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University of MiamiScholarly Repository
Open Access Theses Electronic Theses and Dissertations
2013-03-07
The Role of Personality and Mood in Music-UseDuring a High-Cognitive Demand TaskAndrew PanayidesUniversity of Miami, [email protected]
Follow this and additional works at: https://scholarlyrepository.miami.edu/oa_theses
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Recommended CitationPanayides, Andrew, "The Role of Personality and Mood in Music-Use During a High-Cognitive Demand Task" (2013). Open AccessTheses. 399.https://scholarlyrepository.miami.edu/oa_theses/399
UNIVERSITY OF MIAMI
THE ROLE OF PERSONALITY AND MOOD IN MUSIC-USE DURING A HIGH-COGNITIVE DEMAND TASK
By
Andrew G. Panayides
A THESIS
Submitted to the Faculty of the University of Miami
in partial fulfillment of the requirements for the degree of Master of Music
Coral Gables, Florida
May 2013
©2013 Andrew G. Panayides All Rights Reserved
UNIVERSITY OF MIAMI
A thesis submitted in partial fulfillment of the requirements for the degree of
Master of Music
THE ROLE OF PERSONALITY AND MOOD IN MUSIC-USE DURING A HIGH-COGNITIVE DEMAND TASK
Andrew G. Panayides
Approved: ________________________ ________________________ Teresa Lesiuk, Ph.D. M. Brian Blake, Ph.D. Associate Professor, Music Therapy Dean of the Graduate School ________________________ ________________________ Shannon K. de l’Etoile, Ph.D. Carlos Abril, Ph.D. Associate Professor, Music Therapy Associate Professor, Music Education ________________________ Mitsunori Ogihara, Ph.D. Professor, Department of Computer Science
PANAYIDES, ANDREW G. (M.M., Music Therapy)
The Role of Personality and (May 2013) Mood in Music-Use During a High-Cognitive Demand Task Abstract of a thesis at the University of Miami. Thesis supervised by Professor Teresa L. Lesiuk. No. of pages in text. (145)
The purpose of this thesis was to investigate the ways in which individuals use
music during a high-cognitive demand task – computer programming. This thesis also
examined relationships among music-use, personality, and mood. Thirty-four university
students with varying levels of computer programming experience participated in the
study. Initially, participants completed a demographic questionnaire and personality
inventory during an individual meeting with the researcher. The second portion of the
study was completed using a study webpage, in which participants submitted responses to
a mood scale, task assessment, and music-use questionnaire. The mood scale was
completed immediately prior to a computer programming task accompanied by music
listening, and the music-use questionnaire was completed immediately after the task. The
music-use questionnaire consisted of a music-use scale, two open-ended items, and
questions about the listening experience.
Music-use during a computer programming task appears to be a complex process,
being impacted by individual differences and contextual factors. Bivariate correlations
were used to examine relationships between study variables. Results indicated several
significant relationships. First, the personality factor of Openness was positively
correlated with both Cognitive and Emotional-use of music, and the relationship between
Openness and Cognitive-use was supported in a predictive model. No significant
correlations were found between any of the mood and music-use variables. However,
some of the demographic and contextual factors were significantly correlated with music-
use. Computer programming proficiency was positively correlated with Emotional-use
of music. Next, music activity level, listening duration, and music focus were each
positively correlated Cognitive-use of music, while computer programming background
and task difficulty were each negatively correlated with Cognitive-use. An analysis of
variance revealed a significant effect of computer programming background on
Cognitive-use of music.
The themes that emerged in open-ended responses from this study generally
supported the quantitative results obtained. Participant statements typically related to one
of the music-use categories, and the distribution of responses was similar to the
distribution of scores on the music-use scale. In addition to utilizing words related to the
music-use categories, participants employed specific language to describe the type of
music they chose and its influence on overall productivity.
ACKNOWLEDGEMENTS
I would first like to thank the members of my committee for their guidance and
support: Dr. Teresa Lesiuk, Dr. Shannon de l’Etoile, Dr. Mitsunori Ogihara, and Dr.
Carlos Abril. Thanks also to Corinne Huggins, for her ever-gracious assistance with the
research design and data analysis. I would like to extend special thanks to Dr. Teresa
Lesiuk for her patience and compassion throughout the development and completion of
this thesis, and much appreciation as well to Dr. Shannon de l’Etoile for her thoughtful
advice during my entire graduate school experience.
I would also like to thank Sarah Zaharako for volunteering an extra set of eyes to
the editing process, Bob Ladue for sharing with me one of your many talents, web-
design, and Carolyn Dachinger for your mentorship.
Naturally, my mother and father have a great deal to do with this project. Thank
you and much love to you both for imparting in me the values of kindness and dedication,
without stifling my imagination and creativity. Thank you also to my sisters and my
brothers for being totally different, showing me the values of patience and openness.
And thanks as well to my stepmother and her extended family for always welcoming me
and encouraging me. To those I have lost in this life, I feel your foundation beneath me.
I am also grateful for support from a list of companions: Thanks to Jessie and
Shelva for always letting me in when I show up at your house, to Greg for walking next
to me, to Richard for staying alive, to Rajan for calling me, to Nat for packages in the
mail, to Jeff for bicycles, to Brian for taking breaks to watch sports, to Brent for flying
coast-to-coast, and to my roomies, Lika and Ed, for reminding me to eat.
Finally, thank you to the students who volunteered to participate in this study.
iii
TABLE OF CONTENTS
Page
LIST OF FIGURES ..................................................................................................... vi LIST OF TABLES ....................................................................................................... vii Chapter 1 INTRODUCTION ........................................................................................... 1 Statement of the Problem ................................................................................. 1 Definition of Terms.......................................................................................... 4 Need for the Study ........................................................................................... 6 Purpose Statement ............................................................................................ 9 2 REVIEW OF LITERATURE .......................................................................... 10 Music Perception ............................................................................................. 10 Everyday Music-Use ........................................................................................ 12 Affect and Cognition........................................................................................ 16 Computer Programming and High-Cognitive Demand ................................... 21 Personality and Music-Use .............................................................................. 28 The Effect of Music on Affect ......................................................................... 30 The Effect of Personality and Music on Cognition ......................................... 33 Summary of Literature Review ........................................................................ 39 Research Questions .......................................................................................... 43 3 METHOD ........................................................................................................ 44 Participants ....................................................................................................... 44 Design and Variables ....................................................................................... 44 Measures .......................................................................................................... 45 Procedure ......................................................................................................... 52 Data Collection ................................................................................................ 55 Data Analysis ................................................................................................... 55 4 RESULTS ........................................................................................................ 59 Descriptive Results .......................................................................................... 59 Inferential Results ............................................................................................ 70 Content Analyses ............................................................................................. 77
iv
5 DISCUSSION .................................................................................................. 83 Review of the Research Questions .................................................................. 83 Review of the Content Analyses ...................................................................... 91 Limitations of the Study................................................................................... 93 Theoretical Implications .................................................................................. 95 Clinical Implications ........................................................................................ 96 Recommendations for Future Research ........................................................... 99 Summary and Conclusions .............................................................................. 100 REFERENCES ............................................................................................................ 103 APPENDIX A: Demographic Questionnaire............................................................... 111 APPENDIX B: NEO-FFI Instructions & Sample Items .............................................. 113 APPENDIX C: Job Affect Scale.................................................................................. 114 APPENDIX D: Task Assessment ................................................................................ 115 APPENDIX E: Music-Use Questionnaire ................................................................... 117 APPENDIX F: Study Advertisement .......................................................................... 122 APPENDIX G: Informed Consent Form ..................................................................... 123 APPENDIX H: Other Significant Relationships ......................................................... 126 APPENDIX I: Open-Ended Responses to Music-Use Questionnaire ......................... 133 APPENDIX J: Participant Music Selections Reported ................................................ 140
v
LIST OF FIGURES
Page
FIGURE 1: Diagram Depicting Study Variables .................................................... 46 FIGURE 2: Flow Chart Depicting Sequence of Study Measures ........................... 47 FIGURE 3: Pie Chart Depicting Proportions of Participants’ Computer Programming Proficiency ................................................... 64 FIGURE 4: Scatterplots Depicting Correlations between Openness and Cognitive and Emotional-uses of Music ....................................... 72 FIGURE 5: Pie Charts Depicting Results of Uses of Music Inventory and Directed Content Analysis ............................................................ 78
vi
LIST OF TABLES
Page
TABLE 1: Frequency of Participant Demographics ................................................. 61 TABLE 2: Frequency of Participant Programs of Study .......................................... 62 TABLE 3: Frequency of Participant Computer Programming Experience .............. 63 TABLE 4: Frequency of Participant Computer Programming Task Characteristics ................................................................................ 66 TABLE 5: Frequency of Participant Listening Experience Characteristics ............. 67 TABLE 6: t-tests for Personality Factor Sample Means and Typical Means .......... 68 TABLE 7: Means and Standard Deviations for Mood Variables and Subscales ..... 69 TABLE 8: Means and Standard Deviations for Music-Use Variables ..................... 70 TABLE 9: Correlations for Music-Use Categories with Personality Factors........... 71 TABLE 10: Multiple Regression Analysis for Personality Factors Predicting Cognitive-use of Music ......................................................... 73 TABLE 11: Multiple Regression Analysis for Personality Factors Predicting Emotional-use of Music ........................................................ 73 TABLE 12: Correlations for Music-Use Variables with Mood Variables and Subscales ....................................................... 74 TABLE 13: Correlations for Music-Use Variables with Demographics, Computer Programming Task Characteristics, and Listening Experience Variables ....................................................... 75 TABLE 14: Analysis of Variance for Effect of Computer Programming Background on Cognitive-use of Music ................................................. 76 TABLE 15: Frequency of Responses to First Open-Ended Item on Music-Use Questionnaire................................................................... 77 TABLE 16: Frequency of Responses to Second Open-Ended Item on Music-Use Questionnaire................................................................... 77
vii
TABLE 17: Conventional Content Analysis for First Open-Ended Item on Music-Use Questionnaire .......................................................... 80 TABLE 18: Conventional Content Analysis for Second Open-Ended Item on Music-Use Questionnaire .......................................................... 81 TABLE 19: Summative Content Analysis for Open-Ended Items on Music-Use Questionnaire................................................................... 82
viii
Chapter One
Introduction
Many individuals listen to music in conjunction with work tasks. Personal music
libraries are now highly accessible via mobile devices and the internet, and music-use is
more widely accepted in the workplace than in the past. What influences individuals to
use music in these instances, and how exactly is the music being used? Finite answers to
these daunting questions are not yet possible. A body of research must be conducted in a
number of domains before conclusions may be drawn. This study supports such an aim
by supplying data from computer programmers that listen to music while working.
This thesis specifically explores the relationships between music-use, personality,
and mood within the specific context of a computer programming task. By examining
various uses of music and collecting responses from student computer programmers, an
informed observation will be documented concerning the role of music in the workplace.
Such data will be a valuable contribution to the field of computer programming and
similar professions as it has, until now, been rare in literature.
Statement of the Problem
Individuals choose and utilize music for different reasons at different times.
Listening experiences depend on musical, environmental, psychological, and social
factors. Behavior and affect are influenced by these factors, and corresponding music
choices are developed for each listening experience (Cassidy & MacDonald, 2007;
Rentfrow, Goldberg, & Levitin, 2011). Active listening tends to occur during moments
of leisure, when one has the opportunity to focus entirely on the music. Occurring more
frequently, passive listening takes place when one uses music to accompany a
1
2
nonmusical event (Sloboda, 2010). For example, many students and professionals listen
to music as they complete their work. Their personalities, mood, and the nonmusical task
at hand all play a role in how, when, or if they utilize the music within the context of their
work. Listening to music likely plays a role in the quality of work that ensues, but
productivity is often difficult to quantify. Moreover, measurement of productivity is
particularly challenging when evaluating the work of individuals who complete more
cognitively demanding tasks.
Relationships between personality, mood, and context may influence why and
how a particular person makes use of music. Before applied research can assess the
effect of music-use on productivity in high-cognitive demand tasks, basic research must
explore existing relationships between music-use and a list of factors. Contextual forces,
trait inclinations, and state preferences all play a role in music-use (Sloboda & O’Neill,
2001). Contextual forces include situational goals and constraints, such as task
directives, setting, and deadlines. Trait inclinations are reflective of personality,
knowledge, and experience, while state preferences align with a particular mood or
emotion. Recent studies have begun to explore these individual differences and
contextual limitations, and there is a call for more research (Chamorro-Premuzic &
Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, & Maakip, 2009; Lesiuk, Polak,
Stutz, & Hummer, 2011; Rentfrow et al., 2011).
The present-day information age offers new and unique challenges to the
professionals responsible for creating efficient infrastructure for information. They are
asked to provide instant access to knowledge that would have been difficult to conceive
during the previous industrial age. The tasks of computer programmers require them to
3
learn new languages quickly and devise creative solutions under tight deadlines
(Sonnentag, Niessen, & Volmer, 2006; Woszczynski, Guthrie, & Shade, 2005). These
demands place a high amount of stress on computer programmers. For the companies
who employ these individuals, stress can lead to high turnover and absenteeism, low
morale, decreased productivity, workplace conflict, and poor teamwork (Longenecker,
Schaffer, & Scazzero, 1999). For the computer programmers, stress can hinder energy
levels, attitude, health, cooperation, loyalty, and commitment. They may experience
feelings of frustration, anger, aggression, helplessness, mood swings, sleeplessness,
depression, and lack of motivation. Computer programmers must be able to identify
causes of stress and develop problem-solving skills to suppress it. Such skills may be
part of a proactive personal development plan that is often missing from the job
descriptions of information technology professionals. Management and employees must
work together to conceive new solutions to improve the workplace environment,
including positive changes in morale and productivity (Longenecker et al., 1999).
Music-use during computer programming may be one simple method for
improving quality of life at work. Investigating the role of music within the context and
demands of a computer programming task has the potential to reveal the interactions
between personality, mood, and music-use. Computer programmers may have certain
personality traits that allow them to meet the high demands of their work. Their mood
may be reflective of this personality, and together these trait and state characteristics can
influence their music choices at any given time. Additionally, the high-cognitive demand
4
of computer programming tasks may have an anticipatory influence on the incoming
mood of computer programmers, manipulating the way in which these individuals choose
and utilize music to accompany their work.
Definition of Terms
High-cognitive demand. High-cognitive demand is generally defined as a “need
for focus and selective attention to systematic analysis and creative problem solving”
(Lesiuk, 2010b, p.1). Systematic analysis involves translation of information from
various domains into a working unit. Abstract planning, such as pattern recognition, is a
component of cognitive demand. During a high-cognitive demand task, relations must be
identified between domains, and organized connections must be made using domain-
specific knowledge and metacognitive knowledge. Metacognition may be described as
thinking about thinking (Sonnentag et al., 2006). Next, creative problem-solving requires
generation of ideas beyond information given. Pertinent knowledge from past
experiences is retrieved from memory and combined with the given information to form a
cohesive plan (Forgas, 1998; Forgas & George, 2001). According to Piaget's cognitive
development theory, systematic analysis and creative problem-solving fall into the formal
operations level of cognition, which is the highest level one can reach. Formal operations
involve logic, abstract thinking, hypothesis generation, systematic problem-solving, and
mental manipulation (Nairne, 2009a). Because individual cognitive skills exist on a
continuum, the amount of cognitive demand necessary to be deemed 'high' is relative
(Lesiuk, 2010b).
5
Music-use. Music-use describes why, how, when, and where music is used. This
thesis comprises three music-uses, including 1) background or social, 2) cognitive,
intellectual, or rational, and 3) emotional (Chamorro-Premuzic & Furnham, 2007).
Music-use for background and social purposes may be used while working, studying,
socializing, or performing a task. Music-use for cognition may be used in an intellectual
way, including analysis and evaluation. Emotional-use of music involves regulation of
mood (Chamorro-Premuzic et al., 2009). Music-use also includes the circumstances
under which an individual uses music. Music may be played using a portable device
through headphones or on an external device through speakers. Individuals may listen to
music while completing various tasks in their home, at work, in the library, or on-the-go.
Music may be used before, during, or after a task. People listen to music in specific
sequences for various lengths of time (Lesiuk et al, 2011).
Personality. Personality consists of stable trait characteristics that are both
behavioral and emotional. Personal goals influence behavior, and motivation is believed
to underlie these characteristics (Depue & Collins, 1999). Individuals display varying
degrees of intensity for each personality trait. These individual differences define each
unique personality, and this individual makeup may be summarized using five
dimensions: neuroticism, extraversion, openness to experience, agreeableness, and
conscientiousness. The dimensions comprise the Big Five personality factors, as
theorized by Costa and McCrae (1992). First, neuroticism involves a tendency to
experience negative affect and have irrational ideas. These qualities are linked to poor
impulse control and coping skills. Second, extraversion is associated with active social
tendencies, loquaciousness, outward confidence, and cheerful disposition. Third,
6
openness to experience involves active imagination, curiosity about oneself and the
world, and independence of judgment that is often associated with creativity. Fourth,
agreeableness is concerned with interpersonal tendencies, including sympathy, empathy,
and willingness to help others. Last, conscientiousness includes willpower,
determination, self-discipline, and reliability. These qualities help one to control
impulsiveness (Costa & McCrae, 1992).
Mood. Mood is impacted by context, or state dependent motivating factors.
These factors include a level of activation, intensity, or arousal. Hedonic value, or
pleasure, is another component of mood. Mood may be described as positive or pleasant,
or by contrast, negative or unpleasant (Berlyne, 1971a, 1971b; Gfeller, 2005; Thaut,
2005). Mood is a psychological event with a neurophysiological response and an
expressive reaction. A conscious subjective experience or feeling occurs congruently
with these responses and reactions (Nairne, 2009b). This thesis defines mood as a level
of positive or negative affect intensity. Positive affect is further categorized using
subscales for relaxation and enthusiasm. Negative affect subscales include nervousness
and fatigue (Oldham, Cummings, Mischel, Schmidtke, & Zhou, 1995).
Need for the Study
Theoretical relevance. This thesis has both theoretical and practical relevance.
Theoretically, this research enhances knowledge about why individuals naturally use
music in relation to a cognitive task. The study design allows for “actual, behaviorally-
determined music usage” based on situational restraints (Chamorro-Premuzic, Swami,
Furnham, & Maakip, 2009, p. 26). Results of this thesis further the understanding of the
relationship between music, personality, mood, and cognition. By providing knowledge
7
about their personalities, moods, music-uses, and the contexts within which student
computer programmers work, this thesis informs future studies that attempt to measure
the effects of music on behavior and affect (Cassidy & MacDonald, 2007). Research that
investigates the relationship of personality, mood, and music-use among computer
programmers is limited. A research model has been proposed, however, which accounts
for the influence of personality differences in the interaction between person and
environment, with regard to mood and music-use (Lesiuk et al, 2009). Personality testing
increases knowledge of trends among computer programmers, revealing diverse ways of
thinking and working (Woszczynski et al., 2005). An assessment of mood prior to
music-use and the music listening choices that follow provide a supplement to existing
research on the mood and music interaction (Rentfrow et al., 2011). Furthermore, studies
that collect data on music-use in general are scarce in the literature.
Additionally, through a review of current research literature, this thesis provides
information on the nature of computer programming, including the cognitive demands,
associated stress, and its role in the systems development life cycle. The literature review
also explores the relationships between mood and cognition, music and mood, music and
cognition, and personality and mood.
Practical relevance. The thesis topic informs the areas of music therapy,
cognitive psychology, organizational psychology, information technology, music
marketing, and industrial and organizational design in the workplace. Practically, this
research provides insight into the role of music-use within the context of a high-cognitive
demand work task, including the impact of one’s personality and incoming mood state.
This study explores the music-use and working context of student computer programmers
8
(Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, &
Maakip, 2009). The information provided in this thesis is valuable to individuals
interested in music as a commodity and music as a resource. Studies of individual
differences and music-use have relevance to music recommendation services. Analysis
of past listening choices may allow personal music libraries to be grouped in terms of
structure, energy, emotion, and intended use. Consequently, these categorized music
libraries may become a resource for improving well-being, productivity, and generally,
bettering quality of work and life. Songs may be automatically selected for the age,
gender, education, and income of the listener (Rentfrow et al., 2011).
Music may also be used as a resource for computer programmers, influencing
human factors that affect their work. Music-use has been linked with increases in
positive mood and subsequent improvement in work performance of computer
information systems developers (Lesiuk, 2005, 2010b). Quality of work improved and
task length decreased when participants were able to choose the music and the
circumstances under which they listened. They reported that music was valuable for
mood, perception, and thought enhancement (Lesiuk, 2010a). Through an examination
of personality, mood, and music-use within the context a high-cognitive demand task,
this thesis is taking into consideration the person-environment fit (Lesiuk et al., 2011).
Such a fit occurs when an individual successfully adjusts to their surroundings,
constructing a preferred environment or adapting it to their needs in the moment.
9
Purpose Statement
The purpose of this study was to investigate the ways in which student computer
programmers use music while programming. Personality, mood, and music-use data
were collected in connection with a high-cognitive demand task – computer
programming. Uses of music were related to personality and mood variables.
Additionally, other variables were considered as factors related to music-use. These
variables included demographics, computer programming and musical experience, and
contextual factors.
Chapter Two
Review of Literature
This chapter will review research literature pertinent to understanding the role of
personality and mood in music-use during a high-cognitive demand task – computer
programming. The review will begin by explaining music perception and clarifying
everyday music-use. Personality and affective response models will also be described as
they apply to cognition. Additionally, the cognitive demands of computer programming
will be described in detail. Literature that explores relationships between personality,
affect, music, and cognition will then be reviewed. The final section of the chapter will
summarize research that tests the effect of music on high-cognitive demand task
performance. This literature review will provide rationale for surveying the everyday use
of music during high-cognitive demand tasks, including personality and mood accounts
from the individuals who use the music.
Music Perception
Music is processed bilaterally in several areas of the brain. Researchers identify
these areas by monitoring physiological responses, utilizing brain imaging techniques,
and studying the development of affective responses to music (Trainor & Schmidt, 2003).
Music perception comprises neocortically mediated cognitive processes and subcortically
mediated affective responses.
Neocortically, music activates areas in the temporal and frontal cortices, including
Broca’s area in the left hemisphere (Peretz & Coltheart, 2003; Peretz & Zatorre, 2005).
The frontal cortex is an area associated with high levels of cognition, or executive
functions. Creative problem-solving and abstract thinking are examples of executive
10
11
functions. This neural evidence also suggests that listening to music involves cognitive
processes of attention and working memory (Lesiuk, 2010b). Additionally, the
cerebellum and basal ganglia work together with motor cortical areas to process time
relations in music (Peretz & Coltheart, 2003; Peretz & Zatorre, 2005).
Affective response to music involves subcortical structures and psychological
theory. Music activates areas of the brain associated with pleasure and reward. These
areas include the anterior cingulate gyrus, dorsal midbrain, ventral tegmental, nucleus
accumbens in the ventral striatum, orbitofrontal cortex, amygdala, hippocampus and
insula (Blood & Zatorre, 2001). In addition to music, this brain reward system responds
to other highly rewarding stimuli, such as food, sex, and drugs. When music has high
hedonic value, arousal occurs. With arousal, increased physiological activity is expressed
in the autonomic nervous system, neuroendocrine system, and central nervous system.
Examples of physiological functions include cerebral blood flow and endorphin release
(Goldstein, 1980; Rickard, 2004; Trainor & Schmidt, 2003). The arousal system is
comprised of the brain stem reticular formation, hypothalamus, and the thalamus. The
thalamus provides a relay between subcortical structures and neocortical structures
(Blood & Zatorre, 2001).
Humans seek arousal, and according to Berlyne’s (1971a) optimal arousal theory,
music’s value or emotional meaning is derived from the arousal properties of structures
in the music itself. Three music stimulus properties have been determined. First,
psychophysical properties are the structural aspects of music, including spatial and timing
ranges, which portray levels of energy. Tempo and amplitude are examples of
psychophysical properties. Next, collative properties are patterned descriptors, such as
12
complexity and novelty. These musical patterns allow a listener to build expectations,
providing opportunities for surprise or familiarity, depending on how the music stimuli
progresses. According to Meyer’s (2001) expectation theory, arousal occurs when
expectation is inhibited. An individual’s experience of music is derived from his or her
affective response to the music, which is a function of a relationship within the music
itself. Listeners bring with them a vast body of musical experiences that condition their
response to music as it unfolds. Music’s evocative power derives from its capacity to
generate, suspend, prolongate, or violate these expectations. The final music stimulus
property, ecological, refers to the extramusical associations one makes with the music,
which assist in the perception and interpretation of emotional processing (Berlyne,
1971b).
Everyday Music-Use
Music-use cannot be accurately evaluated without the inclusion of context
(Rentfrow et al., 2011). Sloboda and O'Neill (2001) recommend that studies of music-
use include contextual factors, trait preferences, and state preferences. In this thesis, a
difficult computer programming task is part of the context in which personality, mood,
and music-use interact. These social, psychological, musical, and environmental factors
all have an influence on the effects of music on behavior and affect.
Sloboda (2010) has established 10 dimensions of “everyday” music, each of
which contributes to defining the context of music-use. Everyday music-use tends to be
relatively mundane and trivial, providing accompaniment to some other nonmusical
activity (North, Hargreaves, & Hargreaves, 2004). Each dimension of everyday music is
thought to have influence, nonetheless, on affective response to music.
13
The first two dimensions are related to one’s past use of music in everyday
contexts. First, frequency of occurrence, refers to the regularity of music-use. This
dimension’s influence on affect is dependent on the experience and habits of the listener.
For example, music-use that occurs frequently lacks the element of surprise, and
therefore may elicit a weaker affective response (Meyer, 2001; Sloboda, 2010). Second,
ordinariness versus specialness of the context or experience, refers to the social or
cultural weight of music-use. When music is used in everyday situations, its personal
significance to the listener and the associated affective response are likely to be
attenuated. Consequently, memory for this type of music experience may be diminished
(Bower, 1981; Levene, 1997; Levine & Safer, 2002).
The next three dimensions of everyday music are dependent on factors in the
present that impact music-use. First, location of occurrence, refers to the setting in which
music-use takes place. Everyday music-use occurs at home and in public places, where
significant distractions and changes in experience may occur. Affective responses are not
predictable in situations with such fluctuations (Sloboda, 2010). Next, circumstance of
exposure: the role of choice, refers to the listener’s hierarchical adjustment of musical
choice, as a result of factors outside of the listener’s control. Individuals are not always
given the choice of what to hear in everyday life. Even when music is chosen, the
unpredictable nature of everyday experience may lower the priority of that choice. Such
lack of control over music choice may yield a negative affective response (Sloboda,
2010). Nature of transmission, the fifth dimension, refers to the physical source of the
14
music. Most everyday music is recorded, being transmitted by an audio device.
Therefore, listeners are not pondering the person or instrument producing the music, and
social emotions are not included in their affective response (Sloboda, 2010).
The centrality of music to the experience and the salience of the context comprise
the sixth dimension of everyday music. The nonmusical activity requires more attention
relative to the music in everyday circumstances. Thus, the concurrent affective response
is likely to be less dependent on the music and more reflective of the nonmusical activity.
Additionally, individual variation in affective response is to be expected, because the
interaction between music activity and nonmusical activity differs for each individual
(Sloboda, 2010).
The seventh dimension, the nature of the music, involves the imprecise distinction
between art music and vernacular music. An assumption is that everyday music-use
typically includes vernacular music or art music that has been in some way sliced into
smaller sections. A second assumption is that a function of vernacular music is to
provide recognizable symbols with clear affective meaning. Given these assumptions,
the aesthetic affective response to everyday music may be understated (Sloboda, 2010).
Research considerations and strategies are also addressed when investigating
everyday use of music. First, the method of investigation, Sloboda’s (2010) eighth
dimension, recommends post-hoc interviews or questionnaires to capture affective
response to everyday music. Next, the intellectual stance of writer/research suggests that
researchers take a specific perspective when assessing everyday music-use. Investigators
should take the point of view of the consumer, who is as interested in activities where
music is accompaniment as he is in activities where music is the focal point. Finally, the
15
contextual specificity of judgment obtained distinguishes studies of everyday music-use
from typical studies of musical preference. In everyday music-use studies, preferences
must be linked to context. Furthermore, a final assumption is that everyday music plays a
functional role in an individual’s affective goal achievement. Thus, studies of everyday
music-use must measure affective response relative to mood regulation, instead of
affective response related to stable traits or attitudes (Sloboda, 2010).
To summarize, music does not appear to have a homogenous effect on the
listener. The context determines the value of the musical experience, and reasons for
using music change based on one’s present motivation for listening (Lamont & Greasley,
2009). Each listening experience involves cultural preconceptions about which type of
music is suitable for a particular circumstance, and several reciprocal feedback
relationships exist between stimulus characteristics, the listener, and the situation.
Additionally, affect-optimization may underlie the reasons for using music, in order to
maximize the listening experience (Sloboda, 2010). Consequently, individuals may
decide to use music in various situations with differing levels of engagement. Choice of
music within a context occurs with little thought, nonetheless, providing a cognitively
undemanding accompaniment to a task (North et al., 2004).
Everyday music-use may be categorized in terms of function. Three music-use
categories have been established: background or social use of music; cognitive,
intellectual, or rational use of music; emotional use of music (Chamorro-Premuzic,
Fagan, & Furnham, 2010; Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic,
Gomà-i-Freixanet, Furnham, & Muro, 2009; Chamorro-Premuzic, Swami, Furnham, &
Maakip, 2009). These categories describe why music is used within a context, and they
16
were derived from the Uses of Music Inventory (Chamorro-Premuzic & Furnham, 2007).
The items on this self-report questionnaire were created through a review of literature and
qualitative pilot testing. During focus groups and open interview sessions, open-ended
and non-directive questions were used to elicit opinions about music, when it was
listened to, and why. Thematic analysis then took place to derive categories. Thus, the
authors determined that music-use can be divided into three categories, including
background to other activities, cognitive/intellectual use of music, and
manipulation/regulation of emotions. Examples of background-use include listening to
music while working, studying, socializing, or performing a task. Cognitive-use involves
listening to music in an intellectual way to improve focus and increase cognitive
efficiency. Emotional-use refers to the extent to which an individual uses music to
regulate emotions (Chamorro-Premuzic & Furnham, 2007).
Affect and Cognition
Affect, whether positive or negative, is a general term that encompasses both
emotion and mood. Emotions are brief and tied to a stimulus event (Sloboda & Juslin,
2010). Moods are generally longer lasting than emotions, providing a tonic-affective
background that can change the likelihood that a particular emotion will occur. Mood
may occur as a result of its significance, meaning, and reward or aversive nature in
connection with a neural response (Davies, 2010). A circumplex model has been used to
place mood variables along a horizontal axis of activation and vertical axis of valence
(Russell, 1980; Sloboda & Juslin, 2010; Zentner & Eerola, 2010). Additionally, a
connection between the thalamus and the amygdala, which bypasses the thalamocortical
17
pathway, provides evidence that mood may be experienced in a preconscious or
automatic manner. Therefore, one does not have to be conscious of mood to experience
an affective response (Sloboda & Juslin, 2001).
Literature has linked affective response with cognition in a number of domains.
Mild positive affect may be induced in subtle and common ways, using conventional
methods, and having a constructive impact on social behaviors and thought processes.
Examples of affect induction methods include the presentation of a gift or reward,
suggestive thinking, and affect-laden stories, videos, or music (Amabile, Barsade,
Mueller, & Staw, 2005). When individuals experience mild positive affect, they are able
to think more clearly to make choices that are socially responsible and helpful. These
individuals tend to enjoy what they are doing, thus finding more motivation and openness
to accomplish goals (Isen, 2009).
Mild positive affect increases the availability of cognitive material for processing,
which in turn increases the number of cognitive elements available for association (Isen,
2009). Attention is influenced by affect, particularly in terms of breadth. Mild positive
affect helps to defocus attention, expanding the cognitive context, consequently adding to
the scope of available cognitive elements. Mild positive affect may also influence
flexibility focus, a process by which one simultaneously takes a broader focus without
losing focus on task details (Isen, 2009).
Individuals with mild positive affect perceive information more carefully and
fully, and they are able to consider numerous aspects of a situation at once. These
individuals also choose and evaluate behaviors in light of the situation and task demands
(Ashby, Isen, & Turken, 1999; Isen, 2009). Flexible thinking describes the ability to
18
identify reasonable connections, consider multiple perspectives and solutions, and
prioritize as needed to address a problem. These cognitive processes are necessary for
problem-solving that involves multiple goal considerations. Mild positive affect
facilitates flexible thinking, which translates into creativity and effective problem-solving
(Estrada, Isen, & Young, 1997; Forgas, 1998; Forgas & George, 2001). Creative
problem-solving requires the generation of ideas beyond information given. In this
process, information is retrieved from memory and systematically combined with the
given information (Isen, 2009).
Mild positive affect also has a relationship with expectation, particularly in terms
of motivation. Expectancy is a cognitive process by which one determines the valence of
an outcome. Cognitive effort is then calibrated, based on the evaluation (Isen, 2009).
Expectancy theory predicts that individuals are motivated by the expectation of obtaining
a positive outcome, or reward. When an individual’s expectation has a negative outcome,
motivation is less likely to occur. Motivation is also tied to cognitive performance. In a
study that explored expectancy and motivation in university students, positive affect was
induced and tested for its effect on cognitive performance (Erez & Isen, 2002). Affect
was measured using a Likert scale, which assessed the degree to which participants
experienced different feelings. Motivation was calculated in three components, including
valence, expectancy, and instrumentality. Expectancy refers to the appraisal of rewards
and involves estimations about the strength of relationships between effort and
performance. Instrumentality refers to similar evaluations and hypotheses about links
between performance and outcome. Participants in this study solved anagrams to
measure cognitive performance. Results showed that positive affect facilitated both
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motivation and performance. Given their propensity to anticipate a positive outcome,
individuals with mild positive affect were more effortful and persistent (Erez & Isen,
2002).
The relationship between negative affect and cognition has also been documented.
Negative affect is associated with pervasive negative emotionality and self-concept.
Individuals with high negative affect tend to focus excessively on negative aspects of
themselves, others, and the world. Compared to individuals with low negative affect,
these individuals are more likely to experience significant levels of distress in any
situation (Brief, Burke, George, Robinson, & Webster, 1988; Isen, 2009; Watson &
Clark, 1984). Negative affect also includes a prevalence of unpleasant arousal.
Unpleasantness and overly high levels of arousal narrow attention and prevent processing
resources from effectively influencing a decision task (Mano, 1992; Watson & Tellegen,
1985). Additionally, negative affect is more narrowly represented in long-term memory
than positive affect. In other words, memories with a positive affective tone are more
prevalent in long-term storage. Therefore, negative affect is not as useful as a memory
retrieval cue, when compared to positive affect (Isen, 2009). Also, in a study that
explored the association between affect and creativity in adult professionals, positive
affect was more commonly associated with creativity than negative affect (Amabile et al.,
2005).
A dopaminergic theory also provides evidence for the relationship between affect
and cognition. Increases of the neurotransmitter, dopamine, in the anterior cingulate
gyrus occur in relation to mild positive affect. The anterior cingulate gyrus is involved in
episodic long-term memory as well as working memory functions. A rush of dopamine
20
to this area also influences frontal brain regions involved in problem-solving, cognitive
flexibility, and other neural areas identified with thinking and working memory (Ashby et
al., 1999; Isen, 2009).
Stressful situations play a role in the relationship between affect and cognition
(Brief, Butcher, & Roberson, 1995). In a study of negative affect and job related stress in
adult professionals, several significant correlations resulted. Negative life stress was
negatively correlated with life satisfaction and positively correlated with symptoms of
depression. Negative affect was positively correlated with negative life stress. Negative
affect was also positively correlated with indices of distress and negatively correlated
with indices of satisfaction on both the life and job satisfaction measures (Brief et al,
1988). A later study also addressed the connection between affect and job satisfaction
among adult professionals. As hypothesized, a significant negative correlation occurred
between negative affect and job satisfaction, and a significant positive correlation
occurred between positive affect and job satisfaction (Fisher, 2000).
Personality, affect, and cognition. Trait personality and state dependent
affective response appear to interact. Dopaminergic theory also accounts for individual
differences in personality, including an association with affect. Individuals who are high
in extraversion are assumed to have relatively stable levels of positive emotionality,
particularly in the form of incentive motivation. These individuals may have increased
dopamine activity in the ventral tegmental area, which is part of the brain reward system,
but this theory is still under review (Depue & Collins, 1999). Therefore, more research is
necessary to explain the relationship between personality and affect.
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Rusting (1999) studied the interaction between personality and affect, including
an examination of their influence on emotion-congruent memory and judgment, in
university students. The study measured personality in terms of extraversion and
neuroticism and affect in terms of positive affectivity and negative affectivity. Findings
revealed that extraversion and positive affectivity were linked to positive memory
retrieval and a tendency to make positive judgments. Neuroticism and negative
affectivity were linked to negative memory retrieval and a tendency to make negative
judgments. Due to several significant personality and mood effects on judgment and
memory, the influence of personality and mood did not appear to be independent of one
another (Rusting, 1999). Personality and affect indices tend to be correlated with one
another, in fact, making it difficult to distinguish the independent effects of stable
personality and transient mood on cognition (Rusting, 2001). Again, the relationship
between these variables must continue to be tested.
Computer Programming and High-Cognitive Demand
A computer program consists of objects, calculations, and procedures. In object-
oriented programming, which is the most prominent programming paradigm, a computer
program is a hierarchy of so-called “objects,” each of which is composed of data fields
and methods. Each of the data fields are either a primitive data value or an instantiation
of an object, and each of the methods is a series of operations on the data fields.
Computer programming is the process of translating calculation or procedure
specifications into such a hierarchy (Dennis & Wixom, 2000).
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To understand a program and the underlying problem, computer programmers
must find relations and create connections between various cognitive domains.
Grammatical rules exist for creating a program, requiring knowledge and efficient
retrieval of syntax guidelines, semantics, and schemata. Computer programming requires
“focus and selective attention to systematic analysis and creative problem-solving”
(Lesiuk, 2010b, p. 137). Computer programmers must be able to organize domain-
specific knowledge and meta-cognitive knowledge. Then, they must also engage in
domain-specific problem-solving focused on abstract concepts and goals, including
abstract planning and evaluation (Sonnentag et al., 2006). Computer programming
strategies are diverse, having been described as top-down versus bottom-up, forward
versus backward development, or breadth-first versus depth-first (Détienne & Bott,
2002).
Based on present-day cognitive theories, computer programming is a high-
cognitive demand task. According to Piaget’s cognitive development theory, the formal
operations level of cognition is necessary to complete computer programming tasks
(White & Sivitanides, 2005). This level of cognitive development is the highest level one
can reach. Formal operations cognition involves logic, abstract thinking, hypothesis
generation, systematic problem-solving, and mental manipulation (Nairne, 2009a).
The cognitive demand of computer programming may also be described using
cognitive load theory, which is built on the limitations of working memory and involves
three types of cognitive load (Garner, 2002). First, intrinsic cognitive load refers to the
mental demands of a task. When separate schemata are able to be processed serially,
rather than simultaneously, low intrinsic cognitive load occurs. Computer programming
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has high intrinsic cognitive load, as it requires interactive processing between multiple
schemata at once. Next, extraneous cognitive load is dependent on the format of
instruction used to teach and learn a process. Computer programming training methods
have been developed to reduce extraneous cognitive load, because a combination of high
extraneous and high intrinsic cognitive load may exceed the capabilities of working
memory. Extraneous cognitive load may be lessened, for example, by using computer
programming textbooks that integrate diagrams and text. Last, germane cognitive load
refers to the conscious processing of unused working memory to construct schemata in a
particular domain. When adequate instructional design has limited extraneous cognitive
load, conscious processing of abstract schemata is made possible, increasing germane
cognitive load. High germane cognitive load is desirable for computer programming, and
training techniques have been developed to promote this type of cognitive load. A
method of encouraging high germane cognitive load is the use of incomplete worked
examples, which require students to use abstract thinking to fill in missing schemata
(Garner, 2002).
The high-cognitive demand tasks of a computer programmer are part of the
systems development life cycle (Valacich, George, & Hoffer, 2006). This ongoing cycle
of activities can be divided into four phases. During the initial systems planning and
selection phase, systems analysts identify, prioritize, and organize system needs into a
written plan for development. Then, during the systems analysis phase, systems analysts
and developers study, dissect, and combine existing systems to produce alternatives that
may be considered within the cost, labor, and technical levels of the project. Next, during
the systems design phase, systems developers and software engineers describe a new
24
system, including a logical design and a physical design. The logical design shows the
system’s function in a business organization, and the physical design shows the technical
specifications to be followed when programming and constructing the system. During
the final systems implementation and operation phase, the system is coded, tested, and
installed by computer programmers. The system is also maintained and repaired as
necessary to keep it running and useful for the end user (Valacich et al., 2006).
Computer programmers do the bulk of their work during the systems
implementation and operation phase, but they may also contribute during the systems
analysis and systems design phases. In essence, computer programmers translate a
software program design that they receive from computer software engineers and systems
analysts into a series of instructions that the computer can comprehend (Valacich et al.,
2006). Computer programmers write (or code) these instructions using a number of
computer programming languages, and they choose a language based on the design and
personal preferences. Commonly used programming languages include C++, JAVA, and
Python (Bureau of Labor Statistics, U.S. Department of Labor, 2009).
Computer programmers must also update, modify, expand, and repair existing
programs. They may use automated computer applications and libraries of basic code to
increase productivity and reliability. Computer programmers may also work in teams to
complete a program. Like software engineers and systems analysts, computer
programmers must interact with the end users. Computer programmers receive feedback
from these individual or commercial users, and they may be asked to design or redesign
part of the program to meet customer needs (Bureau of Labor Statistics, U.S. Department
of Labor, 2009; Norton, 1997; Woszczynski et al., 2005).
25
The personality of computer programmers. Some personality traits are typical
among computer programmers. Although music psychologists often use a five-factor
personality inventory to collect personality data, information technology studies that
utilize this structure are scarce in the literature. The Myers-Briggs Type Indicator
(MBTI) is another prevalent personality scale, and this measure has been used in
information technology research (Myers, 1962; Myers, McCaulley, Quenk, & Hammer,
2003). The MBTI consists of four personality dichotomies: extraversion (E)/introversion
(I), sensing (S)/intuition (N), thinking (T)/feeling (F), and judging (J)/perception (P).
Preferences for the E/I dichotomy are called attitudes, S/N and T/F preferences are called
functions, and J/P preferences are called lifestyle. Individuals with a preference for
extraversion are social, action-oriented, and pursue quantity over quality. Individuals
with a preference for introversion are independent, thoughtful, and detail-oriented. The
functions preference pairs are dichotomous, and each pair is also related to a lifestyle
preferences. First, the sensing/intuition functions are related to perception, in that they
define how an individual comprehends new information. Individuals with a preference
for sensing trust what they perceive using the five senses, relying on fact over instinct,
while individuals with a preference for intuition place trust in theory and possibilities.
Next, the thinking/feeling functions are related to judging, in that they define how an
individual makes decisions. Individuals with a preference for thinking base judgments on
reason and logic, while individuals with a preference for feeling make choices using
empathy and seeking harmony. Completion of the MBTI results in one of 16 personality
“types,” which includes four letters, one from each of the dichotomies (e.g. ISTP).
26
Results of the MBTI identify inherent personality tendencies, and trained administrators
of the measure explain how to either accept and foster these tendencies, or compensate
for and modify them as necessary (Myers et al., 2003).
The cognitive ability to problem-solve is related to the sensing/intuition and
thinking/feeling functions (Myers et al., 2003). In a study that related the personality
profiles of novice computer programming students to final average grades in an entry
level computer programming course, the specific preferences of intuitive thinkers were
more abundant than the other preferences, including intuitive feelers, sensor thinkers,
and sensor feelers (Woszczynski et al., 2005). Based on an analysis of variance
(ANOVA), students with different personality profiles scored differently in programming
principles I, which is a typical introductory course in computer programming. The
intuitive thinkers’ final average was higher than averages for intuitive feelers, sensor
thinkers, and sensor feelers preferences respectively. The intuitive thinkers’ final average
was also significantly higher than the sensor feelers’ average at the 0.05 level. Thus, the
intuitive thinker preference may be common among computer programming students who
progress beyond the novice level of proficiency. Since no other significant differences
occurred in this study, however, diverse personality profiles are to be expected in this
population (Woszczynski et al., 2005)
To assess differences between novice and expert computer programmers, a study
used the MBTI to compare the personality profiles of undergraduate computer
programmers to professional computer programmers (Kenner, 1993). Significant
differences emerged between undergraduates and professionals in the sensing-feeling and
27
introversion-intuition-thinking-judging preferences. Results showed that the groups were
similar, however, for introversion, thinking, judging, and intuition-feeling preferences.
Again, results of this study increase the expectation for diverse personality profiles
among computer programmers, and diversity may occur at various levels of proficiency
(Kenner, 1993).
Another exploratory pilot study that used the MBTI to examine 32 computer
information systems developers found a prevalence of introversion, thinking, and judging
preferences (Lesiuk, Pons, & Polak, 2009). Also, introversion preferences outnumbered
extraversion preferences two to one in this sample, which is opposite to the trend of the
general population. Individuals with a preference for introversion are reported to select
occupations that demand sustained attention to and interest in concepts and ideas, while
individuals with a preference for extraversion tend to select occupations that have a
primary focus on people (Myers et al., 2003). These natural inclinations may make the
field of information technology more attractive to individuals with a preference for
introversion, with positive ramifications on quality of work. In a later study of systems
analysts, in fact, individuals with a preference for introversion performed better on a
work performance task (Lesiuk et al., 2011). The relationship between personality and
mood in information technology professionals is another recently documented subject of
research. The exploratory pilot study by Lesiuk et al. (2009) examined personality and
mood in computer information systems developers. The negative trait mood was greater
for individuals with preferences for introversion and feeling. Then, Lesiuk et al. (2011)
found a link between personality and mood in the sample of computer systems analysts.
When individuals who scored high on the conscientiousness personality factor listened to
28
music, which was hypothesized to improve mood, they reported a significant reduction in
fatigue over time. Decreased fatigue is an indicator of reduced negative affect (Lesiuk et
al., 2011).
To summarize, some personality preferences may be expected among information
technology professionals, including introversion, intuition, thinking, and judging (Lesiuk
et al., 2009; Woszczynski et al., 2005). Introversion has an association with an
individual’s propensity to choose occupations that require interest in and sustained
attention to concepts and ideas (Myers et al., 2003). A positive relationship between
introversion and work performance has also been shown (Lesiuk et al., 2011). Intuition
and thinking preferences represent the MBTI functions, which are related to one’s ability
to problem-solve (Myers et al., 2003). Additionally, personality has an interaction with
mood. Introversion and feeling preferences have been positively linked with negative
trait mood, and the conscientiousness personality factor has been negatively linked with
fatigue (Lesiuk et al., 2009, 2011).
Personality and Music-Use
A series of studies were conducted across cultures using the Big Five personality
variables and the Uses of Music Inventory music-use categories (Chamorro-Premuzic &
Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;
Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). In these studies, university
students in America, Great Britain, Spain, and Malaysia completed measures of
personality and music-use. General findings revealed that of the Big Five personality
variables, neuroticism, extraversion, and openness to experience were most closely
related to music-use.
29
In the first study with university students in America and Great Britain,
neuroticism was positively correlated with emotional-use of music, extraversion was
negatively correlated with emotional-use of music, and openness to experience was
positively correlated with cognitive-use of music. Additionally, conscientiousness was
negatively correlated with emotional-use of music. All relationships were significant
with p values less than 0.01 (Chamorro-Premuzic & Furnham, 2007).
In a replication study in Spain, neuroticism was positively correlated with
emotional-use, and openness to experience was positively correlated with cognitive-use.
Contrary to the results of the first study, however, extraversion was positively rather than
negatively correlated with emotional-use in the second study. Extraversion was also
positively correlated with background-use of music. All relationships were significant
with p values less than 0.01 (Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,
2009). The results of a replication study in Malaysia were the same as the results in
Spain, but only the p value for the correlation between neuroticism and emotional-use of
music was less than 0.01. The other correlations had p values less than 0.05 (Chamorro-
Premuzic, Swami, Furnham, & Maakip 2009).
Personality also plays a role in aspects of music-use outside the three music-use
categories. In typical individuals, Cassidy and MacDonald (2007) explored perceptions
of the effect of music-use using open-ended questions. Individuals with a preference for
introversion reported choosing music to reduce anxiety, including both psychological and
physical symptoms. Extraverted types were less aware of these positive anxiolytic effects
of music, when compared to introverted types. Individuals with a preference for
extraversion also reported being less distracted by background music, when compared to
30
introverted types (Cassidy & MacDonald, 2007). Later, Lesiuk et al. (2009) monitored
listening habits in a pilot study of computer systems designers, and daily average
listening time differed between personality types. Individuals with preferences for
extraversion listened to twice as much music as introverted types, and feeling types
listened to twice as much as thinking types.
The Effect of Music on Affect
To strengthen the neurologic and theoretical association between music and
affective response, researchers have utilized various methods of investigation to measure
the effect of music on state affect. Eich, Ng, Macaulay, Percy, and Grebneva (2007)
explored methods of modifying mood, including endogenous (e.g., naturally occurring)
and exogenous (e.g., induced/experimental) moods. The investigators listed desirable
attributes of mood-induction as criteria for a specific mood-modification technique,
abbreviated MCI, which was tested in a series of experiments. The technique was
assessed by measuring these qualities, including success rate, time to criterion, ratings of
pleasure and arousal, ratings of positive and negative affect, and ratings of mood
genuineness. Ratings were expected to remain stable over time, and moods were
expected to be reliably induced more than once (Eich, et al., 2007).
Using the MCI method for each experiment, participants were induced into very
pleasant or very unpleasant moods by listening to merry versus melancholic music while
contemplating elating or depressing thoughts about real or imaginary people, places, or
events. Data were collected periodically, and each experiment was conducted twice.
Participants logged their pleasure and arousal levels using a visual matrix, and they also
31
provided self-ratings for feeling very pleasant or very unpleasant regardless of arousal.
Additionally, participants rated positive affect, negative affect, and mood genuineness
throughout each experiment (Eich et al., 2007).
The MCI method was 87 percent successful, having a strong effect on mood,
arousal, positive affect, and negative affect. High ratings of mood genuineness suggested
that authentic moods were being experienced during the experiments. Induced moods
were deemed strong, stable, sincere, and reproducible. The authors concluded by making
suggestions for improvement with future mood-modification techniques, including the
selection of more appropriate musical selections or music that is preferential to the
participants (Eich et al., 2007).
Mitterschiffthaler, Fu, Dalton, Andrew, and Williams (2007) used Western
classical music to induce happy, sad, and neutral moods, and they monitored activity in
the brain using functional magnetic resonance imagining (fMRI). Results showed that
responses to happy music activated the ventral and dorsal striatum, anterior cingulate
gyrus, parahippocampal gyrus, and auditory association areas. Response to sad music
was represented in the hippocampus, amygdala, and auditory association areas. Neutral
music activated the insula and auditory association areas. Many of these structures have
been identified with general affective response (Blood & Zatorre, 2001). Specifically,
the medial temporal areas are associated with the appraisal and processing of emotions.
Additionally, the ventral and dorsal striatum are active during reward experience and
movement, and the anterior cingulate gyrus is involved in targeting attention.
(Mitterschiffthaler et al., 2007).
32
Knight and Rickard (2001) investigated the effect of relaxing music on subjective
and physiological response to stress and anxiety. Undergraduate students were exposed
to silence or Pachelbel’s Canon in D major while completing a stressor task – preparation
for an oral presentation. Subjective anxiety, heart rate, blood pressure, cortisol level, and
salivary content were measured at rest and after presentation of the stressor. Subjective
anxiety, heart rate, and systolic blood pressure increased significantly with the stressor.
These increases did not occur when music was present. Even in the absence of stressor,
baseline salivary IgA significantly decreased with music, which is an indication of
reduced stress. This study provides evidence for music as an anxiolytic treatment for
physiological symptoms (Knight & Rickard, 2001).
The relationship between music and affect has also been studied by measuring the
effect of mood on music-use. Greenwood and Long (2009) utilized a series of measures
to test whether individual differences in mood were predictive of mood-specific music-
use in typical individuals. Participants rated how often they used media, such as music or
television, when they recalled the experience of various mood states. Individual
differences in emotion regulation were also measured, along with an assessment of
rumination and reflection tendencies. Three music-use categories emerged during factor
analysis, including music-use in a positive mood, music-use in a negative mood, and
music-use when one is bored. The findings suggest that some individuals are generally
inclined to use music for mood regulation, regardless of whether they are in a positive or
negative mood. Also, predictive relationships emerged between both the negative mood
and bored conditions and music-use for mood regulation (Greenwood & Long, 2009).
33
The Effect of Personality and Music on Cognition
A few studies have been conducted to observe the relationship between
personality, music, and cognitive tasks. In a study of personality and the effect of
background music and background noise on task performance, participants completed an
immediate and delayed recall test, and the Stroop Neuropsychological Screening Test
(Cassidy & MacDonald, 2007). Each individual completed the tests with no music,
background music, and background noise. The Stroop test required participants to read
and vocalize a list of color names printed in a non-concurrent ink color. They were given
a mark for each correct answer completed within time, and the task was negatively
marked for incorrect answers. Negative marking is part of an evaluation process in
which marks are deducted from the actual score for every wrong answer. Results of
multivariate analyses of variance (MANOVA) showed a main effect of personality,
indicating significant differences in performance. Introverts performed significantly
better than extraverts on the immediate recall and delayed recall tests in all listening
conditions. Extraverts performed significantly better than introverts on the negatively
marked task of the Stroop test in the background music and background noise conditions
(Cassidy & MacDonald, 2007).
A similar study used three cognitive measures, including a test of abstract
reasoning, general cognitive ability, and verbal reasoning, to investigate the effect of
personality, background music, and background noise on task performance (Dobbs,
Furnham, & McClelland, 2011). Again, participants completed the tests with no music,
background music, and background noise. Significant positive correlations emerged
between extraversion and performance on all three measures. On the abstract reasoning
34
test, multiple regression analyses showed significant main effects of both extraversion
and background sound. Performance on the abstract reasoning test was significantly
better in silence than with music, and performance with music was significantly better
than with noise. A significant interaction between personality and background sound was
also revealed. During the music and noise conditions, extraversion was a significant
predictor of performance on the abstract reasoning test (Dobbs et al., 2011).
The results were similar on the cognitive ability test, with a few exceptions.
Significant main effects of both extraversion and background sound were found, and a
significant interaction emerged between personality and background sound. Performance
on the cognitive ability test was significantly better both in silence and with music, when
compared to performance with noise. Performance in silence was not, however,
significantly better than performance with music. Last, extraversion was a significant
predictor of performance on the cognitive ability test in all three background sound
conditions (Dobbs et al., 2011).
An earlier study explored the effect of personality and recorded vocal and
instrumental music on cognitive task performance (Furnham, Trew, & Sneade, 1999).
Student participants ages 16 to 18 completed a reading comprehension test, logic
problem, and coding task while listening to music. Despite a lack of significant
interactions, the cognitive task performance of individuals with a preference for
introversion was generally impaired by music in the environment, and the performance of
extraverted types was generally enhanced. Additionally, extraversion had a significant
main effect on reading comprehension (Furnham et al., 1999).
35
The effect of music on high-cognitive demand task performance. A series of
studies have been conducted to test music’s influence on high-cognitive demand task
performance. The relationship between music and affect is an important element in all of
these studies, and their interaction is supported by such research. Recently, personality
has also been included as a possible factor (Lesiuk, 2008; Lesiuk et al., 2009, 2011).
Most of the applied research is limited by reliance on self-reports to measure
productivity, but a recent study of systems analysts utilized expert evaluation. Thus, the
study employed an objective measure of productivity (Lesiuk et al., 2011).
Lesiuk (2000) examined affective response in university students during computer
programming tasks and found that state anxiety decreased in response to music. Students
who listened to music prior to and during the tasks had significantly lower levels of state
anxiety than students who used no music. Additionally, in a later study with computer
systems designers, three significant correlations emerged. Anxiety was positively
correlated with listening time during the day, and depression was positively correlated
with listening time during the day and listening time at work. Therefore, as negative
affect increased, participants listened to more music (Lesiuk et al., 2009). This
relationship is supported by an earlier finding in which everyday music listening
experiences were found to be mostly positive (Sloboda & O’Neill, 2001).
Lesiuk (2008) explored music listening and anxiety in another high-cognitive
demand occupation, air traffic control, and personality variables were included.
Personality was represented by extraversion and introversion, and anxiety was divided
into low and high-trait anxiety. Participants either listened to preferred music or sat in
silence. Results showed that state anxiety decreased significantly with and without
36
music, but individuals with high-trait anxiety and a preference for introversion had no
decrease in state anxiety (Lesiuk, 2008). A later study with computer information
systems developers included MBTI personality variables and trait mood variables.
Results of this study showed that negative trait mood was high in individuals with
preferences for introversion and feeling (Lesiuk et al., 2009).
Music-use has also been effective at improving state mood at work, with impacts
on productivity, and familiar music appears to be more influential than unfamiliar music.
In particular, a relaxed mood has been linked to a relationship between music-use and
productivity (Oldham et al., 1995). Another study by Lesiuk (2005) tested the effect of
music on positive affect and work performance in computer information systems
developers. State positive affect was measured prior to and following music listening,
and work performance was assessed for quality-of-work and time-on-task. Data were
collected over a five week period, and music listening occurred on the first, second, third,
and fifth week. Results showed that trait positive affect was high in this population. Pre
to post, state positive affect increased significantly during the music weeks, and the
greatest increase occurred during the week following the week without music. Also, as
trait positive affect increased, listening duration increased. State positive affect was
lowest in the week without music and highest in the third week. Analysis of state
positive affect over time revealed a significant difference between pre-test scores and the
third week and between the third and fourth week (no music). Quality-of-work scores
decreased significantly from baseline to the week without music, and returned to higher
levels when music listening returned in the final week. Additionally, time-on-task was
longer than anticipated during the week without listening. Time-on-task differences were
37
significant between baseline and the week without music, between the third week and the
week without music, and between the week without music and the final week. To
summarize, music-use corresponded with increases in state positive affect, which
appeared to have a positive influence on quality-of-work and time-on-task (Lesiuk,
2005).
A recent study by Lesiuk (2010b) measured the effect of preferred music on mood
and work performance in professional computer information systems developers.
Computer information systems design requires generative processing, or creative
problem-solving, which was the specific cognitive process being tested. The study took
place over a three-week period, and the participants listened to at least 30 minutes of
music during each workday in weeks one and three. A diverse music library was
provided, and/or participants could listen to music from their personal collection. A
narrative work stress questionnaire asked participants to identify and classify the stress
inducers present in this setting. Affect was measured using the Job Affect Scale, which
pairs positive affect with enthusiasm and negative affect with unpleasant arousal (Brief et
al., 1988). Cognitive performance was assessed using a self-assessment questionnaire. A
final music listening questionnaire was given after the study to capture each participant’s
most liked and least liked music listening experience (Lesiuk, 2010b).
Participants identified stressors related to “time pressures, unrealistic deadlines,
volume of work, not knowing how to do something, co-worker problems, client
problems, and layoffs of co-workers” (Lesiuk, 2010b, p. 145). Such stressors have a
negative effect on cognition and decision making (Longenecker et al, 1999). Positive
affect was significantly higher during weeks one and three than during week two.
38
Moreover, negative affect was significantly lower during those weeks, when compared to
the week with no music. Furthermore, higher self-ratings of cognitive performance
occurred during the weeks with music. Four themes for why music was used were
revealed in a final music listening questionnaire: mood, nostalgia, relieving stress, and
work efficiency. Lesiuk highlights a participant comment that emphasizes the choice of
music for focusing: “The right type of music allows me to focus on my work and be free
of distractions. The wrong type of music annoys me and distracts me from my work”
(Lesiuk, 2010b, p. 148). Finally, Lesiuk recommends that employers empower their
employees by increasing awareness of affective response. Researchers are encouraged to
seek more evidence to strengthen the relationship between music and affect in the
workplace, and a theoretical model is requested (Lesiuk, 2010b).
Another recent study explored the effect of music and personality on state mood
and work performance in systems analysts (Lesiuk et al., 2011). All of the participants
completed the personality inventory, mood scale, and a work performance task, and half
of the participants also listened to music prior to the task. Personality was assessed prior
to the task, and mood was assessed immediately before music listening, after music
listening before the task, and after the task. Results showed that extraversion had a
significant main effect on work performance, with higher extraversion being associated
with lower scores on the work performance task. Mood was represented by positive and
negative affect. Negative affect decreased after music listening and increased after the
task, while positive affect increased steadily over the three time points with music
listening (Lesiuk et al, 2011).
39
Summary of Literature Review
Music perception occurs bilaterally in the brain, involving both neocortically
mediated cognitive processes and subcortically mediated affective responses (Blood &
Zatorre, 2001; Peretz & Coltheart, 2003; Peretz & Zatorre, 2005). Many of the neural
areas identified in music perception are involved in the cognitive processes of attention,
working memory, and executive function (Lesiuk, 2010b). The brain reward system is
also activated by music, facilitating arousal (Goldstein, 1980; Rickard, 2004; Trainor &
Schmidt, 2003). Additionally, psychological theories link music stimulus properties to
arousal and expectation (Berlyne, 1971a, 1971b; Meyer 2001).
Everyday music-use is influenced by contextual factors and individual trait and
state preferences (Lamont & Greasley, 2009; Rentfrow et al., 2011; Sloboda & O’Neill,
2001). Everyday music is defined by 10 dimensions, each of which relates to the context
of music-use and has an impact on individual affective response. These dimensions
comprise musical, social, psychological, and environmental factors (Sloboda, 2010).
Recent studies have defined music-use in terms of function, and three categories have
been established: background, cognitive, and emotional-use (Chamorro-Premuzic, et al.,
2010; Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet,
Furnham, & Muro, 2009; Chamorro-Premuzic, Swami, Furnham, & Maakip, 2009).
Affect includes both emotion and mood (Sloboda &Juslin, 2010). Mild positive
affect promotes efficient and flexible cognitive processes (Ashby et al., 1999; Estrada et
al., 1997; Forgas, 1998; Forgas & George, 2001; Isen, 2009). Mild positive affect is also
associated with expectation, specifically in terms of motivation, and a link exists between
motivation and cognitive performance (Erez & Isen, 2002; Isen, 2009). Furthermore, a
40
body of research shows that negative affect is detrimental to cognition, impacting
perception, attention, memory, decision-making skills, and creativity (Amabile et al.,
2005; Brief, et al., 1988; Isen, 2009; Mano, 1992; Watson & Clark, 1984; Watson &
Tellegen, 1985). Movement of the neurotransmitter, dopamine, in the anterior cingulate
gyrus also helps to explain the relationship between affect and memory, problem-solving,
and cognitive flexibility (Ashby et al., 1999; Isen, 2009). Affect appears to interact with
personality as well, with influences on cognition, but this theory necessitates further
research (Depue & Collins, 1999; Rusting, 1999, 2001).
Computer programming is part of an ongoing process, called the systems
development life cycle (Valacich et al., 2006). Computer programming is a process
wherein a series of computer design specifications are translated into an organized
working unit, or program (Dennis & Wixom, 2000). Computer programmers must utilize
high level cognitive processes, including focused and selective attention, creative-
problem solving, and abstract planning (Lesiuk, 2010b; Sonnentag et al., 2006). Thus,
according to present-day cognitive theories, computer programming is a high-cognitive
demand task (Garner, 2002; White & Sivitanides, 2005).
Some personality preferences have been identified as typical among computer
programmers, including intuition and thinking (Woszczynski et al., 2005). These
preferences represent the MBTI functions, which are related to one’s ability to problem-
solve (Myers et al., 2003). Other information technology professionals showed a
preference for introversion and judging (Lesiuk et al., 2009). Introversion is associated
with an individual’s propensity to choose occupations that require interest in and
sustained attention to concepts and ideas (Myers et al., 2003). Furthermore, introversion
41
has been shown to have a positive relationship with work performance (Lesiuk et al.,
2011). Other research evidence suggests, nonetheless, that diverse personality profiles
are likely among computer programmers (Kenner, 1993; Woszczynski et al., 2005).
Personality also has an interaction with mood. Information technology professionals with
preferences for introversion and feeling showed higher negative trait mood (Lesiuk et al.,
2009). Additionally, conscientiousness had a negative relationship with fatigue, and
reduced fatigue is an indication of decreased negative affect (Lesiuk et al., 2011).
General results of a series of studies that explored the role of personality in music-
use suggest that preferences for neuroticism, extraversion, and openness are likely
connected to music-use. Specifically, neuroticism had a positive relationship with
emotional-use of music, extraversion had a positive relationship with background-use of
music, and openness had a positive relationship with cognitive-use of music. (Chamorro-
Premuzic et al., 2007, 2009ab). Individual personality preferences are also related to
other music-use characteristics. Introversion was associated with using music to control
anxiety in typical adults (Cassidy & MacDonald, 2007). Additionally, information
technology professionals with preferences for extraversion and feeling listened to music
for longer periods of time, when compared to introverted and thinking types (Lesiuk et
al., 2009).
Music appears to have an effect on affective response, as evidenced by its role in
successful mood-induction techniques (Eich et al., 2007). Brain imaging data also show
the effect of music in mood-induction (Blood & Zatorre, 2001; Mitterschiffthaler et al.,
2007). Furthermore, music has an anxiolytic effect on physiological symptoms of stress
42
and anxiety (Knight & Rickard, 2001). From another perspective, affect appears to
impact music-use, with certain individuals being inclined to use music to for mood
regulation (Greenwood & Long, 2009).
Music also interacts with personality, with impacts on cognitive performance. In
the presence of background music and noise, typical individuals with a preference for
extraversion performed better than introverted types on a Stroop test. Introverts
performed better than extraverts, however, on immediate recall and delayed recall tests in
background music, background noise, and no music conditions (Cassidy & MacDonald,
2007). Extraversion and background sound also had effects on the cognitive performance
of typical individuals on an abstract reasoning test and a cognitive ability test.
Performance with background music was significantly better than with background noise,
and extraversion was a predictor of performance in the background music and
background noise conditions (Dobbs et al., 2011). Additionally, in a study that tested the
effect of personality and music on cognitive performance in typical individuals,
extraversion had a main effect on reading comprehension (Furnham et al., 1999).
Studies that explore the effect of music on high-cognitive demand tasks include
both mood and personality variables. Students that listened to music during computer
programming tasks reported lower levels of anxiety than students that used no music
(Lesiuk, 2000). A study with air traffic controllers included personality and state anxiety
as variables in music use. Individuals with a preference for introversion and high-trait
anxiety had no decrease in state anxiety with and without music (Lesiuk, 2008).
Additionally, negative trait mood was high in computer information systems developers
with preferences for introversion and feeling (Lesiuk et al., 2009). Moreover, in
43
computer systems designers, increases in negative affect were associated with longer
durations of music-use (Lesiuk et al., 2009). The effect of music on mood also has
implications on productivity at work. Computer information systems developers showed
an increase in state positive mood during music-use, with positive ramifications on
quality-of-work and time-on-task (Lesiuk, 2005, 2010b). A similar study with systems
analysts also included personality variables. Negative affect decreased and positive
affect increased with music, and individuals with a preference for extraversion tended to
have lower scores on a work performance task (Lesiuk et al., 2011).
Research Questions
This study is designed to address the following research questions in regard to
music-use during high-cognitive demand computer programming tasks:
1. What is the relationship between personality and music-use?
2. What is the relationship between mood and music-use?
3. What demographic and/or contextual factors are related to music-use?
Chapter Three
Method
This chapter describes the research design, variables, and procedures used to test
the research questions. Participant eligibility is described, and recruitment data are
included. Next, measurement scales are described in detail, and the recruitment and data
collection procedure is presented. Finally, data analysis techniques are explained.
Participants
Thirty-four university students participated in this study, including students at the
undergraduate, masters, and doctoral levels. Participants were recruited from University
of Miami in Coral Gables, Florida, during the Spring 2012 semester. Students from the
Department of Computer Science, Electrical and Computer Engineering Department,
Visual Journalism program in the School of Communication, and Music Engineering
Technology program in the Frost School of Music were included. Students were eligible
to participate in the study if they were enrolled in computer programming courses within
these academic areas. Recruitment was limited to courses with curriculum that required
students to complete regular computer programming tasks. Female and male students 18
years and older were eligible to participate. All racial and ethnic groups were included,
and both musicians and nonmusicians were accepted. Last, only those students who
usually listened to music while computer programming were eligible to participate.
Design and Variables
This study utilized a cross-sectional survey design. This design was chosen to
describe trends and attitudes of student computer programmers who listen to music while
they work (Creswell, 2009; Fraenkel & Wallen, 2009). A cross-sectional survey design,
44
45
including self-administered and Web-based questionnaires, was the preferred type of data
collection, as it allowed for economy of design and a rapid turnaround. Specifically, the
study design was developed to examine relationships between personality, mood, and
music-use (see Figure 1). Personality was comprised of five factors: Neuroticism,
Extraversion, Openness to Experience (Openness), Agreeableness, and Conscientiousness
(Costa & McCrae, 1992). Mood was expressed as Positive Affect and Negative Affect,
and subscales existed for each variable. Positive Affect subscales included Relaxation
and Enthusiasm. Negative Affect subscales included Nervousness and Fatigue (Oldham
et al., 1995). Music-use was comprised of Background, Cognitive, and Emotional-use
variables (Chamorro-Premuzic & Furnham, 2007).
Measures
Measurement tools were administered in two study phases. During the first study
phase, participants met with the researcher to complete a demographic questionnaire and
an inventory for personality. During the second study phase, participants completed a
standardized mood questionnaire, a computer programming task assessment, and music-
use questionnaire on a study webpage. Both phases were completed within a two week
period. The participants completed a computer programming task of their choosing while
listening to preferred music during the second phase. The task and listening occurred
after the mood scale, and it was immediately followed by the computer programming task
assessment and music-use questionnaire. Figure 2 includes the duration of each measure.
Demographic questionnaire. The researcher-generated demographic
questionnaire first asked participants to report their age, gender, ethnicity, and race (see
Appendix A). The descriptors for ethnicity were “Hispanic or Latino” and “Not Hispanic
46
Figure 1. Study Variables.
Personality & Mood Music-Use
Openness
Conscientiousness
Extroversion Personality
Agreeableness Background
Neuroticism Music-Use Cognitive
EmotionalPositive Affect
RelaxationMood
Enthusiasm
Negative Affect
Nervousness
Fatigue
47
Figure 2. Measures Flow Chart.
Phase Measure Duration
Demographic questionnaire 5 min↓
NEO Five-Factor Inventory 15 min
Job Affect Scale 3 min↓
Computer programming task 20+ min↓
Computer programming task assessment 2 min↓
Music use questionnaire 20-30 min
Total: 65-75+ min
Phase One
Phase Two
48
or Latino.” The descriptors for race were “American Indian or Alaska Native,” “Asian,”
“Black or African American,” “Native Hawaiian or Other Pacific Islander,” and “White.”
Then, participants indicated their current level of education and degree being pursued.
Additionally, the participants reported years of computer programming background,
average number of hours spent daily on computer programming, and most prevalently
used computer programming language. Last, the participants rated their level of
proficiency in computer programming using a 5-point scale ranging from 1 (absolute
beginner) to 5 (power user). More specifically, an absolute beginner had little or no
knowledge, a novice had created a few simple computer programs, an intermediate
computer programmer was moderately proficient, an advanced computer programmer
had created complex computer programs, and a power user was an expert in computer
programming (Lesiuk et al., 2011).
NEO Five-Factor Inventory. This personality measure, called the NEO Five-
Factory Inventory (NEO-FFI) consisted of 60 items rated on a 5-point Likert scale (Costa
& McCrae, 1992). Each item on the NEO-FFI was a statement with which the
participants agree or disagree (see Appendix B). Participants responded on a scale from
1 (strongly disagree) to 5 (strongly agree). The NEO-FFI measured the Big Five
personality traits, including Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness. For each of these traits or dimensions, participants received a score
on a continuum from very low to very high. Individuals with a high Neuroticism score
have a tendency to experience negative affect and have irrational ideas. They have little
control of their impulses with poor coping skills. Extraverted personalities tend to be
very social with a cheerful disposition. They are described as active, talkative, and
49
assertive. Individuals with a high Openness score tend to have an active imagination,
curiosity about themselves and the world around them, and independence of judgment
that is often associated with creativity. The trait of Agreeableness is concerned with
interpersonal relationship abilities. Individuals with a high degree of Agreeableness tend
to be sympathetic toward others, and they are willing to help those around them. High
Conscientiousness scorers tend to be strong-willed, determined, powerful, and reliable.
They are usually able to control impulses. Reported reliability coefficients are 0.79 for
Neuroticism, 0.79 for Extraversion, 0.80 for Openness, 0.75 for Agreeableness, and 0.83
for Conscientiousness.
Job Affect Scale. The Job Affect Scale is a 12-item measurement of state mood
in the work setting (see Appendix C). Each item has a single word to describe a feeling
one might have while working. Word examples include “calm,” “excited,” “scornful,”
and “drowsy.” The original scale included 20 items, but an adapted scale that consists of
12 items was later created for the purpose of reducing time spent on questionnaires
during work (Oldham et al., 1995). Participants respond in conjunction with a work task
utilizing a 7-point Likert scale ranging from 1 (extremely slightly) to 7 (extremely
strongly). Responses may occur before, during, or after a work task (Brief et al., 1988).
Six items have words relating to Positive Affect, and six items have words relating to
Negative Affect. Scores range from 6 to 42 for each state affect, with higher scores
representing higher affect intensity. In the Oldham et al. (1995) adaptation, subscales
have been created for each state affect. High Positive Affect is represented by
Enthusiasm, and low Positive Affect is represented by Relaxation. High Negative Affect
is represented by Nervousness, and low Negative Affect is represented by Fatigue. These
50
subscales add a valence dimension to the results. The adapted JAS has reliability
coefficients ranging from 0.66 to 0.91. For the purpose of this study, participants
completed the JAS immediately prior to the computer programming task. They were
instructed to rate each word to describe how they feel at the current moment.
Task assessment. The researcher-generated task assessment collected details
about the computer programming task (see Appendix D). This assessment was included
to explore relationships between computer programming task characteristics and music-
use. First, a 5-point scale ranging from 1 (extremely easy) to 5 (extremely difficult)
measured the complexity of the computer programming task. Participants were then
asked to indicate the length of time in minutes spent on the task. Next, they specified
where they completed the task, choosing from “home, work, library,” or “other.” Last,
participants identified the day of the week and when they completed the task, choosing
from “morning, afternoon,” or “night.”
Music-use questionnaire. The music-use questionnaire gathered information
about the type and role of music that participants choose to accompany their computer
programming task (see Appendix E). The multifaceted questionnaire was primarily
designed to measure music-use, and it also allowed the researcher to collect a playlist and
explore relationships between the listening experience and music-use. Additionally, this
questionnaire included questions about the participants’ musical experience. To conclude
the questionnaire and complete participation in the study, all participants were given an
opportunity to make general comments.
51
Music choices. The participants listed up to 10 songs that they listened to during
the computer programming task. They also reported the artist/band and musical style for
each of the songs. Additionally, participants reported generally how active the music was
that they chose using a 5-point scale ranging from 1 (extremely inactive [very low
energy]) to 5 (extremely active [very high energy]).
Listening experience. The participants reported in minutes the length of time
spent listening to music. They also indicated how focused they were on the music during
the task using a 5-point scale ranging from 1 (extremely unfocused) to 5 (extremely
focused). Additionally, participants reported the device they used to play and listen to the
music and whether or not they used headphones. These data were collected as possible
environmental factors to consider as part of the listening experience.
Music-use. Participants were given an opportunity to generate their own
descriptions of the reasons for listening while programming. They answered two open-
ended questions. The first question asked them to explain why they chose their particular
music to accompany the task. The second question asked them how they thought music
listening influenced them and their work. Participants then completed the Uses of Music
Inventory, which is described in detail next.
Uses of Music Inventory. This measure of music-use, called the Uses of Music
Inventory consists of 15 items rated on a 5-point Likert scale (Chamorro-Premuzic &
Furnham, 2007). Each item consists of a statement which a person may use to describe
their feelings about listening to music (see Appendix E). Participants respond on a scale
from 1 (strongly disagree) to 5 (strongly agree). Examples of statements include “I
enjoy listening to music while I work, Listening to music is an intellectual experience for
52
me,” and “Listening to music really affects my mood.” The Uses of Music Inventory
categories include Background, Cognitive, and Emotional-use. Five items have
statements relating to Background-use, five items have statements relating to Cognitive-
use, and five items have statements relating to Emotional-use. Scores range from 5 to 25
for each category, with higher scores representing a higher likelihood of using music in
this way. Background-use assesses the extent to which an individual uses music while
working, studying, socializing, or performing a task. Cognitive-use is an indication of
the degree to which an individual listens to music in an intellectual way. Emotional-use
refers to the extent to which an individual uses music for emotional regulation.
Cronbach’s α ranges from 0.61 to 0.64, and 0.6 is an acceptable value for 5-item scales
(Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, &
Maakip, 2009).
Musical experience. Two questions collected the musical experience of each
participant. They reported the number of years they have played a musical instrument or
sung in a choir. Participants also choose from six spans of time that they typically spend
listening to music each day, ranging from “0-1 hours” to “10+ hours.”
Procedure
University students were recruited from class meetings with permission from
course instructors. Professors were contacted via email or in person during office hours.
A study advertisement was attached to email correspondences, delivered by hand, and
posted on approved department bulletin boards (see Appendix F). For each course, the
researcher made a study announcement and distributed study advertisements during the
first five minutes of an initial class meeting. Immediately following this class, the
53
researcher met with students to field questions and schedule individual appointments for
the first phase of the study. One week later, the researcher returned to each class prior to
its start time to follow-up with students, address any questions or concerns, and schedule
new appointments.
Study Phase 1. Participants met individually with the researcher at various
campus locations to complete the first phase of the study. Locations included library
study rooms, computer labs, and student common areas. Initially, each participant signed
an informed consent form (see Appendix G) and was given a participant number.
Participant email addresses and phone numbers were included on the informed consent
form for the purpose of communicating an assigned study website login. Only participant
numbers were included on each subsequent measure. Names and contact information
were omitted to protect the identity of each student. Then, participants completed the
researcher-generated demographic questionnaire and the NEO-FFI measure of
personality to conclude the first phase of the study. This meeting occurred in person to
help establish a connection with each participant and ensure that he or she understood the
study requirements. Furthermore, the NEO-FFI requires permission and a fee to
administer via a study website. Each meeting lasted approximately 20 to 30 minutes.
Study Phase 2. To initiate the second phase of the study, the researcher sent an
email to each participant containing a unique link to the study website. By providing
each individual participant with a dedicated link to the study website, participants were
only granted access to their own online measures and responses. This security measure
54
ensured the privacy of each participant. On the study website, each individual measure
began by requesting the participant number, allowing the researcher to link website data
to data collected during the first phase of the study.
The second phase of the study was completed by each participant at a time of
their choosing. The researcher asked participants to finish the second phase within two
weeks of the first phase to reduce the threat of mortality. On the study website,
participants began by completing the Job Affect Scale of state mood. Then, they were
instructed to complete a computer programming task of their choice, which lasted a
minimum of 20 minutes without interruption. This length of time has been reported to be
typical for a continuous computer programming task (M. Ogihara, personal
communication, October 5, 2011). The researcher requested that each participant be
prepared with a “difficult coding task” prior to the study, as research suggests that the
likelihood of music having an effect on task performance is higher with more complex
mental tasks (Cassidy & MacDonald, 2007; M. Ogihara, personal communication,
November 4, 2011). A “difficult” computer programming task was relative for each
participant, based on their level of expertise. During this task, participants were also
expected to listen to at least 10 minutes of music. Research studies have indicated that
mood can be affected within this amount of time (Barnes-Holmes, Barnes-Holmes,
Smeets, & Luciano, 2004; Eich et al., 2007; Smith & Noon, 1998; Standage, Ashwin, &
Fox, 2010).
55
Upon completion of the computer programming task, participants returned to the
study website. Next, they completed the computer programming task assessment to
gather data about each individual task. Finally, they finished the second phase and
concluded the study by completing the music-use questionnaire. The second phase of the
study lasted 45 minutes or more, depending on the length of the chosen computer
programming task. Total participation in the study lasted a minimum of 65 minutes (see
Figure 2).
Data Collection
The researcher collected demographic information and NEO-FFI data during the
first phase of the study, utilizing hard copies of the demographic questionnaire and the
NEO-FFI Test Booklet-Form S (Adult). The Job Affect Scale, computer programming
task assessment, and music-use questionnaire data were collected via the study website.
Access to the study website was username/password protected and given only to the
researchers. The study website, www.UMmusicwhileyouwork.info, was hosted on
www.hostpapa.com. All data were stored in a locked file cabinet in the researcher’s
home office. Electronic data was stored on a password-protected computer. Data was
entered by participant number with no identifying information.
Data Analysis
Data collected were analyzed using several methods. Frequencies, means, and
standard deviations were calculated for the demographic, personality, mood, and music-
use data. Frequencies were calculated for computer programming task data and listening
experience data. The NEO-FFI table of mean age-matched scores was used for
comparison to the study sample (Costa & McCrae, 1992). Thus, t-tests for independent
56
means were conducted to compare the study sample means for each of the personality
factors to means of age-matched adults (Creswell, 2009; Gravetter & Wallnau, 2011).
The test was appropriate, because the researcher wished to identify significant differences
between mean scores from two dissimilar groups on the same measure (Fraenkel &
Wallen, 2009).
To address research questions 1 and 2, bivariate Pearson product-moment
correlation coefficients were calculated for the purpose of ascertaining the degree of
relatedness amongst the continuous variables, including each of the personality factors,
mood variables, mood subscales, and music-use variables. Pearson correlations
measured the degree and direction of the linear relationship between two of the variables
at a time (Creswell, 2009; Gravetter & Wallnau, 2011). Scatterplots were constructed to
illustrate the data visually. The analyses were appropriate, because the researcher was
exploring relationships between quantitative variables within one group (Fraenkel &
Wallen, 2009). Further, multiple regression analyses were used to test significant
correlations in a predictive model. The analysis was appropriate to test the correlation
between a criterion music-use variable and two or more predictor variables (Fraenkel &
Wallen, 2009). For research question 3, Pearson product-moment correlation coefficients
were calculated for the music-use variables and other continuous variables, such as age,
musical background, task duration, and listening duration.
Next, Spearman rank correlation coefficients were calculated between the
continuous and ordinal variables. Ordinal variables included school level, computer
programming background, computer programming proficiency, computer programming
hours per day, computer programming task difficulty, music activity level, listening hours
57
per day, and music focus. The analysis was applicable, because correlated data from an
ordinal scale may yield results that show a consistent direction, but not necessarily a
linear relationship (Creswell, 2009; Gravetter & Wallnau, 2011).
To conclude the statistical analysis, a one-way analysis of variance (ANOVA)
was used to determine whether a significant effect of computer programming background
on music-use existed (Creswell, 2009; Gravetter & Wallnau, 2011). The analysis was
possible because two similarly sized groups could be formed from the study sample
based on computer programming background. The test allowed the researcher to analyze
variation both within and between each group. Since this ANOVA only compared two
groups, an F test was sufficient to determine significance (Fraenkel & Wallen, 2009).
In addition to quantitative methods, content analysis was utilized to code
responses to open-ended items on the music-use questionnaire (Creswell, 2009; Fraenkel
& Wallen, 2009). To reduce the threat of researcher bias, four random adult volunteers
rated the responses. Raters were given the definitions of background, cognitive, and
emotional-use of music, and they were instructed to choose the best placement of the
each participant comment into each of the music use categories. Raters were also given
the option of choosing “other” when none of the provided descriptions were applicable.
These directed content analyses were conducted to compare open-ended responses on the
music-use questionnaire to the Uses of Music Inventory data (Hsieh & Shannon, 2005).
The researcher also employed conventional and summative content analyses.
These qualitative methods were utilized to identify themes in responses to open-ended
items. The conventional content analysis used categories that were derived directly from
58
the content for coding. In summative content analysis, key terms or phrases were
counted and compared for interpretation (Fraenkel & Wallen, 2009; Hsieh & Shannon,
2005).
Chapter Four
Results
The recruitment and data collection phase lasted approximately three months,
from February 2012 through May 2012. A total of 34 participants completed all tasks
and measures of the study, and the results are presented in group aggregate below. This
chapter begins by presenting descriptive results for the total sample on each variable.
Inferential results are reported next, based on the research questions. Finally, results of
content analyses are reported at the end of the chapter.
Statistical analyses were completed using the software Statistical Package for
Social Sciences (SPSS) v. 16.0. Certain measurements collected ordinal data. For
example, some items contained spans of time (e.g., 0-1 year, 2-3 years, 4-5 years, etc.),
and other items had levels (e.g., novice, intermediate, advanced, etc.). These data are
displayed in frequency tables, and they were also coded into whole numbers (e.g., 1, 2, 3,
etc.) in SPSS in order to determine group measures of central tendency.
Descriptive Results
The descriptive results report frequencies, means, and standard deviations for the
demographics, including musical and computer programming background data.
Frequencies are also shown for the computer programming task data and listening
experience data. Additionally, personality, mood, and music-use data are summarized.
Demographics. Demographic characteristics of this sample are summarized in
Table 1. The final sample included 34 participants, comprised of 31 males and 3 females.
This distribution of sexes is representative of the student computer programming
population (M. Ogihara, personal communication, November 1, 2012). The mean age of
59
60
the total sample was 23.03 years (SD = 6.28), with ages ranging from 18 to 50 years.
Ethnicity and race were recorded as separate variables. Four of the participants were
Hispanic or Latino, and all four were White. Thirty of the participants were not Hispanic
or Latino. Of the participants who were not Hispanic or Latino, 23 were White, 3 were
Asian, 2 were Black or African American, 1 was American Indian or Alaska Native, and
1 was Native Hawaiian or Other Pacific Islander. The participants were university
students pursuing a variety of degrees. Programs of study among these students included
computer science, electrical engineering, music engineering, computer engineering, math,
and others (see Table 2). Twenty-seven participants were undergraduates, four were
master’s degree candidates, and three were doctoral students. Participants’ musical
background, as shown in the number of years playing a musical instrument or singing,
had a mean of 9.12 years (SD = 5.87), with a range from 2 to 25 years. Four participants
had no musical background. Last, participants most frequently reported listening to 2-3
hours of music daily, and the group average was also 2-3 hours daily.
Computer programming experience. The computer programming experience
of this sample is summarized in Table 3. The participants most frequently reported 1-2
years of computer programming background, and the group averaged 3-4 years of
background. Participant self-ratings for level of computer programming proficiency were
most frequently at the level of intermediate, and the group also averaged an intermediate
proficiency (see Figure 3). Participants most frequently reported completing 0-1 or 2-3
hours of computer programming each day, and the group average was 2-3 hours. Finally,
Java, C++, and MATLAB were the most frequently reported computer programming
languages preferred by this sample.
61
Table 1
Demographic Characteristics of Sample (n = 34)
Frequency Distribution f % Gender
Female
3 8.8
Male 31 91.2 Ethnicity
Hispanic or Latino
4 11.8
Not Hispanic or Latino 30 88.2 Race
American Indian or Alaska Native
1 2.9
Asian
3 8.8
Black or African American
2 5.9
Native Hawaiian or Other Pacific Islander
1 2.9
White 27 79.4 School Level
Undergraduate
27 79.4
Masters
4 11.8
Doctoral 3 8.8 Daily Music Listening (hours)
0-1
4 11.8
2-3
21 61.8
4-5
8 23.5
6-7
0 0.0
8-9
1 2.9
10+ 0 0.0 Note. The sum of distribution percentage values may not equal 100%, due to rounding.
62
Table 2
Programs of Study Reported (n = 34)
Frequency Distribution f % Computer Science
10 29.4
Electrical Engineering
5 14.7
Music Engineering
5 14.7
Computer Engineering
3 8.8
Math
3 8.8
Applied Physics
2 5.9
Music Composition
2 5.9
Communications
1 2.9
Economics
1 2.9
Environmental Engineering
1 2.9
German 1 2.9 Note. The sum of distribution percentage values may not equal 100%, due to rounding.
63
Table 3
Computer Programming Experience of Sample (n = 34)
Frequency Distribution f % Background (years) Less than 1
4 11.8
1-2
12 35.3
3-4
9 26.5
5-6
2 5.9
7-8
1 2.9
More than 8 6 17.6 Proficiency
Absolute Beginner
0 0.0
Novice
7 20.6
Intermediate
13 38.2
Advanced
12 35.3
Power User 2 5.9 Programming Hours/Day
0-1
14 41.2
2-3
14 41.2
4-5
2 5.9
6-7
1 2.9
8-9 3 8.8
64
Figure 3. Levels of computer programming proficiency reported. (x) = number of times level was reported.
(7) Novice
(13) Intermediate
(12) Advanced
(2) Power User
65
Computer programming task characteristics. Participants completed a
computer programming task of their choosing. They were instructed to complete a
difficult coding task lasting at least 20 minutes without interruption. They could
complete the task at any location, during any time of day, and on any day of the week.
The computer programming task characteristics of the group are summarized in Table 4.
The participants most frequently reported completing tasks of moderate difficulty, and
the group average was also moderate. Computer programming task durations had a mean
of 70.79 minutes (SD = 64.38), with a range from 15 to 240 minutes. Participants most
frequently completed their task at home. Other locations included computer labs, the
library, work, and study rooms. Most participants did their work in the evening, as
opposed to the morning. They worked every day of the week, but less than 15 percent of
the participants completed their tasks on Fridays and Saturdays.
Listening experience characteristics. Participants listened to music of their
choosing while they completed the computer programming task. They were instructed to
listen to at least 10 minutes of music. Music-use durations had a mean of 56.12 minutes
(SD = 43.84), with a range from 15 to 200 minutes. Participants could listen using an
audio device of their choosing, with headphones or without. Thirty-two participants
listened to music on their personal computer. One participant used a portable mp3 audio
player, and one participant used his or her phone. Twenty of the participants used
headphones. Other music-use characteristics of the group are summarized in Table 5.
The participants most frequently reported listening to music that was active (high
energy), and the group average was also active. They were asked to report how focused
they were on the music using a 5-point scale ranging from 1 (extremely unfocused) to 5
66
(extremely focused). Participants most frequently reported being focused on the music,
but the group average was neutral.
Table 4
Computer Programming Task Characteristics (n = 34)
Frequency Distribution f % Task Difficulty
Extremely Easy
1 2.9
Easy
7 20.6
Moderate
14 41.2
Difficult
9 26.5
Extremely Difficult 3 8.8 Task Location
Home
19 55.9
Work
2 5.9
Library
4 11.8
Other 9 26.5 Task Time of Day
Morning
5 14.7
Evening 29 85.3 Task Day of Week
Sunday
6 17.6
Monday
6 17.6
Tuesday
4 11.8
Wednesday
6 17.6
Thursday
7 20.6
Friday
2 5.9
Saturday 3 8.8 Note. The sum of distribution percentage values may not equal 100%, due to rounding.
67
Table 5
Listening Experience Characteristics (n = 34)
Frequency
Distribution f % Music Activity Level
Extremely Inactive (very low energy)
0 0.0
Inactive (low energy)
1 2.9
Moderate
13 38.2
Active (high energy)
17 50.0
Extremely Active (very high energy) 3 8.8 Music Focus
Extremely Unfocused
1 2.9
Unfocused
6 17.6
Neutral
13 38.2
Focused
14 41.2
Extremely Focused 0 0.0 Note. The sum of distribution percentage values may not equal 100%, due to rounding.
Personality factors. The results of the NEO-FFI were distributed among five
personality factors, and scores indicated the intensity of each factor. Scores between 0
and 48 were possible for Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness. The sample means for each of these factors are summarized in Table
6. Additionally, an inferential statistical test was used to compare how the personality of
this sample differs from the personality of typical adults. The sample means were
compared with age-matched adult means, as reported in the NEO PI-R professional
manual (Costa & McCrae, 1992). Results of one-sample t-tests showed one significant
difference (see Table 6). A significant difference between groups emerged in the
68
Openness factor, with the study group having a higher mean score for Openness (M =
34.71, SD = 6.02) than the typical mean score for Openness (M = 27.03, SD = 5.84); t(33)
= 7.43, p = 0.001. This result shows that Openness scores in this sample are significantly
different from Openness scores among typical adults. Specifically, Openness scores in
this sample are higher than in typical adults.
Table 6
NEO-FFI Factor Means for Sample and Typical Adults
Mean Score Factor Sample Typical t df Neuroticism
17.91 (6.75)
19.07 (7.68)
-1.00
33
Extraversion
28.03 (6.57)
27.69 (5.85)
0.30 33
Openness
34.71 (6.02)
27.03 (5.84)
7.43*** 33
Agreeableness
31.47 (6.55)
32.84 (4.97)
-1.22 33
Conscientiousness
33.00 (6.14)
34.57 (5.88)
-1.49 33
Note. *** p <.001, two-tailed. Standard Deviations appear in parentheses below means. Mood variables and subscales. The results of the Job Affect Scale are provided
for Positive Affect and Negative Affect. Two mood subscales exist for each of the
variables. Relaxation and Enthusiasm are subscales of Positive Affect, and Nervousness
and Fatigue are subscales of Negative Affect. Scores between 6 and 42 were possible for
each variable, and scores between 3 and 21 were possible for each subscale. The means
and standard deviations for each of the variables and subscales are summarized in Table
7. Differences between mean scores for Positive Affect and Negative Affect were
69
negligible, and both scores were near the value of 21. Positive Affect was nearly evenly
divided between Relaxation and Enthusiasm, with both scores falling near the value of
10.5. Negative Affect was not evenly divided between its subscales. In fact, Fatigue
accounted for 77 percent of the total Negative Affect score. Mean scores for Fatigue
were near the value of 17. Mean scores for Nervousness were near the value of 5.
Table 7
Mood Results
Score Variable Subscale M SD Positive Affect
20.94
3.92
Relaxation
10.38 3.77
Enthusiasm
10.56 3.04
Negative Affect
21.68
3.09
Nervousness
5.03 2.70
Fatigue
16.65 2.90
Note. Variable scores were possible between 6 and 42. Subscale scores were possible between 3 and 21. Music-use variables. The results of the Uses of Music Inventory were
distributed between three music-use variables, Background, Cognitive, and Emotional.
Scores between 5 and 25 were possible for each variable. The group means and standard
deviations for each of these variables are summarized in Table 8. The scores were nearly
evenly represented among the music-use variables, with mean scores for Cognitive-use
70
being slightly less than scores for Background and Emotional-use. Mean scores for
Background and Emotional-use were both near the value of 16. Mean scores for
Cognitive-use were near the value of 15.
Table 8
Music-Use Results
Score Variable M SD Background
16.38
2.88
Cognitive
15.06 3.90
Emotional
16.24 2.80
Note. Scores were possible between 5 and 25.
Inferential Results
This section presents results of inferential analyses, based on the research
questions. For each question, correlation coefficients were calculated. Multiple
regression analyses of significant relationships were utilized whenever possible, and
scatterplots are provided to support these relationships. Other significant relationships
not pertaining to the research questions are presented in Appendix H.
Research Question 1: What is the relationship between personality and
music-use? Bivariate Pearson product-moment correlation coefficients were calculated
for the continuous variables, including each of the personality factors and the music-use
variables (see Table 9). Two significant correlations were found. Openness was
positively correlated with both Cognitive (p < 0.01) and Emotional-use of music (p <
0.05). Figure 4 shows graphical representations of these relationships. Multiple
regression analyses were used to test these correlations in a predictive model, testing for
71
the combined and unique relationships of the personality factors on each of the music-use
categories separately. The results of these analyses, which include Cognitive or
Emotional-use of music as a dependent variable and the five personality factors as
simultaneous independent variables, are found in Table 10 and Table 11. Openness
significantly predicted Cognitive-use of music scores, β = 0.53, t(34) = 2.84, p < 0.01.
Openness also explained a significant proportion of variance in Cognitive-use of music
scores, R2 = 0.35, F(1, 34) = 3.03, p < 0.05. As can be seen in Table 11, however, none
of the Personality factors significantly predicted or explained a significant proportion of
variance in Emotional-use of music. Additionally, two other significant relationships
emerged during correlational analyses. Musical background was positively correlated
with both Neuroticism (p < 0.05) and Openness (p < 0.01) (see Table A.4, Appendix H).
Table 9
Pearson’s Product Moment Correlations (r) for Music-Use Categories with Personality Factors Music-Use Categories Background Cognitive Emotional Neuroticism
-0.04
0.20
0.20
Extraversion
0.11 -0.04 0.01
Openness
-0.04 0.49** 0.35*
Agreeableness
-0.22 -0.26 -0.23
Conscientiousness
0.14 -0.27 -0.02
Note. * p < .05, two-tailed; ** p < .01, two-tailed. n = 34 for all analyses.
72
Figure 4. Scatterplots for Openness factor with Cognitive and Emotional-uses of music.
0
5
10
15
20
25
30
0 10 20 30 40 50
Cog
nitiv
e-U
se o
f Mus
ic
Openness
0
5
10
15
20
25
0 10 20 30 40 50
Em
otio
nal-U
se o
f Mus
ic
Openness
73
Table 10
Summary of Multiple Regression Analysis for Personality Factors Predicting Cognitive-use Cognitive-use of Music Factor B SE B β Neuroticism
0.00
0.11
0.01
Extraversion
-0.10 0.12 -0.16
Openness
0.34 0.12 0.53**
Agreeableness
-0.06 0.10 -0.10
Conscientiousness
-0.14 0.10 -0.22
R2
0.35
F
3.03*
Note. * p < .05. ** p < .01.
Table 11
Summary of Multiple Regression Analysis for Personality Factors Predicting Emotional-use Emotional-use of Music Factor B SE B β Neuroticism
0.05
0.09
0.12
Extraversion
0.00 0.10 0.01
Openness
0.14 0.10 0.30
Agreeableness
-0.08 0.08 -0.19
Conscientiousness
0.01 0.08 0.02
R2
0.18
F
1.19
74
Research Question 2: What is the relationship between mood and music-use?
Bivariate Pearson product-moment correlation coefficients were calculated for the
continuous variables, including each of the mood variables, mood subscales, and music-
use variables (see Table 12). Significant correlations were not found between any of the
mood variables or subscales and any of the music-use variables.
Table 12
Pearson’s Product Moment Correlations (r) for Music-Use Variables with Mood Variables and Subscales Music-Use Variable Subscales Background Cognitive Emotional Positive Affect
0.10
-0.30
0.13
Relaxation
-0.06 0.06 0.10
Enthusiasm 0.20 -0.11 0.10 Negative Affect
-0.23
-0.14
-0.14
Nervousness
-0.20 -0.01 -0.07
Fatigue
-0.06 -0.14 -0.08
Research Question 3: What demographic and/or contextual factors are
related to music-use? Correlation coefficients were also calculated between music-use
variables and other continuous and ordinal study variables. Initially, bivariate Pearson
product-moment correlation coefficients were calculated between the music-use variables
and the other continuous variables, including age, musical background, task duration, and
listening duration. No significant correlations were found among these variables. Next,
Spearman rank correlation coefficients were calculated to determine relationships among
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the music-use variables and the ordinal variables, including school level, computer
programming background, computer programming proficiency, computer programming
hours per day, computer programming task difficulty, music activity level, listening hours
per day, and music focus. Several significant correlations are reported (see Table 13).
First, computer programming proficiency was positively correlated with Emotional-use
(p < 0.05). Cognitive-use was included in five other significant correlations. Computer
programming background (p < 0.01) and task difficulty (p < 0.05) were both negatively
correlated with Cognitive-use. Music activity level (p < 0.01), listening hours per day (p
< 0.05), and music focus (p < 0.05) were all positively correlated with Cognitive-use.
Table 13
Spearman’s Rank Correlations (rs) for Music-Use Variables with Ordinal Variables
Music-Use Variables Background Cognitive Emotional School Level
-0.06 -0.01 0.05
Computer Programming Proficiency
0.12 -0.33 0.34*
Computer Programming Background
-0.09 -0.44** 0.23
Computer Programming Hours/Day
-0.00 -0.13 0.04
Computer Programming Task Difficulty
0.04 -0.40* -0.22
Music Activity Level
0.01 0.54** 0.18
Listening Hours/Day
0.24 0.39* 0.34
Music Focus 0.09 0.41* -0.03 Note. * p < .05, two-tailed; ** p < .01, two-tailed.
Other relationships. Several other significant correlations were found during the
data analysis process. Relationships that did not involve the music-use variables are
shown in Tables A.1 through A.9 in Appendix H. In addition to correlational analyses, a
76
one-way analysis of variance (ANOVA) was used to determine whether a significant
effect of computer programming background on music-use existed for two groups. Of
the 34 participants, 16 students had two years or less of computer programming
background, and 18 students had three years or more of computer programming
background. Therefore, two similarly sized groups were analyzed for between group
differences using an ANOVA procedure. The mean Cognitive-use score for participants
with two years or less of computer programming background was 16.81 (SD = 3.80), and
the mean Cognitive-use score for participants with three or more years of computer
programming background was 13.50 (SD = 3.37). ANOVA showed a significant effect
of computer programming background on Cognitive-use of music at the p < 0.05 level for
two groups [F(1, 32) = 7.27, p = 0.011] (see Table 14). As a determination of practical
significance, Cohen's d was calculated to evaluate the effect size between means. The
Cohen’s d value was 0.92, and when the magnitude of d is 0.80 or above, a large effect
size is present (Gravetter & Wallnau, 2011).
Table 14
Summary of ANOVA Results for Effect of Computer Programming Background on Cognitive-use of Music Sum of Squares df Mean Square F Between Groups
92.95
1
92.95
7.27*
Within Groups
408.94 32 12.78
Total 501.88 33 Note. * p < .05.
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Content Analyses
Prior to completing the Uses of Music Inventory, participants answered two open-
ended questions about their music-use. Four volunteer raters coded responses to these
items for comparison to the Uses of Music Inventory data. Results of these directed
content analyses are summarized in Table 15 and Table 16. Figure 5 shows how these
results compare to the music-use variable means obtained from the Uses of Music
Inventory. Complete responses to the open-ended items are also included in Appendix I.
Table 15
Responses to “Please explain why you chose the music you listened to.”
Frequency Distribution f % Background
40 29.63
Cognitive
38 28.15
Emotional
46 34.07
Other 11 8.15
Table 16
Responses to “How do you think music listening influenced you and your work?”
Frequency Distribution f % Background
20 14.71
Cognitive
52 38.24
Emotional
56 41.18
Other 8 5.88 Note. The sum of distribution percentage values may not equal 100%, due to rounding.
78
Figure 5. Directed content analysis and Uses of Music Inventory results
comparison.
Background 30%
Cognitive 28%
Emotional 34%
Other 8%
Responses to“Please explain why you chose the music you listened to.”
Background 15%
Cognitive 38%
Emotional 41%
Other 6%
Responses to “How do you think music listening influenced you and your work?”
Background 34%
Cognitive 32%
Emotional 34%
Uses of Music Inventory Mean Scores
79
The researcher also reviewed responses to open-ended items on the music-use
questionnaire using conventional and summative content analyses. In conventional
analysis, the researcher identified unanimity in the volunteer raters’ responses. Only
open-ended responses that received the same rating from all four volunteers were
included in the analysis, and the statements were organized by type of music-use (see
Table 17 and Table 18). For responses to “Please explain why you chose the music you
listened to,” raters unanimously agreed that two statements were representative of
Background-use, one statement was representative of Cognitive-use, and five statements
were representative of Emotional-use. For responses to “How do you think music
listening influenced you and your work?,” raters did not unanimously agree that any
statements were representative of Background-use. Raters did unanimously agree,
however, that seven statements were representative of Cognitive-use, and 10 statements
were representative of Emotional-use.
In summative content analysis, the researcher categorized and counted key words
and phrases in the open-ended responses. Five categories were determined, and they
included background, cognitive, emotional, productivity, and music. Emotional words
were used most often, followed in descending order by cognitive, music, productivity,
and background (see Table 19). Ten words or phrases were each utilized eight or more
times. In the cognition category, “focus” was used 24 times, and “distract” was used 23
times. In the productivity category, “help” was used 18 times and “productive” was used
eight times. In the music category, “lyrics,” “vocals,” and “words” were together used 14
times. In the emotional category, “relax,” “energy,” and “mood” were each used 13
times. Additionally, “calm” and “enjoy” were each used eight times.
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Table 17
Conventional Content Analysis Results for Open-Ended Item #6 on the Music-Use Questionnaire
Responses to “Please explain why you chose the music you listened to.”
Background I chose this music from a pre-existing playlist that I had knowing that it would be music I liked but at the same time was not so busy that it would be distracting from my assignment. It's very ambient. This band has a very specific style, where every song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden underneath textures rather than the focus of the song. It's easy to not pay attention to what they're saying, but still have something to groove to. Cognitive I listened to recordings I made of myself improvising at the piano. I like the music very much and I like how my brain lights up as I remember making the music, it seems to organize and calm my mind. Emotional So Lonely by the Police is a great feel-good song and always gets me in a good mind set. It calms me and does not distract me. BT is one of my foremost musical idols. I haven't listened to his "Dreaming" remix compilation EP, and the song usually puts me into a mellow, yet energized mood. The song itself has a deep existentialist-like meaning to me as well. It seemed like the right choice for my current state. I chose the jazz tracks (Mehldau) because those are my favorite to actively listen to. The rock track (Gabe Dixon) I chose because I believe that it generally puts me in a more upbeat mood. The alternative tracks (Iron and Wine) were chosen because I enjoy the relaxed vibe that they give me. I listened to the Nicolas Jaar BBC Radio 1 Essential Mix DJ set 05-19-12. It had these songs in it (besides the last two). There were other songs in the set but those were the ones that stood out most to me. I listened to that set because I knew it would be a mix of calming yet interesting works. I chose the music based more on coming home from a long day of work and sitting down with a glass of wine to work with, rather than specifically being related to the task at hand. Yet it helped me get through the task with a more relaxed demeanor.
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Table 18
Conventional Content Analysis Results for Open-Ended Item #7 on the Music-Use Questionnaire
Responses to “How do you think music listening influenced you and your work?” Cognitive
I think that it helps me to code for longer periods of time due to the focus I can maintain while I work. I can be easily distracted at times, and as a result my coding time is very fragmented. Having a solid block of time to sit down and work helps me get an entire task complete rather than try to get bits and pieces done over a couple of days wherein I loose time trying to remember why I did a particular thing, or how a function I wrote was intended to work.
It excites my mind, almost as if it’s pushing any mental block I have in the way.
I don’t know. I prefer to do homework and other activities with music mostly because it helps me lose focus on my surroundings and focus on the task I'm trying to complete.
I think it made me better able to focus on my work for a long time without feeling bored or distracted. It kept me energized and motivated.
It helped me to focus on my task more. In quiet rooms, I tend to get distracted by every sound that crops up. When I play music, I zone out while listening to that, and ignore all the other "unexpected" sounds a lot more. It also helps to keep my energy level moderately high.
Mostly I find that listening to music helps me block out distractions around me especially if I'm in a public place like the library. I also feel like I get "in the zone" when I've got the right kind of music playing. It's hard to explain but having a steady, driving beat can help me stay productive, and focus on problems more easily.
I like to think that it kept my mind stimulated at times when repeating menial, repetitive tasks. It probably also distracted me a bit, but I was okay with that.
Emotional
I believe that the music helped me remain relaxed and allowed me to enjoy the programming assignment.
It made me not freak out when I was working on this project. I can get pretty frustrated when I don't understand exactly why a program is not working. I think the music just makes me say to myself: "Okay, it's all good. What is not working here?" I would say it helps me keep my cool.
The music added a mood to my programming task, which is often bland when unaccompanied by music. This helped at certain points but hindered at others and I had to pause the song for a few minutes to focus on troubleshooting errors.
I think it helped me to stay in a relaxed mode so I wouldn't get frustrated when I encountered problems with my code.
The music relaxed me and let me gather my focus and direct it towards the task at hand. I find working in silence gives me anxiety and that I need music at least in the background to work well.
It alleviates some tedium that might have set in, and also improves my mood. It's difficult to tell if it improved my thinking or quality of work in any way.
It definitely calmed me down; I was a bit jittery before the programming. I don't think it necessarily helped me to focus, though.
relaxed me, also covered some outside noise
It certainly puts me in a better mood, which I can describe as cheerful or up-lifted, plus keeps me awake. Because of its high beat, I tend to act more focused and fast and get things done quicker. If I really need to think on things and plan the flow of the code, I choose to pause the music for a while.
I think the music calmed down a little. I don't usually listen to music and program, but I feel like its occupying a part of my brain that might be stressing out normally.
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Table 19
Summative Content Analysis Results for Open-Ended Items #6 and #7 on the Music-Use Questionnaire
Words Utilized in the Discussion of Music-Use During Computer Programming Tasks f f
Background 15 Productivity 52 background 6 help 18 noise 5 productive 8 quiet/silent 4 easy 6
Cognitive 68 flow 5
focus 24 allow (me to) 5 distract 23 (get in the) zone 3 attention 5 stay 3 block out 3 maintain 2 concentrate 3 awake 2 cognitive 2 Music 57 aware 2 lyrics/vocals/words 14 conscious 2 tempo (fast) 6 mental 2 upbeat 5 stimulate 2 beat 5
Emotional 82 rhythm 5
relax 13 pace 4 energy 13 loop 4 mood 13 instrumental 3 calm 8 mellow 3 enjoy 8 shuffle 3 favorite 5 steady 3 motivate 4 unique 2 interesting 4 frustrated 4 excites 2 inspiration 2 state 2 emotion 2 bored 2
Note. f = number of times the word was used.
Chapter Five
Discussion
The purpose of this study was to investigate the ways in which individuals use
music while working. Personality, mood, and music-use data were collected in
connection with a high-cognitive demand computer programming task. Data were
analyzed to identify relationships between music-use, personality, and mood variables.
Statistical relationships between music-use variables, demographics, experience, and
contextual factors were also explored. Additionally, participants’ years of computer
programming background were categorized into either a less experienced group or more
experienced group. Inferential analyses revealed a significant effect of computer
programming background on type of music-use.
An interpretation of the results of this study will be presented in this chapter,
reviewing each research question. Explanations for other emerging relationships in this
study will follow the research questions. Later, the theoretical and clinical implications of
this study will be discussed. Finally, study limitations and recommendations for future
research studies will be identified.
Review of the Research Questions
The relationship between personality and music-use. The results of this study
revealed significant positive correlations between the personality factor of Openness and
both Cognitive and Emotional-use of music. Openness also emerged as a significant
predictor of Cognitive-use of music. Therefore, the findings provide evidence that a
tendency for Openness in student computer programmers is not only related to a trend to
use music for cognitive reasons, Openness also predicts Cognitive-use of music.
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Although Openness did not predict the probability of Emotional-use of music, the
evidence suggests that a strong preference for Openness in computer programmers is also
related to a propensity to use music for emotional reasons.
The results of this study are similar to past research with other individuals, which
found that Openness has a positive relationship with Cognitive-use of music (Chamorro-
Premuzic et al., 2010; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;
Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, & Cermakova,
2012; Chamorro-Premuzic, Swami, Furnham, & Maakip 2009; Isaacson, 2007). The link
between Openness and Cognitive-use of music has been associated with a link between
Openness and intelligence (Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,
2009). Individuals with a preference for Openness may use music to help generate
experiences that enhance cognition (Chamorro-Premuzic & Furnham, 2005). Similarly,
open and intelligent individuals were inclined to listen to music described as ‘complex’
and ‘reflective’ in explorations of personality and music preference (Chamorro-Premuzic
et al., 2010; Rentfrow & Gosling, 2003). Presently, studies that examine the relationship
between personality and music-use in computer programmers or other information
technology professionals are scarce in research literature.
Previous research with typical individuals provides evidence that additional
relationships may exist between personality and music-use. According to past research,
Emotional-use of music has a positive relationship with Neuroticism and a negative
relationship with Conscientiousness. (Chamorro-Premuzic et al., 2010; Chamorro-
Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,
2009; Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). A positive association
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between Extraversion and Background-use of music has also been shown (Chamorro-
Premuzic et al., 2012; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;
Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). In one study, Extraversion was
positively linked with Emotional-use of music and negatively linked with Cognitive-use
of music (Chamorro-Premuzic et al., 2012). Although none of these relationships
between personality and music-use were statistically significant in the present study with
student computer programmers, the positive versus negative directions of the correlations
that emerged were alike.
Incidentally, the student computer programmers in this study scored significantly
higher on the Openness factor, when compared to typical adults. The high degree of
Openness in this sample may be related to a number of factors. The task of computer
programming may be attractive to individuals with a preference for Openness. Perhaps
abstract thinking and creative problem-solving demands Openness. The average age and
musical experience of this sample may also be connected with Openness. University
students tend to be explorative in their search for identity and independence (Nairne,
2009a). The participants in this study had an average of over nine years of musical
background and reported listening to two to three hours of music daily. With music
playing such a strong role in these individuals’ lives, it is likely that musical background
and daily listening duration are associated with Openness. In fact, a significant positive
relationship was found between musical background and Openness in this study. A high
degree of Openness is also unsurprising, given that the individuals who agreed to
participate in this study did so without incentive. Therefore, Openness to Experience is
to be expected in this sample.
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The results are consistent with previous research in terms of computer
programmer personality. Past research with university students provided evidence that
the personality preference for intuitive-thinking is more typical in computer
programmers, when compared to the other MBTI personality preferences (Woszczynski
et al., 2005). The NEO-FFI Openness dimension corresponds to the MBTI preference of
intuition (Costa & McCrae, 1992). Additionally, an earlier study showed a MBTI
preference for intuition in students majoring in computer science (Pocius, 1991).
Past research has also shown that introversion, thinking, and judging preferences
were also typical among information technologists. Introversion and thinking
preferences were apparent in an early study of students majoring in computer science
(Pocius, 1991). Similarly, introversion, thinking, and judging preferences were prevalent
in a study with computer information systems developers (Lesiuk et al., 2009). The
MBTI introversion-extraversion dichotomy corresponds to the NEO-FFI Extraversion
dimension, the thinking-feeling dichotomy corresponds with Agreeableness, and the
judging-perceiving dichotomy corresponds with Conscientiousness (Costa & McCrae,
1992). Although the scores for Extraversion, Agreeableness, and Conscientiousness were
not significantly different from age-matched adults in the current study, diverse
personality profiles are to be expected in this population.
The relationship between mood and music-use. Significant correlations did not
emerge between any of the mood variables or subscales and any of the music-use
categories in the current study. The strongest non-significant correlation appeared
between Positive Affect and Cognitive-use of music. A negative correlation value was
calculated between these variables, so using music for cognitive reasons may become less
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likely as Positive Affect increases. Given the assumption that affect is related to arousal,
it is possible that when music becomes too arousing, or incites too much pleasure, it gets
rejected as an aid to cognition.
Studies that explore the relationship between mood and music-use in computer
programmers are scarce in current research literature. However, a predictive relationship
between music listening and mood has been examined, and a few studies have measured
this association in information technology professionals. Greenwood and Long (2009)
found that some individuals were inclined to use music for mood regulation in general,
regardless of whether they were in a positive or negative mood. Furthermore, when
individuals were bored or experiencing a negative mood, the probability of using music
for mood regulation increased. More anecdotal evidence to support the relationship
between mood and music-use was presented by Lonsdale and North (2011), who found
that mood management was a prominent theme in participant responses for reasons why
they listen to music. Subthemes included mood control, arousal management, positive
mood creation, mood enhancement, emotional expression, and emotional exploration.
Although similar results were not shown in the current study, mood is still likely to
interact with music-use during high-cognitive demand tasks.
A series of studies were conducted with information technology professionals to
explore the effect of music-use on mood and subsequent work productivity (Lesiuk,
2010a, 2010b; Lesiuk et al., 2009, 2011). Several effects of music-use on positive and
negative mood states emerged during these studies, and open-ended responses revealed
that these individuals used music specifically to control, enhance, or regulate their
moods. These results suggest that these individuals used music for emotional reasons,
88
based on their current mood, and findings were similar in the current study, based on
content analyses of open-ended responses. When participants were asked to explain why
they chose the music they did, 34 percent of the responses were emotional in content.
Likewise, 41 percent of the responses were emotional in content when participants
expressed how they thought music listening influenced them and their work.
One mood subscale emerged as prevalent in the current study. Fatigue accounted
for 77 percent of the total Negative Affect score. Therefore, Negative Affect scores can
be mostly attributed to Fatigue, rather than Nervousness. Fatigue is to be expected in
university students. A low Nervousness score was no surprise, either, since participants
completed the mood scale and computer programming task on their own and at a time
and place of their choosing. Furthermore, participants completed a computer
programming task of their choosing. In other words, the study was specifically designed
to involve a natural unrestricted work situation.
The relationship between demographics, experience, contextual factors and
music-use. Correlations between demographic data and music-use variables revealed no
significant relationships. Significant correlations were found, however, between
computer programming experience, computer programming-related contextual factors
and music-use variables. First, participants’ level of computer programming expertise
was positively correlated with Emotional-use of music. This finding provides evidence
that advanced student computer programmers are more drawn to use music for emotional
reasons, when compared to less advanced student computer programmers. Additionally,
programming background and level of computer programming task difficulty were
negatively correlated with Cognitive-use of music. These findings provide evidence that
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students with more years of computer programming background are less inclined to use
music for cognitive reasons. Likewise, during more difficult computer programming
tasks, Cognitive-use of music is less preferred. Perhaps more complex computer
programming tasks require greater attention and focus, inhibiting Cognitive music-use.
Such a theory is supported by Sloboda’s (2010) sixth dimension of everyday music use,
referred to as centrality of music to the experience and the salience of the context, in
which the nonmusical activity requires more attention relative to the music.
Significant correlations also emerged between listening-related contextual factors
and music-use variables. The level of music activity, duration of daily listening, and
participants’ level of focus on the music were all positively correlated with Cognitive-use
of music. It appears that when student computer programmers listen to music for
cognitive reasons, they choose music that is highly active, rather than music with a low
activity level. This interpretation is supported by Berlyne’s (1971a) optimal arousal
theory, in which arousal and subsequent attention are attributed to psychophysical
properties in the music. Furthermore, student computer programmers who use music in a
cognitive way during computer programming tasks are inclined to listen to longer
durations of music in their daily lives. Last, the evidence suggests that student computer
programmers who use music for cognitive reasons are more highly focused on the music
during computer programming tasks, when compared to peers who use music for
background or emotional reasons. Therefore, Cognitive-use of music appears to demand
a high degree of focus on the music stimulus.
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Studies linking computer programming experience and computer programming-
related contextual factors to music-use are scarce in current research literature.
Furthermore, music-use has not been specifically explored in terms of listening-related
contextual factors. Relationships between demographic variables and music-use have
been measured in past studies, though. Past researchers found that age and school level
were both negatively correlated with Background-use of music in typical individuals.
Specifically, as age and school level increased, the likelihood of using music for
background reasons decreased (Chamorro-Premuzic et al., 2012). Although similar
results were not revealed in the current study, age and school level may still correlate
with music-use during high-cognitive demand tasks.
Another recent study used several methods to explore the way music-use changes
with age in typical individuals (Lonsdale & North, 2011). Although music-use was not
divided into the same quantifiable categories as this study, age had notable relationships
with various aspects of music-use. The past findings provided evidence that music’s
importance in daily life decreases significantly after the age of 30. Individuals over 50
years of age spent significantly less money per month on music, when compared to
individuals between the ages of 18 and 49. Additionally, individuals under the age of 30
spent significantly more time listening to music (Lonsdale & North, 2011). Similarly, a
significant negative correlation emerged between age and music listening duration in a
recent study with computer information systems analysts (Lesiuk, 2005).
The effect of computer programming background on Cognitive-use of music.
By placing participants into two groups, based on years of computer programming
background, the relationship between computer programming background and music-use
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was further tested for effect. In this study, less experienced student computer
programmers had a stronger preference for Cognitive-use of music, when compared to
more experienced programmers. The results showed a significant effect of computer
programming background on Cognitive-use of music for the two groups, providing
evidence that using music for cognitive reasons is directly influenced by a student
computer programmer’s background experience. Perhaps less experienced programmers
are drawn to use music for cognitive reasons because they have yet not acquired enough
computer programming skills to work efficiently. With less computer programming
resources at their disposal, music-use may be an external method to improve focus. Over
time, enhanced attention may aid in the acquisition of a desired computer skill. Then as
these individuals gain computer programming experience, cognitive music-use may lose
importance in light of new knowledge. Related findings are not yet present in research
literature, due to the unique nature of this research question. Future research is requested
to test the interaction between computer programming background and Cognitive-use of
music.
Review of the Content Analyses
Content analyses of open-ended responses regarding music-use were conducted to
compare qualitative and quantitative data and identify trends. The results of directed
content analysis were mostly compatible with results from the Uses of Music Inventory.
In particular, the raters’ distribution of responses to, “Please explain why you chose the
music you listened to,” were quite similar to average scores on each of the Uses of Music
Inventory categories. First, 30 percent of the responses to this item related to
Background-use, and mean scores for Background-use represented 34 percent of total
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scores on the Uses of Music Inventory. Next, 28 percent of the responses to this open-
ended item related to Cognitive-use, and mean scores for Cognitive-use represented 32
percent of scores on the Uses of Music Inventory. Last, 34 percent of responses to this
item related to Emotional-use, and mean scores for Emotional-use represented 34 percent
of scores on the Uses of Music Inventory. The responses to, “How do you think music
listening influenced you and your work?” were more heavily distributed between
Cognitive (38%) and Emotional-use (41%).
The results of conventional content analysis revealed characteristic responses for
each of the music-use categories. A response that was clearly representative of
Background-use was, “It's very ambient. This band has a very specific style, where every
song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden
underneath textures rather than the focus of the song. It's easy to not pay attention to
what they're saying, but still have something to groove to.” It seems that this individual
uses music with specific psychophysical characteristics, meant to accompany, but not
alter his or her work. For Cognitive-use, one participant stated, “Mostly I find that
listening to music helps me block out distractions around me especially if I'm in a public
place like the library. I also feel like I get ‘in the zone’ when I've got the right kind of
music playing. It's hard to explain but having a steady, driving beat can help me stay
productive, and focus on problems more easily.” For this individual, the right type of
music facilitates focus. Last, a response that was representative of Emotional-use was,
“It made me not freak out when I was working on this project. I can get pretty frustrated
when I don't understand exactly why a program is not working. I think the music just
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makes me say to myself: ‘Okay, it's all good. What is not working here?’ I would say it
helps me keep my cool.” It appears that this individual uses music to remain calm during
moments of frustration.
Finally, summative content analysis of open-ended responses resulted in five
subject categories and several commonly used words. In addition to background,
cognitive, and emotional categories, words were sorted into productivity and music
groups. Emotional words were used most often, followed in descending order by
cognitive, music, productivity, and background words. Emotional words were more
diverse as well, with the words “relax,” “energy,” “mood,” “calm,” and “enjoy” each
occurring at least eight times. The words “focus” and “distract” dominated the cognitive
category, each occurring at least 23 times. Music words had two primary themes, one
regarding the presence of lyrics in the music, and another regarding musical tempo. The
most widely used word relating to productivity was “help,” and an argument could be
made for this word being too general to be categorized. More definitive in this category
was the word “productive,” which was used eight times.
Limitations of the Study
Limitations of this study included small sample size and an uneven number of
participants in the computer programming background groups. A larger sample size
would have increased the statistical power of the correlations between personality, mood,
and music-use. Furthermore, a larger sample size may have revealed other relationships
between demographics, experience, contextual factors, and music-use. A larger sample
size would have also increased the statistical power needed to identify other effects of
94
computer programming background on music-use. Moreover, a larger sample size may
have enabled participants to be divided into other types of groups to investigate the effect
of different variables on music-use.
Another possible limitation of this study was the use of the Job Affect Scale,
which may not have adequately captured mood. The Job Affect Scale was chosen for its
quick administration, its application to the workplace, and its use in similar research
studies. Since this scale included a list of 12 words that a person may use to describe his
or her feelings while working, it may not have been the most appropriate scale to use in
an educational setting. This scale was also not as long as some other prevalent mood
scales. For instance, the Profile of Mood States (POMS) Brief assessment is an
alternative method of assessing active mood states, consisting of 30 words that describe
feelings people may have in any context (McNair, Lorr, & Droppleman, 1971).
Additionally, location threats were possible, given that participants completed
both phases of the study in various locations. During the first phase, participants met
with the researcher at several locations, over a number of days, and at different times of
day. However, only the demographic questionnaire and personality inventory were
completed during the first phase. These measures gathered static data, so participant
responses should not be variable due to location. During the second study phase,
participants completed online questionnaires in a variety of locations. Since these
measures surveyed state-dependent attitudes, location characteristics may have affected
participant responses. For example, a participant working in a shared computer lab may
have been prone to use music for cognitive reasons, in order to block out distractions in
the room. Therefore, alternate explanations for the results are possible. Yet, the study
95
was specifically designed to capture participant responses in a natural setting, and the
location, day of week, and time of day were accounted for on the computer programming
task assessment.
Theoretical Implications
This study indicates that a relationship exists between personality and music-use
during a high-cognitive demand task. A significant positive correlation emerged between
Openness and both Cognitive and Emotional-use of music, and a predictive relationship
was found between Openness and Cognitive-use of music. These results suggest that
individuals with preference for Openness tend to use music for cognitive reasons during
high-cognitive demand tasks.
This study indicates that a relationship may not exist, however, between initial
mood and music-use. The mood variables in this study were not significantly correlated
with any of the music-use variables. Furthermore, a significant relationship was not
found between mood and personality in this study. The absence of these relationships in
this study does not suggest, though, that interactions among these variables are
implausible in other individuals and in different contexts. Mood change in relation to
music-use is another possible interaction that was not explored in the current study
The findings also provide evidence of several other relationships with music-use.
First, a significant positive correlation emerged between computer programming
proficiency and Emotional-use of music. This result suggests that as computer
programming proficiency increases, so does the likelihood of using music for emotional
reasons. Next, Cognitive-use of music was involved in several significant correlations.
Music activity level, listening hours per day, and music focus were each positively
96
correlated with Cognitive-use of music. These results indicate that Cognitive-use of
music increases the likelihood of using music that is highly active and demands more
intense focus on the music. Likewise, the results show that individuals who use music
for cognitive reasons listen to longer durations of music in their daily life. Additionally,
a negative correlation emerged between computer programming task difficulty and
Cognitive-use of music, suggesting that the likelihood of using music for cognitive
reasons decreases as computer programming tasks become more difficult. Last,
computer programming background
was also negatively correlated with Cognitive-use of music. This result suggests that as
years of computer programming background increase, the likelihood of using music for
cognitive reasons decreases.
This study also indicates that computer programming background has an effect on
Cognitive-use of music. Less experienced student computer programmers were more
drawn to use music for cognitive reasons, when compared to more experienced student
computer programmers. This result suggests that the use of music for cognitive reasons
changes with experience. Music-use may lose priority as a cognitive aid when computer
programmers gain other skills more specific to the task. Acquisition of a new coding
language, for example, may be more effective than using music to improve cognition.
Clinical Implications
The results of this study provide implications for the use of music during
computer programming tasks. Regardless of music’s effect on productivity in this
domain, certain individuals find it necessary to listen to music while they work. All of
the participants in this study listen to music while completing computer programming
97
tasks. A higher degree of Openness was found among these individuals, when compared
to the typical population. Therefore, the specific personality trait of Openness may be
associated with music-use in general among computer programmers. Furthermore,
individuals in the typical population who have a preference for Openness may be drawn
to use music when they complete work tasks. Music therapists, as well as employers,
may suggest music-use in the workplace for individuals who possess this personality
preference.
Cegielski (2006) found that personality is predictive of computer programming
performance in an undergraduate object-oriented programming course. Compared to past
research, which found that cognitive ability and personality are equally predictive of
computer programming ability, Cegielski found that personality is the stronger predictor.
In particular, performance may be dependent on personality factors related to self-esteem
and self-efficacy (Cegielski, 2006). Self-esteem is determined by one’s evaluation of
worth, and self-efficacy involves the independent assessment of one's ability to complete
tasks and achieve goals. Similarly, the NEO-FFI Openness dimension includes attention
to inner feelings and independence of judgment (Costa & McCrae, 1992). These findings
suggest that employers of computer programmers should pay particular attention to
aspects of personality when assessing a potential employee.
The results of this study also have implications outside the workplace. Music
therapists may use music to facilitate cognitive goals in educational settings, where
abstract thinking and creative problem-solving are often in demand. The use of music in
school may be beneficial to a variety of populations, but individuals with attention-deficit
disorders and other developmental disorders may find music-use to be particularly
98
advantageous. Participants in this study reported using music to maintain focus and
block out distractions. Individuals who struggle with selective and sustained attention
may listen to preferred music to improve and increase focus on written work and other
visual stimuli.
Other participants in this study reported using music for emotional reasons during
a high-cognitive demand task. Again, various people in various contexts may use music
as a means of regulating emotions, but certain individuals may benefit from music
listening as a means of functional emotional goal development. Individuals with mood
disorders, for instance, may experience a pervasive undesirable mood or extreme
fluctuations in mood. A disorder implies that these experiences impair one’s
occupational functioning and social skills, and such impairments affect multiple aspects
of daily life (American Psychiatric Association., 2000). Music therapists may use music
listening to help individuals with mood disorders vector and stabilize their mood, thus,
avoiding disturbances in daily life.
Finally, the results of this study may have implications in the commercial domain.
One general theme emerged from this study: Certain individuals use music listening as a
resource, and they appear to understand how and why. For developers of music
recommendation software, this study provides a new music therapy perspective. Instead
of focusing on what is similar or different about the music people choose and trying to
guess what they want to hear next, these developers may turn their attention to the
reasons why listeners choose certain music in relation to a type of activity or task at hand.
A listener may use the software initially to report his or her mood and the impending
99
activity. As the program receives more input from the listener and tracks the music that
is chosen, it can begin to identify trends in the listener’s music-use. Later, the listener
can ask the program to recommend music, based on a given mood and upcoming event.
Rather than recommending new music to these listeners, the software may be designed to
hand-pick music from their preferred music library, based on past input.
Recommendations for Future Research
To further explore the role of personality and mood in music-use during high-
cognitive demand tasks, future research should be conducted with a larger sample size.
Additional research that examines the effect of computer programming background on
music-use is also requested, including a larger sample size and an even number of
participants placed in each level of computer programming background. Additionally,
future research could explore all of these interactions in professional computer
programmers, also accounting for workplace demands, such as deadlines and
performance evaluations.
A similar study could measure mood before and after a computer programming
task, analyzing for relationships between, personality, mood change, and music-use.
Such a study may reveal an interaction between mood change and Emotional-use of
music, for instance. One would expect that individuals who report using music for
emotional reasons would also report a change in mood. An exploratory study was
conducted to examine mood change and its association with individual descriptions of the
functions of music in specific contexts (Sloboda, O'Neill, & Ivaldi, 2001). Data were
collected from participants in two-hour increments using a paging device, so mood
change was not measured concurrent to a specific task. Results showed, however, that
100
individuals in various contexts used music to control, enhance, or regulate their present
mood (Sloboda et al., 2001). Later, Lesiuk (2005) collected narrative data regarding
mood change from computer information systems analysts, working under the
assumption that an increase in state positive affect has positive effects on work
performance. Participant responses indicated music-use for positive mood change and
improved perception on work tasks (Lesiuk, 2005).
A future study could also test mood and music-use variables on more than one
occasion, accounting for differences in computer programming task difficulty. Anecdotal
interpretation of participant responses in this study suggests that music-use may vary
within one individual, based on state mood and level of task difficulty. Several studies
have taken this more longitudinal view of mood and music-use, although research does
not yet exist to explore this interaction in parallel with computer programming tasks
(Greasley & Lamont, 2011; Greenwood & Long, 2009; Isaacson, 2007; Lonsdale &
North, 2011). A few studies have investigated mood and music-use over time in
computer systems information analysts, but variable difficulty in work tasks was not
accounted for in these studies (Lesiuk, 2005; Lesiuk et al., 2009).
Summary and Conclusions
The purpose of this study was to investigate the ways in which individuals use
music while working. Personality, mood, and music-use data were collected in
connection with a high-cognitive demand computer programming task. Participants were
involved in a computer programming task of their choosing, and they each listened to
music from their personal collection during the task. Personality and demographic data
101
were collected before and separate from the computer programming task. Mood data
were collected immediately prior to the task, and music-use data were collected
immediately following the task, both via a study webpage. Based on years of computer
programming background, each participant was placed into a less experienced group or a
more experienced group for analysis purposes.
The findings indicated several significant relationships. In particular reference to
the research questions, positive correlations emerged between the personality factor of
Openness and both Cognitive and Emotional-use of music, and the relationship between
Openness and Cognitive-use was supported in a predictive model. Additionally,
computer programmers in this study scored significantly higher than typical adults on
Openness. No significant correlations were found between any of the mood and music-
use variables. However, some of the demographic, experience, and contextual factor
variables were significantly correlated with music-use. Computer programming
proficiency was positively correlated with Emotional-use of music. Next, music activity
level, listening duration, and music focus were each positively correlated with Cognitive-
use of music. Contrastingly, computer programming background and task difficulty were
each negatively correlated with Cognitive-use of music. Last, the findings also indicated
a significant effect of computer programming background on Cognitive-use of music.
As a result, individuals with a preference for Openness appear to use music for
both cognitive and emotional reasons, but the bond between Openness and Cognitive-use
of music may be stronger. Also, as an individual becomes more proficient at computer
programming, he or she may be more likely to use music for emotional reasons.
Additionally, Cognitive-use of music appears to demand increased focus on the music
102
stimulus as well as music with a high activity level. Individuals who use music in a
cognitive way also tend to listen to longer durations of music in their daily lives. The
likelihood of using music for cognitive reasons appears to decrease, however, as the
concurrent computer programming task increases in complexity. Similarly, Cognitive-
use of music appears less likely as years of computer programming background increase.
Furthermore, computer programming background appears to have a differential effect on
Cognitive-use of music. Less experienced student computer programmers showed a
significantly stronger preference for the Cognitive-use of music, when compared to more
experienced computer programmers.
The themes that emerged in open-ended responses from this study generally
supported the quantitative results obtained. Participant statements typically related to one
of the music-use categories, and the distribution of responses was similar to the
distribution of scores on the Uses of Music Inventory. In addition to utilizing words
related to the music-use categories, participants employed specific language to describe
the type of music they chose and its influence on overall productivity.
To conclude, everyday music-use is a complex process, being impacted by
contextual factors and individual differences. Contextual factors differ for each listening
experience, as do mood states. These variables should not be overlooked in future
explorations of music-use. Personality, although more stable than context and mood,
appears to play an important role as well. Thus, similar research considerations should
include all three of these elements as significant contributors to everyday music-use.
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Appendix A
Demographic Questionnaire
Participant Number: ________
Please respond to each item accurately and honestly. Also, please do not skip any items.
Thank you for your participation.
1. Age: ________ 2. Gender: ________________ 3. What is your ethnicity? (select one) [ ] Hispanic or Latino [ ] Not Hispanic or Latino 4. What is your race? Mark one or more races to indicate what you consider yourself to be. [ ] American Indian or Alaska Native [ ] Asian [ ] Black or African American [ ] Native Hawaiian or Other Pacific Islander [ ] White 5. School Level (circle one): Undergraduate Masters Doctoral
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6. Current degree being pursued: ________________________________________________ 7. Years of computer programming experience (circle one): 0 1-2 3-4 5-6 7-8 9+ 8. Average number of hours spent daily on computer programming (choose one): [ ] 0 – 1 hours [ ] 6 – 7 hours [ ] 2 – 3 hours [ ] 8 – 9 hours [ ] 4 – 5 hours [ ] 10+ hours 9. Most prevalent computer programming language used: ____________________________ 10. Level of proficiency in computer programming (check one): ________ 0 = Absolute Beginner (I have little or no knowledge.) ________ 1 = Novice (I have created a few simple computer programs.) ________ 2 = Intermediate (I am moderately proficient.) ________ 3 = Advanced (I have created complex computer programs.) ________ 4 = Power User (I consider myself an expert in computer programming.)
Appendix B
NEO-FFI Instructions
Write only where indicated in the booklet. Carefully read all of the instructions
before beginning. This questionnaire contains 60 statements. Read each statement
carefully. For each statement, fill in the circle with the response that best represents your
opinion. Make sure that your answer is in the correct box.
Fill in (SD) if you strongly disagree or the state is definitely false.
Fill in (D) if you disagree or the statement is mostly false.
Fill in (N) if you are neutral on the statement, if you cannot decide, or if the statement is about equally true and false
Fill in (A) if you agree or the statement is mostly true.
Fill in (SA) if you strongly agree or the statement is definitely true.
Fill in only one response for each statement. Respond to all of the statements,
making sure that you fill in the correct response. If you need to change an answer, make
an “X” through the incorrect response, and then fill in the correct response. Note that the
responses are numbered in rows.
NEO-FFI Sample Items
8. Once I find the right way to do something, I stick to it.
20. I try to perform all the tasks assigned to me conscientiously.
32. I often feel as if I’m bursting with energy.
44. I’m hard-headed and tough-minded in my attitudes.
51. I often feel helpless and want someone else to solve my problems.
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Appendix C
Job Affect Scale Below is a list of words that a person may use to describe their feelings while working.
Please use the scale provided, and indicate how you feel at this time. Please be open
and honest with your responses. Also, please do not skip any items.
1 2 3 4 5 6 7 Extremely Fairly Slightly Moderately Fairly Strongly Extremely Slightly Slightly Strongly Strongly
________ 1. Calm ________ 2. Sleepy ________ 3. Strong ________ 4. Excited ________ 5. Scornful ________ 6. Hostile ________ 7. Relaxed ________ 8. At rest ________ 9. Nervous ________ 10. Drowsy ________ 11. Elated ________ 12. Sluggish
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Appendix D
Task Assessment 1. Please rate the complexity of the computer programming task that you just
completed (choose one answer only). [ ] Extremely Easy [ ] Easy [ ] Moderate [ ] Difficult [ ] Extremely Difficult 2. What was the length of time in minutes that you spent on the computer programming
task? ________________ minutes 3. Where did you complete the computer programming task? (select one) [ ] Home [ ] Work [ ] Library [ ] Other: ________________________ 4. During what time of day did you complete the computer programming task?
(select one) [ ] Morning [ ] Afternoon [ ] Night
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5. During what day of the week did you complete the computer programming task? (select one) [ ] Sunday [ ] Monday [ ] Tuesday [ ] Wednesday [ ] Thursday [ ] Friday [ ] Saturday
Appendix E
Music-Use Questionnaire Please respond to each item accurately and honestly. This questionnaire completes the
research study. When you have finished the questionnaire, please logoff the study
website. Thank you for your participation.
1. List the music that you listened to during the task: Song/Piece Artist/Band Style _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ 2. Generally, how active was the music that you chose? (choose one) [ ] Extremely Inactive (very low energy) [ ] Inactive (low energy) [ ] Moderate [ ] Active (high energy) [ ] Extremely Active (very high energy)
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3. What was the length of time in minutes that you spent listening to music while working on the computer programming task?
________________ minutes 4. How focused were you on the music during the computer programming task? (choose one) [ ] Extremely Focused [ ] Focused [ ] Neutral [ ] Unfocused [ ] Extremely Unfocused 5. What type of device did you use to play and listen to the music? For example, “MP3 Player” _________________________ Did you use headphones? ________ 6. Please explain why you chose the music you listened to. You may want to refer
to a specific song, artist, or style in your answer. ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________
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7. How do you think music listening influenced you and your work? Please provide a description.
______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________
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8. Uses of Music Inventory Below there is a list of statements which a person may use to describe one’s
feelings about listening to music. Use the scale provided, and answer each item.
Please be open and honest with your responses. Also, please do not skip any
items.
___________________________________________________________________________ ________ a). I often feel very lonely if I don’t listen to music. ________ b). I often enjoy analyzing complex musical compositions. ________ c). Whenever I want to feel happy, I listen to a happy song. ________ d). Listening to music is an intellectual experience for me. ________ e). I don’t enjoy listening to pop music because it’s very primitive. ________ f). Music is very distracting so whenever I study I need to have silence. ________ g). I enjoy listening to music in social events. ________ h). When I listen to sad songs I feel very emotional. ________ i). Listening to music really affects my mood. ________ j). Rather than relaxing, when I listen to music I like to concentrate on it. ________ k). I enjoy listening to music while I work. ________ l). I am not very nostalgic when I listen to old songs I used to listen to. ________ m). I seldom like a song unless I admire the technique of the musicians. ________ n). If I don’t listen to music while I’m doing something, I often get bored. ________ o). Almost every memory I have is associated with a particular song.
1 2 3 4 5 Strongly Disagree Neutral Agree Strongly Disagree Agree
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9. How many years have you played a musical instrument or sung in a choir? _____________ years 10. How much time do you typically spend listening to music each day (choose one)? [ ] 0 – 1 hours [ ] 6 – 7 hours [ ] 2 – 3 hours [ ] 8 – 9 hours [ ] 4 – 5 hours [ ] 10+ hours 11. Any additional comments about the music you listened to while coding? ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________
Appendix F
Study Advertisement
Looking for student computer programmers!
Do you listen to music while you code?
If so, a project is taking place on campus that
needs participants just like you.
The music therapy program is exploring the role of personality and
mood in music-use during a computer programming task. Participants
will initially complete brief personality and demographic
questionnaires under the supervision of the researcher. Later at the
convenience of the participant, Web-based mood and music
questionnaires will be completed in conjunction with a coding task of
their choice. Total participation should last about one hour and no
more than two hours, including the computer programming task.
Interested in participating?
Please contact the investigator, Andy Panayides, at 813-992-0988 or [email protected].
You may also contact Dr. Mitsunori Ogihara at 305-284-2308 or [email protected].
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Appendix G
University of Miami CONSENT TO PARTICIPATE IN A RESEARCH STUDY
THE ROLE OF PERSONALITY AND MOOD IN MUSIC-USE DURING A HIGH-COGNITIVE DEMAND TASK
The following information describes the research study in which you are being asked to participate. Please read the information carefully. At the end, you will be asked to sign if you agree to participate. You will also be asked to provide your email address and phone number, so that you may be contacted regarding the second portion of this research study. PURPOSE OF STUDY: You are being asked to participate in a research study. This study will investigate the ways in which individuals use music while working. Personality, mood, and music-use data will be collected in connection with a high-cognitive demand task - computer programming. Uses of music will be related to personality and mood variables. You are being asked to be in the study because you usually listen to music while completing computer programming tasks. PROCEDURES: This study requires participation at two separate times, and the second portion must be completed within two weeks of the first. During the first portion and in sequence:
1. You will complete a personality inventory, which takes approximately 15 minutes. 2. You will complete a demographic questionnaire, which takes approximately 5
minutes. During the second portion and in sequence:
1. You will receive an email containing a website address and a participant number. Once you are logged in, you will complete the Job Affect Scale, which will ask you to rate your current mood on a given numerical scale. This assessment takes approximately 3 minutes.
2. You will complete a computer programming task. Specifically, you should complete a difficult coding task that lasts a minimum of 20 minutes without interruption. During this task, you are expected to listen to at least 10 minutes of music. Whenever possible, you will use music software that allows you to track and refer back to a playlist. If you do not typically listen to preferred music while programming, you should not participate in this study.
3. Upon completion of the computer programming task, you will return to the research study website. You will rate the complexity of the computer programming task that you just completed and indicate the length of time that you spent on the task. You will also specify where and when you completed the task. This task assessment will take no longer than 2 minutes.
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4. On the website, you will also complete a music-use questionnaire, which will ask you open-ended and guided questions about the music that you listened to during the computer programming task. It takes approximately 20-30 minutes. It will be helpful if you are able to refer back to the music that you listened to during the computer programming task to accurately and thoroughly complete this final questionnaire. You will also be given the opportunity to upload your playlist to the website.
Participation in the study will last a minimum of 75 minutes, including the computer programming task. The maximum length of time that you are expected to participate is 2 hours. RISKS AND/OR DISCOMFORTS: We do not anticipate that you will experience any personal risk or discomfort from taking part in this study. There may be uncommon or unknown risks. You should report any problems to the researcher. BENEFITS: No direct benefit can be promised to you from your participation in this study. CONFIDENTIALITY: The investigators and their assistants will consider your records confidential to the extent permitted by law. All data will be stored in a secure location within the music therapy department at the University of Miami. Names and individual demographic information will not be reported in the study. The U.S. Department of Health and Human Services (DHHS) may request to review and obtain copies of your records. Your records may also be reviewed for audit purposes by authorized University or other agents who will be bound by the same provisions of confidentiality. COMPENSATION: There will not be compensation for participation in this study. RIGHT TO DECLINE OR WITHDRAW: Your participation in this study is voluntary. You are free to refuse to participate in the study or withdraw your consent at any time during the study. The investigator reserves the right to remove you without your consent at such time that they feel it is in the best interest for you. If you are an employee or student at the University of Miami, your desire not to participate in this study or request to withdraw will not adversely affect your status as an employee or grades at the University of Miami. CONTACT INFORMATION: Teresa Lesiuk, Ph.D., Associate Professor of Music Therapy, at 305-284-3650, will gladly answer any questions you may have concerning the purpose, procedures, and outcome of this project. If you have questions about your rights as a research subject you may contact Human Subjects Research Office at the University of Miami, at (305) 243-3195.
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PARTICIPANT AGREEMENT: I have read the information in this consent form and agree to participate in this study. I have had the chance to ask any questions I have about this study, and they have been answered for me. I am entitled to a copy of this form after it has been read and signed. ____________________________ __________________ Signature of Participant Date ____________________________ Participant Email Address ____________________________ Participant Phone Number ____________________________ __________________ Signature of person obtaining consent Date Participant Number: ________
Appendix H
Other Significant Relationships
Table A.1
Pearson’s Product Moment Correlations (r) Between Personality Factors Personality Factors Extraversion Openness Agreeableness Conscientiousness Neuroticism
-0.40*
0.21
-0.11
-0.03
Extraversion
0.36* 0.31 0.17
Openness
-0.12 -0.00
Agreeableness
0.21
Note. * p < .05, two-tailed.
Table A.2
Pearson’s Product Moment Correlations (r) for Mood Variables with Mood Subscales Variables Subscales Positive Affect Negative Affect Relaxation
0.69**
0.16
Enthusiasm
0.43* 0.05
Nervousness
0.41* 0.51**
Fatigue
-0.18 0.60**
Note. * p < .05, two-tailed; ** p < .01, two-tailed.
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Table A.3
Pearson’s Product Moment Correlations (r) between Mood Subscales Subscales Enthusiasm Nervousness Fatigue Relaxation
-0.36*
0.51**
-0.31
Enthusiasm
-0.11 0.15
Nervousness
-0.39*
Note. * p < .05, two-tailed; ** p < .01, two-tailed.
Table A.4
Pearson’s Product Moment Correlations (r) for Personality Factors with Other Continuous Variables
Personality Factors Neuroticism Extraversion Openness Agreeableness Conscientiousness
Age
-0.08
0.13
-0.11
0.15
0.10
Musical Background
0.38*
0.20
0.63**
-0.01
-0.16
Task Duration
-0.00 -0.28 -0.15 -0.14 -0.12
Listening Duration
-0.18 -0.12 -0.08 -0.15 -0.11
Note. * p < .05, two-tailed; ** p < .01, two-tailed.
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Table A.5
Pearson’s Product Moment Correlations (r) Between Other Continuous Variables
Other Continuous Variables Musical Background Task Duration Listening Duration
Age
0.09
-0.04
0.10
Musical Background
-0.34* -0.13
Task Duration
0.75**
Note. * p < .05, two-tailed; ** p < .01, two-tailed.
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Table A.6
Spearman’s Rank Correlations (rs) for Personality Factors with Ordinal Variables Personality Factors Neuroticism Extraversion Openness Agreeableness Conscientiousness School Level
-0.08
0.18
0.18
0.07
-0.13
Computer Programming Proficiency
0.13
-0.11
-0.13
-0.31
0.06
Computer Programming Background
-0.26
-0.11
-0.22
-0.25
-0.08
Computer Programming Hours/Day
-0.09
-0.04
0.03
-0.17
0.12
Computer Program Task Difficulty
0.04
-0.02
-0.21
0.04
0.06
Music Activity Level
0.15
0.35*
0.35*
-0.10
-0.23
Listening Hours/Day
-0.05
0.18
0.41*
-0.23
-0.02
Music Focus
-0.39* 0.22 0.09 -0.23 -0.08
Note. * p < .05, two-tailed.
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Table A.7
Spearman’s Rank Correlations (rs) for Mood Variables and Subscales with Ordinal Variables Mood Variables Mood Subscales Positive Negative Relaxation Enthusiasm Nervousness Fatigue School Level
-0.02
0.09
-0.24
0.22
0.07
0.14
Computer Programming Proficiency
0.03
0.12
-0.15
0.16
0.02
0.12
Computer Programming Background
-0.03
0.15
-0.20
0.14
0.06
0.18
Computer Programming Hours/Day
0.54**
0.07
0.19
0.40*
0.12
0.10
Computer Program Task Difficulty
0.19
0.26
-0.06
0.23
0.29
0.09
Music Activity Level
-0.05 0.26 0.04 -0.17 -0.01 0.23
Listening Hours/Day
0.20 -0.26 0.12 0.14 0.03 -0.20
Music Focus
0.05 0.04 -0.13 0.21 0.16 0.03
Note. * p < .05, two-tailed; ** p < 0.01, two-tailed.
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Table A.8
Spearman’s Rank Correlations (rs) for Other Continuous Variables with Ordinal Variables Other Continuous Variables
Age Musical Background
Computer Program Task Duration
Listening Duration
School Level
0.68**
0.14
0.20
0.28
Computer Programming Proficiency
0.27 -0.13 0.32 0.27
Computer Programming Background
0.61** -0.18 0.34* 0.34*
Computer Programming Hours/Day
0.18 -0.02 0.23 0.23
Computer Programming Task Difficulty
0.12 -0.12 0.18 0.08
Music Activity Level
-0.17 0.17 0.10 0.02
Listening Hours/Day
-0.26 0.09 0.21 0.22
Music Focus
0.10 -0.17 0.27 0.27
Note. * p < .05, two-tailed; ** p < 0.01, two-tailed.
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Table A.9
Spearman’s Rank Correlations (rs) Between Ordinal Variables
Ordinal Values
Computer Programming Proficiency
Computer Programming Background
Computer Programming
Hours/Day
Music Focus
School Level
0.22
0.47**
0.17
0.29
Computer Programming Proficiency
0.76** 0.51** 0.20
Computer Programming Background
0.38* 0.16
Music Activity Level
0.35*
Note. * p < .05, two-tailed; ** p < .01, two-tailed.
Appendix I
Open-Ended Responses to Music-Use Questionnaire
Item #6: Please explain why you chose the music you listened to. You may want to
refer to a specific song, artist, or style in your answer.
1. So Lonely by the Police is a great feel-good song and always gets me in a good mind set.
2. Techno music in general has repetitive sounds that allow me to focus on what I'm doing instead of the music I listen to. I find I get more work done listening to techno than I do with other types of music such as pop, rock, etc.
3. I am listening to Pandora a long time. I believe they have turned my favorite radio stations to my music liking. I wouldn't surprise on this as, they use machine learning to identify the users attitude towards music, using feedback.
4. I listened to recordings I made of myself improvising at the piano. I like the music very much and I like how my brain lights up as I remember making the music, it seems to organize and calm my mind.
5. It calms me and does not distract me. 6. I chose this music from a pre-existing playlist that I had knowing that it would be
music I liked but at the same time was not so busy that it would be distracting from my assignment
7. I prefer the jam or free flowing music that it easy to get lost in. While I'm coding, I tend to alternate getting caught up in the code, and then caught up in the music. The music itself is easy to space out to on its own, so I find it doesn't distract much. I would say it’s there when I need it, but easily forgotten when I don't. Phish, Grateful Dead, and Government Mule are my ideal programming artists. Generally I'll just leave a live concert running.
8. I choose it because I was feeling upbeat as the programming was going well or at least the music made it feel like I was making progress, because I was moving to the music. The time of day all so meant I want to play more uptempo music.
9. I felt like listening to some jazz to set an easy, smooth feel while working on this difficult assignment.
10. I chose to listen to this to block out the noise around me. I find television, my neighbors, and music with words to be very distracting, so in order to concentrate I need to block all of these sounds out. Listening to this type of white noise is perfect for this. I chose this particular thunderstorm soundscape because I really like the sound of rain and stormy weather.
11. I just like this music. It makes me feel pretty chilled out and confident. It's got some soul to it. I like the first song the most: "O Que Sobrou do Céu." These are Brazilian songs, so I get to exercise a little bit of my Portuguese while I am working. It may be helpful to specify that I was working on a program to quiz me on Portuguese words while I listened to this music. I thought it was appropriate.
12. I started out with Kyng because they are a new band I'm listening to. I switched to Toxic Holocaust because I recently started loving them and Lord of the Wasteland is a real headbanger. It's almost like the next level from Kyng. Then I picked a song I
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never listened to by a band that I have listened to in the past. This was pure thrash. Then I went back to a favorite of mine from Toxic Holocaust which is also fast. The vocals are real interesting also. Then I finished with something epic and glorious which was Blood Brothers by Iron maiden. It's full of melody and great vocals/lyrics.
13. Songs like Universal Mind, and Why I Am, Firewall, Chromazone, Inertiatic ESP and Sir Duke have a lot of energy. Their color in their songs and the live upbeat rhythms invigorates me to push forward. Songs like Cissy Strut, #41, Kid A, Paranoid Android are a lot mellower than the other songs. While still having very catchy rhythms and or a lot of musical activity going on, they allow me to relax and retain a sense of motivation at the same time. Progressive music always excites me the most because of my background as a musician I know what's going on and can take feeling from those elements in those songs. It also clarifies my mind- as if understanding time changes and polyrhythms always me to figure out a solution to a program.
14. I like listening to instrumental music when working. Songs with words distract me from what I’m working on and I like explosions in the sky specifically because it’s almost inspirational to finish my task
15. I chose the Black Seeds to relax and read the instructions/get started. The Bibio song gets me energized but not distracted because it has no words and a simple beat. The other songs put me in a similar mood.
16. I had heard of Childish Gambino and wanted to check out his music. I am a huge fan of U2 and Johnny Cash. I'm also a huge fan of Kings of Leon and I've found that they really help me to relax when I am writing code.
17. Most of the music I chose had rhythmic, energetic instrumentals at a medium pace, with sparse lyrics or reduced lyrical emphasis (Interpol's "C'mere" being the exception). I believe that great instrumentals can subconsciously boost mood and productivity, without the cognitive distractions of lyrical recognition.
18. I chose high energy high tempo music without lyrics so that I could listen to the music and focus on my work at the same time. Music with lyrics tends to distract me from the programming task, and low-impact music tends to make me feel unproductive.
19. It's very ambient. This band has a very specific style, where every song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden underneath textures rather than the focus of the song. It's easy to not pay attention to what they're saying, but still have something to groove to.
20. I wanted to listen to this album (Viva la Vida) in particular. I usually listen to rock while programming and today I was in a Coldplay mood. The following songs I put on loop for a bit because I enjoy them more than the rest of the album: Life in Technicolor, Viva la Vida and Death and All His Friends. Other music I listen to occasionally while programming includes OneRepublic, Queen, Billy Joel, and sometimes classical (preferably Beethoven). Mostly rock though. I like straight eighths. Also, when it's a piece/song I really like and put it on loop I find that I focus more on the music-as if I'm more aware of the sounds, but I still maintain focus on the program.
21. I enjoy listening to Bach, and since I already knew these pieces I thought they would not distract me as much as a new piece of music might.
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22. This was just a playlist of about 500 of my favourite songs on shuffle. I did not necessarily pick the order of the music, but it is all music that I enjoy.
23. I chose the music that I did because it was what I wanted to listen to at that moment. The changes in style were just me sifting through my library and picking what sounded good, so there wasn't a whole lot of conscious thought that went into the process.
24. I set Itunes on shuffle, so I didn't specifically choose any of the songs. In general, I choose music because it has interesting harmonies, multiple layers to focus on, and unique timbres.
25. BT is one of my foremost musical idols. I haven't listened to his "Dreaming" remix compilation EP, and the song usually puts me into a mellow, yet energized mood. The song itself has a deep existentialist-like meaning to me as well. It seemed like the right choice for my current state.
26. I "always" listen to music when I do work. I like country music when I'm relaxing or driving. I like rap/heavier dubstep when I'm working out. I like music by or resembling Explosions in the Sky when I'm reading or learning new material. I like mashups, especially new ones when I'm writing a paper. The change of pace really helps get the ideas flowing. If I'm programming, or just doing homework (things I already know how to do, I just have to sit down and crank it out), there is no better music than Electronic/Progressive House. I prefer a majority of uptempo artists like Swedish House Mafia and Kaskade. I save the more demonic sounding music (i.e. Skrillex, ect.) for when I'm working out or getting ready for world war 3.
27. Relaxed listening, low volume, no heavy beats 28. I picked a few songs that were a bit up tempo to get me going. I also find work
inspiration in songs that present a concept or situation that has one emotion but expresses it in a different way. American Pie is about three famous and influential musicians dying in a plane crash. McLean presents some very sad concepts but does so in somewhat of an upbeat manner. I like that. It's sad but it's happy. I'm a big classical fan, and being a brass player at heart, I love the stuff all the eastern Europeans have composed during the various classical eras. I find 1812 very majestic and triumphant (hopefully making my study session the same). I've always enjoyed playing Firebird and I like the composition. I went back to American Pie because I hadn't heard it in awhile, and I was in the mood for another iteration.
29. I usually choose high energy music while coding because it keeps me awake and also because it is sort of an isolation mechanism. (I think that is why I choose to use headphones and high volume.) I also think that it sort of puts me in the mood of fighting and achieving things, sort of a workout mood where you have to do things, and prove that you can do it.
30. It came up on shuffle, and when i hear one song from a musical i usually want to hear the next one.
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31. When I chose music to listen too while working, I look for songs that have a fairly mellow and light sound, but also have a steady, moderate - upbeat tempo, and some energy. I try to stay away from stuff like rock with harsh sounds, and prefer instead things with a lot of airy, ethereal components like synthesized sounds or strings. Even the alternative stuff I choose like Phoenix or the Flaming Lips tend to have some almost electronic sounds to them. I will also occasionally listen to jazz, especially jazz piano, as I find it has a unique sound that has a lot of energy and movement to it, even in ballads or slower pieces.
32. I chose the jazz tracks (Mehldau) because those are my favorite to actively listen to. The rock track (Gabe Dixon) I chose because I believe that it generally puts me in a more upbeat mood. The alternative tracks (Iron and Wine) were chosen because I enjoy the relaxed vibe that they give me.
33. I did not choose this music in relationship to the task I was performing, but rather, I chose music that I wanted to listen to because they were on the tip of my brain. I would say that I chose the music that put the juice in my boots. I'm really into this new album by Kyarypamyupamyu. It's pretty rad. I think it is super cool. I didn't listen to all of it. About the first 5 or 6 tracks or something. Nine inch nails is always a good choice. Especially the fragile. It was nice to revisit this album this morning that I have listened to sooo many times. Rumba is always nice. I went looking for this song "Chano", which I did not find but I found several named "chano pozo", which I think are different. Anyway. I let this collection play through several tracks before moving on. I've listened to the new Meshuggah album Koloss a whole lot. It's nice to compare to their slightly older stuff. Catch 33. They have really come a long way since that album. It's very surprising how much better their song writing has become. After writing this I am listening now listening to Koloss!!!
34. I listened to the Nicolas Jaar BBC Radio 1 Essential Mix DJ set 05-19-12. It had these songs in it (besides the last two). There were other songs in the set but those were the ones that stood out most to me. I listened to that set because I knew it would be a mix of calming yet interesting works. I chose the music based more on coming home from a long day of work and sitting down with a glass of wine to work with, rather than specifically being related to the task at hand. Yet it helped me get through the task with a more relaxed demeanor.
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Item #7: How do you think music listening influenced you and your work? Please
provide a description.
1. It motivated me to go with the flow. 2. I think that it helps me to code for longer periods of time due to the focus I can
maintain while I work. I can be easily distracted at times, and as a result my coding time is very fragmented. Having a solid block of time to sit down and work helps me get an entire task complete rather than try to get bits and pieces done over a couple of days wherein I loose time trying to remember why I did a particular thing, or how a function I wrote was intended to work.
3. Sometimes music is relaxing, sometimes it’s not. It all depends on the mood i am in. 4. It relaxed and energized me, made me more aware of my posture and mental state,
gave me some stimulation to distract my sexual desires...provided a right-brained counterpart to the left-brained work I was doing...
5. It calms me, and allows me to concentrate better. 6. I feel that the music gave me background noise I could control so I could drown out
other noises such as my roommate talking or the traffic outside. 7. The music is there when I need it. If I'm stumped on a problem or frustrated I can sit
back and listen to a few minutes of calming, easy flowing music. On the other hand when I really get into it, the music is easy to tune out. Of course, when a favorite song comes on my mind pretty much drops everything to pay attention to that, but generally the music is just there.
8. I got less frustrated with the programming when errors occurred as I was enjoying the music. It also made the time seem to pass quicker when programming. I don't think I was as productive however as I would have been without the music.
9. I believe that the music helped me remain relaxed and allowed me to enjoy the programming assignment.
10. I think that it helped me to focus more on what I was doing. I felt like I was alone in my own little world since I couldn't hear anything besides the soundtrack. If I hadn't been listening to it I would definitely been distracted by what was going on around me.
11. It made me not freak out when I was working on this project. I can get pretty frustrated when I don't understand exactly why a program is not working. I think the music just makes me say to myself: "Okay, it's all good. What is not working here." I would say it helps me keep my cool.
12. I don't think it influenced it much. Although it did slow me down because I had to go and choose the next song. Also, sometimes I would find myself focusing too much on the music and that loses time.
13. It excites my mind, almost as if its pushing any mental block I have in the way. 14. I don’t know. I prefer to do homework and other activities with music mostly because
it helps me lose focus on my surroundings and focus on the task I’m trying to complete.
15. The music added a mood to my programming task, which is often bland when unaccompanied by music. This helped at certain points but hindered at others and I had to pause the song for a few minutes to focus on troubleshooting errors.
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16. I think it helped me to stay in a relaxed mode so I wouldn't get frustrated when I encountered problems with my code.
17. The music prevents me from becoming terribly bored of my current task and thus staves off the need to seek distractions. The steady rhythmic pace also grants me a certain energy level where, in my opinion, I become more productive.
18. I think it made me better able to focus on my work for a long time without feeling bored or distracted. It kept me energized and motivated.
19. It helped me to focus on my task more. In quiet rooms, I tend to get distracted by every sound that crops up. When I play music, I zone out while listening to that, and ignore all the other "unexpected" sounds a lot more. It also helps to keep my energy level moderately high.
20. When I listen to something I enjoy, I will often put it on loop. Somehow this focuses my attention. If that background "sound" changes, I notice, even if that sound is an entire composition of music. Today I was debugging a particularly annoying program which meant a lot of analysis and looking for that one line that's messing everything up. The added focus from an album allows me to focus on just these two things, the music and the program. It keeps me on track so to speak. Although I've never tested it, my mind probably wanders more without music. I also notice that I'm a tiny bit calmer while listening to a good piece in general. Coincidently I found the bad lines of code while listening to my favorite song on loop. In truth it was kind of awesome for them to coincide. Finding a bug in my code always brings a rush of elation but good music in background makes it better.
21. I found listening to music kept me going at a constant pace (rather than going on and off as I sometimes do while programming) but the speed at which I coded seemed slightly slower than usual. I would notice the music more prominently when I was trying to figure out something about the program that I was unsure of, and it was slightly distracting.
22. The music relaxed me and let me gather my focus and direct it towards the task at hand. I find working in silence gives me anxiety and that I need music at least in the background to work well.
23. I think the music mostly distracted me from my work, because I wanted to sit back and listen to it. I found that while programming I was either really concentrating on what I was doing or really listening to the music, but I couldn't do both at the same time. As a result, I think I ended up with a more fragmented train of thought than normal.
24. It alleviates some tedium that might have set in, and also improves my mood. It's difficult to tell if it improved my thinking or quality of work in any way.
25. It definitely calmed me down; I was a bit jittery before the programming. I don't think it necessarily helped me to focus, though.
26. When I'm working hard or at least "*trying* to, I prefer to work by myself, in a quiet place, with my headphones blaring. I don't like silence. It bothers me, especially when I'm doing work. I honestly can't remember a time I wrote a paper or sat down to do homework without music (I'm still listening to music right now). It could just be a placebo effect, but whatever...that shit still works. Sometimes, when I'm in the zone, I'll feel like the music has my full attention and my fingers are just typing on autopilot. I feel like listening to music keeps me interested in my current task and
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increases my overall productivity. If I get to library and realize I forgot my headphones, I'll leave. I'd rather go home and comeback with headphones than waste time feeling unproductive.
27. relaxed me, also covered some outside noise 28. Twisted Sisters was a bit distracting and loud. It got my energy up but didn't really
help me stay focused on my coding. I felt American Pie put me in the mood to work and I focused better with it on. I was most productive during 1812 and Firebird. This is partially due to the absence of words to distract. Both are also very emotional and motivational pieces so I felt obligated to get work done when they were playing. American Pie the second time was also good for focuses, but not as much as the first listening session was.
29. It certainly puts me in a better mood, which I can describe as cheerful or up-lifted, plus keeps me awake. Because of its high beat, I tend to act more focused and fast and get things done quicker. If I really need to think on things and plan the flow of the code, I choose to pause the music for a while.
30. I think the music calmed down a little. I don't usually listen to music and program, but i feel like its occupying a part of my brain that might be stressing out normally.
31. Mostly I find that listening to music helps me block out distractions around me especially if I'm in a public place like the library. I also feel like I get "in the zone" when I've got the right kind of music playing. It's hard to explain but having a steady, driving beat can help me stay productive, and focus on problems more easily.
32. As an aspiring jazz musician, I feel as though the jazz trio tracks somewhat distracted me from my programming. This makes sense to me, given that I consider playing jazz piano as an cognitively-demanding task alongside programming. I feel as though, during the rock and alternative tracks, I got the most work done - pushing the music into the background, more or less.
33. I'm not really sure. Probably distracted me more towards the music. I am fairly active and analytical listener and I get all juiced up about different stuff. Definitely not applicable to the task at hand. I like to think about the music. I find that if I am not paying attention to the music I usually turn it off.
34. I like to think that it kept my mind stimulated at times when repeating menial, repetitive tasks. It probably also distracted me a bit, but I was okay with that.
Appendix J
Participant Music Selections Reported
Artist Song Style/Genre Adele Set Fire To The Rain Pop Adele Someone Like You Pop Adventure Club Daisy(Adventure Club Remix) Electronic Adventure Club Youth(Adventure Club Remix) Electronic Aerosmith Dream On alternative/rock Alexisonfire Hey, it's your funeral post-hardcore Alexisonfire No Transitory post-hardcore Aphex Twin Jynwythek Ylow Electronic Aphex Twin Nannou Electronic Aphex Twin Bbydhyonchord calm IDM Bach Contrapunctus I Classical Bach Contrapunctus II Classical Bach Contrapunctus III Classical Bach Contrapunctus IV Classical Baywood I Can Breath Again Folk Becca Stevens Band My Girls Folk Bibio Lovers' Carvings Electronic Bill Evans I Love You Porgy Jazz Blue Sky Black Death Our Hearts of Ruin Progressive Rock Blue Sky Black Death And Stars, Ringed Progressive Rock Blue Sky Black Death To The Ends Of The Earth Progressive Rock Blue Sky Black Death Farewell To The Former World Progressive Rock Blue Sky Black Death Falling Short Progressive Rock Blue Sky Black Death Gold In Gold Out Progressive Rock Blue Sky Black Death Where Do We Go Progressive Rock Blue Sky Black Death In The Quiet Absence of God Progressive Rock Blue Sky Black Death Where The Sun Beats Progressive Rock Blue Sky Black Death Starry Progressive Rock Boards of Canada Dayvan Cowboy Electronic Bob Brookmeyer Small Band You'd Be So Nice to Come Home to Jazz Bon Iver Bon Iver folk Brad Mehldau When it Rain Jazz Brad Mehldau Ruckblick Jazz Brad Mehldau Trio Nobody Else But Me Jazz Brad Mehldau Trio Exit Music (For A Film) Jazz Brad Mehldau Trio River Man Jazz Brahms Violin concerto in D Mayor, Op 77 Academic BT Dreaming Electronic Dance
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Artist Song Style/Genre BT Dreaming (Eric Kupper Mix) Electronic Dance BT Dreaming (Libra Mix) Electronic Dance Cake Wheels Rock/alt/country Cake No Phone Rock/alt/country Caribou Odessa Electronic Chick Corea Waltz for Debby Jazz Chiddy Bang Opposite of Adults Rap Childish Gambino Heartbeat Rap Christopher Smith Endless Earth Piano Improvisation Christopher Smith For Every Thing That Lives is Holy Piano Improvisation Christopher Smith Paranoi Piano Improvisation Christopher Smith Death Worship Piano Improvisation Christopher Smith Summer Darkness Piano Improvisation Cobra Starship You Make Me Feel dance Cobra Starship Good Girls Go Bad dance Coldplay Life In Technicolor Indie Rock Coldplay Cemeteries of London Indie Rock Coldplay Lost! Indie Rock Coldplay 42 Indie Rock Coldplay Lovers in Japan Indie Rock Coldplay Yes Indie Rock Coldplay Viva la Vida Indie Rock Coldplay Violet Hill Indie Rock Coldplay Strawberry Swing Indie Rock Coldplay Death and All His Friends Indie Rock Damien Jurado Sheets folk Dark Dark Dark Daydreaming Folk Rock Dave Brubeck Tritonis Jazz Dave Brubeck Benjamin Christopher Davis Brubeck Jazz Dave Brubeck Loverman Jazz Dave Brubeck Tokyo Traffic Jazz Dave Matthews Band #41 Jazz/World Dave Matthews Band Why I Am Rock/World David Guetta Where Them Girls At dance Deadmau5 Ghosts n' Stuff House Death Cab for Cutie Your New Twin Sized Bed Indie Rock Disasterpiece Club Wolf Electronic Don McLean American Pie Folk/Soul Don McLean American Pie Folk/Soul Dubba Johnny All In Dubstep explosions in the sky Your hand in mine instrumental Explosions in the Sky Your Hand In Mine Rock/Electronic
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Artist Song Style/Genre Family of the Year St. Croix Indie Rock Feist Caught A Long Wind indie singer/songwriter Flaming Lips One More Robot Alternative Flo Rida Good Feeling pOP Flux Pavillion Bass Cannon Electronic Foals Miami Indie Galactic Go Go Instrumental Gorillaz Stylo Electronic Rock Igor Stravinsky Firebird Classical Interpol C'mere Alternative Rock Interpol Length of Love Alternative Rock Iron & Wine Love and Some Verses Alternative Iron & Wine Jezebel Alternative Iron Maiden Blood Brothers Metal Jason DeRulo Whatcha Say Pop Jesus Christ Super Star Overture Musical Jesus Christ Super Star Heaven on their minds Musical Jesus Christ Super Star What's the Buzz/Strange thing mystifying Musical Jesus Christ Super Star Everything's Alright Musical Jesus Christ Super Star Jesus must die Musical Jet Cold Hard Bitch Dance/garage rock John Mayer (by Radiohead) Kid A John Mayer Trio (The Meters) Cissy Strut Funk Johnny Cash When The Man Comes Around Country/Rock Johnny Cash Help Me Country/Rock Johnny Greenwood There Will Be Blood Dissonant contemporary Joshua Radin Today folk Justice D.A.N.C.E. Electronic Katy Perry Last Friday Night (T.G.I.F.) Pop Keith Jarrett Toyko Encore, 1974 solo piano jazz Kenny G The Moment Jazz Kenny G The Champion's Theme Jazz Kenny G Passages Jazz Kenny G Northern Lights Jazz Kenny G Moonlight Jazz Kenny G Innocence Jazz Kenny G Havana Jazz Kenny G Gettin' On The Step Jazz Kenny G Eastside Jam Jazz Kenny G Always Jazz Kesha Blow dance Kings of Leon The End Rock
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Artist Song Style/Genre Kings of Leon Pyro Rock Kings of Leon Back Down South Rock Klaypex Dubstep Guns Dubstep Klaypex Lights Dubstep Kyarypamyupamyu pamyupamyurevolution (album) JPOP Kyng Trampled Sun Heavy Rock/Metal Kyng Trails in Veins Heavy Rock/Metal Lauryn Hill Ex-Factor Neo Soul Layz Replay Pop Linkin Park Numb alternative Liquid Tension Experiemnt Universal Mind Progressive Metal luminary Youth Jets To Bangalore Electronic Maceo Plex Gravy Train (Nicolas Jaar Remix) spacey electronic dance Maroon5 & Christina Aguilera Moves Like Jagger dance Meiko Reasons To Love You folk Meshuggah Catch 33 (album) Metal MGMT Time to Pretend Electronic/Indie Rock Michael Dulin Clair De Lune folk Miike Snow The Devil's Work Electronic Rock Mike Stern/Bob Berg Band Chromazone Fusion Modeselektor Berlin Electronic Modest Mouse Dark Center of the Universe Indie Rock Municipal Waste Wolves of Chernobyl Thrash Metal Nick Drake River Man Alternative Nine Inch Nails The Fragile (left) (album) Rock / Metal O Rappa O Que Sobrou Do Céu Samba/Funk O Rappa Se Não Avisar o Bicho Pega Samba/Funk O Rappa Minha Alma [A Paz Que Eu Não Quero] Samba/Funk O Rappa Lado B Lado A Samba/Funk Outkast So Fresh So clean hip hop OutKast Crumblin' Erb Hip-Hop Pearson Sound Footloose leftfield breakbeat Phantogram When I'm Small Phish Backwards down the number line Jam Phish Stealing time from the faulty plan Jam Phish Joy Jam Phish Sugar Shack Jam Phish Ocelot Jam Phish Kill Devil Falls Jam Phish Light Jam Phoenix Countdown Alternative Phoenix Lasso Alternative
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Artist Song Style/Genre Pitbull International Love Pop Pixies Bone Machine Alternative rock Pixies Alec Eiffel Alternative rock Plaid Crumax Rins synth IDM Plaid Upona synth IDM Port O'Brien I Woke Up Today Rock Radiohead Paranoid Android Progressive/Grunge Rock Radiohead Paranoid Android Rock Radiohead No Surprises Rock Radiohead/De la Soul Itsowezee hip hop/Alternative rock Ricardo Villalobos What You Say Is More Than I Can Say weird house Rihanna We found love dance simplynoise.com Thunderstorm Stephen Swartz Ft. Joni Fatora Bullet Train Electronic Steve Aoki WARP Electronic Steve Vai Firewall Funk/Progressive Rock Stevie Wonder Sir Duke Funk Strike911 Trance Insurgency Part 1 Techno Strike911 Trance Insurgency Part 2 Techno Strike911 Death of a Hero Part 1 Techno Strike911 Death of a Hero Part 2 Techno Strike911 The Entrance Techno Strike911 Ayane's Winter (DoA2) Techno Strike911 Helena's Trance (DoA2) Techno Strike911 Sixth Gear Techno Strike911 Chaotic Dreamer (The Attack) Techno Strike911 Victory Techno Swedish House Mafia Save The World Electronic Tchaikovsky 1812 Overture Classical The Black Keys I Got Mine Alternative Rock The Black Seeds Don’t Turn Around Reggae The Black Seeds Dust and Dirt Reggae The Concept D-D-Dance Alternative Pop The Dead Weather Die By The Drop Alternative rock The Field The Little Heart Beats so Fast minimal tech house The Gabe Dixon Band One To The World Rock The Killers Shadowplay Alternative Rock The Lonely Island Threw It On The Ground Comedy The Mars Volta Inertiatic ESP Progressive/Grunge Rock The Middle East Blood Indie Rock The Morning Benders Excuses Alternative The Police So Lonely Classic Rock/Ska
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Artist Song Style/Genre The Police Can't Stand Losing You Classic Rock The Raconteurs Level alternative The White Stripes The Denial Twist Garage Rock Revival Toxic Holocaust Lord of the Wasteland Heavy Rock/Metal/Thrash Toxic Holocaust Burn Thrash Metal Twisted Sisters We're not gonna take it Heavy Metal U2 Beautiful Day Rock U2 Stuck in a Moment You Can't Get Out Of Rock U2 Elevation Rock U2 Stay (So Close, So Far Away) Rock Usher OMG Pop Washed Out Feel It All Around Rhythmic/Electronic Weepies Nobody Knows Me at ALl Wilco Pot Kettle Black Alternative Wildlife Control Analog or Digital Indie Rock various Repertório Rumba (album) Rumba