What is content analysis?

How to perform a qualitative research study using content analysis

For more best practices see our method overview
Content
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Introduction to content analysis

As Krippendorff (1980)defines it, “content analysis is a research technique for making replicable and valid inferences from data to their context, with the purpose of providing knowledge, new insights, a representation of facts, and a practical guide to action.” This technique offers you a way to systematically analyze written, verbal, or visual communication messages (Cole, 1988), through which you can quantify the presence of certain themes or explore deeper meanings.
Already used in the 19th century to analyze newspaper articles, and political speeches to research societal interests and biases (Harwood & Garry, 2003), content analysis has grown to become a widely accepted research method across fields including communication, journalism, sociology, psychology, and business. In modern research, content analysis allows you to distill large amounts of textual or visual data into manageable categories, enabling you to derive meaningful insights through a type of qualitative data analysis.

With QDAcity, you can easily apply content analysis to data from diverse sources. Whether the goal is to explore public opinion, analyze media trends, or examine relationships within datasets of your case study or Qualitative Survey, QDAcity's tools provide a flexible and structured approach to adapt to your research objectives.


A structured and flexible approach to content analysis

Content analysis allows you to analyze data both quantitatively and qualitatively, offering a flexible framework based on your research question and study design. Whether you're counting word frequencies or exploring relationships between concepts, QDAcity supports both inductive and deductive approaches.
  • Inductive analysis moves from specific observations to broader generalizations, ideal when little prior knowledge exists about the phenomenon. This involves identifying themes directly from the data, categorizing them, and generating abstract concepts that summarize your findings.
  • Deductive analysis is theory-driven, testing predefined concepts or models against new data. It’s often used when the goal is to validate existing theories or compare categories across different datasets. In QDAcity, you can develop a categorization matrix that enables structured analysis based on existing models.
With either approach, the process typically includes three main phases: preparation, organizing, and reporting. You start by selecting a unit of analysis, which could be anything from a single word to an entire conversation. QDAcity's tools make it easy to organize and visualize the data, keeping your analysis streamlined and efficient.


Developing and applying your coding scheme

At the core of content analysis is the coding process, which organizes your data into meaningful categories. Using QDAcity, you can create and apply a coding scheme that reflects the research questions you're investigating. The coding process will help you systematically break down the data. You might follow the Grounded Theory style of coding with open, axial and selective coding, or follow some other method. The overall process usually is some form of the following activities:
  1. Initial Coding: During this phase, you generate as many labels as needed to describe the new theoretical constructs you identify in your data. QDAcity allows you to easily create new codes, and create codebook entries for each of them containing a definition and disambiguation to other codes you may already have.
  2. Identifying Relationships: You will then want to define the relationships between the constructs you identified. These relationships are a crucial part of your theory. One type of relationship is built by developing common categories. You can do this easily in QDAcity through drag and drop. Other relationships can be documented during memo writing or in the codebook.
  3. Comparing and Refining Categories: After the initial coding, and categorization, you can analyze your categories in terms of broader Themes and in what way they are relevant to your research question. By comparing different categories you may want to redefine some of them, or merge them together with a different category or code. QDAcity can support you with this by allowing you to move all or some of the codings from one code to another.
  4. Identify gaps and iterate: as a last step you usually search for any gaps in your theory that need further investigation. If you have not reached theoretical saturation, you’ll gather more data and start the analysis process again from step 1 to find more theoretical constructs, improve the categorization, the definitions, find new relationships, etc.
As you refine your coding scheme, QDAcity supports collaborative coding and multiple rounds of intercoder agreement sessions, enabling teams to work together in organizing coding rules and refining the definitions of each theoretical construct to be modeled in the theory. This collaborative approach is particularly useful for team projects, ensuring that everyone follows the same standards and contributing to a more efficient, reliable, and consistent coding process.


From coding to insights: analysis and theory writing

Once your data is coded, QDAcity’s statistical tools, such as the analysis of overlapping coding instances, offer valuable insights to support your research. Whether you're looking for trends, patterns, or insights, QDAcity offers a wide range of options to explore and present your data effectively.
QDAcity also supports the creation and refinement of codebooks, helping you systematically organize and manage your coding scheme as your research evolves. The codebook definitions as well as the memos you wrote during the analysis are immensely helpful when you go from coding to theory writing.
By focusing on collaborative work and ensuring coding consistency, QDAcity equips you with the tools needed to conduct thorough, reliable analyses. As you wrap up your project, you can export the results and integrate them into comprehensive reports, ensuring your findings are well-documented and accessible for your audit trail.


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