We could only write our book because we have taught quantitative methods (mostly to historians, also to other humanists and social scientists) for more than 15 years. In France as elsewhere, however, there are few courses specifically aimed at historians or humanists – or they only address specific methods (GIS, network analysis, text analysis, etc.) and seldom discuss inputting and categorizing data. Therefore, our first intention when we wrote the initial, French version of the book in 2007 was to offer a self-help handbook: something that beginning graduate students (and any person beginning a research) could use to learn the basics on their own; the book and the companion website would point them to further readings and tutorials. The English book of 2019 can be used in the same way (here are some ways to read it on your own, depending on your needs).
If, however, you are teaching quantitative history, or digital humanities, or methods generally, and you want to devote at least a few lessons to the contents of our book, this post will give you a general idea of our own teaching formats. We are very willing to discuss ways to teach quantitative methods to diverse audiences (in comments here, over e-mail, in conferences, etc.). Continue reading “Teaching with our book”
Years of experience have convinced us that you don’t need a more sophisticated tool than a spreadsheet editor (i.e. Excel or Calc, the Open/Libreoffice equivalent) if you want to properly store (input) and analyze (categorize and create visualizations and calculation based on) historical data for a research project. For a PhD dissertation, say, or for any other project that is personal or involves just a few collagues. (Sharing data with the general public is a different matter)
So you won’t find advice on this blog about database managers or the TEI. Nothing wrong with those, and please do use them if you know how to (you’ll still have to export .csv files if you want to quantify). What we just say is that learning them is not necessary to quantify properly, and that they are more difficult to learn, in our experience, than the proper use of a spreadsheet editor. (We discuss this with more details in Chapter 3) So here is a list of functions that you should learn to use in order to store and analyze your data using a spreadsheet editor. This is the more practical complement to our “ten commandments of inputting data.” (those are the core of our book, in our view, and they seem to have helped a lot of users of the French version)
Continue reading “Things that you should learn to do with a spreadsheet editor”
Joanna Drucker’s “Humanities Approaches to Graphical Display,” a classic among critical digital humanists, is ostensibly a paper on visualization, with nice, original figures. The author discusses the implicits of “objective” visualizations; those often endorse simple conceptions of time (e.g. linear or even with no past) and categories (exclusive from one another, with firm boundaries, etc.). Doing this, she in fact addresses the implicits of “objective” categorization generally – and she explicity makes the point that data are capta (constructed), that humanists know this and that using computers/quantification should not make them forget it. Her paper might free your imagination for the categorization of your own data. If you want to begin with an even shorter piece, she makes some of the same points with incisive clarity in this interview (with Miriam Posner, by Miriam Kienle). Continue reading “Categorization: Principles and examples”
Here, we have too many options! As a relatively recent, marginal technique, promoted by sociologists who like fine-grained descriptions and thinking about time, sequence analysis has produced many papers that we like – not just because they explain the technique rather clearly, but because they tend to care about their sources and categories. So if you want to read just one paper after reading our overview, you should choose among the following depending on your substantive research interests. They are all clear and accessible as regards sequence analysis per se. Continue reading “Favorite papers in sequence analysis”