Peter Miksza, Kenneth Elpus

Design and Analysis for Quantitative Research in Music Education (eBook, PDF)

• Format: PDF

Produktbeschreibung

In recent years, academics and professionals in the social sciences have forged significant advances in quantitative research methodologies specific to their respective disciplines. Although new and sophisticated techniques for large-scale data analyses have become commonplace in general educational, psychological, sociological, and econometric fields, many researchers in music education have yet to be exposed to such techniques. Design and Analysis of Quantitative Research in Music Education is a comprehensive reference for those involved with research in music education and related fields, providing a foundational understanding of quantitative inquiry methods. Authors Peter Miksza and Kenneth Elpus update and expand the set of resources that music researchers have at their disposal for conceptualizing and analyzing data pertaining to music-related phenomena. This text is designed to familiarize readers with foundational issues of quantitative inquiry as a point of view, introduce and elaborate upon issues of fundamental quantitative research design and analysis, and expose researchers to new, innovative, and exciting methods for dealing with complex research questions and analyzing large samples of data in a rigorous and thorough manner. With this resource, researchers will be better equipped for dealing with the challenges of the increasingly information-rich and data-driven environment surrounding music education. An accompanying companion website provides valuable supplementary exercises and videos.

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Produktdetails

• Verlag: Oxford University Press
• Seitenzahl: 272
• Erscheinungstermin: 1. Februar 2018
• Englisch
• ISBN-13: 9780199391929
• Artikelnr.: 54569967

Autorenporträt

Peter Miksza is Associate Professor of Music (Music Education) at the Indiana University Jacobs School of Music and an affiliate member of the Indiana University Cognitive Science Program. He teaches courses in instrumental music education, the pyschology of music, learning processes in music, measurement and assessment in music, and research methods. He earned his PhD and Master of Music degrees from the Indiana University Jacobs School of Music and his Bachelor of Music degree and K-12 teaching credential from The College of New Jersey. Dr. Miksza's scholarship is focused on self-regulated music learning, musical skill acquisition, music teacher preparation, and music education policy. Kenneth Elpus is Assistant Professor of Music Education at the University of Maryland, where he teachers courses in choral music education, research methods, and conducts the university Treble Choir. Dr. Elpus earned his PhD and Master of Music degrees from Northwestern University where he held a fellowship in the Center for the Study of Education and the Musical Experience. He earned his Bachelor of Music degree and K-12 teaching credential from The College of New Jersey. Dr. Elpus's scholarship is focused on issues related to music education and public policy, the demographics of music students, the process of selection into music education, and music education as a context for adolescent development.

Inhaltsangabe

* Table of Contents
* List of Illustrations
* Acknowledgements
* About the Companion Website
* 1. Prelude
* 2. Characteristics of Scientific Inquiry
* Part I. Foundational Concepts for Quantitative Inquiry in Music
Education
* 3. Descriptive Research Design
* 4. Descriptive Analysis
* 5. Inferential Analysis
* 6. Correlational Design and Analysis
* 7. Causation and Causal Claims
* 8. Design and Analysis of Experimental Research I
* 9. Design and Analysis of Experimental Research II
* 10. Regression
* Part II. Advanced Concepts for Quantitative Inquiry in Music
Education
* 11. Analyses of Complex Survey Data
* 12. Multilevel Models
* 13. Exploratory and Confirmatory Factor Analysis
* 14. Structural Equation Modeling
* 15. Advances in Quasi-Experimental Methods
* 16. Postlude
* Appendix: Inferential Analysis with Non-parametric Tests
* Index