Content Analysis vs. Thematic Analysis Explained with Examples

So you’re staring at a stack of interview transcripts, survey responses, social media posts, or survey reports and wondering what to do next. Content and thematic analysis help you make sense of all that data, but how do you choose between them?

These terms sound similar and get tossed around in the research communities, but they serve different purposes.

In today’s guide, we explain both approaches, explore use cases, and discuss how to choose the most relevant one for your research.

TL;DR – Content Analysis vs. Thematic Analysis

Only got a minute to spare?

Here’s a breakdown of content and thematic analysis, their pros and cons, and what each method is best for.

Content Analysis	Thematic Analysis
A systematic method to quantify and analyze patterns or themes in textual, audio, or visual content.	A qualitative method to identify and interpret patterns or themes in data.
Pros	Pros
Offers historical and cultural insights into communication patterns. It’s reliable and replicable due to the standardized coding schemes. Effective for analyzing large datasets and tracking trends. Quantifies data systematically as it offers an organized structure. Provides direct insights into communication without participant input.	Enables the emergence of natural themes without pre-set categories. It’s accessible for beginners with a straightforward step-by-step process. It uncovers more profound insights and hidden meanings. It’s flexible and adaptable to various research questions. It’s well-suited for understanding emotions, perspectives, and the “why” questions.
Cons	Cons
It only focuses on content, which may miss broader contextual details. It’s limited by the availability and type of data being analyzed. May concentrate on superficial content, missing the underlying narratives.It requires rigorous development and validation of coding schemes.There’s potential for misinterpretation due to overlooking deeper meanings or messages.	It’s difficult to replicate and achieve consistent results. It may overlook subtle refinements in complex relationships between data points. It’s time-consuming due to the need for detailed data engagement. It relies on the researcher’s interpretation, leading to potential bias. It’s less suitable for managing large datasets effectively.
Best For	Best For
Content analysis serves researchers, professionals, and organizations across media studies, sociology, and marketing, offering quantitative and qualitative insights.	Thematic analysis helps explore subjective experiences, perspectives, and meanings in qualitative data, such as interviews, open-ended surveys, or social media posts, in fields like psychology, education, and market research. It offers a flexible, systematic way to uncover patterns and underlying motivations for both beginners and experts.

If you want an easy way to conduct content and thematic analysis, look no further than Marvin. 

Our software lets you auto-tag keywords, organize insights into themes, and collaborate with your team to group themes. 

Try Marvin today for free and turn your research or interviews into insights in minutes.

What is Content Analysis?

Did you know the content analytics market is projected to grow from $6.94 billion (2025) to more than $11 billion by 2030, driven by demand for data-driven insights in sectors like healthcare and finance?

Content analysis is a research method for analyzing patterns in recorded communication, such as visuals, texts, or even audio data, to identify themes, recurring patterns, and meanings within the content.

To conduct content analysis, you must systematically collect data from oral, visual, or written sources, such as books and newspapers, speeches, photographs, or social media content.

There are two key elements of content analysis:

• Coding: Assigning data into predefined categories, such as keywords.
• Thematic Identification: Analyzing the frequency and relationship of these codes to identify patterns throughout the text.

With these elements, content analysis can still be done in different types, depending on your research goals.

Here are some of the types under content analysis:

1. Directed: Where you test a hypothesis using predefined codes, such as measuring the frequency of emotional rhetoric in speeches.
2. Summative: Involves quantifying trends, such as tracking ‘climate change’ mentions in the news over decades.
3. Conventional: Where you explore analysis without pre-set categories, such as identifying themes in customer feedback.

Is Content Analysis Qualitative or Quantitative?

The short answer: content analysis can be both qualitative and quantitative. 

Whether you use content analysis in a qualitative or quantitative approach will depend on your research goals and the nature of the data you want to examine.

• Quantitative Content Analysis: You can count and measure specific elements, such as phrases. You can use this method to test hypotheses or address questions generated from existing theories.

For example, you can identify trends by counting the times ‘climate change’ appears in news articles annually.

• Qualitative Content Analysis: Alternatively, you can use this analysis to interpret and understand the meaning of data, themes, and patterns. You can use this approach to explore the context and subjective meanings behind communication.

For example, analyzing interview transcripts to uncover recurring themes about mental health experiences.

This dual nature makes content analysis a flexible and powerful research tool across disciplines.

Purpose of Content Analysis

So, as a researcher, why should you bother with content analysis in the first place?

Let’s check out its uses:

• To simplify the content for better comprehension.
• To identify the functional applications of language.
• To find out the context, behavior, and reactions of the speaker.
• To analyze the trends and associations between textual content and multimedia.
• To investigate the relationships between language and cultures, interpersonal relationships, and communication.
• To achieve a thoroughly refined, in-depth meaning of the language.
• To get a comprehensive understanding of the concept.
• To know the impact of language on society.

How to Do Content Analysis

To conduct a content analysis, you must follow some steps for a successful result. 

After starting with a clear and direct research question, follow these four steps:

1. Organize the Data to Analyze

You can choose the content you want to analyze depending on your research objectives. 

Data sources might include interviews, videos, and social media posts.

2.  Have a Set of Rules for Coding

It’s essential to have a set of rules before you start coding, especially with more conceptual categories, to be aware of what will and won’t be included. This will ensure complete consistency in the coding.

Coding rules are most important if you work with multiple researchers, but they’re still highly beneficial for practical analysis even when working alone.

3. Code the Text

Go through all the text and note all the categories according to specific characteristics, such as ‘aged 20 to 30’ or ‘parent.’ 

You can do this manually or use Marvin, our AI analytics tool, to speed up your coding process.

4. Interpret and Compile the Results

After coding is completed, review the results, find trends, and draw conclusions that align with the research question.

Content Analysis Examples

Next, let’s walk through some examples to see how content analysis plays out in practice:

1. Academic Research: A scholar examining gender representation in children’s books might group characters according to gender roles and explore their frequencies to reveal potential disparities.
2. Social Media Analysis: A business analyzing customer feedback on Facebook or X could employ content analysis to identify common themes, such as satisfaction with products, customer service complaints, or brand loyalty.
3. Political Campaigns: Researchers analyzing election campaigns might study speeches, social media posts, or advertisements to determine the frequency of keywords, such as ‘progress,’ and assess their appeal to voters.
4. Market Research: Analyzing customer reviews on e-commerce platforms to reveal recurring themes, such as value for money, durability, or delivery experiences.
5. Healthcare: Analyzing thousands of social media posts to identify patient concerns like ‘long wait’ or ‘billing shock,’ leading to improved clinical support.

When to Use Content Analysis

Here are scenarios where this method is the perfect fit for your project:

1. Marketing: Brands analyze social media posts, customer reviews, and competitor communications to understand consumer preferences and market trends.
2. Media Studies: Researchers use content analysis to inspect media biases and trends in news coverage.
3. Education: Inspectors in the education sector inspect curriculum materials, students’ writings, and discussions to improve teaching methods.
4. Healthcare: Medical professionals use content analysis to study medical literature, patient records, and health communications campaigns.
5. Political Science: Analysts examine political speeches, public discourse, and policy documents to understand political strategies and public opinion.
6. Sociology: Sociologists inspect cultural artifacts, historical documents, and social media interactions to understand social trends.
7. Psychology: Psychologists use content analysis to study patterns in therapy sessions.

These are all practical applications illustrating when to use content analysis.

What is Thematic Analysis?

Thematic analysis is a method of analyzing and identifying patterns in qualitative data. 

You can apply it to texts such as interviews or transcripts, where you closely examine the data to identify recurring topics, ideas, and patterns, which are then developed into themes.

It generally follows these six steps, which we shall expound on later.

1. Familiarize yourself with the data, read through the text, and take the initial notes.
2. Code the data by highlighting specific text sections and assigning codes (short labels) that describe the content.
3. Generate themes by grouping codes into broader categories that share a familiar pattern.
4. Review themes to ensure their accuracy in representing the data.
5. Write up the findings of the themes in an organized manner.
6. Create a detailed report with the conclusions of the research.

Types of Thematic Analysis

Thematic analysis is not a one-size-fits-all. It can be done in several ways, depending on what you’re trying to understand. 

Here are four common types:

1. Deductive Thematic Analysis: You start with a theory or framework, which you will use to identify the themes and test the hypothesis, instead of allowing themes to emerge.

Example: A researcher may hypothesize that interviews with refugees will raise themes such as ‘safety’ and ‘belonging.’ They examine the interview data and code the refugees’ experiences according to these themes.

2. Inductive Thematic Analysis: In this research method, analytical themes emerge organically from the data. You don’t have to pre-set themes around a specific premise; instead, you synthesize themes from the data.

Example: A study on motherhood may find that the themes ‘mental health’ and ‘deep cultural belief’ emerge purely from the participants’ words.

3. Semantic Thematic Analysis: You focus on explicit content and surface-level meanings. This approach is suitable for organizing straightforward feedback into actionable categories because it emphasizes what is already stated rather than digging into hidden meanings.

Example: Customer reviews are categorized into themes such as ‘Satisfaction with Shipping Speed’ or ‘Concerns About Product Quality’

4. Latent Thematic Analysis: This approach explores underlying ideas, assumptions, and conceptual patterns within the data. It focuses more on what is implied than just stated.

Example: Your employees’ internal feedback might have surface-level comments like ‘working overtime’ that might reveal latent themes such as ‘feeling undervalued.’

How to Conduct Thematic Analysis

Ready to dive into your data and pull out meaningful insights?

Here’s how to get started with it:

Step 1: Familiarize Yourself with Data

You need to understand and review your data. 

Document audio entries, make copies of original documents, and then study the materials to comprehend them.

Step 2: Create Initial Codes

The next step is to create a qualitative research codebook. 

Take note of initial patterns and ideas that stand out. Highlight sections of these phrases and sentences. These short labels are called codes. They help you easily identify patterns in your resource as you categorize them according to their meanings.

For example, you might create a code like ‘uncertainty’ for sections of texts with ‘I don’t know why and how’ and ‘I’m not sure.’

Step 3: Generate Themes

You should now turn the codes we created above into themes. Typically, themes are broader categories that you make by combining codes.

For example, you could have a ‘misinformation’ theme for codes like ‘incorrect facts’ and ‘biased media sources.’

Step 4: Review Themes

The next step is to ensure that your themes are accurate and useful. Revisit the themes and compare them with the datasets, ensuring they accommodate all the data you want to evaluate, and looking for ways to improve them. You can discard, split, combine, or create new ones to make them useful.

Step 5: Write Your Analysis of the Data

Interpret the themes in your data by writing an accurate explanation or definition of what each theme entails and the information about what they contain.

Step 6: Create a Detailed Report

Craft an analysis of your data in a detailed report involving an introductory paragraph that enlightens readers about your research question and the analysis performed. Also, highlight how you collected data and explain how you conducted the thematic analysis.

Finally, explain the key takeaways and how the findings answer the research question. 

Thematic Analysis Examples

Theory is great, but nothing beats a good example. Let’s see thematic analysis in real-life scenarios:

• Studying Political Speeches: Thematic analysis can help unravel underlying ideologies implied in a politician’s speech, rather than those directly stated, by interpreting the data for hidden assumptions and conceptual patterns.
• Examining Customer Experiences: In a study about airline customers’ experiences, a researcher can identify codes such as ‘delayed resolution of issues’ and ‘lack of empathy’ and apply them to the complaints and requests received.
• Analyzing Remote Learning from Students and Parents: You can tag user research insights directly from their experiences. For example, if you encounter concerns like ‘It was hard to focus at home,’ you can assign a code like ‘distractions at home.’
• Reflecting on Your Background and Biases: When analyzing interviews with immigrants, acknowledge how personal perspectives shape the development. This might lead to themes like ‘loss of identity’ and ‘emotional fatigue.’

When to Use Thematic Analysis

While we’ve seen thematic analysis in action, the main question is when to implement it.

Here’s when it’s right for the job:

• Examining Complex Datasets: If you want to simplify complex datasets into manageable and interpretable categories, regardless of their size.

• Flexible with all Data Sources: When dealing with diverse data sources, including interviews, conversations, open-ended survey responses, and social media posts. 
• Comparing Experiences Across Groups: Thematic analysis can be used when dealing with questions exploring subjective experiences and opinions. For example, understanding the experiences of in-person and remote students will help develop new learning formats.

Detailed Comparison: Thematic Analysis vs. Content Analysis

Now let’s check out the differences between content and thematic analysis (we’ll dig further in the next section):

Criteria	Thematic Analysis	Content Analysis
Objective	To identify, analyze, and interpret themes or patterns of meaning for a deeper understanding of the underlying message.	To systematically categorize and quantify content elements to identify the frequency of trends and patterns.
Approach	A flexible, qualitative, and interpretive approach that emphasizes subjective understanding and narrative exploration.	A structured and often quantitative approach focuses on objective and replicable coding and counting.
Data Focus	Focuses on unstructured qualitative data like interviews. Suitable for small to medium datasets.	It applies to structured and unstructured data, including texts and images, and is suitable for large datasets.
Output	Rich narrative insights describing themes, subthemes, and their interrelationships.	Quantitative metrics like count frequencies, statistical trends, and broad overviews.
Best Used For	Exploring subjective experiences, developing theories, understanding nuanced meanings, and revealing complex patterns.	Measuring trends, verifying hypotheses, comparing content across sources, and analyzing large datasets for objective patterns.

Whether you want an AI assistant for your thematic or content analysis, Marvin will help you with any qualitative data analysis.

Our AI software can help you with thematic analysis by summarizing your qualitative data into key points, highlighting recurring patterns, and suggesting initial themes.

You can also use it for content analysis to automatically tag text, create a codebook, and count the frequency.

Book a free Marvin demo today and see how to transform your raw data into cleanly coded insights faster and with less strain!

Similarities and Differences   

Content and thematic analysis may seem similar, but they differ. 

Let’s discuss the similarities and differences between the two for a better understanding:

Thematic Analysis and Content Analysis Similarities

Both these methods help extract insights, and they also share other common traits:

• Analyzing Techniques: They primarily explore and extract insights from textual or qualitative data in research or applied settings.
• Common Goal: Both require systematic coding processes to analyze and categorize data segments. They also produce organized results that help explain data patterns.
• Application: Both can be applied across various fields and data types.

Thematic Analysis and Content Analysis Differences

Although, on the surface, these two methods look the same, they aren’t. 

Let’s find out what makes thematic analysis different from content analysis:

• Focus on Purpose: Thematic analysis is suitable for exploring subjective experiences and theory development, while content analysis is suitable for trend tracking and hypothesis verification.
• Depth of Analysis: Thematic analysis focuses on latent meanings, while quantitative content analysis focuses on content to count and categorize.
• Generalizability: Thematic analysis is context-specific and less generalizable. On the other hand, content analysis supports broader generalization.
• Types of Insights: Thematic analysis provides rich, narrative insights. Content analysis yields quantifiable and measurable data.
• Data Type Suitability: Thematic analysis focuses on unstructured qualitative data, while content analysis can handle structured and unstructured data.
• Analytical Focus: Thematic analysis interprets themes or patterns, unlike content analysis, which categorizes and quantifies content elements.

Frequently Asked Questions (FAQs)

Lastly, let’s explore some commonly asked questions on the topic:

How Long Does Content Analysis and Thematic Analysis Typically Take?

Both content analysis and thematic analysis are time-consuming. However, the specific duration depends on the data’s complexity, the study’s breadth, and the methods used. 

Content analysis of a large dataset can take several weeks or even months. Thematic, more qualitative analysis can also be time-intensive, mainly during the coding and theme development stages.

You don’t have to analyze your data manually; you can do this with Marvin in minutes and save hours of work.

Are Content Analysis and Thematic Analysis Valid for Mixed-Methods Research?

They are both completely valid and frequently used in mixed methods research.

Thematic analysis helps uncover the underlying reasons behind quantitative findings, offering deeper insights into mixed-method studies.

Content analysis complements this by systematically identifying patterns in the data, bridging both qualitative and quantitative aspects.

What Training is Needed to Conduct Content and Thematic Analysis?

Researchers should have a strong understanding of the methodologies and qualitative data analysis process to perform effective analyses.

This calls for training on coding techniques, learning about research design, and qualitative data analysis software.

What Are the Best Tools for Content and Thematic Analysis?

Some popular choices include NVivo and ATLAS.ti, QDA Miner, and AI-powered platforms like Marvin, Thematic, and Looppanel.

Marvin outshines all these tools when analyzing your data. Our AI research assistant can help you automatically create transcripts from your interviews, tag themes, and collaborate with your research team. 

The Bottom Line

We hope our guide on content analysis vs. thematic analysis helps you understand these different approaches and how and when to use them.

Both methods are necessary and suitable, but you must be specific about the data you’re dealing with. 

If your primary goal is to quantify frequencies of specific elements across very large datasets, content analysis is often more appropriate. If you aim for in-depth understanding of experiences and meanings from qualitative data, thematic analysis is generally well-suited.If your primary goal is to quantify frequencies of specific elements across very large datasets, content analysis is often more appropriate. If you aim for an in-depth understanding of experiences and meanings from qualitative data, thematic analysis is generally well-suited.

Whether you choose content or thematic analysis, Marvin can speed up your workflow and simplify the analysis process.

Our tool can auto-detect all tags and keywords and organize them for easy review. You can also easily search for specific words and quotes.

Sign up for our free account, and let Marvin do the heavy lifting so you can quickly get actionable insights.