What is content analysis?

When you're conducting qualitative research, you'll find yourself analyzing various texts. Perhaps you'll be evaluating transcripts from audio interviews you've conducted. Or you may find yourself assessing the results of a survey filled with open-ended questions.

Whether you want to incorporate letters, survey responses, feedback, literary works, or other texts into your study, you need a structured and consistent analytical approach. One of the best ways to approach these documents is through a research method known as content analysis.

Content analysis is a research method used to identify the presence of various concepts, words, and themes in different texts. Two types of content analysis exist: conceptual analysis and relational analysis. In the former, researchers determine whether and how frequently certain concepts appear in a text. In relational analysis, researchers explore how different concepts are related to one another in a text. 

Both types of content analysis require the researcher to code the text. Coding the text means breaking it down into different categories that allow it to be analyzed more easily.

You can use content analysis to analyze many forms of text, including:

• Speeches

• Interview and discussion transcripts

• Newspaper articles and headline

• Literary works

• Nonfiction

• Essays

• Letters

• Historical documents

• Government reports

• Academic papers

• Music lyrics

Researchers commonly use content analysis to draw insights and conclusions from literary works. Historians and biographers may apply this approach to letters, papers, and other historical documents to gain insight into the historical figures and periods they are writing about. Market researchers can also use it to evaluate brand performance and perception.

Some researchers have used content analysis to explore differences in decision-making and other cognitive processes. While researchers traditionally used this approach to explore human cognition, content analysis is also at the heart of machine learning approaches currently being used and developed by software and AI companies.

Conceptual analysis is more commonly associated with content analysis than relational analysis. 

In conceptual analysis, you're looking for the appearance and frequency of different concepts. Why? This information can help further your qualitative or quantitative analysis of a text. It's an inexpensive and easily understood research method that can help you draw inferences and conclusions about your research subject. And while it is a relatively straightforward analytical tool, it does consist of a multi-step process that you must closely follow to ensure the reliability and validity of your study.

When you're ready to conduct a conceptual analysis, refer to your research question and the text. Ask yourself what information likely found in the text is relevant to your question. You'll need to know this to determine how you'll code the text. Then follow these steps:

1. Determine whether you're looking for explicit terms or implicit terms.

Explicit terms are those that directly appear in the text, while implicit ones are those that the text implies or alludes to or that you can infer. 

Coding for explicit terms is straightforward. For example, if you're looking to code a text for an author's explicit use of color,  you'd simply code for every instance a color appears in the text. However, if you're coding for implicit terms, you'll need to determine and define how you're identifying the presence of the term first. Doing so involves a certain amount of subjectivity and may impinge upon the reliability and validity of your study.

2. Next, identify the level at which you'll conduct your analysis.

You can search for words, phrases, or sentences encapsulating your terms. You can also search for concepts and themes, but you'll need to define how you expect to identify them in the text. You must also define rules for how you'll code different terms to reduce ambiguity. For example, if, in an interview transcript, a person repeats a word one or more times in a row as a verbal tic, should you code it more than once? And what will you do with irrelevant data that appears in a term if you're coding for sentences? 

Defining these rules upfront can help make your content analysis more efficient and your final analysis more reliable and valid.

3. You'll need to determine whether you're coding for a concept or theme's existence or frequency.

If you're coding for its existence, you’ll only count it once, at its first appearance, no matter how many times it subsequently appears. If you're searching for frequency, you'll count the number of its appearances in the text.

4. You'll also want to determine the number of terms you want to code for and how you may wish to categorize them.

For example, say you're conducting a content analysis of customer service call transcripts and looking for evidence of customer dissatisfaction with a product or service. You might create categories that refer to different elements with which customers might be dissatisfied, such as price, features, packaging, technical support, and so on. Then you might look for sentences that refer to those product elements according to each category in a negative light.

5. Next, you'll need to develop translation rules for your codes.

Those rules should be clear and consistent, allowing you to keep track of your data in an organized fashion.

6. After you've determined the terms for which you're searching, your categories, and translation rules, you're ready to code.

You can do so by hand or via software. Software is quite helpful when you have multiple texts. But it also becomes more vital for you to have developed clear codes, categories, and translation rules, especially if you're looking for implicit terms and concepts. Otherwise, your software-driven analysis may miss key instances of the terms you seek.

7. When you have your text coded, it's time to analyze it.

Look for trends and patterns in your results and use them to draw relevant conclusions about your research subject.

In a relational analysis, you're examining the relationship between different terms that appear in your text(s). To do so requires you to code your texts in a similar fashion as in a relational analysis. However, depending on the type of relational analysis you're trying to conduct, you may need to follow slightly different rules.

Three types of relational analyses are commonly used: affect extraction, proximity analysis, and cognitive mapping.

Affect extraction

This type of relational analysis involves evaluating the different emotional concepts found in a specific text. While the insights from affect extraction can be invaluable, conducting it may prove difficult depending on the text. For example, if the text captures people's emotional states at different times and from different populations, you may find it difficult to compare them and draw appropriate inferences.

Proximity analysis

A relatively simpler analytical approach than affect extraction, proximity analysis assesses the co-occurrence of explicit concepts in a text. You can create what's known as a concept matrix, which is a group of interrelated co-occurring concepts. Concept matrices help evaluate and determine the overall meaning of a text or the identification of a secondary message or theme.

Cognitive mapping

You can use cognitive mapping as a way to visualize the results of either affect extraction or proximity analysis. This technique uses affect extraction or proximity analysis results to create a graphic map illustrating the relationship between co-occurring emotions or concepts.

Conducting a relational analysis

To conduct a relational analysis, you must start by determining the type of analysis that best fits the study: affect extraction or proximity analysis. 

Complete steps one through six as outlined above. When it comes to the seventh step, analyze the text according to the relational analysis type they've chosen. During this step, feel free to use cognitive mapping to help draw inferences and conclusions about the relationships between co-occurring emotions or concepts. And use other tools, such as mental modeling and decision mapping as necessary, to analyze the results.

Content analysis provides researchers with a robust and inexpensive method to qualitatively and quantitatively analyze a text. By coding the data, you can perform statistical analyses of the data to affirm and reinforce conclusions you may draw. And content analysis can provide helpful insights into language use, behavioral patterns, and historical or cultural conventions that can be valuable beyond the scope of the initial study.

When content analyses are applied to interview data, the approach provides a way to closely analyze data without needing interview-subject interaction, which can be helpful in certain contexts. For example, suppose you want to analyze the perceptions of a group of geographically diverse individuals. In this case, you can conduct a content analysis of existing interview transcripts rather than assuming the time and expense of conducting new interviews.

Content analysis is a research method that helps a researcher explore the occurrence of and relationships between various words, phrases, themes, or concepts in a text or set of texts. The method allows researchers in different disciplines to conduct qualitative and quantitative analyses on a variety of texts.

Content analysis is used in multiple disciplines, as you can use it to evaluate a variety of texts. You can find applications in anthropology, communications, history, linguistics, literary studies, marketing, political science, psychology, and sociology, among other disciplines.

Content analysis may be either conceptual or relational. In a conceptual analysis, researchers examine a text for the presence and frequency of specific words, phrases, themes, and concepts. In a relational analysis, researchers draw inferences and conclusions about the nature of the relationships of co-occurring words, phrases, themes, and concepts in a text.

Content analysis typically uses a descriptive approach to the data and may use either qualitative or quantitative analytical methods. By contrast, a thematic analysis only uses qualitative methods to explore frequently occurring themes in a text.