content and discourse analysis research example

What (Exactly) Is Discourse Analysis? A Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Discourse analysis is one of the most popular qualitative analysis techniques we encounter at Grad Coach. If you’ve landed on this post, you’re probably interested in discourse analysis, but you’re not sure whether it’s the right fit for your project, or you don’t know where to start. If so, you’ve come to the right place.

Overview: Discourse Analysis Basics

In this post, we’ll explain in plain, straightforward language :

• What discourse analysis is
• When to use discourse analysis
• The main approaches to discourse analysis
• How to conduct discourse analysis

What is discourse analysis?

Let’s start with the word “discourse”.

In its simplest form, discourse is verbal or written communication between people that goes beyond a single sentence . Importantly, discourse is more than just language. The term “language” can include all forms of linguistic and symbolic units (even things such as road signs), and language studies can focus on the individual meanings of words. Discourse goes beyond this and looks at the overall meanings conveyed by language in context .  “Context” here refers to the social, cultural, political, and historical background of the discourse, and it is important to take this into account to understand underlying meanings expressed through language.

A popular way of viewing discourse is as language used in specific social contexts, and as such language serves as a means of prompting some form of social change or meeting some form of goal.

Now that we’ve defined discourse, let’s look at discourse analysis .

Discourse analysis uses the language presented in a corpus or body of data to draw meaning . This body of data could include a set of interviews or focus group discussion transcripts. While some forms of discourse analysis center in on the specifics of language (such as sounds or grammar), other forms focus on how this language is used to achieve its aims. We’ll dig deeper into these two above-mentioned approaches later.

As Wodak and Krzyżanowski (2008) put it: “discourse analysis provides a general framework to problem-oriented social research”. Basically, discourse analysis is used to conduct research on the use of language in context in a wide variety of social problems (i.e., issues in society that affect individuals negatively).

For example, discourse analysis could be used to assess how language is used to express differing viewpoints on financial inequality and would look at how the topic should or shouldn’t be addressed or resolved, and whether this so-called inequality is perceived as such by participants.

What makes discourse analysis unique is that it posits that social reality is socially constructed , or that our experience of the world is understood from a subjective standpoint. Discourse analysis goes beyond the literal meaning of words and languages

For example, people in countries that make use of a lot of censorship will likely have their knowledge, and thus views, limited by this, and will thus have a different subjective reality to those within countries with more lax laws on censorship.

When should you use discourse analysis?

There are many ways to analyze qualitative data (such as content analysis , narrative analysis , and thematic analysis ), so why should you choose discourse analysis? Well, as with all analysis methods, the nature of your research aims, objectives and research questions (i.e. the purpose of your research) will heavily influence the right choice of analysis method.

The purpose of discourse analysis is to investigate the functions of language (i.e., what language is used for) and how meaning is constructed in different contexts, which, to recap, include the social, cultural, political, and historical backgrounds of the discourse.

For example, if you were to study a politician’s speeches, you would need to situate these speeches in their context, which would involve looking at the politician’s background and views, the reasons for presenting the speech, the history or context of the audience, and the country’s social and political history (just to name a few – there are always multiple contextual factors).

Discourse analysis can also tell you a lot about power and power imbalances , including how this is developed and maintained, how this plays out in real life (for example, inequalities because of this power), and how language can be used to maintain it. For example, you could look at the way that someone with more power (for example, a CEO) speaks to someone with less power (for example, a lower-level employee).

Therefore, you may consider discourse analysis if you are researching:

• Some form of power or inequality (for example, how affluent individuals interact with those who are less wealthy
• How people communicate in a specific context (such as in a social situation with colleagues versus a board meeting)
• Ideology and how ideas (such as values and beliefs) are shared using language (like in political speeches)
• How communication is used to achieve social goals (such as maintaining a friendship or navigating conflict)

As you can see, discourse analysis can be a powerful tool for assessing social issues , as well as power and power imbalances . So, if your research aims and objectives are oriented around these types of issues, discourse analysis could be a good fit for you.

Discourse Analysis: The main approaches

There are two main approaches to discourse analysis. These are the language-in-use (also referred to as socially situated text and talk ) approaches and the socio-political approaches (most commonly Critical Discourse Analysis ). Let’s take a look at each of these.

Approach #1: Language-in-use

Language-in-use approaches focus on the finer details of language used within discourse, such as sentence structures (grammar) and phonology (sounds). This approach is very descriptive and is seldom seen outside of studies focusing on literature and/or linguistics.

Because of its formalist roots, language-in-use pays attention to different rules of communication, such as grammaticality (i.e., when something “sounds okay” to a native speaker of a language). Analyzing discourse through a language-in-use framework involves identifying key technicalities of language used in discourse and investigating how the features are used within a particular social context.

For example, English makes use of affixes (for example, “un” in “unbelievable”) and suffixes (“able” in “unbelievable”) but doesn’t typically make use of infixes (units that can be placed within other words to alter their meaning). However, an English speaker may say something along the lines of, “that’s un-flipping-believable”. From a language-in-use perspective, the infix “flipping” could be investigated by assessing how rare the phenomenon is in English, and then answering questions such as, “What role does the infix play?” or “What is the goal of using such an infix?”

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Approach #2: Socio-political

Socio-political approaches to discourse analysis look beyond the technicalities of language and instead focus on the influence that language has in social context , and vice versa. One of the main socio-political approaches is Critical Discourse Analysis , which focuses on power structures (for example, the power dynamic between a teacher and a student) and how discourse is influenced by society and culture. Critical Discourse Analysis is born out of Michel Foucault’s early work on power, which focuses on power structures through the analysis of normalized power .

Normalized power is ingrained and relatively allusive. It’s what makes us exist within society (and within the underlying norms of society, as accepted in a specific social context) and do the things that we need to do. Contrasted to this, a more obvious form of power is repressive power , which is power that is actively asserted.

Sounds a bit fluffy? Let’s look at an example.

Consider a situation where a teacher threatens a student with detention if they don’t stop speaking in class. This would be an example of repressive power (i.e. it was actively asserted).

Normalized power, on the other hand, is what makes us not want to talk in class . It’s the subtle clues we’re given from our environment that tell us how to behave, and this form of power is so normal to us that we don’t even realize that our beliefs, desires, and decisions are being shaped by it.

In the view of Critical Discourse Analysis, language is power and, if we want to understand power dynamics and structures in society, we must look to language for answers. In other words, analyzing the use of language can help us understand the social context, especially the power dynamics.

While the above-mentioned approaches are the two most popular approaches to discourse analysis, other forms of analysis exist. For example, ethnography-based discourse analysis and multimodal analysis. Ethnography-based discourse analysis aims to gain an insider understanding of culture , customs, and habits through participant observation (i.e. directly observing participants, rather than focusing on pre-existing texts).

On the other hand, multimodal analysis focuses on a variety of texts that are both verbal and nonverbal (such as a combination of political speeches and written press releases). So, if you’re considering using discourse analysis, familiarize yourself with the various approaches available so that you can make a well-informed decision.

How to “do” discourse analysis

As every study is different, it’s challenging to outline exactly what steps need to be taken to complete your research. However, the following steps can be used as a guideline if you choose to adopt discourse analysis for your research.

Step 1: Decide on your discourse analysis approach

The first step of the process is to decide on which approach you will take in terms. For example, the language in use approach or a socio-political approach such as critical discourse analysis. To do this, you need to consider your research aims, objectives and research questions . Of course, this means that you need to have these components clearly defined. If you’re still a bit uncertain about these, check out our video post covering topic development here.

While discourse analysis can be exploratory (as in, used to find out about a topic that hasn’t really been touched on yet), it is still vital to have a set of clearly defined research questions to guide your analysis. Without these, you may find that you lack direction when you get to your analysis. Since discourse analysis places such a focus on context, it is also vital that your research questions are linked to studying language within context.

Based on your research aims, objectives and research questions, you need to assess which discourse analysis would best suit your needs. Importantly, you  need to adopt an approach that aligns with your study’s purpose . So, think carefully about what you are investigating and what you want to achieve, and then consider the various options available within discourse analysis.

It’s vital to determine your discourse analysis approach from the get-go , so that you don’t waste time randomly analyzing your data without any specific plan.

Step 2: Design your collection method and gather your data

Once you’ve got determined your overarching approach, you can start looking at how to collect your data. Data in discourse analysis is drawn from different forms of “talk” and “text” , which means that it can consist of interviews , ethnographies, discussions, case studies, blog posts.  

The type of data you collect will largely depend on your research questions (and broader research aims and objectives). So, when you’re gathering your data, make sure that you keep in mind the “what”, “who” and “why” of your study, so that you don’t end up with a corpus full of irrelevant data. Discourse analysis can be very time-consuming, so you want to ensure that you’re not wasting time on information that doesn’t directly pertain to your research questions.

When considering potential collection methods, you should also consider the practicalities . What type of data can you access in reality? How many participants do you have access to and how much time do you have available to collect data and make sense of it? These are important factors, as you’ll run into problems if your chosen methods are impractical in light of your constraints.

Once you’ve determined your data collection method, you can get to work with the collection.

Step 3: Investigate the context

A key part of discourse analysis is context and understanding meaning in context. For this reason, it is vital that you thoroughly and systematically investigate the context of your discourse. Make sure that you can answer (at least the majority) of the following questions:

• What is the discourse?
• Why does the discourse exist? What is the purpose and what are the aims of the discourse?
• When did the discourse take place?
• Where did it happen?
• Who participated in the discourse? Who created it and who consumed it?
• What does the discourse say about society in general?
• How is meaning being conveyed in the context of the discourse?

Make sure that you include all aspects of the discourse context in your analysis to eliminate any confounding factors. For example, are there any social, political, or historical reasons as to why the discourse would exist as it does? What other factors could contribute to the existence of the discourse? Discourse can be influenced by many factors, so it is vital that you take as many of them into account as possible.

Once you’ve investigated the context of your data, you’ll have a much better idea of what you’re working with, and you’ll be far more familiar with your content. It’s then time to begin your analysis.

Step 4: Analyze your data

When performing a discourse analysis, you’ll need to look for themes and patterns .  To do this, you’ll start by looking at codes , which are specific topics within your data. You can find more information about the qualitative data coding process here.

Next, you’ll take these codes and identify themes. Themes are patterns of language (such as specific words or sentences) that pop up repeatedly in your data, and that can tell you something about the discourse. For example, if you’re wanting to know about women’s perspectives of living in a certain area, potential themes may be “safety” or “convenience”.

In discourse analysis, it is important to reach what is called data saturation . This refers to when you’ve investigated your topic and analyzed your data to the point where no new information can be found. To achieve this, you need to work your way through your data set multiple times, developing greater depth and insight each time. This can be quite time consuming and even a bit boring at times, but it’s essential.

Once you’ve reached the point of saturation, you should have an almost-complete analysis and you’re ready to move onto the next step – final review.

Step 5: Review your work

Hey, you’re nearly there. Good job! Now it’s time to review your work.

This final step requires you to return to your research questions and compile your answers to them, based on the analysis. Make sure that you can answer your research questions thoroughly, and also substantiate your responses with evidence from your data.

Usually, discourse analysis studies make use of appendices, which are referenced within your thesis or dissertation. This makes it easier for reviewers or markers to jump between your analysis (and findings) and your corpus (your evidence) so that it’s easier for them to assess your work.

When answering your research questions, make you should also revisit your research aims and objectives , and assess your answers against these. This process will help you zoom out a little and give you a bigger picture view. With your newfound insights from the analysis, you may find, for example, that it makes sense to expand the research question set a little to achieve a more comprehensive view of the topic.

Let’s recap…

In this article, we’ve covered quite a bit of ground. The key takeaways are:

• Discourse analysis is a qualitative analysis method used to draw meaning from language in context.
• You should consider using discourse analysis when you wish to analyze the functions and underlying meanings of language in context.
• The two overarching approaches to discourse analysis are language-in-use and socio-political approaches .
• The main steps involved in undertaking discourse analysis are deciding on your analysis approach (based on your research questions), choosing a data collection method, collecting your data, investigating the context of your data, analyzing your data, and reviewing your work.

If you have any questions about discourse analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your analysis, book an initial consultation with a friendly Grad Coach to see how we can help.

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30 Comments

This was really helpful to me

I would like to know the importance of discourse analysis analysis to academic writing

In academic writing coherence and cohesion are very important. DA will assist us to decide cohesiveness of the continuum of discourse that are used in it. We can judge it well.

Thank you so much for this piece, can you please direct how I can use Discourse Analysis to investigate politics of ethnicity in a particular society

Fantastically helpful! Could you write on how discourse analysis can be done using computer aided technique? Many thanks

I would like to know if I can use discourse analysis to research on electoral integrity deviation and when election are considered free & fair

I also to know the importance of discourse analysis and it’s purpose and characteristics

Thanks, we are doing discourse analysis as a subject this year and this helped a lot!

Please can you help explain and answer this question? With illustrations,Hymes’ Acronym SPEAKING, as a feature of Discourse Analysis.

What are the three objectives of discourse analysis especially on the topic how people communicate between doctor and patient

Very useful Thank you for your work and information

thank you so much , I wanna know more about discourse analysis tools , such as , latent analysis , active powers analysis, proof paths analysis, image analysis, rhetorical analysis, propositions analysis, and so on, I wish I can get references about it , thanks in advance

Its beyond my expectations. It made me clear everything which I was struggling since last 4 months. 👏 👏 👏 👏

Thank you so much … It is clear and helpful

Thanks for sharing this material. My question is related to the online newspaper articles on COVID -19 pandemic the way this new normal is constructed as a social reality. How discourse analysis is an appropriate approach to examine theese articles?

This very helpful and interesting information

This was incredible! And massively helpful.

I’m seeking further assistance if you don’t mind.

Found it worth consuming!

What are the four types of discourse analysis?

very helpful. And I’d like to know more about Ethnography-based discourse analysis as I’m studying arts and humanities, I’d like to know how can I use it in my study.

Amazing info. Very happy to read this helpful piece of documentation. Thank you.

is discourse analysis can take data from medias like TV, Radio…?

I need to know what is general discourse analysis

Direct to the point, simple and deep explanation. this is helpful indeed.

Thank you so much was really helpful

really impressive

Thank you very much, for the clear explanations and examples.

It is really awesome. Anybody within just in 5 minutes understand this critical topic so easily. Thank you so much.

Thank you for enriching my knowledge on Discourse Analysis . Very helpful thanks again

This was extremely helpful. I feel less anxious now. Thank you so much.

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• Methodology

Content Analysis | A Step-by-Step Guide with Examples

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

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers, and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group, or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analysing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

• The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
• The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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Luo, A. (2022, December 05). Content Analysis | A Step-by-Step Guide with Examples. Scribbr. Retrieved 29 April 2024, from https://www.scribbr.co.uk/research-methods/content-analysis-explained/

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How to do thematic analysis | guide & examples, data collection methods | step-by-step guide & examples, qualitative vs quantitative research | examples & methods.

From Content Analysis to Discourse Analysis: Using Systematic Analysis of Meanings and Discourses

• First Online: 29 July 2022

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• Wendy Olsen 2  

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This chapter illustrates the analysis of texts and discourses in a mixed-methods context, starting with a content-analysis example. For systematic mixed-methods research, it can be advantageous to use a wide lens that allows social inequality or other broader macro issues to be linked to a project. This chapter then explains and illustrates the finding of ‘micro’, ‘meso’, and ‘macro’ mechanisms (or entities) in primary field research. In the annex, practical techniques are given for qualitative textual databases. To discern mechanisms that operate upon people, and which affect people even if they are not aware of it, goes beyond ordinary hermeneutics in the following sense. Instead of drawing upon only what was said and what it means, one can also draw up conclusions about what mechanisms were at work. This is a realist step of deriving a knowledge of social institutions, social structures, and other meso and macro mechanisms from qualitative data. Using survey data proved helpful. Through coding of evidence, deep linkage of mixed-methods data can be achieved. The particular examples here show actual codes, which are compared and grouped into key topics. This chapter also notes competing discourses, and shows how to code intertextuality. Re-theorising the situation could occur as a synthesising moment. This chapter thus underpins the use of qualitative data in mixed-methods research.

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Archer, Margaret (2009), Realist Social Theory: The Morphogenic Approach , Cambridge UK: Cambridge University Press.

Google Scholar  

Archer, Margaret (2014) Structure, Agency and the Internal Conversation , Cambridge University Press.

Bhaskar, Roy (1997) A Realist Theory of Science (2nd ed.). London: Verso.

Blaikie, Norman, and Jan Priest (2013) Designing Social Research. 3rd Edition , Cambridge: Polity Press (2nd ed. 2009).

Blaikie, Norman W.H. (2000) Designing Social Research: The logic of anticipation . Cambridge, UK; Malden, MA: Polity Press: Blackwell.

Bowell, T., & Kemp, G. (2015) Critical Thinking: A Concise Guide . 4th ed., London: The University of Chicago Press.

Carter, Bob, and Alison Sealey (2009), “Reflexivity, Realism, and the Process of Casing”, Chapter 3 in Byrne and Ragin, eds., The Sage Handbook of Case-Based Methods, 2009.

Charmaz, Kathy (2014) Constructing Grounded Theory , 2nd ed., London: Sage.

Danermark, Berth, Mats Ekstrom, Liselotte Jakobsen, and Jan Ch. Karlsson, (2002; 1st published 1997 in Swedish language) Explaining Society: Critical Realism in the Social Sciences , London: Routledge.

Fairclough, Norman (1992) Discourse and Social Change , Cambridge: Polity Press.

Fairclough, Norman (2001), Language and Power , London: Orient Longman.

Flyvbjerg, Bent (2011) Making Social Science Matter: Why Social Inquiry Fails and How it Can Succeed Again , Cambridge, UK: Cambridge University Press.

Gibbs, Graham R. (2002), Qualitative Data Analysis: Explorations with NVivo , Buckingham: Open University Press.

Jeffery, Patricia, and Roger Jeffery (1996), Don’t Marry Me To A Plowman! Women’s Everyday Lives In Rural North India , London: Routledge.

Layder, Derek (1993) New Strategies in Social Research, Cambridge: Blackwell Publishers. Also Repr. 1995, 1996, Oxford: Polity Press.

Layder, Derek (1998) “The Reality of Social Domains: Implications for Theory and Method”, ch. 6 in May and Williams, eds. (1998), pp. 86–102.

Lewins, Ann, and Silver, Christina (2007) Using Software in Qualitative Research Step-By-Step Guide, London: Sage Publications.

Book   Google Scholar  

Lewis, P. A. (2000). “Realism, Causality and the Problem of Social Structure”, Journal for the Theory of Social Behavior 30: 249–268.

Lopez, Jose J. and Scott, John (2000) Concepts in the Social Sciences, Social Structure, Buckingham: Open University Press.

Mäenpää T., & Vuori A. (2021). “Broaching Overweight and Obesity at Maternity and Child Health Clinics”. International Journal of Nursing Practice , e12958. https://doi.org/10.1111/ijn.12958 , accessed May 2021.

Maxwell, Joseph A. (2012) A Realist Approach for Qualitative Research, London: Sage.

Neff, D. (2009), PhD, University of Manchester.

Olsen, Wendy (2006), “Globalisation, Liberalisation And A Paradox Of Social Exclusion In Sri Lanka”, chapter in A.H. Carling, ed., pages 109–130 in Globalisation and Identity: Development and Integration in a Changing World, London: I.B. Tauris.

Olsen, Wendy (2009a) “Non-Nested and Nested Cases in a Socio-Economic Village Study”, chapter in D. Byrne and C. Ragin, eds. (2009), Handbook of Case-Centred Research Methods , London: Sage.

Olsen, Wendy (2009b) “Beyond Sociology: Structure, Agency, and Strategy Among Tenants in India”, Asian Journal of Social Science , 37:3, 366–390.

Article   Google Scholar  

Olsen, Wendy (2010) “Realist Methodology: A Review”, Chapter 1 in Olsen, W.K., ed., Realist Methodology , volume 1 of 4-volume set, Benchmarks in Social Research Methods Series. London: Sage. Pages xix–xlvi. URL https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:75773&datastreamId=SUPPLEMENTARY-1.PDF , accessed 2021.

Röhwer, Götz (2011), “Qualitative Comparative Analysis: A Discussion of Interpretations”, European Sociological Review 27:6, 728–740, https://doi.org/10.1093/esr/jcq034 .

Sayer, Andrew (2000) Realism and Social Science . London: Sage.

Scott, James C. (1977) The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia , Ithaca, NY: Yale University Press.

Tashakkori, Abbas, and John W. Creswell (2007), “The New Era of Mixed Methods”, Journal of Mixed Methods Research, 1: 3, 3–7, https://doi.org/10.1177/2345678906293042 .

Teddlie, Charles, and Abbas Tashakkori (2009) Foundations of Mixed Methods Research , London: Sage.

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Summary of Arguments in an inductive content analysis paper. (Developed from Mäenpää and Vuori 2021 ). Notes: Induction is shown by lines drawn from premises P towards conclusions C. In addition, matters for reflection and theoretical elaboration are shown as ontic level, in dotted-line circles, and in the rectangular box. One role played by quantitative evidence might be to clarify aspects of this rectangular box. The conclusion would then develop a clear link with additional evidence

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Olsen, W. (2022). From Content Analysis to Discourse Analysis: Using Systematic Analysis of Meanings and Discourses. In: Systematic Mixed-Methods Research for Social Scientists. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-93148-3_8

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19 Content Analysis

Lindsay Prior, School of Sociology, Social Policy, and Social Work, Queen's University

• Published: 02 September 2020
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In this chapter, the focus is on ways in which content analysis can be used to investigate and describe interview and textual data. The chapter opens with a contextualization of the method and then proceeds to an examination of the role of content analysis in relation to both quantitative and qualitative modes of social research. Following the introductory sections, four kinds of data are subjected to content analysis. These include data derived from a sample of qualitative interviews ( N = 54), textual data derived from a sample of health policy documents ( N = 6), data derived from a single interview relating to a “case” of traumatic brain injury, and data gathered from fifty-four abstracts of academic papers on the topic of “well-being.” Using a distinctive and somewhat novel style of content analysis that calls on the notion of semantic networks, the chapter shows how the method can be used either independently or in conjunction with other forms of inquiry (including various styles of discourse analysis) to analyze data and also how it can be used to verify and underpin claims that arise from analysis. The chapter ends with an overview of the different ways in which the study of “content”—especially the study of document content—can be positioned in social scientific research projects.

What Is Content Analysis?

In his 1952 text on the subject of content analysis, Bernard Berelson traced the origins of the method to communication research and then listed what he called six distinguishing features of the approach. As one might expect, the six defining features reflect the concerns of social science as taught in the 1950s, an age in which the calls for an “objective,” “systematic,” and “quantitative” approach to the study of communication data were first heard. The reference to the field of “communication” was nothing less than a reflection of a substantive social scientific interest over the previous decades in what was called public opinion and specifically attempts to understand why and how a potential source of critical, rational judgment on political leaders (i.e., the views of the public) could be turned into something to be manipulated by dictators and demagogues. In such a context, it is perhaps not so surprising that in one of the more popular research methods texts of the decade, the terms content analysis and communication analysis are used interchangeably (see Goode & Hatt, 1952 , p. 325).

Academic fashions and interests naturally change with available technology, and these days we are more likely to focus on the individualization of communications through Twitter and the like, rather than of mass newspaper readership or mass radio audiences, yet the prevailing discourse on content analysis has remained much the same as it was in Berleson’s day. Thus, Neuendorf ( 2002 ), for example, continued to define content analysis as “the systematic, objective, quantitative analysis of message characteristics” (p. 1). Clearly, the centrality of communication as a basis for understanding and using content analysis continues to hold, but in this chapter I will try to show that, rather than locate the use of content analysis in disembodied “messages” and distantiated “media,” we would do better to focus on the fact that communication is a building block of social life itself and not merely a system of messages that are transmitted—in whatever form—from sender to receiver. To put that statement in another guise, we must note that communicative action (to use the phraseology of Habermas, 1987 ) rests at the very base of the lifeworld, and one very important way of coming to grips with that world is to study the content of what people say and write in the course of their everyday lives.

My aim is to demonstrate various ways in which content analysis (henceforth CTA) can be used and developed to analyze social scientific data as derived from interviews and documents. It is not my intention to cover the history of CTA or to venture into forms of literary analysis or to demonstrate each and every technique that has ever been deployed by content analysts. (Many of the standard textbooks deal with those kinds of issues much more fully than is possible here. See, for example, Babbie, 2013 ; Berelson, 1952 ; Bryman, 2008 , Krippendorf, 2004 ; Neuendorf, 2002 ; and Weber, 1990 ). Instead, I seek to recontextualize the use of the method in a framework of network thinking and to link the use of CTA to specific problems of data analysis. As will become evident, my exposition of the method is grounded in real-world problems. Those problems are drawn from my own research projects and tend to reflect my academic interests—which are almost entirely related to the analysis of the ways in which people talk and write about aspects of health, illness, and disease. However, lest the reader be deterred from going any further, I should emphasize that the substantive issues that I elect to examine are secondary if not tertiary to my main objective—which is to demonstrate how CTA can be integrated into a range of research designs and add depth and rigor to the analysis of interview and inscription data. To that end, in the next section I aim to clear our path to analysis by dealing with some issues that touch on the general position of CTA in the research armory, especially its location in the schism that has developed between quantitative and qualitative modes of inquiry.

The Methodological Context of Content Analysis

Content analysis is usually associated with the study of inscription contained in published reports, newspapers, adverts, books, web pages, journals, and other forms of documentation. Hence, nearly all of Berelson’s ( 1952 ) illustrations and references to the method relate to the analysis of written records of some kind, and where speech is mentioned, it is almost always in the form of broadcast and published political speeches (such as State of the Union addresses). This association of content analysis with text and documentation is further underlined in modern textbook discussions of the method. Thus, Bryman ( 2008 ), for example, defined CTA as “an approach to the analysis of documents and texts , that seek to quantify content in terms of pre-determined categories” (2008, p. 274, emphasis in original), while Babbie ( 2013 ) stated that CTA is “the study of recorded human communications” (2013, p. 295), and Weber referred to it as a method to make “valid inferences from text” (1990, p. 9). It is clear then that CTA is viewed as a text-based method of analysis, though extensions of the method to other forms of inscriptional material are also referred to in some discussions. Thus, Neuendorf ( 2002 ), for example, rightly referred to analyses of film and television images as legitimate fields for the deployment of CTA and by implication analyses of still—as well as moving—images such as photographs and billboard adverts. Oddly, in the traditional or standard paradigm of CTA, the method is solely used to capture the “message” of a text or speech; it is not used for the analysis of a recipient’s response to or understanding of the message (which is normally accessed via interview data and analyzed in other and often less rigorous ways; see, e.g., Merton, 1968 ). So, in this chapter I suggest that we can take things at least one small step further by using CTA to analyze speech (especially interview data) as well as text.

Standard textbook discussions of CTA usually refer to it as a “nonreactive” or “unobtrusive” method of investigation (see, e.g., Babbie, 2013 , p. 294), and a large part of the reason for that designation is because of its focus on already existing text (i.e., text gathered without intrusion into a research setting). More important, however (and to underline the obvious), CTA is primarily a method of analysis rather than of data collection. Its use, therefore, must be integrated into wider frames of research design that embrace systematic forms of data collection as well as forms of data analysis. Thus, routine strategies for sampling data are often required in designs that call on CTA as a method of analysis. These latter can be built around random sampling methods or even techniques of “theoretical sampling” (Glaser & Strauss, 1967 ) so as to identify a suitable range of materials for CTA. Content analysis can also be linked to styles of ethnographic inquiry and to the use of various purposive or nonrandom sampling techniques. For an example, see Altheide ( 1987 ).

The use of CTA in a research design does not preclude the use of other forms of analysis in the same study, because it is a technique that can be deployed in parallel with other methods or with other methods sequentially. For example, and as I will demonstrate in the following sections, one might use CTA as a preliminary analytical strategy to get a grip on the available data before moving into specific forms of discourse analysis. In this respect, it can be as well to think of using CTA in, say, the frame of a priority/sequence model of research design as described by Morgan ( 1998 ).

As I shall explain, there is a sense in which CTA rests at the base of all forms of qualitative data analysis, yet the paradox is that the analysis of content is usually considered a quantitative (numerically based) method. In terms of the qualitative/quantitative divide, however, it is probably best to think of CTA as a hybrid method, and some writers have in the past argued that it is necessarily so (Kracauer, 1952 ). That was probably easier to do in an age when many recognized the strictly drawn boundaries between qualitative and quantitative styles of research to be inappropriate. Thus, in their widely used text Methods in Social Research , Goode and Hatt ( 1952 ), for example, asserted that “modern research must reject as a false dichotomy the separation between ‘qualitative’ and ‘quantitative’ studies, or between the ‘statistical’ and the ‘non-statistical’ approach” (p. 313). This position was advanced on the grounds that all good research must meet adequate standards of validity and reliability, whatever its style, and the message is well worth preserving. However, there is a more fundamental reason why it is nonsensical to draw a division between the qualitative and the quantitative. It is simply this: All acts of social observation depend on the deployment of qualitative categories—whether gender, class, race, or even age; there is no descriptive category in use in the social sciences that connects to a world of “natural kinds.” In short, all categories are made, and therefore when we seek to count “things” in the world, we are dependent on the existence of socially constructed divisions. How the categories take the shape that they do—how definitions are arrived at, how inclusion and exclusion criteria are decided on, and how taxonomic principles are deployed—constitute interesting research questions in themselves. From our starting point, however, we need only note that “sorting things out” (to use a phrase from Bowker & Star, 1999 ) and acts of “counting”—whether it be of chromosomes or people (Martin & Lynch, 2009 )—are activities that connect to the social world of organized interaction rather than to unsullied observation of the external world.

Some writers deny the strict division between the qualitative and quantitative on grounds of empirical practice rather than of ontological reasoning. For example, Bryman ( 2008 ) argued that qualitative researchers also call on quantitative thinking, but tend to use somewhat vague, imprecise terms rather than numbers and percentages—referring to frequencies via the use of phrases such as “more than” and “less than.” Kracauer ( 1952 ) advanced various arguments against the view that CTA was strictly a quantitative method, suggesting that very often we wished to assess content as being negative or positive with respect to some political, social, or economic thesis and that such evaluations could never be merely statistical. He further argued that we often wished to study “underlying” messages or latent content of documentation and that, in consequence, we needed to interpret content as well as count items of content. Morgan ( 1993 ) argued that, given the emphasis that is placed on “coding” in almost all forms of qualitative data analysis, the deployment of counting techniques is essential and we ought therefore to think in terms of what he calls qualitative as well as quantitative content analysis. Naturally, some of these positions create more problems than they seemingly solve (as is the case with considerations of “latent content”), but given the 21st-century predilection for mixed methods research (Creswell, 2007 ), it is clear that CTA has a role to play in integrating quantitative and qualitative modes of analysis in a systematic rather than merely ad hoc and piecemeal fashion. In the sections that follow, I will provide some examples of the ways in which “qualitative” analysis can be combined with systematic modes of counting. First, however, we must focus on what is analyzed in CTA.

Units of Analysis

So, what is the unit of analysis in CTA? A brief answer is that analysis can be focused on words, sentences, grammatical structures, tenses, clauses, ratios (of, say, nouns to verbs), or even “themes.” Berelson ( 1952 ) gave examples of all of the above and also recommended a form of thematic analysis (cf., Braun & Clarke, 2006 ) as a viable option. Other possibilities include counting column length (of speeches and newspaper articles), amounts of (advertising) space, or frequency of images. For our purposes, however, it might be useful to consider a specific (and somewhat traditional) example. Here it is. It is an extract from what has turned out to be one of the most important political speeches of the current century.

Iraq continues to flaunt its hostility toward America and to support terror. The Iraqi regime has plotted to develop anthrax and nerve gas and nuclear weapons for over a decade. This is a regime that has already used poison gas to murder thousands of its own citizens, leaving the bodies of mothers huddled over their dead children. This is a regime that agreed to international inspections then kicked out the inspectors. This is a regime that has something to hide from the civilized world. States like these, and their terrorist allies, constitute an axis of evil, arming to threaten the peace of the world. By seeking weapons of mass destruction, these regimes pose a grave and growing danger. They could provide these arms to terrorists, giving them the means to match their hatred. They could attack our allies or attempt to blackmail the United States. In any of these cases, the price of indifference would be catastrophic. (George W. Bush, State of the Union address, January 29, 2002)

A number of possibilities arise for analyzing the content of a speech such as the one above. Clearly, words and sentences must play a part in any such analysis, but in addition to words, there are structural features of the speech that could also figure. For example, the extract takes the form of a simple narrative—pointing to a past, a present, and an ominous future (catastrophe)—and could therefore be analyzed as such. There are, in addition, several interesting oppositions in the speech (such as those between “regimes” and the “civilized” world), as well as a set of interconnected present participles such as “plotting,” “hiding,” “arming,” and “threatening” that are associated both with Iraq and with other states that “constitute an axis of evil.” Evidently, simple word counts would fail to capture the intricacies of a speech of this kind. Indeed, our example serves another purpose—to highlight the difficulty that often arises in dissociating CTA from discourse analysis (of which narrative analysis and the analysis of rhetoric and trope are subspecies). So how might we deal with these problems?

One approach that can be adopted is to focus on what is referenced in text and speech, that is, to concentrate on the characters or elements that are recruited into the text and to examine the ways in which they are connected or co-associated. I shall provide some examples of this form of analysis shortly. Let us merely note for the time being that in the previous example we have a speech in which various “characters”—including weapons in general, specific weapons (such as nerve gas), threats, plots, hatred, evil, and mass destruction—play a role. Be aware that we need not be concerned with the veracity of what is being said—whether it is true or false—but simply with what is in the speech and how what is in there is associated. (We may leave the task of assessing truth and falsity to the jurists). Be equally aware that it is a text that is before us and not an insight into the ex-president’s mind, or his thinking, or his beliefs, or any other subjective property that he may have possessed.

In the introductory paragraph, I made brief reference to some ideas of the German philosopher Jürgen Habermas ( 1987 ). It is not my intention here to expand on the detailed twists and turns of his claims with respect to the role of language in the “lifeworld” at this point. However, I do intend to borrow what I regard as some particularly useful ideas from his work. The first is his claim—influenced by a strong line of 20th-century philosophical thinking—that language and culture are constitutive of the lifeworld (Habermas, 1987 , p. 125), and in that sense we might say that things (including individuals and societies) are made in language. That is a simple justification for focusing on what people say rather than what they “think” or “believe” or “feel” or “mean” (all of which have been suggested at one time or another as points of focus for social inquiry and especially qualitative forms of inquiry). Second, Habermas argued that speakers and therefore hearers (and, one might add, writers and therefore readers), in what he calls their speech acts, necessarily adopt a pragmatic relation to one of three worlds: entities in the objective world, things in the social world, and elements of a subjective world. In practice, Habermas ( 1987 , p. 120) suggested all three worlds are implicated in any speech act, but that there will be a predominant orientation to one of them. To rephrase this in a crude form, when speakers engage in communication, they refer to things and facts and observations relating to external nature, to aspects of interpersonal relations, and to aspects of private inner subjective worlds (thoughts, feelings, beliefs, etc.). One of the problems with locating CTA in “communication research” has been that the communications referred to are but a special and limited form of action (often what Habermas called strategic acts). In other words, television, newspaper, video, and Internet communications are just particular forms (with particular features) of action in general. Again, we might note in passing that the adoption of the Habermassian perspective on speech acts implies that much of qualitative analysis in particular has tended to focus only on one dimension of communicative action—the subjective and private. In this respect, I would argue that it is much better to look at speeches such as George W Bush’s 2002 State of the Union address as an “account” and to examine what has been recruited into the account, and how what has been recruited is connected or co-associated, rather than use the data to form insights into his (or his adviser’s) thoughts, feelings, and beliefs.

In the sections that follow, and with an emphasis on the ideas that I have just expounded, I intend to demonstrate how CTA can be deployed to advantage in almost all forms of inquiry that call on either interview (or speech-based) data or textual data. In my first example, I will show how CTA can be used to analyze a group of interviews. In the second example, I will show how it can be used to analyze a group of policy documents. In the third, I shall focus on a single interview (a “case”), and in the fourth and final example, I will show how CTA can be used to track the biography of a concept. In each instance, I shall briefly introduce the context of the “problem” on which the research was based, outline the methods of data collection, discuss how the data were analyzed and presented, and underline the ways in which CTA has sharpened the analytical strategy.

Analyzing a Sample of Interviews: Looking at Concepts and Their Co-associations in a Semantic Network

My first example of using CTA is based on a research study that was initially undertaken in the early 2000s. It was a project aimed at understanding why older people might reject the offer to be immunized against influenza (at no cost to them). The ultimate objective was to improve rates of immunization in the study area. The first phase of the research was based on interviews with 54 older people in South Wales. The sample included people who had never been immunized, some who had refused immunization, and some who had accepted immunization. Within each category, respondents were randomly selected from primary care physician patient lists, and the data were initially analyzed “thematically” and published accordingly (Evans, Prout, Prior, Tapper-Jones, & Butler, 2007 ). A few years later, however, I returned to the same data set to look at a different question—how (older) lay people talked about colds and flu, especially how they distinguished between the two illnesses and how they understood the causes of the two illnesses (see Prior, Evans, & Prout, 2011 ). Fortunately, in the original interview schedule, we had asked people about how they saw the “differences between cold and flu” and what caused flu, so it was possible to reanalyze the data with such questions in mind. In that frame, the example that follows demonstrates not only how CTA might be used on interview data, but also how it might be used to undertake a secondary analysis of a preexisting data set (Bryman, 2008 ).

As with all talk about illness, talk about colds and flu is routinely set within a mesh of concerns—about causes, symptoms, and consequences. Such talk comprises the base elements of what has at times been referred to as the “explanatory model” of an illness (Kleinman, Eisenberg, & Good, 1978 ). In what follows, I shall focus almost entirely on issues of causation as understood from the viewpoint of older people; the analysis is based on the answers that respondents made in response to the question, “How do you think people catch flu?”

Semistructured interviews of the kind undertaken for a study such as this are widely used and are often characterized as akin to “a conversation with a purpose” (Kahn & Cannell, 1957 , p. 97). One of the problems of analyzing the consequent data is that, although the interviewer holds to a planned schedule, the respondents often reflect in a somewhat unstructured way about the topic of investigation, so it is not always easy to unravel the web of talk about, say, “causes” that occurs in the interview data. In this example, causal agents of flu, inhibiting agents, and means of transmission were often conflated by the respondents. Nevertheless, in their talk people did answer the questions that were posed, and in the study referred to here, that talk made reference to things such as “bugs” (and “germs”) as well as viruses, but the most commonly referred to causes were “the air” and the “atmosphere.” The interview data also pointed toward means of transmission as “cause”—so coughs and sneezes and mixing in crowds figured in the causal mix. Most interesting, perhaps, was the fact that lay people made a nascent distinction between facilitating factors (such as bugs and viruses) and inhibiting factors (such as being resistant, immune, or healthy), so that in the presence of the latter, the former are seen to have very little effect. Here are some shorter examples of typical question–response pairs from the original interview data.

(R:32): “How do you catch it [the flu]? Well, I take it its through ingesting and inhaling bugs from the atmosphere. Not from sort of contact or touching things. Sort of airborne bugs. Is that right?” (R:3): “I suppose it’s [the cause of flu] in the air. I think I get more diseases going to the surgery than if I stayed home. Sometimes the waiting room is packed and you’ve got little kids coughing and spluttering and people sneezing, and air conditioning I think is a killer by and large I think air conditioning in lots of these offices.” (R:46): “I think you catch flu from other people. You know in enclosed environments in air conditioning which in my opinion is the biggest cause of transferring diseases is air conditioning. Worse thing that was ever invented that was. I think so, you know. It happens on aircraft exactly the same you know.”

Alternatively, it was clear that for some people being cold, wet, or damp could also serve as a direct cause of flu; thus: Interviewer: “OK, good. How do you think you catch the flu?”

(R:39): “Ah. The 65 dollar question. Well, I would catch it if I was out in the rain and I got soaked through. Then I would get the flu. I mean my neighbour up here was soaked through and he got pneumonia and he died. He was younger than me: well, 70. And he stayed in his wet clothes and that’s fatal. Got pneumonia and died, but like I said, if I get wet, especially if I get my head wet, then I can get a nasty head cold and it could develop into flu later.”

As I suggested earlier, despite the presence of bugs and germs, viruses, the air, and wetness or dampness, “catching” the flu is not a matter of simple exposure to causative agents. Thus, some people hypothesized that within each person there is a measure of immunity or resistance or healthiness that comes into play and that is capable of counteracting the effects of external agents. For example, being “hardened” to germs and harsh weather can prevent a person getting colds and flu. Being “healthy” can itself negate the effects of any causative agents, and healthiness is often linked to aspects of “good” nutrition and diet and not smoking cigarettes. These mitigating and inhibiting factors can either mollify the effects of infection or prevent a person “catching” the flu entirely. Thus, (R:45) argued that it was almost impossible for him to catch flu or cold “cos I got all this resistance.” Interestingly, respondents often used possessive pronouns in their discussion of immunity and resistance (“my immunity” and “my resistance”)—and tended to view them as personal assets (or capital) that might be compromised by mixing with crowds.

By implication, having a weak immune system can heighten the risk of contracting colds and flu and might therefore spur one to take preventive measures, such as accepting a flu shot. Some people believe that the flu shot can cause the flu and other illnesses. An example of what might be called lay “epidemiology” (Davison, Davey-Smith, & Frankel, 1991 ) is evident in the following extract.

(R:4): “Well, now it’s coincidental you know that [my brother] died after the jab, but another friend of mine, about 8 years ago, the same happened to her. She had the jab and about six months later, she died, so I know they’re both coincidental, but to me there’s a pattern.”

Normally, results from studies such as this are presented in exactly the same way as has just been set out. Thus, the researcher highlights given themes that are said to have emerged from the data and then provides appropriate extracts from the interviews to illustrate and substantiate the relevant themes. However, one reasonable question that any critic might ask about the selected data extracts concerns the extent to which they are “representative” of the material in the data set as a whole. Maybe, for example, the author has been unduly selective in his or her use of both themes and quotations. Perhaps, as a consequence, the author has ignored or left out talk that does not fit the arguments or extracts that might be considered dull and uninteresting compared to more exotic material. And these kinds of issues and problems are certainly common to the reporting of almost all forms of qualitative research. However, the adoption of CTA techniques can help to mollify such problems. This is so because, by using CTA, we can indicate the extent to which we have used all or just some of the data, and we can provide a view of the content of the entire sample of interviews rather than just the content and flavor of merely one or two interviews. In this light, we must consider Figure 19.1 , which is based on counting the number of references in the 54 interviews to the various “causes” of the flu, though references to the flu shot (i.e., inoculation) as a cause of flu have been ignored for the purpose of this discussion. The node sizes reflect the relative importance of each cause as determined by the concept count (frequency of occurrence). The links between nodes reflect the degree to which causes are co-associated in interview talk and are calculated according to a co-occurrence index (see, e.g., SPSS, 2007 , p. 183).

What causes flu? A lay perspective. Factors listed as causes of colds and flu in 54 interviews. Node size is proportional to number of references “as causes.” Line thickness is proportional to co-occurrence of any two “causes” in the set of interviews.

Given this representation, we can immediately assess the relative importance of the different causes as referred to in the interview data. Thus, we can see that such things as (poor) “hygiene” and “foreigners” were mentioned as a potential cause of flu—but mention of hygiene and foreigners was nowhere near as important as references to “the air” or to “crowds” or to “coughs and sneezes.” In addition, we can also determine the strength of the connections that interviewees made between one cause and another. Thus, there are relatively strong links between “resistance” and “coughs and sneezes,” for example.

In fact, Figure 19.1 divides causes into the “external” and the “internal,” or the facilitating and the impeding (lighter and darker nodes). Among the former I have placed such things as crowds, coughs, sneezes, and the air, while among the latter I have included “resistance,” “immunity,” and “health.” That division is a product of my conceptualizing and interpreting the data, but whichever way we organize the findings, it is evident that talk about the causes of flu belongs in a web or mesh of concerns that would be difficult to represent using individual interview extracts alone. Indeed, it would be impossible to demonstrate how the semantics of causation belong to a culture (rather than to individuals) in any other way. In addition, I would argue that the counting involved in the construction of the diagram functions as a kind of check on researcher interpretations and provides a source of visual support for claims that an author might make about, say, the relative importance of “damp” and “air” as perceived causes of disease. Finally, the use of CTA techniques allied with aspects of conceptualization and interpretation has enabled us to approach the interview data as a set and to consider the respondents as belonging to a community, rather than regarding them merely as isolated and disconnected individuals, each with their own views. It has also enabled us to squeeze some new findings out of old data, and I would argue that it has done so with advantage. There are other advantages to using CTA to explore data sets, which I will highlight in the next section.

Analyzing a Sample of Documents: Using Content Analysis to Verify Claims

Policy analysis is a difficult business. To begin, it is never entirely clear where (social, health, economic, environmental) policy actually is. Is it in documents (as published by governments, think tanks, and research centers), in action (what people actually do), or in speech (what people say)? Perhaps it rests in a mixture of all three realms. Yet, wherever it may be, it is always possible, at the very least, to identify a range of policy texts and to focus on the conceptual or semantic webs in terms of which government officials and other agents (such as politicians) talk about the relevant policy issues. Furthermore, insofar as policy is recorded—in speeches, pamphlets, and reports—we may begin to speak of specific policies as having a history or a pedigree that unfolds through time (think, e.g., of U.S. or U.K. health policies during the Clinton years or the Obama years). And, insofar as we consider “policy” as having a biography or a history, we can also think of studying policy narratives.

Though firmly based in the world of literary theory, narrative method has been widely used for both the collection and the analysis of data concerning ways in which individuals come to perceive and understand various states of health, ill health, and disability (Frank, 1995 ; Hydén, 1997 ). Narrative techniques have also been adapted for use in clinical contexts and allied to concepts of healing (Charon, 2006 ). In both social scientific and clinical work, however, the focus is invariably on individuals and on how individuals “tell” stories of health and illness. Yet narratives can also belong to collectives—such as political parties and ethnic and religious groups—just as much as to individuals, and in the latter case there is a need to collect and analyze data that are dispersed across a much wider range of materials than can be obtained from the personal interview. In this context, Roe ( 1994 ) demonstrated how narrative method can be applied to an analysis of national budgets, animal rights, and environmental policies.

An extension of the concept of narrative to policy discourse is undoubtedly useful (Newman & Vidler, 2006 ), but how might such narratives be analyzed? What strategies can be used to unravel the form and content of a narrative, especially in circumstances where the narrative might be contained in multiple (policy) documents, authored by numerous individuals, and published across a span of time rather than in a single, unified text such as a novel? Roe ( 1994 ), unfortunately, was not in any way specific about analytical procedures, apart from offering the useful rule to “never stray too far from the data” (p. xii). So, in this example, I will outline a strategy for tackling such complexities. In essence, it is a strategy that combines techniques of linguistically (rule) based CTA with a theoretical and conceptual frame that enables us to unravel and identify the core features of a policy narrative. My substantive focus is on documents concerning health service delivery policies published from 2000 to 2009 in the constituent countries of the United Kingdom (that is, England, Scotland, Wales, and Northern Ireland—all of which have different political administrations).

Narratives can be described and analyzed in various ways, but for our purposes we can say that they have three key features: they point to a chronology, they have a plot, and they contain “characters.”

All narratives have beginnings; they also have middles and endings, and these three stages are often seen as comprising the fundamental structure of narrative text. Indeed, in his masterly analysis of time and narrative, Ricoeur ( 1984 ) argued that it is in the unfolding chronological structure of a narrative that one finds its explanatory (and not merely descriptive) force. By implication, one of the simplest strategies for the examination of policy narratives is to locate and then divide a narrative into its three constituent parts—beginning, middle, and end.

Unfortunately, while it can sometimes be relatively easy to locate or choose a beginning to a narrative, it can be much more difficult to locate an end point. Thus, in any illness narrative, a narrator might be quite capable of locating the start of an illness process (in an infection, accident, or other event) but unable to see how events will be resolved in an ongoing and constantly unfolding life. As a consequence, both narrators and researchers usually find themselves in the midst of an emergent present—a present without a known and determinate end (see, e.g., Frank, 1995 ). Similar considerations arise in the study of policy narratives where chronology is perhaps best approached in terms of (past) beginnings, (present) middles, and projected futures.

According to Ricoeur ( 1984 ), our basic ideas about narrative are best derived from the work and thought of Aristotle, who in his Poetics sought to establish “first principles” of composition. For Ricoeur, as for Aristotle, plot ties things together. It “brings together factors as heterogeneous as agents, goals, means, interactions, circumstances, unexpected results” (p. 65) into the narrative frame. For Aristotle, it is the ultimate untying or unraveling of the plot that releases the dramatic energy of the narrative.

Characters are most commonly thought of as individuals, but they can be considered in much broader terms. Thus, the French semiotician A. J. Greimas ( 1970 ), for example, suggested that, rather than think of characters as people, it would be better to think in terms of what he called actants and of the functions that such actants fulfill within a story. In this sense, geography, climate, and capitalism can be considered characters every bit as much as aggressive wolves and Little Red Riding Hood. Further, he argued that the same character (actant) can be considered to fulfill many functions, and the same function may be performed by many characters. Whatever else, the deployment of the term actant certainly helps us to think in terms of narratives as functioning and creative structures. It also serves to widen our understanding of the ways in which concepts, ideas, and institutions, as well “things” in the material world, can influence the direction of unfolding events every bit as much as conscious human subjects. Thus, for example, the “American people,” “the nation,” “the Constitution,” “the West,” “tradition,” and “Washington” can all serve as characters in a policy story.

As I have already suggested, narratives can unfold across many media and in numerous arenas—speech and action, as well as text. Here, however, my focus is solely on official documents—all of which are U.K. government policy statements, as listed in Table 19.1 . The question is, How might CTA help us unravel the narrative frame?

It might be argued that a simple reading of any document should familiarize the researcher with elements of all three policy narrative components (plot, chronology, and character). However, in most policy research, we are rarely concerned with a single and unified text, as is the case with a novel; rather, we have multiple documents written at distinctly different times by multiple (usually anonymous) authors that notionally can range over a wide variety of issues and themes. In the full study, some 19 separate publications were analyzed across England, Wales, Scotland, and Northern Ireland.

Naturally, listing word frequencies—still less identifying co-occurrences and semantic webs in large data sets (covering hundreds of thousands of words and footnotes)—cannot be done manually, but rather requires the deployment of complex algorithms and text-mining procedures. To this end, I analyzed the 19 documents using “Text Mining for Clementine” (SPSS, 2007 ).

Text-mining procedures begin by providing an initial list of concepts based on the lexicon of the text but that can be weighted according to word frequency and that take account of elementary word associations. For example, learning disability, mental health, and performance management indicate three concepts, not six words. Using such procedures on the aforementioned documents gives the researcher an initial grip on the most important concepts in the document set of each country. Note that this is much more than a straightforward concordance analysis of the text and is more akin to what Ryan and Bernard ( 2000 ) referred to as semantic analysis and Carley ( 1993 ) has referred to as concept and mapping analysis.

So, the first task was to identify and then extract the core concepts, thus identifying what might be called “key” characters or actants in each of the policy narratives. For example, in the Scottish documents, such actants included “Scotland” and the “Scottish people,” as well as “health” and the “National Health Service (NHS),” among others, while in the Welsh documents it was “the people of Wales” and “Wales” that figured largely—thus emphasizing how national identity can play every bit as important a role in a health policy narrative as concepts such as “health,” “hospitals,” and “well-being.”

Having identified key concepts, it was then possible to track concept clusters in which particular actants or characters are embedded. Such cluster analysis is dependent on the use of co-occurrence rules and the analysis of synonyms, whereby it is possible to get a grip on the strength of the relationships between the concepts, as well as the frequency with which the concepts appear in the collected texts. In Figure 19.2 , I provide an example of a concept cluster. The diagram indicates the nature of the conceptual and semantic web in which various actants are discussed. The diagrams further indicate strong (solid line) and weaker (dashed line) connections between the various elements in any specific mix, and the numbers indicate frequency counts for the individual concepts. Using Clementine , the researcher is unable to specify in advance which clusters will emerge from the data. One cannot, for example, choose to have an NHS cluster. In that respect, these diagrams not only provide an array in terms of which concepts are located, but also serve as a check on and to some extent validation of the interpretations of the researcher. None of this tells us what the various narratives contained within the documents might be, however. They merely point to key characters and relationships both within and between the different narratives. So, having indicated the techniques used to identify the essential parts of the four policy narratives, it is now time to sketch out their substantive form.

Concept cluster for “care” in six English policy documents, 2000–2007. Line thickness is proportional to the strength co-occurrence coefficient. Node size reflects relative frequency of concept, and (numbers) refer to the frequency of concept. Solid lines indicate relationships between terms within the same cluster, and dashed lines indicate relationships between terms in different clusters.

It may be useful to note that Aristotle recommended brevity in matters of narrative—deftly summarizing the whole of the Odyssey in just seven lines. In what follows, I attempt—albeit somewhat weakly—to emulate that example by summarizing a key narrative of English health services policy in just four paragraphs. Note how the narrative unfolds in relation to the dates of publication. In the English case (though not so much in the other U.K. countries), it is a narrative that is concerned to introduce market forces into what is and has been a state-managed health service. Market forces are justified in terms of improving opportunities for the consumer (i.e., the patients in the service), and the pivot of the newly envisaged system is something called “patient choice” or “choice.” This is how the story unfolds as told through the policy documents between 2000 and 2008 (see Table 19.1 ). The citations in the following paragraphs are to the Department of Health publications (by year) listed in Table 19.1 .

The advent of the NHS in 1948 was a “seminal event” (2000, p. 8), but under successive Conservative administrations, the NHS was seriously underfunded (2006, p. 3). The (New Labour) government will invest (2000) or already has (2003, p. 4) invested extensively in infrastructure and staff, and the NHS is now on a “journey of major improvement” (2004, p. 2). But “more money is only a starting point” (2000, p. 2), and the journey is far from finished. Continuation requires some fundamental changes of “culture” (2003, p. 6). In particular, the NHS remains unresponsive to patient need, and “all too often, the individual needs and wishes are secondary to the convenience of the services that are available. This ‘one size fits all’ approach is neither responsive, equitable nor person-centred” (2003, p. 17). In short, the NHS is a 1940s system operating in a 21st-century world (2000, p. 26). Change is therefore needed across the “whole system” (2005, p. 3) of care and treatment.

Above all, we must recognize that we “live in a consumer age” (2000, p. 26). People’s expectations have changed dramatically (2006, p. 129), and people want more choice, more independence, and more control (2003, p. 12) over their affairs. Patients are no longer, and should not be considered, “passive recipients” of care (2003, p. 62), but wish to be and should be (2006, p. 81) actively “involved” in their treatments (2003, p. 38; 2005, p. 18)—indeed, engaged in a partnership (2003, p. 22) of respect with their clinicians. Furthermore, most people want a personalized service “tailor made to their individual needs” (2000, p. 17; 2003, p. 15; 2004, p. 1; 2006, p. 83)—“a service which feels personal to each and every individual within a framework of equity and good use of public money” (2003, p. 6).

To advance the necessary changes, “patient choice” must be and “will be strengthened” (2000, p. 89). “Choice” must be made to “happen” (2003), and it must be “real” (2003, p. 3; 2004, p. 5; 2005, p. 20; 2006, p. 4). Indeed, it must be “underpinned” (2003, p. 7) and “widened and deepened” (2003, p. 6) throughout the entire system of care.

If “we” expand and underpin patient choice in appropriate ways and engage patients in their treatment systems, then levels of patient satisfaction will increase (2003, p. 39), and their choices will lead to a more “efficient” (2003, p. 5; 2004, p. 2; 2006, p. 16) and effective (2003, p. 62; 2005, p. 8) use of resources. Above all, the promotion of choice will help to drive up “standards” of care and treatment (2000, p. 4; 2003, p. 12; 2004, p. 3; 2005, p. 7; 2006, p. 3). Furthermore, the expansion of choice will serve to negate the effects of the “inverse care law,” whereby those who need services most tend to get catered to the least (2000, p. 107; 2003, p. 5; 2006, p. 63), and it will thereby help in moderating the extent of health inequalities in the society in which we live. “The overall aim of all our reforms,” therefore, “is to turn the NHS from a top down monolith into a responsive service that gives the patient the best possible experience. We need to develop an NHS that is both fair to all of us, and personal to each of us” (2003, p. 5).

We can see how most—though not all—of the elements of this story are represented in Figure 19.2. In particular, we can see strong (co-occurrence) links between care and choice and how partnership, performance, control, and improvement have a prominent profile. There are some elements of the web that have a strong profile (in terms of node size and links), but to which we have not referred; access, information, primary care, and waiting times are four. As anyone well versed in English healthcare policy would know, these elements have important roles to play in the wider, consumer-driven narrative. However, by rendering the excluded as well as included elements of that wider narrative visible, the concept web provides a degree of verification on the content of the policy story as told herein and on the scope of its “coverage.”

In following through on this example, we have moved from CTA to a form of discourse analysis (in this instance, narrative analysis). That shift underlines aspects of both the versatility of CTA and some of its weaknesses—versatility in the sense that CTA can be readily combined with other methods of analysis and in the way in which the results of the CTA help us to check and verify the claims of the researcher. The weakness of the diagram compared to the narrative is that CTA on its own is a somewhat one-dimensional and static form of analysis, and while it is possible to introduce time and chronology into the diagrams, the diagrams themselves remain lifeless in the absence of some form of discursive overview. (For a fuller analysis of these data, see Prior, Hughes, & Peckham, 2012 ).

Analyzing a Single Interview: The Role of Content Analysis in a Case Study

So far, I have focused on using CTA on a sample of interviews and a sample of documents. In the first instance, I recommended CTA for its capacity to tell us something about what is seemingly central to interviewees and for demonstrating how what is said is linked (in terms of a concept network). In the second instance, I reaffirmed the virtues of co-occurrence and network relations, but this time in the context of a form of discourse analysis. I also suggested that CTA can serve an important role in the process of verification of a narrative and its academic interpretation. In this section, however, I am going to link the use of CTA to another style of research—case study—to show how CTA might be used to analyze a single “case.”

Case study is a term used in multiple and often ambiguous ways. However, Gerring ( 2004 ) defined it as “an intensive study of a single unit for the purpose of understanding a larger class of (similar) units” (p. 342). As Gerring pointed out, case study does not necessarily imply a focus on N = 1, although that is indeed the most logical number for case study research (Ragin & Becker, 1992 ). Naturally, an N of 1 can be immensely informative, and whether we like it or not, we often have only one N to study (think, e.g., of the 1986 Challenger shuttle disaster or of the 9/11 attack on the World Trade Center). In the clinical sciences, case studies are widely used to represent the “typical” features of a wider class of phenomena and often used to define a kind or syndrome (as in the field of clinical genetics). Indeed, at the risk of mouthing a tautology, one can say that the distinctive feature of case study is its focus on a case in all of its complexity—rather than on individual variables and their interrelationships, which tends to be a point of focus for large N research.

There was a time when case study was central to the science of psychology. Breuer and Freud’s (2001) famous studies of “hysteria” (originally published in 1895) provide an early and outstanding example of the genre in this respect, but as with many of the other styles of social science research, the influence of case studies waned with the rise of much more powerful investigative techniques—including experimental methods—driven by the deployment of new statistical technologies. Ideographic studies consequently gave way to the current fashion for statistically driven forms of analysis that focus on causes and cross-sectional associations between variables rather than ideographic complexity.

In the example that follows, we will look at the consequences of a traumatic brain injury (TBI) on just one individual. The analysis is based on an interview with a person suffering from such an injury, and it was one of 32 interviews carried out with people who had experienced a TBI. The objective of the original research was to develop an outcome measure for TBI that was sensitive to the sufferer’s (rather than the health professional’s) point of view. In our original study (see Morris et al., 2005 ), interviews were also undertaken with 27 carers of the injured with the intention of comparing their perceptions of TBI to those of the people for whom they cared. A sample survey was also undertaken to elicit views about TBI from a much wider population of patients than was studied via interview.

In the introduction, I referred to Habermas and the concept of the lifeworld. Lifeworld ( Lebenswelt ) is a concept that first arose from 20th-century German philosophy. It constituted a specific focus for the work of Alfred Schutz (see, e.g., Schutz & Luckman, 1974 ). Schutz ( 1974 ) described the lifeworld as “that province of reality which the wide-awake and normal adult simply takes-for-granted in an attitude of common sense” (p. 3). Indeed, it was the routine and taken-for-granted quality of such a world that fascinated Schutz. As applied to the worlds of those with head injuries, the concept has particular resonance because head injuries often result in that taken-for-granted quality being disrupted and fragmented, ending in what Russian neuropsychologist A. R. Luria ( 1975 ) once described as “shattered” worlds. As well as providing another excellent example of a case study, Luria’s work is also pertinent because he sometimes argued for a “romantic science” of brain injury—that is, a science that sought to grasp the worldview of the injured patient by paying attention to an unfolding and detailed personal “story” of the individual with the head injury as well as to the neurological changes and deficits associated with the injury itself. In what follows, I shall attempt to demonstrate how CTA might be used to underpin such an approach.

In the original research, we began analysis by a straightforward reading of the interview transcripts. Unfortunately, a simple reading of a text or an interview can, strangely, mislead the reader into thinking that some issues or themes are more important than is warranted by the contents of the text. How that comes about is not always clear, but it probably has something to do with a desire to develop “findings” and our natural capacity to overlook the familiar in favor of the unusual. For that reason alone, it is always useful to subject any text to some kind of concordance analysis—that is, generating a simple frequency list of words used in an interview or text. Given the current state of technology, one might even speak these days of using text-mining procedures such as the aforementioned Clementine to undertake such a task. By using Clementine , and as we have seen, it is also possible to measure the strength of co-occurrence links between elements (i.e., words and concepts) in the entire data set (in this example, 32 interviews), though for a single interview these aims can just as easily be achieved using much simpler, low-tech strategies.

By putting all 32 interviews into the database, several common themes emerged. For example, it was clear that “time” entered into the semantic web in a prominent manner, and it was clearly linked to such things as “change,” “injury,” “the body,” and what can only be called the “I was.” Indeed, time runs through the 32 stories in many guises, and the centrality of time is a reflection of storytelling and narrative recounting in general—chronology, as we have noted, being a defining feature of all storytelling (Ricoeur, 1984 ). Thus, sufferers both recounted the events surrounding their injury and provided accounts as to how the injuries affected their current life and future hopes. As to time present, much of the patient story circled around activities of daily living—walking, working, talking, looking, feeling, remembering, and so forth.

Understandably, the word and the concept of “injury” featured largely in the interviews, though it was a word most commonly associated with discussions of physical consequences of injury. There were many references in that respect to injured arms, legs, hands, and eyes. There were also references to “mind”—though with far less frequency than with references to the body and to body parts. Perhaps none of this is surprising. However, one of the most frequent concepts in the semantic mix was the “I was” (716 references). The statement “I was,” or “I used to” was, in turn, strongly connected to terms such as “the accident” and “change.” Interestingly, the “I was” overwhelmingly eclipsed the “I am” in the interview data (the latter with just 63 references). This focus on the “I was” appears in many guises. For example, it is often associated with the use of the passive voice: “I was struck by a car,” “I was put on the toilet,” “I was shipped from there then, transferred to [Cityville],” “I got told that I would never be able …,” “I was sat in a room,” and so forth. In short, the “I was” is often associated with things, people, and events acting on the injured person. More important, however, the appearance of the “I was” is often used to preface statements signifying a state of loss or change in the person’s course of life—that is, as an indicator for talk about the patient’s shattered world. For example, Patient 7122 stated,

The main (effect) at the moment is I’m not actually with my children, I can’t really be their mum at the moment. I was a caring Mum, but I can’t sort of do the things that I want to be able to do like take them to school. I can’t really do a lot on my own. Like crossing the roads.

Another patient stated,

Everything is completely changed. The way I was … I can’t really do anything at the moment. I mean my German, my English, everything’s gone. Job possibilities is out the window. Everything is just out of the window … I just think about it all the time actually every day you know. You know it has destroyed me anyway, but if I really think about what has happened I would just destroy myself.

Each of these quotations, in its own way, serves to emphasize how life has changed and how the patient’s world has changed. In that respect, we can say that one of the major outcomes arising from TBI may be substantial “biographical disruption” (Bury, 1982 ), whereupon key features of an individual’s life course are radically altered forever. Indeed, as Becker ( 1997 , p. 37) argued in relation to a wide array of life events, “When their health is suddenly disrupted, people are thrown into chaos. Illness challenges one’s knowledge of one’s body. It defies orderliness. People experience the time before their illness and its aftermath as two separate entities.” Indeed, this notion of a cusp in personal biography is particularly well illustrated by Luria’s patient Zasetsky; the latter often refers to being a “newborn creature” (Luria, 1975 , pp. 24, 88), a shadow of a former self (p. 25), and as having his past “wiped out” (p. 116).

However, none of this tells us about how these factors come together in the life and experience of one individual. When we focus on an entire set of interviews, we necessarily lose the rich detail of personal experience and tend instead to rely on a conceptual rather than a graphic description of effects and consequences (to focus on, say, “memory loss,” rather than loss of memory about family life). The contents of Figure 19.3 attempt to correct that vision. Figure 19.3 records all the things that a particular respondent (Patient 7011) used to do and liked doing. It records all the things that he says he can no longer do (at 1 year after injury), and it records all the consequences that he suffered from his head injury at the time of the interview. Thus, we see references to epilepsy (his “fits”), paranoia (the patient spoke of his suspicions concerning other people, people scheming behind his back, and his inability to trust others), deafness, depression, and so forth. Note that, although I have inserted a future tense into the web (“I will”), such a statement never appeared in the transcript. I have set it there for emphasis and to show how, for this person, the future fails to connect to any of the other features of his world except in a negative way. Thus, he states at one point that he cannot think of the future because it makes him feel depressed (see Figure 19.3 ). The line thickness of the arcs reflects the emphasis that the subject placed on the relevant “outcomes” in relation to the “I was” and the “now” during the interview. Thus, we see that factors affecting his concentration and balance loom large, but that he is also concerned about his being dependent on others, his epileptic fits, and his being unable to work and drive a vehicle. The schism in his life between what he used to do, what he cannot now do, and his current state of being is nicely represented in the CTA diagram.

The shattered world of Patient 7011. Thickness of lines (arcs) is proportional to the frequency of reference to the “outcome” by the patient during the interview.

What have we gained from executing this kind of analysis? For a start, we have moved away from a focus on variables, frequencies, and causal connections (e.g., a focus on the proportion of people with TBI who suffer from memory problems or memory problems and speech problems) and refocused on how the multiple consequences of a TBI link together in one person. In short, instead of developing a narrative of acting variables, we have emphasized a narrative of an acting individual (Abbott, 1992 , p. 62). Second, it has enabled us to see how the consequences of a TBI connect to an actual lifeworld (and not simply an injured body). So the patient is not viewed just as having a series of discrete problems such as balancing, or staying awake, which is the usual way of assessing outcomes, but as someone struggling to come to terms with an objective world of changed things, people, and activities (missing work is not, for example, routinely considered an outcome of head injury). Third, by focusing on what the patient was saying, we gain insight into something that is simply not visible by concentrating on single outcomes or symptoms alone—namely, the void that rests at the center of the interview, what I have called the “I was.” Fourth, we have contributed to understanding a type, because the case that we have read about is not simply a case of “John” or “Jane” but a case of TBI, and in that respect it can add to many other accounts of what it is like to experience head injury—including one of the most well documented of all TBI cases, that of Zatetsky. Finally, we have opened up the possibility of developing and comparing cognitive maps (Carley, 1993 ) for different individuals and thereby gained insight into how alternative cognitive frames of the world arise and operate.

Tracing the Biography of a Concept

In the previous sections, I emphasized the virtues of CTA for its capacity to link into a data set in its entirety—and how the use of CTA can counter any tendency of a researcher to be selective and partial in the presentation and interpretation of information contained in interviews and documents. However, that does not mean that we always must take an entire document or interview as the data source. Indeed, it is possible to select (on rational and explicit grounds) sections of documentation and to conduct the CTA on the chosen portions. In the example that follows, I do just that. The sections that I chose to concentrate on are titles and abstracts of academic papers—rather than the full texts. The research on which the following is based is concerned with a biography of a concept and is being conducted in conjunction with a Ph.D. student of mine, Joanne Wilson. Joanne thinks of this component of the study more in terms of a “scoping study” than of a biographical study, and that, too, is a useful framework for structuring the context in which CTA can be used. Scoping studies (Arksey & O’Malley, 2005 ) are increasingly used in health-related research to “map the field” and to get a sense of the range of work that has been conducted on a given topic. Such studies can also be used to refine research questions and research designs. In our investigation, the scoping study was centered on the concept of well-being. Since 2010, well-being has emerged as an important research target for governments and corporations as well as for academics, yet it is far from clear to what the term refers. Given the ambiguity of meaning, it is clear that a scoping review, rather than either a systematic review or a narrative review of available literature, would be best suited to our goals.

The origins of the concept of well-being can be traced at least as far back as the 4th century bc , when philosophers produced normative explanations of the good life (e.g., eudaimonia, hedonia, and harmony). However, contemporary interest in the concept seemed to have been regenerated by the concerns of economists and, most recently, psychologists. These days, governments are equally concerned with measuring well-being to inform policy and conduct surveys of well-being to assess that state of the nation (see, e.g., Office for National Statistics, 2012 )—but what are they assessing?

We adopted a two-step process to address the research question, “What is the meaning of ‘well-being’ in the context of public policy?” First, we explored the existing thesauri of eight databases to establish those higher order headings (if any) under which articles with relevance to well-being might be cataloged. Thus, we searched the following databases: Cumulative Index of Nursing and Allied Health Literature, EconLit, Health Management Information Consortium, Medline, Philosopher’s Index, PsycINFO, Sociological Abstracts, and Worldwide Political Science Abstracts. Each of these databases adopts keyword-controlled vocabularies. In other words, they use inbuilt statistical procedures to link core terms to a set lexis of phrases that depict the concepts contained in the database. Table 19.2 shows each database and its associated taxonomy. The contents of Table 19.2 point toward a linguistic infrastructure in terms of which academic discourse is conducted, and our task was to extract from this infrastructure the semantic web wherein the concept of well-being is situated. We limited the thesaurus terms to well-being and its variants (i.e., wellbeing or well being). If the term was returned, it was then exploded to identify any associated terms.

To develop the conceptual map, we conducted a free-text search for well-being and its variants within the context of public policy across the same databases. We orchestrated these searches across five time frames: January 1990 to December 1994, January 1995 to December 1999, January 2000 to December 2004, January 2005 to December 2009, and January 2010 to October 2011. Naturally, different disciplines use different words to refer to well-being, each of which may wax and wane in usage over time. The searches thus sought to quantitatively capture any changes in the use and subsequent prevalence of well-being and any referenced terms (i.e., to trace a biography).

It is important to note that we did not intend to provide an exhaustive, systematic search of all the relevant literature. Rather, we wanted to establish the prevalence of well-being and any referenced (i.e., allied) terms within the context of public policy. This has the advantage of ensuring that any identified words are grounded in the literature (i.e., they represent words actually used by researchers to talk and write about well-being in policy settings). The searches were limited to abstracts to increase the specificity, albeit at some expense to sensitivity, with which we could identify relevant articles.

We also employed inclusion/exclusion criteria to facilitate the process by which we selected articles, thereby minimizing any potential bias arising from our subjective interpretations. We included independent, stand-alone investigations relevant to the study’s objectives (i.e., concerned with well-being in the context of public policy), which focused on well-being as a central outcome or process and which made explicit reference to “well-being” and “public policy” in either the title or the abstract. We excluded articles that were irrelevant to the study’s objectives, those that used noun adjuncts to focus on the well-being of specific populations (i.e., children, elderly, women) and contexts (e.g., retirement village), and those that focused on deprivation or poverty unless poverty indices were used to understand well-being as opposed to social exclusion. We also excluded book reviews and abstracts describing a compendium of studies.

Using these criteria, Joanne Wilson conducted the review and recorded the results on a template developed specifically for the project, organized chronologically across each database and timeframe. Results were scrutinized by two other colleagues to ensure the validity of the search strategy and the findings. Any concerns regarding the eligibility of studies for inclusion were discussed among the research team. I then analyzed the co-occurrence of the key terms in the database. The resultant conceptual map is shown in Figure 19.4.

The position of a concept in a network—a study of “well-being.” Node size is proportional to the frequency of terms in 54 selected abstracts. Line thickness is proportional to the co-occurrence of two terms in any phrase of three words (e.g., subjective well-being, economics of well-being, well-being and development).

The diagram can be interpreted as a visualization of a conceptual space. So, when academics write about well-being in the context of public policy, they tend to connect the discussion to the other terms in the matrix. “Happiness,” “health,” “economic,” and “subjective,” for example, are relatively dominant terms in the matrix. The node size of these words suggests that references to such entities is only slightly less than references to well-being itself. However, when we come to analyze how well-being is talked about in detail, we see specific connections come to the fore. Thus, the data imply that talk of “subjective well-being” far outweighs discussion of “social well-being” or “economic well-being.” Happiness tends to act as an independent node (there is only one occurrence of happiness and well-being), probably suggesting that “happiness” is acting as a synonym for well-being. Quality of life is poorly represented in the abstracts, and its connection to most of the other concepts in the space is very weak—confirming, perhaps, that quality of life is unrelated to contemporary discussions of well-being and happiness. The existence of “measures” points to a distinct concern to assess and to quantify expressions of happiness, well-being, economic growth, and gross domestic product. More important and underlying this detail, there are grounds for suggesting that there are in fact a number of tensions in the literature on well-being.

On the one hand, the results point toward an understanding of well-being as a property of individuals—as something that they feel or experience. Such a discourse is reflected through the use of words like happiness, subjective , and individual . This individualistic and subjective frame has grown in influence over the past decade in particular, and one of the problems with it is that it tends toward a somewhat content-free conceptualization of well-being. To feel a sense of well-being, one merely states that one is in a state of well-being; to be happy, one merely proclaims that one is happy (cf., Office for National Statistics, 2012 ). It is reminiscent of the conditions portrayed in Aldous Huxley’s Brave New World , wherein the rulers of a closely managed society gave their priority to maintaining order and ensuring the happiness of the greatest number—in the absence of attention to justice or freedom of thought or any sense of duty and obligation to others, many of whom were systematically bred in “the hatchery” as slaves.

On the other hand, there is some intimation in our web that the notion of well-being cannot be captured entirely by reference to individuals alone and that there are other dimensions to the concept—that well-being is the outcome or product of, say, access to reasonable incomes, to safe environments, to “development,” and to health and welfare. It is a vision hinted at by the inclusion of those very terms in the network. These different concepts necessarily give rise to important differences concerning how well-being is identified and measured and therefore what policies are most likely to advance well-being. In the first kind of conceptualization, we might improve well-being merely by dispensing what Huxley referred to as “soma” (a superdrug that ensured feelings of happiness and elation); in the other case, however, we would need to invest in economic, human, and social capital as the infrastructure for well-being. In any event and even at this nascent level, we can see how CTA can begin to tease out conceptual complexities and theoretical positions in what is otherwise routine textual data.

Putting the Content of Documents in Their Place

I suggested in my introduction that CTA was a method of analysis—not a method of data collection or a form of research design. As such, it does not necessarily inveigle us into any specific forms of either design or data collection, though designs and methods that rely on quantification are dominant. In this closing section, however, I want to raise the issue as to how we should position a study of content in our research strategies as a whole. We must keep in mind that documents and records always exist in a context and that while what is “in” the document may be considered central, a good research plan can often encompass a variety of ways of looking at how content links to context. Hence, in what follows, I intend to outline how an analysis of content might be combined with other ways of looking at a record or text and even how the analysis of content might be positioned as secondary to an examination of a document or record. The discussion calls on a much broader analysis, as presented in Prior ( 2011 ).

I have already stated that basic forms of CTA can serve as an important point of departure for many types of data analysis—for example, as discourse analysis. Naturally, whenever “discourse” is invoked, there is at least some recognition of the notion that words might play a part in structuring the world rather than merely reporting on it or describing it (as is the case with the 2002 State of the Nation address that was quoted in the section “Units of Analysis”). Thus, for example, there is a considerable tradition within social studies of science and technology for examining the place of scientific rhetoric in structuring notions of “nature” and the position of human beings (especially as scientists) within nature (see, e.g., work by Bazerman, 1988 ; Gilbert & Mulkay, 1984 ; and Kay, 2000 ). Nevertheless, little, if any, of that scholarship situates documents as anything other than inert objects, either constructed by or waiting patiently to be activated by scientists.

However, in the tradition of the ethnomethodologists (Heritage, 1991 ) and some adherents of discourse analysis, it is also possible to argue that documents might be more fruitfully approached as a “topic” (Zimmerman & Pollner, 1971 ) rather than a “resource” (to be scanned for content), in which case the focus would be on the ways in which any given document came to assume its present content and structure. In the field of documentation, these latter approaches are akin to what Foucault ( 1970 ) might have called an “archaeology of documentation” and are well represented in studies of such things as how crime, suicide, and other statistics and associated official reports and policy documents are routinely generated. That, too, is a legitimate point of research focus, and it can often be worth examining the genesis of, say, suicide statistics or statistics about the prevalence of mental disorder in a community as well as using such statistics as a basis for statistical modeling.

Unfortunately, the distinction between topic and resource is not always easy to maintain—especially in the hurly-burly of doing empirical research (see, e.g., Prior, 2003 ). Putting an emphasis on “topic,” however, can open a further dimension of research that concerns the ways in which documents function in the everyday world. And, as I have already hinted, when we focus on function, it becomes apparent that documents serve not merely as containers of content but also very often as active agents in episodes of interaction and schemes of social organization. In this vein, one can begin to think of an ethnography of documentation. Therein, the key research questions revolve around the ways in which documents are used and integrated into specific kinds of organizational settings, as well as with how documents are exchanged and how they circulate within such settings. Clearly, documents carry content—words, images, plans, ideas, patterns, and so forth—but the manner in which such material is called on and manipulated, and the way in which it functions, cannot be determined (though it may be constrained) by an analysis of content. Thus, Harper’s ( 1998 ) study of the use of economic reports inside the International Monetary Fund provides various examples of how “reports” can function to both differentiate and cohere work groups. In the same way. Henderson ( 1995 ) illustrated how engineering sketches and drawings can serve as what she calls conscription devices on the workshop floor.

Documents constitute a form of what Latour ( 1986 ) would refer to as “immutable mobiles,” and with an eye on the mobility of documents, it is worth noting an emerging interest in histories of knowledge that seek to examine how the same documents have been received and absorbed quite differently by different cultural networks (see, e.g., Burke, 2000 ). A parallel concern has arisen with regard to the newly emergent “geographies of knowledge” (see, e.g., Livingstone, 2005 ). In the history of science, there has also been an expressed interest in the biography of scientific objects (Latour, 1987 , p. 262) or of “epistemic things” (Rheinberger, 2000 )—tracing the history of objects independent of the “inventors” and “discoverers” to which such objects are conventionally attached. It is an approach that could be easily extended to the study of documents and is partly reflected in the earlier discussion concerning the meaning of the concept of well-being. Note how in all these cases a key consideration is how words and documents as “things” circulate and translate from one culture to another; issues of content are secondary.

Studying how documents are used and how they circulate can constitute an important area of research in its own right. Yet even those who focus on document use can be overly anthropocentric and subsequently overemphasize the potency of human action in relation to written text. In that light, it is interesting to consider ways in which we might reverse that emphasis and instead to study the potency of text and the manner in which documents can influence organizational activities as well as reflect them. Thus, Dorothy Winsor ( 1999 ), for example, examined the ways in which work orders drafted by engineers not only shape and fashion the practices and activities of engineering technicians but also construct “two different worlds” on the workshop floor.

In light of this, I will suggest a typology (Table 19.3 ) of the ways in which documents have come to be and can be considered in social research.

While accepting that no form of categorical classification can capture the inherent fluidity of the world, its actors, and its objects, Table 19.3 aims to offer some understanding of the various ways in which documents have been dealt with by social researchers. Thus, approaches that fit into Cell 1 have been dominant in the history of social science generally. Therein, documents (especially as text) have been analyzed and coded for what they contain in the way of descriptions, reports, images, representations, and accounts. In short, they have been scoured for evidence. Data analysis strategies concentrate almost entirely on what is in the “text” (via various forms of CTA). This emphasis on content is carried over into Cell 2–type approaches, with the key differences being that analysis is concerned with how document content comes into being. The attention here is usually on the conceptual architecture and sociotechnical procedures by means of which written reports, descriptions, statistical data, and so forth are generated. Various kinds of discourse analysis have been used to unravel the conceptual issues, while a focus on sociotechnical and rule-based procedures by means of which clinical, police, social work, and other forms of records and reports are constructed has been well represented in the work of ethnomethodologists (see Prior, 2011 ). In contrast, and in Cell 3, the research focus is on the ways in which documents are called on as a resource by various and different kinds of “user.” Here, concerns with document content or how a document has come into being are marginal, and the analysis concentrates on the relationship between specific documents and their use or recruitment by identifiable human actors for purposeful ends. I have pointed to some studies of the latter kind in earlier paragraphs (e.g., Henderson, 1995 ). Finally, the approaches that fit into Cell 4 also position content as secondary. The emphasis here is on how documents as “things” function in schemes of social activity and with how such things can drive, rather than be driven by, human actors. In short, the spotlight is on the vita activa of documentation, and I have provided numerous example of documents as actors in other publications (see Prior, 2003 , 2008 , 2011 ).

Content analysis was a method originally developed to analyze mass media “messages” in an age of radio and newspaper print, well before the digital age. Unfortunately, CTA struggles to break free of its origins and continues to be associated with the quantitative analysis of “communication.” Yet, as I have argued, there is no rational reason why its use must be restricted to such a narrow field, because it can be used to analyze printed text and interview data (as well as other forms of inscription) in various settings. What it cannot overcome is the fact that it is a method of analysis and not a method of data collection. However, as I have shown, it is an analytical strategy that can be integrated into a variety of research designs and approaches—cross-sectional and longitudinal survey designs, ethnography and other forms of qualitative design, and secondary analysis of preexisting data sets. Even as a method of analysis, it is flexible and can be used either independent of other methods or in conjunction with them. As we have seen, it is easily merged with various forms of discourse analysis and can be used as an exploratory method or as a means of verification. Above all, perhaps, it crosses the divide between “quantitative” and “qualitative” modes of inquiry in social research and offers a new dimension to the meaning of mixed methods research. I recommend it.

Abbott, A. ( 1992 ). What do cases do? In C. C. Ragin & H. S. Becker (Eds.), What is a case? Exploring the foundations of social inquiry (pp. 53–82). Cambridge, England: Cambridge University Press.

Google Scholar

Google Preview

Altheide, D. L. ( 1987 ). Ethnographic content analysis.   Qualitative Sociology, 10, 65–77.

Arksey, H. , & O’Malley, L. ( 2005 ). Scoping studies: Towards a methodological framework.   International Journal of Sociological Research Methodology, 8, 19–32.

Babbie, E. ( 2013 ). The practice of social research (13th ed.) Belmont, CA: Wadsworth.

Bazerman, C. ( 1988 ). Shaping written knowledge. The genre and activity of the experimental article in science . Madison: University of Wisconsin Press.

Becker, G. ( 1997 ). Disrupted lives. How people create meaning in a chaotic world . London, England: University of California Press.

Berelson, B. ( 1952 ). Content analysis in communication research . Glencoe, IL: Free Press.

Bowker, G. C. , & Star, S. L. ( 1999 ). Sorting things out. Classification and its consequences . Cambridge, MA: MIT Press.

Braun, V. , & Clarke, V. ( 2006 ). Using thematic analysis in psychology.   Qualitative Research in Psychology, 3, 77–101.

Breuer, J. , & Freud, S. ( 2001 ). Studies on hysteria. In L. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 2). London, England: Vintage.

Bryman, A. ( 2008 ). Social research methods (3rd ed.). Oxford, England: Oxford University Press.

Burke, P. ( 2000 ). A social history of knowledge. From Guttenberg to Diderot . Cambridge, MA: Polity Press.

Bury, M. ( 1982 ). Chronic illness as biographical disruption.   Sociology of Health and Illness, 4, 167–182.

Carley, K. ( 1993 ). Coding choices for textual analysis. A comparison of content analysis and map analysis.   Sociological Methodology, 23, 75–126.

Charon, R. ( 2006 ). Narrative medicine. Honoring the stories of illness . New York, NY: Oxford University Press.

Creswell, J. W. ( 2007 ). Designing and conducting mixed methods research . Thousand Oaks, CA: Sage.

Davison, C. , Davey-Smith, G. , & Frankel, S. ( 1991 ). Lay epidemiology and the prevention paradox.   Sociology of Health & Illness, 13, 1–19.

Evans, M. , Prout, H. , Prior, L. , Tapper-Jones, L. , & Butler, C. ( 2007 ). A qualitative study of lay beliefs about influenza.   British Journal of General Practice, 57, 352–358.

Foucault, M. ( 1970 ). The order of things. An archaeology of the human sciences . London, England: Tavistock.

Frank, A. ( 1995 ). The wounded storyteller: Body, illness, and ethics . Chicago, IL: University of Chicago Press.

Gerring, J. ( 2004 ). What is a case study, and what is it good for?   The American Political Science Review, 98, 341–354.

Gilbert, G. N. , & Mulkay, M. ( 1984 ). Opening Pandora’s box. A sociological analysis of scientists’ discourse . Cambridge, England: Cambridge University Press.

Glaser, B. G. , & Strauss, A. L. ( 1967 ). The discovery of grounded theory. Strategies for qualitative research . New York, NY: Aldine de Gruyter.

Goode, W. J. , & Hatt, P. K. ( 1952 ). Methods in social research . New York, NY: McGraw–Hill.

Greimas, A. J. ( 1970 ). Du Sens. Essays sémiotiques . Paris, France: Ėditions du Seuil.

Habermas, J. ( 1987 ). The theory of communicative action: Vol.2, A critique of functionalist reason ( T. McCarthy , Trans.). Cambridge, MA: Polity Press.

Harper, R. ( 1998 ). Inside the IMF. An ethnography of documents, technology, and organizational action . London, England: Academic Press.

Henderson, K. ( 1995 ). The political career of a prototype. Visual representation in design engineering.   Social Problems, 42, 274–299.

Heritage, J. ( 1991 ). Garkfinkel and ethnomethodology . Cambridge, MA: Polity Press.

Hydén, L-C. ( 1997 ). Illness and narrative.   Sociology of Health & Illness, 19, 48–69.

Kahn, R. , & Cannell, C. ( 1957 ). The dynamics of interviewing. Theory, technique and cases . New York, NY: Wiley.

Kay, L. E. ( 2000 ). Who wrote the book of life? A history of the genetic code . Stanford, CA: Stanford University Press.

Kleinman, A. , Eisenberg, L. , & Good, B. ( 1978 ). Culture, illness & care, clinical lessons from anthropologic and cross-cultural research.   Annals of Internal Medicine, 88, 251–258.

Kracauer, S. ( 1952 ). The challenge of qualitative content analysis.   Public Opinion Quarterly, Special Issue on International Communications Research (1952–53), 16, 631–642.

Krippendorf, K. ( 2004 ). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, CA: Sage.

Latour, B. ( 1986 ). Visualization and cognition: Thinking with eyes and hands. Knowledge and Society, Studies in Sociology of Culture, Past and Present, 6, 1–40.

Latour, B. ( 1987 ). Science in action. How to follow scientists and engineers through society . Milton Keynes, England: Open University Press.

Livingstone, D. N. ( 2005 ). Text, talk, and testimony: Geographical reflections on scientific habits. An afterword.   British Society for the History of Science, 38, 93–100.

Luria, A. R. ( 1975 ). The man with the shattered world. A history of a brain wound ( L. Solotaroff , Trans.). Harmondsworth, England: Penguin.

Martin, A. , & Lynch, M. ( 2009 ). Counting things and counting people: The practices and politics of counting.   Social Problems, 56, 243–266.

Merton, R. K. ( 1968 ). Social theory and social structure . New York, NY: Free Press.

Morgan, D. L. ( 1993 ). Qualitative content analysis. A guide to paths not taken.   Qualitative Health Research, 2, 112–121.

Morgan, D. L. ( 1998 ). Practical strategies for combining qualitative and quantitative methods.   Qualitative Health Research, 8, 362–376.

Morris, P. G. , Prior, L. , Deb, S. , Lewis, G. , Mayle, W. , Burrow, C. E. , & Bryant, E. ( 2005 ). Patients’ views on outcome following head injury: A qualitative study,   BMC Family Practice, 6, 30.

Neuendorf, K. A. ( 2002 ). The content analysis guidebook . Thousand Oaks: CA: Sage.

Newman, J. , & Vidler, E. ( 2006 ). Discriminating customers, responsible patients, empowered users: Consumerism and the modernisation of health care,   Journal of Social Policy, 35, 193–210.

Office for National Statistics. ( 2012 ). First ONS annual experimental subjective well-being results . London, England: Office for National Statistics. Retrieved from http://www.ons.gov.uk/ons/dcp171766_272294.pdf

Prior, L. ( 2003 ). Using documents in social research . London, England: Sage.

Prior, L. ( 2008 ). Repositioning documents in social research.   Sociology. Special Issue on Research Methods, 42, 821–836.

Prior, L. ( 2011 ). Using documents and records in social research (4 vols.). London, England: Sage.

Prior, L. , Evans, M. , & Prout, H. ( 2011 ). Talking about colds and flu: The lay diagnosis of two common illnesses among older British people.   Social Science and Medicine, 73, 922–928.

Prior, L. , Hughes, D. , & Peckham, S. ( 2012 ) The discursive turn in policy analysis and the validation of policy stories.   Journal of Social Policy, 41, 271–289.

Ragin, C. C. , & Becker, H. S. ( 1992 ). What is a case? Exploring the foundations of social inquiry . Cambridge, England: Cambridge University Press.

Rheinberger, H.-J. ( 2000 ). Cytoplasmic particles. The trajectory of a scientific object. In Daston, L. (Ed.), Biographies of scientific objects (pp. 270–294). Chicago, IL: Chicago University Press.

Ricoeur, P. ( 1984 ). Time and narrative (Vol. 1., K. McLaughlin & D, Pellauer, Trans.). Chicago, IL: University of Chicago Press.

Roe, E. ( 1994 ). Narrative policy analysis, theory and practice . Durham, NC: Duke University Press.

Ryan, G. W. , & Bernard, H. R. ( 2000 ). Data management and analysis methods. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 769–802). Thousand Oaks, CA: Sage.

Schutz, A. , & Luckman, T. ( 1974 ). The structures of the life-world (R. M. Zaner & H. T. Engelhardt, Trans.). London, England: Heinemann.

SPSS. ( 2007 ). Text mining for Clementine . 12.0 User’s Guide. Chicago, IL: SPSS.

Weber, R. P. ( 1990 ). Basic content analysis . Newbury Park, CA: Sage.

Winsor, D. ( 1999 ). Genre and activity systems. The role of documentation in maintaining and changing engineering activity systems.   Written Communication, 16, 200–224.

Zimmerman, D. H. , & Pollner, M. ( 1971 ). The everyday world as a phenomenon. In J. D. Douglas (Ed.), Understanding everyday life (pp. 80–103). London, England: Routledge & Kegan Paul.

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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:

Download free trial

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.

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.

Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

This type of content analysis involves the systematic and objective counting and categorization of the content of a particular form of communication, such as text or video. The data obtained is then subjected to statistical analysis to identify patterns, trends, and relationships between different variables. Quantitative content analysis is often used to study media content, advertising, and political speeches.

Qualitative Content Analysis

This type of content analysis is concerned with the interpretation and understanding of the meaning and context of the content. It involves the systematic analysis of the content to identify themes, patterns, and other relevant features, and to interpret the underlying meanings and implications of these features. Qualitative content analysis is often used to study interviews, focus groups, and other forms of qualitative data, where the researcher is interested in understanding the subjective experiences and perceptions of the participants.

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

This method involves analyzing the meanings of key concepts used in the content being analyzed. The researcher identifies key concepts and analyzes how they are used, defining them and categorizing them into broader themes.

Content Analysis by Frequency

This method involves counting and categorizing the frequency of specific words, phrases, or themes that appear in the content being analyzed. The researcher identifies relevant keywords or phrases and systematically counts their frequency.

Comparative Analysis

This method involves comparing the content of two or more sources to identify similarities, differences, and patterns. The researcher selects relevant sources, identifies key themes or concepts, and compares how they are represented in each source.

Discourse Analysis

This method involves analyzing the structure and language of the content being analyzed to identify how the content constructs and represents social reality. The researcher analyzes the language used and the underlying assumptions, beliefs, and values reflected in the content.

Narrative Analysis

This method involves analyzing the content as a narrative, identifying the plot, characters, and themes, and analyzing how they relate to the broader social context. The researcher identifies the underlying messages conveyed by the narrative and their implications for the broader social context.

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

• Define your research question or objective: Before starting your content analysis, you need to define your research question or objective clearly. This will help you to identify the content you need to analyze and the type of analysis you need to conduct.
• Select your sample: Select a representative sample of the content you want to analyze. This may involve selecting a random sample, a purposive sample, or a convenience sample, depending on the research question and the availability of the content.
• Develop a coding scheme: Develop a coding scheme or a set of categories to use for coding the content. The coding scheme should be based on your research question or objective and should be reliable, valid, and comprehensive.
• Train coders: Train coders to use the coding scheme and ensure that they have a clear understanding of the coding categories and procedures. You may also need to establish inter-coder reliability to ensure that different coders are coding the content consistently.
• Code the content: Code the content using the coding scheme. This may involve manually coding the content, using software, or a combination of both.
• Analyze the data: Once the content is coded, analyze the data using appropriate statistical or qualitative methods, depending on the research question and the type of data.
• Interpret the results: Interpret the results of the analysis in the context of your research question or objective. Draw conclusions based on the findings and relate them to the broader literature on the topic.
• Report your findings: Report your findings in a clear and concise manner, including the research question, methodology, results, and conclusions. Provide details about the coding scheme, inter-coder reliability, and any limitations of the study.

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

• Media Research: Content analysis is commonly used in media research to examine the representation of different groups, such as race, gender, and sexual orientation, in media content. It can also be used to study media framing, media bias, and media effects.
• Political Communication : Content analysis can be used to study political communication, including political speeches, debates, and news coverage of political events. It can also be used to study political advertising and the impact of political communication on public opinion and voting behavior.
• Marketing Research: Content analysis can be used to study advertising messages, consumer reviews, and social media posts related to products or services. It can provide insights into consumer preferences, attitudes, and behaviors.
• Health Communication: Content analysis can be used to study health communication, including the representation of health issues in the media, the effectiveness of health campaigns, and the impact of health messages on behavior.
• Education Research : Content analysis can be used to study educational materials, including textbooks, curricula, and instructional materials. It can provide insights into the representation of different topics, perspectives, and values.
• Social Science Research: Content analysis can be used in a wide range of social science research, including studies of social media, online communities, and other forms of digital communication. It can also be used to study interviews, focus groups, and other qualitative data sources.

Examples of Content Analysis

Here are some examples of content analysis:

• Media Representation of Race and Gender: A content analysis could be conducted to examine the representation of different races and genders in popular media, such as movies, TV shows, and news coverage.
• Political Campaign Ads : A content analysis could be conducted to study political campaign ads and the themes and messages used by candidates.
• Social Media Posts: A content analysis could be conducted to study social media posts related to a particular topic, such as the COVID-19 pandemic, to examine the attitudes and beliefs of social media users.
• Instructional Materials: A content analysis could be conducted to study the representation of different topics and perspectives in educational materials, such as textbooks and curricula.
• Product Reviews: A content analysis could be conducted to study product reviews on e-commerce websites, such as Amazon, to identify common themes and issues mentioned by consumers.
• News Coverage of Health Issues: A content analysis could be conducted to study news coverage of health issues, such as vaccine hesitancy, to identify common themes and perspectives.
• Online Communities: A content analysis could be conducted to study online communities, such as discussion forums or social media groups, to understand the language, attitudes, and beliefs of the community members.

Purpose of Content Analysis

The purpose of content analysis is to systematically analyze and interpret the content of various forms of communication, such as written, oral, or visual, to identify patterns, themes, and meanings. Content analysis is used to study communication in a wide range of fields, including media studies, political science, psychology, education, sociology, and marketing research. The primary goals of content analysis include:

• Describing and summarizing communication: Content analysis can be used to describe and summarize the content of communication, such as the themes, topics, and messages conveyed in media content, political speeches, or social media posts.
• Identifying patterns and trends: Content analysis can be used to identify patterns and trends in communication, such as changes over time, differences between groups, or common themes or motifs.
• Exploring meanings and interpretations: Content analysis can be used to explore the meanings and interpretations of communication, such as the underlying values, beliefs, and assumptions that shape the content.
• Testing hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the effects of media on attitudes and behaviors or the framing of political issues in the media.

When to use Content Analysis

Content analysis is a useful method when you want to analyze and interpret the content of various forms of communication, such as written, oral, or visual. Here are some specific situations where content analysis might be appropriate:

• When you want to study media content: Content analysis is commonly used in media studies to analyze the content of TV shows, movies, news coverage, and other forms of media.
• When you want to study political communication : Content analysis can be used to study political speeches, debates, news coverage, and advertising.
• When you want to study consumer attitudes and behaviors: Content analysis can be used to analyze product reviews, social media posts, and other forms of consumer feedback.
• When you want to study educational materials : Content analysis can be used to analyze textbooks, instructional materials, and curricula.
• When you want to study online communities: Content analysis can be used to analyze discussion forums, social media groups, and other forms of online communication.
• When you want to test hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the framing of political issues in the media or the effects of media on attitudes and behaviors.

Characteristics of Content Analysis

Content analysis has several key characteristics that make it a useful research method. These include:

• Objectivity : Content analysis aims to be an objective method of research, meaning that the researcher does not introduce their own biases or interpretations into the analysis. This is achieved by using standardized and systematic coding procedures.
• Systematic: Content analysis involves the use of a systematic approach to analyze and interpret the content of communication. This involves defining the research question, selecting the sample of content to analyze, developing a coding scheme, and analyzing the data.
• Quantitative : Content analysis often involves counting and measuring the occurrence of specific themes or topics in the content, making it a quantitative research method. This allows for statistical analysis and generalization of findings.
• Contextual : Content analysis considers the context in which the communication takes place, such as the time period, the audience, and the purpose of the communication.
• Iterative : Content analysis is an iterative process, meaning that the researcher may refine the coding scheme and analysis as they analyze the data, to ensure that the findings are valid and reliable.
• Reliability and validity : Content analysis aims to be a reliable and valid method of research, meaning that the findings are consistent and accurate. This is achieved through inter-coder reliability tests and other measures to ensure the quality of the data and analysis.

Advantages of Content Analysis

There are several advantages to using content analysis as a research method, including:

• Objective and systematic : Content analysis aims to be an objective and systematic method of research, which reduces the likelihood of bias and subjectivity in the analysis.
• Large sample size: Content analysis allows for the analysis of a large sample of data, which increases the statistical power of the analysis and the generalizability of the findings.
• Non-intrusive: Content analysis does not require the researcher to interact with the participants or disrupt their natural behavior, making it a non-intrusive research method.
• Accessible data: Content analysis can be used to analyze a wide range of data types, including written, oral, and visual communication, making it accessible to researchers across different fields.
• Versatile : Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research.
• Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives.

Limitations of Content Analysis

While content analysis has many advantages, there are also some limitations to consider, including:

• Limited contextual information: Content analysis is focused on the content of communication, which means that contextual information may be limited. This can make it difficult to fully understand the meaning behind the communication.
• Limited ability to capture nonverbal communication : Content analysis is limited to analyzing the content of communication that can be captured in written or recorded form. It may miss out on nonverbal communication, such as body language or tone of voice.
• Subjectivity in coding: While content analysis aims to be objective, there may be subjectivity in the coding process. Different coders may interpret the content differently, which can lead to inconsistent results.
• Limited ability to establish causality: Content analysis is a correlational research method, meaning that it cannot establish causality between variables. It can only identify associations between variables.
• Limited generalizability: Content analysis is limited to the data that is analyzed, which means that the findings may not be generalizable to other contexts or populations.
• Time-consuming: Content analysis can be a time-consuming research method, especially when analyzing a large sample of data. This can be a disadvantage for researchers who need to complete their research in a short amount of time.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Discourse analysis

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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.

 Three approaches to discourse analysis

• View inline

 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 related to patient care by looking at [computer generated] summaries alone.” 5

What is empirical discourse analysis?

Researchers using empirical discourse analysis 4 do not use highly structured methods to code individual words and utterances in detail. Rather, they look for broad themes and functions of language in action using approaches called conversation analysis (the study of “talk-in-interaction”) 6 and genre analysis (the study of recurrent patterns, or genres of language that share similar structure and context—such as the case report, the scientific article). 7

Conversation analysis and genre analysis give more prominence to sociological uses of language than to grammatical or linguistic structures of words and sentences and are used to study human conversations or other forms of communication in order to elucidate the ways in which meaning and action are created by individuals producing the language. 4 Lingard and colleagues, for example, studied communication between nurses and surgeons during 128 hours of observing 35 different procedures in the operating room and categorised recurrent patterns of communication. They then used their findings to draw links between interpersonal tensions, the use of language, and the occurrence of errors in the operating room. 8 Genre analysis is presented in detail in box 1.

Box 1 An empirical discourse analysis (genre analysis) of case presentations by medical students*

This study took place at a tertiary care teaching hospital in Canada. It was conducted in the context of a medical student rotation in paediatrics. The aim of the study was to gain understanding of how the formal linguistic structure of the case presentation is used in academic medical settings.

The researchers conducted 21 in-depth interviews with medical students and faculty members. Pairs of researchers also observed 16 oral case presentations as well as the teaching exchanges that surrounded them. All of these encounters were tape recorded and transcribed (for a total of 555 pages of text); the transcriptions were iteratively analysed. The analysis was structured to allow themes to emerge from the data (that is, as indicated by multiple examples of such themes throughout the data). However, it particularly focused on themes that helped to illuminate the rules around certain modes of case presentation and on the role of these rules in teaching and learning.

The study showed a pronounced tension between the educational (“schooling”) uses and clinical (“workplace”) functions of case presentations. For example, students saw the case presentation as a school mode and emphasised that they wanted to get through their presentations without being asked any questions. Faculty, on the other hand, understood the case presentation as a way for professionals to jointly create shared knowledge. Their cross-purposes affected the effectiveness of faculty feedback to the students about their case presentations.

*Description based on study by Lingard et al 9

What is critical discourse analysis?

Researchers in cultural studies, sociology, and philosophy use the term critical discourse analysis to encompass an even wider sphere that includes all of the social practices, individuals, and institutions that make it possible or legitimate to understand phenomena in a particular way, and to make certain statements about what is “true.” Critical discourse analysis is particularly concerned with power and is rooted in “constructivism.” Thus the discourse analyses of Michel Foucault, for example, illustrated how particular discourses “systematically construct versions of the social world.” 4 Discourse analysis at this level involves not only the examination of text and the social uses of language but also the study of the ways in which the very existence of specific institutions and of roles for individuals to play are made possible by ways of thinking and speaking.

Foucault’s study of madness, for example, uncovered three distinct discourses that have constructed what madness is in different historical periods and in different places: madness as spiritual possession, madness as social deviancy, and madness as mental illness. 10 In a similarly oriented study, Speed showed how different discourses about mental health service in use today construct individuals’ identities as “patients,” “consumers,” or “survivors” and are made possible by specific institutional practices and ways for individuals to “be.” 11

In a different context, Stone contrasted the specific discourses used in the education literature for diabetes patients (“patient self care” and “autonomy”) with the medical literature’s use of doctor centred discourses (“compliance” and “adherence”). Stone related the resulting tension (and the important implications for patients’ behaviours) to the ways in which the roles that physicians and patients play are historically determined by different and conflicting models of what disease and healing are. 12

Finally, Shaw and colleagues used a discourse analysis to illustrate the many ways in which research itself can be defined (for example, by a lay person, a medical editor, the World Medical Association, a hospital, the taxman) and how these various definitions are linked to the power and objectives of particular institutions. 13

In these examples of critical discourse analysis, the language and practices of healthcare professionals and institutions are examined with the aim of understanding how these practices shape and limit the ways that individuals and institutions can think, speak, and conduct themselves. Table 2 ⇑ illustrates how a critical discourse approach to diabetes education would compare with discourse analyses using other linguistic and empirical approaches to research.

Although our categorisation (tables 1 ⇑ and 2 ⇑ ) emphasises the distinctions between these approaches to discourse analysis, in practice researchers often use more than one of the approaches together in a study. For example, genre analysts may invoke critical theorists in order to study the origins of the sanctioned methods of communication, asking, for example, “What historical and contextual factors led to the adoption of the scientific journal article as a legitimate form of expression of medical ‘truth’ rather than the adoption of another format?”

What should we be looking for in a discourse analysis?

Given the wide variety of approaches to discourse analysis, the elements that constitute a high quality study vary. Rogers has argued that some discourse analysis research suffers from scanty explanation of the analytical method used. 14 Thus one should expect clear documentation of the sources of information used and delimitation of data sources 3 (including a description of decisions made with regard to selection of groups or individuals for interviews, focus groups, or observation) and, importantly, a description of the context of the study. The method of analysis should be clearly explained, including assumptions made and methods used to code and synthesise data. Finally, given that the goal of critical discourse analysis is to illuminate and critique structures of power, it is especially important that researchers describe the ways in which their own individual sociocultural roles may influence their perspectives.

Discourse analysis is an effective method to approach a wide range of research questions in health care and the health professions. What underpins all variants of discourse analysis is the idea of examining segments, or frames of communication, and using this to understand meaning at a “meta” level, rather than simply at the level of actual semantic meaning. In this way, all of the various methods of discourse analysis provide rigorous and powerful approaches to understanding complex phenomena, ranging from the nature of on-the-ground human communication to the inner workings of systems of power that construct what is “true” about health and health care. While these methods are gaining popularity, much remains to be done to develop a widespread appreciation for the use, funding, and publication of discourse analyses. As a start, we hope this article will help readers who encounter these approaches to understand the basic premises of discourse analysis. Box 2 offers further reading for those interested in learning more or undertaking discourse analytical research.

Box 2 Further reading

Fairclough N. Language and power . London: Longman, 1989.

Foucault. The archaeology of knowledge and the discourse on language . New York: Random House, 1972.

Jaworski A, Coupland N, eds. The discourse reader . London: Routledge, 1999.

Kendall G, Wickham G. Using Foucault’s method . London: Sage, 2003.

Mills S. Discourse . London: Routledge, 2004.

Journal articles

Barnes R. Conversation analysis: a practical resource in the health care setting. Med Educ 2005;39:113-5.

Ford-Sumner S. Genre analysis: a means of learning more about the language of health care. Nurse Researcher 2006;14(1):7-17.

Roberts C, Sarangi S. Theme-oriented discourse analysis of medical encounters. Med Educ 2005;39:632-40.

Summary points

Discourse analysis is an effective method for approaching a wide range of research questions in health care and the health professions

Discourse analysis is about studying and analysing the uses of language

A vast array of data sources is available to the discourse analyst

The various methods of discourse analysis provide rigorous and powerful approaches to understanding complex phenomena, ranging from the nature of on-the-ground human communication to the inner workings of systems of power that construct what is “true” about health and health care

Cite this as: BMJ 2008;337:a879

• Related to doi: , 10.1136/bmj.a288
• doi: , 10.1136/bmj.39602.690162.47
• doi: , 10.1136/bmj.a1020
• doi: , 10.1136/bmj.a949
• doi: 10.1136/bmj.a1035

This is the fourth in a series of six articles that aim to help readers to critically appraise the increasing number of qualitative research articles in clinical journals. The series editors are Ayelet Kuper and Scott Reeves.

For a definition of general terms relating to qualitative research, see the first article in this series.

Contributors: All authors contributed to the conception and drafting of the article and its revisions, and all approved the final version.

Competing interests: None declared.

Provenance and peer review: Commissioned; externally peer reviewed.

• Harris ZS. Methods in structural linguistics . Chicago: University Press, 1951 .
• Sacks H. Lectures on conversation . Jefferson G, ed. Cambridge, MA: Blackwell, 1995 .
• ↵ Foucault M. The archaeology of knowledge and the discourse on language . New York: Random House, 1972 .
• ↵ McHoul A, Grace W. A Foucault primer: discourse, power and the subject . New York: New York University Press, 1993 .
• ↵ Lacson RC, Barzilay R, Long WJ. Automatic analysis of medical dialogue in the home hemodialysis domain: structure induction and summarization. J Biomed Informatics 2006 ; 39 : 541 -55. OpenUrl CrossRef PubMed Web of Science
• ↵ Ten Have P. Medical ethnomethodology: an overview. Human Studies 1995 ; 18 : 245 -261. OpenUrl CrossRef Web of Science
• ↵ Ford-Sumner S. Genre analysis: a means of learning more about the language of health care. Nurse Researcher 2006 ; 14 (1): 7 -17. OpenUrl PubMed
• ↵ Lingard L, Espin S, Whyte S, Regehr G, Baker GR, Reznick R, et al. Communication failures in the operating room: an observational classification of recurrent types and effects. Qual Saf Health Care 2004 ; 13 : 330 -4. OpenUrl Abstract / FREE Full Text
• ↵ Lingard L, Schryer C, Garwood K, Spafford M. “Talking the talk”: school and workplace genre tensions in clerkship case presentations. Med Educ 2003 ; 37 : 612 -20. OpenUrl CrossRef PubMed Web of Science
• ↵ Foucault M. Madness and civilization; a history of insanity in the age of reason [Howard R, translation]. New York: Vintage Books, 1988. (Original work published in 1961 .)
• ↵ Speed E. Patients, consumers and survivors: a case study of mental health service user discourses. Soc Sci Med 2006 ; 62 (1): 28 -38. OpenUrl CrossRef PubMed Web of Science
• ↵ Stone MS. In search of patient agency in the rhetoric of diabetes care. Technical Communication Quarterly 1997 :6:201-17.
• ↵ Shaw S, Boynton PM, Greenhalgh T. Research governance: where did it come from, what does it mean? J R Soc Med 2005 ; 98 : 496 -502. OpenUrl Abstract / FREE Full Text
• ↵ Rogers R, Malancharuvil-Berkes R, Mosley M, Hui D, O’Garro JG. Critical discourse analysis in education: a review of the literature. Rev Educ Res 2005 ; 75 : 365 -416. OpenUrl CrossRef

• How it works

Discourse Analysis – A Definitive Guide With Steps & Types

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

What is Discourse Analysis?

Discourse analysis is an essential aspect of studying a language and its uses in day-to-day life.

It aims to gain in-depth knowledge about the language and identify its association with society, culture, and people’s perception.

It is used in various social science and humanities disciplines, such as linguistic, sociolinguistics, and psycholinguistics.

Aims of Discourse Analysis

It focuses on

• The clear, in-depth meaning of the language.
• The uses of language and its effects.
• The association of the language with cultures, interpersonal relationships, and communication.
• Various components of the language like vocabulary, grammar, pronunciation, tone of voice, fonts, and written form.

Uses of Discourse Analysis

Discourse analysis is

• Used to study the language and its applications in texts and contexts.
• It focuses on the entire conversation and real text instead of constructed or artificial text.
• It helps linguists to know the role of language in improving the understanding of people.
• It enables teachers to learn many language strategies to teach students writing/speaking skills better.

Materials Used in Discourse Analysis

The material includes

Types of Discourse

What to analyse, does your research methodology have the following.

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Discourse Analysis?

While conducting discourse analysis, you need to focus on the following points.

• Purpose of the writer
• The context of the speech/passage
• Type of the language used.
• The organisation of the text

You need to interpret the meaning and context of the discourse based on the available material and resources. There are various methods to conduct discourse analysis, but we are discussing the most basic method below.

Step1: Develop a Research Question

Like any other research in discourse analysis, it’s essential to have a  research question  to proceed with your study.  After selecting your research question, you need to find out the relevant resources to find the answer to it. Discourse analysis can be applied to smaller or larger samples depending on your research’s aims and requirements.

Example : If you want to find out the impact of plagiarism on the credibility of the authors. You can examine the relevant materials available on the topic from the internet, newspapers, and books published during the past 5-10 years.

Step 2: Collect Information and Establish the Context

After formulating a research question, you can  review the literature and find out the details about the source material, such as:

• Who is the author?
• What is the year and date of publication?
• What’s the name of the publication?
• What country and place is it from?
• What language is used?
• How and where did you find it?
• How can others get access to the same source?
• What kind of impact did it make on its audience?
• What’s the association between discourse material and real life?

These questions enable you to construct a strong evidence-based theory about your study.

Example: While investigating the history and origin of a particular religion. You also have to research the political events, culture, language of the people, and their association with society.

Generally, details about the publication and production of the material are available in the  about section on their online websites. If you don’t find the relevant information online, don’t hesitate to contact the editor or publication via email, phone calls, etc. 

Step 3: Analyse the Content

In this step, you should analyse various aspects of the materials such as:

• Sentence structure
• Inter-relationship between the text
• Layout and Page quality (if you are using offline materials)
• Links, comments, technical excellence, readability, multimedia content (if you are using online material)
• The genre of the source (a news item, political speech, a report, interview, biography, commentary, etc.)

The analysis of these elements gives you a clear understanding, and you can present your findings more accurately.  Once you have analysed the above features, you should analyse the following aspects:

• The structure of the argument
• The role of the introduction and conclusion of the material
• The context of the material
• Patterns and themes
• Discursive statements (arguments, perspective, thoughts of the writer/speaker
• Grammatical features (use of pronouns, adjectives, phrases, active or passive voice, and their meaning)
• Literary figures (idioms, similes, metaphors, allegories, proverbs)

Step 4: Interpret the Data

Now you have all the information, but the question that arises here is: 

What does it all mean?

To answer this question,  compile all your findings  to explain the meaning and context of the discourse.

Step 5: Present your Findings

It’s time to present your results. Throughout the process, you gathered detailed notes of the discourse, building a strong presentation or thesis. You can use the references of other relevant sources as evidence to support your discussion. Always try to make your paper interesting to grab the attention of the reader.

Advantages and Disadvantages of Discourse Analysis

• It provides a way of thinking and analysing the problem.
• It enables us to understand the context and perception of the speaker.
• It can be applied at any given time, place, and people.
• It helps to learn any language its origin and association with society and culture.

Disadvantages

• There are many options available as each tradition has its own concepts, procedures, and a specific understanding of discourse and its analysis.
• Discourse analysis doesn’t help to find out the answer to scientific problems.

Frequently Asked Questions

How to describe the discourse analysis.

Discourse analysis examines language use in context. It studies how communication shapes and reflects social meaning, power dynamics, and cultural norms. By analyzing spoken, written, or visual language, it unveils hidden ideologies, identities, and social structures within various contexts.

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• How It Works

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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10 Content Analysis Examples

Content analysis is a research method and type of textual analysis that analyzes the meanings of content , which could take the form of textual, visual, aural, and otherwise multimodal texts.

Generally, a content analysis will seek meanings and relationships of certain words and concepts within the text or corpus of texts, and generate thematic data that reveals deeper insights into the text’s meanings.

Prasad (2008) defines it as:

:…the study of the content with reference to the meanings, contexts and intentions contained in messages.” (p. 174)

Content analyses can involve deductive coding , where themes and concepts are asserted before the content is created; or, they can involve inductive coding , where themes and concepts emerge during a close reading of the text.

An example of a content analysis would be a study that analyzes the presence of ideological words and phrases in newspapers to ascertain the editorial team’s political biases.

Content Analysis Examples

1. conceptual analysis.

Also called semantic content analysis, a conceptual analysis selects a concept and tries to count its occurrence within a text (Kosterec, 2016).

An example of a concept that you might examine is sentiment, such as positive, negative, and neutral sentiment. Here, you would need to conduct a semantic study of the text to find instances of words like ‘bad’, ‘terrible’, etc. for negative sentiment, and ‘good’, ‘great’, etc. for positive sentiment. A compare and contrast will demonstrate a balance of sentiment within the text.

A basic conceptual analysis has the weakness of lacking the capacity to read words in context, which would require a deeper qualitative analysis of paragraphs, which is offset by other types of analysis in this list.

Example of Conceptual Analysis

A company launches a new product and wants to understand the public’s initial reactions to it. They use conceptual analysis to analyze comments on their social media posts about the product. They could choose specific concepts such as “like”, “dislike”, “awesome”, “terrible”, etc. The frequency of these words in the comments give them an idea about the public’s sentiment towards the product.

2. Relational Analysis

Relational analysis addresses the above-mentioned weakness of conceptual analysis (i.e. that a mere counting of instances of terms lacks context) by examining how concepts in a text relate to one another .

Here, a scholar might analyze the overlap or sequences between certain concepts and sentiments in language (Kosterec, 2016). To combine the two examples from the above conceptual analysis, a scholar might examine all of a particular masthead newspaper’s columns on global warming. In the study, they would examine the proximity between the word ‘global warming’ and positive, negative, and neutral sentiment words (‘good’, ‘bad’, ‘great’, etc.) to ascertain the newspaper’s sentiment toward a specific concept .

Example of Relational Analysis

A political scientist wants to understand the relationship between the use of emotional rhetoric and audience reaction in political speeches. They carry out a relational analysis on a corpus of speeches and corresponding audience feedback. By exploring the co-occurrence of emotive words (“hope”, “fear”, “pride”) and audience responses (“applause”, “boos”, “silence”), they discover patterns in how different types of emotional language affect audience reactions.

3. Thematic Analysis

A thematic analysis focuses on identifying themes or major ideas running throughout the text.

This can follow a range of strategies, spanning from highly quantitative – such as using statistical software to thematically group words and terms – through to highly qualitative, where trained researchers take notes on each paragraph to extract key ideas that can be thematicized.

Many literature reviews take the form of a thematic analysis, where the scholar reads all recent studies on a topic and tries to ascertain themes, as well as gaps, across the recent literature.

Example of Thematic Analysis

A scholar searches on research bases for all published academic papers containing the keyword “back pain” from the past 10 years. She then uses inductive coding to generate themes that span the studies. From this thematic analysis, she produces a literature review on key emergent themes from the literature on back pain, as well as gaps in the research.

4. Narrative Analysis

This involves a close reading of the framing and structure of narrative elements within content. It can examine personal life stories, biographies, journals, and so on.

In literary research, this method generally explores the elements of the story , such as characters, plot, literary themes , and settings. But in life history research, it will generally involve deconstructing a real person’s life story, analyzing their perspectives and worldview to develop insights into their unique situation, life circumstances, or personality.

The focus generally expands out from the story itself to what it can tell us about the individuals or culture from which it originates.

Example of Narrative Analysis

A social work researcher takes a group of their patients’ personal journals and, after obtaining ethics clearance and permission from the patients, deconstructs the underlying messages in their journals in order to extract an understanding of the core mental hurdles each patient faces, which are then analyzed through the lens of Jungian psychoanalysis.

5. Discourse Analysis

Discourse analysis, the research methodology from which I conducted my PhD studies, involves the study of how language can create and reproduce social realities.

Based on the work of postmodern scholars such as Michel Foucault and Jaques Derrida, it attempts to deconstruct how texts normalize ways of thinking within specific historical, cultural, and social contexts .

Foucault, the most influential scholar in discourse analytic research, demonstrated through the study of how society spoke about madness that different societies constructed madness in different ways: in the renaissance era, mad people we spoken of as wise people, during the classical era, language changed, and they were framed as pariahs. Finally, in the modern era, they were spoken about as if they were sick.

Following Foucault (1988), many content analysis scholars now look at the differing ways societies frame different identities (gender, race, social class, etc.) in different times – and this can be revealed by looking at the language used in the content (i.e. the texts) produced throughout different eras (Johnstone, 2017).

Example of Discourse Analysis

A scholar examines a corpus of immigration speeches from a specific political party from the past 10 years and examines how refugees are discussed in the speeches, with a focus on how language constructs and defines refugees. It finds that refugees appear to be constructed as threats, dirty, and nefarious.

See Here for 10 More Examples of Discourse Analysis

6. Multimodal Analysis 

As audiovisual texts became more important in society, many scholars began to critique the fact that content analysis tends to only look at written texts. In response, a methodology called multimodal analysis emerged.

In multimodal analysis, scholars don’t just decode the meanings in written texts, but also in multimodal texts . This involves the study of the signs, symbols, movements, and sounds that are within the text.

This opens up space for the analysis of television advertisements, billboards, and so forth.

For an example, a multimodal analysis of a television advertisement might not just study what is said, but it’ll explore how the camera angles frame some people as powerful (low to high angle) and some people as weak (high to low angle). Similarly, they may examine the colors to see if a character is positioned as sad (dark colors, walking through rain) or joyful (bright colors, sunshine).

Example of Multimodal Analysis

A cultural studies scholar examines the representation of Gender in Disney films, looking not only at the spoken words, but also the dresses worn, the camera angles, and the princesses’ tone of voice when speaking to other characters to assess how Disney’s construction of gender has changed over time.

7. Semiotic Analysis

Semiotic analysis takes multimodal analysis to the next step by providing the specific methods for the analysis of multimodal texts.

Seminal scholars Kress and van Leeuwen (2006) have created a significant repertoire of texts demonstrating how semiotics shape meaning. In their works, they present deconstructions of various modes of address:

• Visual: How images, signs, and symbols create meaning in social contexts. For example, in our modern world, a red octagon has a specific social meaning: stop!
• Textual: How words shape meaning, such as through a sentiment analysis as discussed earlier.
• Motive: How movement can create a sense of pace, distance, the movement of time, and so forth, which shapes meaning.
• Aural: How sounds shape meaning. For example, the words spoken are not the only way we interpret a speech, but also how they’re spoken (shakily, confidently, assertively, etc.)

Example of Semiotic Analysis

A communications studies scholar examines the body language of leaders during meetings at an international political event, using it to explore how the leaders subtly send messages about who they are allied with, where they view themselves in geopolitical terms, and their attitudes toward the event overall.

8. Latent Content Analysis

This involves the interpretation of the underlying, inferred meanings of the words or visuals. The focus here is on what is being implied by the content rather than just what is explicitly said.

For example, in the context of the same newspaper articles, a latent content analysis might examine the way the event is framed, the language or rhetoric used, the themes or narratives that are implied, or the attitudes and ideologies that are expressed or endorsed, either overtly or covertly .

Returning to the work of Foucault, he demonstrated how silence also constructs meaning. The question emerges: what is left unsaid in the content, and how does this shape our understanding of the biases and assumptions of the author?

Example of Latent Content Analysis

A sociologist studying gender roles in films watches the top 10 movies from last year and doesn’t just count instances of words – rather, they analyze the underlying, implicit messages about gender roles. This could include exploring how female characters are portrayed (do they tend to be passive and in need of rescue, or are they active, independent and resourceful?) and how male characters are portrayed (emotional or unemotional?) What kind of occupations do characters of each gender typically have?

9. Manifest Content Analysis

A manifest content analysis is the counterpoint to latent content analysis. It involves a direct and surface-level reading of the visible aspects of the content.

It concerns itself primarily with what is visible, obvious and countable. This approach asserts that we should not read too deeply into anything beyond what is manifest (i.e. present), because the deeper we try to read into the missing or latent elements, the more we stray into the real of guessing and assuming.

Scholars will often do both latent and manifest content analyses side-by-side, exploring how each type of analysis might reveal different interpretations or insights.

Example of Manifest Content Analysis

A researcher is interested in studying bias in media coverage of a particular political event. They might conduct a conceptual analysis where the concept is the tone of language used – positive, neutral, or negative. They would examine a number of articles from different newspapers, tallying up instances of positive, negative, or neutral language to see if there is a bias towards positivity or negativity in coverage of the event.

10. Longitudinal Content Analysis

A longitudinal content analysis analyzes trends in content over a long period of time.

Earlier, I explored the idea in discourse analysis that different eras have different ideas about terms and concepts (consider, for example, evolving ideas of gender and race). A longitudinal analysis would be very useful here. It would involve collecting cross-sectional moments in time , at varying points in time, which would then be compared and contrasted for the representation of varying concepts and terms.

Example of Longitudinal Content Analsis

A scholar might look at newspaper reports on texts from each decade for 100 years, examining environmental terms (‘global warming’, ‘climate change’, ‘recycling’) to identify when and how environmental concepts entered public discourse.

For other Examples of Analysis, See Here

Content analysis is a form of empirical research that uses texts rather than interviews or naturalistic observation to gather data that can then be analyzed. There are a range of methods and approaches to the analysis of content, but their unifying feature is that they involve close readings of texts to identify concepts and themes that might be revealing of core or underlying messages within the content.

The above examples are not mutually exclusive types, but rather different approaches that researchers can use based on their specific goals and the nature of the data they are working with.

Foucault, M. (1988). Madness and civilization: A history of insanity in the age of reason . London: Vintage.

Johnstone, B. (2017). Discourse analysis . London: John Wiley & Sons.

Kosterec, M. (2016). Methods of conceptual analysis. Filozofia , 71 (3).

Kress, G., & Van Leeuwen, T. (2006). The grammar of visual design . London and New York: Routledge.

Prasad, B. D. (2008). Content analysis: A method of Social Science Research . In D.K. Lal Das (ed) Research Methods for Social Work, (pp.174-193). New Delhi: Rawat Publications.

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]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
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Home » Education » Difference Between Content Analysis and Discourse Analysis

Difference Between Content Analysis and Discourse Analysis

Main difference – content analysis vs discourse analysis.

Content analysis and discourse analysis are research tools that are often used in a wide range of disciplines. Although these two terms are very broad and are general terms referring to a quite diverse research approaches and techniques, we’ll attempt to examine them in a general sense.  Content Analysis is a method for studying and/or retrieving meaningful information from documents. Discourse Analysis is the study of the ways in which language is used in texts and contexts. The main difference between content analysis and discourse analysis is that the content analysis is a quantitative analysis whereas discourse analysis is a qualitative method.

Here, we will cover,

1. What is Content Analysis? – Meaning, Features and Uses

2. What is Discourse Analysis?  – Meaning, Features and Uses

3. What is the difference between Content Analysis and Discourse Analysis?

What is Content Analysis

Content analysis is used as an umbrella term for various research approaches and techniques. It can be mainly defined as a research method for studying and/or retrieving meaningful information from documents by determining the occurrence of certain words or concepts within texts or sets of texts. The concept of text here can be broadly defined as books, newspaper headlines and articles, essays, conversations, discussions, speeches, advertising, theater, historical documents, audio-visual texts, etc.

Holsti (1969) states that there are three basic uses of content analysis.

Making inferences about the antecedents of a communication, describing and making inferences about characteristics of a communication and making inferences about the effects of  communication are these three basic uses.

According to Dr. Klaus Krippendorff (2004), every content analysis must address six questions:

• Which data are analysed?
• How are the data defined?
• What is the population from which the data is drawn?
• What is the context relative to which the data are analysed?
• What are the boundaries of the analysis?
• What is the target of the inferences?

What is Discourse Analysis

The term discourse analysis also has different definitions and meanings in various disciplines.  It can be broadly categorized as the study of the ways in which language is used in texts and contexts. Discourse analysis always refers to the analysis of real life discourse or naturally occurring language; the data for discourse is taken from written texts or tape recordings.

Discourse analysis is used in various disciplines in humanities and social sciences, including linguistics, sociology, cultural studies, international relations,   anthropology , social work, education, cognitive psychology , social psychology, area studies, human geography, communication studies, biblical studies, and translation studies.

Discourse analysis involves examining various dimensions of discourse such as style, syntax , tone , intonation , idioms , and gestures , analysing various genres of discourse, the relationship between discourse and context, the relationship between discourse and syntactic structure, etc.

Content Analysis is a method for studying and/or retrieving meaningful information from documents.

Discourse Analysis is the study of the ways in which language is used in texts and contexts.

Content Analysis examines the content.    

Discourse Analysis examines the language.        

Quantitative vs Qualitative

Content Analysis is a quantitative method.

Discourse Analysis is often a qualitative method.

  Holsti, Ole R. (1969). Content Analysis for the Social Sciences and Humanities. Reading, MA: Addison-Wesley.

Krippendorff, Klaus (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Thousand Oaks, CA: Sage. p. 413. ISBN 9780761915454.

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About the Author: Hasa

Hasanthi is a seasoned content writer and editor with over 8 years of experience. Armed with a BA degree in English and a knack for digital marketing, she explores her passions for literature, history, culture, and food through her engaging and informative writing.

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