Data Visualization using Principal Component Analysis within the Digital Humanities

Abstract

The present study investigates the application of principal component analysis (PCA) and the resulting visualizations within the scholarly field of the Digital Humanities. Recent developments in digital technologies, tools and methods have created unprecedented opportunities for the humanities. The increasing use of digital methods and tools calls for a critical understanding and exploration of the ways (visual) information is processed en produced. The current debate on data analysis and the use of digital methods within the Digital Humanities has catalyzed this call. This thesis will therefore investigate the application of principal component analysis and resulting visualizations within the scholarly field of the Digital Humanities. In this study the methodological aspects of processing and visualizing data using principal component analysis in 46 Digital Humanities studies are analyzed in a structural manner, resulted in concerns on the lack of transparency in the implementation of the method and the perils of objectivity in humanities research. My research found that most researchers seem to have an incomplete, reticent and closed position with respect to the method. A more transparent and reliable approach is therefore also proposed. In conclusion I argue for a more critical and informed debate on digital methods in the humanities in general.