content analysis thesis example

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

• What (exactly) is qualitative content analysis
• The two main types of content analysis
• When to use content analysis
• How to conduct content analysis (the process)
• The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a  qualitative analysis method  that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called  unobtrusive  research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with? 

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding? 

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction  and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

• Affect extraction
• Proximity analysis
• Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

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3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

• Recap on your research questions
• Undertake bracketing to identify biases
• Operationalise your variables and develop a coding scheme
• Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

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14 Comments

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Thank you very much. I really enjoyed every word.

please send me one/ two sample of content analysis

send me to any sample of qualitative content analysis as soon as possible

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Could you please send me some sample of textbook content analysis?

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

please could you send me samples of content analysis?

Yes please send

really we enjoyed your knowledge thanks allot. from Ethiopia

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

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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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Using Content Analysis

This guide provides an introduction to content analysis, a research methodology that examines words or phrases within a wide range of texts.

• Introduction to Content Analysis : Read about the history and uses of content analysis.
• Conceptual Analysis : Read an overview of conceptual analysis and its associated methodology.
• Relational Analysis : Read an overview of relational analysis and its associated methodology.
• Commentary : Read about issues of reliability and validity with regard to content analysis as well as the advantages and disadvantages of using content analysis as a research methodology.
• Examples : View examples of real and hypothetical studies that use content analysis.
• Annotated Bibliography : Complete list of resources used in this guide and beyond.

An Introduction to Content Analysis

Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze the presence, meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, historical documents, speeches, conversations, advertising, theater, informal conversation, or really any occurrence of communicative language. Texts in a single study may also represent a variety of different types of occurrences, such as Palmquist's 1990 study of two composition classes, in which he analyzed student and teacher interviews, writing journals, classroom discussions and lectures, and out-of-class interaction sheets. To conduct a content analysis on any such text, the text is coded, or broken down, into manageable categories on a variety of levels--word, word sense, phrase, sentence, or theme--and then examined using one of content analysis' basic methods: conceptual analysis or relational analysis.

A Brief History of Content Analysis

Historically, content analysis was a time consuming process. Analysis was done manually, or slow mainframe computers were used to analyze punch cards containing data punched in by human coders. Single studies could employ thousands of these cards. Human error and time constraints made this method impractical for large texts. However, despite its impracticality, content analysis was already an often utilized research method by the 1940's. Although initially limited to studies that examined texts for the frequency of the occurrence of identified terms (word counts), by the mid-1950's researchers were already starting to consider the need for more sophisticated methods of analysis, focusing on concepts rather than simply words, and on semantic relationships rather than just presence (de Sola Pool 1959). While both traditions still continue today, content analysis now is also utilized to explore mental models, and their linguistic, affective, cognitive, social, cultural and historical significance.

Uses of Content Analysis

Perhaps due to the fact that it can be applied to examine any piece of writing or occurrence of recorded communication, content analysis is currently used in a dizzying array of fields, ranging from marketing and media studies, to literature and rhetoric, ethnography and cultural studies, gender and age issues, sociology and political science, psychology and cognitive science, and many other fields of inquiry. Additionally, content analysis reflects a close relationship with socio- and psycholinguistics, and is playing an integral role in the development of artificial intelligence. The following list (adapted from Berelson, 1952) offers more possibilities for the uses of content analysis:

• Reveal international differences in communication content
• Detect the existence of propaganda
• Identify the intentions, focus or communication trends of an individual, group or institution
• Describe attitudinal and behavioral responses to communications
• Determine psychological or emotional state of persons or groups

Types of Content Analysis

In this guide, we discuss two general categories of content analysis: conceptual analysis and relational analysis. Conceptual analysis can be thought of as establishing the existence and frequency of concepts most often represented by words of phrases in a text. For instance, say you have a hunch that your favorite poet often writes about hunger. With conceptual analysis you can determine how many times words such as hunger, hungry, famished, or starving appear in a volume of poems. In contrast, relational analysis goes one step further by examining the relationships among concepts in a text. Returning to the hunger example, with relational analysis, you could identify what other words or phrases hunger or famished appear next to and then determine what different meanings emerge as a result of these groupings.

Conceptual Analysis

Traditionally, content analysis has most often been thought of in terms of conceptual analysis. In conceptual analysis, a concept is chosen for examination, and the analysis involves quantifying and tallying its presence. Also known as thematic analysis [although this term is somewhat problematic, given its varied definitions in current literature--see Palmquist, Carley, & Dale (1997) vis-a-vis Smith (1992)], the focus here is on looking at the occurrence of selected terms within a text or texts, although the terms may be implicit as well as explicit. While explicit terms obviously are easy to identify, coding for implicit terms and deciding their level of implication is complicated by the need to base judgments on a somewhat subjective system. To attempt to limit the subjectivity, then (as well as to limit problems of reliability and validity ), coding such implicit terms usually involves the use of either a specialized dictionary or contextual translation rules. And sometimes, both tools are used--a trend reflected in recent versions of the Harvard and Lasswell dictionaries.

Methods of Conceptual Analysis

Conceptual analysis begins with identifying research questions and choosing a sample or samples. Once chosen, the text must be coded into manageable content categories. The process of coding is basically one of selective reduction . By reducing the text to categories consisting of a word, set of words or phrases, the researcher can focus on, and code for, specific words or patterns that are indicative of the research question.

An example of a conceptual analysis would be to examine several Clinton speeches on health care, made during the 1992 presidential campaign, and code them for the existence of certain words. In looking at these speeches, the research question might involve examining the number of positive words used to describe Clinton's proposed plan, and the number of negative words used to describe the current status of health care in America. The researcher would be interested only in quantifying these words, not in examining how they are related, which is a function of relational analysis. In conceptual analysis, the researcher simply wants to examine presence with respect to his/her research question, i.e. is there a stronger presence of positive or negative words used with respect to proposed or current health care plans, respectively.

Once the research question has been established, the researcher must make his/her coding choices with respect to the eight category coding steps indicated by Carley (1992).

Steps for Conducting Conceptual Analysis

The following discussion of steps that can be followed to code a text or set of texts during conceptual analysis use campaign speeches made by Bill Clinton during the 1992 presidential campaign as an example. To read about each step, click on the items in the list below:

• Decide the level of analysis.

First, the researcher must decide upon the level of analysis . With the health care speeches, to continue the example, the researcher must decide whether to code for a single word, such as "inexpensive," or for sets of words or phrases, such as "coverage for everyone."

• Decide how many concepts to code for.

The researcher must now decide how many different concepts to code for. This involves developing a pre-defined or interactive set of concepts and categories. The researcher must decide whether or not to code for every single positive or negative word that appears, or only certain ones that the researcher determines are most relevant to health care. Then, with this pre-defined number set, the researcher has to determine how much flexibility he/she allows him/herself when coding. The question of whether the researcher codes only from this pre-defined set, or allows him/herself to add relevant categories not included in the set as he/she finds them in the text, must be answered. Determining a certain number and set of concepts allows a researcher to examine a text for very specific things, keeping him/her on task. But introducing a level of coding flexibility allows new, important material to be incorporated into the coding process that could have significant bearings on one's results.

• Decide whether to code for existence or frequency of a concept.

After a certain number and set of concepts are chosen for coding , the researcher must answer a key question: is he/she going to code for existence or frequency ? This is important, because it changes the coding process. When coding for existence, "inexpensive" would only be counted once, no matter how many times it appeared. This would be a very basic coding process and would give the researcher a very limited perspective of the text. However, the number of times "inexpensive" appears in a text might be more indicative of importance. Knowing that "inexpensive" appeared 50 times, for example, compared to 15 appearances of "coverage for everyone," might lead a researcher to interpret that Clinton is trying to sell his health care plan based more on economic benefits, not comprehensive coverage. Knowing that "inexpensive" appeared, but not that it appeared 50 times, would not allow the researcher to make this interpretation, regardless of whether it is valid or not.

• Decide on how you will distinguish among concepts.

The researcher must next decide on the , i.e. whether concepts are to be coded exactly as they appear, or if they can be recorded as the same even when they appear in different forms. For example, "expensive" might also appear as "expensiveness." The research needs to determine if the two words mean radically different things to him/her, or if they are similar enough that they can be coded as being the same thing, i.e. "expensive words." In line with this, is the need to determine the level of implication one is going to allow. This entails more than subtle differences in tense or spelling, as with "expensive" and "expensiveness." Determining the level of implication would allow the researcher to code not only for the word "expensive," but also for words that imply "expensive." This could perhaps include technical words, jargon, or political euphemism, such as "economically challenging," that the researcher decides does not merit a separate category, but is better represented under the category "expensive," due to its implicit meaning of "expensive."

• Develop rules for coding your texts.

After taking the generalization of concepts into consideration, a researcher will want to create translation rules that will allow him/her to streamline and organize the coding process so that he/she is coding for exactly what he/she wants to code for. Developing a set of rules helps the researcher insure that he/she is coding things consistently throughout the text, in the same way every time. If a researcher coded "economically challenging" as a separate category from "expensive" in one paragraph, then coded it under the umbrella of "expensive" when it occurred in the next paragraph, his/her data would be invalid. The interpretations drawn from that data will subsequently be invalid as well. Translation rules protect against this and give the coding process a crucial level of consistency and coherence.

• Decide what to do with "irrelevant" information.

The next choice a researcher must make involves irrelevant information . The researcher must decide whether irrelevant information should be ignored (as Weber, 1990, suggests), or used to reexamine and/or alter the coding scheme. In the case of this example, words like "and" and "the," as they appear by themselves, would be ignored. They add nothing to the quantification of words like "inexpensive" and "expensive" and can be disregarded without impacting the outcome of the coding.

• Code the texts.

Once these choices about irrelevant information are made, the next step is to code the text. This is done either by hand, i.e. reading through the text and manually writing down concept occurrences, or through the use of various computer programs. Coding with a computer is one of contemporary conceptual analysis' greatest assets. By inputting one's categories, content analysis programs can easily automate the coding process and examine huge amounts of data, and a wider range of texts, quickly and efficiently. But automation is very dependent on the researcher's preparation and category construction. When coding is done manually, a researcher can recognize errors far more easily. A computer is only a tool and can only code based on the information it is given. This problem is most apparent when coding for implicit information, where category preparation is essential for accurate coding.

• Analyze your results.

Once the coding is done, the researcher examines the data and attempts to draw whatever conclusions and generalizations are possible. Of course, before these can be drawn, the researcher must decide what to do with the information in the text that is not coded. One's options include either deleting or skipping over unwanted material, or viewing all information as relevant and important and using it to reexamine, reassess and perhaps even alter one's coding scheme. Furthermore, given that the conceptual analyst is dealing only with quantitative data, the levels of interpretation and generalizability are very limited. The researcher can only extrapolate as far as the data will allow. But it is possible to see trends, for example, that are indicative of much larger ideas. Using the example from step three, if the concept "inexpensive" appears 50 times, compared to 15 appearances of "coverage for everyone," then the researcher can pretty safely extrapolate that there does appear to be a greater emphasis on the economics of the health care plan, as opposed to its universal coverage for all Americans. It must be kept in mind that conceptual analysis, while extremely useful and effective for providing this type of information when done right, is limited by its focus and the quantitative nature of its examination. To more fully explore the relationships that exist between these concepts, one must turn to relational analysis.

Relational Analysis

Relational analysis, like conceptual analysis, begins with the act of identifying concepts present in a given text or set of texts. However, relational analysis seeks to go beyond presence by exploring the relationships between the concepts identified. Relational analysis has also been termed semantic analysis (Palmquist, Carley, & Dale, 1997). In other words, the focus of relational analysis is to look for semantic, or meaningful, relationships. Individual concepts, in and of themselves, are viewed as having no inherent meaning. Rather, meaning is a product of the relationships among concepts in a text. Carley (1992) asserts that concepts are "ideational kernels;" these kernels can be thought of as symbols which acquire meaning through their connections to other symbols.

Theoretical Influences on Relational Analysis

The kind of analysis that researchers employ will vary significantly according to their theoretical approach. Key theoretical approaches that inform content analysis include linguistics and cognitive science.

Linguistic approaches to content analysis focus analysis of texts on the level of a linguistic unit, typically single clause units. One example of this type of research is Gottschalk (1975), who developed an automated procedure which analyzes each clause in a text and assigns it a numerical score based on several emotional/psychological scales. Another technique is to code a text grammatically into clauses and parts of speech to establish a matrix representation (Carley, 1990).

Approaches that derive from cognitive science include the creation of decision maps and mental models. Decision maps attempt to represent the relationship(s) between ideas, beliefs, attitudes, and information available to an author when making a decision within a text. These relationships can be represented as logical, inferential, causal, sequential, and mathematical relationships. Typically, two of these links are compared in a single study, and are analyzed as networks. For example, Heise (1987) used logical and sequential links to examine symbolic interaction. This methodology is thought of as a more generalized cognitive mapping technique, rather than the more specific mental models approach.

Mental models are groups or networks of interrelated concepts that are thought to reflect conscious or subconscious perceptions of reality. According to cognitive scientists, internal mental structures are created as people draw inferences and gather information about the world. Mental models are a more specific approach to mapping because beyond extraction and comparison because they can be numerically and graphically analyzed. Such models rely heavily on the use of computers to help analyze and construct mapping representations. Typically, studies based on this approach follow five general steps:

• Identifing concepts
• Defining relationship types
• Coding the text on the basis of 1 and 2
• Coding the statements
• Graphically displaying and numerically analyzing the resulting maps

To create the model, a researcher converts a text into a map of concepts and relations; the map is then analyzed on the level of concepts and statements, where a statement consists of two concepts and their relationship. Carley (1990) asserts that this makes possible the comparison of a wide variety of maps, representing multiple sources, implicit and explicit information, as well as socially shared cognitions.

Relational Analysis: Overview of Methods

As with other sorts of inquiry, initial choices with regard to what is being studied and/or coded for often determine the possibilities of that particular study. For relational analysis, it is important to first decide which concept type(s) will be explored in the analysis. Studies have been conducted with as few as one and as many as 500 concept categories. Obviously, too many categories may obscure your results and too few can lead to unreliable and potentially invalid conclusions. Therefore, it is important to allow the context and necessities of your research to guide your coding procedures.

The steps to relational analysis that we consider in this guide suggest some of the possible avenues available to a researcher doing content analysis. We provide an example to make the process easier to grasp. However, the choices made within the context of the example are but only a few of many possibilities. The diversity of techniques available suggests that there is quite a bit of enthusiasm for this mode of research. Once a procedure is rigorously tested, it can be applied and compared across populations over time. The process of relational analysis has achieved a high degree of computer automation but still is, like most forms of research, time consuming. Perhaps the strongest claim that can be made is that it maintains a high degree of statistical rigor without losing the richness of detail apparent in even more qualitative methods.

Three Subcategories of Relational Analysis

Affect extraction: This approach provides an emotional evaluation of concepts explicit in a text. It is problematic because emotion may vary across time and populations. Nevertheless, when extended it can be a potent means of exploring the emotional/psychological state of the speaker and/or writer. Gottschalk (1995) provides an example of this type of analysis. By assigning concepts identified a numeric value on corresponding emotional/psychological scales that can then be statistically examined, Gottschalk claims that the emotional/psychological state of the speaker or writer can be ascertained via their verbal behavior.

Proximity analysis: This approach, on the other hand, is concerned with the co-occurrence of explicit concepts in the text. In this procedure, the text is defined as a string of words. A given length of words, called a window , is determined. The window is then scanned across a text to check for the co-occurrence of concepts. The result is the creation of a concept determined by the concept matrix . In other words, a matrix, or a group of interrelated, co-occurring concepts, might suggest a certain overall meaning. The technique is problematic because the window records only explicit concepts and treats meaning as proximal co-occurrence. Other techniques such as clustering, grouping, and scaling are also useful in proximity analysis.

Cognitive mapping: This approach is one that allows for further analysis of the results from the two previous approaches. It attempts to take the above processes one step further by representing these relationships visually for comparison. Whereas affective and proximal analysis function primarily within the preserved order of the text, cognitive mapping attempts to create a model of the overall meaning of the text. This can be represented as a graphic map that represents the relationships between concepts.

In this manner, cognitive mapping lends itself to the comparison of semantic connections across texts. This is known as map analysis which allows for comparisons to explore "how meanings and definitions shift across people and time" (Palmquist, Carley, & Dale, 1997). Maps can depict a variety of different mental models (such as that of the text, the writer/speaker, or the social group/period), according to the focus of the researcher. This variety is indicative of the theoretical assumptions that support mapping: mental models are representations of interrelated concepts that reflect conscious or subconscious perceptions of reality; language is the key to understanding these models; and these models can be represented as networks (Carley, 1990). Given these assumptions, it's not surprising to see how closely this technique reflects the cognitive concerns of socio-and psycholinguistics, and lends itself to the development of artificial intelligence models.

Steps for Conducting Relational Analysis

The following discussion of the steps (or, perhaps more accurately, strategies) that can be followed to code a text or set of texts during relational analysis. These explanations are accompanied by examples of relational analysis possibilities for statements made by Bill Clinton during the 1998 hearings.

• Identify the Question.

The question is important because it indicates where you are headed and why. Without a focused question, the concept types and options open to interpretation are limitless and therefore the analysis difficult to complete. Possibilities for the Hairy Hearings of 1998 might be:

What did Bill Clinton say in the speech? OR What concrete information did he present to the public?

• Choose a sample or samples for analysis.

Once the question has been identified, the researcher must select sections of text/speech from the hearings in which Bill Clinton may have not told the entire truth or is obviously holding back information. For relational content analysis, the primary consideration is how much information to preserve for analysis. One must be careful not to limit the results by doing so, but the researcher must also take special care not to take on so much that the coding process becomes too heavy and extensive to supply worthwhile results.

• Determine the type of analysis.

Once the sample has been chosen for analysis, it is necessary to determine what type or types of relationships you would like to examine. There are different subcategories of relational analysis that can be used to examine the relationships in texts.

In this example, we will use proximity analysis because it is concerned with the co-occurrence of explicit concepts in the text. In this instance, we are not particularly interested in affect extraction because we are trying to get to the hard facts of what exactly was said rather than determining the emotional considerations of speaker and receivers surrounding the speech which may be unrecoverable.

Once the subcategory of analysis is chosen, the selected text must be reviewed to determine the level of analysis. The researcher must decide whether to code for a single word, such as "perhaps," or for sets of words or phrases like "I may have forgotten."

• Reduce the text to categories and code for words or patterns.

At the simplest level, a researcher can code merely for existence. This is not to say that simplicity of procedure leads to simplistic results. Many studies have successfully employed this strategy. For example, Palmquist (1990) did not attempt to establish the relationships among concept terms in the classrooms he studied; his study did, however, look at the change in the presence of concepts over the course of the semester, comparing a map analysis from the beginning of the semester to one constructed at the end. On the other hand, the requirement of one's specific research question may necessitate deeper levels of coding to preserve greater detail for analysis.

In relation to our extended example, the researcher might code for how often Bill Clinton used words that were ambiguous, held double meanings, or left an opening for change or "re-evaluation." The researcher might also choose to code for what words he used that have such an ambiguous nature in relation to the importance of the information directly related to those words.

• Explore the relationships between concepts (Strength, Sign & Direction).

Once words are coded, the text can be analyzed for the relationships among the concepts set forth. There are three concepts which play a central role in exploring the relations among concepts in content analysis.

• Strength of Relationship: Refers to the degree to which two or more concepts are related. These relationships are easiest to analyze, compare, and graph when all relationships between concepts are considered to be equal. However, assigning strength to relationships retains a greater degree of the detail found in the original text. Identifying strength of a relationship is key when determining whether or not words like unless, perhaps, or maybe are related to a particular section of text, phrase, or idea.
• Sign of a Relationship: Refers to whether or not the concepts are positively or negatively related. To illustrate, the concept "bear" is negatively related to the concept "stock market" in the same sense as the concept "bull" is positively related. Thus "it's a bear market" could be coded to show a negative relationship between "bear" and "market". Another approach to coding for strength entails the creation of separate categories for binary oppositions. The above example emphasizes "bull" as the negation of "bear," but could be coded as being two separate categories, one positive and one negative. There has been little research to determine the benefits and liabilities of these differing strategies. Use of Sign coding for relationships in regard to the hearings my be to find out whether or not the words under observation or in question were used adversely or in favor of the concepts (this is tricky, but important to establishing meaning).
• Direction of the Relationship: Refers to the type of relationship categories exhibit. Coding for this sort of information can be useful in establishing, for example, the impact of new information in a decision making process. Various types of directional relationships include, "X implies Y," "X occurs before Y" and "if X then Y," or quite simply the decision whether concept X is the "prime mover" of Y or vice versa. In the case of the 1998 hearings, the researcher might note that, "maybe implies doubt," "perhaps occurs before statements of clarification," and "if possibly exists, then there is room for Clinton to change his stance." In some cases, concepts can be said to be bi-directional, or having equal influence. This is equivalent to ignoring directionality. Both approaches are useful, but differ in focus. Coding all categories as bi-directional is most useful for exploratory studies where pre-coding may influence results, and is also most easily automated, or computer coded.
• Code the relationships.

One of the main differences between conceptual analysis and relational analysis is that the statements or relationships between concepts are coded. At this point, to continue our extended example, it is important to take special care with assigning value to the relationships in an effort to determine whether the ambiguous words in Bill Clinton's speech are just fillers, or hold information about the statements he is making.

• Perform Statisical Analyses.

This step involves conducting statistical analyses of the data you've coded during your relational analysis. This may involve exploring for differences or looking for relationships among the variables you've identified in your study.

• Map out the Representations.

In addition to statistical analysis, relational analysis often leads to viewing the representations of the concepts and their associations in a text (or across texts) in a graphical -- or map -- form. Relational analysis is also informed by a variety of different theoretical approaches: linguistic content analysis, decision mapping, and mental models.

The authors of this guide have created the following commentaries on content analysis.

Issues of Reliability & Validity

The issues of reliability and validity are concurrent with those addressed in other research methods. The reliability of a content analysis study refers to its stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time; reproducibility , or the tendency for a group of coders to classify categories membership in the same way; and accuracy , or the extent to which the classification of a text corresponds to a standard or norm statistically. Gottschalk (1995) points out that the issue of reliability may be further complicated by the inescapably human nature of researchers. For this reason, he suggests that coding errors can only be minimized, and not eliminated (he shoots for 80% as an acceptable margin for reliability).

On the other hand, the validity of a content analysis study refers to the correspondence of the categories to the conclusions , and the generalizability of results to a theory.

The validity of categories in implicit concept analysis, in particular, is achieved by utilizing multiple classifiers to arrive at an agreed upon definition of the category. For example, a content analysis study might measure the occurrence of the concept category "communist" in presidential inaugural speeches. Using multiple classifiers, the concept category can be broadened to include synonyms such as "red," "Soviet threat," "pinkos," "godless infidels" and "Marxist sympathizers." "Communist" is held to be the explicit variable, while "red," etc. are the implicit variables.

The overarching problem of concept analysis research is the challenge-able nature of conclusions reached by its inferential procedures. The question lies in what level of implication is allowable, i.e. do the conclusions follow from the data or are they explainable due to some other phenomenon? For occurrence-specific studies, for example, can the second occurrence of a word carry equal weight as the ninety-ninth? Reasonable conclusions can be drawn from substantive amounts of quantitative data, but the question of proof may still remain unanswered.

This problem is again best illustrated when one uses computer programs to conduct word counts. The problem of distinguishing between synonyms and homonyms can completely throw off one's results, invalidating any conclusions one infers from the results. The word "mine," for example, variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage. For example, one may find 50 occurrences of the word "mine." But, if one is only looking specifically for "mine" as an explosive device, and 17 of the occurrences are actually personal pronouns, the resulting 50 is an inaccurate result. Any conclusions drawn as a result of that number would render that conclusion invalid.

The generalizability of one's conclusions, then, is very dependent on how one determines concept categories, as well as on how reliable those categories are. It is imperative that one defines categories that accurately measure the idea and/or items one is seeking to measure. Akin to this is the construction of rules. Developing rules that allow one, and others, to categorize and code the same data in the same way over a period of time, referred to as stability , is essential to the success of a conceptual analysis. Reproducibility , not only of specific categories, but of general methods applied to establishing all sets of categories, makes a study, and its subsequent conclusions and results, more sound. A study which does this, i.e. in which the classification of a text corresponds to a standard or norm, is said to have accuracy .

Advantages of Content Analysis

Content analysis offers several advantages to researchers who consider using it. In particular, content analysis:

• looks directly at communication via texts or transcripts, and hence gets at the central aspect of social interaction
• can allow for both quantitative and qualitative operations
• can provides valuable historical/cultural insights over time through analysis of texts
• allows a closeness to text which can alternate between specific categories and relationships and also statistically analyzes the coded form of the text
• can be used to interpret texts for purposes such as the development of expert systems (since knowledge and rules can both be coded in terms of explicit statements about the relationships among concepts)
• is an unobtrusive means of analyzing interactions
• provides insight into complex models of human thought and language use

Disadvantages of Content Analysis

Content analysis suffers from several disadvantages, both theoretical and procedural. In particular, content analysis:

• can be extremely time consuming
• is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
• is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
• is inherently reductive, particularly when dealing with complex texts
• tends too often to simply consist of word counts
• often disregards the context that produced the text, as well as the state of things after the text is produced
• can be difficult to automate or computerize

The Palmquist, Carley and Dale study, a summary of "Applications of Computer-Aided Text Analysis: Analyzing Literary and Non-Literary Texts" (1997) is an example of two studies that have been conducted using both conceptual and relational analysis. The Problematic Text for Content Analysis shows the differences in results obtained by a conceptual and a relational approach to a study.

Related Information: Example of a Problematic Text for Content Analysis

In this example, both students observed a scientist and were asked to write about the experience.

Student A: I found that scientists engage in research in order to make discoveries and generate new ideas. Such research by scientists is hard work and often involves collaboration with other scientists which leads to discoveries which make the scientists famous. Such collaboration may be informal, such as when they share new ideas over lunch, or formal, such as when they are co-authors of a paper.

Student B: It was hard work to research famous scientists engaged in collaboration and I made many informal discoveries. My research showed that scientists engaged in collaboration with other scientists are co-authors of at least one paper containing their new ideas. Some scientists make formal discoveries and have new ideas.

Content analysis coding for explicit concepts may not reveal any significant differences. For example, the existence of "I, scientist, research, hard work, collaboration, discoveries, new ideas, etc..." are explicit in both texts, occur the same number of times, and have the same emphasis. Relational analysis or cognitive mapping, however, reveals that while all concepts in the text are shared, only five concepts are common to both. Analyzing these statements reveals that Student A reports on what "I" found out about "scientists," and elaborated the notion of "scientists" doing "research." Student B focuses on what "I's" research was and sees scientists as "making discoveries" without emphasis on research.

Related Information: The Palmquist, Carley and Dale Study

Consider these two questions: How has the depiction of robots changed over more than a century's worth of writing? And, do students and writing instructors share the same terms for describing the writing process? Although these questions seem totally unrelated, they do share a commonality: in the Palmquist, Carley & Dale study, their answers rely on computer-aided text analysis to demonstrate how different texts can be analyzed.

Literary texts

One half of the study explored the depiction of robots in 27 science fiction texts written between 1818 and 1988. After texts were divided into three historically defined groups, readers look for how the depiction of robots has changed over time. To do this, researchers had to create concept lists and relationship types, create maps using a computer software (see Fig. 1), modify those maps and then ultimately analyze them. The final product of the analysis revealed that over time authors were less likely to depict robots as metallic humanoids.

Non-literary texts

The second half of the study used student journals and interviews, teacher interviews, texts books, and classroom observations as the non-literary texts from which concepts and words were taken. The purpose behind the study was to determine if, in fact, over time teacher and students would begin to share a similar vocabulary about the writing process. Again, researchers used computer software to assist in the process. This time, computers helped researchers generated a concept list based on frequently occurring words and phrases from all texts. Maps were also created and analyzed in this study (see Fig. 2).

Annotated Bibliography

Resources On How To Conduct Content Analysis

Beard, J., & Yaprak, A. (1989). Language implications for advertising in international markets: A model for message content and message execution. A paper presented at the 8th International Conference on Language Communication for World Business and the Professions. Ann Arbor, MI.

This report discusses the development and testing of a content analysis model for assessing advertising themes and messages aimed primarily at U.S. markets which seeks to overcome barriers in the cultural environment of international markets. Texts were categorized under 3 headings: rational, emotional, and moral. The goal here was to teach students to appreciate differences in language and culture.

Berelson, B. (1971). Content analysis in communication research . New York: Hafner Publishing Company.

While this book provides an extensive outline of the uses of content analysis, it is far more concerned with conveying a critical approach to current literature on the subject. In this respect, it assumes a bit of prior knowledge, but is still accessible through the use of concrete examples.

Budd, R. W., Thorp, R.K., & Donohew, L. (1967). Content analysis of communications . New York: Macmillan Company.

Although published in 1967, the decision of the authors to focus on recent trends in content analysis keeps their insights relevant even to modern audiences. The book focuses on specific uses and methods of content analysis with an emphasis on its potential for researching human behavior. It is also geared toward the beginning researcher and breaks down the process of designing a content analysis study into 6 steps that are outlined in successive chapters. A useful annotated bibliography is included.

Carley, K. (1992). Coding choices for textual analysis: A comparison of content analysis and map analysis. Unpublished Working Paper.

Comparison of the coding choices necessary to conceptual analysis and relational analysis, especially focusing on cognitive maps. Discusses concept coding rules needed for sufficient reliability and validity in a Content Analysis study. In addition, several pitfalls common to texts are discussed.

Carley, K. (1990). Content analysis. In R.E. Asher (Ed.), The Encyclopedia of Language and Linguistics. Edinburgh: Pergamon Press.

Quick, yet detailed, overview of the different methodological kinds of Content Analysis. Carley breaks down her paper into five sections, including: Conceptual Analysis, Procedural Analysis, Relational Analysis, Emotional Analysis and Discussion. Also included is an excellent and comprehensive Content Analysis reference list.

Carley, K. (1989). Computer analysis of qualitative data . Pittsburgh, PA: Carnegie Mellon University.

Presents graphic, illustrated representations of computer based approaches to content analysis.

Carley, K. (1992). MECA . Pittsburgh, PA: Carnegie Mellon University.

A resource guide explaining the fifteen routines that compose the Map Extraction Comparison and Analysis (MECA) software program. Lists the source file, input and out files, and the purpose for each routine.

Carney, T. F. (1972). Content analysis: A technique for systematic inference from communications . Winnipeg, Canada: University of Manitoba Press.

This book introduces and explains in detail the concept and practice of content analysis. Carney defines it; traces its history; discusses how content analysis works and its strengths and weaknesses; and explains through examples and illustrations how one goes about doing a content analysis.

de Sola Pool, I. (1959). Trends in content analysis . Urbana, Ill: University of Illinois Press.

The 1959 collection of papers begins by differentiating quantitative and qualitative approaches to content analysis, and then details facets of its uses in a wide variety of disciplines: from linguistics and folklore to biography and history. Includes a discussion on the selection of relevant methods and representational models.

Duncan, D. F. (1989). Content analysis in health educaton research: An introduction to purposes and methods. Heatlth Education, 20 (7).

This article proposes using content analysis as a research technique in health education. A review of literature relating to applications of this technique and a procedure for content analysis are presented.

Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc.

This book primarily focuses on the Gottschalk-Gleser method of content analysis, and its application as a method of measuring psychological dimensions of children and adults via the content and form analysis of their verbal behavior, using the grammatical clause as the basic unit of communication for carrying semantic messages generated by speakers or writers.

Krippendorf, K. (1980). Content analysis: An introduction to its methodology Beverly Hills, CA: Sage Publications.

This is one of the most widely quoted resources in many of the current studies of Content Analysis. Recommended as another good, basic resource, as Krippendorf presents the major issues of Content Analysis in much the same way as Weber (1975).

Moeller, L. G. (1963). An introduction to content analysis--including annotated bibliography . Iowa City: University of Iowa Press.

A good reference for basic content analysis. Discusses the options of sampling, categories, direction, measurement, and the problems of reliability and validity in setting up a content analysis. Perhaps better as a historical text due to its age.

Smith, C. P. (Ed.). (1992). Motivation and personality: Handbook of thematic content analysis. New York: Cambridge University Press.

Billed by its authors as "the first book to be devoted primarily to content analysis systems for assessment of the characteristics of individuals, groups, or historical periods from their verbal materials." The text includes manuals for using various systems, theory, and research regarding the background of systems, as well as practice materials, making the book both a reference and a handbook.

Solomon, M. (1993). Content analysis: a potent tool in the searcher's arsenal. Database, 16 (2), 62-67.

Online databases can be used to analyze data, as well as to simply retrieve it. Online-media-source content analysis represents a potent but little-used tool for the business searcher. Content analysis benchmarks useful to advertisers include prominence, offspin, sponsor affiliation, verbatims, word play, positioning and notational visibility.

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

Good introduction to Content Analysis. The first chapter presents a quick overview of Content Analysis. The second chapter discusses content classification and interpretation, including sections on reliability, validity, and the creation of coding schemes and categories. Chapter three discusses techniques of Content Analysis, using a number of tables and graphs to illustrate the techniques. Chapter four examines issues in Content Analysis, such as measurement, indication, representation and interpretation.

Examples of Content Analysis

Adams, W., & Shriebman, F. (1978). Television network news: Issues in content research . Washington, DC: George Washington University Press.

A fairly comprehensive application of content analysis to the field of television news reporting. The books tripartite division discusses current trends and problems with news criticism from a content analysis perspective, four different content analysis studies of news media, and makes recommendations for future research in the area. Worth a look by anyone interested in mass communication research.

Auter, P. J., & Moore, R. L. (1993). Buying from a friend: a content analysis of two teleshopping programs. Journalism Quarterly, 70 (2), 425-437.

A preliminary study was conducted to content-analyze random samples of two teleshopping programs, using a measure of content interactivity and a locus of control message index.

Barker, S. P. (???) Fame: A content analysis study of the American film biography. Ohio State University. Thesis.

Barker examined thirty Oscar-nominated films dating from 1929 to 1979 using O.J. Harvey Belief System and the Kohlberg's Moral Stages to determine whether cinema heroes were positive role models for fame and success or morally ambiguous celebrities. Content analysis was successful in determining several trends relative to the frequency and portrayal of women in film, the generally high ethical character of the protagonists, and the dogmatic, close-minded nature of film antagonists.

Bernstein, J. M. & Lacy, S. (1992). Contextual coverage of government by local television news. Journalism Quarterly, 69 (2), 329-341.

This content analysis of 14 local television news operations in five markets looks at how local TV news shows contribute to the marketplace of ideas. Performance was measured as the allocation of stories to types of coverage that provide the context about events and issues confronting the public.

Blaikie, A. (1993). Images of age: a reflexive process. Applied Ergonomics, 24 (1), 51-58.

Content analysis of magazines provides a sharp instrument for reflecting the change in stereotypes of aging over past decades.

Craig, R. S. (1992). The effect of day part on gender portrayals in television commercials: a content analysis. Sex Roles: A Journal of Research, 26 (5-6), 197-213.

Gender portrayals in 2,209 network television commercials were content analyzed. To compare differences between three day parts, the sample was chosen from three time periods: daytime, evening prime time, and weekend afternoon sportscasts. The results indicate large and consistent differences in the way men and women are portrayed in these three day parts, with almost all comparisons reaching significance at the .05 level. Although ads in all day parts tended to portray men in stereotypical roles of authority and dominance, those on weekends tended to emphasize escape form home and family. The findings of earlier studies which did not consider day part differences may now have to be reevaluated.

Dillon, D. R. et al. (1992). Article content and authorship trends in The Reading Teacher, 1948-1991. The Reading Teacher, 45 (5), 362-368.

The authors explore changes in the focus of the journal over time.

Eberhardt, EA. (1991). The rhetorical analysis of three journal articles: The study of form, content, and ideology. Ft. Collins, CO: Colorado State University.

Eberhardt uses content analysis in this thesis paper to analyze three journal articles that reported on President Ronald Reagan's address in which he responded to the Tower Commission report concerning the IranContra Affair. The reports concentrated on three rhetorical elements: idea generation or content; linguistic style or choice of language; and the potential societal effect of both, which Eberhardt analyzes, along with the particular ideological orientation espoused by each magazine.

Ellis, B. G. & Dick, S. J. (1996). 'Who was 'Shadow'? The computer knows: applying grammar-program statistics in content analyses to solve mysteries about authorship. Journalism & Mass Communication Quarterly, 73 (4), 947-963.

This study's objective was to employ the statistics-documentation portion of a word-processing program's grammar-check feature as a final, definitive, and objective tool for content analyses - used in tandem with qualitative analyses - to determine authorship. Investigators concluded there was significant evidence from both modalities to support their theory that Henry Watterson, long-time editor of the Louisville Courier-Journal, probably was the South's famed Civil War correspondent "Shadow" and to rule out another prime suspect, John H. Linebaugh of the Memphis Daily Appeal. Until now, this Civil War mystery has never been conclusively solved, puzzling historians specializing in Confederate journalism.

Gottschalk, L. A., Stein, M. K. & Shapiro, D.H. (1997). The application of computerized content analysis in a psychiatric outpatient clinic. Journal of Clinical Psychology, 53 (5) , 427-442.

Twenty-five new psychiatric outpatients were clinically evaluated and were administered a brief psychological screening battery which included measurements of symptoms, personality, and cognitive function. Included in this assessment procedure were the Gottschalk-Gleser Content Analysis Scales on which scores were derived from five minute speech samples by means of an artificial intelligence-based computer program. The use of this computerized content analysis procedure for initial, rapid diagnostic neuropsychiatric appraisal is supported by this research.

Graham, J. L., Kamins, M. A., & Oetomo, D. S. (1993). Content analysis of German and Japanese advertising in print media from Indonesia, Spain, and the United States. Journal of Advertising , 22 (2), 5-16.

The authors analyze informational and emotional content in print advertisements in order to consider how home-country culture influences firms' marketing strategies and tactics in foreign markets. Research results provided evidence contrary to the original hypothesis that home-country culture would influence ads in each of the target countries.

Herzog, A. (1973). The B.S. Factor: The theory and technique of faking it in America . New York: Simon and Schuster.

Herzog takes a look at the rhetoric of American culture using content analysis to point out discrepancies between intention and reality in American society. The study reveals, albeit in a comedic tone, how double talk and "not quite lies" are pervasive in our culture.

Horton, N. S. (1986). Young adult literature and censorship: A content analysis of seventy-eight young adult books . Denton, TX: North Texas State University.

The purpose of Horton's content analysis was to analyze a representative seventy-eight current young adult books to determine the extent to which they contain items which are objectionable to would-be censors. Seventy-eight books were identified which fit the criteria of popularity and literary quality. Each book was analyzed for, and tallied for occurrence of, six categories, including profanity, sex, violence, parent conflict, drugs and condoned bad behavior.

Isaacs, J. S. (1984). A verbal content analysis of the early memories of psychiatric patients . Berkeley: California School of Professional Psychology.

Isaacs did a content analysis investigation on the relationship between words and phrases used in early memories and clinical diagnosis. His hypothesis was that in conveying their early memories schizophrenic patients tend to use an identifiable set of words and phrases more frequently than do nonpatients and that schizophrenic patients use these words and phrases more frequently than do patients with major affective disorders.

Jean Lee, S. K. & Hwee Hoon, T. (1993). Rhetorical vision of men and women managers in Singapore. Human Relations, 46 (4), 527-542.

A comparison of media portrayal of male and female managers' rhetorical vision in Singapore is made. Content analysis of newspaper articles used to make this comparison also reveals the inherent conflicts that women managers have to face. Purposive and multi-stage sampling of articles are utilized.

Kaur-Kasior, S. (1987). The treatment of culture in greeting cards: A content analysis . Bowling Green, OH: Bowling Green State University.

Using six historical periods dating from 1870 to 1987, this content analysis study attempted to determine what structural/cultural aspects of American society were reflected in greeting cards. The study determined that the size of cards increased over time, included more pages, and had animals and flowers as their most dominant symbols. In addition, white was the most common color used. Due to habituation and specialization, says the author, greeting cards have become institutionalized in American culture.

Koza, J. E. (1992). The missing males and other gender-related issues in music education: A critical analysis of evidence from the Music Supervisor's Journal, 1914-1924. Paper presented at the annual meeting of the American Educational Research Association. San Francisco.

The goal of this study was to identify all educational issues that would today be explicitly gender related and to analyze the explanations past music educators gave for the existence of gender-related problems. A content analysis of every gender-related reference was undertaken, finding that the current preoccupation with males in music education has a long history and that little has changed since the early part of this century.

Laccinole, M. D. (1982). Aging and married couples: A language content analysis of a conversational and expository speech task . Eugene, OR: University of Oregon.

Using content analysis, this paper investigated the relationship of age to the use of the grammatical categories, and described the differences in the usage of these grammatical categories in a conversation and expository speech task by fifty married couples. The subjects Laccinole used in his analysis were Caucasian, English speaking, middle class, ranged in ages from 20 to 83 years of age, were in good health and had no history of communication disorders.

Laffal, J. (1995). A concept analysis of Jonathan Swift's 'A Tale of a Tub' and 'Gulliver's Travels.' Computers and Humanities, 29 (5), 339-362.

In this study, comparisons of concept profiles of "Tub," "Gulliver," and Swift's own contemporary texts, as well as a composite text of 18th century writers, reveal that "Gulliver" is conceptually different from "Tub." The study also discovers that the concepts and words of these texts suggest two strands in Swift's thinking.

Lewis, S. M. (1991). Regulation from a deregulatory FCC: Avoiding discursive dissonance. Masters Thesis, Fort Collins, CO: Colorado State University.

This thesis uses content analysis to examine inconsistent statements made by the Federal Communications Commission (FCC) in its policy documents during the 1980s. Lewis analyzes positions set forth by the FCC in its policy statements and catalogues different strategies that can be used by speakers to be or to appear consistent, as well as strategies to avoid inconsistent speech or discursive dissonance.

Norton, T. L. (1987). The changing image of childhood: A content analysis of Caldecott Award books. Los Angeles: University of South Carolina.

Content analysis was conducted on 48 Caldecott Medal Recipient books dating from 1938 to 1985 to determine whether the reflect the idea that the social perception of childhood has altered since the early 1960's. The results revealed an increasing "loss of childhood innocence," as well as a general sentimentality for childhood pervasive in the texts. Suggests further study of children's literature to confirm the validity of such study.

O'Dell, J. W. & Weideman, D. (1993). Computer content analysis of the Schreber case. Journal of Clinical Psychology, 49 (1), 120-125.

An example of the application of content analysis as a means of recreating a mental model of the psychology of an individual.

Pratt, C. A. & Pratt, C. B. (1995). Comparative content analysis of food and nutrition advertisements in Ebony, Essence, and Ladies' Home Journal. Journal of Nutrition Education, 27 (1), 11-18.

This study used content analysis to measure the frequencies and forms of food, beverage, and nutrition advertisements and their associated health-promotional message in three U.S. consumer magazines during two 3-year periods: 1980-1982 and 1990-1992. The study showed statistically significant differences among the three magazines in both frequencies and types of major promotional messages in the advertisements. Differences between the advertisements in Ebony and Essence, the readerships of which were primarily African-American, and those found in Ladies Home Journal were noted, as were changes in the two time periods. Interesting tie in to ethnographic research studies?

Riffe, D., Lacy, S., & Drager, M. W. (1996). Sample size in content analysis of weekly news magazines. Journalism & Mass Communication Quarterly,73 (3), 635-645.

This study explores a variety of approaches to deciding sample size in analyzing magazine content. Having tested random samples of size six, eight, ten, twelve, fourteen, and sixteen issues, the authors show that a monthly stratified sample of twelve issues is the most efficient method for inferring to a year's issues.

Roberts, S. K. (1987). A content analysis of how male and female protagonists in Newbery Medal and Honor books overcome conflict: Incorporating a locus of control framework. Fayetteville, AR: University of Arkansas.

The purpose of this content analysis was to analyze Newbery Medal and Honor books in order to determine how male and female protagonists were assigned behavioral traits in overcoming conflict as it relates to an internal or external locus of control schema. Roberts used all, instead of just a sample, of the fictional Newbery Medal and Honor books which met his study's criteria. A total of 120 male and female protagonists were categorized, from Newbery books dating from 1922 to 1986.

Schneider, J. (1993). Square One TV content analysis: Final report . New York: Children's Television Workshop.

This report summarizes the mathematical and pedagogical content of the 230 programs in the Square One TV library after five seasons of production, relating that content to the goals of the series which were to make mathematics more accessible, meaningful, and interesting to the children viewers.

Smith, T. E., Sells, S. P., and Clevenger, T. Ethnographic content analysis of couple and therapist perceptions in a reflecting team setting. The Journal of Marital and Family Therapy, 20 (3), 267-286.

An ethnographic content analysis was used to examine couple and therapist perspectives about the use and value of reflecting team practice. Postsession ethnographic interviews from both couples and therapists were examined for the frequency of themes in seven categories that emerged from a previous ethnographic study of reflecting teams. Ethnographic content analysis is briefly contrasted with conventional modes of quantitative content analysis to illustrate its usefulness and rationale for discovering emergent patterns, themes, emphases, and process using both inductive and deductive methods of inquiry.

Stahl, N. A. (1987). Developing college vocabulary: A content analysis of instructional materials. Reading, Research and Instruction , 26 (3).

This study investigates the extent to which the content of 55 college vocabulary texts is consistent with current research and theory on vocabulary instruction. It recommends less reliance on memorization and more emphasis on deep understanding and independent vocabulary development.

Swetz, F. (1992). Fifteenth and sixteenth century arithmetic texts: What can we learn from them? Science and Education, 1 (4).

Surveys the format and content of 15th and 16th century arithmetic textbooks, discussing the types of problems that were most popular in these early texts and briefly analyses problem contents. Notes the residual educational influence of this era's arithmetical and instructional practices.

Walsh, K., et al. (1996). Management in the public sector: a content analysis of journals. Public Administration 74 (2), 315-325.

The popularity and implementaion of managerial ideas from 1980 to 1992 are examined through the content of five journals revolving on local government, health, education and social service. Contents were analyzed according to commercialism, user involvement, performance evaluation, staffing, strategy and involvement with other organizations. Overall, local government showed utmost involvement with commercialism while health and social care articles were most concerned with user involvement.

For Further Reading

Abernethy, A. M., & Franke, G. R. (1996).The information content of advertising: a meta-analysis. Journal of Advertising, Summer 25 (2) , 1-18.

Carley, K., & Palmquist, M. (1992). Extracting, representing and analyzing mental models. Social Forces , 70 (3), 601-636.

Fan, D. (1988). Predictions of public opinion from the mass media: Computer content analysis and mathematical modeling . New York, NY: Greenwood Press.

Franzosi, R. (1990). Computer-assisted coding of textual data: An application to semantic grammars. Sociological Methods and Research, 19 (2), 225-257.

McTavish, D.G., & Pirro, E. (1990) Contextual content analysis. Quality and Quantity , 24 , 245-265.

Palmquist, M. E. (1990). The lexicon of the classroom: language and learning in writing class rooms . Doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.

Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Tanscripts. Hillsdale, NJ: Lawrence Erlbaum Associates.

Roberts, C.W. (1989). Other than counting words: A linguistic approach to content analysis. Social Forces, 68 , 147-177.

Issues in Content Analysis

Jolliffe, L. (1993). Yes! More content analysis! Newspaper Research Journal , 14 (3-4), 93-97.

The author responds to an editorial essay by Barbara Luebke which criticizes excessive use of content analysis in newspaper content studies. The author points out the positive applications of content analysis when it is theory-based and utilized as a means of suggesting how or why the content exists, or what its effects on public attitudes or behaviors may be.

Kang, N., Kara, A., Laskey, H. A., & Seaton, F. B. (1993). A SAS MACRO for calculating intercoder agreement in content analysis. Journal of Advertising, 22 (2), 17-28.

A key issue in content analysis is the level of agreement across the judgments which classify the objects or stimuli of interest. A review of articles published in the Journal of Advertising indicates that many authors are not fully utilizing recommended measures of intercoder agreement and thus may not be adequately establishing the reliability of their research. This paper presents a SAS MACRO which facilitates the computation of frequently recommended indices of intercoder agreement in content analysis.

Lacy, S. & Riffe, D. (1996). Sampling error and selecting intercoder reliability samples for nominal content categories. Journalism & Mass Communication Quarterly, 73 (4) , 693-704.

This study views intercoder reliability as a sampling problem. It develops a formula for generating sample sizes needed to have valid reliability estimates. It also suggests steps for reporting reliability. The resulting sample sizes will permit a known degree of confidence that the agreement in a sample of items is representative of the pattern that would occur if all content items were coded by all coders.

Riffe, D., Aust, C. F., & Lacy, S. R. (1993). The effectiveness of random, consecutive day and constructed week sampling in newspaper content analysis. Journalism Quarterly, 70 (1), 133-139.

This study compares 20 sets each of samples for four different sizes using simple random, constructed week and consecutive day samples of newspaper content. Comparisons of sample efficiency, based on the percentage of sample means in each set of 20 falling within one or two standard errors of the population mean, show the superiority of constructed week sampling.

Thomas, S. (1994). Artifactual study in the analysis of culture: A defense of content analysis in a postmodern age. Communication Research, 21 (6), 683-697.

Although both modern and postmodern scholars have criticized the method of content analysis with allegations of reductionism and other epistemological limitations, it is argued here that these criticisms are ill founded. In building and argument for the validity of content analysis, the general value of artifact or text study is first considered.

Zollars, C. (1994). The perils of periodical indexes: Some problems in constructing samples for content analysis and culture indicators research. Communication Research, 21 (6), 698-714.

The author examines problems in using periodical indexes to construct research samples via the use of content analysis and culture indicator research. Issues of historical and idiosyncratic changes in index subject category heading and subheadings make article headings potentially misleading indicators. Index subject categories are not necessarily invalid as a result; nevertheless, the author discusses the need to test for category longevity, coherence, and consistency over time, and suggests the use of oversampling, cross-references, and other techniques as a means of correcting and/or compensating for hidden inaccuracies in classification, and as a means of constructing purposive samples for analytic comparisons.

Busch, Carol, Paul S. De Maret, Teresa Flynn, Rachel Kellum, Sheri Le, Brad Meyers, Matt Saunders, Robert White, and Mike Palmquist. (2005). Content Analysis. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=61

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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How to do a content analysis

What is content analysis?

Why would you use a content analysis, types of content analysis, conceptual content analysis, relational content analysis, reliability and validity, reliability, the advantages and disadvantages of content analysis, a step-by-step guide to conducting a content analysis, step 1: develop your research questions, step 2: choose the content you’ll analyze, step 3: identify your biases, step 4: define the units and categories of coding, step 5: develop a coding scheme, step 6: code the content, step 7: analyze the results, frequently asked questions about content analysis, related articles.

In research, content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. Simply put, content analysis is a research method that aims to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data , depending on the specific use case.

As such, some of the objectives of content analysis include:

• Simplifying complex, unstructured content.
• Identifying trends, patterns, and relationships in the content.
• Determining the characteristics of the content.
• Identifying the intentions of individuals through the analysis of the content.
• Identifying the implied aspects in the content.

Typically, when doing a content analysis, you’ll gather data not only from written text sources like newspapers, books, journals, and magazines but also from a variety of other oral and visual sources of content like:

• Voice recordings, speeches, and interviews.
• Web content, blogs, and social media content.
• Films, videos, and photographs.

One of content analysis’s distinguishing features is that you'll be able to gather data for research without physically gathering data from participants. In other words, when doing a content analysis, you don't need to interact with people directly.

The process of doing a content analysis usually involves categorizing or coding concepts, words, and themes within the content and analyzing the results. We’ll look at the process in more detail below.

Typically, you’ll use content analysis when you want to:

• Identify the intentions, communication trends, or communication patterns of an individual, a group of people, or even an institution.
• Analyze and describe the behavioral and attitudinal responses of individuals to communications.
• Determine the emotional or psychological state of an individual or a group of people.
• Analyze the international differences in communication content.
• Analyzing audience responses to content.

Keep in mind, though, that these are just some examples of use cases where a content analysis might be appropriate and there are many others.

The key thing to remember is that content analysis will help you quantify the occurrence of specific words, phrases, themes, and concepts in content. Moreover, it can also be used when you want to make qualitative inferences out of the data by analyzing the semantic meanings and interrelationships between words, themes, and concepts.

In general, there are two types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions. With that in mind, let’s now look at these two types of content analysis in more detail.

With conceptual analysis, you’ll determine the existence of certain concepts within the content and identify their frequency. In other words, conceptual analysis involves the number of times a specific concept appears in the content.

Conceptual analysis is typically focused on explicit data, which means you’ll focus your analysis on a specific concept to identify its presence in the content and determine its frequency.

However, when conducting a content analysis, you can also use implicit data. This approach is more involved, complicated, and requires the use of a dictionary, contextual translation rules, or a combination of both.

No matter what type you use, conceptual analysis brings an element of quantitive analysis into a qualitative approach to research.

Relational content analysis takes conceptual analysis a step further. So, while the process starts in the same way by identifying concepts in content, it doesn’t focus on finding the frequency of these concepts, but rather on the relationships between the concepts, the context in which they appear in the content, and their interrelationships.

Before starting with a relational analysis, you’ll first need to decide on which subcategory of relational analysis you’ll use:

• Affect extraction: With this relational content analysis approach, you’ll evaluate concepts based on their emotional attributes. You’ll typically assess these emotions on a rating scale with higher values assigned to positive emotions and lower values to negative ones. In turn, this allows you to capture the emotions of the writer or speaker at the time the content is created. The main difficulty with this approach is that emotions can differ over time and across populations.
• Proximity analysis: With this approach, you’ll identify concepts as in conceptual analysis, but you’ll evaluate the way in which they occur together in the content. In other words, proximity analysis allows you to analyze the relationship between concepts and derive a concept matrix from which you’ll be able to develop meaning. Proximity analysis is typically used when you want to extract facts from the content rather than contextual, emotional, or cultural factors.
• Cognitive mapping: Finally, cognitive mapping can be used with affect extraction or proximity analysis. It’s a visualization technique that allows you to create a model that represents the overall meaning of content and presents it as a graphic map of the relationships between concepts. As such, it’s also commonly used when analyzing the changes in meanings, definitions, and terms over time.

Now that we’ve seen what content analysis is and looked at the different types of content analysis, it’s important to understand how reliable it is as a research method . We’ll also look at what criteria impact the validity of a content analysis.

There are three criteria that determine the reliability of a content analysis:

• Stability . Stability refers to the tendency of coders to consistently categorize or code the same data in the same way over time.
• Reproducibility . This criterion refers to the tendency of coders to classify categories membership in the same way.
• Accuracy . Accuracy refers to the extent to which the classification of content corresponds to a specific standard.

Keep in mind, though, that because you’ll need to code or categorize the concepts you’ll aim to identify and analyze manually, you’ll never be able to eliminate human error. However, you’ll be able to minimize it.

In turn, three criteria determine the validity of a content analysis:

• Closeness of categories . This is achieved by using multiple classifiers to get an agreed-upon definition for a specific category by using either implicit variables or synonyms. In this way, the category can be broadened to include more relevant data.
• Conclusions . Here, it’s crucial to decide what level of implication will be allowable. In other words, it’s important to consider whether the conclusions are valid based on the data or whether they can be explained using some other phenomena.
• Generalizability of the results of the analysis to a theory . Generalizability comes down to how you determine your categories as mentioned above and how reliable those categories are. In turn, this relies on how accurately the categories are at measuring the concepts or ideas that you’re looking to measure.

Considering everything mentioned above, there are definite advantages and disadvantages when it comes to content analysis:

Let’s now look at the steps you’ll need to follow when doing a content analysis.

The first step will always be to formulate your research questions. This is simply because, without clear and defined research questions, you won’t know what question to answer and, by implication, won’t be able to code your concepts.

Based on your research questions, you’ll then need to decide what content you’ll analyze. Here, you’ll use three factors to find the right content:

• The type of content . Here you’ll need to consider the various types of content you’ll use and their medium like, for example, blog posts, social media, newspapers, or online articles.
• What criteria you’ll use for inclusion . Here you’ll decide what criteria you’ll use to include content. This can, for instance, be the mentioning of a certain event or advertising a specific product.
• Your parameters . Here, you’ll decide what content you’ll include based on specified parameters in terms of date and location.

The next step is to consider your own pre-conception of the questions and identify your biases. This process is referred to as bracketing and allows you to be aware of your biases before you start your research with the result that they’ll be less likely to influence the analysis.

Your next step would be to define the units of meaning that you’ll code. This will, for example, be the number of times a concept appears in the content or the treatment of concept, words, or themes in the content. You’ll then need to define the set of categories you’ll use for coding which can be either objective or more conceptual.

Based on the above, you’ll then organize the units of meaning into your defined categories. Apart from this, your coding scheme will also determine how you’ll analyze the data.

The next step is to code the content. During this process, you’ll work through the content and record the data according to your coding scheme. It’s also here where conceptual and relational analysis starts to deviate in relation to the process you’ll need to follow.

As mentioned earlier, conceptual analysis aims to identify the number of times a specific concept, idea, word, or phrase appears in the content. So, here, you’ll need to decide what level of analysis you’ll implement.

In contrast, with relational analysis, you’ll need to decide what type of relational analysis you’ll use. So, you’ll need to determine whether you’ll use affect extraction, proximity analysis, cognitive mapping, or a combination of these approaches.

Once you’ve coded the data, you’ll be able to analyze it and draw conclusions from the data based on your research questions.

Content analysis offers an inexpensive and flexible way to identify trends and patterns in communication content. In addition, it’s unobtrusive which eliminates many ethical concerns and inaccuracies in research data. However, to be most effective, a content analysis must be planned and used carefully in order to ensure reliability and validity.

The two general types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions.

In qualitative research coding means categorizing concepts, words, and themes within your content to create a basis for analyzing the results. While coding, you work through the content and record the data according to your coding scheme.

Content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. The goal of a content analysis is to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data, depending on the specific use case.

Content analysis is a qualitative method of data analysis and can be used in many different fields. It is particularly popular in the social sciences.

It is possible to do qualitative analysis without coding, but content analysis as a method of qualitative analysis requires coding or categorizing data to then analyze it according to your coding scheme in the next step.

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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.

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Data & Finance for Work & Life

Qualitative Content Analysis: a Simple Guide with Examples

Content analysis is a type of qualitative research (as opposed to quantitative research) that focuses on analyzing content in various mediums, the most common of which is written words in documents.

It’s a very common technique used in academia, especially for students working on theses and dissertations, but here we’re going to talk about how companies can use qualitative content analysis to improve their processes and increase revenue.

Whether you’re new to content analysis or a seasoned professor, this article provides all you need to know about how data analysts use content analysis to improve their business. It will also help you understand the relationship between content analysis and natural language processing — what some even call natural language content analysis.

Don’t forget, you can get the free Intro to Data Analysis eBook , which will ensure you build the right practical skills for success in your analytical endeavors.

What is qualitative content analysis, and what is it used for?

Any content analysis definition must consist of at least these three things: qualitative language , themes , and quantification .

In short, content analysis is the process of examining preselected words in video, audio, or written mediums and their context to identify themes, then quantifying them for statistical analysis in order to draw conclusions. More simply, it’s counting how often you see two words close to each other.

For example, let’s say I place in front of you an audio bit, a old video with a static image, and a document with lots of text but no titles or descriptions. At the start, you would have no idea what any of it was about.

Let’s say you transpose the video and audio recordings on paper. Then you use a counting software to count the top ten most used words, excluding prepositions (of, over, to, by) and articles (the, a), conjunctions (and, but, or) and other common words like “very.”

Your results are that the top 5 words are “candy,” “snow,” “cold,” and “sled.” These 5 words appear at least 25 times each, and the next highest word appears only 4 times. You also find that the words “snow” and “sled” appear adjacent to each other 95% of the time that “snow” appears.

Well, now you have performed a very elementary qualitative content analysis .

This means that you’re probably dealing with a text in which snow sleds are important. Snow sleds, thus, become a theme in these documents, which goes to the heart of qualitative content analysis.

The goal of qualitative content analysis is to organize text into a series of themes . This is opposed to quantitative content analysis, which aims to organize the text into categories .

Types of qualitative content analysis

If you’ve heard about content analysis, it was most likely in an academic setting. The term itself is common among PhD students and Masters students writing their dissertations and theses. In that context, the most common type of content analysis is document analysis.

There are many types of content analysis , including:

• Short- and long-form survey questions
• Focus group transcripts
• Interview transcripts
• Legislature
• Public records
• Comments sections
• Messaging platforms

This list gives you an idea for the possibilities and industries in which qualitative content analysis can be applied.

For example, marketing departments or public relations groups in major corporations might collect survey, focus groups, and interviews, then hand off the information to a data analyst who performs the content analysis.

A political analysis institution or Think Tank might look at legislature over time to identify potential emerging themes based on their slow introduction into policy margins. Perhaps it’s possible to identify certain beliefs in the senate and house of representatives before they enter the public discourse.

Non-governmental organizations (NGOs) might perform an analysis on public records to see how to better serve their constituents. If they have access to public records, it would be possible to identify citizen characteristics that align with their goal.

Analysis logic: inductive vs deductive

There are two types of logic we can apply to qualitative content analysis: inductive and deductive. Inductive content analysis is more of an exploratory approach. We don’t know what patterns or ideas we’ll discover, so we go in with an open mind.

On the other hand, deductive content analysis involves starting with an idea and identifying how it appears in the text. For example, we may approach legislation on wildlife by looking for rules on hunting. Perhaps we think hunting with a knife is too dangerous, and we want to identify trends in the text.

Neither one is better per se, and they each have carry value in different contexts. For example, inductive content analysis is advantageous in situations where we want to identify author intent. Going in with a hypothesis can bias the way we look at the data, so the inductive method is better

Deductive content analysis is better when we want to target a term. For example, if we want to see how important knife hunting is in the legislation, we’re doing deductive content analysis.

Measurements: idea coding vs word frequency

Two main methodologies exist for analyzing the text itself: coding and word frequency. Idea coding is the manual process of reading through a text and “coding” ideas in a column on the right. The reason we call this coding is because we take ideas and themes expressed in many words, and turn them into one common phrase. This allows researchers to better understand how those ideas evolve. We will look at how to do this in word below.

In short, coding in the context qualitative content analysis follows 2 steps:

• Reading through the text one time
• Adding 2-5 word summaries each time a significant theme or idea appears

Word frequency is simply counting the number of times a word appears in a text, as well as its proximity to other words. In our “snow sled” example above, we counted the number of times a word appeared, as well as how often it appeared next to other words. There’s are online tool for this we’ll look at below.

In short, word frequency in the context of content analysis follows 2 steps:

• Decide whether you want to find a word, or just look at the most common words
• Use word’s Replace function for the first, or an online tool such as Text Analyzer for the second (we’ll look at these in more detail below).

Many data scientists consider coding as the only qualitative content analysis, since word frequency turns to counting the number of times a word appears, making is quantitative.

While there is merit to this claim, I personally do not consider word frequency a part of quantitative content analysis. The fact that we count the frequency of a word does not mean we can draw direct conclusions from it. In fact, without a researcher to provide context on the number of time a word appears, word frequency is useless. True quantitative research carries conclusive value on its own.

Measurements AND analysis logic

There are four ways to approach qualitative content analysis given our two measurement types and inductive/deductive logical approaches. You could do inductive coding, inductive word frequency, deductive coding, and deductive word frequency.

The two best are inductive coding and deductive word frequency. If you would like to discover a document, trying to search for specific words will not inform you about its contents, so inductive word frequency is un-insightful.

Likewise, if you’re looking for the presence of a specific idea, you do not want to go through the whole document to code just to find it, so deductive coding is not insightful. Here’s simple matrix to illustrate:

Qualitative content analysis example

We looked at a small example above, but let’s play out all of the above information in a real world example. I will post the link to the text source at the bottom of the article, but don’t look at it yet . Let’s jump in with a discovery mentality , meaning let’s use an inductive approach and code our way through each paragraph.

Qualitative Content Analysis Example Download

*Click the “1” superscript to the right for a link to the source text. 1

How to do qualitative content analysis

We could use word frequency analysis to find out which are the most common x% of words in the text (deductive word frequency), but this takes some time because we need to build a formula that excludes words that are common but that don’t have any value (a, the, but, and, etc).

As a shortcut, you can use online tools such as Text Analyzer and WordCounter , which will give you breakdowns by phrase length (6 words, 5 words, 4 words, etc), without excluding common terms. Here are a few insightful example using our text with 7 words:

Perhaps more insightfully, here is a list of 5 word combinations, which are much more common:

The downside to these tools is that you cannot find 2- and 1-word strings without excluding common words. This is a limitation, but it’s unlikely that the work required to get there is worth the value it brings.

OK. Now that we’ve seen how to go about coding our text into quantifiable data, let’s look at the deductive approach and try to figure out if the text contains a single word we’re looking for. (This is my favorite.)

Deductive word frequency

We know the text now because we’ve already looked through it. It’s about the process of becoming literate, namely, the elements that impact our ability to learn to read. But we only looked at the first four sections of the article, so there’s more to explore.

Let’s say we want to know how a household situation might impact a student’s ability to read . Instead of coding the entire article, we can simply look for this term and it’s synonyms. The process for deductive word frequency is the following:

• Identify your term
• Think of all the possible synonyms
• Use the word find function to see how many times they appear
• If you suspect that this word often comes in connection with others, try searching for both of them

In my example, the process would be:

• Parents, parent, home, house, household situation, household influence, parental, parental situation, at home, home situation
• Go to “Edit>Find>Replace…” This will enable you to locate the number of instances in which your word or combinations appear. We use the Replace window instead of the simply Find bar because it allows us to visualize the information.
• Accounted for in possible synonyms

The results: 0! None of these words appeared in the text, so we can conclude that this text has nothing to do with a child’s home life and its impact on his/her ability to learn to read. Here’s a picture:

Don’t Be Afraid of Content Analysis

Content analysis can be intimidating because it uses data analysis to quantify words. This article provides a starting point for your analysis, but to ensure you get 90% reliability in word coding, sign up to receive our eBook Beginner Content Analysis . I went from philosophy student to a data-heavy finance career, and I created it to cater to research and dissertation use cases.

Content analysis vs natural language processing

While similar, content analysis, even the deductive word frequency approach, and natural language processing (NLP) are not the same. The relationship is hierarchical. Natural language processing is a field of linguistics and data science that’s concerned with understanding the meaning behind language.

On the other hand, content analysis is a branch of natural language processing that focuses on the methodologies we discussed above: discovery-style coding (sometimes called “tokenization”) and word frequency (sometimes called the “bag of words” technique)

For example, we would use natural language processing to quantify huge amounts of linguistic information, turn it into row-and-column data, and run tests on it. NLP is incredibly complex in the details, which is why it’s nearly impossible to provide a synopsis or example technique here (we’ll provide them in coursework on AnalystAnswers.com ). However, content analysis only focuses on a few manual techniques.

Content analysis in marketing

Content analysis in marketing is the use of content analysis to improve marketing reach and conversions. has grown in importance over the past ten years. As digital platforms become more central to our understanding and interaction with others, we use them more.

We write out ideas, small texts. We post our thoughts on Facebook and Twitter, and we write blog posts like this one. But we also post videos on youtube and express ourselves in podcasts.

All of these mediums contain valuable information about who we are and what we might want to buy . A good marketer aims to leverage this information in three ways:

• Collect the data
• Analyze the data
• Modify his/her marketing messaging to better serve the consumer
• Pretend, with bots or employees, to be a consumer and craft messages that influence potential buyers

The challenge for marketers doing this is getting the rights to access this data. Indeed, data privacy laws have gone into play in the European Union (General Data Protection Regulation, or GDPR) as well as in Brazil (General Data Protection Law, or GDPL).

Content analysis vs narrative analysis

Content analysis is concerned with themes and ideas, whereas narrative analysis is concerned with the stories people express about themselves or others. Narrative analysis uses the same tools as content analysis, namely coding (or tokenization) and word frequency, but its focus is on narrative relationship rather than themes. This is easier to understand with an example. Let’s look at how we might code the following paragraph from the two perspectives:

I do not like green eggs and ham. I do not like them, Sam-I-Am. I do not like them here or there. I do not like them anywhere!

Content analysis : the ideas expressed include green eggs and ham. the narrator does not like them

Narrative analysis : the narrator speaks from first person. He has a relationship with Sam-I-Am. He orients himself with regards to time and space. he does not like green eggs and ham, and may be willing to act on that feeling.

Content analysis vs document analysis

Content analysis and document analysis are very similar, which explains why many people use them interchangeably. The core difference is that content analysis examines all mediums in which words appear , whereas document analysis only examines written documents .

For example, if I want to carry out content analysis on a master’s thesis in education, I would consult documents, videos, and audio files. I may transcribe the video and audio files into a document, but I wouldn’t exclude them form the beginning.

On the other hand, if I want to carry out document analysis on a master’s thesis, I would only use documents, excluding the other mediums from the start. The methodology is the same, but the scope is different. This dichotomy also explains why most academic researchers performing qualitative content analysis refer to the process as “document analysis.” They rarely look at other mediums.

Content Gap Analysis

Content gap analysis is a term common in the field of content marketing, but it applies to the analytical fields as well. In a sentence, content gap analysis is the process of examining a document or text and identifying the missing pieces, or “gap,” that it needs to be completed.

As you can imagine, a content marketer uses gap analysis to determine how to improve blog content. An analyst uses it for other reasons. For example, he/she may have a standard for documents that merit analysis. If a document does not meet the criteria, it must be rejected until it’s improved.

The key message here is that content gap analysis is not content analysis. It’s a way of measuring the distance an underperforming document is from an acceptable document. It is sometimes, but not always, used in a qualitative content analysis context.

• Link to Source Text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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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]

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