Christmas music is everywhere at the moment, so I thought I would look at the history of it. In the British Library Music Catalogue, of the one million or so total publications, almost 10,000 – very nearly 1% – have the words ‘Christmas’, ‘Noel’ or ‘Weihnacht’ in the title. This chart shows the proportion by publication date…Continue reading →
Many datasets of composers tell us relatively little about them, so we sometimes have to guess details from the information available – such as the composer’s name. Forenames, for example, are often a good indicator of gender, as described in this previous article. Titles – associated with the church, aristocracy or royalty – can also reveal gender, and tell us about occupation or social class. This article looks at what names can tell us about nationality – based on a recent attempt to identify Italian composers among the many obscure and unknown names listed in the British Library’s music catalogue.Continue reading →
Following this previous article, a friend got in touch to thank me for disproving some astrological ‘nonsense’. I replied that I had not disproved anything – I had just failed to find supporting evidence – but it did get me wondering about the nature of the conclusions that can be drawn from this sort of analysis.
Suppose, for the sake of argument, that people born under Aquarius do show a significantly higher propensity to become composers than those born under Virgo. Consider these three possible explanations… Continue reading →
If you go to the British Library online catalogue, search for music scores published in each year from 1650 to 1920, and plot the number of ‘hits’ by year, the result looks like this. Continue reading →
In what ways can statistical techniques be used to investigate topics in historical musicology? I think there are four main approaches – hypothesis testing, quantification, modelling and exploration. Their use depends on the topic, the data, and the type of question you are trying to answer.
These four types often overlap. It is hard to do modelling without some exploration and quantification, for example. Also, after you have spent so long collecting the data, cleaning it, and getting it into a form for statistical analysis, why not squeeze the most out of it and do some general exploration after testing your hypotheses? Continue reading →