Statistics in Historical Musicology
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Open Research Online The Open University’s repository of research publications and other research outputs Statistics in Historical Musicology Thesis How to cite: Gustar, Andrew James (2014). Statistics in Historical Musicology. PhD thesis The Open University. For guidance on citations see FAQs. c 2014 Andrew Gustar Version: Version of Record Link(s) to article on publisher’s website: http://dx.doi.org/doi:10.21954/ou.ro.0000a37b Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online’s data policy on reuse of materials please consult the policies page. oro.open.ac.uk STATISTICS IN HISTORICAL MUSICOLOGY by Andrew James Gustar MA (Music, Open University, 2009) MA (Mathematics, Cambridge, 1989) Submitted 2nd May 2014 for the degree of Doctor of Philosophy Faculty of Arts Open University Andrew Gustar Statistics in Historical Musicology Page 3 of 297 Abstract Statistical techniques are well established in many historical disciplines and are used extensively in music analysis, music perception, and performance studies. However, statisticians have largely ignored the many music catalogues, databases, dictionaries, encyclopedias, lists and other datasets compiled by institutions and individuals over the last few centuries. Such datasets present fascinating historical snapshots of the musical world, and statistical analysis of them can reveal much about the changing characteristics of the population of musical works and their composers, and about the datasets and their compilers. In this thesis, statistical methodologies have been applied to several case studies covering, among other things, music publishing and recording, composers’ migration patterns, nineteenth-century biographical dictionaries, and trends in key and time signatures. These case studies illustrate the insights to be gained from quantitative techniques; the statistical characteristics of the populations of works and composers; the limitations of the predominantly qualitative approach to historical musicology; and some practical and theoretical issues associated with applying statistical techniques to musical datasets. Quantitative methods have much to offer historical musicology, revealing new insights, quantifying and contextualising existing information, providing a measure of the quality of historical sources, revealing the biases inherent in music historiography, and giving a collective voice to the many minor and obscure works and composers that have historically formed the vast majority of musical activity but who have been largely absent from the received history of music. Acknowledgements I am grateful to my supervisors, Professor David Rowland and Professor Kevin McConway, for their advice and encouragement, and for allowing me to pursue what appeared, at first, to be a somewhat unusual line of research. I also appreciate the many individuals at the Open University and elsewhere who have given me insights, ideas, feedback and comments at presentations or in individual discussions, without which this research would certainly have been more difficult and less coherent. I particularly thank my wife Gill for her support and patience during the last four years. Total word count: 91,253 Andrew Gustar Statistics in Historical Musicology Page 4 of 297 Contents 1 A Methodological Blind-Spot .............................................................................................. 6 2 Research Objectives and Approach ................................................................................... 27 2.1 The Statistical Approach 32 2.2 The Case Studies 34 3 Musical Datasets ................................................................................................................. 47 3.1 What to Look for in a Musical Dataset 51 3.2 The Characteristics of Musical Datasets 58 4 The Statistical Methodology .............................................................................................. 80 4.1 The Key Concepts of Statistics 82 4.2 Defining the Question 94 4.3 Sampling 100 4.4 Creating Usable Data 120 4.5 Understanding and Exploring the Data 136 4.6 Quantifying the Data 147 4.7 Hypothesis Testing 160 4.8 Presenting and Interpreting Statistical Results 169 5 Musicological Insights ...................................................................................................... 178 5.1 Composers’ Lives 179 5.2 Compositions 196 5.3 Dissemination 217 5.4 Survival, Fame and Obscurity 230 6 Quantitative and Qualitative Research in Music History ............................................... 243 Appendix A: Further detail of Case Studies ........................................................................... 259 A1. Pazdírek Case Study 259 A2. Macdonald Case Study 262 A3. Piano Keys Case Study 267 A4. Recordings Case Study 271 A5. Biographical Dictionaries Case Study 274 A6. Composer Movements Case Study 276 A7. ‘Class of 1810’ and ‘Class of 1837’ Case Studies 278 Appendix B: Musical Datasets ................................................................................................ 282 B1. Historical Datasets 282 B2. Current Datasets 287 Bibliography ............................................................................................................................. 290 Datasets 290 Literature 295 Analytical Tools 297 Andrew Gustar Statistics in Historical Musicology Page 5 of 297 Table of Figures The numbered Figures listed here include graphs, diagrams and tables used as illustrations. Other tables that are integral to the text are not numbered. Figure 1: Normal Distribution.................................................................................................. 88 Figure 2: Cross-tabulation of Penguin Guides and genres .................................................... 138 Figure 3: Average key signatures ............................................................................................. 140 Figure 4: Destinations of composers ....................................................................................... 141 Figure 5: British Library music holdings by attributed date .................................................. 142 Figure 6: Example correlation coefficients ............................................................................. 143 Figure 7: Average key by age of composer .............................................................................. 144 Figure 8: Modularity classes of composer movements network ............................................ 146 Figure 9: Composers per page in Pazdírek (1904–10) ............................................................ 148 Figure 10: Works per composer (random composers) ........................................................... 155 Figure 11: Works per composer (random works) ................................................................... 156 Figure 12: Implied probability of publication ........................................................................ 158 Figure 13: ‘Rich chart’ of composer exports ........................................................................... 174 Figure 14: Confidence ranges for composer exports and imports ........................................ 175 Figure 15: Distribution of works and composers in Pazdírek’s Universal Handbook .............. 176 Figure 16: Distribution of technical difficulty of piano works ............................................... 177 Figure 17: Composers’ linguistic groupings by period ........................................................... 182 Figure 18: Distribution of number of composer moves......................................................... 185 Figure 19: Duration between composer moves ...................................................................... 185 Figure 20: Distribution of age at time of composer moves .................................................... 185 Figure 21: Top composer destinations .................................................................................... 186 Figure 22: Average length of stay by destination .................................................................... 187 Figure 23: Share of composer imports and exports ............................................................... 187 Figure 24: Metre code vs composition date ............................................................................ 199 Figure 25: Distribution of time signatures ............................................................................. 200 Figure 26: Metre code of song vs other genres ....................................................................... 201 Figure 27: Keyboard works in extreme keys ............................................................................ 203 Figure 28: Average key signature (all genres) by date ............................................................. 203 Figure 29: Average key of keyboard works by composer’s age ............................................... 205 Figure 30: Average key of keyboard works by composer status .............................................. 206 Figure 31: Proportion of major keys (all genres) by date ........................................................ 207 Figure 32: Major keys by region and date............................................................................... 208 Figure 33: Bar of first non-diatonic accidental: mean and sample points ............................ 209 Figure 34: Distribution of changes of key