| Kommentar |
The rise of personal computers, Excel sheets, and, more recently, large language models such as ChatGPT has completely reshaped research in the social sciences and the humanities. The course aims to introduce history students to the quantitative methods used in the social sciences within the framework of economic history. Each session will focus on a specific method/tool, addressing the theory behind it and discussing applications from the historical literature. As case studies, we will investigate the rise of modern states in the 19th and 20th centuries and the problem of income and wealth inequality.
The course is meant for history students with little to no background in mathematics. Previous knowledge of statistics is not required. The course is structured in two parts. In the first part, we will cover the main methods and read together historical literature where these methods are implemented. Topics include basic quantitative notions (collecting data, Microsoft Excel, OCR and transcription methods), basic statistics (manipulating variables, mean, variance, distributions, logs and differences), data visualisation (in Excel and the software R), regression analysis, causal inference, drawing maps, network analysis (all in R). The second part of the course will primarily consist of students’ projects based on the methods learned in part I. Most classes require the use of a laptop. If that is not possible, please contact the lecturer. |
| Literatur |
Feinstein, C. H. and Thomas, M., Making History Count: A Primer in Quantitative Methods for Historians (Cambridge: Cambridge University Press, 2002)
Hudson, P. and Ishizu, M., History by Numbers: An Introduction to Quantitative Approaches, Second edition (London, New York: Bloomsbury Academic an imprint of Bloomsbury Publishing Plc, 2017)
Huntington-Klein, N., The Effect: An Introduction to Research Design and Causality (Boca Raton, London, New York: CRC Press, 2022)
The Programming Historian https://programminghistorian.org/ |
