11.3: Content Analysis

Content analysis is a type of unobtrusive research that involves the study of human communications and visual representations. Another way to think of content analysis is as a way of studying texts and their meaning as well as artifacts and their meanings. Thus, the units of analysis here are social interactions and artifacts.

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).Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth.

The material 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).Reinharz, S. (1992). Feminist methods in social research. New York, NY: Oxford University Press. 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)Ferree, M. M., & Hall, E. J. (1990). Visual images of American society: Gender and race in introductory sociology textbooks. Gender & Society, 4(4), 500–533. 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. 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)Goolsby, A. (2007). U.S. immigration policy in the regulatory era: Meaning and morality in state discourses of citizenship (Unpublished master’s thesis). Department of Sociology, University of Minnesota, Minneapolis, MN. 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),Schaller, M. (1997). The psychological consequences of fame: Three tests of the self-consciousness hypothesis. Journal of Personality, 65, 291–309. 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).Houle, J. (2008). Elliott Smith’s self referential pronouns by album/year. Prepared for teaching SOC 207, Research Methods, at Pennsylvania State University, Department of Sociology.

Sampling in Content Analysis

Typically content analysis uses probability sampling methods, which include all of the types discussed in the sampling chapter.

Analysis of Content Analysis

Material you would like to analyze, the next step is to figure out how you’ll analyze them. This step requires that you determine your procedures for coding, understand the difference between manifest and latent content, and understand how to identify patterns across your coded data. We’ll begin by discussing procedures for coding.

While the coding procedures used for written documents obtained unobtrusively may resemble those used to code interview data, many sources of unobtrusive data differ dramatically from written documents or transcripts. What if your data are sculptures or worn paths, or perhaps kitchen utensils, as in the previously discussed example? The idea of conducting open coding and focused coding on these sources as you would for a written document sounds a little silly, not to mention impossible. So how do we begin to identify patterns across the sculptures or worn paths or utensils we wish to analyze? One option is to take field notes as we observe our data and then code patterns in those notes. Let’s say, for example, that we’d like to analyze kitchen utensils. Taking field notes might be a useful approach were we conducting observations of people actually using utensils in a documentary or on a television program. (Remember, if we’re observing people in person then our method is no longer unobtrusive.)

If rather than observing people in documentaries or television shows our data include a collection of actual utensils, note taking may not be the most effective way to record our observations. Instead, we could create a code sheet to record details about the utensils in our sample. A code sheet, sometimes referred to as a tally sheet in quantitative coding, is the instrument an unobtrusive researcher uses to record observations.

In the example of kitchen utensils, perhaps we’re interested in how utensils have changed over time. If we had access to sales records for utensils over the past 50 years, we could analyze the top-selling utensil for each year. To do so, we’d want to make some notes about each of the 50 utensils included in our sample. For each top-rated utensil, we might note its name, its purpose, and perhaps its price in current dollar amounts. We might also want to make some assessment about how easy or difficult it is to use or some other qualitative assessment about the utensil and its use or purpose. To rate the difficulty of use we could use a 5-point scale, with 1 being very easy to use and 5 being very difficult to use. We could even record other notes or observations about the utensils that may not occur to us until we actually see the utensils. Our code sheet might look something like the sample shown in Table 11.2. Note that the sample sheet contains columns only for 10 years’ worth of utensils. If you were to conduct this project, obviously you’d need to create a code sheet that allows you to record observations for each of the 50 items in your sample.

As you can see, our code sheet will contain both qualitative and quantitative data. Our “ease of use” rating is a quantitative assessment; we can therefore conduct some statistical analysis of the patterns here, perhaps noting the mean value on ease of use for each decade we’ve observed. We could do the same thing with the data collected in the row labeled Price, which is also quantitative. The final row of our sample code sheet, containing notes about our impressions of the utensils we observe, will contain qualitative data. We may conduct open and focused coding on these notes to identify patterns across those notes. In both cases, whether the data being coded are quantitative or qualitative, the aim is to identify patterns across the coded data.

The Purpose row in our sample code sheet provides an opportunity for assessing both manifest and latent content. Manifest content is the content we observe that is most apparent; it is the surface content. This is in contrast to latent content, which is less obvious. Latent content refers to the underlying meaning of the surface content we observe. In the example of utensil purpose, we might say a utensil’s manifest content is the stated purpose of the utensil. The latent content would be our assessment of what it means that a utensil with a particular purpose is top rated. Perhaps after coding the manifest content in this category we see some patterns that tell us something about the meanings of utensil purpose. Perhaps we conclude, based on the meanings of top-rated utensils across five decades, that the shift from an emphasis on utensils designed to facilitate entertaining in the 1960s to those designed to maximize efficiency and minimize time spent in the kitchen in the 1980s reflects a shift in how (and how much) people spend time in their homes.

Strengths and Weaknesses of Content Analysis

A major strength of this method is that it is very cheap to do and relatively quick to do. It also allows you to study a process that is occurring over a long period of time. Also since it is an unobtrusive measure there is no subject impacts to consider. However, no method is perfect and the biggest issue with this method is that it is limited to what information is recorded, which in and of itself can be biased.

KEY TAKEAWAYS

• Content analysts study human communications.
• The texts that content analysts analyze include actual written texts such as newspapers or journal entries as well as visual and auditory sources such as television shows, advertisements, or movies.
• Content analysts most typically analyze primary sources, though in some instances they may analyze secondary sources.
• Indirect measures that content analysts examine include physical traces and material artifacts.
• Manifest content is apparent; latent content is underlying.
• Content analysts use code sheets to collect data.

Exercises

1. Identify a research question you could answer using unobtrusive research. Now state a testable hypothesis having to do with your research question. Next identify at least two potential sources of data you might analyze to answer your research question and test your hypothesis.
2. Create a code sheet for each of the two potential sources of data that you identified in the preceding exercise.

This page titled 11.3: Content Analysis is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by Anonymous.

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