Grounded Theory vs. Qualitative Content Analysis: What's the Difference?

 
 

Understanding the differences between grounded theory and qualitative content analysis helps you choose the right approach for your research. Both methods analyze textual data but have different purposes and results. This article explores these differences, highlighting the strengths and uses of these popular qualitative research methods.

Overview of Grounded Theory and Qualitative Content Analysis

Grounded Theory Overview

Grounded theory is a qualitative research approach where you develop a new theory directly from your data. You start without a preconceived theory and let the patterns in your data guide your understanding of it, not any preexisting ideas or studies. You recruit participants and collect data, often through interactive rounds of interviews or observations, and gradually build a theory based on the information you gather. This continues until you reach a point in your theory development where no new insights come from your data. While recruiting and adding new data takes time and flexibility, it leads to thorough theory development.

 
 

→ To learn more, check out The Practical Guide to Grounded Theory. 

Qualitative Content Analysis Overview

Qualitative content analysis is a research method used to systematically categorize and interpret textual data. It organizes large amounts of data to identify patterns, concepts, keywords, categories, and themes. This method (an umbrella term for several sub-methods) can be applied to various materials, including interviews, historical documents, books, and other textual sources. A few sub-methods are summative, directed, and conventional content analysis.

You can perform inductive content analysis, where categories are derived from the data, or deductive content analysis, where predefined categories are applied to the data.  Some submethods are better suited to one than another. The latter is the more common of the two approaches. The research process in both cases involves:

  1. Familiarizing yourself with the data.

  2. Generating codes.

  3. Grouping these codes into themes.

  4. Refining these themes to develop your analysis.

Because themes keep coming up, it's worth noting that qualitative content analysis differs from thematic analysis in several ways. For instance, it quantifies data and focuses on mostly deductive, manifest content. This means it tends to focus on predefined categories to analyze data (deductive) and looks more at the surface-level meaning of the content (manifest).

→ For a detailed comparison of these two research methods, see our article Content Analysis vs. Thematic Analysis. 

Key Differences: Grounded Theory vs. Qualitative Content Analysis

Understanding the differences between grounded theory and qualitative content analysis will help you choose the right approach for your research. Here's a detailed comparison of their scope, recruiting, process, coding methods, and outcomes.

Recruiting:

  • Grounded Theory: Recruiting is cyclical and involves theoretical sampling. Participants are recruited in batches throughout the study based on emerging findings, allowing for the theory's iterative development.

  • Qualitative Content Analysis: You might not need to recruit participants at all since it often uses existing textual data like news stories. If recruiting is necessary, it typically happens once at the beginning. The analysis involves revising your themes, but you usually don't recruit more participants later.

Scope:

  • Grounded Theory: Covers the entire research process from data collection to theory development. This approach is useful when no existing theory adequately explains the phenomenon or current theories are insufficient. In terms of time investment, grounded theory generally takes longer and needs a much more flexible timeline due to iterative recruiting (theoretical sampling).

  • Qualitative Content Analysis: Focuses on identifying and describing patterns, concepts, and themes within the data. It's more of an analytical tool rather than a comprehensive methodology. This broader method has a variety of submethods, each offering different depths and approaches to analyzing textual data. It is best for studies that identify patterns and themes without necessarily developing new theoretical frameworks.

Process:

  • Grounded Theory: Involves an iterative data collection and analysis cycle, including recruiting participants in batches based on findings. You collect data through interviews, observations, and documents, analyze it, and then collect more data based on their findings. This cycle continues using the constant comparison method until theoretical saturation is reached—when new data no longer provides additional insights.

  • Qualitative Content Analysis: Follows a more step-by-step process. You start by familiarizing yourself with the data, then generate initial codes, search for themes, review and define these themes, and finally write up their findings. While some iteration exists in checking and refining themes, it is not as ingrained as grounded theory.

Coding:

  • Grounded Theory: Coding happens in three iterative phases: open coding (breaking down data into discrete parts), axial coding (identifying relationships among open codes), and selective coding (integrating and refining the theory). All the coding maps to one overarching core category that leads to the final theory.

  • Qualitative Content Analysis: Involves coding data segments to identify patterns. You generate initial codes, which are then grouped into themes. This approach is more flexible than grounded theory, allowing for inductive and deductive approaches. It also doesn't necessarily follow the structured phases of open, axial, and selective coding.

Outcomes:

  • Grounded Theory: Develops a theoretical framework that explains the phenomenon being studied. It digs deep into the data to uncover underlying processes and behaviors, resulting in a comprehensive theory that can be applied to similar contexts.

  • Qualitative Content Analysis: Results in a systematic categorization of themes and patterns within the data. It provides a detailed description of the content's meaning and context, often including both qualitative insights and quantitative aspects like frequency of themes.

When to Use Grounded Theory vs. Qualitative Content Analysis

Choosing between grounded theory and qualitative content analysis comes down to your research goals and your data type. Here's a quick guide on when to consider using each method:

Use grounded theory when you want to:

  • Develop new theories tightly connected to your data, not existing ones.

  • Represent real-world settings through methods like in-depth interviews and observation.

  • Have a specific strategy for maintaining structure in your analysis.

  • Collect and analyze data together to ensure you have enough information.

  • Prevent confirmation bias by following what comes directly from the data.

Use qualitative content analysis when you want to:

  • Explore complex phenomena like attitudes, beliefs, and social interactions.

  • Analyze large volumes of textual data in a systematic way.

  • Understand the meaning and context of communication in various forms of textual data.

  • Identify patterns by quantifying the frequency of themes.

  • Have a flexible method that can be applied to various types of text.

Qualitative analysis doesn't have to be overwhelming.

Take Delve's free online course. → Get started here.

Grounded Theory vs. Qualitative Content Analysis: Real-World Example

Let's see a quick example of how these two methods can work in the field. In our example, Becca is a graduate student who wants to study how remote learning affects student engagement in high school. She is considering either grounded theory or qualitative content analysis for her research. Depending on the method she uses, here's how her research might play out.

Using Grounded Theory:

Becca begins by conducting interviews with a small group of high school students. She transcribes these interviews and starts with open coding, identifying key points related to student engagement, such as changes in motivation and participation. Later, she does another round and realizes she also needs to interview parents because this is creating a gap in her theory.

As she collects more data, she uses the constant comparison method to notice recurring patterns. She starts axial coding to link these patterns and understand broader categories, like the impact of the home environment and teacher interaction.

She continues this iterative process, conducting more interviews based on her findings and refining her categories as she goes. This additional recruiting is more time-intensive and expensive than qualitative content analysis because it requires adjusting recruitment strategies based on what she learns.

Eventually, she reaches theoretical saturation, where new interviews do not provide new insights. Through this cyclical process, Becca develops a new theory about how remote learning impacts different aspects of student engagement, explaining how factors like household distractions and the lack of face-to-face interaction influence students' motivation and participation.

Using Qualitative Content Analysis:

Becca starts by collecting 75 newspaper articles from across the country on remote learning and its impact on student engagement. She familiarizes herself with the data by reading through these articles and making notes on her initial impressions and potential codes.

Next, she uses summative content analysis, systematically coding the data by looking for significant keywords and phrases that frequently appear in the articles. Some of these keywords might include "remote learning," "student engagement," "distractions at home," and "parental involvement." She groups these codes into themes like "distractions at home," "lack of social interaction," and "flexibility in learning."

Becca reviews and refines these themes to ensure they accurately represent the data. She then writes her findings, describing each theme in detail and providing quotes from the articles to support her analysis. She may also include frequency counts of certain themes or patterns to highlight how often these topics are mentioned.

Through her qualitative content analysis, Becca identifies key issues affecting student engagement in remote learning. She provides a detailed narrative of her findings in her final write-up, offering insights into the meaning and context of the students’ experiences.

Tools for Grounded Theory and Qualitative Content Analysis Coding

 
 

Now, let's explore the tools for conducting grounded theory and qualitative content analysis. Managing numerous codes and memos can be challenging for both methodologies. Here are some approaches to coding for these research methods:

1. Coding by Hand

Simple tools like pen, paper, scissors, and highlighters allow for a hands-on coding approach. Print out your transcripts, perform open coding by cutting up the transcripts into individual excerpts, and organize these papers into piles to create your codes and categories. This method, while tactile, can become very time-consuming, especially with large data sets. Keeping track of comparisons and managing extensive amounts of paper can also be challenging. Additionally, collaboration with remote team members is not feasible with this method.

2. Coding Using a Word Processor

Word processors like Microsoft Word or Google Docs can be used to add excerpts and comments for open coding. To create categories, copy and paste excerpts labeled by category names into different documents. This digital approach helps organize your analysis but can still be cumbersome with constant copying and pasting. Tracking specific snippets or codes can also be time-consuming without a system for quickly recalling them.

3. Coding Using Qualitative Data Analysis Software

Qualitative coding software like Delve is designed to support both grounded theory and qualitative content analysis processes. Delve helps you keep track of excerpts and codes, organizes your thoughts, and simplifies the constant comparison method. The digital interface makes managing large data sets easier and maintains the many comparisons needed. Other features, such as demographic filters and search functionality, streamline the grounded theory and qualitative content analysis processes, ensuring a thorough and efficient analysis.

Using any combination of these tools, you can effectively manage your qualitative data, making the coding and analysis process more efficient and insightful. Whether coding by hand, using word processors, or employing specialized software, each method has advantages and challenges. Choosing the right tool depends on your preferences and the research project's specific needs.

Wrapping Up Grounded Theory vs. Content Analysis

Choosing between grounded theory and qualitative content analysis can be challenging, but managing your data doesn't have to waste time. Both methods involve extensive coding and memo writing, which can become overwhelming without the right tools.

Streamline Your Qualitative Research with Delve

Delve, a web-based qualitative coding tool, simplifies your research process whether you're using grounded theory or content analysis:

  • Efficiently organize codes, memos, and excerpts

  • Manage large datasets with ease

  • Track your work seamlessly

  • Derive more insightful findings

 
 

💡 Delve Reviews: A Favorite Among Professors, PhD Candidates and Doctoral Students

  • “Delve was simple to use, intuitive, and cost-effective. I highly recommend Delve to anyone who is doing a qualitative analysis! [It] made coding, discovering themes, and data synthesis simple and efficient. These words and phrases appeared from the transcriptions quickly.” - Tommy G. (Professor)

  • “It's intuitive (easy to learn and master), sleek (looks stylish, and I take great pleasure in using it and love showing the tool off to friends), and simple (my ideas should be complex and comprehensive, not my software).” - Dara M. (PhD Candidate)

  • “Just what I needed for my dissertation. Was priced well for a doctoral student and was easy to set up for my specific needs.” - Laura B. (Dean of Students)

  • “I had enough on my plate with theoretical/conceptual frameworks, literature review, references, data collection, APA, developing research and interview questions...IRB approval. I had zero time to learn a software in order to analyze my data and report the findings in my dissertation. Delve is so user-friendly my niece could code!” - Asha T. (Doctorate Candidate)

  • “Delve is very user-friendly. Its quite intuitive, so its easy for new users, including students, to pick it up. It is also very affordable and easy to cancel. I was able to work with undergraduate students during the school year, then pause our subscription until we could resume after the summer and winter break.” - Rosario M. (Postdoctoral Research Associate)

  • “The in-depth training course provided by Delve enabled me to quickly apply various coding options and find themes in support of grounded analysis.” - Halida. D. (Doctoral Candidate)

Ready to elevate and streamline your research? Experience the Delve difference in your grounded theory or content analysis project!

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Cite This Blog Post:

Content Analysis, Grounded TheoryDaniel Politz