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thesis entitled
The Relationships Among Characteristic Moods,

Music Preference, and Music-Listening Habits:
A Survey Study

presented by

Jin-Hi Choi, MT-BC

has been accepted towards fulfillment
of the requirements for

Masters degree in Music Therapy

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Date April 28, 2003

 

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5/08 K'IProjIAcc&Pres/ClRC/DateDue.indd

THE RELATIONSHIPS AMONG CHARACTERISTIC MOODS, MUSIC
PREFERENCE, AND MUSIC-LISTENING HABITS: A SURVEY STUDY
By

J in-Hi Choi, MT-BC

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF MUSIC
Department of Music

2003

 

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ABSTRACT
THE RELATIONSHIPS AMONG CHARACTERISTIC MOODS, MUSIC
PREFERENCE, AND MUSIC-LISTENING HABITS: A SURVEY STUDY
By

J in-Hi Choi, MT-BC

The purpose Of this study was to expand the current understanding Of the connection
between music and characteristic moods by exploring the relationships among general
characteristic moods, music preference, and music-listening habits in an everyday setting.
Two hundred thirty-one people with a wide range Of ages participated in this study by
completing the Multiple Affect Adjective Check List — Revised (MAACL-R) and a
questionnaire designed by the researcher. People who were in more anxiety or more
depression spent more time listening to music in their everyday life. People who liked
hard rock/ heavy metal music spent more time listening to music in their everyday life (p
< .01.). Younger people liked and actually listened to hard rock/ heavy metal music and
rap/ hip hOp music more than older people did (p < .01.). Older people liked classical
music and country music more than younger people did (p < .01.). More educated people
liked soft rock/ pop music and rap/ hip hop music less, and they spent less time on rap/
hip hop music listening (p < .01.). More educated people liked classical music and jazz
more (p < .01), and they also spent more time on classical music listening than less
educated people did (p < .05.). Further study done with a large sample in which age and
levels of education are tightly controlled is recommended. Qualitative study of this

research topic is also recommended.

Copyright by
J IN-HI CHOI
2003

To all dedicated music therapists, music educators,

and people who believe the power Of music in their emotional lives.

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ACKNOWLEDGMENTS

Without the support and warm encouragement of the special people in my life, I
could never have attempted and completed this project. First, I’d like to thank Dr. Albert
LeBlanc, my research advisor, for his help, patience, dedication, and wisdom not only for
this study, but also for my academic growth.

Dr. Frederick Tims, my graduate advisor, listened to me, and supported me with
warm encouragement and enormous consideration throughout my graduate schooling at
Michigan State University. The modest professor Roger Smeltekop inspired me to keep
going on with music therapy from my undergraduate days at Michigan State University.

I will always respect and believe that he is a model which all music therapists should
follow.

I’d like to express a special thank to Dr. David Stowe who showed his interest in my
study, as well as music therapy, and kindly allowed me to survey students enrolled in one
of his classes at Michigan State University.

I’d also like to express a great deal of appreciation to all the people who volunteered
to participate in my study including undergraduate and graduate students at Michigan
State University and the senior citizens in Burcham Hills, MI. Without them, this study
would not have been possible.

I will always be indebted to my family. Without their tangible and intangible
support, I could never have successfully completed my study abroad at Michigan State

University. I remember their prayers for me, and they were always there for me. I’d like

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to thank my parents who encouraged me to develop my intellectual curiosities since my
childhood and handed over inborn health and strong will, which were the basis of my
academic search. Without them, I wouldn’t be where I am today.

And, I return my thanks to WOO-Young, who gave me a wake-up call every
morning, and showed tons of patience and understanding for me.

I always thank God for accompanying and guiding me through my life and for
allowing me to meet these especially precious people. I also appreciate his plan for me,

in hopes that I can fully be qualified to fulfill his project for me.

vi

TABLE OF CONTENTS

LIST OF TABLES .................................................................................... ix

CHAPTER 1

INTRODUCTION ................................................................................... 1
Purpose ..................................................................................... 4
Problems ................................................................................... 5
Definitions ................................................................................. 5
Assumptions ............................................................................... 8
Limitations .................................................................................... 9

CHAPTER 2

RIVIEW OF LITERATURE ...................................................................... 10
Music and Characteristic Moods ...................................................... 10
Music Preference and Characteristic Moods...................... 15
Music-Listening Habits and Characteristic Moods 19

CHAPTER 3

METHOD ............................................................................................ 21
Participants .................................................................................. 22
Survey Development .................................................................... 25
Procedure ................................................................................... 29
Data Analysis ............................................................................ 34

CHAPTER 4

RESULTS ........................................................................................... 38
Descriptive Statistics .................................................................... 38
Correlations between Characteristic Moods and Music Preferences ............. 4O
Correlations between Characteristic Moods and Music-Listening Habits. 42
Listening Media ......................................................................... 44
Correlations between Music Preferences and Music-Listening Habits .......... 45
Correlations between Ages and Music Preferences ................................. 46
Correlations between Ages and Music-Listening Habits ........................... 47
Music Preference and Education ...................................................... 49
Music-Listening Habits and Education ............................................... 50

CHAPTER 5

DISCUSSION ....................................................................................... 52
The Relationship between Characteristic Moods and Music Preference. ............ 52
The Relationship between Characteristic Moods and Music-Listening Habits... 54

vii

 

 

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Chapter 1

INTRODUCTION

Since ancient times there has been a persistent interest in the relationship between
the mind and music among philosophers, music psychologists, and music educators.
Plato believed that because music affected the formation of a human being’s mind in an
enormous way, a human being should be educated with music that was morally
acceptable. Plato recommended frequent uses of Dorian and Phrygian mode, arguing
against the use of Lydian and Mixolydian mode, which he believed were not good for the
mind.

The relationship between the mind and music appears to still remain an area in need
Of research. While considerable research has been conducted on this topic, and the
results of these studies, as well as personal experiences, support a connection between the
mind and music, more remains to be learned. Saying that both music and people are
multidimensional, Wheeler (1985) invited researchers to examine the influence of
characteristics Of both the listener and the music in order to better understand reactions to
music.

How do people listen to, feel, and understand music? Like Plato’s belief, does
music significantly influence the process of forming a person’s mind, including general
mood and personality, either in a positive way or in a negative way? Does a person listen

to a certain type of music because he or she is in a particular mood?

 

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In relation tO the issue of ‘music and the mind,’ Thaut (1989, p.56) pointed out that
the majority of the studies he examined provided strong evidence for the following four
conclusions: (a) music evokes affective-mood reactions, including emotional peak
experiences; (b) music can alter a listener’s mood; (c) affective-mood responses to music
are accompanied by physiological changes in the individual; and (d) existing mood,
music preference, cultural expectations, and arousal needs also play a role in determining
affective-mood responses to a given music stimulus. Considering Thaut’s comment, it is
evident that music influences people both physically and psychologically. In addition,
such a connection between music and affective-mood responses evokes the question of
what elements are involved in the connection between music and characteristic moods.

LeBlanc (1987) provided an important theoretical basis for the connection between
music and characteristic moods. According to LeBlanc’s (1982) interactive theory of
music preference, music preference decisions are based upon the interaction of input
information and the characteristics of the listener, with input information consisting of
the musical stimulus and the listener’s cultural environment. For the listener’s
characteristics, LeBlanc mentioned auditory sensitivity, musical ability, musical training,
personality, sex, ethnic group, sociO-economic status, maturation, memory, physiological
enabling conditions, basic attention, and current affective state. Here, LeBlanc pointed
out the crucial role of the listener’s current affective state in relation to his or her reaction
to input information. He explained that current affective state was best conceptualized as

the listener’s current mood, which would function more as a filter than a gate, but it

 

 

 

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would always have some influence upon the listener’s reaction to input information
(LeBlanc, 1987).

Wheeler (1985) investigated the influence Of mood, personality characteristics,
musical taste, musical training, college major, gender, and age, plus hearing music live or
recorded, on listeners’ moods after hearing music and on their enjoyment of the music.
She found that the effect music had on mood was dependent upon both the nature of
people’s moods and their liking of the music. Specifically, Wheeler reported that mood
improved for people who began in a sad mood and heard music they liked, while mood
became worse for those who began in a happy mood and heard music they did not like.
On the other hand, people who began in a sad mood and heard music they did not like
remained in a sad mood, while those who began in a happy mood and heard music they
liked remained happy. Wheeler’s study did give an insight into the fact that an
individual’s music preference, as one of the factors with which music interacts,
influences a person’s mood; however, this study was conducted in an experimentally
designed setting as opposed to in a daily life situation.

Studies on the connection between music and characteristic moods have rarely been
conducted in a daily life situation. Most of the studies exploring this topic have been
conducted in intentionally designed experimental settings in which immediate mood
responses to carefully selected music stimuli are examined. As an exception to this,
Stratton and Zalanowski (1997) were interested in exploring the relationship between

characteristic moods and the types of music most commonly listened to on a daily basis.

 

 

 

 

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Their study was the pioneer in exploring how everyday music-listening preferences and

exposure to music were related to overall persistent moods; however, the researchers

were primarily interested in seeing how different people used music listening as a mood

induction technique in their everyday life according to different educational levels.

Studies that explored how a person’s music-listening habits were related to his or her

general characteristic moods had not been found in the ‘music and mood’ research area.
The present study focused on how people’s general characteristic moods were

related to the types of music they usually listened to on a daily basis, and how their music

 

preferences were related to their general characteristic moods and their usual music-
listening habits as well. “Music-listening habits,” in this study, were defined as how
many minutes per week people listened to each type Of music, how many minutes per
week people listened to all kinds of music considered together, and the methods through
which people listened to music. The results of this study do not show causality between
these variables, but it is expected that exploring the relationship between characteristic
moods and music can provide music therapists and music educators with some
meaningful implications in their use of music for therapeutic and educational purposes.
Also, professionals in the fields of advertising, aesthetics, education, marketing, music,

psychology, and sociology are likely to benefit from greater knowledge in this area.

Pu_rpose

The purpose Of this study was to expand the current understanding Of the

relationships between music and characteristic moods by focusing on an everyday life
setting and people’s general persistent characteristic moods rather than on structured

experimental settings and immediate mood responses.

Problems

The following were Specific research foci of this study:
(1) To explore the relationship between music preference and general characteristic
moods in an everyday setting.
(2) To explore the relationship between music-listening habits and general characteristic
moods in an everyday setting.

(3) To explore the relationship between music preference and music-listening habits.

Definitions

I wish to present clear and understandable definitions of relevant terms used in this
study. And, in some cases, I believe that general dictionary definitions are very helpful.
1. Mood: The new American Webster handy college dictionary (Morehead, & Morehead
(Eds), 1981) defined the term Of mood as a state of mind or feeling, and/ or disposition.
Also, the American college dictionary (Branhart & Stein (Eds), 1963) defined mood as a
frame of mind, or state of feeling, at a particular time. In a dictionary of psychology, the
term mood was defined as a temporary but relatively sustained and pervasive affective

state, often contrasted in psychology and psychiatry with a more specific and short-term

 

 

 

 

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emotion (Coleman, 2001). LeBlanc (1987) defined mood as a current affective state,
which, in relation to the listener’s music preference, is best conceptualized as the
listener’s current mood. According to Lazarus (1984, p.125), moods usually refer to
sustained general states, such as sadness and contentment, that may or may not be
considered emotions depending on theoretical and definitional convention. Stratton and
Zalanowski (1989) also suggested that moods should be less intense physiologically than
emotion, reflected more in subjective responses, cognition, and behavior, and can most
appropriately be measured along these dimensions.

For the purpose of this study, “mood” was defined as the participant’s long term and
characteristic affective state as measured by the Multiple Affective Adjective Check List-

Revised (MAACL-R) trait form (Zuckerman and Lubin, 1985).

2. This study used the Multiple Affect Adjective Check List-Revised (MAACL-R) trait
form (Zuckerman & Lubin, 1985) to measure participants’ general characteristic moods.
The MAACL-R is a versatile instrument for the measurement of five categories of affect:
anxiety, depression, hostility, positive affect, and sensation seeking. Therefore, the
following moods were defined:

2.]. Anxiety: A term used with many shades of meaning and in many different areas of
psychology. It is generally held to be an unpleasant emotional state resulting from stress
or conflict and characterized by fear and apprehension. If the fear and apprehension are
vague and diffuse and not attached to a specific Obj ect, or if they seem excessive, the

anxiety is considered neurotic (Statt, 1998).
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2.2. Depression: One of the most common forms Of emotional disturbance which can
vary in intensity from an everyday attack of “the blues” to a psychotic condition Of
paralyzing hopelessness. It is characterized by anxiety, dej ection and a general lowering
Of activity. There is a difference of Opinion as to whether (or to what extent) the causes
of depression are to be found in unconscious conflict or in biochemical malfunctioning of
the brain (Statt, 1998).

2.3. Hostility: The American college dictionary (Branhart & Stein (Eds), 1963) defined
hostility as a hostile state, enmity, and antagonism. It illustrated “animosity, ill will,
unfriendliness, and Opposition” as synonyms Of the term of hostility.

2. 4. Affect: The term aflect means that which arouses emotion rather than cognition or
thought and the resulting diffuse mental condition. It is a fundamental controlling
element when a person is in an emotional state. In psychology, affect is more frequently
used as a description of a response — a mental or emotional state. Psychologists think
affect is causal, and it influences perception, cognition, and behavioral action. In
addition, the term affect is used to describe any motivational condition, whether an
emotion or a drive state like sexual urge, hunger, thirst, the need to eliminate, and
physical pain (Kazdin, 2000).

2.5. Sensation seeking: The concept Of sensation-seeking has been described at length in
an article by Zuckerman, Eysenck, and Eysenck (1978). An instrument called the
Sensation-Seeking Scale was developed by Zuckerman and associates (Zuckerman,
Kolin, Price, & Zoob, 1964) in an attempt to provide a measure for the construct “Optimal

level

of stimulation,” which these authors stated could also be viewed as “optimal level of
arousal.” The Sensation-Seeking Scale derived from factor analyses of items reflecting a
positive reaction to or a desire for stimulating, exciting, and novel kinds of experiences.
Zuckerman (1971) described the following four factors in the dimension of sensation
seeking (in Zuckerman, Eysenck, & Eysenck, 1978, p. 140): “The first factor was called
Thrill and Adventure Seeking (TAS), and it contained items expressing a desire to engage
in sports or other activities involving speed or danger. The second factor was called
Experience Seeking (ES), and it represented the seeking of experience through the mind
and senses, travel, and a nonconforming life-style. The third factor was labeled
Disinhibition (Dis), which seemed to represent the desire for social and sexual
disinhibition as expressed in social drinking, partying, and variety in sexual partners. The
fourth factor, called Boredom Susceptibility (BS), represented an aversion to repetition,

routine, and dull people, and restlessness when things are unchanging.”

Assumptions

I assumed that the Six styles of music that were carefully defined in this study not
only represented the most commonly recognized categories, but also would not cause any
confusion or misunderstandings among participants in responding.

I assumed that participants really knew what these styles of music sounded like,
even when they did not hear them but only saw the name of the style in print.

I assumed that participants would tell the truth when responding to the mood scale,

the MAACL-R.

Limitations

Perhaps the most important limitation of this study was the fact that I did not play
actual music examples for participants to hear.

There might have been a possibility that the actual number of listening minutes per
week reported by the participants were not as accurate as would have been desirable.

There might also have been a possibility that some participants did indicate their
current moods during the survey rather than their general persistent characteristic moods.

However, this study was based on general persistent characteristic moods.

 

Chapter 2

REVIEW OF LITERATURE

Music and Characteristic Moods

Many studies (Rohner & Miller, 1980; Hanser, Larson, & O’Connell, 1983; Stratton
& Zalanowski, 1989; Thaut & De l’Etoile, 1993; Carter, Wilson, Lawson, & Bulik, 1995;
Kerr, Walsh, & Marshall, 2001; De l’Etoile, 2002) and personal experiences show that
music can change moods, either in a positive or a negative way. The mood-modifying
properties Of music can play a significant role in one’s affective system.

Rohner and Miller (1980) found a trend that sedative music had some anxiety-
reducing effects upon high state anxiety participants. Stratton and Zalanowski (1989)
examined the ability of music and paintings to change moods in college students who
were enrolled in introductory level psychology and music courses. The music and
paintings used in this study were chosen to reflect the mood states of depression, positive
affect, and neutrality. They found that paired music and paintings did have significant
effects on changing moods with the mood change always going in the direction of the
music. With their findings, Stratton and Zalanowski suggested that music appears to be
dominant in determining the direction of mood change.

According to De l’Etoile (2002), in the mid-19703 psychologists began to recognize

the profound effect of emotional states on cognitive processes. Research from the field

10

 

 

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of cognitive science has demonstrated that emotion and moods constrain a number of
cognitive processes such as memory, learning, and perception (Kerr, Walsh, & Marshall,
2001). Memory, learning, and perception are especially critical cognitive processes in
the context of therapy. In terms of memory performance, research findings in this area
have contributed to the identification of a cognitive phenomenon known as mood state-
dependent recall. Thaut and De l’Etoile’s study (1993) supported the influence of
musical mood induction procedures on a cognitive process of memory retrieval. Musical
mood induction means using musical selections to produce affect change (Durand &
Mapstone, 1998). Thaut and De l’EtOile (1993) investigated the effects Of music used as
background stimulus or as a mood induction technique on a cognitive learning task.
They sought to determine if music can produce a mood-state dependent recall effect in
which music is integrated as part Of the internal environmental context of the learning
situation. They compared the effect of background music on encoding and recall of a list
of words with the effect Of musical mood induction on the same processes. The
researchers expected that the induced mood would become associated with the cognitive
information Of a list of words in these processes. Thaut and De l’EtOile reported that
subjects who participated in the mood induction condition recalled significantly more
information than did those in the no music condition and the condition with background
music present only during encoding.

Later, De l’EtOile (2002) conducted another experiment to replicate and expand

upon this study by examining the effect Of a musical mood induction procedure on mood

 

 

 

 

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state-dependent word retrieval. In this experiment, to assess the effect of the musical
mood induction procedure on mood-state dependent word retrieval, the participants were
randomly assigned to one of the following four conditions: (a) musical mood induction
prior to encoding only, (b) musical mood induction prior to recall only, (e) no musical
mood induction prior to encoding or recall, and (d) musical mood induction prior to both
encoding and recall. Providing additional evidence for mood state-dependent effects on
memory, De l’Etoile reported that the high success rate (85%) for this technique of
musical mood induction procedure concurred with the results Obtained previously by
Thaut and De l’EtOile (1993). Through the results from both studies, the researchers
proposed clinical applications of affect modification through music for cognitive and
behavior therapy, and for music therapy respectively. In addition, it will be useful for
music therapists to explore musical mood induction techniques in their music
psychotherapy approaches to address clients’ clinical disorders such as depression.
However, it cannot be assumed that the same piece of music will be equally effective at
eliciting a certain mood state across a range of individuals (Carter, Wilson, Lawson, &
Bulik, 1995). The importance of individualizing music for subjects of the musical mood
induction procedure should be highlighted.

The assumption that a relationship may exist between music and behavior has long
been intuitively held. How does music affect human behavior? This question has been
systematically investigated in a variety of settings as evidenced by research conducted in

music therapy and psychology as well as other fields Of study. Some studies (Wass,

12

Miller, & Redditt, 1991; Harris, Bradley, & Titus, 1992; Wooten, 1992; Gowensmith &
Bloom, 1997; Durand & Mapstone, 1998) suggest that certain types of music, for
example, heavy metal music, may induce negative and antisocial emotions that could lead
to destructive behaviors. Harris and Bradley (1992) compared the inappropriate behavior
Of clients at a state mental health hospital when hard rock and rap music were played for
21 days, followed by easy listening and country and western music for 21 days. The
results Of this study demonstrated that more inappropriate behavior was observed under
the condition in which hard rock and rap music were played than when easy listening and
country western music were played.

Duran and Mapstone (1998) studied the experimental manipulation of conditions
associated with affect change and the influence of affect change on challenging
behaviors. They intended to examine how musical manipulations that were associated
with certain affective responses influenced challenging behaviors such as aggression and
self-injurious behavior (SIB) among the mentally retarded. The researchers designed two
experiments. Experiment 1 was designed to identify stimulus conditions predictive of
high rates of the participant’s challenging behavior and these conditions were used in
Experiment 2 as the baselines. In Experiment 2, the high rate condition determined in
Experiment 1 was repeated with the addition of fast and slow beat music. Duran and
Mapstone reported that participants displayed differential expressions of affect in these
conditions. They said that playing fast beat music during conditions previously associated

with high rates of challenging behavior resulted in reliable reductions in these behaviors.

Meanwhile, it was reported that rates of challenging behavior became higher than in the
previously assessed high rate condition when slow beat music was played. Duran and
Mapstone suggested that varying music may serve as an establishing operation for
challenging behavior in that it can moderate the effects Of immediate stimulus conditions
such as task demands or tangible reinforcers. “Establishing operations” mean situations
that affect the reinforcing properties of other events.

Thaut (1989) mentioned that considering the influence of affect on cognition and
behavior, research in music therapy must investigate the potential of music as an affective
stimulus to influence behavioral learning and change in therapeutic context. Emotional
experience is often at the center of many of the problems that bring people to a therapy
situation. Investigators studying the therapy process have repeatedly found that primary
emotional change is correlated with therapeutic outcome; therefore, they say that
affective processes are critical to understanding and promoting lasting therapeutic change
(Kerr, Walsh, & Marshall, 2001). The behavioral effects of music such as relaxation and
anxiety-reduction have been investigated. Using a sample of 40 anxious adults, Kerr,
Walsh, and Marshall examined the use of music to increase affect modification and
emotional restructuring in a cognitive refrarning intervention. Reframing is one
technique that could potentially benefit from the mood stimulating properties of music.

In the reframing process, the therapist or client generate new positive cognitive
understandings or frames of reference for a given situation or experience. Any emotional

change is understood as a consequence Of shifts made in the client’s cognitive awareness.

l4

Kerr, Walsh, and Marshall found that the music-assisted reframing intervention was more
efficacious than the typical reframing intervention in reducing anxiety, modifying affect,
and promoting imagery-vividness. In addition, the results showed that subjects in the
music-assisted reframing group reported more positive affective reactions than subjects in
the standard reframing group. Kerr, Walsh, and Marshall suggested that the success of
the music-assisted intervention in this study indicates that music may be effectively used
to promote emotional change. Hanser and Larson (1983) also found the effectiveness Of
musical stimuli to reduce anxiety and/ or increase relaxation. They reported that a
combination Of patient-selected and experimenter-chosen music reduced pain perception
and increased relaxation in expectant mothers. These studies encourage therapists tO
employ music systematically to elevate mood. In music therapy, whether music should
match the client’s present mood or oppose it in order to influence the client most

effectively is a central issue (Stratton & Zalanowski, 1989).

Music Preference and Characteristic Moods

There has been strong evidence that existing mood, music preference, cultural
expectations, and arousal needs play a role in determining affective-mood responses to a
given music stimulus (Thaut, 1989). Researchers (Stratton & Zalanowski, 1984;
Wheeler,1985; Litle & Zuckerman, 1986; Davis & Thaut, 1989; McNamara & Ballard,
1990; Amett, 1991; Metzger, 1991; Wass, Miller, & Redditt, 1991; Martin, Clarke, &

Pearce, 1993; Singer, Levine, & Jou, 1993; Thaut & Davis, 1993; Lester, Whipple, &

Melissa, 1996; Forsyth, Barnard, & McKeganey, 1997) have investigated how music
preference influences affective-mood and behavioral responses to a given music stimulus.
Stratton and Zalanowski (1984) suggested that individual preferences must be considered
when using music to aid relaxation. Thirty-six college freshmen and sophomores who
were enrolled in an introductory psychology course participated in their study designed to
examine the role of listening to music in relaxation. Stratton and Zalanowski reported
that no single type of music was found to lead to significantly more relaxation; rather, the
most important factor in relaxation was the degree Of liking for the music. They
mentioned that precategorized soothing music may not always be soothing. Davis and
Thaut (1989) also emphasized the importance Of music preference in reducing anxiety
and/ or increasing relaxation. They conducted a study to measure physiological and
psychological responses to preferred and relaxing music. The results of this study
Showed that listening to preferred music was effective in reducing state anxiety and in
enhancing relaxation. According to Davis and Thaut, it is important to note that very
different types of music are effective in bringing about positive change, possibly because
preference, familiarity, or past experience with the music may have been more important
than type of music in determining relaxation and/ or anxiety reduction potential.

Meanwhile, Litle and Zuckerman (1986) reported that sensation seeking, that is, a
trait characterized by the need for varied, novel, and complex sensations and experience,
and the willingness to take physical and social risks for the sake of such experiences

(McNamara & Ballard, 1990, p.231), was positively correlated with a music preference

for all types of rock music and negatively correlated with a preference for bland
soundtrack music. McNamara and Ballard (1990) also found that people who preferred
more arousing types of music had higher scores on the Sensation Seeking Scale
(Zuckerman, Eysenck, & Eysenck, 1978) than people who preferred more soothing types
Of music.

The negative influences Of heavy metal music on adolescents’ moods and behaviors
have continuously been emphasized by researchers. Amett (1991) intended to delineate
some of the characteristics of adolescents who like heavy metal music. He found a
significant relationship between a preference for heavy metal music and reckless and
dangerous activities in adolescents. According to Amett, boys who like heavy metal
music reported a higher rate of a wide range of reckless behavior, including driving
behavior, sexual behavior, and drug use. Amett also reported that girls who like heavy
metal music were more reckless in the areas of shoplifting, vandalism, sexual behavior,
and drug use, and had lower self-esteem. Regarding drug use, Amett also found that
adolescents who preferred heavy metal music were more likely to use, but not abuse,
illegal drugs than those who did not like heavy metal music. Similarly, Forsyth and
Barnard (1997) reported that a relationship existed between adolescent drug use and
identification with styles of music linked to Specific youth culture. Forsyth and Barnard
found fans of dance music were more likely to have used drugs than those who preferred
other types of music. Singer, Levine, and Jon (1993) examined the relationship between

a preference for heavy metal music and delinquency as well as delinquent associations.

They reported that heavy metal music did have an effect on delinquency when parental
control was low.

Some researchers (Metzger, 1991; Martin, Clarke, & Pearce, 1993; Lester &
Whipple, 1996) have investigated the relationship between music preference and
psychological vulnerability such as depression and suicidal preoccupation. Lester and
Whipple (1996) explored the question of whether or not individuals who prefer country
and Western music or heavy metal music have stronger suicidal tendencies than those
preferring other kinds of music. The results showed that a preference for country and
Western music was not associated with depression and suicidal preoccupation; however,
although preference for heavy metal music was not associated with current depression
and suicidal ideation, those with a stronger preference for such music were more likely to
have considered suicide in the past. Interestingly, Lester and Whipple reported that
psychoticism scores were associated with a stronger preference for jazz and rhythm and
blues, whereas neuroticism scores were not associated with music preferences. Martin,
Clarke, and Pearce (1993) investigated possible relationships between adolescents’ music
preference and aspects of their psychological health and life style. They reported that
significant associations appeared to exist between a preference for rock/ metal and
suicidal thoughts, acts of deliberate self-harm, depression, delinquency, drug taking, and
family dysfunction. In addition, the researchers suggested that people who feel sadder
after listening to rock and heavy metal music appear to be most vulnerable to acting out

the lyrics or themes from the music. Martin, Clarke, and Pearce believed that a

 

 

 

 

 

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preference for rock/ heavy metal music particularly among young females may be an
indicator for vulnerability, as were depression, suicidal thoughts, deliberate self-harm,

risk taking, drug and alcohol abuse, and family psychopathology.

Music-Listening Habits and Characteristic Moods

The theory of mood management explains that people choose sensory materials in
order to regulate their affective experiences and mood states in desirable ways (Zillmann,
1988). Considering the theory of mood management, musical mood management can be
frequently Observed in daily life. Schwartz and Fouts (1999, in Woody & Burns, 2001 ,
p.57) reported that among adolescents, the most common motives for listening to music
were tO relieve boredom and to entertain themselves. According to them, “to feel better,”
“to keep me in the mood I am in,” and “to help me get into a particular mood” were the
frequently mentioned motives for listening to music among adolescents. How then are
most commonly listened to types of music related to characteristic moods? There has
been little research exploring the relationship between music-listening habits and
characteristic moods. Stratton and Zalanowski (1997) conducted a survey to determine
people’s characteristic moods and the types of music they typically listened to. College
students, college faculty and staff, and noncollege adults participated in this survey.
Stratton and Zalanowski reported the following: Among the college students, listening to

rock was positively correlated with anxiety, depression, and sensation seeking, and

negatively correlated with positive affect. Among the faculty-staff, classical music-

19

 

listenin
music l
{3091in
music a

and me

listening was positively correlated with depression, anxiety, and hostility, while total
music listening was positively correlated with depression and negatively correlated with
positive affect. Among the noncollege adults, only a small correlation between classical
music and positive affect was found. Stratton and Zalanowski suggested that younger

and more educated individuals use music more for mood management.

20

Chapter 3

METHOD

1 employed two different survey forms to examine participants’ general
characteristic moods and their music preference and music-listening habits. For the
survey on general characteristic moods, the Multiple Affect Adjective Check List-
Revised (MAACL-R) trait form (Zuckerman & Lubin, 1985) was used. MAACL-R was
a versatile instrument for the measurement of affect traits. It presented the participants
with sixty-six scorable adjectives which measured five categories of affect: anxiety,
depression, hostility, positive affect, and sensation seeking. The individual MAACL-R
scale scores were Obtained by summing the number Of adjectives checked on each of the
five respective scales. Please, refer to information appearing in Appendix A. Because
the publisher’s standard permission for graduate research allowed me to use up to five
sample items from the MAACL-R, I chose five adjectives, and reproduced them in
Appendix A. Each adjective represents one of the five respective characteristic mood
scales. If you are interested in MAACL-R, please, contact the publisher directly referring
to the contact information shown in Appendix A. In addition, to examine music
preferences and music-listening habits, I designed my own survey (See Appendix B).
The responses from the surveys served as the data in this study, and I developed a coding
system and utilized the Statistical Package for the Social Science (SPSS) to analyze the

responses.

21

Participants

Two hundred seventy-one people with a wide range of ages voluntarily participated
in this study. However, 23 of them did not complete their surveys, and 17 of them made
errors while they were responding to their surveys. Therefore, a total of 231 people
actually completed all aspects Of data collection. Ages ranged from 18 to 93, with an
average age of 32.7. Most of the participants were college students. Table 1 shows the
distribution of the participants’ ages.

Table 1

Distribution of the Participants ’ Ages

 

 

 

Age Frequency (people) Rate (%)
18 ~ 203 150 64.9
30$ 20 8.7
403 22 9.5
SOS 17 7.4
603 3 1.3
703 5 2.1
805 12 5.2
903 2 0.9
Total 231 100

 

22

 

 

 

 

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Among the participants, 94 were males and 137 were females, and the majority Of
them were Caucasians. Table 2 shows the distribution of the participants’ ethnicities.
‘Other’ included Eastern Indian, Afghan, and Arabic. ‘Asian, not American’ included
Koreans, Japanese, and Chinese. Plus, the participants whose parents were interracial
couples reported their ethnicity as ‘Mixed’.

Table 2

Distribution of the Participants’ Ethnicities

 

 

 

Ethnicity Frequency (people) Rate (%)

Caucasian 168 72.7
African-American 1 0 4.3
Latino(a)/ Chicano(a) 7 3.0
Asian/ Pacific American 12 5.2
Native American 2 .9
Other 3 1.3

Mixed 9 3.9

Asian, not American 20 8.7
Total 231 100

 

The participants reported their highest levels of education completed. The lowest

level of education was] 2th grade and the highest one was doctor’s degree or in a doctoral

23

program. The majority of the participants were college students.

participants’ levels of education.
Table 3

Participants’ Levels of Education

Table 3 shows the

 

 

 

Education Frequency (people) Rate (%)
Less than 12‘h grade 0 0
12th grade 13 5.6
College student 122 52.8
College graduate 41 17.7
Master’s degree or In 33 14.3
master’s program
Doctor’s degree or In 22 9.5
doctor’s program
Total 231 100

 

Among 231 participants, 88 people reported that they were trained musicians or

music major students. Table 4 shows the participants’ musical training.

24

Table 4

Participants’ Musical Training

 

 

 

Musical Training Frequency (people) . Rate (%)
Yes 88 38.1
No 143 69.9
Total 231 100

 

Survey Development

Two survey questionnaires were used. One was for measuring general characteristic
moods, and the other was for checking music preference and music-listening habits. For
the mood questionnaire, the Multiple Affect Adjective Check List-Revised (MAACL-R)
trait form (Zuckerman and Lubin, 1985; see Appendix A) was used. MAACL-R consists
of 132 adjectives and assesses people’s general feeling in their everyday life in terms Of
five different mood categories: anxiety (A), depression (D), hostility (H), positive affect
(PA), and sensation seeking (SS). The reliability of the Trait Form of the MAACL-R has
been reported in terms of internal consistency, alpha coefficients and test-retest
reliability. All Trait MAACL-R scales show adequate internal reliability. The alpha
coefficients range from .69 to .95. Table 5 Shows the internal consistency reliabilities
(alpha) Of the Trait Form of the MAACL-R for the general population, college students,

and the elderly.

25

Table 5

Internal Consistency (Alphas) of the MAA CL-R Trait Form

 

 

Sample General Population College Elderly
N ’s 1 5 43 1259 858
Scales

A .79 .82 .69
D .81 .82 .81

H .84 .87 .80
PA .74 .92 .89
SS .69 .78 .72

 

The Trait Form Of the MAACL-R has also been evaluated in terms of test-retest
reliability. The authors believe that most reliabilities of the Trait Form of the MAACL-R
are satisfactory even after an 8 week retest interval. Table 6 shows retest reliabilities for

the Trait version of the MAACL-R for intervals of 2 and 8 weeks. These are all college

samples.

26

Table 6

Retest Reliabilities ofMAA CL-R Trait Form

 

 

Retest Interval 2 wks. 8 wks.
N’s 23 52
Scales

A 89** 64**
D 77** 61 **
H 84’” 53**
PA 41 ** 39**
SS 45** 60**

 

*p< .05, **p< .01.

In addition to MAACL-R, I created a music related questionnaire (see Appendix B)
that measured participants’ music preferences and their listening-habits on a daily basis.
This music related questionnaire included six styles Of music and this categorization Of
music styles was carefully defined by me with the consultation of an experienced
professional in this area, Dr. Albert LeBlanc, Professor of Music Education at Michigan
State University. Thus, these six styles of music not only represented the most
commonly recognized categories, but also were unlikely to cause any confusion or
misunderstandings among participants in responding. The six styles of music were: hard
rock/ heavy metal, soft rock/ pop, country music, jazz, rap/ hip hop, and classical music.

The participants were asked to rank the types Of music in order according to how well
27

they liked each one Of them. Participants responded with #1 being used to indicate the
most liked music and #6 being used to indicate the least liked music. The participants
were also asked to estimate the number of minutes per week they listened to each type of
music, and the total amount Of minutes per week they listened to any kind of music.
Participants were also asked to report the number Of minutes per week they listened to
any kind of music on each of the given media. The listening media suggested in the
survey included radio, CDS, DVDS, cassette tapes, LPS, TV, intemet, and live.

According to LeBlanc’s (1982, p.29) interactive theory of music preference, music
preference decisions are based upon the interaction of input information and the
characteristics of the listener, with input information consisting of the musical stimulus
and the listener’s cultural environment. LeBlanc regarded the listener’s sex, ethnic group
membership, and the maturation variable as a part Of the characteristics of the listener
that played an important role in music preference decisions; therefore, I asked the
participants to report their gender, ethnicity, and age on the music related questionnaire to
explore how different music preferences were associated with people’s gender, ethnicity,
and age. In addition, l asked the participants to check their educational levels in
consideration of not only LeBlanc’s (1982) theory but also the results of Stratton and
Zalanowski’s study (1997). Musical training and maturation mentioned by LeBlanc were
directly connected to the concept of educational level. In addition, Stratton and
Zalanowski (1997) suggested that music-listening should be more related to mood among

more educated individuals. Considering these suggestions, I also asked the participants

28

to report their highest levels of education completed to examine how educational levels
affected people’s music preferences and music-listening habits on a daily basis.

A panel of three experts in survey design and psychology of music served as judges
to determine clarity of the instruction and how well the survey addressed the topic (music
preference and music-listening habits). The panel members consisted Of Dr. Albert
LeBlanc, Professor of Music Education at Michigan State University, Dr. Frederick
Tims, Professor and Chair Of Music Therapy at Michigan State University, and Mr.

Roger Smeltekop, Associate Professor of Music Therapy at Michigan State University.

Procedure

This study proceeded according to the following steps.
1) I Obtained copies of the MAACL-R and its manual by ordering them from Educational
and Industrial Testing Service (EleS) after my qualifications had been verified in
March, 2002.
2) I created and pilot tested a survey to examine music preferences and music-listening
habits with twenty Michigan State University students being the subjects Of this pilot
study in March, 2002.
3) After examining the completed pilot test results to check for clarity, I made changes in
the categorization Of the types of music and the types Of media to improve the music-

related survey questionnaire.

29

4) I received permission and approval to conduct this current study from the Michigan
State University Committee on Research Involving Human Subjects (UCRIHS) on
February 2, 2003.

5) The survey was conducted from February 6, 2003 through February 24, 2003. I
initially intended to recruit a total Of one hundred twenty people, with an approximately
equal number of males and females with a wide range of ages as volunteers for the study.
But, actually, a total of 271 people participated in this study in a voluntary way, and of
that number, 231 surveys proved usable as the data. Only those who were defined as
adults by UCRIHS were surveyed for this study. A person whose age was over eighteen,
or who was a college student, was approved as an adult by UCRIHS. Even though I
asked the participants to report if they were musically trained or not on the survey, I
randomly recruited volunteers regardless of their majors, without considering their
musical training, on the logical basis of LeBlanc’S (1984) suggestion that those who lack
musical training Often have difficulty naming the physical properties of music they hear,
but their reaction to properties that please or displease them seems to be as strong as that
of the trained musician.

6) I used several different ways to recruit participants. First, I advertised for volunteers
to participate this study by speaking to junior and senior MSU music therapy students in
their class with the instructor’s permission. While advertising, I explained the purpose of
this study and the approximate amount of time needed to complete the survey, and

indicated the time and the place the survey would be conducted. The volunteers were

30

 

surveyed at the place announced on a prearranged day, as scheduled.

Second, I requested participation in this study in the Jazz History class at Michigan
State University.

Third, I put the survey questionnaires in every Michigan State University music
therapy student’s mailbox in the music therapy clinic with a brief announcement about
this survey, asking both undergraduate and graduate music therapy students to respond to
this survey. m

Fourth, I was allowed to survey 36 college students enrolled in one of American

—-*-

History courses at Michigan State University by the professor who was in charge Of that
class.

Fifth, I directly contacted individuals I would like to survey at various places on the
Michigan State University campus, such as the Union building, dormitories, cafeterias,
libraries, and classrooms.

Sixth, I visited a large retirement community in the Lansing area, Michigan State to
recruit senior subjects, after receiving the permission of the recreation manager. She
allowed me to make a direct contact with the residents at any place in the building. Once
a resident showed his or her agreement to participating in the survey, I individually
helped him or her complete the survey, staying with him or her because their age ranges
were in the 703, 805, or 905.

Seventh, I visited the Community Music School at Michigan State University after

getting permission Of the responsible authorities to ask if parents who brought their

31

children to music lessons were interested in participating in the survey while they were
waiting for their children to finish their lessons.

Eighth, I also had a chance to make a direct contact with parents Of high school
seniors who applied to Michigan State University School of Music on their audition day,
February 14, 2003.

Last, I went to a large shopping mall in the Lansing area, Michigan State to recruit
participants. I was able to approach individuals who were taking a break, Sitting on a
bench during their shopping, and those who took a rest in the food court area after having

a snack. Table 7 summarizes the survey process.

32

Table 7

 

 

 

The Process of Survey
Case Withdrawal Completion Usable N
Errors responses
Music Therapy class 0 0 4 4
Jazz History class 10 0 6 16
Music Therapy Clinic 7 O 8 15
American History class 0 2 34 36
MSU campus 0 8 102 110
Retirement Home 3 2 20 25
Community Music School 0 1 4 5
Audition Day 0 2 32 34
Mall 3 2 21 26
Total 23 17 231 271
Rate (%) 8.5 6.3 85.2 100

 

7) Testing was conducted in the following manner: I first explained the purpose Of the
study and what the survey was about, and asked individuals if they were interested in
participating in the survey. Once they decided to participate in the survey, 1 presented
two copies of the informed consent form, allowing time for reading them and asking any

questions. I asked participants to sign both copies of the informed consent form and to

33

turn in one of them to me, but they could keep the other for themselves. After collecting
the Signed consent forms, I provided participants with the mood questionnaire along with
the music questionnaire concerning music preferences and music-listening habits, and
asked them to complete each questionnaire. 1 did not play recorded music examples for
participants. Not playing music examples made the survey easier and quicker to
administer; therefore, I not only could get more participants, but also could get a wider
sampling Of people. All participants were surveyed anonymously, and they were not
supposed to write their name on the survey questionnaires; instead, I put a unique number
on each participant’s data in order to guarantee anonymity to all of the participants

surveyed. All data were kept in a secure area, accessed only by me.

Data Wis

In this study T intended to explore how general characteristic moods were related to
music preferences and music-listening habits. The responses from the surveys served as
the data in this study, and a coding system was used to prepare the data for computerized
statistical analysis. I used the Statistical Package for the Social Science (SPSS) to
analyze the data.

I did a frequency analysis on the demographic data, including gender, ethnicity, age,
and educational level, using charts as well as tables to report this, if appropriate. I also
computed basic descriptive statistics, including the mean, standard deviation, and
minimum and maximum score, for each mood scale and for the appropriate items of the

music listening questionnaire.

34

I scored each characteristic mood scale following the manual for the MAACL-R:
Every time an adjective on a scale was checked by the participant, that added one point to
the participant’s score on that scale. A larger score meant that that mood was more
prominent for that participant.

Each participant automatically generated a rank order for the six styles of music
listed on the music questionnaire, with a smaller number indicating a greater preference
for a style. However, when I was analyzing the rank order data using SPSS, I
transformed the direction of the number to make a larger number indicate a greater
preference for a style, because a bigger number usually represents a higher degree of
something. Some participants did not choose to rank every type Of music, saying that
they did not know certain types of music, or they did not listen to such types Of music at
all. In this case, I gave the unranked types Of music an average rank score for the
remaining the types of music.

I used this rank order data and the raw scores on the mood scales to compute
Spearman correlation coefficients between each characteristic mood and the ranked
preference for each style Of music. SPSS automatically converted the raw scores for
mood into rank order data when the Spearman procedure was carried out. For example,
if a result showed that hard rock/ heavy metal was positively correlated to sensation
seeking, this meant that people who reported more sensation seeking also reported that
they liked hard rock/ heavy metal more. If a result showed that soft rock/ pop was

negatively correlated to depression, this meant that people who were more depressed

35

reported that they liked soft rock/ pop less. Also, if a result showed that there was a zero
correlation between classical music and hostility, this meant that there was no
relationship between classical music and hostility.

I also calculated Pearson correlations between each score Of characteristic mood and
the amounts of listening to each type of music and between each score of characteristic
mood and total listening time per week. I did this to explore the relationship between
characteristic mood, music style preference, and music-listening habits. If a result
Showed, for example, that there was a positive correlation between hard rock/ heavy
metal listening and reported anxiety, this meant that people spending more time listening
to hard rock/ heavy metal were more likely to be high in anxiety. If a result showed that
soft rock/ pop listening was negatively correlated to depression, this meant that people
who spent more time listening to soft rock/ pop reported less depression. And, in terms
of correlation between each score of characteristic mood and total listening time per
week, if a positive correlation was found between total amount Of music listening and
positive affect, this meant that people who spent more time listening to music in their
everyday life were more likely to report positive affect. This also meant that the more
time people spent listening to music, the more positive mood they might feel.
Meanwhile, if a negative correlation was found between total amount of music listening
and anxiety, this meant people who had more anxiety were less likely to spend time
listening to music in their everyday life. In addition, zero correlation always meant that

there was no positive or negative relationship between the two variables.

36

I calculated Spearman correlation coefficients between the total amount of music
listening per week and the rank order of music preference to see how people’s music
preferences were related to general music-listening habits in their everyday life. For
example, if a positive correlation was found between soft rock/ pop and the total amount
of music listening per week, this meant that generally people who preferred soft rock/
pop were likely to spend more time listening to music in their everyday life. Meanwhile,
if a negative correlation was found between rap and the total amount of music listening
per week, this meant that generally people who spend more time listening to music in

their everyday life did not like rap music.

37

Chapter 4

RESULTS

Two hundred seventy-one people who indicated interest in voluntarily participating
in this study as the participants were initially surveyed. Of the total number, 17 surveys
were unusable due to gross completion errors, and 23 surveys were returned not
completed, or the participants withdrew in the middle of being surveyed. Therefore, two

hundred thirty-one surveys (85%) were used as the data for this study. t

 

Descriptive Statistics

Table 8 shows the mean score of the rank order for each type of music reported by
the participants. During the survey, the participants were supposed to generate a rank
order for the six styles of music with a smaller number indicating a greater preference for
a style. However, during analysis every number reported was converted in its direction to
make a larger number indicate a greater preference for a style. Therefore, in Table 8, a
larger number in rank orders indicates more preference. Soft Rock/ Pop Music was
ranked as the most liked music among the participants; meanwhile, Country Music was
the least liked music. According to Kendall’s W, which was .167, the participants’

agreement in their rank order of music preference was statistically significant beyond the

.01 level.

38

Table 8

Means and Standard Deviations of Preference Rankings given to Each Type of Music

 

 

Type of Music M SD
Soft Rock/ Pop 4.706 1.309
Classical Music 4.030 1.614

Jazz 3.591 1.448

Rap/ Hip Hop 3.006 1.728

Hard Rock/ 2.840 1.688

Heavy Metal

Country Music 2.814 1.509

 

Note. Kendall’s W = .167, Chi-Square = 192.867, df= 5, Asymp. Sig. = .000.
The rank order of music preference (1 = least liked, 6 = most liked)
N = 231

Table 9 shows the average number of minutes spent listening to each type of music
per week, as reported by the participants. The results indicated that the participants
listened to Soft Rock/ Pop Music most in their everyday life; meanwhile, they listened to
Country Music least. The standard deviations are rather large, indicating that the
participants differed widely in the amount Of time they spent listening to music. It is
interesting to note that Soft Rock/ Pop Music was ranked as the most liked music among
the participants (See Table 8), and Soft Rock/ Pop Music also proved to be the type of

music that the participants listened to most in their everyday life. The participants ranked

Country Music as the least liked music, and Country Music also proved to be the least

39

 

listened to music in their everyday life. I considered this to be an indication of the

validity of their preference responses.

Table 9

Average Number of Minutes of Listening Per Week

 

 

 

 

Type of Music Min. Max. M SD
Soft Rock/ Pop 0 2100 158.50 265.14
Classical Music 0 2500 141.61 288.00
Hard Rock/ 0 2000 74.11 203.81

Heavy Metal
Jazz 0 1440 67.40 178.46
Rap/ Hip Hop 0 1000 67.18 144.32
Country Music 0 1200 39.60 129.57
Note. N = 231

Correlations between Characteristic Moods and Music Preferences

Each characteristic mood scale was scored following the manual for the MAACL-R.
I used Pearson correlation when both variables were measured at interval level and
Spearman correlation when at least one variable was measured at ordinal level.
Correlations were computed between each characteristic mood and the ranked preference
for each type of music. Table 10 shows Spearman correlation coefficients between the

scores on each mood subscale and the rank orders of the types of music reported by the

participants.

40

Table 10

Spearman Correlations between Characteristic Moods and the Rank Orders of the Types

of Music

 

Spearman Type of Music Anxiety Depression Hostility Positive Sensation

 

 

Correlation Affect Seeking
Hard Rock/ .028 -.058 .117 -.111 .072
Heavy Metal
Soft Rock/ -.022 -.034 -.055 -.024 -.l67*
Pop
Country -.134* -.128 -.190* .002 .042
Jazz .043 .058 .041 .014 -.046
Rap/ .060 .070 .100 -.01 1 .136*
Hip Hop
Classical -.015 .043 -.062 .139* -. 100
Note. N = 231

* Correlation is significant at the .05 level (2-tailed).

Anxiety and a preference for Country Music were negatively correlated with each
other. Their relationship was statistically significant at the .05 level. Hostility and a
preference for Country Music were also negatively correlated with each
other, also the .05 level. A positive correlation was found between Positive Affect and a
preference for Classical Music. They, were significantly correlated with each other at the
.05 level. Sensation Seeking and a preference for Soft Rock/ Pop Music were negatively

correlated with each other, significant at the .05 level. Meanwhile, Sensation Seeking

41

 

was positively related with a preference for Rap/ Hip Hop Music, and this positive

correlation was statistically significant at the .05 level.

Correlations between Characteristic Moods and Music-Listening Habits

Correlations were computed between each characteristic mood and the number of
minutes Of listening to each type of music, and between each characteristic mood and the
total amount Of minutes of listening to any type of music per week reported by the
participants. Table 11 shows Pearson correlation between the scores on each mood
subscale and minutes Of listening to each type of music per week. Table 12 Shows
Pearson correlation between the scores on each mood subscale and the total amount of

minutes of listening to any type of music per week.

42

Table 11
Pearson Correlations between Characteristic Moods and Minutes of Listening to Each

Type of Music Per Week

 

 

 

 

Pearson Type of Characteristic Moods
Correlatio Music Anxiety Depression Hostility Positive Sensation
n Affect Seeking
Hard Rock/ .055 .035 .096 -.008 .008
Heavy Metal
Soft Rock/ .021 .023 -.021 -.021 -.040
Pop
Country -.048 -.023 -.O7l .007 .070
Jazz .073 .128 .072 .091 .039
Rap/ .015 .029 .035 .045 .154*
Hip Hop
Classical .194** .086 .015 .056 .010
Note. N = 231

* Correlation is significant at the .05 level (2-tailed).
** Correlation is significant at the .01 level (2-tailed).

A positive correlation was found between Rap/ Hip Hop listening and reported

Sensation Seeking. This correlation was statistically significant at the .05 level.

43

Table 12
Pearson Correlations between Characteristic Moods and the Total Number of Minutes of

Listening to Any Type of Music Per Week

 

 

Characteristic Mood Pearson Correlation
Anxiety .100
Depression .129
Hostility .008
Positive Affect .040
Sensation Seeking .005

 

Nate. N = 231

NO statistically significant correlation was found between each characteristic mood
and the total amount of minutes of listening to any type Of music. However, even though
it was not a statistically significant correlation, the total listening time was fairly closely
related with reported Anxiety and Depression in a positive way in comparison to the other

mood subscales.

Listening Media

Table 13 shows the average minutes of listening to music through each of the
mediums named in this study. The results showed that CDs, DVDs, Cassette tapes, or
LPs were the most commonly used mediums when the participants listened to music.
However, the standard deviations are extremely large. The subjects differed widely in

terms Of listening mediums they used.
44

 

Table 13

Average Number of Minutes of Listening Through Each Medium Per Week

 

 

 

Type of Medium Min. Max. M SD
CDs, DVDS, Cassette 0 3600 206.79 360.36
tapes, or LPs
Radio 0 2430 174.70 310.64
Internet 0 2450 121.36 341.18
Live 0 2450 57.67 233.83
TV 0 1200 43.22 1 15.60
Note. N = 231

Correlations between Music Preferences and Music-Listening Habits

Correlations were computed between the total amounts of music listening and the
rank order of music preference to see how people’s music preferences are related to
general music-listening habits in their everyday life. Table 14 shows Spearman
correlation coefficients between the total number of minutes of listening to any type of

music per week and the rank orders of the types of music reported by the participants.

45

Table 14
Spearman Correlations between the Total Number of Minutes of Listening to Any Type of

Music Per Week and the Rank Order of Preferences for the Types of Music

 

 

 

The Rank Order of Each Type of Music Spearman Correlation
Soft Rock/ Pop -.055
Classical Music -.071
Jazz .077
Rap/ Hip Hop .063
Hard Rock/ Heavy Metal .216**
Country Music -.288**
Note. N = 231

** Correlation is significant at the .01 level (2-tailed).

The total amount of music listening was positively related to a preference for Hard
Rock/ Heavy Metal Music. This correlation was statistically significant at the .01 level.
Meanwhile, the total amount of music listening and a preference for Country Music were

negatively correlated with each other, statistically significant at .01 level.

Correlations between Ages and Music Preferences

Correlations were computed between the participants’ ages and the rank order of
music preference to see how people’s music preferences are related to their ages. Table
15 shows Spearman correlation coefficients between the age of the participants and the

rank order Of the types of music reported by the participants.
46

Table 15

Spearman Correlations between the Participants ’ Ages and Music Preference

 

 

 

Rank Order of Each Type of Music Spearman Correlation
Soft Rock/ Pop -.106
Classical Music .426**
Jazz .115
Rap/ Hip Hop -.552**
Hard Rock/ Heavy Metal -.253**
Country Music .455**
Note. N = 231

** Correlation is significant at the .01 level (2-tailed).

Positive correlations were found between Age and a preference for Country Music
and for Classical Music. Both correlations were statistically Significant at the .01 level.
Meanwhile, Age was negatively related to a preference for Hard Rock/ Heavy Metal
Music and for Rap/ Hip Hop Music as well. These negative correlations also had

statistical significance at the .01 level.

Correlations between Ages and Music-Listening Habits

Correlations were also computed between the participants’ ages and the minutes Of
listening to each type of music per week, and between the participants’ ages and the total
amount of minutes of listening to any type of music per week to see how different music-

listening habits in everyday life might be for people Of different ages. Table 16 shows

47

Pearson correlations between the participants’ ages and the minutes of listening to each
type Of music per week.

Table 16

Pearson Correlations between Participants ’ Ages and Minutes of Listening to Each Type

of Music Per Week

 

 

 

Minutes of Listening to Each Type of Music Pearson Correlation

Soft Rock/ Pop -.23l**

Classical Music -.043
Hard Rock/ Heavy Metal -.181**

Jazz -.O40
Rap/ Hip Hop -.289**

Country -.088

Note. N = 231

** Correlation is significant at the .01 level (2-tailed).

Negative correlations were found between Age and Hard Rock/ Heavy Metal Music
listening, Soft Rock/ Pop Music listening, and Rap/ Hip Hop Music listening. These
correlations were statistically significant at the .01 level.

The participants’ Ages and Total minutes of music listening were negatively
correlated with each other, yielding a Pearson correlation Of -.282. This correlation was
statistically significant at the .01 level.

Correlations were also computed between the participants’ ages and the number of

minutes of listening to music through each type of medium named in this study to see

48

how the listener’s age might be related to different music-listening mediums. Table 17
shows Pearson correlations between the participants’ ages and estimated listening time to
each listening medium.

Table 17

Pearson Correlation between Participants ’ Ages and the Number of Minutes of Listening

 

 

to Each Type of Medium
Listening Time to Each Type of Medium Pearson Correlation
Radio .007
CDs, DVDS, Cassette tapes, or LPs -.190**
TV -.117
Internet -.240**
Live -.O79

 

*"‘ Correlation is significant at the .01 level (2-tailed).

CDs, DVDs, Cassette tapes, or LPs and Internet were negatively related to the

listener’s Age, significant at the .01 level.

Music Preference and Education

Correlations were computed between the participants’ levels Of education and the
rank order for each type of music to see how different the participants’ music preferences
were according to their levels Of education. Table 18 shows Spearman correlation
coefficients between the levels of education and the rank orders for each type of music

reported by the participants.

49

Table 18

Spearman Correlations between Level of Education and Rank Orders for Each Type of

 

 

 

Music
The Rank Order of Each Type of Music Spearman Correlation
Soft Rock/ Pop -.l71**
Classical Music .370**
Jazz .171**
Rap/ Hip Hop -.307**
Hard Rock/ Heavy Metal -.l67*
Country Music .125
Note. N = 231

** Correlation is significant at the .01 level (2-tailed).
* Correlation is significant at the .05 level (2-tailed).

Negative correlations were found between the level of education and a preference
for Hard Rock/ Heavy Metal Music, Soft Rock/ Pop Music, and Rap/ Hip Hop Music.
The correlation with Hard Roe/d Heavy Metal Music was significant at the .05 level, and
the other negative correlations were significant at the .01 level. Meanwhile, the level of
education was positively correlated with a preference for Jazz and Classical Music,

Significant at the .01 level.

Music-Listening Habits and Education
Correlations were computed between the level of education and minutes of listening

to each type of music per week to see how different the participants’ music-listening
50

 

habits were according to their education levels. Table 19 shows Pearson Correlations
between the level of education and minutes of listening to each type of music reported by
the participants.

Table 19

Pearson Correlations between Level of Education and Minutes of Listening to Each Type

of Music Per Week

 

 

 

Minutes of Listening to Each Type of Music Pearson Correlation
Soft Rock/ Pop -.146*
Classical Music .155*
Hard Rock/ Heavy Metal -.1 19
Jazz .054
Rap/ Hip Hop -.180**
Country -.103
Note. N = 231

** Correlation is significant at the .01 level (2-tailed).
* Correlation is significant at the .05 level (2-tailed).

A negative correlation was found between the level of education and listening time
for Soft Rock/ POp Music and Rap/Hip Hop Music. These correlations were Significant
at the .05 and .01 level, respectively. The level of education was positively correlated

with classical music listening, significant at the .05 level.

51

Chapter 5

DISCUSSION

The idea that connections exist among characteristic moods, music preference, and
music-listening habits was supported by the results of this study. However, it is hard to
say that all of the results of this study could be generalized to every situation, because L
music affects different people in different ways, and music preference and music-

listening habits were involved with a variety Of elements such as, age, ethnicity,

1) -

 

education, and musical training.

The Relationship between Characteristic Moods and Music Preference

In terms of the relationship between characteristic moods and music preference, the
results showed that a preference for country music was negatively related to negative
affect such as, anxiety and hostility (See Table 10). This means people who liked country
music reported less anxiety and less hostility. These negative relationships were
statistically significant; however, it could not be said that their relationships strongly
affect actual performance because of their small shared variances. A percentage of
shared variance between two variables, which is also called the coefficient of
determination, is shown by the squared correlation. Here, the shared variances between a
preference for country music and anxiety, and between a preference for country music

and hostility were .018 and .036, respectively. This means that there is only a 2% chance

52

of correctly predicting that people who prefer country music are less likely to be in a
mood of anxiety. In addition, the possibility that people who prefer country music are
less likely to be in a mood of hostility can be correctly predicted with a 3% chance of
success in actual practice.

The results showed that a preference for rap/ hip hop music was positively
correlated with sensation seeking (See Table 10). This means that people who liked rap/
hip hop music were more likely to be in a sensation seeking mood. This result can be
understood in the context of age. Correlations were computed between age and sensation
seeking, and between age and a preference for rap/ hip hop music. The participants’ ages
and sensation seeking were negatively correlated with each other, yielding a Pearson
correlation Of-.136. This correlation was statistically significant at the .05 level. The
participants’ ages and a preference for rap/ hip hop music were negatively correlated with
each other, yielding a Spearman correlation coefficient of -.5 52. This correlation was
statistically significant at the .05 level. This means that the younger the person, the more
often a sensation seeking mood was reported, and that the younger the person, the more
preference for rap/ hip hop music was reported. Essentially, younger people liked rap/
hip hop music more than Older people did, and generally younger people were likely to be
in a sensation seeking mood. Therefore, we may consider age to be a mediator for the
relationship between sensation seeking and a preference for rap/ hip hop music. It should

be noted that rap/ hip hop music was the youngest style of music presented in this study.

53

 

The Relationship between Characteristic Moods and Music-Listening Habits

 

A positive correlation was also found between sensation seeking and minutes of
listening to rap/ hip hop music (See Table l 1). This means that people who spent more
time listening to rap/ hip hop music reported more sensation seeking. Like the
relationship between sensation seeking and a preference for rap/ hip hop, this positive
relationship between sensation seeking and rap/ hip hop listening can also be understood
in the context of age. Correlation between age and minutes of rap/ hip hop listening was
computed. The participants’ ages and minutes of listening to rap/ hip hop music were
negatively correlated with each other, yielding a Pearson correlation of -.289. This
correlation was statistically significant at the .01 level. This means that the younger the
person, the more rap/ hip hop music listening was reported. Also, as mentioned above,
generally younger people were more likely to be in a sensation seeking mood than older
people. Therefore, considering the context Of age, it can be understood that people
spending more time listening to rap/ hip hop music were more likely to be in sensation
seeking mood.

In this study, no Significant correlation was found between minutes Of listening to
hard rock/ heavy metal music and any negative affect such as, anxiety, depression, and
hostility (See Table 11). This particular result might be attributed to the participants’
ages and levels of education. This study surveyed only the adult population with a large
range of age; in addition, educational levels of the majority of participants were college

and/ or above college education. But, if adolescents and/ or young adults had been the

54

 

only population group surveyed, the findings might have been different, and might have
supported other studies which pointed out a possible negative impact Of hard rock/ heavy
metal music on people listening to it in an emotional and a behavioral way. However, the
results of this study suggested another possibility Of interpreting the influences of hard
rock/ heavy metal music on its listeners. These types Of music could influence different
populations in different ways, rather than producing mainly negative effects on everyone
who listens to it.

It is interesting to speculate on the relationship between total music-listening time
and negative affects including anxiety and depression. Even though in this study the
correlations between total music-listening time and both anxiety and depression were not
statistically significant, total music-listening time was more closely correlated with these
negative affects in a positive way in comparison to its relationship with the other mood
subscales (See Table 12). Especially, in the case Of depression, their relationship was
almost significant at the .05 level. This result suggested that people who were in more
anxiety or more depression spent more time listening to music in their everyday life.
Actually, it is hard to conclude the causality Of such relationships; however, it could be
assumed that it is not music listening itself that leads to negative affect or increases the
degree of such negative affect; rather, people may listen to music more frequently when
they have negative affects, including anxiety and depression, to change their mood
toward a more positive state. Of course, people select their music listening for a variety

of different reasons; for example, to express their emotions, to identify their emotions, to

55

 

clarify their emotion, or sometimes, to experience empathy from music according to their
mood state, as Opposed to music’s making people feel in a negative or a positive way by
itself. Therefore, it may not be argued with confidence that certain types of music, for
example, hard rock/ heavy metal music, provoke negative and aggressive moods like
hostility or anxiety. Instead, it may be reasonably assumed that people choose certain

types Of music that match their moods.

The Relationship between Music Preference and Music-ListenigHabits i
The results show that total minutes of listening to music were positively correlated :

with a preference for hard rock/ heavy metal music, and negatively correlated with a

preference for country music (See Table 14). These results can be understood in the

context of age. Total music-listening time was negatively correlated with age, and age

and a preference for hard rock/ heavy metal music were negatively correlated with each

other (See Table 15). This means that the younger the person, the more preference for

hard rock/ heavy metal music was reported, and generally younger people spent more

time listening to music in their everyday life. in this context, the result that people who

liked more hard rock/ heavy metal music spent more time listening to music may be

understandable. With the same logic, the result that people who liked more country

music spent less time listening to music in their life can be understood. The Older the

person, the more preference for country music was reported (See Table 15), and generally

Older people spent less time listening to music in their everyday life.

56

It is interesting to note that the most frequently used medium for music listening was
‘CDs, DVDS, Cassette tapes, or LP.’ The popularity of these media was followed by
radio, intemet, live, and TV in order (See Table 13). However, there was a possibility
that the participants may not have had accurate recollection of exactly how long they

spent listening to each medium.

Age and Music Preference & Music-Listening Habits

The results showed that younger people not only more liked but also actually more
listened to hard rock/ heavy metal music and rap/ hip hop music than older people did
(See Table 15 and Table 16). Assuming the fact that people would like to listen to music
intentionally to change their moods, and would like to choose the types of music which
are meaningful for them in their specific mood states, hard rock/ heavy metal music and
rap/ hip hop music can be considered meaningful social codes among young people in
relation to characteristic moods and music. Meanwhile, for older people, country music
and classical music can be considered meaningful in their emotional life (See Table 15).

It suggested that older people were likely to Spend less time listening to music in
their everyday life, and they were less likely to use intemet and CD3, DVDS, Cassette

tapes, or LPS for listening (See Table 17).

57

 

Education and Music Preference & Music-Listening Habits

The more educated the person, the less preference for soft rock/ pop music and rap/
hip hop music was reported (See Table 18). Plus, it suggested that more educated people
spent less time for soft rock/ pop music as well as rap/ hip hop music listening in their
everyday life than relatively less educated people did (See Table 19). Meanwhile, the
more educated the person, the more preference for jazz and classical music was reported
(See Table 18). In addition, more educated people also spent more time listening to
classical music in their everyday life than less educated people did (See Table 19). Jazz
and classical music have their value in school education. Jazz and classical music have
been taught through school educational programs for anyone who is interested in them,
regardless of his or her major; however, the other genres Of music, such as hard rock/
heavy metal music, soft rock/ pop music, and rap/ hip hop music have rarely been taught
in school, even though there exist some music schools that have educational curriculums
for popular music to teach it in a professional way. It could be assumed that more
educated people have more opportunities than less educated people to experience jazz
and classical music through schooling, which, in turn, influences their music preference
for these genres Of music. This interpretation would be more reasonable than making a
connection between more educated people’s intelligence and their music preference for

jazz and classical music.

58

 

Chapter 6

Implications and Recommendations

It is difficult to make decisive conclusions regarding how people’s general
characteristic moods are related to their music preference and music-listening habits in
everyday life. In addition, statistical analysis Of correlations among variables does not
give clear answers as to their causalities. However, this study Showed several meaningful
results that could support people’s general assumptions or ideas on the role of music in
daily emotional life by disclosing actual trends of relationships among characteristic
moods, music preference, and music-listening habits. Among those results, some can be
reasonably understood, whereas others lead to challenging questions which require
further study.

The value of this study may lie not so much in its findings, but in what it left
unsolved. It is difficult to find clues for interpreting some Of the interesting results of this
study. Why did people who were more in anxiety and hostility moods report less
preference for country music? Could this be explained by age? Actually, age was
negatively related to anxiety and hostility, yielding a Pearson correlation Of -.216 and -
.211, respectively. These correlations were statistically significant at the .01 level.
Because the Older the person, the less anxiety and hostility were felt in this study, and the
older the person, the more preference for country music were reported, does a preference

for country music always become negatively correlated with anxiety and hostility? It can

59

 

not be said, on the basis Of this study alone, that all people feel less anxious or less hostile
as they become Older. Also, why are country and classical music preferred by Older
people rather than younger people? Why do people spend less time listening to music in
their everyday life, as they get Older? The reason for this might be that Older people are
simply busier than younger people.

Why do people who prefer soft rock/ pop music feel less sensation seeking in their
daily lives, whereas people who prefer and spend more time listening to rap/ hip hop
music feel more sensation seeking? Could this be true because the lyrics of rap/ hip hop
music are more suggestive of sensation seeking? All of these questions are worth further
research through future studies. In some perspectives, these unsolved questions could
find their answers in the context Of every single person’s emotional experiences in
relation to musical stimuli. Music, as a social phenomenon, is experienced within a
listener’s personal and social environments, and such individualized musical experiences,
in turn, influence the listener’s music preference and also contribute to shaping his or her
music-listening habits in daily life. Therefore, future studies that approach these
questions with phenomenological research methods including interview could be
recommended in the hope that a phenomenological approach may be able to give specific
answers to such Open questions, and/ or it may also be able to provide crucial clues to
figure out any causality regarding how people’s moods, music preference, and listening
habits interact with one another.

Considering the fact that music is a social phenomenon and an important cultural

60

 

code in our society, it would be reasonable to study how music preference and music-
listening habits might be different depending on ethnic groups to which people belong.
Such research could provide understanding of music preference and music-listening
habits with ethnomusicological perspectives by guiding researchers to interpret in the
cultural and historical context. For example, it may possibly be assumed that African-
Americans would not only show more preference for rap/ hip hop music, but also report
more listening time for this type Of music than any other ethnic groups. This is in fir,
consideration of the fact that rap/ hip hop music finds its origin in an underground

cultural movement called Hip Hop, which started in the early 19703 in New York, and

 

represents an African-American urban culture that is also connected to other aspects of
African-American music.

It would also be appropriate that future research should deal with a gender issue in
relation to music preference and music-listening habits. For example, it could be
meaningful to explore whether certain types of music are presented more through the
male perspective, and/ or whether certain types of music reflect predominantly female
attitudes, such that females identify more with such styles Of music. Dealing with gender
differences in relation to music preference and music-listening habits would be a
worthwhile contribution to the social psychology of music.

Further studies with large samples that divide participants into more specific
subgroups, for example, in terms of age, gender, or level of education would be useful.

In addition, grouping participants by different characteristics may make it possible to

61

Show how differently music-listening is used as sensory material for mood management
by people in various demographic groups.

Even though it was not statistically significant, this study suggested that people who
reported more negative moods, such as depression and anxiety, were likely to spend more
time listening to music in their everyday life. This result supports an assumption that
people choose music in order to regulate their affective experiences and mood states in
desirable ways. This assumption has an important implication for music therapy and
music education. The use of music as a therapeutic tool for people’s mental health and as
an educational method for emotional and behavioral modification for emotionally and
behaviorally disturbed children could be supported by such a result. Therefore, future
research that shows how people use music listening for mood management in their
emotional life in a concrete way, introducing their motives for music listening and
musical experiences, is recommended. In this study people who felt more positive affect
reported more preference for classical music; whereas, people who felt more anxiety
reported that they spent more time listening to classical music. Through this result, it
could be assumed that people may use classical music with an intention to reduce their
anxiety, accessing more positive mood. This shows an interesting dynamic among
characteristic moods, music preference, and music listening habits, leading us to
speculate on how people use music listening for their mood management in daily
emotional life.

It is beyond the scope of this study to make clear conclusions regarding how

people’s moods influence their music-listening habits, or how music-listening habits

62

 

affect people’s moods, and what kind of role music preference plays in people’s music
listening in certain moods. However, this study is useful in that it shows many possibly
meaningful relationships among characteristic moods, music preference, and music-
listening habits. This study is also valuable in that it poses several important open
questions that need to be further researched to gain better understanding on the dynamics
among characteristic moods, music preference, and music-listening habits. In addition,
by providing more expanded understanding of the role Of music in people’s daily
emotional life, this study makes one small contribution to the knowledge building in the
field of psychology of music. Music therapists and music educators may refer to the
findings of this study in their practical use of music for therapeutic and educational

purposes.

63

 

 

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APPENDICES

68

Appendix A

Multiple Affect Adjective Check List -— Revised (MAACL-R)
Trait / In general Form

By Marvin Zuckerman
And
Bernard Lubin
DIRECTIONS: On this sheet you will find words which describe different kinds of

moods and feelings. Mark an ‘x’ in the boxes beside the words which describe how y_(_)t_1

generally feel. Some of the words may sound alike, but we want you to check a_11 IE

 

words that describe your feelings. Work rapidly.

 

Adventurous
Discouraged
Furious
Nervous

Pleasant

Reproduced by permission. MAACL-R © 1965 EleS Publishers.
Educational and Industrial Testing Service (EdITS)

P.O. Box 7234 San Diego, CA 92107
Phone: (800) 416-1666 or (619) 222-1666

69

 

Appendix B

A Survey for Music Preference and Everyday Music Listening Habits

1. What is your age? ( )

 

2. Are you male or female? (Please, check one)

Male Female

3. What is your ethnic background?
Caucasian
African - American
Latino(a)/ Chicano(a)
Asian/ Pacific American
Native American

Other (Please, identify)

 

4. Please, indicate your highest level of education:
Less than 12th grade
12th grade (high school diploma)
College student (In bachelor’s program)
College graduate (Bachelor’s degree)
Master’s degree or in Master’s program

Doctor’s degree or in Doctor’s program

5. Are you a trained musician or a music major student? (Please, check one)

Yes NO

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6. Please rank the types Of music listed below according to how well you like them. (#1 is
most liked, #6 is least liked)

Hard Rock/ Heavy Metal Jazz
Soft Rock/ Pop Rap/ Hip Hop
Country Music Classical Music

7. Please, estimate the number Of minutes per week you listen to each type Of music.
Minutes Hard Rock/ Heavy Metal
Minutes Soft Rock/ Pop
Minutes Country Music

 

Minutes Jazz
Minutes Rap/ Hip Hop

Minutes Classical Music

8. Please, estimate the number Of minutes per week you listen to any kind of music.

Average # of minutes

9. Please, estimate the number of minutes per week you listen to any kind Of music on the
following media and/ or live performance.

Minutes Radio

Minutes CDs, DVD, Cassette tapes, or LP

Minutes TV

Minutes Internet

Minutes Live

Thank you very much for your cooperation!

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