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Discourse Analysis – Methods, Types and Examples

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Discourse Analysis

Definition:

Discourse Analysis is a method of studying how people use language in different situations to understand what they really mean and what messages they are sending. It helps us understand how language is used to create social relationships and cultural norms.

It examines language use in various forms of communication such as spoken, written, visual or multi-modal texts, and focuses on how language is used to construct social meaning and relationships, and how it reflects and reinforces power dynamics, ideologies, and cultural norms.

Types of Discourse Analysis

Some of the most common types of discourse analysis are:

Conversation Analysis

This type of discourse analysis focuses on analyzing the structure of talk and how participants in a conversation make meaning through their interaction. It is often used to study face-to-face interactions, such as interviews or everyday conversations.

Critical discourse Analysis

This approach focuses on the ways in which language use reflects and reinforces power relations, social hierarchies, and ideologies. It is often used to analyze media texts or political speeches, with the aim of uncovering the hidden meanings and assumptions that are embedded in these texts.

Discursive Psychology

This type of discourse analysis focuses on the ways in which language use is related to psychological processes such as identity construction and attribution of motives. It is often used to study narratives or personal accounts, with the aim of understanding how individuals make sense of their experiences.

Multimodal Discourse Analysis

This approach focuses on analyzing not only language use, but also other modes of communication, such as images, gestures, and layout. It is often used to study digital or visual media, with the aim of understanding how different modes of communication work together to create meaning.

Corpus-based Discourse Analysis

This type of discourse analysis uses large collections of texts, or corpora, to analyze patterns of language use across different genres or contexts. It is often used to study language use in specific domains, such as academic writing or legal discourse.

Descriptive Discourse

This type of discourse analysis aims to describe the features and characteristics of language use, without making any value judgments or interpretations. It is often used in linguistic studies to describe grammatical structures or phonetic features of language.

Narrative Discourse

This approach focuses on analyzing the structure and content of stories or narratives, with the aim of understanding how they are constructed and how they shape our understanding of the world. It is often used to study personal narratives or cultural myths.

Expository Discourse

This type of discourse analysis is used to study texts that explain or describe a concept, process, or idea. It aims to understand how information is organized and presented in such texts and how it influences the reader’s understanding of the topic.

Argumentative Discourse

This approach focuses on analyzing texts that present an argument or attempt to persuade the reader or listener. It aims to understand how the argument is constructed, what strategies are used to persuade, and how the audience is likely to respond to the argument.

Discourse Analysis Conducting Guide

Here is a step-by-step guide for conducting discourse analysis:

• What are you trying to understand about the language use in a particular context?
• What are the key concepts or themes that you want to explore?
• Select the data: Decide on the type of data that you will analyze, such as written texts, spoken conversations, or media content. Consider the source of the data, such as news articles, interviews, or social media posts, and how this might affect your analysis.
• Transcribe or collect the data: If you are analyzing spoken language, you will need to transcribe the data into written form. If you are using written texts, make sure that you have access to the full text and that it is in a format that can be easily analyzed.
• Read and re-read the data: Read through the data carefully, paying attention to key themes, patterns, and discursive features. Take notes on what stands out to you and make preliminary observations about the language use.
• Develop a coding scheme : Develop a coding scheme that will allow you to categorize and organize different types of language use. This might include categories such as metaphors, narratives, or persuasive strategies, depending on your research question.
• Code the data: Use your coding scheme to analyze the data, coding different sections of text or spoken language according to the categories that you have developed. This can be a time-consuming process, so consider using software tools to assist with coding and analysis.
• Analyze the data: Once you have coded the data, analyze it to identify patterns and themes that emerge. Look for similarities and differences across different parts of the data, and consider how different categories of language use are related to your research question.
• Interpret the findings: Draw conclusions from your analysis and interpret the findings in relation to your research question. Consider how the language use in your data sheds light on broader cultural or social issues, and what implications it might have for understanding language use in other contexts.
• Write up the results: Write up your findings in a clear and concise way, using examples from the data to support your arguments. Consider how your research contributes to the broader field of discourse analysis and what implications it might have for future research.

Applications of Discourse Analysis

Here are some of the key areas where discourse analysis is commonly used:

• Political discourse: Discourse analysis can be used to analyze political speeches, debates, and media coverage of political events. By examining the language used in these contexts, researchers can gain insight into the political ideologies, values, and agendas that underpin different political positions.
• Media analysis: Discourse analysis is frequently used to analyze media content, including news reports, television shows, and social media posts. By examining the language used in media content, researchers can understand how media narratives are constructed and how they influence public opinion.
• Education : Discourse analysis can be used to examine classroom discourse, student-teacher interactions, and educational policies. By analyzing the language used in these contexts, researchers can gain insight into the social and cultural factors that shape educational outcomes.
• Healthcare : Discourse analysis is used in healthcare to examine the language used by healthcare professionals and patients in medical consultations. This can help to identify communication barriers, cultural differences, and other factors that may impact the quality of healthcare.
• Marketing and advertising: Discourse analysis can be used to analyze marketing and advertising messages, including the language used in product descriptions, slogans, and commercials. By examining these messages, researchers can gain insight into the cultural values and beliefs that underpin consumer behavior.

When to use Discourse Analysis

Discourse analysis is a valuable research methodology that can be used in a variety of contexts. Here are some situations where discourse analysis may be particularly useful:

• When studying language use in a particular context: Discourse analysis can be used to examine how language is used in a specific context, such as political speeches, media coverage, or healthcare interactions. By analyzing language use in these contexts, researchers can gain insight into the social and cultural factors that shape communication.
• When exploring the meaning of language: Discourse analysis can be used to examine how language is used to construct meaning and shape social reality. This can be particularly useful in fields such as sociology, anthropology, and cultural studies.
• When examining power relations: Discourse analysis can be used to examine how language is used to reinforce or challenge power relations in society. By analyzing language use in contexts such as political discourse, media coverage, or workplace interactions, researchers can gain insight into how power is negotiated and maintained.
• When conducting qualitative research: Discourse analysis can be used as a qualitative research method, allowing researchers to explore complex social phenomena in depth. By analyzing language use in a particular context, researchers can gain rich and nuanced insights into the social and cultural factors that shape communication.

Examples of Discourse Analysis

Here are some examples of discourse analysis in action:

• A study of media coverage of climate change: This study analyzed media coverage of climate change to examine how language was used to construct the issue. The researchers found that media coverage tended to frame climate change as a matter of scientific debate rather than a pressing environmental issue, thereby undermining public support for action on climate change.
• A study of political speeches: This study analyzed political speeches to examine how language was used to construct political identity. The researchers found that politicians used language strategically to construct themselves as trustworthy and competent leaders, while painting their opponents as untrustworthy and incompetent.
• A study of medical consultations: This study analyzed medical consultations to examine how language was used to negotiate power and authority between doctors and patients. The researchers found that doctors used language to assert their authority and control over medical decisions, while patients used language to negotiate their own preferences and concerns.
• A study of workplace interactions: This study analyzed workplace interactions to examine how language was used to construct social identity and maintain power relations. The researchers found that language was used to construct a hierarchy of power and status within the workplace, with those in positions of authority using language to assert their dominance over subordinates.

Purpose of Discourse Analysis

The purpose of discourse analysis is to examine the ways in which language is used to construct social meaning, relationships, and power relations. By analyzing language use in a systematic and rigorous way, discourse analysis can provide valuable insights into the social and cultural factors that shape communication and interaction.

The specific purposes of discourse analysis may vary depending on the research context, but some common goals include:

• To understand how language constructs social reality: Discourse analysis can help researchers understand how language is used to construct meaning and shape social reality. By analyzing language use in a particular context, researchers can gain insight into the cultural and social factors that shape communication.
• To identify power relations: Discourse analysis can be used to examine how language use reinforces or challenges power relations in society. By analyzing language use in contexts such as political discourse, media coverage, or workplace interactions, researchers can gain insight into how power is negotiated and maintained.
• To explore social and cultural norms: Discourse analysis can help researchers understand how social and cultural norms are constructed and maintained through language use. By analyzing language use in different contexts, researchers can gain insight into how social and cultural norms are reproduced and challenged.
• To provide insights for social change: Discourse analysis can provide insights that can be used to promote social change. By identifying problematic language use or power imbalances, researchers can provide insights that can be used to challenge social norms and promote more equitable and inclusive communication.

Characteristics of Discourse Analysis

Here are some key characteristics of discourse analysis:

• Focus on language use: Discourse analysis is centered on language use and how it constructs social meaning, relationships, and power relations.
• Multidisciplinary approach: Discourse analysis draws on theories and methodologies from a range of disciplines, including linguistics, anthropology, sociology, and psychology.
• Systematic and rigorous methodology: Discourse analysis employs a systematic and rigorous methodology, often involving transcription and coding of language data, in order to identify patterns and themes in language use.
• Contextual analysis : Discourse analysis emphasizes the importance of context in shaping language use, and takes into account the social and cultural factors that shape communication.
• Focus on power relations: Discourse analysis often examines power relations and how language use reinforces or challenges power imbalances in society.
• Interpretive approach: Discourse analysis is an interpretive approach, meaning that it seeks to understand the meaning and significance of language use from the perspective of the participants in a particular discourse.
• Emphasis on reflexivity: Discourse analysis emphasizes the importance of reflexivity, or self-awareness, in the research process. Researchers are encouraged to reflect on their own positionality and how it may shape their interpretation of language use.

Advantages of Discourse Analysis

Discourse analysis has several advantages as a methodological approach. Here are some of the main advantages:

• Provides a detailed understanding of language use: Discourse analysis allows for a detailed and nuanced understanding of language use in specific social contexts. It enables researchers to identify patterns and themes in language use, and to understand how language constructs social reality.
• Emphasizes the importance of context : Discourse analysis emphasizes the importance of context in shaping language use. By taking into account the social and cultural factors that shape communication, discourse analysis provides a more complete understanding of language use than other approaches.
• Allows for an examination of power relations: Discourse analysis enables researchers to examine power relations and how language use reinforces or challenges power imbalances in society. By identifying problematic language use, discourse analysis can contribute to efforts to promote social justice and equality.
• Provides insights for social change: Discourse analysis can provide insights that can be used to promote social change. By identifying problematic language use or power imbalances, researchers can provide insights that can be used to challenge social norms and promote more equitable and inclusive communication.
• Multidisciplinary approach: Discourse analysis draws on theories and methodologies from a range of disciplines, including linguistics, anthropology, sociology, and psychology. This multidisciplinary approach allows for a more holistic understanding of language use in social contexts.

Limitations of Discourse Analysis

Some Limitations of Discourse Analysis are as follows:

• Time-consuming and resource-intensive: Discourse analysis can be a time-consuming and resource-intensive process. Collecting and transcribing language data can be a time-consuming task, and analyzing the data requires careful attention to detail and a significant investment of time and resources.
• Limited generalizability: Discourse analysis is often focused on a particular social context or community, and therefore the findings may not be easily generalized to other contexts or populations. This means that the insights gained from discourse analysis may have limited applicability beyond the specific context being studied.
• Interpretive nature: Discourse analysis is an interpretive approach, meaning that it relies on the interpretation of the researcher to identify patterns and themes in language use. This subjectivity can be a limitation, as different researchers may interpret language data differently.
• Limited quantitative analysis: Discourse analysis tends to focus on qualitative analysis of language data, which can limit the ability to draw statistical conclusions or make quantitative comparisons across different language uses or contexts.
• Ethical considerations: Discourse analysis may involve the collection and analysis of sensitive language data, such as language related to trauma or marginalization. Researchers must carefully consider the ethical implications of collecting and analyzing this type of data, and ensure that the privacy and confidentiality of participants is protected.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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21 Great Examples of Discourse Analysis

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Discourse analysis is an approach to the study of language that demonstrates how language shapes reality. It usually takes the form of a textual or content analysis .

Discourse is understood as a way of perceiving, framing, and viewing the world.

For example:

• A dominant discourse of gender often positions women as gentle and men as active heroes.
• A dominant discourse of race often positions whiteness as the norm and colored bodies as ‘others’ (see: social construction of race )

Through discourse analysis, scholars look at texts and examine how those texts shape discourse.

In other words, it involves the examination of how the ‘ways of speaking about things’ normalizes and privileges some frames of thinking about things while marginalizing others.

As a simple example, if movies consistently frame the ideal female as passive, silent, and submissive, then society comes to think that this is how women should behave and makes us think that this is normal , so women who don’t fit this mold are abnormal .

Instead of seeing this as just the way things are, discourse analysts know that norms are produced in language and are not necessarily as natural as we may have assumed.

Examples of Discourse Analysis

1. language choice in policy texts.

A study of policy texts can reveal ideological frameworks and viewpoints of the writers of the policy. These sorts of studies often demonstrate how policy texts often categorize people in ways that construct social hierarchies and restrict people’s agency .

Examples include:

2. Newspaper Bias

Conducting a critical discourse analysis of newspapers involves gathering together a quorum of newspaper articles based on a pre-defined range and scope (e.g. newspapers from a particular set of publishers within a set date range).

Then, the researcher conducts a close examination of the texts to examine how they frame subjects (i.e. people, groups of people, etc.) from a particular ideological, political, or cultural perspective.

3. Language in Interviews

Discourse analysis can also be utilized to analyze interview transcripts. While coding methods to identify themes are the most common methods for analyzing interviews, discourse analysis is a valuable approach when looking at power relations and the framing of subjects through speech.

4. Television Analysis

Discourse analysis is commonly used to explore ideologies and framing devices in television shows and advertisements.

Due to the fact advertising is not just textual but rather multimodal , scholars often mix a discourse analytic methodology (i.e. exploring how television constructs dominant ways of thinking) with semiotic methods (i.e. exploration of how color, movement, font choice, and so on create meaning).

I did this, for example, in my PhD (listed below).

5. Film Critique

Scholars can explore discourse in film in a very similar way to how they study discourse in television shows. This can include the framing of sexuality gender, race, nationalism, and social class in films.

A common example is the study of Disney films and how they construct idealized feminine and masculine identities that children should aspire toward.

6. Analysis of Political Speech

Political speeches have also been subject to a significant amount of discourse analysis. These studies generally explore how influential politicians indicate a shift in policy and frame those policy shifts in the context of underlying ideological assumptions.

9. Examining Marketing Texts

Advertising is more present than ever in the context of neoliberal capitalism. As a result, it has an outsized role in shaping public discourse. Critical discourse analyses of advertising texts tend to explore how advertisements, and the capitalist context that underpins their proliferation, normalize gendered, racialized, and class-based discourses.

11. Analyzing Lesson Plans

As written texts, lesson plans can be analyzed for how they construct discourses around education as well as student and teacher identities. These texts tend to examine how teachers and governing bodies in education prioritize certain ideologies around what and how to learn. These texts can enter into discussions around the ‘history wars’ (what and whose history should be taught) as well as ideological approaches to religious and language learning.

12. Looking at Graffiti

One of my favorite creative uses of discourse analysis is in the study of graffiti. By looking at graffiti, researchers can identify how youth countercultures and counter discourses are spread through subversive means. These counterdiscourses offer ruptures where dominant discourses can be unsettled and displaced.

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The Origins of Discourse Analysis

1. foucault.

French philosopher Michel Foucault is a central thinker who shaped discourse analysis. His work in studies like Madness and Civilization and The History of Sexuality demonstrate how our ideas about insanity and sexuality have been shaped through language.

The ways the church speaks about sex, for example, shapes people’s thoughts and feelings about it.

The church didn’t simply make sex a silent taboo. Rather, it actively worked to teach people that desire was a thing of evil, forcing them to suppress their desires.

Over time, society at large developed a suppressed normative approach to the concept of sex that is not necessarily normal except for the fact that the church reiterates that this is the only acceptable way of thinking about the topic.

Similarly, in Madness and Civilization , a discourse around insanity was examined. Medical discourse pathologized behaviors that were ‘abnormal’ as signs of insanity. Were the dominant medical discourse to change, it’s possible that abnormal people would no longer be seen as insane.

One clear example of this is homosexuality. Up until the 1990s, being gay was seen in medical discourse as an illness. Today, most of Western society sees that this way of looking at homosexuality was extremely damaging and exclusionary, and yet at the time, because it was the dominant discourse, people didn’t question it.

2. Norman Fairclough

Fairclough (2013), inspired by Foucault, created some key methodological frameworks for conducting discourse analysis.

Fairclough was one of the first scholars to articulate some frameworks around exploring ‘text as discourse’ and provided key tools for scholars to conduct analyses of newspaper and policy texts.

Today, most methodology chapters in dissertations that use discourse analysis will have extensive discussions of Fairclough’s methods.

Discourse analysis is a popular primary research method in media studies, cultural studies, education studies, and communication studies. It helps scholars to show how texts and language have the power to shape people’s perceptions of reality and, over time, shift dominant ways of framing thought. It also helps us to see how power flows thought texts, creating ‘in-groups’ and ‘out-groups’ in society.

Key examples of discourse analysis include the study of television, film, newspaper, advertising, political speeches, and interviews.

Al Kharusi, R. (2017). Ideologies of Arab media and politics: a CDA of Al Jazeera debates on the Yemeni revolution. PhD Dissertation: University of Hertfordshire.

Alaazi, D. A., Ahola, A. N., Okeke-Ihejirika, P., Yohani, S., Vallianatos, H., & Salami, B. (2021). Immigrants and the Western media: a CDA of newspaper framings of African immigrant parenting in Canada. Journal of Ethnic and Migration Studies , 47 (19), 4478-4496. Doi: https://doi.org/10.1080/1369183X.2020.1798746

Al-Khawaldeh, N. N., Khawaldeh, I., Bani-Khair, B., & Al-Khawaldeh, A. (2017). An exploration of graffiti on university’s walls: A corpus-based discourse analysis study. Indonesian Journal of Applied Linguistics , 7 (1), 29-42. Doi: https://doi.org/10.17509/ijal.v7i1.6856

Alsaraireh, M. Y., Singh, M. K. S., & Hajimia, H. (2020). Critical DA of gender representation of male and female characters in the animation movie, Frozen. Linguistica Antverpiensia , 104-121.

Baig, F. Z., Khan, K., & Aslam, M. J. (2021). Child Rearing and Gender Socialisation: A Feminist CDA of Kids’ Popular Fictional Movies. Journal of Educational Research and Social Sciences Review (JERSSR) , 1 (3), 36-46.

Barker, M. E. (2021). Exploring Canadian Integration through CDA of English Language Lesson Plans for Immigrant Learners. Canadian Journal of Applied Linguistics/Revue canadienne de linguistique appliquée , 24 (1), 75-91. Doi: https://doi.org/10.37213/cjal.2021.28959

Coleman, B. (2017). An Ideological Unveiling: Using Critical Narrative and Discourse Analysis to Examine Discursive White Teacher Identity. AERA Online Paper Repository .

Drew, C. (2013). Soak up the goodness: Discourses of Australian childhoods on television advertisements, 2006-2012. PhD Dissertation: Australian Catholic University. Doi: https://doi.org/10.4226/66/5a9780223babd

Fairclough, N. (2013). Critical discourse analysis: The critical study of language . London: Routledge.

Foucault, M. (1990). The history of sexuality: An introduction . London: Vintage.

Foucault, M. (2003). Madness and civilization . New York: Routledge.

Hahn, A. D. (2018). Uncovering the ideologies of internationalization in lesson plans through CDA. The New English Teacher , 12 (1), 121-121.

Isti’anah, A. (2018). Rohingya in media: CDA of Myanmar and Bangladesh newspaper headlines. Language in the Online and Offline World , 6 , 18-23. Doi: http://repository.usd.ac.id/id/eprint/25962

Khan, M. H., Adnan, H. M., Kaur, S., Qazalbash, F., & Ismail, I. N. (2020). A CDA of anti-Muslim rhetoric in Donald Trump’s historic 2016 AIPAC policy speech. Journal of Muslim Minority Affairs , 40 (4), 543-558. Doi: https://doi.org/10.1080/13602004.2020.1828507

Louise Cooper, K., Luck, L., Chang, E., & Dixon, K. (2021). What is the practice of spiritual care? A CDA of registered nurses’ understanding of spirituality. Nursing Inquiry , 28 (2), e12385. Doi: https://doi.org/10.1111/nin.12385

Mohammadi, D., Momeni, S., & Labafi, S. (2021). Representation of Iranians family’s life style in TV advertising (Case study: food ads). Religion & Communication , 27 (58), 333-379.

Munro, M. (2018) House price inflation in the news: a CDA of newspaper coverage in the UK. Housing Studies, 33(7), pp. 1085-1105. doi: 10.1080/02673037.2017.1421911

Ravn, I. M., Frederiksen, K., & Beedholm, K. (2016). The chronic responsibility: a CDA of Danish chronic care policies. Qualitative Health Research , 26 (4), 545-554. Doi: https://doi.org/10.1177%2F1049732315570133

Sengul, K. (2019). Critical discourse analysis in political communication research: a case study of right-wing populist discourse in Australia. Communication Research and Practice , 5 (4), 376-392. Doi: https://doi.org/10.1080/22041451.2019.1695082

Serafis, D., Kitis, E. D., & Archakis, A. (2018). Graffiti slogans and the construction of collective identity: evidence from the anti-austerity protests in Greece. Text & Talk , 38 (6), 775-797. Doi: https://doi.org/10.1515/text-2018-0023

Suphaborwornrat, W., & Punkasirikul, P. (2022). A Multimodal CDA of Online Soft Drink Advertisements. LEARN Journal: Language Education and Acquisition Research Network , 15 (1), 627-653.

Symes, C., & Drew, C. (2017). Education on the rails: a textual ethnography of university advertising in mobile contexts. Critical Studies in Education , 58 (2), 205-223. Doi: https://doi.org/10.1080/17508487.2016.1252783

Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education , 46 (2), 25-44. Doi: https://doi.org/10.1080/17508480509556423

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
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Introducing Discourse Analysis for Qualitative Research

Qualitative researchers often try to understand the world by listening to how people talk, but it can be really revealing to look at not just what people say, but how. This is how discourse analysis (DA) can be used to examine qualitative data.

Daniel Turner

Qualitative research often focuses on what people say: be that in interviews , focus-groups , diaries , social media or documents . Qualitative researchers often try to understand the world by listening to how people talk, but it can be really revealing to look at not just what people say, but how. Essentially this is the how discourse analysis (DA) can be used to examine qualitative data. Discourse is the complete system by which people communicate, it’s the widest interpretation of what we call ‘language’. It includes both written, verbal and non-verbal communication, as well as the wider social concepts that underpin what language means, and how it changes. For example, it can be revealing to look at how some people use a particular word, or terms from a particular local dialect. This can show their upbringing and life history, or influences from other people and workplace culture. It can also be interesting to look at non-verbal communication: people’s facial expressions and hand movements are an important part of the context of what people say. But language is also a dynamic part of culture, and the meanings behind terms change over time. How we understand terms like ‘fake news’ or ‘immigration’ or ‘freedom’ tells us a lot, not just about the times we live in or the people using those terms, but groups that have power to change the discourse on such issues. We will look at all these as separate types of discourse analysis. But first it’s important to understand why language is so important; it is much more than just a method of communication.

“Language allows us to do things. It allows us to engage in actions and activities. We promise people things, we open committee meetings, we propose to our lovers, we argue over politics, and we “talk to God”…

Language allows us to be things. It allows us to take on different socially significant identities. We can speak as experts—as doctors, lawyers, anime aficionados, or carpenters—or as ‘everyday people’. To take on any identity at a given time and place we have to ‘talk the talk’…”         - Gee 2011

Language is more than a neutral way of communicating, it’s deeply connected with actions and personal identity, and can even shape the way we think about and understand the world. Who we are, what we do, and our beliefs are all shaped by the language we use. This makes it a very rich avenue for analysis.

Types of discourse analysis Just like so many blanket qualitative terms , there are a lot of different practices and types of analysis called ‘discourse’ analysis, and many different ways of applying them. Hodges et al. (2008) identify 3 meta-types, broadly going from more face-value to conceptual analysis:      • Formal linguistic (basically looking at words/phrases, grammar or semantics)      • Empirical (social practice constructed through text)              • Critical (language constructing and limiting thought)

Tannen et al., 2015 categorise three similar broad types of analysis, again becoming increasingly socially conceptual:

• language use

• anything beyond the sentence

• a broader range of social practice that includes non-linguistic and non-specific instances of language

However Gee (2011) only recognises two main categories, essentially those that look at the use of words, and ‘critical discourse analysis’: like the latter of both groupings above, this is analysis of how language is situated in cultural and contextual power dynamics. But before we get there, let’s start with an example of some more obvious linguistic level discourse analysis.

Example Imagine the following scenario from your favourite fictional medical drama. A patient is wheeled into the ER/casualty unit, conscious but suffering from burns. The doctor attending says three things:

To Patient: “We’re just going to give you a little injection to help with the pain.”

To Nurse: “10cc’s of sodium pentothal, stat!”

To Surgeon: “We’ve got severe second-degree chemical burns, GA administered”

In this situation, the doctor has said essentially the same thing 3 times, but each time using a different response for each recipient. Firstly, when talking to the patient, the doctor doesn’t use any medical terminology, and uses calming and minimising language to comfort the patient. This is a classic type of discourse we are familiar with from medical TV dramas, the ‘good bed-side manner’.

To the nurse, the doctor has a different tone, more commanding and even condescending. It’s a barked command, finished with the term ‘stat!’ - a commonly used medial slang word (actually from the Latin word ‘statum’ meaning immediately, that’s your linguistic analysis!). This is interesting, because it’s not a term you’d hear used in other professional places like a busy kitchen. It shows there is a specific discourse for the setting (a hospital) and for different people in the setting. The ‘10cc of sodium pentothal’ is a commonly used anaesthetic: the same ‘something to help with the pain’ but now with a (trademarked) pharmacological name and dose.

Finally, to the surgeon the same prescription is described by the doctor as an abbreviation (GA for General Anaesthetic). Between senior health professionals, abbreviations might be used more often, in this case actually hiding the specific drug given, perhaps on the basis that the surgeon doesn’t need to know. It could also imply that since only that basic first step has been made, there has been little assessment or intervention so far, telling to an experienced ear what stage of the proceedings they are walking in on. The use of the term ‘we’ might imply the doctor and surgeon are on the same level, as part of the team, a term not used when addressing the nurse.

Even in this small example, there are a lot of different aspects of discourse to unpack. It is very contextually dependent, none of the phrases or manners are likely to be adopted by the doctor in the supermarket or at home. This shows how the identity and performativity of the doctor is connected to their job (and shaped by it, and contextual norms). It also shows differences in discourse between different actors, and power dynamics which are expressed and created through discursive norms.

At a very basic level, we could probably do an interesting study on TV shows and the use of the term ‘stat!’. We could look at how often the term was used, how often it was used by doctors to nurses (often) and by nurses to doctors (rarely). This would probably be more like a basic linguistic analysis, possibly even quantitative. It’s one of the few occasions that a keyword search in a qualitative corpus can be useful – because you are looking at the use of a single, non-replaceable word. If someone says ‘now please’ or ‘as soon as you can’ it has a very different meaning and power dynamic, so we are not interested in synonyms here. However, we probably still want to trawl through the whole text to look at different phrases that are used, and why ‘stat!’ was not the command in all situations. This would be close to the ‘formal linguistic’ approach listed above.

But a more detailed, critical and contextual examination of the discourse might show that nurses struggle with out-moded power dynamics in hospitals (eg Fealy and McNamara 2007 , Turner et al 2007 ). Both of these papers are described as ‘critical’ discourse analysis. However, this term is used in many different ways.

Critical discourse analysis is probably the most often cited, but often used in the most literal sense – that it looks at discourse critically, and takes a comparative and critical analytic stance. It’s another term like ‘grounded theory’ that is used as a catch-all for many different nuanced approaches. But there is another ‘level’ of critical discourse analysis, influenced by Foucault (1972, 1980) and others, that goes beyond reasons for use and local context, to examine how thought processes in society influenced by the control of language and meanings.

Critical discourse analysis (hardcore mode)

“What we commonly accept as objective or obviously true is only so because of negotiated agreement among people” – Gee (2011)

Language and discourse are not absolute. Gee (2011) notes at least three different ways that the positionality of discourse can be shown to be constructed and non-universal: meanings and reality can change over time, between cultures, and finally with ‘discursive construction’ – due to power dynamics in setting language that controls how we understand concepts. Gee uses the term ‘deconstruction’ in the Derridian sense of the word, advocating for the critical examining and dismantling of unquestioned assumptions about what words mean and where they come from.

But ‘deep’ critical discourse analysis also draws heavily from Foucault and an examination of how language is a result of power dynamics, and that the discourse of society heavily regulates what words are understood to mean, as well as who can use them. It also implies that because of these systems of control, discourse is used to actually change and reshape thought and expression. But the key jump is to understand and explain that “what we take to be the truth about the world importantly depends on the social relationships of which we are a part” (Gergen 2015). This is social construction, and a key part of the philosophy behind much critical discourse analysis.

Think of the use of the term ‘freedom’ in mainstream and political discourse in the United States. It is one of the most powerful words used by politicians, and has been for centuries (eg Chanley and Chanley 2015 ) However, it’s use and meaning have changed over time, and what different people from different parts of the political spectrum understand to be enshrined under this concept can be radically different, and even exclusionary. Those in powerful political and media positions are able to change the rhetoric around words like freedom, and sub-terms like ‘freedom of speech’ and ‘freedom of religion’ are both being shifted in public discourse, even on a daily basis, and taking our own internal concepts and ideas with them. It may be that there has never been an age when so much power to manipulate discourse is concentrated in so few places, and able to shift it so rapidly.

Doing Discourse

So do we ‘do’ discourse analysis? How can we start examining complex qualitative data from many voices from a point of view of discourse? Like so many qualitative analytical techniques , researchers will usually adopt a blend of approaches: doing some elements of linguistic analysis, as well as critical discourse analysis for some parts or research questions. They may also draw on narrative and thematic analysis . But discourse analysis is often comparative, it lends itself to differences in the use of language between individuals, professionals and contexts.

From a practical point of view, it can be started by a close reading of key words and terms, especially if it is not clear from the outset what the important and illustrative ones are going to be. For building a complete picture of discourse, a line-by-line approach can be adopted, but it’s also useful to use ‘codes’ or ‘themes’ to tag every use of some terms, or just significant ones. A qualitative software tool like Quirkos can help you do this.

For critical discourse analysis, examination of primary data is rarely enough – it needs to be deeply contextualised within the wider societal or environmental norms that govern a particular subset of discourse. So policy and document analysis are often entwined and can be analysed in the same project. From here, it’s difficult to describe a single technique further, as it will greatly vary by type of source. It is possible in discourse analysis for a single sentence or word to be the major focus of the study, or it may look widely across many different people and data sources.

The textbooks below are all classic works on discourse analysis, each a rabbit hole in itself to digest (especially the new edition of Gergen (2015) which goes much wider into social construction). However, Hodges et al. (2008) is a nice short, practical overview to start your journey.

If you are looking for a tool to help your qualitative discourse analysis, why not give Quirkos a try? It was designed by qualitative researchers to be the software they wanted to use, and is flexible enough for a whole number of analytical approaches, including discourse analysis. Download a free trial , or read more about it here .

Gee, J., P., 2011. An Introduction to Discourse Analysis . Routledge, London.

Gergen, K. J., 2015, An invitation to Social Construction . Sage, London.

Hodges, B. D., Kuper, A., Reeves, S. 2008. Discourse Analysis. BMJ , a879.

Johnstone, B., 2017. Discourse Analysis . Wiley, London.

Paltridge, B., 2012. Discourse Analysis: An Introduction . Bloomsbury.

Tannen, D., Hamilton, H., Schiffrin, D. 2015. The Handbook of Discourse Analysis . Wiley, Chichester.

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• Knowledge Base
• Methodology
• Critical Discourse Analysis | Definition, Guide & Examples

Critical Discourse Analysis | Definition, Guide & Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Discourse analysis is a research method for studying written or spoken language in relation to its social context. It aims to understand how language is used in real-life situations.

When you do discourse analysis, you might focus on:

• The purposes and effects of different types of language
• Cultural rules and conventions in communication
• How values, beliefs, and assumptions are communicated
• How language use relates to its social, political, and historical context

Discourse analysis is a common qualitative research method in many humanities and social science disciplines, including linguistics, sociology, anthropology, psychology, and cultural studies. It is also called critical discourse analysis.

Table of contents

What is discourse analysis used for, how is discourse analysis different from other methods, how to conduct discourse analysis.

Conducting discourse analysis means examining how language functions and how meaning is created in different social contexts. It can be applied to any instance of written or oral language, as well as non-verbal aspects of communication, such as tone and gestures.

Materials that are suitable for discourse analysis include:

• Books, newspapers, and periodicals
• Marketing material, such as brochures and advertisements
• Business and government documents
• Websites, forums, social media posts, and comments
• Interviews and conversations

By analysing these types of discourse, researchers aim to gain an understanding of social groups and how they communicate.

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Unlike linguistic approaches that focus only on the rules of language use, discourse analysis emphasises the contextual meaning of language.

It focuses on the social aspects of communication and the ways people use language to achieve specific effects (e.g., to build trust, to create doubt, to evoke emotions, or to manage conflict).

Instead of focusing on smaller units of language, such as sounds, words, or phrases, discourse analysis is used to study larger chunks of language, such as entire conversations, texts, or collections of texts. The selected sources can be analysed on multiple levels.

Discourse analysis is a qualitative and interpretive method of analysing texts (in contrast to more systematic methods like content analysis ). You make interpretations based on both the details of the material itself and on contextual knowledge.

There are many different approaches and techniques you can use to conduct discourse analysis, but the steps below outline the basic structure you need to follow.

Step 1: Define the research question and select the content of analysis

To do discourse analysis, you begin with a clearly defined research question . Once you have developed your question, select a range of material that is appropriate to answer it.

Discourse analysis is a method that can be applied both to large volumes of material and to smaller samples, depending on the aims and timescale of your research.

Step 2: Gather information and theory on the context

Next, you must establish the social and historical context in which the material was produced and intended to be received. Gather factual details of when and where the content was created, who the author is, who published it, and whom it was disseminated to.

As well as understanding the real-life context of the discourse, you can also conduct a literature review on the topic and construct a theoretical framework to guide your analysis.

Step 3: Analyse the content for themes and patterns

This step involves closely examining various elements of the material – such as words, sentences, paragraphs, and overall structure – and relating them to attributes, themes, and patterns relevant to your research question.

Step 4: Review your results and draw conclusions

Once you have assigned particular attributes to elements of the material, reflect on your results to examine the function and meaning of the language used. Here, you will consider your analysis in relation to the broader context that you established earlier to draw conclusions that answer your research question.

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Introduction

What is discourse analysis?

Forms of discourse analysis, how to conduct discourse analysis.

• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative data analysis software

Discourse analysis

The way that people speak, write, and otherwise communicate with others has a profound effect on the meaning being conveyed.

We expect a recipe in a cookbook to have a particular format. A job interview has a certain structure that is different from a friendly conversation. People speak to their boss in a way that is different from how they speak to their parents.

Discourse analysis focuses on these types of texts, be they in written or spoken language, or other modes of meaning-making. Discourse analysis is a crucial methodological approach in qualitative research that examines both the content of communication and the way in which it is conducted, including the social context and cultural context surrounding the use of language. Those who conduct discourse analysis on a given text analyze both their linguistic content and language use.

Discourse analysis aims to examine language in its social context. It examines how social realities are constructed and understood through language, going beyond just the words spoken or written. It aims to reveal the cultural, political, and sociological dimensions within a communication, whether it's an informal chat, a novel, a business meeting, or a social media post .

In discourse analysis, language is not just a neutral means of conveying information. Instead, it plays an active role in shaping our understanding of the world and social relations. By studying the nuances of language, such as its structure, style, and context, we can reveal more about societal structures, power dynamics, and ideologies.

What is an example of discourse analysis?

Let's consider an example to illustrate this concept. Suppose a team in a corporate setting conducts a series of meetings to discuss a potential project. In these meetings, the language used, the topics discussed, the dynamics of the conversation, and the hierarchy of speaking will all form part of the discourse.

A discourse analyst studying this scenario would not only focus on the words spoken but also on how they are said, who says them, in what order, and with what intent. They may explore patterns such as whether junior team members speak less frequently or if certain ideas are immediately dismissed. Such patterns could reveal underlying power dynamics or ideological bias within the team.

Real-world applications of discourse analysis

Discourse analysis is widely applicable across various fields due to its interdisciplinary nature. In sociology, it helps reveal societal norms and values. In politics, it can help researchers decode political speeches to understand underlying messages and strategies. In marketing, it enables the comprehension of consumer attitudes and perceptions.

One real-world application of discourse analysis is in the field of media studies. Discourse analysis can help with deconstructing the news, identifying biases and highlighting how language is used to shape public opinion. Another application could be in the field of healthcare, where it can assist in understanding patient-doctor communication, thereby contributing to improved care.

Discourse analysis made easy with ATLAS.ti

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Discourse analysis is a common qualitative research method . Researchers use discourse analysis for various purposes. As such, there are several different forms of discourse analysis, each focusing on different aspects of language and its use in context. Some of these forms are explained below.

Narrative analysis

Narrative analysis is an approach within discourse analysis that centers on the study of stories. These stories, or narratives, can be drawn from a variety of sources, such as interviews , observations , or written texts.

This form of analysis looks at how individuals structure their experiences and make sense of the world through storytelling. It acknowledges that the way people narrate their experiences is not merely a reflection of those experiences but a constructive process that provides meaningful insight into their social and psychological realities.

There are several elements that a narrative analysis might focus on, such as the structure of the narrative, the characters involved, and the sequencing of events. For instance, an analyst might look at how a person positions themselves within their story, whether as a protagonist, a victim, or a bystander. These roles can reveal a lot about an individual’s self-perception and worldview.

Likewise, the way in which events are sequenced can provide insights into how the individual sequences and assigns significance to different events. Narrative analysis, therefore, serves as a powerful tool to understand individual perceptions, social roles, and cultural norms.

Rhetorical analysis

Rhetorical analysis is another specialized form of discourse analysis that scrutinizes the methods and strategies of persuasion employed in a piece of text or speech. This method acknowledges that language is not just a passive conveyor of ideas but an active tool designed to influence and persuade audiences.

In this analysis, an analyst might look at a variety of elements, such as the use of figurative language (metaphors, similes, analogies), the logical structure of arguments, and the use of emotional appeals.

For instance, a rhetorical analysis of a political speech might examine how a politician uses specific metaphors or analogies to frame issues in a way that resonates with their audience's values and beliefs. It might also analyze how the speaker structures their argument, considering whether they employ logical reasoning, emotional appeals, or ethical arguments to persuade their audience.

This approach is often used in fields like politics, advertising, and literature, where understanding the art of persuasion is crucial. However, it can also be useful in everyday contexts, enabling individuals to engage critically with the persuasive messages they encounter daily.

Conversation analysis

Conversation analysis (CA) is another common branch of discourse analysis that primarily focuses on the study of talk-in-interaction. It emphasizes understanding the structure and processes of social interaction that happen in everyday conversation.

Other forms of discourse analysis may look at larger social or cultural issues. However, CA focuses more closely on how people talk to each other in real-life situations, and it typically involves close analysis of each individual turn, including intonation.

Conversation analysts often work with recordings of natural conversation to examine in fine-grained detail the mechanisms and patterns of social interaction. They consider the order of turns in conversation, the length of pauses, the use of different speech acts, and non-verbal cues.

Each of these components plays a critical role in how a conversation unfolds and how meaning is made. For example, discourse analysts may investigate how questions and answers are structured in a conversation, who initiates a particular topic and who gets to speak more often, or how interruptions are managed.

The study of these patterns can offer insights into social roles, norms, and expectations that govern conversations.

Critical discourse analysis

Critical discourse analysis (CDA) is a form of discourse analysis that critically examines the relationship between language and power . It posits that language is not neutral but intimately connected with social power dynamics and ideologies.

According to CDA, discourses – everyday conversations, media reports, or political speeches – can subtly reproduce and reinforce societal power structures and ideologies. In this case, conducting critical discourse analysis is about the changes that the discourse effects, which researchers can study in addition to the actual meaning conveyed.

When conducting a critical discourse analysis, an analyst will look at a variety of factors. For instance, they might examine who has the authority to speak, who remains silent, and what topics are considered legitimate or illegitimate. They might also look at the use of specific words or phrases and consider how these language choices reflect and reinforce certain ideologies.

For example, a CDA of a news report might uncover biases in the way the report frames certain issues, highlighting how these biases serve to uphold certain power structures. Similarly, a CDA of a company's internal communications might reveal how the company's language practices reinforce certain hierarchical relations or marginalize certain groups.

By revealing these hidden power dynamics of discourse, CDA serves as a useful tool for promoting social justice and equity. A critical discourse analysis of qualitative data has the potential to challenge the assumptions of knowledge conveyed through analysis of power relations and point to how such a power imbalance can be remedied through language use.

Conducting discourse studies is an iterative and multifaceted process that requires careful planning, execution, and interpretation . The general process, however, can be divided into several sequential steps:

Define your research questions

The first and most crucial step in discourse analysis is formulating clear and concise research questions . What are you hoping to uncover from the discourse? Are you interested in understanding power dynamics , revealing social norms, exploring the construction of identities, or analyzing the mechanisms of persuasion? The answers to these questions will guide your choice of data, your method of analysis, and the conclusions you draw.

Choose an analysis strategy

The research question determines the most appropriate unit of analysis for your study. If you are studying a classroom context, for example, you might look specifically at a teacher's questions and the responses by students. This might involve dividing your transcripts into episodes and analyzing each episode for the type of question posed and the responses elicited for the purpose of identifying what is being taught and learned.

In another example, imagine analyzing discourse involving speakers who are in the process of learning a language. You might consider identifying instances in a discourse where speakers struggle with the language while communicating (e.g., searching for the right word, resorting to another language) to examine their extent of success in navigating the challenges of communication. You may also choose to analyze how their interlocutors respond when they navigate those challenges (e.g., are they belittled or treated with patience?).

Whatever strategy you employ, it's important to reduce the data to a coherent set of analytical units that are relevant to the research question you are seeking to address.

Analyze the discourse

The heart of discourse analysis lies in the detailed examination of your material, as is the case with all qualitative methodologies . The specific focus of your analysis will depend on your research questions and the type of discourse analysis you are conducting.

For instance, if you are conducting a narrative analysis , you might analyze the structure of the narratives, the roles of different characters, and the sequencing of events. A thematic approach to discourse might examine the patterns and recurring themes inherent to a particular interaction. If you're analyzing rhetoric, you'll focus on methods of persuasion, such as the use of figurative language and emotional appeals.

In a CA approach, you'll concentrate on the mechanisms of social interaction, such as the order of turns and the use of different speech acts. If you are using video data , you can also analyze body language in conjunction with spoken utterances and other non-verbal cues accompanying word choice.

While conducting your analysis , it's essential to continually link your findings back to your research questions, and you may reshape your research question as you engage in data collection and analysis since qualitative research often supports adapting the study to emerging findings. Also, consider how your findings relate to the broader social and cultural context of the discourse.

Develop and refine interpretations

As you conduct your detailed analysis, you also begin interpreting your findings . What do they reveal about your research questions? How do they help you understand the broader context you're interested in? In your interpretations, strive to balance the specific details of your material with the broader conceptual or theoretical frameworks you may be using in your discourse analysis.

The process of discourse analysis is rarely linear. As you delve into your material, new insights may emerge that prompt you to revisit your research questions, your material, or your analysis.

Don't be afraid to refine your analysis in light of these new insights. The final review of findings is never truly final until answers to your research questions have been sufficiently developed.

You can also apply your findings to new data to confirm what you have learned from previous discourse analyses to further refine your understanding of the specific context you are examining.

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• Brian David Hodges , associate professor, vice chair (education), and director 1 ,
• Ayelet Kuper , assistant professor 2 ,
• Scott Reeves , associate professor 3
• 1 Department of Psychiatry, Wilson Centre for Research in Education, University of Toronto, 200 Elizabeth Street, Eaton South 1-565, Toronto, ON, Canada M5G 2C4
• 2 Department of Medicine, Sunnybrook Health Sciences Centre, and Wilson Centre for Research in Education, University of Toronto, 2075 Bayview Avenue, Room HG 08, Toronto, ON, Canada M4N 3M5
• 3 Department of Psychiatry, Li Ka Shing Knowledge Institute, Centre for Faculty Development, and Wilson Centre for Research in Education, University of Toronto, 200 Elizabeth Street, Eaton South 1-565, Toronto, ON, Canada M5G 2C4
• Correspondence to: B D Hodges brian.hodges{at}utoronto.ca

This articles explores how discourse analysis is useful for a wide range of research questions in health care and the health professions

Previous articles in this series discussed several methodological approaches used by qualitative researchers in the health professions. This article focuses on discourse analysis. It provides background information for those who will encounter this approach in their reading, rather than instructions for conducting such research.

What is discourse analysis?

Discourse analysis is about studying and analysing the uses of language. Because the term is used in many different ways, we have simplified approaches to discourse analysis into three clusters (table 1 ⇓ ) and illustrated how each of these approaches might be used to study a single domain: doctor-patient communication about diabetes management (table 2 ⇓ ). Regardless of approach, a vast array of data sources is available to the discourse analyst, including transcripts from interviews, focus groups, samples of conversations, published literature, media, and web based materials.

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 Three approaches to discourse analysis

 Three approaches to a specific research question: example of doctor-patient communications about diabetes management

What is formal linguistic discourse analysis?

The first approach, formal linguistic discourse analysis, involves a structured analysis of text in order to find general underlying rules of linguistic or communicative function behind the text. 4 For example, Lacson and colleagues compared human-human and machine-human dialogues in order to study the possibility of using computers to compress human conversations about patients in a dialysis unit into a form that physicians could use to make clinical decisions. 5 They transcribed phone conversations between nurses and 25 adult dialysis patients over a three month period and coded all 17 385 words by semantic type (categories of meaning) and structure (for example, sentence length, word position). They presented their work as a “first step towards an automatic analysis of spoken medical dialogue” that would allow physicians to “answer questions …

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Chapter 23: Discourse analysis

Tess Tsindos

Learning outcomes

Upon completion of this chapter, you should be able to:

• Describe discourse analysis.
• Understand how to conduct discourse analysis.
• Identify the strengths and limitations of discourse analysis.

What is discourse analysis?

Discourse analysis is a field of qualitative analysis that has its origins in disciplines such as linguistics, philosophy, psychology, anthropology. 1 It is an interdisciplinary field that deals with ‘language’ and meaning. 2

According to Jaworski and Coupland, the purpose of discourse analysis is that it ‘offers a means of exposing or deconstructing the social practices that constitute ‘social structure’ and what we might call the conventional meaning structures of social life. It is a sort of forensic activity’. 3 ( p5 ) There are three domains of discourse analysis: the study of social interaction; the study of minds, selves and sense-making; and the study of culture and social relations. 4 ( p5 )

Discourse analysis is the study of texts such as transcribed interviews, websites, forums, books, newspapers, government documents (and many more), and the analysis of those texts to understand different accounts and the meanings behind those accounts. Qualitative researchers strive to understand the relationships between text (discourse) and social constructs. As text is analysed, the meaning behind the text is also explored, often as the ‘voices’ in the text. For example, when a participant is asked about their eating habits and they discuss their joy in eating as well as feelings of guilt from eating high-calorific foods, they may be voicing their parents’ disapproval of this eating behaviour. The relationship between text and social constructs can also be seen in alcohol advertising: an advertisement may be promoting alcohol consumption as a fun behaviour, but also cautions listeners to drink ‘responsibly’, because the advertiser is required to do so by advertising standards authorities. This inherent contradiction in the advertising is part of the meaning-making regarding alcohol consumption. This meaning-making is contextual and differs between countries, such as Australia (a high alcohol consumption culture) and Canada (a lower alcohol consumption culture). Another example of context is in the use of the word ‘just’ by an interview participant; the term can mean many things, but if the researcher is asking about job title, ‘just’ may the participant’s implication or inference that the title does not reflect an important position (e.g. ‘I’m just an editor’). In discourse analysis, texts, meanings and inferences are important.

Following is an example of media articles and two distinct discourses about violence towards women. The first media article, published by The Guardian on 15 June 2018 , 5 presents a discourse about how it is the responsibility of women to prevent men from being violent towards them. The second article about the same incident, published by The Age on 25 May 2019, 6 presents a discourse that it is the responsibility of men not to be violent towards women.

Meanings of texts are particularly important when participants use metaphors. The researcher needs to examine the implications of the metaphor, deliberate or inadvertent. For example, when the researcher asks the participant how they felt about their life and the participant replies, ‘life is a highway’, the researcher needs to look beyond what was said to understand the participant’s meaning.

As an interdisciplinary method, discourse analysis can be complex and intricate. Gee 7 provides 72 tools to assist with various types of discourse analysis, ranging from identifying what is being said and what is not being said, to examining ‘how the person is using language, as well as ways of acting, interacting, believing, valuing, dressing, and using various objects, tools, and technologies in certain sorts of environments to enact a specific socially recognizable identity and engage in one or more socially recognizable activities’. 7 ( p201 ) Gee also includes a helpful table (see Table 23.1) populated with his 7 building tasks for researchers to examine their discourses, and provides the answers. 8

Table 23.1. Seven Building Tasks and associated discourse analysis questions

How to conduct discourse analysis

Discourse analysis, as in all other qualitative methods, is used depending on the research topic and question(s) or aim(s). The following steps are recommended:

Step 1: Have a clearly defined topic and research question, because this informs the types of research materials that will be used.

Step 2: Conduct wide-ranging searches for materials that will inform the research topic.

Step 3: Determine which theory and framework will be used as the underpinning foundation for the analyses (see Section 1 chapters 1–4).

Step 4: Analyse the content of the materials. This analysis is different (but similar) to content analysis, which is a research technique to systematically classify codes and identify themes or patterns within the data. Discourse analysis is concerned with identifying themes and patterns within the texts that relate to the social contexts reflected in the research topic and within the theoretical lens chosen for analyses.

Step 5: Interpret and draw conclusions. Reflect on your work and examine how the various texts use language within the context of the research topic to answer the research question(s).

As an example, Table 23.3 includes a study on girls’ experience of competitive dancing . 9 The authors progressed through the steps as follows:

Step 1: The topic is eating disorders and young dancers. The research question is ‘ How does experience in the world of competitive dance shape the relationship that young girls have with their bodies ?’

Step 2: The author conducted wide – ranging literature searches on eating disorders, ballet dancers, body image, thinness, Western culture, dieting, media influences and many more topics.

Step 3: Feminism was the theoretical underpinning of the text ual analys i s. As described by the authors, ‘ a feminist post structural approach was chosen to provide a critical lens to explore the beliefs, values, and practices of young dancers… aimed to provide an understanding of the dominant and competing discourses present in the world of dance and discover how these discourses are constituted, perpetuated, and form ways of knowing in relation to body and body image.’ 9(p 7 )

Ste p 4: T he transcripts were analysed in 5 steps , following Aston 10 a nd presented in table 23.2 :

Table 23.2. A guide to using feminist poststructuralism informed by discourse analysis

*Note: This table is from an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits copy and redistribution of material in any medium or format, remix, transform and build upon the material for any purpose, even commercially provided the original work is properly cited.

Step 5: Results were first interpreted within an ‘environmental’ context (competitive culture, ideal dancer’s body, mirrors, and dance attire and costumes) , which was predominately negative due to the competitive culture. The second context was ‘parents’ , which encompassed body monitoring, joking, and parents and support. Although most of the dancers stated that their parents did not influence their relationship with their body, discourse analysis demonstrated that parents did influence them. The third context was ‘ coaches’ . Coaches had a very strong influence on participants’ body image. While the dancers believed their coaches were supportive, the discourse demonstrated that most coach es’ comments were negative. ‘Peers’ represented in the final context for analysis. Again, the dancers believed their peers were supportive ; however , discourse analysis demonstrated that many peer comments were negative. The conclusions drawn from the research were that ‘ all participants experienced negative physical, mental, and/or emotional repercussions throughout their competitive dance experience. It was also determined that environment, parents, coaches, and peers largely shaped the dancer’s relationship with body and body image in the world of dance. These influences generated and perpetuated the dominant negative body image discourse that dancers were often unable to resist, and consequently their relationship with body and body image suffered.’ 9(p p22-23 )

This is a good example of situating a topic (body image) within a context (young women dancing) underpinned by a theoretical framework that explores the dancers’ beliefs, values and practices.

Table 23.3. Discourse analysis examples

Advantages and challenges of discourse analysis

Discourse analysis can be used to analyse small and large data sets with homogenous and heterogenous samples. It can be applied to any type of data source, from interviews and focus groups to diary entries, news reports and online discussion forums. However, interpretation in discourse analysis can lead to limitations and challenges that tend to occur when discourse analysis is misapplied or done poorly. Discourse analysis can be highly flexible and is best used when anchored in a theoretical approach. Because discourse analysis involves subjective interpretation, training and support from a qualitative researcher with expertise in the method is required to ensure that the interpretation of the data is meaningful. Finally, discourse analysis can be time-consuming when analysing large volumes of texts.

Discourse analysis is a process whereby texts are examined and interpreted. It looks for the meanings ‘behind’ text in cultural and social contexts. Discourse analysis is flexible, and the researcher has scope to interpret the text(s) based on the research topic and aim(s). Having a theoretical approach assists the researcher to position the discourse in cultural and social grounding.

• Schiffrin D, Tannen D et al . , ed s . The Handbook of Discourse Analysis . Blackwell ; 2001.
• Jaworski A, Coupland N. eds. The Discourse Reader . 2nd ed. Routledge; 2006.
• Jaworski A, Coupland N. Introduction: perspectives on discourse analysis. In: Jaworski A, Coupland N, eds. The Discourse Reader . 2nd ed. Routledge; 2006.
• Wetherell M, Taylor S, Yates S. (2001) Discourse Theory and Practice: A Reader . 2nd ed. Sage. 2001.
• Davey M. ‘Men need to change’: anger grows over police response to Eurydice Dixon’s murder. Guardian . June 15, 2018. Accessed April 28, 2023. https://www.theguardian.com/australia-news/2018/jun/15/men-need-to-change-anger-grows-over-police-response-to-comedians#:~:text=Melbourne
• Fowler M. ‘This is about men’s behaviour’, says top policy offer after another woman’s murder . Age . May 25, 2019. Accessed April 28, 2023. https://www.theage.com.au/national/victoria/this-is-about-men-s-behaviour-says-top-police-officer-after-another-woman-s-murder-20190525-p51r46.html
• Gee J. How t o d o Discourse Analysis: A Toolkit .  2nd ed. Routledge; 2014.
• Gee J. An Introduction to Discourse Analysis: Theory and Method . 3rd ed. Routledge; 2011.
• Doria N, Numer M. Dancing in a culture of disordered eating: a feminist poststructural analysis of body and body image among young girls in the world of dance. PLoS ONE . 2022;17(1): e0247651. doi:10.1371/journal.pone.0247651
• Aston M. Teaching feminist poststructuralism: founding scholars still relevant today.  Creative Education . 2016;7(15):2251-2267. doi: 10.4236/ce.2016.715220
• Öhman A, Burman M, Carbin M et al . ‘The public health turn on violence against women’: analysing Swedish healthcare law, public health and gender-equality policies.  BMC Public Health . 2020;20:753. doi:10.1186/s12889-020-08766-7
• Carrasco JM, Gómez-Baceiredo B, Navas A et al. Social representation of palliative care in the Spanish printed media: a qualitative analysis. PLoS ONE . 2019;14(1):e0211106. doi:10.1371/journal.pone.0211106

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Tess Tsindos is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Discourse analysis: Step-by-step guide with examples

What is a discourse analysis, the application of discourse analysis in the academic thesis, discourse analysis with maxqda.

• Step 1: Importing data
• Step 2: Coding data
• Step 3: Creating Codebook
• Step 4: Visualize data

Literature about MAXQDA

Tuesday, September 19, 2023

MAXQDA supports various methodological approaches, including discourse analysis. This guide will introduce you to the tools of MAXQDA, which are ideal for performing discourse analysis with MAXQDA quickly and easily. MAXQDA is a qualitative data analysis software that helps you import, code, and identify patterns in your discourse.

Discourse analysis is a multidisciplinary method used in the humanities and social sciences to develop a deeper understanding of the interactions between language, society, and culture. It focuses on the study of linguistic expressions, structures, and practices in order to capture social meanings and power dynamics. Both verbal and nonverbal communication are considered. The overarching goal of discourse analysis is to explore how discourses influence the construction of knowledge, identities, and social relations. It enables the study of the role of language and communication in shaping and influencing social reality. Overall, discourse analysis makes a valuable contribution to the study of social phenomena and processes by providing an in-depth understanding of how language and communication are used to create meanings, shape social relationships, and establish social power dynamics. Discourse analysis contributes to critical reflection and knowledge acquisition in various academic disciplines.

A primary motivation for using discourse analysis is the ability to uncover dominant discourses, ideological assumptions, and power structures in texts, media content, or political speeches. Discourse analysis allows researchers to better understand and critically reflect on the role of language and discourse in society. Another important area of application of discourse analysis in dissertations is the study of the relationship between discourses and identity constructions. For example, gender roles, ethnic identities, or sexual orientations can be studied. Discourse analysis can help to understand how identities are negotiated, constructed, and reproduced in specific social contexts. Another area of application in dissertations is the study of discourses in the media. The analysis of media discourses makes it possible to identify, critically expose and reflect on patterns and trends in reporting. This can contribute to a better understanding of the media’s role in constructing and disseminating discourses. In summary, discourse analysis offers a valuable methodological perspective for the study of complex social phenomena in the context of academic work.

Researchers typically follow these steps in discourse analysis: defining the research question, selecting relevant textual data, coding and categorizing the data, analyzing patterns and meanings within the discourse, interpreting the results, and documenting their findings in written form. The specific steps may vary depending on the research question and methodology.

As mentioned earlier, there are clear advantages to using software like MAXQDA to conduct discourse analysis. With MAXQDA, you can segment data, code it, and develop analytical ideas all at the same time. This makes the process more efficient and allows you to refine your theoretical approaches in real time. If you do not have a MAXQDA License yet, download the free 14-day trial to get started:

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Step 1 of the discourse analysis with MAXQDA: Importing data

Importing data into MAXQDA is a crucial step in beginning the analysis of qualitative data. MAXQDA provides several options for importing data into the program, allowing you to effectively organize your research materials. You can import different types of data, such as text documents, transcripts, media content, or existing MAXQDA Projects. MAXQDA gives you the flexibility to import both individual files and entire folders of data, which is especially helpful when working with large data sets. The import process is designed to be simple and user-friendly, making it easier for you to work with your data. Another advantage of MAXQDA is that it supports a wide variety of file formats. You can import files in various formats, including TXT, DOC, PDF, MP3, MP4 and many more. This versatility allows you to work with different types of data and incorporate different media into your analysis. Importing your data into MAXQDA makes it structured and accessible for further analysis. Within MAXQDA, you can organize, code, and link your data with other analytical tools. This makes it easier to navigate and access relevant information during the analysis process. Overall, importing data into MAXQDA is an efficient way to manage your qualitative research materials and prepare them for analysis. It serves as a critical first step in launching your project in MAXQDA and taking full advantage of the program’s extensive analytical capabilities.

Importing data into MAXQA plays a crucial role in conducting discourse analysis. With MAXQDA, you can segment your data into documents and annotate them with relevant metadata such as title, author, and date. This allows you to organize your texts during the analysis phase. You can sort, filter, and group your data based on various criteria to access specific texts. In addition, MAXQDA provides the ability to annotate the imported text with notes, comments, or memos. This feature is invaluable for capturing important information, thoughts, or interpretations that arise during analysis. You can document your observations and insights directly in MAXQDA, thus fostering a comprehensive understanding of the discourse being analyzed. In MAXQDA, you can assign meaningful titles to your data and include relevant metadata such as author and date in the document names. This ensures a clear organization of your texts during the analysis phase. You can sort, filter, and group your data according to various criteria to access specific texts. In addition, MAXQDA allows you to annotate the imported texts with comments and notes using memos. This feature is very useful for capturing key information, thoughts, or interpretations that emerge during the analysis. You can document your observations and insights directly in MAXQDA and develop a thorough understanding of the discourse being analyzed. Importing data into MAXQDA is fundamental to conducting a systematic and comprehensive discourse analysis.The structured organization of data in MAXQDA facilitates the effective application of various analysis methods and techniques. You can create codes to identify and analyze important themes, terms, or patterns within the discourse. Importing data into MAXQDA provides a central platform where you can manage, analyze, and interpret your data. This greatly streamlines the entire process of discourse analysis, allowing you to make informed statements about social meanings, power dynamics, and identity constructions within the discourse you are analyzing.

Step 2 of the discourse analysis with MAXQDA: Coding data

Coding data in MAXQDA plays a critical role in the analysis process. Coding involves identifying and marking specific themes, categories, or concepts within the data. This allows researchers to systematically organize and extract relevant information from the data. In MAXQDA, different types of data can be coded, such as text passages, images, videos, or audio files. Codes can be used to associate these data segments with specific content or meanings. Researchers can use codes to identify and mark certain phenomena or themes in the data, allowing for targeted access later. Coding in MAXQDA allows researchers to identify complex relationships and patterns within the data. By linking and combining codes and organizing them hierarchically, researchers can establish relationships between different elements. These connections provide new insights and help understand the relationships within the data. The coded data can be further used in MAXQDA for additional analysis. For example, complex queries or filters can be applied to examine specific aspects of the discourse in detail. By analyzing the coded data, researchers can identify patterns, trends, and significant relationships that lead to valuable insights. MAXQDA provides an intuitive and easy-to-use platform to efficiently perform the coding and analysis process. The program offers several tools and features that allow researchers to customize the coding process and tailor the analysis to their specific needs. Overall, coding data in MAXQDA is a critical step in analyzing and understanding qualitative data.

Coding data in MAXQDA allows researchers to identify and analyze specific discursive elements such as themes, arguments, or language strategies in the texts under study. To code data in MAXQDA, researchers can select relevant text passages and assign them codes that represent specific meanings or categories. These codes can be organized hierarchically to illustrate relationships between different discursive elements. In addition to coding, MAXQDA offers features such as text annotation, the ability to create memos, and options for visual data presentation at later stages. These features facilitate the organization and interpretation of coded data, enabling researchers to gain deep insights into the discourse under study and to visualize their findings. MAXQDA provides a comprehensive and efficient platform for coding and analyzing data in discourse analysis.

Step 3 of the discourse analysis with MAXQDA: Creating Codebook

A Codebook in MAXQDA defines codes for units of meaning within data. It enables structured and consistent coding, improves traceability and reproducibility, increases the efficiency of data analysis, facilitates comparisons and cross-references between codes and data, and provides flexibility and adaptability. In summary, a codebook promotes structured, consistent, and efficient data analysis, improving traceability and identification of relationships and patterns.

A Codebook is also very useful for discourse analysis in MAXQDA. Here are some reasons why:

• Structured coding of discourse features: A Codebook establishes uniform rules and definitions for coding data. This ensures that coding is structured and consistent across researchers and stages of analysis. This increases the reliability of results and facilitates the comparison and integration of data.
• Improved traceability and reproducibility: By clearly defining the codes and their use in the Codebook, the traceability of the coding process is improved. Other researchers can understand and trace the coding, increasing the reproducibility of the analysis. In addition, a Codebook facilitates effective collaboration and sharing of data and analysis among researchers.
• Identification and comparison of discourse patterns: A Codebook allows for the systematic identification and comparison of discourse patterns. This makes it possible to identify connections, patterns, and differences in the data, thus facilitating the interpretation of the results.
• Efficient data analysis: A Codebook provides a structured view of the codes used and their meanings. This allows researchers to work more efficiently by applying the codes quickly and specifically to relevant data. Using a codebook saves time and makes it easier to organize and navigate the coded data.
• Flexibility and adaptability: A Codebook in MAXQDA is flexible and customizable. Researchers can add, modify, or remove codes to meet the needs of their specific research questions. This allows for dynamic and iterative data analysis, where the Codebook can be continually updated and expanded.

In summary, a well-designed codebook in MAXQDA promotes structured, consistent, and efficient data analysis.

Step 4 of the discourse analysis with MAXQDA: Visualize data

MAXQDA offers a wide range of visualization tools to help you present your research data in an engaging and meaningful way. These include not only different types of charts, such as bar or pie charts for visualizing numerical data, but also other innovative visualization tools that help you identify and analyze complex relationships.

Code Matrix Browser

With the Code Matrix Browser , in MAXQDA, you can visually display and analyze the occurrence of codes in your data. This feature is invaluable for identifying similarities, differences, and patterns in discourse. Here are some of the ways the Code Matrix Browser can help you:

• Visualization of codings: The Code Matrix Browser displays a matrix where codes are arranged along the rows and documents along the columns. This visual representation allows you to quickly see which codes were used in which documents. This allows you to identify similarities and differences in the coding, which makes it easier to make connections.
• Pattern recognition: By analyzing codings in the Code Relations Browser, you can identify patterns in discourse. For example, you can observe which codes are particularly prevalent in certain documents. These patterns may indicate important themes, arguments, or language strategies, helping you to develop a more comprehensive understanding of the discourse.
• Comparison: With the Code Matrix Browser, you can compare how often certain codes were assigned in each document and display the corresponding information in the matrix. This allows you to analyze relationships between different elements in the discourse and to make connections between different topics or arguments.

Code Relations Browser

The Code Relations Browser , in MAXQDA allows you to visually display and analyze the connections and dependencies between the codes in your discourse. This feature is extremely valuable for understanding the interactions and hierarchy between codes. Here are some of the ways the Code Relations Browser can help you:

• Visualize code relationships: The Code Relations Browser visually displays the relationships between codes. You can see which codes are linked and how they are related to each other. These relationships can be hierarchical, associative, or several other types. This visual representation helps you better understand the structure and organization of codes within the discourse.
• Analyze interactions: The Code Relations Browser lets you analyze the interactions between codes. You can observe which codes occur frequently or how they influence each other. This can help you identify specific themes, arguments, or concepts in the discourse and examine their interrelationships. Analyzing these interactions can provide a deeper understanding of the discourse and the connections between codes.

The Code Map in MAXQDA visualizes selected codes as a map, showing the similarity of codes based on overlaps in the data material. Each code is represented by a circle, and the distance between the circles indicates their similarity. Larger circles represent more instances of coding with the code. Colors can highlight group membership, and connecting lines indicate overlap between codes, with thicker lines indicating more significant overlap. Visualizing the similarities between codes in the data provides an overview of different discursive elements. Grouping codes into clusters allows for the identification of specific discourse themes or dimensions. The connecting lines also show how codes interact and which codes frequently appear together. This allows for a detailed examination of the relationships between discursive elements, facilitating the interpretation and analysis of the discourse.

Document Map

The Document Map visualizes selected documents like a map. The positioning of the circles on the map is based on the similarity of the code assignments between the documents. Documents with similar code mappings are placed closer together, while those with different code mappings are placed further apart. Variable values from the documents can be used to determine similarity. Optionally, similar documents can be color-coded. Larger circles represent documents with more of the analyzed codes. The Document Map is a useful tool for visually grouping cases and can be used for typing or further investigation of the identified groups. The Document Map can be used in several ways in discourse analysis:

• Discourse group identification: By positioning documents on the map based on their code assignments, similar discourse groups can be identified. Documents with similar code assignments are placed closer together, indicating common discursive features.
• Recognition of discourse patterns: The visual representation of documents and their similarities on the map allows for the detection of patterns in discourse. Clusters of documents with similar codings may indicate common themes, arguments, or language patterns.
• Exploration of discourse dynamics: The use of connecting lines between codes on the map can reveal which codes overlap within documents. Thick connecting lines indicate frequent overlap and may suggest discursive relationships or connections.”
• Typification: The Document Map can serve as a basis for typology in discourse analysis. By grouping documents with similar code assignments, different discourse types can be identified and described”.

Profile Comparison Chart

The Profile Comparison Chart MAXQDA allows you to select multiple documents and compare the use of codes within those documents. This comparison allows you to identify differences or similarities in discourse between the selected documents. Below are some steps for using the Profile Comparison Chart:

• Document selection: Select the documents you want to compare. You can choose single documents or a group of documents. These documents should represent the discourse you want to analyze.
• Code selection: Select the codes you wish to compare in the selected documents. These can be specific themes, concepts or discursive elements that are of interest in the discourse.
• Create the comparison chart: Create the comparison graph in MAXQDA. The graph shows the occurrence of codes in individual paragraphs of the documents.
• Analysis of the chart: Analyze the comparison chart to identify differences or similarities in the discourse of the selected documents. Examine the assignment of codes in the paragraphs of the documents. Different patterns or variations in frequency may indicate differences in discourse, while similar patterns may indicate similarities in discourse.

Document Portrait

The Document Portrait feature in MAXQDA allows you to visually represent important features, themes, or characteristics of a document by visualizing the sequence of coding within that document. This feature allows you to identify relevant aspects of the discourse and analyze their weight in this particular document. Below are some steps for using the Document Portrait:

• Document Selection: Select the document for which you want to create a document portrait. The document selected should be representative of the discourse you are analyzing.
• Identify relevant features: Identify the codes that you want to visualize. These may be specific relevant features, themes or characteristics of the document, or other elements relevant to the discourse.
• Weighting of Features: The length of the segment is used as a weighting factor for the Document Portrait.
• Creation of the Document Portrait: Generate the Document Portrait in MAXQDA. The portrait visualizes the identified features and their weighting in the selected document. As a result, you obtain a visual representation of the sequence of coding performed within the document.
• Analysis of the Portrait: Analyze the Document Portrait to identify important features, themes, or characteristics of the document. This allows you to locate and understand relevant aspects of the discourse within a particular document.

The Codeline is a powerful tool in MAXQDA that allows you to visually represent the use of different codes within a document. By displaying the sequence of codes, you can see the flow and development of the discourse. With the Codeline, you can not only see which codes were used in specific sections of the document, but you can also track the progression of codings within a document. This allows you to identify crucial stages, turning points, or focal points in the discourse. The Codeline also allows you to analyze coded segments over time. You can examine specific codes and their occurrences or changes over time. This allows you to examine and interpret trends, patterns, or changes in the discourse more closely. The Codeline is therefore a valuable tool for considering the temporal progression and development of discourse in your analysis. By analyzing coded segments over time, you can gain a deeper understanding of the dynamics and context of the discourse, leading to more informed interpretations.

The Word Cloud is a powerful visualization tool in MAXQDA that helps you visually represent frequently occurring words or terms in the discourse. By looking at the size or weight of the words in the Word Cloud, you can quickly see which terms are particularly prevalent or significant in the discourse. By analyzing the Word Cloud, you can identify key terms in the discourse and examine their weight or frequency in relation to other terms. This allows you to identify and understand important themes, trends, or focuses in the discourse. In addition, you can use the Word Cloud to identify connections between different terms. If certain words occur frequently together or are used in similar contexts, you can identify associations or links in the discourse. The Word Cloud is thus a valuable tool for getting a quick and clear representation of the most common words or terms in the discourse. By analyzing the key terms and their weighting, you can gain important insights into the content and structure of the discourse and make a well-informed interpretation.

We offer a variety of free learning materials to help you get started with MAXQDA. Check out our Getting Started Guide to get a quick overview of MAXQDA and step-by-step instructions on setting up your software and creating your first project with your brand new QDA software. In addition, the free Literature Reviews Guide explains how to conduct a literature review with MAXQDA.

Getting Started with MAXQDA

Literature Reviews with MAXQDA

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Methods and Approaches of Discourse Analysis

• by Discourse Analyzer
• March 31, 2024 May 3, 2024

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“Methods and Approaches of Discourse Analysis” article serves as a gateway for readers interested in the complex ways that language influences and reflects social structures. The article details various analytical frameworks and methodologies used in Discourse Analysis (DA), ranging from Content Analysis and Conversation Analysis to more critical perspectives like Foucauldian Discourse Analysis and Critical Discourse Analysis . Each approach is carefully outlined to show how it contributes to understanding language in texts and social interactions, whether through quantitative measurement of language features or qualitative interpretations of textual meanings. Additionally, the article addresses the significance of methodological diversity in DA, including mixed methods approaches that combine qualitative depth with quantitative breadth, offering a richer, more comprehensive understanding of discourse. This introductory guide not only equips readers with the knowledge of different DA methods but also emphasizes the importance of rigorous data collection, ethical considerations, and the thoughtful analysis necessary to explore the powerful role of language in shaping human experience and social order.

1) Content Analysis

2) conversation analysis (ca), 3) critical discourse analysis (cda), 4) ethnography of communication, 5) foucauldian discourse analysis, 6) narrative analysis, 7) multimodal discourse analysis, 8) corpus linguistics, 1) qualitative approaches, 2) quantitative approaches, 3) differences between qualitative and quantitative approaches, 4) mixed methods in da, 1) data collection and analysis, 2) coding and categorizing data, 3) ethical considerations, frequently asked questions, 1. analytical frameworks.

Discourse Analysis (DA) encompasses a variety of methods and approaches for examining language use across texts, talks, and social practices . These methods vary widely depending on the theoretical perspective and the specific objectives of the research. Below are some key methods and approaches used in Discourse Analysis:

This method involves systematically categorizing the content of texts (which could be written texts, speech, or other forms of communication) to quantify certain aspects, such as the frequency of certain words, phrases, themes, or concepts. Content analysis can be both qualitative and quantitative and is useful for analyzing large volumes of text to identify patterns or trends.

CA is a methodological approach that focuses on the detailed, systematic study of the talk in interaction . It examines the sequential organization of speech to understand how participants in a conversation manage turn-taking, repair, openings, closings, and how they achieve mutual understanding. CA is particularly interested in the procedural aspects of conversation and how social actions are accomplished through talk.

CDA is an approach that aims to understand the relationship between discourse and social power . It analyzes how discourse structures (such as texts, talks, or visual images) serve to establish, maintain, or challenge power relations within society. CDA pays close attention to the ways in which language is used to represent different social groups and interests, often focusing on issues of ideology , identity , and hegemony.

This approach combines ethnographic methods with the analysis of discourse, focusing on the ways in which language use is embedded within cultural contexts . Researchers adopting this method study communication practices within their socio-cultural settings to understand the norms, values, and expectations that govern how language is used in specific communities.

Inspired by the work of Michel Foucault , this approach examines how discourses construct subjects, objects, and knowledge within specific historical and social contexts . It is concerned with the rules and practices that produce discourses, how discourses are related to power and knowledge , and the effects they have on society and individual subjects.

Narrative analysis focuses on the ways in which people use stories to make sense of their experiences and the world around them. This method examines the structure, content, and function of narratives to understand how individuals construct identities and social realities through storytelling.

With the recognition that communication is not only verbal but also involves other modes (such as visual, audio, gestural), multimodal discourse analysis studies how these different modes interact and contribute to the meaning-making process. It is particularly relevant in the analysis of digital media, advertising, and other forms of communication that use multiple semiotic resources.

While not exclusively a method of discourse analysis, corpus linguistics involves analyzing large collections of texts (corpora) using computational tools to identify patterns, frequencies, collocations, and other linguistic features. This method can support discourse analysis by providing empirical evidence of language use across different contexts .

Each of these methods and approaches brings a unique perspective to the study of discourse, allowing researchers to explore the complex ways in which language shapes and is shaped by social reality . The choice of method often depends on the research questions, the data available, and the theoretical framework guiding the analysis.

2. Qualitative, Quantitative, and Mixed Methods Approaches

Discourse Analysis (DA) can be approached through qualitative, quantitative, or mixed methods, depending on the research objectives, the nature of the data, and the theoretical framework adopted. Understanding these different approaches and how they can be integrated provides a comprehensive toolkit for researchers in the field.

Qualitative approaches to DA focus on the interpretation of textual or spoken data to understand the underlying meanings, themes, and patterns within a discourse. This method is less about counting occurrences and more about understanding the context, the social practices, and the power relations that discourse reflects and constructs. Qualitative DA is deeply concerned with the nuances of language use, such as metaphors, narrative structures, and the ways in which language constructs identities and social realities.

Applications: Qualitative DA is often used in studies where the goal is to explore the complexities of discourse in shaping social phenomena, such as identity formation, social inequality , or cultural practices. Methods like Critical Discourse Analysis (CDA) and Conversation Analysis (CA) typically adopt a qualitative approach.

Quantitative approaches to DA involve the systematic coding and counting of features within texts or spoken language to identify patterns, frequencies, and correlations. This method relies on statistical analysis to draw conclusions about the data, offering a more objective measurement of discourse patterns.

Applications: Quantitative DA is suitable for studies aiming to generalize findings from a larger corpus of text or speech. It can be used to track changes in discourse over time, compare discourse across different groups, or measure the prevalence of certain linguistic features. Content analysis and corpus linguistics are examples of methods that can be applied quantitatively.

• Objective vs. Subjective: Quantitative DA is often viewed as more objective, relying on statistical methods to analyze data, while qualitative DA is more subjective, focusing on the interpretation of texts and contexts.
• Data Representation: Quantitative methods result in numerical data, graphs, and tables, whereas qualitative methods produce detailed descriptions, themes, and narrative accounts.
• Focus: Quantitative DA tends to focus on the frequency and distribution of certain elements within discourse, whereas qualitative DA focuses on the content, meaning , and context of discourse.
• Scope: Quantitative approaches can handle large volumes of data, making them suitable for broad analyses. Qualitative approaches, while potentially more time-consuming, provide deep insights into smaller datasets.

Mixed methods involve the combination of qualitative and quantitative approaches in the analysis of discourse. This integration allows for a more comprehensive understanding of discourse by leveraging the strengths of both methodologies.

Applications: Mixed methods can be particularly useful when researchers seek to explore a complex research question that requires both an in-depth understanding of contextual meanings (qualitative) and the generalizability or measurement of certain features across a larger dataset (quantitative). For example, a mixed-methods study might first use qualitative methods to explore the themes and narratives within a set of interviews and then apply quantitative methods to measure how frequently certain themes appear across a broader range of texts.

Advantages: Mixed methods in DA offer a robust framework for research, allowing researchers to validate findings through triangulation, enrich the analysis by combining insights from different methodological perspectives, and provide a more nuanced understanding of the phenomena under study.

In summary, the choice between qualitative, quantitative, and mixed methods in Discourse Analysis depends on the research questions, the nature of the data, and the goals of the study. Each approach offers unique insights and has its place in the comprehensive study of discourse.

3. Data Collection and Analysis

Discourse Analysis (DA) involves a meticulous process of data collection and analysis, with careful consideration of the types of texts or corpora selected, the methodologies employed for coding and categorizing data, and adherence to ethical standards. Here’s an overview:

In DA, data can comprise a wide variety of texts, including written documents (books, articles, social media posts), spoken language (interviews, conversations, speeches), or multimodal texts (videos, images with captions). The choice of data depends on the research question and the theoretical framework guiding the analysis.

Selecting Texts and Corpora The selection of texts or corpora is a critical step in DA. Researchers must choose texts that are representative of the discourse being studied, considering factors such as genre, context, and the social practices they reflect. For instance, a study on political discourse might analyze speeches and social media posts of political figures, while research on medical discourse might examine patient-doctor conversations and medical textbooks. It’s essential to justify the selection of texts to ensure the study’s relevance and reliability.

Analyzing the Data Analysis in DA varies widely across different approaches but generally involves closely reading and interpreting the text to uncover patterns, themes, meanings, and structures. This might involve identifying discourse strategies, narrative structures, rhetorical devices, or specific uses of language that reveal underlying ideologies, power relations, or social identities .

Coding involves systematically labeling segments of the text to identify specific features or themes. This can be done manually or with the help of software. Coding can be inductive, emerging from the data itself, or deductive, based on pre-existing theoretical frameworks.

Categorizing involves grouping coded segments into broader categories that reflect major themes, concepts, or discourse strategies identified in the analysis. This process helps in structuring the analysis and facilitating the interpretation of how language functions within the texts.

Ethical considerations in DA are paramount, especially when dealing with sensitive topics or personal data. Key ethical concerns include:

• Consent: Ensuring that participants in studies involving spoken discourse or private texts have given informed consent for their data to be used in research.
• Anonymity and Confidentiality: Protecting the identity of participants by anonymizing data and maintaining confidentiality, especially when dealing with sensitive information.
• Impact: Considering the potential impact of the research on participants and communities, including avoiding harm and misrepresentation.
• Bias and Reflexivity: Researchers should be aware of their own biases and the power dynamics in the research process, striving for reflexivity in how their perspectives and choices may influence the analysis.

Overall, DA requires a thoughtful and rigorous approach to data collection, analysis, coding, and ethical practices. These steps ensure that the research is robust, reliable, and respectful of the communities and discourses it aims to understand.

In conclusion, the analytical frameworks of Discourse Analysis (DA) present a rich tapestry of methodologies that enable researchers to delve into the complexities of language and its role in shaping social phenomena. From qualitative approaches that unveil nuanced meanings embedded within discourse to quantitative methods that uncover patterns and frequencies, each framework contributes to a comprehensive understanding of language use. Moreover, the integration of mixed methods offers a holistic approach, bridging the qualitative-depth and quantitative-breadth to provide multifaceted insights into discourse analysis. As researchers navigate the terrain of data collection, analysis, and ethical considerations, they engage in a rigorous process that not only illuminates the mechanisms of discourse but also upholds principles of integrity and respect. Ultimately, these analytical frameworks serve as invaluable tools for unraveling the multifaceted nature of language and its profound impact on society, paving the way for deeper insights and transformative understanding.

DA is a field that examines language use across texts, talks, and social practices to uncover how language shapes and is shaped by social reality. It incorporates various methods and approaches, influenced by theoretical perspectives and research objectives.

Key methods include Content Analysis, Conversation Analysis, Critical Discourse Analysis, Ethnography of Communication , Foucauldian Discourse Analysis, Narrative Analysis, Multimodal Discourse Analysis, and Corpus Linguistics. Each method offers a unique lens for analyzing discourse.

Content Analysis systematically categorizes text content to quantify aspects like word frequencies, themes, or concepts. It can be qualitative or quantitative and is ideal for analyzing large volumes of text to identify patterns.

CA focuses on the detailed study of talk in interaction, examining how participants manage conversation through turn-taking, repair, and achieving mutual understanding. It emphasizes the procedural aspects of conversation and social action accomplishment.

CDA aims to understand the relationship between discourse and social power, analyzing discourse structures to see how they establish, maintain, or challenge power relations. It explores language use in representing social groups and focuses on ideology, identity, and hegemony.

This approach merges ethnographic methods with discourse analysis, studying how language use is embedded in cultural contexts. It aims to understand the norms, values, and expectations governing language use in specific communities.

Inspired by Michel Foucault, this approach examines how discourses construct subjects, objects, and knowledge within historical and social contexts. It focuses on discourse production rules, power-knowledge relations, and societal effects.

Narrative Analysis studies how people use stories to construct identities and realities, examining narrative structure, content, and function to understand storytelling’s role in experience interpretation.

Recognizing that communication involves various modes (visual, audio, gestural), this analysis studies how different modes interact and contribute to meaning-making, especially in digital media and advertising.

Although not exclusively for DA, Corpus Linguistics analyzes large text collections using computational tools to identify linguistic patterns, frequencies, and features, providing empirical language use evidence across contexts.

Qualitative approaches focus on interpreting textual or spoken data to understand underlying meanings and contexts. In contrast, quantitative approaches involve systematic coding and counting of text features to identify patterns and correlations. Mixed methods combine both to offer a comprehensive discourse understanding.

Ethical considerations include obtaining informed consent, ensuring anonymity and confidentiality, considering research impact, and being reflexive about biases and power dynamics in the research process.

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

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Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

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I certainly hope to hear from you

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• How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analyzing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Discourse analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Caulfield, J. (2023, June 22). How to Do Thematic Analysis | Step-by-Step Guide & Examples. Scribbr. Retrieved September 13, 2024, from https://www.scribbr.com/methodology/thematic-analysis/

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Discourse Analysis as a Qualitative Approach to Study Information Sharing Practice in Malaysian Board Forums

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