Content Analysis With Tableau: Intro

Bit of a change of pace here at Raise High the Roofbeam, Carpenters. Over these next few blog posts, I will write about how a method borrowed from the harder social sciences called content analysis might be useful for humanities scholars.

Content analysis takes qualitative data, boils it down into numbers, then analyzes it. A lot of humanities scholars are already doing content analysis in some form, largely without realizing it. This post will hopefully begin to bridge the gap between the ad hoc content analysis strategies of digital humanists and the formal content analysis of sociologists and psychologists.

Before we start, you might be asking what the pay-off of all this work is.

My current research uses content analysis to look at 18th century Christmas. I looked through more than 250 diaries for entries written on or around Christmas day, which I then coded. Here is a visualization which displays at the percentage of diary entries mentioning a given code, by decade. You can find that here.

Please keep in mind this is only a working example—results are not to be cited or circulated except as an example of this method.
The following blog series will give you step-by-step instructions into how you can turn your research question into a data visualization like the one above.

Broadly speaking, content analysis studies communicative activity by turning qualitative data into quantitative data. At its simplest, a scholar counts the number of times a particular thing happens in a particular set of documents across a particular span of time. Kimberly Neuendorf, author of the most thorough content analysis textbook I’ve read, defines the method like this: “Content analysis may be briefly defined as the systematic, objective, quantitative analysis of message characteristics.” The field is thriving—the number of articles mentioning the method has skyrocketed over the past decade, in part due to the vast expansion of the number of machine-readable documents researchers can now access.

The appeal of this method for humanists is obvious. In some ways, content analysis simply formalizes the narrative synthesis humanists are already so good at.
But despite this promise, it’s hard to know where the humanist can start with content analysis. Textbooks are pitched towards sociologists, psychologists and scholars in media studies struggling with that wonderful non-stop fire-hose of present-day data. Furthermore, content analysis is pitched towards the harder social sciences, which wrestle with very different questions and hold very different theories of change and action than historians and literary scholars. Unlike other digital humanities methods, like text analysis, social network analysis, or geospatial analysis, there is no single out-of-the-box technical solution for content analysis projects—as far as I know. Finally, there is the problem with the name content analysis itself. It is neither evocative nor punchy. It barely describes what the method does. Frankly, it feels boring, overly technical, and scientistic.
These blog posts will hopefully go some ways to provide the interested humanist with some essential background and tools that will overcome these problems.

A warning first: I am an interested amateur, and these blogs represent merely what I’ve gleaned from trying out my own content analysis projects. There’s a certain here’s what I learned on my Summer Vacation quality to all of this. Experts in content analysis will likely find many faults with what follows. Other historians will certainly offer feedback about how I can make better questions and collect more comprehensive corpora. I look forward to their corrections.

Table of Contents

Part 1: A Humanistic Map To A Content Analysis Project

Part 2: Data Collection

Part 3: Coding: How To Fit The Qualitative World Into The QuantitativeNut-shell

Part 4: Visualizing Your Data With Tableau