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RES-850-RS-LuckettQuantDissertation-W2.pdf
The Relationship between Musical Aptitude and Academic Achievement among
Beginning Band Students
Submitted by
Theodus Luckett III
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctorate of Education
Grand Canyon University
Phoenix, Arizona
December 11, 2015
© by Theodus Luckett III, 2015
All rights reserved.
GRAND CANYON UNIVERSITY
The Relationship between Musical Aptitude and Academic Achievement among
Beginning Band Students
I verify that my dissertation represents original research, is not falsified or plagiarized,
and that I have accurately reported, cited, and referenced all sources within this
manuscript in strict compliance with APA and Grand Canyon University (GCU)
guidelines. I also verify my dissertation complies with the approval(s) granted for this
research investigation by GCU Institutional Review Board (IRB).
__________________________________ ______________________
Theodus Luckett III Date
Abstract
The purpose of this quantitative correlational study was to examine if and to what degree
a correlation existed between the musical aptitude measured by Intermediate Measures of
Music Audiation (IMMA), and reading and mathematics scores on the State of Texas
Assessment of Academic Readiness (STAAR) among sixth grade beginning band students.
Two research questions guided the study: (1) was there a correlation between the level of
musical aptitude on the IMMA and the composite and sub-categorical performance levels
of the mathematics scores on the STAAR among beginning band students? and (2) was
there a correlation between the level of musical aptitude and the composite and sub-
categorical performance levels of the reading scores on the STAAR among beginning band
students? The theoretical foundation was Gardner’s theory of multiple intelligences
supported by Gordon’s music learning theory. Results from regression analyses indicated
there was a statistically significant relationship between students’ musical aptitude and
mathematics achievement composite scores, R = .673, R2 = .453, F (1, 63) = 52.132, p <
.001, between students’ musical aptitude scores and five mathematics achievement
subscales, R = .709, R2 = .502, F (5, 59) = 11.915, p < .001, between students’ musical
aptitude and reading achievement composite scores, R = .848, R2 = .718, F(1, 63) =
160.722, p < .001. Results from the multiple regression analysis indicated a significant
relationship between students’ musical aptitude scores and three reading achievement
subscales, R = .861, R2 = .740, F(3, 61) = 58.022, p < .001. The implications suggest that
music programs designed to increase musical aptitude may have a positive effect on
reading and mathematics achievement among middle school students.
Keywords: Musical aptitude, academic achievement, reading, mathematics
vi
Dedication
This dissertation is dedicated to the wonderful people who helped me along this
doctoral journey. First, I would like to dedicate this dissertation to the Lord, who is my
helper, friend, and Savior! Without Him, I am nothing. Second, I would like to thank my
lovely wife and wish her the best as she embarks on her doctoral journey. I couldn’t have
done it without her support and advice. Third, I would like to thank my sister (Dr. Pamela
Luckett) for paving the way to my dream. Fourth, I would like thank my parents (Emma
Lee Luckett and Theodus Luckett Jr.) for their sacrifices. They have made me into the
man I am today. Lastly, I would like to dedicate this dissertation to my son (Theodus
Luckett IV) who has pushed me to complete this degree! I love you, son!
-If I am for you, who can be against you!
vii
Acknowledgements
I must acknowledge and thank my entire dissertation team: Dr. Dolores Kelly,
Chair, my friend and colleague Dr. Richard Holsomback, Dr. James Lehmann, and Dr.
Dorina Miron. You made this journey not only possible, but also enjoyable. I cannot
thank you enough for all of your time and dedication thought this process. Thank you!
viii
Table of Contents
List of Tables .................................................................................................................... xii
List of Figures .................................................................................................................. xiii
Chapter 1: Introduction to the Study ....................................................................................1
Introduction ....................................................................................................................1
Background of the Study ...............................................................................................3
Problem Statement .........................................................................................................5
Purpose of the Study ......................................................................................................6
Research Questions and Hypotheses .............................................................................7
Advancing Scientific Knowledge ..................................................................................9
Significance of the Study .............................................................................................11
Rationale for Methodology ..........................................................................................13
Nature of the Research Design for the Study ...............................................................14
Definitions of Terms ....................................................................................................16
Assumptions, Limitations, Delimitations ....................................................................18
Assumptions ........................................................................................................18
Limitations ..........................................................................................................19
Delimitations .......................................................................................................19
Summary and Organization of the Remainder of the Study ........................................19
Chapter 2: Literature Review .............................................................................................22
Introduction and Background ......................................................................................22
Theoretical Framework ................................................................................................26
Theory of multiple intelligences .........................................................................26
ix
Music learning theory .........................................................................................27
Review of the Literature ..............................................................................................28
Overview of musical aptitude .............................................................................29
Academic achievement .......................................................................................33
Music and academic achievement.......................................................................39
Musical aptitude and academic achievement ......................................................48
Methodology. ......................................................................................................52
Instrumentation. ..................................................................................................54
Summary ......................................................................................................................55
Chapter 3: Methodology ....................................................................................................58
Introduction ..................................................................................................................58
Statement of the Problem .............................................................................................59
Research Questions and Hypotheses ...........................................................................60
Research Methodology ................................................................................................62
Research Design...........................................................................................................64
Population and Sample Selection.................................................................................66
Instrumentation ............................................................................................................67
IMMA. ................................................................................................................68
STAAR................................................................................................................68
Validity ........................................................................................................................69
IMMA validity ....................................................................................................69
STAAR validity ..................................................................................................69
Reliability .....................................................................................................................70
x
IMMA reliability .................................................................................................70
STAAR reliability ...............................................................................................71
Data Collection Procedures ..........................................................................................72
Data Analysis Procedures ............................................................................................74
Ethical Considerations .................................................................................................76
Limitations ...................................................................................................................77
Summary ......................................................................................................................78
Chapter 4: Data Analysis and Results ................................................................................81
Introduction ..................................................................................................................81
Descriptive Data...........................................................................................................82
Data Analysis Procedure ..............................................................................................85
Research Question 1: data evaluation .................................................................87
Research Question 2: data evaluation .................................................................90
Results ..........................................................................................................................93
Results of Research Question 1 ..........................................................................93
Results of Research Question 2 ..........................................................................97
Summary ....................................................................................................................102
Chapter 5: Summary, Conclusions, and Recommendations ............................................104
Introduction ................................................................................................................104
Summary of the Study ...............................................................................................105
Summary of Findings and Conclusion .......................................................................108
Implications................................................................................................................113
Theoretical implications ....................................................................................113
xi
Practical implications ........................................................................................114
Future implications ...........................................................................................115
Strengths and weaknesses .................................................................................116
Recommendations ......................................................................................................116
Recommendations for future research ..............................................................116
Recommendations for future practice ...............................................................117
References ........................................................................................................................119
Appendix A. Site Authorization Letter ............................................................................136
Appendix B. Permission to Use Intrument ......................................................................137
Appendix C. IMMA Instrument ......................................................................................138
Appendix D. Informed Consent .......................................................................................147
Appendix E. Computation of Minimum Sample Size .....................................................149
Appendix F. IRB Approval Letter ...................................................................................150
xii
List of Tables
Table 1. Intermediate Measures of Music Audiation Reliabilities, Standard
Errors of Measurement, and Standard Errors of a Difference ........................... 71
Table 2. STAAR Grade 6 Total Group Descriptive Data ................................................ 72
Table 3. The Study Population: Gender and Ethnicity .................................................... 84
Table 4. Summary of Variables and Statistical Tests used to Evaluate
Research Questions 1 and 2 ............................................................................... 87
Table 5. Descriptive Statistics of the Criterion and Predictor Variables ......................... 88
Table 6. Skewness and Kurtosis Statistics of the Criterion and Predictor Variables
Used to Evaluate Research Question 1 .............................................................. 89
Table 7. Summary of Correlations between Predictor Variables used in Research
Question 1 .......................................................................................................... 90
Table 8. Descriptive Statistics of the Criterion and Predictor Variables used to
Evaluate Research Question 2 ........................................................................... 91
Table 9. Skewness and Kurtosis Statistics of the Criterion and Predictor Variables
Used to Evaluate Research Question 2 .............................................................. 92
Table 10. Summary of Correlations between Predictor Variables used in Research
Question 2 .......................................................................................................... 93
Table 11. Model Summary of Regression for Research Question 1 ................................ 95
Table 12. Model Summary of Multiple Regression for Research Question 1 ................. 97
Table 13. Model Summary of Regression for Research Question 2 ................................ 99
Table 14. Model Summary of Multiple Regression for Research Question 2 ............... 101
Table 15. Summary of Results for Hypotheses 1 and 2 ................................................. 103
xiii
List of Figures
Figure 1. District enrollment percentages by ethnicity for 2013-2014 school year.......... 83
Figure 2. Scatterplot depicting the relationship between mathematics achievement
and musical aptitude. ......................................................................................... 96
Figure 3. Scatterplot depicting the relationship between reading achievement
and musical aptitude. ....................................................................................... 100
1
Chapter 1: Introduction to the Study
Introduction
Since the beginning of the 21st century, public education in the United States
could be characterized as a decade of increased emphasis on school accountability
(Duncan, 2011; William, 2010). The accountability standards mandated by federal and
state legislature require public school districts to acquire passing scores on standardized
tests. Yet, standardized assessments across the nation suggest that more than one-third of
American students are not proficient in reading and mathematics (Campbell & Malkus,
2011; Hemmings, Grootenboer, & Kay, 2011; Martin, 2012; National Assessment of
Educational Progress, 2009). In addition, the level of academic proficiency for minority
students is even lower (Arbona & Jimenez, 2014; Morales, 2010; Olszewski-Kubilius &
Thomson, 2010; Williams, 2011).
These alarming statistical data prompted music educators across the United States
to examine musical aptitude as it relates to academic achievement. Similar empirical
studies have examined the relationship between musical aptitude and academic
achievement in Texas. Holsomback (2001) conducted a quantitative correlational study
that examined the relationship between musical aptitude and the Texas Assessment of
Knowledge and Skills (TAKS) for beginning band students. The study was conducted
with 104 sixth grade band students in an east Texas school district. The researcher found
that a strong statistical relationship existed between musical aptitude and academic
achievement. The following year, Holsomback (2002) conducted a similar study that
examined the relationship between the musical aptitude and academic achievement of 74
seventh grade band students. That second study identified a positive correlation between
2
musical aptitude, as measured by the Selmer Music Guidance Survey, and academic
achievement, as measured by the results of the Texas Assessment of Academic Skills
(TAAS). In addition, Holsomback (2002) suggested that further research should
investigate the relationship between musical aptitude and other standardized assessments.
Although compelling empirical evidence correlates musical aptitude to earlier
standardized assessments in Texas (Holsomback, 2002; 2001), as of 2015 there has been
no research concerning the correlation between musical aptitude and the State of Texas
Assessments of Academic Readiness (STAAR) among sixth grade beginning band
students. This gap in the literature justifies the need for this study. By investigating such
relationships, school administrators, teachers, and policy holders are capable of
identifying tactics that may improve test results. In addition, the data extended the
literature relating music to cognitive abilities by examining the correlation of musical
aptitude to specific areas of academic performance. Therefore, the purpose of this
quantitative correlational study was to examine if, and to what degree, a correlation exists
between musical aptitude and the reading and mathematics sections of the STAAR
among beginning band students.
Chapter 1 includes the background to the study, the problem statement, the
purpose of the study, and the research questions and corresponding hypotheses. A
discussion of how it will advance scientific knowledge and the significance of the study
is also presented. The researcher provides the rationale for the selected methodology and
research design. The chapter concludes with definitions of research terms, assumptions,
limitations, and the study’s delimitations.
3
Background of the Study
Over the past decade, the educational reform movement has been a major societal
and political debate in the United States (Knoeppel & Brewer, 2011; McGuinn, 2006).
Although many attempts have been made at school reform, minimal evidence of
academic advancement has been presented (Good, 2010; Ravitch & Cortese, 2009). In
1983, a highly controversial reform, A Nation at Risk: The Imperative for Education
Reform, suggested that more homework, extended school days, and extended school
years would increase academic achievement (U.S. Department of Education, 1983).
However, researchers suggested that the reform advocated by A Nation at Risk was
partially right and partially wrong (Burdick, 2012; Good, 2010; Palmer, Davis, Moore, &
Hilton, 2010). Kitsantas, Cheema, and Ware (2011) conducted a mixed method study that
examined the amount of homework spent, self-efficacy, and academic achievement. The
study consisted of 3,776 students and 221 schools. The findings suggested that more
homework does not yield higher academic achievement.
Supportively, Good (2010) suggested that if learning achievements were low,
perhaps something different was needed—not more of the same. Yet, Johanningmeier
(2010) suggested that A Nation at Risk brought national awareness for academic
excellence in the United States. Additionally, Burdick (2012) suggested that various
accountability measurements may provide impetus for increased academic achievement.
Although various educational reform acts have attempted to improve public
education in the United States, in 2001, federally mandated policies such as the No Child
Left Behind Act (NCLB) required school districts to meet Adequate Yearly Progress
(AYP). NCLB urged schools to “establish challenging educational standards, to develop
4
aligned assessments, and to build accountability systems for districts and schools” (U.S.
Department of Education, 2010, p.1). Therefore, school districts have scrutinized every
educational program to determine their usefulness in helping educators meet NCLB
standards (Circle, 2005; Grey, 2010; Johnson, 2010; Morse, 2010).
Nevertheless, music programs are often ignored. Abril and Gault (2006)
suggested that the NCLB mandate has pejoratively affected arts programs in schools. Yet,
the arts programs are defined as a core academic subject under the NCLB mandate. West
(2012) suggested that the NCLB act is adversely affecting school music programs,
particularly schools that have made AYP. Additionally, West argued that many music
education programs are being reduced or eliminated. Moreover, Spohn (2008)
incorporated a mixed method case study that investigated teachers’ perspectives of the
NCLB policy and its effect on arts programs, particularly music programs. The sample
population consisted of 20 elementary and secondary visual arts teachers and 26
elementary and secondary music teachers. The data collected revealed that administrative
decisions made to improve standardized tests and the accommodations of regulations
mandated by NCLB have threatened arts education. Although researchers (Abril & Gault,
2006; Major, 2013; Proctor Duax, 2013) suggested that music is subjective in nature and
lacks quantifiable evidence of performance, other empirical evidence correlates students
involved in music programs to overall academic performance (Gadberry, 2010; Hall,
2013).
While Gordon (2003) and Kuhlman (2005) suggested that students involved in
music programs increases musical aptitude, other researchers suggested that a
relationship exists between musical aptitude and academic achievement (Holsomback,
5
2001; Kuhlman, 2005; Rubinson, 2010). Holsomback (2001) conducted a quantitative
correlational study that examined the relationship between musical aptitude and Texas
Assessment of Knowledge and Skills (TAKS) for beginning band students. The study
consisted of 104 sixth grade band students in an east Texas school district and suggested
that a strong statistical relationship existed between musical aptitude and academic
achievement. Additionally, the study indicated that further research should be conducted
to investigate the relationship between musical aptitude and other standardized
assessments.
Problem Statement
It was not known if, and to what degree a correlation existed between the level of
musical aptitude and the level of reading and mathematics scores on the STAAR among
beginning band students. Empirical research has examined the relationship between
musical aptitude and various academic assessments (Cavanagh, 2009; Holsomback,
2004). E. A. Geist, K. Geist, and Kuznik (2012) suggested that musical elements such as
tempo, rhythm, and steady beat enhance mathematical concepts such as spatial properties,
counting, and sequencing. Supportively, Oare and Bernstorf (2010) suggested that music
instruction enhances phonological processes that assist in developing good readers and
writers.
Although empirical evidence correlates musical elements to academic
achievement, minimum evidence has been presented on the relationship between musical
aptitude and the reading and mathematics sections of the STAAR. Holsomback (2001,
2002) indicated that a strong statistical relationship existed between musical aptitude and
academic achievement. Yet, he suggested that further research should examine the
6
relationship between musical aptitude and other standardized assessments. For this
reason, it was necessary to determine whether a relationship exists between musical
aptitude and the current academic standardized assessment in Texas.
Understanding the relationship between musical aptitude and academic
achievement is essential in finding effective ways to utilize music instruction to enhance
reading and mathematics achievement of middle school students. In addition, this study
contributed to solving the problem by providing a quantitative analysis on the
relationship between the level of musical aptitude and the level of reading and
mathematics scores on the STAAR among beginning band students in Texas. Therefore,
this study investigated the composite and sub-categorical performance levels of the
Intermediate Measures of Musical Audiation (IMMA) and the STAAR. The 2013-2014
sixth grade class consists of 65 students, all of whom are required to enroll in a beginning
band course and take a musical aptitude assessment. Therefore, the target population and
the sample consisted of 65 sixth grade band students.
Purpose of the Study
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the level of musical aptitude assessed through
IMMA and the reading and mathematics scores on the STAAR among sixth grade
beginning band students in northeast Texas. The study utilized archival data deriving
from the results of the 2013-2014 IMMA assessment and the 2013-2014 reading and
mathematics sections of the STAAR. The independent variable, musical aptitude, was
defined as the potential for musical achievement (Gordon, 2007). The dependent variable,
STAAR, was defined as sequences of state mandated standardized assessments currently
7
used in Texas public schools to evaluate student achievement and knowledge in each
grade level. Specifically, the STAAR includes annual assessments for grades 3-8 in
reading and mathematics; assessments in writing at grades 4 and 7; in science at grades 5
and 8; and in social studies at grade 8. In addition, the STAAR includes end-of-course
assessments for English I, English II, Algebra I, Biology, and U.S. History (Texas
Education Agency, 2014). The study then evaluated the results to determine if a
statistically significant relationship existed between musical aptitude and the reading and
mathematics sections of the STAAR among sixth grade beginning band students. The
study was conducted with 65 students from a middle school band program in northeast
Texas.
The findings of this study advanced the understanding of the relationship between
musical aptitude and academic achievement among beginning band students. The
collection of data from this study added to the literature in this area by broadening the
knowledge surrounding the problem statement. The data extended the literature relating
music to cognitive abilities by examining the correlation of musical aptitude to specific
areas of academic performance. The findings contributed to the existing research by
providing a quantitative analysis on musical aptitude and the current academic
standardized assessment in northeast Texas. In addition, this study contributed to the field
by providing new information and resources relevant to musical aptitude and academic
achievement in reading and mathematics among middle school students.
Research Questions and Hypotheses
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the level of musical aptitude and the reading and
8
mathematics scores on the STAAR among beginning band students in Texas. This
research was framed in the theoretical context that learning, and more specifically
musical learning, is a person's ability to understand and process sound, rhythm, patterns
in sound, relationships between sounds, and ability to process rhymes and other auditory
information. This theoretical context was based on Gardner’s (2006) theory of multiple
intelligences and Gordon’s (1986) music learning theory. In order to understand various
relationships between musical aptitude and academic achievement among beginning band
students, appropriate research questions are essential. In addition, the research questions
and hypotheses were related to the problem statement by examining the relationship
between the level of musical aptitude and the reading and mathematics scores on the
STAAR. The following research questions and hypotheses guided this study:
R1: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics scores on the STAAR
among beginning band students?
H1a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
H10: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
R2: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading scores on the STAAR among
beginning band students?
9
H2a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
H20: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
Advancing Scientific Knowledge
This study advanced scientific knowledge by providing a quantitative
correlational analysis on musical aptitude and academic achievement. Compelling
empirical evidence has examined musical aptitude (Gordon, 2007; Karma, 2007;
Rutkowski, 1996; Ukkola-Vuoti et al., 2013) and academic achievement (Cheema &
Galluzzo; 2013; Maltese, Tai, & Xitao, 2012; Musa, 2013; Rowe, Miller, Ebenstein, &
Thompson, 2012; Schutz, Simon, & Musgrave, 2013; Stanley & Stanley, 2011; Talley &
Scherer, 2013; Toldson, 2012; Young, Hyuck, Sunyoung, & You Kyung, 2012).
However, few studies have examined the relationship between musical aptitude and
academic achievement (Holsomback; 2002; Holsomback, 2004; Kuhlman, 2005;
Rubinson, 2010). Holsomback (2001) conducted a quantitative correlational study that
examined the relationship between musical aptitude and Texas Assessment of Knowledge
and Skills (TAKS) of beginning band students. The study consisted of 104 sixth grade
band students in an east Texas school district. The study suggested that a strong statistical
relationship existed between musical aptitude and academic achievement. The following
year, Holsomback (2002) conducted a similar study that examined the relationship
between musical aptitude and academic achievement of 74 seventh grade band students.
10
The study suggested that a positive correlation existed between musical aptitude, as
measured by the Selmer Music Guidance Survey, and academic achievement, as
measured by the results of the Texas Assessment of Academic Skills (TAAS). In
addition, Holsomback (2002) suggested that further research should investigate the
relationship between musical aptitude and other standardized assessments.
While empirical evidence has examined relationships between musical aptitude
and academic achievement, previous studies identified a gap in the literature and allowed
this study to analyze the relationship between musical aptitude and the composite and
sub-categorical performance levels of the STAAR among sixth grade beginning band
students. The researcher evaluated the gaps in the literature and added to the existing
research by providing a quantitative analysis on musical aptitude and academic
achievement in reading and mathematics. Although similar studies have examined the
relationship between musical aptitude and previous state academic assessments in Texas
(Holsomback, 2001; 2004), a gap existed in the literature concerning the relationship
between musical aptitude, as measured by the IMMA, and the current standardized
assessment, as measured by the reading and mathematics sections of the STAAR
(Holsomback, 2002).
This research was framed in the theoretical context that learning in general, and
more specifically musical learning, is a person's ability to understand and process sound,
rhythm, patterns in sound, relationships between sounds, and process rhymes and other
auditory information. Gardner’s theory of multiple intelligence classified intelligence into
seven distinct categories (musical intelligence, bodily-kinesthetic intelligence, logical-
mathematical intelligence, linguistic intelligence, spatial intelligence, interpersonal
11
intelligence, and intrapersonal intelligence). Although each intelligence is relatively
independent (Feldman, 2010), Gardner suggested these separate intelligences operate
together and not in isolation, depending on the type of activity in which individuals are
engaged (Gardner, 2006; Gardner & Moran, 2006). Since Gardner identified musical
intelligence as one component, it was advantageous to examine musical learning and its
contribution to overall intellectual capacity.
Gordon’s music learning theory is a stage specific theoretical model that
introduces musical learning processes and presents effective teaching methods (Gordon,
1986). music learning theory employs the following three basic learning sequences: skill
learning, tonal content, and rhythm content. As a method of instruction, the learning
sequences are combined in various learning sequence activities which can be combined
with classroom activities. In this method, a skill level cannot be achieved except in
combination with a tonal or rhythm content level. Both Gordon’s music learning theory
and Gardner’s theory of multiple intelligence guided this study by serving as foundations
in the development of the research questions and hypotheses. This study advanced each
theory by providing a quantitative analysis that supports the theoretical foundation on
which the study is built. Specifically, this study advanced each theory by examining the
relationship between musical aptitude and the composite and sub-categorical
performance levels of the STAAR among sixth grade beginning band students.
Significance of the Study
The significance of this quantitative correlational study was to understand the role
of musical aptitude better as it relates to reading and mathematics achievement among
middle school students. Recent empirical evidence indicated that arts programs,
12
particularly music, are being eradicated at an exponential rate (Major, 2013; Slaton,
2012; Spohn, 2008). Although this study does not seek to demonstrate causation,
investigating possible relationships may help educators find effective ways of enhancing
reading and mathematics achievement of middle school students. Prior research generally
shows a positive relationship between musical aptitude and academic achievement
(Holsomback; 2002; Holsomback, 2004; Kuhlman, 2005; Rubinson, 2010). Although
Holsomback (2001; 2002) examined the relationship between musical aptitude and the
previous academic assessment in Texas, a defined need or gap exists in the literature
concerning the relationship between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading and mathematical section of the
STAAR among beginning band students. By investigating such relationships, school
administrators, teachers, and policy holders are capable of identifying tactics that may
improve test results.
The collection of data from this study added to the literature in this area by
broadening the knowledge surrounding the problem statement. In addition, the data
extended the literature relating music to cognitive abilities by examining the correlation
of musical aptitude to specific areas of academic performance. The findings contributed
to the existing research by providing a quantitative analysis on musical aptitude and the
current academic standardized assessment in Texas. By addressing the problem, this
study provided educators with new detailed information and resources relevant to musical
aptitude and academic achievement in reading and mathematics among middle school
students. Ultimately, this study could also impact educators by providing valuable data
that may help district leaders identify tactics that may improve test results.
13
Rationale for Methodology
This study utilized a quantitative approach to determine if, and to what degree, a
correlation existed between musical aptitude and the STAAR among sixth grade
beginning band students. Previous research has employed a quantitative methodology to
determine the relationship between musical aptitude and standardized assessments
(Holsomback, 2001; Kuhlman, 2005; Rubinson, 2010). A quantitative methodology
involves empirical analysis of data that has been collected from a sample of individuals
from specific populations to make a generalizable observation based on the measure of
relationships. Additionally, quantitative research seeks to establish relationships between
variables and attempts to clarify a phenomenon by performing a statistical analysis of a
body of numerical data (Fraenkel, Wallen, & Hyun, 2012). A quantitative methodology
was appropriate for this study since quantitative research involves statistical analysis of
quantitative data. Proper selection of methodology was imperative in comprehending and
interpreting the results based on the research questions and hypotheses (Yin, 2009).
Fraenkel, Wallen, and Hyun (2012) noted that quantitative methodology
comprises of explicit hypotheses. Additionally, the quantitative approach utilizes
objective instruments such as multiple choice standardized assessments, questionnaires,
personality scales, and aptitude assessments. Qualitative research expands the range of
knowledge and understanding of the world beyond the researchers themselves. It often
helps one see why a situation is the way it is, rather than just presenting a phenomenon
(Fraenkel, Wallen, Hyun, 2012). In addition, qualitative research is “intuitive in nature
and expands the scope of research to finding out the why and how of things that happen
in addition to the what, where, and when things happen” (p. 43). Since qualitative
14
research attempts to investigate naturally occurring phenomena in all their complexity, a
qualitative methodology would be inappropriate for evaluating the research questions and
hypotheses in the current study (Yin, 2009).
Nature of the Research Design for the Study
A correlational research design was employed in this study. According to
Fraenkel, Wallen, and Hyun (2012), correlational research design “seeks to investigate
the extent to which one or more relationships of some type exist” (p. 11). A correlational
study was the most appropriate design to identify the degree to which there is a
relationship between the level of musical aptitude and the mathematics and reading
scores on the STAAR among sixth grade beginning band students. As the study did not
“seek to determine reasons or causes for preexisting differences in groups of individuals”
(Fraenkel, Wallen, & Hyun, 2012, p. 365), a causal-comparative research design was
inappropriate for this study. In experimental research, variables are manipulated, and the
effects of this manipulation are measured upon the dependent variable (Fraenkel, Wallen,
& Hyun, 2012). Additionally, in experimental research, a treatment is deliberately
imposed on a group of objects or participants (Fraenkel, Wallen, Hyun, 2012). As this
study did not attempt to manipulate the variables, an experimental research design was
inappropriate for the study.
The sample selection for a correlational study, as in any type of study, should be
carefully planned (Yin, 2009). The minimum acceptable sample size for a correlational
study is considered by most researchers to be no less than 30 (Fraenkel, Wallen, & Hyun,
2012). In addition, a statistical power analysis was conducted in order to determine
adequate sample size. The parameter statistical power was set at 0.8, the significance
15
level at 0.05 and the effect size at 0.5. The analysis suggested that a minimum of 85
students was required for this study (Appendix E).
A structured process was used to collect the data. According to Fraenkel, Wallen,
and Hyun (2012), quantitative research is prevalent in developing procedures relating to
the comparison of variables, groups, or relating factors about individuals or groups in
experiments through correlational studies and surveys. Collecting data and analyzing
numbers that measure distinct attributes of individuals and groups is a trend that is
prevalent in today’s studies (Fraenkel, Wallen, & Hyun, 2012). Prior to collecting the
data, the researcher obtained written permission from the school district. Additionally,
Grand Canyon University Institutional Review Board (IRB) approval was required prior
to the data collection process (See Appendix F). The 2013-2014 sixth grade class
consisted of 65 students, all of whom were required to enroll in a beginning band course
and take a musical aptitude assessment. This study was conducted with the entire
population of 65 sixth grade band students available in the school district selected for
convenience as a research site for this study. A statistical power analysis was conducted
in order to determine adequate sample size. The analysis suggested that a minimum of 85
students were required for this study. Archival data, results from the 2013-2014 IMMA
assessment, and the results from the 2013-2014 reading and mathematics sections of the
STAAR, was analyzed in an attempt to answer two overarching research questions: (1) Is
there a correlation between musical aptitude and the composite and sub-categorical
performance levels of the mathematics section of the STAAR among sixth grade
beginning band students? (2) Is there a correlation between musical aptitude and the
16
composite and sub-categorical performance levels of the reading section of the STAAR
among sixth grade beginning band students?
The data collection procedures consisted of several steps to ensure proper
collection and storage of the data. The researcher obtained each student’s performance
data once IRB approval was received. Identifiers such as student names, identification
numbers, dates of birth, and addresses were removed by the school district and assigned
an alphabet to minimize a breach of confidentiality. All information regarding the
participants remained in the possession of the researcher and kept in a lock box located in
the counselor’s office for three years. Data collected was exported into the Statistical
Package for Social Science (SPSS) 23.0 software.
Definitions of Terms
The terms listed within this section are terms that are frequently used within the
study. This section defined the study construct and provided a common understanding of
technical terminology, variables, and concepts utilized within the scope of the study. The
following terms were used operationally in this study:
Academic achievement. A student’s ability to excel in an academic subject and
gain the necessary skills and knowledge required to contribute in a global society. It is
often measured by a student’s grade point average (GPA) and/or a standardized
assessment (U.S. Department of Education, 2010). This term was also identified as the
dependent variable in the current study.
Adequate Yearly Progress (AYP). A measure of a school or school system’s
ability to meet federal benchmarks with specified performance standards (U.S.
Department of Education, 2010).
17
Composite musical aptitude. A composite musical aptitude is the combination of
tonal and rhythmic aptitude scores (Gordon, 2003).
Intermediate Measures of Musical Audiation (IMMA). Developed by Edwin E.
Gordon and based on an extensive body of research and practical field-testing, the IMMA
is a musical aptitude test for grades 4-6. It was designed to act as objective aids to
teachers by providing students with appropriate opportunities and instruction. The IMMA
was developed because of the need for a more advanced version of the Primary Measures
of Music Audiation (PMMI) test. Administration of the test requires approximately
twenty minutes for the rhythm and tonal subtests (Gordon, 2007).
Middle school. Schools that are typically configured to begin with six grade and
end with eighth grade (Texas Education Agency, 2014).
Musical aptitude. Musical aptitude is the potential for musical achievement in
which one can learn music (Gordon, 2007). This term was also identified as the
independent variable in the current study.
No Child Left Behind (NCLB). NCLB is a law passed in 2001 which proposes to
raise student achievement and eliminate the achievement gap between students from
different backgrounds (U.S. Department of Education, 2010).
Rhythmic aptitude. Rhythmic aptitude is the ease in which one learns rhythm
(Gordon, 1986).
Standardized assessment. An assessment that is identical for each individual and
are usually made to correlate to specific state academic standards. They are often multiple
choice and have identical testing conditions (time limits, instructions, and scoring) for all
students (U.S. Department of Education, 2010).
18
The State of Texas Assessment of Academic Readiness (STAAR). The STAAR
are sequences of state mandated standardized assessments currently used in Texas public
schools to evaluate student achievement and knowledge in each grade level. Specifically,
the STAAR includes annual assessments for grades 3-8 in reading and mathematics;
assessments in writing at grades 4 and 7; in science at grades 5 and 8; and in social
studies at grade 8. In addition, the STAAR includes end-of-course assessments for
English I, English II, Algebra I, Biology, and U.S. History (Texas Education Agency,
2014).
Tonal aptitude. Tonal aptitude is the ease with which one learns melody (Gordon,
1986).
Assumptions, Limitations, Delimitations
Assumptions. Assumptions are any important assertion presumed to be true but
not actually verified; major assumptions should be described in any research proposal or
report (Fraenkel, Wallen, & Hyun, 2012). The following assumptions are present in this
study:
1. It was assumed that the musical components of tonal and rhythmic aptitude were accurately measured by the musical aptitude assessment. The rationale behind this
assumption is that the musical aptitude assessment was administered properly
according to the musical aptitude assessment manual.
2. It was assumed that the participants answered the assessment honestly, and to the best of their ability.
3. It was assumed that the reading and mathematics achievement of study participants was accurately measured by the reading and mathematics section of
the STAAR. The rationale behind this assumption is that only certified educators
are allowed to administer the STAAR according to the rules and regulations set
forth by Texas Education Agency (TEA). A month prior to test administration, all
test examiners are mandated to complete training in testing procedures. In
addition, all examiners and examinees are required to sign an oath of
confidentiality and test security prior to test administration (Texas Education
Agency, 2014).
19
Limitations. According to Fraenkel, Wallen, and Hyun (2012), knowledge
concerning the limitations of a study may assist other researchers in evaluating the degree
to which the findings can be generalized. The researcher identified the following
limitations:
1. The researcher’s access to secondary data was limited to one school district in northeast Texas that has only one middle school. As a result, the researcher
identified the following consequences:
a. Population size for the grade of interests (6th grade) was N = 65, which did not meet the minimal sample size needed to capture medium-size correlations.
The study can be identified as significant only if a large correlation exists.
b. Population may not be typical for the entire state of Texas (low external validity).
c. Since there are significant educational differences among states, the results of this study cannot be generalized to other states.
2. The scope of this study was limited by the scope of the instrument used to measure musical aptitude and the scope of the mathematics and reading tests.
While the study presented several unavoidable limitations, it did not negatively
affect the results of the study.
Delimitations. Delimitations are simply defined as boundaries set by the
researcher(s) to control the study (Fraenkel, Wallen, & Hyun, 2012). The researcher
made the following delimitations:
1. A longitudinal study may reveal different or similar relationship patterns, however, this study was limited to the 2013-2014 school year.
2. Use of Pearson correlation analysis: Correlational studies do not investigate cause and effect (Yin, 2009), causal conclusions cannot be drawn from the study.
Summary and Organization of the Remainder of the Study
This chapter introduced the study, which focuses on the relationship between
musical aptitude and academic achievement among sixth grade students in northeast
20
Texas. Previous empirical research has examined the relationship between musical
aptitude and various academic assessments (Cavangh, 2009; Holsomback, 2001; 2004;
Rubinson, 2010). Yet, a defined need or gap exists in the literature concerning the
relationship between musical aptitude, as measured by the IMMA, and academic
achievement, as measured by the reading and mathematics sections of the STAAR
(Holsomback, 2002).
The findings of this study may help school administrators, music specialists, and
reading and mathematics instructors find effective ways to utilize music instruction to
enhance reading and mathematics achievement of middle school students. In addition,
this study contributed to the field by providing new information and resources relevant to
musical aptitude and academic achievement among middle school students. Thus, the
purpose of this quantitative correlational study was to examine if, and to what degree, a
correlation existed between the level of musical aptitude and the reading and mathematics
scores on the STAAR among sixth grade beginning band students in Texas.
Chapter 2 will present an organized literature review covering the background to
the problem, the theoretical framework providing the foundation to the study, and various
topics and themes within those topics related to the study. Those topics will include: (a)
the history of musical aptitude, (b) academic achievement, (c) music in relation to
academic achievement, (d) musical aptitude and academic achievement, and (e)
methodological strengths and weaknesses followed by a summary. Chapter 3 will present
a detailed description of the methodology of the study beginning with the restatement of
research questions and hypotheses, followed by the research design, population and
sample selection, instrumentation, validity, reliability, data collection procedures, data
21
analysis procedures, and ethical considerations. Chapter 4 will present the data summary
and statistical analysis of the study data. Finally, Chapter 5 will present the summary of
the study, findings, implications, and recommendations from the study.
22
Chapter 2: Literature Review
Introduction and Background
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation exists between musical aptitude and the reading and mathematics
sections of the State of Texas Assessment of Academic Readiness (STAAR) among sixth
grade beginning band students. Specifically, this quantitative correlational study
examined the relationship between musical aptitude, as measured by the Intermediate
Measures of Music Audiation (IMMA Gordon, 2002), and the reading and mathematics
performance levels of the STAAR. Administrators and policy makers have investigated
pedagogical methods for improving academic achievement in schools. Thus, the intent of
the literature review was to provide a comprehensive review of peer-reviewed journal
articles, books, professional publications, and internet sources relevant to the study.
The literature review was conducted utilizing various sources and approaches.
Empirical journal articles on relevant subjects were discovered by searching available
academic databases to include: ABI/INFORM Complete, EBSCOhost, JSTOR, ERIC,
and ProQuest. Doctoral dissertations were accessed by using the ProQuest Dissertations
& Thesis: The Humanities and Social Sciences Collection. Additional supporting
resources were found on the internet to include: the Office of Personnel Management
website, Google Scholar, and a number of educational websites. Keywords associated
with these websites include: musical aptitude, academic achievement, reading
achievement, and mathematics achievement. In addition, the review of the literature
included a snowballing technique (Marshall, 1998) which compiled references from each
23
document. This technique led to the identification of over 300 scholarly journal articles,
books, and dissertations relevant to the study.
This chapter addresses the Background to the Problem, which provides the
justification for this study based on the literature. Additionally, the historical background
behind the study is presented. Second, this chapter presents the theoretical framework,
which serves as the foundation of this research study and is utilized to help develop the
research question and data collection approach. This section presents the models to be
used behind the study variables. Next, this chapter discusses the Review of Literature and
other topics relevant to the study. These topics include: (a) the history of musical
aptitude, (b) academic achievement, (c) music in relation to academic achievement, (d)
musical aptitude and academic achievement, and (e) methodological strengths and
weaknesses in similar studies. Lastly, a summary of the literature review further
synthesizes the literature review and identifies the problem statement from the
Background to the Problem, the research questions based on the Theoretical Foundation,
the design from the review of similar designs, and data collection approaches and
instruments.
Throughout the twentieth century, music psychologists have designed tests to
measure music constructs such as talents, ability, and aptitude (Gordon, 1986; Seashore;
1938; Stanton, 1922). By using their tests, educators were capable of predicting musical
ability and possibly achievement. While some music educators were skeptical about the
value of objective information yielded by test scores, other educators appreciated and
utilized such tests. However, in the early 1970’s, researchers questioned whether the
results from musical aptitude assessments correlated with academic achievement
24
(Rainbow, 1963; Young, 1971). Young (1971) conducted a quantitative correlational
study that examined the relationship between musical aptitude and academic achievement
of elementary students in Chicago. The researcher employed the Musical Aptitude Profile
of Gordon, the Lorge-Thorndike Intelligence Test, and the Iowa Test of Basic Skills
(1962) to determine the extent of the relationship between musical aptitude and academic
achievement. The study suggested that a strong correlation existed between musical
aptitude and academic achievement. The researcher suggested that future studies should
explore the relationship between musical aptitude and academic achievement among
various demographics and socioeconomic backgrounds.
Similarly, Hobbs (1985) conducted a correlational study that examined the
relationship between musical aptitude, scholastic achievement, and academic
achievement. The sample consisted of 72 first, second, and third graders. The study found
low correlations (r = .33) between musical aptitude and scholastic aptitude, indicating
that musical aptitude and scholastic aptitude tests measure different aspects of cognition.
However, higher correlations were found between music aptitude and academic
achievement (r = .56). The study indicated that further research should examine the
relationship between musical aptitude and standardized academic assessments in other
states.
In the late 1990s to early 2000s, researchers began to examine the relationship
between musical aptitude and standard assessments in various states (Barrett, 1993;
Holsomback; 2002; 2004). Barrett (1993) conducted a quantitative, non-experimental
study that investigated the relationship between musical aptitude and academic
standardized assessment in Florida. The study consisted of 76 sixth, seventh, and eighth
25
graders. The study determined that no correlation existed between musical aptitude and
academic achievement. Conversely, Holsomback (2001) conducted a similar study that
examined the relationship between musical aptitude and Texas Assessment of Knowledge
and Skills (TAKS) of beginning band students. The study consisted of 104 sixth grade
band students in an east Texas school district. The study suggested that a strong statistical
relationship existed between musical aptitude and academic achievement. Additionally,
the study indicated that further research should be conducted to investigate the
relationship between musical aptitude and other standardized assessments.
From 2003 to 2014, one study, to the researcher’s knowledge, has examined the
relationship between musical aptitude and academic standardized assessments. Rubinson
(2010) conducted a quantitative correlational study that investigated the relationship
between musical aptitude and developing reading abilities of kindergarten students. The
researcher employed the tonal and rhythmic components of the Primary Measures of
Music Audiation (PMMA) to determine the amount of musical aptitude of kindergarten
students. Reading achievement was measured by various subtests of Dynamic Indicators
of Basic Early Literacy Skills (DIBELS), a standardized assessment of early literacy
development that evaluates the alphabetic abilities, and phonological awareness of
kindergarten students. The study consisted of 80 kindergarten students from an
elementary school in central Connecticut. The study suggested that a strong correlation
existed between musical aptitude and the phonological awareness of kindergarten
students.
While research on the relationship between musical aptitude and academic
achievement has declined significantly within the last decade, Holsomback (2002)
26
suggested that future research should investigate the relationship between musical
aptitude and current academic standardized assessment in Texas. Based on this gap, the
study will explore whether musical aptitude is closely related to reading and mathematics
achievement and could serve as a predictor of reading and mathematics achievement in
middle school students. The findings from this study may assist teachers, administrators,
and educational policy makers in developing effective methods for increasing reading
and mathematics achievement of middle school students.
Theoretical Framework
Previous research conducted on musical aptitude and academic achievement has
utilized Gardner’s theory of multiple intelligences (Gardner, 2006) and Gordon’s music
learning theory (Gordon, 1986) as a theoretical foundation. This research was framed in
the theoretical context that learning in general, and more specifically musical learning, is
a person's ability to understand and process sound, rhythm, patterns in sound,
relationships between sounds, and ability to process rhymes and other auditory
information. This framework was based on Gardner’s (2006) theory of multiple
intelligences and Gordon’s (1971) music learning theory. Multiple theoretical models
influenced the research questions in this study as they provided a rationale on the
relationship between musical aptitude and academic achievement. Each theoretical model
provided the foundation for the study.
Theory of multiple intelligences. American neuropsychologist and educator,
Howard Gardner, defined intelligences as “the ability to create an effective product that is
valued in a culture” (Gardner, 2006, p. 6). Rather than viewing intelligence as dominated
by a single general ability, Gardner pluralized the theoretical concept of intelligence.
27
Gardner’s theory of multiple intelligence classified intelligence into seven distinct
categories (musical intelligence, bodily-kinesthetic intelligence, logical-mathematical
intelligence, linguistic intelligence, spatial intelligence, interpersonal intelligence, and
intrapersonal intelligence). Although each intelligence is relatively independent
(Feldman, 2010), Gardner suggested these separate intelligences operate together and not
in isolation, depending on the type of activity in which we are engaged (Gardner, 2006;
Gardner & Moran, 2006). Since Gardner identified musical intelligence as one
component, it would be advantageous to examine musical learning and its contribution to
overall intellectual capacity. In addition, Music participation and learning, which
incorporates instruction geared toward the plurality of various intelligences, may
ultimately have a positive effect on reading and mathematical achievement. Gardner’s
(2006) multiple intelligences describes music and movement as equal to and unique from
reading and mathematical learning. This theory motivated educators to investigate the
relationship between musical aptitude and academic achievement.
Music learning theory. This theory was developed by Edwin E. Gordon and
based on an extensive body of research and practical field-testing. music learning theory
is a stage-specific theoretical model that introduces musical learning processes and
presents effective teaching methods. In 1971, the theoretical model was introduced in his
book, The Psychology of Music Teaching, and has been revised in his subsequent
publications. The teaching model is sequential and utilizes the concept of audiation—
hearing music in the mind with understanding—music learning theory employs the
following three sequential learning activities: (a) skill learning, (b) tonal content, and (c)
rhythm content. In this theoretical context, musical aptitude is considered to be normally
28
distributed within the general population, with relatively few people having high or low
musical aptitude and the majority having average musical aptitude (Gordon, 2002).
Specifically, Gordon (1999) suggested that:
Music Learning Theory is unique among music teaching methods in
accounting directly for students’ differing potentials to achieve in music.
Students of average aptitude are taught more tonal content and rhythm
content than low aptitude students, and high aptitude students learn more
content than average aptitude students. By teaching to students’ individual
differences, teachers lessen the risk of boring students with high potential
and frustrating students with lower potential. (p. 2)
Although Gordon’s music learning theory analyzes musical learning
processes, it is ultimately designed to enhance musical intelligence. Based on
these theoretical models, this study adds to the existing research by providing a
quantitative analysis on musical aptitude and the current standardized assessment
in northeast Texas.
Review of the Literature
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between musical aptitude and the reading and mathematics
sections of the STAAR among sixth grade beginning band students. The review of
literature provides an overview of various thematic topics relevant to the study. These
topics include: (a) an overview of musical aptitude, (b) academic achievement, (c) music
in relation to academic achievement, and (d) musical aptitude and academic achievement.
29
Overall, the review of literature provides the foundation of the study to examine the
relationship between musical aptitude and academic achievement.
Overview of musical aptitude. The overview of musical aptitude examination is
a component of the historical nature of music education in the United States (Gordon,
2007; Seashore, 1938). Some of the early musical aptitude assessments included the
Seashore Measures of Musical Talent, the Kwalwasser-Dykema Music Tests, and the
Music Aptitude Profile of Gordon. Motivated by the standardization movement, musical
aptitude has been defined as the potential for music achievement. Music achievement is
the actual attainment of musical knowledge and ability.
Early in the twentieth century, musicologist Carl E. Seashore indicated that
“When the proximate physiological threshold has been obtained, practice is of no avail”
(Seashore, 1919, p. 60). Musically, Seashore (1919) suggested that once an individual has
attained their maximum physiological ability, musical training was impractical. Yet,
Seashore believed that musical aptitude was a God-given gift that one was either born
with or without. Seashore supported the notion that practice and training in music would
not affect one’s physiological ability, but may affect one’s cognitive abilities. Seashore
then realized that musical aptitude was not only an innate ability, but was also determined
genetically.
Many researchers believed that musical aptitude was inherited and that it
stabilizes at birth (Stanton, 1922; Seashore, 1919). However, James Mursell, a prolific
author in the field of music, psychology, and education, became suspicious of the genetic
disposition theory of musical aptitude. Mursell popularized his view in an exchange of
letters with Seashore that was published in the Music Educators Journal during the early
30
1930’s. Over the next several years, the nature vs. nurture disposition eventually evolved
into a major controversy among music educators across the nation and debated by
Mursell in The Psychology of Music of 1937. Mursell suggested that “if indeed musical
aptitude is innate and if it cannot be altered with practice and training, why are the
majority of students in American public schools required to receive instruction in general
music” (Mursell, 1937, p.6). He also suggested that “If Seashore is correct, training in
music is all but useless for those born without a high level of musical aptitude” (p.5).
Mursell (1937) suggested that Seashore was largely involved in the identification and
instruction of the musically talented and to a much lesser extent and pedagogical
practices regarding students’ individual musical abilities. Yet, Mursell’s views became a
controversial topic concerning musical aptitude in the late 1930s.
Intermediate Measures of Musical Audiation (IMMA). Developed by Edwin E.
Gordon and based on an extensive body of research and practical field-testing, the IMMA
was designed to act as objective aids to teachers by providing students with appropriate
opportunities and instruction. The IMMA was developed due to the need for a more
advanced version of Gordon’s earlier musical aptitude assessment, the Primary Measures
of Music Audiation (PMMA, Gordon, 2002). The IMMA ultimately serves two purposes:
(a) to identify children with overall musical aptitude so that they can be encouraged to
participate in special musical activities and (b) to diagnose individual musical strengths
and weaknesses. The assessment may be administered to children from ages six through
nine as well as from 10 through 11, though children’s musical aptitudes generally
stabilize around age nine (Gordon, 2002). The level of musical aptitude a child attains by
the age of nine remains ostensibly the same throughout life. Although a child’s aptitude
31
results increases from year to year, his or her percentile ranking remain relatively stable.
Additionally, Gordon (2003) suggested:
The IMMA is intended to be used in a group where 50% or more of the children
scored 80% or above on the Tonal subtest, Rhythm subtest, or both on the
PMMA. The IMMA is most appropriate for children with above average and high
developmental aptitudes, whereas PMMA is most appropriate for children with
average and low developmental musical aptitudes. (p. 10)
It would be erroneous for researchers to evaluate variations of developmental
music aptitude by comparing the students’ scores on the PMMA to those of the IMMA.
Rather, researchers should compare the dissimilarities of the students’ scores on two or
more of the same assessment to ensure accurate interpretation.
The Seashore Measures of Musical Talents. Developed by Carl E. Seashore, the
Seashore Measures of Musical Talents is a musical aptitude assessment that initially
appeared in 1919 and was revised extensively in 1939. The assessment provided a
reference against which to compare other approaches to assess musical abilities.
Although contemporary constructs and outlooks contributed to the decline of the
Seashore Measures of Musical Talents, the assessment exemplified the belief that musical
ability, particularly in the aptitude aspect, is based on psychoacoustical discriminations
(Radocy & Boyle, 2003).
The Seashore Measures of Musical Talents (1919) consists of two aural
discrimination rhythm tasks. In the rhythm test, the respondents indicate whether paired
tapped patterns are the same or different. The time test requires an indication of whether
the second of the paired tones are longer or shorter than the first. Although Mursell and
32
other theorists question the tests’ validity as predictors of musical talent, they are
unquestionably valid measures of both discrimination tasks (Radocy & Boyle, 2003).
Kwalwasser-Dykema Music Tests. While Seashore was between the publication
of the first and second edition of Seashore Measures of Musical Talents, in 1930, Jacob
Kwalwasser and Peter Dykema developed and published the Kwalwasser-Dykema Music
Tests, commonly known as the K-D tests. Both Kwalwasser-Dykema Music Tests and
the Seashore Measures of Musical Talents assessments display similarities (Radocy &
Boyle, 2003). Perhaps it is because the ultimate level of attainment of musically trained
students is best predicted by past musical achievement that Kwalwasser and Dykema,
unlike Seashore, included musical achievement measures in their assessment. Clearly,
they were more interested in the practical aspects of Seashore’s assessment than in the
search for a viable description of musical aptitude. Kwalwasser-Dykema Music Tests
were comprised of ten subtests. Six of the ten subtests were created to measure similar
factors found in the Seashore Measures of Musical Talents assessment, although they
were developed and titled differently. However, Kwalwasser-Dykema employed
orchestral instruments and the Duo-Art Reproducing Piano as stimuli for many of the
subtests.
The phenomenon of musical aptitude assessment is a component of the historical
nature of music education in the United States (Gordon, 2007; Seashore, 1938).
Furthermore, musical aptitude assessments are designed to assist music teachers in
providing students with appropriate instruction and learning opportunities. Many music
educators employ tests such as the Intermediate Measures of Musical Audiation (1986),
Kwalwasser-Dykema Music Tests (1930), and The Seashore Measures of Musical
33
Talents (1919) to measure musical aptitude. Yet, Gordon’s Intermediate Measures of
Musical Audiation (1986) is the only brief, longitudinally valid music aptitude test for
Grades 1 through 6. Based on this information, the IMMA would be the most appropriate
assessment to measure musical aptitude among middle school band students.
Academic achievement. Since the beginning of the 21st century, the educational
reform movement has been a major societal and political debate in the United States
(Davies, 2007; McGuinn, 2006). Although many attempts have been made at school
reform, minimal evidence of academic advancement has been presented (Good, 2010;
Ravitch & Cortese, 2009). In 1983, a highly controversial reform, A Nation at Risk: The
Imperative for Education Reform, suggested that more homework, extended school days,
and extended school years would increase academic achievement (U.S. Department of
Education, 1983). However, researchers suggested that the reform advocated by A Nation
at Risk was partially right and partially wrong (Burdick, 2012; Good, 2010; Palmer,
Davis, Moore, & Hilton, 2010). Cheema, Kitsantas, and Ware (2011) conducted a mixed
method study that examined the amount of homework spent, self-efficacy, and academic
achievement. The study consisted of 3,776 students and 221 schools. The findings
suggested that more homework does not yield higher academic achievement. In addition,
Good (2010) suggested that if learning achievements were low, perhaps something
different was needed, not more of the same. Moreover, he suggested that differentiated
instruction might yield higher learning achievements. Yet, Johanningmeier (2010) noted
that A Nation at Risk brought national awareness for academic excellence in the United
States and various accountability measurements may provide the impetus for increased
academic achievement.
34
Measurement of academic achievement. Recent empirical studies have
investigated effective methods of measuring students’ academic performance in schools
(Burns, 2010; Stubnisky, Perry, Hall, & Guay, 2012; Weaver, 2011). Although the
investigation of academic advancement has become a critical method for measuring the
effectiveness of educational organizations, federal and state authorities continue to
develop assessments that attempt to measure academic achievement. Weaver (2011)
suggested that standardized testing has become a key factor in measuring academic
achievement. However, Jorgenson (2012) affirmed that standardized testing does not
adequately measure academic achievement. While some researchers contend that
standardized testing inadequately assesses academic performance, various states have
developed standardized assessments that examine academic achievement.
In 2003, the Texas Education Agency (TEA), in conjunction with Pearson and
various Texas educators collaborated to develop TAKS, the Texas Assessment of
Knowledge and Skills (TEA, 2003). While the assessment evaluated reading, writing,
mathematics, science, and social studies, it failed to provide sufficient academic readiness
for Texas students. Thus, the TEA, in collaboration with the Texas Higher Education
Coordinating Board (THECB), developed a new appraisal system that focuses on
enhancing the postsecondary readiness of high school students. Additionally, the new
appraisal system ensures that Texas students are academically competitive both
nationally and internationally (TEA, 2009). On May 5, 2011, the TEA systematically
adopted a new method of measuring academic achievement. The State of Texas
Assessment of Academic Readiness (STAAR) was developed to enhance the rigidity of
the assessment as well as to evaluate knowledge and skills effectively. Some of the
35
components of the STAAR assessment include the transition of grade-based assessments
to course-based assessments, the establishment of academic readiness standards for
Algebra II and English III, and the utilization of validation studies to ensure that
performance standards are linked appropriately (TEA, 2012). Although the TEA contends
that the STAAR assessment will adequately measure academic achievement, minimal
empirical research has been conducted validating such assessment. Thus, further research
is necessary to investigate various relationships between the results from the TAKS and
STAAR.
School accountability. School accountability has become synonymous with
federal, state, and local efforts to measure and monitor academic achievement adequately
(Elmore & Furhman, 2001). The United States Department of Education mandated that
each elementary and secondary school meet Adequate Yearly Progress (AYP).
Undoubtedly, AYP, a mandate of the No Child Left Behind Act (NCLB), and the more
recent "Race to the Top" proposal (United States Department of Education, 2009), are
based on accountability frameworks. Although such accountability frameworks were
designed to improve academic achievement in the long term, researchers suggested that
the accountability measurement had a negative effect on public schools.
Mathis (2004) suggested that AYP and NCLB are statistically impossible. He
suggested that “high” standards are considered high because a limited number of people
achieve them. Obviously, if everyone achieved such standards, they would become low
standards. Powell, Higgins, Aram, and Freed (2009) conducted a qualitative study that
investigated the NCLB act as it relates to the decision-making of rural teachers and
administrators. Statistics were collected from 101 rural elementary school administrators
36
in Missouri and 76 rural elementary teachers in Maine. The results indicated that there
were significant changes in the use of instructional time for teaching reading and some
subjects and non-instructional time for recess and kindergarten naptime. Administrators
affirmed that professional development hours were used exclusively for maintaining
AYP. Maine teachers reported that NCLB benefits some groups of students more than
NCLB benefits others and that it has a harmful effect on student motivation. Similarly,
educationalists are discovering that the strategy is imperfect, developmentally
inappropriate, ill-funded, and ultimately is leaving more students, teachers, and schools
behind than ever before (Eisele-Dyrli, 2010; Krumenaker, 2009).
In the State of Texas, school achievement is evaluated through the state-mandated
accountability system in which ratings are given to individual school campuses and to
school districts based on academic performance. To earn the highest rating in the state,
Exemplary, schools must meet the following guidelines: no more than a 0.2% dropout
ratio, at least 90% of students passing the TAKS, as well as subgroups, and 95% of the
completion standard met (Texas Education Agency, 2008). To earn the second highest
rating, Academically Recognized, these criteria must be met: no greater than a 0.7%
dropout ratio, at least 75% of students passing the TAKS, as well as various subgroups,
and 85% of the completion standard (TEA, 2006, 2007, 2008).
Receiving the ranking of Academically Acceptable means having data indicating:
no greater than a 1.0% dropout rate, at least 65% of students passing the English
Language Arts (ELA), Writing, and Social Studies sections of the TAKS, as well as
subgroups, at least 45% of students passing the TAKS Math, including subgroups, at least
40% of students pass the TAKS Science measure, including subgroups, and 75% of the
37
completion standard met (TEA, 2008). To earn the lowest accountability ranking,
Academically Unacceptable, Individual school campuses and districts that do not meet
the basic requirement stated above are ranked as academically unacceptable.
Additionally, the state of Texas has mandated consequences for schools and school
districts that are categorized as academically unacceptable. Although the state of Texas is
currently replacing the TAKS with the STAAR, the accountability measurements are
identical.
No Child Left Behind and music education. In 2001, federally mandated policies
such as the NCLB act have negatively affected music education in public schools (Spohn,
2008; West, 2012; Abril & Gault, 2006). West (2012) suggested that the NCLB act is
adversely affecting school music programs, particularly schools that have made AYP.
Additionally, West argued that many music education programs are being reduced or
eliminated. Spohn (2008) incorporated a mixed method case study that investigated
teachers’ perspectives of the NCLB policy and its effect on arts programs, particularly
music programs. The sample population consisted of 20 elementary and secondary visual
arts teachers and 26 elementary and secondary music teachers. The data collected
revealed that administrative decisions made to improve standardized tests and the
accommodations of regulations mandated by NCLB have threatened arts education.
Similarly, Abril and Gault (2006) affirmed that NCLB budgets, standardized tests,
and scheduling presented adverse changes amongst music programs as stated by school
administrators. Circle (2005) suggested that music curricula across the United States have
been reduced due to increased time allotment in reading and math. Controversially,
examinations of NCLB have provided limited quantitative data suggesting the decline or
38
elimination of music education programs (Ashford, 2004; Colwell, 2005; Mishook &
Kornhaber, 2006).
Consequently, Chapman (2004) and Meyer (2005) projected that minimal time
would be available for electives as limited states fail to incorporate various electives such
as art, music, theatre, and physical education into their accountability systems. Anecdotal
evidence suggested that high-stakes accountability has affected scheduling practices of
public schools (Beveridge, 2010; West, 2012). Hence, Beveridge (2010) argued that
adverse scheduling practices caused by NCLB mandate are more far-reaching than mere
student class placement. Oftentimes, school districts would eliminate a student’s elective
if he or she did not perform adequately on state assessments. The elective course is often
replaced with remedial academic core subjects intended to increase standardized test
scores. Additionally, remedial teachers, as well as administrators, utilize enrichment
subjects such as music, art, and theatre to entice students to perform well on standardized
assessments.
While other methods could be adopted, such as after school tutoring, which
minimizes the least amount of disruptions to the school day (Beveridge, 2010),
administrators argue that pullouts from non-core subjects are the logical solution (Grey,
2010). Although empirical research has suggested that controversial connections exist
between arts education and NCLB mandates, further research could investigate budgets
and time allotment before and after the NCLB mandate.
Since its inception in 2011, Texas public schools have administered the STAAR
to measure academic achievement in students. While prior academic assessments failed
to provide sufficient academic readiness for Texas students, the STAAR was designed to
39
enhance the rigidity of the previous assessment. Additionally, this standardized academic
assessment meets the criteria mandated by the NCLB and is the most current and valid
academic standardized assessment for the state of Texas (TEA, 2012). Based on this
information, the STAAR was the most appropriate assessment to measure academic
achievement among middle school students in Texas.
Music and academic achievement. Empirical research suggested that an
inextricable relationship exists between music education and academic achievement
(Cavangh, 2009; Cox & Stephens, 2006). Argabright (2005) proposed that participation
in music education programs increase academic achievement. Additionally, Argabright
suggested that students who participated in private instrumental instruction significantly
enhanced academic learning. Moreover, Gadberry (2010) conducted a qualitative study
that examined music and the academic success of students. Surveys were distributed to
adults and children who were choir members and another was conducted with 500
parents and 300 educators. The findings implied that students who were choir participants
had higher grades and some of their parents and teachers suggested that their academic
achievement increased considerably after joining the choir.
Kelly (2012) conducted a quantitative correlational study that examined academic
achievement between 12th grade students who participated in fine arts courses,
particularly music, and students who did not enroll in fine arts courses. The data
represented all 12th grade students as provided by the Florida Department of Education,
consisting of the Florida Comprehensive Assessment Test (FCAT), grade point averages,
and the SAT scores of 12th grade students between 2007-2008 and 2010-2011. The
findings indicated that a strong and reliable relationship exists between individuals who
40
participated in fine arts programs and higher academic achievement. Furthermore, the
findings suggested that students participating in fine arts courses for eight or more
semesters demonstrated higher academic success. Scores on the FCAT for reading,
mathematics, and writing of individuals enrolled in fine arts courses remained consistent
when comparing the results from the years of 2010-2011 to 2007-2008. Differentiation
between the years of 2007-2008 and 2010-2011 FCAT scores were not statistically
significant (p = .05); thus, demonstrating stability.
Conversely, Elpus (2012) conducted a quantitative correlational study that
examined the college entrance assessment results of music and non-music students across
the United States. The researcher extracted data from the Educational Longitudinal Study
of 2002 (ELS), a nationally representative educational study facilitated by the National
Center of Education Statistics. Data collected from the 2004 graduates in the United
States indicated that approximately 36% of high school graduates earned at least one
credit in music-related courses. Elpus (2012) employed fixed-effect regression
procedures to compare standardized test results of music students who were defined as
being enrolled in at least one music-related course to non-music students while
controlling extraneous variables such as prior academic achievement, disposition towards
school, and demographic nature. The findings revealed that music students did not
academically surpass non-music students on the SAT. While Kelly’s (2012) findings
support the significance of music programs in schools, Elpus (2012) argued that musical
participation does not correlate to higher academic achievement.
After examining the methodological approaches from both studies, Kelly (2012)
and Elpus (2012) employed a quantitative correlational research design while Gadberry
41
(2010) employed a qualitative case study. Both Kelly (2012) and Elpus (2012) utilized
archival data to retrieve information regarding both variables in the studies. Elpus’ (2012)
sample consisted of students across the United States while the examples of Kelly (2012)
and Gadberry (2010) consisted of participants from their respective states. Gadberry’s
(2010) study lacked quantifiable evidence due to its scope. Elpus (2012) employed the
results from SAT scores to measure academic achievement, while Kelly (2012) employed
several instruments such as FCAT, grade point averages, and SAT scores to measure
academic achievement, which suggests a stronger analysis.
Music and the brain. Although compelling empirical research supports the value
of music education in schools, many music educators find themselves fighting for its
existence (Cole, 2011; Fujioka, 2006; Phillips-Silver, 2009). This timely new resource
has encouraged neuroscientists such as Howard Gardner, Erika Skoe, Nina Kraus, Diana
Deutsch, and Laurel Trainor to investigate the neurological components of the brain as
they relate to music.
Butler and Trainor (2012) indicated that musical training appears to modify the
brain’s auditory cortex, which processes sound. Additionally, the researchers suggested
that musical training leads to advanced levels of memorization, attention, and cognitive
development. However, music practitioners question whether those auditory and
cognitive processes are similar in academic learning. On May 5, 2009 at the Learning,
Arts, and the Brain Summit, various neuroscientists from Harvard, Princeton, and Yale
conducted a quantitative study that examined musical training as it relates to various
mathematical concepts. Although the neuroscientists emphasized that the study only
focused on relationships rather than cause and effect (as cited in Cole, 2011), the
42
researchers indicated that a strong relationship exists between prolonged musical training
and geometrical shapes. Similarly, Michael Posner (as cited in Cole, 2011) suggested that
the arts, particularly instrumental music, support continuous motivation that “provides the
cognitive benefit of strengthening executive attention networks in the brain, such as those
found in the midline and lateral frontal areas” (p.17). Additionally, the researcher
suggested that the arts programs can “help students to pay better attention in school due
to structural brain modifications developed when the students were engaged in practicing
their art forms” (as cited in Cole, 2011, p.21).
Furthermore, Francois, Chobert, Besson, and Schon (2013) indicated that musical
learning connects, develops, and refines the neurological and motor brain systems.
Posner, Rothbart, Sheese, and Kieras (2008) conducted a quantitative study that
examined arts participation influences cognitive processes through underlying
mechanism of attention, conflict resolution, and motivation. Posner et al. (2008)
theorized that:
a) there are specific brain networks for different art form; b) there is a
general interest for the arts; c) participants who display high interest for
the arts, training/support in those arts, develop high motivation; d)
motivation sustains attention; and e) high sustained motivation, while
engaging in conflict-related tasks, improve cognition. (Posner et al., 2008,
p.1)
The researchers tested their hypothesis by randomly assigning participants into a
control group, which performed a basic task, and an experimental group, in which
participants performed specific tasks under motivating conditions (a reward system). The
43
findings suggested that high levels of motivation yielded stronger improvements in tasks
performance, especially when motivation was sustained for extended periods. The
researchers indicated that the results ultimately support the idea that interest in the arts,
particularly music, allows for continuous motivation.
Hyde et al. (2009) conducted a longitudinal study that investigated the effects of
musical training on structural brain development. The study consisted of two control
groups. The first group consisted of 16 children with an average age of approximately
5.90 years old at the start of the study; these children did not receive any instrumental
music preparation during a 15-month period, but did partake in a bi-weekly group music
class in school. The second group consisted of 15 children with an average age of
approximately 6.32 years old at the start of the study; these children received private
piano instruction for 15 consecutive months. The findings suggested that children who
received private piano instruction exhibited greater behavioral improvements over 15
months on the finger motor, melody, and rhythmic tasks.
Neurological differentiations between students with prior musical training and
students with little or any musical training can be found in many parts of the brain.
Additionally, students with prior musical training tend to display higher mathematical
and phonemic abilities than students with little or any musical training (Butler & Trainor,
2012). Musacchia, Sams, Skoe, and Kraus (2007) argued that a musician’s basic sensory
mechanisms for coding visual and auditory processes might also be specialized in the
brain. The findings from their study provided empirical evidence that musicians display
stronger aural and visual brainstem reactions to speech than non-musicians. For this
44
reason, it is logical to assume that musical training may enhance phonological awareness
among students.
Music and reading. Empirical evidence suggested that a strong correlation exists
between music training and reading achievement (Cole, 2011; Michener & Fishoff, 2012;
Pane & Salmon, 2011; Piro, 2009). Pane and Salmon (2011) proclaimed that music is
interconnected with thought and aids children in drawing, talking, and reading, which are
all components of literacy development. While Waller (2010) suggested that music
teachers rarely provide opportunities for writing music, Oare and Bernstorf (2010)
suggested that music instruction enhances phonological processes that assist in
developing good readers and writers. Standley (2008) indicated that music education
curriculum enhances reading achievement among elementary students. Brian Wandell (as
cited in Cole, 2011) conducted a quantitative study to determine whether a correlation
existed between reading fluency and musical training. The study consisted of 49 children
from the ages of 7 to 12. The study indicated that the children who read rhythms and
recognized pitches would most likely demonstrate fluency when reading books or stories.
Piro and Ortiz (2009) conducted a quantitative quasi-experimental study that
examined the relationship between musical training and literacy skills amongst second
grade students. The study selected two elementary schools located in the same
geographic vicinity with similar ethnic and socioeconomic qualities. As a part of a
comprehensive instructional intervention program, one elementary school (n=46)
engaged in musical learning by studying piano formally for three consecutive years.
Conversely, children attending the control school (n=57) received no musical training
from their respective school. Both elementary schools were engaged in a comprehensive
45
balanced literacy programme that engaged children in reading, writing, speaking, and
listening. All children were tested to evaluate their literacy development at the beginning
and at the ending of a standard school year utilizing the Structure of Intellect (SOI). The
findings indicated that children who studied piano formally had significantly higher
vocabulary and phonological awareness than children with no formal musical training.
Supportively, Ho, Cheug, and Chan (2003) conducted a similar study that
examined the relationship between musical instruction and phonemic reasoning. The
study consisted of 90 males between the ages of 6 and 15 from the same geographical
location. The study suggested that the participants with prior musical training had
significantly higher phonological awareness and retention abilities than those with little
or no musical training. Additionally, a follow-up study determined that the effect was
causal. Butzlaff (2000) conducted a meta-analysis of 25 correlational studies that
examined reading achievement and music training. The sample sizes ranged from 50
students to 500,000 students. The study indicated that a strong and reliable relationship
existed between reading comprehension and music participation. While Ho, Cheug, and
Chan (2003) found that musical instruction enhances phonological awareness, the study
only involved male participants. If the researchers would have included female
participants, it may have yielded different outcomes.
Deutsch, Dougherty, Shachar, Tsang, and Wandell (2009) conducted a
longitudinal study that investigated the development of reading ability and the brain. The
study consisted of 49 participants from the ages of 7 to 12. Although questionnaires were
sent to the participant’s guardian concerning their involvement in the arts, only the
parents of 41 children responded to the survey. The amount of musical training was then
46
correlated with the participant’s assessment scores in literacy development and
phonological awareness over three consecutive years. The study suggested that a
statistically significant correlation existed between the amount of musical training and the
participants’ reading fluidity.
Music and mathematics. More than two-thirds of students in the United States
are not proficient in mathematics (Campbell, Malkus, 2011; Hemmings, Grootenboer,
Kay, 2011; National Assessment of Educational Progress, 2009). This overwhelming
evidence has prompted researchers to investigate current trends to promote awareness in
public schools.
Empirical research indicated that math and music are related in the brain from
very early in life (E.A. Geist, K. Geist, Kuznik, 2012; Linder, Powers-Costello, &
Stegelin 2011; Zenter, Eerola, 2010). Geist et al. (2012) suggested that musical elements
such as tempo, rhythm, and steady beat enhance mathematical concepts such as spatial
properties, counting, and sequencing. McLelland (2005) conducted a quantitative study
that investigated participation in music on academic achievement. The researcher
examined standardized assessment data, mathematics, and reading from multiple school
years (2001-2002 and 2003-2004) from approximately 356 fifth grade students. The
researcher identified a statistically significant difference in reading and mathematics
performance between the fifth grade music participants and non-participants.
Additionally, students who participated in instrumental music had a mean score that was
7.9191 points higher in reading and 8.590 points higher in math.
Deere (2010) conducted a mixed method study that examined the influence of
music education programs on reading and mathematics achievement. The study consisted
47
of 57 fourth grade students and 63 eighth grade students. Additionally, the study
investigated the perception of music education programs by surveying administrators,
school board members, and teachers. Quantitatively, the author analyzed the reading and
mathematics scores on the Tennessee Comprehensive Assessment Program (TCAP). The
findings suggested that 76% of the respondents agreed on the importance of music
education programs in schools. Additionally, the findings indicated that fourth grade
participants who had prior musical training scored significantly higher than participants
who had no musical training on the TCAP. Yet, eighth grade students who had prior
music training only scored significantly higher than their peers who had no musical
training on the reading section of the TCAP. Further research is needed to determine if
the relationship between music participation and academic achievement is causal.
Spelke (2009) conducted a quantitative experimental study that examined the
relationship between musical training and cognitive systems in mathematics. Two
experiments examined the participants’ mathematical ability on a district-standardized
assessment. The first experiment consisted of participants from the ages of 5 to 17 from a
Massachusetts community. All participants attended either a Saturday musical training or
an athletic training event. Both groups were examined utilizing a multiple regression.
Predictors such as musical and athletic training were found to be poor predictors for
mathematical ability. Age was found to be a more reliable predictor.
Furthermore, Spelke’s (2009) second experiment examined 61 students, ages 8 to
13, while receiving intense musical instruction from music schools in Boston to a group
of students with little or no musical background. Participants were assessed on six
different mathematical abilities, equivalent to the assessment incorporated in the first
48
experiment. A statistical analysis, two-way ANCOVA with age and verbal IQ, were
applied to determine its relationship. The study indicated that children with intense
musical instruction performed higher on all measures of geometric abilities than children
with little or no musical instruction. However, the experiment did reveal that a
statistically significant relationship did not exist with musical training and spatial ability.
Indeed, empirically researched data supports the notion that music improves
mathematical achievement. However, Cox and Stephens (2006) conducted a quantitative
study that compared high school students who acquired music credits to those who had
none. The researcher concluded that no statistically significant difference was found in
their mean math grade averages.
With ever-increasing demands on academic performance in schools, researchers
have examined the value of music education programs. Although Spelke (2009), Deere
(2010), McLelland (2005), and Deutsch, Dougherty, Shachar, Tsang, and Wandell (2009)
found positive correlations between students who participated in music programs and
higher academic achievement, Cox and Stephens (2006) found no differences between
music participation and academic achievement. Interestingly, most of the empirical
evidence that displayed strong correlations between music participation and academic
achievement consisted of samples from a diverse population which exhibited a higher
generalizability. However, the sample of Cox and Stephens (2006) was limited to
predominantly one ethnic population and socioeconomic status. In addition, the study
consisted of primarily female participants, which limits the scope of the study.
Musical aptitude and academic achievement. Compelling empirical evidence
has examined musical aptitude (Gordon, 2007; Karma, 2007; Rutkowski, 1996; Ukkola-
49
Vuoti et al., 2013) and academic achievement (Cheema & Galluzzo; 2013; Maltese, Tai,
& Xitao, 2012; Musa, 2013; Rowe, Miller, Ebenstein, & Thompson, 2012; Schutz,
Simon, & Musgrave, 2013; Stanley & Stanley, 2011; Talley & Scherer, 2013; Toldson,
2012; Young, Hyuck, Sunyoung, & You Kyung, 2012). However, few studies have
examined the relationship between musical aptitude and academic achievement
(Holsomback; 2002; 2004; Kuhlman, 2005; Rubinson, 2010).
Holsomback (2001) conducted a quantitative correlational study that examined
the relationship between musical aptitude and various academic achievement measures of
beginner instrumental music students. The standardized academic performance
assessments utilized in the study were the composite performance levels of the reading
and mathematics sections of the Iowa Test of Basic Skills Test (ITBS), The Otis-Lemon
School Abilities Test (OLSAT), the Metropolitan Achievement Test (MAT), and the
Texas Assessment of Academic Skills Test (TAAS). The musical aptitude assessment
utilized in the study was the Selmer Music Guidance Survey. Since no research was
found in the literature with regard to the Selmer Music Guidance Survey, the reliability
coefficient was established for the composite scores.
The study consisted of 104 sixth grade band students in an east Texas school
district. The study employed a purposive sampling technique. Students were administered
the Selmer Music Guidance Survey to serve as a guide to instrument assignment and
performance indicator of the individual needs of the selected band students at the
beginning of the school year. Additionally, students were interviewed for physical
characteristics to identify strengths and weaknesses that may enhance instrumental music
training. Although the researcher hypothesized that no relationship exists between the
50
variables, the study revealed that a strong statistical relationship existed between musical
aptitude and academic achievement. Additionally, the study indicated that further
research should be conducted to investigate the relationship between musical aptitude and
other standardized assessments.
Similarly, Rubinson (2010) conducted a quantitative correlational study that
investigated the relationship between musical aptitude and the developing reading
abilities of kindergarten students. The researcher employed the tonal and rhythmic
components of the PMMA to determine the amount of musical aptitude of kindergarten
students. Reading achievement was measured by various subtests of Dynamic Indicators
of Basic Early Literacy Skills (DIBELS), a standardized assessment of early literacy
development that evaluates the alphabetic abilities, and phonological awareness of
kindergarten students. The study consisted of 80 kindergarten students from an
elementary school in central Connecticut. The convenience sample consisted of
kindergarten students in the Fall Semester of the 2008-2009 school year.
The study employed both bivariate and multivariate correlational statistics to
determine the relationship between musical aptitude and reading achievement scores.
Multiple regression analyses, yielding multiple correlation coefficients (R), were
employed to investigate the correlation between phonological awareness and tonal and
rhythmic aptitude. The findings suggested that musical aptitude is reliably and strongly
associated with phonological awareness and early reading development of kindergarten
students. Additionally, further research is recommended to determine whether the
relationships established in this study are causal.
51
Peynircioglu, Durgunoglu, and Uney-Kusefoglu (2002) conducted a quantitative
study that investigated the relationship between musical aptitude and phonological
awareness among pre-school children. The study consisted of 31 Turkish children and 29
American children ranging from 4 to 6 years of age. In experiment one, Turkish children,
and in experiment two, American children, completed multiple phoneme tasks with
words in their respective languages and with pseudo-words. Additionally, the children
completed a tone deletion task by listening to snippets of melodies. Due to limited
reading skills, both assessments were evaluated largely on pure auditory abilities.
The reading assessment consisted of reading simple phrases and then sequentially
simpler words. Participants who were capable of identifying even the simplest words
were excluded from the study. The musical aptitude assessment focused on pitch and
rhythm. The assessment consisted of singing back various melodies including major and
minor intervals and reproducing several rhythms varying in length and complexity. The
participants’ responses were recorded and two independent judges with expertise in
music evaluated each participant’s musical aptitude. In both experiments, the findings
concluded that a statistically significant correlation existed between musical aptitude and
overall phonological awareness. Particularly, children who demonstrated high musical
aptitude performed better on the verbal phonological awareness than children with low
musical aptitude (Peynircioglu, Durgunoglu, & Uney-Kusefoglu, 2002).
Empirical studies suggested that a positive correlation exists between musical
aptitude and academic achievement (Holsomback, 2001; Peynircioglu, Durgunoglu, &
Uney-Kusefoglu, 2002; Rubinson, 2010). Each study employed various academic and
52
musical aptitude assessments relevant to their population. Additionally, each study
contributed vital methodological information that influenced this study.
Methodology. The majority of the methodology from the research described
throughout this literature review section has been quantitative. Holsomback (2001)
employed a quantitative methodology followed with a correlational design. The study
investigated the relationship between musical aptitude, as measured by the Selmer Music
Guidance Survey, and academic achievement, as measured by the composite scores of the
ITBS, OLSAT, MAT, and TAAS. The study consisted of 104 sixth grade band students.
The data from the study was analyzed with the use of four Pearson r correlation
coefficients to determine the linear relationship between both variables. The study
indicated that moderate relationship existed between musical aptitude and academic
achievement (correlations ranging from .393 to .472).The study could have demonstrated
a stronger analysis by analyzing both composite and sub dimensions of all variables
presented in the study. Additionally, a longitudinal study may have revealed different or
similar relationship patterns, but this study was limited to the 1999-2000 school year.
Rubinson (2010) employed a quantitative study that examined the relationship
between the musical aptitude and phonological awareness of kindergarten students. The
study consisted of 62 kindergarten students. The data from the study was analyzed with
the use of a Pearson r correlation coefficient to determine the linear relationship between
both variables. The study indicated that moderate-sized correlation existed between
musical aptitude and academic achievement (correlations ranging from .27 to .38).The
study produced robust analyses by examining composite and sub dimensions
performance levels of both variables in the study. However, the study was limited by a
53
small sample of participants from only one public elementary school. Additionally, the
research findings may not be applicable to different student populations, geographical
locations, and grade levels. If the study consisted of sample populations from different
ethnic groups and socioeconomic status, the study may have benefited schools with
diverse populations. As correlational studies do not investigate cause and effect, causal
conclusions cannot be drawn from the study (Yin, 2009).
Peynircioglu, Durgunoglu, and Uney-Kusefoglu (2002) employed a quantitative
methodology with a correlational design to determine the relationship between musical
aptitude and phonological awareness of preschool children. The study consisted of 31
Turkish children and 29 American children ranging from 4 to 6 years of age. The study
indicated that a large correlation (ranging from .45 to .52) existed between musical
aptitude and phonological awareness. The study produced strong analyses by correlating
sub-dimensions of phonemes, initial consonant, and initial vowel to musical aptitude.
However, the study did not utilize a standard musical aptitude assessment. Rather, two
independent judges with expertise in music evaluated each participant’s musical aptitude.
This ultimately questions the consistency of the scoring; thus, bringing into question the
validity of the study.
The methodological strengths and weaknesses presented in this section provided
important information regarding the relationship between musical aptitude and academic
achievement. Each researcher (Holsomback, 2001; Peynircioglu, Durgunoglu, and Uney-
Kusefoglu, 2002; Rubinson, 2010) employed a quantitative methodology using a
correlational design to determine the extent of the relationship between both variables.
However, each study employed different assessments to measure musical aptitude and
54
academic achievement. Ultimately, their research identified the gaps in the literature and
allowed this study to analyze the relationship between musical aptitude and the composite
and sub-categorical performance levels of the STAAR among beginning band students.
Instrumentation. For the purpose of this research study, two sources of data were
used. Both sources of data have been successfully used in other empirical studies to
measure musical aptitude and academic achievement. Many music educators employ
tests such as Intermediate Measures of Musical Audiation (1986), Kwalwasser-Dykema
Music Tests (1930), and The Seashore Measures of Musical Talents (1919) to measure
musical aptitude. Yet, Gordon’s Intermediate Measures of Musical Audiation (1986) is
the only brief, longitudinally valid music aptitude test for Grades 1 through 6. In addition,
several empirical studies (Brophy, 2005; Hodges & O’Connell, 2005; Hornbach &
Taggart, 2005; Tomei, 2010) have employed the IMMA assessment to measure the level
of musical aptitude. Based on this information, the IMMA was the most appropriate
assessment to measure musical aptitude among middle school band students.
Developed in 2011, Texas public schools have administered the STAAR to
measure academic achievement in students. While prior academic assessments failed to
provide sufficient academic readiness for Texas students, the STAAR was designed to
enhance the rigidity of the previous assessment. This standardized academic assessment
meets the criteria mandated by the NCLB and is the most current and valid academic
standardized assessment for the state of Texas (TEA, 2012). Although few studies
(Johnson, Johnson, & Johnson, 2012; Johnson, Wilson, & Rossi, 2013) have utilized the
STAAR to measure academic achievement, it is the most current academic standardized
55
assessment in Texas. Based on this information, the STAAR was the most appropriate
assessment to measure academic achievement among middle school students in Texas.
Several validation studies were conducted on both, the IMMA and STAAR
instruments and demonstrated acceptable internal consistency across the studies and
yielded an acceptable split-half reliability estimate and acceptable test-retest reliability
coefficient alpha ranging from .76 to .91. Both of the scales’ reliabilities were generally
high, exceeding the conventional standard of .70 for internal consistency recommended
in the literature (Fraenkel, Wallen, and Hyun, 2012). Utilizing the data from Gordon
(1986) and Texas Education Agency (2014) validity testing, both instruments were valid
and acceptable for use in this study.
Summary
This chapter provided a broad overview of the existing literature related to the
variables in the study. Additionally, this study was formed primarily by the theoretical
foundations of Gordon’s music learning theory and Gardner’s theory of multiple
intelligences. This research advanced these theories by providing empirical data on
musical aptitude and academic achievement. The research questions in this study aligned
with multiple theoretical models as they provided a rationale on the relationship between
musical aptitude and academic achievement. Each theoretical model provided the
foundation for this study. Five thematic ideas discussed in the literature review were (a)
overview of musical aptitude, (b) academic achievement, (c) music in relation to
academic achievement, (d) musical aptitude and academic achievement, and (e)
methodological strengths and weakness from similar studies. These themes are related to
the focus of this study as they contribute to the overall understanding of the research
56
topic. The study addressed if, and to what degree, a correlation existed between musical
aptitude and the composite and sub-categorical performance levels of the STAAR among
sixth grade beginning band students.
A quantitative approach followed with a correlational design was chosen for the
purpose of collecting and analyzing numerical data regarding the relationship between
musical aptitude and academic achievement of sixth grade beginning band students.
Many of the previous studies on this topic employed quantitative methods and provided a
basis for the continued use of this approach (Holsomback, 2002; 2004; Rubinson, 2010;
Tomei, 2010). Additionally, these studies have utilized the IMMA assessment in order to
measure the level of musical aptitude.
Empirical evidence has identified strong relationships between musical learning,
musical aptitude and academic achievement (Deutsch et al., 2009; Gadberry, 2010; Ho et
al., 2003; Holsomback; 2002; Holsomback, 2004; Kuhlman, 2005; Piro & Ortiz, 2009
Rubinson, 2010). While it is reasonable to believe that a high quality music program
intended to increase musical aptitude may also increase standardized assessments, a gap
in the literature existed concerning the relationship between musical aptitude and the
composite and sub-categorical performance levels of the STAAR among beginning band
students. Additionally, future research should be conducted to determine if the
relationship is causal.
Previous studies suggested that a strong and reliable relationship existed between
musical aptitude and academic achievement (Holsomback; 2002; 2004; Rubinson, 2010;
Tomei, 2010). Additionally, previous studies identified a gap in the literature and allowed
this study to analyze the relationship between musical aptitude and the composite and
57
sub-categorical performance levels of the STAAR among beginning band students
(Holsomback, 2002; 2004). The study evaluated the gaps in the literature and added to
the existing research of musical aptitude and academic achievement. Chapter 3 will
provide a detailed discussion of the methodology that will be employed in the study.
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Chapter 3: Methodology
Introduction
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the level of musical aptitude and the reading and
mathematics scores of the STAAR among beginning band students. Specifically, this
quantitative correlational study examined the relationship between musical aptitude, as
measured by the Intermediate Measures of Musical Audiation (IMMA), and the reading
and mathematics performance levels of the STAAR. Previous empirical research has
examined the relationship between musical aptitude and various academic assessments
(Cavangh, 2009; Holsomback, 2004; Rubinson, 2010). Yet, a gap existed in the literature
concerning the relationship between musical aptitude, as measured by the IMMA, and the
current standardized assessment, as measured by the reading and mathematics sections of
the STAAR (Holsomback, 2002).
Previous research has defined a gap in the literature and justified the need for this
study. The findings of this study advanced the understanding of the relationship between
musical aptitude and academic achievement among sixth grade beginning band students.
In addition, the findings may assist teachers, administrators, and educational policy
makers in determining the relationship of music instruction to reading and mathematics
achievement of middle school students.
This chapter focused on the quantitative research method to examine the
relationship between the level of musical aptitude and the level of academic achievement
among middle school students. This chapter included a brief description of the problem,
research questions and hypotheses, population and sampling procedures, instrumentation,
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and data collection and analysis procedures, validity, reliability, ethical considerations,
and potential limitations.
Statement of the Problem
It was not known if, and to what degree, a correlation existed between the level of
musical aptitude assessed on the IMMA and the level of reading and mathematics scores
on the STAAR among beginning band students. Empirical research has examined the
relationship between musical aptitude and various academic assessments (Cavangh,
2009; Holsomback, 2004). E. A. Geist, K. Geist, and Kuznik (2012) suggested that
musical elements such as tempo, rhythm, and steady beat enhance mathematical concepts
such as spatial properties, counting, and sequencing. Supportively, Oare and Bernstorf
(2010) suggested that music instruction enhances phonological processes that assist in
developing good readers and writers.
Although empirical evidence correlates musical elements to academic
achievement, minimum evidence has been presented on the relationship between musical
aptitude and the reading and mathematics sections of the STAAR. Holsomback (2002)
indicated that a strong statistical relationship existed between musical aptitude and
academic achievement. Yet, he suggested that further research should examine the
relationship between musical aptitude and other standardized assessments. For this
reason, it was necessary to determine whether a relationship existed between musical
aptitude and the current academic standardized assessment in Texas.
Understanding the relationship between musical aptitude and academic
achievement was essential in finding effective ways to utilize music instruction to
enhance reading and mathematics achievement of middle school students. In addition,
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this study contributed to solving the problem by providing a quantitative analysis on the
relationship between the level of musical aptitude and the level of reading and
mathematics scores on the STAAR among beginning band students in Texas. Therefore,
this study investigated the composite and sub-categorical performance levels of the
IMMA and the STAAR. The study consisted of a middle school band program from a
rural school district in northeast Texas.
Research Questions and Hypotheses
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the level of musical aptitude and the reading and
mathematics scores on the STAAR among beginning band students in Texas. This
research was framed in the theoretical context that learning in general, and more
specifically musical learning, is a person's ability to understand and process sound,
rhythm, patterns in sound, relationships between sounds, and process rhymes and other
auditory information. In order to understand various relationships between musical
aptitude and academic achievement among beginning band students, appropriate research
questions were essential. In addition, the research questions and hypotheses related to the
problem statement by examining the relationship between the level of musical aptitude
and the reading and mathematics scores on the STAAR. The following research questions
and hypotheses guided this study:
R1: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics scores of the STAAR
among beginning band students?
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H1: A correlation exists between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics section of the STAAR
among beginning band students.
H01: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the mathematics section
STAAR among beginning band students.
R2: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading scores of the STAAR among
beginning band students?
H2: A correlation does exist between the level of musical aptitude and the composite
and sub-categorical performance levels of the reading section of the STAAR
among beginning band students.
H02: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
In a correlational study, there are no true independent and dependent variables as
the design is non-experimental. In this study, variable 1 was measured by the IMMA
assessment. The IMMA is a 20-minute musical aptitude assessment that measures the
potential musical ability in an individual (Gordon, 2003). Variable 2 was measured by the
STAAR. The STAAR is a sequence of state mandated standardized assessments currently
used in Texas public schools to evaluate student achievement and knowledge in each
grade level. The STAAR includes annual assessments for grades three to eight in reading
and mathematics; assessments in writing at grades four and seven; in science at grades
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five and eight and in social studies at grade eight. In addition, the STAAR includes end-
of-course assessments for English I, English II, Algebra I, Biology, and U.S. History
(Texas Education Agency, 2011). Both the IMMA and the STAAR are secondary data.
Research Methodology
A quantitative methodology was employed to determine if, and to what degree, a
correlation existed between musical aptitude and the STAAR among beginning band
students. Quantitative studies examine empirical theories utilizing numerical variables
designed to represent the theoretical concepts so that mathematical relationships can be
revealed (Yin, 2009). Fraenkel, Wallen, and Hyun (2012) suggested that quantitative
methodology comprises of explicit hypotheses. Additionally, the quantitative approach
utilizes objective instruments such as multiple choice standardized assessments,
questionnaires, personality scales, and aptitude assessments. The rationale of selecting a
quantitative methodology was to develop an objective way of testing theories by
examining relationships between variables that can be measured and analyzed using
statistical procedures, resulting in numerical results (Fraenkel, Wallen, & Hyun, 2012).
Prior empirical research has utilized a quantitative methodology to determine the
relationship between musical aptitude and standardized assessments (Holsomback, 2001;
Kuhlman, 2005; Rubinson, 2010). Quantitative research involves testing objective
theories by examining the relationships among measurable variables so the researcher can
utilize statistical procedures to analyze the numerical data. In addition, quantitative
research involves an empirical analysis of data collected from a sample of individuals
from specific populations to make a generalizable observation for the whole based on the
measure of relationships (Fraenkel, Wallen, & Hyun, 2012). Since this study sought to
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investigate the relationship between musical aptitude and academic achievement, a
quantitative methodology was most appropriate.
Qualitative research expands the range of knowledge and understanding of the
world beyond the researchers themselves. It often helps one see why a situation is the
way it is, rather than just presenting a phenomenon (Yin, 2009). Since qualitative
research attempts to investigate naturally occurring phenomena in all their complexity
(Yin, 2009), a qualitative methodology was inappropriate for the research questions and
hypotheses. In a mixed methodology, qualitative and quantitative methods are used in
tandem to strengthen the study. Since this study involved the analysis of exclusively
secondary interval and ratio data and there was no interaction between the student and the
researcher, a mixed methodology was inappropriate for the current study. Proper
selection of methodology is imperative in comprehending and interpreting the results
based on the research questions and hypotheses (Fraenkel, Wallen, & Hyun, 2012).
The study utilized the results from the 2013-2014 IMMA assessment and the
results from the 2013-2014 reading and mathematics sections of the STAAR. The study
evaluated the results to determine if a statistically significant relationship existed between
musical aptitude and the reading and mathematics sections of the STAAR among
beginning band students. The study consisted of a middle school band program from a
rural school district in northeast Texas. The independent variable, musical aptitude, was
measured by IMMA assessment. The dependent variable, academic achievement, was
measured by the results from the reading and mathematics sections of the STAAR.
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Research Design
A correlational research design was utilized for this study. Previous empirical
research has utilized a correlational research design to determine if a correlation exists
between musical aptitude and academic achievement (Holsomback, 2001; 2002;
Rubinson, 2010). Holsomback (2001; 2002) employed a quantitative correlational design
that examined the relationship between the independent variable, musical aptitude, and
the dependent variable, academic achievement, of sixth grade band students. Both studies
found strong correlations between musical aptitude and academic achievement. However,
Rubinson (2010) employed a correlational research design that examined the musical
aptitude and phonemic awareness of kindergarten students. The results revealed a strong
relationship existed between musical aptitude and phonemic awareness. As this
dissertation study did not “seek to determine reasons or causes for preexisting differences
in groups of individuals” (Fraenkel, Wallen, & Hyun, 2012, p. 365), a causal-comparative
research design was inappropriate for this study.
In experimental research, variables are manipulated, and the effects of this
manipulation are measured upon the dependent variable. Additionally, in experimental
research a treatment is deliberately imposed on a group of objects or participants
(Fraenkel, Wallen, Hyun, 2012). As this study did not attempt to manipulate the
variables, an experimental research design was inappropriate for the study.
According to Fraenkel, Wallen, and Hyun (2012), a correlational research design
“seeks to investigate the extent to which one or more relationships of some type exist”
(p.11). Furthermore, a correlational research design is useful to researchers who are
interested in determining to what degree two or more variables are related; however, a
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correlational design does not determine causation. Therefore, a correlational study was
the most appropriate design to identify a strong and reliable relationship between musical
aptitude and the STAAR among beginning band students.
Examining the correlation between one independent variable and one dependent
variable will simplify the data analysis process (Yin, 2009). Additionally, the researcher
correlated the sub dimensions of each variable in order to obtain a more robust analysis.
The data from the study was analyzed with the use of a Pearson r correlation coefficient,
which provided a numerical summary of the data. According to Bluman (2012), the
correlation coefficient usually determines the strength of the relationships between
variables and the direction of that relationship. The range of the coefficient lies between -
1 and +1; the closer the correlation coefficient is to +1, the stronger the positive
relationship. Meanwhile, the closer the correlation coefficient is to -1, the stronger the
negative relationship. A positive relationship means that as one value increases, so does
the other; a negative relationship means that as one value increases the other decreases.
Furthermore, the purpose of a quantitative study usually predicts, explains, or controls
phenomena through a precise process of collecting numeric data (Fraenkel, Wallen, &
Hyun, 2012). As this study investigated the linear relationship between variables, the use
of a Pearson r correlation coefficient was most appropriate.
The researcher utilized archival data, results from the 2013-2014 IMMA
assessment, and the results from the 2013-2014 reading and mathematics sections of the
STAAR. The researcher evaluated the results to determine if a statistically significant
relationship existed between musical aptitude and the reading and mathematics sections
of the STAAR among beginning band students. The study consisted of 65 sixth grade
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students from a rural school district in northeast Texas. The independent variable,
musical aptitude, was measured by IMMA assessment. The dependent variable, academic
achievement, was measured by the results from the reading and mathematics sections of
the STAAR.
Population and Sample Selection
The setting for this study was in a small rural east Texas public school district.
According to the 2013-2014 district’s annual report, the enrollment consisted of
approximately 1,100 students in grades kindergarten through 12, where 10% were
African American students, 81% were Caucasian American students, 9% were Hispanic
American students, and 71% of the total students were categorized as economically
disadvantaged. The district comprises three schools, one elementary school, one middle
school, and one high school. The 2013-2014 sixth grade class consisted of 65 students,
who were all required to enroll in a beginning band course and take a musical aptitude
assessment. Therefore, the target population (N = 65) consisted of sixth grade band
students enrolled in the school district. An a priori power statistical analysis was
conducted using G* Power 3.0.10. The analysis suggested that a minimum of 85 students
are required for this study. The researcher set statistical significance as α = .05 and power
at .8 (β = .8) for this study. Since this study did not meet minimum sample size, the study
was identified as significant only if a large correlation existed. After the data analysis
process, the researcher conducted a post hoc analysis to identify patterns not specified in
an a priori analysis. Post hoc power for all tests were calculated at greater than 0.8 (See
Appendix E). As all students were required to take both IMMA and STAAR assessments,
a randomization sample selection was not applicable to this study. Total population
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sampling was appropriate for this study in order to identify a sample with certain
characteristics relevant to the population of interest (Fraenkel, Wallen, & Hyun, 2012).
Consent to conduct this study was attained from an east Texas school district and
Grand Canyon University Institutional Review Board. Student identity and
confidentiality was preserved through an alpha numeric coding system so that the
identification of participants was confidential. The data from the IMMA and STAAR
assessments was obtained from archival school records located in the counselor’s office
for the school year. The data from both the IMMA and STAAR assessments was
provided within the data spreadsheet. Identifiers such as student names, identification
numbers, dates of birth, and addresses was removed by the counselor and assigned a
numeric code to ensure anonymity. Then, the raw data was imported into SPSS for
analysis.
Instrumentation
For the purpose of this research study, two sources of data were used. Both
sources of data were successfully used in other empirical studies to measure musical
aptitude and academic achievement. IMMA (Gordon, 1986) was utilized to determine
overall musical aptitude. The STAAR (Texas Education Agency, 2014) was utilized to
evaluate the reading and mathematics achievements of study participants. The IMMA
was chosen because it is a well-established tool for measuring musical aptitude. The
STAAR was chosen because it is the most current assessment for evaluating academic
achievement in Texas. Both the STAAR (shown in Table 1) and the IMMA (shown in
Table 2) assessments have adequate reliability and validity, the composite alpha
coefficient ranging from .889 to .92 and yielding continuous data.
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IMMA. The IMMA, according to Gordon (2003), is the only brief longitudinally
valid musical aptitude assessment for kindergarten through sixth grade students. The
IMMA was designed to measure developmental musical aptitude. The IMMA produces
three scores: Tonal, Rhythmic, and Composite. The IMMA assessment is divided into
two sub sections, Tonal and Rhythm, and each section takes 20 to 25 minutes to
administer. As specified in the test manual procedures, each subtest is administered on
different days with the Tonal subtest given first. Each subtest consists of approximately
40 pairs of short tape-recorded tonal or rhythmic sequential aural excerpts. The tonal
excerpts are played without rhythmic patterns. The duration of pitches are equal lengths.
The rhythmic excerpts are played on only one pitch. Each excerpt both Tonal and
Rhythmic are played twice. Participants are asked to distinguish by indicating if the first
and second excerpts are the same or different. A composite score is developed once the
results from both the Tonal and Rhythmic subtests are combined and yield continuous
data.
STAAR. The STAAR is a sequence of state mandated standardized assessments
currently used in Texas public schools to evaluate student achievement in each grade
level (TEA, 2009). Specifically, the STAAR includes annual assessments for grades three
through eight in reading and mathematics. The students will have four hours to complete
each assessment. Students will begin the assessment after instructions from their test
administrator. The four hour time limit stops only for a 30 minute lunch break. Both the
reading and mathematics sections of the STAAR for grade six consist of approximately
45 multiple choice questions and are identified as continuous data.
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Validity
According to Freankel, Wallen, Hyun (2012), validity is the process of evaluating
whether an instrument is designed and successful at measuring what it is designed to
measure. Validity coefficients are considered statistically significant at .05 or .01 levels.
IMMA validity. The IMMA instrument has initial construct validity, longitudinal
validity, congruent validity, have at least some validity and some are more valid for
certain purposes than others. Both subjective considerations and objective evidence are
presented in support of validity for the IMMA.
Many research studies have been conducted to ensure the validity of the IMMA.
Gordon (1986) conducted several validation studies and included different samples.
Content validity was established through taxonomic research. Longitudinal predictive
validity was undertaken at a private academy for boys in Chestnut Hill, Pennsylvania.
The longitudinal predictive validity coefficients ranged from .64 to .90. This information
provided objective evidence for establishing concurrent validity. Gordon (1986)
established congruent validity by correlating two tests that were designed to measure the
same factor. The author examined the correlations between the Primary Measures of
Musical Audiation (PMMA) and the Musical Aptitude Profile (MAP). The correlation
coefficients ranged from .47 to .71. In addition, the IMMA was found to have congruent
validity with the PMMA. The correlation between the composite score of IMMA and the
composite score of PMMA is .61.
STAAR validity. The results of the STAAR assessment are used to guide
educational planning related to the knowledge and skills that students are acquiring in
each academic content area. Validity evidence from the STAAR originated from a variety
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of sources. External and internal validity was collected to inform standard settings and
support interpretations of performance standards. Results on each assessment are
interconnected across grades to performance on similar assessments. One method by
which Texas provides validity evidence for the STAAR assessment is by examining the
relationship between the performance on the STAAR and the performance on other
standardized assessments. The process is commonly referred to as criterion-related
validity. Several validation studies were conducted and demonstrated acceptable internal
consistency across the studies and yielded an acceptable split-half reliability estimate and
acceptable test-retest reliability coefficient alpha of .76 (Texas Education Agency, 2014).
Utilizing the data from Gordon (1986) and Texas Education Agency (2014) validity
testing, both instruments were valid and acceptable for use in the study.
Reliability
The use of a reliable instrument is essential to a strong research study (Fraenkel,
Wallen, Hyun, 2012). In addition, the stability to test results is best interpreted through
reliability information. Subtle and extraneous factors that contribute to bias and
unreliability in human judgment have no effect on objective test scores. The less test
results are affected by individual physical and psychological deviations and by
distractions within and outside of the classroom, the more reliable the test (Gordon,
2003). Test reliability varies with type, content, and length. However, reliability
coefficients generally range between .00 and .95 (Frankel, Wallen, & Hyun, 2012).
IMMA reliability. Each reliability coefficient for the IMMA (summarized in
Table 1) is an index of the stability of the test scores. However, the split-halves
coefficient derived from only one administration of each test is more influenced by the
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homogeneity of test content, and the test-retest coefficient is more influenced by physical
and psychological changes in the individual and by different environmental conditions.
Thus, test- retest coefficients are typically lower than split-halves coefficients for a given
test. Test-retest reliabilities for all grade levels for Tonal, Rhythm, and Composite scores
ranged from .76 to .80, .80 to .88, and .80 to .91 (Gordon, 1986), which exceeds the
conventional standard of .70 recommended in the literature (Fraenkel, Wallen, & Hyun,
2012).
Table 1
Intermediate Measures of Music Audiation Reliabilities, Standard Errors of
Measurement, and Standard Errors of a Difference
Tonal Rhythm Composite Standard error
of a difference Reliability Reliability Reliability
Grade 1-4
2.5
Split-Halves .76 .70 .80
Test-Retest with
Raw Scores .88 .84 .91
Test-retest with
Criterion Scores .78 .76 .81
Standard Error of
Measurement 1.8 1.7 2.4
Note: Adopted from “The primary measures of music audiation and the intermediate
measures of music audiation” by E. E. Gordon, 2010, p.12. GIA Publications Manual.
STAAR reliability. TEA utilized two types of internal consistency measures:
Kuder-Richardson 20 (KR20) 33 was used for tests with only multiple-choice items and
the stratified coefficient alpha was employed for tests containing a mixture of multiple-
choice and constructed-response items. According to TEA, the STAAR reading and
mathematics assessments (summarized in Table 2) have a stratified alpha reliability
ranging from 0.87 to 0.903, showing a high level of internal consistency among the
survey items. Both of the scales’ reliabilities were generally high, exceeding the
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conventional standard of .70 for internal consistency recommended in the literature
(Fraenkel, Wallen, & Hyun, 2012).
Table 2
STAAR Grade 6 Total Group Descriptive Data
Subject Score
Points N Mean SD Alpha* SEM
Mean P-
Value**
Reading 40 327918 25.9 8.02 0.897 2.647 64.967
Mathematics 46 337288 30.05 9.39 0.903 2.83 65.34
SD = standard deviation
N represents population
SEM = Structural Equation Modeling
* Stratified Alpha Reliability computed for tests/reporting categories involving short-answer and/or
essay questions, KR-20 reliability computed for all others
** Multiple-choice and gridded items (if applicable) only
Data Collection Procedures
According to Frankel and Wallen (2012), quantitative research is prevalent in
developing procedures relating to the comparison of variables, groups, or relating factors
about individuals or groups in experiments through correlational studies and surveys.
Collecting data and analyzing numbers that measure distinct attributes of individuals and
groups is a trend that is prevalent in today’s studies (Frankel, Wallen, & Hyun, 2012).
The data collection procedures consisted of multiple steps to ensure the ethical validity of
data collection. First, permission was obtained from the superintendent at a northeast
Texas school district. This step involved obtaining a letter of authorization that validated
the ability to conduct research within the confines of the school district (see Appendix C).
After obtaining the letter of authorization from the superintendent, an Institutional
Review Board (IRB) application was submitted to Grand Canyon University (see
Appendix G). Once approval was granted, the researcher retrieved archival performance
73
data of the 2013-2014 STAAR and IMMA from the counselor’s office. Identifiers such as
student names, identification numbers, dates of birth, and addresses were removed by the
school district and assigned an alphabet to minimize a breach of confidentiality.
The researcher assumed that proper data collection methods were taken during
both the IMMA and STAAR assessments. The IMMA assessment packet included a
paper that allowed the test administrator to run the students’ answer documents through a
photocopier, which would reveal the correct answers marked with an elongated black
oval. The test administrator then counted the number of correct responses by indicating
which student-circled answers corresponded with the black ovals. The copy-machine
method was used in grading the answer sheets in this study because of its greater
efficiency and accuracy.
Unlike the IMMA data collection procedures, the STAAR data collection
procedures required state-mandated protocols to minimize a breach of security and to
ensure the validity of the data. The test administrator began by issuing each student a
sealed test booklet and a corresponding answer document. Next, the test administrator
instructed the students to break the seal of their test booklet using their pencils. Then, the
test administrator read the instructions and directed students to begin the test. After the
four-hour time limit expired, the test administrator collected all the test booklets and
answer documents. Once collected, the test administrator reported all testing materials to
the district testing coordinator to be mailed to the state for scoring. The state assessment
division loaded each answer document into the Texas Student Data System (TSDS) to
generate the test results. Once the test results were generated, the state assessment
division notified the school district and mailed each student’s performance data.
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Once the archived data of the IMMA and STAAR was retrieved from the
counselor, it remained in the possession of the researcher and kept in a lock box located
in the counselor’s office for three years. After three years, the researcher will shred and
dispose all documents (including electronic data) in accordance to current research
standards. Data collected was exported into the Statistical Package for Social Science
(SPSS) 23.0 software.
Data Analysis Procedures
The raw data was provided by the junior high counselor and was organized and
prepared for descriptive analysis in several ways. First, the data collected from the
IMMA and the STAAR was coded utilizing an alpha numeric coding system to minimize
a breach of confidentiality. The data from both the IMMA and STAAR assessments was
provided within the data spreadsheet. Both descriptive and inferential statistical analyses
were performed. Data screening was performed to ensure data accuracy and to confirm
the adequacy of the planned statistical test. Descriptive statistics included first testing for
normal distribution by examining the modes, means, and median for the musical aptitude
variables (from the IMMA) and academic achievement variables (from the STAAR).
Then, scatterplots was employed to identify possible outliers. According to Fraenkel,
Wallen, and Hyun (2012) scatterplots are “pictorial representations of the relationship
between two quantitative variables” (p.201). The assumption of multicollinearity was
tested by calculating correlations between predictor variables and collinearity statistics
(tolerance and variance inflation factor). In addition, a homoscedasticity analysis was
conducted to determine whether a regression model's ability to predict a variable is
consistent across all values of that variable (Fraenkel, Wallen, and Hyun, 2012).
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Two-tailed tests and an alpha level of .05 were used for all inferential statistical
tests. This means the probability of obtaining such an outcome is only five times (or less)
in 100 (Frankel, Wallen, & Hyun, 2012). The first research question of this study was: Is
there a correlation between the level of musical aptitude and the composite and sub-
categorical performance levels of the mathematics scores on the STAAR among
beginning band students? The null hypothesis to be tested is:
H01: A correlation does not exist between musical aptitude and the composite and
sub-categorical performance levels of the mathematics section of the STAAR
among beginning band students.
To test this null hypothesis, Pearson correlations were computed. Pearson
correlations are used to measure the degree of linear relationship between two variables.
The Pearson product-moment correlation coefficient analysis is parametric in nature and
requires a population with a normal distribution. When the data for both variables is
expressed in a quantifiable method, the Pearson r is the appropriate correlation
coefficient to utilize (Fraenkel, Wallen, & Hyun, 2012). Additionally, the Pearson
product-moment coefficient establishes the possibility of relationship without
determining the relationship is causation. This statistical technique measures the strength
of a relationship between two variables within a sample.
For the first null hypothesis, one Pearson correlation was computed between the
Mathematics score from the STAAR and the composite musical aptitude scale from the
IMMA. If any of the correlations were statistically significant, the null hypothesis was
rejected.
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The second research question was: Is there a correlation between the level of
musical aptitude and the composite and sub-categorical performance levels of the reading
scores on the STAAR among beginning band students? The corresponding null
hypothesis was:
H02: A correlation does not exist between musical aptitude and the composite and
sub-categorical performance levels of the reading section of the STAAR among
beginning band students.
This null hypothesis was tested using one additional Pearson correlation. For this
null hypothesis, the correlation between STAAR Reading test scores and scores on the
composite musical aptitude scale were computed. If any of the correlations were
statistically significant, the null hypothesis was rejected.
Ethical Considerations
Confidentiality and privacy are essential in any quality research study (Yin,
2009). This study was conducted within all the ethical considerations of the Belmont
Principles (Grand Canyon University, 2013). The participants’ standardized test scores
are protected under the Family Educational Rights and Privacy Act (FERPA), which is
intended to protect the privacy and confidentiality of student’s educational records. Grand
Canyon University Institutional Review Board (IRB) approval was required prior to the
data collection process. Once IRB approval was granted, the researcher retrieved the
participants’ standardized assessment data from the school district. In addition, site
authorization was obtained in writing from the school district.
The researcher obtained each student’s performance data. The counselor, who
managed the data and replaced with numeric IDs to ensure anonymity, removed
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identifiers such as student names, identification numbers, dates of birth, and addresses.
Since the sample consisted of students from a vulnerable population, all information
regarding the participants remained in the possession of the researcher and kept in a lock
box located in the researcher’s office for three years to protect the confidentiality of the
students. After three years, the researcher will shred and dispose all documents, including
electronic data, in accordance with current research standards. Data collected was
imported into the Statistical Package for Social Science (SPSS) 23.0 software. The results
of this study will be published in ProQuest and presented without the identification of the
school district, selected campus, or participants. Moreover, this study will be available to
the school district and any others who wish to receive the results. Copyrights for this
study will belong to the researcher.
Limitations
According to Fraenkel, Wallen, and Hyun (2012), knowledge concerning the
limitations of a study may assist other researchers in evaluating the degree to which the
findings can be generalized. Although none of the limitations are expected to pejoratively
affect the study, the researcher identified several limitations. First, the researcher’s access
to secondary data was limited to one school district in Texas with one middle school. As
a result, the researcher identified the following consequences: (a) population size for the
grade of interests (6th grade) is N = 65, which does not meet the minimal sample size
needed to capture medium-size correlations. The study can be identified as significant
only if a large correlation exists, (b) the population may not be typical for the entire state
of Texas (low external validity), and (c) since there are significant educational
differences among states, the results of this study cannot be generalized to other states.
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Researchers could minimize negative consequences by increasing the sample size.
Participants could be selected from several school districts in Texas. Larger sample sizes
allow researchers to better determine the average values of their data (Fraenkel, Wallen,
& Hyun, 2012). Second, the scope of this study was limited by the confines of the
instrument used to measure musical aptitude and the scope of the mathematics and
reading tests. While the study presented several unavoidable limitations, it will not
negatively affect the results of the study.
Delimitations are simply defined as boundaries set by the researcher(s) to control
the study (Fraenkel, Wallen, & Hyun, 2012). The researcher made the following
delimitations: (a) a longitudinal study may reveal different or similar relationship
patterns, however, this study was limited to the 2013-2014 school year, and (b)
correlational studies do not investigate cause and effect (Yin, 2009), therefore, causal
conclusions cannot be drawn from the study.
Summary
An overview of the selected methodology for this study was provided in this
chapter. The study employed a quantitative, correlational research methodology to
address the research questions and hypotheses. The primary focus of this research study
was to examine if, and to what degree, a correlation existed between the level of musical
aptitude and the reading and mathematics sections of the STAAR among beginning band
students. This quantitative correlational study examined the relationship between musical
aptitude, as measured by the results from the 2013-2014 IMMA, and the results from the
2013-2014 reading and mathematics performance levels of the STAAR. The sample
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population for this study consisted of approximately 65 beginning band students from an
east Texas middle school.
A correlational research design was employed for this study. According to
Fraenkel, Wallen, and Hyun (2012), correlational research design “seeks to investigate
the extent to which one or more relationships of some type exist” (p.11). Furthermore, a
correlational research design is useful to researchers who are interested in determining to
what degree two or more variables are related. Correlation coefficients were calculated
using the Pearson r in order to analyze the strength of the linear relationships between
both variables (Fraenkel, Wallen, Hyun, 2012). A bivariate analysis was applied to
describe the statistical relationship between the independent variable, musical aptitude,
and the dependent variable, academic achievement. The findings of this study may help
school administrators, music specialists, reading and mathematics instructors find
effective ways to utilize music instruction to enhance reading and mathematics
achievement of middle school students. In addition, this study contributed to the field by
providing new information and resources relevant to musical aptitude and academic
achievement among middle school students.
Chapter 3 included all of the necessary methods in developing this research study,
including the problem statement, research questions and hypotheses. Additionally, the
chapter discussed the organization that was targeted for this study, the sample population,
and sample participants. The chapter also identified the rationale for the quantitative
methodology, instrumentation, validity, and reliability. Finally, the chapter discussed the
data collection procedures, data analysis, ethical considerations, and limitations that were
identified within the study. Chapter 4 presents the results obtained from the data
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collection procedures in this methodology chapter. In addition, Chapter 4 describes in
detail the statistical and non-statistical information obtained from the administration of
the researcher’s instruments to the selected sample and all raw data collected from this
study.
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Chapter 4: Data Analysis and Results
Introduction
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the musical aptitude, reading and mathematics
scores of the STAAR among beginning band students. Specifically, this quantitative
correlational study examined the relationship between musical aptitude, as measured by
the Intermediate Measures of Musical Audiation (IMMA), and the reading and
mathematics performance levels of the STAAR. Previous empirical research has
examined the relationship between musical aptitude and various academic assessments
(Cavangh, 2009; Holsomback, 2004; Rubinson, 2010). Yet, a gap existed in the literature
concerning the relationship between musical aptitude, as measured by the IMMA, and the
current standardized assessment, as measured by the reading and mathematics sections of
the STAAR (Holsomback, 2002). Based on this gap, this study examined the relationship
between musical aptitude and academic achievement of 65 sixth grade beginning band
students in northeast Texas. In addition, the following research questions and hypotheses
guided this study:
R1: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics scores on the STAAR
among beginning band students?
H1a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
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H10: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
R2: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading scores on the STAAR among
beginning band students?
H2a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
H20: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
The remainder of this chapter is divided into three sections that provide a
summary of the results gleaned from the data analysis. First, a summary of the descriptive
statistics was tabulated regarding demographic characteristics of the study population.
Second, an explanation and description of the procedures is used to analyze data collected
from participants. Lastly, a presentation of the results is provided, summarizing the
results that were revealed from the statistical analysis procedures for each of the research
questions.
Descriptive Data
This study was conducted within a single school district in northeast Texas.
Permission to conduct the study was obtained from the district superintendent. According
to the 2013-2014 district’s annual report, the enrollment consisted of approximately 1,100
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students in grades kindergarten through 12, among whom 10% were African American,
81% were Caucasian, and 9% were Hispanic. In addition, approximately 71% of the
student population was categorized as economically disadvantaged. The district comprises
three schools, one elementary school, one middle school, and one high school. The 2013-
2014 sixth grade class consisted of 65 students, who were all required to enroll in a
beginning band course and take a musical aptitude assessment. Therefore, the target
population (N = 65) consisted of sixth grade band students enrolled in the school district.
Figure 1 displays the ethnic diversity of the population from which the sample
was extracted. Three pie slices in the figure depict ethnic background and respective
percentages (%). As displayed in the figure, Caucasians represented a majority in the
population (81.2%).
Figure 1. District enrollment percentages by ethnicity for 2013-2014 school year.
From the sample of 6th grade students, 65 students participated. Based on the
ethnic profile of the sample, 8 were African American, 50 were Caucasian, and 7 were
9.8%
81.2%
9.0%
African American Caucasian Hispanic
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Hispanic. In addition, there were 30 boys and 35 girls. Moreover, 50 students were
economically disadvantaged. Table 3 depicts the number of students by ethnicity and
gender along with respective totals and percentages (%).
Three columns were specified in Table 3; these columns headings were ethnicity,
gender, and total. For ethnicity, African American, Caucasian, and Hispanics were
represented. For gender, both boys and girls were represented. As shown in the table,
Caucasians represented the majority of students with 50 of 65 or 76.9%. Boys and girls
were mostly equally distributed with girls holding a slight majority of 53.8%.
Table 3
The Study Population: Gender and Ethnicity
Ethnicity Gender
Total/%
Boys/% Girls/%
African American 3 (37.5) 5 (62.5) 8 (12.3)
Caucasian American 23 (46.0) 27 (54.0) 50 (76.9)
Hispanic American 4 (57.1) 3 (42.9) 7 (10.8)
Total 30 (46.2) 35 (53.8) 65
For the purpose of this research study, two sources of data were used. Both
sources of data have been successfully used in other empirical studies to measure musical
aptitude and academic achievement. The study utilized archival data deriving from the
results of the 2013-2014 IMMA assessment and the 2013-2014 composite and sub-
categorical performance levels of the reading and mathematics sections of the STAAR.
Consent to conduct this study was attained from an east Texas school district and Grand
Canyon University Institutional Review Board. Student identity and confidentiality was
preserved through an alpha numeric coding system so that the identification of
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participants cannot be breached. The data from the IMMA and STAAR assessments was
from archival school records located in the counselor’s office for the school year. The
data from both the IMMA and STAAR assessments was provided within the data
spreadsheet. Identifiers such as student names, identification numbers, dates of birth, and
addresses were removed by the counselor and assigned a numeric code to ensure
anonymity. Then, the raw data was imported into SPSS for analysis.
Data Analysis Procedure
Inferential statistics were used to draw conclusions from the sample tested. The
Statistical Package for the Social Sciences (SPSS) was used to code and tabulate scores
collected from the survey and provide summarized values where applicable including the
mean, variance, and standard deviation. A power statistical analysis was conducted using
G* Power 3.0.10. The analysis suggested that a minimum of 85 students are required for
this study. The researcher set statistical significance as α = .05 and power at .8 (β = .8) for
this study. Since this study did not meet minimum sample size, the study was identified
as significant only if a large correlation existed. After the data analysis process, the
researcher conducted a post hoc analysis to identify patterns not specified in an a priori
analysis. Post hoc power for all tests were recalculated at greater than 0.80 (See
Appendix E). Regression and multiple regression analyses were used to evaluate the two
research questions. The research questions and hypotheses were:
RQ1: Is there a correlation between the level of musical aptitude and the composite
and sub-categorical performance levels of the mathematics scores of the STAAR
among beginning band students?
86
Ho1: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the mathematics section
STAAR among beginning band students.
HA1: A correlation exists between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics section of the STAAR
among beginning band students.
RQ2: Is there a correlation between the level of musical aptitude and the composite
and sub-categorical performance levels of the reading scores of the STAAR
among beginning band students?
Ho2: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
HA2: A correlation does exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
Prior to analyzing the research question, data cleaning and data screening were
undertaken to ensure the variables of interest met appropriate statistical assumptions.
Thus, the following analyses were assessed using an analytic strategy in that the variables
were first evaluated for missing data, univariate and multivariate outliers, normality,
linearity, homoscedasticity, and multicollinearity. Finally, regression and multiple
regression analyses were run to determine if any relationships existed between the
variables of interest. The variables and statistical tests used to evaluate research questions
1 and 2 were displayed in Table 4.
87
Table 4
Summary of Variables and Statistical Tests used to Evaluate Research Questions 1 and 2
Hypothesis Criterion
Variable Predictor Variable Analysis
RQ1a
Musical
Aptitude
Composite
Mathematical Achievement Composite Regression
RQ1b
Musical
Aptitude
Composite
Quantitative Reasoning, Algebraic
Reasoning, Spatial Reasoning, Measurement
Statistics
Multiple
Regression
RQ2a
Musical
Aptitude
Composite
Reading Achievement Composite Regression
RQ2b
Musical
Aptitude
Composite
Understanding across Genres, Understanding
of Literary Text, Understanding of
Informational Text
Multiple
Regression
Research Question 1: data evaluation. Research question 1 was evaluated using
regression and multiple regression analyses to determine if any significant correlations
existed between students’ level of musical aptitude and the composite and sub-categorical
performance levels of the mathematics section of the STAAR. The criterion variable was
students’ musical aptitude composite scores as measured by the Intermediate Measures of
Music Addiction (IMMA). The predictor variable used in the regression analysis was
students’ mathematics achievement composite scores as measured by the State of Texas
Assessments of Academic Readiness (STAAR). For the multiple regression analysis, the
predictor variables were students’ scores on the five mathematics achievement subscales
including quantitative reasoning, algebraic reasoning, spatial reasoning, measurement,
and statistics scores.
Data cleaning. Data were collected from a sample of 65 sixth grade band students
enrolled in the school district. However, before the assumptions were assessed, the data
88
were screened for missing data, univariate outliers, and multivariate outliers. Missing
data were investigated using frequency counts and no cases were found to have missing
data. The data were screened for univariate outliers by transforming raw scores to z-
scores and comparing z-scores to a critical value of +/- 3.29, p < .001 (Tabachnick &
Fidell, 2007). Z-scores that exceed this critical value are more than three standard
deviations away from the mean and thus represent outliers. The distributions were
evaluated and no cases with univariate outliers were found.
For the multiple regression analysis, multivariate outliers were evaluated using
Mahalanobis distance. Mahalanobis distances were computed for each variable and these
scores were compared to a critical value from the chi-square distribution table.
Mahalanobis distance for five predictor variables indicates a critical value of 20.52 and
no cases were found to exceed this value. Thus, 65 responses from participants were
received and 65 were evaluated by the regression and multiple regression models (n =
65). Descriptive statistics of the criterion and predictor variables are displayed in Table 5.
Table 5
Descriptive Statistics of the Criterion and Predictor Variables
Variable N Min Max Mean Std.
Deviation Skewness Kurtosis
Musical Aptitude Composite 65 39 80 65.169 8.774 -0.668 0.203
Mathematics Achievement
Composite 65 1399 1874 1599.631 115.088 0.297 -0.510
Quantitative Reasoning 65 3 16 9.800 2.954 -0.001 -0.426
Algebraic Reasoning 65 0 11 6.185 2.984 -0.232 -0.879
Spatial Reasoning 65 1 8 5.292 2.075 -0.318 -0.847
Measurement 65 1 8 4.539 1.888 -0.220 -0.918
Statistics 65 1 8 4.923 2.041 -0.291 -1.122
Note. Total N = 65
Normality. Before the research question was analyzed, basic parametric
assumptions were assessed. That is, for the criterion (musical aptitude composite) and
89
predictor variables (mathematics achievement composite, quantitative reasoning,
algebraic reasoning, spatial reasoning, measurement, and statistics) assumptions of
normality, linearity, homoscedasticity, and multicollinearity were tested. The variables
were analyzed for linearity and homoscedasticity using scatterplots and the distributions
met the assumptions. To test if the distributions were normally distributed, the skew and
kurtosis coefficients were divided by the skew/kurtosis standard errors, resulting in z-
skew/z-kurtosis coefficients. This technique was recommended by Tabachnick and Fidell
(2007). Specifically, z-skew/z-kurtosis coefficients exceeding the critical range between -
3.29 and +3.29 (p < .001) may indicate non-normality. Thus, based on the evaluation of
the z-skew/z-kurtosis coefficients, no variables exceeded the critical range. Therefore,
normality was assumed for all distributions. Displayed in Table 6 are skewness and
kurtosis statistics of the criterion and predictor variables used to evaluate research
question 1.
Table 6
Skewness and Kurtosis Statistics of the Criterion and Predictor Variables Used to
Evaluate Research Question 1
Variable Skewness Skewness
Std. Error
z-
skew Kurtosis
Kurtosis
Std. Error
z-
kurtosis
Musical Aptitude Composite -0.668 0.297 -2.249 0.203 0.586 0.346
Mathematics Achievement
Composite 0.297 0.297 1.000 -0.510 0.586 -0.870
Quantitative Reasoning -0.001 0.297 -0.003 -0.426 0.586 -0.727
Algebraic Reasoning -0.232 0.297 -0.781 -0.879 0.586 -1.500
Spatial Reasoning -0.318 0.297 -1.071 -0.847 0.586 -1.445
Measurement -0.220 0.297 -0.741 -0.918 0.586 -1.567
Statistics -0.291 0.297 -0.980 -1.122 0.586 -1.915
Note. Total N = 65
Multicollinearity. The assumption of multicollinearity was tested by calculating
correlations between predictor variables (quantitative reasoning, algebraic reasoning,
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spatial reasoning, measurement, and statistics) and collinearity statistics (tolerance and
variance inflation factor). Results indicated that correlations between predictor variables
did not exceed the critical value of .80. Tolerance was calculated using the formula T = 1
– R2 and variance inflation factor (VIF) is the inverse of Tolerance (1 divided by T).
Commonly used cut-off points for determining the presence of multicollinearity are T <
.10 and VIF > 10. Results indicated that the predictor variables did not exceed the critical
values. Thus, since the correlation, tolerance, and VIF coefficients did not exceed their
critical values, the presence of multicollinearity was not assumed. Displayed in Table 7
are summary details of the correlations between predictor variables used to evaluate
research question 1.
Table 7
Summary of Correlations between Predictor Variables used in Research Question 1
Predictor Variable Quantitative
Reasoning
Algebraic
Reasoning
Spatial
Reasoning Measurement Statistics
Quantitative Reasoning 1.000 .712 .634 .594 .697
Algebraic Reasoning 1.000 .713 .631 .726
Spatial Reasoning 1.000 .641 .670
Measurement 1.000 .737
Statistics 1.000
Note. Total N = 65
Research Question 2: data evaluation. Research question 2 was evaluated using
regression and multiple regression analyses to determine if any significant correlations
existed between students’ level of musical aptitude and the composite and sub-categorical
performance levels of the reading section of the STAAR. The criterion variable was
students’ musical aptitude scores as measured by the Intermediate Measures of Music
Addiction (IMMA). The predictor variable used in the regression analysis was students’
reading achievement composite scores as measured by the State of Texas Assessments of
91
Academic Readiness (STAAR). For the multiple regression analysis, the predictor
variables were students’ scores on the three reading achievement subscales including
understanding/analysis across genres, understanding of literary text, and
understanding/analysis of informational text.
Data cleaning. Before the assumptions were assessed, the data were screened for
missing data, univariate outliers and multivariate outliers. Results indicated there were no
cases with missing data and no univariate outliers were found. For the multiple regression
analysis, multivariate outliers were evaluated using Mahalanobis distance. Mahalanobis
distance for three predictor variables indicates a critical value of 16.27 and no cases were
found to exceed this value. Therefore, responses from 65 participants received and 65
were evaluated by the regression and multiple regression models (N = 65). Descriptive
statistics of the criterion and predictor variables are displayed in Table 8.
Table 8
Descriptive Statistics of the Criterion and Predictor Variables used to Evaluate Research
Question 2
Predictor Variable Min Max Mean
Std.
Deviatio
n
Skewnes
s
Kurtosi
s
Musical Aptitude Composite 39 80 65.169 8.774 -0.668 0.203
Reading Achievement Composite 1327 1864 1591.01
5 109.845 0.112 -0.070
Understanding Analysis across Genres 1 10 6.692 2.106 -0.490 -0.240
Understanding of Literary Text 2 20 13.385 3.896 -0.403 -0.214
Understanding Analysis of Informational
Text 5 17 12.015 3.319 -0.263 -0.862
Note. Total N = 65
Normality. Before the research question was analyzed, basic parametric
assumptions were assessed. That is, for the criterion (musical aptitude composite) and
predictor variables (understanding across genres, understanding of literary text, and
92
understanding of informational text) assumptions of normality, linearity,
homoscedasticity, and multicollinearity were tested. The variables were analyzed for
linearity and homoscedasticity using scatterplots and the distributions met the
assumptions. To test if the distributions were normally distributed, the skew and kurtosis
coefficients were divided by the skew/kurtosis standard errors, resulting in z-skew/z-
kurtosis coefficients. Results indicated that no variables exceeded the critical range (< -
3.29 and > 3.29). Therefore, normality was assumed for all distributions. Displayed in
Table 9 are skewness and kurtosis statistics of the criterion and predictor variables used
to evaluate research question 2.
Table 9
Skewness and Kurtosis Statistics of the Criterion and Predictor Variables Used to
Evaluate Research Question 2
Variable Skewness Skewness
Std. Error z-skew Kurtosis
Kurtosis
Std.
Error
z-kurtosis
Musical Aptitude Composite -0.668 0.297 -2.249 0.203 0.586 0.346
Reading Achievement
Composite 0.112 0.297 0.377 -0.070 0.586 -0.119
Understanding Analysis across
Genres -0.490 0.297 -1.650 -0.240 0.586 -0.410
Understanding of Literary Text -0.403 0.297 -1.357 -0.214 0.586 -0.365
Understanding Analysis of
Informational Text -0.263 0.297 -0.886 -0.862 0.586 -1.471
Note. Total N = 65
Multicollinearity. The assumption of multicollinearity was tested by calculating
correlations between predictor variables (understanding across genres, understanding of
literary text, and understanding of informational text) and collinearity statistics (tolerance
and variance inflation factor). Results indicated that correlations between predictor
variables did not exceed the critical value of .80. For tolerance and variance inflation
factor (VIF), results indicated that the predictor variables did not exceed the critical
93
values (T < .10 and VIF > 10). Thus, since the correlation, tolerance, and VIF
coefficients did not exceed their critical values, the presence of multicollinearity was not
assumed. Displayed in Table 10 are summary details of the correlations between
predictor variables used to evaluate research question 2.
Table 10
Summary of Correlations between Predictor Variables used in Research Question 2
Pearson Correlation
Predictor Variable 1 2 3
Understanding Analysis across Genres (1) 1.000 0.632 0.765
Understanding of Literary Text (2) 1.000 0.683
Understanding Analysis of Informational Text (3) 1.000
Note. Total N = 65
Results
This study consisted of two overarching research questions, each associated with
a pair of null and alternative hypothesis. In this section, the researcher utilizes the results
of the statistical analyses to answer each research question.
Results of Research Question 1. Regression and multiple regression analyses
using the general linear model were used to determine if any significant correlations
existed between students’ level of musical aptitude and the composite and sub-categorical
performance levels of the mathematics section of the STAAR. The criterion variable was
specified as students’ musical aptitude composite scores as measured by the Intermediate
Measures of Music Addiction (IMMA). The predictor variable specified in the regression
analysis was students’ mathematics achievement composite scores as measured by the
State of Texas Assessments of Academic Readiness (STAAR). The predictor variables
for the multiple regression analysis were students’ scores on the five mathematics
achievement subscales including quantitative reasoning, algebraic reasoning, spatial
94
reasoning, measurement, and statistics scores while the criterion variable was musical
aptitude composite scores as measured by the Intermediate Measures of Music Addiction
(IMMA). The null and alternative hypotheses were:
Ho1: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the mathematics section
STAAR among beginning band students.
HA1: A correlation exists between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics section of the STAAR
among beginning band students.
Using SPSS 23, regression and multiple regression analyses was used to
determine if students’ musical aptitude scores were significantly correlated to the
composite and sub-categorical performance levels of the mathematics section of the
STAAR. Results from the regression analysis indicated there was a significant
relationship between students’ musical aptitude and mathematics achievement composite
scores, R = .673, R2 = .453, F (1, 63) = 52.132, p < .001. That is, 45.3% (R2 = .453) of the
variance observed in the criterion variable (musical aptitude) was due to the predictor
variable (mathematic achievement composite). Based on findings, the null hypothesis
was rejected in favor of the alternative. A model summary of the regression analysis was
displayed in Table 11.
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Table 11
Model Summary of Regression for Research Question 1
Source R R2 Standard Error F Sig.
(p)
Omnibus .673 .453 6.542 52.132 < .001
Unstandardized
Coefficients
Standardized
Coefficients
Source B Std.
Error Beta t
Sig.
(p)
Part
Correlation
(Constant) -16.895 11.395 -1.483 .143
Mathematic Achievement
Composite 0.051 0.007 0.673 7.220 < .001 .673
Note. Criterion variable = musical aptitude; N = 65
Figure 2 displays the regression plot depicting the relationship between
mathematics and musical aptitude. The scatterplot displays a strong and significant
relationship between the two variables. The regression equation is Ý = -16.895 + .051*x.
This means that for every one unit increase in mathematics scores, musical aptitude
increases by .051.
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Figure 2. Scatterplot depicting the relationship between mathematics achievement and
musical aptitude.
Results from the multiple regression analysis indicated there was a significant
relationship between students’ musical aptitude scores and five mathematics achievement
subscales (quantitative reasoning, algebraic reasoning, spatial reasoning, measurement,
and statistics), R = .709, R2 = .502, F (5, 59) = 11.915, p < .001. That is, 50.2% (R2 =
.502) of the variance observed in the criterion variable (musical aptitude) was due to a
model containing five predictor variables. Thus, the null hypothesis for research question
1 was rejected in favor of the alternative hypothesis. A model summary of the multiple
regression analysis is displayed in Table 12.
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Table 12
Model Summary of Multiple Regression for Research Question 1
Source R R2 Standard Error F Sig. (p)
Omnibus .709 .502 6.446 11.915 < .001
Unstandardized
Coefficients
Standardized
Coefficients
Source B Std. Error Beta t Sig. (p) Part
Correlation
(Constant) 47.191 2.927 16.124 < .001
Quantitative Reasoning 0.477 0.425 0.161 1.124 .266 .103
Algebraic Reasoning 0.341 0.465 0.116 0.734 .466 .067
Spatial Reasoning 1.807 0.606 0.427 2.981 .004 .274
Measurement -0.450 0.664 -0.097 -0.678 .500 -.062
Statistics 0.746 0.707 0.174 1.055 .296 .097
Note. Criterion variable = musical aptitude; N = 65
The contribution of each predictor variable when the others were controlled for
was evaluated using the standardized Beta coefficient. That is, results indicated that one
predictor variable (spatial reasoning) made a significantly unique contribution in
explaining the criterion variable (standardized B = 0.427, p = .004). Specifically, there
was a significant and positive relationship between students’ musical aptitude and spatial
reasoning scores. The part correlation coefficient (part correlation = .274) indicated that
7.5% (part correlation2 = .075) of the variance observed in the criterion variable (musical
aptitude) was due to students’ spatial reasoning scores. The remaining predictor variables
did not make a significantly unique contribution in explaining the criterion variable
(quantitative reasoning p = .266, algebraic reasoning p = .466, measurement p = .500, and
statistics p = .296).
Results of Research Question 2. Research question 2 was evaluated using
regression and multiple regression analyses to assess if any significant correlations
existed between students’ level of musical aptitude and the composite and sub-categorical
98
performance levels of the reading section of the STAAR. The null and alternative
hypotheses were:
Ho2: A correlation does not exist between the level of musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
HA2: A correlation does exist between the level of musical aptitude and the composite
and sub-categorical performance levels of the reading section of the STAAR
among beginning band students.
Using SPSS 23, regression and multiple regression analyses was used to
determine if students’ musical aptitude scores were significantly correlated to the
composite and sub-categorical performance levels of the reading section of the STAAR.
Results from the regression analysis indicated there was a significant relationship
between students’ musical aptitude and reading achievement composite scores, R = .848,
R2 = .718, F(1, 63) = 160.722, p < .001. That is, 71.8% (R2 = .718) of the variance
observed in the criterion variable (musical aptitude) was due to the predictor variable
(reading achievement composite). A model summary of the regression analysis is
displayed in Table 13.
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Table 13
Model Summary of Regression for Research Question 2
Source R R2 Standard Error F Sig.
(p)
Omnibus .848 .718 4.693 160.722 <
.001
Unstandardized
Coefficients
Standardized
Coefficients
Source B Std.
Error Beta t
Sig.
(p)
Part
Correlation
(Constant) -42.548 8.517 -4.996 <
.001
Reading Achievement
Composite 0.068 0.005 0.848 12.678
<
.001 .848
Note. Criterion variable = musical aptitude; N = 65
Figure 3 displays the regression plot depicting the relationship between reading
achievement and musical aptitude. The scatterplot displays a strong and significant
relationship between the two variables. The regression equation is Ý = -42.548 + .068*x.
This means that for every one unit increase in mathematics scores, musical aptitude
increases by .068.
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Figure 3. Scatterplot depicting the relationship between reading achievement and musical
aptitude.
Results from the multiple regression analysis indicated there was a significant
relationship between students’ musical aptitude scores and three reading achievement
subscales (understanding across genres, understanding of literary text, and understanding
of informational text), R = .861, R2 = .740, F(3, 61) = 58.022, p < .001. That is, 74.0%
(R2 = .740) of the variance observed in the criterion variable (musical aptitude) was due
to a model containing three predictor variables. Thus, the null hypothesis for research
question 2 was rejected in favor of the alternative hypothesis. A model summary of the
multiple regression analysis is displayed in Table 14.
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Table 14
Model Summary of Multiple Regression for Research Question 2
Source R R2 Standard
Error F
Sig.
(p)
Omnibus .861 .740 4.578 58.022 < .001
Unstandardized
Coefficients
Standardized
Coefficients
Source B Std.
Error Beta t
Sig.
(p)
Part
Correlation
(Constant) 36.166 2.285 15.829 < .001
Understanding across Genres 1.129 0.434 0.271 2.602 .012 .170
Understanding of Literary Text 0.687 0.207 0.305 3.324 .002 .217
Understanding of
Informational Text 1.019 0.292 0.386 3.486 .001 .227
Note. Criterion variable = musical aptitude; N = 65
The contribution of each predictor variable when the others were controlled for
was evaluated using the standardized Beta coefficient. Results indicated that all three
predictor variables (understanding across genres, understanding of literary text, and
understanding of informational text) made significantly unique contributions in
explaining the criterion variable (musical aptitude). Specifically, students’ understanding
of informational text scores made the strongest unique contribution (standardized B =
0.386, p = .001) followed by understanding of literary text scores (standardized B =
0.305, p = .002), and then understanding across genres scores (standardized B = 0.271, p
= .012). That is, there were significant and positive relationships between students’
musical aptitude and all three reading achievement subscales. The part correlation
coefficients indicated that 2.9% (part correlation = .170, part correlation2 =.029) of the
variance observed in the criterion variable (musical aptitude) was due to students’
understanding across genre scores and 4.7% was due to understanding of literary text
scores (part correlation = .217, part correlation2 =.047). Lastly, 5.2% of the variance
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observed in the criterion variable was due to students’ understanding of informational
text scores (part correlation = .227, part correlation2 = .052).
Summary
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the level of musical aptitude, reading, and
mathematics scores of the STAAR among beginning band students. The study consisted
of 65 sixth grade beginning band students whom are all required to enroll in a beginning
band course and take a musical aptitude assessment. Secondary data (IMMA and
STAAR) were obtained from a northeast Texas school district.
This chapter presented the descriptive statistics and statistical analysis such as
regression, and multiple regression analyses to measure the relationship between
variables. The chapter began by describing the sample population using descriptive
statistics followed by data analysis procedures. In addition, there were no data limitations
that emerged based on data analysis. Findings from analyzing the data were significant:
For H1a, results from the regression analysis indicated there was a significant
relationship between students’ musical aptitude and mathematics achievement composite
scores. For H1b, results from the multiple regression analysis indicated there was a
significant relationship between students’ musical aptitude scores and five mathematics
achievement subscales (quantitative reasoning, algebraic reasoning, spatial reasoning,
measurement, and statistics). For H2a, results from the regression analysis indicated there
was a significant relationship between students’ musical aptitude and reading
achievement composite scores. For H2b, results from the multiple regression analysis
indicated there was a significant relationship between students’ musical aptitude scores
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and three reading achievement subscales (understanding across genres, understanding of
literary text, and understanding of informational text). Table 15 displays summary
findings for Hypotheses 1 and 2. Post hoc power for all tests were calculated at greater
than 0.8.
Table 15
Summary of Results for Hypotheses 1 and 2
Hypothesis Criterion
Variable Predictor Variable Analysis Sig. (p)
Post Hoc
Power
H1a
Musical
Aptitude
Composite
Math Achievement
Composite Regression < .001 0.801
H1b
Musical
Aptitude
Composite
Quantitative Reasoning,
Algebraic Reasoning,
Spatial Reasoning,
Measurement, Statistics
Multiple
Regression < .001 0.855
H2a
Musical
Aptitude
Composite
Reading Achievement
Composite Regression < .001 0.801
H2b
Musical
Aptitude
Composite
Understanding across
Genres, Understanding of
Literary Text,
Understanding of
Informational Text
Multiple
Regression < .001 0.855
Note. Total N = 65
In the final chapter of this study, Chapter 5 will provide an overview of the
importance of this study and its contribution to the understanding of the topic. Chapter 5
will also reiterate the two research questions, and provide conclusions and
recommendations based on the description of the data findings related to the research
questions and hypotheses. Chapter 5 will also discuss the specific findings of this study,
the theoretical and future implications, suggestions on musical training, and
recommendations for future research.
104
Chapter 5: Summary, Conclusions, and Recommendations
Introduction
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the musical aptitude, reading and mathematics
scores of the STAAR among sixth grade beginning band students from a middle school in
northeast Texas. Similar empirical studies have examined the relationship between
musical aptitude and previous standardized assessments in Texas (Holsomback, 2001;
Holsomback, 2002). Yet, a gap in the literature existed concerning the relationship
between the levels of musical aptitude and the current standardized assessment in Texas.
Based on this gap in the literature, the following research questions and hypotheses
guided this study:
R1: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics scores on the STAAR
among beginning band students?
H1a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
H10: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the mathematics section of
the STAAR among beginning band students.
R2: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading scores on the STAAR among
beginning band students?
105
H2a: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
H20: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
By examining such relationships, school administrators, teachers, and policy
holders are capable of identifying tactics that may improve test results. Understanding the
relationship between musical aptitude and academic achievement was essential in finding
effective ways to utilize music instruction to enhance reading and mathematics
achievement of middle school students. The findings from this study have opened
opportunities for further research in this area.
This chapter provides a summary of the study, conclusions, limitations, and future
theoretical and practical implications. The conclusions and findings associated with each
research question and hypothesis were examined. The researcher’s observations and
summary of the study were noted. In addition, this chapter concludes with a discussion of
recommendations for future practice and research.
Summary of the Study
The researcher embarked on this study in an attempt to understand the
relationship between the levels of musical aptitude and the current standardized
assessment in Texas. This study focused exclusively on 65 sixth grade beginning band
students in one northeast Texas school as the district requires all sixth grade students to
enroll in a beginning band course and take the State of Texas Assessment of Academic
106
Readiness (STAAR). Although compelling empirical evidence examined the relationship
between the levels of musical aptitude and previous standardized assessments
(Holsomback, 2001; Rubinson, 2010), as of 2015 there has been no research concerning
the correlation between musical aptitude and the State of Texas Assessments of
Academic Readiness (STAAR) among sixth grade beginning band students. Therefore,
the purpose of this quantitative correlational study was to examine if, and to what degree,
a correlation existed between the musical aptitude and reading and mathematics scores of
the STAAR among beginning band students.
The findings of this study advanced the understanding of the relationship between
musical aptitude and academic achievement among beginning band students. The
collection of data from this study added to the literature in this area by broadening the
knowledge surrounding the problem statement. Moreover, the data extended the literature
relating music to cognitive abilities by examining the correlation of musical aptitude to
specific areas of academic performance.
Chapter 1 provided an introduction and rationale for the study and presented the
research questions that were used to justify the purpose and address the current lack of
research on musical aptitude and academic achievement. A discussion of how it will
advance scientific knowledge and the significance of the study was also presented. The
researcher provided the rationale for the selected methodology and research design. The
chapter concluded with definitions of research terms, assumptions, limitations, and the
study’s delimitations.
Chapter 2 synthesized the foundational and current literature related to musical
aptitude and academic achievement. In addition, chapter 2 presented an organized
107
literature review covering the background to the problem, the theoretical framework
providing the foundation to the study, and various topics and themes within those topics
related to the study. Specifically, chapter 2 included a discussion of musical aptitude in
relation to reading and mathematics achievement. Chapter 2 provided a synopsis of the
gaps in prior research and methodological strengths and weaknesses found in earlier
studies.
Chapter 3 presented an overview of the selected methodology and research
design. A quantitative methodology and a correlational research design were employed as
they are useful to researchers who are interested in determining the extent to which two
or more variables are related. The researcher utilized secondary data from one school
district in northeast Texas to determine the extent of the relationship between the levels
of musical aptitude and the composite and sub-categorical performance levels of the
STAAR. Chapter 3 outlined the data analysis procedures needed to process the data and
answer each research question and hypothesis.
Chapter 4 presented the descriptive statistics and statistical analysis such as
regression and multiple regression analyses to measure the relationship between
variables. The chapter began by describing the sample population using descriptive
statistics followed by data analysis procedures. Results from the data analyses were then
used to answer the research questions and hypotheses.
Chapter 5 provides an overview of the importance of this study and its
contribution to the understanding of the topic. Chapter 5 reiterates the two research
questions, and provided conclusions and recommendations based on the description of
the data findings related to the research questions and hypotheses. Chapter 5 discusses the
108
specific findings of this study, the theoretical and future implications, suggestions on
musical training, and recommendations for future research.
Summary of Findings and Conclusion
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the musical aptitude, reading and mathematics
scores of the STAAR among beginning band students. Specifically, this quantitative
correlational study examined the relationship between musical aptitude, as measured by
the Intermediate Measures of Music Audiation (IMMA Gordon, 2002), and the reading
and mathematics performance levels of the STAAR. In order to attain this objective, this
study presented two research questions, each supported by an alternative hypothesis and a
null hypothesis.
The first research question was:
R1: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the mathematics scores of the STAAR
among beginning band students?
The following hypotheses were associated with this research question:
H1: A statistically significant correlation exists between the level of musical aptitude
and the composite and sub-categorical performance levels of the mathematics
section STAAR among beginning band students.
H10: There is no statistically significant correlation between the level of musical
aptitude and the composite and sub-categorical performance levels of the
mathematics section of the STAAR among beginning band students.
Research question 1 was evaluated using regression and multiple regression
109
analyses to determine if any significant correlations existed between students’ level of
musical aptitude and the composite and sub-categorical performance levels of the
mathematics section of the STAAR. The criterion variable was students’ musical aptitude
composite scores as measured by the Intermediate Measures of Music Addiction
(IMMA). The predictor variable used in the regression analysis was students’ mathematic
achievement composite scores as measured by the State of Texas Assessments of
Academic Readiness (STAAR). For the multiple regression analysis, the predictor
variables were students’ scores on the five mathematic achievement subscales including
quantitative reasoning, algebraic reasoning, spatial reasoning, measurement, and statistics
scores. Results from the regression analysis indicated there was a significant relationship
between students’ musical aptitude and mathematics achievement composite scores, R =
.673, R2 = .453, F (1, 63) = 52.132, p < .001. That is, 45.3% (R2 = .453) of the variance
observed in the criterion variable (musical aptitude) was due to the predictor variable
(mathematic achievement composite). In addition, results from the multiple regression
analysis indicated there was a significant relationship between students’ musical aptitude
scores and five mathematics achievement subscales (quantitative reasoning, algebraic
reasoning, spatial reasoning, measurement, and statistics), R = .709, R2 = .502, F (5, 59) =
11.915, p < .001. That is, 50.2% (R2 = .502) of the variance observed in the criterion
variable (musical aptitude) was due to a model containing five predictor variables. Based
on findings, the null hypothesis was rejected in favor of the alternative.
The second research question was:
110
R2: Is there a correlation between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading scores of the STAAR among
beginning band students?
H2: A statistically significant correlation exists between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
H20: There is no statistically significant correlation between musical aptitude and the
composite and sub-categorical performance levels of the reading section of the
STAAR among beginning band students.
Research question 2 was evaluated using regression and multiple regression
analyses to determine if any significant correlations existed between students’ level of
musical aptitude and the composite and sub-categorical performance levels of the reading
section of the STAAR. The criterion variable was students’ musical aptitude scores as
measured by the Intermediate Measures of Music Addiction (IMMA). The predictor
variable used in the regression analysis was students’ reading achievement composite
scores as measured by the State of Texas Assessments of Academic Readiness (STAAR).
For the multiple regression analysis, the predictor variables were students’ scores on the
three reading achievement subscales including understanding/analysis across genres,
understanding of literary text, and understanding/analysis of informational text. Results
from the regression analysis indicated there was a significant relationship between
students’ musical aptitude and reading achievement composite scores, R = .848, R2 =
.718, F(1, 63) = 160.722, p < .001. That is, 71.8% (R2 = .718) of the variance observed in
the criterion variable (musical aptitude) was due to the predictor variable (reading
111
achievement composite). In addition, results from the multiple regression analysis
indicated there was a significant relationship between students’ musical aptitude scores
and three reading achievement subscales (understanding across genres, understanding of
literary text, and understanding of informational text), R = .861, R2 = .740, F(3, 61) =
58.022, p < .001. That is, 74.0% (R2 = .740) of the variance observed in the criterion
variable (musical aptitude) was due to a model containing three predictor variables. Thus,
the null hypothesis for research question 2 was rejected in favor of the alternative
hypothesis.
In conclusion, the findings of this study suggested a strong correlation existed
between the level of musical aptitude and the composite and sub-categorical performance
levels of the reading and mathematics section of the STAAR among sixth grade
beginning band students. The findings of this study may help school administrators,
music specialists, and reading and mathematics instructors find effective ways to utilize
music instruction to enhance reading and mathematics achievement of middle school
students. In addition, this study contributed to the field by providing new information and
resources relevant to musical aptitude and academic achievement among middle school
students.
The study provided empirical evidence that correlations do exist between musical
aptitude and academic achievement, which was predictable under Gardner’s theory of
multiple intelligence (2006) and Gordon’s music learning theory (1971) as they both are
ultimately designed to enhance musical intelligence. Although Gordon’s (2003) and
Kuhlman’s (2005) premise were not supported in this research, Holsomback’s, (2001);
Kuhlman’s, (2005); and Rubinson’s (2010) inferences were partially supported.
112
Specifically, although the omnibus test revealed a significant relationship between
mathematics achievement and musical aptitude, not all of the five mathematics
achievement subscales (quantitative reasoning, algebraic reasoning, spatial reasoning,
measurement, and statistics) yielded significant predicative findings. That is, only spatial
reasoning was found to significantly predict musical aptitude. Quantitative reasoning,
algebraic reasoning, measurement, and statistics were not found to predict musical
aptitude.
Interestingly, and consistent with other studies, Dastjerdi, Ozker, Foster, ,
Rangarajan, , and Parvizi (2013) found that electrical activity in a particular group of
nerve cells in the intraparietal sulcus spiked when, and only when, volunteers were
performing calculations. The area of the brain responsible for this activity is different
from the area of the brain that is responsible for spatial reasoning and reading
(Anthamatten, 2010). This evidence suggests that there is, perhaps, a biological reason
why math ability may not be as related to musical aptitude as reading or spatial
reasoning. Findings from this study, in conjunction with Dastjerdi et al. (2013) findings,
may in fact provide the impetus to change researcher’s perception about the relationship
between mathematics, reading, and music aptitude.
Comparatively, findings from testing hypothesis 2 supported the premise that
musical aptitude is related to knowledge and skills, as posited by Holsomback (2001).
Findings from hypothesis 2 also provided some credibility to Gordon’s (2003) and
Kuhlman’s (2005) assertion that music programs positively affect musical aptitude.
Specifically, this study confirmed a relationship between reading (and associated reading
113
sub-constructs) and musical aptitude. This study does not confirm music program
efficacy, which may affect musical aptitude and promote reading achievement.
Implications
The purpose of this quantitative correlational study was to examine if, and to what
degree, a correlation existed between the musical aptitude, reading, and mathematics
scores of the STAAR among beginning band students. There are a number of
implications based on the results of this study. Some of the implications are associated
with the theoretical framework upon which the research was built. Moreover, practical
and future implications should be considered as they may be meaningful to researchers
and educators.
Theoretical implications. The theoretical implications encompass the
interpretation of data in terms of the research questions. In addition, the theoretical
implications encompass the interpretation of findings in the existing literature framework.
The research study was guided by two overarching research questions. Both questions
examined the relationship between the level of musical aptitude and the composite and
sub-categorical performance levels of the reading and mathematics scores, respectively,
on the STAAR among beginning band students. The results provided an answer to both
research questions, revealing some theoretical implications. The findings indicated that a
positive correlation existed between level of musical aptitude and the composite and sub-
categorical performance levels of the STAAR among beginning band students. The
study’s findings did support scholarly research concerning the relationship between
musical aptitude and academic achievement (Holsomback 2001; Holsomback, 2002;
Rubinson, 2010). In addition, Gardner’s theory of multiple intelligence (2006) identified
114
musical intelligence as one of eight different types of intelligence. Musical learning and
participation, which incorporates instruction geared toward the plurality of various
intelligences and aptitudes, may ultimately have a positive effect on reading and
mathematical achievement. Supportively, Gordon’s (1971) musical learning theory
analyzes musical learning processes; it was ultimately designed to enhance musical
intelligence. Both theoretical implications supported the research findings that
correlations existed between musical aptitude and reading and mathematical performance
levels of the STAAR among beginning band students. The results of this study are
consistent with Holsomback’s (2001) and Rubinson’s (2010) findings that positive
correlations existed between musical aptitude and various standardized academic
assessments. The study’s theoretical implications and findings may assist teachers,
administrators, and educational policy makers in developing effective methods for
increasing reading and mathematics achievement of middle school students.
Practical implications. As previously stated in the literature review, more than
two-thirds of students in the United States are not proficient in reading and mathematics
(Campbell, Malkus, 2011; Hemmings, Grootenboer, Kay, 2011; National Assessment of
Educational Progress, 2009). More staggering, the statistical evidence of academic
proficiency for minority and lower socioeconomic students is even lower (Arbona &
Jimenez, 2014; Morales, 2010; Olszewski-Kubilius & Thomson, 2010). This evidence
prompted the researcher to examine the relationship between the level of musical aptitude
and academic achievement among beginning band students.
This study indicated a strong and statistical relationship existed between the level
of musical aptitude and both reading and mathematics achievement. The findings of this
115
study have several practical implications regarding musical aptitude testing and its
relationship to reading and mathematical achievement. First, Gordon (2001) argued that a
person’s musical aptitude would greatly influence how he or she learns to develop
audiation skills. The primary purpose of musical aptitude testing, then, is “to enable
teachers and parents to adapt music guidance and instruction to each child’s individual
needs” (Gordon, 2003, p. 13). Assessing a student’s musicality based upon his or her
academic achievement is usually misleading, and musical aptitude tests can prove
especially useful when scores do not match an educator’s expectations. The awareness of
a student’s musical aptitude and academic achievement, in comparison to his or her peers
as well as identifying a student’s strengths and weaknesses (such as differences in tonal
and rhythmic aptitude) allows teachers and administrators to provide aligned learning
objectives and activities.
Future implications. There are several future implications from the findings of
this research. Based on the finding of this study and similar studies previously mentioned
in the Literature Review, researchers should examine school districts with different
demographics and ethnic backgrounds. Analyzing the same variables with different
populations would provide expanded insight about the merit of these findings.
Furthermore, analyzing other grade levels would provide insight on the significance of
the variables. Another implication is the need for school districts to support music
education programs. Recent empirical evidence indicated that arts programs, particularly
music, are being eradicated at an exponential rate (Major, 2013; Slaton, 2012; Spohn,
2008). If early skills in reading are strongly related to the auditory processing of speech
components, then strengthening auditory analysis skills necessary for increasing musical
116
aptitude may be a valuable tool in reinforcing reading and mathematics comprehension of
middle school students (Rubinson, 2010). This notion ultimately may have a positive
effect on standardized assessments.
Strengths and weaknesses. This study displayed strengths and weaknesses based
on the methodology, research design, and data. This study provided a robust analysis by
examining the composite and sub-dimensions of the reading and mathematics sections of
the STAAR to the level of musical aptitude. However, the researcher’s accessibility to
secondary data is limited to one school district in Texas that has only one middle school
which affected the sample size. It was determined that a larger sample size was required.
Population size for the grade of interest (6th grade) was N = 65, which did not meet the
minimal sample size needed to capture medium-size correlations. The findings were
identified as significant only as a large correlation existed between the level musical
aptitude and the composite and sub-categorical performance levels of the reading and
mathematics sections of the STAAR. In addition, as there are significant educational
differences among states, the results of this study cannot be generalized to other states.
Recommendations
Recommendations for future research. Although there are many research
studies that examine musical aptitude and academic achievement respectively, there are
limited studies correlating the level of musical aptitude to academic achievement. As a
result of limited research regarding the findings of this study, the recommendations for
further research are as follows:
1. Perform a similarly designed study with a significantly larger sample. Students could be selected from several school districts in Texas. Larger sample sizes
allow researchers to better determine the average values of their data (Fraenkel,
Wallen, Hyun, 2012).
117
2. Replicate this study with a different academic assessment. As states adopt new academic standards, different states are creating new standardized assessments for
reading and mathematics for all grade levels. As a result, this study needs to be
replicated using a new academic assessment from respective states.
3. Perform a similarly designed study with a different grade level to check for similarities in findings. Broadening the scope could show more dynamics of
information regarding the relationship between musical aptitude and academic
achievement.
4. Conduct a longitudinal study to examine if the level of musical aptitude correlates to reading and mathematics achievement over a period of years.
5. Perform a similarly designed study utilizing a point biserial correlation coefficient to determine the relationship between musical aptitude and academic achievement
among students of different genders, ethnic, and socioeconomic backgrounds. By
investigating populations form different social and ethnic backgrounds, the
findings could be generalized to school with similar demographics in Texas.
6. Replicate this study using the more advanced version of the IMMA (Advanced Measures of Musical Audiation, AMMA) to measure the level of musical
aptitude. It would be interesting to see if similar correlations exist between the
reading and mathematics sections of the STAAR and a more advanced version of
the musical aptitude assessment.
Recommendations for future practice. There are three key recommendations
for practice based on the results of this study.
1. Raise school district awareness. The researcher will share his findings with the administration in the school district that authorized this study. This study will be
summarized in a scholarly article and submitted to the Texas Music Educator
Research in order to make the information more accessible for all stakeholders.
The findings of this study showed a strong statistical correlation between the level
of musical aptitude and the reading and mathematics sections of the STAAR. By
investigating such relationships, school administrators, teachers, and policy
holders are capable of identifying tactics that may improve test results.
2. Administrators and policy makers need to support music education programs. The results of this study indicated that a strong correlation existed between the level of
musical aptitude and reading and mathematics section of the STAAR. With high
quality music instruction, musical aptitude can be raised to its highest possible
level (Gordon, 2003). Therefore, it is reasonable to believe that high quality music
programs designed to increase musical aptitude may also have a positive effect on
reading and mathematics achievement.
118
3. Incorporate musical aptitude testing periodically in every music classroom. Musical aptitude testing is designed to act as objective aids to teachers and
parents by providing students with appropriate opportunities and instruction
(Gordon, 2003). Specifically, musical aptitude testing can be beneficial to music
educators by adapting formal and informal instructions to the individual musical
needs of students with high, average, and low music aptitudes.
These recommendations are provided as practical suggestions to improve
academic achievement among middle school students. As noted earlier, with high quality
music instruction, musical aptitude can be raised to its highest possible level (Gordon,
2003). Hence, it is reasonable to believe that high quality music programs designed to
increase musical aptitude may also have a positive effect on standardized assessments.
119
References
Abril, C. R., & Gault, B. M. (2006). The state of music in the elementary school: The
principals’ perspective. Journal of Research in Music Education, 54(1): 6-20.
Retrieved December 9, 2012 from EBSCOhost database.
Allen, K. (2011). Mathematics as thinking. Democracy & Education, 19(2): 1-7.
Anthamatten, P. (2010). Spatial thinking concepts in early grade-level geography
standards. Journal of Geography, 109(5), 169-180.
doi:10.1080/00221341.2010.498898
Arbona, C., & Jimenez, C. (2014). Minority stress, ethnic identity, and depression among
Latino/a college students. Journal of Counseling Psychology, 61(1): 162-168. doi:
10.1037/a0034914
Argabright, R. (2005). Connecting with music. General Music Today, 18(2): 5-6.
Ashford, E. (2004). NCLB’s unfunded art programs seek refuge. Education Digest,
70(2): 22–26.
Barrett, D.L. (1993). Relationship of musical audiation to musical training, musical
proficiency and scholastic achievement among advantaged and disadvantaged 6-
to-8 year olds. (Doctoral dissertation, Columbia University Teachers College).
Dissertation Abstracts International, 54 (12), 4383A.
Beveridge, T. (2010). No child left behind and fine arts classes. Arts Education Policy
Review, 111(1): 4-7. doi:10.1080/10632910903228090
Bluman, A. G. (2012). Elementary statistics: A step by step approach. New York:
McGraw-Hill.
120
Brophy, T. S. (2005). A longitudinal study of selected characteristics of children's
melodic improvisations. Journal of Research in Music Education, 53(2), 120-133.
Retrieved from http://search.proquest.com/docview/214476818?accountid=737
Burdick, J. (2012). The legacy of "A Nation at Risk". Online Submission.
Burns, T. G. (2010). Wechsler Individual Achievement Test-III: What is the 'gold
standard' for measuring academic achievement? Applied Neuropsychology, 17(3):
234-236.
Butler, B. E., & Trainor, L. J. (2012). Sequencing the cortical processing of pitch-
evoking stimuli using EEG analysis and source estimation. Frontiers in
Psychology, 3, 1-13.
Butzlaff, R. (2000). Can music be used to teach reading? Journal of Aesthetic Education,
34(3-4): 167-178. doi:10.1080/09084282.2010.499803
Campbell, P. F., & Malkus, N. N. (2011). The impact of elementary mathematics coaches
on student achievement. Elementary School Journal, 111(3): 430-454.
Cavanagh, S. (2009). Music and academic achievement. Education Week, 28(23): 4.
Circle, D. (2005). To test or not to test? Music Educators Journal, 92(1): 4.
Chapman, L. (2004). No child left behind in art? Arts Education Policy Review, 106 (2):
3–17.
Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in math achievement:
Evidence from a large-scale US sample. Research in Education, (90), 98-112.
doi:10.7227/RIE.90.1.7
121
Cheema, J., Kitsantas, A., & Ware, H. W. (2011). Mathematics achievement: The role of
homework and self-efficacy beliefs. Journal of Advanced Academics, 22(2): 310-
339.
Cole, K. (2011). Professional notes: Brain-based-research music advocacy. Music
Educators Journal, 98(1): 26-30. doi: 10.1177/0027432111416574
Colwell, R. (2005). Whither programs and arts policy? Arts Education Policy Review 106
(6): 19–29.
Cox, H. A., & Stephens, L. J. (2006). The effect of music participation on mathematical
achievement and overall academic achievement of high school students.
International Journal of Mathematical Education in Science & Technology,
37(7): 757-763. doi: 10.1080/00207739050013781
Dastjerdi, M., Ozker, M., Foster, B. L., Rangarajan, V., & Parvizi, J. (2013). Numerical
processing in the human parietal cortex during experimental and natural
conditions. Nature Communications, 4.
Davies, G. (2007). See government grow: Education politics from Johnson to Reagan.
Lawrence: University Press of Kansas.
Deere, K. B. (2010). The impact of music education on academic achievement in reading
and math. (Order No. 3425720, Union University). ProQuest Dissertations and
Theses,135. Retrieved from
http://search.proquest.com/docview/759833158?accountid=7374. (759833158).
Deutsch, G. K., Dougherty, R. F., Scachar, M. B., Tsang, J. M., & Wandell, B. (2009).
Training in the arts, reading, and brain imaging. Washington, DC: Dana Press.
122
Duncan, C. (2011). Re/Viewing student success in an era of accountability. Journal of
Unschooling & Alternative Learning, 5(10): 58-99.
Eisele-Dyrli, K. (2010). Study finds impact of NCLB on art ed generally negative.
District Administration, 46(3): 18.
Elmore, R. F., & Fuhrman, S. H. (2001). Research finds the false assumption of
accountability. Education Digest, 67(4): 9.
Elpus, K. (2012). Is it the music or is it selection bias? A nationwide analysis of music
and non-music students’ SAT scores. Journal of Research in Music Education.
Fieldman, R. S. (2010). Child development. Upper Saddle River: Prentice Hall.
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate
research in education. New York: McGraw-Hill Higher Education.
Francois, C., Chobert, J., Besson, M., & Schon, D. (2013). Music training for the
development of speech segmentation. Cerebral Cortex, 23(9): 2038-2043. doi:
10.1093/cercor/bhs180
Fujioka, T. (2006).One year of musical training affects development of auditory cortical
evoked fields in young children. Brain, 129, 2593–2608.
Gadberry, D. (2010). Music participation and academic success. Kodaly Envoy, 36(4):
13-14.
Gardner, H. (2006). Multiple intelligences: New horizons. New York: BasicBooks.
Gardner, H., & Moran, S. (2006). The Science of multiple intelligences theory: A
response to Lynn Waterhouse. Educational Psychologist, 41(4): 227-232. doi:
10.1207/s15326985ep4104_2
123
Geist, K., Geist, E. A., & Kuznik, K. (2012). The Patterns of music: Young children
learning mathematics through beat, rhythm, and melody. Young Children, 67(1):
74-79.
Good, C. J. (2010). A nation at risk. American Educational History Journal, 37(1/2):
367-386.
Gordon, E. (1971). The psychology of music teaching. Englewood Cliffs: Prentice-Hall.
Gordon, E. (1986). Learning sequences in music. Chicago: GIA
Gordon, E. E. (1999). All about audiation and music aptitudes. Music Educators Journal,
86(2): 41.
Gordon, E. E. (2002). Developmental and stabilized music aptitudes: Further evidence of
the duality. Chicago: GIA Publications.
Gordon, E. (2003). Learning sequences in music: Skill, content, and patterns: A music
learning theory. Chicago: GIA Publications.
Gordon, E. (2007). Learning sequences in music; Skill, content and patterns. Chicago;
GIA Publications.
Gordon, E.E. (2011). Roots of music learning theory and audiation. Chicago: GIA
Publications.
Grand Canyon University. (2013). Institutional review board handbook.
Grey, A. C. (2010). No child left behind in art education policy: A review of key
recommendations for arts language revisions. Arts Education Policy Review,
111(1): 8-15. doi:10.1080/10632910903228132
Hall, K. (2013). The Carnegie Hall Royal Conservatory Achievement Program. Journal
of Singing, 69(4): 467-470.
124
Hemmings, B., Grootenboer, P., & Kay, R. (2011). Predicting mathematics achievement:
The influence of prior achievement and attitudes. International Journal of Science
& Mathematics Education, 9(3): 691-705. doi:10.1007/s10763-010-9224-5
Ho, Y., Cheung, M., & Chan, A. (2003). Music training improves verbal but not visual
memory: Cross-Sectional and Longitudinal Explorations in Children.
Neuropsychology, 17(3), 439–450. doi:10.1037/0894-4105.17.3.439.
Hobbs, C. (1985). A Comparison of the music aptitude, scholastic aptitude, and academic
achievement of young children. Psychology of Music, 13(2): 93-98. doi:
10.1177/0305735685132003
Hodges, D. A. & O’Connell, D. S. (2005). The impact of music education on academic
achievement. Chapter 2 in Sounds of learning status report. International
Foundation for Music Research,
Holsomback, J. R. (2001). Testing the relationship between musical aptitude and
standardized test scores of beginner band students, Reports of Research in Music
Education, Duke, Robert A. (Ed.), Texas Music Educators Association, Austin.
Retrieved from www.tmea.org/080_College/Research/Hol2001.pdf
Holsomback, J. R. (2002). Testing the relationship between musical aptitude and
standardized test scores of beginner band students: Results of the second-year
study, Reports of Research in Music Education, Duke, Robert A. (ed.) Texas
Music Educators Association, Austin. Retrieved from
www.tmea.org/080_College/Research/Hol2002.pdf
Holsomback, J. R. (2004). Testing the relationship between musical aptitude and
standardized test scores of beginner band students: Results of the second-year
125
study, Reports of Research in Music Education, Duke, Robert A. (ed.) Texas
Music Educators Association. Austin. Retrieved from
www.tmea.org/080_College/Research/Hol2002.pdf
Hornbach, C. & Taggart, C. (2005). The relationship between developmental tonal
aptitude and singing achievement among kindergarten, first-, second-, and third-
grade students. Journal of Research in Music Education, 53(4), 322-331.
Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C., & Schlaug, G.
(2009). The effects of musical training on structural brain development. Annals of
the New York Academy of Sciences, 1169: 182-186. doi:10.1111/j.1749-
6632.2009.04852.x
Johanningmeier, E. V. (2010). "A nation at risk" and "Sputnik": Compared and
reconsidered. American Educational History Journal, 37(2): 347-365.
Johnson, D. L. (2010). Teaching band in a "program improvement" (NCLB) district.
School Band & Orchestra, 13(10): 44-46.
Johnson, K.A., Wilson, C.M. Wilson, & Williams-Rossi, D. (2014). All reading tests are
not created equal: A comparison of the state of Texas assessment of academic
readiness (STAAR) and the Gray Oral Reading Test-4 (GORT-4). Texas Journal
of Literacy Education, 1(1), 13-22.
Johnson, W. L., Johnson, A. M., & Johnson, J. (2012). Predicting student success on the
Texas chemistry STAAR Test: A logistic regression analysis. Online Submission.
Jorgenson, O. (2012). What we lose in winning the test score race. Principal, 91(5), 12-
15.
126
Karma, K. (2007). Musical aptitude definition and measure validation: Ecological
validity can endanger the construct validity of musical aptitude tests.
Psychomusicology: A Journal of Research in Music Cognition, 19(2): 79-90.
doi:10.1037/h0094033
Kelly, S. N. (2012). Fine arts-related instruction’s influence on academic success.
Journal of Research in Music Education, 54(4): 293-307.
Kitsantas, A., Cheema, J., & Ware, H. W. (2011). Mathematics achievement: The role of
homework and self-efficacy beliefs. Journal of Advanced Academics, 22(2): 310-
339.
Knoeppel, R. C., & Brewer, C. A. (2011). Education reform, equal opportunity and
educational achievement: Do trend data adequately report progress? Education
Policy Analysis Archives, 19(10): 1-27.
Krumenaker, L. (2009). The modern U.S. high school astronomy course, its status and
makeup, and the effects of No Child Left Behind. Astronomy Education Review,
8(1): 010107-1-010107-8. doi: 10.3847/AER2009042
Kuhlman, K. (2005). Musical aptitude versus academic ability as a predictor of beginning
instrumental music achievement and retention: Research and implications.
UPDATE: Applications of Research in Music Education, 24(1): 34-43.
Linder, S. M., Powers-Costello, B., & Stegelin, D. A. (2011). Mathematics in early
childhood: research-based rationale and practical strategies. Early Childhood
Education Journal, 39(1): 29-37. doi: 10.1007/s10643-010-0437
127
Major, M. L. (2013). How they decide: A case study examining the decision-making
process for keeping or cutting music in a K–12 public school district. Journal of
Research in Music Education, 61(1): 5-25. doi:10.1177/0022429412474313
Maltese, A. V., Tai, R. H., & Xitao, F. (2012). When is homework worth the time?
Evaluating the association between homework and achievement in high school
science and math. High School Journal, 96(1): 52-72.
Marshall, D. (1998). "snowballing technique." A Dictionary of Sociology. Retrieved from
http://www.encyclopedia.com/doc/1O88-snowballingtechnique.html
Martin, D. (2012). Rich assessment in a first-year, teacher education (primary)
mathematics education subject. International Journal of Pedagogies & Learning,
7(1): 62-72.
Mathis, W. J. (2004). NCLB and high-stakes accountability: A cure? Or a symptom of
the Disease? Educational Horizons, 82(2): 143-152.
McGuinn, P. J. (2006). No child left behind and the transformation of federal education
policy, 1956-2005. Lawrence: University Press of Kansas
McLelland, M. (2005). The impact of instrumental music on student achievement in
reading and mathematics. Doctoral dissertation, Wilmington College, Delaware.
Meyer, L. (2005). The complete curriculum: Ensuring a place for the arts in America’s
schools. Arts Education Policy Review, 106 (3): 35–392005. UMI No. 3156552.
Michener, D., & Fishoff, J. (2012). Surprising insights: Literacy and music. California
Reader, 45(3): 25-32.
Mishook, J., and M. Kornhaber. (2006). Arts integration in an era of accountability. Arts
Education Policy Review, 107(4): 3–11.
128
Morales, E. E. (2010). Linking strengths: Identifying and exploring protective factor
clusters in academically resilient low-socioeconomic urban students of color.
Roeper Review, 32(3): 164-175. doi:10.1080/02783193.2010.485302
Morse, L. L. (2010). Involvement in sixth-grade beginner band: A relational study of the
impact on mathematics achievement. The University of Mississippi. ProQuest
Dissertations and Theses, 86. Retrieved from
http://search.proquest.com/docview/858094089?accountid=7374 (858094089).
Mursell, J. L. (1937). What about music tests? Music Educators Journal, 24(2): 16. doi:
10.2307/3385164
Musa, A. J. (2013). Gender, geographic locations, achievement goals and academic
performance of secondary school students from Borno State, Nigeria. Research in
Education, (90), 15-31. doi:10.7227/RIE.90.1.2
Musacchia, G., Sams, M., Skoe, E., & Kraus, N. (2007). Musicians have enhanced
subcortical auditory and audiovisual processing of speech and music. Proceedings
of the National Academy of Sciences, 104(40), 15894-15898. doi:
10.1073/pnas.0701498104
National Assessment of Educational Progress. (2009): Math assessment. ED Review, 5.
Oare, S., & Bernstorf, E. (2010). Literacy in the instrumental classroom. Kodaly Envoy,
37(1): 17-20.
Olszewski-Kubilius, P., & Thomson, D. L. (2010). Gifted programming for poor or
minority urban students: Issues and lessons learned. Gifted Child Today, 33(4):
58-64.
129
Palmer, R. T., Davis, R. J., Moore III, J. L., & Hilton, A. A. (2010). A nation at risk:
Increasing college participation and persistence among African American males
to stimulate U.S. global competitiveness. Journal of African American Males in
Education, 1(2): 105-124.
Pane, D., & Salmon, A. (2011). Author's camp: Facilitating literacy learning through
music. Journal of Reading Education, 36(2): 36-42.
Peynircioglu, Z. F., Durgunoglu, A. Y., & Uney-Kusefoglu, B. (2002). Phonological
awareness and musical aptitude. Journal of Research in Reading, 25(1): 68-80.
doi: 10.1111/1467-9817.00159
Phillips-Silver, J. (2009). On the meaning of movement in music, development and the
brain. Contemporary Music Review, 28(3): 293-314.
doi:10.1080/07494460903404394
Piro, J. (2009). Music training and literacy development. Literacy Today, 59, 32-34.
Piro, J., & Ortiz, C. (2009). Music education can help children improve reading skills.
Journal of Psychology of Music, 65 (1): 32-37.
Posner, M., Rothbart, M., Sheese, B., & Kieras, J., (2008) How arts training influences
cognition. Learning, Arts and the Brain (pp.1-11). Dana Consortium Report on
Arts and Cognition.
Powell, D., Higgins, H. J., Aram, R., & Freed, A. (2009). Impact of no child left behind
on curriculum and instruction in rural schools. Rural Educator, 31(1): 19-28.
Proctor Duax, K. (2013). How important is music education? Journal of Singing, 69(3):
257-259.
130
Radocy, R. E., & Boyle, J. D. (2003). Psychological foundations of musical behavior.
Springfield: Thomas.
Rainbow, E. L. (1965). A pilot study to investigate constructs of musical aptitude.
Journal of Research in Music Education, 13, 3-14.
Ravitch, D., & Cortese, A. (2009). Why we’re behind: What top nations teach their
students but we don't. Education Digest, 75(1): 35-38.
Rowe, E. W., Miller, C., Ebenstein, L. A., & Thompson, D. F. (2012). Cognitive
predictors of reading and math achievement among gifted referrals. School
Psychology Quarterly, 27(3): 144-153. doi:10.1037/a0029941
Rubinson, L. E. (2010). A correlational study of the relationships between music aptitude
and phonemic awareness of kindergarten children. Capella University). ProQuest
Dissertations and Theses, 137. Retrieved from
http://search.proquest.com/docview/305243668?accountid=7374 (305243668).
Rutkowski, J. (1996). The effectiveness of individual/small group singing activities on
kindergartners' use of singing voice and developmental music aptitude. Journal of
Research in Music Education, 44(4): 353-68.
Schutz, D., Simon, D. M., & Musgrave, R. (2013). Toward educational testing reform:
Inside reading achievement tests. Education Policy Analysis Archives, 21(90): 1-
28.
Seashore, C. E. (1919). The psychology of musical talent. Boston: Silver, Burdett and
Company.
Seashore, C. E. (1938). The Psychology of music. XIII. Musical intelligence. Music
Educators Journal, 24(5): 32. doi: 10.2307/3385427
131
Simmons, J. C. H. (1981). An investigation of relationships among primary-level student
performance on selected measures of music aptitude, scholastic aptitude, and
academic achievement. (Doctoral dissertation, Peabody College for Teachers of
Vanderbilt University). Abstract retrieved from Dissertations & Theses: A&I
database.
Slaton, E. (2012). Collegiate connections: Music education budget crisis. Music
Educators Journal, 99(1): 33-35. doi:10.1177/0027432112454837
Spelke, E. S. (2009). Effects of music instruction on developing cognitive systems at the
foundations of mathematics and science. Learning, Arts and the Brain: The Dana
Consortium Report on Arts and Cognition. NY/Washington D.C.: Dana Press.
Spohn, C. (2008). Teacher perspectives on no child left behind and arts education: A case
study. Arts Education Policy Review, 109(4): 3-12.
Standley, J. M. (2008). Does music instruction help children learn to read? Evidence of a
meta-analysis. Update: Applications of Research in Music Education, 27(1): 17-
32. doi: 10.1177/8755123308322270
Stanley, N., & Stanley, L. (2011). Predicting FCAT reading scores using the reading-
level indicator. Reading Psychology, 32(2): 99-112.
doi:10.1080/02702711.2010.507529
Stanton, H. M. (1922). The inheritance of specific musical capacities. Cold Spring
Harbor.
Stubnisky, R. H., Perry, R. P., Hall, N. C., & Guay, F. (2012). Examining perceived
control level and instability as predictors of first-year college students' academic
achievement. Contemporary Educational Psychology, 37(2): 81-90.
132
Talley, C. P., & Scherer, S. (2013). The enhanced flipped classroom: Increasing
academic performance with student-recorded lectures and practice testing in a
"flipped" STEM course. Journal of Negro Education, 82(3): 339-347.
Texas Education Agency. (2003). Enrollment in Texas public schools, 2002–03
(Document No. GE09 601 06). Austin: Author.
Texas Education Agency. (2006). 2006 accountability system state summary. Retrieved
March 2, 2014 from http://www.tea.state.tx.us/accountability/
Texas Education Agency. (2007). 2007 accountability system state summary. Retrieved
March 2, 2014 from http://www.tea.state.tx.us/accountability/
Texas Education Agency. (2008). 2008 accountability system state summary. Retrieved
March 2, 2014 from http://www.tea.state.tx.us/accountability/
Texas Education Agency. (2009). Snapshot 2009. Austin: Author. Retrieved from
http://ritter.tea.state.tx.us/perfreport/snapshot/2008/state.html
Texas Education Agency. (2012). Snapshot 2012. Austin, TX: Author. Retrieved from
http://www.tea.state.tx.us/index4.aspx?id=2147505027
Texas Education Agency. (2014, January 9). STAAR® resources. Texas Education
Agency. Retrieved from http://www.tea.state.tx.us/student.assessment/staar/
Toldson, I. A. (2012). Editor's comment: When standardized tests miss the mark. Journal
of Negro Education, 81(3): 181-185.
Tomei, M. C. (2010). The relationship between musical aptitude and academic
achievement of third-grade students (Order No. 1480763). Available from
ProQuest Dissertations & Theses Full Text: The Humanities and Social Sciences
133
Collection. (755055207). Retrieved from
http://search.proquest.com/docview/755055207?accountid=7374
Ukkola-Vuoti, L., Kanduri, C., Oikkonen, J., Buck, G., Blancher, C., Raijas, P., Karma,
K., Lahdesmaki, H., & Järvelä, I. (2013). Genome-wide copy number variation
analysis in extended families and unrelated individuals characterized for musical
aptitude and creativity in music. Plos ONE, 8(2): 1-9.
doi:10.1371/journal.pone.0056356
United States Department of Education. (1983). A nation at risk: The imperative for
educational reform (National Commission on Excellence in Education).
Washington, DC: Author.
United States Department of Education. (2009). United States Department of Education
FY 2009 Annual Performance Report. United States Department of Education.
U.S. Department of Education. (1983). A nation at risk: The imperative for educational
reform: A report to the Nation and the Secretary of Education, United States
Department of Education. Washington, D.C.: National Commission on
Excellence in Education.
U.S. Department of Education. (2010). United States Department of Education FY 2009
annual performance report. U.S. Department of Education.
Wadsworth, B. J. (1996). Piaget's theory of cognitive and affective development:
Foundations of constructivism. White Plains: Longman USA.
Waller, D. (2010). Language literacy and music literacy: A pedagogical asymmetry.
Philosophy of Music Education Review, 18(1): 26-44.
134
Weaver, K. (2011). Standardized testing: Measurement of academic achievement. Online
Submission.
Webb, M. N. A. (1984). An investigation of the relationship of musical aptitude and
intelligence of students at the third grade level. (Doctoral dissertation, The
University of North Carolina at Greensboro). Abstract retrieved from
Dissertations & Theses: A&I database.
West, C. (2012). Teaching music in an era of high-stakes testing and budget reductions.
Arts Education Policy Review, 113(2): 75-79.
doi:10.1080/10632913.2012.656503
William, D. (2010). Standardized testing and school accountability. Educational
Psychologist, 45(2): 107-122. doi:10.1080/00461521003703060
Williams, A. (2011). A Call for change: Narrowing the achievement gap between white
and minority students. Clearing House, 84(2): 65-71.
doi:10.1080/00098655.2010.511308
Yin, R. K. (2009). Case study research: Design and methods, 4th edition. London: SAGE
Publishing.
Young, W. T. (1971). The Role of Musical Aptitude, Intelligence, and Academic
Achievement in Predicting the Musical Attainment of Elementary Instrumental
Music Students. Journal of Research in Music Education, 19(4): 385.
Young Ju, J., Hyuck, S., Sunyoung, J., & You Kyung, L. (2012). The effects of academic
self-efficacy, learning strategies, and perceived instructional strategies on high
and low achievers' in the middle school Korean language. KEDI Journal of
Educational Policy, 9(2): 239-257.
135
Zentner, M., & Eerola, T. (2010). Rhythmic engagement with music in infancy.
/Proceedings of the National Academy of Sciences, 107(13): 5768-5773. doi:
10.1073/pnas.1000121107
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Appendix A
Site Authorization Letter
137
Appendix B
Permission to Use Instrument
138
Appendix C
IMMA Instrument
139
140
141
142
143
144
145
146
147
Appendix D
Informed Consent
148
149
Appendix E
Computation of Minimum Sample Size
Exact - Correlation: Bivariate normal model
Options: exact distribution
Analysis: A priori: Compute required sample size
Input: Tail(s) = Two
Correlation ρ H1 = 0.475
α err prob = 0.05
Power (1-β err prob) = 0.8
Correlation ρ H0 = 0
Output: Lower critical r = -0.2759365
Upper critical r = 0.2759365
Total sample size = 85
Actual power = 0.9507848
t-tests - Linear bivariate regression: One group, size of slope
Analysis: Post hoc: Compute achieved power
Input: Tail(s) = One
Slope H1 = 0.15
α err prob = 0.05
Total sample size = 65
Slope H0 = 0
Std dev σ_x = 1
Std dev σ_y = 1
Output: Noncentrality parameter δ = 1.2231777
Critical t = 1.6694022
Df = 63
Power (1-β err prob) = 0.8018498
150
Appendix F
IRB Approval Letter
