RELATIONSHIPS BETWEEN EARLY CHILDHOOD MUSICEXPERIENCES AND MUSIC APTITUDE

A DissertationSubmitted to

the Temple University Graduate Board

in Partial Fulfillmentof the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ByEric P. Rasmussen

May, 2004

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©

Eric P. Rasmussen

2004

All Rights Reserved

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ABSTRACT

Relationships between Early Childhood Music Experiences and Music Aptitude

By Eric P. RasmussenTemple University, 2004

Doctor of PhilosophyMajor Advisor: Dr. Darrel L. Walters

The problems of this study are to 1) identify early childhood musical experiences

(birth to 18 months) that predict music aptitude scores as measured in first grade and 2)

learn whether school type (public/private) and school setting (urban/suburban) contribute

to the predictions.

The researcher administered the Intermediate Measures of Music Audiation

(IMMA) (Gordon, 1986) to children in first grade. Parents completed the Musical

Experiences Questionnaire (MEQ), designed to ascertain the richness of children’s

musical experiences. The reliabilities of the measures were from .72 to .92. A factor

analysis on the MEQ yielded six factors: musical behaviors of parents, music and

movement classes, prenatal music exposure, live instrumental experiences, music from

television, and live music and radio.

After performing two series of multiple regression analyses, the researcher found

no significant relationships between MEQ factors and IMMA scores. The researcher did

find that when the MEQ factors were combined with the school variables, they accounted

for a statistically significant amount of variance in common with IMMA tonal scores. The

t-tests revealed that the difference between urban and suburban school settings (favoring

suburban) contributes substantially to the variance accounted for in the IMMA tonal

scores, p = .004.

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The researcher conducted open interviews with seven parents of children with

high music aptitudes and low MEQ scores. Interviewees demonstrated a high level of

interest in the music development of their children. All but one provided their children

with music lessons or classes, and all but one attended church weekly with their children.

The researcher concluded that the MEQ did not discriminate between

environments that sufficiently nurture a child's musical development and those that do

not. Regarding the difference in tonal aptitude scores between the urban and suburban

groups, the researcher suggests that because melody and harmony is lacking in much

urban music, children would seemingly suffer some degree of musical malnutrition given

lesser opportunity to hear music with more tonal substance. Challenges facing urban

music educators may also play a role in the discrepancy—that is if the music experiences

children receive after reaching school age can indeed influence aptitude scores.

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ACKNOWLEDGEMENTS

I express my deep appreciation for the many contributors to this study. Without

your tenacious support, skillful guidance, hard work, and inspiration, this project never

would have come to fruition.

I gratefully acknowledge the administrators, teachers, and parents for granting me

access to the children. I especially thank those parents whom I interviewed, for the

candor and enthusiasm brought to those conversations. Thank you moms, dads,

grandparents, and all of you who contribute to the musical richness in your families.

School music programs can never replace you.

To Dr. John M. Holahan, former master’s thesis adviser and statistician

extraordinaire, I thank you for your brilliant capacity to stretch my thinking repeatedly

beyond its limits. Our conversations and your specific guidance regarding the analysis

provided me with a deeper understanding of research.

To Dr. Sook Won Kim, I acknowledge you for the level of integrity you brought

to the peer reviewing process. You collaborated as if the study were your own.

To the advisory committee—Dr. Beth Bolton, Dr. Maurice Wright, and Dr. Karen

Bond—I thank you for your insights into the forest when the trees were in my way. I

appreciated all of the direction.

To Dr. Darrel Walters, to say that I enjoyed your guidance over the years is a gross

understatement. I thrived under it. You continually demanded excellence from me; I

would have had it no other way. Thank you.

Finally, to my wife, Nancy, and daughter, Sarah, I acknowledge you for your

humor, constant support, inspiration, and unconditional love. You are my life and joy.

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DEDICATION

I dedicate this study to Mom and Dad. In unique ways, you have contributed to

whom I have become and the possibility that the future holds for me. Words will never

express adequately my appreciation for all your work, worry, and especially your love.

May this study represent a small triumph for you.

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TABLE OF CONTENTS

Page

...................................................................................................................ABSTRACT iv

...........................................................................................ACKNOWLEDGEMENTS vi

...............................................................................................................DEDICATION vii

...........................................................................................................LIST OF TABLES x

CHAPTER

1. INTRODUCTION, PURPOSE, AND PROBLEMS

..........................................................................................................Introduction 1.................................................Neuroscience and Early Childhood Experiences 2

..............................................Fetal Development and Infant Response to Music 7......................................................................................Early Musical Behaviors 9

...............................................................Early Childhood Music Development 12...................................................................................................Music Aptitude 14

...........................................................................................................In Closing 16........................................................................................Purpose and Problems 17

2. RELATED RESEARCH

Effects of Music and Movement Instruction on Developmental Music.............................................................................................................Aptitude 18

................................................................................... The Pickett Study 18..................................................................................... The Flohr Study 19

.................................................................................. The Stamou Study 21............................................................................... The Blesedell Study 23

...............Relationships between Home Environment and Music Achievement 24.................................................................................. The Shelton Study 24

........................................................................... The Kirkpatrick Study 25................................................................................... The Moore Study 26

............................................................................... The Reynolds Study 28....Relationships between Home and School Environments and Music Aptitude 29

............................................................................... The Kehrberg Study 29.................................................................................. The Gordon Study 30

.................................................................................... The Brand Study 31....................................................................................... The Farr Study 33

...........................................................................................................In Closing 34

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3. DESIGN AND ANALYSIS

........................................................................................Purpose and Problems 36...............................................................................................................Sample 36................................................................................................................Design 37

..........................................................................................................Procedures 38.............................................................................................................Analysis 41

4. RESULTS AND INTERPRETATIONS

...........................................................................................Quantitative Results 44 The Intermediate Measures of Music Audiation ..................... (IMMA) 44

................................... The Musical Experiences Questionnaire (MEQ) 46.. Regression Analyses: Relationships between MEQ and IMMA Scores 52

.............................................................................................Qualitative Results 55........................................................................ Case Study Presentation 57

............................................... Qualitative Analysis of the Case Studies 67

5. CONCLUSIONS........................................................................................Purpose and Problems 73

.............................................................................................Design and Analysis 73...................................................................................Quantitative Conclusions 75

.....................................................................................Qualitative Conclusions 81

..........................................................................................................BIBLIOGRAPHY 91

....................................................................................................................APPENDIX 96

....................................................A. MUSICAL EXPERIENCES QUESTIONNAIRE 96................................................................B. RESEARCH SOLICITATION LETTER 101

C. PERMISSION SLIP 102

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LIST OF TABLES

Page

TABLE

.......................................................................................................1. Sample Summary 37

2. Descriptive Statistics for IMMA Administrations for MEQ Submitters Versus.........................................................................................................Non-Submitters 45

............................................3. Descriptive Statistics of the Pilot Sample for the MEQ 47

......4. Means, Standard Deviations, and Discrimination Values for Items of the MEQ 48

5. Factor Analysis of the MEQ using a Principal Components Extraction Method ....................................... with an Orthotran/Orthogonal Quartimax Transformation 50

...............................................................6. Descriptive Statistics for the MEQ Factors 51

...................................7. Intercorrelations and Reliability Estimates for MEQ Factors 51

8. Results of the Multiple Regression Analyses (Six Factor Model) between MEQ .....................................Factors and Tonal, Rhythm, and Composite IMMA Scores 52

9. Results of the Multiple Regression Analyses (Eight Factor Model) between MEQ...................................... Factors and Tonal, Rhythm, and Composite IMMA Scores 53

10. Regression Coefficients for the Eight Factor Model and the IMMA Tonal.......................................................................................................................Scores 54

1CHAPTER 1

REVIEW OF THE LITERATURE

Introduction

Music educators are often frustrated by their inability to lead every child to

musical competence in terms of being able to sing or play with a sense of tonality, meter,

and style. One such music teacher might say, “Sally just doesn’t ever use her singing

voice and Joseph can’t keep a steady beat. Why can’t I seem to make a difference with

them as I have with the other children?” Such comments, if not spoken aloud, are likely

in the minds of music teachers across the country regularly.

What accounts for low levels of music achievement in some children? What

accounts for high levels? How much of the challenge of teaching music to any child is

predetermined by that child’s potential? What role do music teachers and others have in

the development of that potential?

These questions are fundamental to understanding how to raise the quality of

music education. Whether that education takes place inside or outside the classroom, the

musical futures of children are in large part entrusted to us, the professional music

educators. Ultimately, we may want to extend our role beyond traditional K-12

educational settings to contribute fully to musical expression in society at large.

Research in neuroscience, fetal development, infant response to music, early

childhood music development, and music aptitude may lend insight into how children

develop the capacity to become musical.

2Neuroscience and Early Childhood Experiences

Recent research in neuroscience indicates that the first years of life are critical to

brain development. According to many neuroscientists, prenatal and newborn infants

grow connective webs of neurons in the brain at intense rates. These webs, in turn, form

the foundation for learning. At various stages, some of these neural communication

networks are truncated through the natural processes of the developing brain, leaving

only the most stable connections unaltered. The quality and quantity of a child’s

experiences account for the stability of connections. That is, the more an infant receives

particular stimuli, the more stable the connections in the brain will be relative to those

stimuli (Hodges, 1996; Restak, 1995; Changeux, 1985).

Neuroscientists have defined learning as the creation, connection, stabilization,

and subsequent truncation of neural networks. Without them, learning cannot occur.

According to neuroscience pioneer Jean-Pierre Changeux (1985),

The 10,000 or so synapses per cortical neuron [brain cell located within the outer shell of the brain] are not established immediately. On the contrary, they proliferate in successive waves from birth to puberty in man. With each wave, there is a transcient [sic] redundancy and selective stabilization. This causes a series of critical periods when activity exercises a regulatory effect. . . . One has the impression that the system becomes more and more ordered as it receives “instructions” from the environment. If the theory proposed here is correct, spontaneous or evoked activity is effective only if neurons and their connections already exist before interaction with the outside world takes place. Epigenetic selection acts on preformed synaptic substrates. To learn is to stabilize preestablished synaptic combinations, and to eliminate the surplus. (p. 248)

The quality of a child’s early environment could have an enormous impact on

brain development and subsequent learning. Hodges (1996) writes, “In the first few years

3of life, neurons grow in size and in the number of connections. It is believed that a rich

sensory environment stimulates these connections, while an impoverished one inhibits

cognitive growth” (pp. 199-200). Neuroanatomists have shown that neurons that develop

in the brains of children exposed to impoverished environments are structurally very

different—having considerably fewer and shorter dendritic branches (neuronal message

receivers) than those of children exposed to enriched environments (Greenough and

Juraska, 1986). Greenough and Juraska, pioneers in enrichment studies, suggest that

experience determines which synapses (neuronal connectors) are shed and which are

retained. Lewontin (2000), an evolutionary biologist, states that the leading theory of

brain development is selective theory. According to this theory, neurons form random

connections before they can be stabilized by experience—without which, the connections

will decay or eventually disappear.

Early childhood experiences are believed to affect infant temperament, language

development, intelligence, social and health problems. According to Goldstein (1997),

infant temperament is determined partly by genetics and partly by the demeanor of

infant-parent communication set from birth on. Lewontin (2000) suggests that

environments can indeed modify intellectual ability. Regarding language development, he

states, “Human beings can speak because they have the right genes and the right social

environment” (p. 29). Goldstein (1997), a researcher in sociology, believes that good

quality early experiences are key to the healthy development of a child. He cites studies

of child resiliency that indicate social and health problems have their roots in the first

three years of a child’s development:

4It is now known that there is an elaborate interplay between human physiology and environmental conditions: the nervous system responds to the environment by secreting particular hormones, and these hormones in turn effect [sic] how and when different genes are expressed in the central nervous system. Recent breakthroughs in neurological research have shown that through this combination of ‘nature’ and ‘nurture,’ infants not only learn from their experiences, but these experiences influence the organization of critical portions of the brain. (pp. 9-10)

As important as the first years of life apparently are, prenatal experiences may

play an equally significant role in the development of the brain. Consider that there is a

phase during fetal brain development when two and a half million neurons per minute are

created, and that most of the child’s brain cells are produced between the fourth and

seventh month of gestation. After the seventh month, this stage is complete and cell

elaboration begins (Santrock, 1997). Santrock (1997) defines cell elaboration as the stage

when axons (neuronal transmitters) and dendrites (neuronal receivers) begin connecting

to other neurons. At birth, a child has over one hundred billion neurons, each with

potentially thousands of connections to other neurons (Restak, 2001). The numbers, and

the possibility behind them, stagger the imagination. Is it possible that, even as these

neuronal structures are being generated and interconnected within the fetal brain,

environmental influences are helping to determine how well they will ultimately

function?

How the brain develops before and after the birth of the child seems to depend

partially upon environmental stimuli. Without such stimuli, the brain would have only

preprogrammed genetic factors governing its structural development. The degree to

which both genetic and environmental factors contribute to any one human ability is still

in question.

5Neuroscientists believe that genetics contributes significantly to the development

of a child’s brain, but how it contributes is not fully understood. Senft (1997) suggests

that although the genome is the system that is primarily responsible for programming

brain development, the scope of a gene’s influence could be somewhat limited. He

elaborates,

We are virtually certain that the genome specifies neither the detailed pattern of the connectivity for each cell in the brain nor the precise number of cells in all regions. . . . It seems possible that there exists in the genome only a finite repertoire of potential neuritic forms. . . and that each cell group developmentally expresses differing subsets from this morphogenetic roster. (pp. 43-44)

Cziko (1995) concurs with Senft. He states that neural and molecular scientists

have concluded that genes cannot contain enough information to specify how the billions

of neural connections are established in the human neocortex. Cziko believes that genes

simply do not have the capacity to account for such detail in the developing brain. He

writes, “The genome provides the general structure of the central nervous system, and

nervous system activity and sensory stimulation provide the means by which the system

is fine-tuned and made operational” (p. 66).

The earlier position of Changeux (1985) is similar. He states,

It seems difficult to imagine a differential distribution of genetic material from a single nucleus to each of these tens of thousands of synapses unless we conjure up a mysterious ‘demon’ who selectively channels this material to each synapse according to a preestablished code! The differential expression of genes cannot alone explain the extreme diversity and specificity of connections between neurons. (pp. 216-217)

6The nature versus nurture debate has waned in favor of the understanding that

most human abilities are a product of nature with nurture. There is now little question that

both genetic and environmental factors influence a child’s growth and development.

Regarding the potential to achieve in music, the staunch positions of both Revesz

and Lundin can be considered moot. Revesz (1954) stated “there are so many examples

of direct inheritance of musical aptitude from the parents that the laws of heredity in the

field of music cannot be questioned” (p. 192). Lundin (1967) wrote, “musical talent is the

result of previously acquired skills and not inherited genius” (p. 222). Each theory has

been shown to be less than adequate without the other.

The work of Pribram (1971) helps to explain how musical behaviors may take

place at the neuronal level. Like any sensory input, music stimulates neural structures of

the brain. According to Pribram, during neural stimulation, ribonucleic acid (RNA) is

produced by deoxyribonucleic acid (DNA), the genetic memory molecule, and begins to

appear in glial cells embedded in the brain tissue. Over time, these structures stabilize,

allowing subsequent sensory information to be compared to the information already

stored. Pribram suggests that this process may serve as the neural foundation for long-

term memory. He states,

Experimental evidence shows that, at any moment, current sensory excitation is screened by some representative record of prior experience; this comparison—the match between current excitation and representative record—guides attention and action. (p. 49)

Radocy and Boyle (1988) believe that music behaviors are guided by this very activity at

the neural level. Whether one is attending to music or performing it, without neural

7structures to support the activity, any behavioral response would either become haphazard

or cease altogether.

Most researchers agree that one’s potential to achieve in music stems from both

genetic and environmental factors. Former president of the Human Genome Project,

Walter Bodmer, states that “musical aptitude . . . is inextricably bound up both with

environmental influences and a person’s genetic heritage” (Bodmer and McKie, 1994, p.

5). Apparently, musically enriched environments contribute to the growth of a ‘musical

brain,’ and thus establish the parameters for a child’s capacity to be musical. What

remains in question is the degree to which an optimal musical environment contributes to

a child’s potential to achieve in music. Is there an optimal window of opportunity within

which to influence a child’s potential to achieve in music? And if so, when is that window

open?

Fetal Development and Infant Response to Music

Researchers have investigated the development of the auditory system in human

fetuses. Ando and Hattori (1970) found that newborn infants responded differently to

noise stimulus based on the presence or absence of the stimulus during the months prior

to their birth. Newton and Modahl (1978) found that when third-trimester fetuses were

exposed to loud noises or classical music, their heart rates changed. Elliot (1995), writes

that

the auditory powers of the human fetus are already considerable before birth and. . . the sounds of the mother’s body and voice together with the music and speech of her culture penetrate the womb and the nascent consciousness of the fetus. . . . Perhaps the stages of fetal development are most likely imbued with auditory information that the fetus cognizes to some degree. On the basis of these thoughts, I make bold to suggest that

8sound (including musical sound) has few if any rivals for the attention of the human fetus. (pp. 127-128)

Lecanuet (1996) shows that a) a large variety of acoustical stimuli reaches the

fetal auditory system; b) the auditory system of the fetus is functional three to four

months before birth; and c) fetuses 28 and 30 weeks gestational age react to auditory

stimuli. He points out that the nervous pathway of the human auditory system is

influenced by prenatal exposure to acoustical stimulation. In turn, such stimuli seem to

have structural and functional effects on the development of the auditory system. He

states,

Prenatal familiarization to specific sounds or classes of sounds may contribute to the development of a particular sensitivity to these stimuli and to the formation of a preference for 1) a given speaker (mother); 2) some particular prosodic sequence when read or sung by the mother during the last weeks of her pregnancy; 3) some particular musical sequence; 4) a given language (maternal). (p. 24)

This research suggests that because fetuses respond to sound very early in life, they may

be influenced by music stimuli even several weeks prior to birth.

Wilkin (1995) measured the musical responses of fetuses at 38 weeks gestational

age and—after exposing them to a piece of music daily from 32 weeks gestational age

until birth—measured the same children again at six weeks postpartum. She found that

movement responses and heartbeat decelerations of the test-group fetuses were

significantly greater than those of the control-group fetuses who did not receive the daily

music exposure treatment. She also found that the test-group newborns had significantly

more movement responses than the control group. Wilkin found that the newborns

exposed prenatally to daily music responded to the music stimuli more actively,

9receptively, and alertly than did the newborns who did not receive the daily music

exposure treatment.

The effects of auditory cortical plasticity on language skill development might

have implications for our understanding the basis for music development. Restak (2001)

has shown that people of various cultures develop preferences for phonemes unique to

their language. If the American “r” phoneme were missing from the early sound

environment, a child would have great difficulty learning to say words using that

phoneme. According to Rauschecker (1999), such predispositions are not genetically

programmed, but are learned very early in life. He believes that the environment

influences the formation of specific neurons in the brain. Noteworthy though is that

children across cultures do not develop preferences for the phonemes of their native

language until six months of age.

Early Musical Behaviors

Research on prenatal, infant, and early childhood musical behaviors is abundant.

Many researchers have presented evidence that musical development is rooted in infancy

(Jusczyk and Krumhansl, 1993; Hargreaves, 1986; Papousek and Papousek, 1981;

Trehub, 1987; Thorpe, Trehub and Cohen, 1986; Trehub and Chang, 1977; Moog, 1976;

Miller, Schweinitz and Goetzinger, 1963). By the end of the first year, children respond to

music with a variety of musical behaviors including singing, chanting, moving, and

listening. Hargreaves (1986) cites researchers who believe that the vocalizations of very

young infants are the precursor to spontaneous song.

10Some researchers believe that crying, smiling, gazing and other behaviors are

evidence that the infant is exploring or responding to the musical environment. Moog

(1976) investigated the effects of six types of musical and sound stimuli on children

between the ages of three months and five years. He observed the responses of

approximately 500 children to a) nursery songs sung by children, b) words chanted

rhythmically, c) rhythms performed on instruments, d) instrumental music, e)

harmonically dissonant versions of the same instrumental music, and f) non-musical

sounds such as a vacuum cleaner and traffic noise. Among his observations, Moog

reported that six-month-old infants usually stopped their current behavior—including

nursing—to gaze, turn toward the sound, or smile in response to many of the stimuli.

More attention was given to the songs and instrumental music than to the others.

Papousek and Papousek (1981) believe that infants as young as two months can

match pitch, intensity, and melodic contour of songs sung by their mother; and by four

months, they also match rhythmic structure. In a separate observational study, Papousek

(1982) suggests that infants are predisposed even more to aspects of the musical

environment than to elements of speech (Rogers, 1990).

Miller, Schweinitz and Goetzinger (1963) showed that five-month-old infants

respond to various noises significantly more than four-month-old infants who, in turn,

respond significantly more than three-month-old infants. The infants expressed their

responses by turning their heads, moving their eyes, spreading their toes, wiggling their

noses, or other consistent actions. This research suggests that infants develop a rapidly

increasing awareness of their sound environment as they age.

11Trehub and Chang (1977) present evidence that five-month-old infants are

sensitive to changes in tones. After infants were habituated to six-tone patterns, they

reacted with decelerated heartbeats when exposed to patterns in scrambled order.

Transposition of the pattern did not alter their reactions. The authors concluded that

infants recognize selected differences in patterns, but did not draw conclusions about

whether infants recognize similarities.

Thorpe, Trehub, and Cohen (1986) showed that infants also discriminate among

contrasting performances of given rhythm patterns. Infants distinguished between two

rhythm patterns that were the same even when researchers presented those patterns at

various tempi and rates. Trehub (1987) states, “Just as infants, under most conditions,

encode pitch relations (contour) within a melody independent of specific pitches, so do

they encode relational aspects of temporal structure independent of specific durations” (p.

640).

Jusczyk and Krumhansl (1993) presented evidence that infants between 17 and 22

weeks old respond to elements of musical phrase structure. Infants attended significantly

less to versions of minuets that had pauses inserted into the middles of phrases than to

versions that had pauses inserted at the ends of phrases (p < .01). They also found that

infants did not attend significantly differently to versions with pauses inserted at the ends

of phrases than to versions with no pauses.

Other research suggests that parents can help infants develop their ability to hear.

According to Restak (1979), infants who were consistently rocked tended to develop

12hearing earlier. This suggests that movement activities, such as dancing with infants,

might have a profound effect on their musical development.

Given the body of research, little question remains that newborns and fetuses

respond in various ways to sound and musical stimuli. Do these experiences prepare

children for future music learning, and if so, how influential are they? Theories of early

childhood music development might contribute to a fuller understanding of how musical

experiences influence musical growth.

Early Childhood Music Development

Fridman (1973), Michel (1973), Moorhead and Pond (1977), and Gordon (1997a)

have developed theories about early childhood music development. Each believes that the

first years of life are critical.

After studying the cries of children two to fourteen months old, Fridman (1973)

purports that the first cry of the newborn is the generator of spoken language, musicality,

movement, and rhythm. She believes that “a child must be musically nourished from

birth. His interest in a sonorous world must be awakened through songs, rhythmical

movement, and different timbres and pitches of sounds” (p. 267).

Michel’s (1973) theory of early childhood music development consists of three

stages of musical productivity: a) Reproductive/interpretive achievement accompanied by

corresponding development of skills; b) Productive achievement; c) Productive-creative

achievement. He does not explain these stages, but does state that the first stage may be

attained early in life. Michel concludes that “early musical development during the first

13years of life in the home and in nursery schools, and the musical education in schools

built upon such foundations, are the Alpha and Omega of our musical culture” (p. 19).

Moorhead and Pond (1977) conducted extensive observational research on how

children between 18 months and eight years old grow musically in a non-directive

environment. Their landmark studies created the foundation for further research in early

childhood music development. Pond (1992) states,

Deeply rooted awareness of the auditory phenomena is primary, and it is the young child’s innate possession from the first moment of his or her existence. Surely nothing can be more basic to emerging musicality. First, a child becomes aware of sounds, then he or she experiences wonder and delight, and then an insatiable exploration begins as wide as the environment can provide. (p. 40)

Gordon (1997a) has developed an extensive theory of early childhood music

development. He describes the early stages of a child’s musical growth as a general stage

of “music babble,” analogous to the babble stage in language development. In this stage,

a child does not understand the syntax of the adult musical world and thus cannot yet

audiate. Gordon defines audiation as the ability to hear and comprehend music when the

sound is not physically present. Two analogies help to clarify its meaning and its

implications for music understanding. Artists visualize in the mind’s eye and musicians

audiate in the mind’s ear, or as Gordon himself states, “Thinking is to language what

audiation is to music” (p. 11).

According to Gordon, a child’s progression through the stages of preaudiational

development into a stage of understanding music is dependent upon a combination of

early musical experiences and inborn potential for music achievement. He believes that

14appropriate early childhood musical experiences are especially crucial between birth and

eighteen months, at which point the child exits the preverbal stage of development.

Gordon (1997a) defines three types of preparatory audiation, which encompass

seven stages. The types of preparatory audiation are acculturation, imitation, and

assimilation. In acculturation, the child absorbs the musical sounds of the environment,

and then randomly—and later purposefully—responds with musically unrelated

movement and vocalizations. In imitation, the child recognizes that these movements and

vocalizations do not relate to the music. With that realization, the child can then begin

imitating tonal and rhythm patterns with some degree of accuracy. In assimilation, after

recognizing a lack of coordination, the child then learns to coordinate singing and

chanting with breathing and movement.

Gordon (1997a) believes that acculturation is fundamental to all subsequent music

learning. Without having developed a sufficient listening vocabulary in acculturation, a

child will be limited in the subsequent stages of preparatory audiation. With that in mind,

he states that because most children spend their early years at home, parents are the most

important teachers children will ever have.

Music Aptitude

Gordon (1987) has performed extensive research into the phenomenon of music

aptitude. He defines music aptitude as the potential to achieve in music (inner possibility)

and distinguishes it from music achievement (outer actuality). Gordon (1997b) believes

that one’s level of music aptitude is a product of two fundamental factors, namely innate

capacity and early environmental influences.

15According to Gordon, because it is in part a product of environmental factors, a

child’s music aptitude is considered developmental until approximately age nine. It

fluctuates from birth—or before birth—in relation to the quality and quantity of early

musical experiences that child receives. After age nine, that child’s aptitude becomes

stabilized, and remains at a consistent level throughout life. A study by DeYarman (1975)

corroborates Gordon’s assertion that music aptitude stabilizes, but its findings suggest

that it may stabilize even earlier, when the child reaches the age of five or six.

Gordon believes that because a child’s music aptitude is more malleable early in

life, musical stimuli are especially influential during that time. If early musical influences

are insufficient, the child’s level of music aptitude may decline. If that situation is

corrected, that child’s level of aptitude will begin to rise. Accordingly, how high that

child’s developmental aptitude will rise depends upon the child’s innate potential. How

low it falls depends upon environmental influences. Further, the rate at which it rises or

falls depends not only upon the child’s innate potential and environmental factors, but

also upon the child’s age. The closer to age nine, the slower the rate of change will be

despite optimal or absent musical influences; the closer to birth, the faster the rate of

change will be.

Gordon has established that there are at least two measurable dimensions of

developmental music aptitude, namely tonal and rhythm. Although there may be more,

none have been reliably measured. To the degree that a child can determine whether two

tonal patterns are the same or different, or two rhythm patterns are the same or different,

that is the level of that child’s tonal or rhythm aptitude. To retain a pattern and compare it

16to a second requires a fundamental level of audiation—one that is not a function of

achievement.

The developmental music aptitude measure of Gordon (1986), the Intermediate

Measures of Music Audiation (IMMA), is designed to measure the tonal and rhythm

aptitudes of children ages six through nine. The tonal and rhythm subtests of IMMA each

consist of forty pairs of items. After hearing each pair, the child determines whether the

two tonal patterns or the two rhythm patterns are the same or different. Depending on the

conclusion drawn, the child then circles either a box containing two smiling faces (same)

or a box containing a frowning face and a smiling face (different).

The IMMA has been shown to be reliable and valid. The range of split-halves and

test-retest reliability coefficients is .70 on the rhythm subtest for first-grade children to .

91 on the composite for first and second-grade children. Longitudinal predictive validity

research has shown that IMMA scores predict the singing and instrumental performance

scores of fourth-grade students. Other studies of the congruent validity of IMMA have

shown similar results (Gordon, 1986).

In Closing

Research in neuroscience, fetal development, early musical behaviors, early

childhood music development, and music aptitude supports the theory that early music

experiences likely have a profound effect on music development in general, and music

aptitude specifically, of infants and young children. Because innate factors are likely to

remain out of our control, music educators are well advised to make the difference we

can with the children we have by providing stellar musical environments. Even then,

17music education may be missing an important opportunity to influence music

development when it is most plastic—during the first year or two of life.

Given their low level of music achievement, Sally and Joseph simply might not

have been born with the innate capacity or had sufficient and appropriate early musical

experiences to be able to achieve at the same rate as the other children in the class. Given

similar circumstances, music educators make daily choices of whether to spend

disproportionate amounts of time—at the expense of others—with children who are

exceptionally low music achievers. If rich musical environments were provided for

infants and young children prior to their entering school, the number of children lacking

basic musical skills might decrease substantially. With that, rather than have their

teaching efforts compromised, music educators would likely experience greater

effectiveness and satisfaction in the classroom.

Purpose and Problems

The purpose of this study is to examine relationships between early childhood

musical experiences and later potential to achieve in music.

The problem of this study is to identify factors and combinations of factors

involving early childhood musical experiences (birth to 18 months), as reported by

parents, that predict a child’s scores of music aptitude (tonal, rhythm, and composite) as

measured in first grade. A secondary problem of this study is to learn whether school type

(public/private) and school setting (urban/suburban) contribute to the prediction of music

aptitude scores beyond music experience factors.

18CHAPTER 2

RELATED RESEARCH

Research closely related to the present study consists of investigations about a)

effects of music and movement instruction on developmental music aptitude, b)

relationships between home environment and music achievement and c) relationships

between home and school environments and music aptitude. The first group of studies

shows that certain types of instruction, when administered early enough in a child’s life,

may raise music aptitude scores. The second group of studies was chosen from among the

more competently conducted research studies on relationships between home

environmental factors and music achievement. As this group shows a positive

relationship between those variables, it lies in stark contrast to the results of the third

group of studies—one in which the relationship between environments and music

aptitude is shown to be interestingly elusive, and to which the present study is most

closely related.

Effects of Music and Movement Instruction on Developmental Music Aptitude

The Pickett Study

Pickett (1997) described the music aptitudes of children ages five to seven. The

problem of the study was to investigate the effects of two types of instruction on the

developmental music aptitudes of 94 kindergarten and first-grade children in a rural

elementary school in Kentucky.

Intact classes of children were randomly assigned to one of two treatments. One

first-grade class and one kindergarten class received instruction for several months from a

music teacher who used the “Jump Right In” music curriculum (Gordon and Woods,

191986). Likewise, one first-grade and one kindergarten class received instruction from a

music teacher who used “Music and You” music series (McMillan, 1988). Prior to

instruction, both the tonal and rhythm portions of the Primary Measures of Music

Audiation (PMMA) (Gordon, 1986) were administered to measure the children’s music

aptitude. After instruction, PMMA was readministered to the children.

Pickett found no significant differences between the pre-test and post-test music

aptitude scores of children who received instruction for several months from a music

teacher who used either the “Jump Right In” music curriculum or “Music and You.”

Pickett did report that there was a non-significant increase in tonal aptitude scores and no

increase in rhythm aptitude scores when children were taught using “Jump Right In.” No

aptitude score increases were reported for children who were taught using “Music and

You.”

The Flohr Study

Flohr (1981) investigated the effects of two types of music instruction on the

developmental music aptitudes of Kindergarten children. Five-year-old children (N = 29)

in a Texas university childcare center were randomly assigned to three groups—two

treatment groups and a control group. The children in the treatment groups received

either Orff-based instruction including several kinds of creativity and improvisation

experiences (Music-I) or traditional classroom music experiences including singing,

dancing, playing percussion instruments, and playing games (Music-II).

Prior to the treatment period, all subjects received weekly music instruction for

three months. During the treatment period, the two experimental groups received two

20twenty-five minute periods of instruction each week for twelve weeks. A sophomore and

junior music major team-taught the two experimental groups. The former had no prior

teaching experience while the latter had one semester of prior teaching experience. The

control group received no instruction during the treatment period.

A pre-post-test design was used to determine the effects on composite (tonal and

rhythm scores combined) music aptitude scores as measured by the Primary Measures of

Music Audiation (PMMA) (Gordon, 1979). Using the pretest means as the covariate

variable in an analysis of covariance (ANCOVA), Flohr found no significant differences

among the three groups in the initial analysis. However, by combining the two

experimental groups and running a second ANCOVA, he did find that children who

received any music instruction scored significantly higher than the children in the control

group. Flohr suggested that the instruction was responsible for the difference.

Flohr conducted additional analyses comparing pre-test with post-test means

using t-tests. This analysis revealed that there was a significant difference between pre-

and post-test means of the combined groups of children who received either kind of

music instruction. In this additional analysis, Flohr found that the mean of the control

group decreased nonsignificantly on the post-test.

Flohr suggests that a small sample size coupled with the attrition in one of the

experimental groups may have contributed to the initial findings. He cautions against

making any judgments about the relative effectiveness of either type of music instruction

on the music aptitudes of young children. Furthermore, he suggests that because some

young children do not understand the concept of same and different, those children are

21distinctly disadvantaged in responding to the items on PMMA. That being the case, the

scores of children who did not comprehend the directions of the test should be left out of

the data. Questionable research practices call into question the validity of these results.

Specifically, conducting additional t-tests increases the risk of committing a Type I error.

In conclusion, Flohr suggests that music instruction can raise developmental

music aptitudes and that teachers should expect statistically significant increases even

after short instructional periods. He recommends that music educators question the

validity of, and make changes to, instruction that does not raise the music aptitude scores

of young children.

The Stamou Study

Stamou (1998) investigated the effects of Suzuki music instruction on the

developmental music aptitudes of beginning string students. The sample consisted of 116

children ages five to eight from four communities in Michigan. The experimental group

consisted of 43 children enrolled in one of three established Suzuki music programs. The

control group consisted of 73 children in kindergarten through third grade who went to a

middle-to-upper-class public elementary school.

Prior to the treatment period, the Primary Measures of Music Audiation (PMMA)

(Gordon, 1986) was administered to the children in both groups. During the treatment

period, each child in the experimental group received one 20-30 minute private Suzuki

lesson and one 45-60 minute group Suzuki lesson each week for 22 weeks. For the same

22 weeks, the control group received general music class instruction twice a week for 30

minutes as part of their regular school music curriculum. Some of the children in the

22experimental group might have also received general music instruction during the

treatment period, but Stamou did not control for that in this study.

Following instruction, PMMA was administered again to the children in both

groups. Because the mean age of the children in the control group was significantly

higher, their mean music aptitude score was also higher. To balance the data, Stamou

paired children in the experimental group with children in the control group using age

first and aptitude scores second as the criteria. Using dependent-samples t-tests, Stamou

then compared tonal, rhythm, and composite means between groups and across the four

age levels.

Stamou found no significant differences between pre-test and post-test scores

across all levels of music aptitude scores (tonal, rhythm, and composite) and at any age

level (5, 6, 7, or 8). Still, she did report that the post-instruction composite means tended

to be higher—albeit nonsignificantly—for the Suzuki students then for the control group

at all age levels except for the eight-year-old students. The reported results might be

attributable to the small sample sizes as they ranged from four to eleven across the age

levels.

Of further interest is Stamou’s use of a questionnaire to determine the early

childhood music experiences of the subjects. From the information gathered from the

subjects’ parents, she compared groups who had and did not have early music instruction

(before five years old). Stamou found no significant differences in violin performance

rating scores between those children who had early musical instruction and children who

had no early musical instruction. Although the former group did score higher, the

23differences may have been insignificant due to the inadequate sample size in the latter

group (n = 3).

The Blesedell Study

Blesedell (1991) studied the effects of two types of movement instruction on the

rhythm achievement, movement achievement, and rhythm aptitude of three- and four-

year-old children. Fifty-one children from four intact classes—two each from two private

preschools in suburban Philadelphia—participated in the study.

Prior to the treatment period, Blesedell administered Audie (Gordon, 1989) a

measure of music aptitude developed for children ages three to five, to each child

individually. Blesedell then assigned the four intact classes—one three-year-old and one

four-year-old class from each preschool—to either of two movement treatment groups.

One group received Laban-based movement instruction and the other received Dalcroze-

based movement instruction. After ten thirty-minute classes of movement instruction,

Audie was administered again to each child in both groups.

By conducting a one-dimensional Analysis of Variance, Blesedell found that both

types of movement instruction yielded significant increases (p < .05) in post-test scores

for the rhythm and melody dimensions and for the composite score of the music aptitude

measure. Blesedell concluded that both Laban-based and Dalcroze-based movement

instruction had a positive effect on the music aptitude of three- and four-year-old

children. Although the results were significant, they might have been due to a maturation

effect.

24Relationships between Home Environment and Music Achievement

The Shelton Study

Shelton (1965) investigated the relationship between what he termed preschool

“home” musical environments (including kindergarten and church school attendance) and

the musical responses of first-grade children. The children used in this study were chosen

by music teachers in five schools in Missouri. After two months experience with the

children, the teachers purposefully selected eighteen musical and twelve unmusical

children. The following factors were used as criteria: singing in tune, discriminating

between pitches, determining melodic direction, responding to rhythms using body

movement and by playing rhythm instruments, responding to contrasting tempos and

moods while listening to music, and a score of general musical temperament.

To determine whether the selected children came from musical, unmusical, or

average musical environments, Shelton interviewed the children’s parents in their homes,

and conferred with kindergarten and church school teachers. He found that 75 percent of

the “musical” homes produced “musical” children and that 73 percent of the “unmusical”

homes produced “unmusical” children. A Chi Square test yielded a significant

relationship (p < .05) between the musical responses of children and their musical

environment classification. Shelton identified specific factors that appeared to have

contributed to the musical responses of the first-grade children: the children’s opportunity

to participate in, and to hear, music; the parents’ musical background, ability, and

participation; the parents listening to music in the home; and the children’s kindergarten

and church school experience. Shelton recommended that parents provide children with

25frequent opportunities to hear music and should encourage them to participate in musical

activities.

The Kirkpatrick Study

Kirkpatrick (1962) investigated relationships between the singing ability of five-

year-old children and their home musical environment. The 116 children who participated

in this study were between 56 and 96 months old (approximately five to eight years) and

represented a predominantly middle-class community from southern California. The

researcher audio tape-recorded each child’s singing of a standard repertoire of children’s

songs. From those recordings, he classified each child as fitting into one of three groups:

singers, partial singers, or non-singers. Singers were those children who sang 90 percent

or more of the correct tones without changing tonality. Partial singers sang 75 to 89

percent of the correct tones with at least one tonality change. Nonsingers sang less than

74 percent of the correct tones without establishing tonality. Additionally, the researcher

compared and described the vocal ranges and voice quality among the three groups.

Kirkpatrick also classified the preschool home music environments of the children

into three categories—excellent to good, good to fair, and poor—using several criteria,

including a survey of home musical environment. From this information, he found that

the singing ability of children varied significantly with environment (p < .005). He found

that excellent to good musical environments produced singers and partial singers, with

few nonsingers. Poor environments produced most of the nonsingers and many partial

singers. Interestingly, Kirkpatrick also reported that there were some “singers” who were

26from “unmusical” environments. He suggested that this might be due to a strong genetic

factor.

Kirkpatrick also noted relationships between singing ability and five specific

factors: mothers who sang to their children, other adults who musically guided children,

parents who taught songs to their children, families who participated in singing and

instrument playing, and parents who had musical backgrounds. These factors were all

related positively to singing ability beyond the .05 level of confidence.

The Moore Study

Moore (1973) studied effects of early musical experiences on the pitch and

rhythm responses of five-year-old children. The subjects were 101 five-year-old

Caucasian, middle-class children enrolled in kindergarten at a school district in

Minnesota. The investigator invited children to “play some games,” and those who

accepted the invitation participated in the study. Moore acknowledged that some musical

but shy children may have been overlooked in the sampling process.

The researcher constructed three rhythm subtests and three tonal subtests to

measure the children’s rhythm and tonal achievement during their first week of

kindergarten. The tests measured pitch accuracy, vocal range, ability to keep a steady beat

to recorded music, and tonal and rhythm pattern identification and imitation.

Moore used a parent-completed questionnaire as the instrument for measuring the

home musical environment. Items on the questionnaire consisted of: a) the availability

and use of musical instruments, record players, tape recorders, televisions and radios in

the home; b) the parents’ participation with their child in musical activities such as

27singing, moving, and going to concerts; c) the presence of older siblings assessed as

either musical or unmusical; d) the musical background of the parents; and e) attendance

at church and nursery school.

Moore used stepwise multiple regressions to analyze the data. After an initial

analysis using the pitch and rhythm subtest scores separately, Moore grouped the number

of dependent variables that correlated significantly with each other (p < .05). She then

combined the reduced number of pitch and rhythm achievement scores into composite

scores for each of the six subtests. Moore regressed individual subtest scores with the

predictor variables obtained from the musical environment survey. In each of the six

regressions, Moore found that between nine and 25 predictor variables accounted for

between 36 and 64 percent of the variance in common between the predictor variables

and the corresponding music achievement subtest.

Moore reported specific factors of the home environment that were significantly

related to the level of music achievement (p < .05). These were a) having and hearing

musical instruments in the home, b) having parents and siblings who had been or were

currently participating in musical activities and showed that interest by singing, playing

and going to concerts, c) having parents help the children sing in tune and move to music,

and d) having the opportunity to hear various kinds of recorded music. One noteworthy

result mentioned by Moore is that seven to ten percent of the children who scored

average or above on the music achievement test were from homes designated as

“unmusical.” Last, the researcher suggests that multiple nursery and church school

28factors present in several of the regressions appear to contribute positively to children’s

musical achievement.

The Reynolds Study

Reynolds (1960) examined relationships between factors in the home

environment and the musicality of children entering kindergarten. Children who sang in a

“definite tonality with melodic and rhythm delineation” were considered musically

“awakened.” Children who did not were considered “unawakened.” More specifically,

Reynolds investigated the nature of and commonalities among home musical

environments that led children to be either musically “awakened” or “unawakened.”

The 85 children who participated in the study were from six kindergarten classes

located in rural Illinois communities. Recordings of familiar songs sung by each child

were evaluated by five musicians on a five-point rating scale. Children who scored above

2.50 were considered by the researcher to be “musically awakened.” Children whose

average score was above 3.75 were placed in one group. Those who scored between 2.50

and 3.74 were placed in another group. The “unawakened” children—those below 2.50—

were placed in two final groups. Reynolds does not state how they were separated.

Using a standard group of questions, Reynolds then interviewed each of the

families of the children to determine the characteristics of the musical environment. He

assigned responses to one of six categories of information: a) the children’s opportunities

to listen to music; b) the children’s opportunities to participate in music; c) the parents’

musical background; d) the parents’ musical participation; e) the parental attitude toward

29music; and f) the basis of the children’s interest, their family status, and the timbre of the

parents’ speaking voices.

From the information gathered in the interviews, Reynolds calculated response

percentages and assigned them to the appropriate group. From these percentages,

Reynolds concluded that the following factors contributed to a child’s singing ability: a)

mothers who sang, played piano, and helped to operate a record player for the child; b)

parents who provided quality children’s records, took them to concerts, provided other

listening opportunities, and had themselves received some musical training; c) parents

who appreciated music and were interested in their child’s musical growth; d) parents

who provided a permissive home atmosphere and who encouraged their child’s musical

expression; e) mothers who contributed to awakening the child’s musical interest; and f)

parents who had a piano and record player at home.

Relationships between Home and School Environments and Music Aptitude

The Kehrberg Study

Kehrberg (1984) examined the nature of the relationship between students’

outside-of-school environment and five other factors: music aptitude, general music

achievement, attitude toward music, school music participation, and school music

achievement. Of specific interest to Kehrberg was the relationship between outside-of-

school factors and scores on a measure of music aptitude.

The subjects for this study were 169 fourth through twelfth grade public school

students from a small, rural German-Mennonite community in Kansas. Kehrberg

administered the Musical Aptitude Profile (Gordon, 1965) a measure of stabilized music

30aptitude. The parents of the students then completed surveys to provide information about

97 elements of the musical environment and 14 nonmusical elements, including

demographic variables. Additionally, Kehrberg investigated the relationship between

music aptitude and general music achievement. He analyzed data by using multiple

regression analyses.

In a pilot study, Kehrberg performed an item analysis on the survey, adjusted

weak items, and obtained acceptable reliability coefficients above .80. Of 16 results, two

relate to music aptitude. The first was that there were no conclusive results about the

relationship between music aptitude and outside-of-school factors. Second, Kehrberg

found that music aptitude was not related to the frequency in which students participate in

nonmusical activities or listening to music. He did find that musical style preferences and

attendance at concerts were moderately related to a student’s music aptitude.

The Gordon Study

In a three-year longitudinal predictive validity study of the Musical Aptitude

Profile (MAP), Gordon (1967) investigated relationships between selected environmental

factors and stabilized music aptitude. Approximately 250 fourth and fifth-grade students

from four school districts—one in Wisconsin and three in Iowa—participated in the

study.

Gordon administered the three subtests of MAP, yielding tonal, rhythm, musical

sensitivity, and composite aptitude scores for each student. Using a 23-item

questionnaire, Gordon then conducted individual student interviews and examined school

31records to obtain information about environmental factors. These factors were then

correlated to the student’s scores on the three subtests and the composite of MAP.

Of the 23 environmental factors measured by the questionnaire, seven correlated

significantly across all four music aptitude scores: the student a) likes to practice; b) plays

an instrument in school other than in lessons; c) takes lessons in the summer; d) plays

another instrument; e) has a piano at home; f) attends school or community concerts; g)

tends to have a higher socioeconomic status (p < .05). These correlations ranged from .15

to .29, with the strongest relationship being between taking summer lessons and the

composite aptitude score. Gordon concluded that the relationships between music

environmental factors and music aptitude are evidence that stabilized music aptitude

scores are not a function of formal music training.

The Brand Study

Brand (1986) examined the relationship between home musical environment and

musical attributes of second-grade children. The 116 seven-year-old children who

participated in the study were taken from a large urban, primarily Mexican-American

school district in Texas.

The specific musical attributes measured by Brand consisted of tonal and

rhythmic aural perception, and general music class achievement. The measure of aural

perception was Primary Measures of Music Audiation (PMMA) (Gordon, 1979). From

formal and informal observations, the general music teacher recorded the children’s

music achievement using a Music Achievement Assessment Form (MAAF) (Brand,

1986). The MAAF was a twelve-item Likert-type scale questionnaire that categorized

32four student behaviors: musical knowledge (music symbols, terms and instruments), skill

in performance (singing or playing), music reading, and degree of interest and

motivation. Brand reported an alpha reliability coefficient of .73 for the MAAF.

Brand assessed the children’s home musical environment using his own Home

Musical Environmental Scale (HOMES) (Brand, 1986), a parent self-reporting measure

of the musical environments of lower elementary school children. The HOMES measured

four factors: a) parental attitude toward music and musical involvement with the child; b)

parental concert attendance; c) parent-child ownership and use of musical equipment and

recorded music; and d) parental ownership and playing of musical instruments. Brand

reported an alpha reliability coefficient of .86 for the HOMES.

To analyze the relationship between the two music attributes and the music

environment, Brand performed three multiple regression (setwise) analyses. Each

analysis regressed one of the three music attributes (tonal aptitude, rhythm aptitude, and

music achievement) with the four factors of the HOMES questionnaire. Brand found no

significant relationship between aptitude scores (tonal and rhythm) and the four HOMES

factors. He did find a significant relationship between music achievement scores and one

of the HOMES factors (p < .001), namely parental attitudes toward music and musical

involvement with the child. This relationship accounted for 20 percent of the common

variance with music achievement. Brand presented evidence for a strong relationship

between home music environments and the music achievement of second-grade children.

33The Farr Study

Farr (1993) investigated relationships between music aptitude and home musical

environments of children ages three to seven. The sample consisted of 43 children from a

primarily Caucasian suburban community. Farr administered either Audie (Gordon, 1989)

or Gordon’s Primary Measures of Music Audiation (PMMA) (Gordon, 1979) to each

child, depending on which measure was age appropriate. Farr reported Kuder-Richardson

reliability coefficients of .90 and .82 for the tonal and rhythm subtests of PMMA

respectively; and reliability coefficients of .65 and .60 for the melody and rhythm subtests

of Audie, respectively.

To measure the children’s early childhood music environment, Farr used Behavior

Profiles, a questionnaire for parents designed by Fox (1991). Behavior Profiles measured

four attributes of their child’s music achievement: a vocal score, a movement score, an

instrument playing score, and a participation score. No reliability coefficients were

reported for the Behavior Profiles measure.

For each age group, Farr calculated Pearson correlations between PMMA or

Audie scores—tonal, rhythm and composite—and the four factors of the Behavior Profile.

The range of correlations between the Audie tonal and rhythm scores and the Behavior

Profile scores was -.22 to .20 (n = 29). The range of correlations between PMMA

composite scores and the Behavior Profile scores was near zero to .24 (n = 16). The

strongest relationship reported by Farr, between the PMMA tonal subtest scores and the

instrumental performance score, accounted for less than seven percent of variance in

34common. Unfortunately, due to insufficient sample size in most of the correlation

matrices (n < 11), most of the results of this study are rendered untrustworthy.

In Closing

Although all but one investigation of the effect of music or movement instruction

on music aptitude yielded non-significant differences in pre- and post-test music aptitude

scores, it would be short-sighted to conclude that instruction (or other more informal

music-learning environments) will not raise the music aptitudes of children. Perhaps

improved aptitude scores are difficult to achieve for several reasons.

First, the reliability and validity of measures of music aptitude for children

younger than four years old have not been well established. Children’s scores on Audie

commonly vary from week to week. Are such changes due to actual fluctuations in

aptitude brought about by fluctuations in the music environment or are they due

extraneous factors? Firm answers should be subject to scrutiny. It is unfortunate that by

the time a child’s aptitude can be measured reliably—by four or five years old—the child

is already leaving the most plastic stage of developmental music aptitude. To cause a

difference in music aptitude scores of children in the later stages of developmental music

aptitude would be, by definition, operationally very difficult. Although the aptitudes of

five to eight-year-old children are in the developmental stage and are by definition still

malleable, they are probably fairly well formed as they approach the stage of stabilized

music aptitude at around nine years old. Also, music aptitude scores are likely to be

difficult to raise within the relatively short treatment periods used in most of the above

studies. Raising them becomes even more problematic if the conclusions of DeYarman

35(1975) are valid—that music aptitudes stabilize in children as young as five or six years

old.

Still, in the Blesedell study, the treatment period was only ten weeks long and yet

the pre- and post-test rhythm aptitude scores were significantly different. What accounted

for this result? One possibility is that because the subjects in the Blesedell study were one

to two years younger than the subjects of the other studies, their aptitudes were in a more

plastic state. The comparison of these studies suggests that the younger the child is, the

more the child’s music aptitude is susceptible to environmental influences. Gordon’s

definition of developmental music aptitude lends agreement to this assertion.

36CHAPTER 3

DESIGN AND ANALYSIS

Purpose and Problems

The purpose of this study is to examine relationships between early childhood

musical experiences and later potential to achieve in music.

The problem of this study is to identify factors and combinations of factors

involving early childhood musical experiences (birth to 18 months), as reported by

parents, that predict a child’s scores of music aptitude (tonal, rhythm, and composite) as

measured in first grade. A secondary problem of this study is to learn whether school type

(public/private) and school setting (urban/suburban) contribute to the prediction of music

aptitude scores beyond music experience factors.

Sample

More than 275 first-grade children were invited to participate in this study. Of

those children, 151 submitted a researcher-developed questionnaire designed to measure

each child’s early childhood music experiences. Of those that submitted questionnaires,

140 received scores on the tonal portion of the measure of music aptitude, 132 received

rhythm scores, and 128 received composite scores. For each case on the aptitude

measure, the child presented no evidence that the scores were invalid. (i.e. Their answer

sheets were complete and had no obvious occurrences of pattern marking.) Although not

all questionnaires were complete, most were retained for the purpose of gathering as

much information as possible about the constructs of the measure. Of those children who

submitted complete questionnaires, 126 received scores in the tonal aptitude measure, 118

37received scores in the rhythm aptitude measure, and 114 received scores in both

dimensions of music aptitude. These numbers were used in the final analyses.

The children who participated in this study represented public and private school

populations from urban and suburban school settings in southeastern Pennsylvania. The

sample breakdown for the children who completed all components of this study is shown

in Table 1. Of those children accounted for in Table 1, a purposive sample of seven

parents participated in open interviews for use in the qualitative portion of this study.

Table 1

Sample Summary_______________________________________________________________________

School Type _______________________________________________________________________

Public Private Total_______________________________________________________________________

School Setting Urban 30 19 49

Suburban 29 36 65

Total 59 55 114_______________________________________________________________________

Design

The researcher administered the tonal and rhythm subtests of the Intermediate

Measures of Music Audiation (IMMA) (Gordon, 1986) to 275 children in first grade.

After the aptitude measure was administered, parents were asked to complete and return a

38researcher-developed questionnaire, the Musical Experiences Questionnaire (MEQ)

(Appendix A), designed to ascertain the quality and quantity of the child’s musical

experiences between birth and eighteen months.

Following data collection, the researcher conducted in-depth personal interviews

with a purposive sample of parents (n = 7). Parents were selected for interviews based on

the following criteria: the child had a high music aptitude in at least one dimension, the

child’s MEQ score represented a relatively impoverished musical environment, and the

quality of the open responses on the questionnaire indicated a parents willingness to be

interviewed. The researcher then quantitatively and qualitatively analyzed the

information obtained from the questionnaires (total sample) and the interviews (purposive

sample) in relationship to the tonal, rhythm, and composite music aptitude scores from

the IMMA administrations.

Procedures

Prior to formal data collection, the researcher conducted a pilot study to test for

the workability of the procedures, the internal consistency of the researcher-developed

Musical Experiences Questionnaire (MEQ) (Appendix A), and the suitability of the

Intermediate Measures of Music Audiation (IMMA) (Gordon, 1986) for first grade

children. The researcher investigated the effectiveness of two procedures for collecting

the questionnaires: having parents mail them to the researcher in self-addressed stamped

envelopes or having students return them to their classroom teachers.

The researcher determined from the pilot sample that the MEQ and IMMA were

satisfactorily stable. For the MEQ, a Chronbach’s alpha reliability coefficient was

39calculated: r = .88 (n = 25). For the IMMA, Spearman Brown corrected split-halves

reliability coefficients were calculated for the tonal, rhythm, and composite scores of the

children in the pilot sample (n = 39). For the tonal subtest, r = .83. For the rhythm

subtest, r = .79. For the two subtests combined, r = .88.

After adjusting the MEQ collection procedures, the researcher solicited the

administrators of public and private schools from urban and suburban areas in

Southeastern Pennsylvania for permission to conduct the study with first-grade children

and their parents. (See Appendix B for the solicitation letter.) After the administrators of

the various schools granted permission, the teachers and parents of the first-grade

children were asked to participate. Parents signed a permission slip (Appendix C)

agreeing to complete the MEQ and be available for potential interviews on the condition

of confidentiality. In the agreement, parents were asked if they wanted to be informed of

their child’s music potential after data collection was complete.

Two 20-minute periods within one week were scheduled at each school, during

which the researcher administered the tonal and rhythm subtests of IMMA to intact first-

grade classes. Immediately after each IMMA administration, the researcher asked the

children to take home a copy of the MEQ, which parents had previously agreed to

complete and return. The IMMA answer sheets were then hand-scored as per the

instructions in the test manual.

Depending on who was most knowledgeable about their child’s early childhood

musical experiences, one or both parents completed the MEQ and mailed it back to the

researcher in a self-addressed stamped envelope. In an attempt to raise the percentage of

40questionnaire returns, the researcher periodically reminded children and followed up with

classroom teachers. Further, as an incentive, a chance to win 50 dollars was offered to

parents if they completed and returned the MEQ by the requested time. After terminating

MEQ data collection, the researcher awarded the 50 dollars by lottery, read the

questionnaires, converted items with quantitative properties to five-point ratings, and

computed the results.

The researcher then selected parents for open interviews by determining whose

children had at least one exceptionally high music aptitude—tonal, rhythm, or composite

at the 90th percentile or greater—and also had a low combined score on the MEQ. After

finding the relatively small number of cases that met these criteria, he examined and took

into account the parent's written responses to the open items on the MEQ. Parents whose

writing presented evidence of an openness to be interviewed were favored.

He then scheduled telephone interviews with seven parents, conducted and

recorded those interviews, transcribed the recordings, and photocopied the transcripts. He

studied one set of the transcripts of the recorded interviews and categorized the case

information. To organize the categorization process, the researcher copied the transcripts

onto color-coded paper using a different color to represent each interview. From those

transcripts, fragments of the conversations were cut apart, studied, and grouped to reveal

commonalities and unique qualities among and within the cases. After several weeks, the

researcher repeated the categorization procedure with the duplicate set of the color-coded

interview transcripts. Substantial overlap of information between the two readings

indicated stability in the process, though a few unique potential categories were revealed.

41He categorized the qualitative information gathered from the MEQs for the purposive

sample as well as for the total sample. Using the above information, the researcher wrote

brief accounts for each of the seven children. When those accounts were complete, the

researcher asked each child’s parents to verify accuracy.

After studying information from both samples (purposive and total) and its

relationships to the tonal, rhythm, and composite scores obtained from the

administrations of the aptitude measure, the researcher wrote a rich description of his

findings. A peer reviewer verified that the researcher’s written accounts were fair and his

findings reasonable. Corrections and clarifications were made based on mutual agreement

between the peer reviewer and the researcher.

Analysis

Means, standard deviations, and Spearman Brown corrected split-halves

reliability coefficients were calculated for the tonal, rhythm, and composite scores for

each of the IMMA administrations, for each of the sample populations, and for all sample

populations combined.

Further, because a substantial number of parents from some schools failed to

return an MEQ, the possibility of sample bias was investigated by calculating the same

IMMA statistics for children whose parents submitted the MEQ versus those who did not

submit the MEQ. This answers the question as to whether the IMMA scores of children

whose parents submitted the MEQ were different from those of children whose parents

did not submit the MEQ—as they would self-select themselves to be excluded from the

final data analyses.

42For the MEQ, number of responses, means, and standard deviations were

calculated for each item and for the composite scores. Alpha reliability coefficients were

also calculated for each of the subgroups and for the subgroups combined. Prior to the

study, the same statistics were calculated using a pilot sample (n = 25). Because the pilot

sample yielded acceptably stable results for the MEQ (and IMMA), those scores were

included in the final data set. To further substantiate the consistency of the MEQ, the

researcher calculated a test-retest reliability coefficient (n = 17) using a second series of

questionnaires, which were distributed to a portion of both the pilot and study sample.

To investigate what questionnaire items, if any, might be combined and used in

the final data analyses, the researcher performed a factor analysis on the MEQ.

Correlations among the factors and reliability estimates for each were also calculated.

From an examination of those statistics, the researcher determined which factors would

be included in the final data analyses.

The researcher then performed two series of three multiple regression analyses.

The first series included the selected MEQ factors alone as potential predictors of the

tonal, rhythm, and composite IMMA scores. The second series included factors of school

type (public/private) and school setting (urban/suburban) in combination with the MEQ

factors as potential predictors of the IMMA scores. The dependent variables for both

series were the tonal, rhythm, and composite music aptitude scores. From those

regression analyses, percentages of variance in common between the IMMA scores

(tonal, rhythm, and composite) and MEQ scores—with and without school factors—were

read and interpreted.

43The researcher used all of the information gathered from the MEQs and

interviews in a qualitative analysis. He then reported in rich description his findings on

the relationships between early childhood musical environments and tonal, rhythm, and

composite music aptitude scores.

44CHAPTER 4

RESULTS AND INTERPRETATIONS

Quantitative Results

The Intermediate Measures of Music Audiation

Descriptive statistics for the administrations of Intermediate Measures of Music

Audiation (IMMA) (Gordon, 1986) are reported in Table 2. Because some parents did not

submit the Musical Experiences Questionnaire (MEQ), the researcher checked for any

relationship between children’s aptitude scores and parent’s ability or willingness to

submit the form. Should any IMMA score differences be found—within a subgroup—

between those who completed the MEQ (submitters) and those who did not (non-

submitters), the researcher should examine whether the self-selecting sample accurately

represents its subgroup. Mean differences between submitters and their subgroups were

investigated by comparing the mean of submitter groups with the mean of the sample it

represents.

Among suburban public children, submitters had higher tonal, rhythm, and

composite means than did non-submitters. They also had smaller standard deviations for

tonal and composite scores than did non-submitters. The same can be noted for urban

private submitters relative to non-submitters for the same group, but the sample size for

non-submitters is too small to make any differences meaningful. Urban public submitters

had means similar to those of non-submitters, but were more heterogeneous. For the

suburban private group, mean comparisons would be meaningless due to the minimal

non-submitter rate.

45Table 2

Descriptive Statistics for IMMA Administrations for MEQ Submitters (S) Versus

Non-Submitters (NS)___________________________________________________________________

Tonal Rhythm Composite

n M SD ra n M SD r n M SD rSub-Pub S 35 33.6 2.5 .80 29 30.7 3.1 .78b 29 64.2 4.3 .85

NS 53 31.8 4.5 .88 50 29.4 3.2 .69 47 61.7 6.6 .86Urb-Pub S 41 29.9 5.6 .92 40 28.2 5.1 .86 36 59.4 8.8 .91

NS 75 29.9 5.1 .85 75 28.1 4.0 .79 67 58.2 7.8 .86Sub-Pvt S 44 31.9 3.6 .79 43 28.2 3.9 .73 43 60.4 5.3 .81

NS 1 - - - 2 - - - 1 - - -Urb-Pvt S 20 31.1 3.1 .82 20 29.9 4.4 .82 20 61.0 6.7 .91

NS 6 28.8 2.9 - 5 27.4 3.5 - 4 57.5 3.8 -Public S 76 31.6 4.9 .90 69 29.2 4.6 .81 65 61.5 7.6 .90

NS 128 30.7 4.9 .89 125 28.6 3.9 .74 114 59.7 7.5 .90Private S 64 31.7 3.4 .78 63 28.8 4.1 .76 63 60.6 5.8 .79

NS 7 29.7 3.5 - 7 28.9 3.8 - 5 59.2 4.8 -Suburban S 79 32.7 3.3 .80 72 29.2 3.8 .72 72 61.9 5.3 .83

NS 54 31.9 4.5 .88 52 29.6 3.2 .65 48 61.8 6.5 .85Urban S 61 30.3 5.0 .91 60 28.8 5.0 .84 56 59.9 8.2 .85

NS 81 29.8 4.9 .89 80 28.0 4.2 .75 71 58.2 7.6 .89All S 140 31.6 4.3 .85 132 29.0 4.4 .78 128 61.1 6.8 .85

NS 135 30.6 4.9 .89 132 28.6 3.9 .72 119 59.6 7.4 .85Combined 275 31.1 4.6 .87 264 28.8 4.2 .72 247 60.4 7.1 .87Norms 675 31.1 4.5 .76 675 29.2 4.0 .70 675 60.3 7.2 .80

___________________________________________________________________

Note. Sub = suburban. Urb = urban. Pub = public. Pvt = private. aSplit halves (corrected for length) bThe rhythm

subtest statistics for the Sub-Pub group are from a second administration necessitated by a low reliability obtained in

the first.

For the submitter groups combined, the means are only slightly higher and the

standard deviations slightly smaller than those for the non-submitter groups combined.

Given that, the researcher determined that sample bias for the combined submitter group

should not be a significant factor.

46The split-halves reliability coefficients for the tonal and rhythm subtests and for

the composite were acceptably high across all groups with one notable exception. The

reliability coefficient for the initial rhythm subtest administration to suburban public

children was unacceptably low (r = .56). This may have been due to either test fatigue or

a lack of motivation, as the researcher administered the rhythm subtest on a Friday

afternoon, 48 hours after the tonal subtest administration. For the other subgroups, the

researcher administered the rhythm subtest one week after the tonal subtest

administrations. The researcher readministered the rhythm subtest to suburban public

submitters after several days. The reliability from that administration was acceptably

higher (r = .78).

Overall, the IMMA functioned exceptionally well with the children in the study

sample. The reliability coefficients, means, and standard deviations for the subgroups and

the groups combined were comparable to those reported for the norms sample by the test

author (cited at the bottom of Table 2).

The Musical Experiences Questionnaire

Prior to formal data collection, the researcher conducted a preliminary analysis of

the Musical Experiences Questionnaire (MEQ) using a pilot sample. Descriptive statistics

are reported in Table 3. Although the size of the pilot sample was small, the mean,

standard deviation, and reliability coefficients were within an acceptable range relative to

theoretical constructs. That is, by taking the total number of items (22) and the maximum

score for each item (5), one can calculate the theoretical maximum score (110). The

theoretical minimum is 22. Therefore, the theoretical mean is halfway between (66).

47Dividing the difference between the maximum score and the theoretical mean by three

yields the theoretical standard deviation (14.7).

Table 3

Descriptive Statistics of the Pilot Sample for the MEQ_______________________________________________________________________ N M SD αa test-retest _______________________________________________________________________ Obtained 20 60.6 15.0 .88 .84

[theoretical] [66.0] [14.7]______________________________________________________________________________________

Note. aChronbach’s alpha. For the test-retest reliability coefficient, n = 17.

Using the entire study sample, the researcher conducted an item analysis for MEQ

items with quantitative properties. Means, standard deviations, and discrimination values

for those items are reported in Table 4. The reported statistics showed that the MEQ had

properties similar to those expected of a good measurement instrument—a wide range of

means with low to moderate positive discrimination values. The strongest exception was

item 16, which had an exceptionally high mean, a small standard deviation, and a

discrimination value close to zero. Item 16 was also the only item in the MEQ that

pertained to early childhood experiences after the child passed 18 months of age. For

those reasons, the researcher eliminated item 16 from subsequent quantitative analyses.

Table 4

Means, Standard Deviations, and Discrimination Values for Items of the MEQ ______________________________________________________________________________________

item # M SD Disc. values

481 3.60 1.14 .21

1a 2.63 1.08 .19

2 3.41 1.05 .21

3 2.81 1.21 .26

4 2.94 1.23 .32

5 2.62 1.17 .27

5a 2.16 1.40 .32

6 3.29 1.19 .25

6a 2.52 1.23 .24

7 4.01 1.07 .16

7a 3.30 1.12 .21

8 1.46 .85 .12

8a .99 1.53 .24

10 2.79 1.18 .18

11 2.63 1.12 .11

12 2.08 1.08 .17

13b .76 1.36 .20

13c .80 1.43 .20

14 2.39 1.04 .12

16 4.18 .81 .04

17 3.85 1.29 .23

17a 3.35 1.57 .23______________________________________________________________________________________

49To investigate what factors, if any, were being measured by the MEQ, the

researcher conducted a factor analysis using all items with quantitative properties

excepting item 16. He limited the number of factors extracted to those with Eigenvalues

greater than 1.0. The resultant six factors and their names are reported in Table 5.

The descriptive statistics for the six factors are presented in Table 6. Factors 2 and

4 have low means and large standard deviations relative to theoretical constructs. This

was to be expected, as those factors measure aspects missing in most children’s early

musical experiences—namely music and movement classes and live instrumental

modeling. The other factors have means closer to theoretical values, and standard

deviations somewhat larger than theoretical values. The discrimination values for the

factors were low to moderately low, ranging from .10 to .24.

Intercorrelations among and reliability estimates for those factors are presented in

Table 7. Variance in common among the factors (r2) was less than nine percent, with three

exceptions. Factor 1 shared fourteen percent of the variance with Factors 3 and 4, and

twenty percent with Factor 6. Because factors 1, 3, and 4 were highly reliable relative to

those intercorrelations, these factors can be retained for the final analysis with some

degree of confidence. The low reliability for Factor 6 and its high intercorrelation with

Factor 1 weakens it as a potential and unique predictor of music aptitude scores. Still,

because its intercorrelations with the other factors are relatively weak, eliminating it from

subsequent analysis could risk a loss of information. Factor 5 consisted of a single

questionnaire item, so its reliability could not be estimated.

Table 5

50Factor Analysis of the MEQ using a Principal Components Extraction Method

with an Orthotran/Orthogonal Quartimax Transformation_______________________________________________________________________________________________

Item # Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 (music behaviors (music/move. (prenatal (live inst’l. (music from (live music of parents) classes) music exp.) experiences) television) and radio)_______________________________________________________________________________________________

1 .88 .12 -.38 -.10 -.04 -.34

1a .61 -.19 .17 .04 -.28 .13

2 .72 -.18 -.01 -.05 .30 .02

3 .70 -.01 -.02 -.06 .42 .04

4 .85 .01 -.03 .05 .19 -.13

5 .71 -.08 -.05 -.03 .09 .24

5a .62 -.09 .15 -.08 -.09 .31

6 .83 .08 -.24 -.01 .12 -.22

6a .62 -.07 .20 .03 -.26 -.03

7 .81 .24 -.28 -.16 -.34 -.23

7a .69 -.01 .23 -.07 -.49 -.09

8 .03 .04 .00 .95 -.01 .00

8a .11 -.03 .06 .90 -.01 -.01

10 .25 -.05 .30 -.14 .09 .49

11 .13 .01 .04 -.03 .69 .00

12 .21 .20 -.29 .18 .20 .44

13b -.03 .96 .09 -.02 -.01 .06

13c -.03 .95 .08 .02 .00 .04

14 .03 .07 -.17 .00 -.05 .86

17 .24 .07 .69 -.01 .04 -.12

17a .00 .12 .83 .07 .00 -.11

__________________________________________________________________________________________

Note. High factor loadings are in bold.

51Table 6

Descriptive Statistics for MEQ Factors_________________________________________________________________________________

F1 F2 F3 F4 F5 F6 (music behaviors (mus./move. (prenatal (live inst’l. (music from (live music of parents) classes) music exp.) experiences) television) and radio)_________________________________________________________________________________

No. of items 11 2 2 2 1 3

n 151 151 141 151 150 149

M 33.3 1.6 7.2 2.5 2.6 7.3

theoretical M 33.0 6.0 6.0 6.0 3.0 9.0

SD 9.0 2.8 2.5 2.3 1.1 2.4

theoretical SD 7.3 1.3 1.3 1.3 0.6 2.0

disc. values .24 .20 .23 .18 .10 .15_________________________________________________________________________________

Table 7

Intercorrelations and Reliability Estimates for MEQ Factors ______________________________________________________________________________________

F1 F2 F3 F4 F5 F6 (music behaviors (mus./move. (prenatal (live inst’l. (music from (live music of parents) classes) music exp.) experiences) television) and radio)______________________________________________________________________________________

F1 .89

F2 .17 .97

F3 .38 .16 .71

F4 .38 .10 .30 .85

F5 .15 .06 .12 .10 -

52 F6 .45 .14 .28 .24 .17 .46______________________________________________________________________________________

Note. Factor reliability coefficients are in bold. (Factor 5 consisted of a single item.)

Regression Analyses: Relationships between MEQ and IMMA Scores

To ascertain the degree to which MEQ factors (alone and combined with school

type/setting factors) account for the variance in IMMA scores (tonal, rhythm, and

composite), the researcher performed two series of three multiple regression analyses. In

the first series, all six of the factors were forced to determine if MEQ factors would

predict tonal, rhythm, and composite IMMA scores. If they did, their potency as

predictors could be ascertained from the regression coefficients for each factor. Similarly

for the second series, all eight factors were again forced—this time including school type

and school setting variables. The results of the Six Factor Model (without school

variables) and the Eight Factor Model (with school variables) are reported in Tables 8 and

9 respectively. R2 and Adjusted R2 values are reported, the latter removing error generated

by commonalities among overlapping factors.

Table 8

Results of the Multiple Regression Analyses (Six Factor Model) between MEQ Factors

and Tonal, Rhythm, and Composite IMMA Scores

_________________________________________________________________________________

N |R| R2 Adjusted R2 p_________________________________________________________________________________

53

Tonal 126 .24 .06 .01 .327

Rhythm 118 .26 .07 .02 .267

Composite 114 .23 .06 .00 .411_______________________________________________________________________

Table 9

Results of the Multiple Regression Analyses (Eight Factor Model) between MEQ Factors

and Tonal, Rhythm, and Composite IMMA Scores__________________________________________________________________________________________

N |R| R2 Adjusted R2 p__________________________________________________________________________________________

Tonal 126 .35* .12 .06 .049

Rhythm 118 .26 .07 .01 .440

Composite 114 .27 .07 .00 .425

- - - - - - - - - - - -

Tonal

df SS MS F __________________________________________________________________________________________

Regression 8 283.3 35.4 2.02*

Residual 117 2047.2 17.5

Total 125 2330.5 __________________________________________________________________________________________

* p < .05

54As can be determined from the analyses, neither MEQ factors nor school factors

are related to either the rhythm or composite IMMA scores beyond that which could be

accounted for by sampling error. Likewise, MEQ factors by themselves did not account

for any variance in IMMA tonal scores beyond that of chance. However, when the MEQ

factors were combined with the school variables, they accounted for a statistically

significant amount of variance in common with IMMA tonal scores |R| = .35; F (8, 117)

= 2.02, p < .05.

The R2 value showed that a maximum of 12 percent of the variance of the IMMA

tonal scores could be accounted for by the eight factors combined. That estimate, though,

is positively biased and does not account for the average proportion of variance more

likely representative of the sample population. The adjusted R2, which removes that

positive bias, was calculated at 6.2 percent.

The t-tests for the regression coefficients in the eight-factor model (tonal) indicate

that the difference between urban and suburban school settings (favoring suburban)

contributes substantially to the variance accounted for in the IMMA tonal scores. No

other regression coefficients were significant at or beyond the .05 level. The regression

coefficients for the eight factors are presented in Table 10.

Table 10

Regression Coefficients for the Eight-Factor Model and the IMMA Tonal Scores___________________________________________________________________

B SE B ß t ___________________________________________________________________

Factor 1 0.01 0.05 0.02 0.21

55Factor 2 -0.12 0.14 -0.08 -0.82

Factor 3 0.14 0.17 0.08 0.86

Factor 4 0.18 0.18 0.10 1.00

Factor 5 -0.41 0.38 -0.10 -1.07

Factor 6 -0.13 0.18 -0.07 -0.71

School Type 0.20 0.79 0.02 0.26

School Setting 2.43 0.84 0.29 2.93**_________________________________________________________________________________

** p = .004

Although IMMA tonal score means were higher among suburban children whose

parents submitted the survey (submitters) than among those who did not (non-

submitters), an almost identical finding was detected among urban children. In fact, the

difference in tonal score means between the suburban submitters and urban submitters

relative to their combined groups within school setting (suburban and urban groups

respectively) was only .03. Therefore, the researcher can confidently discard the issue of

submitter versus non-submitter bias for the purpose of his interpretations.

Qualitative Results

Given the wealth of information regarding early childhood music experiences that

could not be thoroughly ascertained through the MEQ, the researcher conducted a

qualitative analysis to shed additional light on relationships between those experiences

and music aptitudes. The researcher believes that notable qualitative differences present

among the musical environments of young children—even among those with similar

richness—could not be accounted for fully by use of quantitative research methods.

56Further, when quantitative differences in richness were not found among environments,

deeper probing using qualitative methods of data analysis usually showed that

considerable differences were present. It is important that these differences be recognized

and accounted for in their power, or lack thereof, to predict music aptitude scores. This

second research perspective can only contribute to a more comprehensive understanding

of the relationships between early childhood musical experiences and later potential to

achieve in music.

Another purpose for presenting the following case studies is simply to give the

reader a personal sense of the musical exposure that a small purposive sample of children

(n = 7) received early in life. For each child, one of the parents or guardians participated

in an open interview with the researcher. In each case, the child had at least one

exceptionally high aptitude—either tonal (as in cases 1, 2 and 3), rhythm (as in cases 4

and 5), or both tonal and rhythm (as in cases 6 and 7). Case studies 1-6 were also chosen

from among children whose MEQ scores indicated somewhat lacking music experiences

in their first 18 months of life. Case study 7 was chosen from among children whose

aptitudes and MEQ scores were both exceptionally high. This last case affords the reader

a mark for comparison.

The researcher chose these cases to examine and describe environments that were

measured as relatively impoverished and yet were apparently rich enough to support the

development and maintenance of a high music aptitude in at least one dimension. Further,

because all but one of these environments were represented as less than rich as measured

by the quantitative items of the MEQ, the researcher used the information gathered in the

57interviews to a) determine if some parents were prone to underreport the amount of

musical stimulation their child received, and b) question the validity of the MEQ as a

measure of environmental musical richness.

In choosing the sample, the researcher also took into consideration that

differences might be found between environments from which children developed high

tonal aptitudes (and average or below rhythm) and those from which they developed high

rhythm aptitudes (and average or below tonal). Simply asked, can any qualitative

difference be identified between the musical environments that produced children with

high tonal aptitude and ones that produced children with high rhythm aptitude?

Case Study Presentation

Following the presentation of the summarized information gathered from the

interviews, the researcher will interpret his findings.

Case study #1. This child—call him Andre—was selected from the urban public

population. His paternal grandmother completed the interview. Andre’s tonal aptitude

score was at the 99th percentile; his rhythm aptitude was above average. His combined

factor score on the MEQ was relatively low.

Andre went to live with his grandmother when he was six months old. Soon

afterward, “he had a short stay in the hospital and . . . when he came around, he would

sing the Barney song, so the nurses thought he was older—he was large for his age. So at

about 6-7 months of age, he would do ‘When You’re Happy and You Know It, Clap Your

Hands,’ and from then on, any song that would come by, he could pick up on it and he

58loved to listen to music. . . .” When he was a year or two old, he would clap his hands or

play the tambourine during church services, which he attended regularly throughout his

childhood. In fact, the minister commented on his ability to clap the beat to the

musicians. Andre knew all of the songs that the choir sang and he would often sing along

with them.

A few months after his “adoption,” and for the next four years, he attended day

care. To and from the day care, his grandmother would sing in the car while listening to

gospel music on the radio. At the day care, the teachers sang much of the standard

children’s repertoire, mostly that from television shows. They also encouraged the use of

toy percussion instruments with which the children marched around the room on many

occasions.

Andre also learned songs and nursery rhymes mostly from television, but his

grandmother often would perform them for and with him. She did not believe that she

was musically inclined. Still, Andre often encouraged her as well as his father to sing

with him. According to the grandmother, he was always the one dragging them along.

Andre listened to and enjoyed all styles of music. He mostly listened to gospel, but his

grandmother recalls that he was especially impressed with a piano student who played a

piece by Beethoven. She said, “There’s no music that I’ve seen that he does not like.”

Case study #2. This child—call her Brianna—was selected from the suburban

public population. Her mother completed the interview. Brianna’s tonal aptitude score

was one point higher than the 99th percentile for children in first grade; her rhythm

59aptitude score was above average. Her combined factor score on the MEQ was

exceptionally low.

Brianna’s mother was born in Korea and spoke with very broken English. Still,

her communication was quite clear. She told me that when Brianna was a baby, she sang

to her almost every day—for five, sometimes thirty, minutes. Some of the songs she

taught her were Korean, but most were children’s songs in English. I asked her to sing

one of the Korean songs for me and she obliged. Her performance of this lullaby was

beautiful—flowing, impeccably in tune, and very sweet.

From a very young age, Brianna showed an affinity for watching others play the

piano. She also loved church and attended regularly. There she would often do “sing-

talking” with her mother. When I asked, “Does everyone in your family sing?” she said,

“We like it.” Then she chuckled and reiterated, “Yes, we like it.” She said that she hopes

that Brianna gets the benefit of music exposure from everyone in the family singing. At

the end of the interview, she said, “I’d like to take piano lessons. I hope Brianna will like

to take lessons too.”

Case study #3. This child—call her Cynthia—was selected from the urban private

school population. Her mother completed the interview. Cynthia had a tonal aptitude

score one point higher than the 99th percentile for children in the first grade. Her rhythm

aptitude was below average. Her combined factor score on the MEQ was relatively low.

Everyone in Cynthia’s family liked to listen to music. According to her mother,

she was exposed continuously to music prior to her birth through her infancy. From the

60time she was one until she was three years old, she was also exposed to the informal early

childhood music classes her brother attended at a private music school.

Cynthia’s mother said that what also might contribute to her daughter’s aptitude is

that “We’re a silly family. We do a lot of sing-songy, rhymy games, you know, just

kidding around. That’s always been a big part. Even my father does that a lot with the

kids—little ditties and again through her early years. That was always a big part of play

time.”

Cynthia’s family listened and danced to a variety of recorded music, including

some jazz and classical, but rock and roll artists such as Billy Joel and Elton John were

staples. They would push the furniture back and just dance, especially when her extended

family got together.

Cynthia’s mother never sang any traditional repertoire for her children. She said,

“to sing formal songs—like a lullaby, even a rock and roll song that I knew—that

wouldn’t be a part of it. It was made up, mostly silly things.” When she was still very

young, Cynthia often would stand to the side and just absorb the action when the family

was involved in music making. Her mother said that she could tell Cynthia was usually

“chomping at the bit” to join in.

At the time of the interview, Cynthia was taking piano lessons at school. She had

also recently been in ballet and jazz dance classes offered by a community arts center.

Case study #4. This child—call her Debbie—was selected from the urban private

school population. Her mother completed the interview. Debbie had a rhythm aptitude

61score at the 99th percentile and an average tonal aptitude score. Her combined factor

score on the MEQ was relatively low.

When Debbie was born, her older brother—call him Brad—was three years old.

They were always together, so when the mother sang to Brad, she believed that Debbie

was getting the benefit.

Most of the music exposure Debbie received was from the television, and

included shows like Barney and Mr. Rogers. Beyond that, the family would listen to a

fairly wide range of popular radio stations—including light rock, top 40, hip-hop, and

danceable music from the 1970s and 80s.

When Debbie was still in utero, the mother sang to Brad quite often. During that

time, she was also frequently exposed to the loud and heavy beat of dance club music. (A

friend of the mother was a disc jockey.) This exposure ended prior to entering the last

trimester of her pregnancy.

Debbie’s maternal grandparents watched her frequently after school, and they

sang to her and her brother frequently too. But when Sesame Street came on, both would

stop whatever they were doing to watch. When they watched, Debbie would sit

completely still. She exhibited the same behavior when watching movies, even after

having seen them many times before. Her favorite video was Disney’s “Hocus Pocus,”

starring Bette Midler. The family was using their third copy of the tape because the first

two had been badly worn from repeated use. At the time of the interview, Debbie listened

to the typical repertoire for her generation: Britney Spears, the Back Street Boys, In Sync,

and Christina Aguilera.

62Debbie’s mother took extreme interest in what the children learned to sing. She

would learn the words to all the songs they listened to on the radio and made sure that the

children sang them correctly. If they sang any wrong words, she always would correct

them. Interestingly, they never minded being corrected. In fact, they always asked for

their mother’s help in figuring out what the words were. Any song with questionable

content prompted the immediate changing of the radio station. The mother was especially

passionate when talking about this part of their music exposure.

Because Debbie’s parents were separated, the father saw the children every other

weekend. According to the mother, he played a lot of music she would never allow, i.e.

hard rock, and songs with “a lot of language like Eminem.” She preferred that they listen

to the music she liked: the Bee Gee’s, the Village People, Cyndi Lauper, Rod Stewart,

Madonna, Donna Summer and Whitney Houston. Debbie listened to almost no classical,

jazz, gospel, hip-hop, or rap. Out of a ten-hour day, she would listen to music at least six

hours.

Her mother continued. “They just like to sing. As a matter of fact the other day,

we go to church, and they do a lot of singing at their mass. And my mother’s always

opening up the songbook for her—even though she [the grandmother] can’t sing, she still

likes to sing. And she’s always nudging Debbie to go along with the words. So my Mom

does that, makes her do that.”

I invited the mother to ask Debbie and her brother to perform something for me

over the phone. When she did, they immediately ran to get their favorite tape, put it in the

tape player, giggled a bit, but eventually sang an energetic version of a Disney-like song.

63They sang with rhythmic accuracy and considerable style, but they did not use their

singing voices consistently. They certainly enjoyed themselves singing for me though.

Case study #5. This child—call him Edward—was selected from the suburban

private school population. His adopting mother completed the interview. Edward had a

rhythm aptitude score at the 95th percentile and an average tonal score. His combined

factor score on the MEQ was relatively low.

Edward’s family did not consider themselves to be musical—just the opposite. A

major impetus for his mother taking a high interest in Edward’s music development was

due to the fact that his older sister (also adopted) had parents who were learning-disabled.

The mother believed that if she started Edward’s sister with music early on, she would

learn to develop focusing and listening skills. As a consequence, only a few weeks after

they adopted Edward, he started coming along to her classes in a research-based early

childhood program at a major university. [Coincidentally, the program at the time of the

interview was based on the early childhood music learning theory of Gordon.] When

Edward was big enough to walk, he began attending the same classes for himself.

Edward was born with a hereditary malfunction of his Eustachian tubes that

caused ear infections and early hearing loss. A family friend who was partially deaf

herself and taught double bass told the mother to “get him involved with an instrument he

could feel, like the double bass or guitar, something string that you hold and feel the

vibrations.” This added to her determination to keep Edward involved in music. The

mother never had a music class; she felt compelled to give her children what she herself

never had.

64When Edward began day care, he was attending professionally taught music

classes twice per week. Beyond that experience and the early childhood music program

previously mentioned, he also was involved in two other early childhood music programs

when he was a little older.

At home, Edward’s mother chanted nursery rhymes and read books in rhythm

(Chicka Chicka Boom Boom), using the extreme ends of her vocal range. She did not do

much singing because she felt inadequate, but she recalled chanting rhythms for him

—“you know that Gordon thing: ‘ba bah bah ba ba bah.’ I actually have them and I have

the tape. They’re hard to remember because they don’t have words and that’s one reason

why we switched over [to another program] and stopped going.”

Edward was rarely, if ever, exposed to recorded music in the house. His mother

did not know even if their stereo system was working. They did listen to light jazz on the

radio whenever they were in the car. They also listened to the Disney radio station and to

tapes provided by one of the early childhood music programs they attended. Unlike his

sister, Edward had little interest in television, but his favorite video was Walt Disney’s

“Fantasia.”

At Edward’s seven-year medical checkup, the doctor asked him what his favorite

subject in school was. The doctor trotted out a few subjects, but Edward did not answer

him. Then Edward said, “I only go for the music.” At the time of the interview, Edward

was taking guitar lessons and sleeping at night with an electronic keyboard in his bed.

Case study #6. This child—call him Frank—was selected from the urban private

school population. His mother completed the interview. Frank had a composite aptitude

65score at the 93rd percentile. His tonal score was at the 75th and his rhythm score was at

the 95th percentile. His combined factor score on the MEQ was exceptionally low.

Frank was born 10-12 weeks premature. During his subsequent 54-day stay in the

hospital, he was exposed to the music that the nurses “always” played there (mostly

radio). During the few hours each day that the mother could spend time with Frank, she

sang to him “all of the lullaby songs” like “Twinkle, Twinkle Little Star.” She continued

this practice until he was fourteen months old, at which point he no longer slept in his

parent’s bed. “He had a lot of attention paid to him—all the way around, involving

everything.” Considerable effort was given to ensure the best future for a child who was

born so early.

According to the mother, his father is “not a singing type of person.” He did rock

him back and forth to music. He listened to the Grateful Dead almost exclusively. From

the age of two and a half, Frank received a lot of exposure from television and tape

recordings. He watched a lot of Barney, Sesame Street, and Mr. Rogers. He also watched

all of the Disney movies. “Peter Pan” and “The Jungle Book” were among his favorites.

“I used to always sing and try to get him to sing with me too. And he used to do it

too. It was really funny because he was pretty young—two and a half I think. I would

sing that oldie, ‘See You in September.’ Then there’s that part where you hold your voice,

you know, hold the note and go ‘ahhhh.’ He’d do that. It was really cute.”

After the family received Frank’s scores from the aptitude testing, they signed

him up for piano lessons with a piano teacher at his school. At the time of the interview,

his lessons were going well.

66Case study #7. This child—call her Georgia—was selected from the urban public

school population. Her mother completed the interview. She had a composite music

aptitude score at the 93rd percentile. Her tonal aptitude score was at the 95th percentile;

her rhythm aptitude score was at the 85th percentile. Her combined factor score on the

MEQ was exceptionally high.

Before Georgia was born, her mother would use a portable tape player at work,

placing the headphones on her abdomen for the baby to hear. She did this every day up

until her ninth month. She played a considerable amount of Andre Hawkins (gospel

singer) and classical music, especially Beethoven. She said that the baby often responded

by kicking when the music was playing. Whenever it stopped, the baby became less

active. When I asked her what gave her the idea to use headphones, she said that when

she had expressed concern at her Lamaze classes that her baby was not moving much, a

couple there suggested trying them. “So I just tried it and it worked. She moved around

all the time. I couldn’t get her to stop moving.”

In addition to playing recorded music, Georgia’s mother sang a considerable

amount prior to her birth. In our conversation, she stated that children learn to recognize

their parent’s voices while still in the womb. I asked how she knew this information and

she informed me that she was certified in parent education for children zero to five years

old.

After Georgia was born, her older sister (who was in nursery school at the time)

sang much of the standard preschool repertoire to Georgia. The mother said that “Paddy

Cake” was her first song. Her sister’s repertoire consisted primarily of nursery rhymes

67with hand movements. Other repertoire included “Mary Had a Little Lamb,” “Twinkle,

Twinkle Little Star”, “Row, Row, Row Your Boat,” and “The Itsy Bitsy Spider,” which

was Georgia’s favorite.

Georgia began singing at about 15 months old. At two, she knew “Candy Coated

Raindrops,” although the words were twisted in toddler-like fashion. Georgia’s mother

said that her family was always quite musically active: she and her father sang in a

Baptist church choir, two cousins were taking piano lessons, and her brother was in a

band when she was young. Another cousin was planning to become a classical pianist.

She said, “He is so great. And he caught on so fast. Because at first—he’s like my brother

—my brother used to play by ear before he even knew what a note was. He played by ear.

He could just grab any song. And my nephew picked the same thing up, but my brother

wanted him to expand. . . . We’d go to every concert and she [Georgia] loved to hear

[him] play.”

Georgia went to church every Sunday from the time she was born, and so was

exposed to live gospel music weekly. When she was older she enjoyed listening to the

music from the tape recordings that accompanied children read-along books, including

those from Barney, Blue’s Clues, and Sesame Street.

Georgia’s mother went to the same school as Georgia, where she had loved her

music teacher: “I don’t understand for the life of me why they don’t have music there

anymore. I went into my other daughter’s school and they had computers in there to help

them do music. I just thought that was so bizarre! That’s taking the fun away from it to

me. . . . I think I’m going to do something about that.”

68Qualitative Analysis of the Case Studies

If nothing else, the reported case studies show clearly that children with high

music aptitudes came from remarkably diverse musical environments. The diversity

notwithstanding, the researcher examined the studies to derive commonalities among, as

well as unique qualities within, the environments that may have contributed to the

maintenance and development of the high aptitudes of the children in the purposive

sample.

Commonalities. Perhaps the most notable qualitative commonality among the

cases was the surprisingly high level of interest that all of the interviewees demonstrated

in speaking about the music development of their children. Parents were happy to take

the opportunity to speak with the researcher at length. In every case, the conversations

lasted as long as the researcher needed. No one asked to leave the conversation.

Additionally, not one of the parents had any difficulty scheduling or keeping an interview

time. All interviewees made themselves easily available. One parent even requested to be

called at a beach house on her vacation. This factor is perhaps a testimony to the level of

interest the parents had in their child’s music development.

Another commonality was that, in all but one case, the parents provided their

children with formal music lessons or classes. Even in the excepted case, the mother

volunteered a statement during her interview saying that she hoped that her daughter—as

well as herself—would take piano lessons. For children to be involved in formal music

lessons by the first grade is exceptional. Again, this factor points to uncommonly high

parental involvement across the purposive sample. Although this level of formal music

69exposure is noteworthy for indicating a high level of parental interest, it all occurs after

the first 18 months of life. The richness of environments after the first 18 months of life

was a factor poorly measured by the MEQ.

A third commonality among the environments was that from very early in life, all

but one of the children attended church weekly with their families. In some cases, the

music exposure from these sources was especially active and energetic; in all, the

exposure to live music in large groups is not gotten easily, either elsewhere or so

regularly. Albeit with a probable lack of intention in this case, parents were providing fair

amounts of musical stimulation for their children. This factor may be exceptionally

beneficial to the children considering the quality of, as well as the quantity within, their

informal music environments.

Other factors that may have been influential to the child's music development

came out of the interviews and surveys, but none with more than a moderate frequency.

Three of these are presented briefly here, though they were covered by items on the

MEQ. Of the seven families representing the purposive sample, three stated that they

listened to compact disks regularly, three musically stimulated their child while in utero,

and two provided their children with early childhood music classes.

Unique qualities. Besides identifying environmental commonalities that may

have had some influence on the aptitudes of the children within the sample, the researcher

examined the distinct qualities within each case that may have likewise been influential.

The researcher recognizes that the ideas presented here cannot be corroborated within this

70study due to the unique nature of each case. Still, he believes they are worth considering

for their potential value.

According to his grandmother, Andre exhibited noteworthy musical behaviors at

the exceptionally early age of six or seven months—considerably younger than most

children, and certainly younger than the other children in the purposive sample. Andre

also had shown himself to be the musical ringmaster of his family, often encouraging his

father and grandmother to sing with him. Both he and Georgia (case study 7) were

exposed to a high level of musical participation at weekly Baptist church services. With

the exception of this last item, these behaviors set Andre’s early musical experiences

apart from the other case studies.

For Brianna, the unique feature of her early childhood environment was the

exceptional musical quality of her mother’s singing. Although other parents from the case

studies sang for their children, Brianna’s mother had a vocal timbre that many early

childhood music educators contend is an ideal model for young children. Her sound was

light, clear, and childlike. Furthermore, her intonation, phrasing, and musical expression

were likewise exemplary.

Two environmental qualities were unique to Cynthia’s case. First, her family and

extended family were very musically active. Whenever they gathered, which was

regularly, they pushed the furniture back and danced to a variety of recorded music.

Second, her family rarely sang any songs from traditional children’s repertoire. Rather,

they created music spontaneously throughout the day, generally being “silly.” For

Cynthia, these activities provided musical stimulation in an environment that was quite

71participatory and highly energetic. None of the other cases matched this level of

consistent family involvement.

Unlike the other cases, Debbie’s musical diet consisted primarily of music in the

popular culture: Disney movies on videotape, popular radio stations, and children’s

television shows. Although these experiences may not seem to be as advantageous or as

balanced as many music educators might prescribe, Debbie’s mother did report an

exceptionally large amount of daily exposure, averaging about six hours. Another notable

and unique factor is that up until the mother’s last trimester of pregnancy, she frequently

visited dance clubs. As the reader may remember, the previous cases received high scores

only in the tonal portion of the aptitude measure, not in the rhythm.

Edward’s early childhood history is unique to the others in two ways: the

extensive amount of specialized early childhood music instruction he received, and the

number of rhythm activities performed by his mother in the home. Beginning soon after

his adoption at just five days old and continuing throughout his early childhood, Edward

attended five early childhood music programs, including a research-based program at a

major university during his first year. While in day care, Edward was often attending two

such programs a week. No other case had near as much professional early childhood

instruction. In recalling his musical experiences at home, Edward’s mother stated that she

chanted nursery rhymes, read books in rhythm, and chanted short rhythm patterns as she

was taught in one of his music classes. She did not do much, if any, singing, perhaps due

to her self-declared musical deficiency.

72Frank’s case study is distinguished from the others primarily by the circumstances

of his premature birth. For the eight initial weeks he spent in the hospital, his mother sang

to him during the few hours each day that she was allowed to be with him. During that

time, he also was exposed to music from the radio that the nurses played. After leaving

the hospital, he continued to be sung to by his mother for hours each day for at least

another twelve months.

Georgia’s musical environment was extremely rich and, unlike the other cases,

was reflected in a high score on her MEQ. Her music exposure came in many forms,

including a) prenatally for several months through her mother’s own singing voice as

well as from recorded music regularly played through headphones placed on the mother’s

abdomen, b) from an older sibling who often sang to the baby, c) through weekly

attendance in services at a Baptist church, d) through family musicians, some of whom

were highly trained, and e) from a fairly wide variety of recorded music.

73CHAPTER 5

CONCLUSIONS

Purpose and Problems

The purpose of this study is to examine relationships between early childhood

musical experiences and later potential to achieve in music.

The problem of this study is to identify factors and combinations of factors

involving early childhood musical experiences (birth to 18 months), as reported by

parents, that predict a child’s scores of music aptitude (tonal, rhythm, and composite) as

measured in first grade. A secondary problem of this study is to learn whether school type

(public/private) and school setting (urban/suburban) contribute to the prediction of music

aptitude scores beyond music experience factors.

Design and Analysis

The researcher conducted this study using qualitative and quantitative research

methods. Summaries of the design and analysis for each are presented here.

For the quantitative portion of this study, the researcher administered the tonal

and rhythm subtests of the Intermediate Measures of Music Audiation (IMMA) (Gordon,

1986) to children in first grade. After the aptitude measure was administered, parents

were asked to complete and return a researcher-developed questionnaire, the Musical

Experiences Questionnaire (MEQ) (Appendix A), designed to ascertain the quality and

quantity of the child’s musical experiences between birth and eighteen months.

Means, standard deviations, and Spearman Brown corrected split-halves

reliability coefficients were calculated for the tonal, rhythm, and composite scores for

each of the IMMA administrations. For the MEQ, means and standard deviations were

74calculated for each item and for the composite scores. Alpha reliability coefficients were

also calculated.

To investigate what questionnaire items were to be combined and used in the final

data analyses, the researcher performed a factor analysis on the MEQ. Correlations

among the factors and reliability estimates for each were also calculated. The researcher

then performed two series of three multiple regression analyses. The first series included

the selected MEQ factors alone as potential predictors of the tonal, rhythm, and

composite IMMA scores. The second series included factors of school type (public/

private) and school setting (urban/suburban) in combination with the MEQ factors as

potential predictors of tonal, rhythm, and composite IMMA scores. From those regression

analyses, percentages of variance in common between the IMMA scores (tonal, rhythm,

and composite) and MEQ scores—with and without school factors—were read and

interpreted.

For the qualitative portion of this study, the researcher conducted open interviews

with a purposive sample of parents (n = 7). He selected parents for interviews by

determining whose children had at least one exceptionally high music aptitude—tonal,

rhythm, or composite at the 90th percentile or greater—and also had a low combined

factor score on the MEQ. He then scheduled and conducted open telephone interviews

with the seven parents, recorded those interviews, transcribed the recordings, studied the

transcripts, and categorized the case information. Using the above information, the

researcher wrote brief accounts for each of the seven children. When those accounts were

complete, the researcher asked each child’s parents to verify their accuracy.

75After studying the case information and its relationship to the tonal, rhythm, and

composite scores obtained from the administrations of the aptitude measure, the

researcher wrote a rich description of his findings. His findings included commonalities

among, as well as unique qualities within, the cases. A peer reviewer verified that the

researcher’s written accounts were fair and his findings reasonable. Corrections and

clarifications were made based on mutual agreement between the peer reviewer and the

researcher.

Quantitative Conclusions

Two results from this study need to be considered. The first is that no statistically

significant relationships between early childhood music experiences and music aptitude

were found. The second is that the school setting variable (favoring suburban over urban)

contributed significantly to the variance in IMMA tonal scores. Both results pose

interesting questions. Why were early childhood factors as measured by the MEQ not

found to contribute to tonal, rhythm, or composite music aptitude scores? Is it because

they do not contribute or because they simply were not found? What can be concluded

about how the two measurement instruments functioned? Are there problems, and if so,

what are they, and what would it mean to the results if they were corrected? Why did the

school setting variable contribute to IMMA tonal scores and not to IMMA rhythm scores?

What is the source of the difference between the tonal scores of urban and suburban

populations? Is this finding a peculiarity, or can a conclusion be drawn that urban

children generally have lower tonal aptitudes than suburban children? Potential answers

to these questions are explored below.

76If the first finding is true (that no relationship exists between the factors), then

only one possibility can be considered—that innate factors determine the development of

a child's music aptitude regardless of the level of richness of the music environment.

Common sense renders this position weak, if not insupportable. A child removed from

any musical nurturing whatsoever, regardless of how high his innate aptitude is, has little,

if any, chance of developing musically—just as a child left unacculturated in a language

has little, if any, chance of developing linguistically. Given the unlikelihood that innate

influences solely determine music aptitudes, and the likelihood that MEQ scores are not

valid for the purposes of this study, the researcher believes that the first finding (that no

relationship exists between the two factors) was a Type II error.

Assuming relationships between early childhood music experiences and music

aptitude exist, then why were none found in the present study? Given the results

uncovered in the qualitative analysis, and those enumerated below, the researcher

believes that the MEQ does not discriminate between environments (and factors within

those environments) that sufficiently nurture a child's musical development and those that

do not. Knowing that reliability is the prime pre-requisite for validity, the researcher

made extensive efforts through pilot work to make the MEQ reliable. A full view of the

validity, however, was revealed only by the extensive information yielded in the study’s

interviews. In essence, this study became, in part, a test of the MEQ’s validity—a test that

the MEQ failed.

Several issues regarding the validity of the MEQ need to be considered. First,

because parents were asked to recall their child's experiences six or more years in their

77past, they may not have reported actuality. Instead, they reported their best notions about

the length and frequency of music experiences provided for their children. Parents who

inflated their responses, influenced by the natural tendency to want to be perceived as a

better music provider than otherwise would be more accurate, may have skewed the

MEQ data upward. Alternately, a few parents showed during the interviews that they

skewed the data in the opposite direction, being conservative in their estimate of the

length and frequency of their child's music experiences. In fact, one such case was

discovered in the interviewing process. Cultural differences may play a role in this.

Second, the researcher sees evidence that high scores on a minimal number of items from

the MEQ (leaving the total score low) can represent music environments that are

sufficiently rich to sustain a child's innate level of music aptitude. That is, some children

with low overall MEQ scores still get adequate levels of music stimulation. Third, MEQ

scores do not account for the attitude a family has toward music, a factor that may have

strong implications for the nurturing quality of the environment. (These last two points

are discussed in the qualitative portion of this chapter.) Each of the issues stated above

endangers the validity of the MEQ as a discriminating measure of early childhood music

experiences. Taken together, they have the power to render the MEQ impotent as a

predictor of music aptitude scores.

There is one more compelling issue of validity regarding the MEQ. The scores

from the MEQ cannot account for the quality of music performance by the musical role

models, or of much of the music to which the children are exposed. This raises important

questions. Can small amounts of high quality music be more beneficial to a child than

78large amounts of lower quality music? Do parents who sing poorly actually hinder a

child's musical growth, or is the absence of a live musical role model a less acceptable

alternative? Answers to these questions will be the province of future research.

The validity of the IMMA deserves brief attention. To question the objective

validity of the IMMA may not bear fruit, as that validity has been well established. On a

subjective basis, one could rightfully disagree with its content and constructs, but to do so

might rob music education of a valuable tool. Information from valid music aptitude test

scores guides music teachers in their decisions of whom to teach what and when. Still,

questions could be raised about the particular administrations of the test. These will be

discussed shortly.

The second finding of this study—that the school setting factor (favoring

suburban over urban) contributed significantly to the variance in IMMA tonal scores—

presents some meaningful issues to be considered by the music education community. If

the finding is a Type I error, then any difference in tonal music aptitude mean scores

between suburban and urban populations can be summarily disregarded. Replicating the

study would begin to mount evidence for the error or corroborate the result of the current

study.

On the other hand, if the finding is not in error, then certain questions need to be

posed. What gives rise to the discrepancy in tonal aptitude scores between suburban and

urban first-grade children? What is either present or absent in the suburban environment

that could be a factor in fostering tonal aptitude scores? Alternately, what is either present

79or absent in the urban environment that could be a factor in suppressing tonal aptitude

scores? Finally, what are potential solutions to correct the discrepancies?

Although potentially controversial, the ideas presented here are purely

speculative. As is almost always the case, further research will support or weaken their

validity. At any rate, the researcher hopes that topics explored here are sufficiently

interesting to spur research of practical value.

Given the stated finding, one readily apparent thought to consider is the difference

between the music generally heard in urban environments and that heard in suburban

environments, and the role it might play in the development of tonal aptitudes. Although

the researcher fully acknowledges the popularity of “urban” music in suburban areas, he

found distinct listening habits between the two groups in the qualitative data of this study.

When asked to report the type of music their children listened to most, 13% of urban

respondents reported that their children listened to rap music. On the same question, none

of the suburban respondents reported likewise. Does this factor account for, or contribute

to, the difference in tonal aptitude scores? Without research to support this, and

considering any number of factors that could contribute to the finding, the researcher

recognizes the frailty of the position. Still, it would be a disservice to not consider it as

having some influence on children's tonal aptitudes in particular and musical growth in

general.

Acknowledging the inherent generalization in this statement, the researcher

contends that the tonal element—namely melody and harmony—is extremely limited in

much rap and hip-hop music. Since their emergence as the predominant styles of urban

80music, other styles of music with more sophisticated tonal elements have been displaced.

The point here is not to invalidate rap or hip-hop as art forms, but rather to illuminate a

potential consequence of music that has such limited value from the standpoint of a

musical purist—social and cultural values notwithstanding. Without having the same

degree of exposure to the tonal elements, children would seemingly suffer some degree of

musical malnutrition given lesser opportunity to supplement their diet with music having

a higher degree of tonal substance. In theory, the present idea is easily suggested. In

practice, balanced scrutiny and future research will help to support or weaken its validity.

Another possibility should be considered as to why tonal aptitude scores differed

between urban and suburban children. If musical experiences provided for children after

they reach school age are consequential to music aptitude scores, then the relative states

of urban and suburban music education can be considered relevant.

In large part, music education faces more challenges in urban schools than in

suburban schools. The general level of support music educators receive from within

urban school systems is often highly questionable, if not sometimes even disturbing. As a

result, urban music personnel are less stable—either changing schools, finding jobs

elsewhere, or leaving teaching altogether. Even for those who do remain, continual

hardships contribute to a state of cynicism and hopelessness within the teaching

community. Such are the natural responses many teachers use to cope with the daily

adversities they encounter, whether it is the unruly behaviors of their children or the same

The researcher defines “musical purist” as someone who believes that when extra-musical elements (e.g. words) are subtracted from music, it should retain a level of musical interest for the listener.

81problems do not exist in suburban schools, just that they are generally less prevalent or

less serious.

If higher quality music education were available in the urban city schools—given

an amount of correction to the above problems—perhaps the gap in tonal aptitude scores

could be narrowed. Whether the climate of music education in urban versus suburban

schools is at cause in the discrepancy between tonal music aptitude scores is difficult to

determine. Still, the root causes of the issue are best considered from a larger perspective.

The researcher admits that any or all of the above points may or may not be at play in the

schools in which data was collected for the present study. Either way, he believes they are

worth considering.

Last, regarding the finding in question, aptitude scores may not represent

accurately the tonal aptitudes of some of the children in the urban group. Instead, children

with deficient test taking skills could have skewed the results downward. Although the

researcher believes this is highly unlikely, to not raise the possibility would be an act of

omission. If such were the case, any discussion of musical influences on the tonal score

discrepancies between urban and suburban children would become moot.

Qualitative Conclusions

The results of the qualitative portion of this study include observations made on

seven case studies purposively selected from among children whose tonal, rhythm, or

composite aptitude scores were exceptionally high and, with one exception, whose MEQ

scores were average or below average. The case study analysis showed that the seven

children a) came from remarkably diverse musical environments, b) had parents who

82demonstrated a high level of interest in their child's music development—freely granting

information in the interviews and, in all but one case, providing formal music lessons, c)

with one exception, attended weekly church services, and d) came from families that

demonstrated their enjoyment of music.

The diversity of the environments from which the children came is easily seen in

the case accounts reported in the previous chapter. The various family situations, cultural

differences, school settings, and school types represented by the children, and the types of

music to which they were exposed, contributed to the diversity. Although these factors

are evidence that children who have at least one high aptitude (tonal or rhythm) can come

from diverse backgrounds—musical and otherwise—the commonalities among the

environments were of primary interest to the researcher.

Among the commonalities was a high level of interest demonstrated by the

parents in the phone interviews. If this interest were reflective of the interest taken in the

child's actual musical development, then despite a less than rich musical environment—at

least as measured by the MEQ—the overall attitude toward music could be characterized

as indeed rich. Although their interest may have been a reaction to hearing good news

about their child's level of music aptitude, to having been chosen to participate in the

study, or to other factors, my sense is that the enthusiasm they brought to the

conversations is a valid indicator of an intrinsic interest in their children's music

development. Such interest could represent an unmeasured and important dimension of

the overall musical environment—one that provides for the fertile ground in which a

child's high music aptitude can be sufficiently nurtured.

83Further evidence supports the assertion that the environments for the entire

purposive sample were qualitatively rich even when the quantitative richness was

reportedly lacking. The rate of occurrence of formal music instruction among the cases is

inordinately high, especially considering these children had not yet completed their first

grade year. Making the commitments in time, money, and resources for young children

points to an exceptional attitude toward the value that music likely holds for those

families within the purposive sample. To reiterate the previous point, these attitudes may

contribute to the musical environment something beyond that which music exposure

alone provides.

Regarding the weekly church services attended by all but one family in these case

studies, such regular music exposure may also be an influential factor on the development

and maintenance of a high music aptitude. Despite the differences in the level of musical

participation among the represented denominations, simply being exposed weekly to

relatively large groups of people singing, if not also moving to music, very young

children have the opportunity to absorb and assimilate their musical surroundings in ways

not gotten otherwise. An hour or more of music exposure in an active environment every

week may make a healthy contribution to a child’s musical diet.

A highly demonstrative enjoyment of music may be a staple in all of the case

studies. For Andre, even though his grandmother did not consider herself musical, she did

demonstrate her love for music consistently by singing to the gospel radio station most

times on the way to Andre's day care center. Furthermore, because of his own

demonstrative appetite for singing and participating musically, Andre may have been a

84rare child who took the responsibility for getting his own musical nurturing in more ways

than most children, or even his caregivers, could be. Without this apparently insatiable

drive, the researcher believes he probably would not have received the attention he did.

Despite the relatively low combined factor score on the MEQ, Andre's environment

seemed to be more than adequately rich for him to have received a high music aptitude

score.

Similarly for cases 2-6, the qualitative richness of the environment was not

necessarily represented by high or, in some cases, even moderate MEQ scores. Given

that, the researcher believes that any given component of a child’s music environment—

whether it be an item or factor measured by the MEQ or not—may in itself represent a

sufficiently fertile ground from which a high music aptitude can be nurtured. For Brianna,

that factor could have been the beautiful musical model her mother provided in her

singing; for Cynthia, her highly musically active family; for Debbie, the extensive diet of

popular music from movies, radio, and children’s television; for Edward, the considerable

number of early childhood music programs; and for Frank, the intensive musical attention

he received from his mother during his first fourteen months. Georgia’s high MEQ scores

represent several factors, any or all of which may have been sufficiently influential.

In examining the differences between environments that produced children with

high tonal scores (cases 1-3) and those that produced children with high rhythm scores

(cases 4 and 5), the researcher found that the latter two families reported activities that

were highly rhythmic. No such activities were reported by the other cases. The mother in

case 4 stated that she frequently visited dance clubs late in her pregnancy. The loud

85incessant beats that accompany such music would easily overpower other musical

elements to which the fetus would have been exposed. The mother in case 5 reported on

rhythm activities she performed for her child: reading books to rhythm and chanting

rhythm patterns to the baby as she had learned in an early childhood music class.

Although these two cases could be seen as relatively meaningless by quantitative research

standards, the researcher finds it somewhat compelling that the parents in these cases

reported experiences that were more rhythm-specific than any of those reported by the

parents in the other cases.

As the researcher began to analyze the data qualitatively, he recognized an

apparent disconnection between the results of the MEQ and the information gleaned from

the interviews. When individual MEQ item scores were extrapolated out into what

occurred in reality, it becomes clear that any number of them by themselves—and

especially when they are combined with an accruing number of others—can represent a

sufficiently rich environment to support a high music aptitude. In short, the researcher

believes that factor scores taken from the MEQ, either combined or separate, cannot

indicate with a sufficient degree of validity the richness of the musical environment

experienced by the children in this study. As long as MEQ scores do not approach their

absolute lower limits, and none in the entire study sample did, the level of musical

stimulation for most children with a high innate level of music aptitude still may be

sufficient for them to achieve a high score on the IMMA. Stated yet another way, even

moderate scores on a small portion of the items on the MEQ could represent a sufficiently

rich influence on the developing aptitudes of children having strong innate potential.

86The scores from the MEQ do not necessarily correlate with the attitude the family

has toward the importance of music. The musical support that children receive from their

families may not necessarily be so much a function of saturation through time as of

making impressions at key moments in time, perhaps during particular windows of

opportunity when the child's ability to process the music environment is piqued. Parents

who have a keen interest in music, but do not necessarily demonstrate that interest in

ways that were measured by the MEQ, may be tuned into their children and thus are

poised to nurture them at the appropriate times. Another possibility is that these parents

simply underreported the amount of stimulation their child received. If they did report

accurately, then other possibilities need to be considered, including that a child born with

a high innate level of aptitude can make the most out of musical experiences—even

experiences that contribute only marginally to scores by the MEQ.

Music exposure received in the first 18 months of life may not be crucial. Perhaps

subsequent exposure is potent enough to allow for the recovery of a high aptitude despite

the absence of a highly stimulating musical environment during those early months. Of

the parents who reported low combined scores on the MEQ, and whose children had at

least one high aptitude, most also reported that their children were exposed to richer

environments after their first 18 months. Unfortunately, this item on the MEQ was found

to contribute almost nothing due to its minimal variability. Why it did not discriminate

well may be due to a number of factors, one of which could be that the parents seemed to

equate music exposure more with formal instruction than with any form of informal

music exposure. A substantial number of musically influential adults—including those

87who do not consider themselves musical only for lack of formal musical training—tend

to discount the importance of the informal music environment they provide. They favor

classes, lessons, or other interactions with those they consider as “qualified” music

makers. To these parents, learning music may not be so much about becoming musical as

it is about having traditional music training as they believe most musicians would have.

In part because of their high level of innate music aptitude, children may

demonstrate musical behaviors especially early in life (during the first year or so), and

this in turn may lead parents to seek out classes or lessons that will enrich the talent they

have observed. This may especially be the case when the parents themselves feel

somewhat inadequate in their musical abilities. In the purposive sample, it is noteworthy

that as many parents considered themselves (as well as their spouses) to be musical as

unmusical. In the latter cases, there was an extra motivational factor to provide for their

children what they themselves either were denied or for which they felt some degree of

inadequacy.

Such a premium has been placed on learning to read and perform written music in

the culture of music education that many in the general population, no matter how

musical they are, may tend to think they cannot be influential in another's music

development unless they have had some type of formal training. Unfortunately, such

training often means only gaining some knowledge of music theory.

The researcher believes that relationships between early music experiences and

music aptitude are complex and dynamic. They are either disguised in the data, leaving

the possibility for alternate quantitative methods of data analysis, or simply are not

88present in the study data. To take this last position is to acknowledge that the MEQ was

not valid for the intended purpose of this study, even though the reliability measures were

reasonably good. This point has already been explored and it remains the strongest.

Consider an analogy using the size of a fisherman's net as the degree of innate

music aptitude and the number of fish in the area as the degree of richness in the music

environment. Regardless of the size of net used, few fish can be caught in an environment

that is not well stocked. Conversely, regardless of how well stocked the environment is,

those with smaller nets will not have the same success as those better equipped. Given the

unique size of each child's net (innate aptitude) and an extraordinarily wide variance in

fish populations (musical stimulation) from area to area, to reconcile the relationships

may be a more complex statistical challenge than could be met with the techniques used

in this study. Further challenges surface when a dynamic interplay between net size and

the number of fish caught is considered. For instance, fishermen with small nets that catch

fish early in their lives may adapt strategies to keep their catch rate as high as possible.

The size, number, and quality of the fish may be enhanced by adjustments made to the

mesh size of the net (neuronal network). Conversely, those with large nets but who fail at

the catch may turn their nets into tools for other purposes. In other words, a child with

high innate aptitude may sustain it only when there are enough stimuli to feed it. For

those with less-than-high inborn levels of music aptitude, the likelihood is poor, if not

absent, that rich musical environments can ever compensate for the size of the net.

Because almost all children experience music regularly, and given that the mean

and normal distribution of music aptitudes has remained unchanged for almost twenty

89years, the researcher believes that although innate aptitude plays a dominant role, it alone

cannot account for the potential a child has for being musical later in life. The only

conclusion that can be drawn is that nature and nurture each contribute—to what degree

each influences the other, and the value of the quality and quantity of the music, remain

important unanswered questions.

Perhaps it is fortunate that the environments of contemporary society are saturated

with music. Although much of it is low quality, it serves as an important stimulus

compared to the alternative of no stimulus at all. To state this point another way, the

researcher believes that most children gain something from the ever-present musical

stimulus, albeit less diverse and less sophisticated than would be optimal, if only from a

“music for music's sake” standpoint. He does not deny its function or validity, but he

wishes the musical affect would match the power of the social affect delivered in so much

contemporary music. The ready abundance will never compensate for the lack of

sophistication, especially among those with enough innate potential to make good use of

it.

The researcher believes that understanding the interplay between nature and

nurture holds exceptional value for music educators. Making music achievement as

natural and fluid as possible for the children in our classrooms—and perhaps even before

they become our professional responsibility—requires a thorough understanding of the

unique qualities within each child. The fruits of thoughtful work are best demonstrated

when all children—regardless of their levels of music aptitude, but also because of our

understanding of those levels—can come to value music through its understanding. Last,

90the researcher hopes that future research will provide valuable and practical answers to

some of the questions revealed in this study. The musical futures of the many Josephs and

Sallies we teach may depend on it.

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96APPENDIX A

MUSICAL EXPERIENCES QUESTIONNAIRE

Questionnaire Instructions for the Parents/Caregiversof First-Grade Children at Elementary School

Please give this questionnaire to the caregiver most knowledgeable about the musical experiences provided for your first-grade child between the time the child was born and 18 months of age. If there is more than one person who is knowledgeable about the early childhood musical experiences your child had, then complete this questionnaire together.

• Please read every item carefully and use pencil. You may want to change your answers.

• Some of the information that you will provide might be difficult to remember. Be as diligent and as truthful as you can.

• Answer every question to the best of your ability.

PLEASE COMPLETE THE QUESTIONNAIRE AND RETURN IT IN THE ENCLOSED ENVELOPE BY “Date”. THE INFORMATION THAT YOU ARE PROVIDING IS VERY VALUABLE.

All information will be kept confidential. Neither the school’s nor your child’s name will be used in any portion of the completed study. If you have any questions or concerns, please feel free to call me at home. My phone number is (215) 555-5512. Thank you.Sincerely,

Eric RasmussenPh.D. CandidateDepartment of Music EducationEsther Boyer College of MusicTemple University

97MUSICAL EXPERIENCES QUESTIONNAIRE

Instructions:! Please give this questionnaire to the person who knows most about your first-grade child’s musical experiences before he/she was 18 months old. Please complete and return this questionnaire in the enclosed envelope.

SECTION I - Basic Information____________________ ______________________! Child’s Name! School’s Name!

________________________! Name of person filling out this questionnaire!___________________ ____________________! Phone numbers: ! (Home) ! (Work)

SECTION II - Musical Role Models - When you or someone else was being musical around your child.

(Circle the most appropriate answer.)On average,

1) how often did you or someone else sing to your child?

! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! (If never, go to q. #2)! per week! per week! per week! times/week!

! 1a) how long each time did you sing to your child?!

less than 2 mins.! 2-5 mins. ! 6-10 mins.! 11-15 mins.! more than 15 mins.! ! ! !

2) how often did your child sing, or try to sing, with you?

! Never! Rarely! Sometimes! Usually ! Always

3) how often did your child sing, or try to sing, independently?

! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! ! per week! per week! per week! times/week

4) how often did you encourage your child to sing?!

! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! ! per week! per week! per week! times/week

5) how often did you sing together as a family (or the child with a sibling)?

98! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! (If never, go to q. #6)! per week! per week! per week! times/week!

! 5a) how long each time did you sing as a family?

less than 2 mins.! 2-5 mins. ! 6-10 mins.! 11-15 mins.! more than 15 mins.

6) how often did you chant! nursery rhymes?!!! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! (If never, go to q. #7)! per week! per week! per week! times/week

6a) how long each time did you chant nursery rhymes?

less than 2 mins.! 2-5 mins. ! 6-10 mins.! 11-15 mins.! more than 15 mins.!

!7) how often did you rock or dance with your child?

! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! (If never, go to q. #8)! per week! per week! per week! times/week!7a) how long each time did you rock or dance?

less than 2 mins.! 2-5 mins. ! 6-10 mins.! 11-15 mins.! more than 15 mins.!

!8) how often did you play a musical instrument when your child was close by?

! Never! 1-3 times! 4-8 times! 9-11 times! 12 or more! (If never, go to q. #9)! per week! per week! per week! times/week!8a) how long each time did you play?

less than 2 mins.! 2-5 mins. ! 6-10 mins.! 11-15 mins.! more than 15 mins.!

!SECTION III – Musical Listening Experiences – When your child was listening to music but was not around anyone being musical.

9) What one type of music was your child most exposed to?

99

Classical! Country ! Folk! Gospel! Jazz! New Age! Pop! R&B

______________Rap! Rock ! Soul! TV! Other! Can’t say

On average,10) how many hours each day was your child exposed to recorded music or the music on the radio?!(Exclude music from TV)

1/2 hour! between 1/2! between 1! between 2! over 4 hrs.! or less/day! and 1 hour! and 2 hours! and 4 hours! per day

11) how many hours each day was your child exposed to music from TV shows?

1/2 hour! between 1/2! between 1! between 2! over 4 hrs.! or less/day! and 1 hour! and 2 hours! and 4 hours! per day

12) how many times did your child attend live music or dancing performances? !

! Never! 1-3 times! 4-7 times! 8-12 times! more than 12 times

SECTION IV – Other Adult Musical Guidance

13) Have other adults guided your child musically?

Yes! No (If no, go to question #14)

_____________________________________________________! If yes, briefly describe. !_____________________________________________________________! !

! 13a) Did you and your child ever attend a music or movement class?

Yes! No (If no, go to question #14)

! 13b) If yes, how often did you go?

once/month! twice/month! once/week! twice/wk. ! more than twice/wk.

! 13c) How long were the sessions?

15 mins. or less! 15-30 mins. ! 30-45 mins. ! 45-60 mins. ! more than 1 hour

10014) How often did you attend a regular meeting or other function with your child where there was singing or dancing (i.e. church, synagogue, etc.)?

! never! 1-2 times/month! 1 time/week! 2 times/week ! more than 2 times/week! ! ! ! ! ! !

_______________________________________! If church, what denomination? ! !

15) Which best describes your household between the time your child was born and 18 months. Circle all that apply.

! One working parent! Single parent household! Two working parents

_______! Nanny or daily babysitter! One stay-at-home parent! Other (specify! )

16) As compared to the musical environment your child experienced between birth and 18 months old, your child’s musical environment after the age of 18

_________months has been ! .

! much richer! somewhat richer! about the same! somewhat less rich! less rich

___________________________! If there was a change, please describe. !_____________________________________________________________! !

16a) IMPORTANT: Elaborate in your answer to question #16. Please describe any musical influences (family members, babysitters, friends, etc.) added to, or subtracted from, the child’s environment after the age of 18 months._____________________________________________________________________________!

_____________________________________________________________________________!

_____________________________________________________________________________!

_____________________________________________________________________________!!NOTE: Since we know that fetuses can hear sounds from inside the womb, consider the music environment your child was exposed to especially in the last trimester of pregnancy.

During the later stages of pregnancy, on average,17) how often did you or someone else sing or play music near or to your prenatal child? !

101

! Never! 1-3 times! 1-3 times! 4-6 times! 7 or more!(If never, go to q. #18)! per month! per week ! times per week! times per week

17a) how long each time did you or someone else sing or play to your prenatal child?

! Less than 2 minutes! 2-5 mins. ! 6-10 mins. ! 11-15 mins.! More than 15 mins.!18) In the space below, please write down any other information that you think might be beneficial to this research. (Feel free to use the back of this page.)_________________________________________________________________________________!

_________________________________________________________________________________!

_________________________________________________________________________________!

_________________________________________________________________________________!

_________________________________________________________________________________!

_________________________________________________________________________________!

102APPENDIX B

RESEARCH SOLICITATION LETTER

RE: Music research study for first-grade children and their parentsDATE: “date”

To whom it may concern:

As a veteran music teacher and early childhood music specialist, Eric Rasmussen is beginning groundbreaking research on the relationship between brain development and early childhood music experiences. Eric is currently completing his Ph.D. in Music Education at the Esther Boyer College of Music, Temple University, Philadelphia, PA.

Children in first grade and their parents are eligible to participate in this study. With the permission of the school principal, the researcher will:

• Obtain parental permission for children to participate in the study. Parents who agree to participate will complete a ten-minute questionnaire and be available for an hour-long interview. (See attached parental permission letter.) Those chosen to interview will depend on the results of the questionnaires and the child’s test scores.

• Administer a standardized music aptitude test to the children in first grade. The test is the most objective way of determining a child’s potential to achieve in music and has been given to tens of thousands of children. It is administered in two 20-minute sessions within a time span of one week. The content of the test is as follows: After hearing two very short musical examples, the child circles either 1) a box containing two smiley faces if the short examples are heard as the “same” or 2) a box containing a smiley and a frowney face if the short examples are heard as “different.” The administration of the test will yield tonal, rhythm and composite scores of musical potential for each child. The researcher will maintain strict confidentiality of all results.

• Provide valuable information to the school principal regarding the musical aptitudes of all children who complete the test battery. Musically gifted children will also be identified so that they can be encouraged to participate in musical activities in and out of school. The researcher will also provide valuable information to the school principal describing the music aptitude of the school’s first-grade population in relationship to nationally standardized norms.

The advisor for this project is Darrel Walters, Chair, Department of Music Education, Esther Boyer College of Music, Temple University. He can be reached at (215) 555-5555. Eric Rasmussen can be reached at (215) 555-5512. If you have any questions, please call either of the above phone numbers.

Thank you very much.Eric Rasmussen

103Encl.

APPENDIX CPERMISSION SLIP

Date

__________Dear Parents and Caregivers of First-Grade Children at Elementary School,As a veteran music educator and early childhood music specialist, I am very

pleased to invite your child to be part of a groundbreaking research study about the music development of young children. Given recent research in both music and brain development, I am interested in finding out more about how adults contribute in important ways to the musical ability of children.

What does your child need to do for this?Take two twenty-minute tests over the course of two weeks. The tests are the most objective way to measure your child’s potential for music achievement. The times will be arranged with your child’s school principal and the classroom teacher.

What do you need to do? 1) Sign and return the permission form below and complete a 10-minute

questionnaire. The questionnaires will be sent home after the permission slip is received. (Those who return the completed survey by “date” are eligible to win $50.00 in a drawing.)

2) Be willing to spend an hour in a personal interview with me. Very few will actually be interviewed. (All information will be kept confidential.)

If you have any questions or concerns, please feel free to call me at home. My phone number is (215) 555-5512. Thank you very much.----------------------------------------------------------------------------------------------------------

__________________________I give my permission for to take part in the study (child’s name)I agree to complete and return a questionnaire in a researcher-provided envelope by

“date.”I agree to be available for potential interviews scheduled at my convenience within the

next few weeks. (All information will be kept confidential.)I would like to know the test results of my child’s music potential. Yes____ No____

______________________________________ ________________Signature Date

104______________________ ___________ __________Name Phone (H) Phone (W)