Triangulation is a research technique that involves looking at the same thing from two different perspectives. In surveying, it enables positions and distances to be calculated by measuring angles from two locations. In the social sciences, it can increase the reliability of conclusions if they are found by two (or more) different methods. And in statistical historical musicology, looking for the same works or composers in two or more datasets can tell us a lot about the characteristics of the datasets, and about the works’ patterns of survival or dissemination. Continue reading →
Often in statistical analysis we need to select things at random. For example, if it is impractical to work with a complete dataset, the only option might be to use a random sample. The science of statistics tells us how to analyse a sample in order to reach conclusions about the entire dataset, and gives us ways to calculate margins of error based on the size of the sample. But I digress.
So, how might we pick a random composer? Continue reading →
I have recently been trying to collect data from the Listening Experience Database (LED) in order to put together a proposal for a conference paper. The LED is a nicely constructed database using linked open data and a structure based on something called the ‘Semantic Web’. Rather than traditional databases that have a hierarchical ‘tree’ structure, the Semantic Web concept is a true ‘network’, where anything can be linked to anything else. The LED, for example, includes links to data on a number of other databases. Have a look at the LED and follow a few links and you will see what this means – a very rich and flexible means of linking data together. 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 →