Content Analysis

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One way of conducting unobtrusive research is to analyze texts. Texts come in all kinds of formats. At its core, content analysis addresses the questions of “Who says what, to whom, why, how, and with what effect?” (Babbie, 2010, pp. 328–329). 1 Content analysis is a type of unobtrusive research that involves the study of human communications. Another way to think of content analysis is as a way of studying texts and their meaning. Here we use a more liberal definition of text than you might find in your dictionary. The text that content analysts investigate includes such things as actual written copy (e.g., newspapers or letters) and content that we might see or hear (e.g., speeches or other performances). Content analysts might also investigate more visual representations of human communication such as television shows, advertisements, or movies. The following table provides a few specific examples of the kinds of data that content analysts have examined in prior studies. Which of these sources of data might be of interest to you?

One thing you might notice about "Table 11.1" is that the data sources represent primary sources. That is, they are original. Secondary sources, on the other hand, are those that have already been analyzed. Shulamit Reinharz offers a helpful way of distinguishing between these two types of sources in her methods text. She explains that while primary sources represent the “‘raw’ materials of history,” secondary sources are the “‘cooked’ analyses of those materials” (1992, p. 155). 7 The distinction between primary and secondary sources is important for many aspects of social science, but it is especially important to understand when conducting content analysis. While there are certainly instances of content analysis in which secondary sources are analyzed, I think it is safe to say that it is more common for content analysts to analyze primary sources.

In those instances where secondary sources are analyzed, the researcher’s focus is usually on the process by which the original analyst or presenter of data reached his conclusions or on the choices that were made in terms of how and in what ways to present the data. For example, Ferree and Hall (1990) 8 conducted a content analysis of introductory sociology textbooks, but their aim was not to learn about the content of sociology as a discipline. Instead, the researchers sought to learn how students are taught the subject of sociology and understand what images are presented to students as representative of sociology as a discipline.

Sometimes students new to research methods struggle to grasp the difference between a content analysis of secondary sources and a review of literature, which is discussed in "Research Design". In a review of literature, researchers analyze secondary materials to try to understand what we know, and what we don’t know, about a particular topic. The sources used to conduct a scholarly review of the literature are typically peer-reviewed sources, written by trained scholars, published in some academic journal or press, and based on empirical research that has been conducted using accepted techniques of data collection for the discipline (scholarly theoretical pieces are included in literature reviews as well). These sources are culled in a review of literature in order to arrive at some conclusion about our overall knowledge about a topic. Findings are generally taken at face value.

Conversely, a content analysis of scholarly literature would raise questions not raised in a literature review. A content analyst might examine scholarly articles to learn something about the authors (e.g., Who publishes what, where?), publication outlets (e.g., How well do different journals represent the diversity of the discipline?), or topics (e.g., How has the popularity of topics shifted over time?). A content analysis of scholarly articles would be a “study of the studies” as opposed to a “review of studies.” Perhaps, for example, a researcher wishes to know whether more men than women authors are published in the top-ranking journals in the discipline. The researcher could conduct a content analysis of different journals and count authors by gender (though this may be a tricky prospect if relying only on names to indicate gender). Or perhaps a researcher would like to learn whether or how various topics of investigation go in and out of style. She could investigate changes over time in topical coverage in various journals. In these latter two instances, the researcher is not aiming to summarize the content of the articles but instead is looking to learn something about how, why, or by whom particular articles came to be published.

Content analysis can be qualitative or quantitative, and often researchers will use both strategies to strengthen their investigations. In qualitative content analysis the aim is to identify themes in the text being analyzed and to identify the underlying meaning of those themes. A graduate student colleague of mine once conducted qualitative content analysis in her study of national identity in the United States. To understand how the boundaries of citizenship were constructed in the United States, Alyssa Goolsby (2007)  9 conducted a qualitative content analysis of key historical congressional debates focused on immigration law. Quantitative content analysis, on the other hand, involves assigning numerical values to raw data so that it can be analyzed using various statistical procedures. One of my research collaborators, Jason Houle, conducted a quantitative content analysis of song lyrics. Inspired by an article on the connections between fame, chronic self-consciousness (as measured by frequent use of first-person pronouns), and self-destructive behavior (Schaller, 1997), 10 Houle counted first-person pronouns in Elliott Smith song lyrics. Houle found that Smith’s use of self-referential pronouns increased steadily from the time of his first album release in 1994 until his suicide in 2003 (2008). 11 We’ll elaborate on how qualitative and quantitative researchers collect, code, and analyze unobtrusive data in the final portion of this section.