Introduction

Last updated on 2025-04-23 | Edit this page

Overview

Questions

  • What is the purpose of quantitative data analysis in the humanities?
  • When is it meaningful to use quantitative data analysis in humanities research?
  • What kinds of operations can be performed in quantitative data analysis?
  • How can these operations be performed using Python programming?

Objectives

  • Learn the basic principles and methods of quantitative data analysis for the humanities, regardless of your programming experience
  • Understand which aspects of different types of datasets can be analyzed quantitatively in humanities research
  • Learn how to perform basic analyses on various types of data using Python
  • Gain an understanding of the fundamental principles of writing Python scripts

Why take this lesson?

This lesson is designed for absolute beginners in digital humanities research. Its goal is to help humanities scholars understand when, why, and how programming can be a valuable tool for data analysis in their work.

By the end of this lesson, you will have learned the basic principles of data analysis with a focus on quantitative research in the humanities using Python. You will be better equipped to determine whether incorporating programming and quantitative methods is meaningful for analyzing your research data. You’ll also be able to evaluate whether digitizing, processing, and publishing existing analog or material data could benefit your own research and contribute to the broader scholarly community.

Where does this lesson fit within the broader spectrum of the so-called “digital humanities”?

In the field commonly known as digital humanities, there are generally two major directions you can pursue:

  1. Work at a GLAM institution (Gallery, Library, Archive, Museum), where you digitize texts, images, and objects, and create or enrich digital catalogs for them. In this area, skills such as data management, knowledge of metadata standards, and understanding the FAIR principles are particularly valuable.

  2. Analyze data gathered by yourself, other individuals, or institutions to derive insights that go beyond the capacity of human time or intellect due to the large volume of data. This is the domain of data analysis for humanities research — and this is where we will focus.

Critical Reflections

Although the term “digital scholarship” is becoming increasingly widespread and popular, I avoid using it for several reasons:

  1. What exactly does “digital” mean in this context? Does simply using a computer to create text, images, diagrams or tables transform “analog” scholarship into a “digital” one?

  2. Is “digitality” — however it’s defined in this context — a method, a tool, or something that could give rise to entirely new areas of study, such as the so-called “digital humanities”?

Instead of the vague term “digital scholarship,” I prefer to use “quantitative data analysis.” By “quantitative data analysis,” I refer to a range of methods, including:

  • Counting
  • Comparing
  • Searching
  • Pattern recognition
  • Classification
  • Graphical representation

Quantitative data analysis is a task that can be automated using a computer. It can be performed with existing software designed for specific analysis tasks or by writing your own code using a programming language. In this lesson, we will be taking the second approach, using Python.

When is it legitimate to practice quantitative data analysis?

In humanities research, quantitative data analysis is only logical and legitimate when:

It reveals new insights: This method should uncover information that was previously unknown. If your research merely confirms what is already well-established, it lacks value. For example, we already know that women have historically been underrepresented in the documentation of human achievements. If we take a book on the history of art, create a list of all the artists mentioned, count how many are women, and conclude that female artists are less frequent in the book than male artists, our effort provides no new knowledge.

The data is too extensive for manual analysis: The data to be analyzed is so vast that it is either impossible for human intellect to process it in a reasonable time frame, or it would require an impractical amount of resources for individuals to analyze it. For instance, counting how many of the 50 names in a list are female does not require programming; it can easily be done by an individual. However, analyzing large datasets, like millions of records, would require quantitative methods.

Avoiding the temptation to overuse “digital analysis”: In today’s academic environment, there is a temptation to include “digital analysis” in grant proposals or PhD theses simply to make them sound modern and sophisticated. It’s crucial not to fall into this trap and produce work that, in the end, won’t be meaningful or taken seriously. Remember: merely adding a table or graph to your research doesn’t automatically make you a digital humanist or a quantitative data analyst.

Key Points

  • Determine whether the type and volume of your research data, as well as your research question, justify the use of quantitative data analysis.

  • If you choose to pursue quantitative data analysis, consider what insights you want to extract from your data and how you can achieve this using Python programming.