How should I read this book?

This book is intended for many different audiences. We hope that you will read it bit by bit, but return to it often. Here are some ideas on how to do it.

We also hope that you will read papers, books, tutorials, or blog entries afterwards: the book is not a self-contained object (even with its accompanying blog). It is an invitation to discover quantitative research that respects the core values of humanistic research – and is engaging! In this blog, we point you to our favorite papers/books cited in each chapter, so that you can plan further readings.

But first, how show you read the book? It depends, of course, on what you are looking for. We have stuff for persons who just want to understand an analysis that they found in a paper or book; and if you want to practice quantification, we have advice for unconvinced beginners; for non-historians; for persons who think they could quantify but don’t know how to create data for their sources (our specialty); for beginners generally; for advanced digital humanists and those who would like to become one; and even for advanced quantifiers.

If you have ever read a paper or book that you were interested in for substantive reasons, but were stopped or intimidated by its opaque presentation of the results of a regression, a network analysis, or some other bizarre technique, our book might help you. We present, in non-mathematical terms, the principles of each method, the important things to look for in the results, as well as the main dangers of over-interpretation, so as to equip humanists with the tools to assess quantitative research. The index will tell you where to look for the method that you encountered, but basically, Chapter 2 addresses sampling, percentages, and tests; Chapter 4 addresses regression and factor analysis; Chapter 5 addresses network analysis, sequence analysis, and event history analysis; Chapter 6 addresses, network graphs, maps, and GIS; Chapter 7 addresses text analysis (e.g. topic modeling).

If you have never practiced quantification and wonder (or are even skeptical) about what it could bring to your field, in which it is not common to use numbers, graphs, maps, etc., you can perhaps begin with the index. We have created entries for topics that are not often thought of as suited to quantification, like “art history,” “gender,” or “literature.” They will guide you to discussions of works that we like and that address the topic with some type of quantification. (in the future, we might offer suggestions here as to what to read if you want to read just one paper in quantitative art history, quantitative gender studies, etc.)

By the way, if you are not an historian, you will quickly notice that we are. But we really think that our book can be useful in other disciplines in the humanities and the social sciences. (it is our experience with a previous, French version of the book that it has been used in many other disciplines; its title said “for historians” but was, correctly, read as “for dummies”) The index will point you to pages where we more directly address, art history, archaeology, political science, etc.

If you think that something could be quantified in your sources, but have no idea on what to do to this effect, this is the best book for you! As far as we know, it is the only book that addresses the actual work of translating an archival or printed source (or an image, etc.) into a database without losing sights of the main tenets of humanistic interpretation. You want to start with Chapter 3.

Before or after, if you are looking for the basic mistakes to avoid and for basic methods that are useful in almost any case of quantification in the humanities, you should read Chapter 2 for advice on sampling and explanations about tables and percentages; and the first part of Chapter 6 for general advice on visualization. (you can then look here for further readings, then practical advice)

If you define yourself as a digital humanist, you will find familiar ground in Chapters 5 (networks), 6 (maps and GIS), and 7 (texts), but you might be surprised by how we address these methods, by some works and methods that we cite and others that we don’t. If you want to understand how (uncomfortably) we situate ourselves vis-√†-vis digital humanities, you can have a look at Chapter 1. Still, if you want to learn some basics of digital humanities, Chapters 5, 6, and 7, (with the additional pointers listed here – we advise you to go to further readings instead of directly jumping to practical advice), will work as a gentle introduction!

If you are a quantitative social scientist, or a historian well-versed in quantification – perhaps an economic historian or historical demographer -, you might think that we don’t go far enough, especially on regression. But you might be interested in learning the basics of less standard methods, such as factor analysis if you are not francophone (Chapter 4), network analysis and sequence analysis if you are not a sociologist (Chapter 5), or text analysis (Chapter 7) (you can then look here for further readings, then practical advice). Or perhaps what you are looking for is not how to analyze data (you know enough in this department) but how to go from an archive (or some other type of document that you are not used to) to you regular “clean” dataset (.csv file, “tidy” file, or whatever). Chapter 3 is here for you.


Author: Claire Lemercier

CNRS research professor of history in Paris / Directrice de recherche au CNRS en histoire, au Centre de sociologie des organisations

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