Within the arena of qualitative research, there is a wide range of methodologies that analysts have at their disposal. It is fair to say that the key approach falls into a decision of narrative analysis vs thematic analysis. Both approaches are invaluable when it comes to examining and extracting meaning from language in media and border communication, yet both work in different ways and can produce different kinds of insights, therefore serving different research goals.
Having an understanding of how and when you should apply each narrative analysis tool is vital for those who have been tasked with investigating and evaluating complex communication spaces.
Narrative analysis is the study of how stories are told. It is a method that aims to dig into the form, structure and purpose behind any communication, particularly focusing on how wider experiences, events and perspectives are being framed. Instead of isolating repeated ideas, this method prefers to interpret the fuller arc of a story including characters, context and sequencing.
Some of the key elements of narrative analysis include:
Is the story being told chronologically? Or is it fragmented or cyclical instead?
Which story participants are being cast as heroes, villains and victims?
What emotions are being targeted for stirring? Are any specific linguistic cues being used?
What is the state of the current cultural or social backdrop that is influencing the story?
In summary, this type of content analysis seeks to go beyond the surface to get to the heart of intent. Who is doing the speaking? Who is their target audience? What is the intended outcome? Questions like these are what make narrative research so important in uncovering the motivations and influences behind potentially powerful words.
In almost complete contrast, the concern of thematic analysis is to work on identifying repeated patterns across any body of material. Instead of regarding any communication as a structured story, this approach treats the data in its entirety and scans it for things like recurring topics, opinions and attitudes. This way of analyzing is particularly suitable when working with larger volumes of unstructured communication, social media comments or survey responses, for example. The ultimate goal is to record and understand the frequency of any emerging themes.
Traditional thematic analysis will often involve these steps:
Full immersion into a dataset in order to get the best initial overview.
Marking all and any relevant data extracts with appropriate labels that reflect the emerging concepts being identified.
The process of sorting codes in groups of broader patterns and themes.
Which story participants are being cast as heroes, villains and victims?
Finally, presenting the themes complete with supporting quotes and examples.
Whilst it is clear that in thematic vs narrative analysis both methods deal with meaning, thematic analysis does not rely on things like tone, structure or character framing. Instead, it is better for mapping consensus or divergence within a sample population.
The ability to understand the difference between narrative analysis vs thematic analysis is vital when it comes to knowing which tools to apply for your needs.
Some key bullet points to keep in mind on this matter include:
• Narrative analysis examines how something is said, and why it is being framed in a specific way.
• Thematic analysis examines what is actually being said, with focus on frequency and consistency.
• The main question that narrative analysis asks is ‘what storyline is being constructed, and what purpose is it serving?’.
• Thematic analysis reserves that lens, instead asking ‘what themes are most common across this content?’.
In a nutshell, narrative analysis is about form and function, whilst thematic analysis is about pattern and frequency.
If the purpose of your research is to understand the nature of framing and persuasion, or to influence strategies, then narrative analysis is the appropriate choice. The method is best used for things like dissecting political speeches, marketing campaigns or media articles to discern intention and impact.
Some of the most beneficial situations include:
• Analyzing how a story can shift over time to reflect new priorities.
• Identifying strategic language being used in leadership based communication.
• Gaining an understanding of how people, nations or companies are positioned globally and culturally.
This method is particularly effective when executed using the kinds of advanced tools offered by the Osavul platform, designed specifically to decode narrative thematic analysis at large scale. In tracking how storytelling evolves over time and where the most impactful messaging occurs, much deeper insights and narrative control can be gained compared to basic keyword tracking.
On the other hand, thematic analysis can shine under a different set of circumstances. It remains the better choice if handling large datasets that don’t particularly lend themselves to natural storytelling. Customer feedback, public consultation comments and customer or employee satisfaction surveys, for example.
Thematic analysis is best suited when:
• The goal is to evaluate data for any emerging concerns and/or trends.
• You want to create a visual map of apparent public sentiment.
• The structure of the communication at hand isn’t as crucial as the actual content.
This method is best thought of as a radar for ideas and concerns. If the key aim of your research is to know what people are saying most often about a certain brand, policy or product, then thematic analysis provides quick answers.
There is a danger in confusing narrative analysis vs thematic analysis, and that comes in the form of risking coming to incomplete or shallow conclusions. When the goal is to monitor a set of complex messaging campaigns or to examine strategic communication, then a simple surface level review of repetition in them isn’t going to be enough. Decoding of the story is needed, rather than just a listing of its elements.
If the wrong set of tools are selected, you risk missing things like subtle manipulation, coordinated framing and slight changes in messaging over a period of time. The best way to avoid these oversights is by utilizing tools like those provided by Osavul, purpose built for in depth narrative analysis.
Overall, though narrative analysis and thematic analysis are frequently mentioned together, the truth is that they do serve distinct and different functions. Think of the two approaches as complementary methods rather than competing methods. Each approach uncovers different aspects of communication, and when utilized in conjunction they can provide the fullest possible picture in terms of meaning, impact and motivation.
In essence:
• Narrative analysis is used for structure, framing and intention.
• Thematic analysis is used to identify repeated themes and topics across large datasets.
The question of narrative analysis vs thematic analysis isn’t a matter of which approach is better, it is a matter of which is best for your specific goal.
