). Adolescent informal music participation embodies a vast array
of musical activities such as listening to music, playing/singing music with friends,
composing original music, playing musical video games, creating and discussing online
musical content, etc. It is important to note that some musical activities may include
aspects of formal, nonformal, and informal music learning practices (Wright, 2016). For
the purposes of this study, each musical activity will be classified according to the
majority of the learning practices present in the activity.
43
Demographic, Environmental, and Personal Belief Factors
There are many reasons secondary students choose to participate in musical
activities. This section will review three factors related to musical participation. The first
section will consider the influence of personal attitudes toward involvement in music.
The second section will evaluate the influence of musical ability beliefs. Finally, to
address the construct of subjective norms, the last section will review empirical research
related to the influence of parents and peers on involvement with musical activities.
Student Attitudes Toward Music
Interest, enjoyment, and positive/negative attitudes toward school music programs
are commonly cited reasons for participation or nonparticipation in school music
programs. Waters, McPherson, and Schubert (2014) recently examined the choice
between music electives and sports electives at an all-boys independent school in
Australia. Results showed 42.7% of the boys were motivated to choose a sport elective,
4.8% selected music, and 49.8% chose neither. Interest was cited as a primary indicator
in students’ decision to select music vs. sport with the participants expressing much less
interest in music class.
Several other studies have reported students’ musical enjoyment is a primary
reason for enrolling in a music program (Gouzouasis et al, 2008; Wolfe, 1969). Scholars
examining retention and attrition in music programs have also found interest toward
music class is a significant predictor of retention (Corenblum & Marshall, 1998; Horne,
2007; Martignetti, 1965; Morehouse, 1987; Rawlins, 1979; Siebenaler, 2006; Wolfe,
1969). In summary, researchers have demonstrated adolescents’ attitudes and interest
44
towards school-based music programs is one factor that influences their decision to enroll
or not enroll in a music course.
Perceived Musical Ability Beliefs
Music education scholars have noted beliefs about one’s musical ability is one
driver of musical motivation (Evans, 2016; Hallam, 2016). When an individual has
positive beliefs towards their musical ability, they will likely experience higher
motivation for music participation (Austin, 1990, 1991) and may continue music
participation for longer periods of time, especially in a supportive musical environment
(Creech et al., 2008; Long, 2013; McPherson et al., 2012). Much of the research on music
ability beliefs is related to sustained participation, practice, and the development of
musical expertise. There is far less information concerning the relationship between
music ability beliefs and intention to enroll in a school music program.
Researchers have investigated the relationship between musical ability beliefs and
school-based music enrollment using a variety of constructs such as musical self-esteem,
musical self-concept, and musical possible selves. Austin (1990) evaluated the musical
self-esteem of 252 fifth- and sixth-grade students using the Self-Esteem of Music Ability
scale. Results showed music self-esteem was a significant predictor of participation in
both in-school and out-of-school music activities. In a similar study, Fiedler and
Spychiger (2017) examined the relationship between musical self-concept and interest in
school music for 516 students (age 8– 18) in South Germany. Results indicated those
with strong musical self-concepts were related to interest in school music. Finally,
Campbell (2009) considered the construct of musical possible selves for 199 eighth-grade
45
general music students. She found students with a positive vision of their musical self
also envisioned a positive future of musical involvement. Despite the limited research on
music ability beliefs and intention to enroll in a school music program, extant research
seems to indicate a link between adolescents’ beliefs about their musical abilities and
interest in school music programs. While adolescent musical beliefs seem to influence
enrollment in music programs, the influence of parents and peers may also influence
students’ decision to join a music program.
Parent and Peer Influence
Parental influence on music course enrollment. McPherson (2009) explains
parents have an active role in shaping adolescents sense of musical competence, identity,
and “…the continuing desire to participate, exert effort, overcome obstacles, and
succeed” (p. 95). Several empirical studies support the notion that parents have a
powerful role in children’s decision to enroll in a school music program. Ryan, Boulton,
O’Neill, and Sloboda (2000) investigated the motivational social factors that influenced
participation in music for 1,209 British school-age children (age 10–11). The students
were initially divided into three comparative groups: (1) current music participants, (2)
former music participants, and (3) those who had never played an instrument before.
Analysis revealed significantly higher support from parents, teachers, and friends for
students currently participating in music. Former music participants experienced less
support and those who never played felt the least support. Lucas (2007) examined the
reasons middle school males decide to enroll in music classes (N = 226). The students for
this study were from four U.S. public schools. The researchers administered a
46
questionnaire concerning students and parents’ attitudes toward joining a music class.
Results showed students’ intention to enroll in a music class was significantly influenced
by their parents’ opinions on enrollment in the school music program. Extant research
seems to indicate parents have an influence on whether or not a student enrolls in a
school music program as well as their subsequent musical development (McPherson,
2009; McPherson et al., 2012).
Peer influence on music course enrollment. Several studies have documented
the close social connections between members of school music ensembles (Abril, 2013;
Adderley et al., 2003; Patrick et al, 1999). Patrick and colleagues (1999) conducted 41 indepth interviews with high-school student musicians and athletes regarding the sense of
community within music ensembles and sports teams. Findings suggest close, personal
relationships within these contexts help sustain involvement, expand social networks, and
increase enjoyment in musical and athletic activities. The study also indicates music
participants are aware that students outside of the large ensemble have both negative and
positive views of their involvement in school music. Other studies have also shown
adolescents readily acknowledge their peers positive and negative perceptions of school
music involvement (Adderley et al., 2003; Gouzouasis et al., 2008). While these findings
do not directly impact enrollment, it is important to note social interactions and
perceptions of peers are a factor some students are aware of as they become involved in
school-based music programs.
While secondary students may be aware of their peers’ perceptions of schoolbased music involvement, there is little evidence adolescents’ perceptions of peer opinion
directly influences enrollment in school-based music programs. Castelli (1986)
47
considered the variables that influence male student enrollment in a high school choir
program. The participants were 342 male public high school students from the State of
Maryland. They competed a questionnaire concerning their reasons for participation or
nonparticipation in school-based music programs. Peer influence was rated as one of the
lowest indicators of nonparticipation while sex-role endorsement was rated the highest. It
seems factors beyond peer influence played a role in participants’ decision to enroll or
not enroll in a school music program.
In a study of elementary school students, Mizener (1993) evaluated students’
attitudes towards choir in relation to their gender, grade level, classroom singing
activities, previous musical experience, self-perceived singing skill, and peer attitudes.
The results showed peer attitudes did not have a direct effect on students’ decision to
participate in choir.
Comprehensive Studies of Music Course Enrollment
Several recent studies have considered how multiple factors collectively predict
enrollment in school-based music programs. Ruybalid (2016) examined a sample of fifthgrade students’ (N = 278) intention to enroll in a middle school music program for the
next school year. Several factors were considered as predictors of intention to enroll such
as perceived behavioral control, subjective norms, attitudes toward school music classes,
parental involvement and peer influence. Results showed parental support to enroll,
subjective socials norms, and attitudes towards school music were the three significant
predictors of intention to enroll in a music program. Among all the variables, attitudes
toward school music (enjoyment, interest, etc.) emerged as the most significant predictor
48
of intention to enroll in a school music program the following year. Peer influence did not
significantly affect the students’ intentions.
Similarly, Hawkinson (2015) conducted a mixed-methods study that examined
student’s reasons for nonparticipation in a school music program at a Midwestern high
school (N = 319). Quantitative results indicated perceptions and attitudes toward music,
musical task difficulty, conflicting activities, and personal perceptions were all significant
predictors of school music nonparticipation. As part of the mixed-methods design,
qualitative data were collected from a sample of 12 high school students from the same
school. The results indicated several of the students were musicians outside-of-school
instead of in-school, the decision to participate in music was reflective of personal values,
and students had a desire for alternative music courses not currently offered at their
school.
Chapter Summary
Music participation may be defined as an individual’s involvement in musical
activities such as performing music, creating music, or responding to music, both alone or
with others (Chang & Coster, 2014; Shuler et al., 2014). Veblen (2012) explains the
nature of different musical activities are delineated by a spectrum of music learning
practices related to formal, nonformal, and informal music. Each domain of music
learning practices (i.e., formal, nonformal, informal) is qualified by context, learning
style, ownership (i.e., the person in control of the learning), intentionality of participants
(i.e., to play music or to learn music), and modes for transmission (i.e., notation, aural,
etc.). Formal music instruction is characterized by school or institutional contexts led by
49
a teacher or leader with planned and sequenced instruction. The musical learning that
occurs in formal contexts is intentional because participants focus their attention on
learning how to play or sing instead of just playing or singing for fun (Veblen, 2012).
Nonformal music experiences often have identifiable learning goals established through a
collaborative process between the teacher/mentor and the participants. The learning
process is flexible, contextualized, and tailor made with a teacher/mentor acting as a
guide (Higgins, 2016). This process can occur in a variety of contexts including
community, religious, and school-based music programs (Veblen, 2012). Finally,
informal music participation is characterized by experiential learning (Veblen, 2012).
Informal music learning occurs in unofficial, casual settings where the learning process
happens through the interactions of the participants (Folkestad, 2006). The individuals
control the learning process and the learning may be incidental or even accidental (Green,
2008). In summary, adolescents participate in a wide array of activities typified by the
three music learning domains of formal, nonformal, and informal music participation.
Adolescent formal music participation typically occurs in school-based music
courses and private music instruction. A recent study indicated that from 1982 to 2009 an
average of 34% of all U.S. students enrolled in at least one music course in high school
(Elpus, 2014). Other research reports have demonstrated many students participate in outof-school music instruction, such as private music lessons, in addition to school-based
music courses (ABRSM, 2014; NEA, 2013). Most students in school-based music
programs participate in large-performing ensembles (29.33% in 2009) but some also
participate in alternative music courses (12.53% in 2009) such as secondary general
music, piano, guitar, music theory, music technology, popular music ensembles and IB
50
music (Clements, 2010; Elpus, 2014; Powell et al., 2015).
Adolescent nonformal and informal music participation is more difficult to
quantify than formal music participation. Regarding nonformal participation, many
adolescents are involved in music instruction and ensembles at community-based music
centers (Higgins 2016; Higgins & Willingham, 2017). Religious musical involvement is
another way some adolescents may engage in nonformal music activities (Ingalls, 2008;
Ingalls & Yong, 2015; Miller & Miller, 2013). Regarding informal music participation,
adolescents may listen to music (Rideout, 2015), perform in popular-music ensembles
(MacDonald et al., 2005; Miell & Littleton, 2008; Moir, 2017), write/arrange/produce
original music (Tobias, 2013b, 2015), or play music-based video games (O’Leary &
Tobais, 2017). For adolescents, the manners and contexts of nonformal and informal
music participation is quite diverse.
Extant research indicates numerous factors influence adolescent musical
involvement. For example, several scholars suggest an individual’s attitudes toward
music activities have a significant effect on their decision to participate (Corenblum &
Marshall, 1998; Hawkinson, 2015; Horne, 2007; Waters et al., 2014). Additionally,
adolescents’ perceptions of their musical abilities also seem to relate positively to musical
involvement (Austin, 1990, 1991; Creech et al., 2008; Fiedler & Spychiger, 2017). Other
individuals such as parents have an effect on adolescents’ value for music and their
decision to participate in music activities (Lucas, 2007; McPherson et al., 2012; Ryan et
al., 2000). Finally, environmental factors such as school activities, busy lives, and
perceived control over one’s life seem to influence musical engagement (Hawkinson,
2015; Ruybalid, 2016). Chapter 3 presents the research methodology for this study,
51
including information about the conceptual design, instruments used to measure key
constructs, and the study procedures.
52
CHAPTER 3
METHOD
Adolescent music participation is a key factor many music education scholars
consider when developing music research and pedagogy. Music education researchers
may examine differences on a range of variables according to individuals’ music
participation (e.g., academic achievement, social-benefits of music-making, music
perception, etc.) (Elpus, 2017). Scholars tend to categorize music participation
dichotomously. For example, researchers may compare students in school music
programs with those who are not (Butzlaff, 2000; Elpus, 2013, 2014; Elpus & Abril,
2011; Fitzpatrick, 2006; Kinney, 2008; Rawlings, 2016, 2017; Rawlings & Stoddard,
2017) or those who have formal music training and those that do not (Chobert et al.,
2011; Corrigall & Trainor, 2011; Costa-Giomi, 1999, 2004). However, this dichotomous
classification may be overly simplistic considering the range of musical activities a
student might be involved with at school, in the community, and at home.
One prevalent issue in music education academic literature related to music
participation is that of enrollment in secondary school-based music programs. Some
scholars are alarmed that 34% of high school students nationally are involved in schoolbased music programs, believing more students should be enrolled (Kratus, 2007; D. A.
Williams, 2011; D.B. Williams, 2011). As music education scholars work to determine
53
effective ways to serve the entire student population, it may be necessary to first
investigate the musical activities of adolescents inside and outside music programs to
determine the degree to which their musical experience, demographic characteristics,
environment, and personal musical beliefs are associated with different types of music
participation.
In this study, I utilize a researcher designed questionnaire and several other selfreport measures to examine the music participation profiles of secondary public school
students and the effect of music participation on several key demographic,
environmental, and personal-belief variables. This chapter describes the research
methodology and design for the dissertation. I will present the purpose, research
questions, participants, scales, procedures and statistical analyses.
Purpose of the Study
The purpose of this study was two-fold: (1) to identify profiles of music
participation from an adolescent school population; and (2) to examine the demographic,
environmental and personal belief differences of adolescents according to music
participation profile. The four research questions were:
1. What music participation profiles exist from the sampled population?
2. What is the relationship between music participation profiles and demographic
variables such as gender, grade, GPA, and parent education?
3. To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life
activity involvement, and past experiences with school music classes?
54
4. To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity,
music ability beliefs, and attitudes towards secondary school music classes?
Research Setting
The Salt Lake City School District (SLCSD) was chosen as a site location for the
study. An Institutional Review Board (IRB) application at The University of Utah and an
external research application for the SLCSD were submitted and approved (see Appendix
for approval letters). The SLCSD fine arts supervisor notified the school principals of his
support for the project. Dates and times for data collection were negotiated among school
principals and teachers.
Sample and Participants
Participants for this study were comprised of a convenience sample from one
middle school (MS) and two high schools in the SLCSD. The school sites were chosen
because they represented secondary students from grades 7 through 12 who attended
schools with active school-based music classes and ensembles. High school A (HS A)
served as the site for the pilot study. Demographics for each school are listed in Table
3.1. The principals for each school requested I ask teachers’ permission to administer the
questionnaire in their classrooms. In an effort to gather the broadest possible sample of
the school population, I contacted language arts, social studies, science, and health
teachers because they each taught courses that were required of every student in the
school. Six teachers at HS A, one teacher at HS B, and four teachers at MS gave me
55
Table 3.1
Demographic Data of Participating Schools
Demographic Category
HS A
HS B
MS
N, Total school population
2,361
1,681
475
7th grade
–
–
248
8th grade
–
–
219
9th grade
592
465
–
10th grade
637
408
–
11th grade
559
386
–
12th grade
474
356
–
60.53%
41.34%
36.63%
Hispanic
44.2%
23.6%
17.1%
Black
5.4%
4.3%
3.2%
Asian
9.3%
3.6%
4.8%
Pacific Islander
4.0%
4.2%
2.5%
Native American
0.8%
2.1%
1.9%
Caucasian
57.3%
32.9%
65.9%
Other
3.5%
4.9%
4.6%
% Low income
Racial composition
permission to administer the questionnaire in their classrooms. Additionally, I purposely
sampled five music classes at HS A to ensure school music students were adequately
represented in the sample.
For the pilot study at the high school level, I chose to administer the questionnaire
with two of the language arts teachers, resulting in eight classes. These classes included
an adequate number of students to meet the minimum required sample size based on the
power analysis. For the primary study at HS A, I administered the questionnaire to a total
of 19 classes. At HS B, six classes of students completed the questionnaire. Finally, 14
56
classes completed the questionnaire at the middle school. This sampling strategy ensured
a robust sample size for the analysis.
Description of the Instrument and Measures
All scales and measurement instruments are located in the Appendix of this
document.
Demographic Variables
Self-reports of gender, age, GPA, and a parents’ highest level of education were
elicited to determine demographic characteristics (Diemer, Mistry, Wadsworth, López, &
Reimers, 2013).
Pilot Study – Development of the Music Participation Index
Extant research indicates that music participation is often considered when
investigating social, cognitive, and emotional issues in music education research (Elpus,
2017). Adolescents are often classified dichotomously as in-school/out-of-school music
participants or trained/untrained musicians. Yet, this generalization may mask
meaningful variations of music participation within a study population. Given the diverse
musical experiences of adolescents, I created a measure to quantify adolescent music
participation in three domains (i.e., formal, nonformal, and informal) with each domain
addressing creative, performative, and responsive musical activities (NCCAS, 2014;
Veblen, 2012).
Many researchers, especially those in health sciences, have worked to quantify the
57
concept of participation to address issues of physical activity, rehabilitation, and
disability (Sylvia, Bernstein, Hubbard, Keating, & Anderson, 2013). Scholars use a
variety of tools (e.g., self-report questionnaires, diaries, observation, accelerometers) to
study participation over different periods of time, domains (leisure, occupation, school,
etc.), intensity, frequency, breadth, and attitude (Chang & Coster, 2014; Chang, Coster, &
Helfrich, 2013; Sylvia et al., 2013). Each type of measurement has strengths and
weaknesses. For example, self-report measures are easy to distribute and collect large
amounts of data but are not as precise as pedometers or accelerometers (Sylvia et al.,
2013). This study will exclusively consider best practices in self-report measures.
Participation can be measured from both an objective and subjective perspective.
Objective participation is measured by observable indicators that enable researchers to
compare participants across groups (Chang & Coster, 2014; Forsyth & Jarvis, 2002). For
self-report measures, measuring objective participation is typically accomplished by
asking individuals about the frequency and breadth of activities in certain contexts
(Chang & Coster, 2014; Chang, Chang, Liou, & Whiteneck, 2017; DiStefano et al., 2016;
Eyssen, Steultjens, Dekker, Terwee, 2011). Subjective participation concerns how one
perceives their own participation. Whiteneck and Dijkers (2009) argue researchers who
exclusively use objective measures presume that all people should participate in activities
equally when, in actuality, different people may be satisfied with different levels of
participation. There is some evidence to indicate that a person’s personal level of
satisfaction with their participation is more highly correlated with their global quality of
life than objective measures (Brown, Dijkers, Gordon, Ashman, & Charatz, 2004).
Participation scholars have used a number of indicators to measure subjective
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participation including perceived difficulties, importance, satisfaction, and desire for
change (Chang & Coster, 2014). In summary, participation scholars recommend both
objective and subjective participation are included on self-report questionnaires (Chang
& Coster, 2014; Whiteneck & Dijkers, 2009).
Similar to other recently developed measures of participation, I sought to develop
a measure of adolescent music participation that addressed the multiple contexts, settings,
and types of youth involvement in music (Chang et al., 2017). Each item represents an
objective measure of participation. The objective items refer to the level of involvement
in a musical activity associated with a specific musical domain (i.e., formal, nonformal,
informal), mode (i.e., creating, performing, responding), and category (see Figure 3.1).
Additional items represent a subjective measure of music participation which refer to an
individual’s desire for more participation. Subjective participation will be referred to as
desire for the remainder of this paper.
The music participation index (MPI) is comprised of three separate
questionnaires, each characterized by a different domain of participation (i.e., formal,
nonformal, informal) (see Table 2.1) (Veblen, 2012). Each domain is delineated by three
modes of music participation which correspond to the three artistic processes outlined in
the National Core Arts Standards (NCCAS, 2014) (i.e., perform, create, respond).
Finally, the activities within each mode are categorized as follows: (1) perform – sing,
play an instrument, perform, practice (2) create – write, arrange, improvise (3) respond –
perceive (e.g., listen, attend performances), analyze/evaluate (e.g., discuss, write about),
support (e.g., musical ventures, industry, etc.), active response (e.g., dance, musical
theatre, etc.) See Figure 3.1 for a visual representation of this framework.
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Figure 3.1. Visual representation of the Music Participation Index framework.
I began the process of developing the MPI using Veblen’s (2012)
conceptualization of music learning domains. Before building scale items, I determined
the measure would best be suited using a formative measurement model instead of a
reflective measurement model (Kline, 2012) (see Figure 3.2). A description of both
formative and reflective measurement models are described below.
In classical test theory, reflective measures are often developed to evaluate a
specific latent construct. A latent construct is an unobserved trait such as anxiety,
depression or self-esteem. Latent constructs are unobserved because they are very
difficult to see or count (Streiner, Norman, & Cairney, 2015). For example, anxiety is a
latent construct and a well-documented mental health condition. To determine the
presence or severity of anxiety, a researcher or clinician must rely on observable
indicators caused by anxiety, such as feelings of worry or irritability. Since an individual
can report their feelings of worry and irritability, a researcher or clinician can ask them
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Reflective
Sweatiness
Formative
Income
Worry
Anxiety
SES
Occupational
status
Sleep
disturbance
Education
level
Irritability
Figure 3.2. Reflective vs. formative indicator (Brown, 2015; Streiner et al., 2015).
questions about such topics using a questionnaire. Each item of the questionnaire
generally addresses an observable or reportable indicator caused by the latent construct
(e.g., anxiety) (Streiner, Norman, & Cairney, 2015). When developing a reflective
measure, several items are created and then tested to see if each item is indeed caused by
the latent construct (Brown, 2015). If items share a common cause, they will be
intercorrelated and, theoretically, interchangeable (Brown, 2015). For example, a person
experiencing anxiety will exhibit both irritability and sweatiness (see Figure 3.2). In
summary, a reflective measurement model is based on the assumption that an unobserved
factor (latent construct) causes several observable behaviors or feelings and that a
researcher can measure the unobserved construct by evaluating the observable behaviors.
In many scenarios, it may be unnatural to define a construct in a reflective manner
(Streiner et al., 2015). Instead, a formative model may be more appropriate. One common
61
example of a formative model is the construct of socioeconomic status (SES). Income,
education level, and occupational status are typical indicators of SES (Brown, 2015).
Unlike a reflective model, it seems unlikely that SES causes income, education level, and
occupation but instead the reverse is true; the three indicator variables cause SES. Brown
explains that “…in this case, it seems more reasonable to assert that these three variables
are the cause of one’s SES than the converse, which would be the claim that income,
education level, and occupation status are interrelated because they share the common
underlying cause of SES” (p. 322). Therefore, in the case of SES, the three indicator
variables independently cause SES instead of SES causing the indictor variables (See
Figure 3.2). It is therefore more appropriate to conceive of measures of SES as formative
because the indicators form the construct instead of serving as a reflection (reflective
model) of the construct.
The Music Participation Index (MPI) was conceptualized as a formative model
instead of a reflective model for several reasons. First, the causal flow of the indicators is
formative and not reflective. That is, music participation does not cause involvement in a
musical activity. Instead, it is the involvement in a variety of musical activities that
collectively causes music participation. Second, there is little reason that the amount of
time an individual spends on different musical activities must be the same. Therefore,
each music activity uniquely contributes to the construct of music participation. For
example, an individual’s active involvement in their school choir program does not
require they are also involved in other activities within the same domain (e.g., composing
music for school, performing in the school orchestra) or in other domains (e.g., playing
guitar informally with friends, participating in a community choir). Certainly, musical
62
activities may sometimes intercorrelate. However, just because an individual is involved
in one musical activity does not mean they must be involved in another.
To generate items for the MPI, I drew upon the framework described above and
research literature on adolescents’ formal (Abril, 2013; Clements, 2010; Dammers, 2012;
Elpus & Abril, 2011; Fonder, 2014; Miksza, 2013; Parker, 2018; Shuler, 2011b);
nonformal (Higgins, 2016; Higgins & Willingham, 2017; Moir, 2017); and informal
music activities (Campbell et al., 2007; Cayari, 2017; Green, 2002, 2008; Hallam et al.,
2017; O’Leary & Tobias, 2017; O’Neil, 2017; Rideout, 2015; Tobias, 2013a, 2015). I
created several items for each mode (i.e., performing, creating, responding). For example,
items related to the creative mode of music participation addressed musical activities for
composing, arranging, and improving. It was important that each item not only indicate a
specific category or mode of music participation, but also was characteristic of the
context, learning style, ownership, and intentionality defined by the domain of
participation (see Table 2.1) (i.e., formal, nonformal, informal).
For each objective participation item, participants are asked to indicate the
frequency of an activity. For each desire item, participants are asked to indicate their
desire to perform activities in each domain less or more (subjective). The frequency of
each activity is answered on a seven-point frequency scale (objective) and desire on a
five-point Likert-type scale (subjective). The MPI is scored by summing each performing
item, creating item, and responding item (see Figure 3.3). This additive process results in
a summed performing score, creating score, and responding score for each domain (i.e.,
formal, nonformal, informal). The desire items in each domain are also summed. This
process results in four scores for each index: objective performing, objective creating,
63
Figure 3.3. Model for formal, nonformal, and informal Music Participation Indices. Each
formative indicator (bottom) represents the domain (F, N, I; formal, nonformal, informal)
and the mode (P, C, R; perform, create, respond).
objective responding, a desire score. If each index is included (i.e., formal, nonformal,
informal), each participant will have twelve summed scores in total. Three reflective
indicators are also included for each domain and will be instrumental in the statistical
validation of the scale. Validation procedures are described later in this chapter.
Primary Study – Environmental Factors
Several measures were used to evaluate participants’ perceptions of
environmental factors related to music participation. I used an adaptation of the
Peer/Parent Subjective Norm scale (Fishbein & Ajzen, 2010) to evaluate the social
pressure from parents and peers to participate in musical activities. These scales were
adapted from previous studies within music education (Ruybalid, 2016) and outside
64
music education (Fitchten et al., 2014). The subjective norm scales were chosen for this
study because they have been used for more than three decades to evaluate the
relationship between social norms and a wide-variety of behviors (Ajzen, 1991; Fishbein
& Ajzen, 2010). Therefore, the subjective norm scale was deemed appropriate because
one of the concerns of this study was the relationship between peer/parent subjective
norms and music participation. For the Peer/Parent Subjective Norm scale, participants
indicated their level of agreement on a six-point, Likert-type scale. The Peer/Parent
Subjective Norm Scale, includes statements such as: Most of my close friends think I
should participate in music activities, and My parents would be disappointed if I did not
participate in music activities. Previous studies have shown both scales to be valid and
reliable (Davis, Ajzen, Saunders, & Williams, 2002; Fitchen et al., 2014). School/life
activity involvement was measured with several items pertaining to students’
involvement in other school elective courses, sports teams, after-school jobs, and
church/religious activities.
Primary Study – Personal Belief Scales
An adaptation of the Perceived Competence Scale (Williams, Freedman, & Deci,
1996) was used to assess participants perceived competence for singing and playing
music. This short four-item questionnaire concerns participants’ sense that they are able
to sing/play an instrument, learn to sing/play an instrument, achieve their goals on
singing/playing an instrument, and meet the challenges of singing/playing an instrument.
Participants responded to each competence statement on a six-point scale ranging from
strongly disagree to strongly agree. Previous studies have shown this measure to be
65
reliable and valid (Williams et al., 1996, 1998).
I used an adaptation of the Theory of Planned Behavior Attitude Scale to assess
participants’ attitudes towards four music classes: large music ensemble, piano/guitar
class, popular music ensemble, and a composition/technology-based music class
(Fishbein & Ajzen, 2010). These four classes were chosen because they represented
common music classes such as large music ensembles and piano/guitar classes as well as
emerging music classes like popular music ensembles and composition with technology
classes (Dammers, 2012; Elpus & Abril, 2011; Powell, Krikun, Pignato, 2015). This scale
was also used to evaluate participants’ attitudes toward their prior school music
experiences. For each class, the respondents indicated if participating in the class would
be good, fun, or interesting. Participants rated each item using a six-point Likert-type
scale ranging from strongly disagree to strongly agree. Researchers in the field of music
education (Ruybalid, 2016) and outside music education (Hagger, Chatzisarantis, Biddle,
& Orbell, 2001) have demonstrated the effectiveness of the theory of planned behavior
attitude scale in measuring participants’ positive or negative feelings towards various
activities.
Finally, long-term musical identity was measured with a single item. The item
read: “How long do you think you will sing, play an instrument, or write your own
music?” Response options included: I don’t sing, play or write my own music; until the
end of the school year; until the end of middle school; for some of high school; until the
end of high school; after I graduate high school (college/young adult); for the rest of my
life. Long-term musical identity is based on a longitudinal study that revealed children
with a long-term vision for their musical involvement continued music participation
66
longer than children with a short-term vision for their musical involvement (McPherson,
Davidson, & Faulkner, 2012).
Procedures
IRB
The Institutional Review Board (IRB) of The University of Utah and the Salt
Lake City School District (SLCSD) external research department approved this study.
Both the University of Utah and SLCSD waived the requirement for active consent to
participate in the study because the questionnaire did not require any identifying
information. Application approval letters are located in the appendix of this document.
Procedures for notifying parents are described below.
Survey Administration
As part of the IRB and SLCSD research application, I requested a waiver of
active parental consent for this study. Instead, a letter notifying parents of the survey was
sent home a week prior to survey administration. Parents returned the letter with their
signature if they preferred their child not participate in the study. I collected data for the
pilot study and the primary study over the course of 3 weeks: week 1 – pilot study data
collection, week 2 – pilot study data analysis and edits to the MPI, week 3– primary study
data collection. Multiple safeguards were implemented at each study site to ensure the
protection of participants. First, an assent script was read aloud to participants that
indicated they may leave their survey questionnaire blank without penalty if they do not
wish to participate in the study. Second, participants were not prompted to offer any
67
identifying information either on the survey instrument or by the survey administrator.
All answers were anonymous. The survey administrator, teachers, school administrators,
parents/guardians, and peers did not have access to participants’ answers. The students
took the survey using paper and pencil. To transfer the responses from paper to a
computer database, I utilized Snap Survey 11 paper scanning software (Snap Survey
11.23, 2018).
Pilot Study - Data Analysis
Several procedures were used to analyze the data collected in this pilot study. This
section details the processes and statistical analyses for answering each pilot research
question.
Pilot research question one. To what degree, if any, do adolescents find the
content of the music participation index (MPI) relevant, interpretable, and representative
of broad music participation? Content validity was established through a cognitive
interview process with a small focus group of adolescents (Devellis, 2017; Johnson &
Morgan, 2016). Participants reviewed the music participation index and offered their
thoughts about the relevance and interpretation of the measure. I edited the survey based
on this feedback before collecting data with a larger group of secondary public-school
students.
Pilot study power analysis. Prior to data collection and analysis, I conducted an
a priori power analysis to determine the minimum sample size required given the use of a
MIMIC confirmatory factor analysis. Using models A, B, and C in Figure 3.3, I assumed
an intended test power of .80 and calculated the degrees of freedom (9) (same for each
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model) by subtracting the estimated parameters (12) from the elements in the observed
variance-observed covariance matrix (21) (calculated as, k(k + 1)/2 where k is the number
of observed variables). The analysis was conducted with the R statistical language (R
Core Team, 2013) using code developed by Gnambs (2017) and MacCallum and Hong
(1997). Results of the power analysis suggest a minimum sample size of N = 191.
Pilot research question two. To what extent, if any, do the internal structure and
items of the MPI represent the music participation of middle and high school students? In
order to answer this research question, I utilized the procedures described previously in
this chapter to collect data in HS A. Item frequencies, means, and distributions from each
MPI were examined. Detailed procedures for data cleaning and screening will be
described in the primary study section of this chapter.
I employed a multiple indicators, multiple causes (MIMIC) confirmatory factor
analysis (CFA) model to investigate the construct validity of each MPI. Brown (2015)
describes CFA as “…an indispensable analytic tool for construct validation in the social
and behavioral sciences” (p. 2). CFA is often used as a statistical tool to examine the
relationship between a number of observable indicators (e.g., items on a questionnaire)
and the latent structure of the test instrument. For example, in Figure 3.3, each MPI
includes three reflective indicators (denoted with a Y). These indicators represent the
hypothesis that, collectively, three indicators measure the construct of formal music
participation, three measure the construct of nonformal music participation, and an
additional three measure the construct of informal music participation. However, for the
MPI, formative indicators (i.e., FP, FC, FR, NP, NC, NR, IP, IC, IR) are also included to
fully measure the construct which slightly alters the theory and procedures of the
69
common factor model (Brown, 2015).
As explained previously, I do not believe that reflective indicators alone are
adequate for measuring adolescent musical involvement because of the varied nature of
music participation, which is why each MPI includes three formative indicators (summed
totals of frequency of musical activity) and three reflective indicators. Theoretically,
formative indicators are independent of each other and may not intercorrelate in the same
manner as reflective indicators. Therefore, the CFA model must be specified differently
in order to examine the convergent validity of the MPI. In order to examine construct
validity, a MIMIC CFA specifies the formative indicators as covariates of the reflective
indicators. In theory, if the formative indicators adequately predict the reflective
indicators, they are predicting the same construct and the measurement is therefore valid
(Kline, 2014). Several scholars have recommended MIMIC-type specifications for
formative models (Brown, 2015; Kline, 2014).
Brown (2015) recommends conducting a MIMIC CFA in a two-step process.
First, “establish a viable measurement model using the full sample” and reflective
indicators (Brown, 2015, p. 273). As in most confirmatory factor models, the results
should include the factor loadings of each indicator, estimates of factor variances and
covariances, and the measurement error for each indicator (Brown, 2015). Each reflective
indicator should have a relatively high standardized factor loading (>.70) and the
correlations between the reflexive indicators should not be unreasonably high (<.90)
(Kline, 2016). Researchers should also examine model fit to ensure the factor solution is
reasonable and good-fitting to the sample. Second, Brown (2015) recommends adding
“…one or more covariates to the model to examine their direct effects on the factors and
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selected indicators” (p. 273). In this case, the formative indicators are added to the model
to predict the reflective indicators. If the formative indicators do indeed predict the
reflective indicators, then the model fit should be reexamined (Brown, 2015). If the
model is reasonable and good-fitting then validity of the measure can be established.
Based on the data screening process and the MIMIC CFA, the MPI will be edited once
more to ensure a valid and reliable instrument for the primary study.
Primary Study - Data Analysis
Primary study power analysis. Prior to data collection, I determined the
necessary sample size required for each statistical analysis cited below (i.e., ANOVA,
MANOVA, and latent profile analysis). To determine the necessary sample size for the
multivariate analysis of variance (MANOVA), I conducted an a priori power analysis
using G*Power software, version 3.1.9.2 (Faul, Erdfelder, Lang, & Buchner, 2007). I
assumed an intended test power of .80, which is considered the “industry standard”
(Overland, 2014, p. 45), a significance level of α = .01 and an effect size of .25 (Cohen,
1988). Results of the power analysis suggest a minimum sample size of N = 61. For the
latent profile analysis, Park and Yu (2017) recommend 30 individuals per latent profile.
While the number of latent profiles is yet unknown, I estimated eight latent profiles
would adequately describe the music participation of the sample population. Therefore, a
sample size of N = 240 would likely be adequate for estimating a latent profile model of
music participation.
Given the minimum sample size for each of the statistical tests above, the primary
study required a minimum sample size of N = 240. It was expected that the sampling
71
procedure described earlier in this chapter would result in at least 250 participants in the
middle school and 350 participants in the high schools. Notably, this sampling procedure
represents a larger sample size than is called for based on the power analysis. I decided to
gather a larger sample size because I wished to detect even very small groups of
individuals that might be present in the sample (Acock, 2018).
Preliminary data analysis. I conducted preliminary data analyses in Stata 15
(StataCorp, 2017). Using STATA 15, I created a data set from the collected surveys,
checking for errors in data entry. After the data file was prepared for analysis, I made
additional decisions regarding missing data. For example, I screened all items for
response bias and patterns of missingness. I summed the appropriate variables and then
examined the data for univariate normality, multivariate normality, linearity, and
collinearity. I conducted data transformations if violations of normality occurred (Mertler
& Reinhart, 2017). Finally, I conducted an analysis to determine if school, gender,
parent’s education, grade or GPA should be included as covariates in the primary
analysis.
Primary data analysis. To answer research question one, Stata 15 was used to
conduct a latent profile analysis of adolescent music participation (Morin & Wang, 2016;
Wang & Wang, 2012). Latent profile analysis is a statistical procedure used to identity
groups of participants that are not directly observable (Wang & Wang, 2012). In other
words, the groups are hidden or not relatively apparent. To answer research questions two
through four, Stata 15 was used to conduct a series of statistical tests including chi-square
tests for independence, ANOVA, and MANOVA.
Research question one. What music participation profiles exist from the sampled
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population? Studies in music education have yet to establish a method of quantitatively
representing the varied nature of music participation within a sample population. One
quantitative method for uncovering and describing the unique profiles of music
participants is through the use of mixture models. Mixture models are a person-centered
instead of variable centered data analytic approach useful for identifying unobserved
subgroups of participants within a sample (Wang & Wang, 2012). This technique enables
the researcher to group the data into classes that are not directly observable (Oberski,
2016). Oberski (2016) describes this process as “…the art of unscrambling eggs:
recovering hidden groups from observed data” (p. 275). For example, a researcher may
be interested in studying patterns of drug-use among a certain population to examine how
addiction treatment strategies might be tailored to specific groups (Wang & Wang, 2012).
Instead of classifying individuals a priori by the drug they use most, the investigator
might consider conducting a mixture model to see if some groups of people engage in a
distinctive combination of drug-using habits (a latent, unobservable categorization of
drug habit). The categories that emerge from the mixture model will likely better
represent the population and, therefore, more effective treatments might be designed.
Scholars have described mixture modeling as a person-centered approach to data analysis
because researchers who utilize this methodology aim to “…identify relatively
homogeneous subgroups of participants, also called latent classes or profiles, differing
qualitatively and quantitatively from one another…” (Morin & Wang, 2016, p. 184). The
most common types of mixture models include latent class analysis and latent profile
analysis (Morin & Wang, 2016).
A latent class analysis is a type of mixture model comprised of categorical
73
observed variables and a latent profile analysis is a mixture model comprised of
continuous observed variables (Morin & Wang, 2016; Obserski, 2016). However,
categorical and continuous variables can be combined in the same model which makes
the classification between latent class and latent profile analysis unnecessary (Collins &
Lanza, 2010). The term latent profile analysis will be used for the remainder of this study.
Statistical procedures for a latent profile analysis enable a researcher to identify patterns
of responses among participants. Participants with similar response patterns are grouped
into (latent) profiles. For this study, I conducted a latent profile analysis of adolescent
musical participation to determine: (1) categories of adolescent music participation
beyond dichotomous classifications like in-school/out-of-school music participation, (2)
the number of participants in each category, (3) and the nature of each profile (i.e., how
formal, nonformal, and informal music activities interact to form a unique music
participation profile). This music participation latent profile analysis (LPA) was
originally designed to include a total of twelve observed variables for each study
participant as measured with the MPI. The frequency of performing, creating, and
responding activities for each domain of music participation (i.e., formal, nonformal,
informal) represent nine of the variables. The remaining three variables are measures of
participation desire within each domain (see Figure 3.4).
Wang and Wang (2012) outline several steps to estimate a latent profile model.
First, the researcher must determine the “optimal number of latent classes…” (Collins &
Lanza, 2010; Wang & Wang, 2012, p. 292). To determine the number of latent classes,
the researcher conducts a series of successive models, each with an increasing number of
latent profiles (e.g., model A – 1 latent profile, model B – 2 latent profiles, model C –
74
Figure 3.4. Latent profile of music participation model
latent profiles, etc.). A number of fit indices such as the Bayesian Information Criterion
(BIC) or the Aikeke Information Criterion (AIC) are compared for each model (Collins &
Lanza, 2010). The model that best represents the data according to the fit indices signifies
the preferred number of latent profiles (Wang & Wang, 2012). Morin and Wang (2016)
encourage researchers to examine the final model for theoretical consistency in addition
to the fit indices. Each profile should consist of a meaningful combination of indicators.
Second, individual participants are classified into the latent profiles based on the
probability their response pattern matches the latent profile (Wang & Wang, 2012).
Finally, the participants assigned to each profile are quantified to demonstrate the
percentage of the sample population in each latent profile (Morin & Wang, 2016). With
the latent profiles of music participant verified and participants categorized into their
75
most likely profile, the latent profiles may be used as a grouping variable for subsequent
research questions.
Research question two. What is the relationship between music participation
profiles and demographic variables such as gender, grade, GPA, and parent education? I
conducted a series of chi-square tests of independence to determine if there was an
association between music participation profile and gender or parent’s level of education.
To further examine differences in demographic characteristics, I conducted a two oneway analysis of variance (ANOVA) with music participation profile as the independent
variable and GPA, followed by grade as the dependent variables.
Research question three. To what extent, if any, do differences exist among
categories of music participation and environmental factors such as parent/peer norms,
school/life activity involvement, and past experiences with school music classes? I
performed a one-way multivariate analysis of variance (MANOVA) to determine if
parent or peer norms (dependent variables) varied as a function of music participation
profile (independent variable). I conducted a one-way ANOVA to determine if past
experiences with school music classes (dependent variable) varied according to music
participation profile (independent variable). Finally, a chi-square test of independence
was conducted to test the association between amount of school/life activity and music
participation profile.
Research question four. What is the relationship between categories of music
participation and various personal beliefs such as music ability beliefs, long-term musical
identity, and attitudes towards secondary school music classes? To determine if music
ability beliefs vary as a function of music participation profile, I conducted a one-way
76
ANOVA with music participation profile as the independent variable and music ability
beliefs as the dependent variable. A chi-square test for independence was used to test the
association between long-term musical identity and music participation profile. Finally, I
performed a one-way MANOVA with music participation profile as the independent
variable and attitudes towards the four types of music classes (i.e., large ensemble,
guitar/piano, music composition, popular music ensemble) as each dependent variable.
77
CHAPTER 4
RESULTS
The purpose of this study was two-fold: (1) to identify profiles of music
participation from an adolescent school population; and (2) to examine the demographic,
environmental and personal belief differences of adolescents according to music
participation profile.
Data were examined to assess the latent profiles of music participation
represented in the sample population. Additionally, comparative analyses were used to
determine differences in demographic, environmental, and self-belief factors. The four
research questions were:
1. What music participation profiles exist from the sampled population?
2. What is the relationship between music participation profiles and demographic
variables such as gender, grade, GPA, and parent education?
3. To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life
activity involvement, and past experiences with school music classes?
4. To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity,
music ability beliefs, and attitudes towards secondary school music classes?
78
For the pilot study, this chapter contains and displays the following: (1) sample
demographics, (2) information about missing data, and (3) a multiple indicators, multiple
causes (MIMIC) confirmatory factor analysis (CFA). For the primary study, this chapter
contains and displays the following: sample demographics, information about missing
data, descriptive statistics on music participation, latent profile analysis of music
participation, and a comparative analysis of demographic, environmental, and self-belief
factors according to music participation profile.
Pilot Study
Sample Demographics
Self-reported demographics were collected for gender, grade, parent education,
and grade point average (GPA). The school district did not allow ethnicity or freereduced lunch status to be included on the questionnaire. Demographic characteristics of
the sample compared with the school population appear in Table 4.1.
Missing Data
Initially, 228 students participated in the pilot study and returned the
questionnaire. Nine participants were eliminated from the data set because the
questionnaires they submitted were less than 50% complete (n = 219). To determine
patterns of missing data, I first summed the appropriate questionnaire items for each of
the nine formative variables (i.e., formal performing, creating, and responding; nonformal
performing, creating, and responding; and informal performing, creating, and
responding). Listwise deletion was employed to ensure individuals who left at least one
79
Table 4.1
Demographic Characteristics of Primary Study Participants and HS A Population
Sample
Characteristic
High School A
n
%
n
%
228
–
2,767
–
Female
99
43.4
1,335
48.2
Male
127
55.6
1,432
51.8
2
1
–
–
7
–
–
103
3.7
8
–
–
85
3.1
9
94
41.2
764
27.6
10
36
15.8
594
21.5
11
73
32
639
23.1
12
23
10.1
582
21
No response
1
–
–
–
Hispanic
–
–
1,218
44.01
Caucasian
–
–
969
35.01
Asian
–
–
229
8.2
Black
–
–
134
4.8
Pacific Islander
–
–
91
3.3
American Indian
–
–
17
.6
Multi Race
–
–
109
3.9
1,530
55.3
Total
Gender
Nonbinary
Grade
Ethnicity
Economically
disadvantaged
Parent Education
Did not complete HS
60
26.3
–
–
GED/High-School
52
22.8
–
–
Vocational certificate
4
1.8
–
–
Associates degree
14
6.1
–
–
80
Table 4.1 continued
Sample
Characteristic
High School A
n
%
n
%
Bachelor’s degree
22
9.6
–
–
Master’s degree
16
7
–
–
Doctoral degree
17
7.5
–
–
Did not answer
43
18.9
–
–
Mostly A’s
47
20.8
Mostly A’s and B’s
43
19.1
–
–
Mostly B’s
45
20
–
–
Mostly B’s and C’s
31
13.8
–
–
Mostly C’s
30
13.3
–
–
D’s and F’s
29
12.7
–
–
Unsure/did not answer
3
1.3
–
–
GPA
81
question blank for a particular mode of participation also received a blank score for the
final variable. The formative portion of the data set was reduced from 58 separate
questionnaire items concerning different levels of music participation to nine formative
music participation variables. In total, the data set included nine formative music
participation variables and nine reflective music participation variables.
Overall, 17 (94.4%) of the 18 measured variables included some missing data,
with 35 (16%) out of 219 respondents having some level of missingness. However, the
missingness of the total sample was relatively low, with only 4.1% (161) missing from
the measured items (3,942) by total number of respondents. Overall, missingness per item
ranged from 0 to 5.48%. A missingness rate of 5% or less is generally acceptable
(Bennett, 2001; Schafer, 1999). Still, a full information maximum likelihood (FIML)
estimator was used in all subsequent analyses to accommodate for the missing data
(Allison, 2003; Brown, 2015).
Pilot Research Study Question One
To what degree, if any, do adolescents find the content of the music participation
index (MPI) relevant, interpretable, and representative of broad music participation?
Before administering the survey to a sample population, I conducted a focus group
cognitive interview with six adolescents to review the face validity of the music
participation index (Johnson & Morgan, 2016). Three participants were enrolled in
middle school and three in high school. The participants completed the MPI and then
engaged in conversation with the researcher regarding its content and structure.
Several themes emerged from the focus group discussion, including index
82
organization, confusion on some survey items, length of the index, and the scope of the
index. First, several participants recommended the index broken into sections with
similar items grouped together. The participants reported this organization would make
each individual item easier to interpret because the section heading would contextualize
each item. Second, focus group members recommended some items be simplified by
removing excessive qualifiers. The focus group participants found interpretation difficult
with too many qualifiers in one item. Third, the participants indicated that the index was
long; however, they did not find it arduous to complete the whole questionnaire. Most of
the participants completed the index in 15 minutes or less. Finally, the participants
indicated that the questionnaire covered the majority of their musical activities and did
not recommend any additional topics.
In response to the focus group, several changes were made to the music
participation index. First, I divided the index into four subsections to better organize the
questionnaire. Second, I removed excess qualifiers and illustrations from items. For
example, one item originally read: Just for fun, casually sing, rap, DJ or play an
instrument (including electronic instruments) on your own or with friends and family
(e.g., sing in car, shower, sing along with CD’s, karaoke, family reunion, etc.). The focus
group participants indicated this item had too much information and too many qualifiers.
Therefore, the item was changed to: Just for fun, casually sing, rap, DJ or play an
instrument on your own (including electronic instruments). Third, the index was reduced
from 60 to 57 objective participation items by combining redundant items. Moreover, I
also reduced the length of the MPI by including 12 separate desire items instead of one
desire participation item for every objective participation item.
83
Pilot Research Study Question Two
To what extent, if any, do the internal structure and items of the MPI represent the
music participation of middle and high school students? This research question concerned
the convergent validity of the music participation index. To address this question, I
conducted a multiple indicators, multiple causes (MIMIC) confirmatory factor analysis
(CFA) for each music participation construct (i.e., formal, nonformal, informal) (see
Figure 4.1). A MIMIC CFA model is similar to a CFA model, with reflective indicators
collectively measuring a latent construct. However, a MIMIC CFA model includes
additional indicators as covariates of the measured construct. For this study, the
covariates are the formative indicators generated from the music participation index
(MPI) (e.g., formal performing, nonformal responding, informal creating, etc.). In theory,
if both the reflective indicators (typical CFA) and formative indicators (covariates for the
MIMIC CFA model) predict the construct, then an acceptable level of convergent validity
is established for the MPI (Kline, 2012). For the current study, each MIMIC CFA model
includes three formative indicators and three reflective indicators. The formative
indicators are the summed items from the music participation index and, collectively,
form the construct of music participation. The reflective indicators are likert-type items
that indicate levels of involvement or importance for each domain of music participation
(i.e., formal, nonformal, informal) and collectively reflect the construct of music
participation. The overall MIMIC CFA model represents the hypothesis that the
formative indicators will predict the construct of music participation (in practical terms,
predict the reflective indicators). A relationship between the formative indicators and the
music participation construct represents an adequate level of convergent validity for the
84
Figure 4.1. Model for formal, nonformal, and informal music participation indices. Each
formative indicator (bottom) represents the domain (F, N, I; formal, nonformal, informal)
and the mode (P, C, R; perform, create, respond).
use of the music participation index in the primary study (Johnson & Morgan, 2016;
Kline, 2012).
Assumptions. Before conducting each of the three MIMIC CFA models, I
examined each of the 18 variables for normality and linear relationships. Analysis of
univariate normality revealed a positive skew for each formal and nonformal formative
indicator. This result was expected because many of the students surveyed were not
involved in school music classes (e.g., school, lessons, etc.). Due to the positive skew of
the formal and nonformal formative indicators, as well as additional groupings in the
distribution, I decided to re-scale each formative variable from a continuous variable to
an ordinal variable. Miksza and Elpus (2018) recommended inspecting data to ensure the
level of measurement intended before data collection (e.g., nominal, ordinal, ratio,
intervallic). While the intention was to collect continuous data, conceptually, the
85
formative indicators are best represented as ordered categories (ordinal). Therefore, each
formal and nonformal formative indicator (i.e., performing, creating, and responding)
was recoded into three levels: (1) those who do not participate in any formal/nonformal
music activities, (2) those who participate in moderate levels of formal/nonformal music
activities, (3) and those who are highly involved in formal/nonformal music activities.
Distribution analyses also revealed positive skew for the formal and nonformal reflective
indicators. To encourage a more normal distribution of the data, I conducted a
logarithmic transformation for each formal and nonformal reflective variables
(Tabachnick & Fidell, 2007). Normality for each of the reflective indicators was greatly
improved. Finally, informal music participation variables did not violate assumptions of
normality. This result was expected because informal music activities occur in social and
home environments. Therefore, the data are expected to follow a normal distribution. The
assumption of linearity for all eighteen variables was met.
Procedure. Once the data were cleaned, screened, and the appropriate data
transformations were conducted, I employed a three-step process for conducting each
MIMIC CFA. First, I evaluated the measurement model with only the reflective
indicators to ensure the model fits the data. Second, I added the formative indicators as
covariates of the model to determine if the participation construct varies according to the
formative measure (music participation index). Third, I use modification indices to
determine additional improvements to the fit of the model. Procedures, data
modifications, correlations, factor loadings, and fit statistics for the three different models
are described in the subsections below.
Formal music participation – CFA MIMIC model. Following Brown’s (2015)
86
procedure for conducting a MIMIC CFA, I first examined a model of formal music
participation that only included the reflective indicators to ensure the model is reasonable
and good-fitting before adding the formative indicators (see Table 4.2). Missing data
were estimated using FIML, which uses all available information (Acock, 2013; Allison,
2003).
The first formal music participation model (N = 219) with only reflective
indicators demonstrated strong internal consistency (a = .92) (see Table 4.3). Next, I
added the formal performing, formal creating, and formal responding formative
indicators as predictors of the formal music participation construct. Results indicated that
formal performing was a significant and strong predictor of formal music participation (B
= .16, b = .69, p < .001) while formal creating (B = .01, b = .07, p = .25) and formal
responding (B = .003, b = .01, p = .78) were not significant predictors of formal music
participation. Although some fit indices showed good model fit, overall the Formal
MIMIC CFA model showed a poor fit to the data (c2 = 28.28 (6), p < .001, RMSEA = .13
(90% CI = .08–.18), CFI = .96, TLI = .93) (see Table 4.3). To improve model fit, I
followed Acock’s (2013) procedure for examining postestimation modification indices.
Acock (2013) explains: “A modification index is an estimate of how much the chi-square
Table 4.2
Correlations for Formal CFA Analysis
Observed Variable
1
2
3
1. Active
2. Spend time
3. Important part of my life
1
.87
.75
1
.76
1
Note. N = 219
87
will be reduced if we estimated a particular extra parameter” (p. 26). Based on the results
of the modification index, I fit a final MIMIC CFA model but structured the formal
creating formative indicator as a predictor of both the construct of formal music
participation and the reflective indicator Important part of my life. Results indicated
formal creating was a significant predictor of the reflective indicator Important part of my
life (B = .06, b = .22, p < .001). The final CFA MIMIC model also revealed an improved
and good-fit to the data: (c2 = 6.79 (5), p = .23, RMSEA = .04 (90% CI = .00–.10), CFI =
.99, TLI = .99) (see Table 4.3).
In summary, the results indicated that the performing portion of the music
Table 4.3
Comparison of Fit Statistics for Each Stage of the Formal, Nonformal, and Informal
models
Model
c2 (df), p
RMSEA
(90% CI)
CFI
TLI
Reliability
(a)
–
–
.92
.96
.93
.79
.99
.99
.79
Formal models
1. Formal CFA Model
–
.13
2. Formal MIMIC Model 28.28 (6), p < .001
(.08-.18)
3. Final MIMIC Model*
–
6.79 (5), p > .23
.04
(.00-.10)
Nonformal models
1. Nonformal CFA Model
2. Nonformal MIMIC
Model
–
–
–
–
.91
14.02 (6), p = .02
.07
(.02-.13)
.98
.97
.76
–
–
.66
.64
.28
.68
Informal models
1. Informal CFA Model
2. Informal MIMIC
Model
–
–
.25
93.27 (6), p < .001
(.21-.31)
Note. The first model is just-identified and, therefore, has no degrees of freedom available to perform fit
statistics. In a just-identified model, the chi-square and RMSEA values will always be zero and the CFI
and TLI will always be one.
88
participation index was a strong predictor of formal music participation (see Table 4.4).
While there was not a significant relationship between formal music creating and formal
music participation there was a significant relationship between formal music creating
and the reflective item Important part of my life. This relationship may suggest that
students who create music as part of their music classes, lessons, or practice sessions,
while not viewing composition as a core part of their formal music participation, may feel
that it enhances the value they have for formal music participation. There was no
relationship between formal responding and formal music participation (see Figure 4.2).
Table 4.4
Estimates for the Formal and Nonformal Music Participation MIMIC CFA Models
Unstandardized
Indicator
Standardized
Estimate
SE
Estimate
SE
Reflective - Active
Reflective - Spend time
Reflective - Important
1
.94
.74
–
.01
.05
.93
.92
.69
.01
.01
.05
Formative - Performing
Formative - Creating
.16
.01
.02
.02
.71
.03
.06
.07
Formative - Responding
Reflective – Important
Formative - Creating
.004
.01
.01
.06
.06
.01
.22
.05
1
–
.85
.04
Reflective - Spend time
Reflective - Important
Formative performing
.88
1.01
.10
.05
.05
.02
.88
.92
.47
.03
.03
.07
Formative creating
Formative responding
.01
.04
.01
.01
.05
.22
.08
.07
Formal Music Participation
Nonformal Music Participation
Reflective - Active
Note. N = 219
89
Formative
Performing
Active
.71
.63
.93
.44
.12
1
E4
Formative
Responding
.01
Formal music
participation
Spend
time .66
.92
.14
2
.69
.03
Formative
Creating
.22
Important
.65
3
.30
Figure 4.2. Final MIMIC CFA model for formal music participation.
Nonformal music participation – CFA MIMIC model. Similar to the formal
music participation model, I first examined a model of nonformal music participation (N
= 219) that only included the reflective indicators. This step is conducted to ensure the
model is reasonable and good-fitting before adding the formative indicators (see Table
4.5). Missing data were estimated using FIML (Acock, 2013; Allison, 2003).
The first formal music participation model with only reflective indicators
demonstrated strong internal consistency (a = .91) (see Table 4.3). Next, I added the
nonformal performing, nonformal creating, and nonformal responding formative
indicators as predictors of the nonformal music participation construct. Results indicated
nonformal performing (B = .10, b = .47, p < .001) and nonformal responding (B = .04, b
= .22, p < .003) were significant predictors of nonformal music participation while
nonformal creating was not (B = .01, b = .07, p = .25). Overall, the Nonformal MIMIC
CFA model showed a “reasonably close fit” to the data (c2 = 14.02 (6), p = .02, RMSEA
= .07 (90% CI = .02–.13), CFI = .98, TLI = .97) (Acock, 2013, p. 24).
90
Table 4.5
Correlations for Nonformal CFA Analysis
Observed Variable
1
2
1. Active
2. Spend time
1
.75
1
3. Important part of my life
.78
.82
3
1
Note. N = 219
In summary, results showed the performing and responding portion of the music
participation index were predictors of nonformal music participation (see Table 4.4).
There was no relationship between nonformal creating and nonformal music
participation. These findings may be indicative of the types of music activities
adolescents participate in throughout their community, namely performing (e.g., singing,
playing, etc.) and responding (e.g., listening, selecting, discussing, dancing, etc.) (see
Figure 4.3).
Informal music participation – CFA MIMIC model. To begin, I fit a model of
informal music participation (N = 219) that only included informal reflective indicators to
ensure the informal model is reasonable and good-fitting before adding the formative
indicators (see Table 4.3). Similar to previous analyses, missing data were estimated
using FIML (Acock, 2013; Allison, 2003). I also included the vce(robust) option, which
relaxes the normality assumption in the maximum likelihood estimator (Acock, 2013;
Williams, Allison, & Moral-Benito, 2018). The vce(robust) option was included because
the data distribution of the formative informal creating variable was slightly skewed.
Several scholars have noted it is acceptable to use full information maximum likelihood
with a vce(robust) option to accommodate for normality violations (Asparouhov, 2002;
Brown, 2015, p. 340; Williams et al., 2018).
91
Nonformal
Performing
Active
.47
.02
.17
2
Spend
time .15
3
.85
.01
E1
Nonformal
Responding
.22
Nonformal music
participation
.88
.01
.92
.05
Nonformal
Creating
Important
.14
4
.005
Figure 4.3. Final MIMIC CFA model for nonformal music participation.
The first informal music participation model with only reflective indicators
demonstrated moderate internal consistency (a = .66) (see Table 4.6). In addition, factor
loadings for Casually playing and Writing were less than .70 (Kline, 2016). Although the
model was weak, I decided to add the informal performing, creating, and responding
formative indicators to determine the whole model fit (formative and reflective
indicators). Results showed a poor fit when the formative indicators were added (c2 =
93.27 (6), p < .001, RMSEA = .25 (90% CI = .21–.31), CFI = .64, TLI = .28) and factor
loadings remained low. With poor model fit, I decided informal music participation was
likely not a single-factor construct but, instead, each dimension of informal music
participation (i.e., performing, creating, responding) is best represented individually as a
single factor by itself. Therefore, the final convergent validity of the informal portion of
the music participation index will be addressed in the Primary Study section of this
chapter.
92
Table 4.6
Correlations for Informal CFA Analysis
Observed Variable
1
2
1. Listening
2. Casually playing
1
.51
1
3. Writing
.27
.39
3
1
Note. N = 219
Pilot Study Summary
To summarize, both the formal and nonformal MIMIC models showed acceptable
fit demonstrating convergent validity of the formal and nonformal music participation
indices. For the formal model, formal performing activities predicted formal music
participation while formal creating and responding activities did not. This finding is
realistic considering one of the primary aims of music lessons and school music classes is
performance. For the nonformal model, nonformal performing and responding activities
predicted nonformal music participation while creating activities did not. Similar to
formal music participation, the aim of many after-school, community, and religious
musical organizations is performance. The results of the pilot test will be taken into
consideration when determining how to best conduct a latent profile analysis for the first
research question of the primary study.
The informal MIMIC model did not demonstrate good model fit. It is likely that
informal music participation is likely too diverse to be represented as a single construct.
To test this hypothesis, I composed additional reflective items and included them on the
primary study questionnaire (see below). The convergent validity of the informal music
participation index was retested in the primary portion of this study using separate
reflective indicators for each mode of informal music participation (i.e., performing,
93
creating, responding).
Primary Study
Sample Demographics
Self-reported demographics were collected for gender, grade, parent education,
and grade point average (GPA). The school district did not permit collecting data on
ethnicity or free-reduced lunch status. Demographic characteristics of the sample from
each school appear in Table 4.7.
Variables
Several data transformations were required to combine questionnaire items into
their designated variables. Listwise deletion was employed for all mathematical
operations to ensure individuals who left at least one question blank also received a blank
score for the final variables described above. First, I summed the appropriate
questionnaire items for nonformal and informal music participation resulting in five
variables (i.e., nonformal performing and responding; informal performing, creating, and
responding). Second, I created new variables based on each participant’s mean score for
questionnaire items related to past attitudes towards music class, large ensemble attitudes,
piano/guitar attitudes, pop ensemble attitudes, music composition attitudes, peer
subjective norms, parent subjective norms, and music ability beliefs. Third, I summed the
appropriate questionnaire items to create formal, nonformal, informal performing,
informal creating, and informal responding desire variables. Based upon results from the
pilot tests, formal creating, formal responding, and nonformal creating indicators were
94
Table 4.7
Demographic Characteristics of Primary Study Participants
HS A
(n = 450)
Characteristic
HS B
(n = 125)
MS
(n = 280)
n
%
n
%
n
%
Female
207
46.0
53
42.4
132
47.1
Male
233
51.8
70
56.0
142
50.7
Nonbinary
10
2.2
2
1.6
6
2.1
7
36
8.0
–
–
191
68.2
8
5
1.1
–
–
85
30.4
9
121
26.9
10
8.0
–
–
10
189
42.0
83
66.4
–
–
11
74
16.4
19
15.2
–
–
12
21
4.7
12
9.6
–
–
No answer
4
0.9
1
0.8
4
1.4
Did not complete HS
126
28.0
30
24.0
30
10.7
HS diploma/GED
67
14.8
32
25.6
35
12.5
45
10.0
10
8.0
31
11.1
Bachelor’s degree
62
13.8
21
16.8
56
20.0
Graduate degree
(Masters/Doctorate)
140
31.1
29
23.2
106
37.9
Unsure/no answer
10
2.2
3
2.4
22
7.9
Mostly A’s
147
32.6
25
20.0
109
38.9
Mostly B’s
175
38.9
54
43.2
115
41.1
Mostly C’s
104
23.1
34
27.2
46
16.4
Mostly C’s/D’s/F’s
22
4.9
10
8.0
8
2.9
Unsure/no answer
2
0.4
2
1.6
2
0.7
Gender
Grade
Parent Education
Associates/Vocational
GPA
Note. N = 855
95
not used in this analysis.
After examining the frequencies and distributions for the formal questionnaire
items, I decided to slightly alter the additive process of creating the final formative
indicators. First, it was clear that on the first questionnaire item (sing or play an
instrument in a school music class), participants fell into one of two groups: no school
music participation or regular school music participation (one or more times a week).
Therefore, I reduced this question from a range of seven response options to only two
response options. Second, data for the second and third item (sing or play an instrument
in more than one school music class, sing in a musical theatre class during school) were
largely distributed into three groups: not at all, monthly, and weekly. Therefore, the
second and third questionnaire items were reduced from seven options to three. Finally, it
was clear that formal music participants represented three groups of formal music
participation overall: (1) only school music, (2) only private lessons, and (3) both school
music and private lessons. Therefore, I created two formative indicators from the
appropriate questionnaire items. One represented school formal music participation and
the other formative indicator signified private lessons. In summary, the formal music
participation data were summed and categorized in a slightly different manner than
originally intended to accommodate for the nature of the data collected (Miksza & Elpus,
2018).
Overall, the data set was reduced from 108 separate questionnaire items to 33
total variables (i.e., demographics, objective music participation indicators, desire
indicators, parent/peer norms, school/life activity involvement, past experiences with
school music classes, attitudes toward school music classes, music ability beliefs, long
96
term musical identity). Procedures for missing data are described in the next section.
Missing Data
Initially, 871 students participated in the survey and turned in a questionnaire.
Sixteen participants were eliminated from the data set because the questionnaire they
submitted was less than 50% complete (n = 855). Overall, 31 (95.6%) of the 33 measured
variables included some missing data, with 205 (29.2%) out of 855 respondents having
some level of missingness. However, the missingness of the total sample was relatively
low, with only 2.2% (621) missing from the measured items (28,215) by total number of
respondents. Overall, missingness per item ranged from 0 to 5.61%. I also examined the
data for patterns of missingness. Results showed the only pattern of missing data
concerned a small group (2.0% of sample) that did not complete the following
demographic variables: grade, GPA, parent education, amount of elective classes, and
whether or not they held an after-school job. In general, a missingness rate of 5% or less
is acceptable (Bennett, 2001; Schafer, 1999). Still, a full information maximum
likelihood (FIML) estimator was used to accommodate for the missing data for any
structural models (confirmatory factor analysis) (Allison, 2003; Brown, 2015). For all
linear analyses (e.g., latent profile analysis, ANOVA, MANOVA, etc.), I utilized listwise
case deletion, which was deemed appropriate because the data set contained a substantial
sample size and any comparison groups for multivariate analyses will remain an adequate
size despite the case deletions (Mertler & Vannatta Reinhart, 2017).
97
Differences Between Adolescents Enrolled in HS A, HS B, and MS
To investigate differences between adolescents at each participating school, c2
tests for independence and one-way analysis of variance were calculated. There were no
statistically significant differences between the schools on the basis of gender. However,
there was a statistically significant difference for all other demographic variables (i.e.,
grade, parent education, GPA), indicating the samples were not similar. Table 4.8
displays the results from these calculations. Effect size calculations revealed small effects
for grade, parent education, and GPA (Gravetter & Wallnau, 2004). Still, effects were
large enough that school will be included as a covariate in the latent profile analysis and
subsequent multivariate statistical procedures.
Informal Music Participation – MIMIC CFA Models
Sample. The sample for the Informal MIMIC CFA model was comprised of only
students who attended HS A and HS B (n = 575) (see Table 4.7). The additional
reflective indicators made the primary questionnaire longer and were only included on
the high school version of the questionnaire. I was concerned the middle school students
would not complete a questionnaire substantially lengthened by additional items. High
school was included as a covariate in each analysis to ensure school did not affect the
results of the model.
Assumptions. Before conducting each of the three MIMIC CFA models, I
examined each of the 12 variables for normality and linear relationships. Analysis of
univariate normality revealed a positive skew for two performing, three creating, and one
responding reflective variable. I conducted a square root transformation (Tabachnick &
98
Table 4.8
Differences in Adolescents for HS A, HS B, and MS
Demographic
Variables
SS
MS
F
c2
df
Effect
Size
Gender
–
–
–
1.08
4
V = .02
Gradea
Parent Education
22.11
–
22.11
–
18.18*
–
–
44.84*
1, 568
8
η2 = .01
V = .16
GPA
14.44
7.22
10.01
–
2, 846
η2 = .01
Note. Gravetter & Wallnau (2004) explain the standards for interpreting Cramer’s V. The interpretation of
the effect size is based on the variable in the analysis with the fewest number of categories. If the variable
has two categories (gender), these values are small (.06), medium (.30), and large (.50). If the variable has
four or more categories (parent education, GPA, grade), these values are small (.06), medium (.17), and
large (.29).
a
Only HS A and HS B were compared on grade because comparing the high schools and middle school
on the basis of grade showed an extreme difference because the high schools do not serve many 7th- or 8thgrade students.
* represents significance detected at the p < .01 level
Fidell, 2007) but the transformation reversed the direction of the skew. Therefore, I
decided to proceed with model analysis employing the vce(robust) option to
accommodate for nonnormality (Acock, 2013). Analysis of univariate normality also
revealed a positive skew for the formative creating indicator. I conducted a logarithmic
transformation procedure to correct the positive skew. Results showed the transformation
was successful with the formative creating variable meeting requirements of normality.
The assumption of linearity was met for all 12 variables.
Informal performing – MIMIC CFA Model. Following Brown’s (2015)
procedure for conducting a MIMIC CFA, I first examined a model of informal
performing music participation that only included the reflective indicators to ensure the
model is reasonable and good-fitting before adding the formative indicators (see Table
4.9). Missing data were estimated using FIML (Acock, 2013; Allison, 2003).
The first informal performing model (N = 575) with only reflective indicators
99
Table 4.9
Correlations for Informal Performing CFA Analysis
Observed Variable
1
2
1. Sing/play alone
2. Sing play/family & friends
1
.69
1
3. Perform
.23
.34
3
1
Note. N = 575
demonstrated moderate internal consistency (a = .69) (see Table 4.10). Next, I added the
informal performing formative indicator and the school variable (covariate) as predictors
of the informal performing construct. Results indicated informal performing was a
significant, moderate predictor of the informal performing construct (B = .10, b = .52, p <
.001). The school variable was not a significant predictor of informal performing (B =
.10, b = .18, p = .16). Although some fit indices showed model fit, overall the informal
performing MIMIC CFA model showed a poor fit to the data (c2 = 49.88 (4), p < .001,
RMSEA = .14 (90% CI = .10–.18), CFI = .92, TLI = .83) (see Table 4.10). To improve
model fit, I followed Acock’s (2013) procedure for examining postestimation
modification indices. Based on the results of the modification index, I fit a final MIMIC
CFA model but structured the informal performing formative indicator as a predictor of
both the construct of informal performing and the reflective indicator Perform. Results
indicated formal creating was a significant predictor of the reflective indicator Perform
(B = .07, b = .30, p < .001). The final CFA MIMIC model also revealed an improved and
good-fit to the data: (c2 = 7.38 (3), p = .06, RMSEA = .05 (90% CI = .00–.09), CFI = .99,
TLI = .97) (see Table 4.10).
In summary, results showed the informal performing portion of the music
100
Table 4.10
Comparison of Fit Statistics for the Informal Performing, Informal Creating, and
Informal Responding MIMIC CFA Models
Model
c2 (df), p
RMSEA
(90% CI)
CFI
TLI
Reliability
(a)
Informal performing models
1. Informal Performing
CFA
2. Informal Performing
MIMIC Model
3. Final Informal
Performing
MIMIC Model
–
–
–
–
.69
49.88 (4), p < .001
.14
(.10-.18)
.92
.83
.42
7.38 (3), p = .06
.05
(.00-.09)
.99
.97
.42s
Informal creating models
1. Informal Creating CFA
2. Informal Creating
MIMIC Model
–
–
–
–
.78
16.60 (4), p < .05
.07
(.04-.11)
.98
.96
.72
Informal responding models
1. Informal Responding
CFA
2. Informal Responding
MIMIC Model
–
–
–
–
.62
3.64 (4), p < .001
.00
(.00-.06)
1.00
1.00
.30
Note. The first model is just-identified and, therefore, has no degrees of freedom available to perform fit
statistics. In a just-identified model, the chi-square and RMSEA values will always be zero and the CFI
and TLI will always be one.
participation index was a moderate predictor of formal music participation (see Table
4.11). There was also a significant relationship between the performing formative
variable and the performing reflective item Performing. This item went a step beyond
casually singing or playing music to actually performing music for others. The
relationship between the formative variable of informal performing (frequency of
playing/singing music) and the reflective item Performing indicates not all individuals
who enjoy singing or playing music informally also enjoy performing music in front of
others (see Figure 4.4).
101
Table 4.11
Estimates for the Informal Performing, Informal Creating, and Informal Responding
MIMIC CFA Models
Unstandardized
Indicator
Standardized
Estimate
SE
Estimate
SE
1
–
.76
.03
1.23
.09
.90
.03
.27
.07
.21
.05
.09
.01
.49
.04
.17
.13
.05
.04
.07
.01
.30
.05
1
–
.83
.02
.91
.50
.06
.05
.79
.59
.03
.04
4.13
.27
.74
.03
.05
.10
.01
.03
1
–
.48
.04
Reflective – Discuss
Reflective – Dance
1.88
2.02
.24
.26
.68
.67
.03
.02
Formative – Informal
Responding
.04
.01
.80
.04
School
.01
.07
.003
.04
Informal Performing
Reflective – Sing/play alone
Reflective – Sing/play with
friends/family
Reflective – Perform
Formative – Informal
Performing
School
Reflective – Perform
Informal Performing
Informal Creating
Reflective –
Writing/arranging
Reflective – Technology
Reflective – Post songs
Formative – Informal
Creating
School
Informal Responding
Reflective – Listen
Note. N = 575
102
Perform
.30
Informal
Performing
.38
2
.79
.76
.22
Informal Performing
Music Participation
.05
.90
Sing/play
alone 1.7
4
.41
School
E1
.73
.21
Sing/play
others 1.1
3
.18
Figure 4.4. Final MIMIC CFA model for informal performing music participation.
Informal creating – MIMIC CFA Model. Similar to the previous model, I first
examined a model of informal creating (N = 575) that only included the reflective
indicators to ensure the model is reasonable and good-fitting before adding the formative
indicators (see Table 4.12). Missing data were estimated using FIML (Acock, 2013;
Allison, 2003).
The first informal creating model (N = 575) with only reflective indicators
demonstrated internal consistency (a = .78) (see Table 4.10). Next, I added the informal
creating formative indicator and the school variable (covariate) as predictors of the
informal creating construct. Results indicated formative informal creating was a
significant and strong predictor of the informal creating construct (B = 4.13, b = .74, p <
.001) (see Table 4.11). The school variable was not a significant predictor of informal
creating (B = .05, b = .02, p = .6). The model also revealed a good fit to the data: (c2 =
16.60 (4), p < .05, RMSEA = .07 (90% CI = .04–.1), CFI = .98, TLI = .96) (see Table
4.10) (see Figure 4.5).
103
Table 4.12
Correlations for Informal Creating CFA Analysis
Observed Variable
1
2
1. Writing/arranging
2. Technology
1
.64
1
3. Post songs
.47
.53
3
1
Note. N = 575
Writing/
arranging
4
.43
.45
Informal
Performing
.83
.74
Informal Creating
Music Participation
.01
Tech
.79
.48
3
.36
School
E1
.47
.59
Post
songs .72
2
.64
Figure 4.5. Final MIMIC CFA model for informal creating music participation.
Informal responding – MIMIC CFA Model. I first examined the informal
responding model with only reflective indicators (N = 575). Missing data were estimated
using FIML (Acock, 2013). This model (N = 575) demonstrated internal consistency (a =
.62) (see Table 4.13). Next, I added the formative indicator and school variable as
predictors of the informal responding construct. Results indicated formative informal
creating was a significant predictor of the informal responding construct (B = .04 b = .81,
p < .001) (see Table 4.11). The school variable was not a predictor of informal creating
(B = .01, b = .04, p = .9). The model showed a good fit to the data: (c2 = 3.64 (4), p <
.001, RMSEA = .00 (90% CI = .00–.06), CFI = 1.00, TLI = 1.00) (see Table
104
Table 4.13
Correlations for Informal Responding CFA Analysis
Observed Variable
1
2
1. Listen
2. Discussing
1
.34
1
3. Dancing
.27
.45
3
1
Note. N = 575
4.10) (see Figure 4.6).
Summary informal music participation models. Overall, the informal music
participation performing, creating, and responding models fit the data. Each of the
formative indicators (informal performing, creating, and responding), which were
summed from the music participation index, predicted the respective music participation
constructs. With this result, I proceeded to use the informal music participation formative
variables in the subsequent latent profile analysis (research question one).
Primary Study – Research Question One
What are the music participation profiles of secondary-school students and how
many participants are represented in each profile? This study employs a data analytic
technique called latent profile analysis (LPA) to uncover and describe unique profiles of
music participation based upon the variables listed in Table 4.14. Morin and Wang
(2016) explain: “Rather than classifying participants based on scores on a set of
indicators, [LPA] can be used to extract subgroups of participants differing from one
another in regard to the relations between sets of variables” (p. 187). Somewhat similar to
confirmatory factor analysis (CFA), LPA researchers estimate latent (unobserved)
variables from a set of categorical or continuous observed indicators (Wang & Wang,
105
Listen
4
Discuss
3
Dance
2
2.3
Informal
Performing
.78
.48
.80
Informal Responding
Music Participation
.003
.68
-.11
.53
School
E1
.67
.34
-.15
.54
Figure 4.6. Final MIMIC CFA model for informal responding music participation.
2012). The latent variables in CFA are continuous while the latent variables in LPA are
categorical (e.g., classes or profiles) (Wang & Wang, 2012). Some scholars have
identified this method as a “person-centered analytic approach” because participant
groupings are not determined a priori. In this analysis, I used the music participation
indicators listed in Table 4.14 to identify latent subgroups (profiles) of music
participation (see Figure 4.7).
Each LPA model is comprised of two types of indicators: objective and desire.
First, the objective participation indicators (the frequency of engagement in a particular
activity) include: formal school performing, formal lesson performing, nonformal
performing, nonformal responding, and informal performing, creating, and responding.
Second, this study also includes subjective participation measures (desire) which indicate
the degree to which individuals wish to participate in each activity (more or less). The
desire variables are formal desire, nonformal desire, informal performing desire, informal
creating desire, and informal responding desire. Notably, the objective indicators for
106
Table 4.14
Means and Standard Deviations for Model Variables
Frequencies
Variable
Mean
SD
None
Low
High
Formal school performinga
Formal lesson performinga
1.40
1.40
.73
.72
640
626
86
108
129
121
Nonformal performinga
Nonformal respondinga
1.63
1.72
.77
.83
469
451
227
190
159
214
Informal performing
Informal creating
Informal responding
16.31
7.43
42.02
7.63
5.16
13.28
19.23
–
–
7.50
–
–
15.32
–
–
Formal desire
Nonformal desire
9.77
6.48
4.32
3.29
–
–
–
–
–
–
Informal performing desire
Informal creating desire
3.41
5.53
1.44
2.67
–
–
–
–
–
–
Informal responding desire
6.04
2.14
–
–
–
Note. N = 855
a
Represents categorical variables
Music Participation
Latent Profiles
FPS
FPL
NP
NR
IP
IC
IR
FSu
NSu
IPSu ICSu IRSu
Figure 4.7. Final music participation latent profile model. Each formative indicator
(bottom) represents the domain (F, N, I; formal, nonformal, informal) and the mode (PS –
perform school; PL – perform lesson; C – create; R – respond; Su – desire).
107
formal creating, formal responding, and nonformal creating are not represented in this
analysis because pilot results showed they did not adequately predict music participation.
As noted in previous analyses, the data for several indicators were skewed
positive because many participants indicated they did not engage in certain types of
musical activities. While this result is to be expected, it presents a challenge in analyzing
the data continuously. Means, standard deviations, and z-scores are central to interpreting
some of the data points in this analysis, each of which is influenced by the distribution of
the data. Therefore, I transformed the variables with a strong positive skew to ordinal
variables with three levels (i.e., formal school performing, formal lesson performing,
nonformal performing, nonformal responding). All other variables remained continuous
(see Table 4.14).
Identifying music participation profiles. The latent profile analysis included the
entire sample (N = 855). Wang and Wang (2012) describe several steps for conducting a
LPA. First, the researcher must determine the “optimal number of latent [profiles]…” (p.
293). To determine the number of profiles, the researcher conducts a series of successive
models, each with an increasing number of latent profiles (e.g., model A = 1 latent
profile, model B = 2 latent profiles, model C = 3 latent profiles, etc.). A number of fit
indices such as the Bayesian Information Criterion (BIC) or the Aikeke Information
Criterion (AIC) are compared for each model (Collins & Lanza, 2010). Additionally, an
entropy value is calculated to determine “the precision with which the cases are classified
into the profiles” (Morin & Wang, 2016, p. 189). The model that best represents the data
according to the fit indices signifies the preferred number of latent profiles (Wang &
Wang, 2012). Second, study participants are classified into each of the latent profiles. To
108
accomplish this step, the probabilities of individual participants belonging in each profile
are calculated (posterior probability). Typically, participants are assigned to the profile
associated with their largest posterior probability (Pastor, Barron, Miller, & Davis, 2007).
Assignment into a latent class depends on the probability that a participant answered in a
similar pattern to the latent profile (Morin & Wang, 2016). Finally, each profile is
defined or named based on how the observed indicators loaded on each profile (Morin &
Wang, 2016). That is, the latent profile is defined by the pattern of responses of the
participants in the profile. Each step of this analysis process is described below.
To begin, LPA was utilized to derive the optimal number of music participation profiles
for each grouping below (i.e., all participants, only school music participants, participants
outside of school music classes). I investigated the plausibility of a 1-, 2-, 3-, 4-, 5-, 6-,
and 7-profile solution. Profiles were added iteratively to determine the best model fit
according to both statistical indices and theoretical interpretation (Morin & Wang, 2016).
Model fit was evaluated using the Akaike Information Criterion (AIC) (Akaike, 1974),
Bayesian information Criterion (BIC) (Schwarz, 1978), entropy statistic, and uniqueness
of profile. Optimal model fit was defined as a lower AIC or BIC score than the previous
model (see Table 4.15). Entropy statistics were calculated to determine classification
accuracy of the model. Morin and Wang (2016) explain the entropy statistic is useful for
examining “…the precision with which cases are classified into the profiles, with larger
values (closer to 1) indicating fewer classification errors” (p. 189). An entropy value of
.80 or higher is acceptable (Grimm, Ram, & Estabrook, 2017). I ran models with an
increasing number of profiles (beginning with one) until the models would no longer
converge or distinctions between the profiles lacked theoretical meaning.
109
Table 4.15
Profile Fit Indices
All Participants Models
df
AIC
BIC
Entropy
1-profile
2-profile
24
41
43268.90
41147.75
43382.93
41342.55
–
.86
3-profile
4-profile
5-profile
58
75
92
40431.61
39999.75
39778.23
40707.18
40356.08
40215.33
.84
.85
.86
6-profile
7-profile
109
126
39487.69
39329.04
40005.56
39927.68
.87
.93
Note. N = 855
Latent profile analysis. LPA revealed the seven-profile solution showed better fit
than all previous solutions, evidenced by the lowest AIC values, BIC values, high entropy
statistic, and the theoretical differences between each of the classes. However, the
unconditional probability (the proportion of the sample population expected to belong to
each latent profile) for two of the seven profiles were very low (unconditional probability
£ .04, n £ 34). Several scholars suggest that profiles with few participants may indicate
spurious results (Morin & Wang, 2016; Wang & Wang, 2012). Therefore, I deemed the
six-profile solution as the best fitting model. Individuals were represented in each profile
as follows: Profile 1, n = 183 (21%); Profile 2, n = 209 (24%); Profile 3, n = 184 (22%);
Profile 4, n = 64 (8%); Profile 5, n = 144 (17%); Profile 6, n = 71 (8%) (see Table 4.16). I
also included school (i.e., HS A, HS B, MS) as a covariate in the latent profile analysis to
determine if school had an effect on the nature of the profiles. Only participants within
Profile 5 showed differences on some indicators based on school membership. Post-hoc
analysis revealed this effect was largely due to Profile 5 students from HS B participating
in school-based music courses and outside of school music lessons less than Profile 5
Low, High
None
Nonformal responding
Low, High
None
Nonformal performing
Low, High
None
Formal lesson performing
Low, High
None
Formal school performing
Variable
.22, .25
.53
.26, .19
.55
.13, .14
.73
.10, .15
.75
Sample
(N = 855)
.13, .12
.75
.13, .04
.83
.05, .03
.92
.07, .03
.90
.21
Profile 1
(n = 183)
Profile 3
(n = 184)
Profile 4
(n =64)
.27, .13
.60
.28, .06
.66
.11, .05
.84
.22
.20, .06
.74
.21, .02
.77
.10, .10
.80
.11, .17
.72
.66
.08
.32, .38
.30
.33, .37
.30
.22, .23
.55
.14, .20
Conditional probability
.11, .05
.84
.24
Unconditional probability
Profile 2
(n = 209)
Latent profile
Table 4.16
Six-Profile Model - Unconditional Probabilities, Conditional Probabilities, and Means
.29, .59
.12
.44, .55
.01
.23, .35
.42
.10, .34
.56
.17
Profile 5
(n = 144)
.16, .63
.21
.31, .51
.19
.16, .31
.53
.10, .28
.62
.08
Profile 6
(n = 71)
110
110
9.77
6.48
3.41
5.53
6.04
Formal desire
Nonformal desire
Informal performing desire
Informal creating desire
Informal responding desire
Notes. N = 855
42.02
Informal responding
Sample
(N = 855)
7.43
Informal creating
Variable
16.31
Informal performing
Table 4.16 continued
3.67
2.66
1.36
3.43
5.10
30.93
Profile 1
(n = 183)
4.70
9.68
6.02
4.51
3.99
4.19
7.39
42.88
Profile 2
(n = 209)
5.57
14.77
6.89
6.76
3.85
8.73
12.84
40.82
Profile 3
(n = 184)
5.54
14.73
Means
5.98
6.70
3.20
4.98
8.19
49.71
Profile 4
(n =64)
16.11
22.00
6.90
6.87
4.04
9.00
13.23
45.52
Profile 5
(n = 144)
6.98
20.77
6.04
5.53
3.41
6.48
15.55
57.59
Profile 6
(n = 71)
17.91
27.55
111
111
112
students from HS A and MS. This difference may be explained by two factors: (1) HS B
had a smaller music program than both HS A and MS; (2) I was not granted permission
to directly sample music students in HS B as I was in HS A. Therefore, it stands to reason
that there may be fewer formal music participants from HS B within Profile 5 when
compared to those from HS A and MS.
Description of latent profiles. To substantively interpret the six-profile model,
probabilities for each categorical music participation variable and means for each
continuous music participation variable were calculated (see Table 4.16). The categorical
item probabilities indicate the probability that a member of a given profile would endorse
a specific category (e.g., high formal, no nonformal, etc.). The means for each continuous
variable represent the average score of the individuals within a respective profile. To
facilitate interpretation, graphic representations of each profile are provided in Figure 4.8.
For the informal and desire portion of each graphic, the zero line marks the overall
sample mean for the informal and desire participation variables. The profile mean for
each informal and desire variable were transformed into z-scores and represented as a bar
on the graph. Therefore, the graph represents the profile means in terms of the number of
standard deviations (z-score) above or below the overall sample mean (zero line). The
formal and nonformal participation variables are categorical. Therefore, the graph
indicates the probability that a member of the profile would endorse a particular formal or
nonformal category.
All informal and desire participation means for Profile 1 (21%) were lower than
the overall sample mean. The categorical indicators showed very few adolescents in this
profile participate in formal or nonformal music activities. Thus, this profile is referred to
113
Informal
Participation
Create
Perform
Respond
Subjective
Participation
Formal
Informal
Participation
High
Respond
Create
Perform
Subjective
Participation
Informal Informal Informal
Perform Create Respond
Formal/Nonformal
Participation
Informal
Create
Participation
Perform
Nonformal
Low
None
Respond
Subjective
Formal
Non- Participation
Informal Informal Informal
Formal Respond
Nonformal Perform Create
Informal Informal Informal
formal
Formal/Nonformal
Participation
High
Formal/Nonformal
Participation
Low
None
High
Perform
Create
Respond
Low
None
Figure 4.8. Graphic representations of each profile in the 6-profile LPA model.
113
114
114
Figure 4.8. Continued.
115
as Low Participation. Interpretation of the informal and desire participation measures
indicated that Profile 2 (24%) reflected individuals who informally respond to music
(e.g., listen to music, dance, talk about music, etc.) and wish for more informal
performing activities. Formal and nonformal indicators show participants in Profile 2
have a 30–40% chance of being involved in nonformal music activities but are unlikely to
be involved in formal music activities. Accordingly, this class was referred to as
Primarily Music Listeners. Means and probabilities for Profile 3 (22%) reflected
individuals with low informal music participation, low formal, nonformal and informal
music participation. However, these adolescents also indicated high desire towards each
mode of music participation (i.e., formal, nonformal, informal), and thus are referred to
as Music Listeners with High Desire.
For Profile 4 (8%), participants expressed high rates of informal music
participation (especially creating) as well as nonformal music participation. However,
measures of formal and nonformal participation desire were lower than the overall
sample mean while desire informal measures were similar or higher than the sample
mean (especially informal performing desire). Therefore, Profile 4 is referred to as High
Informal–Nonformal Participants. Interpretation of the probabilities and means indicated
that Profile 5 (17%) reflected individuals with the highest rates of formal and nonformal
participation compared to other profiles. Individuals in Profile 5 indicated, on average,
higher rates of all desire participation variables when compared to the overall sample
mean. Informal performing and informal responding variables were also higher than the
overall sample mean; however, informal creating was lower. Accordingly, Profile 5 is
referred to as High Formal–Nonformal Participants. Finally, individuals in Profile 6
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rated all informal participation and participation desire measures higher than the overall
sample mean. The categorical indicators showed moderate rates of formal music
participation and high rates of nonformal music participation. Therefore, Profile 6 is
referred to as High Participation.
Primary Study – Research Question Two
What is the relationship between music participation profiles and the demographic
variables gender, grade, GPA, and parent education? A Chi-square test for independence
indicated a significant association between gender and music participation latent profile
(MPLP), c2(5, n = 837) = 31.87, p < .01, V = .19. Examination of frequency tables
revealed males were represented at approximately twice the rate of females in both
Profile 1 (low participation) and Profile 4 (high informal-nonformal participants).
Additionally, Profile 5 (high formal-nonformal participants) contained more females than
males. Another Chi-square test for independence revealed a significant association
between parent education and MPLP, c2(8, n = 820) = 61.83, p < .01, V = .13. Frequency
tables showed the parents of participants in Profile 3 (low participation/high desire) and
Profile 5 (high formal-nonformal participants) had higher levels of education than the
parents of participants in Profile 1 (low participation).
To determine if there were grade or GPA differences based on MPLP, I conducted
two univariate analyses of variance. Main effect results revealed GPA was significantly
different among participants from different MPLP, F(5, 843) = 10.73, p < .01, partial η2 =
.01. Post-hoc analyses revealed participants in Profile 5 (high formal-nonformal
participants) reported higher GPAs than those in Profile 1 (low participation), Profile 2
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(music listeners with high desire), and Profile 4 (high informal-nonformal participants).
There was no main effect for grade.
Primary Study - Research Question Three
To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life activity
involvement, and past experiences with school music classes? Parent and peer norms
concern the degree to which an individual feels their parents or peers expect them to
participate in musical activities. Scores for parent and peer norms ranged from 1 to 6. To
address parent/peer norms, I conducted a MANCOVA with one between-subjects
variable (MPLP), two variates (peer norms and parent norms), and one covariate
(school). MANCOVA procedures carry assumptions of normality, homoscedasticity,
random selection, a linear relationship among variables, and absence of multicollinearity
(Mertler & Vannatta, 2017). Correlations among variables ranged from .29 to .65. Box’s
Test revealed a violation of homoscedasticity (p < .01), so I employed the more
conservative Pillai’s Trace for all multivariate analyses. I found a significant main effect
for MPLP, F(10, 1636) = 37.32, p <.001, partial η2 = .18. Subsequent univariate analyses,
adjusted using the Bonferroni correction for multiple comparisons (p = .02), indicated
that MPLP significantly affected both peer norms, F(5, 818) = 74.86, p <.001, partial η2 =
.31, and parent norms, F(5, 818) = 63.21, p <.001, partial η2 = .28. The covariate (school)
significantly influenced the variates, F(2, 817) = 3.91, p <.05, partial η2 = .01. Overall,
participants reported higher expectations of music participation from peers and parents as
profiles increased in formal and nonformal music participation (see Table 4.17).
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Table 4.17
Means and Standard Deviations for Peer and Parent Norms
Variable
Profile 1
Profile 2
Profile 3
Profile 4
Profile 5 Profile 6
Peer norms
1.46
(.82)
2.13
(1.22)
2.32
(1.14)
3.02
(1.42)
3.49
(1.44)
4.09
(1.60)
Parent norms
1.83
(1.36)
2.34
(1.38)
3.04
(1.54)
3.35
(1.59)
4.08
(1.48)
4.51
(1.46)
Note. Scores on parent and peer norm scales range from 1 to 6.
Next, I conducted a univariate ANCOVA to determine if past experiences with
school music classes (positive or negative) were affected by MPLP. Main effect results
revealed that past experiences with school music classes were significantly different
among participants from different MPLP, F(5, 842) = 11.89, p < .001, partial η2 = .06.
Scores ranged from one to six. Participants from Profile 1 (low participation) rated their
past school music experiences the lowest (M = 3.40, SD = 1.42) while participants in
Profile 6 (high participation) rated their past school music experiences highest (M = 4.38,
SD = 1.32). However, none of the profiles rated their past school music experiences less
than a three, which have would indicated a negative experience. Therefore, while
attitudes towards past school music classes varied, participants generally rated their
experiences as positive. The covariate (school) did not significantly effect past school
music experience.
Finally, to determine if school or other life activities are associated with MPLP, I
considered four variables: (1) the number of sports teams individuals participated in, (2)
the number of school elective classes, (3) involvement in church/religious activities, and
(4) after-school jobs. First, I conducted a univariate ANCOVA to determine if the
participation in sports teams was affected by MPLP with school as a covariate. Results
revealed no main effect for MPLP on sports team involvement. Second, I performed a
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univariate ANCOVA to determine if an individual’s number of elective classes was
related to MPLP. Results showed no main effect for MPLP on number of elective classes.
Third, I employed chi-square tests for independence to determine associations between
MPLP, church/religious involvement, and after-school job. Results indicated an
association between MPLP and church/religious involvement for Profiles 3 and 5 c2(5, n
= 842) = 28.53, p < .001, V = .18. Profile 5 (high formal-nonformal participants) included
almost twice as many participants involved in church/religious activities as those who
were not. In contrast, Profile 3 (music listeners with high desire) included almost twice as
many participants not involved with church/religious activities as involved. The
remaining profiles were largely balanced between those who participated in
church/religious activities and those that did not. Finally, a chi-square test of
independence revealed some association between MPLP and after-school job, c2(5, n =
818) = 19.78, p < .01, V = .15. While most participants indicated they did not have an
after-school job, two profiles exhibited high percentages of participants with after-school
employment. Forty-one percent of participants in Profile 4 and 43% of participants in
Profile 6 reported employment in an after-school position.
Primary Study - Research Question Four
To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity, music
ability beliefs, and attitudes towards secondary school music classes? Long-term musical
identity represents individuals’ predictions concerning how long they will sing, play, or
compose music. A Chi-square test for independence was used to determine if MPLP was
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associated with the categories of long-term musical identity (see Table 4.18). Results
from the chi-square test revealed a significant association between long-term musical
identity and MPLP, c2(30, n = 848) = 310.01, p < .001, V = .27. Overall, participants in
Profile 3, 4, 5, and 6 predicted they would participate in musical activities longer than
those in Profiles 1 and 2. Individuals in Profile 6 predicted the longest engagement in
musical activities with 87.3% reporting that they intended to sing, play, or compose
music for the rest of their lives.
I performed a univariate ANCOVA to investigate the effect of MPLP on music
ability beliefs. Results revealed a significant main effect for MPLP on music ability
beliefs, F(5, 834) = 78.57, p < .001, partial η2 = .32. Individuals in Profiles 4, 5, and 6
scored higher on music ability beliefs than those in Profiles 1, 2, and 3 (see Table 4.19).
The covariate (school) did have a significant effect on the variates, F(1, 834) = 23.48, p
<.01, partial η2 = .01.
Table 4.18
Percentages of Long-Term Musical Identity Within Music Participation Latent Profiles
Variable
I don’t sing, play, or
compose
Until the end of the
school year
Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6
76.8%
61.1%
44.0%
18.8%
14.58%
4.2%
1.6%
4.0%
2.2%
4.7%
2.8%
1.4%
For some of high school
Until the end of high
school
After I graduate high
school (college/young
adult)
9.0%
10.6%
8.7%
12.5%
4.2%
2.8%
4.0%
4.0%
5.4%
6.25%
9.7%
2.8%
2.0%
3.4%
7.1%
9.4%
2.8%
1.4%
For the rest of my life
6.8%
17.3%
32.6%
48.4%
66.0%
87.3%
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Table 4.19
Means and Standard Deviations for Music Ability Beliefs
Variable
Music Ability
Beliefs
Profile 1
Profile 2
Profile 3
Profile 4
2.21
(1.39)
3.40
(1.39)
3.76
(1.37)
4.19
(1.32)
Profile 5 Profile 6
4.64
(1.16)
5.03
(1.12)
To address attitudes towards secondary music courses, I conducted a MANCOVA
with one between-subjects variable (MPLP), four variates (large ensemble, piano/guitar,
popular music group, and composition with technology), and one covariate (school).
Correlations among variables ranged from .34 to .61. Box’s Test revealed a violation of
homoscedasticity (p < .001), so I employed the more conservative Pillai’s Trace for all
multivariate analyses. Results indicated a main effect for MPLP, F(20, 3312) = 14.21, p
<.001, partial η2 = .08. Subsequent univariate analyses, adjusted using the Bonferroni
correction for multiple comparisons (p = .01), indicated that MPLP significantly affected
large ensemble, F(5, 828) = 37.97, p <.001, partial η2 = .18, piano/guitar, F(5, 828) =
30.81, p <.001, partial η2 = .16, popular music group, F(5, 828) = 33.23, p <.001, partial
η2 = .17, and composition with technology, F(5, 828) = 47.08, p <.001, partial η2 = .22.
The covariate (school) did not significantly influence the variates. Overall, participants in
Profile 5 and 6 had more positive attitudes towards all music courses when compared to
individuals in Profiles 1, 2, 3, and 4 (see Table 4.20). Notably, individuals in Profiles 1,
2, and 3 indicated the most positive attitudes towards piano/guitar class (see Figure 4.9).
Participants in Profile 5 expressed the most positive attitudes towards large ensembles.
Finally, individuals in Profiles 4 and 6 had the most positive attitudes towards music
composition with technology.
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Table 4.20
Means and Standard Deviations for Secondary Music Courses
Variable
Profile 1
Profile 2
Profile 3
Profile 4
Profile 5 Profile 6
Large Ensemble
3.04
(1.44)
3.89
(1.35)
4.16
(1.16)
3.92
(1.42)
4.78
(1.15)
4.88
(1.14)
Piano/Guitar
3.22
(1.50)
4.19
(1.38)
4.40
(1.25)
4.04
(1.63)
4.63
(1.24)
5.19
(1.07)
Popular Music
Group
Music
Composition
with Technology
2.97
(1.56)
2.98
(1.59)
3.96
(1.39)
4.03
(1.30)
4.02
(1.33)
4.21
(1.28)
4.05
(1.49)
4.67
(1.46)
4.50
(1.23)
4.72
(1.18)
5.11
(1.15)
5.38
(1.18)
6
5
4
3
2
Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6
Figure 4.9. Mean attitudes for secondary music courses according to Music Participation
Latent Profile.
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Conclusion
This chapter presented the results of this dissertation study examining latent
profiles of music participation and the demographic, environmental, and personal-beliefs
associated with them. Latent profile analysis revealed six theoretically distinct profiles of
music participation: Profile 1 – Low participation; Profile 2 – Primarily music listeners;
Profile 3 – Low participation with high desire; Profile 4 – High informal-nonformal
participants; Profile 5 – High formal-nonformal participants; Profile 6 – High
participation. I examined differences among the profiles based on various demographic,
environmental, and personal-belief variables. For demographic variables, participants in
Profile 3 and 5 indicated that their parents possessed higher levels of education than the
parents of children in Profile 1. Males were overrepresented in Profiles 1 and 4 and
underrepresented in Profile 5. Participants in Profile 5 also reported higher GPAs than
those in Profile 1, 2, and 4.
For environmental factors, participants reported higher expectations for music
participation from peers and parents as profile rates of formal and nonformal music
participation increased. Students in Profiles 4 and 6 reported higher rates of after-school
employment. Adolescents in Profile 5 indicated very high rates of church/religious
involvement whereas those in Profile 3 reported low rates. For self-belief variables, the
majority of participants in Profiles 5 and 6 believed they will participate in music for the
rest of their life while those in Profiles 1, 2, 3, and 4 intend to participate for less time.
Concerning music ability beliefs, participants in Profiles 4, 5, and 6 expressed greater
confidence in their musical abilities than those in Profiles 1, 2, and 3. Results also
showed participants from Profile 5 and 6 had the most positive attitudes towards all
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music classes while those in Profile 1 had the least positive attitudes. Overall,
piano/guitar class and music composition with technology were rated the highest among
all of the profiles except for Profile 5, which rated large ensembles the highest. The
implications of these findings are discussed in Chapter 5.
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CHAPTER 5
SUMMARY AND CONCLUSIONS
The following chapter summarizes the results of this study as presented in
Chapter 4. Specifically, this chapter reviews the results of each research question and
interprets the findings within the context of past research. This chapter also highlights
implications for theory, research, music education pedagogy, and music teacher
education. Finally, the chapter concludes with suggestions for future research and
limitations of the current study.
Purpose and Research Questions
The purpose of this study was two-fold: (1) to identify profiles of music
participation from an adolescent school population; and (2) to examine the demographic,
environmental and personal belief differences of adolescents according to music
participation profile. The four research questions were:
1. What music participation profiles exist from the sampled population?
2. What is the relationship between music participation profiles and demographic
variables such as gender, grade, GPA, and parent education?
3. To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life
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activity involvement, and past experiences with school music classes?
4. To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity,
music ability beliefs, and attitudes towards secondary school music classes?
Pilot Study
Summary of Pilot Study Results
The purpose of the pilot study was to establish face and convergent validity for
the music participation index (MPI). First, adolescents from the focus group portion of
the pilot study recommended that the text of the MPI be simplified and that organization
features be added before administering the measure to a broader population of students.
The focus group participants also indicated that the MPI addressed the majority of
musical activities in their lives, and they did not suggest any additional items. Second, the
MPI was administered to a larger group of high school students to examine convergent
validity. Several models (MIMIC CFA) were examined to determine if the items from the
MPI were associated with five separate domains of music participation: formal,
nonformal, informal performing, informal creating, and informal responding.
Formal music participation. The results from the formal music participation
model indicated formal performing activities were the primary indicators of formal music
participation (FMP). Formal creating and formal responding were not significantly
associated with FMP. It is possible that creating and responding activities did not predict
FMP because school music classes and private lessons are often directed towards
performance activities (Allen, 2001; Holt, 2008; Lautzenheiser, 2000; Randles &
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Stringham, 2013). Still, while formal creating activities did not predict FMP overall, they
were associated with a reflective item concerning the importance of formal music
participation to oneself. In other words, participants who reported more formal creating
activities also self-reported formal music activities were very important to them. Finally,
examination of the data revealed that formal performing activities were best expressed as
ordered categories (ordinal) instead of continuous data.
Nonformal music participation. Results from the nonformal music participation
(NMP) model were similar to the formal music participation model. Nonformal
performing activities bore the strongest relationship with NMP. Nonformal responding
activities showed a weak association with NMP, while nonformal creating activities were
not significantly related to NMP. Data analysis revealed comparable results between
formal performing activities and nonformal performing activities–both are best expressed
as categorical, not continuous variables.
Informal music participation. Initial results suggested informal music
participation was best conceptualized as three separate constructs–informal performing,
informal creating, and informal responding. The model for informal performing
demonstrated acceptable fit but only a weak association between informal performing
activities and the informal performing music participation (IPMP) construct. Informal
performing activities also predicted a specific reflective item concerning performing
music. This result suggested those who indicated more informal performing activities are
also more likely to perform music for friends and family or in public venues. Both
informal creating and informal responding activities were strongly associated with their
respective constructs.
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Explanation and Alignment With Past Research
Overall, results from the formal, nonformal, and informal music participation
measurement models are consistent with extant research. Several scholars note large
ensembles such as band, chorus, and orchestra are the primary vehicles for music
participation in secondary public schools (Abril & Gault, 2008; Elpus, 2014; Williams,
2007). Large ensemble courses typically feature instruction that is teacher led with a
focus on musical development, particularly in performance (Allen, 2001; Holt, 2008;
Lautzenheiser, 2000). Outside of school, one of the most prevalent forms of formal music
instruction is through private instrumental and vocal music lessons (ABRSM, 2014;
NEA, 2013). Nonformal music activities, while perhaps more diverse than formal music
activities, also tend to emphasize music performance (Higgins & Willingham, 2017;
Music for Everyone, 2018; Welcome, 2018). The prevalence of performance-based
practices in formal and nonformal music activities may explain why performing activities
like school music classes, private lessons, and performances were the primary predictors
of formal and nonformal music participation.
While involvement in formal and nonformal performing activities was measured
continuously (frequency of involvement), examination of the data revealed that these
activities were best represented as categories (i.e., none, low involvement, high
involvement) (Miksza & Elpus, 2018). This categorical representation is due, in large
part, to the fact that many participants do not participate in formal or nonformal activities
at all. This lack of participation also created a clear group division and reduced the
variability among the participants who were involved in formal and nonformal activities.
These results suggest that it may be beneficial to measure formal and nonformal music
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participation by the number of activities in which participants are involved instead of a
frequency measure of those activities.
In contrast to formal and nonformal music participation, informal music
participation showed variability for each mode (i.e., performing, creating, responding).
This finding is consistent with extant research, which suggests that informal music
participation is comprised of a diverse and numerous array of musical activities (Cayari,
2017; Miell & Littleton, 2008; O’Leary & Tobias, 2017; Rideout, 2015; Tobias, 2013b,
2015). It seems that the diversity of informal music participation found in the current
study and in previous studies warrants the continued measurement of informal music
activity as a continuous indicator for each mode of music participation (i.e., performing,
creating, responding).
Research Question One
What music participation profiles exist from the sampled population? I conducted
a latent profile analysis using the objective and subjective (desire) music participation
indicators generated from the music participation index. The results yielded six distinct
music participation profiles. Participants in Profile 1 (low participation, 21%) reported
low participation in every category. Individuals in Profile 2 (music listeners with
informal performing desire, 24%) indicated average amounts of informal responding
(e.g., listening, dancing, etc.) and an above average desire for informal performing
opportunities; however, they did not report actual engagement in any type of musical
activity beyond informal responding. Participants from Profile 3 (low participation / high
desire, 22%) also expressed little involvement in formal, nonformal, or informal music
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activities; however, they did express a desire for more music participation in each
domain, especially formal and nonformal music. Overall, Profiles 1, 2, and 3 are
characterized by very little actual musical participation. The key differences between the
three profiles concern the amount of informal responding, with only Profiles 2 and 3
showing near average levels of responding. The participants in Profile 3 also expressed a
strong desire for more participation.
Adolescents from Profiles 4, 5, and 6 generally displayed higher amounts of
music activity involvement. Profile 4 (high informal–nonformal participants, 8%)
represented a small subset of the sample population. Adolescents from Profile 4 indicated
above average involvement in informal music activities (especially creating). They
displayed little desire for more formal or nonformal music participation and some desire
for increased informal creating participation. Concerning formal and nonformal
participation, approximately two-thirds reported nonformal music participation and onethird report formal music participation. Participants in Profile 5 (high formal–nonformal
participants, 17%) reported the most involvement in formal and nonformal activities out
of all six profiles, with over half indicating formal involvement and almost 100%
reporting nonformal involvement. They expressed positive attitudes towards increased
involvement in all musical domains, especially formal and nonformal. Concerning
informal participation, these participants displayed above average amounts of informal
performing, and below average amounts of informal creating. Finally, individuals in
Profile 6 (high participation, 8%) reported high degrees of informal and nonformal
participation with moderate levels of formal music participation. Students in Profile 6
also indicated positive attitudes toward increased involvement in every domain of music
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participation. The key differences between individuals in Profile 4, 5, and 6 include the
amount of informal creating (high in Profile 4 and 6, low in Profile 5) and the desire for
more or less music participation (more in Profiles 5 and 6, less or the same in Profile 4).
Explanation and Alignment With Past Research
Results seem to confirm the effectiveness of using a music participation
framework based on domains (i.e., formal, nonformal, informal) and modes (i.e.,
performing, creating, responding) of musical engagement (Folkestad, 2006; Veblen,
2012). Each indicator (e.g., formal performing, informal creating, etc.) showed
variability, demonstrating individuals in this sample are involved in musical activities
characterized by different domains and modes of participation. Moreover, scholars
explain that formal, nonformal, and informal music participation exists on a continuum
(Folkestad, 2006). Individuals may engage in one type or multiple types of music-making
across the continuum (Espeland, 2010; Green, 2008). The results from this study confirm
the belief that music participation is driven by a continuum and not binary categories.
The music participation profiles represented individuals involved in different domains
and at varying levels of music activity.
While the profiles support the usefulness of the underlying music participation
theoretical framework, several scholars have stressed the belief that latent profiles must
reflect theoretically meaningful differences between the study participants (Morin &
Wang, 2016; Oberski, 2016). The latent profile analysis for the current study
demonstrates theoretically significant differences between profiles. For example, the
analysis identified clear groups such as: (1) adolescents involved in very few musical
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activities (Profile 1); (2) adolescents highly involved in a wide array of musical activities
(Profile 6); (3) individuals who engage in some types of informal music activities and not
others (Profiles 4 and Profile 5); (4) and adolescents who are not very involved in music
activities but wish to be (Profile 2 and Profile 3). Indeed, additional delineations within
profiles might be made. Several profiles (e.g., Profiles 4, 5, and 6) include a mixture of
participants, with some involved in formal and nonformal music activities and others not.
Therefore, scholars and practitioners alike may be curious to know more specifically
about only students involved in music programs or only students involved in nonformal
music activities. Several extensions of latent profile analysis (e.g., group LPA, multilevel
LPA) might improve meaningful distinctions between profiles will be discussed in the
implications sections of this chapter.
Research Question Two
What is the relationship between music participation profiles and demographic
variables such as gender, grade, GPA, and parent education? This research question
addresses the association between music participation profiles and demographic
variables. The results from this study broadly mirror and extend those of other
demographic studies of secondary school students (Elpus & Abril, 2011; Elpus, 2014).
For example, students in Profile 5 reported higher GPAs than those in Profile 1 (low
participation), Profile 2 (primarily music listeners), and Profile 4 (high informalnonformal participants). This finding corroborates previous national demographic
profiles that indicate that students who do not participate in music classes tend to score
lower on standardized tests (Elpus & Abril, 2011; Elpus, 2014).
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There was also a significant association between gender and music participation
profile. Students in Profile 1 (low participation) and Profile 4 (high-informal-nonformal
participants) were predominantly male while students in Profile 5 (high formal-nonformal
participants) were largely female. Elpus (2015) showed that females have been
consistently over-represented in all large ensemble music classes since 1982. This
national trend may explain why males are over-represented in Profile 1 (low
participation) and Profile 4 while underrepresented in Profile 5 (high formal performing
participation). The students in Profile 4 (predominantly male) indicated they spent
substantial amounts of time creating music, informally. The students in Profile 5
(predominantly female) indicated high levels of formal, informal, and nonformal music
performance and low levels of creating. It is difficult to know from this study why males
were underrepresented in Profile 5 (high formal performing) and overrepresented in
Profile 4 (high informal creating). Still, as the underrepresentation of males in school
music programs is a persistent issue in music education programs nationally (Elpus,
2015), music education scholars might explore if this gender-based phenomenon exists in
other school populations and what the implications of such a phenomenon might mean
for recruiting males into school-based music programs.
Finally, results showed differences in parent education based on music
participation profile. Frequency tables showed the parents of participants in Profile 3
(music listeners with high desire) and Profile 5 (high formal-nonformal participants) have
higher levels of education than the parents of participants in Profile 1 (low participation).
Similar to the results above, this finding corroborates previous national demographic
profiles of music participation, with parents of students in music classes showing higher
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levels of education than those outside music classes (Elpus & Abril, 2011). Notably,
findings from the current sample show students in Profile 3 (whose parents are highly
educated), while not participating in school music classes, did demonstrate a desire to
become involved in formal, nonformal, and informal music instruction. It is unclear why
profiles of students with highly educated parents had more positive views or higher
participation in formal music classes. These findings should be explored further to better
understand if these results are consistent across populations, why such differences exist,
and how music educators might respond.
Research Question Three
To what extent, if any, do differences exist among categories of music
participation and environmental factors such as parent/peer norms, school/life activity
involvement, and past experiences with school music classes? Findings from research
question three indicated relationships between music participation profile and parent/peer
norms. Norms were measures of parent and peer expectations for involvement (Fishbein
& Ajzen, 2010). For each music participation profile, parent norms were rated higher
than peer norms, indicating participants generally felt stronger expectations of music
participation from their parents than peers. Individuals in Profiles 1, 2, and 3 reported
very little expectation from parents or peers to be involved in music activities. Profiles
that contained adolescents with the highest rates of formal and nonformal involvement
(Profile 4, 5, and 6) indicated neutral to strong expectations from parents but only
moderate expectations from peers.
Research question three also addressed past school music experiences, sports
135
involvement, elective involvement, after-school jobs, and religious involvement.
Regarding past-school music experiences, participants reported neutral to positive
feelings about elementary and middle school music classes with adolescents in Profile 6
reporting the strongest positive feelings. Adolescents in Profile 5 reported high levels of
church/religious involvement while those in Profile 3 reported very little involvement.
Participants contained in Profile 4 and Profile 6 indicated the highest rates of after-school
employment. Finally, the amount of sports involvement and elective classes did not show
any relationship to music participation profile.
Explanation and Alignment With Past Research
Results from research question three confirm extant scholarship concerning the
influence of parents, peers, and other environmental factors on music participation. For
example, several scholars have found parent value for music and encouragement to
participate seem to promote involvement with music (Lucas, 2007; McPherson, 2009;
McPherson, Davidson, & Faulkner, 2012; Ryan et al., 2000). Additionally, findings from
the current study also confirmed extant research regarding peers. Music participation was
more strongly associated with parent expectations than peer expectations (Castelli, 1986;
Mizener, 1993).
Both church involvement and after-school employment also seem to be consistent
with profiles associated with high nonformal involvement (Profiles 5 and 6). For
example, Profile 5 included the highest rates of nonformal music participation while
Profile 3 reflected the lowest. Therefore, it stands to reason that church/religious
involvement would be higher in Profile 5 because church arenas are often a venue for
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nonformal music involvement. Similarly, individuals in Profiles 4 and 6 reported the
highest levels of informal music involvement and the highest levels of after-school
employment. It is possible that members of these profiles generally participate in a higher
number of out-of-school activities.
Research Question Four
To what extent, if any, do differences exist among categories of music
participation and various personal beliefs such as long-term musical identity, music
ability beliefs and attitudes towards secondary school music classes? This research
question concerns the attitudes and beliefs of participants on three constructs: (1) longterm musical identity, (2) musical ability beliefs, and (3) attitudes towards secondary
music classes. Long-term musical identity (LTMI) concerns an individual’s perception of
what their musical involvement may be like in the future (Evans & McPherson, 2015).
LTMI steadily increased from Profile 1 to Profile 6. Eighty-seven percent of adolescents
in Profile 6 reported they intended to sing, play, or compose music for the rest of their
life while only 66% reported the same in Profile 5 and 48% in Profile 4. Regarding
musical ability beliefs, students in Profile 6 expressed the strongest belief in their abilities
to learn and perform music while those in Profile’s 3, 4, and 5 expressed less confidence.
Individuals in Profiles 1 and 2 expressed the least confidence.
The final portion of research question four concerned participants’ attitudes
towards secondary music courses based upon music participation profile. Attitude was
measured by asking individuals the degree to which they felt each class would be good,
fun, and interesting. Four secondary music courses were considered: (1) large ensemble,
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(2) piano/guitar class, (3) popular music group, and (4) music composition with
technology class. Students in Profile 1 had a largely negative view of all four classes.
Students from Profile 2 had a neutral view towards each class. Students in Profile 3
expressed attitudes that were slightly more positive than Profile 2 but remained relatively
neutral. Participants in Profile 4 expressed neutral attitudes towards each music class
except composition with technology, which they viewed more favorably than the rest.
The most positive views were expressed by students in Profile 5 and Profile 6. Students
from Profile 5 regarded the large ensemble and music composition as the most desirable
music classes. Students from Profile 6 viewed the large ensemble with less positivity than
both piano/guitar and popular music groups. Individuals in Profile 6 had the most positive
attitudes toward composition.
Explanation and Alignment With Past Research
Musicians with a long-term view of their musical involvement and a basic
commitment to practice tend to internalize music participation within their personal
identity (Evans & McPherson, 2015). Long-term musical identity was included in the
current study because it may serve as an indicator of life-long commitment to music
making. While all the students in Profiles 4, 5, and 6 indicated some commitment to longterm musical involvement, individuals in Profile 6 expressed the most (87.3%), followed
by Profile 5 (66%), and Profile 4 (44%). The primary differences between these Profiles
concerns the breadth of music participation. Individuals from Profile 6 indicate relatively
high levels of musical involvement in each domain with positive attitudes toward
additional musical activities. Students in Profile 5 seem to lack involvement in informal
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music activities, while participants in Profile 4 engage in a variety of activities but do not
have a desire to increase participation. The association between students in Profile 6 and
high, long-term musical identity may suggest that they have taken a long-term
perspective of their personal and musical identity. That is, they see involvement in music
as a vital part of who they are now and who they will be in the future. However, it is
important to note that a causal relationship between broad music participation and longterm musical identity cannot be established from this study. Still, this finding may
warrant further research into the causes of long-term musical identity and if breadth of
music participation is a key factor in the development of long-term musical identity.
Several scholars have noted that positive beliefs about one’s ability to sing or play
an instrument is a key factor for generating and sustaining motivation to sing and play
(Evans, 2016; Hallam, 2016). Findings from the current study seem to confirm previous
research. That is, participants in the current study who reported high involvement in
musical activities also reported the strongest music ability beliefs. Still, it is difficult to
determine if the breadth or frequency of participants’ current musical involvement caused
the music ability beliefs or if other factors in their past increased their musical
confidence. Additional research is needed to determine the role of music ability beliefs as
both an antecedent and consequent of different types of music participation.
The final variable of the current study concerned individuals’ attitudes towards a
range of secondary music courses. Overall, piano/guitar and music composition with
technology tended to be rated among the top one or two classes each profile preferred.
Still, attitudes toward music classes were reflected by the characteristics of each profile.
Adolescents in profiles 1, 2, 3, and 4 held the least positive attitudes toward music classes
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as well as the lowest rates of current music participation. Still, the participants in Profile
3, while showing very little current music participation, demonstrated more positivity
towards large ensembles, piano/guitar, and popular music groups than participants in
Profile 4. This difference is likely a reflection of the high desire ratings in Profile 3.
Students in Profile 4 were primarily interested in music composition with technology,
which is a reflection of their high informal creating scores. While students in both Profile
5 and Profile 6 rated all the music classes positively, students in Profile 5 rated the large
ensemble the highest (reflecting their high formal musical involvement) and students in
Profile 6 rated music composition with technology, piano/guitar, and popular music
group the highest (reflecting their high informal music participation). In summary, it may
be that offering music courses that reflect secondary students current involvement (both
formal and informal) may result in positive attitudes towards school music involvement.
Implications
The previous sections of this chapter summarized and discussed the findings for
each research question of the current study. This section of the chapter is intended to
clarify the implications of the current study. I will organize this discussion into four
separate sections: 1) theoretical implications, 2) research implications, 3) implications for
music teacher education, and 4) implications for music education pedagogy.
Theoretical Implications
The current study draws upon two theoretical frameworks for conceptualization
and measurement: participation and domains of music learning. At the outset of the
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study, participation was defined as “involvement in a life situation” (World Health
Organization, 2007, p. xvi). Participation, in this context, strictly concerns the frequency
and breadth of involvement in musical activities (Chang & Coster, 2014; Forsyth &
Jarvis, 2002). Scholars who study adolescent participation postulate two types of
involvement: objective and subjective participation (Chang & Coster, 2014). Objective
participation concerns the frequency of engagement while subjective participation
addresses an individual’s perception of their own participation (Whiteneck & Dijkers,
2009). Taken altogether, objective and subjective participation allow for the measurement
of activity frequency (objective) as well as individuals’ satisfaction with their level of
involvement (desire/subjective). While scholars have measured participation in a number
of ways, questionnaire-based participation research typically includes self-report
measures of activity frequency, breadth, and personal satisfaction.
Overall, considering both objective and subjective music participation proved to
be an effective and advantageous framework for understanding adolescent musical
involvement. While results from the current study show clear music participation profiles
with varying levels of objective participation, several profiles were identified with
contrasting subjective (desire) participation levels as well. For example, adolescents in
Profiles 1, 2, and 3 all indicated below average levels of formal and nonformal music
participation. The differences between the profiles were primarily driven by levels of
informal participation and the desire to participate in musical activities. For example,
individuals in Profile 3, while expressing very little objective participation beyond
informal responding, indicated above average levels of participation desire. In contrast,
participants in Profile 1 showed below average rates of objective participation and
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participation desire in any domain. Participants in Profile 2 showed similar levels of
objective participation to those in Profile 3 but only average rates of participation desire.
By including several domains of music participation, objective participation, and desire
to participate, it was possible to identify unique subgroups of students even among those
with very little formal or nonformal music participation.
Domains of music learning (Veblen, 2012) was the second framework for the
current study. I considered music participation through the framework of formal,
nonformal, and informal domains of music learning (Folkestad, 2006). I designed, tested,
and implemented a music participation index (MPI) structured around the three domains
of music learning in order to address the multiple contexts, settings, and types of youth
music involvement (Chang et al., 2017). Each domain was further delineated by three
modes of music participation based upon three artistic processes from the National Core
Arts Standards: performing, creating, and responding (NCCAS, 2014). Each mode was
divided into the categories of activities characteristic of the three modes. Finally, specific
musical activities based upon research literature were generated for each category within
their respective modes and domains.
In general, formal, nonformal, and informal music learning practices provided a
useful and effective framework for conceptualizing music participation. This framework
allowed for a wide array of activities within each domain/mode to be represented on the
MPI. This wide array ensured an ample amount of data for the current study. The
structure and detail of the MPI also allowed for a broad picture of each adolescent’s
musical involvement. Considering music participation across domains allowed for the
subsequent music participation profiles obtained through latent profile analysis to include
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unique combinations of each domain and mode. Both the frameworks of participation and
music learning domains may be ways to quantitatively capture the diversity of musical
activity in a person’s life.
Methodological Implications
Music education researchers should continue to develop rigorous methodological
approaches to studying adolescent music participation. Questions concerning how
adolescents develop and reshape their music participation over the course of their lives as
well as the effects of different types of music participation remain rich areas for inquiry.
The current study provides potential frameworks (i.e., participation, music learning
domains); measurements (i.e., music participation index); and analysis tools (i.e., latent
profile analysis) for other music education scholars to utilize and develop. Still, multiple
improvements to the conceptualization, measurement, and analysis processes from this
study will likely enhance the validity of future studies. This section reviews the
measurement and analysis process of the current study with implications and
recommended improvements for future studies.
Measuring music participation. The music participation index (MPI) is
structured as a formative measure of the music participation construct. Many
questionnaires that evaluate latent constructs are predicated on a reflective measurement
model. Reflective measures included indicators or questionnaire items considered
reflections of the unobserved latent construct (Streiner et al., 2015). For example, anxiety
(an unobservable latent construct) may cause observable behaviors such as irritability and
sweatiness (reflections of anxiety). Some constructs, however, are better measured
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formatively. Brown (2015) explains that, for some constructs, it is more reasonable to
assume the indicators cause the construct rather than the construct causing the indicators.
In the current study, I theorized an individual’s music participation was caused by a
composite of the musical activities they choose to be involved with and, therefore, the
measurement model should be formative.
Results from the five MIMIC CFA models confirmed the validity of a formative
approach to music participation measurement. For each model, the formative indicators
adequately predicted the latent construct of music participation. Music education scholars
who wish to use formative models should proceed with care because these models are
more difficult to validate than are reflective models, and they can cause issues with
identification in structural models (Kline, 2012). Still, several scholars address these
concerns (Bagozzi, 2007; Bollen, 2007) and many leading researchers provide
information on using formative models effectively (Acock, 2013; Brown, 2015; Kline,
2012, 2016).
While results from the pilot portion of the current study confirmed the validity of
a music participation formative measurement model, results also revealed that the formal
and nonformal portions of the MPI should be modified in several ways to improve
efficiency and validity. First, creating and responding were not substantial predictors of
formal/ nonformal music participation. Results seemed to indicate performing activities
were the primary indicators of formal and nonformal participation. It may be that creating
and responding activities are indicators of content within a particular formal/nonformal
activity instead of separate, standalone activity. Second, the formative performing
indicators for formal/nonformal were transformed from continuous to categorical
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variables because the distributions were skewed. Scores on the formal/nonformal
performing variables also tended to cluster into categories. While the variables were
originally intended to be continuous, it was clear that the data collected were categorical.
Based upon these findings, future measurements of formal/nonformal music activities
may be improved by conducting a frequency-count of formal/nonformal activities using
yes/no response options (e.g., Do you participate in a school music class?; Do you take
weekly music lessons?; Do you sing in a church choir?) instead of degrees of frequency
for each activity.
Validity of the music participation index. The validity of the music
participation index (MPI) was tested in primarily two ways for this investigation. First, I
conducted a focus group with six adolescents to record their questions, concerns, and
suggestions on the MPI. Second, I tested the convergent validity of the MPI using a
MIMIC CFA model (Brown, 2015). Results from each model indicated that the formative
participation indicators (composite scores of music activities listed on the MPI)
significantly predicted the construct of music participation (comprised of three reflective
effect indicators). This process demonstrated convergent validity because the formative
measures showed a substantial relationship to the reflective indicators (Johnson &
Morgan, 2016).
The measures of validity described above provided enough evidence to proceed
with the current study. Still, additional testing is needed to firmly establish the content
validity of the MPI. One common way to establish the content validity of a measure in
participation research is to ask participants to keep a journal of their activities, complete
the measure, and then compare the journal entries to the questionnaire to see if the two
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agree (Rachele, McPhail, Washington, & Cuddihy, 2012). Adolescent activity patterns
are often more variable than those of adults which means they are less likely to make
accurate self-report assessments (Baquet, Stratton, Van Praagh, & Berthoin, 2007; Stallis,
1991). Journaling one’s activities helps ensure that there is an accurate, in the moment,
account of participation. Another method for determining stronger content validity is to
conduct focus groups with adolescents from different geographic regions, cultures,
ethnicities, age groups, etc. This process would ensure the maximum amount of music
participation items are represented on the MPI. While the current study established a
degree of content and convergent validity for the MPI, additional validity tests may be
useful to firmly establish the trustworthiness of the measure.
One threat to validity that is currently unresolved is the classification of activities
that belong to more than one domain of music participation. For example: Is an afterschool show choir a formal or nonformal activity? It occurs at school and is likely
supervised by a teacher but the teaching processes within the after-school club are likely
nonformal. That is, the teacher may guide instruction but the group members ultimately
decide the choreography, performance venues, and literature. Further still, consider a
middle school student that writes a song (creating), plays it on the ukulele (performing),
video records and edits the performance (responding), posts the song on YouTube
(performing) and then engages in a dialogue about their composition after they post the
recording to their Facebook account. This process is comprised of a mixture of informal
performing, creating, and responding activities that is difficult to untangle. This issue
may be resolved by increasing the specificity of each item on the MPI or by counting the
item two or three times, once in each domain that it corresponds to. Additional research is
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needed to better understand how to handle activities assigned to multiple domains.
Latent profile analysis and music education. Latent profile analysis proved to
be a useful method for identifying unobserved subgroups within the sample population.
The six-profiles demonstrated unique, theoretically meaningful differences and displayed
discriminant associations on a number of variables (research questions two, three, and
four). Still, this analysis is not without shortcomings. One notable deficiency with the
current analysis is all participants were analyzed as a single group. While the results were
still meaningful, some confusion does exist within the profiles. For example, Profile 4
shows 34% of participants are in music classes, 50% are in private lessons, 70% are
involved in nonformal performing activities, and 62% participate in nonformal
responding activities. It is difficult to know, from this analysis, which participants are
involved in only one of the categories or multiple categories. This reality slightly
dampens the interpretation because the analyses does not account for the naturally nested
arrangement of the participants (e.g., students in music classes, students outside of music
classes, students in HS A, students in HS B, etc.). In future studies, it may be beneficial
to conduct multigroup (McCutcheon, 2002) or multilevel latent profile analysis (Henry &
Muthén, 2010) to account for the nested nature of the profiles (Miksza & Elpus, 2018).
Implications for Music Education Pedagogy
Music education policy-makers, scholars, and researchers from a diverse array of
disciplines have expressed their commitment to ensuring secondary music programs
serve all students (Miksza, 2013; Odegaard, 2018; Randles, 2015; Shuler, 2011b). This
renewed commitment to ensuring all students have a place in a secondary music program
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is likely an outgrowth of recent studies that indicate approximately one-third of
secondary public high school students participate in music programs while the remaining
two-thirds of students do not (Elpus, 2014; Elpus & Abril, 2018). Music education
scholars discuss and debate the best means to reach students outside of school music
programs (Fonder, 2014; Kratus, 2007; Odegaard, 2018; Shuler, 2011b; Williams, 2011).
However, discussions that concern recruiting or attracting new students into
school music programs operate on an underlying presumption: that students wish to be or
can be recruited into a school music program. That is, students outside of school-based
music programs would enroll in a music course if they were provided access to a music
program that reflected their interests, values, and preferences. This belief may be based
on research indicating listening to music is among the top activities for adolescents
(Rideout, 2015) and that many adolescents find listening to music a meaningful activity
(McFerran, 2010; McFerran & Saarikallio, 2014). Based on this research, music
educators may assume that all students would be interested in music participation since
listening to music is such a large part of their life. However, it is unclear if listening to
music also accompanies performing or creating music at school, in the community or at
home. This study sheds light on the music participation habits for one population of
adolescents and may hold implications for how music educators conceive of increasing
enrollment in school-based music programs.
For the population of students in this study, results seem to indicate that not all
students perform music, create music, or respond to music, regardless of the participation
domain (i.e., formal, nonformal, informal). Similarly, the students from this study
reported different levels of desire to participate in more musical activities, regardless of
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the domain of music participation. For example, students who primarily listened to music
represented 67% of the sample population (Profiles 1, 2, and 3). Of those students,
approximately one-third reported listening to music much less than their peers (Profile 1),
one-third listened to music at a rate similar to their peers (Profile 2), and another third
listened to music at average rates but also expressed a desire to participate in more
musical activities (Profile 3). These findings indicate that 45% of the sample population
listened to music at rates similar to or below their peers and were generally disinclined to
change their level of music participation in any domain (Profile 1 and Profile 2). In
contrast, 33% of the students in this study reported active musical lives, participating in
musical activities at different levels and in different ways across the three domains
(Profile 4, Profile 5, and Profile 6).
The results from this study indicate that, for this population, many students do not
participate in musical activities beyond listening nor do they wish to do more. Certainly,
for some students, performing and creating music is an important part of their lives. Still,
for large portions of the sample, music participation did not seem to be particularly
prevalent nor did they express a strong desire to increase participation in any domain. For
music educators, these findings may challenge the assumption that all students would
enroll in a music program if they were granted equal access and/or provided instruction
that aligned with their interests or values. To be clear, it still may be that there are many
students who wish to participate in music programs but cannot or do not for reasons of
access, interest, or curricular relevance. Instead, the results from this study may raise
questions regarding the size or proportion of students not already enrolled in a music
program that might enroll in the future.
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As music education stakeholders search for ways to involve more students in
secondary music programs, it may be advantageous to (1) tailor recruitment efforts
towards specific groups of students and (2) consider recruitment strategies that address
the full range of factors that are associated with music participation. For example, music
educators might focus their efforts on recruiting students in Profiles 3 and 4 which
represent students with interest in school music programs or who reported involvement in
performing and creating activities outside of school. Results from this study also showed
that a number of factors were associated with each music participation profile. For
example, it may be that those who participate very little in music activities do not feel
confident in their musical abilities, are not expected to participate in musical activities by
parents or peers, or music participation does not match their cultural norms. Music
education stakeholders may consider recruiting interventions that deal specifically with
the factors listed above and target such interventions towards students with music
participation profiles that seem open to increasing their music participation.
It may be advantageous to continue to gain more information about students not
currently enrolled in music classes as music education stakeholders work to develop
effective strategies to increase enrollment in school-based music programs. A
comprehensive understanding of the music participation habits of adolescents inside and
outside school music programs is one important piece of information but there are others
that may be equally useful. In addition to rates of music participation, researchers might
gather and examine information on musical self-concept, motivation, cultural norms, and
school, district, and state level factors. Researchers might examine how interactions
between these factors encourage or discourage enrollment in school music programs.
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With a more comprehensive understanding of school music enrollment, music education
stakeholders may have more of the necessary information to create music programs that
serve as many students as possible.
Implications for Music Teacher Education
The varied and diverse nature of the music participation profiles of participants in
this study may have implications for music teacher education programs. Overall,
evidence from this study confirms extant research, which suggests that young people
engage in a range of musical activities (Green, 2002; Lamont et al., 2003) and that the
most prevalent activity is music listening (Rideout, 2015). In light of the diverse
adolescent musical participation represented by the six profiles in this study, music
teacher preparation programs may consider redoubling efforts to ensure that graduates are
capable of engaging any student in any domain of music learning practice. While music
teacher training programs have historically prepared graduates to instruct within the
formal music paradigm, several academic leaders have encouraged curricular expansion
to include other domains of music learning in the undergraduate curriculum (CMSTF,
2014; Kaschub & Smith, 2014). Undergraduate music education majors equipped to
effectively deliver instruction using any domain of music learning (i.e., formal,
nonformal, formal) may be better prepared to engage all students within a school
population.
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Suggestions for Future Research
This section of the chapter highlights suggestions for future research examining
music participation. Several recommendations are made including validation procedures
for the music participation index, additional music participation profiles, longitudinal
studies, and recommendations for examining participation and nonparticipation in school
music classes.
Future researchers may wish to conduct additional studies testing the validation
and application of the music participation index (MPI). Several recommendations for
additional validity testing were made previously in this chapter such as music activity
journals and adolescent focus groups that represent adolescents from various geographic
regions, cultural backgrounds, ethnicities, and age groups. Content and convergent
validity testing using multiple methodologies in multiple contexts will likely increase the
efficiency and accuracy of the measure. Researchers may also wish replicate the current
study, using the MPI to profile the musical habits of adolescents in different regions.
Studies of this nature would likely enhance our understanding of adolescent music
participation on a national scale.
Music education researchers have conducted few longitudinal studies to
determine the origins of music participation in childhood, it’s establishment in
adolescence, and its continuation in adulthood (McPherson, Davidson, & Faulkner,
2012). It is impossible to know how the participants in this study arrived at their current
rates of music participation and how their music participation will change over the course
of their lives. These studies might be conducted in a number of ways. First, one might use
the music participation index to classify students and then follow up with qualitative
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inquiry over time to examine how music participation began and how it changes. Second,
quantitative methodologies such as latent transition analysis and growth mixture
modeling allow researchers to examine how latent profiles shift over time (Grimm, Ram,
& Estabrook, 2017). These methodologies may be beneficial for increasing music
education scholars’ understanding of the origins of music participation and life-long
music learners.
Finally, future researchers may consider examining how music participation is
related to the other numerous variables that seem to affect participation in school music
programs. Researchers may consider how social-psychological constructs such as
identity, motivation, competence, social norms, and ecological environment interact with
demographic factors and music participation profile to influence participation in school
music programs. Social-psychologists have established a robust field of research into how
behavior is influenced and changed that may be useful to understanding why some
students participate in music programs and others do not. Several models such as the
Theory of Planned Behavior or the Integrated Behavior Change Model serve as models
that predict behavior and guide interventions to change behavior (Ajzen, 1991; Hagger &
Chatzisarantis, 2014). Extant social-psychological research on behavior change and the
development of music participation profiles may prove fruitful in understanding why
some students participate in music and others do not.
Limitations
There are several important limitations to discuss that pertain to the overall
study’s design and validity. First, the primary portion of this study (music participation
153
profiles) was conducted with a researcher designed questionnaire (MPI), tested once
before implementation in the primary study. Results based upon the MPI should be
interpreted with caution as additional validity procedures are likely necessary to
determine the effectiveness of using the MPI to measure music participation. Second, this
study is limited to a survey of two high schools and one middle school located in Salt
Lake City, Utah. While efforts were made to ensure the sample adequately represented
the overall population of each school, it is still possible the sample is biased and does not
represent the overall population of each school. Third, sampling procedures did not allow
for statistical procedures such as multigroup or multilevel analysis of the data. The
students sampled were treated as one group. This large group may function as a
confounding factor when interpreting the music participation profiles and related
variables.
Conclusion
The purpose of this study was two-fold: (1) to identify profiles of music
participation from an adolescent school population; and (2) to examine the demographic,
environmental and personal belief differences of adolescents according to music
participation profile. Music education stakeholders from policy, research, and
pedagogical backgrounds have expressed concern for the subpopulations of secondary
school students who do not enroll in school-based music courses (Elpus & Abril, 2018;
Odegaard, 2018; D. A. Williams, 2011). Extant research indicates that many factors
related to demographics, music ability beliefs, and environmental pressures also
contribute to how a young person decides to engage in musical activity (Elpus, 2014;
154
Hawkinson, 2015; McPherson et al., 2012; Ruybalid, 2016). This study was an attempt to
examine the music participation habits of an adolescent population as well as the
interplay between music participation and related demographic, environmental, and
personal factors.
Findings from this dissertation indicate that multiple variables are indeed
associated with the type and frequency of musical involvement an adolescent chooses.
For example, there was a clear association between participants’ musical choices and
their attitudes toward school music classes. If students chose to participate in very few
music activities informally or nonformally, then they also showed poor attitudes toward
any type of formal school music involvement, regardless of whether the courses were
traditional or emerging. Similarly, those who composed their own music informally were
most interested in music composition with technology courses. Yet, results also showed
that adolescents with little musical involvement were less confident in their musical
abilities, felt very little expectation from their parents/peers to engage in musical
activities, and seemed to be influenced by the social norms of their gender or community.
The implications of this dissertation study may encourage music teachers, music
teacher educators, policy-makers, and researchers to adapt formal musical engagement
strategies based on the needs, attitudes, and perceptions of adolescents in specific
communities. It seems likely that a number of socio-ecological and psycho-social factors
uniquely influence informal, nonformal, formal music engagement among adolescents
from different cultures and regions. Music education stakeholders may benefit from
comprehensive interventions that address the multiple factors affecting adolescent music
participation when working to engage more students in school-based music programs.
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APPENDIX
From:
Subject:
Date:
To:
Cc:
irb@hsc.utah.edu
ERICA IRB New Study Approval
January 10, 2018 at 1:50 PM
seth.pendergast@gmail.com
j.rawlings@utah.edu
IRB:
IRB_00107953
PI:
Seth Pendergast
Title:
Music participation and motivation: The development and validation of the music participation
index
Date:
1/10/2018
The above-referenced protocol has received an IRB exemption determination and may begin
the research procedures outlined in the University of Utah IRB application and supporting
documents.
EXEMPTION DOCUMENTATION
Review Type: Exemption Review
Exemption Category(ies): Category 11
Exemption Date: 1/10/2018
Note the following delineation of categories:
Categories 1-6: Federal Exemption Categories defined in 45 CFR 46.101(b)
Categories 7-11: Non-Federal Exemption Categories defined in University of Utah IRB policy in
Investigator Guidance Series, Exempt Research
You must adhere to all requirements for exemption described in University of Utah IRB policy
in (Investigator Guidance Series, Exempt Research). This includes:
All research involving human subjects must be approved or determined exempt by the IRB before the
research is conducted.
All research activities must be conducted in accordance with the Belmont Report and must adhere to
principles of sound research design and ethics.
Orderly accounting and monitoring of research activities must occur.
Ongoing Submissions for Exempt Projects
Continuing Review: Since this determination is not an approval, the study does not expire or need
continuing review. This determination of exemption from continuing IRB review only applies to the
research study as submitted to the IRB. You must follow the protocol as proposed in this application
Amendment Applications: Substantive changes to this project require an amendment application
to the IRB to secure either approval or a determination of exemption. Investigators should
contact the IRB Office if there are questions about whether an amendment consists of
substantive changes. Substantive changes include, but are not limited to
Changes to study personnel (to secure Conflict of Interest review for all personnel on the
study)
Changes that increase the risk to participants or change the risk:benefit ratio of the study
156
Changes that increase the risk to participants or change the risk:benefit ratio of the study
Changes that affect a participant’s willingness to participate in the study
Changes to study procedures or study components that are not covered by the Exemption
Category determined for this study (listed above)
Changes to the study sponsor
Changes to the targeted participant population
Changes to the stamped consent document(s)
Report Forms: Exempt studies must adhere to the University of Utah IRB reporting requirements for
unanticipated problems and deviations: http://irb.utah.edu/submit-application/forms/index.php
Final Project Reports for Study Closure: Exempt studies must be closed with the IRB once the
research activities are complete: http://irb.utah.edu/submit-application/final-project-reports.php
SUPPORTING DOCUMENTS
Parental Permission Forms
Parent Consent Letter
Surveys, etc.
Music Participation Index
Motivation Scales
Literature Cited/References
References
Other Documents
MPI Teacher Instructions 01.10.18.pdf
Click IRB_00107953 to view the application.
Please take a moment to complete our customer service survey. We appreciate your
opinions and feedback.
156
157
ASSESSMENT & EVALUATION
440 East 100 South
Salt Lake City, Utah 84111
p 801.578.8249
f 801.578.8681
February 1, 2018
Seth Pendergast
University of Utah
School of Music
1375 East Presidents Circle
Salt Lake City, Utah 84103Avenue
Salt Lake City, UT 84103
Re: Music Participation, Motivation, and School Music Courses
Salt Lake City School District is committed to the advancement of educational
research, and we receive and consider many requests for research every year. All
requests are reviewed to see if they fit with the goals we have defined for the district,
and we are very cautious about taking instructional time from our teachers and our
students.
We have reviewed your recent application for external research for your dissertation
study titled Music Participation, Motivation, and School Music Courses.
We can see the value of the focus of your research request and the information that
would be gained from such research. You have permission to conduct research at
schools that agree to participate from February 2018 to June 2018.
In addition, you will be required to submit your findings to the Assessment and
Evaluation Department at the conclusion of your research no later than September
2018. We look forward to hearing from you regarding your research.
Sincerely,
Michelle Amiot
Michelle Amiot
Director, Assessment and Evaluation
Salt Lake City School District
One Goal – One Purpose: Student Learning
www.slcschools.org
158
Parental Consent Opt-Out Letter
Title: Music Participation Index for Adolescents
Dear Parent/Guardian,
With the support of Adam Eskelson (Fine Arts Supervisor for Salt Lake City School District)
and local school principals, we are conducting a research study concerning adolescent students
(middle/high school) participation in different types of music activities and why they are
motivated to engage in those activities.
Students currently participating in a school music program and those that are not participating in
a school music program will be asked to participate in this study. This will help us better
understand how adolescents engage with music both in school and out of school.
The reason we are conducting this study is because research has shown students who participate
in musical activities often exhibit positive social and emotional outcomes. However, much of
this research has only considered those students who participate in school-based music activities
and not community-based or independent music activities. We believe inquiry regarding how
adolescents’ music participation and the reasons they participate may be useful for school and
community-based music programs. Your child’s participation in this survey will aid the
development of music education programs both locally and nationally.
If you provide consent for your son or daughter to participate in this survey, the following
process will occur.
Step 1: The survey will be distributed during a specified period on the designated day of the
survey.
Step 2: The teacher will distribute the survey and read the following prompt.
Today you are being asked to complete a survey about your musical activity in everyday
life. This includes music classes, music lessons, learning an instrument on YouTube,
playing music on your own for fun or even just listening to music. This is a completely
private survey, so DO NOT write your name on this survey. No one will know how you
answered each question so be sure to give your honest opinion. Once I pass out the
survey, read the directions carefully. Start at the beginning and fil out each answer
159
completely until you reach the end. Participating in this survey is optional, so if you
prefer not to take the survey, simply DO NOT write anything on it, and turn it in with the
rest of the class.
Step 3: The teacher will distribute the survey to all the students.
Step 4: The teacher will allow adequate time for each student to fill out the survey
(approximately 10 – 15 minutes).
Step 5: The teacher will collect the surveys, place them back in the packet for collection.
If you have any questions complaints or if you feel you have been harmed by this research please
contact Seth Pendergast, University of Utah, School of Music. Phone: 717-991-4417 Email:
u0999348@umail.utah.edu
Contact the Institutional Review Board (IRB) if you have questions regarding your rights as a
research participant. Also, contact the IRB if you have questions, complaints or concerns, which
you do not feel you, can discuss with the investigator. The University of Utah IRB may be
reached by phone at (801) 581-3655 or by e-mail at irb@hsc.utah.edu.
It should take ten to fifteen minutes for your child to complete the questionnaire. Participation in
this study is voluntary. This questionnaire DOES NOT address any sensitive or personal
information and is completely anonymous. If you DO NOT wish for your child to participate in
this study, simply email the teacher that sent this letter home directing them to withhold the
survey from your and your child will be automatically opted out of the study. Also, your child is
free to decline participation in the study at their own discretion. You will have one week from
the time of receipt of this letter to opt your child/ren from this survey.
Thank you so much in advance for your consideration to allow your son or daughter to
participate in this survey. We believe this study will provide valuable information and insight to
the music education profession regarding how students engage with music in their lives and why
they choose to do so.
Sincerely,
Seth Pendergast
PhD Candidate, Music Education
University of Utah
160
MUSIC SURVEY
TEACHER INSTRUCTIONS
Dear Classroom Teacher,
Thank you for taking time out of your class for students to complete this questionnaire. This
study is being conducted by Seth Pendergast with the oversight of Dr. Jared Rawlings from the
University of Utah, School of Music. We believe this research will benefit all students, those currently
enrolled in music classes and those who are not. Please follow each step below and read the prompt
exactly as written.
Sincerely,
Seth Pendergast
PhD candidate, Music Education
University of Utah
________________________________________________________________________________
Please complete the following:
How many students are enrolled in this class? _____
________________________________________________________________________________
Step 1: Pass out the survey to all the students and read the following prompt exactly as written.
Today you are being asked to complete a survey about your musical activity in everyday life. This includes music classes,
music lessons, learning an instrument on YouTube, playing music on your own for fun or even just listening to music. This is
a completely private survey, so DO NOT write your name on this survey. No one will know how you answered each question
so be sure to give your honest opinion. Once I pass out the survey, read the directions carefully. Start at the beginning and
fil out each answer completely until you reach the end. Participating in this survey is optional, so if you prefer not to take
the survey, simply DO NOT write anything on it, and turn it in with the rest of the class. Any student that is over 18 years of
age should not take this survey as it only applies to those who are 17 or younger.
Step 2: Allow adequate time for each student to fill out the survey (approx. 15 minutes)
Step 3: Collect the surveys, place them back in the packet and send one student with them to the
office. I (Seth) will be in the office receiving the survey packets.
Additional Information: (Provided so you can assist students with questions they may have.)
•
•
We are asking students be as objective as possible.
This is a general survey about music activity for ALL students. Even students who only listen to
music and nothing else should still participate in this survey.
161
Music Survey
What is this survey?
We are trying to find out about your musical activity at school and in everyday life. We are also trying to find
out some of your experiences and opinions about music-making at school, home, and in your community.
SECTION 1
Read each prompt. Mark an "X" in the box that best describes you.
Q1
Q5
Gender?
How many elective classes are you taking this
school year (e.g., art, dance, woodworking,
child development)?
Male
Female
0
Non-binary/third gender
Q2
Q3
1
2
Grade?
3
7
10
8
11
9
12
4
5
6+
Mark the average grade you typically receive on
your report card.
Q6
Do you have an after-school or weekend job?
A
C-
Yes
A-
D
No
B
D-
B-
F
Q7
Do you participate in a school music class?
C
Q4
Yes
No
What is the highest level of education that one
or both of your parents/guardians hold?
Q8
Did not complete high school
Do you participate in more than one school
music class?
GED
High-school diploma
Yes
Vocational certificate (e.g., electrician, plumber,
HVAC, etc.)
No
Associates degree (2-year college degree)
Bachelor’s degree (4-year college degree)
Q10
Q9
Do you take private music lessons?
Master’s degree
Yes
Doctoral degree (e.g., Ph.D., MD, JD., etc.)
No
How many sports teams do you participate in throughout an entire year?
0
1
2
Q11 Are you involved in church/religious activities?
Yes
No
3
4
5
6+
162
SECTION 2
Q12 How long do you think you will sing, play an instrument, or write your own music?
I don't sing, play,
or write my own Until the end of the
music
school year
Until the end of
middle school
For some of high
school
Until the end of
high school
After I graduate
high school
(college/young
adult)
For the rest of my
life
Q13 Read each statement about school music and mark an "X" for your level of agreement.
Strongly Moderately Slightly
disagree disagree disagree
Slightly Moderately Strongly
agree
agree
agree
My past experiences in elementary and middle school music
classes were good.
My past experiences in elementary and middle school music
classes were fun.
My past experiences in elementary and middle school music
programs were interesting.
Q14 Participating in a school music ensemble like band, chorus, or orchestra would be...
Strongly disagree
Moderately
disagree
Slightly disagree
Slightly agree
Moderately
agree
Strongly agree
Slightly agree
Moderately
agree
Strongly agree
...good.
...fun.
...interesting.
Q15 Participating in a school piano or guitar class would be...
Strongly disagree
Moderately
disagree
Slightly disagree
...good.
...fun.
...interesting.
Q16 Particpiating in a school popular music group like rock band, hip-hop group, or iPad band would be...
Strongly disagree
Moderately
disagree
Slightly disagree
Slightly agree
Moderately
agree
Strongly agree
...good.
...fun.
...interesting.
Q17 Participating in a school music-writing class like music recording/production, songwriting, or writing
music with computers and iPads would be...
Stronly disagree
...good.
...fun.
...interesting.
Moderately
disagree
Slightly disagree
Slightly agree
Moderately
agree
Strongly agree
163
SECTION 3: School Music and Private Lesson Activities
Q18 Read each musical activity and mark an "X" for how often you do each one.
Never
Sing or play an instrument in a school music class
Sing or play an instrument in more than one school
music class
Sing in a musical theatre class during school
Take a private music lesson (voice or instrument)
Practice music at home for a school music class or a
private lesson
Write/arrange your own music during a school music
class or for a class assignment
Write or arrange your own music as part of your music
lesson
Improvise new music during a music class or private
lesson
Improvise new music while practicing at home for school
or lessons
Listen to music during a music class or private music
lesson
Discuss/write about music you listen to in music class or
music lesson
Participate in a music technology or video production
class at school
Participate in a dance class at school
Less than
4 or more
once a
1 time a 2–3 times 1 time a 2-3 times times a
month
month
a month
week
a week
week
164
SECTION 4: After-School and Community Music Activities
Q19 Read each musical activity and mark an "X" for how often you do each one.
Never
Sing or play an instrument with an after-school music
group
Sing or play an instrument with a music group at a
church or religious institution
Sing or play an instrument with a community music
group outside of school
Practice music at home for after-school, religious, or
community music group
Write or arrange music for a school group
Write or arrange music for a church or religious music
group
Write or arrange music for a community music group
Write or arrange music for fun with help from a teacher
or adult
Improvise new music with an after-school, religious, or
community music group
Listen to music with your after-school, religious or
community music group
Select performance music for your after-school,
religious, community music group
Discuss music with members of an after-school,
religious, community music group
Work at a music store or community music center
Participate in a dance program that meets after-school
or at a private dance studio
Less than
4 or more
once a
1 time a 2–3 times 1 time a 2-3 times times a
month
month
a month
week
a week
week
165
SECTION 5: - At-Home Music Activities
Q20 Read each musical activity and mark an "X" for how often you do each one.
Never
Just for fun, causally sing, rap, DJ or play an instrument
on your own (including electronic instruments)
Post videos of yourself singing, playing, rapping, or
DJing on social media, YouTube or other places online.
Practice singing, rapping, DJing, or playing an
instrument on your own, to try and get better.
Sing, rap, DJ or play an instrument with a group of
friends/family
Write/arrange songs or other types of music on your
own or with friends/family
Use technology like tablets/computers to record songs
or prodcue remixes, mashups, beats, etc.
Post your songs, remixes, mashups, or beats on social
media or other online platforms (e.g., YouTube,
SoundCloud, etc.)
Improvise new music by singing, rapping, DJing or
playing an instrument
Listen to music in the background while you are at
home, at a friend's house, or in the car
Listen to music while doing your homework
Listen to music on purpose when you want to change
your mood, get your day started, or excercise
Write or share comments about music on social media,
YouTube, blogs or other online platforms
Listen to and talk about music with friends or family
Create tutorial or explainer videos that show others how
to play, sing, rap or record something
Modify the music in the video games you play
Create videos with music playing in the background
Just for fun, dance to music alone or with friends/family
Play music based video games (e.g., Guitar Hero, Rock
Band, DJ Hero, etc.)
Make lip-sync, dance, or funny videos using music in
some way
Sing along with the crowd/congregation during a church
or religious service
Less than
4 or more
once a
1 time a 2-3 times 1 time a 2-3 times times a
month
month
a month
week
a week
week
166
SECTION 6: Long-Term Musical Activities
Q21 Read each musical activity and mark an "X" for how often you do each one. Be aware - the options have
changed.
Never
Perform in concerts with a school music
group
Perform in concerts or recitals by yourself
(solo)
Take trips or attend concerts as a music
class or private lesson studio
Perform in concerts or other types of
performances with an after-school, religious,
or community music group
Perform solo at your religious institution,
church, school, or other community venues
Take trips or attend concerts with an afterschool, religious or community music group
Run the lighting board, live sound board, or
other technology for musical productions at
school, church/religious services or for
community events
Perform (sing, play, rap, or DJ) for friends or
family members
Perform (sing, play, rap, or DJ) in places like
clubs, parties, coffee shops, restaurants,
school dances or other performance venues
Attend music concerts alone, with friends, or
with family
Once each Once every Once every
year
6 months 3 months
Once a
month
One or
2-3 times a more times
month
per week
167
SECTION 7
Q22 Read each statement and mark an "X" for your level of agreement?
Strongly
disagree
Moderately
disagree
Slightly
disagree
Slightly
agree
Moderately
agree
Strongly
agree
Most of my close friends think I should participate
in music activities.
Most of my close friends would be disappointed if I
did not participate in music activities.
Most of my close friends expect me to participate in
music activities.
My parents think I should participate in music
activities.
My parents would be disappointed if I did not
participate in music activities.
My parents expect me to participate in music
activities.
I feel confident in my ability to sing or play a
musical instrument.
I am capbable of learning how to sing or play a
musical instrument.
I am able to achieve my musical goals when
singing or playing a musical instrument.
I feel able to meet the challenge of performing well
as a singer or with an instrument.
Q23 Would you like to participate in each activity below less, more, or the same?
Much
Less
Sing or play an instrument in a school music class.
Sing or play an instrument in a private music lesson.
Write/arrange/improvise my own music in a school music class or lesson.
Listen to and/or discuss music in a school music class.
Sing or play an instrument in an after-school, church, or community group.
Write/arrange/improvise my own music with an after-school, church, or
community group.
Listen to and discuss music in an after-school, church, or community group.
Just for fun, sing or play an instrument at home alone or with family/friends.
Just for fun, write my own music at home.
Use technology at home to write and record my own music.
Listen to music on my own and with friends.
Create videos with music or about music and post them online.
Less
Same
More
Much
More
168
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